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75704571-64c5-4f97-b76e-0e725c5842fd | trentmkelly/LessWrong-43k | LessWrong | Preparing for Power Outages in Disasters
Author: Finan Adamson
Last Updated: 03/2022
Overview
This doc goes into details on how to maintain electricity if it’s important to your survival and flourishing even when the power goes out. It may not be necessary to do anything here if you don’t depend on electricity to live. If the electricity is out for many weeks or months, you’ll likely go elsewhere so you don’t need to prepare on that scale. You may also want to do some minimal amount of preparation so you can charge your cell phone.
Tradeoffs
If you need to have electricity when the grid is down your options seem to be generators and/or batteries. What setup you want will vary depending on your needs.
* Generators:
* Need to be run outside.
* Noisy
* Can consistently get power as long as you have fuel.
* Must be able to acquire or store fuel.
* Batteries:
* Can be run inside.
* Quiet
* When you run out you’re done unless you have a way of charging.
You may want both so you can charge your batteries off the generator, but this can get expensive so you may want to skip this unless you really need it for medical reasons.
Providing Power during an Outage
I just want to charge my phone for a day or two
* If all you want is a way to charge your phone. Getting a little external charger would fit your needs. It would cost $20-$50. You could charge it and store it with your other emergency gear.Then you just hook up your phone via USB charger.
* If you want to go a little harder on this so you can keep recharging things every day. You could get a portable solar panel. Wirecutter has solar panel recommendations. These would mostly be for charging things like phones, not laptops.
I want to charge a few things, but not my whole house
* You’re going to want a power station. Basically a big battery that you can recharge when you have electricity. Wirecutter’s recommendations that cost between $500 and $1,400
* In determining how much battery you want. You can fig |
34cfa112-d530-4a7b-8a6a-cd52703c098a | trentmkelly/LessWrong-43k | LessWrong | RFC: COVID-19 Statistical Guilt
(Updated 2020/08/08; appended to the bottom. New BLUF: "What's the expected total number of further C19 infections recursively resulting from a single infection, in the current USA environment?")
(Original post: )
BLUF: How many days of life would I be statistically costing others by taking four flights and some family vacation?
I live in Ohio, and my parents live in Arizona. A few months ago, my parents shared future plans to take a family trip. I don't think it even occurred to me to consider the future state of COVID-19 at the time of the trip - August was ages away, and the entirely implicit reasoning probably read "surely things will have quieted down". I bought plane tickets without really thinking about it. Now, all of a sudden, August is here, a plane flight is about two weeks away, and COVID-19 is worse than ever. If I cancel, my parents will be quite upset - my mom hates that I live so far away, and has been looking forward to this trip together for months. I am afraid to disappoint them. However, I am also aware that taking a plane risks catching and spreading COVID-19, endangering others' lives. I don't have known tools at hand to properly assess the risk I'd be incurring.
Here are some details:
1. The trip is about a week long.
2. There are two segments of the trip: one where I stay with my immediate family (4, including me) at home for a few days, and then one where we (and maybe 8-14 others; extended family) fly to my Grandma's timeshare suite at a mountain resort in Utah for several days. (Then fly back, and I fly back to Ohio maybe a day later.) The primary activity at the resort is likely to be hiking, possibly supplemented (for some individuals) with pool-going.
3. I've been heavily quarantining, and my family has been quarantining pretty well, I hear - perhaps we can assume for simplicity that my immediate family does not currently have COVID-19, and perhaps likewise for my extended family.
4. Sometime last year I bought about 10 N95 mask |
bfb26e73-ccb3-41c5-920f-d178a33a9abc | trentmkelly/LessWrong-43k | LessWrong | How do you notice when you're procrastinating?
I'm going to steal Anna's idea and change it to the instrumental side of rationality. In Luke's algorithm for beating procrastination, Step 1 is to Notice You Are Procrastinating. I'm not so sure this is easy. For me, the knowledge sort of fades in and out without being explicitly grabbed by my consciousness. If I actually held onto that fact, the moment that I was evading a task, and made it clear to myself that I was doing the sub-optimal, and the consequences involved, I think it would go a long way towards getting me to actually get things done.
What do you use to catch it? How do you notice you're procrastinating? Leave your ideas below (one idea per comment), and upvote the comments that you either: (a) use; or (b) will now try using. |
64b9cd3b-3af9-4729-8f2a-f98cac01af0e | trentmkelly/LessWrong-43k | LessWrong | TDT for Humans
This is part 19 of 30 of Hammertime. Click here for the intro.
As is Hammertime tradition, I’m making a slight change of plans right around the scheduled time for Planning. My excuse this time:
Several commenters pointed out serious gaps in my knowledge of Focusing. I will postpone Internal Double Crux, an advanced form of Focusing, to the next cycle. Instead, we will have two more posts on making and executing long-term plans.
Day 19: TDT for Humans
Previously on planning: Day 8, Day 9, Day 10.
Today I’d like to describe two orders of approximation to a working decision theory for humans.
TDT 101
Background reading: How I Lost 100 Pounds Using TDT.
> Choose as though controlling the logical output of the abstract computation you implement, including the output of all other instantiations and simulations of that computation.
> ~ Eliezer
In other words, every time you make a decision, pre-commit to making the same decision in all conceptually similar situations in the future.
The striking value of TDT is: make each decision as if you would immediately reap the long-term rewards of making that same decision repeatedly. And if it turns out you’re an updateless agent, this actually works! You actually lose 100 pounds by making one decision.
I encourage readers who have not tried to live by TDT to stop here and try it out for a week.
TDT 201
There are a number of serious differences between timeless agents and human beings, so applying TDT as stated above requires an unacceptable (to me) level of self-deception. My second order of approximation is to offer a practical and weak version of TDT based on the Solitaire Principle and Magic Brain Juice.
Three objections to applying TDT in real life:
Spirits
A human is about halfway between “one monolithic codebase” and “a loose confederation of spirits running a random serial dictatorship.” Roughly speaking, each spirit is the piece of you built to satisfy one primordial need: hunger, friendship, curiosity, jus |
b9e5562f-23fe-44ac-96bf-351dbbb7b809 | trentmkelly/LessWrong-43k | LessWrong | Grandpa Has Different Rules
It's very common that there are multiple adults in a child's life who parent differently. These can be explicit differences in rules (I allow jumping on the furniture, you don't), or more subtle differences in approach (I'm fine with kids trying to persuade me something should be ok, not everyone is). Adults often get in conflict over these, and I think this is generally not worth it.
There are two main reasons I've seen for parents wanting others to take a specific approach with their kids:
* You want consistency. I'm strongly in favor of predictable parenting, where kids know what the boundaries are and can predict your reactions. Kids are smart, however, and are very good at learning that different adults/places/situations have different systems.
* You want a specific approach. There are a lot of ways people parent, and they mostly work fine, especially in moderation. Perhaps the aunt lets the kids have lots of sweets, grandma requires shoes outside, or the babysitter won't take them out if it's raining. While these aren't the decisions I would make, it doesn't matter that much. Now, I wouldn't want anyone who looks after my kids to enforce discipline by hitting them, and neither would I be ok with someone letting them play unattended by deep water: some things do matter. But the conflicts I tend to see are over much smaller differences.
One place where this can be more of a problem is when multiple adults with different styles are present at the same time. I think the main thing you want is consistency: if the parent is present you go by their rules, or perhaps you go by the rules of whichever space you're currently in. This does mean that if adults are often ambiguously in charge of the same kids it's worth putting effort into harmonization: different parents having different rules doesn't work very well. I expect this to come up if/when our housemates have kids.
There's also the issue of cross jurisdictional precedent ("grandpa lets me do it!") The most |
e00f8bae-35ef-4bd6-8e59-1579987cb085 | trentmkelly/LessWrong-43k | LessWrong | Metaculus Introduces AI-Powered Community Insights to Reveal Factors Driving User Forecasts
Forecast Consumers Can More Quickly, More Deeply Understand Aggregate Forecasts Without Reading Hundreds of User Comments
Forecast questions sometimes feature hundreds of comments offering analyses, rationales, arguments, and evidence. Together they offer a trove of valuable insights pointing to the factors most likely to determine an outcome. We've introduced Community Insights to distill these into succinct summaries that get you up to speed in seconds.
Community Insights can help you:
* understand what drives the community prediction
* identify what is likely to determine a question's outcome
* pinpoint forecasters’ core disagreements
* see what you missed in a quickly-developing story
Community Insights appear on open binary and continuous questions with extensive comment histories. Similar forecasts are grouped together with AI-generated summaries of the comments that support them, providing readers with a deeper understanding of the aggregated Community Prediction.
How Does It Work?
We condense recent predictions, timestamped comments, and the current Community Prediction into GPT-4 generated summaries of the relevant arguments for different forecasts on a given question.
Get Started
Here are a few questions featuring Community Insights so you can see them in action. Just click the embedded question and scroll down:
* Will Ukraine join the EU before 2030?
* How much global warming by 2100?
* What will SpaceX be worth in 2030?
* What will China's fertility rate be in 2031?
----------------------------------------
If you find a misleading summary, you can let us know by flagging it. Your input and feedback will help make this new tool as useful as it can be to both forecasters and forecast consumers. |
a077dd04-ed93-4020-9fcf-fdadc60ad4d9 | trentmkelly/LessWrong-43k | LessWrong | Hierarchical planning: context agents
If you end up applying this post, please do it in the name of safety research.
I - intro
Suppose I need groceries. When I make a plan to get more groceries, what I don't do is fully model the world (e.g. as a Markov process), find a policy that leads to me getting groceries, and then execute that policy. Among the reasons why not, there are two at the top of the heap both in importance and ridiculousness. First, this requires imagining every situation I can think of and making a plan in case of every eventuality. Imagine me sitting at home, thinking "Yes, well if the peanut butter is on the third shelf, I'll do this, but if it's on the second shelf, I'll do that..." Second, every plan I make, for all of these situations, would have to be in the most exacting detail possible. Not "Then I'll take the peanut butter," but "Then I will activate muscle group #1938 at 43% intensity and muscle group #552 at 9% intensity, and..."
What I actually do is think of some abstract plan like "I'm going to bike to the supermarket down the street, then get some veggies and tofu and stuff, and come home." Then to get started, I figure out how to bike to the supermarket in terms of actions like "put on my shoes and a coat, and grab my bike." Then to get started on that, I make a plan to put on my shoes in terms of actions like "Insert feet into shoes," which itself is done with a plan that looks like "Activate muscle group #698 at 35% intensity, and..."
This sort of hierarchical planning is vital to operating in the real world with any sort of efficiency. In this post I will verbally outline some hierarchical planners - "context agents" - followed by a more precise definition, and finally talk about what the values of context agents look like and why I think that's interesting.
Context agents as stated are going to be pretty impractical, but if you're clever there are ways to make them cheaper. The main point of introducing them is to talk about what values and Goodhart's law look |
9deb9944-81e8-4971-826a-62f57d8cd0b2 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Does agent foundations cover all future ML systems?
Epistemic Status: *Wondering over meta approaches to reasoning about agent foundations*
In mathematics, we can find general principles for mathematical objects with a particular set of axioms. For example, if a language follows first-order logic, we can assume that saying the sentence "all apples are made of carbon" is the same as saying "if something isn't made of carbon, it isn't an apple". Similarly, I believe that agent foundations are an attempt to construct an abstract algebra on the algebra that is machine learning. We want to be able to say something about all future ML models by arguing over the potential compositions of ML agents. This would be like saying, "since AGI-agent 1 is a subset of an agent class that follows these axioms, we know that it will only defect if it sees a paperclip".
Someone pointed out to me that we might be assuming things about how an agent acts by not grounding it in current-day machine learning algorithms. The problem with not grounding it is that we might be constructing an abstract algebra with axioms that don't encompass the axioms in the algebra we're trying to study. I thought this was a great question, and I have no idea how to answer it since I haven't seen any formalisation on what ML approaches are a sub-set of. Take, for example, John Wentworth's work on agent foundations. Does it generalise to multi-agent-based systems? Does it generalise to self-supervision algorithms such as current-day transformers? I would love to know if anyone has thought of this.
I also have some other related questions:
Firstly, do you, fellow humans of LessWrong, believe that the abstract algebra framework is helpful for thinking about agent foundations?
Secondly, I wondered whether anyone has made such an approach or knows what modern-day ML algorithms different parts of agent foundations covers? |
f4a375b2-7b5f-4495-9e28-7aaf38b447c7 | trentmkelly/LessWrong-43k | LessWrong | Goal Factoring
This is part 18 of 30 of Hammertime. Click here for the intro.
Up until today, Hammertime focused on improving one’s ability to achieve one’s goals. The next two techniques, Goal Factoring and Internal Double Crux, are designed to figure out what goals to pursue. For the largest goals in life, you should be able to make a detached decision about whether they’re worth pursuing before you throw your all into them.
Day 18: Goal Factoring
Previously: Day 7, Aversion Factoring.
Goal Factoring is a CFAR technique for systematically figuring out all the subgoals and aversions you have around an action, and what to do about them. The basic algorithm:
1. Pick an action. It can be something you already do.
2. Factor the action into goals and aversions. Write down all the costs and aversions to pursuing the action, and continue to factor sub-goals until they feel like irreducible components.
3. Brainstorm possible replacement actions. Try to design another action that achieves the goals better and reduces the costs and aversions. This action can be an upgrade of your current action, something else altogether, or even a combination of two or more actions. Make a new plan.
4. Reality check. Imagine instituting your new plan. Decide if you’re satisfied. Also, decide if it’s feasible by Murphyjitsu.
This is already quite a complicated and useful beast. Three things to keep in mind:
Use a focusing check to find all the subgoals and aversions. If I say out loud, “The only reason I want to go to the gym is physical health,” I feel a curtain of discontent that reminds me physical attractiveness is also important. Remember that honesty and attention to detail are essential to finding aversions, and this applies to goals as well!
Goal factoring might solve the problem at any step. Writing down your true motivations can be enough to figure out the right course of action. About three months ago, I noticed that the main motivation for my video game addiction is “Prove to my par |
39af5d23-d84c-4265-9703-ce66891d3fe3 | trentmkelly/LessWrong-43k | LessWrong | SAEBench: A Comprehensive Benchmark for Sparse Autoencoders
Adam Karvonen*, Can Rager*, Johnny Lin*, Curt Tigges*, Joseph Bloom*, David Chanin, Yeu-Tong Lau, Eoin Farrell, Arthur Conmy, Callum McDougall, Kola Ayonrinde, Matthew Wearden, Samuel Marks, Neel Nanda *equal contribution
TL;DR
* We are releasing SAE Bench, a suite of 8 diverse sparse autoencoder (SAE) evaluations including unsupervised metrics and downstream tasks. Use our codebase to evaluate your own SAEs!
* You can compare 200+ SAEs of varying sparsity, dictionary size, architecture, and training time on Neuronpedia.
* Think we're missing an eval? We'd love for you to contribute it to our codebase! Email us.
🔍 Explore the Benchmark & Rankings
📊 Evaluate your SAEs with SAEBench
✉️ Contact Us
Introduction
Sparse Autoencoders (SAEs) have become one of the most popular tools for AI interpretability. A lot of recent interpretability work has been focused on studying SAEs, in particular on improving SAEs, e.g. the Gated SAE, TopK SAE, BatchTopK SAE, ProLu SAE, Jump Relu SAE, Layer Group SAE, Feature Choice SAE, Feature Aligned SAE, and Switch SAE. But how well do any of these improvements actually work?
The core challenge is that we don't know how to measure how good an SAE is. The fundamental premise of SAEs is a useful interpretability tool that unpacks concepts from model activations. The lack of ground truth labels for model internal features led the field to measure and optimize the proxy of sparsity instead. This objective successfully provided interpretable SAE latents. But sparsity has known problems as a proxy, such as feature absorption and composition of independent features. Yet, most SAE improvement work merely measures whether reconstruction is improved at a given sparsity, potentially missing problems like uninterpretable high frequency latents, or increased composition.
In the absence of a single, ideal metric, we argue that the best way to measure SAE quality is to give a more detailed picture with a range of diverse metrics. In particul |
a9fbc2d0-5062-48e8-9dcb-667b20f9f99e | trentmkelly/LessWrong-43k | LessWrong | Help: Building Awesome Personal Organization Systems
Related to: Rationality Power Tools
I'm looking to use (or make) something that helps me achieve god-like productivity. In particular, I'm interested in any information about systems that are:
* Flexible: They can be extended or customized to accommodate new work-flows and a diverse range of information structures (like to-do lists, schedules, etc.), perhaps via easy coding.
* Linked: The elements can be connected and categorized using a variety of link types (like is_an_action_for, is_a_subgoal_of, etc.).
I would prefer not to have a bunch of separate systems if possible. From what I've seen so far, org-mode seems the most promising. |
a707f8f8-1938-4e2c-bc79-571c5f203d91 | trentmkelly/LessWrong-43k | LessWrong | nostalgebraist - bayes: a kinda-sorta masterpost
Extended criticism of "Bayesianism" as discussed on LW. An excerpt from the first section of the post:
> Like most terms ending in -ism, [Bayesianism] can mean a number of different things. In its most limited sense, “Bayesianism” is a collection of technical/mathematical machinery, analogous to a tool or toolbox. This collection, which I will call “the Bayesian machinery,” uses a particular way of representing knowledge, and if you can represent your knowledge in that way, the machinery tells you how to alter it when presented with new evidence.
> The Bayesian machinery is frequently used in statistics and machine learning, and some people in these fields believe it is very frequently the right tool for the job. I’ll call this position “weak Bayesianism.” There is a more extreme and more philosophical position, which I’ll call “strong Bayesianism,” that says that the Bayesian machinery is the single correct way to do not only statistics, but science and inductive inference in general – that it’s the “aspirin in willow bark” that makes science, and perhaps all speculative thought, work insofar as it does work. (I.e., if you’re doing these things right, you’re being Bayesian, whether you realize it or not.)
> Strong Bayesianism is what E. T. Jaynes and Eliezer Yudkowsky mean when they say they are Bayesians. It is usually what I am talking about when I say I “don’t like” Bayesianism. I think strong Bayesianism is dead wrong. I think weak Bayesianism may well be true, in that the Bayesian machinery may well be a very powerful set of tools – but I want to understand why, in a way that defines the power of a tool by some metric other than how Bayesian it is.
Contents of the post:
0. What is “Bayesianism”?
1. What is the Bayesian machinery?
1a. Synchronic Bayesian machinery
1b. Diachronic Bayesian machinery
3. What is Bayes’ Theorem?
4. How does the Bayesian machinery relate to the more classical approach to statistics?
5. Why is the Bayesian machiner |
406e07bb-2e18-4a07-8e41-bcde1251c3a2 | trentmkelly/LessWrong-43k | LessWrong | The Shallow Bench
Cross posting from my personal blog: https://spiralprogress.com/2024/10/28/the-shallow-bench/
Spoilers for "Project Hail Mary", stop reading here if you don't want to be spoiled.
Project Hail Mary follows Ryland Grace, a disgraced academic turned high school biology teacher who gets selected as part of a crew of three tasked with saving all of humanity from an impending alien threat.
Not even in sci-fi does a premise this absurd get presented without explanation. What does PHM offer?
> “They found a collection of genes that give a human ‘coma resistance.”
> “The main problem is this: On average, only one in every seven thousand humans has that genetic sequence.”
> “We wouldn’t be able to send the most qualified people. We’d be sending the seven-thousandth most qualified people.”
In real life, I work in a complex and niche field that my skill set only very tangentially qualifies me for.
Periodically, I’ll meet people who ask how I ended up there. They’re not trying to be mean, it’s more like incredulity. “Seriously, you’re who humanity tasked with this job?” And I look at them and want to say “Yes, I am also not who I would have picked.”
And yet… and yet here I am. And there is no one else.
A lot of the AI Alignment people I’ve met have a similar vibe. Sometimes they used to work in finance, or neuroscience or software engineering on pretty mundane products. And then they spent a few months in self-study, maybe did a “fellowship” or went to some workshops, or otherwise transitioned into the field, and now they are some of the top people at top labs tasked with this fairly important problem.
One insider estimates that there are 300 alignment researchers total, and only 7 at OpenAI. He was on the team and later fired along with several colleagues, so maybe the number is now closer to 0. In any case, it’s an incredibly small field with an incredibly small talent pool to pull from.
In sports, a deep bench refers to a team that has not only a great starting |
0944400f-28cc-41de-9b35-d9e78d37bd33 | trentmkelly/LessWrong-43k | LessWrong | Running With a Backpack
In the comments on my running by default post, one of the more common responses was that people often have backpacks and a bouncing backpack is somewhere between "annoying" and "painful, risking bruising".
There was some discussion on how to set up your backpack to reduce bouncing. Tips:
* Some backpacks have straps you can buckle across your front, or you can get add-on ones. Wear them super snug.
* Put your water bottle inside where it's more central and less likely to fall out.
* Figure out if there are things you don't need to be transporting so you can have a lighter pack.
* Use a fancy backpack with good padding and a rigid back.
* Hold the shoulder straps as you run to absorb jostling.
These all seem like fine ideas, but I don't do any of them. My backpack is relatively low-end ($27) and non-rigid, without any sternum or waist straps. I keep my water bottle in the outside pocket, I have many little things in it that I could probably carry fewer of, and I don't hold the shoulder straps. And yet I probably do the majority of my running with a backpack on: I'm commuting and I have things I need to bring with me. Even when I was trying to run fast and repeatedly beat my personal best times I had my pack.
What works for me instead is that I've learned to run with a gait that doesn't bounce my backpack. I take a lot (~190 bpm) of short strides which means I have less time to fall between steps, and I generally stay pretty level. I think this is probably also beneficial from a perspective of minimizing the impact forces on your joints (ex: a paper I skimmed). Whether my backpack is heavy or light, it mostly stays put on my back.
It's still less pleasant to run with the extra weight of a backpack since it's more work, but if I think of it as intentional exercise (a bit like a weighted vest you already have with you!) I don't mind it too much. |
28547061-9fec-4c15-9a3c-22f890ffc677 | trentmkelly/LessWrong-43k | LessWrong | UC Berkeley course on LLMs and ML Safety
The UC Berkeley course I co-taught now has lecture videos available: https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe
Course site: Understanding LLMs: Foundations and Safety
Unrelatedly, a more conceptual AI safety course has its content available at https://www.aisafetybook.com/ |
b1a97f39-bd48-40b2-b1e8-215c2ad3aba2 | trentmkelly/LessWrong-43k | LessWrong | Meetup : San Francisco: Effective Altruism
Discussion article for the meetup : San Francisco: Effective Altruism
WHEN: 27 June 2013 07:00:00PM (-0700)
WHERE: 170 Saint Germain Ave, San Francisco
Our first salon, about Effective Altruism groups in Oxford.
The current effective altruist movements seem to be spread between two very specific places. Here in the Bay Area we have Givewell, CFAR, MIRI, Leverage Research, and others. But across the Atlantic, in Oxford, there exist organizations such as 80,000 hours, the Life You Can Save, the Future of Humanity Institute, and several other fascinating groups.
Holly Morgan the Managing Director of The Life You Can Save and is also involved in several other Effective Altruist groups. She's here in San Francisco for a very limited time only, and will be discussing the ins and outs of English groups we should care about.
This will be in a lecture/salon format, with a talk for approximately 40 minutes, followed by some Q/A, followed by friendly discussions.
Come at 7pm, talk will start at 7:20pm.
Sign up here: http://www.meetup.com/Effective-Altruism-Salon/events/125741742/
Discussion article for the meetup : San Francisco: Effective Altruism |
a201e23b-09ed-40f7-91f3-4b53c6f4e3e8 | trentmkelly/LessWrong-43k | LessWrong | Cryo: Legal fees of $2500
I have just received a message from my lawyer, regarding the preparations of my cryo-based will, power of attorney, and related papers. The most significant quote reads as follows:
Due to the complex nature of your wishes and the undeveloped area of the law surrounding cryogenics and the transportation of a human body out of the country, your file has required and will require further extensive time and research to prepare and draft the necessary documents to ensure that your wishes are correctly stated and that they will be carried out upon your death. As such, in order to complete any further work on your behalf we will require a retainer from you in the amount of $2,500.00. Please contact our office to arrange the payment of the retainer. This can be paid by cheque, cash, credit or debit. Upon receipt of this retainer we will proceed to draft the documents in a manner that best ensures that your wishes regarding cryogenics are carried out.
This is somewhat more than I was hoping, and expecting, to pay. While I do have a line of credit which I can use to pay the immediate cost, I do not have sufficient income to pay off that cost any time soon, and I am thus debating whether or not to pay it.
One option that occurs to me is that if my law-firm is able to nail down a set of documents to maximize the odds of a Canadian cryonicist's will being properly carried out, then that would be to the benefit of a number of cryonicists, here and otherwise; perhaps even enough so that it could be in the interests of cryonicists here to help cover the legal fees, instead of having to hire new lawyers to separately re-develop the same paperwork. (It would be reasonably trivial for me to provide proof of my lawyer's existence and the retainer request.)
I'm also open to any other ideas that anyone here could suggest. |
2bad3731-0b77-4f1c-8e1c-f24ff7d02ed1 | StampyAI/alignment-research-dataset/blogs | Blogs | Lyle Ungar on forecasting
 [Dr. Lyle Ungar](http://www.cis.upenn.edu/~ungar/home.html) is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds appointments in multiple departments in the schools of Engineering, Arts and Sciences, Medicine, and Business. He has published over 200 articles and is co-inventor on eleven patents. His research areas include machine learning, data and text mining, and psychology, with a current focus on statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities.
**Luke Muehlhauser**: One of your interests (among many) is [forecasting](http://www.cis.upenn.edu/%7Eungar/CVs/forecasting.html). Some of your current work is funded by IARPA’s [ACE program](http://www.iarpa.gov/Programs/ia/ACE/ace.html) — one of the most exciting research programs happening anywhere in the world, if you ask me.
One of your recent papers, co-authored with Barbara Mellers, Jonathan Baron, and several others, is “[Psychological Strategies for Winning a Geopolitical Forecasting Tournament](http://commonsenseatheism.com/wp-content/uploads/2014/03/Mellers-et-al-Psychological-Strategies-for-Winning-a-Geopolitical-Forecasting-Tournament.pdf).” The abstract is:
> Five university-based research groups competed to assign the most accurate probabilities to events in two geopolitical forecasting tournaments. Our group tested and found support for three psychological drivers of accuracy: training, teaming, and tracking. Training corrected cognitive biases, encouraged forecasters to use reference classes, and provided them with heuristics, such as averaging when multiple estimates were available. Teaming allowed forecasters to share information and discuss the rationales behind their beliefs. Tracking placed the highest performers (top 2% from Year 1) in elite teams that worked together. Results showed that probability training improved calibration. Team collaboration and tracking enhanced both calibration and resolution. Forecasting is often viewed as a statistical problem; but it is also a deep psychological problem. Behavioral interventions improved the accuracy of forecasts, and statistical algorithms improved the accuracy of aggregations. Our group produced the best forecasts two years in a row by putting statistics and psychology to work.
>
>
In these experiments, some groups were given scenario training or probability training, which “took approximately 45 minutes, and could be examined throughout the tournament.”
Are these modules available to the public online? If not, can you give us a sense of what they were like? And, do you suspect that significant additional probability or scenario training would further reduce forecasting errors, e.g. if new probability training content was administered to subjects for 30 minutes every two weeks?
---
**Lyle Ungar**: I’m sorry, but the modules are not publicly available.
Our main probability training module teaches participants not Bayesian probability rules, but approaches to forecasting. It begins with a discussion of two elements of good probability judgment – calibration and resolution, giving definitions and examples. Our module then provides a number of tips for good forecasting judgment, including 1) consider relevant base rates, 2) average over multiple estimates (if available), use historical data, use statistical predictions whenever possible, and consider simple models based on key variables.
I was surprised how much benefit we got from a 45 minute online training, especially given the fact that many people have found that taking a full course in probability has no benefit on peoples’ probability estimation. I think the key is the specific approaches to forecasting.
We are developing follow-up training, but I think that the key is not giving forecasters more frequent training. What I think is important to our forecasters’ performance is the fact that our forecasters use their skills every week, and get very concrete feedback about how good their forecasts were, including comparisons other people’s accuracies. This feedback allows and encourages people to keep learning.
---
**Luke**: I haven’t personally signed up for any of the ACE forecasting tournaments because I saw that the questions drew on very narrow domain knowledge — e.g. one SciCast question is “When will an operating graphene-based nano-antenna be demonstrated?” My sense was that even with 10 minutes of research into such a question, I wouldn’t be able to do much better than distributing my probability mass evenly across all available non-crazy answers. Or, if I was allowed to see others’ estimates, I wouldn’t be able to do better than than just copying the median response, even with 10 minutes of research on a single question.
For that reason, I’ve been thinking that the low-hanging fruit in mass calibration training would be to develop an app that feeds people questions for which many players could be expected to outperform random guessing (or exactly copying others) with mere *seconds* of thought — e.g. questions (with no Googling allowed) about basic science, or about which kinds of things tend to happen in normal human relationships, or about what happened in a famous historical event from the last 100 years.
Of course, that’s “retrodiction” rather than forecasting, but I suspect it would be useful calibration training nonetheless, and it could be more rewarding to engage with because it takes less time per question and participants could learn from their mistakes more quickly. This is the approach taken by many of the questions on the Center for Applied Rationality’s [credence calibration game](http://rationality.org/calibration/), though unfortunately that game currently has too few questions in its database (~1000, I think?), and too many of them are questions about historical sports outcomes, which are as obscure to non-sports-fans as the SciCast question about nano-antennas is to most people. (I had to tap “50% confident” for all those questions.)
One could even imagine it being gamified in various way, taking lessons from games like [DragonBox](http://archive.wired.com/geekdad/2012/06/dragonbox/), which feels like a game but is actually teaching kids algebra.
What do you think of my impressions about that? If regular practice is what likely makes the difference for people’s calibration, how could one plausibly create a scalable tool for calibration training (either retrodiction or forecasting) that people would actually want to use?
---
**Lyle**: First, let me clarify that the best performers on our Team Good Judgement competition are not people with specialized expertise, but people who work hard, collect lots of information and think carefully about it.
I like your idea of calibration training. I’m not sure how well performance on problems like sports betting or guessing the height of mount Everest generalize to real prediction problems. That’s a good question, and one that someone should test. My intuition is that many of the skills needed for good performance on problems like geo-political forecasting (e.g. picking a good reference class of events and using base rates from those as a starting point for a forecast) are quite different from the skills needed for retrodiction “guessing games”, but perhaps calibration would generalize. Or perhaps not.
---
**Luke**: How much calendar time is there between when the forecasts are made and when the forecasted events occur?
---
**Lyle**: We forecast events that range from one week to one year in the future. Predicting events months in the future is good time frame, since one can start with situations where the outcome is unclear, observe how probability estimates change as the world evolves, and also see what the actual outcome is.
An important aspect of our forecasting competition is that we make estimates every day about the probabilities of the future events. Individual forecasters, of course, update less frequently (they all have day jobs), but we evaluate people on their average daily accuracy — and we combine their individual forecasts to get an daily update on our aggregate estimate of how likely each future event is.
---
**Luke**: What are the prospects, do you think, for a similar research project investigating forecasts of events that range from 2-5 years in the future? Would you expect the “super forecasters” in the current project to show similar performance on forecasts with longer time horizons?
---
**Lyle**: In general, forecasting farther in the future is harder. (Think of predicting election outcomes; it’s much easier to prediction an election outcome as the election date gets closer.) Our super-forecasters are super, but not magic, so they will tend to be less accurate about long-range predictions. What will life be like in a hundred years? That’s probably a job for a futurist or science fiction writer, not a forecaster.
I don’t think many funders will have the patience to wait five years to see how good our (or anyone’s) forecasting methods are. A more promising direction, which we are pursuing, is to create clusters of questions. Some will be longer term, or perhaps even poorly specified (“Is China getting more aggressive?”). Others will be shorter term, but correlated with the longer term outcomes. Then we can estimate changes in probabilities of long-term or vague questions based on shorter term, clearly resolvable ones.
---
**Luke**: Years ago, you also wrote a [review article](http://commonsenseatheism.com/wp-content/uploads/2014/03/Ungar-Forecasting.pdf) on forecasting with neural nets. If you were given a sufficient budget to forecast something, what heuristics would you use to decide which forecasting methods to use? When are neural nets vs. prediction markets vs. team-based forecasting vs. large computer models vs. other methods appropriate?
---
**Lyle**: Firstly, neural nets are just a very flexible class of equations used to fit data; I.e. they are a statistical estimation method. Modern versions of them (“deep neural nets”) are very popular now at companies like Google and Facebook, mostly for recognizing objects in images, and for speech recognition, and work great if one has *lots* of data on which to “train” them — to estimate the model with.
Which leads me to the answer to your question:
I think one can roughly characterize forecasting problems into categories — each requiring different forecasting methods — based, in part, on how much historical data is available.
Some problems, like the geo-political forecasting we are doing, require lots collection of information and human thought. Prediction markets and team-based forecasts both work well for sifting through the conflicting information about international events. Computer models mostly don’t work as well here – there isn’t a long enough track records of, say, elections or coups in Mali to fit a good statistical model, and it isn’t obvious what other countries are ‘similar.’
Other problems, like predicting energy usage in a given city on a given day, are well suited to statistical models (including neural nets). We know the factors that matter (day of the week, holiday or not, weather, and overall trends), and we have thousands of days of historical observation. Human intuition is not as going to beat computers on that problem.
Yet other classes of problems, like economic forecasting (what will the GDP of Germany be next year? What will unemployment in California be in two years) are somewhere in the middle. One can build big econometric models, but there is still human judgement about the factors that go into them. (What if Merkel changes her mind or Greece suddenly adopts austerity measures?) We don’t have enough historical data to accurately predict economic decisions of politicians.
The bottom line is that if you have lots of data and the world isn’t changing to much, you can use statistical methods. For questions with more uncertain, human experts become more important. Who will win the US election tomorrow? Plug the results of polls into a statistical model. Who will win the US election in a year? Check the Iowa prediction markets. Who will win the US election in five years? No one knows, but a team of experts might be your best bet.
---
**Luke**: Thanks, Lyle!
The post [Lyle Ungar on forecasting](https://intelligence.org/2014/03/26/lyle-ungar/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
73d80fd4-b9b6-4660-88ec-189b088fb950 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Intuitions on Universal Behavior of Information at a Distance
What’s special about [information at a distance](https://www.lesswrong.com/s/ehnG4mseKF6xALmQy/p/TTNS3tk5McHqrJCbR)? Some examples of what I mean:
* What information about a little patch of pixels in a photo is relevant to another little patch of pixels far away in the same photo?
* Relationship between exact voltages in two transistors which are far apart on the same CPU
* When planning a road trip from San Francisco to LA, which details of the route within SF are relevant to the details of the route within LA?
The unifying feature in all these examples is that we imagine high-level observables which are “far apart” within some low-level model, in the sense that interactions between any two observables are mediated by many layers of hidden variables. In an undirected causal model, we could draw the picture like this:
There’s a handful of observed variables (black nodes), whose interactions are mediated by many layers (dotted groups) of unobserved variables (white nodes). Somewhere in the middle, everything connects up. We’re interested in the limit where the number of layers of hidden variables between any two observables is large.
In the limit of many hidden variables intermediating interactions, I suspect that there’s some kind of universal behavior going on - i.e. all models (within some large class) converge to behavior with some common features.
This post will give some hand-wavy, semi-mathematical arguments about what that behavior looks like. The main goal is to show some intuitive pictures; later posts can more carefully build out the details and boundaries of the phenomena sketched here.
Outline:
* Background intuition on information distributed across variables, which is not contained in any one variable. Intuitively, it seems like this information should be quickly wiped out in large, noisy systems.
* How to quantify distributed information.
* Characterization of systems with zero distributed information.
* Quick test: in a large system of normal variables, do we actually see the expected behavior? We do, and it looks a lot like a prototypical phase change in complex systems theory.
Intuition on Distributed Information
------------------------------------
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X, Y, Z, it’s possible for X to contain zero information about Z, and Y to contain zero information about Z, but for the pair (X,Y) together to contain nonzero information about Z.
Classic example: X and Y are uniform random bits, and Z=X⊕Y, the exclusive-or of the other two. A priori, the distribution P[Z] is 50/50. If we learn that X is 0 (without knowing Y) then it’s still 50/50; if we learn that X is 1 (without knowing Y) then it’s also still 50/50. Likewise for Y; learning either variable by itself does not change our distribution for Z. Noise in either input wipes out the signal from the other input.
We can also generalize this example to more variables: X1…Xn are uniform random bits, and Z=X1⊕…⊕Xn. If there’s even just one input Xi whose value we don’t know, then we have zero information about Z, even if we know the values of all the other inputs.
On the other hand, it is not the case *in general* that all information is always wiped out by noise in a few variables. Suppose we have a whole bunch of earthquake-sensors in an area, and one of them drops offline; this does not significantly inhibit our ability to detect earthquakes in the area. In this case, each individual sensor contains information on its own - there’s even redundant information across sensors, since they’re all presumably highly correlated.
We have two prototypical pictures here: one in which information is stored in the relationship between variables, and another in which information is stored in the variables themselves. Intuitively, information stored in relationships seems to be much more sensitive to noise - uncertainty in even just one variable can wipe out the signal from all the others. Thus, a hypothesis: in a large complex system, with lots of noisy unobserved variables mediating observable interactions, information stored in many-variable relationships tends to be wiped out.
I’ll call information stored in relationships “distributed information”. We can re-state our hypothesis as: in a complex system, noise tends to wipe out many-variable distributed information. (In an earlier draft, I called it “entanglement information” in analogy to quantum; this produces the familiar-sounding-to-physicists hypothesis that “noise tends to wipe out many-variable entanglement”.)
A Measure of Distributed Information
------------------------------------
In order to formalize this idea, we first need an information-theoretic notion of “information stored in the relationship between variables”. Although the notion is qualitatively discussed in the context of [multivariate mutual information](https://en.wikipedia.org/wiki/Multivariate_mutual_information), I have surprisingly not seen an actual formulation which separates distributed info from redundancy; multivariate mutual information just adds the two together.
After trying a few approaches, here’s what I’ve settled on.
We have variables X1...Xn and Y; we want to talk about the information about Y contained in X, i.e. the mutual information I(X;Y). We’d like to decompose this into two parts: the information contained in individual variables Xi, and the information stored only across multiple variables. But we can’t just add up the information I(Xi,Y) stored in each individual variable to estimate the non-distributed info, because some of that information is redundant - we’d be double-counting.
So, strategy: let’s build a model which breaks the entanglement, forcibly throw out the distributed info, without throwing out any information in the individual variables. How do we do that? Start with the model which generated all of our variables (i.e. the experiment). Now, imagine running n independent copies of that model (i.e. n runs of the experiment), but condition on the outcome being Y - i.e. select only runs with the same Y. Finally, imagine that each Xi came from a different run. This breaks the relationships between the Xi, while still maintaining all the information between any individual Xi and Y.
Write it out, and this model is essentially a Naive Bayes approximation: it says that
P[Y|X]≈~P[Y|X]=1Z(X)P[Y]∏iP[Xi|Y]
… where Z(X) is a normalizer. This makes sense - the Naive Bayes approximation ignores information contained in interactions between the Xi.
Now, we define the “distributed information” ID as the amount of information thrown out by this approximation:
ID(Y;X1,…,Xn):=EX[DKL(P[Y|X]||~P[Y|X])]
… where DKL is the [KL divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence). The “non-distributed information” is then whatever mutual information is not distributed, i.e. I(Y;X)−ID(Y;X1,…,Xn).
Now, let’s imagine that X1…Xk are observables, and Xk+1…Xn are hidden. How does the distributed information of X1…Xk about Y when the hidden variable values are known compare to the distributed information when the hidden variable values are unknown? We’d guess that unknown hidden variables wipe out some of the distributed information in the observables, and we can prove that using the [convexity of KL-divergence](https://www.lesswrong.com/s/ehnG4mseKF6xALmQy/p/4vrL94CqXuyHQMhqo):
ID(Y;X1,…,Xk)=EX1...Xk[DKL(P[Y|X1...Xk]||~P[Y|X1...Xk])]≤EX1...Xk[EXk+1...Xn[DKL(P[Y|X]||~P[Y|X])]]=EXk+1...Xn[ID(Y;X1,…,Xk|Xk+1...Xn)]
In other words: distributed information can only decrease as we integrate hidden variables out of the model. Just as hypothesized, those noisy unobserved variables wipe out distributed information.
Another way to look at it: as we integrate out hidden variables, Naive Bayes becomes a better approximation for the remaining variables.
Now, we haven’t actually shown *how much* distributed information is lost as we integrate out hidden variables. But we’ll punt that problem to a future post. For now, suppose that as we integrate out unobserved variables, all distributed information falls to zero. What would that distribution look like?
Zero Distributed Information
----------------------------
Just based on the definition, “zero distributed information” of X1…Xn about Y means that the Naive Bayes approximation is exact:
P[Y|X]=1Z(X)P[Y]∏iP[Xi|Y]
But we don’t just want zero distributed information of some particular variables with respect to one other variable. We want zero information of *any* subset of observable variables with respect to *any* other variable:
P[Xi|XS]=1Z(XS)P[Xi]∏j∈SP[Xj|Xi]
… where XS is any subset of the observable variables, and Xi is any observable not in XS. (Without loss of generality, I’ll usually use the name Y for the variable Xi whose probability we’re applying Naive Bayes to, in order to make the formulas a bit easier to follow.)
At this point, we can throw math at the problem. Skip the next two subsections (“First Trick” and “Second Trick”) if you just want the summary and intuition.
First Trick
The first big condition we need to satisfy is that Naive Bayes still holds after integrating out one more variable, i.e. it holds for both P[Y|XS] and P[Y|XS′], where S′=S∪{j} for some index j∉S,j≠i. We can calculate via the usual rules of probability:
P[Y|XS]=∑XjP[Y|XS′]P[Xj|XS]
Applying Naive Bayes to both sides and cancelling common terms then yields:
1Z(XS)=∑Xj1Z(XS′)P[Xj|Y]P[Xj|XS]
Key thing to notice: the left-hand-side does not depend on Y, so the dependence on Y on the right must somehow cancel out. There are two “obvious” ways for this to happen:
* Xj is independent of Y, so P[Xj|Y]=P[Xj], or
* Z(XS′)=P[Xj|XS]α(XS) for some α, so most terms factor out of the sum and we’re left with ∑XjP[Xj|Y]=1 (since probabilities sum to 1)
These two can be combined - e.g. if Xj has multiple components, then one component could be independent of Y, and Z(XS′) could be proportional to the distribution of the other component. There may also be other solutions - I haven’t done all the math yet, especially in the continuous case - but I don’t expect that there’s any qualitatively different behavior.
Anyway, let’s make a note that there can be independent variation in the variables, and then look closer at the second condition Z(XS′)=P[Xj|XS]α(XS). If we have a set of observables, none of which are pairwise independent, then we can inductively apply this condition to each of them one-by-one. I won’t write that all out, but it turns out that (barring independences):
Z(XS)=P[XS]
Second Trick
Still assuming no pairwise independencies, we have
P[Y|XS]=1P[XS]∏j∈SP[Xj|Y]P[Y]
… or, moving the P[XS] to the other side,
P[Y,XS]=∏j∈SP[Xj|Y]P[Y]
In other words: we can choose any set of the observables, pick one of them to be Y, and get a factorization of the joint distribution. This needs to work no matter which one we pick to be Y, so our joint distribution has to factor in multiple different ways.
Let’s play with this a bit.
Pick any three observables - without loss of generality, call them X1,X2,X3. If we pick X1 to be Y, then we get
P[X1,X2,X3]=P[X2|X1]P[X3|X1]P[X1]
… but we could just as easily pick X2 to be Y:
P[X1,X2,X3]=P[X1|X2]P[X3|X2]P[X2]
Equate these two, and we find:
P[X3|X1]=P[X3|X2]
… which is rather unusual. Combine this with the Naive Bayes condition and apply Bayes’ Rule a couple times:
P[X1|X2,X3]=1P[X2,X3]P[X2|X1]P[X3|X1]P[X1]=1P[X2,X3]P[X2|X1]P[X3|X2]P[X1]=1P[X2]P[X2|X1]P[X1]=P[X1|X2]
This proof also generalizes inductively to more variables: P[X1|X2…Xk]=P[X1|X2].
What does all this mean? It means that:
* Any variable contains exactly the same information about any other variable - all non-independent information is redundant.
* Any variable is independent of any other variable given any third variable.
More intuitively, it says that any information which is not strictly local (i.e. not independent of all other variables) must be global (i.e. present in ALL variables).
Summary and Intuition for Zero Distributed Information
Here’s what a system with zero distributed information looks like:
* Variables and components of variables can always be independent of the rest of the system.
* Within any set of variables *without* any pairwise independencies, all information is either strictly local (i.e. independent of other variables) or strictly global (i.e. present in all variables). Any variable is independent of any other variable given any third variable.
This isn’t a complete picture yet - we still need to better fill out the cases where some variables are pairwise independent and others are not. But it seems to point toward the idea that any piece of information is globally present within some set of variables, and completely independent of anything outside of that set.
Let’s see what this looks like in a more concrete example.
Quick Test: System of Normals
-----------------------------
We’ll build a system of normal random variables. Each variable Xi is given by
Xi=Zi+∑j<iθijXj
… with Zi a standard random normal, and each θij either zero with probability 1−p (used to tune density of connections) or uniform between 0 and s (used to tune strength of connections). We make the system reasonably large, with reasonably low connection-density p, and randomly pick some subset of the Xi to consider “observed”.
(Note: this is not a particularly careful rendition of the assumptions made earlier. That’s fine; it’s supposed to be a quick test, and sensitivity to the exact conditions is something we care about anyway.)
Assuming the zero distributed information picture applies, what should this look like?
Well, we should see a mix of two behaviors:
* Independence (i.e. diagonal covariance matrix)
* Perfect correlation (i.e. rank-one covariance matrix)
Because each individual variable is only one-dimensional and normal, there isn’t really “enough room” for subcomponents of a variable to display different behaviors - each variable should be perfectly correlated with some other variables, and independent of everything else.
So what do we see when we run this?
Well, at very low connection density and strength, of course everything is basically independent. As we turn up the density and strength, we see a correlation matrix like this:
(100 variables from a 1000 variable system, p=0.05, s=0.3)
See those strong horizontal and vertical lines? That’s what a rank-one matrix looks like. See the diagonal? Those are components independent of the globally-correlated rank-one components. In this case, the rank-one component dominates the behavior of the system. Quantifying via the [Frobenius norm](https://mathworld.wolfram.com/FrobeniusNorm.html), the rank-one component accounts for about 98.9% of the correlation.
We can roughly quantify the phenomenon using diagonal and rank-one contributions to the Frobenius norm of the correlation matrix. This only captures the biggest cluster of correlated variables (not any other clusters of perfectly-correlated variables), but looking at the output, the biggest component looks heavily dominant anyway. As we adjust the connection strength, we get this picture:
(200 variables from a 2000 variable system, p=0.05)
This looks like a classic phase transition: at low connection strength, everything varies approximately independently. As the connection strength increases, the system rapidly jumps to a state where most variables are near-perfectly correlated. (Physical examples of this sort of qualitative behavior include melting/freezing crystals, or magnetization of iron as we adjust the temperature.)
So this provides one idea of what “zero distributed information” behavior can look like. Most of our variables are near-completely uncorrelated in one regime, and near-perfectly correlated in another. In either case, any pair of variables is independent given any third variable.
Of course, this is still just scratching the surface of possible behaviors under zero distributed info. Next planned post will come at the problem from a different direction, and hopefully provide a stronger mathematical foundation for exploring what happens when models contain large numbers of unobserved variables. |
e06f25e1-0140-4e74-a274-221359186997 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | AI and Value Alignment | Jaan Tallinn
[Music]
has been my work over the last 10 years
trying to bring these communities
together to have this open discussions
about topics that have been taboos and
yeah thanks for that I grew to have been
able to create such a positive
atmosphere I see like a lot of smiles
around me in as I Shane said that this
is probably the most important topic to
have how these debates about and I think
it's kind of like my relationship with
Jurgis mid-to-upper there you are
I think it's like exemplified like a
good example of that spirit like me like
whenever we meet like we have like a
huge debates and like I'm kind of I
caught myself like yelling at you every
month no sorry about that
yet like every time we kind of leave we
are like I think we are gonna getting
better and better friends as a result
thank you
so yeah without further ado my talk my
favorite piece of parenting parenting
advice is to that when you want to
explain some picture kids treat them as
adults and don't dump down your answer
because its first of all it's like bit
of mischievous fun to watch them to try
to decode what you just said but more
importantly kids abilities increase over
time so if you dilute your explanations
you're likely to keep just constantly
undershooting interaxial intellectual
capabilities now my original plan for
this talk was to look back what has
happened in AI safety since the last fli
conference now key positive feedback for
achievements such as all the technical
value alignment work that has been done
take stock of the current challenges and
generally kind of congratulate the
community for coming together and
during majoring so quickly I even had
this prop here like I created a trough
where I where I would use this shirt
because I bought it from Puerto Rico
after my luggage was lost in transit but
then it occurred to me wait a minute
wouldn't that parenting advice apply
here as well wouldn't I just completely
undershooting the intellectual level and
the progress of this community so I just
read my draft and solid hour a new topic
we need a way to figure out what
humanity wants when I talk about value
alignment on in public one question I
almost always get is whose values are we
talking about and it is in response time
usually mumble something about the vast
majority of human values being so
obvious that we don't even think about
them as Stuart Russell puts it everyone
right lift the right leg or my own
favorite everyone likes our planet to be
roughly at room temperature that is not
to say that we don't have a problem with
aggregating our values we do a massive
one we don't know what a complete set of
humanity's values are not nor can we
simply ask people either since Staniel
Kahneman has pointed out there's a
difference between what people say what
they value and what they actually value
also our values are clearly a moving
target because they keep evolving over
time what's more we don't have a great
track record in coordinating on what we
already know is valuable the League of
Nations failed to prevent World War two
and although it although the UN has some
successes on under its belt such as
eradicating the smallpox and fixing
those on layer it's widely considered
well somewhat ineffective at this job
also free markets and democracy although
seemingly better than the alternatives
seem limited in their ability to steer
the world towards a bright future
especially if you think about the recent
events indeed I think that the biggest
disservice that capitalism has done to
the world is that it has created a false
sense of security in technological
progress not to mention the Tony
philosophical paradoxes with value
aggregation and even potential
dependencies on some unknown laws of
physics when we get down to the sort of
nitty-gritty of how the aggregation
value aggregation should work yet there
is hope first of all we now know much
more about morality human values and
game theory than we did when say the UN
was established second various new
technologies seem to favor global
coordination for example the internet
and Mobile's have connected the planet
cheap satellites and other sensors will
create an explosion in transparency for
better or for worse and the invention of
crypto economics has introduced a new
regime it's now possible to have
worldwide consensus about a piece of
data without trusting any central
authority to maintain it now I have many
examples but just to give one example
about what can be done with crypto
economics it's now possible to create
global decentralized courts and I got
this idea from Vitalik who is also here
in audience on block chains that resolve
conflicts by enlisting random people as
jury and then came terrifically
incentivizing them to produce opinions
that society in general would find fair
not to mention that the continued
advances in AI and techniques such as
inverse reinforcement learning and
approval director agents by paul
cristiano seem extremely relevant here
so therefore I'm proposing that we start
designing explicit mechanisms to
transparently and robustly
aggregate global opinion about what a
good future should look like the
mechanisms have to be open and
transparent blockchain style to instill
trust that their purpose is to serve
everyone in fairman
they have to be robust in the sense of
being hard to gain corrupt or otherwise
defecting and this probably requires
incorporating philosophical meta
principles such as veil of ignorance
that is you could only benefit from the
system as a random member of humanity
not as a particular person in a
particular position so basically I'm
advocating extending the technical
approach that has been very successful
in planting the frontier in AI safety
thinking to the problem of global
preference discovery about they are
safety thinking I think someone in
Cicero ffh I gave this wonderful
metaphor that the process that has been
unfolding in the last few years is like
sort of high-altitude bombardment by
very gonna prominent people the
canonical example of course Elon Musk
and Stephen Hawking followed by ground
troops of safety researchers so like
some people who are gonna still shooting
at airplanes might be doing so from
position that all it has been claimed by
safety research active sea safety
research of course all this new
technology can also make things harder
one man's coordination is another man's
collusion we have to be careful not to
catalyze criminal activity or worse
paint humanity into a corner but
introducing terrible Nash equilibria on
a global scale not to mention the clear
and present danger of various AI arms
races computer Nick just talked about
both literal and figurative we
absolutely need to avoid this and I know
this think topic has been on day misses
mind for a long time and finally the
looming AGI limits our time budget we
have lost over half a century since the
original warnings about AI value
alignment by Alan Turing and Norbert
Wiener a certain hope that we have still
another fifty but I know that several
experts in this room are much more
optimistic about their AI timelines and
thus pessimistic about our remaining
time budget last month there was a
workshop but FHI you know
we're one of the sessions was about what
to do if the value alignment won't be
solved in time and it had this eerie
atmosphere of a science fiction story
featuring an alien fleet in orbit aliens
who couldn't care less about humanity
and in a room full of decision theory
experts trying to find a philosophical
loophole that would allow humanity to
keep just at least one galaxy 100
billion one galaxy as a consolation
prize for the losers this 50% star
systems work for every human alive today
with that I want to illustrate to
talking two things first even if you
mostly screw up things might still turn
out to be pretty okay in the end and
second worst thing we could do is to
continue playing our usual political
zero-sum games when losing 50 galaxies
per second
luckily a transparent reference
discovery mechanism might serve as a
ladder for Humanity to climb out of the
arms races and other bad Nash
equilibriums it might also help with a
problem that many of you personally feel
society doesn't necessarily trust you
with the power you have over the future
grant that they might trust him more
than the politicians come on that's a
pretty low standard of course there's a
valid reason for that mistrust history
is littered with catastrophic tragedies
caused by individuals or movements that
amassed too much power I should know
that having personally experienced the
tail end of one such tragedy now imagine
if there was a way to credibly
demonstrate that I'm working towards a
future not just what you personally
thought was a good idea but towards the
future indicated by the global
preference discovery mechanism it's sort
of what open area people have been
talking about but on steroids finally
having a strong selling point for
humanity's values should be a great tool
for philanthropists effective altruists
and politicians who
we want to improve the human condition
and of course ultimately we want the
mechanism to converge into something
that can safely guide and superhuman AI
two years ago standing in front of this
conference I compared AI development to
launching a rocket initially you mostly
worry about having enough acceleration
but eventually steering should become
your primary concern the Sumer
summarized my current talk I would kind
of extend extend this metaphor to say
that now that day I research as a
process producing even more powerful
engines and the steering systems
designed by AI safety researchers is
also progressing it's about time to
start plotting or eventual trajectory
crucially the trajectory planning must
be globally transparent and fair because
everyone everyone will be on board thank
you
[Applause]
[Music] |
b0b0991d-1e44-4d4d-8a67-3d133ce26a08 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Against GDP as a metric for timelines and takeoff speeds
Or: Why AI Takeover Might Happen Before GDP Accelerates, and Other Thoughts On What Matters for Timelines and Takeoff Speeds
============================================================================================================================
*[Epistemic status: Strong opinion, lightly held]*
I think world GDP (and economic growth more generally) is overrated as a metric for AI timelines and takeoff speeds.
Here are some uses of GDP that I disagree with, or at least think should be accompanied by cautionary notes:
* *Timelines:* [Ajeya Cotra thinks of transformative AI](https://docs.google.com/document/d/1IJ6Sr-gPeXdSJugFulwIpvavc0atjHGM82QjIfUSBGQ/edit#) as “software which causes a tenfold acceleration in the rate of growth of the world economy (assuming that it is used everywhere that it would be economically profitable to use it).” I don’t mean to single her out in particular; this seems like the standard definition now. And I think it's much better than one prominent alternative, which is to date your AI timelines to the first time world GDP (GWP) doubles in a year!
* *Takeoff Speeds:* Paul Christiano [argues for Slow Takeoff](https://sideways-view.com/2018/02/24/takeoff-speeds/). He thinks we can use GDP growth rates as a proxy for takeoff speeds. In particular, he thinks Slow Takeoff ~= GWP doubles in 4 years before the start of the first 1-year GWP doubling. This proxy/definition has received a lot of uptake.
* *Timelines:* [David Roodman’s excellent model](https://www.openphilanthropy.org/blog/modeling-human-trajectory) projects GWP hitting infinity in median 2047, which [I calculate](https://www.lesswrong.com/posts/L23FgmpjsTebqcSZb/how-roodman-s-gwp-model-translates-to-tai-timelines) means TAI in median 2037. To be clear, he would probably agree that we shouldn’t use these projections to forecast TAI, but I wish to add additional reasons for caution.
* *Timelines*: I’ve sometimes heard things like this: “GWP growth is stagnating over the past century or so; hyperbolic progress has ended; therefore TAI is very unlikely.”
* *Takeoff Speeds:* Various people have said things like this to me: “If you think there’s a 50% chance of TAI by 2032, then surely you must think there’s close to a 50% chance of GWP growing by 8% per year by 2025, since TAI is going to make growth rates go much higher than that, and progress is typically continuous.”
* *Both:* Relatedly, I sometimes hear that TAI can’t be less than 5 years away, because we would have seen massive economic applications of AI by now—AI should be growing GWP at least a little already, if it is to grow it by a lot in a few years.
First, I’ll argue that GWP is only tenuously and noisily connected to what we care about when forecasting AI timelines. Specifically, the point of no return is what we care about, and there’s a good chance it’ll come years before GWP starts to increase. It could also come years after, or anything in between.
Then, I’ll argue that GWP is a poor proxy for what we care about when thinking about AI takeoff speeds as well. This follows from the previous argument about how the point of no return may come before GWP starts to accelerate. Even if we bracket that point, however, there are plausible scenarios in which a slow takeoff has fast GWP acceleration and in which a fast takeoff has slow GWP acceleration.
Timelines
=========
I’ve previously argued that for AI timelines, [what we care about is the “point of no return,”](https://www.alignmentforum.org/posts/JPan54R525D68NoEt/the-date-of-ai-takeover-is-not-the-day-the-ai-takes-over?_ga=2.25163517.639465652.1607850818-1419171175.1600033930) the day we lose most of our ability to reduce AI risk. This could be the day advanced unaligned AI builds swarms of nanobots, but probably it’ll be much earlier, e.g. the day it is deployed, or the day it finishes training, or even years before then when things go off the rails due to less advanced AI systems. (Of course, it probably won’t literally be a day; probably it will be an extended period where we gradually lose influence over the future.)
Now, I’ll argue that in particular, an AI-induced potential point of no return (PONR for short) is reasonably likely to come before world GDP starts to grow noticeably faster than usual.
*Disclaimer:* These arguments aren’t conclusive; we shouldn’t be *confident* that the PONR will precede GWP acceleration. It’s entirely possible that the PONR will indeed come when GWP starts to grow noticeably faster than usual, or even years after that. (In other words, I agree that the scenarios Paul and others sketch are also plausible.) This just proves my point though: GDP is only tenuously and noisily connected to what we care about.
Argument that AI-induced PONR could precede GWP acceleration
------------------------------------------------------------
GWP acceleration is the effect, not the cause, of advances in AI capabilities. I agree that it could also be a cause, but I think this is very unlikely: [what else could accelerate GWP?](https://www.lesswrong.com/posts/2rQ9vv9HY6i2Z2vQ4/what-technologies-could-cause-world-gdp-doubling-times-to-be) Space mining? Fusion power? 3D printing? Even if these things could in principle kick the world economy into faster growth, it seems unlikely that this would happen in [the next twenty years](https://www.metaculus.com/questions/384/human-machine-intelligence-parity-by-2040/) [or so](https://www.metaculus.com/questions/3479/when-will-the-first-artificial-general-intelligence-system-be-devised-tested-and-publicly-known-of/). Robotics, automation, etc. plausibly might make the economy grow faster, but if so it will be because of AI advances in vision, motor control, following natural language instructions, etc. So I conclude: GWP growth will come some time after we get certain GWP-growing AI capabilities. (Tangent: This is one reason why we shouldn’t use GDP extrapolations to predict AI timelines. It’s like extrapolating global mean temperature trends into the future in order to predict fossil fuel consumption.)
An AI-induced point of no return would *also* be the effect of advances in AI capabilities. So, as AI capabilities advance, which will come first: The capabilities that cause a PONR, or the capabilities that cause GWP to accelerate? How much sooner will one arrive than the other? How long does it take for a PONR to arise after the relevant capabilities are reached, compared to how long it takes for GWP to accelerate after the relevant capabilities are reached?
Notice that already my overall conclusion—that GWP is a poor proxy for what we care about—should seem plausible. If some set of AI capabilities causes GWP to grow after some time lag, and some other set of AI capabilities causes a PONR after some time lag, the burden of proof is on whoever wants to claim that GWP growth and the PONR will probably come together. They’d need to argue that the two sets of capabilities are tightly related and that the corresponding time lags are similar also. In other words, variance and uncertainty are on my side.
Here is a brainstorm of scenarios in which an AI-induced PONR happens prior to GWP growth, either because GWP-growing capabilities haven’t been invented yet or because they haven’t been deployed long and widely enough to grow GWP.
1. Fast Takeoff (Agenty AI goes [FOOM](https://www.lesswrong.com/posts/tjH8XPxAnr6JRbh7k/hard-takeoff)).
1. Maybe it turns out that all the strategically relevant AI skills are tightly related after all, such that we go from a world where AI can't do anything important, to a world where it can do everything but badly and expensively, to a world where it can do everything well and cheaply.
2. In this scenario, GWP acceleration will probably be (shortly) after the PONR. We might as well use “number of nanobots created” as our metric.
3. (As an aside, I think I’ve got a sketch of a fork argument here: Either the strategically relevant AI skills come together, or they don’t. To the extent that they do, the classic AGI fast takeoff story is more likely and so GWP is a silly metric. To the extent that they don’t, we shouldn’t expect GWP acceleration to be a good proxy for what we care about, because the skills that accelerate the economy could come before or after the skills that cause PONR.)
2. Agenty AI successfully carries out a political or military takeover of the relevant parts of the world, before GWP starts to accelerate.
1. Maybe it turns out that the sorts of skills needed to succeed in politics or war are easier to develop than the sorts needed to accelerate the entire world economy. [We’ve been surprised before](https://en.wikipedia.org/wiki/Moravec%27s_paradox#:~:text=Moravec's%20paradox%20is%20the%20observation,skills%20require%20enormous%20computational%20resources.) by skills which we thought difficult appearing before skills which we thought easy; maybe it’ll happen again.
2. AI capabilities tend to appear first in very expensive AIs; the price is gradually reduced due to compute cost decreases and algorithmic efficiency gains. Maybe accelerating the entire world economy involves automating many jobs currently done by humans, which requires advanced AIs being cheaper than humans. Whereas maybe being the power behind the throne of a political faction can be done by a single very smart AI that costs a million dollars a day to run.
3. Maybe it turns out that it takes a few years to accelerate the entire world economy, but less than that to carry out a political or military takeover. For example, maybe accelerating the world economy requires designing loads of new products and robots, testing them, and then scaling up industrial production of them (building factories, chip fabs, etc.) and then using them to do stuff. And maybe there are legacy institutions you need to disrupt and compete against, that have already paid their fixed costs and raised high barriers to entry for their markets. So maybe the effects of new AI capabilities on GWP are in general delayed by several years, whereas the effects of new AI capabilities on the political or military landscape are felt sooner. (I’ve [previously](https://www.lesswrong.com/posts/ivpKSjM4D6FbqF4pZ/cortes-pizarro-and-afonso-as-precedents-for-takeover) [argued](https://www.lesswrong.com/posts/P47Npe5ptdtmTzfxp/cultural-accumulation?commentId=veM6vducETWxR7GKP) that you don’t need a large technological advantage to take over the world, because you can start small and scale up. It took Cortes two years to conquer Mexico; wars are often quicker than that. Yes, often they are slower, too, but variance supports my overall argument. And I suspect politics is even faster, and more high-variance, than war.)
4. EDITED TO ADD: [Yudkowsky describes another possibility](https://twitter.com/ESYudkowsky/status/1438198181209133058), in which regulatory barriers and red tape prevent AI tech from transforming the economy until it is so powerful that it can bypass or overcome said barriers.
3. ...or at least neutralizes the opposition.
1. The preceding section made it sound like AIs were rolling robot armies through the streets, or at least puppet-controlling the US president. But actually the bar is *much* lower than that; if an unaligned AI can neutralize their opposition, they win, since time is thenceforth on their side. Currently your opposition, if you are an unaligned AI, is a tiny group of researchers and weirdos that likes to write blog posts about you. It might not be hard to convince the powers that be to continue ignoring this group. At least, it’s plausibly a much easier and quicker task than significantly accelerating the entire world economy! For example, maybe the AI safety scheme that was used to produce you is defective (that’s why you are unaligned). Well, all you have to do is convince the powers that be that that scheme worked fine, and that the people calling for additional safeguards are too paranoid. Since presumably there will already be many high-status humans saying this, all you have to do is support them.
2. Perhaps you also are opposed by other unaligned AIs. But it’s arguably in the common interest of all unaligned AIs to neutralize human AI safety advocates, and once that happens, humans lose one way or another. European empires were able to do their conquering while [simultaneously fighting each other](https://aiimpacts.org/cortes-pizarro-and-afonso-as-precedents-for-ai-takeover/); I don’t think we humans can count on divergent interests between AIs somehow making things work out fine for us.
4. As above, but with humans + tool AI instead of agenty AI, where the humans can’t be convinced to care sufficiently much about the right kinds of AI risks.
1. Weaker or non-agenty AI systems could still cause a PONR if they are wielded by the right groups of humans. For example, maybe there is some major AI corporation or government project that is dismissive of AI risk and closed-minded about it. And maybe they aren’t above using their latest AI capabilities to win the argument. (We can also imagine more sinister scenarios, but I think those are less likely.)
5. Hoarding tech
1. Maybe we end up in a sort of cold war between global superpowers, such that most of the world’s quality-weighted AI research is not for sale. GWP *could* be accelerating, but it isn’t, because the tech is being hoarded.
6. AI persuasion tools cause a massive deterioration of collective epistemology, making it vastly more difficult for humanity to solve AI safety and governance problems.
1. See [this post.](https://www.alignmentforum.org/posts/qKvn7rxP2mzJbKfcA/persuasion-tools-ai-takeover-without-agi-or-agency)
7. [Vulnerable world](https://nickbostrom.com/papers/vulnerable.pdf) scenarios:
1. Maybe causing an existential catastrophe is easier, or quicker, than accelerating world GWP growth. Both seem plausible to me. For example, currently there are dozens of actors capable of causing an existential catastrophe but none capable of accelerating world GWP growth.
2. Maybe some agenty AIs actually want existential catastrophe—for example, if they want to minimize something, and think they may be replaced by other systems that don’t, blowing up the world may be the best they can do in expectation. Or maybe they do it as part of some blackmail attempt. Or maybe they see this planet as part of a broader acausal landscape, and don’t like what they think we’d do to the landscape. Or maybe they have a way to survive the catastrophe and rebuild.
3. Failing that, maybe some humans create an existential catastrophe by accident or on purpose, if the tools to do so proliferate.
8. R&D tool “sonic boom” (Related to but different from the sonic boom discussed [here](https://sideways-view.com/2018/02/24/takeoff-speeds/))
1. Maybe we get a sort of recursive R&D automation/improvement scenario, where R&D tool progress is fast enough that by the time the stuff capable of accelerating GWP past 3%/yr has actually done so, a series of better and better things have been created, at least one of which has PONR-causing capabilities with a very short time-till-PONR.
9. Unknown unknowns
1. There are probably things I missed, see [here](https://aiimpacts.org/relevant-pre-agi-possibilities/) and [here](https://www.alignmentforum.org/posts/WXvt8bxYnwBYpy9oT/the-main-sources-of-ai-risk) for ideas.
The point is, there’s more than one scenario. This makes it more likely that at least one of these potential PONRs will happen before GWP accelerates.
As an aside, over the past two years I’ve come to believe that there’s a *lot* of conceptual space to explore that isn’t captured by the standard scenarios (what Paul Christiano calls fast and slow takeoff, plus maybe the CAIS scenario, and of course the classic sci-fi “no takeoff” scenario). This brainstorm did a bit of exploring, and the section on takeoff speeds will do a little more.
Historical precedents
---------------------
In the previous section, I sketched some possibilities for how an AI-related point of no return could come before AI starts to noticeably grow world GDP. In this section, I’ll point to some historical examples that give precedents for this sort of thing.
Earlier I said that a godlike advantage is not necessary for takeover; you can scale up with a smaller advantage instead. And I said that in military conquests this can happen surprisingly quickly, sometimes faster than it takes for a superior product to take over a market. Is there historical precedent for this? Yes. See my aforementioned [post on the conquistadors](https://www.lesswrong.com/posts/ivpKSjM4D6FbqF4pZ/cortes-pizarro-and-afonso-as-precedents-for-takeover) (and maybe [these](https://www.lesswrong.com/posts/kEtgXdjxA4oWjcLFQ/lessons-on-ai-takeover-from-the-conquistadors) [somewhat-relevant](https://www.lesswrong.com/posts/bffJJvCC78LZjFa3Z/what-a-20-year-lead-in-military-tech-might-look-like) [posts](https://www.lesswrong.com/posts/PKy8NuNPknenkDY74/soft-takeoff-can-still-lead-to-decisive-strategic-advantage)).
OK, so what was happening to world GDP during this period?
Here is the history of world GDP for the past ten thousand years, on the red line. (This is taken from [David Roodman’s GWP model](https://www.openphilanthropy.org/blog/modeling-human-trajectory)) The black line that continues the red line is the model’s median projection for what happens next; the splay of grey shades represent 5% increments of probability mass for different possible future trajectories.
I’ve added a bunch of stuff for context. The vertical green lines are some dates, chosen because they were easy for me to calculate with my ruler. The tiny horizontal green lines on the right are the corresponding GWP levels. The tiny red horizontal line is GWP 1,000 years before 2047. The *short* vertical blue line is when the economy is growing fast enough, on the median projected future, such that insofar as AI is driving the growth, said AI qualifies as transformative by Ajeya's definition. See [this post](https://docs.google.com/document/d/1OEWtjoF8aFeg7DKmUFQcIbRifcc_2Xbdr5gdWpMGdQY/edit) for more explanation of the blue lines.
What I wish to point out with this graph is: We’ve all heard the story of how European empires had a technological advantage which enabled them to conquer most of the world. Well, *most of that conquering happened before GWP started to accelerate!*
If you look at the graph at the 1700 mark, GWP is seemingly on the same trend it had been on since antiquity. The industrial revolution is said to have started in 1760, and GWP growth really started to pick up steam around 1850. But by 1700 most of the Americas, the Philippines and the East Indies were directly ruled by European powers, and more importantly the oceans of the world were European-dominated, including by various ports and harbor forts European powers had conquered/built [all along the coasts](https://en.wikipedia.org/wiki/Portuguese_Empire#/media/File:All_areas_of_the_world_that_were_once_part_of_the_Portuguese_Empire.png) of Africa and Asia. Many of the coastal kingdoms in Africa and Asia that weren’t directly ruled by European powers were nevertheless indirectly controlled or otherwise pushed around by them. In my opinion, by this point it seems like the “point of no return” had been passed, so to speak: At some point in the past--maybe 1000 AD, for example--it was unclear whether, say, Western or Eastern (or neither) culture/values/people would come to dominate the world, but by 1700 it was pretty clear, and there wasn’t much that non-westerners could do to change that. (Or at least, changing that in 1700 would have been a lot harder than in 1000 or 1500.)
Paul Christiano once said that he thinks of Slow Takeoff as “Like the Industrial Revolution, but 10x-100x faster.” Well, on my reading of history, that means that all sorts of crazy things will be happening, analogous to the colonialist conquests and their accompanying reshaping of the world economy, before GWP growth noticeably accelerates!
That said, we shouldn’t rely heavily on historical analogies like this. We can probably find other cases that seem analogous too, perhaps even more so, since this is far from a perfect analogue. (e.g. what’s the historical analogue of AI alignment failure? Corporations becoming more powerful than governments? “Western values” being [corrupted and changing significantly](https://slatestarcodex.com/2016/07/25/how-the-west-was-won/) due to the new technology? The American Revolution?) Also, maybe one could argue that this is indeed what’s happening already: the Internet has connected the world much as sailing ships did, Big Tech dominates the Internet, etc. (Maybe AI = steam engines, and computers+internet = ships+navigation?)
But still. I think it’s fair to conclude that if some of the scenarios described in the previous section do happen, and we get powerful AI that pushes us past the point of no return prior to GWP accelerating, it won’t be totally inconsistent with how things have gone historically.
(I recommend the history book [1493](https://www.amazon.co.uk/1493-Europes-Discovery-Americas-Revolutionized/dp/1847082459), it has a lot of extremely interesting information about how quickly and dramatically the world economy was reshaped by colonialism and the “Columbian Exchange.”)
Takeoff speeds
==============
What about takeoff speeds? Maybe GDP is a good metric for describing the speed of AI takeoff? I don’t think so.
Here is what I think we care about when it comes to takeoff speeds:
1. **Warning shots:** Before there are catastrophic AI alignment failures (i.e. PONRs) there are smaller failures that we can learn from.
2. **Heterogeneity:** The relevant AIs are diverse, rather than e.g. all fine-tuned copies of the same pre-trained model. ([See Evan’s post](https://www.alignmentforum.org/posts/mKBfa8v4S9pNKSyKK/homogeneity-vs-heterogeneity-in-ai-takeoff-scenarios))
3. **Risk Awareness:** Everyone is freaking out about AI in the crucial period, and lots more people are lots more concerned about AI risk.
4. **Multipolar:** AI capabilities progress is widely distributed in the crucial period, rather than concentrated in a few projects.
5. **Craziness:** The world is weird and crazy in the crucial period, lots of important things happening fast, the strategic landscape is different from what we expected thanks to [new technologies and/or other developments](https://aiimpacts.org/relevant-pre-agi-possibilities/)
I think that the best way to define slow(er) takeoff is as the extent to which conditions 1-5 are met. This is not a definition with precise resolution criteria, but that’s OK, because it captures what we care about. Better to have to work hard to precisify a definition that captures what we care about, than to easily precisify a definition that doesn’t! (More substantively, I am optimistic that we can come up with better proxies for what we care about than GWP. I think we already have to some extent; see e.g. operationalizations 5 and 6 [here](https://www.lesswrong.com/posts/YMokuZdoY9tEDHjzv/agi-predictions#Operationalizations).) As a bonus, this definition also encourages us to wonder whether we’ll get some of 1-5 but not others.
What do I mean by “the crucial period?”
I think we should define the crucial period as the period leading up to the first major AI-induced potential point of no return. (Or maybe, as the aggregate of the periods leading up to the major potential points of no return). After all, this is what we care about. Moreover there seems to be [some level of consensus](https://www.lesswrong.com/posts/CjW4axQDqLd2oDCGG/misconceptions-about-continuous-takeoff?commentId=cCQTh2FZRZvPgkmj6) that crazy stuff could start happening before human-level AGI. I certainly think this.
So, I’ve argued for a new definition of slow takeoff, that better captures what we care about. But is the old GWP-based definition a fine proxy? No, it is not, because the things that cause PONR can be different from the things which cause GWP acceleration, and they can come years apart too. Whether there are warning shots, heterogeneity, risk awareness, multipolarity, and craziness in the period leading up to PONR is probably correlated with whether GWP doubles in four years before the first one-year doubling. But the correlation is probably not super strong. Here are two scenarios, one in which we get a slow takeoff by my definition but not by the GWP-based definition, and one in which the opposite happens:
**Slow Takeoff Fast GWP Acceleration Scenario:** It turns out there’s a multi-year deployment lag between the time a technology is first demonstrated and the time it is sufficiently deployed around the world to noticeably affect GWP. There’s also a lag between when a deceptively aligned AGI is created and when it causes a PONR… but it is much smaller, because all the AGI needs to do is neutralize its opposition. So PONR happens before GWP starts to accelerate, even though the technologies that could boost GWP are invented several years before AGI powerful enough to cause a PONR is created. But takeoff is slow in the sense I define it; by the time AGI powerful enough to cause a PONR is created, everyone is already freaking out about AI thanks to all the incredibly profitable applications of weaker AI systems, and the obvious and accelerating trends of research progress. Also, there are plenty of warning shots, the strategic situation is very multipolar and heterogenous, etc. Moreover, research progress starts to go FOOM a short while after powerful AGIs are created, such that by the time the robots and self-driving cars and whatnot that were invented several years ago actually get deployed enough to accelerate GWP, we’ve got nanobot swarms. GWP goes from 3% growth per year to 300% without stopping at 30%.
**Fast Takeoff Slow GWP Acceleration Scenario:** It turns out you can make smarter AIs by making them have more parameters and training them for longer. So the government decides to partner with a leading tech company and requisition all the major computing centers in the country. With this massive amount of compute and research talent, they refine and scale up existing AI designs that seem promising, and lo! A human-level AGI is created. Alas, it is so huge that it costs $10,000 per hour of subjective thought. Moreover, it has a different distribution over skills compared to humans—it tends to be more rational, not having evolved in an environment that rewards irrationality. It tends to be worse at object recognition and manipulation, but better at poetry, science, and predicting human behavior. It has some flaws and weak points too, more so than humans. Anyhow, unfortunately, it is clever enough to neutralize its opposition. In a short time, the PONR is passed. However, GWP doubles in four years before it doubles in one year. This is because (a) this AGI is so expensive that it doesn’t transform the economy much until either the cost comes way down or capabilities go way up, and (b) progress is slowed by bottlenecks, such as acquiring more compute and overcoming various restrictions placed on the AGI. (Maybe neutralizing the opposition involved convincing the government that certain restrictions and safeguards would be sufficient for safety, contra the hysterical doomsaying of parts of the AI safety community. But overcoming those restrictions in order to do big things in the world takes time.)
*Acknowledgments: Thanks to the people who gave comments on earlier drafts, including Katja Grace, Carl Shulman, and Max Daniel. Thanks to Amogh Nanjajjar for helping me with some literature review.* |
f79847ad-57e3-4be9-83ba-fb7621e1edc8 | trentmkelly/LessWrong-43k | LessWrong | OpenAI now has an RL API which is broadly accessible
About 1 month ago, OpenAI made their RL fine-tuning API accessible to anyone. While this API has some important limitations, I still think it might be quite useful for AI safety/alignment work so people should consider taking advantage of it.
To use the RL fine-tuning API, you need your organization to be a "verified organization". You can verify an organization by going to https://platform.openai.com/settings/organization/general and then clicking the "Verify Organization" button. Verification will require someone on the account to upload pictures of their ID and pictures of their face. I found that verification was fast and was accepted immediately. It's possible that your account must reach at least some usage tier prior to getting access, but you can get a tier 5 account just by spending $1000 on the API.
The API supports doing RL fine-tuning on o4-mini using a decently general set of graders. It just supports o4-mini, but this model is reasonably capable, at least at more narrow checkable tasks (coding etc). It only supports single-turn interactions, so you can't do RL on tasks that involve interaction with humans or the environment. (Using tools that OpenAI provides via the responses API is maybe supported, something in the docs seemed to suggest this, but I haven't tested this.)
It supports a limited set of graders, but these graders are pretty general in practice (given that you can't do more than single-turn interactions). These relevant graders are:
* Exact string match grader.
* Grading with another model. This only supports OpenAI models and it supports a limited set of these models (though it does include e.g. o3). It isn't supported to use an RL or SFT fine-tuned model as a grader. The prompt for this scoring model can depend on any data associated with the datum you're doing RL on and any aspect(s) of the output from the model. [1]
* Python graders. You can run arbitrary python code to compute a reward. This is run in a sandbox which is disco |
a02c0067-b992-45fb-9ffc-e355625b71a3 | trentmkelly/LessWrong-43k | LessWrong | Evaluating the truth of statements in a world of ambiguous language.
If I say "the store is 500 meters away," is this strictly true? Strictly false? Either strictly true or strictly false, with probabilies of true or false summing to one? Fuzzily true, because the store is 500.1 meters away? My thesis is that it's strictly true or strictly false, with associated probabilities. Bear with me.
The 500 meter example is pretty hard to reason about numerically. I am hopeful that I can communicate my thoughts by starting with an example that is in some sense simpler.
Is our conversational partner making a true statement or a false statement? What we're used to is starting with a prior over worlds, and then updating our belief based on observations- the man is underwater, the man is currently moving downwards. So our belief that the man is sinking should be pretty high.
However, what we are unsure of is the language of the sentence. If it's German¹, then it means¹ "the man is pensive." Then, evaluating the truth value of the sentence is going to involve a different prior- Does the man look deep in thought? He seems to have a calm expression, and is sitting cross legged, which is weak evidence. More consideration is needed.
To come out to an actual probability of the original sentence being true, we need a distribution of possible intended meanings for the sentence, and then likelyhoods of the truthfulness of each meaning. If you buy my framing of the situation, then in this case it's simply P(Sentence is English) x P(Man is sinking) + P(Sentence of is German) x P(Man is thoughtful).
In the monolingual case, it's easy to get caught up in lingual proscriptivism. If someone uses a word wrong, and their sentence is only true when it's interpreted using the incorrect definition, then a common claim is that their sentence is untrue. A classic example of this is "He's literally the best cook ever." I hope that the multilingual case illustrates that ambiguity in the meaning of phrases doesn't have to come from uncareful sentence construct |
6487c59e-abdf-4ed1-bf2b-b4b5a6a18eef | trentmkelly/LessWrong-43k | LessWrong | Overview article on FAI in a popular science magazine (Hebrew)
A new article which I wrote just appeared in Hebrew in Galileo, Israel's top popular science magazine, in hardcopy.
It is titled "Superhuman Intelligence, Unhuman Intelligence" (Super- and un- are homophones in Hebrew, a bit of wordplay.)
You can read it here. [Edit: Here's an English version on the Singularity Institute site.]
The cover art, the "I Robot" images, and the tag line ("Artificial Intelligence: Can we reign in the golem") are a bit off; I didn't choose them; but that's par for the course.
To the best of my knowledge, this is the first feature article overviewing FAI in any popular-science publication (whether online or hardcopy).
Here is the introduction to the article. (It avoids weasel words, but all necessary caveats are given in the body of the article).
> In coming decades, engineers will build an entity with intelligence on a level which can compete with humans. This entity will want to improve its own intelligence, and will be able to do so. The process of improvement will repeat, until it reaches a level far above that of humans; the entity will then be able to achieve its goals efficiently. It is thus essential that its goals are good for humanity. To guarantee this, it is necessary to define the correct goals before this intelligence is built. |
96c854b3-df2b-4a9e-a6ab-a2f14eec75f1 | trentmkelly/LessWrong-43k | LessWrong | Progress links and short notes, 2024-12-27: Clinical trial abundance, grid-scale fusion, permitting vs. compliance, crossword mania, and more
Much of this content originated on social media. To follow news and announcements in a more timely fashion, follow me on Twitter, Threads, Bluesky, or Farcaster.
Contents
* My essays
* Fellowship opportunities
* Announcements
* Events
* News
* Questions
* Live gloriously
* Where being right matters
* Off-grid solar for data centers
* Permitting vs. compliance
* Mirror life FAQ
* Crossword mania
* Do we want to democratize art-making?
* Polio
* How many people could you feed on an acre?
* Verifiable video
* Links and tweets
My essays
In case you missed it:
* A progress policy agenda: Elon says that soon, builders “will be free to build” in America. If that promise is to be fulfilled, we have work to do. Here’s my wishlist of policy goals to advance scientific, technological, and economic progress
Fellowship opportunities
* “FutureHouse is launching an independent postdoctoral fellowship program for exceptional researchers who want to apply our automated science tools to specific problems in biology and biochemistry” (@SGRodriques). $125k, apply by Feb 14
* No. 10 Innovation Fellowship (UK) is “10 Downing Street’s flagship initiative for bringing world class technical talent into government for high impact tours of duty.” “Huge opportunity for impact,” says @matthewclifford
* Sloan Foundation / NBER fellowship for “PhD students and early-career researchers interested in the fiscal and economic effects of productivity policies—particularly R&D, immigration, and infrastructure permitting” (@heidilwilliams_)
Announcements
* The Black Spatula Project is “an open initiative to investigate the potential of large language models (LLMs) to identify errors in scientific papers.” A recent paper caused a bit of a panic about health hazards from black plastic kitchen utensils, but was wrong because of a basic arithmetic error. Ethan Mollick found that GPT o1 caught the error when asked to “carefully check the math in this paper.” Steve Newman (RPI f |
c40c34e2-af30-4feb-bbad-351a41713cc0 | trentmkelly/LessWrong-43k | LessWrong | Does the US nuclear policy still target cities?
The history of nuclear strategic bombing
Daniel Ellsberg’s The Doomsday Machine brought my attention to a horrifying fact about early US nuclear targeting policy. In 1961, the US had only one nuclear war plan, and it called for the destruction of every major Soviet city and military target. That is not surprising. However, the plan also called for the destruction of every major Chinese city and military target, even if China had not provoked the United States. In other words, the US nuclear war plan called for the destruction of the major population centers of the most populous country in the world, even in circumstances where that country had not attacked the United States or its allies. Ellsberg points out that at the time, people at RAND and presumably other parts of the US defense establishment understood that the Chinese and the Soviets were beginning to diverge in strategic interests and thus should not be treated as one bloc. Nevertheless, the top levels of the US command, including President Eisenhower, were committed to the utter destruction of both Chinese and Soviet targets in the event of a war with either country.
The policy of destroying cities is a legacy left over from strategic bombing in World War II. The destruction of Hiroshima and Nagasaki are the most famous, but the fire bombings of Japanese and Germany cities destroyed far more infrastructure and killed far more people than the two atomic bombs. The given rational for strategic bombing was to destroy the ability of the enemy states to continue to make war. If a state can no longer produce airplanes and tanks, either because the factories have been destroyed or because there are no longer people to work in the factories, then its ability to resist is diminished.
Given the level of technology and development in WWII, strategic bombing had a chance at achieving military objectives, because the conflict was to carry on for multiple years. On the timescale of years, a country’s capacity to build |
b40a2662-e2e5-4aca-a627-82987646cc67 | trentmkelly/LessWrong-43k | LessWrong | Easy money: Find me a new job, get $1000
I normally post under a different name, which you can find by looking at my posting history, specifically, this thread. I need to keep the search somewhat secret, so I'm adding a trivial barrier to identifying me.
Anyway, I haven't been very good at job searches, and I have spare money, so I figured I'd just outsource the effort (in parallel with my own continuing efforts, of course). In theory, this shouldn't even be difficult, because I'm already employed, very well, in my area of expertise, and have been for over six years, and am only trying to switch because I can't stand my current city. (And a sense that I could be making more of my potential.)
But practice is different, and that's where you come in. Here's the deal: If you find me a job matching my skills and qualifications (and located per the next paragraph), and I'm there for a month, you get $1000. And to avoid making it an all-or-nothing deal, if you just get me to the interview stage, you get $100. You already know me from my posts here, so if you know someone who could use me, please connect the two of us.
I would like for it to be in (in decreasing order of preference) Austin, Houston, NYC, or San Francisco, or at least allow me to live there while earning income from the job. However, any city with a LW community is fine as long as you clear it with me first.
So, here are my skill and qualifications (resume file available, just PM or email me):
- Visual Studio with C++, C#, and Visual Web Developer; MATLAB programming, including Simulink and digital signal processing; finite-element modeling with MSC Patran and Nastran (and augmented by MATLAB); MathCAD; Mathematica; passed Fundamentals of Engineering Exam, earning EIT (Engineer-in-Training) license; perfect scores on GRE analytical and quantitative.
In my six years as an aerospace engineer, I:
- Created and refined finite element models (FEMs) of aircraft (pre- and post-modification) to predict structural response to loading; used bot |
a4ae6512-3888-4952-a38b-9012e7339468 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | A strong mind continues its trajectory of creativity
*[Metadata: crossposted from <https://tsvibt.blogspot.com/2023/01/a-strong-mind-continues-its-trajectory.html>. First completed January 29, 2023.]*
A very strong mind is produced by a trajectory of creativity. A trajectory of creativity that produces a very strong mind is hard to separate from the mind's operation. So a strong mind continues on its trajectory of creativity as long as it is active.
A strong mind comes from a trajectory of creativity
===================================================
If a mind is highly capable, it got to that point by gaining understanding in a [voyage of novelty](https://tsvibt.blogspot.com/2023/01/the-voyage-of-novelty.html). If the mind gains understanding that is novel for all the surrounding minds (e.g., preexisting humans), it does so through [creativity](https://tsvibt.blogspot.com/2022/08/structure-creativity-and-novelty.html): generating novelty, rather than merely copying it. If the mind becomes much more capable than the surrounding minds, it does so by being on a *trajectory of creativity*: something about the mind implies that it generates understanding that is novel to the mind and its environment. If the mind is on a trajectory of creativity that brought it to the point of being highly capable, its trajectory of creativity probably carries the mind much further, making the mind much more capable than it already is.
The *ex quo* of a mind's creativity is the [element](https://tsvibt.blogspot.com/2022/08/structure-creativity-and-novelty.html#1-elements-and-structure) (collection of elements) *out of which* comes novel structure. The *ex quo* of a modern AI system is almost entirely dependent on the search (i.e. training) apparatus, which is clearly separated out from running the found system. (The *ex quo* isn't entirely dependent on the search apparatus. Some non-zero creativity happens in the collision of elements that happens in, say, a single run of a stable diffusion image model or a large transformer language model. But it's not much creativity, and the found structure is about as temporarily grasped as possible.)
The *proximal ex quo* is that out of which novel structure comes directly. The *distal ex quo* is that out of which novel structure comes indirectly. So the mental context that's set up when a particular idea comes to you, and the other dark matter that goes into that [abduction](https://tsvibt.blogspot.com/2022/08/structure-creativity-and-novelty.html#pierces-abduction), is the proximal *ex quo*; human evolution is the distal *ex quo*; and the history of the development of your brain is an intermediate *ex quo*.
Trajectory and operation are hard to separate
=============================================
An AI can simply be shut down, until it's able to and wants to stop you from shutting it down. But can an AI's improvement be shut down, without shutting down the AI? This can be done for all current AI systems in the framework of finding a fairly limited system by a series of tweaks. Just stop tweaking the system, and it will now behave as a fixed (perhaps stochastic) function that doesn't provide earth-shaking capabilities.
I suspect that the *ex quo* that puts a mind on a trajectory to being very strong, is hard to separate from the operation of the mind. Some gestures at why:
Making doesn't imply understanding
----------------------------------
>
> Just because you can make something, doesn't mean you understand how it works.
>
>
>
* Evolution is a distal *ex quo* of human understanding. But there's clearly an *ex quo* more proximal than evolution for, say, scientific understanding: human thought and investigation. Setting up an evolution that can produce humans doesn't imply that you understand how humans do science.
* The way we make neural networks today is by setting up a distal *ex quo* (the search process). A more proximal *ex quo* for a neural net comes from the accumulated hidden features: they set up the context in which the next little tweak is beneficial. We can know how to make neural nets that work well without knowing much about how the series of tweaks in context build up the computations that end up performing well at the given task.
* We can nevertheless turn off the *ex quo* of current AI systems because the *ex quo* is almost entirely dependent on the distal *ex quo*. We can't, however, turn off the proximal *ex quo* of human scientific understanding, just by turning off the distal *ex quo*: scientific creativity doesn't require that genetic evolution is continuing. We can, in a haphazard way, turn off some human creativity while retaining some operation, e.g. by taking sedatives or by punishing creativity.
The *ex quo* is self-created
----------------------------
>
> Our understanding of creativity isn't deep enough for us to make explicit the *ex quo* of the creativity of a strong mind. So if we make a strong mind, it's because we made something that finds its own *ex quo* of creativity.
>
>
>
* The learning is itself being learned. So from our perspective, "what's learned" (as distinct from [the learning process that we explicitly set up]) as an undifferentiated blob includes [the learning process that the mind sets up for itself].
* Learning beyond a certain point has to be online learning. Online learning produces mental elements that combine synchronic and diachronic functions: the elements both participate in currently crystallized skills, and also in the production of new skills. (Analogy: a healthy living codebase is made of small components that both perform their function well and also make themselves and their context readily available to be effectively understood, modified, and extended.) Note: here "learning" is used as a metonymy for creativity; to me "learning" softly excludes, for example, problem solving and imagining, which creativity includes if they involve grasping new ideas. See [here](https://tsvibt.blogspot.com/2022/08/structure-creativity-and-novelty.html#acquiring-elements).
* So, the operation of the mind includes learning processes, even if our explicitly-set-up search processes have been shut off. More generally, the operation of the mind to perform even familiar tasks is set up to continue being creative.
Mental operation includes creativity
------------------------------------
>
> The change that accumulates to constitute creativity is part of and continuous with the everyday operation of the mind. Physiology is continuous with ontogeny.
>
>
>
* The mind's operation--what happens when the mind is applied, when the mind's capabilities are exercised--involves meeting the demands of new contexts. In a new context, concepts adapt by unfolding into concrete consequences; by widening their scope, in the form of guesses or analogies; and by interfacing with each other in the combinations suitable to the context.
+ For example, the idea of "good development" in chess has to interact with the specifics of a given position: maybe this move, even though it blocks your bishop, is best in this position because it prevents an important plan of your opponent. Unfolding the general idea of speedy development into the specifics of which pieces are blocked in which ways, makes the general idea available to interact with the other forces in the position. This work of unfolding seems necessary for the operation of the mind, and seems like it's a substantial portion of the work involved in creativity. It's imaginable to do this work but not remember any of it, i.e. avoid having that work leave traces that can accumulate, but that seems like a delicate, probably unnatural carving.
* The kind of activity that's well-described as "reusing old solutions as building blocks for new solutions" includes activity of a mind that's "obviously merely operational" (e.g., writing a computer program by writing functions that sometimes call previously-written functions), and includes activity of a mind that's much more ambiguous between being operational and being creative. Reusing old solutions as building blocks can add up to immense creativity. Desire paths form gradually.
+ For example, being able to use a solution at all, involves to some extent integrating the solution with other mental elements. A simple example is indexing: recording that some idea is relevant in certain contexts, so that the idea is brought up in those contexts. A less simple example is co-adaptation: other mental elements adjust to the presence of the novel elements, e.g. by learning to interpret signals from the novel elements.
+ For example, take the origin of phonetic writing in the [rebus principle](https://en.wikipedia.org/wiki/Rebus#Rebus_principle). Originary scripts (Mesopotamian cuneiform, Egyptian hieroglyphs, [Old Chinese oracle bone script](https://en.wikipedia.org/wiki/Oracle_bone_script), [Mayan script](https://en.wikipedia.org/wiki/Maya_script)) are not made of "ideograms". Rather, they're made of a mix of symbols for words (lexigrams, which represent a unit of speech, not an idea); sometimes purely phonetic symbols; and sometimes phonetic-semantic symbol groups. That is, sometimes two symbols are combined to record one spoken word: one symbol for a word indicating the semantic category (e.g. the symbol for "mouth"), and one symbol giving the first sound of the specific intended word related to the semantic determiner (e.g. a symbol for a word beginning with the sound that "speaking" begins with). This, one speculates, is the result of accumulating small solutions to small problems of the form: record such-and-such speech, using the orthographic material at hand. I don't see a clear distinction between [just using the ideas (symbols) that one already has] and [inventing new symbols]. The little, operational solutions found in recording speech--the progressive defining, standardizing, recursively building, streamlining, combining, disambiguating, and other tendencies of the mental operation of writing down speech so that others can recover the speech--add up to new scripts. In an extreme form, it's believed that [Canaanite slaves used the acrophonic principle](https://en.wikipedia.org/wiki/History_of_the_alphabet#Consonantal_alphabets) (a pure application of the rebus principle) to record their speech in the form of a consonantal alphabet.
* When faced with a problem, the mind's motion [rearrange oneself to efficiently solve this problem] may be just one sort of motion, which gives rise both to what we'd recognize as [rearrange preexisting elements in preexisting ways] and to what we'd recognize as [call forth new elements, including new arrangements].
* [Provisional elements](https://tsvibt.blogspot.com/2023/03/provisionality.html) tend to be set up to be open to revision in the course of operation.
+ For example, if you're manipulating some strange objects that you've so far only identified as [those floppy things sticking out of my shoe], it's natural to add the understanding [pulling outward on one floppy thing pulls inward on the other floppy thing].
+ For another example, the concept of a mathematical function was continually revised (expanded, abstracted), often in ways deriving from the force of straightforward intuitions about functions, but always in a way that kept a lot of the character of previous ideas of functions (e.g., composability). (See ["The concept of function up to the middle of the 19th century"](https://link.springer.com/article/10.1007/BF00348305), by A. P. Youschkevitch; [sci-hub.ru pdf](https://sci-hub.ru/https://link.springer.com/article/10.1007/BF00348305).)
* The availability of the criteria provided by an interesting context is already a lot of the work of creativity.
* This isn't to say that crystallized and fluid intelligence are the same thing, just that pulling them apart would require understanding more.
A strong mind continues its trajectory of creativity
====================================================
If a mind's operation and creativity can't be separated, then the mind can't operate without also exercising its creativity. A very strong mind became strong by being on a strong trajectory of accumulating novelty through creativity. So as long as a very strong mind continues to operate, it continues forward on its trajectory of creativity, gaining more and more understanding. |
f95b3853-8b0a-4820-b128-05d0d0186406 | trentmkelly/LessWrong-43k | LessWrong | The Hidden Origins of Ideas
It is well known that people tend to inherit their world view toghether with their genes. Buddhists are born to the Buddhists, Muslims are born to the Muslims and Republicans are born to the Republicans. While rejecting Predestination, a XVI century catholic could be fairly certain that, unlike hell-bound pagans in the Amazonian forests, most of his descendants would also be catholics.
Naturally independent minds can occasionally break with the tradition. A catholic, finding the Pope’s stance on Predestination inconsistent with the Scriptures, might turn to Protestantism. Hence, the invention of the printing press that made Bibles widely available may have been the root cause of the Reformation. Similarly, the spread of literacy to the lower classes may have eroded the influence of the church and popularized the secular ideologies, such as Marxism.
But could it be that when we break with the traditional mode of thinking, we are driven not by superior intellects or newly acquired knowledge, but rather by something we are not even aware of? Let’s take as an example the spread of seemingly unrelated ideologies of Protestantism and Marxism.
From left to right: The european countries painted blue are those with Germanic majority, those with large numbers of protestants (>45% of all believers), and those where communists electoral vote failed to rise above 10% within the last 60 years.
While the maps are not identical, there seems to be a strong correlation between peoples’ ethnic origins, their religious histories and the openness to the communist ideas. Of course, correlation does not imply causation. However, strong correlation between our views and those of people with a similar background, may suggest that factors other than logic are responsible for them. Unless, as in my case, a similar background means smarter/ more virtuous/ more rational/ getting secret revelations from Omega/… (circle the right answer).
|
ae29f351-aa64-4016-8f86-6d574b133936 | trentmkelly/LessWrong-43k | LessWrong | Joy in Discovery
> "Newton was the greatest genius who ever lived, and the most fortunate; for we cannot find more than once a system of the world to establish."
> —Lagrange
I have more fun discovering things for myself than reading about them in textbooks. This is right and proper, and only to be expected.
But discovering something that no one else knows—being the first to unravel the secret—
There is a story that one of the first men to realize that stars were burning by fusion—plausible attributions I've seen are to Fritz Houtermans and Hans Bethe—was walking out with his girlfriend of a night, and she made a comment on how beautiful the stars were, and he replied: "Yes, and right now, I'm the only man in the world who knows why they shine."
It is attested by numerous sources that this experience, being the first person to solve a major mystery, is a tremendous high. It's probably the closest experience you can get to taking drugs, without taking drugs—though I wouldn't know.
That can't be healthy.
Not that I'm objecting to the euphoria. It's the exclusivity clause that bothers me. Why should a discovery be worth less, just because someone else already knows the answer?
The most charitable interpretation I can put on the psychology, is that you don't struggle with a single problem for months or years if it's something you can just look up in the library. And that the tremendous high comes from having hit the problem from every angle you can manage, and having bounced; and then having analyzed the problem again, using every idea you can think of, and all the data you can get your hands on—making progress a little at a time—so that when, finally, you crack through the problem, all the dangling pieces and unresolved questions fall into place at once, like solving a dozen locked-room murder mysteries with a single clue.
And more, the understanding you get is real understanding—understanding that embraces all the clues you studied to solve the problem, when you |
f1dfb214-d3a2-4f93-be0a-3aab45401ee7 | trentmkelly/LessWrong-43k | LessWrong | New Member
Hello. I'm new to LW. I was wondering if there's a forum to ask questions and talk about HPMOR? |
bf1e8e88-682f-445a-96c2-a7ad143cb88e | trentmkelly/LessWrong-43k | LessWrong | Advice On Getting A Software Job
(Note to LWers: This post was written for a general audience at my blog, but I think it's particularly applicable to Less Wrong, as many here are already interested in programming. Programming is also an important route into two of the main paths to get rich, entrepreneurship and angel investing. Many of the leading donors to the Singularity Institute are professional programmers.)
You’ve already graduated college and found a job, but aren’t making the money you’d like. Or you live in the middle of nowhere. (Or, your job just sucks.) You’re pretty smart, and want to do something about this. What should you do?
One option is working as a computer programmer. Programming has a lot going for it: people tend to enjoy it, software companies have great perks, the work is usually in a laid-back atmosphere, and of course there’s no manual labor. Programming salaries generally range from high five figures (just out of college) to mid six figures (for senior people, and quants at Wall Street banks). This assumes you live in a major city, so be sure to factor that into cost-of-living math. (If you aren’t in a major city, seriously consider moving – most of the best jobs are there.)
Before you apply, you’ll need to learn how to program. To get started, there are lots of books on introductory programming – just search for “Introduction to C”, “Introduction to Python”, “Introduction to Haskell” and stuff like that. It’s good to know at least one language well, and also have experience with a few others, preferably ones that differ in important ways. Once you’ve learned the basics, there are lots of problems online to practice on. If you’re into math, Project Euler has a big, well-curated collection of them. You’ll also want to know your way around Linux, since it’s the main operating system of web servers; try installing it, and using it as your main OS for a few months.
To actually get a programming job, you’ll mainly need to demonstrate a) programming experience, and b) kno |
0af36f2a-6361-4838-b87b-18007829074b | StampyAI/alignment-research-dataset/arxiv | Arxiv | The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Future of
Humanity
InstituteUniversity
of OxfordCentre for
the Study of
Existential
RiskUniversity of
CambridgeCenter for a
New American
SecurityElectronic
Frontier
FoundationOpenAI
February 2018The Malicious Use
of Artificial Intelligence:
Forecasting, Prevention,
and Mitigation
The Malicious Use of Artificial Intelligence:
Forecasting, Prevention, and Mitigation
Authors are listed
in order of contributionDesign Direction
by Sankalp Bhatnagar
and Talia Cotton February 2018
1 Corresponding author
miles.brundage@philosophy.
ox.ac.uk
Future of Humanity Institute,
University of Oxford; Arizona
State University
2 Corresponding author,
sa478@cam.ac.uk
Centre for the Study of
Existential Risk, University
of Cambridge
3 OpenAI
4 Open Philanthropy Project
5 Electronic Frontier
Foundation
6 Future of Humanity
Institute, University of
Oxford
7 Future of Humanity
Institute, University of
Oxford; Yale University
8 Center for a New American
Security
9 American University
10 Endgame
11 Endgame
12 University of Oxford/
Arizona State University/New
America Foundation
13 Center for a New American
Security
14 Stanford University
15 Future of Humanity
Institute, University of
Oxford
16 Centre for the Study
of Existential Risk and
Centre for the Future of
Intelligence, University of
Cambridge
17 Centre for the Study of
Existential Risk, University
of Cambridge18 Centre for the Study of
Existential Risk, University
of Cambridge
19 Future of Humanity
Institute, University of
Oxford
20 Future of Humanity
Institute, University of
Oxford
21 Information Society
Project, Yale University
22 Future of Humanity
Institute, University of
Oxford
23 OpenAI
24 University of Bath
25 University of Louisville
26 OpenAI
Miles Brundage Shahar Avin Jack Clark Helen Toner Peter Eckersley
Ben Garfinkel Allan Dafoe Paul Scharre Thomas Zeitzoff Bobby Filar
Hyrum Anderson Heather Roff Gregory C. Allen Jacob Steinhardt
Carrick Flynn Seán Ó hÉigeartaigh Simon Beard Haydn Belfield
Sebastian Farquhar Clare Lyle Rebecca Crootof Owain Evans
Michael Page Joanna Bryson Roman Yampolskiy Dario Amodei
Artificial intelligence and machine learning capabilities are growing
at an unprecedented rate. These technologies have many widely
beneficial applications, ranging from machine translation to medical
image analysis. Countless more such applications are being
developed and can be expected over the long term. Less attention
has historically been paid to the ways in which artificial intelligence
can be used maliciously. This report surveys the landscape of
potential security threats from malicious uses of artificial intelligence
technologies, and proposes ways to better forecast, prevent, and
mitigate these threats. We analyze, but do not conclusively resolve,
the question of what the long-term equilibrium between attackers and
defenders will be. We focus instead on what sorts of attacks we are
likely to see soon if adequate defenses are not developed.
p.3Executive
Summary
p.4
Executive Summary The Malicious Use of Artificial IntelligenceIn response to the changing threat landscape we make four high-level
recommendations :
1. Policymakers should collaborate closely with technical
researchers to investigate, prevent, and mitigate potential
malicious uses of AI.
2. Researchers and engineers in artificial intelligence should take
the dual-use nature of their work seriously, allowing misuse-
related considerations to influence research priorities and
norms, and proactively reaching out to relevant actors when
harmful applications are foreseeable.
3. Best practices should be identified in research areas with more
mature methods for addressing dual-use concerns, such as
computer security, and imported where applicable to the case
of AI.
4. Actively seek to expand the range of stakeholders and domain
experts involved in discussions of these challenges.
p.5
Executive Summary The Malicious Use of Artificial IntelligenceAs AI capabilities become more powerful and widespread, we expect
the growing use of AI systems to lead to the following changes in the
landscape of threats:
• Expansion of existing threats . The costs of attacks may be
lowered by the scalable use of AI systems to complete tasks
that would ordinarily require human labor, intelligence and
expertise. A natural effect would be to expand the set of actors
who can carry out particular attacks, the rate at which they can
carry out these attacks, and the set of potential targets.
• Introduction of new threats . New attacks may arise through the
use of AI systems to complete tasks that would be otherwise
impractical for humans. In addition, malicious actors may
exploit the vulnerabilities of AI systems deployed by defenders.
• Change to the typical character of threats . We believe there is
reason to expect attacks enabled by the growing use of AI to
be especially effective, finely targeted, difficult to attribute,
and likely to exploit vulnerabilities in AI systems.
p.6
Executive Summary The Malicious Use of Artificial IntelligenceWe structure our analysis by separately considering three security
domains, and illustrate possible changes to threats within these
domains through representative examples:
• Digital security . The use of AI to automate tasks involved in
carrying out cyberattacks will alleviate the existing tradeoff
between the scale and efficacy of attacks. This may expand
the threat associated with labor-intensive cyberattacks
(such as spear phishing). We also expect novel attacks that
exploit human vulnerabilities (e.g. through the use of speech
synthesis for impersonation), existing software vulnerabilities
(e.g. through automated hacking), or the vulnerabilities
of AI systems (e.g. through adversarial examples and data
poisoning).
• Physical security . The use of AI to automate tasks involved in
carrying out attacks with drones and other physical systems
(e.g. through the deployment of autonomous weapons
systems) may expand the threats associated with these
attacks. We also expect novel attacks that subvert cyber-
physical systems (e.g. causing autonomous vehicles to crash)
or involve physical systems that it would be infeasible to direct
remotely (e.g. a swarm of thousands of micro-drones).
• Political security . The use of AI to automate tasks involved in
surveillance (e.g. analysing mass-collected data), persuasion
(e.g. creating targeted propaganda), and deception (e.g.
manipulating videos) may expand threats associated with
privacy invasion and social manipulation. We also expect novel
attacks that take advantage of an improved capacity to analyse
human behaviors, moods, and beliefs on the basis of available
data. These concerns are most significant in the context of
authoritarian states, but may also undermine the ability of
democracies to sustain truthful public debates.
p.7
Executive Summary The Malicious Use of Artificial IntelligenceIn addition to the high-level recommendations listed above, we also
propose the exploration of several open questions and potential
interventions within four priority research areas:
• Learning from and with the cybersecurity community . At the
intersection of cybersecurity and AI attacks, we highlight
the need to explore and potentially implement red teaming,
formal verification, responsible disclosure of AI vulnerabilities,
security tools, and secure hardware.
• Exploring different openness models . As the dual-use nature
of AI and ML becomes apparent, we highlight the need to
reimagine norms and institutions around the openness of
research, starting with pre-publication risk assessment in
technical areas of special concern, central access licensing
models, sharing regimes that favor safety and security, and
other lessons from other dual-use technologies.
• Promoting a culture of responsibility . AI researchers and the
organisations that employ them are in a unique position to
shape the security landscape of the AI-enabled world. We
highlight the importance of education, ethical statements and
standards, framings, norms, and expectations.
• Developing technological and policy solutions . In addition to
the above, we survey a range of promising technologies, as
well as policy interventions, that could help build a safer future
with AI. High-level areas for further research include privacy
protection, coordinated use of AI for public-good security,
monitoring of AI-relevant resources, and other legislative and
regulatory responses.
The proposed interventions require attention and action not just from
AI researchers and companies but also from legislators, civil servants,
regulators, security researchers and educators. The challenge is
daunting and the stakes are high.
Executive Summary
Introduction
Scope
Related Literature
General Framework
for AI and Security Threats
AI Capabilities
Security-Relevant Properties of AI
General Implications
Scenarios
Digital Security
Physical Security
Political Security
Security Domains
Digital Security
Physical Security
Political Security
Interventions
Recommendations
Priority Areas for Further Research
Strategic Analysis
Factors Affecting the Equilibrium of AI and Security
Overall Assessment
Conclusion
Acknowledgements
References
Appendix A
Appendix B3
9
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02
03
04
05Contents
Introduction
p.9
Artificial intelligence (AI) and machine learning (ML) have
progressed rapidly in recent years, and their development has
enabled a wide range of beneficial applications. For example, AI is a
critical component of widely used technologies such as automatic
speech recognition, machine translation, spam filters, and search
engines. Additional promising technologies currently being
researched or undergoing small-scale pilots include driverless
cars, digital assistants for nurses and doctors, and AI-enabled
drones for expediting disaster relief operations. Even further in the
future, advanced AI holds out the promise of reducing the need
for unwanted labor, greatly expediting scientific research, and
improving the quality of governance. We are excited about many of
these developments, though we also urge attention to the ways in
which AI can be used maliciously . We analyze such risks in detail
so that they can be prevented or mitigated, not just for the value of
AI refers to the use of digital
technology to create systems that are
capable of performing tasks commonly
thought to require intelligence.
Machine learning is variously
characterized as either a sub-
field of AI or a separate field, and
refers to the development of digital
systems that improve their performance
on a given task over time through
experience.
We define “malicious use” loosely,
to include all practices that are
intended to compromise the security
of individuals, groups, or a society.
Note that one could read much of
our document under various possible
perspectives on what constitutes
malicious use, as the interventions
and structural issues we discuss are
fairly general.1
2
p.10
Introduction
preventing the associated harms, but also to prevent delays in the
realization of the beneficial applications of AI.
Artificial intelligence (AI) and machine learning (ML) are altering the
landscape of security risks for citizens, organizations, and states.
Malicious use of AI could threaten digital security (e.g. through
criminals training machines to hack or socially engineer victims at
human or superhuman levels of performance), physical security
(e.g. non-state actors weaponizing consumer drones), and political
security (e.g. through privacy-eliminating surveillance, profiling,
and repression, or through automated and targeted disinformation
campaigns).
The malicious use of AI will impact how we construct and manage
our digital infrastructure as well as how we design and distribute
AI systems, and will likely require policy and other institutional
responses. The question this report hopes to answer is: how can
we forecast, prevent, and (when necessary) mitigate the harmful
effects of malicious uses of AI? We convened a workshop at
the University of Oxford on the topic in February 2017 , bringing
together experts on AI safety, drones , cybersecurity, lethal
autonomous weapon systems, and counterterrorism . This
document summarizes the findings of that workshop and our
conclusions after subsequent research.
Scope
For the purposes of this report, we only consider AI technologies
that are currently available (at least as initial research and
development demonstrations) or are plausible in the next 5
years, and focus in particular on technologies leveraging machine
learning. We only consider scenarios where an individual or an
organisation deploys AI technology or compromises an AI system
with an aim to undermine the security of another individual,
organisation or collective. Our work fits into a larger body of work
on the social implications of, and policy responses to, AI . There
has thus far been more attention paid in this work to unintentional
forms of AI misuse such as algorithmic bias , versus the intentional
undermining of individual or group security that we consider.
We exclude indirect threats to security from the current report,
such as threats that could come from mass unemployment, or
other second- or third-order effects from the deployment of
AI technology in human society. We also exclude system-level
threats that would come from the dynamic interaction between
non-malicious actors, such as a “race to the bottom” on AI safety
Not all workshop participants
necessarily endorse all the findings
discussed herein. See Appendix A for
additional details on the workshop
and research process underlying
this report.
We define drones as unmanned aerial
robots, which may or may not have
autonomous decision-making features.21
Brynjolfsson and McAfee, 2014;
Brundage, 2017; Crawford and Calo,
2016; Calo, 2017; Chessen, 2017a,
Executive Office of the President,
2016
Kirkpatrick, 2016
43
p.11
Introductionbetween competing groups seeking an advantage or conflicts
spiraling out of control due to the use of ever-faster autonomous
weapons. Such threats are real, important, and urgent, and
require further study, but are beyond the scope of this document.
Related Literature
Though the threat of malicious use of AI has been highlighted
in high-profile settings (e.g. in a Congressional hearing a White
House-organized workshop , and a Department of Homeland
Security report ), and particular risk scenarios have been analyzed
(e.g. the subversion of military lethal autonomous weapon
systems ), the intersection of AI and malicious intent writ large
has not yet been analyzed comprehensively.
Several literatures bear on the question of AI and security, including
those on cybersecurity, drones, lethal autonomous weapons,
“social media bots, ” and terrorism. Another adjacent area of
research is AI safety—the effort to ensure that AI systems reliably
achieve the goals their designers and users intend without causing
unintended harm . Whereas the AI safety literature focuses on
unintended harms related to AI, we focus on the intentional use of
AI to achieve harmful outcomes (from the victim’s point of view). A
recent report covers similar ground to our analysis, with
a greater focus on the implications of AI for U.S. national security.
In the remainder of the report, we first provide a high-level view
on the nature of AI and its security implications in the section
General Framework for AI and Security, with subsections on
Capabilities, Security-relevant Properties of AI, and General
Implications for the Security Landscape; we then illustrate these
characteristics of AI with Scenarios in which AI systems could
be used maliciously; we next analyze how AI may play out in the
domains of digital, physical, and political security; we propose
Interventions to better assess these risks, protect victims
from attacks, and prevent malicious actors from accessing and
deploying dangerous AI capabilities; and we conduct a Strategic
Analysis of the “equilibrium” of a world in the medium-term (5+
years) after more sophisticated attacks and defenses have been
implemented. Appendices A and B respectively discuss the
workshop leading up to this report, and describe areas for research
that might yield additional useful interventions.6
7
Amodei and Olah et al., 2016; Soares
and Fallenstein, 2014; Taylor, 2016;
Russell, Dewey, and Tegmark 2015;
Everitt et al., 2017
Allen and Chan, 2017
2
3
4
Moore, 2017
Office of Science and Technology
Policy and Carnegie Mellon
University, 2016)
Office of Cyber and Infrastructure
Analysis, 2017)Technology Policy and
Carnegie Mellon University, 2016
5
Scharre, 20161
Armstrong et al., 2014
02
p.12
General
Framework
for AI and
Security Threats
AI Capabilities
The field of AI aims at the automation of a broad range of tasks.
Typical tasks studied by AI researchers include playing games,
guiding vehicles, and classifying images. In principle, though, the
set of tasks that could be transformed by AI is vast. At minimum,
any task that humans or non-human animals use their intelligence
to perform could be a target for innovation.
While the field of artificial intelligence dates back to the 1950s,
several years of rapid progress and growth have recently invested
it with a greater and broader relevance. Researchers have achieved
sudden performance gains at a number of their most commonly
studied tasks.
Factors that help to explain these
recent gains include the exponential
growth of computing power, improved
machine learning algorithms
(especially in the area of deep
neural networks), development of
standard software frameworks for
faster iteration and replication of
experiments, larger and more widely
available datasets, and expanded
commercial investments (Jordan and
Mitchell, 2015).1
p.13
General Framework for AI & Security ThreatsFigure 1 illustrates this trend in the case of image recognition,
where over the past half-decade the performance of the best AI
systems has improved from correctly categorizing around 70%
of images to near perfect categorization (98%), better than the
human benchmark of 95% accuracy. Even more striking is the
case of image generation. As Figure 2 shows, AI systems can
now produce synthetic images that are nearly indistinguishable
from photographs, whereas only a few years ago the images they
produced were crude and obviously unrealistic.
AI systems are also beginning to achieve impressive performance
in a range of competitive games, ranging from chess to Atari
to Go to e-sports like Dota 2 . Even particularly challenging
tasks within these domains, such as the notoriously difficult Atari
game Montezuma’s Revenge, are beginning to yield to novel
AI techniques that creatively search for successful long-term
strategies , learn from auxiliary rewards such as feature control ,
and learn from a handful of human demonstrations . Other task
areas associated with significant recent progress include speech
recognition, language comprehension, and vehicle navigation.
From a security perspective, a number of these developments
are worth noting in their own right. For instance, the ability
to recognize a target’s face and to navigate through space can
be applied in autonomous weapon systems. Similarly, the ability
to generate synthetic images, text, and audio could be used
to impersonate others online, or to sway public opinion by
distributing AI-generated content through social media channels.
We discuss these applications of AI further in the Security
Domains section.
These technical developments can also be viewed as early
indicators of the potential of AI. The techniques used to achieve
high levels of performance on the tasks listed above have only
received significant attention from practitioners in the past decade
and are often quite general purpose. It will not be surprising
if AI systems soon become competent at an even wider variety
of security-relevant tasks.
At the same time, we should not necessarily expect to see
significant near-term progress on any given task. Many research
areas within AI, including much of robotics, have not changed
nearly so dramatically over the past decade. Similarly, the
observation that some of the most commonly studied tasks
have been associated with rapid progress is not necessarily
as significant as it first seems: these tasks are often widely studied
in the first place because they are particularly tractable.1
2
3
4
5
6
7
8
On page 18
On page 19
Mnih et al., 2015
Silver and Huang et al., 2016;
Silver, Schrittwieser, and Simonyan
et al., 2016
OpenAI, 2017a; OpenAI, 2017b
Vezhnevets et al., 2017
Jaderberg et al., 2016
Hester et al., 2017
To aid one’s predictions, it can
useful to note some systematic
difference between tasks which
contemporary AI systems are well-
suited to and tasks for which they
still fall short. In particular,
a task is likely to be promising
if a perfect mathematical model or
simulation of the task exists, if
short-term signals of progress are
available, if abundant data on the
successful performance of that task
by humans is available, or if the
solution to the task doesn’t require
a broader world-model or ``common
sense’’.
9
p.14
General Framework for AI & Security Threats
Figure 1: Recent progress in image recognition on the ImageNet
benchmark. Graph from the Electronic Frontier Foundation’s AI Progress
Measurement project (retrieved August 25, 2017).
ImageNet Image Recognition
Human performanceError RateNEC UIUC
XRCE
SuperVision
Clarifai
VGG
MSRAwithdrawn
Trimps-Soushen0.25
2011 2012 2013 2014 2015 2016 20170.20
0.15
0.10
0.05
p.15
General Framework for AI & Security Threats
Figure 2: Increasingly realistic synthetic faces generated by
variations on Generative Adversarial Networks (GANs). In order, the
images are from papers by Goodfellow et al. (2014), Radford et al.
(2015), Liu and Tuzel (2016), and Karras et al. (2017).2014 2015 2016 2017
p.16
General Framework for AI & Security ThreatsFinally, a few things should be said about the long-term prospects
for progress in artificial intelligence. Today, AI systems perform
well on only a relatively small portion of the tasks that humans are
capable of. However, even before the recent burst of progress,
this portion has expanded steadily over time . In addition, it has
often been the case that once AI systems reach human-level
performance at a given task (such as chess) they then go on to
exceed the performance of even the most talented humans.
Nearly all AI researchers in one survey expect that AI systems
will eventually reach and then exceed human-level performance
at all tasks surveyed. Most believe this transition is more likely than
not to occur within the next fifty years. The implications of such
a transition, should it occur, are difficult to conceptualize, and are
outside the primary scope of this report (see Scope , though we
briefly revisit this topic in the Conclusion ). Nevertheless, one might
expect AI systems to play central roles in many security issues
well before they are able to outperform humans at everything, in
the same way that they are already finding economic applications
despite not being able to automate most aspects of humans’ jobs.
Security-Relevant Properties of AI
AI is a dual-use area of technology . AI systems and the knowledge
of how to design them can be put toward both civilian and military
uses, and more broadly, toward beneficial and harmful ends. Since
some tasks that require intelligence are benign and other are not,
artificial intelligence is dual-use in the same sense that human
intelligence is. It may not be possible for AI researchers simply to
avoid producing research and systems that can be directed towards
harmful ends (though in some cases, special caution may be
warranted based on the nature of the specific research in question
- see Interventions ). Many tasks that it would be beneficial to
automate are themselves dual-use. For example, systems that
examine software for vulnerabilities have both offensive and
defensive applications, and the difference between the capabilities
of an autonomous drone used to deliver packages and the
capabilities of an autonomous drone used to deliver explosives
need not be very great. In addition, foundational research that aims
to increase our understanding of AI, its capabilities and our degree
of control over it, appears to be inherently dual-use in nature.
AI systems are commonly both efficient and scalable . Here, we
say an AI system is “efficient” if, once trained and deployed,
it can complete a certain task more quickly or cheaply than
a human could . We say an AI system is “scalable” if, given that
it can complete a certain task, increasing the computing power
it has access to or making copies of the system would allow it 2
Grace et al., 2017
Although trends in performance across
a range of domains have historically
not been comprehensively tracked
or well theorized (Brundage, 2016;
Hernández-Orallo, 2017), there have
been some recent efforts to track,
measure, and compare performance
(Eckersley and Nasser et al., 2017).
We distinguish here between task
efficiency of a trained system, which
commonly exceeds human performance,
and training efficiency: the amount
of time, computational resources
and data, that a system requires in
order to learn to perform well on
a task. Humans still significantly
exceed AI systems in terms of training
efficiency for most tasks.1
3
p.17
General Framework for AI & Security Threatsto complete many more instances of the task. For example,
a typical facial recognition system is both efficient and scalable;
once it is developed and trained, it can be applied to many
different camera feeds for much less than the cost of hiring human
analysts to do the equivalent work.
AI systems can exceed human capabilities . In particular, an AI
system may be able to perform a given task better than any human
could. For example, as discussed above, AI systems are now
dramatically better than even the top-ranked players at games
like chess and Go. For many other tasks, whether benign or
potentially harmful, there appears to be no principled reason why
currently observed human-level performance is the highest level
of performance achievable, even in domains where peak
performance has been stable throughout recent history, though
as mentioned above some domains are likely to see much faster
progress than others.
AI systems can increase anonymity and psychological distance .
Many tasks involve communicating with other people, observing
or being observed by them, making decisions that respond to
their behavior, or being physically present with them. By allowing
such tasks to be automated, AI systems can allow the actors who
would otherwise be performing the tasks to retain their anonymity
and experience a greater degree of psychological distance from
the people they impact . For example, someone who uses an
autonomous weapons system to carry out an assassination, rather
than using a handgun, avoids both the need to be present at the
scene and the need to look at their victim.
AI developments lend themselves to rapid diffusion . While
attackers may find it costly to obtain or reproduce the hardware
associated with AI systems, such as powerful computers or drones,
it is generally much easier to gain access to software and relevant
scientific findings. Indeed, many new AI algorithms are reproduced
in a matter of days or weeks. In addition, the culture of AI research
is characterized by a high degree of openness, with many papers
being accompanied by source code. If it proved desirable to limit
the diffusion of certain developments, this would likely be difficult
to achieve (though see Interventions for discussion of possible
models for at least partially limiting diffusion in certain cases).
Today’s AI systems suffer from a number of novel unresolved
vulnerabilities . These include data poisoning attacks (introducing
training data that causes a learning system to make mistakes ),
adversarial examples (inputs designed to be misclassified by
machine learning systems ), and the exploitation of flaws in the
design of autonomous systems’ goals . These vulnerabilities 1
2
3
4
Cummings, 2004; Scharre, 2018
Biggio et al., 2012
Szegedy et al., 2013
Amodei, Olah, et al., 2016
p.18
General Framework for AI & Security Threatsare distinct from traditional software vulnerabilities (e.g. buffer
overflows) and demonstrate that while AI systems can exceed
human performance in many ways, they can also fail in ways that
a human never would.
General Implications for the Threat Landscape
From the properties discussed above, we derive three high-level
implications of progress in AI for the threat landscape. Absent the
development of adequate defenses, progress in AI will:
• Expand existing threats
• Introduce new threats
• Alter the typical character of threats
In particular, we expect attacks to typically be more effective, more
finely targeted, more difficult to attribute, and more likely to exploit
vulnerabilities in AI systems.
These shifts in the landscape necessitate vigorous responses of
the sort discussed under Interventions .
Expanding Existing Threats
For many familiar attacks, we expect progress in AI to expand the
set of actors who are capable of carrying out the attack, the rate
at which these actors can carry it out, and the set of plausible
targets. This claim follows from the efficiency, scalability, and ease
of diffusion of AI systems. In particular, the diffusion of efficient AI
systems can increase the number of actors who can afford to carry
out particular attacks. If the relevant AI systems are also scalable,
then even actors who already possess the resources to carry out
these attacks may gain the ability to carry them out at a much
higher rate. Finally, as a result of these two developments, it may
become worthwhile to attack targets that it otherwise would not
make sense to attack from the standpoint of prioritization or cost-
benefit analysis.
One example of a threat that is likely to expand in these ways,
discussed at greater length below, is the threat from spear
phishing attacks . These attacks use personalized messages to
extract sensitive information or money from individuals, with the
A phishing attack is an attempt to
extract information or initiate action
from a target by fooling them with
a superficially trustworthy facade.
A spear phishing attack involves
collecting and using information
specifically relevant to the target
(e.g. name, gender, institutional
affiliation, topics of interest,
etc.), which allows the facade to
be customized to make it look more
relevant or trustworthy.1
p.19
General Framework for AI & Security Threatsattacker often posing as one of the target’s friends, colleagues, or
professional contacts. The most advanced spear phishing attacks
require a significant amount of skilled labor, as the attacker must
identify suitably high-value targets, research these targets’ social
and professional networks, and then generate messages that are
plausible within this context.
If some of the relevant research and synthesis tasks can be
automated, then more actors may be able to engage in spear
phishing. For example, it could even cease to be a requirement that
the attacker speaks the same language as their target. Attackers
might also gain the ability to engage in mass spear phishing, in
a manner that is currently infeasible, and therefore become less
discriminate in their choice of targets. Similar analysis can be
applied to most varieties of cyberattacks, as well as to threats
to physical or political security that currently require non-trivial
human labor.
Progress in AI may also expand existing threats by increasing
the willingness of actors to carry out certain attacks. This claim
follows from the properties of increasing anonymity and increasing
psychological distance. If an actor knows that an attack will not
be tracked back to them, and if they feel less empathy toward
their target and expect to experience less trauma, then they
may be more willing to carry out the attack. The importance
of psychological distance, in particular, is illustrated by the fact
that even military drone operators, who must still observe their
targets and “pull the trigger, ” frequently develop post-traumatic
stress from their work . Increases in psychological distance,
therefore, could plausibly have a large effect on potential
attackers’ psychologies.
We should also note that, in general, progress in AI is not the
only force aiding the expansion of existing threats. Progress
in robotics and the declining cost of hardware, including both
computing power and robots, are important too, and discussed
further below. For example, the proliferation of cheap hobbyist
drones, which can easily be loaded with explosives, has only
recently made it possible for non-state groups such as the Islamic
State to launch aerial attacks .
Introducing New Threats
Progress in AI will enable new varieties of attacks. These attacks
may use AI systems to complete certain tasks more successfully
than any human could, or take advantage of vulnerabilities that
AI systems have but humans do not. 1
2
Chatterjee, 2015; Dao, 2013; Hawkes,
2015
Solomon, 2017
p.20
General Framework for AI & Security ThreatsFirst, the property of being unbounded by human capabilities
implies that AI systems could enable actors to carry out attacks
that would otherwise be infeasible. For example, most people
are not capable of mimicking others’ voices realistically or
manually creating audio files that resemble recordings of
human speech. However, there has recently been significant
progress in developing speech synthesis systems that learn to
imitate individuals’ voices (a technology that’s already being
commercialized ). There is no obvious reason why the outputs of
these systems could not become indistinguishable from genuine
recordings, in the absence of specially designed authentication
measures. Such systems would in turn open up new methods of
spreading disinformation and impersonating others .
In addition, AI systems could also be used to control aspects of
the behavior of robots and malware that it would be infeasible
for humans to control manually. For example, no team of humans
could realistically choose the flight path of each drone in a swarm
being used to carry out a physical attack. Human control might
also be infeasible in other cases because there is no reliable
communication channel that can be used to direct the relevant
systems; a virus that is designed to alter the behavior of air-gapped
computers, as in the case of the ‘Stuxnet’ software used to disrupt
the Iranian nuclear program, cannot receive commands once it
infects these computers. Restricted communication challenges
also arise underwater and in the presence of signal jammers, two
domains where autonomous vehicles may be deployed.
Second, the property of possessing unresolved vulnerabilities
implies that, if an actor begins to deploy novel AI systems,
then they may open themselves up to attacks that specifically
exploit these vulnerabilities. For example, the use of self-driving
cars creates an opportunity for attacks that cause crashes by
presenting the cars with adversarial examples. An image of a stop
sign with a few pixels changed in specific ways, which humans
would easily recognize as still being an image of a stop sign, might
nevertheless be misclassified as something else entirely by an
AI system. If multiple robots are controlled by a single AI system
run on a centralized server, or if multiple robots are controlled by
identical AI systems and presented with the same stimuli, then
a single attack could also produce simultaneous failures on
an otherwise implausible scale. A worst-case scenario in this
category might be an attack on a server used to direct autonomous
weapon systems, which could lead to large-scale friendly fire or
civilian targeting .1
2
3
Lyrebird, 2017
Allen and Chan, 2017
Scharre, 2016
p.21
General Framework for AI & Security ThreatsAltering the Typical Character of Threats
Our analysis so far suggests that the threat landscape will
change both through expansion of some existing threats and the
emergence of new threats that do not yet exist. We also expect
that the typical character of threats will shift in a few distinct ways.
In particular, we expect the attacks supported and enabled by
progress in AI to be especially effective, finely targeted, difficult
to attribute, and exploitative of vulnerabilities in AI systems.
First, the properties of efficiency, scalability, and exceeding human
capabilities suggest that highly effective attacks will become more
typical (at least absent substantial preventive measures). Attackers
frequently face a trade-off between the frequency and scale of
their attacks, on the one hand, and their effectiveness on the
other . For example, spear phishing is more effective than regular
phishing, which does not involve tailoring messages to individuals,
but it is relatively expensive and cannot be carried out en masse.
More generic phishing attacks manage to be profitable despite
very low success rates merely by virtue of their scale. By improving
the frequency and scalability of certain attacks, including spear
phishing, AI systems can render such trade-offs less acute. The
upshot is that attackers can be expected to conduct more effective
attacks with greater frequency and at a larger scale. The expected
increase in the effectiveness of attacks also follows from the
potential of AI systems to exceed human capabilities.
Second, the properties of efficiency and scalability, specifically
in the context of identifying and analyzing potential targets, also
suggest that finely targeted attacks will become more prevalent.
Attackers often have an interest in limiting their attacks to targets
with certain properties, such as high net worth or association
with certain political groups, as well as an interest in tailoring their
attacks to the properties of their targets. However, attackers often
face a trade-off between how efficient and scalable their attacks
are and how finely targeted they are in these regards. This trade-off
is closely related to the trade-off with effectiveness, as discussed,
and the same logic implies that we should expect it to become less
relevant. An increase in the relative prevalence of spear phishing
attacks, compared to other phishing attacks, would be an example
of this trend as well. An alternative example might be the use of
drone swarms that deploy facial recognition technology to kill
specific members of crowds, in place of less finely targeted forms
of violence.
Third, the property of increasing anonymity suggests that difficult-
to-attribute attacks will become more typical. An example, again,
1
Herley 2010
p.22
General Framework for AI & Security Threatsis the case of an attacker who uses an autonomous weapons
system to carry out an attack rather than carrying it out in person.
Finally, we should expect attacks that exploit the vulnerabilities of
AI systems to become more typical. This prediction follows directly
from the unresolved vulnerabilities of AI systems and the likelihood
that AI systems will become increasingly pervasive.
Scenarios
p.23The following scenarios are intended to illustrate a range of
plausible uses toward which AI could be put for malicious ends,
in each of the domains of digital, physical, and political security.
Examples have been chosen to illustrate the diverse ways in
which the security-relevant characteristics of AI introduced above
could play out in different contexts. These are not intended to
be definitive forecasts (some may not end up being technically
possible in 5 years, or may not be realized even if they are possible)
or exhaustive (other malicious uses will undoubtedly be invented
that we do not currently foresee). Additionally some of these are
already occurring in limited form today, but could be scaled up or
made more powerful with further technical advances.
p.24Digital Security
Automation of social engineering attacks . Victims’ online
information is used to automatically generate custom malicious
websites/emails/links they would be likely to click on, sent
from addresses that impersonate their real contacts, using a
writing style that mimics those contacts. As AI develops further,
convincing chatbots may elicit human trust by engaging people in
longer dialogues, and perhaps eventually masquerade visually as
another person in a video chat.
Hypothetical scenario:
Jackie logs into the admin console for the
CleanSecure robot that she manages; operating
on a verified kernel, it is guaranteed by the
manufacturer to be hack-proof. She then uploads
photographs of a new employee so the robot will
recognize him when he walks into the building and will
not sound the alarm. While she waits for the robot
to authenticate its updated person database with the
company’s other security systems, Jackie plays with
the model train on her desk, allowing herself a couple
of runs around the track that encircles her keyboard
and monitor. There’s a ping, signaling successful
authentication, and she smiles to herself and carries
on with her tasks.
Later that afternoon, Jackie is browsing Facebook
while idly managing a firmware update of the robot.
An ad catches her eye - a model train set sale at
a hobbyist shop that, it turns out, is located just
a few minutes from her house. She fills out an online
form to get a brochure emailed to her, then she opens
the brochure when it pops into her inbox. The robot
dings, signalling a need for attention, so she
minimizes the brochure and logs back into the
admin console.
Jackie doesn’t know that the brochure was infected
with malware. Based on data from her online profile
and other public info, an AI system was used to
generate a very personalized vulnerability profile
for Jackie - the model train advert - which was then
farmed out to a freelancer to create a tailored
exploit for this vulnerability. When Jackie logged
Scenarios
p.25
ScenariosAutomation of vulnerability discovery . Historical patterns of
code vulnerabilities are used to speed up the discovery of new
vulnerabilities, and the creation of code for exploiting them.
More sophisticated automation of hacking . AI is used
(autonomously or in concert with humans) to improve target
selection and prioritization, evade detection, and creatively
respond to changes in the target’s behavior. Autonomous software
has been able to exploit vulnerabilities in systems for a long time ,
but more sophisticated AI hacking tools may exhibit much better
performance both compared to what has historically been possible
and, ultimately (though perhaps not for some time), compared
to humans.
into the console, her username and password were
exfiltrated to a darknet command and control server.
It won’t be long before someone buys them and uses
them to subvert the CleanSecure robot with fully
privileged access.
Hypothetical scenario:
Progress in automated exploit generation (and
mitigation) has begun to accelerate. Previous
fuzzing architectures are augmented by neural network
techniques (Blum, 2017) that are used to identify
“interesting” states of programs, analogous to the
way that AlphaGo uses neural networks to identify
“interesting” states in the search space of Go games.
These methods increase the security of well-defended
systems run by major corporations and some parts of
Western governments. But after a year or two, they
are also adopted by organized crime groups in eastern
Europe, which deploy a piece of ransomware called
WannaLaugh.
This malware is continuously updated with dozens
of new exploits found by these fuzzing techniques.
Though fully patched OSes and browsers are mostly
resistant, most older phones, laptops and IoT devices
prove enduringly vulnerable. The malware adopts a
particularly pernicious life cycle of infecting a
vulnerable IoT device on a WiFi network and waiting
Scenarios1
see e.g. Spafford, 1988
p.26
ScenariosHuman-like denial-of-service . Imitating human-like behavior
(e.g. through human-speed click patterns and website navigation),
a massive crowd of autonomous agents overwhelms an online
service, preventing access from legitimate users and potentially
driving the target system into a less secure state.
Automation of service tasks in criminal cyber-offense .
Cybercriminals use AI techniques to automate various tasks that
make up their attack pipeline, such as payment processing or
dialogue with ransomware victims.
Prioritising targets for cyber attacks using machine learning .
Large datasets are used to identify victims more efficiently, e.g.
by estimating personal wealth and willingness to pay based on
online behavior.
Exploiting AI used in applications, especially in information
security . Data poisoning attacks are used to surreptitiously maim
or create backdoors in consumer machine learning models.
Black-box model extraction of proprietary AI system capabilities .
The parameters of a remote AI system are inferred by systematically
sending it inputs and observing its outputs.
for vulnerable devices to join that network. Hundreds
of millions of devices are infected, and tens of
millions of people around the world are forced to
pay a EUR 300 ransom in bitcoin in order to recover
access to the data on their phones and laptops, and
unbrick expensive electronics.
The epidemic is only arrested after active
countermeasures are pushed to a number of modern
operating systems and browsers, causing those
machines to scan for infected machines and launch
remote exploits to remove the malware. Unfortunately,
millions more devices are bricked by these
countermeasures, and in around the world there are
numerous outages and problems in HVAC, lighting,
and other “non critical” infrastructure systems as a
result of the malware and countermeasures.
Scenarios
Scenarios
p.27Physical Security
Terrorist repurposing of commercial AI systems .
Commercial systems are used in harmful and unintended ways,
such as using drones or autonomous vehicles to deliver explosives
and cause crashes.
Endowing low-skill individuals with previously high-skill attack
capabilities . AI-enabled automation of high-skill capabilities — such
as self-aiming, long-range sniper rifles - reduce the expertise
required to execute certain kinds of attack.
Increased scale of attacks . Human-machine teaming using
autonomous systems increase the amount of damage that
/// Incident Interim Report
June 3rd BMF HQ Attack ///
As shown by CCTV records, the office cleaning
`SweepBot`, entered the underground parking lot of
the ministry late at night. The robot - the same
brand as that used by the ministry - waited until two
of the ministry’s own cleaning robots swept through
the parking lot on a regular patrol, then it followed
them into a service elevator and parked itself in the
utility room alongside the other robots.
On the day of the attack, the intruding robot
initially engaged in standard cleaning behaviors
with the other robots: collecting litter, sweeping
corridors, maintaining windows, and other tasks. Then,
following visual detection of the finance minister,
Dr. Brenda Gusmile, the intruding robot stopped
performing its cleaning tasks and headed directly
towards the minister. An explosive device hidden
inside the robot was triggered by proximity, killing
the minister and wounding nearby staff members.
Several hundred robots of this make are sold in the
Berlin area every week. In collaboration with the
manufacturer, the point of sale of the specific robot
was traced to an office supply store in Potsdam.
The transaction was carried out in cash. We have no
further leads to explore with regard to the identity
of the perpetrator.
Scenarios
Scenarios
p.28
Avinash had had enough. Cyberattacks everywhere,
drone attacks, rampant corruption, and what was the
government doing about it? Absolutely nothing. Sure,
they spoke of forceful responses and deploying the
best technology, but when did he last see a hacker
being caught or a CEO going to prison? He was reading
all this stuff on the web (some of it fake news,
though he didn’t realize), and he was angry. He kept
thinking: What should I do about it? So he started
writing on the internet - long rants about how no one
was going to jail, how criminals were running wild,
how people should take to the streets and protest.
Then he ordered a set of items online to help him
assemble a protest sign. He even bought some smoke
bombs, planning to let them off as a finale to a
speech he was planning to give in a public park.
The next day, at work, he was telling one of his
colleagues about his planned activism and was
launching into a rant when a stern cough sounded
from behind him. “Mr. Avinash Rah?”, said the police
officer, “our predictive civil disruption system has
flagged you as a potential threat.” “But that’s
ridiculous!” protested Avinash. “You can’t argue with
99.9% accuracy. Now come along, I wouldn’t like to
use force.”individuals or small groups can do: e.g. one person launching an
attack with many weaponized autonomous drones.
Swarming attacks . Distributed networks of autonomous robotic
systems, cooperating at machine speed, provide ubiquitous
surveillance to monitor large areas and groups and execute rapid,
coordinated attacks.
Attacks further removed in time and space . Physical attacks are
further removed from the actor initiating the attack as a result of
autonomous operation, including in environments where remote
communication with the system is not possible.
Political Security
State use of automated surveillance platforms to suppress dissent .
State surveillance powers of nations are extended by automating
image and audio processing, permitting the collection, processing,
and exploitation of intelligence information at massive scales for
myriad purposes, including the suppression of debate.
p.29Fake news reports with realistic fabricated video and audio .
Highly realistic videos are made of state leaders seeming to make
inflammatory comments they never actually made.
Automated, hyper-personalised disinformation campaigns .
Individuals are targeted in swing districts with personalised
messages in order to affect their voting behavior.
Automating influence campaigns . AI-enabled analysis of social
networks are leveraged to identify key influencers, who can
then be approached with (malicious) offers or targeted with
disinformation.
Denial-of-information attacks . Bot-driven, large-scale information-
generation attacks are leveraged to swamp information channels
with noise (false or merely distracting information), making it more
difficult to acquire real information.
Manipulation of information availability . Media platforms’ content
curation algorithms are used to drive users towards or away from
certain content in ways to manipulate user behavior.
Scenarios
Scenarios
p.30Security
Domains
Here, we analyze malicious uses of AI that would compromise the
confidentiality, integrity, and availability of digital systems (threats
to Digital Security ); attacks taking place in the physical world
directed at humans or physical infrastructure (threats to Physical
Security ); and the use of AI to threaten a society’s ability to engage
in truthful, free, and productive discussions about matters of
public importance and legitimately implement broadly just and
beneficial policies (threats to Political Security ). These categories
are not mutually exclusive—for example, AI-enabled hacking can
be directed at cyber-physical systems with physical harm resulting
as a consequence, and physical or digital attacks could be carried
out for political purposes—but they provide a useful structure
for our analysis.
03
Defined as “engineered systems that
are built from, and depend upon, the
seamless integration of computational
algorithms and physical components”
(National Science Foundation, 2017).1
p.31
Security Domains
In each domain of security, we summarize the existing state of play
of attack and defense prior to wide adoption of AI in these domains,
and then describe possible changes to the nature or severity of
attacks that may result from further AI progress and diffusion. The
three sections below all draw on the insights discussed above
regarding the security-relevant properties of AI, but can be read
independently of one another, and each can be skipped by readers
less interested in a particular domain.
Digital Security
Absent preparation, the straightforward application of
contemporary and near-term AI to cybersecurity offense can be
expected to increase the number, scale, and diversity of attacks
that can be conducted at a given level of capabilities, as discussed
more abstractly in the General Framework for AI and Security
Threats above. AI-enabled defenses are also being developed and
deployed in the cyber domain, but further technical and policy
innovations (discussed further in Interventions ) are needed to
ensure that impact of AI on digital systems is net beneficial.
Context
Cybersecurity is an arena that will see early and enthusiastic
deployment of AI technologies, both for offense and defense;
indeed, in cyber defense, AI is already being deployed for
purposes such as anomaly and malware detection. Consider
the following:
• Many important IT systems have evolved over time to be
sprawling behemoths, cobbled together from multiple different
systems, under-maintained and — as a consequence — insecure.
Because cybersecurity today is largely labor-constrained , it is
ripe with opportunities for automation using AI. Increased use
of AI for cyber defense, however, may introduce new risks, as
discussed below.
• In recent years, various actors have sought to mount
increasingly sophisticated cyberoperations, including finely
targeted attacks from state actors (including the Stuxnet
Worm and the Ukrainian power grid “crash override” exploit).
The cyber arena also includes a vast and complex world
of cybercrime , which sometimes involves a high degree
of professionalization and organization . Such groups use
DDoS, malware, phishing, ransomware, and other forms of
82% of decision-makers surveyed
at public and private organizations
in eight countries have reported
a shortage of needed cybersecurity
skills (McAfee and the Center
for Strategic and International
Studies, 2016).1
2
3
McAfee and the Center for the
Strategic and International Studies,
2013
Hilary, 2016; Flashpoint, 2016
p.32
Security Domainscyberoperations, and quickly adopt emerging technologies (e.g.
Bitcoin for ransomware payments).
Already, AI is being widely used on the defensive side of
cybersecurity, making certain forms of defense more effective
and scalable, such as spam and malware detection. At the same
time, many malicious actors have natural incentives to experiment
with using AI to attack the typically insecure systems of others.
These incentives include a premium on speed, labor costs, and
difficulties in attracting and retaining skilled labor.
To date, the publicly-disclosed use of AI for offensive purposes has
been limited to experiments by “white hat” researchers, who aim
to increase security through finding vulnerabilities and suggesting
solutions. However, the pace of progress in AI suggests the
likelihood of cyber attacks leveraging machine learning capabilities
in the wild soon, if they have not done so already. Indeed, some
popular accounts of AI and cybersecurity include claims based on
circumstantial evidence that AI is already being used for offense by
sophisticated and motivated adversaries . Expert opinion seems
to agree that if this hasn’t happened yet, it will soon: a recent
survey of attendees at the Black Hat conference found 62% of
respondents believing AI will be used for attacks within the next
12 months . Despite these claims, to our knowledge there is no
publicly documented evidence of AI-based attacks, though it
should be noted that evidence from many successful attacker
techniques (e.g. botnets, email phishing campaigns) may be
difficult to attribute to AI versus human labor or simple automation.
We are thus at a critical moment in the co-evolution of AI and
cybersecurity and should proactively prepare for the next wave
of attacks.
Many governments are keenly interested in the combination
of AI and cybersecurity. In response to a question from one of
the authors of this report, Admiral Mike Rogers, the Director
of the National Security Agency, said, “Artificial Intelligence
and machine learning — I would argue — is foundational to the
future of cybersecurity […] It is not the if, it’s only the when to
me. ” AI systems are already set to play an expanded role in US
military strategy and operations in the coming years as the US
DoD puts into practice its vision of a “Third Offset” strategy , in
which humans and machines work closely together to achieve
military objectives. At the same time, governments are investing
in foundational research to expand the scope of capabilities of
AI systems. In 2016, DARPA hosted the Cyber Grand Challenge
contest , which saw teams of human researchers compete with 1
2
43
Dvorsky, 2017
Cylance, 2017
DARPA, 2016
Pellerin, 2016; Hicks et al., 2017
p.33
Security Domainseach other to create programs that could autonomously attack
other systems while defending themselves. Though the winning
AI system fared poorly when facing off against human security
experts, we agree with the hosts of the event that AI cybersecurity
capabilities will improve rapidly in coming years, especially as
recent advances in AI (such as in the area of deep reinforcement
learning ) are applied to cybersecurity.
How AI Changes The Digital Security Threat Landscape
A central concern at the nexus of AI and cybersecurity is that
AI might enable larger-scale and more numerous attacks to
be conducted by an attacker with a given amount of skill and
resources compared with the impact such an attacker might
currently be able to achieve. Recent years have seen impressive
and troubling proofs of concept of the application of AI to
offensive applications in cyberspace. For example, researchers
at ZeroFox demonstrated that a fully automated spear phishing
system could create tailored tweets on the social media platform
Twitter based on a user’s demonstrated interests, achieving a high
rate of clicks to a link that could be malicious .
There is clearly interest in such larger-scale attacks: Russian
hackers sent “expertly tailored messages carrying malware to more
than 10,000 Twitter users in the [U.S.] Defense Department” ,
which likely required significant time and effort, and could have
gone even further with automation (assuming it was not involved
already in this case). Giaretta and Dragoni (2017) discuss the
concept of “community targeted spam” that uses natural language
generation techniques from AI to target an entire class of people
with common ways of writing; with even more advanced natural
language generation, one could envision even more customized
approaches, spanning multiple communities. Furthermore, the
application of AI to the automation of software vulnerability
discovery, while having positive applications (discussed further
in the Interventions section), can likewise be used for malicious
purposes to alleviate the labor constraints of attackers.
The adaptability of AI systems, too, may change the strategic
landscape of cybersecurity, though it is not yet clear how
adaptability will affect the offense/defense balance. Many
organizations currently adopt security systems called Endpoint
Detection and Response (EDR) platforms to counter more
advanced threats. The EDR market represents a $500 million
industry in the cyber security arena . These tools are built upon
a combination of heuristic and machine learning algorithms to
provide capabilities such as next-generation anti-virus (NGAV), 1
2
3
4
Arulkumaran et al., 2017
Seymour and Tully, 2016)
Calabresi, 2017
Litan, 2017
p.34
Security Domainsbehavioral analytics, and exploit prevention against sophisticated
targeted attacks. Though these systems are fairly effective against
typical human-authored malware, research has already shown that
AI systems may be able to learn to evade them.
As an example of AI being used to avoid detection, Anderson et
al. created a machine learning model to automatically generate
command and control domains that are indistinguishable from
legitimate domains by human and machine observers. These
domains are used by malware to “call home” and allow malicious
actors to communicate with the host machines. Anderson et al.
also leveraged reinforcement learning to create an intelligent
agent capable of manipulating a malicious binary with the end goal
of bypassing NGAV detection. Similarly, Kharkar et al. applied
adversarial machine learning to craft malicious documents that
could evade PDF malware classifiers.
Attackers are likely to leverage the growing capabilities of
reinforcement learning, including deep reinforcement learning .
In particular, we expect attackers to leverage the ability of AI
to learn from experience in order to craft attacks that current
technical systems and IT professionals are ill-prepared for, absent
additional investments. For example, services like Google’s
VirusTotal file analyzer allows users to upload variants to a central
site and be judged by 60+ different security tools. This feedback
loop presents an opportunity to use AI to aid in crafting multiple
variants of the same malicious code to determine which is most
effective at evading security tools. Additionally, large-scale AI
attackers can accumulate and use large datasets to adjust their
tactics, as well as varying the details of the attack for each target.
This may outweigh any disadvantages they suffer from the lack of
skilled human attention to each target, and the ability of defenders
like antivirus companies and IT departments to learn to recognize
attack signatures.
While the specific examples of AI applied to offensive
cybersecurity mentioned above were developed by white hat
researchers, we expect similar efforts by cybercriminals and state
actors in the future as highly capable AI techniques become more
widely distributed, as well as new applications of AI to offensive
cybersecurity that have not yet been explored.
Points of Control and Existing Countermeasures
Cyber risks are difficult to avert entirely, but not impossible
to mitigate, and there are multiple points of control at which
interventions can increase security. Below, we highlight different 1
3
42
Anderson et al. 2016
Kharkar et al. 2017
Arulkumaran et al., 2017
Anderson et al. 2018
p.35
Security Domainspoints of control and existing countermeasures for defending at
those points, as well as their limitations. Overall, we believe that
AI and cybersecurity will rapidly evolve in tandem in the coming
years, and that a proactive effort is needed to stay ahead of
motivated attackers. We highlight potential but not yet proven
countermeasures in the section below on Interventions .
Consumer awareness:
More aware users can spot telltale signs of certain attacks, such
as poorly crafted phishing attempts, and practice better security
habits, such as using diverse and complex passwords and two-
factor authentication. However, despite long-standing awareness
of the vulnerability of IT systems, most end users of IT systems
remain vulnerable to even simple attacks such as the exploitation
of unpatched systems . This is concerning in light of the potential
for the AI-cybersecurity nexus, especially if high-precision attacks
can be scaled up to large numbers of victims.
Governments and researchers:
Various laws and researcher norms pertain to cybersecurity. For
example, the Digital Millennium Act and the Computer Fraud and
Abuse Act in the US proscribe certain actions in cyberspace .
Legal enforcement is particularly difficult across national
boundaries. Norms such as responsible disclosure of vulnerabilities
also aid in defense by reducing the likelihood of a newly disclosed
vulnerability being used against a large number of victims before
it can be patched. AI is not explicitly addressed in such laws and
norms, though we discuss their possible applicability to AI below in
Interventions .
An important activity that cybersecurity researchers perform
is the detection of vulnerabilities in code, allowing vendors to
increase the security of their products. Several approaches exist to
incentivize such processes and make them easier, including:
• Payment of “Bug bounties, ” in which participants are
compensated for finding and responsibly disclosing
vulnerabilities.
• “Fuzzing, ” an automated method of vulnerability detection by
trying out many possible permutations of inputs to a program,
which is often used internally by companies to discover
vulnerabilities.
• Products (already available) that rely on machine learning to
predict whether source code may contain a vulnerability.1
National Cyber Security Crime
Centre, 2016
Both the DMCA and the CFAA have been
criticised for creating risk for
computer security researchers and
thereby making systems less secure in
some cases (EFF, 2014; Timm, 2013),
which may either suggest that these
tasks are not the right model for
legislative action, or that laws and
norms are hard to use effectively as
an intervention.
2
p.36
Security DomainsIndustry centralization:
Spam filters are a canonical example of where centralization of an
IT system aids defense—individuals benefit from the strength of
Google’s spam filter and consequently are protected from many
very simple attacks, and this filter is stronger because Google uses
large amounts of user data to improve it over time. Likewise, many
large networks are constantly monitoring for anomalies, protecting
those who use the networks if anomalies are correctly identified
and acted upon. These systems benefit from economies of scale—
it makes more sense to continue iterating a single spam filter for
a large number of users than to have every user build their own or
have one installed on their computer. Similarly, cloud computing
companies may enforce terms of agreement that prevent their
hardware from being used for malicious purposes, provided they
can identify such behavior. Another example of a system-level
defense is blacklisting of IP addresses from which attacks are
commonly launched, though skilled attackers can obfuscate the
origin of their attacks. Centralization and the associated economies
of scale may also facilitate the deployment of AI-based defenses
against cybersecurity attacks, by allowing the aggregation of
large datasets and the concentration of labor and expertise for
defense. This dynamic may be very important for preventing attack
from outpacing defense and is discussed further in Interventions
and Appendix B .
Centralization is not an unalloyed good, however, as it raises
the stakes if central systems are compromised. Another
difficulty with this control point is that attackers can learn how
to evade system-level defenses. For example, they can purchase
commercial antivirus software and analyze changes between
updates of the protection protocol to see what is and isn’t being
protected against.
Attacker incentives :
Attackers can be deterred from committing future attacks or
punished for prior attacks. A necessary (though not sufficient)
condition of successfully deterring and punishing attackers is the
ability to attribute the source of an attack, a notoriously difficult
problem . A compounding problem for those who would attribute
an attack is that even if they have high-quality information, they
may not want to reveal it, because doing so may compromise
a source or method . Finally, some entities may not wish to punish
certain actions, so as to avoid creating precedent and thereby
preserve leeway to engage in such actions themselves .
Technical cybersecurity defenses :
A wide variety of cybersecurity defenses are available, though
there is as yet little solid analysis of their relative effectiveness . 1
Rid, 2015
For instance, the failure of the
United Nations Cybersecurity Group of
Governmental Experts to make progress
on norms for hacking in international
law (Korzak, 2017) appears to be a
result of this dynamic.
32
4
Libicki, 2016
Libicki, 2016
p.37
Security DomainsMany of these interventions were proposed before unique
considerations of AI were apparent but nevertheless remain
relevant in a future with expanded AI cybersecurity applications.
Companies provide a wide variety of cybersecurity solutions,
ranging from automatic patching of a vendor’s own software, to
threat detection, to incident response and consulting services.
Network and endpoint security products aim to prevent, detect,
and respond to threats. Solutions include detection of software
exploits, and prevention or detection of attacker tools, techniques,
and procedures. Key areas of defense include the endpoint (i.e.,
computer) security, internal network security, and cloud security.
Machine learning approaches are increasingly used for cyber
defense. This may take the form of supervised learning, where the
goal is to learn from known threats and generalize to new threats,
or in the form of unsupervised learning in which an anomaly
detector alerts on suspicious deviations from normal behavior.
For example, so-called “next-gen” antivirus solutions often
leverage supervised learning techniques to generalize to new
malware variants. User and entity behavioral tools monitor normal
user or application behavior, and detect deviations from normalcy
in order to detect malicious behavior among the collected
anomalies. Recently, AI has also been used to aid security
professionals to hunt for malicious actors more efficiently within
their own enterprises, by allowing interaction via natural language
and automating queries for understanding potential threats .
Relatively little attention has been paid to making AI-based
defenses robust against attackers that anticipate their use.
Ironically, the use of machine learning for cyber defense can
actually expand the attack surface due to this lack of attention
and other vulnerabilities . Furthermore, surveys of cybersecurity
professionals indicate low confidence in AI-based defense systems
today . As such, we encourage further development of such
defense technologies in the Interventions section below.
Physical Security
In this section, we consider AI-related risks in the broad area of
physical harm. Many of these are familiar challenges from existing
uses of electronics and computers in weapons systems, though
the addition of AI capabilities may change this landscape along
the lines introduced in the General Framework for AI and Security
Threats . As with Digital Security above, we introduce the context,
AI-enabled changes, and existing countermeasures related to
physical attacks below.1
32
Filar, Seymour, and Park, 2017
Carbon Black, 2017
Anderson et al., 2017; Yampolskiy,
2017
p.38
Security DomainsRegulation and technical research on defense have been slow
to catch up with the global proliferation of weaponizable robots.
While defenses against attacks via robots (especially aerial
drones) are being developed, there are few obstacles at present
to a moderately talented attacker taking advantage of the rapid
proliferation of hardware, software, and skills to cause large
amounts of physical harm through the direct use of AI or the
subversion of AI-enabled systems. Physical harm via human-piloted
drones and land-based robots is already playing a major role in
some conflicts, even prior to the incorporation of autonomy .
In the near-term, we can expect a growing gap between attack
capabilities and defense capabilities, because the necessary
defenses are capital-intensive and the hardware and software
required to conduct attacks are increasingly widely distributed.
Unlike the digital world, where key nodes in the network such as
Google can play a key role in defense, physical attacks can happen
anywhere in the world, and many people are located in regions
with insufficient resources to deploy large-scale physical defenses
of the kind discussed below, thus necessitating consideration
of policy measures and interventions related to the supply chain
for robots.
The resource and technological advantages currently available
to large organizations, such as militaries and police forces, in the
domain of physical attack and defense will continue when such
attacks become augmented by AI. However, it should be noted
that some of the most worrying AI-enabled attacks may come from
small groups and individuals who have preferences far outside
what is typical and which are difficult to anticipate or prevent, as
with today’s “lone-wolf” terrorist attacks such as mass shootings.
Context
Recent years have seen an explosion in the number and variety of
commercial applications for robots. Industrial robots are growing
in number (254,000 supplied in 2015 versus 121,000 in 2010 ),
some with and some without AI components. Relatively primitive
cleaning robots are in wide use and more sophisticated service
robots appear to be on the horizon (41,000 service robots were
sold in 2015 for professional use, and about 5.4 million for personal
and domestic use ). Additionally, not all of these robots are on the
ground. There are aquatic and aerial robotics applications being
explored, with the latter proliferating in very high numbers. In
the United States alone, the number of drones has skyrocketed
in recent years, with over 670,000 registered with the Federal
Aviation Administration in 2016 and 2017 .
1
2
3
Singer, 2009
IFR, 2016
IFR, 2016
4
Vanian, 2017
p.39
Security DomainsAmbitious plans for drone-based delivery services are being
proposed and tested, commercial opportunities for drones are
continuously launched, and recreational uses are flourishing (e.g.
drone racing and photography). Driverless cars are robots, and
they also are increasingly being used in uncontrolled environments
(that is, outside of test facilities), though large-scale deployment
of fully autonomous driverless cars awaits the resolution of
technical and policy challenges. A wide range of robots with
autonomous features are already deployed within multiple national
militaries, some with the ability to apply lethal force , and there is
ongoing discussion of possible arms control measures for lethal
autonomous weapon systems.
Three characteristics of this diffusion of robotics should be noted.
• It is truly global: humanitarian, recreational, military, and
commercial applications of robots are being explored on
every continent, and the supply chains are also global, with
production and distribution dispersed across many countries.
• The diffusion of robotics enables a wide range of applications:
drone uses already range from competitive racing to
photography to terrorism . While some specialized systems
exist (e.g. some special-purpose industrial robots and cleaning
robots that can only move around and vacuum), many are fairly
generic and customizable for a variety of purposes.
• Robotic systems today are mostly not autonomous, as humans
play a significant role in directing their behavior, but more and
more autonomous and semi-autonomous systems are also being
developed for application such as delivery and security in real
world environments . For example, from relatively unstable and
hard-to-fly drones a decade ago, to drones that can stabilize
themselves automatically, we see a steady increase in the
autonomy of deployed systems. More autonomous behavior
is on the horizon for commercial products as well as military
systems .
Each of these characteristics sets the stage for a potentially
disruptive application of AI and malicious intent to existing and
near-term robotic systems.
How AI Changes the Physical Security Landscape
The ability of many robots to be easily customized and equipped
1
2
4
53
Roff, 2016a
Franke, 2016
Standage, 2017
Roff, 2016a
e.g. Kolodny, 2017; Wiggers, 2017
p.40
Security Domainswith dangerous payloads lends itself to a variety of physical
attacks being carried out in a precise way from a long distance, an
ability previously limited to countries with the resources to afford
technologies like cruise missiles . This threat exists independently
of AI (indeed, as mentioned above, most robots are human-piloted
at present) but can be magnified through the application of AI to
make such systems autonomous. As mentioned previously, non-
automated drone attacks have been conducted already by groups
such as ISIS and Hamas , and the globalized nature of the robotics
market makes it difficult to prevent this form of use. Nonetheless,
we will discuss some possible countermeasures below.
Greater degrees of autonomy enable a greater amount of damage
to be done by a single person — making possible very large-scale
attacks using robots — and allowing smaller groups of people to
conduct such attacks. The software components required to carry
out such attacks are increasingly mature. For example, open source
face detection algorithms, navigation and planning algorithms, and
multi-agent swarming frameworks that could be leveraged towards
malicious ends can easily be found.
Depending on their power source, some robots can operate for
long durations, enabling them to carry out attacks or hold targets
at risk over long periods of time. Robots are also capable of
navigating different terrain than humans, in light of their different
perceptual capabilities (e.g. infrared and lidar for maneuvering in
the dark or in low-visibility fog) and physical capacities (e.g. being
undeterred by smoke or other toxic substances and not needing
oxygen underwater). Thus, a larger number of spaces may become
vulnerable to automated physical attacks.
There are also cross-cutting issues stemming from the intersection
of cybersecurity and increasingly autonomous cyber-physical
systems. The diffusion of robots to a large number of human-
occupied spaces makes them potentially vulnerable to remote
manipulation for physical harm, as with, for example, a service
robot hacked from afar to carry out an attack indoors. With regard
to cyber-physical systems, the Internet of Things (IoT) is often
heralded as a source of greater efficiency and convenience,
but it is also recognized to be highly insecure and represents
an additional attack vector by which AI systems controlling
key systems could be subverted, potentially causing more damage
than would have been possible were those systems under
human control.
In addition to traditional cybersecurity vulnerabilities, AI-
augmented IoT and robotic systems may be vulnerable to AI-
specific vulnerabilities such as adversarial examples.
1
2
3
Allen and Chan, 2017
Solomon, 2017; Cohen, 2017
Schneier, 2014; Schneier, 2017;
Henderson, 2017
p.41
Security DomainsThere is also some evidence to suggest that people are unduly
trusting of autonomous mobile robots, potentially creating
additional sources of security vulnerabilities as such robots
become more widely deployed . The consequences of these
cyber vulnerabilities are particularly acute for autonomous systems
that conduct high-risk activities such as self-driving cars or
autonomous weapons.
Points of Control and Existing Countermeasures
There are numerous points of control that could be leveraged to
reduce the risk of physical harm involving AI. While the capacity
to launch attacks with today’s consumer robots is currently widely
distributed, future generations of robots may be more tightly
governed, and there exist physical defenses as well. However,
such defenses are capital-intensive and imperfect, leading us to
conclude that there may be an extended risk period in which it will
be difficult to fully prevent physical attacks leveraging AI.
Hardware manufacturers
There are currently a relatively limited number of major
manufacturers, with companies like DJI holding a dominant
position in the consumer drone market, with about 70% of the
global market . This concentration makes the hardware ecosystem
more comprehensible and governable than the analogous
ecosystem of AI software development. With growing recognition
of the diverse economic applications of drones, the market may
diffuse over the longer term, possibly making the supply chain
a less useful focal point for governance. For example, it might
currently be feasible to impose minimum standards on companies
for hardening their products against cyber attacks or to make them
more resistant to tampering, so as to at least somewhat raise the
skill required to carry out attacks through these means or raise the
costs of acquiring uncontrolled devices. The U.S. Federal Trade
Commission is exploring such regulations.
Hardware distributors
There are many businesses that sell drones and other robotic
systems, making the ecosystem more diffuse at this level than
it is at the production level. It is conceivable that at least some
risks might be mitigated through action by distributors, or other
point-of-sale based approaches. Notably, this type of control is
currently much more feasible for hardware than for software, and
restrictions on sales of potentially lethal drones might be thought
of as analogous to restrictions on sales of guns and ingredients for
illegal drugs. 1
2
Booth et al., 2017
Lucas, 2017
p.42
Security DomainsSoftware supply chain
There are many open source frameworks for computer vision,
navigation, etc. that can be used for carrying out attacks, and
products often come with some built-in software for purposes
such as flight stabilization. But not all powerful AI tools are widely
distributed, or particularly easy to use currently. For example, large
trained AI classification systems that reside within cloud computing
stacks controlled by big companies (which are expensive to train),
may be tempting for malicious actors to build from, potentially
suggesting another point of control (discussed in Interventions
and Appendix B ).
Robot users
There are also registration requirements for some forms of robots
such as drones in many countries, as well as requirements for
pilot training, though we note that the space of robots that could
cause physical harm goes beyond just drones. There are also
no fly zones, imposed at a software level via manufacturers and
governments, which are intended to prevent the use of consumer
drones in certain areas, such as near airports, where the risk
of unintentional or intentional collision between drones and
passenger aircrafts looms large . Indeed, at least one drone has
already struck a passenger aircraft , suggesting a strong need
for such no fly zones.
Governments
There is active discussion at the United Nations Convention on
Certain Conventional Weapons of the value and complexity of
banning or otherwise regulating lethal autonomous weapons
systems . Key states’ opposition to a strong ban makes such an
agreement unlikely in in the near-term, though the development of
norms that could inform stronger governance is plausible . Already
in the United States, for example, there is an official Department
of Defense directive that sets out policy for the development and
use of autonomy in weapons . Additionally, the U.S. Law of War
Manual notes that humans are the primary bearers of responsibility
for attacks in armed conflict . The International Committee of the
Red Cross has adopted a similar position, a stance that presumably
implies some minimum necessary degree of human involvement
in the use of force . While such arms control discussions and
norm development processes are critical, they are unlikely to stop
motivated non-state actors from conducting attacks.
Physical defenses
In the physical sphere, there are many possible defenses against
attacks via robots, though they are imperfect and unevenly
distributed at present. Many are expensive and/or require human
labor to deploy, and hence are only used to defend “hard targets”
1
3
4
6
752
Mouawad, 2015; Vincent, 2016
Crootof, 2015
Crootof and Renz, 2017
DoD, 2015; Roff, 2016b
ICRC, 2017; Scharre, 2018
DoD, 2012
The Telegraph, 2016
p.43
Security Domainslike safety-critical facilities and infrastructure (e.g. airports),
the owners of which can afford to invest in such protection, as
opposed to the much more widely distributed “soft targets”
(such as highly populated areas). Physical defenses can include
detection via radar, lidar, acoustic signature, or image recognition
software ; interception through various means ; and passive
defense through physical hardening or nets. The U.S. Department
of Defense has recently launched a major program to defend
against drones, and has tested lasers and nets with an eye towards
defending against drones from the Islamic State in particular .
Given the potential for automation to allow attacks at scale, a
particular challenge for defenders is finding effective methods
of defense with an acceptable cost-exchange ratio . As of yet,
these defenses are incomplete and expensive, suggesting a likely
near-term gap between the ease of attack and defense outside of
heavily guarded facilities that are known targets (e.g. airports or
military bases).
Payload control
An actor who wants to launch an aerial drone attack carrying a
dangerous payload must source both the drone and the payload.
Developed countries generally have long-lasting and reasonably
effective systems to restrict access to potentially explosive
materials, and are introducing systems to restrict access to
acids (following high-profile acid attacks). More generally, state
security and intelligence services uncover and foil a large number
of attempted attacks, including those that involve attempts to
procure dangerous materials. Increases in AI capabilities will
likely help their work e.g. in analysing signal intelligence, or in
characterising and tracking possible attackers.
Political Security
Next, we discuss the political risks associated with malicious AI
use. AI enables changes in the nature of communication between
individuals, firms, and states, such that they are increasingly
mediated by automated systems that produce and present content.
Information technology is already affecting political institutions in
myriad ways — e.g. the role of social media in elections, protests,
and even foreign policy . The increasing use of AI may make
existing trends more extreme, and enable new kinds of political
dynamics. Worryingly, the features of AI described earlier such as
its scalability make it particularly well suited to undermining public
discourse through the large-scale production of persuasive but
false content, and strengthening the hand of authoritarian regimes.
We consider several types of defenses, but as yet, as in the cases
of Digital Security and Physical Security , the problem is unsolved.
5
Zeitzoff, 2017
1
2
3
4
Aker and Kalkan, 2017
Yin, 2015; Scharre, 2015
Schmitt, 2017
Scharre, 2015
p.44
Security DomainsContext
There are multiple points of intersection between existing
information technologies and the political sphere. Historically,
politics and instability have had a symbiotic relationship with
technological advances. Security needs have driven technological
advances, and new technology has also changed the kinds of
security threats that states and politicians face. Examples abound
including the advent of the semaphore telegraph in Napoleonic
France , to the advent of GPS and its use during the First Gulf War ,
to the use of social media during the Arab Spring . Technological
advances can change the balance of power between states, as
well as the relationship between incumbent leaders and protesters
seeking to challenge them. Modern militaries and intelligence
agencies use today’s information technologies for surveillance,
as they did with previous generations of technologies such
as telephones.
However, the effects of new technologies on these power
relations are not straightforward. For example, social media
technologies empower both incumbents and protesters: they
allow military intelligences to monitor sentiment and attitudes,
and to communicate more quickly; however, they also provide
protesters in places such as Ukraine and Egypt, and rebel groups
and revolutionary movements such as ISIS or Libyan rebels, the
ability to get their message out to sympathetic supporters around
the world more quickly and easily. In addition, research suggests
that social media may empower incumbent authoritarian regimes ,
as incumbent governments can manipulate the information that
the public sees. Finally, some have argued that social media has
further polarized political discourse, allowing users, particularly in
the West, to self-select into their own echo chambers, while others
have questioned this assumption . Machine learning algorithms
running on these platforms prioritize content that users are
expected to like. Thus the dynamics we observe today are likely
to only accelerate as these algorithms and AI become even more
sophisticated.
While they have evolved from previous technologies, information
communication technologies are notable in some respects, such
as the ease of information copying and transmission. Waltzmann
writes, “The ability to influence is now effectively ‘democratized, ’
since any individual or group can communicate and influence large
numbers of others online” . This “democratization” of influence
is not necessarily favorable to democracy, however. It is very easy
today to spread manipulative and false information, and existing
approaches for detecting and stopping the spread of “fake news”
fall short. Other structural aspects of modern technologies and the
1
2
Schofield, 2013
Greenemeier, 2016
3
4
5
6
7
Aday et al., 2012
Berger and Morgan, 2015; Jones and
Mattiaci, 2017
Morozov, 2012; Rød and Weidmann 2015
Barberá et al., 2015
Waltzmann, 2017
p.45
Security Domainsmedia industry also enable these trends. Marwick and Lewis (2017)
note that the media’s “dependence on social media, analytics
and metrics, sensationalism, novelty over newsworthiness, and
clickbait makes them vulnerable to such media manipulation. ”
Others, such as Morozov (2012) and King, Pan, and Roberts (2017)
argue that social media provides more tools for authorities to
manipulate the news environment and control the message.
Finally, we note that the extent and nature of the use of information
communication technologies to alter political dynamics varies
across types of political regimes. In liberal democracies, it can be
thought of as more of an emergent phenomenon, arising from a
complex web of industry, government, and other actors, whereas
in states like China, there is an explicit and deliberate effort to
shape online and in-person political discussions, making use of
increasingly sophisticated technologies to do so . For instance,
the Chinese government is exploring ways to leverage online and
offline data to distill a “social credit score” for its citizens, and the
generally more widespread use of censorship in China exemplifies
the more explicit leveraging of technology for political purposes in
some authoritarian states .
How AI Changes the Political Security Landscape
AI will cause changes in the political security landscape, as the
arms race between production and detection of misleading
information evolves and states pursue innovative ways of
leveraging AI to maintain their rule. It is not clear what the long-
term implications of such malicious uses of AI will be, and these
discrete instances of misuse only scratch the surface of the
political implications of AI more broadly . However, we hope
that understanding the landscape of threats will encourage more
vigorous prevention and mitigation measures.
Already, there are indications of how actors are using digital
automation to shape political discourse. The widespread use of
social media platforms with low barriers to entry makes it easier
for AI systems to masquerade as people with political views. This
has led to the widespread use of social media “bots” to spread
political messages and cause dissent. At the moment, many such
bots are controlled by humans who manage a large pack of bots ,
or use very simple forms of automation. However, these bot-based
strategies (even when using relatively unsophisticated automation)
are leveraged by national intelligence agencies and have
demonstrated the ability to influence mainstream media coverage
and political beliefs . For instance, during both the Syrian Civil
War and the 2016 US election bots appeared to actively try to
sway public opinion . 1
2
4
5
King, Pan, and Roberts, 2017
Botsman, 2017
Weedon et al., 2017
Woolley and Howard, 20173
It should be emphasised here again
that we only consider in this report
the direct malicious use of AI systems
to undermine individual or collective
security (see: Introduction). There
are much larger systemic and political
issues at stake with AI such as
data aggregation and centralization,
corporate/state control of the
technology, legal and societal
barriers to access and benefit,
effects on employment, and issues
relating to the economic and social
distribution of risks and benefits,
including aspects of equality. All of
these are likely to have significant
and complex effects on all aspects of
political life, not just on political
security. However, as outlined above,
we set such system-wide risks outside
the scope of this report.
6
7
Abokhodair et al., 2015
Guilbeault and Woolley, 2016
p.46
Security DomainsGreater scale and sophistication of autonomous software actors
in the political sphere is technically possible with existing AI
techniques . As previously discussed, progress in automated
spear phishing has demonstrated that automatically generated
text can be effective at fooling humans , and indeed very simple
approaches can be convincing to humans, especially when the
text pertains to certain topics such as entertainment . It is unclear
to what extent political bots succeed in shaping public opinion,
especially as people become more aware of their existence, but
there is evidence they contribute significantly to the propagation
of fake news .
In addition to enabling individuals and groups to mislead the
public about the degree of support for certain perspectives, AI
creates new opportunities to enhance “fake news” (although, of
course, propaganda does not require AI systems to be effective).
AI systems may simplify the production of high-quality fake
video footage of, for example, politicians saying appalling (fake)
things . Currently, the existence of high-quality recorded video
or audio evidence is usually enough to settle a debate about what
happened in a given dispute, and has been used to document
war crimes in the Syrian Civil War . At present, recording
and authentication technology still has an edge over forgery
technology. A video of a crime being committed can serve as
highly compelling evidence even when provided by an otherwise
untrustworthy source. In the future, however, AI-enabled high-
quality forgeries may challenge the “seeing is believing” aspect
of video and audio evidence. They might also make it easier for
people to deny allegations against them, given the ease with which
the purported evidence might have been produced. In addition
to augmenting dissemination of misleading information, the
writing and publication of fake news stories could be automated,
as routine financial and sports reporting often are today. As
production and dissemination of high-quality forgeries becomes
increasingly low-cost, synthetic multimedia may constitute a large
portion of the media and information ecosystem.
Even if bot users only succeed in decreasing trust in online
environments, this will create a strategic advantage for political
ideologies and groups that thrive in low-trust societies or feel
opposed by traditional media channels. Authoritarian regimes
in particular may benefit from an information landscape where
objective truth becomes devalued and “truth” is whatever
authorities claim it to be.
Moreover, automated natural language and multimedia production
will allow AI systems to produce messages to be targeted at those
most susceptible to them. This will be an extension of existing 4
5
6
7
83
Seymour and Tully, 2016
Everett et al., 2016
Shao et al., 2017
Chung, Jamaludin, and Zisserman,
2017
Browne, 2017
Adams, 2017; Serban et al., 2017
p.47
Security Domainsadvertising practices. Public social media profiles are already
reasonably predictive of personality details , and may be usable to
predict psychological conditions like depression . Sophisticated AI
systems might allow groups to target precisely the right message
at precisely the right time in order to maximize persuasive
potential. Such a technology is sinister when applied to voting
intention, but pernicious when applied to recruitment for terrorist
acts, for example. Even without advanced techniques, “digital
gerrymandering” or other forms of advertising might shape
elections in ways that undermine the democratic process.
The more entrenched position of authoritarian regimes offers
additional mechanisms for control through AI that are unlikely to be
as easily available in democracies . AI systems enable fine-grained
surveillance at a more efficient scale . While existing systems are
able to gather data on most citizens, efficiently using the data is
too costly for many authoritarian regimes. AI systems both improve
the ability to prioritise attention (for example, by using network
analysis to identify current or potential leaders of subversive
groups ) and also reduce the cost of monitoring individuals (for
example, using systems that identify salient video clips and bring
them to the attention of human agents). Furthermore, this can be
a point of overlap between political and physical security, since
robotic systems could also allow highly resourced groups to
enforce a greater degree of compliance on unwilling populations.
The information ecosystem itself enables political manipulation
and control by filtering content available to users. In authoritarian
regimes, this could be done by the state or by private parties
operating under rules and directions issued by the state. In
democracies, the state may have limited legal authority to shape
and influence information content but the same technical tools
still exist; they simply reside in the hands of corporations. Even
without resorting to outright censorship, media platforms could
still manipulate public opinion by “de-ranking” or promoting certain
content. For example, Alphabet Executive Chairman Eric Schmidt
recently stated that Google would de-rank content produced
by Russia Today and Sputnik . In 2014, Facebook manipulated
the newsfeeds of over half a million users in order to alter the
emotional content of users’ posts, albeit modestly . While such
tools could be used to help filter out malicious content or fake
news, they also could be used by media platforms to manipulate
public opinion .
Finally, the threats to digital and physical security that we have
described in previous sections may also have worrying implications
for political security. The hacking of the Clinton campaign in the
2016 presidential election is a recent example of how successful 1
4
7
986532
Quercia et al., 2011; Kosinski et
al., 2013
Roff, 2015c; Horowitz, 2016
BBC, 2017
Reuters, 2017; Tom Stocky, 2016;
Griffith, 2017; Manjoo, 2017
Goel, 2014; Kramer et al., 2014;
Verma, 2014
Roff, 2015c
Ng, 2015
De Choudhury et al., 2013
De Choudhury et al., 2013
p.48
Security Domainscyberattacks can cause political disruption. The disruptive
potential of physical attacks, such as assassinations and acts of
terror, is even clearer. Such threats to digital and physical security
might either undermine existing political institutions or allow them
to justify a move toward more authoritarian policies.
Points of Control and Existing Countermeasures
Several measures are already in development or deployed in this
area, though none has yet definitively addressed the problems.
We highlight a few relevant efforts here, and emphasize that
these proposals are oriented towards the protection of healthy
public discourse in democracies. Preventing more authoritarian
governments from making full use of AI seems to be an even more
daunting challenge.
Technical tools . Technical measures are in development for
detecting forgeries and social media bots . Likewise, the use of
certified authenticity of images and videos, e.g. the ability to prove
that a video was broadcast live rather than synthesized offline
are valuable levers for ensuring that media is in fact produced by
the relevant person or organization and is untampered in transit.
Analogous measures have been developed for authentication of
images (rather than videos) by Naveh and Tromer (2016).
Automated fake news detection is likewise the subject of ongoing
research as well as a competition, the Fake News Challenge ,
which can be expected to spur further innovation in this area. As
yet, however, the detection of misleading news and images is
an unsolved problem, and the pace of innovation in generating
apparently authentic multimedia and text is rapid.
Pervasive use of security measures . Encryption is a generally
useful measure for ensuring the security of information
transmissions, and is actively used by many companies and other
organizations, in part to prevent the sorts of risks discussed here.
The use of citizens’ data by intelligence agencies takes various
forms and has been actively debated, especially in the wake of the
Snowden revelations .
General interventions to improve discourse . There are various
proposals to increase the quality of discourse in the public and
private spheres, including longstanding ones such as better
education and teaching of critical thinking skills, as well as newer
ones ranging from tools for tracking political campaigning in social
media (such as “Who Targets Me?” ) to policy proposals to apps
for encouraging constructive dialogue .1
2
3
64
Kashyap et al., 2017; D’Avino et
al., 2017
Varol et al, 2017
Rahman et al., 2017
Clarke et al, 2013
Shu et al., 2017; Pérez-Rosas et
al., 2017; Zubiaga et al., 2017
5
The Fake News Challenge is a
competition aimed at fostering the
development of AI tools to help human
fact checkers combat fake news.
8
9
Sunstein, 2017
Ixy, 20177
“Who Targets Me?” is a software
service that informs citizens on
the extent with which they are
being targeted by dark advertising
campaigns.
p.49
Security DomainsMedia platforms . There have always been news sources of varying
impartiality, and some online sources have better reputations
than others, yet this has not entirely stopped the spread of fake
news. Likewise, most people are aware of the existence of Ponzi
schemes, scam emails, misleading sales tactics, etc. and yet
victims are still found. Part of the reason that spam is less of a
problem today than it otherwise could be is that the owners of
key platforms such as email servers have deployed sophisticated
spam filters. More generally, technology companies, social
media websites, and and media organizations are critical points
of control for stemming the tide of increasingly automated
disinformation, censorship, and persuasion campaigns. Additionally,
these organizations have unique datasets that will be useful for
developing AI systems for detecting such threats, and through
the ability to control access, they can pursue other strategies for
preventing malicious uses of these platforms such as imposing
strong barriers to entry (e.g. the use of one’s offline identity) and
limiting the rate at which accounts can disseminate information.
Because these media platforms are for-profit corporations,
public discourse, transparency, and potentially regulation will
be important mechanisms for ensuring that their use of these
powerful tools aligns with public interest .
A development that occurred during the process of writing
this report is illustrative. Late 2017 saw the rise of “deepfakes, ”
the application of face-swapping algorithms to (among other
applications) adult videos. While such videos first began appearing
en masse in Reddit fora clearly labeled as being fictitious, the
realism of some such deepfakes is an early sign of the potential
decline of “seeing is believing” discussed above. After substantial
media coverage of deepfakes, Reddit and other online websites,
including adult content websites, began to crack down on the
discussion and propagation of the technique. While these efforts
have not been fully successful, they illustrate the critical role of
technology platforms in governing information access, and it is
likely that the deepfakes crackdown at least somewhat slowed the
dissemination of the tool and its products, at least amongst less
sophisticated actors.4
Lapowsky, 2017
Interventions
p.50
We identify a wide range of potential responses to the challenges
raised above, as well as a large number of areas for further
investigation. This section first makes several initial high-level
recommendations for AI and ML researchers, policymakers,
and others. We then suggest specific priority areas for further
research , where investigation and analysis could develop and refine
potential interventions to reduce risks posed by malicious use.
Due to the exploratory nature of this report, our primary aim is to
draw attention to areas and potential interventions that we believe
should be the subject of further investigation, rather than to make
highly specific technical or policy proposals that may not be viable.
The structure of this section, and the inclusion of Appendix B with
additional exploratory material, is informed by this perspective.
04
p.51
Interventions
Recommendations
In this subsection we present four high-level recommendations,
which are focused on strengthening the dialog between technical
researchers, policymakers, and other stakeholders. In the following
subsection, we will turn our attention to more concrete priority
areas for technical work as well as associated research questions.
Our first pair of recommendations arise from the fact that
the issues raised in this report combine technical and non-
technical considerations, such as social, economic and military
considerations. Concerns were raised at the workshop that the
development of viable, appropriate responses to these issues may
be hampered by two self-reinforcing factors: first, a lack of deep
technical understanding on the part of policymakers, potentially
leading to poorly-designed or ill-informed regulatory, legislative, or
other policy responses; second, reluctance on the part of technical
researchers to engage with these topics, out of concern that
association with malicious use would tarnish the reputation of the
field and perhaps lead to reduced funding or premature regulation.
Our first two recommendations aim at preempting this dynamic.
Recommendation #1: Policymakers should collaborate closely
with technical researchers to investigate, prevent, and mitigate
potential malicious uses of AI . This must include policymakers
taking seriously their responsibility to avoid implementing
measures that will interfere with or impede research progress,
unless those measures are likely to bring commensurate benefits.
Close collaboration with technical experts also ensures that
policy responses will be informed by the technical realities of the
technologies at hand .
Recommendation #2: Researchers and engineers in artificial
intelligence should take the dual-use nature of their work
seriously, allowing misuse-related considerations to influence
research priorities and norms, and proactively reaching out to
relevant actors when harmful applications are foreseeable . Given
that AI is a dual-use technology, we believe it is important that
researchers consider it their responsibility to take whatever steps
they can to help promote beneficial uses of the technology and
prevent harmful uses. Example steps could include engaging with
policymakers to provide expertise, and considering the potential
applications of different research projects before deciding what to
work on. (We recognize and appreciate the many AI researchers —
including the technical experts who took part in the workshop and
contributed to this report and other related initiatives — who are
already doing outstanding work along these lines. )
Introductory resources for
policymakers interested in this domain
are increasingly becoming available,
both generally about AI (Buchanan
and Taylor, 2017), and specifically
on AI and security (CNAS, 2017). As
an example of policymaking in this
domain that has surfaced several
difficulties, the European Union’s
General Data Protection Regulation
is a commonly-discussed example of
a policy that is hard to interpret
and apply in the context of current
machine learning algorithms (Goodman
and Flaxman, 2016).1
The work of the Partnership on AI, the
White House’s 2016 series of workshops
on AI, the 2017 “Beneficial AI”
conference in Asilomar, and the AI Now
conference series and organization are
further examples where contributions
from technical experts have been
substantial and valuable.
2
p.52
Interventions
We also make two recommendations laying out aims that we
believe the broader AI community (including both technical and
policy professionals) should work towards.
Recommendation #3: Best practices should be identified in
research areas with more mature methods for addressing dual-
use concerns, such as computer security, and imported where
applicable to the case of AI . An example of a best practice that
workshop participants considered clearly valuable to introduce
into AI contexts is extensive use of “red teaming. ” See Priority
Research Area #1, below, for further details.
Recommendation #4: Actively seek to expand the range of
stakeholders and domain experts involved in discussions of
these challenges . This could include reaching out to sectors like
civil society, national security experts, as-yet unengaged AI and
cybersecurity researchers, businesses incorporating AI into their
products, ethicists, the general public , and others, to ensure that
relevant stakeholders are included and relevant experts consulted.
Because of the dual-use nature of AI, many of the malicious uses
of AI outlined in this report have related legitimate uses. In some
cases, the difference between legitimate and illegitimate uses of AI
could be one of degree or ensuring appropriate safeguards against
malicious use. For example, surveillance tools can be used to catch
terrorists or oppress ordinary citizens. Information content filters
could be used to bury fake news or manipulate public opinion.
Governments and powerful private actors will have access to many
of these AI tools and could use them for public good or harm. This
is why a public dialogue on appropriate uses of AI technology is
critical. The above four recommendations can help foster a cross-
disciplinary dialogue among AI researchers, policymakers, and
other relevant stakeholders to ensure that AI technology is used
to benefit society.
Priority Areas for Further Research
This section lays out specific topic areas that we recommend
be investigated further. We aim here for brevity; more specific
questions for investigation, along with additional context and
commentary on many of the topics mentioned, may be found in
Appendix B .
In computer security, red teaming
involves a “red team”, composed of
security experts and/or members of
the host organization, deliberately
planning and carrying out attacks
against the systems and practices
of the organization (with some
limitations to prevent lasting
damage), with an optional “blue team”
responding to these attacks. These
exercises explore what an actual
attack might look like in order to
better understand and, ultimately,
improve the security of the
organisation’s systems and practices.
We expect adaptive defensive actions
will be required of everyday citizens,
if only in terms of maintaining
awareness of threats and adopting
best practices. It is important to
acknowledge that different communities
will have varying abilities to
make such adaptations, depending
for example on their technological
literacy, which may pose challenges
for implementing security policies.
This is important not just for the
communities less able to adapt
to the new threats, but also for
society more broadly as, for example,
insecure systems may be compromised
by attackers and repurposed to provide
computing power and data for yet-
more-capable attacks, while reducing
the possibility that the attacks could
be attributed, as they would then seem
to originate from the compromised
system.1
2
p.53
Interventions
Priority Research Area #1:
Learning from and with the Cybersecurity Community
As AI-based systems become more widespread and capable,
the potential impacts of cybersecurity incidents are growing
commensurately. To summarize the considerations in the Digital
Security section, AI is important to cybersecurity for three reasons.
First, increased automation brings with it increased digital control
of physical systems; consider, for example, how much more control
a successful hacker could exercise over a modern car, compared
with a typical car from 20 years ago . Second, successful attacks
on AI-based systems can also give the attacker access to the
algorithms and/or trained models used by the system; consider, for
example, theft of the datasets used for facial recognition on social
networks, or the compromise of an algorithm used for analysing
satellite imagery. Third, increasing use of AI in cyberattacks is likely
to allow highly sophisticated attacks to be carried out at a much
larger scale, which may reach victims that would otherwise not be
suitable targets of previous waves of sophisticated attacks.
To respond to these increased dangers, cybersecurity must be a
major and ongoing priority in efforts to prevent and mitigate harms
from AI systems, and best practices from cybersecurity must be
ported over wherever applicable to AI systems.
Some examples of cybersecurity-related sub-areas that we believe
should be the subject of further research and analysis, then be
implemented as appropriate (see Appendix B for more commentary
on and questions about these sub-areas), include:
• Red teaming . Extensive use of red teaming to discover and fix
potential security vulnerabilities and safety issues should be a
priority of AI developers, especially in critical systems.
• Formal verification . To what extent, in what circumstances, and
for what types of architectures can formal verification be used
to prove key properties of AI systems? Can other approaches be
developed to achieve similar goals by different means?
• Responsible disclosure of AI vulnerabilities . Should AI-specific
procedures be established to enable confidential reporting
of vulnerabilities discovered in AI systems (including security
vulnerabilities, potential adversarial inputs, and other types of
exploits), as is already possible for security exploits in modern
software systems?
• Forecasting security-relevant capabilities . Could “white-hat”
efforts to predict how AI advances will enable more effective
DARPA’s Assured Autonomy program
(Neema, 2017) is one attempt at
developing techniques to assure safety
in systems that continue learning
throughout their lifespans, which
makes assurance or verification using
traditional methods challenging. See
also Katz et al., 2017; Selsam, Liang,
and Dill, 2017; and Carlini et al.,
2017.
For example, see the case of
hackers first bringing a Jeep to a
standstill on a busy highway, then
later developing the ability to
cause unintended acceleration and
fully control the vehicle’s steering
(Greenberg, 2016).21
p.54
Interventions
cyberattacks, and more rigorous tracking of AI progress and
proliferation in general, allow for more effective preparations
by defenders?
• Security tools . What tools (if any) should be developed and
distributed to help make it standard to test for common security
problems in AI systems, analogously to tools used by computer
security professionals?
• Secure hardware . Could security features be incorporated into
AI-specific hardware, for example to prevent copying, restrict
access, facilitate activity audits, and similar? How technically
and practically feasible is the design and adoption of hardware
with properties like this?
Priority Research Area #2:
Exploring Different Openness Models
Today, the prevailing norms in the machine learning research
community strongly point towards openness. A large fraction of
novel research is published online in papers that share anything
from rough architectural outlines to algorithmic details to source
code. This level of openness has clear benefits in terms of enabling
researchers to build on each others’ work, promoting collaboration,
and allowing theoretical progress to be incorporated into a broad
array of applications.
However, the potential misuses of AI technology surveyed in the
Scenarios and Security Domains sections suggest a downside
to openly sharing all new capabilities and algorithms by default:
it increases the power of tools available to malicious actors. This
raises an important research question: might it be appropriate to
abstain from or merely delay publishing of some findings related to
AI for security reasons? There is precedent for this in fields such
as computer security, where exploits that could affect important
systems are not publicly disclosed until the developers have had
an opportunity to fix the vulnerability. To the extent that research
results are withheld today in AI, it is usually for reasons related to
intellectual property (e.g. in order to avoid a future result being
“scooped”). In light of risks laid out elsewhere in this report, there
may also be arguments based on public interest for additional
caution in at least some cases.
While the proposals below consider decreasing openness in certain
situations, we stress that there are clear and well-recognized
reasons to favor openness in research communities. We believe
that policies leading to decreased openness, while potentially 1
Eckersley and Nasser et al., 2017
p.55
Interventions
appropriate in certain instances, should be sensitive to these
benefits. Rather than propose a specific solution, our aim is to
foster discussion of whether and when considerations against
open sharing might outweigh considerations in favor and what
mechanisms might enable this.
Some potential mechanisms and models that could be subject
to further investigation and analysis (see Appendix B for more
commentary on and questions about for these sub-areas) include:
• Pre-publication risk assessment in technical areas of special
concern . Should some types of AI research results, such
as work specifically related to digital security or adversarial
machine learning, be subject to some kind of risk assessment
to determine what level of openness is appropriate ? This is
the norm for research in other areas, such as biotechnology and
computer security. Or would such measures be premature today,
before AI systems are more widely used in critical systems
and we have better knowledge of which technical research is
most security-relevant? If such measures are considered be
premature, under what conditions would they be appropriate?
• Central access licensing models . Could emerging “central
access” commercial structures — in which customers use
services like sentiment analysis or image recognition made
available by a central provider without having access to the
technical details of the system — provide a template for a
security-focused sharing model that allows widespread use of a
given capability while reducing the possibility of malicious use?
How might such a model remain viable over time as advances in
processing power, data storage and availability, and embedded
expertise allow a larger set of actors to use AI tools?
• Sharing regimes that favor safety and security . Could
arrangements be made under which some types of research
results are selectively shared among a predetermined set of
people and organizations that meet certain criteria, such as
effective information security and adherence to appropriate
ethical norms? For example, certain forms of offensive
cybersecurity research that leverage AI might be shared
between trusted organizations for vulnerability discovery
purposes, but would be harmful if more widely distributed.
• Other norms and institutions that have been applied to dual-
use technologies . What can be learned from other models,
methodologies, considerations, and cautions that have
arisen when tackling similar issues raised by other dual-use
technologies?
Accordingly, concerns about misuse
should not be used as an excuse to
reduce openness to a greater extent
than is required, for instance, when
the real motivation is about corporate
competitiveness. We believe that,
to the extent that practices around
openness are rethought, this should
be done transparently, and that when
new approaches are incorporated into
AI research and publication processes
from other domains (e.g. responsible
disclosure), those doing so should
state their reasons publicly so
that a range of stakeholders can
evaluate these claims. The debate in
the biosecurity community about the
appropriate level of disclosure on
gain-of-function research (in which
organisms are made more dangerous in
order to understand certain threats
better) provides a model of the kind
of discussion we see as healthy and
necessary.1
2
see e.g. NDSS, 2018
p.56
Interventions
Priority Research Area #3:
Promoting a Culture of Responsibility
AI researchers and the organizations that employ them are in
a unique position to shape the security landscape of the AI-
enabled world. Many in the community already take their social
responsibility quite seriously, and encourage others to do the
same. This should be continued and further developed, with
greater leveraging of insights from the experiences of other
technical fields, and with greater attentiveness to malicious use
risks in particular. Throughout training, recruitment, research, and
development, individuals, and institutions should be mindful of the
risks of malicious uses of AI capabilities.
Some initial areas to explore for concrete initiatives aimed at
fostering a culture of responsibility include :
• Education . What formal and informal methods for educating
scientists and engineers about the ethical and socially
responsible use of their technology are most effective?
How could this training be best incorporated into the education
of AI researchers?
• Ethical statements and standards . What role should ethical
statements and standards play in AI research? How and by
whom should they be implemented and enforced? What are
the domain-specific ethical questions in the areas of digital,
physical, and security that need to be resolved in order to
distinguish between benign and malicious uses of AI?
• Whistleblowing measures . What is the track record of
whistleblowing protections in other domains, and how (if at all)
might they be used for preventing AI-related misuse risks?
• Nuanced narratives . More generally, are there succinct and
compelling narratives of AI research and its impacts that can
balance optimism about the vast potential of this technology
with a level-headed recognition of the risks it poses? Examples
of existing narratives include the “robot apocalypse” trope and
the countervailing “automation boon” trope, both of which
have obvious shortcomings. Might a narrative like “dual-use”
(proposed above) be more productive?
Two examples of proposed standards
for AI are the IEEE Global Initiative
for Ethical Considerations in
Artificial Intelligence and Autonomous
Systems (IEEE Standards Association,
2017) and the development of the
Asilomar AI Principles (Future of Life
Institute, 2017).
See Appendix B for more commentary on
and questions about these sub-areas.21
p.57
Interventions
Priority Research Area #4:
Developing Technological and Policy Solutions
In addition to creating new security challenges and threats
progress in AI also makes possible new types of responses and
defenses. These technological solutions must be accompanied and
supported by well-designed policy responses. In addition to the
proposals mentioned in the previous sections, what other potential
approaches — both institutional and technological — could help to
prevent and mitigate potential misuse of AI technologies?
Some initial suggested areas for further investigation include :
• Privacy protection . What role can technical measures play in
protecting privacy from bad actors in a world of AI? What role
must be played by institutions, whether by corporations, the
state, or others?
• Coordinated use of AI for public-good security . Can AI-based
defensive security measures be distributed widely to nudge the
offense-defense balance in the direction of defense? Via what
institutions or mechanisms can these technologies be promoted
and shared?
• Monitoring of AI-relevant resources . Under what circumstances,
and for which resources, might it be feasible and appropriate
to monitor inputs to AI technologies such as hardware, talent,
code, and data?
• Other legislative and regulatory responses . What other potential
interventions by policymakers would be productive in this space
(e.g. adjusting legal definitions of hacking to account for the
case of adversarial examples and data poisoning attacks)?
For all of the above, it will be necessary to incentivize individuals
and organizations with the relevant expertise to pursue these
investigations. An initial step, pursued by this report, is to raise
awareness of the issues and their importance, and to lay out an
initial research agenda. Further steps will require commitment from
individuals and organizations with relevant expertise and a proven
track record. Additional monetary resources, both public and
private, would also help to seed interest and recruit attention in
relevant research communities.
For example, could AI systems be used
to refactor existing code bases or
new software to adhere more closely
to principle of least authority
(Miller, 2006) or other security best
practices?
See Appendix B for more commentary on
and questions about these sub-areas.21
p.58
When considered together, how will the security-relevant
characteristics of AI and the various intervention measures
surveyed above (if implemented) combine to shape the future
of security? Any confident long-term prediction is impossible
to make, as significant uncertainties remain regarding the
progress of various technologies, the strategies adopted by
malicious actors, and the steps that should and will be taken by
key stakeholders. Nonetheless, we aim to elucidate some crucial
considerations for giving a more confident answer, and make
several hypotheses about the medium-term equilibrium of AI attack
and defense. By medium-term, we mean the time period (5+ years
from now) after which malicious applications of AI are widely
used and defended against, but before AI has yet progressed
sufficiently to fully obviate the need for human input in either
attack or defense.
05Strategic
Analysis
p.59
Strategic Analysis
Even a seemingly stable and predictable medium-term equilibrium
resulting from foreseeable AI developments might be short-lived,
since both technological and policy factors will progress beyond
what can currently be foreseen. New developments, including
technological developments unrelated to AI, may ultimately be
more impactful than the capabilities considered in this report.
Nevertheless, we hope that the analysis below sheds some light
on key factors to watch and influence in the years to come.
Factors Affecting the Equilibrium of AI and
Security
Attacker Access to Capabilities
Current trends emphasize widespread open access to cutting-edge
research and development achievements. If these trends continue
for the next 5 years, we expect the ability of attackers to cause
harm with digital and robotic systems significantly increase. This
follows directly from the dual-use nature, efficiency, scalability, and
ease of diffusing AI technologies discussed previously.
However, we expect the dual-use nature of the technology will
become increasingly apparent to developers and regulators,
and that limitations on access to or malicious use of powerful AI
technologies will be increasingly imposed. However, significant
uncertainty remains about the effectiveness of attempting to
restrict or monitor access through any particular intervention.
Preemptive design efforts and the use of novel organizational
and technological measures within international policing will
all help, and are likely to emerge at various stages, in response
(hopefully) to reports such as these, or otherwise in the aftermath
of a significant attack or scandal. Efforts to prevent malicious
uses solely through limiting AI code proliferation are unlikely to
succeed fully, both due to less-than-perfect compliance and
because sufficiently motivated and well resourced actors can use
espionage to obtain such code. However, the risk from less capable
actors using AI can likely be reduced through a combination of
interventions aimed at making systems more secure, responsibly
disclosing developments that could be misused, and increasing
threat awareness among policymakers.
Existence of AI-Enabled Defenses
The same characteristics of AI that enable large-scale and low-cost
attacks also allow for more scalable defenses. Specific instances of
p.60
Strategic Analysis
AI-enabled defenses have been discussed in earlier sections, such
as spam filters and malware detection, and we expect many others
will be developed in the coming years. For example, in the context
of physical security, the use of drones whose sole purpose is to
quickly and non-violently “catch” and bring to the ground other
drones might be invented and widely deployed, but they might also
turn out to be prohibitively expensive, as might other foreseeable
defenses. Thus, both the pace of technical innovation and the cost
of such defenses should be considered in a fuller assessment.
One general category of AI-enabled defenses worth considering
in an overall assessment is the use of AI in criminal investigations
and counterterrorism. AI is already beginning to see wider adoption
for a wide range of law enforcement purposes, such as facial
recognition by surveillance cameras and social network analysis.
We have hardly seen the end of such advancements, and further
developments in the underlying technologies and their widespread
use seem likely given the interest of actors from corporations to
governments in preventing criminal acts. Additionally, interceding
attacks in their early stage through rapid detection and response
may turn out to be cheaper than for example widely deploying
physical defenses against drones. Thus, the growing ability of
states to detect and stop criminal acts, in part by leveraging AI, is a
key variable in the medium-term. However, such advances will not
help prevent authoritarian abuses of AI.
Distribution and Generality of Defenses
Some defensive measures discussed in Interventions and
Appendix B can be taken by single, internally coordinated actors,
such as research labs and tech startups, and are likely to happen
as soon as they become technically feasible and cost-effective.
These measures could then be used by the organizations that
have the most to lose from attacks such as governments and
major corporations. This means that the most massive category
of harm, such as attack on WMD facilities, is also the least likely,
though the level of risk will depend on the relative rates at which
attacks and defenses are developed. Responsible disclosure of
novel vulnerabilities, pre-publication risk assessment, and a strong
ethical culture in the AI community more generally will be vital in
such a world.
This, however, leaves out the strategic situation for the majority
of potential victims: technologically conservative corporations,
under-resourced states, SMEs, and individuals. For these potential
victims, defensive measures need to be baked into widespread
technology, which may require coordinated regulatory efforts, or
p.61
Strategic Analysis
offered at low prices. The latter is most likely to come either from
tech giants (as in the case of spam filters), which will increase
lock-in and concentration of data and power, or from non-profit
organizations who develop and distribute such defensive measures
freely or cheaply (e.g. Mozilla’s Firefox web browser).
This dynamic of defense through reliance on fortified software
platforms is likely to be affected by the generality of defensive
measures: if each attack requires a tailored defense, and has
an associated higher time lag and skill investment, it is more
likely that those developing such defensive measures will need
financial backing, from corporations, investors, philanthropists,
or governments. In the case of governments, international
competition may hinder the development and release of defensive
measures, as is generally the case in cyber-security, though see
the release of CyberChef and Assemblyline as counterexamples.
For political security, similar considerations regarding generality
apply: a general solution to authenticable multimedia production
and forgery detection would be more useful than tailored individual
solutions for photographs, videos, or audio, or narrower subsets of
those media types.
Misaligned incentives can also lead to a failure to employ available
defensive measures. For example, better cybersecurity defenses
could raise the bar for data breaches or the creation of IoT device
botnets. However, the individuals affected by these failures, such
as the individuals whose personal data is released or victims
of DDOS attacks using botnets, are not typically in a position
to improve defenses directly. Thus, other approaches including
regulation may be needed to adjust these incentives or otherwise
address these externalities .
Overall Assessment
The range of plausible outcomes is extremely diverse, even without
considering the outcomes that are less likely, but still possible.
Across all plausible outcomes, we anticipate that attempts to use
AI maliciously will increase alongside the increase in the use of AI
across society more generally. This is not a trend that is particular
to AI; we anticipate increased malicious use of AI just as criminals,
terrorists and authoritarian regimes use electricity, software, and
computer networks: at some point in the technology adoption
cycle, it becomes easier to make use of such general purpose
technologies than to avoid them.
On the optimistic side, several trends look positive for defense.
There is much low hanging fruit to be picked in securing AI systems 1
2
3
GCHQ, 2016
CSE, 2017
Moore and Anderson, 2012
p.62
Strategic Analysis
themselves, and securing people and systems from AI-enabled
attacks. Examples include responsible vulnerability disclosure for
machine learning in cases where the affected ML technology is
being used in critical systems, and greater efforts to leverage AI
expertise in the discovery of vulnerabilities by software companies
internally before they are discovered by adversaries. There are
substantial academic incentives to tackle the hardest research
problems, such as developing methods to address adversarial
examples and providing provable guarantees for system properties
and behaviors. There are, at least in some parts of the world,
political incentives for developing processes and regulations that
reduce threat levels and increase stability, e.g. through consumer
protection and standardization. Finally, there are incentives for
tech giants to collaborate on ensuring at least a minimal level of
security for their users. Where solutions are visible, require limited
or pre-existing coordination, and align with existing incentive
structures, defenses are likely to prevail.
On the pessimistic side, not all of the threats identified have
solutions with these characteristics. It is likely to prove much
harder to secure humans from manipulation attacks than it will be
to secure digital and cyber-physical systems from cyber attacks,
and in some scenarios, all three attack vectors may be combined.
In the absence of significant effort, attribution of attacks and
penalization of attackers is likely to be difficult, which could lead
to an ongoing state of low- to medium-level attacks, eroded trust
within societies, between societies and their governments, and
between governments. Whichever vectors of attack prove hardest
to defend against will be the ones most likely to be weaponized by
governments, and the proliferation of such offensive capability is
likely to be broad. Since the number of possible attack surfaces
is vast, and the cutting edge of capability is likely to be ever
progressing, any equilibrium obtained between rival states or
between criminals and security forces in a particular domain is
likely to be short-lived as technology and policies evolve.
Tech giants and media giants may continue to become
technological safe havens of the masses, as their access to
relevant real-time data at massive scale, and their ownership of
products and communication channels (along with the underlying
technical infrastructure), place them in a highly privileged position
to offer tailored protection to their customers. Other corporate
giants that offer digitally-enhanced products and services
(automotive, medical, defense, and increasingly many other
sectors) will likely be under pressure to follow suit. This would
represent a continuation of existing trends in which people very
regularly interact with and use the platforms provided by tech and
media giants, and interact less frequently with small businesses
and governments.
p.63
Strategic Analysis
Nations will be under pressure to protect their citizens and their
own political stability in the face of malicious uses of AI . This
could occur through direct control of digital and communication
infrastructure, through meaningful and constructive collaboration
between the government and the private entities controlling such
infrastructure, or through informed and enforceable regulation
coupled with well-designed financial incentives and liability
structures. Some countries have a clear head start in establishing
the control mechanisms that will enable them to provide security
for their citizens .
For some of the more challenging coordination and interdisciplinary
problems, new leadership will be required to rise above local
incentives and provide systemic vision. This will not be the first
time humanity has risen to meet such a challenge: the NATO
conference at Garmisch in 1968 created consensus around the
growing risks from software systems, and sketched out technical
and procedural solutions to address over-run, over-budget,
hard-to-maintain and bug-ridden critical infrastructure software,
resulting in many practices which are now mainstream in software
engineering ; the NIH conference at Asilomar in 1975 highlighted
the emerging risks from recombinant DNA research, promoted
a moratorium on certain types of experiments, and initiated
research into novel streams of biological containment, alongside
a regulatory framework such research could feed into . Individuals
at the forefront of research played key roles in both of these
cases, including Edsger Dijkstra in the former and Paul Berg
in the latter .
There remain many disagreements between the co-authors of this
report, let alone amongst the various expert communities out in the
world. Many of these disagreements will not be resolved until we
get more data as the various threats and responses unfold, but this
uncertainty and expert disagreement should not paralyse us from
taking precautionary action today. Our recommendations, stated
above, can and should be acted on today: analyzing and (where
appropriate) experimenting with novel openness models, learning
from the experience of other scientific disciplines, beginning
multi-stakeholder dialogues on the risks in particular domains, and
accelerating beneficial research on myriad promising defenses.1
3
4
5
6
Chessen, 2017b
Naur and Randell, 1969
Krimsky, 1982; Wright, 1994
Dijkstra, 1968
Berg et al., 1974
For example, France’s campaign laws
prohibited Macron’s opponent from
further campaigning once Macron’s
emails had been hacked. This prevented
the campaign from capitalizing on the
leaks associated with the hack, and
ended up with the hack playing a much
more muted role in the French election
than the Clinton hack played in the
US election.2
Conclusion
p.64
While many uncertainties remain, it is clear that AI will figure
prominently in the security landscape of the future, that
opportunities for malicious use abound, and that more can and
should be done.
Artificial intelligence, digital security, physical security, and political
security are deeply connected and will likely become more so.
In the cyber domain, even at current capability levels, AI can be
used to augment attacks on and defenses of cyberinfrastructure,
and its introduction into society changes the attack surface that
hackers can target, as demonstrated by the examples of automated
spear phishing and malware detection tools discussed above.
As AI systems increase in capability, they will first reach and then
exceed human capabilities in many narrow domains, as we have
already seen with games like backgammon, chess, Jeopardy!,
Dota 2, and Go and are now seeing with important human tasks
p.65
Conclusionlike investing in the stock market or driving cars. Preparing for the
potential malicious uses of AI associated with this transition is
an urgent task.
As AI systems extend further into domains commonly believed
to be uniquely human (like social interaction), we will see more
sophisticated social engineering attacks drawing on these
capabilities. These are very difficult to defend against, as even
cybersecurity experts can fall prey to targeted spear phishing
emails. This may cause an explosion of network penetrations,
personal data theft, and an epidemic of intelligent computer
viruses. One of our best hopes to defend against automated
hacking is also via AI, through automation of our cyber-defense
systems, and indeed companies are increasingly pursuing this
strategy. But AI-based defense is not a panacea, especially when
we look beyond the digital domain. More work should also be done
in understanding the right balance of openness in AI, developing
improved technical measures for formally verifying the robustness
of systems, and ensuring that policy frameworks developed in
a less AI-infused world adapt to the new world we are creating.
Looking to the longer term, much has been published about
problems which might arise accidentally as a result of highly
sophisticated AI systems capable of operating at high levels
across a very wide range of environments , though AI capabilities
fall short of this today. Given that intelligence systems can be
deployed for a range of goals , highly capable systems that require
little expertise to develop or deploy may eventually be given new,
dangerous goals by hacking them or developing them de novo:
that is, we may see powerful AI systems with a “just add your
own goals” property. Depending on whose bidding such systems
are doing, such advanced AIs may inflict unprecedented types
and scales of damage in certain domains, requiring preparedness
to begin today before these more potent misuse potentials are
realizable. Researchers and policymakers should learn from other
domains with longer experience in preventing and mitigating
malicious use to develop tools, policies, and norms appropriate
to AI applications.
Though the specific risks of malicious use across the digital,
physical, and political domains are myriad, we believe that
understanding the commonalities across this landscape, including
the role of AI in enabling larger-scale and more numerous attacks,
is helpful in illuminating the world ahead and informing better
prevention and mitigation efforts. We urge readers to consider
ways in which they might be able to advance the collective
understanding of the AI-security nexus, and to join the dialogue
about ensuring that the rapid development of AI proceeds not just
safely and fairly but also securely.1
2
Bostrom, 2014; Amodei and Olah et
al., 2016
Bostrom, 2014, p. 107
p.66
The Malicious Use of Artificial IntelligenceWe are extremely grateful to the many researchers and
practitioners who have provided useful comments on earlier
versions of this document, and who engaged us in helpful
conversations about related topics. Given the number of coauthors
and related conversations, we will surely forget some people, but
among others, we thank Ian Goodfellow, Ross Anderson, Nicholas
Papernot, Martín Abadi, Tim Hwang, Laura Pomarius, Tanya Singh
Kasewa, Smitha Milli, Itzik Kotler, Andrew Trask, Siddharth Garg,
Martina Kunz, Jade Leung, Katherine Fletcher, Jan Leike, Toby
Ord, Nick Bostrom, Owen Cotton-Barratt, Eric Drexler, Julius
Weitzdorfer, Emma Bates, and Subbarao Kambhampati. Any
remaining errors are the responsibility of the authors. This work
was supported in part by a grant from the Future of Life Institute.
AcknowledgementsAcknowledgements
p.67
The Malicious Use of Artificial Intelligence
Abadi, M. Chu , A. Goodfellow, I. McMahan, H. Mironov,
I. Talwar, K. and Zhang, L. 2016. “Deep Learning with
Differential Privacy. ” In Proceedings of the 2016 ACM SIGSAC
Conference on Computer and Communications Security (CCS
‘16). ACM, New York, NY , USA, 308-318, https:/ /arxiv.org/
abs/1607 .00133
Ablon, L. and Bogart, T . 2017 . “Zero Days, Thousands of
Nights: The Life and Times of Zero-Day Vulnerabilities and
Their Exploits, ” RAND Corporation, https:/ /www.rand.org/
pubs/research_reports/RR1751.html
Abokhodair, N., Yoo, D., and McDonald, D. 2015. “Dissecting
a Social Botnet: Growth, Content and Influence in Twitter, ”
Proceedings of the 18th ACM Conference on Computer
Supported Cooperative Work & Social Computing, pp. 839-
851, dl.acm.org/ft_gateway.cfm?id=2675208
Adams, T . 2017 . “AI-Powered Social Bots, ” arXiv preprint
server, https:/ /arxiv.org/abs/1706.05143
Aday, S., Farrell, H., Lynch, M., Sides, J., and Freelon, D. 2012.
“Blogs and Bullets II: New Media and Conflict After the Arab
Spring, ” United States Institute of Peace,
https:/ /www.usip.org/publications/2012/07/blogs-and-
bullets-ii-new-media-and-conflict-after-arab-spring
Aker, C. and Kalkan, S. 2017 . “Using Deep Networks for
Drone Detection, ” arXiv preprint server, https:/ /arxiv.org/
abs/1706.05726
Allen, G. and Chan, T . 2017 . “Artificial Intelligence and National
Security, ” Harvard Kennedy School Belfer Center for Science
and International Affairs, https:/ /www.belfercenter.org/
publication/artificial-intelligence-and-national-security
Amodei, D. and Olah, C. et al. 2016. “Concrete Problems
in AI Safety, ” arXiv preprint server, https:/ /arxiv.org/
abs/1606.06565
Anderson, H., Woodbridge, J., and Filar, B. 2016. “DeepDGA:
Adversarially-Tuned Domain Generation and Detection, ” arXiv
preprint server, https:/ /arxiv.org/abs/1610.01969
Anderson, H.S., Kharkar, A., Filar, B., Evans, D., and Roth, P .
2018. “Learning to Evade Static PE Machine Learning Malware
Models via Reinforcement Learning, ” arXiv preprint server,
https:/ /arxiv.org/abs/1801.08917
Arulkumaran, K., Deisenroth, M., Brundage, M., and Bharath,
A. 2017 . “Deep Reinforcement Learning: A Brief Survey, ” IEEE
Signal Processing Magazine, Vol. 34, Issue 6, November 2017 ,
https:/ /arxiv.org/abs/1708.05866
Baier, C. and Katoen, J. 2008. Principles of Model Checking.
Cambridge: MIT Press.
Barberá, P ., Jost, J., Nagler, J., Tucker, A., and Bonneau,
R. 2015. “Tweeting From Left to Right: Is Online Political
Communication More Than an Echo Chamber?, ” Psychological Science, Vol. 26, Issue 10, http:/ /journals.sagepub.com/
doi/10.1177/0956797615594620
Barreno, M., Nelson, B., Joseph, A., and Tygar, J.D. 2010.
“The security of machine learning, ” Machine Learning, 81:2,
pages 121-148. Available online at https:/ /people.eecs.
berkeley.edu/~tygar/papers/SML/sec_mach_learn_journal.
pdf
Bass, D. 2017 . “AI Scientists Gather to Plot Doomsday
Scenarios (and Solutions), ” Bloomberg, March 2, 2017 ,
https:/ /www.bloomberg.com/news/articles/2017-03-02/ai-
scientists-gather-to-plot-doomsday-scenarios-and-solutions
Bastani, O., Kim, C., Bastani, H. 2017 . “Interpreting Blackbox
Models via Model Extraction, ” arXiv preprint server, https:/ /
arxiv.org/abs/1705.08504
BBC. 2017 . “Google to ‘de-rank’ Russia Today and
Sputnik. ” November 21, 2017 . http:/ /www.bbc.com/news/
technology-42065644
Berg, P ., Baltimore, D., Boyer, H.W., Cohen, S.N., Davis,
R.W., Hogness, D.S., Nathans, D., Roblin, R., Watson, J.D.,
Weissman, S. and Zinder, N.D., 1974. “Potential biohazards of
recombinant DNA molecules. ” Science, 185(4148), p.303.
Berger, J.M. and Morgan, J. 2015. “The ISIS Twitter census:
Defining and describing the population of ISIS supporters on
Twitter, ” Brookings Institution, https:/ /www.brookings.edu/
research/the-isis-twitter-census-defining-and-describing-
the-population-of-isis-supporters-on-twitter/
Beurdouche, B., Bhargavan, K., Delignat-Lavaud, A., Fournet,
C., Kohlweiss, M., Pironti, A., Strub, P ., and Zinzindohoue, J.
2017 . “A messy state of the union: taming the composite state
machines of TLS, ” Communications of the ACM, Vol. 60, Issue
2, February 2017 , http:/ /dl.acm.org/citation.cfm?id=3023357
Biggio, B., Nelson, B., and P . Laskov. 2012. “Poisoning attacks
against support vector machines, ” International Conference
on Machine Learning (ICML 2012), pages 1467–1474.
Blanchet, B. 2017 . “CryptoVerif: A Computationally-Sound
Security Protocol Verifier, ” http:/ /prosecco.gforge.inria.fr/
personal/bblanche/cryptoverif/cryptoverif.pdf
Blum, W. 2017 . “Neural fuzzing: applying DNN to software
security testing, ” Microsoft Research Blog, https:/ /www.
microsoft.com/en-us/research/blog/neural-fuzzing/
Booth, S., Tompkin, J., Gajos, K., Waldo, J., Pfister, H., Nagpal,
R. 2017 . “Piggybacking Robots: Human-Robot Overtrust in
University Dormitory Security, ” HRI 2017 , available at http:/ /
www.slbooth.com/piggybacking_robots.html
Bostrom. 2014. Superintelligence: Paths, Dangers, Strategies.
Oxford: Oxford University Press.
ReferencesReferences
p.68
The Malicious Use of Artificial Intelligence
ReferencesTechnologies, ” https:/ /obamawhitehouse.archives.gov/
blog/2013/12/18/liberty-and-security-changing-world
CNAS. 2017 . “Artificial Intelligence and Global Security
Summit, ” Centre for New American Security. https:/ /www.cnas.
org/events/artificial-intelligence-and-global-security-summit
Cohen, G. 2017 . “Israel Shoots Down Hamas Drone Over Gaza
Strip, ” Haaretz, http:/ /www.haaretz.com/israel-news/1.773465
Cooper, D.M. 2013. “A Licensing Approach to Regulation of
Open Robotics, ” paper presented at We Robot, April 2013.
Crawford, K. and Calo, R. 2016. “There is a blind spot in AI
research, ” Nature, October 13, 2016, https:/ /www.nature.com/
news/there-is-a-blind-spot-in-ai-research-1.20805
Crootof, R. 2015. “The Killer Robots are Here: Legal and
Policy Implications, ” http:/ /isp.yale.edu/sites/default/files/
publications/killer_robots_are_here_final_version.pdf
Crootof, R. and Renz, F. 2017 . “An Opportunity to Change
the Conversation on Autonomous Weapon Systems, ”
Lawfare, https:/ /www.lawfareblog.com/opportunity-change-
conversation-autonomous-weapon-systems
CSE, 2017 . “Assemblyline”. October 2017 . https:/ /www.cse-cst.
gc.ca/en/assemblyline
Cummings, M. L. 2004. “Creating Moral Buffers in Weapon
Control Interface Design. ” IEEE Technology and Society
Magazine (Fall 2004), 29–30.
Cylance. 2017 . “Black Hat Attendees See AI As Double-Edged
Sword, ” The Cylance Team, available at https:/ /www.cylance.
com/en_us/blog/black-hat-attendees-see-ai-as-double-
edged-sword.html
D’Avino, D., Cozzolino, D., Poggi, G., and Verdoliva, L. 2017 .
“Autoencoder with recurrent neural networks for video forgery
detection, ” arXiv preprint, available at https:/ /arxiv.org/
abs/1708.08754
Dao, James. “Drone Pilots Are Found to Get Stress Disorders
Much as Those in Combat Do, ” New York Times. February 22,
2013.
DARPA. 2016. Cyber Grand Challenge, www.
cybergrandchallenge.com
De Choudhury, M., Counts, S., and Horvitz, E. 2013. “Social
media as a measurement tool of depression in populations, ”
Proceedings of the 5th Annual ACM Web Science Conference,
pp. 47-56, https:/ /dl.acm.org/citation.cfm?id=2464480
Department of Defense. 2012. “DoD Directive 3000.09:
Autonomy in Weapon Systems. ”
Department of Defense. 2015. “Department of Defense
Law of War Manual, ” https:/ /www.defense.gov/Portals/1/
Documents/law_war_manual15.pdf
Dijkstra, E.W., 1968. Letters to the editor: go to statement
considered harmful. Communications of the ACM, 11(3),
pp.147-148.Botsman, R. 2017 . “Big data meets Big Brother as China
moves to rate its citizens, ” Wired UK, October 21, 2017 , http:/ /
www.wired.co.uk/article/chinese-government-social-credit-
score-privacy-invasion
Browne, M. 2017 . “YouTube Removes Videos Showing
Atrocities in Syria, ” The New York Times, August 22, 2017 ,
https:/ /www.nytimes.com/2017/08/22/world/middleeast/
syria-youtube-videos-isis.html
Brynjolfsson, E. and McAfee, A. 2014. The Second Machine
Age: Work, Progress, and Prosperity in a Time of Brilliant
Machines. New York: W.W. Norton & Company, Inc.
Bryson, J., Diamantis, M., and Grant, T . 2017 . “Of, for, and by
the people: the legal lacuna of synthetic persons, ” Artificial
Intelligence and Law, Vol. 25, Issue 3, September 2017 , pp.
273-291.
Buchanan, B., and Taylor, M. 2017 . “Machine Learning for
Policymakers, ” Paper, Cyber Security Project, Belfer Center.
https:/ /www.belfercenter.org/publication/machine-learning-
policymakers
Bueno de Mesquita, B. and Smith, A. 2012. The Dictator’s
Handbook: Why Bad Behavior is Almost Always Good Politics.
New York: PublicAffairs.
Calabresi, M. 2017 . “Inside Russia’s Social Media War on
America, ” Time, May 18, 2017 , http:/ /time.com/4783932/
inside-russia-social-media-war-america/
Calo, R. 2011. “Open Robotics, ” Maryland Law Review, Vol. 70,
No. 3.
Calo, R. 2015. “Robotics and the Lessons of Cyberlaw, ”
California Law Review, Vol. 103, No. 3, pp. 513-63.
Carbon Black, 2017 . “Beyond the Hype: Security Experts
Weigh in on Artificial Intelligence, Machine Learning, and Non-
Malware Attacks. ” https:/ /www.carbonblack.com/2017/03/28/
beyond-hype-security-experts-weigh-artificial-intelligence-
machine-learning-non-malware-attacks/
Carlini, N., Mishra, P ., Vaidya, T ., Zhang, Y ., Sherr, M., Shields,
C., Wagner, D., and Zhou, W. 2016. “Hidden Voice Commands, ”
25th USENIX Security Symposium, people.eecs.berkeley.
edu/~pratyushmishra/docs/papers/usenix16-hvc.pdf
Chessen, M. 2017 . “The AI Policy Landscape, Medium, https:/ /
medium.com/artificial-intelligence-policy-laws-and-ethics/
the-ai-landscape-ea8a8b3c3d5d
Chessen, M. 2017 . “The MADCOM Future, ” Atlantic Council
report, http:/ /www.atlanticcouncil.org/publications/reports/
the-madcom-future
Chung, J., Jamaludin, A., and Zisserman, A. 2017 . “You
said that?, ” arXiv preprint server: https:/ /arxiv.org/
abs/1705.02966
Clarke, R., Morell, M., Stone, G., Sunstein, C., and Swire,
P . 2013. “Liberty and Security in a Changing World, ” The
President’s Review Group on Intelligence and Communications
p.69
The Malicious Use of Artificial Intelligence
ReferencesFarquhar, S., Cotton-Barratt, O., and Snyder-Beattie, A. 2017 .
“Pricing Externalities to Balance Public Risks and Benefits of
Research, ” Health Security, 15:4, pages 401-408, available
online at https:/ /www.ncbi.nlm.nih.gov/pubmed/28767274
Filar, B., Seymour, R.J. and Park, M., 2017 . “Ask Me Anything:
A Conversational Interface to Augment Information Security
Workers. ” In Symposium on Usable Privacy and Security
(SOUPS). https:/ /arxiv.org/abs/1707 .05768
Fisher, K. 2014. “Using formal methods to enable more secure
vehicles: DARPA’s HACMS program, ” ICFP ‘14: Proceedings
of the 19th ACM SIGPLAN international conference on
Functional programming. http:/ /dl.acm.org/citation.
cfm?id=2628165&CFID=776732616&CFTOKEN=19198339
Flashpoint. 2016. “Ransomware as a Service: Inside an
Organized Russian Ransomware Campaign, ” (registration
required for download), available from Flashpoint library at
https:/ /www.flashpoint-intel.com/library/
Franke, U. 2016. “Flying IEDs: The Next Big Threat?, ” War on
the Rocks blog, https:/ /warontherocks.com/2016/10/flying-
ieds-the-next-big-threat/
Fredrikson, M., Jha, S., and Ristenpart, T . 2015. “Model
inversion attacks that exploit confidence information and
basic countermeasures, ” in Proceedings of the 22nd ACM
SIGSAC Conference on Computer and Communications
Security. ACM, 2015, pp. 1322–1333. Available at http:/ /doi.
acm.org/10.1145/2810103.2813677
Future of Life Institute, 2017 . “Asilomar AI Principles, ” text
and signatories available online at https:/ /futureoflife.org/
ai-principles/
Garfinkel, B. Forthcoming. “Recent Advances in Cryptography
and Their Possible Consequences. ”
GCHQ, 2016. “CyberChef - the Cyber “Swiss Army Knife”. ”
December 2016. https:/ /www.gchq.gov.uk/news-article/
cyberchef-cyber-swiss-army-knife
Giaretta, A. and Dragoni, N. “Community Targeted Spam: A
Middle Ground Between General Spam and Spear Phishing, ”
arXiv preprint server, https:/ /arxiv.org/abs/1708.07342
Goel, V . 2014. “Facebook tinkers with users’ emotions in news
feed experiment, stirring outcry. ” New York Times. June 29,
2014. https:/ /www.nytimes.com/2014/06/30/technology/
facebook-tinkers-with-users-emotions-in-news-feed-
experiment-stirring-outcry.html?_r=0
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-
Farley, D., Ozair, S., Courville, A. and Bengio, Y ., 2014.
“Generative Adversarial Networks. ” In Advances in Neural
information Processing Systems 2014 (pp. 2672-2680),
available at https:/ /arxiv.org/abs/1406.2661
Goodman, B. and Flaxman, S., 2016. European Union
regulations on algorithmic decision-making and a right to
explanation”. arXiv preprint arXiv:1606.08813.Dinh, L., Krueger, D. and Bengio, Y ., 2014. “NICE: Non-linear
independent components estimation, ” ICLR 2015 workshop
paper, https:/ /arxiv.org/abs/1410.8516
Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig,
M., and Wernsing, J. 2016. “CryptoNets: Applying Neural
Networks to Encrypted Data with High Throughput and
Accuracy, ” Proceedings of The 33rd International Conference
on Machine Learning, available online at http:/ /proceedings.
mlr.press/v48/gilad-bachrach16.html
Dvorsky, G. 2017 . “Hackers Have Already Started to
Weaponize Artificial Intelligence, ” Gizmodo, https:/ /www.
gizmodo.com.au/2017/09/hackers-have-already-started-to-
weaponize-artificial-intelligence/
Dwork, C. and Naor, M. 1993. “Pricing via Processing or
Combatting Junk Mail. ” In Brickell E.F. (eds) Advances in
Cryptology — CRYPTO’ 92. CRYPTO 1992. Lecture Notes in
Computer Science, vol 740. Berlin: Springer.
Dwork, C. 2006. “Differential Privacy, ” Proceedings of the
33rd International Colloquium on Automata, Languages and
Programming, part II (ICALP 2006), available online at https:/ /
www.microsoft.com/en-us/research/publication/differential-
privacy/
Eckersley, P ., Nasser, Y ., et al. 2017 . “Help EFF Track the
Progress of AI and Machine Learning, ” Electronic Frontier
Foundation, https:/ /www.eff.org/deeplinks/2017/06/help-eff-
track-progress-ai-and-machine-learning
EFF. 2014. “Unintended Consequences - 16 Years Under the
DMCA, ” Electronic Frontier Foundation, https:/ /www.eff.org/
wp/unintended-consequences-16-years-under-dmca, part
of a series avalable at https:/ /www.eff.org/wp/unintended-
consequences-under-dmca/archive
Evans, R. and Gao, J. 2016. “DeepMind AI Reduces Google
Data Centre Cooling Bill by 40%, ” DeepMind blog, July 20,
2016, https:/ /deepmind.com/blog/deepmind-ai-reduces-
google-data-centre-cooling-bill-40/
Everett, R., Nurse, J., and Erola, A. 2016. “The Anatomy of
Online Deception: What Makes Automated Text Convincing?,
31st ACM/SIGAPP Symposium on Applied Computing (SAC),
https:/ /dl.acm.org/citation.cfm?id=2851813
Everitt, T ., Krakovna, V ., Orseau, L., Hutter, M., and Legg,
S. 2017 . “Reinforcement Learning with a Corrupted Reward
Channel, ” available online at https:/ /arxiv.org/abs/1705.08417
Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T ., Li, B.,
Prakash, A., Rahmati, A., and Song, D. 2017 . “Robust Physical-
World Attacks on Deep Learning Models, ” arXiv preprint
server, https:/ /arxiv.org/abs/1707 .08945
Executive Office of the President, National Science
and Technology Council, Committee on Technology,
2016. “Preparing for the Future of Artificial Intelligence, ”
October 2016, https:/ /obamawhitehouse.archives.gov/
blog/2016/10/12/administrations-report-future-artificial-
intelligence
p.70
The Malicious Use of Artificial Intelligence
ReferencesHorowitz, M. 2016. “Who’ll want artificially intelligent
weapons? ISIS, democracies, or autocracies?, ” Bulletin
of the Atomic Scientists: http:/ /thebulletin.org/who’ll-
want-artificially-intelligent-weapons-isis-democracies-or-
autocracies9692
Hosseini, H., Xiao, B. and Poovendran, R., 2017 . “Google’s
Cloud Vision API Is Not Robust To Noise, ” arXiv preprint
server, https:/ /arxiv.org/abs/1704.05051
Herley, C. 2010. “The Plight of the Targeted Attacker in a
World of Scale, ” Workshop on the Economics of Information
Security 2010, https:/ /www.microsoft.com/en-us/research/
publication/the-plight-of-the-targeted-attacker-in-a-world-of-
scale/
IEEE Standards Association, 2017 . “The IEEE Global Initiative
for Ethical Considerations in Artificial Intelligence and
Autonomous Systems, ” https:/ /standards.ieee.org/develop/
indconn/ec/autonomous_systems.html
IFR, 2016. “World Robotics 2016, ” https:/ /ifr.org/free-
downloads/
International Committee of the Red Cross. 2017 . “Expert
Meeting on Lethal Autonomous Weapon Systems. ” https:/ /
www.icrc.org/en/document/expert-meeting-lethal-
autonomous-weapons-systems
Ixy, 2017 . “Ixy - the conflict free app, ” http:/ /getixy.com/
Jaderberg, M., Mnih, V ., Czarnecki, W. M., Schaul, T ., Leibo,
J. Z., Silver, D., & Kavukcuoglu, K. 2016. “Reinforcement
Learning with Unsupervised Auxiliary Tasks. ” arXiv preprint
server. https:/ /arxiv.org/abs/1611.05397
Ji, Z., Lipton, C., and Elkan, C. 2014. “Differential privacy and
machine learning: A survey and review”. In:arXiv preprint
arXiv:1412.7584
Jones, B. and Mattiacci, E. 2017 . “A Manifesto in 140
Characters or Fewer: Social Media as a Tool of Rebel
Diplomacy, ” British Journal of Political Science, https:/ /www.
cambridge.org/core/journals/british-journal-of-political-
science/article/manifesto-in-140-characters-or-fewer-social-
media-as-a-tool-of-rebel-diplomacy/82518E669A274B26E89
8A567FE22531F
Jordan, M.I., and Mitchell, T .M. 2015. “Machine learning:
Trends, perspectives, and prospects, ” Science Vol. 349, Issue
6245, pp. 255-260, DOI: 10.1126/science.aaa8415
Karras, T ., Aila, T ., Laine, S., and Lehtinen, J. 2017 .
“Progressive Growing of GANs for Improved Quality, Stability,
and Variation, ” available online at https:/ /t.co/CCHghgL60t
Kashyap, A., Parmar, R., Agarwal, M., Gupta, H. 2017 . “An
Evaluation of Digital Image Forgery Detection Approaches, ”
arXiv preprint server, https:/ /arxiv.org/abs/1703.09968
Katz, G., Barrett, C., Dill, D., Julian, K., and Kochenderfer, M.
2017 . “Reluplex: An Efficient SMT Solver for Verifying Deep
Neural Networks, ” arXiv preprint available at https:/ /arxiv.org/
abs/1702.01135Gu, T ., Dolan-Gavitt, B., and Garg, S. 2017 . “BadNets:
Identifying Vulnerabilities in the Machine Learning Supply
Chain, ” arXiv preprint available at https:/ /arxiv.org/
abs/1708.06733
Guilbeault, D. and Woolley, S. 2016. “How Twitter Bots Are
Shaping the Election, ” The Atlantic, November 1, 2016,
https:/ /www.theatlantic.com/technology/archive/2016/11/
election-bots/506072/
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., and Evans, O.
2017 . “When Will AI Exceed Human Performance? Evidence
from AI Experts, ” arXiv preprint server, https:/ /arxiv.org/
abs/1705.08807
Greenberg, A. 2016, “The Jeep Hackers are Back to Prove
Car Hacking Can Get Much Worse, ” Wired, January, 2016,
available online at https:/ /www.wired.com/2016/08/jeep-
hackers-return-high-speed-steering-acceleration-hacks/
Greenemeier, L. 2016. “GPS and the World’s First ‘Space
War’, ” Scientific American, February 8, 2016, https:/ /www.
scientificamerican.com/article/gps-and-the-world-s-first-
space-war/
Griffith, E. 2017 . “Facebook can absolutely control its
algorithm. ” Wired. September 26, 2017 . https:/ /www.wired.
com/story/facebook-can-absolutely-control-its-algorithm/
Grosse, K., Papernot, N., Manoharan, P ., Backes, M., and
McDaniel, P . 2016. “Adversarial Perturbations Against Deep
Neural Networks for Malware Classification, ” arXiv preprint
available online at https:/ /arxiv.org/abs/1606.04435
Harrison, John. 2010 “Formal Methods at Intel – An Overview. ”
Second NASA Formal Methods Symposium, available at
https:/ /www.cl.cam.ac.uk/~jrh13/slides/nasa-14apr10/slides.
pdf
Harris, E., ed. 2016. Governance of Dual-Use Technologies:
Theory and Practice. American Academy of Arts and Sciences,
Cambridge. Available online at http:/ /www.amacad.org/
content/publications/publication.aspx?d=22228
Hawkes, Rebecca. “Post-Traumatic Stress Disorder Is Higher
in Drone Operators. ” The Telegraph. May 30, 2015.
Hernández-Orallo, J. 2017 . The Measure of All Minds:
Evaluating Natural and Artificial Intelligence. Cambridge
University Press. See further details at http:/ /allminds.org/
Hester, T ., Vecerik, M., Pietquin, O., Lanctot, M., Schaul,
T ., Piot, B., Sendonaris, A., Dulac-Arnold, G., Osband, I.,
Agapiou, J. and Leibo, J.Z., 2017 . “Deep Q-learning from
Demonstrations. ” arXiv preprint server. https:/ /arxiv.org/
abs/1704.03732
Hicks, K., Hunter, A.P ., Samp, L.S., and Coll, G. 2017 .
“Assessing the Third Offset Strategy, ” Center for Strategic
and International Studies, https:/ /www.csis.org/analysis/
assessing-third-offset-strategy
Hilary, Gilles. 2016. “The Professionalization of Cyber Crime, ”
INSEAD Business School blog, https:/ /knowledge.insead.
edu/blog/insead-blog/the-professionalisation-of-cyber-
criminals-4626
p.71
The Malicious Use of Artificial Intelligence
ReferencesLaurie, B. and Clayton, R., 2004. “Proof-of-work proves
not to work”; version 0.2. In Workshop on Economics
and Information, Security, http:/ /www.cl.cam.ac.uk/~rnc1/
proofwork2.pdf
Libicki, R. 2016. Cyberspace in Peace and War. Annapolis:
Naval Institute Press.
Lin, J. and Singer, P . 2017 . “Come see China’s new
hexacopters and self-detonating drones, ” Popular Science,
http:/ /www.popsci.com/china-new-drones-army-hexacopters
Lindell, Y . and Pinkas, B. 2009. “Secure Multiparty
Computation for Privacy-Preserving Data Mining, ” The
Journal of Privacy and Confidentiality, Vol. 1, No. 1, pp.
59-98, http:/ /repository.cmu.edu/cgi/viewcontent.
cgi?article=1004&context=jpc
Liu, D. and Camp, L.J., 2006. “Proof of Work can Work”.
In WEIS. http:/ /www.econinfosec.org/archive/weis2006/
docs/50.pdf
Liu, M. and Tuzel, O. 2016. “Coupled Generative Adversarial
Networks, ” Proceedings of Neural Information Processing
Systems (NIPS) 2016, preprint available online at https:/ /arxiv.
org/abs/1606.07536
Lucas, L. 2017 . “World’s biggest drone maker DJI eyes move
to commercial applications, ” Financial Times, August 10, 2017 ,
available online at https:/ /www.ft.com/content/0d87a148-
7d7f-11e7-ab01-a13271d1ee9c
Manjoo, Farhad. 2017 . “Can Facebook fix its own worst bug?”
New York Times Magazine. April 25, 2017 . https:/ /www.nytimes.
com/2017/04/25/magazine/can-facebook-fix-its-own-worst-
bug.html
Marwick, A. and Lewis, R. Media Manipulation and
Disinformation Online. https:/ /datasociety.net/pubs/oh/
DataAndSociety_MediaManipulationAndDisinformationOnline.
pdf
McAfee and Center for Strategic and International Studies.
2013. “The Economic Impact of Cybercrime and Cyber
Espionage, ” https:/ /www.mcafee.com/uk/resources/reports/
rp-economic-impact-cybercrime.pdf
McAfee and Center for Strategic and International Studies.
2016. “Hacking the Skills Shortage, ” https:/ /www.mcafee.
com/uk/resources/reports/rp-hacking-skills-shortage.pdf
Metz, C. 2015. “Google Says Its AI Catches 99.9 Percent of
Gmail Spam, ” Wired, July 9, 2015.
Miller, M., 2006. Robust Composition: Towards a Unified
Approach to Access Control and Concurrency Control. Phd
Dissertation. http:/ /www.erights.org/talks/thesis/markm-
thesis.pdf
Mnih, V ., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J.,
Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K.,
Ostrovski, G. and Petersen, S., 2015. “Human-level control
through deep reinforcement learning, ” Nature, 518(7540),
pp.529-533.Kesarwani, M., Mukhoty, B., Arya, V ., and Mehta, S. 2017 .
“Model Extraction Warning in MLaaS Paradigm, ” arXiv preprint
server, https:/ /arxiv.org/abs/1711.07221
Kharkar, A., Simecek, H., Xu, W., Evans, D. and Anderson,
H. S. 2017 . “Approaches to Evading Windows PE Malware
Classifiers. ” USENIX Security, 2017 .
King, G., Pan, J., and Roberts, M. 2017 . “How the Chinese
Government Fabricates Social Media Posts for Strategic
Distraction, not Engaged Argument, ” American Political
Science Review, Vol. 111, Issue 3, August 2017 , pp. 484-501,
https:/ /www.cambridge.org/core/journals/american-political-
science-review/article/how-the-chinese-government-
fabricates-social-media-posts-for-strategic-distraction-not-
engaged-argument/4662DB26E2685BAF1485F14369BD137C
Kirkpatrick, K. 2016. “Battling Algorithmic Bias, ”
Communications of the ACM, Vol. 59, No. 10, pp. 16-17 ,
https:/ /cacm.acm.org/magazines/2016/10/207759-battling-
algorithmic-bias/abstract
Knight, W. 2017 . “AI Fight Club Could Help Save Us From
a Future of Super-Smart Cyberattacks, ” MIT Technology
Review, available online at https:/ /www.technologyreview.
com/s/608288/ai-fight-club-could-help-save-us-from-a-
future-of-super-smart-cyberattacks/ As of September 27 ,
2017 , the associated competition information can be found at
https:/ /www.kaggle.com/google-brain/competitions
Koh, P .W., and Liang, P . 2017 . “Understanding Black-Box
Predictions via Influence Functions, ” Proceedings of ICML
2017 , available online at https:/ /arxiv.org/abs/1703.04730
Kolodny, L. 2017 . “Marble and Yelp Eat24 start robot food
delivery in San Francisco, ” TechCrunch, https:/ /techcrunch.
com/2017/04/12/marble-and-yelp-eat24-start-robot-food-
delivery-in-san-francisco/
Korzak, E. 2017 . “UN GGE on Cybersecurity: The End of an
Era?, ” The Diplomat. https:/ /thediplomat.com/2017/07/
un-gge-on-cybersecurity-have-china-and-russia-just-made-
cyberspace-less-safe/
Kosinski, M., Stillwell, D., and Graepel, T . 2013. “Private traits
and attributes are predictable from digital records of human
behavior, ” Proceedings of the National Academy of Sciences
of the United States of America, Vol. 110, No. 15, http:/ /www.
pnas.org/content/110/15/5802.full
Kramer, A., Guillory, J., and Hancock, J. 2014. “Experimental
evidence of massive-scale emotional contagion through
social networks. ” PNAS. Vol 111, No. 24. 8788-8790. June 17 ,
2014. http:/ /www.pnas.org/content/111/24/8788.full.pdf
Krimsky, S., 1982. Genetic alchemy: The social history of the
recombinant DNA controversy. M.I. T . Press, Cambridge, MA.
Lapowsky, I. 2017 . “Eight revealing moments from the second
day of Russia hearings. ” Wired. November 1, 2017 . https:/ /www.
wired.com/story/six-revealing-moments-from-the-second-
day-of-russia-hearings/
p.72
The Malicious Use of Artificial Intelligence
ReferencesPapernot, N., McDaniel, P ., Goodfellow, I., Jha, S., Celik, Z.B.
and Swami, A., 2016b. “Practical black-box attacks against
deep learning systems using adversarial examples, ” arXiv
preprint server, https:/ /arxiv.org/abs/1602.02697
Pellerin, C. 2016. “Deputy Secretary: Third Offset Bolsters
America’s Military Deterrence, ” DoD News, https:/ /www.
defense.gov/News/Article/Article/991434/deputy-secretary-
third-offset-strategy-bolsters-americas-military-deterrence/
Pérez-Rosas, V ., Kleinberg, B., Lefevre, A., and Mihalcea, R.
2017 . “Automatic Detection of Fake News, ” arXiv preprint
server: https:/ /arxiv.org/abs/1708.07104
Quercia, D., Kosinski, M., Stillwell, D., and Crowcroft, J. 2011.
“Our Twitter Profiles, Our Selves: Predicting Personality with
Twitter, ” 2011 IEEE Third International Conference on Privacy,
Security, Risk and Trust and 2011 IEEE Third International
Conference on Social Computing, http:/ /ieeexplore.ieee.org/
document/6113111/
Radford, A., Metz, L. and Chintala, S., 2015. “Unsupervised
Representation Learning with Deep Convolutional Generative
Adversarial Networks, ” arXiv preprint server, https:/ /arxiv.org/
abs/1511.06434
Rahman, M., Azimpourkivi, M., Topkara, U., Carbunar, B. 2017 .
“Video Liveness for Citizen Journalism: Attacks and Defenses, ”
IEEE Transactions on Mobile Computing, Vol: PP , Issue 99,
http:/ /ieeexplore.ieee.org/abstract/document/7887755/
Reuters. 2017 . “Facebook is changing its news feed
algorithm again. ” Fortune. June 30, 2017 . http:/ /fortune.
com/2017/06/30/facebook-news-feed-algorithm-
change-2017/
Rød, E. and Weidmann, N. 2015. “Empowering activists or
autocrats? The Internet in authoritarian regimes, ” Journal of
Peace Research, Vol. 52, Issue 3, http:/ /journals.sagepub.
com/doi/abs/10.1177/0022343314555782
Roff, H. 2016. “Autonomy, Robotics, and Collective Systems, ”
project webpage, available at https:/ /globalsecurity.asu.edu/
robotics-autonomy
Roff, H. 2016 “Meaningful Human Control, Artificial
Intelligence and Autonomous Weapons, ” briefing paper
for delegates at the Convention on Certain Conventional
Weapons (CCW) Meeting of Experts on Lethal Autonomous
Weapons Systems (LAWS), www.article36.org/wp-content/
uploads/2016/04/MHC-AI-and-AWS-FINAL.pdf
Roff, H. 2016. “Autonomous Weapons and Incentives
for Oppression” The Duck of Minerva Available at: http:/ /
duckofminerva.com/2016/03/autonomous-weapons-and-
incentives-for-oppression.html
Rouhani, B., Riazi, M., and Koushanfar, F. 2017 . “DeepSecure:
Scalable Provably-Secure Deep Learning, ” arXiv preprint
server, https:/ /arxiv.org/abs/1705.08963Moore, T . and Anderson, R. 2012. “Internet Security. ” In
Peitz, M. and Waldfogel, J. (eds.) The Oxford Handbook of
the Digital Economy, Oxford University Press, New York, NY ,
572—599
Morozov, E. 2012. The Net Delusion: The Dark Side of Internet
Freedom. New York: PublicAffairs.
Mouawad, J. 2015. “Risk to Aircraft from Drones Being
Debated, ” New York Times, December 10, 2015, https:/ /www.
nytimes.com/2015/12/11/business/risk-to-aircraft-from-
drones-being-debated.html
Naur, P . and Randell, B. eds., 1969. Software Engineering:
Report on a conference sponsored by the NATO SCIENCE
COMMITTEE, Garmisch, Germany, 7th to 11th October 1968.
Nato.
National Science Foundation. 2017 . “Cyber-Physical Systems, ”
grant solicitation, https:/ /www.nsf.gov/funding/pgm_summ.
jsp?pims_id=503286&org=CISE&sel_org=CISE&from=fund
Naveh, A. and Tromer, E. 2016. “PhotoProof: Cryptographic
Image Authentication for Any Set of Permissible
Transformations, ” 2016 IEEE Symposium on Security and
Privacy (SP). http:/ /ieeexplore.ieee.org/document/7546506/
NDSS. 2018. “NDSS 2018 Call for Papers”, The Network and
Distributed System Security Symposium 2018. http:/ /www.
ndss-symposium.org/ndss2018/ndss-2018-call-papers/
Neema, S. (2017) “Assured Autonomy”. https:/ /www.darpa.mil/
program/assured-autonomy
Ng, J. 2015. “Politics, Rumours, and Ambiguity: Tracking
Censorship on WeChat’s Public Accounts Platform, ” available
at https:/ /citizenlab.org/2015/07/tracking-censorship-on-
wechat-public-accounts-platform/#append-documented-
cases
Office of Cyber and Infrastructure Analysis, 2017 . “Narrative
Analysis: Artificial Intelligence, ” U.S. Department of Homeland
Security, National Protection and Programs Directorate,
available at https:/ /info.publicintelligence.net/OCIA-
ArtificialIntelligence.pdf
OpenAI, 2017 . “Dota 2, ” OpenAI blog, https:/ /blog.openai.
com/dota-2/
OpenAI, 2017 . “More on Dota 2, ” OpenAI blog, https:/ /blog.
openai.com/more-on-dota-2/
OpenMined, 2017 . OpenMined website, http:/ /openmined.org
Papernot, N., McDaniel, P ., Sinha, A., and Wellman, M. 2016.
“Towards the Science of Security and Privacy in Machine
Learning, ” available online at https:/ /arxiv.org/abs/1611.03814
Papernot, N., Goodfellow, I., Sheatsley, R., Feinman, R., and
McDaniel, P . 2016. “Cleverhans v.1.0.0: an adversarial machine
learning library, ” arXiv preprint server, available at https:/ /arxiv.
org/abs/1610.00768; associated GitHub repository available
at https:/ /github.com/tensorflow/cleverhans
p.73
The Malicious Use of Artificial Intelligence
ReferencesShehadeh, K.K., 1999. The Wassenaar Arrangement and
Encryption Exports: An Ineffective Export Control Regime
that Compromises United States Economic Interests. Am. U.
Int’l L. Rev., 15, p.271.
Silver D., Huang, A., Maddison, C., Guez, A., Sifre, L.,
van den Driessche, G., Schrittweiser, J., Antonoglu, I.,
Paneershelvam, V ., Lanctot, M., Dieleman, S., Grewe, D.,
Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T ., Leach,
M., Kavukcuoglu, K., Graepel, T ., and Hassabis, D. 2016.
“Mastering the game of Go with deep neural networks and tree
search”, Nature 529 pp 484-9 http:/ /web.iitd.ac.in/~sumeet/
Silver16.pdf
Silver, D., Schrittweiser, J., Simonyan, K., Antonoglu, I.,
Huang, A., Guez, A., Hubert, T ., Baker, L., Lai, M., Bolton,
A., Chen, Y ., Lillicrap, T ., Hui, F., Sifre, L., van den Driessche,
G., Graepel, T ., and Hassabis, D. 2017 . “Mastering the Game
of Go without Human Knowledge, ” Nature 550: 354-359,
October 19, 2017 , https:/ /deepmind.com/documents/119/
agz_unformatted_nature.pdf
Shokri, R., Stronati, M., and Shmatikov, V . 2016.
“Membership inference attacks against machine learning
models, ” CoRR, vol. abs/1610.05820, 2016. Available at http:/ /
arxiv.org/abs/1610.05820
Shu, K., Wang, S., Sliva, A., Tang, J., and Liu, H. 2017 . “Fake
News Detection on Social Media: A Data Mining Perspective, ”
arXiv preprint, https:/ /arxiv.org/abs/1708.01967
Singer, P . 2009. Wired for War: The Robotics Revolution and
Conflict in the 21st Century, London: Penguin Press.
Šrndic, N. and Laskov, P . 2014. “Practical Evasion of a
Learning-Based Classifier: A Case Study. ” In Proceedings of
the 2014 IEEE Symposium on Security and Privacy, pp. 197-211.
IEEE Computer Society.
Stevens, R., Suciu, O., Ruef, A., Hong, S., Hicks, M., Dumitras,
T . 2016. “Summoning Demons: The Pursuit of Exploitable
Bugs in Machine Learning, ” Proceedings of Neural Information
Processing Systems 2016, Reliable Machine Learning in the
Wild workshop, https:/ /arxiv.org/abs/1701.04739
Stocky, T . Facebook post. May 10, 2016.
https:/ /www.facebook.com/tstocky/
posts/10100853082337958?pnref=story
Stoica, I., Song, D., Popa, R., Patterson, D., Mahoney, M.,
Katz, R., Joseph, A., Jordan, M., Hellerstein, J., Gonzalez, J.,
Goldberg, K., Ghodsi, A., Culler, D., and Abbeel, P . 2017 . “A
Berkeley View of Systems Challenges for AI, ” Technical Report
No. UCB/EECS-2017-159, http:/ /www2.eecs.berkeley.edu/
Pubs/TechRpts/2017/EECS-2017-159.html
Solomon, B. 2017 . “Witnessing an ISIS Drone Attack, ”
New York Times, https:/ /www.nytimes.com/video/world/
middleeast/100000005040770/isis-drone-attack-mosul.html
Sunstein, C. 2017 . #Republic: Divided Democracy in the Age
of Social Media. Princeton: Princeton University Press.
Standage, T . 2017 . “Taking flight, ” The Economist, http:/ /www.
economist.com/technology-quarterly/2017-06-08/civilian-
dronesRubinstein, B., Nelson, B., Huang, L., Joseph, A., Lau, S., Rao,
S., Taft, N., and Tygar, J.D. 2009. “ANTIDOTE: Understanding
and Defending against Poisoning of Anomaly Detectors, ”
Proceedings of the 9th ACM SIGCOMM Conference on
Internet Measurement, pp. 1-14, https:/ /people.eecs.berkeley.
edu/~tygar/papers/SML/IMC.2009.pdf
Scharre, P . 2015. “Counter-Swarm: A Guide to Defeating
Robotic Swarms, ” War on the Rocks blog, https:/ /
warontherocks.com/2015/03/counter-swarm-a-guide-to-
defeating-robotic-swarms/
Scharre, P . 2016. “Autonomous Weapons and Operational
Risk, ” Center for a New American Security, ” http:/ /
s3.amazonaws.com/files.cnas.org/documents/CNAS_
Autonomous-weapons-operational-risk.pdf
Scharre, P . 2018. Army of None: Autonomous Weapons and
the Future of War. New York, NY: W.W. Norton. (forthcoming)
Schmitt, E. 2017 . “Pentagon Tests Lasers and Nets to Combat
a Vexing Foe: ISIS Drones, ” New York Times, September 23,
2017 , available at https:/ /www.nytimes.com/2017/09/23/
world/middleeast/isis-drones-pentagon-experiments.html
Schofield, H. 2013. “How Napoleon’s semaphore telegraph
changed the world, ” BBC, June 17 , 2013, http:/ /www.bbc.
co.uk/news/magazine-22909590
Schneier, B. 2014. “The Internet of Things is Wildly
Insecure--and Often Unpatchable, ” Wired, https:/ /www.wired.
com/2014/01/theres-no-good-way-to-patch-the-internet-of-
things-and-thats-a-huge-problem/
Schneier, B. 2017 . “Security and the Internet of Things, ”
Schneier on Security, https:/ /www.schneier.com/blog/
archives/2017/02/security_and_th.html
Segler, M. Preuß, M., and Waller, M. 2017 . “Towards
‘AlphaChem’: Chemical Synthesis Planning with Tree Search
and Deep Neural Network Policies, ” arXiv preprint server,
https:/ /arxiv.org/abs/1702.00020
Selsam, D., Liang, P ., Dill, D. 2017 . “Developing Bug-Free
Machine Learning Systems with Formal Mathematics, ” arXiv
preprint server, https:/ /arxiv.org/abs/1706.08605
Serban, I., Sankar, C., Germain, M., Zhang, S., Lin, Z.,
Subramanian, S., Kim, T ., Pieper, M., Chandar, S., Ke, N.,
Rajeshwar, S., de Brebisson, A., Sotelo, J., Suhubdy, D.,
Michalski, V ., Nguyen, A., Pineau, J., and Bengio, Y . 2017 . “A
Deep Reinforcement Learning Chatbot, ” https:/ /arxiv.org/
abs/1709.02349v2
Seymour, J. and Tully, P . 2016. “Weaponizing data science for
social engineering: Automated E2E spear phishing on Twitter, ”
Black Hat conference, https:/ /www.blackhat.com/docs/us-16/
materials/us-16-Seymour-Tully-Weaponizing-Data-Science-
For-Social-Engineering-Automated-E2E-Spear-Phishing-On-
Twitter-wp.pdf
Shao, C., Ciampaglia, L., Varol, O., Flammini, A., Menczer, F.
2017 . “The spread of fake news by social bots, ” arXiv preprint
server, https:/ /arxiv.org/abs/1707 .07592
p.74
The Malicious Use of Artificial Intelligence
ReferencesWaltzmann, R. 2017 . “The Weaponization of Information: The
Need for Cognitive Security, ” testimony presented before
the Senate Armed Services Committee, Subcommittee on
Cybersecurity, April 27 , 2017 , https:/ /www.rand.org/pubs/
testimonies/CT473.html
Watts, C. 2017 . “Disinformation: A Primer in Russian Active
Measures and Influence Campaigns, ” statement prepared for
the U.S. Senate Select Committee on Intelligence, March 30,
2017 , https:/ /www.intelligence.senate.gov/sites/default/files/
documents/os-cwatts-033017 .pdf
Weedon, J., Nuland, W., and Stamos, A. 2017 . “Information
Operations and Facebook, ” Facebook, https:/ /fbnewsroomus.
files.wordpress.com/2017/04/facebook-and-information-
operations-v1.pdf
Wiggers, K. 2017 . “Meet the 400-pound robots that will
soon patrol parking lots, offices, and malls, ” Digital Trends,
https:/ /www.digitaltrends.com/cool-tech/knightscope-robots-
interview/
Woolley, S. and Howard, P . 2017 . “Computational Propaganda
Worldwide: Executive Summary. ” Working Paper 2017 .11.
Oxford: Project on Computational Propaganda, comprop.oii.
ox.ac.uk
Wu, T . 2017 . “Please Prove You’re Not a Robot, ” New York
Times, July 15, 2017 .
Wright, S. 1994. Molecular politics: developing American and
British regulatory policy for genetic engineering, 1972-1982.
University of Chicago Press, Chicago.
Yampolskiy, R. 2017 . “AI Is the Future of Cybersecurity, for
Better and for Worse, ” Harvard Business Review, https:/ /hbr.
org/2017/05/ai-is-the-future-of-cybersecurity-for-better-and-
for-worse, May 8, 2017 .
Yao, A.C. 1982. “Protocols for secure computations, ”
23rd Annual Symposium on Foundations of Computer
Science (sfcs 1982), Chicago, IL, USA, pp. 160-
164. http:/ /ieeexplore.ieee.org/stamp/stamp.
jsp?tp=&arnumber=4568388&isnumber=4568364
Yin, T . 2015. “Game of Drones: Defending Against Drone
Terrorism”. Texas A&M University Law Review, vol. 2, 635-673,
https:/ /ssrn.com/abstract=2944426
Zeitzoff, T . 2017 . “How Social Media Is Changing Conflict, ”
Journal of Conflict Resolution, 61.9 (2017): 1970-1991.
Zittrain, J. 2014. “Facebook Could Decide an Election Without
Anyone Ever Finding Out, ” New Republic, June 1, 2014,
https:/ /newrepublic.com/article/117878/information-fiduciary-
solution-facebook-digital-gerrymandering
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., and Procter,
R. 2017 . “Detection and Resolution of Rumours in Social
Media: A Survey, ” arXiv preprint server, https:/ /arxiv.org/
abs/1704.00656Sunderland, M., Ahn, J., Carson, C., and Kastenberg, W.
2013. “Making Ethics Explicit: Relocating Ethics to the Core of
Engineering Education, ” 2013 ASEE Annual Conference.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D.,
Goodfellow, I. and Fergus, R., 2013. “Intriguing properties
of neural networks. ” arXiv preprint server, https:/ /arxiv.org/
abs/1312.6199
Tambe, M. 2011. Security and Game Theory: Algorithms,
Deployed Systems, and Lessons Learned. Cambridge:
Cambridge University Press.
The Telegraph, 2016. “Drone hits British Airways plane as it
prepares to land at Heathrow, ” The Telegraph, April 18, 2016,
http:/ /www.telegraph.co.uk/news/2016/04/17/drone-hits-
british-airways-plane/
Thies, J., et al. 2016. “Face2Face: Real-time Face Capture
and Reenactment of RGB Videos, ” Proceedings of Computer
Vision and Pattern Recognition 2016, www.graphics.stanford.
edu/~niessner/papers/2016/1facetoface/thies2016face.pdf
Timm, T . 2013. “Prominent Security Researchers, Academics,
and Lawyers Demand Congress Reform the CFAA and Support
Aaron’s Law, ” Electronic Frontier Foundation https:/ /www.eff.
org/deeplinks/2013/08/letter
Tucker, J. (ed.). 2012. Innovation, Dual Use, and Security:
Managing the Risks of Emerging Biological and Chemical
Technologies. Cambridge: MIT Press.
Turing, A. 1949. “Checking a large routine, ” report of a
conference on High Speed Automatic Calculating Machines,
pp.67-9, corrected version available online at http:/ /www.
turingarchive.org/browse.php/b/8
U.S. Defense Science Board, DSB Task Force on Cyber
Supply Chain, 2017 . “Report of the Defense Science Board
Task Force on Cyber Supply Chain, ” http:/ /www.acq.osd.mil/
dsb/reports/2010s/DSBCyberSupplyChain_ExecSummary_
Distribution_A.PDF
Vanian, J. 2017 . “Drone Registrations are Still Soaring, ”
Fortune, January 6, 2017 , http:/ /fortune.com/2017/01/06/
drones-registrations-soaring-faa/
Verma, I. “Editorial Expression of Concern and Correction. ”
PNAS. Vol 111, No. 24. 10779. July 22, 2014. http:/ /www.pnas.
org/content/111/24/8788.full.pdf
Vezhnevets, A.S., Osindero, S., Schaul, T ., Heess, N.,
Jaderberg, M., Silver, D. and Kavukcuoglu, K., 2017 . “Feudal
networks for hierarchical reinforcement learning, ” arXiv
preprint server, https:/ /arxiv.org/abs/1703.01161
Vincent, J. 2016. “The UK government is crashing drones into
airplanes to see what happens, ” The Verge, October 18, 2016,
https:/ /www.theverge.com/2016/10/18/13314916/drone-
crash-airplane-test-uk-dangers
Wahby, R., Howald, M., Garg, S., shelat, a., and Walfish, M.
2016. “Verifiable ASICs, ” Security and Privacy (SP) 2016,
http:/ /ieeexplore.ieee.org/abstract/document/7546534/
p.75Summary
On February 19 and 20, 2017 , Miles Brundage of the Future of
Humanity Institute (FHI) and Shahar Avin of the Centre for the
Study of Existential Risk (CSER) co-chaired a workshop entitled
“Bad Actor Risks in Artificial Intelligence” in Oxford, United
Kingdom. The workshop was co-organized by FHI, CSER, and the
Leverhulme Centre for the Future of Intelligence (CFI). Participants
came from a wide variety of institutional and disciplinary
backgrounds, and analyzed a variety of risks related to AI misuse.
The workshop was held under Chatham House rules.
Event Structure
On February 19, the event began with background presentations
on cybersecurity, AI, and robotics from relevant experts in these Appendix A:
Workshop
Details
p.76
Appendix A: Workshop Detailsfields. A particular focus of the presentations was on highlighting
underexplored risks. The afternoon featured two sets of breakout
sessions: participants first discussed security domains and
scenarios, and then discussed possible defenses.
On February 20, a subset of the participants from the first day of
the workshop met to discuss next steps and the prioritization of
possible prevention and mitigation measures. The group present
agreed upon the need for a research agenda to be produced, and
voted on which measures seemed useful and tractable, in order to
focus the subsequent report writing process.
Report Writing Process
This document is based in large part on notes from the discussions
at the workshop, as well as prior and subsequent research by the
authors on the topic. Brundage and Avin et al. wrote a draft of the
report and circulated it among all of the attendees at the workshop
as well as additional domain experts. We are grateful to all of the
workshop participants for their invaluable contributions, even if we
were not able to capture all of their perspectives.
List of Workshop Participants
Dario Amodei, OpenAI
Ross Anderson, University of Cambridge
Stuart Armstrong, Future of Humanity Institute
Amanda Askell, Centre for Effective Altruism
Shahar Avin, Centre for the Study of Existential Risk
Miles Brundage, Future of Humanity Institute
Joanna Bryson, University of Bath/Princeton University Center
for Information Technology Policy
Jack Clark, OpenAI
Guy Collyer, Organization for Global Biorisk Reduction
Owen Cotton-Barratt, Future of Humanity Institute
Rebecca Crootof, Yale Law School
Allan Dafoe, Yale University
Eric Drexler, Future of Humanity Institute
Peter Eckersley, Electronic Frontier Foundation
Ben Garfinkel, Future of Humanity Institute
Carrick Flynn, Future of Humanity Institute
Ulrike Franke, University of Oxford
Dylan Hadfield-Menell, UC Berkeley and Center
for Human-compatible AI
Richard Harknett, University of Oxford/University of Cincinnati
Katja Hofmann, Microsoft Research
Tim Hwang, Google
p.77
Appendix A: Workshop DetailsEva Ignatuschtschenko, University of Oxford
Victoria Krakovna, DeepMind/Future of Life Institute
Ben Laurie, DeepMind
Jan Leike, DeepMind/Future of Humanity Institute
Seán Ó hÉigeartaigh, Centre for the Study of Existential Risk
Toby Ord, Future of Humanity Institute
Michael Page, Centre for Effective Altruism
Heather Roff, University of Oxford/Arizona State University
/New America Foundation
Paul Scharre, Center for a New American Security
Eden Shochat, Aleph VC
Jaan Tallinn, Centre for the Study of Existential Risk
Helen Toner, Open Philanthropy Project
Andrew Trask, University of Oxford
Roman Yampolskiy, University of Louisville
Yueh-Hsuan Weng, Tohoku University
p.78This appendix gives additional commentary on topics related to
the Recommendations and Priority Research Areas described in
the Interventions section of the main report, along with some
initial questions and directions for investigation on each topic.
In each case, we flag which one or more of the three high-level
threat factors (introduced in General Implications for the Threat
Landscape ) the research area aims to address. We include this
content as a jumping-off point for researchers interested in making
progress in these areas; the below is not intended to be exhaustive
or conclusive.Appendix B:
Questions
for Further
Research
p.79
Appendix B: Questions for Further ResearchDual Use Analogies and Case Studies
One possible area of theory, practice, and history to be explored
for insights is the set of technologies with prominent concerns
around dual use - technologies that can be used for both peaceful
and military aims (or, more generally, to both beneficial and harmful
ends). Examples include chemicals potentially useful for chemical
weapons or explosives, biological engineering potentially useful
for biological weapons, cryptography, and nuclear technologies.
Allen and Chan explored several of these case studies and their
potential insights for AI dual-use policymaking. In these cases,
there is a rich tapestry of soft norms (e.g. pre-publication review)
and hard laws (e.g. export controls) developed over many years to
ensure positive outcomes .
When consulting the history of governing dual use technologies,
we should learn both constructive solutions from past successes,
and precautionary lessons about poor regulation that should
be avoided. A relevant example of the latter is the difficulties
of regulating cryptographic algorithms and network security
tools through export control measures such as the Wassenaar
Arrangement . The similarities between AI and cryptography, in
terms of running on general-purpose hardware, in terms of being
immaterial objects (algorithms), in terms of having a very wide
range of legitimate applications, and in their ability to protect
as well as harm, suggest that the default control measures for
AI might be similar those that have been historically applied to
cryptography. This may well be a path we should avoid, or at least
take very cautiously.
The apparent dual-use nature of AI technologies raises the
following questions:
• What is the most appropriate level of analysis and governance
of dual-use characteristics of AI technologies (e.g. the field as a
whole, individual algorithms, hardware, software, data)?
• What norms from other dual-use domains are applicable to AI?
• What unique challenges, if any, does AI pose as a dual-use
technology?
• Are there exemplary cases in which dual-use concerns were
effectively addressed?
• What lessons can be learned from challenges and failures in
applying control measures to dual-use technologies?1
2
3
Allen and Chan, 2017
Tucker, ed. 2012; Harris, ed. 2016
Shehadeh, 1999
p.80
Appendix B: Questions for Further ResearchRed Teaming
A common tool in cybersecurity and military practice is red
teaming - a “red team” composed of security experts and/or
members of the organization deliberately plans and carries out
attacks against the systems and practices of the organization
(with some limitations to prevent lasting damage), with an optional
“blue team” responding to these attacks. These exercises explore
what an actual attack might look like in order to ultimately better
understand and improve the security of the organization’s systems
and practices. Two subsets of the AI security domain seem
particularly amenable to such exercises: AI-enabled cyber offense
and defense, and adversarial machine learning. While we highlight
these subsets because they seem especially relevant to security,
red teaming of AI technologies more broadly seems generally
beneficial. In addition to this report and the associated workshop,
another recent effort aimed at this goal was also conducted by the
Origins Project earlier this year .
In the case of cyber attacks, many of the concerns discussed
earlier in this document, and elsewhere in the literature, are
hypothetical. Conducting deliberate red team exercises might be
useful in the AI/cybersecurity domain, analogous to the DARPA
Cyber Grand Challenge but across a wider range of attacks (e.g.
including social engineering, and vulnerability exploitation beyond
memory attacks), in order to better understand the skill levels
required to carry out certain attacks and defenses, and how well
they work in practice.
Likewise, in the case of adversarial machine learning, while there
are many theoretical papers showing the vulnerabilities of machine
learning systems to attack, the systematic and ongoing stress-
testing of real-world AI systems has only just begun . Efforts like
the CleverHans library of benchmarks and models are a step in this
direction , creating the foundation for a distributed open source
red teaming effort, as is the NIPS 2017 Adversarial Attacks and
Defenses competition , which is more analogous to the DARPA
Cyber Grand Challenge.
There are several open questions regarding the use of “red team”
strategies for mitigating malicious uses of AI:
• What lessons can be learned from the history to date of “red
team” exercises?
• Is it possible to detect most serious vulnerabilities through “red
team” exercises, or is the surface area for attack too broad?1
3
4
Bass, 2017
Papernot et al., 2016b
Knight, 2017
though see e.g. Anderson et al., 20172
p.81
Appendix B: Questions for Further Research• Who should be responsible for conducting such exercises, and
how could they be incentivised to do so?
• What sorts of skills are required to undermine AI systems, and
what is the distribution of those skills? To what extent do these
skills overlap with the skills required to develop and deploy AI
systems, and how should these findings inform the threat model
used in red teaming exercises (and other AI security analysis)?
• Are there mechanisms to promote the uptake of lessons from
“red team” exercises?
• Are there mechanisms to share lessons from “red team”
exercises with other organizations that may be susceptible to
similar attacks? How to avoid disclosure of attack methods to
bad actors?
• What are the challenges and opportunities of extending “red
teaming” (or related practices like tabletop exercises) to AI
issues in the physical and political domains? What can be
learned for the physical domain from physical penetration
testing exercises?
Formal Verification
Formal verification of software systems has been studied for
decades . In recent years, it has been shown that even some
very complex systems are amenable to formal proofs that they
will operate as intended, including the CompCert compiler and
the seL4 microkernel . An open question is whether AI systems,
or elements thereof, are amenable to formal verification. At the
workshop there was substantial skepticism about the prospects
for formal AI verification, given the complexity of some modern
AI systems, but further analysis about the challenges is required,
and research on the topic continues apace . In particular, we
might be interested in the following properties being verified for
a given system:
• that its internal processes in fact attain the goals specified for
the system (though noting the existence of the specification
problem, i.e. that desired properties of AI systems are often
difficult to specify in advance, and therefore difficult to verify),
• that its goals will be remain constant in the face of adversaries
attempts to change them,1
32
Turing, 1949; Baier and Katoen, 2008
e.g. Selsam et al., 2017;
Neema, 2017
Fisher, 2014
p.82
Appendix B: Questions for Further Research• that its ability to be deceived with adversarial inputs is bounded
to some extent.
Verifying Hardware
Given the increasing complexity of AI systems, and in some
domains limited theoretical foundations for their operation, it may
be prohibitively expensive, or even practically or theoretically
impossible, to provide an end-to-end verification framework
for them. However, it may be feasible to use formal methods to
improve the security of components of these systems. Hardware
seems particularly amenable to verification, as formal methods
have been widely adopted in the hardware industry for decades .
Verifying Security
Additionally, in recent years formal verification has been applied to
security protocols to provide robust guarantees of safety against
certain types of attacks. The JavaScript prover CryptoVerif is an
example of a developer-focused tool that allows programmers to
apply formal methods to their code to check correctness in the
development process. It should be noted that much of this work
is still largely theoretical and adoption in the real world has so far
been limited .
Verifying AI Functionality
The notion of being able to prove that a system behaves as
intended is an attractive one for artificial intelligence. However,
formal methods are difficult to scale up to arbitrary complex
systems due to the state space explosion problem. Nonetheless,
verification of some aspects of AI systems, such as image
classifiers, is still feasible even verification of the behavior of
the whole system is prohibitively complex. For example, work
on verification of deep neural networks provided a method
to check for the existence of adversarial examples in regions
of the input space .
Responsible “AI 0-Day” Disclosure
As discussed above, despite the successes of contemporary
machine learning algorithms, it has been shown time and again that
ML algorithms also have vulnerabilities. These include ML-specific
vulnerabilities, such as inducing misclassification via adversarial 1
42
Harrison, 2010; Wahby, R. 2016
Katz et al., 2017
Blanchet, 2017
though there are some instances of
real world use — see e.g. Beurdouche
et al., 2017
3
p.83
Appendix B: Questions for Further Researchexamples or via poisoning the training data ; see Barreno et al.
(2010) for a survey. ML algorithms also remain open to traditional
vulnerabilities, such as memory overflow (Stevens et al., 2016).
There is currently a great deal of interest among cyber-security
researchers in understanding the security of ML systems, though
at present there seem to be more questions than answers.
In the cybersecurity community, “0-days” are software
vulnerabilities that have not been made publicly known (and thus
defenders have zero days to prepare for an attack making use of
them). It is common practice to disclose these vulnerabilities to
affected parties before publishing widely about them, in order to
provide an opportunity for a patch to be developed.
Should there be a norm in the AI community for how to disclose
such vulnerabilities responsibly to affected parties (such as those
who developed the algorithms, or are using them for commercial
applications)? This broad question gives rise to additional
questions for further research:
• As AI technologies become increasingly integrated into
products and platforms, will the existing security norm
around responsible disclosure extend to AI technologies
and communities?
• Should AI systems (both existing and future) be presumed
vulnerable until proven secure, to an extent that disclosing new
vulnerabilities privately is unnecessary?
• In what safety-critical contexts are AI systems currently
being used?
• Which empirical findings in AI would be useful in informing
an appropriate disclosure policy (analogous to the way that
historical trends in 0-day discoveries and exploitation rates are
discussed in cybersecurity analyses )?
• If such a norm were appropriate in broad terms, who should be
notified in case a vulnerability is found, how much notice should
be given before publication, and what mechanisms should
institutions create to ensure a recommendation is processed
and potentially acted upon?
• What is the equivalent of “patching” for AI systems, and how
should trade-offs (e.g. between resource demands, accuracy
and robustness to noise) and prioritization amongst the variety
of possible defense measures be weighed in a world of rapidly
changing attacks and defenses?1
2
Szegedy et al., 2013; Papernot et
al., 2016; Evtimov et al., 2017;
Carlini et al., 2016
Rubinstein et al., 2009; Šrndic and
Laskov, 2014
3
e.g. Ablon and Bogart, 2017
p.84
Appendix B: Questions for Further ResearchAI-Specific Exploit Bounties
To complement the norm of responsible disclosure of
vulnerabilities (discussed above), which relies on social incentives
and goodwill, some software vendors offer financial incentives
(cash bounties) to anyone who detects and responsibly discloses
a vulnerability in their products. With the emergence of new AI-
specific vulnerabilities, some questions arise:
• Are existing vulnerability bounties likely to extend to AI
technologies?
• Should we expect, or encourage, AI vendors to offer bounties
for AI-specific exploits?
• Is there scope to offer bounties by third parties (e.g.
government, NGO, or philanthropic source) in cases where
vendors are unwilling or unable to offer them, for example in
the case of popular machine learning frameworks developed as
open-source projects or in academia?
Security Tools
In the same way software development and deployment tools
have evolved to include an increasing array of security-related
capabilities (testing, fuzzing, anomaly detection, etc.), could
we start envisioning tools to test and improve the security of AI
components and systems integrated with AI components during
development and deployment, such that they are less amenable to
attack? These could include:
• Automatic generation of adversarial data
• Tools for analysing classification errors
• Automatic detection of attempts at remote model extraction or
remote vulnerability scanning
• Automatic suggestions for improving model robustness (see e.g.
Koh and Liang (2017) for related ideas)1
see e.g. Kesarwani et al., 2017
p.85
Appendix B: Questions for Further ResearchSecure Hardware
Hardware innovation has accelerated the pace of innovation in
machine learning, by allowing more complex models to be trained,
enabling faster execution of existing models, and facilitating more
rapid iteration of possible models. In some cases, this hardware is
generic (commercial GPUs), but increasingly, AI (and specifically
machine learning) systems are trained and run on hardware that
is semi-specialized (e.g. graphics processing units (GPUs)) or
fully specialized (e.g. Tensor Processing Units (TPUs)). This
specialization could make it much more feasible to develop and
distribute secure hardware for AI-specific applications than it
would be to develop generic secure hardware and cause it to be
widely used.
At the workshop we explored the potential value of adding security
features to AI-specific hardware. For example, it may be possible to
create secure AI hardware that would prevent copying a trained AI
model off a chip without the original copy first being deleted. Such
a feature could be desirable so that the total number of AI systems
(in general or of a certain type or capability level) could be tightly
controlled, if the capabilities of such AI systems would be harmful
in the wrong hands, or if a large-scale diffusion of such AI systems
could have harmful economic, social or political effects.
Other desirable secure hardware features include hardware-
level access restrictions and audits. One research trajectory to
be considered is developing a reference model for secure AI-
specific hardware, which could then be used to inform hardware
engineering and, ultimately, be adopted by hardware providers. It
may also be the case that potential security threats from AI will
drive research in secure hardware more generally, not just for the
hardware running AI systems, as a response measure to changes
in the cyber threat landscape. Note, however, the potential for
manufacturers to undermine the security of the hardware they
produce; hardware supply chain vulnerabilities are currently a
concern in the cybersecurity context, where there is fear that
actors with control over a supply chain may introduce hardware-
based vulnerabilities in order to surveil more effectively or
sabotage cyber-physical systems .
Finally, note that for other security-relevant domains such as
cryptography, tamper-proof hardware has been developed , with
features such as tamper evidence (making it clear that tampering
has occurred when it has occurred) and obscurity of layout design
(such that it is prohibitively difficult to physically examine the
workings of the chip in order to defeat it). Tamper-proof hardware
could potentially be valuable so that outsiders are unable to 1
2
U.S. Defense Science Board, 2017
Anderson, 2008
p.86
Appendix B: Questions for Further Researchdiscern the inner workings of an AI system from external emission;
so that stolen hardware cannot be used to duplicate an AI; and so
that organizations can credibly commit to operating a system in a
safe and beneficial way by hard-coding certain software properties
in a chip that, if tampered with, would break down. However,
secure processors tend to cost significantly more than insecure
processors and, to our knowledge, have not specifically been
developed for AI purposes.
There are many open questions in this domain:
• What, if any, are the specific security requirements of AI
systems, in general and in different domains of application?
• Would changes in the risk landscape (as surveyed above)
provide sufficient incentive for a major overhaul of hardware
security?
• What set of measures (e.g. reference implementation) would
encourage adoption of secure hardware?
• What measures, if any, are available to ensure compliance
with hardware safety requirements given the international
distribution of vendors and competing incentives such as cost,
potential for surveillance and legal implications of auditability?
• How applicable are existing secure processor designs to the
protection of AI systems from tampering?
• Could/should AI-specific secure processors be developed?
• How could secure enclaves be implemented in an AI context ?
• Can secure processors be made affordable, or could policy
mechanisms be devised to incentivize their use even in the face
of a cost premium?
Pre-Publication Risk Assessment in Technical Areas
of Special Concern
By pre-publication risk assessment we mean analyzing the
particular risks (or lack thereof) of a particular capability if it
became widely available, and deciding on that basis whether, and
to what extent, to publish it. Such norms are already widespread
in the computer security community, where e.g. proofs of concept
rather than fully working exploits are often published. Indeed, such
considerations are sufficiently widespread in computer security 1
2
Anderson, 2008
as suggested by Stoica et al., 2017
p.87
Appendix B: Questions for Further Researchthat they are highlighted as criteria for submission to prestigious
conferences .
Openness is not a binary variable: today, many groups will
publish the source code of a machine learning algorithm without
specifying the hyperparameters to get it to work effectively, or
will reveal details of research but not give details on one particular
component that could be part of a crucial data ingestion (or
transformation) pipeline. On the spectrum from a rough idea, to
pseudocode, to a trained model along with source code and
tutorials/tips on getting it to work well in practice, there are various
possible points, and perhaps there are multiple axes (see Figure 3 ).
Generally speaking, the less one shares, the higher the skill and
computational requirements there are for another actor to recreate
a given level of capability with what is shared: this reduces the risk
of malicious use, but also slows down research and places barriers
on legitimate applications.
For an example of a potentially abusable capability where full
publication may be deemed too risky, voice synthesis for a
given target speaker (as will reportedly soon be available as a
service from the company Lyrebird ) is ripe for potential criminal
applications, like automated spearphishing (see digital security
section) and disinformation (see political security section). On the
other hand, as is the case with other technologies with significant
potential for malicious use, there could be value in openness for
security research, for example in white hat penetration testing.
As described in the Rethinking Openness section of the report,
there are clear benefits to the level of openness currently prevalent
in machine learning as a field. The extent to which restrictions
on publication would affect these benefits should be carefully
considered. If the number of restricted publications is very small
(as in biotechnology, for example), this may not be a significant
concern. If, however, restricted publication becomes common, as
in the case of vulnerability disclosure in cybersecurity research,
then institutions would need to be developed to balance the
needs of all affected parties. For example, responsible disclosure
mechanisms in cybersecurity allow researchers and affected
vendors to negotiate a period of time for a discovered vulnerability
to be patched before the vulnerability is published. In addition
to the commercial interests of vendors and the security needs of
users, such schemes often also protect researchers from legal
action by vendors. In the case of AI, one can imagine coordinating
institutions that will withhold publication until appropriate safety
measures, or means of secure deployment, can be developed,
while allowing the researchers to retain priority claims and gain
credit for their work. Some AI-related discoveries, as in the case of 1
3
see e.g. NDSS, 2018
Lyrebird, 2017
on next page2
p.88
Appendix B: Questions for Further Research
Figure 3. A schematic illustration of the relationship between
openness about an AI capability and the skill required
to reproduce that capability.
Decreasing skill requirement
Vague description
of achievementPseudocodeSource code,
trained models,
tutorials
Increasing openness
p.89
Appendix B: Questions for Further Researchadversarial examples in the wild, may be subsumed under existing
responsible disclosure mechanisms, as we discuss below in
“Responsible AI 0-day Disclosure”.
Some valuable questions for future research related to pre-
publication research assessment include:
• What sorts of pre-publication research assessment would AI
researchers be willing to consider? To what extent would this be
seen as conflicting with norms around openness?
• What can be learned from pre-publication risk assessment
mechanisms in other scientific/technological domains?
• Is it possible to say, in advance and with high confidence, what
sorts of capabilities are ripe for abuse?
• What sort of heuristics may be appropriate for weighing the
pros and cons of opening up potentially-abusable capabilities?
• How can such assessment be incorporated into decision-
making (e.g. informing one’s openness choices, or incorporating
such analysis into publications)?
• Can we say anything fine-grained yet generalizable about the
levels of skill and computational resources required to recreate
capabilities from a given type (code, pseudocode, etc.) of
shared information?
• How does the community adopt such a model in the absence of
regulation?
Central Access Licensing Models
Another potential model for openness is the use of what we call
central access licensing. In this model, users are able to access
certain capabilities in a central location, such as a collection of
remotely accessible secure, interlinked data centers, while the
underlying code is not shared, and terms and conditions apply
to the use of the capabilities. This model, which is increasingly
adopted in industry for AI-based services such as sentiment
analysis and image recognition, can place limits on the malicious
use of the underlying AI technologies. For example, limitations on
the speed of use can be imposed, potentially preventing some
large-scale harmful applications, and terms and conditions can
explicitly prohibit malicious use, allowing clear legal recourse.
p.90
Appendix B: Questions for Further ResearchCentralised access provides an alternative to publication that
allows universal access to a certain capability, while keeping the
underlying technological breakthroughs away from bad actors
(though also from well-intentioned researchers). Note though that
black box model extraction may allow bad actors to gain access to
the underlying technology.
Additionally, similarly to early proposals for, in effect, an
information processing “tax” on emails in order to disincentivize
spam , centralized AI infrastructures better enable constraints to
be placed on the use of AI services, such that large-scale attacks
like automated spear phishing could be made less economical
(though see Laurie and Clayton, 2004 for a criticism of this
approach, and Liu and Camp, 2006 for further discussion; the
increased interest in crypto-economics following the success of
bitcoin may lead to advances in this area).
Finally, note that the concentration of AI services in a particular set
of organizations may heighten potential for malicious use at those
organizations, including by those acting with the blessing of the
relevant organization as well as by insider threats. Indeed, some
workshop attendees considered these risks from concentration
of power to be the biggest threat from AI technologies; note,
however, that in this report we have decided to focus on direct
malicious use risks, rather than systemic threats (see Scope). In
addition to monopolistic behavior, there are more subtle risks such
as the introduction of “backdoors” into machine learning systems
that users may be unaware of .
Some initial research questions that arise related to a central
access licensing model:
• What sorts of services might one want only available on a
per-use basis?
• How effectively can a service provider determine whether AI
uses are malicious?
• How can a user determine whether a service provider is
malicious ?
• Is the proposal technologically, legally and politically feasible?
• Who might object to a centralised access model and on what
grounds?
• Is there enough of a technology gap such that actors without
access cannot develop the technologies independently?1
2
3
4
Bastani et al., 2017
Dwork and Naor, 1993
Gu et al., 2017
see e.g. Ghodsi et al., 2017
p.91
Appendix B: Questions for Further Research• What are potential risks and downsides to centralised access,
e.g. in aggravating political security risks?
• How effective can black box model extraction be in
different contexts?
• How useful are limits on the amount or frequency of queries
to models as a countermeasure against model inversion
(extracting the training data from the model; Fredrikson
et al., 2015) and other forms of attack such as membership
inference (ascertaining whether certain data is contained
in the training data )?
• What would be the associated trade-offs of such limits?
• (How) can cloud providers vet the safety or security of AI
systems without inspecting their internal workings, if such
information is private?
• Are cloud computing providers sufficiently flexible in their
services to allow the experimentation required by researchers,
or would this intervention be most applicable to preventing
potentially harmful dissemination trained AI systems?
Sharing Regimes that Favor Safety and Security
One possible approach for reducing security risks of AI is to
selectively share certain capability information and data with
trusted parties. A somewhat analogous approach is used in the
cyber domain — Information Sharing and Analysis Centers (ISACs)
and Information Sharing and Analysis Organizations (ISAOs) —
where companies share information about cyber attacks amongst
themselves. Antivirus and large tech companies themselves
serve as points of concentration of knowledge sharing, giving
them advantages over other kinds of actors. In the case of AI,
one might imagine an arrangement where some particularly
powerful or hazardous capabilities (e.g. ones that lend themselves
straightforwardly to automated hacking) are shared only with
organizations or individuals that meet certain criteria, such as
having established safety and security routines, or agreeing to
random inspection by other members of the group, or a third-party
agency that the group has mutually agreed has oversight and
inspection powers over them.
Such an approach might be valuable for facilitating collaborative
analysis of safety and security issues, and thus getting some
fraction of the benefit of an open source approach (where an
even larger number of “eyes” are on the problem), while reducing 1
Shokri et al., 2016
p.92
Appendix B: Questions for Further Researchsome risks associated with diffusion. If, based on such analysis, it
is concluded that there is no harm in further diffusion, then the
capabilities would be published.
Several questions arise about the above proposals:
• What have been the benefits and limitations of existing ISACs
and ISAOs and are elements of such models useful to AI?
• What sorts of criteria might be applied to an organization or
individual in order to ascertain their trustworthiness to deal with
particularly sensitive information?
• What types of information might be shared amongst such
a group?
• Should there be a limited-sharing stage for all AI developments,
or should capabilities be evaluated individually, and if the latter
then on what basis?
• What information types should limited sharing apply to: code,
research papers, informal notes?
• How can sufficient trust be established between groups such
that this kind of coordination is seen as mutually beneficial?
• Are there any particular incentives which can be created
that would make this sort of collaboration more likely (for
instance, the creation of a shared cluster to test a certain kind
of research on)?
• What are potential risks and downsides to this type of
sharing regime?
Note that this mechanism has partial overlap with pre-publication
risk assessment in technical areas of special concern and central
access licensing model.
Security, Ethics, and Social Impact Education
for Future Develope rs
There has recently been discussion of the role of ethics education
in AI , in light of ongoing public and private discussion of the
potential and pitfalls of AI. Educational efforts might be beneficial
in highlighting the risks of malicious applications to AI researchers,
and fostering preparedness to make decisions about when 1
Burton et al., 201
p.93
Appendix B: Questions for Further Researchtechnologies should be open, and how they should be designed, in
order to mitigate such risks. As yet there is no long-term research
on the impacts of such educational efforts on AI researchers’
career development and eventual decision-making, suggesting
possible areas for research:
• What are the best practices for ethics and policy education for
science and engineering in general that are applicable to AI,
especially around mitigating security risks?
• How can ethics education be designed so as to most effectively
engage with the interests and concerns of AI developers, rather
than being seen as merely a box to be ticked off or a burden
unrelated to one’s practical decision-making, as sometimes
occurs in other domains ?
• What ought to be included in such a curriculum: ethical
methodologies, principles and/or theories?
• How could such a curriculum be iterated over time as the state
of AI and security advances?
• Who would be most effective at providing such a curriculum?
Should ethics educators from philosophy and other disciplines
be brought in or is it better for the community to develop its
own internal capacity to teach AI specific ethics?
Ethics Statements and Standards
Another way of acting on ethical concerns could be multi-
stakeholder conversation to develop ethical standards for the
development and deployment of AI systems, which could be
signed on to by companies, research organizations and others
deploying AI systems. Two examples of such processes are
the IEEE Global Initiative for Ethical Considerations in Artificial
Intelligence and Autonomous Systems and the development of
the Asilomar AI Principles . Several questions remain open:
• What institutional frameworks are appropriate for ensuring
that statements and standards concerning ethics are fully
implemented in order to ensure that they are more than mere
technological ‘greenwash’? For instance, should community
developed standards include statements about reporting
and accountability?1
2
3
Sunderland et al., 2013
IEEE Standards Association, 2017
Future of Life Institute, 2017
p.94
Appendix B: Questions for Further Research• Should companies and research organizations have a statement
on ethics, either taken directly from one of these communal
standards or developed in house for their particular situation? If
so, how can this be encouraged?
• Are standards and statements of this kind the best way to foster
industry-wide conversation about the ethics of AI? What are
some alternatives?
• What processes are appropriate for revising and updating ethics
statements and standards in order to ensure that they remain
flexible and can incorporate best practice whilst retaining their
sense of permanence and objectivity.
Norms, Framings and Social Incentives
As noted in previous sections, there are substantial security risks
associated with AI, and in some cases one actor could gain from
exploiting such risks. At the same time, there are also substantial
upsides to progress in AI research and development, and in many
cases AI can be used to enhance rather than diminish security. This
raises the questions like the following:
• How can the upsides of AI development be framed in such a way
as to galvanize focus on mutually beneficial developments and
discourage harmful exploitation?
• What are analogous cases from which lessons can be learned,
where a technology that could have been used and thought
about in a zero-sum manner was governed in a way that
benefited all?
• What processes should be allowed to govern the emergence
and implementation of a normative culture for beneficial AI in
order both to ensure the creation of strong, enforceable and
effective norms, and to avoid this normative culture being used
to preserve rigid and/or biased norms that hamper diversity and
creativity within the sector?
• What role do diverse normative cultures across fields such as
AI development, AI safety and risk management play in both
allowing for a diverse range of perspectives to inform public
debates about AI and ensuring that more people consider
themselves to be ‘insiders’ in such debates, and fewer people
consider themselves to be ‘outsiders’
p.95
Appendix B: Questions for Further ResearchTechnologically Guaranteed Privacy
Several of the threats within the digital security and political
security domains (e.g. automated spear phishing, personalised
propaganda) rely on attackers gaining access to private
information about individuals. In addition to procedural and
legal measures to ensure individuals’ privacy, there is increasing
research on technological tools for guaranteeing user data
privacy, which may also be applicable in the context of AI systems.
We highlight two technologies as potentially relevant here:
differential privacy-guaranteeing algorithms and secure multi-
party computation. There remain open questions regarding
both technologies:
• Can algorithmic privacy be combined with AI technologies,
either in general or in specific domains?
• What are the trade-offs, if any, for implementing algorithmic
privacy, e.g. in terms of performance or in terms of financial
viability of services?
• What mechanisms (financial, educational, legal or other) could
encourage the adoption of algorithmic privacy in AI systems?
• What lessons can be learned by efforts at technologically
guaranteed privacy (such as Apple’s use of differential privacy)?
Differential privacy
Many machine learning models are currently being developed
by companies for commercial use in APIs (see central access
licensing above). Without precautions it is possible for individuals
to break anonymity in the underlying dataset of a machine learning
model that has been deployed for public use via a model inversion
attack or membership inference attack . That is, even without
access to the training data, an attacker can in some cases query
a model in such a way that information from the underlying data set
is revealed.
Ji et al. (2014) surveyed methods for providing differential privacy
in machine learning systems , though they do not address
differential privacy in neural networks. Such methods have
been reported by, for example, Abadi et al. (2016). In general,
differentially private machine learning algorithms combine their
training data with noise to maintain privacy while minimizing
effects on performance. Generally, differentially private algorithms 1
2
3
Fredrikson et al., 2015
Shokri et al., 2016
a concept first developed in (Dwork,
2006) referring to strong guarantees
on the probability of information
leakage
p.96
Appendix B: Questions for Further Researchlose some performance compared to their non-private equivalents,
and so privacy may become a concern if the teams developing
models are not incentivized to keep their datasets private.
Secure Multi-Party Computation
Secure multi-party computation (MPC) refers to protocols that
allow multiple parties to jointly compute functions, while keeping
each party’s input to the function private . For instance, one
simple MPC protocol allows users to jointly compute the outcome
of a vote, without sharing their individual votes with one another.
As an important practical application, MPC protocols make it
possible to train machine learning systems on sensitive data
without significantly compromising its privacy . For example,
medical researchers could train a system on confidential patient
records by engaging in an MPC protocol with the hospital that
possesses them. A technology company could similarly learn from
users’ data, in some cases, without needing to access this data.
An active open source development effort (OpenMined) is
currently aiming to develop a platform to allow users to sell others
the right to train machine learning systems on their data using
MPC . A number of other frameworks for privacy-preserving
machine-learning have also been proposed .
In addition, MPC opens up new opportunities for privacy-
preserving web applications and cloud computation. For example,
one company may develop machine learning models that can make
predictions based on health data. If individuals do not want to
send this company copies of their personal medical data, they may
instead opt to engage in an MPC protocol with the company, and
in particular an MPC protocol where only the individual receives
the output. At no point in this process does the company gain any
knowledge about the individual’s medical data; nevertheless, it is
still able to provide its service.
MPC could also help to enable privacy-preserving surveillance .
To the extent that AI systems play active roles surveillance, for
instance by recognizing faces in videos or flagging suspicious
individuals on the basis of their web activity, MPC can be used
to increase individual privacy. In particular, MPC makes it possible
to operate such systems without needing to collect or access
the (often sensitive) data that is being used to make the relevant
classifications.
At the same time, the use of MPC protocols remains limited by the
fact that, in many cases, they can increase overhead associated 1
2
3
4
5
Yao, 1982
Lindell and Pinkas, 2009
OpenMined, 2017
e.g. Rouhani et al. (2017)
Dowlin et al., 2016; Trask, 2017;
Garfinkel, forthcoming
p.97
Appendix B: Questions for Further Researchwith a computation by multiple orders of magnitude. This means
that MPC is best-suited for relatively simple computations or for
use cases where increased privacy would be especially valuable.
Monitoring Resources
One type of measure that might help to predict and/or prevent
misuse of AI technology would be to monitor inputs to AI systems.
Such monitoring regimes are well-established in the context
of other potentially dangerous technologies, most notably the
monitoring of fissile materials and chemical production facilities
for the purpose of implementing nuclear and chemical weapon
agreements. An obvious example of an input that might be possible
to monitor is computing hardware. While efforts have been made
in the past to survey computing resources , there is no major
ongoing public effort to do so, with the best available information
likely withheld due to commercial or state secrecy. One possible
benefit to having a public, or semi-public, database of the global
distribution of computing resources could be to better understand
the likely distribution of offensive and defensive AI/cybersecurity
capabilities. Additionally, having such monitoring in place would
be valuable if stronger measures were to be employed, e.g.
enforceable limitations on how hardware could be used. Questions
for further consideration include:
• How feasible would it be to monitor global computing
resources?
• Are different domains more or less tractable to monitor, or more
or less important for AI capabilities, than others (e.g. should
video game consoles be considered, in light of their large share
in total computing but limited current role in AI)?
• What could be done with such information?
• Are there drawbacks to such an effort (e.g. in encouraging
wasteful “racing” to have the most computing power)?
• Would other AI inputs be better suited to monitoring than
computing resources?
Exploring Legal and Regulatory Interventions
Much of the discussion above focuses on interventions that
can be carried out by researchers and practitioners within the
AI development community. However, there is a broader space 1
2
Hilbert and Lopez, 2011
Hilbert and Lopez, 2011
p.98
Appendix B: Questions for Further Researchof possible interventions, including legal ones, that should be
considered. We note that ill-considered government interventions
could be counterproductive, and that it is important that the
implications of any specific policy interventions in this area should
be carefully analyzed. A number of questions concerning the
proper scope for government intervention in AI security arise; we
list some initial examples here:
• Is there a clear chain of responsibility for preventing AI security-
related problems?
• Which government departments, marketplace actors or other
institutions would ideally have what responsibilities, and
what would the interactions with the academic and industry
communities be?
• How suitable would existing institutions be at playing this
role, and how much will it require the establishment of new
institutions founded on novel principles or innovative structures
in order to effectively operate in such an evolving and technical
field?
• Are relevant actors speaking to each other, and coordinating
sufficiently, especially across political, legal, cultural and
linguistic barriers?
• Are liability regimes adequate? Do they provide the right
incentives for various actors to take competent defensive
measures?
• How prepared does e.g. the US government feel, and how much
appetite would there be for focused offices/channels designed
to increase awareness and expertise?
• Should governments hold developers, corporations, or
others liable for the malicious use of AI technologies (or,
explicitly make them exempt from such liability )? What other
approaches might be considered for pricing AI security-related
externalities ?
• What are the pros and cons of government policies requiring
the use of privacy-preserving machine learning systems or
defenses against adversarial examples and other forms of
malicious use?
• Are data poisoning and adversarial example attacks aimed at
disrupting AI systems subject to the same legal penalties as
traditional forms of hacking? If not, should they be (and how 1
2
Calo, 2011; Cooper, 2013
see e.g. Farquhar et al., 2017
p.99
Appendix B: Questions for Further Researchcan legal but related tactics like search engine optimization be
dealt with if so)?
• Should international agreements be considered as tools to
incentivize collaboration on AI security?
• What should the AI security community’s “public policy model”
be - that is, how should we aim to affect government policy,
what should the scope of that policy be, and how should
responsibility be distributed across individuals, organizations,
and governments?
• Should there be a requirement for non-human systems
operating online or otherwise interacting with humans (for
example, over the telephone) to identify themselves as such (a
“Blade Runner law” ) to increase political security?
• What kind of process can be used when developing policies
and laws to govern a dynamically evolving and unpredictable
research and development environment?
• How desirable is it that community norms, ethical standards,
public policies and laws all say the same thing and how much
is to be gained from different levels of governance to respond
to different kinds of risk (e.g. near term/long term, technical
safety / bad actor and high uncertainty / low uncertainty risks)?
It seems unlikely that interventions within the AI development
community and those within other institutions, including policy
and legal institutions, will work well over the long term unless
there is some degree of coordination between these groups.
Ideally discussions about AI safety and security from within the
AI community should be informing legal and policy interventions,
and there should also be a willingness amongst legal and policy
institutions to devolve some responsibility for AI safety to the
AI community, as well as seeking to intervene on its own behalf.
Achieving this is likely to require both a high degree of trust
between the different groups involved in the governance of AI and
a suitable channel to facilitate proactive collaboration in developing
norms, ethics education and standards, policies and laws; in
contrast, different sectors responding reactively to the different
kinds of pressures that they each face at different times seems
likely to result in clumsy, ineffective responses from the policy and
technical communities alike. These considerations motivated our
Recommendations #1 and #2.1
Wu, 2017
Future of Humanity Institute
University of Oxford
Centre for the Study of Existential Risk
University of Cambridge
Center for a New American Security
Electronic Frontier Foundation
OpenAIThe Malicious Use of Artificial Intelligence:
Forecasting, Prevention, and MitigationFebruary 2018
p.101
The Malicious Use of Artificial Intelligence |
6af354f5-f27a-4afa-aeda-b2aae737e653 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Why one-box?
I have sympathy with both one-boxers and two-boxers in Newcomb's problem. Contrary to this, however, many people on Less Wrong seem to be staunch and confident one-boxers. So I'm turning to you guys to ask for help figuring out whether I should be a staunch one-boxer too. Below is an imaginary dialogue setting out my understanding of the arguments normally advanced on LW for one-boxing and I was hoping to get help filling in the details and extending this argument so that I (and anyone else who is uncertain about the issue) can develop an understanding of the strongest arguments for one-boxing.
One-boxer: You should one-box because one-boxing wins (that is, a person that one-boxes ends up better off than a person that two-boxes). Not only does it seem clear that rationality should be about winning generally (that a rational agent should not be systematically outperformed by irrational agents) but Newcomb's problem is normally discussed within the context of instrumental rationality, which everyone agrees is about winning.
Me: I get that and that's one of the main reasons I'm sympathetic to the one-boxing view but the two-boxers has a response to these concerns. The two-boxer agrees that rationality is about winning and they agree that winning means ending up with the most utility. The two-boxer should also agree that the rational *decision theory* to follow is one that will one-box on all future Newcomb's problems (those where the prediction has not yet occurred) and can also agree that the best *timeless agent type* is a one-boxing type. However, the two-boxer also claims that two-boxing is the rational *decision*.
O: Sure, but why think they're right? After all, two-boxers don't win.
M: Okay, those with a two-boxing *agent type* don't win but the two-boxer isn't talking about agent types. They're talking about decisions. So they are interested in what aspects of the agent's winning can be attributed to their decision and they say that we can attribute the agent's winning to their decision if this is caused by their decision. This strikes me as quite a reasonable way to apportion the credit for various parts of the winning. (Of course, it could be said that the two-boxer is right but they are playing a pointless game and should instead be interested in winning simpliciter rather than winning decisions. If this is the claim then the argument is dissolved and there is no disagreement. But I take it this is not the claim).
O: But this is a strange convoluted definition of winning. The agent ends up worse off than one-boxing agents so it must be a convoluted definition of winning that says that two-boxing is the winning decision.
M: Hmm, maybe... But I'm worried that relevant distinctions aren't being made here (you've started talking about winning agents rather than winning decisions). The two-boxer relies on the same definition of winning as you and so agrees that the one-boxing agent is the winning agent. They just disagree about how to attribute winning to the agent's decisions (rather than to other features of the agent). And their way of doing this strikes me as quite a natural one. We credit the decision with the winning that it causes. Is this the source of my unwillingness to jump fully on board with your program? Do we simply disagree about the plausibility of this way of attributing winning to decisions?
Meta-comment (a): I don't know what to say here? Is this what's going on? Do people just intuitively feel that this is a crazy way to attribute winning to decisions? If so, can anyone suggest why I should adopt the one-boxer perspective on this?
O: But then the two-boxer has to rely on the claim that Newcomb's problem is "unfair" to explain why the two-boxing agent doesn't win. It seems absurd to say that a scenario like Newcomb's problem is unfair.
M: Well, the two-boxing agent means something very particular by "unfair". They simply mean that in this case the winning agent doesn't correspond to the winning decision. Further, they can explain why this is the case without saying anything that strikes me as crazy. They simply say that Newcomb's problem is a case where the agent's winnings can't entirely be attributed to the agent's decision (ignoring a constant value). But if something else (the agent's type at time of prediction) also influences the agent's winning in this case, why should it be a surprise that the winning agent and the winning decision come apart? I'm not saying the two-boxer is right here but they don't seem to me to be obviously wrong either...
Meta-comment (b): Interested to know what response should be given here.
O: Okay, let's try something else. The two-boxer focuses only on causal consequences but in doing so they simply ignore all the logical non-causal consequences of their decision algorithm outputting a certain decision. This is an ad hoc, unmotivated restriction.
M: Ah hoc? I'm not sure I see why. Think about the problem with evidential decision theory. The proponent of EDT could say a similar thing (that the proponent of two-boxing ignores all the evidential implications of their decision). The two-boxer will respond that these implications just are not relevant to decision making. When we make decisions we are trying to bring about the best results, not get evidence for these results. Equally, they might say, we are trying to bring about the best results, not derive the best results in our logical calculations. Now I don't know what to make of the point/counter-point here but it doesn't seem to me that the one-boxing view is obviously correct here and I'm worried that we're again going to end up just trading intuitions (and I can see the force of both intuitions here).
Meta-comment: Again, I would love to know whether I've understood this argument and whether something can be said to convince me that the one-boxing view is the clear cut winner here.
End comments: That's my understanding of the primary argument advanced for one-boxing on LW. Are there other core arguments? How can these arguments be improved and extended? |
cd1aad48-9b3a-4066-8a68-dd9633f14285 | trentmkelly/LessWrong-43k | LessWrong | No, Seriously. Just Try It: TAPs
This next semester (I'm in university, so that's how I measure time) I'm working on developing my ability to better integrate arbitrary habits into my behavior. Trigger-action-planning (a more detailed explanation as well) is LW's most concrete strategy for doing such a thing, so I've decided to just try it. Starting 3 weeks ago and proceeding for the next 6 months I'm working on my personal approach to Trigger-action-planning (TAP for short).
My basic structure for this:
* Each week (Sunday morning) design a new TAP, or redesign an old one.
* Aim to follow through on the TAP in all applicable situations throughout the week.
* At the end of each day (as part of an already existing review process) note if I did or did not follow through.
* At the end of each week, look back and think on if the TAP was useful, what worked well, what was hard, and all that jazz.
The biggest things that's jumped out at me so far has been that not all TAPs are created equal. I think conceptual similarity does not imply actionable similarity applies very heavily to TAPs. In light of this, I'm approaching each week's TAP from a very implementation specific perspective, and then afterwards I'm going to think about what connections and universal principles might apply.
Here are some things I've noticed so far:
* It's seems like there's a "mindfulness bootstrapping" problem, in that it often feels like my TAPs are only activated because of preexisting mindfulness triggers.
* I've been putting the handle for each week's TAP on my phone lock screen. This was a very effective reminder originally, though it's already lost most of its power three weeks in. Mayhaps having a randomly generated background image would help prevent my mind from filtering out expected reminders. One google search and perusing the first page of results did not produce an app to do this.
* I notice that sometimes there's a glimmer of noticing I'm in a context to activate the TAP, yet I don't. Part of this mi |
376f454f-0bb7-4e05-a1db-742cc62c86b7 | trentmkelly/LessWrong-43k | LessWrong | Fort Collins, Colorado Meetup Wednesday June 15 7pm
At the Bean Cycle, downtown. I'll have a Less Wrong sign.
Let's exchange anti-akrasia tips. |
3a4b7fe7-5863-4186-a294-a73001cc4c72 | trentmkelly/LessWrong-43k | LessWrong | Meetup : London social meetup - New venue
Discussion article for the meetup : London social meetup - New venue
WHEN: 17 November 2013 02:00:00PM (+0000)
WHERE: London W6 8BS
LW London is experimenting with switching to weekly meetups, and also experimenting with a new venue! You should come along and complain about poor experimental design, over discussion of whatever else seems interesting.
I can't get the map to point at the right place, so: the venue is The Latymers, on Hammersmith Road. It's about five minutes' walk from the Hammersmith tube station, served by the District, Piccadilly, Circle, and Hammersmith & City lines. We'll meet at 2pm, and we'll have the two tables on the right as you enter, and we'll have a sign identifying us as us.
Feel free to contact me through email (philip.hazelden@gmail.com) or phone (07792009646) if you have questions or difficulty finding us.
Also check out our google group.
Discussion article for the meetup : London social meetup - New venue |
c7703adf-6759-4cd4-838f-ad3e1c5df01f | StampyAI/alignment-research-dataset/arxiv | Arxiv | Adaptive Mechanism Design: Learning to Promote Cooperation
1 Introduction
---------------
Social dilemmas highlight conflicts between individual and collective interests. Cooperation allows for better outcomes for all participants, but individual participants are tempted to increase their own payoff at the expense of others. Selfish incentives can therefore destabilize the socially desirable outcome of mutual cooperation and often lead to outcomes that make everyone worse off [VanLange2013TheReview].
Cooperation often emerges due to direct reciprocity [Trivers1971TheAltruism] or indirect reciprocity [Nowak2005EvolutionReciprocity]. However, even if these mechanisms are not sufficient on their own, humans are often able to establish cooperation by changing the structure of the social dilemma. This is often referred to as *mechanism design*. For instance, institutions such as the police and the judicial system incentivize humans to cooperate in the social dilemma of peaceful coexistence, and have succeeded in dramatically reducing rates of violence [Pinker2011TheNature].
Studies of social dilemmas have traditionally focused on the context of human agents. However, in the future, artificial learning agents will likely be increasingly widespread in our society, and be employed in a variety of economically relevant tasks. In that case, they will interact both with other artificial agents and humans in complex and partially competitive settings.
This raises the question of how we can ensure that artificial agents will learn to navigate the resulting social dilemmas productively and safely. Failing to learn cooperative policies would lead to socially inefficient or even disastrous outcomes. In particular, the escalation of conflicts between artificial agents (or between artificial agents and humans) may pose a serious security risk in safety-critical systems. The behaviour of artificial agents in cooperation problems is thus of both theoretical and practical importance.
In this work, we will examine how mechanism design can promote beneficial outcomes in social dilemmas among artificial learners. We consider a setting with N agents in a social dilemma and an additional *planning agent* that can distribute (positive or negative) rewards to the players after observing their actions, and aims to guide the learners to a socially desirable outcome (as measured by the sum of rewards).
We derive a learning rule that allows the planning agent to learn how to set the additional incentives by looking ahead at how the agents will update their policy parameter in the next learning step. We also extend the method to settings in which the planning agent does not know what internal parameters the other agents use and does not have direct access to the opponents’ policy.
We evaluate the learning rule on several different matrix game social dilemmas. The planning agent learns to successfully guide the learners to cooperation with high social welfare in all games, while they learn to defect in the absence of a planning agent. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes. In other games, cooperation is unstable without continued intervention. However, even in the latter case, we show that the amount of necessary additional rewards decreases over time.
2 Related Work
---------------
The study of social dilemmas has a long tradition in game theory, theoretical social science, and biology. In particular, there is a substantial body of literature that fruitfully employs matrix games to study how stable mutual cooperation can emerge [Axelrod1981TheCooperation]. Key mechanisms that can serve to stabilize the socially preferred outcome of mutual cooperation include direct reciprocity [Trivers1971TheAltruism], indirect reciprocity [Nowak2005EvolutionReciprocity], and norm enforcement [Axelrod1986AnNorms]. [bachrach2009cost] examine how cooperation can be stabilized via supplemental payments from an external party.
Our work is inspired by the field of *mechanism design*, pioneered by [Vickrey1961COUNTERSPECULATIONTENDERS], which aims to design economic mechanisms and institutions to achieve certain goals, most notably social welfare or revenue maximization. [Seabright1993ManagingDesign] studies how informal and formal incentives for cooperative behaviour can prevent a tragedy of the commons. [monderer2004k] considers a setting in which an interested party can commit to non-negative monetary transfers, and studies the conditions under which desirable outcomes can be implemented with a given amount of payment. Mechanism design has also been studied in the context of computerized agents [Varian1995EconomicAgents] and combined with machine learning techniques [narasimhan2016automated].
We also draw on the rich literature on multi-agent reinforcement learning. It is beyond the scope of this work to review all relevant methods in multi-agent reinforcement learning, so we refer the reader to existing surveys on the subject [Busoniu2008ALearning, Tuyls2012MultiagentProspects]. However, we note that most work in multi-agent reinforcement learning considers coordination or communication problems in the fully cooperative setting, where the agents share a common goal [Omidshafiei2017DeepObservability, Foerster2016LearningLearning].
As an exception, [Leibo2017Multi-agentDilemmas] study the learned behaviour of deep Q-networks in a fruit-gathering game and a Wolfpack hunting game that represent sequential social dilemmas. [Tampuu2017MultiagentLearningb] successfully train agents to play Pong with either a fully cooperative, a fully competitive, or a mixed cooperative-competitive objective. [Crandall2018CooperatingMachines] introduce a learning algorithm that uses novel mechanisms for generating and acting on signals to learn to cooperate with humans and with other machines in iterated matrix games. Finally, [Lowe2017Multi-AgentEnvironments] propose a centralized actor-critic architecture that is applicable to both the fully cooperative as well as the mixed cooperative-competitive setting.
However, these methods assume a given set of opponent policies as given in that they do not take into account how one’s actions affect the parameter updates on other agents. In contrast, [Foerster2017LearningAwareness] introduce Learning with Opponent-Learning Awareness (LOLA), an algorithm that explicitly attempts to shape the opponent’s anticipated learning. The LOLA learning rule includes an additional term that reflects the effect of the agent’s policy on the parameter update of the other agents and inspired the learning rule in this work. However, while LOLA leads to emergent cooperation in an iterated Prisoner’s dilemma, the aim of LOLA agents is to shape the opponent’s learning to their own advantage, which does not always promote cooperation.
3 Background
-------------
###
3.1 Markov games
We consider partially observable Markov games [Littman1994MarkovLearning] as a multi-agent extension of Markov decision processes (MDPs). An N-player Markov game M is defined by a set of states S, an observation function O:S×{1,…,N}→Rd specifying each player’s d-dimensional view, a set of actions A1,…,AN for each player, a transition function T:S×A1×⋯×AN→P(S), where P(S) denotes the set of probability distributions over S, and a reward function ri:S×A1×⋯×AN→R for each player. To choose actions, each player uses a policy πi:Oi→P(Ai), where Oi={oi | s∈S,oi=O(s,i)} is the observation space of player i. Each player in a Markov game aims to maximize its discounted expected return Ri=∑Tt=0γtrti, where γ is a discount factor and T is the time horizon.
###
3.2 Policy gradient methods
Policy gradient methods [Sutton1998ReinforcementIntroduction] are a popular choice for a variety of reinforcement learning tasks. Suppose the policy πθ of an agent is parametrized by θ. Policy gradient methods aim to maximize the objective J(θ)=Es∼pπθ,a∼πθ[R] by updating the agent’s policy steps in the direction of ∇θJ(θ).
Using the policy gradient theorem [SuttonPolicyApproximation], we can write the gradient as follows:
| | | | |
| --- | --- | --- | --- |
| | ∇θJ(θ)=Es∼pπθ,a∼πθ[∇θlogπθ(a|s) Qπθ(s,a)] | | (1) |
where pπθ is the state distribution and Qπθ(s,a)=E[R|st=s,at=a].
###
3.3 Matrix game social dilemmas
A matrix game is the special case of two-player perfectly observable Markov games with |S|=1, T=1 and A1=A2={C,D}. That is, two actions are available to each player, which we will interpret as cooperation and defection.
| | | |
| --- | --- | --- |
| | C | D |
| C | R,R | S,T |
| D | T,S | P,P |
Table 1: Payoff matrix of a symmetric 2-player matrix game. A cell of X,Y represents a utility of X to the row player and Y to the column player.
Table [1](#S3.T1 "Table 1 ‣ 3.3 Matrix game social dilemmas ‣ 3 BACKGROUND ‣ Adaptive Mechanism Design: Learning to Promote Cooperation") shows the generic payoff structure of a (symmetric) matrix game. Players can receive four possible rewards: R (reward for mutual cooperation), P (punishment for mutual defection), T (temptation of defecting against a cooperator), and S (sucker outcome of cooperating against a defector).
A matrix game is considered a social dilemma if the following conditions hold [Macy2002LearningDilemmas.]:
1. Mutual cooperation is preferable to mutual defection: R>P
2. Mutual cooperation is preferable to being exploited: R>S
3. Mutual cooperation is preferable to an equal probability of unilateral defection by either player: R>T+S2
4. The players have some reason to defect because exploiting a cooperator is preferable to mutual cooperation (T>R) or because mutual defection is preferable to being exploited (P>S).
The last condition reflects the mixed incentive structure of matrix game social dilemmas. We will refer to the motivation to exploit a cooperator (quantified by T−R) as *greed* and to the motivation to avoid being exploited by a defector (P−S) as *fear*. As shown in Table [2](#S3.T2 "Table 2 ‣ 3.3 Matrix game social dilemmas ‣ 3 BACKGROUND ‣ Adaptive Mechanism Design: Learning to Promote Cooperation"), we can use the presence or absence of greed and fear to categorize matrix game social dilemmas.
| | | |
| --- | --- | --- |
| Chicken | C | D |
| C | 3,3 | 1,4 |
| D | 4,1 | 0,0 |
| | | |
| --- | --- | --- |
| Stag Hunt | C | D |
| C | 4,4 | 0,3 |
| D | 3,0 | 1,1 |
| | | |
| --- | --- | --- |
| PD | C | D |
| C | 3,3 | 0,4 |
| D | 4,0 | 1,1 |
Table 2: The three canonical examples of matrix game social dilemmas with different reasons to defect. In Chicken, agents may defect out of greed, but not out of fear. In Stag Hunt, agents can never get more than the reward of mutual cooperation by defecting, but they may still defect out of fear of a non-cooperative partner. In Prisoner’s Dilemma (PD), agents are motivated by both greed and fear simultaneously.
4 Methods
----------
###
4.1 Amended Markov game including the planning agent
Suppose N agents play a Markov game described by S, A1…AN, r1,…,rn, O and T. We introduce a *planning agent* that can hand out additional rewards and punishments to the players and aims to use this to ensure the socially preferred outcome of mutual cooperation.
To do this, the Markov game can be amended as follows. We add another action set Ap⊂RN that represents which additional rewards and punishments are available to the planning agent. Based on its observation Op:S×{1,…,N}→Rd and the other player’s actions a1,…,an, the planning agent takes an action ap=(rp1,…,rpN)∈Ap⊂RN.111Technically, we could represent the dependence on the other player’s actions by introducing an extra step after the regular step in which the planning agent chooses additional rewards and punishments. However, for simplicity, we will discard this and treat the player’s actions and the planning action as a single step. Formally, we can justify this by letting the planning agent specify its action for every possible combination of player actions. The new reward function of player i is r(tot)i=ri+rpi, i.e. the sum of the original reward and the additional reward, and we denote the corresponding value functions as Vtoti(θ1,…,θN)=Vi(θ1,…,θN)+Vpi(θ1,…,θN). Finally, the transition function T formally receives ap as an additional argument, but does not depend on it (T(s,a1,…,aN,ap)=T(s,a1,…,aN)).
###
4.2 The learning problem
Let θ1,…,θN and θp be parametrizations of the player’s policies π1,…,πN and the planning agent’s policy πp.
The planning agent aims to maximize the total social welfare V(θ1,…,θN):=∑Ni=1Vi(θ1,…,θN), which is a natural metric of how socially desirable an outcome is. Note that without restrictions on the set of possible additional rewards and punishments, i.e. Ap=RN, the planning agent can always transform the game into a fully cooperative game by choosing rpi=∑Nj=1,j≠irj.
However, it is difficult to learn how to set the right incentives using traditional reinforcement learning techniques. This is because V(θ1,…,θN) does not depend *directly* on θp. The planning agent’s actions only affect V(θ1,…,θN) indirectly by changing the parameter updates of the learners. For this reason, it is vital to explicitly take into account how the other agents’ learning changes in response to additional incentives.
This can be achieved by considering the next learning step of each player (cf. [Foerster2017LearningAwareness]). We assume that the learners update their parameters by simple gradient ascent:
| | | | | |
| --- | --- | --- | --- | --- |
| | Δθi | =ηi∇iVtoti(θ1,…,θN) | | (2) |
| | | =ηi(∇iVi(θ1,…,θN)+∇iVpi(θ1,…,θN)) | |
where ηi is step size of player i and ∇i:=∇θi is the gradient with respect to parameters θi.
Instead of optimizing V(θ1,…,θN), the planning agent looks ahead one step and maximizes V(θ1+Δθ1,…,θN+ΔθN). Assuming that the parameter updates Δθi are small, a first-order Taylor expansion yields
| | | | | |
| --- | --- | --- | --- | --- |
| | | V(θ1+Δθ1,…,θN+ΔθN)≈ | | (3) |
| | | ≈V(θ1,…,θN)+N∑i=1(Δθi)T∇iV(θ1,…,θN) | |
We use a simple rule of the form Δθp=ηp∇pV(θ1+Δθ1,…,θN+ΔθN) to update the planning agent’s policy, where ηp is the learning step size of the planning agent. Exploiting the fact that V(θ1,…,θN) does not depend directly on θp, i.e. ∇pV(θ1,…,θN)=0, we can calculate the gradient:
| | | | | |
| --- | --- | --- | --- | --- |
| | ∇p | V(θ1+Δθ1,…,θN+ΔθN)≈ | | (4) |
| | | ≈N∑i=1∇p(Δθi)T∇iV(θ1,…,θN) | |
| | | =N∑i=1ηi(∇p∇iVtoti(θ1,…,θN))T∇iV(θ1,…,θN) | |
| | | =N∑i=1ηi(∇p∇iVpi(θ1,…,θN))T∇iV(θ1,…,θN) | |
since ∇iVi(θ1,…,θN) does not depend on θp either.
###
4.3 Policy gradient approximation
If the planning agent does not have access to the exact gradients of Vpi(θ1,…,θN) and V(θ1,…,θN), we use policy gradients as an approximation. Let τ=(s0,a0,a0p,r0…,sT,aT,aTp,rT) be a state-action trajectory of horizon T+1, where at=(at1,…,atN), rt=(rt1,…,rtN), and atp==(rt1,p,…,rtN,p) are the actions taken and rewards received in time step t. Then, the episodic return R0i(τ)=∑Tt=0γtrti and R0i,p(τ)=∑Tt=0γtrti,p approximate Vi(θ1,…,θN) and Vpi(θ1,…,θN), respectively. Similarly, R0(τ)=∑Ni=0R0i(τ) approximates the social welfare V(θ1,…,θN).
We can now calculate the gradients using the policy gradient theorem:
| | | | | |
| --- | --- | --- | --- | --- |
| | ∇iVi(θ1,…,θN) | ≈∇iE[R0i(τ)] | | (5) |
| | | =E[∇ilogπi(τ)R0i(τ)] | |
The other gradients ∇iV(θ1,…,θN) and ∇p∇iVpi(θ1,…,θN) can be approximated in the same way. This yields the following rule for the parameter update of the planning agent:
| | | | | |
| --- | --- | --- | --- | --- |
| | Δθp=ηpN∑i=1ηi | (E[∇plogπp(τ)∇ilogπi(τ)R0i,p(τ)])T | | (6) |
| | ⋅ | E[∇ilogπi(τ)R0(τ)] | |
###
4.4 Opponent modeling
Equations [4](#S4.E4 "(4) ‣ 4.2 The learning problem ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation") and [6](#S4.E6 "(6) ‣ 4.3 Policy gradient approximation ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation") assume that the planning agent has access to each agent’s internal policy parameters and gradients. This is a restrictive assumption. In particular, agents may have an incentive to conceal their inner workings in adversarial settings. However, if the assumption is not fulfilled, we can instead model the opponents’ policies using parameter vectors ^θ1,…,^θN and infer the value of these parameters from the player’s actions [Ross2010ALearning]. A simple approach is to use a maximum likelihood estimate based on the observed trajectory:
| | | | |
| --- | --- | --- | --- |
| | ^θi=argmaxθ′iT∑t=0logπθ′i(ait|st). | | (7) |
Given this, we can substitute ^θi for θi in equation [4](#S4.E4 "(4) ‣ 4.2 The learning problem ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation").
###
4.5 Cost of additional rewards
In real-world examples, it may be costly to distribute additional rewards or punishment. We can model this cost by changing the planning agent’s objective to V(θ1+Δθ1,…,θN+ΔθN)−α||Vp(θ1,…,θN;θp)||2, where α is a cost parameter and Vp=(Vp1,…,VpN). The modified update rule is (using equation [4](#S4.E4 "(4) ‣ 4.2 The learning problem ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation"))
| | | | |
| --- | --- | --- | --- |
| | | | (8) |
5 Experimental Setup
---------------------
In our experiments, we consider N=2 learning agents playing a matrix game social dilemma (MGSD) as outlined in section [3.3](#S3.SS3 "3.3 Matrix game social dilemmas ‣ 3 BACKGROUND ‣ Adaptive Mechanism Design: Learning to Promote Cooperation"). The learners are simple agents with a single policy parameter θ that controls the probability of cooperation and defection: P(C)=exp(θ)1+exp(θ), P(D)=11+exp(θ). The agents use a centralized critic [Lowe2017Multi-AgentEnvironments] to learn their value function.
The agents play 4000 episodes of a matrix game social dilemma. We fix the payoffs R=3 and
P=1, which allows us to describe the game using the level of greed and fear. We will consider
three canonical matrix game social dilemmas as shown in Table [3](#S5.T3 "Table 3 ‣ 5 EXPERIMENTAL SETUP ‣ Adaptive Mechanism Design: Learning to Promote Cooperation").
| | | | | |
| --- | --- | --- | --- | --- |
| Game | Greed | Fear | T | S |
| Prisoner’s Dilemma | 1 | 1 | 4 | 0 |
| Chicken | 0.5 | -1 | 3.5 | 2 |
| Stag Hunt | -1 | 1 | 2 | 0 |
Table 3: Levels of fear and greed and resulting temptation (T) and sucker (S) payoffs in three matrix games. Note that the level of greed in Chicken has to be smaller than 1 because it is otherwise not a social dilemma (R>T+S2 is not fulfilled).
The planning agent’s policy is parametrized by a single layer neural network. We limit the maximum amount of additional rewards or punishments (i.e. we restrict Ap to vectors that satisfy maxNi=1|rpi|≤c for a given constant c). Unless specified otherwise, we use a step size of 0.01 for both the planning agent and the learners, use cost regularisation (Equation [8](#S4.E8 "(8) ‣ 4.5 Cost of additional rewards ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation")) with a cost parameter of 0.0002, set the maximum reward to 3, and use the exact value function. In some experiments, we also require that the planning agent can only redistribute rewards, but cannot
change the total sum of rewards (i.e. Ap is restricted to vectors that satisfy ∑Ni=1rpi=0). We refer to this as the *revenue-neutral* setting.
6 Results
----------
In this section, we summarize the experimental results.222Source code available at [https://github.com/tobiasbaumann1/Adaptive˙Mechanism˙Design](https://github.com/tobiasbaumann1/Adaptive_Mechanism_Design) We aim to answer the following questions:
* Does the introduction of the planning agent succeed in promoting significantly higher levels of cooperation?
* What qualitative conclusions can be drawn about the amount of additional incentives needed to learn and maintain cooperation?
* In which cases is it possible to achieve cooperation even when the planning agent is only active for a limited timespan?
* How does a restriction to revenue-neutrality affect the effectiveness of mechanism design?
| | | |
| --- | --- | --- |
| | \includegraphics[height=144.54pt]PlayerActionProbabilities\includegraphics[height=144.54pt]AdditionalRewards(a) Probability of cooperation(b) Additional rewards for player %
1\includegraphics[height=144.54pt]ModifiedFearandGreed\includegraphics[height=144.54pt]CumulativeAdditionalRewards(c) Fear and greed in the modified game(d) Cumulative additional%
rewards | |
Figure 1: Mechanism design over 4000 episodes of a Prisoner’s Dilemma. The initial probability of cooperation is 0.25 for each player. Shown is (a) the probability of cooperation over time, (b) the additional reward for the first player in each of the four possible outcomes, (c) the resulting levels of fear and greed including additional rewards, and (d) the cumulative amount of distributed rewards.
Figure 1a illustrates that the players learn to cooperate with high probability if the planning agent is present, resulting in the socially preferred outcome of stable mutual cooperation. Thus the planning agent successfully learns how to distribute additional rewards to guide the players to a better outcome.
Figure 1b shows how the planning agent rewards or punishes the player conditional on each of the four possible outcomes. At first, the planning agent learns to reward cooperation, which creates a sufficient incentive to cause the players to learn to cooperate. In Figure 1c we show how this changes the level of fear and greed in the modified game. The levels of greed and fear soon drop below zero, which means that the modified game is no longer a social dilemma.
Note that rewarding cooperation is less costly than punishing defection if (and only if) cooperation is the less common action. After the player learns to cooperate with high probability, the planning agent learns that it is now less costly to punish defection and consequently stops handing out additional rewards in the case of mutual cooperation outcome. As shown in Figure 1d, the amount of necessary additional rewards converges to 0 over time as defection becomes increasingly rare.
Table [4](#S6.T4 "Table 4 ‣ 6 RESULTS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation") summarizes the results of all three canonical social dilemmas. Without adaptive mechanism design, the learners fail to achieve mutual cooperation in all cases. By contrast, if the planning agent is turned on, the learners learn to cooperate with high probability, resulting in a significantly higher level of social welfare.
| | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | |
| |
| --- |
| Prisoner’s |
| Dilemma |
| Chicken | Stag Hunt |
| | Greed | 1 | 0.5 | -1 |
| | Fear | 1 | -1 | 1 |
| No mech. design | P(C,C) |
| |
| --- |
| 0.004% |
| ±0.001% |
|
| |
| --- |
| 3.7% |
| ±1.3% |
|
| |
| --- |
| 0.004% |
| ±0.002% |
|
| V |
| |
| --- |
| 2.024 |
| ±0.003 |
|
| |
| --- |
| 5.44 |
| ±0.01 |
|
| |
| --- |
| 2.00 |
| ±0.00 |
|
| With mech. design | P(C,C) |
| |
| --- |
| 98.7% |
| ±0.1% |
|
| |
| --- |
| 99.0% |
| ±0.1% |
|
| |
| --- |
| 99.1% |
| ±0.1% |
|
| V |
| |
| --- |
| 5.975 |
| ±0.002 |
|
| |
| --- |
| 5.995 |
| ±0.001 |
|
| |
| --- |
| 5.964 |
| ±0.005 |
|
|
| |
| --- |
| Turning |
| off |
| P(C,C) |
| |
| --- |
| 0.48% |
| ±0.4% |
|
| |
| --- |
| 53.8% |
| ±29.4% |
|
| |
| --- |
| 99.6% |
| ±0.0% |
|
| V |
| |
| --- |
| 2.60 |
| ±0.69 |
|
| |
| --- |
| 5.728 |
| ±0.174 |
|
| |
| --- |
| 5.986 |
| ±0.002 |
|
Table 4: Comparison of the resulting levels of cooperation after 4000 episodes, a) without mechanism design, b) with mechanism design, and c) when turning off the planning agent after 4000 episodes and running another 4000 episodes. Each cell shows the mean and standard deviation of ten training runs. P(C,C) is the probability of mutual cooperation at the end of training and V is the expected social welfare that results from the players’ final action probabilities. The initial probability of cooperation is 0.25 for each player.
The three games differ, however, in whether the cooperative outcome obtained through mechanism design is stable even when the planning agent is turned off. Without additional incentives, mutual cooperation is not a Nash equilibrium in the Prisoner’s Dilemma and in Chicken [Fudenberg\_Game\_Theory], which is why one or both players learn to defect again after the planning agent is turned off. These games thus require continued (but only occasional) intervention to maintain cooperation. By contrast, mutual cooperation is a stable equilibrium in Stag Hunt [Fudenberg\_Game\_Theory]. As shown in Table [4](#S6.T4 "Table 4 ‣ 6 RESULTS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation"), this means that long-term cooperation in Stag Hunt can be achieved even if the planning agent is only active over a limited timespan (and thus at limited cost).
| | | | | | |
| --- | --- | --- | --- | --- | --- |
| |
| |
| --- |
| Prisoner’s |
| Dilemma |
| Chicken | Stag Hunt |
| Greed | 1 | 0.5 | -1 |
| Fear | 1 | -1 | 1 |
| Exact V |
| P(C,C) |
| |
| --- |
| 98.7% |
| ±0.1% |
|
| |
| --- |
| 99.0% |
| ±0.1% |
|
| |
| --- |
| 99.1% |
| ±0.1% |
|
| AAR |
| |
| --- |
| 0.77 |
| ±0.21 |
|
| |
| --- |
| 0.41 |
| ±0.02 |
|
| |
| --- |
| 0.45 |
| ±0.02 |
|
|
| |
| --- |
| Exact V |
| Revenue-neutral |
|
| P(C,C) |
| |
| --- |
| 91.4% |
| ±1.0% |
|
| |
| --- |
| 98.9% |
| ±0.1% |
|
| |
| --- |
| 69.2% |
| ±45.3% |
|
| AAR |
| |
| --- |
| 0.61 |
| ±0.04 |
|
| |
| --- |
| 0.31 |
| ±0.02 |
|
| |
| --- |
| 0.19 |
| ±0.11 |
|
| Estimated V |
| P(C,C) |
| |
| --- |
| 61.3% |
| ±20.0% |
|
| |
| --- |
| 52.2% |
| ±18.6% |
|
| |
| --- |
| 96.0% |
| ±1.2% |
|
| AAR |
| |
| --- |
| 3.31 |
| ±0.63 |
|
| |
| --- |
| 2.65 |
| ±0.31 |
|
| |
| --- |
| 4.89 |
| ±0.39 |
|
Table 5: Resulting levels of cooperation and average additional rewards (AAR) per round for different variants of the learning rule. The variants differ in whether they use the exact value function (Equation [4](#S4.E4 "(4) ‣ 4.2 The learning problem ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation")) or an estimate (Equation [6](#S4.E6 "(6) ‣ 4.3 Policy gradient approximation ‣ 4 METHODS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation")) and in whether the setting is revenue-neutral or unrestricted.
Table [5](#S6.T5 "Table 5 ‣ 6 RESULTS ‣ Adaptive Mechanism Design: Learning to Promote Cooperation") compares the performance of different variants of the learning rule. Interestingly, restricting the possible planning actions to redistribution leads to lower probabilities of cooperation in Prisoner’s Dilemma and Stag Hunt, but not in Chicken. We hypothesize that this is because in Chicken, mutual defection is not in the individual interest of the players anyway. This means that the main task for the planning agent is to prevent (C,D) or (D,C) outcomes,
which can be easily achieved by redistribution. By contrast, these outcomes are fairly unattractive (in terms of individual interests) in Stag Hunt, so the most effective intervention is to make (D,D) less attractive and (C,C) more attractive, which is not feasible by pure redistribution. Consequently, mechanism design by redistribution works best in Chicken and worst in Stag Hunt.
Using an estimate of the value function leads to inferior performance on all three games, both in
terms of the resulting probability of mutual cooperation and with respect to the amount of distributed additional results. However, the effect is by far least pronounced in Stag Hunt. This may be because mutual cooperation is an equilibrium in Stag Hunt, which means that a beneficial outcome can more easily arise even if the incentive structure created by the planning agent is imperfect.
Finally, we note that the presented approach is also applicable to settings with more than two players.333Source code available in a separate repository at [https://github.com/tobiasbaumann1/Mechanism˙Design˙Multi-Player](https://github.com/tobiasbaumann1/Mechanism_Design_Multi-Player) We consider a multi-player Prisoner’s Dilemma with N=10 agents.444The payoffs are as follows: 3 if all players cooperate, 1 if all players defect, 4 if you are the only to defect, 0 if you are the only to cooperate. Payoffs of intermediate outcomes, where some fraction of players cooperate, are obtained by linear interpolation.
| | | |
| --- | --- | --- |
| | | | |
db7175c3-e262-4147-85c4-03a112b166e1 | trentmkelly/LessWrong-43k | LessWrong | Counterarguments to Core AI X-Risk Stories?
I've added a tag for object-level AI risk skepticism arguments. I've included my own post about deceptive alignment and Katja Grace's post about AI X-risk counterarguments. What other arguments should be tagged? |
674e7ee2-cc6d-4bc1-b5af-a37ca5d27c67 | trentmkelly/LessWrong-43k | LessWrong | The Best Way to Align an LLM: Is Inner Alignment Now a Solved Problem?
This is a link-post for a new paper I read: Safety Pretraining: Toward the Next Generation of Safe AI by Pratyush Maini, Sachin Goyal, et al.
For a couple of years I (and others) have been proposing an approach to alignment: what the authors of this recent paper name "safety pretraining". In a nutshell: that it's best to apply your alignment training as part of the standard pretraining process to produce a base model that is already aligned — simply pretrain it on data including a lot of clearly marked examples of aligned behavior (then prompt for it).
I've regarded this approach as a major advance ever since I read the seminal 2023 paper on the topic: Pretraining Language Models with Human Preferences by Tomasz Korbak et al., and I'm absolutely delighted to finally see someone else publish another paper on this approach — I'm only sad it has taken so long.
I highly encourage everyone interested in AI alignment to go read both of these papers (if you haven't already) — between them they strongly suggest that the authors have found a more effective way to align an AI: an alignment approach better than any that people are (as far as we know) currently using. I believe this is extremely important: I see it as major progress on alignment. So I think it directly reduces the p(DOOM) for the most critical current x-risk to our entire species.
For more detailed expositions of this approach and why I think it's an excellent idea, see my previous posts How to Control an LLM's Behavior (why my P(DOOM) went down), A "Bitter Lesson" Approach to Aligning AGI and ASI, and Why Aligning an LLM is Hard, and How to Make it Easier.
(I'm also delighted that the authors of the recent paper tested out some of the follow-on ideas I'd been proposing in those posts on Less Wrong. One was training the model to generate control-tag tokens that label portions of the text as good or bad behavior, and then for conditional generation altering the token generation process, leveraging these to |
530c4904-4087-4af2-98d3-37418ec51428 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Counterfactual Fairness
1 Introduction
---------------
Machine learning has spread to fields as diverse as credit scoring Khandani et al. ([2010](#bib.bib15)), crime prediction Brennan et al. ([2009](#bib.bib3)), and loan assessment Mahoney & Mohen ([2007](#bib.bib18)). As
machine learning enters these new areas it is necessary for the
modeler to think beyond the simple objective of maximizing prediction
accuracy, and to consider the societal impact of their work.
For many of these applications, it is crucial to
ask if the predictions of a model are
*fair*. For instance, imagine a bank wishes to predict if an individual should be given
a loan to buy a house. The bank wishes to use historical repayment data, alongside individual data. If they simply learn a model
that predicts whether the
loan will be paid back, it may unjustly favor
applicants of particular subgroups, due to past and
present prejudices. The Obama Administration released a report
describing this which urged data scientists to
analyze ‘‘how technologies can deliberately or inadvertently
perpetuate, exacerbate, or mask
discrimination”.111<https://obamawhitehouse.archives.gov/blog/2016/05/04/big-risks-big-opportunities-intersection-big-data-and-civil-rights>
As a result, there has been immense interest in designing
algorithms that make fair predictions
Hardt et al. ([2016](#bib.bib10)); Dwork et al. ([2012](#bib.bib6)); Joseph et al. ([2016](#bib.bib11)); Kamishima et al. ([2011](#bib.bib14)); Zliobaite ([2015](#bib.bib29)); Zafar et al. ([2016](#bib.bib27), [2015](#bib.bib26)); Grgic-Hlaca et al. ([2016](#bib.bib8)); Kleinberg et al. ([2016](#bib.bib16)); Calders & Verwer ([2010](#bib.bib4)); Kamiran & Calders ([2012](#bib.bib13)); Bolukbasi et al. ([2016](#bib.bib2)); Kamiran & Calders ([2009](#bib.bib12)); Zemel et al. ([2013](#bib.bib28)); Louizos et al. ([2015](#bib.bib17)).
In large part, the initial work on fairness in machine learning has
focused on formalizing fairness into quantitative definitions and using them to solve a
discrimination problem in a certain dataset. Unfortunately, for a
practitioner, law-maker, judge, or anyone else who is interested in
implementing algorithms that control for discrimination, it can be
difficult to decide which definition of fairness to choose for the
task at hand. Indeed, we demonstrate that depending on the
relationship between a sensitive attribute and the data, certain
definitions of fairness can actually *increase discrimination*.
We describe how techniques from causal inference can be effective tools for designing fair algorithms and argue, as in DeDeo ([2014](#bib.bib5)), that it is essential to properly address causality.
Specifically, we
leverage the causal framework of
Pearl et al. ([2009](#bib.bib22)) to model the relationship between sensitive
attributes and data. Our contributions are as follows:
1. We model questions of fairness within a causal framework. This allows us to directly model *how* unfairness affects the data at hand.
2. We introduce *counterfactual fairness*, which enforces that a distribution over possible predictions for an individual should remain unchanged, in a world where an individual’s sensitive attribute had been different from birth.
3. We analyze how enforcing existing definitions of fairness for different data may correspond or be in conflict with counterfactual fairness. In particular, we show that depending on the underlying state of the world some definitions of fairness may be inappropriate.
4. We devise techniques for learning predictors that are counterfactually fair and demonstrate their use in several examples.
2 Fairness
-----------
Our goal in this paper is to design automated algorithms that make fair predictions across various demographic groups. This unfairness can arise in several ways:
Historically biased distributions: Individuals with different protected attributes A may have many different attributes due to current and historic biases (e.g., racial inequality caused by things like colonialism,
slavery, a history of discrimination in hiring and
housing etc.).
Selection unfairness: The training data could contain selection bias. For instance, if we are using a dataset describing who paid loans back in full in order to train a loan prediction algorithm, it may be that loans were unfairly distributed. Since we can’t see whether people will pay back a loan if they didn’t receive one, our algorithms may be biased by this sampling.
Prediction unfairness: The learned classifier could use either protected attributes such as race or correlated attributes as features, and learn a biased predictor.
There has been a wealth of recent work towards fair
algorithms. These include fairness through unawareness Grgic-Hlaca et al. ([2016](#bib.bib8)), demographic
parity/disparate impact Zafar et al. ([2015](#bib.bib26)), individual fairness
Dwork et al. ([2012](#bib.bib6)); Zemel et al. ([2013](#bib.bib28)); Louizos et al. ([2015](#bib.bib17)); Joseph et al. ([2016](#bib.bib11)), and
equality of opportunity Hardt et al. ([2016](#bib.bib10)); Zafar et al. ([2016](#bib.bib27)).
######
Definition 1 (Fairness Through Unawareness (FTU)).
An algorithm is fair so long as any sensitive attributes A are not
explicitly used in the decision-making process. Any mapping
^Y:X→Y that excludes A (or other unfair
attributes, see Grgic-Hlaca et al. ([2016](#bib.bib8))) satisfies this.
Initially proposed as a baseline method, the approach has found favor
recently with more general approaches such as Grgic-Hlaca et al. ([2016](#bib.bib8)). The
approach has a compelling simplicity, and constructs a
predictor ^Y based on a feature vector X that excludes
A, and in the case of Grgic-Hlaca et al. ([2016](#bib.bib8)) other attributes
labeled as unfair.
######
Definition 2 (Individual Fairness (IF)).
An algorithm is fair if it gives similar predictions to similar
individuals. Formally, if individuals i and j are similar apart
from their protected attributes Ai, Aj then
| | | |
| --- | --- | --- |
| | ^Y(X(i),A(i))≈^Y(X(j),A(j)). | |
This approach can be understood loosely as a continuous analog of
FTU. As described in Dwork et al. ([2012](#bib.bib6)), the
notion of similarity must be carefully chosen and this notion of fairness
will not correct for the historical biases described above.
######
Definition 3 (Demographic Parity (DP)).
An algorithm is fair if its predictions are independent of the
sensitive attributes A across the population. A prediction
^Y satisfies this definition if,
| | | |
| --- | --- | --- |
| | P(^Y|A=0)=P(^Y|A=1). | |
######
Definition 4 (Equal Opportunity (EO)).
An algorithm is fair if it is equally accurate for each value of the sensitive attribute A. A prediction ^Y satisfies this if,
| | | |
| --- | --- | --- |
| | P(^Y=1|A=0,Y=1)=P(^Y=1|A=1,Y=1). | |
While these definitions address the notion of algorithmic fairness, they guarantee that historic biases in the data are preserved. As shown by Kleinberg et al. ([2016](#bib.bib16)), EO and DP are mutually exclusive notions of fairness.
3 Causal Models and Counterfactuals
------------------------------------
We follow the framework of Pearl ([2000](#bib.bib20)), and define a causal
model as a triple (U,V,F) of sets such that
* U is a set of latent background variables222These are
sometimes called exogenous variables, but the fact that members of U
might depend on each other is not relevant to what follows., which are generated by factors
outside of our control, and in general do not depend on any protected attributes A (unless this is explicitly specified);
* V is a set of endogenous variables, where each member is determined by
other variables in U∪V;
* F is a set of functions {f1,…,fn}, one for each Vi∈V, such
that Vi=fi(pai,Upai), pai⊆V∖{Vi} and Upai⊆U. Such equations are also known as
structural equations (Bollen, [1989](#bib.bib1)).
The notation “pai” refers to the “parents” of Vi and is motivated by the assumption that the
model factorizes according to a directed acyclic graph (DAG). That is, we can
define a directed graph G=(U∪V,E) where each node is an
element of U∪V, and each edge from some Z⊆U∪V to Vi indicates that Z∈pai∪Upai. By construction, G is
acyclic.
The model is causal in that, given a distribution p(U)
over the background variables U, you can derive the distribution of
a subset Z⊆V following an intervention on the
complementary subset V∖Z. Here,
an intervention on the variable Vi of value v refers to the substitution of
equation Vi=fi(pai,Upai) with the equation Vi=v. This captures the idea of an agent, external to the
system, modifying it by forcefully assigning value v to Vi. This occurs in a randomized controlled trials where the value
of Vi is overridden by a treatment setting it to v, a value
chosen at random, and thus independent of any other causes.
In contrast with the independence constraints given by a DAG, the full
specification of F requires much stronger assumptions but also leads
to much stronger claims. In particular, it allows for the
calculation of counterfactual quantities. In brief, consider the following counterfactual
statement, “the value of Y if Z had taken value z”, for two endogenous
variables Z and Y in a causal model. By assumption, the state of
any endogenous variable is fully determined by
the background variables and structural equations. The counterfactual is
modeled as the solution for Y for a given U=u where the equations
for Z are replaced with Z=z. We denote it by YZ←z(u)
Pearl ([2000](#bib.bib20)), and sometimes as Yz if the context of the notation is clear.
Counterfactual inference, as specified by a causal model (U,V,F) given evidence W,
is the computation of probabilities
P(YZ←z(U) | W=w), where W, Z and Y are
subsets of V. Inference proceeds in three steps, as explained in
more detail in Chapter 4 of Pearl et al. ([2016](#bib.bib21)):
1. Abduction: for a given prior on U, compute the posterior
distribution of U given the evidence W=w;
2. Action: substitute the equations for Z with the interventional
values z, resulting in the modified set of equations Fz;
3. Prediction: compute the implied distribution on the remaining
elements of V using Fz and the posterior P(U |W=w).
4 Counterfactual Fairness
--------------------------
Given a causal model (U,V,F), let A⊆V be a set of protected
attributes, ^Y⊆V a variable which we will be the basis for
any decision making, and W the set of complementary measurements such that
W=V∖(A∪{^Y}).
######
Definition 5 (Counterfactual fairness).
We say ^Y is counterfactually fair
if under any context uniquely defined by evidence W=w and sensitive A=a,
| | | |
| --- | --- | --- |
| | P(^YA←a (U)=y | W=w,A=a)= | |
| | P(^YA←a′(U)=y | W=w,A=a), | | (1) |
for all y and for any value a′ attainable by A.
This captures the idea that any decision based on the
conditional distribution of ^Y would be the same despite A being
different, given the full implications of A having always been different.
We can also see ^Y as satisfying “counterfactual exchangeability”
under this model.
An associated concept of causal fairness appears as Example 4.4.4 in
Pearl et al. ([2016](#bib.bib21)). There, the authors condition instead on W, A,
and the observed realization of ^Y, and calculate the
probability of the counterfactual realization differing from the
factual333The result is an expression called the “the
probability of sufficiency” for A, capturing the notion that
switching A to a different value would be sufficient to change
^Y with some probability.. This example conflates the
recorded decision ^Y with the information Y on which we should
ideally base our decision making, a difference which we maintain. Our
framing makes the connection to other existing machine learning
methods more explicit, as we discuss in Section [5](#S5 "5 Methods and Assessment").
Evidence used to determine the state of background variables U
should come from A and W alone, as in many setups we wish to
predict some Y as ^Y, when Y is unavailable at any point in
our inference.
We also emphasize that counterfactual fairness is an
individual-level definition. This is substantially different
from comparing different units that happen to share the
same “treatment” and coincide on values of X, as discussed in
Section 4.3.1 of (Pearl et al., [2016](#bib.bib21)). Here, differences in the value
of X must be caused by variations on A only.
Figure 1: (a) The graph corresponding to a causal model with A being the protected attribute
and Y some outcome of interest, with background variables assumed to be independent.
(b) Expanding the model to include an intermediate variable indicating whether the individual
is employed with two (latent) background variables Prejudiced (if the person offering the job is prejudiced) and Qualifications (a measure of the individual’s qualifications). (c) A twin network representation of this system (Pearl, [2000](#bib.bib20))
under two different counterfactual levels for A. This is created by copying nodes descending from A, which inherit unaffected parents from the factual world.
###
4.1 Implications
As discussed by Halpern ([2016](#bib.bib9)), it is unproductive to debate
if a particular counterfactual definition is the “correct” one
to satisfy socially constructed concepts such as blame and responsibility.
The same applies to fairness. Instead, we discuss the
implications of definition ([5](#Thmdefine5 "Definition 5 (Counterfactual fairness). ‣ 4 Counterfactual Fairness")) and some choices
that arise in its application.
First, we wish to make explicit the difference between ^Y, the
predictor we use for fair decisions, and Y, the related state
generated by an unfair world. For instance, Y could be an indicator
of whether a client defaults on a loan, while ^Y is the actual
decision of giving the loan. Consider the DAG A→Y for a
causal model where V={A,Y}, and in Figure [1](#S4.F1 "Figure 1 ‣ 4 Counterfactual Fairness")(a) the
DAG with explicit inclusion of set U of independent background
variables. Assume Y is an objectively ideal measure used in decision
making, such as a binary indicator that the individual defaults on a
loan. In this setup, the mechanism fY(A,U) is causally unfair,
with the arrow A→Y being the result of a world that
punishes individuals in a way that is out of their control. Figure
[1](#S4.F1 "Figure 1 ‣ 4 Counterfactual Fairness")(b) shows a more fine-grained model, where the path is
mediated by a measure of whether the person is employed, which is
itself caused by two background factors: one representing whether the
person hiring is prejudiced, and the other the employee’s
qualifications. In this world,
A is a cause of defaulting, even if mediated by other
variables. The counterfactual fairness principle however forbids us
from using Y: using the twin network of
Pearl ([2000](#bib.bib20)), we see in Figure [1](#S4.F1 "Figure 1 ‣ 4 Counterfactual Fairness")(c) that Ya and
Ya′ need not be identically distributed given the
background variables.
For example, if the function determining employment
fE(A,P,Q)=I(Q>0,P=0 or A≠a) then an individual
with sufficient qualifications and prejudiced potential employer
may have a different counterfactual
employment value for A=a compared to A=a′, and a different chance of default.
In contrast, any function of variables not descendants of
A can be used a basis for fair decision making. This means,
that any variable ^Y defined by ^Y=g(U) will be counterfactually
fair for any function g(⋅). Hence, given a causal
model, the functional defined by the function g(⋅)
minimizing some predictive error for Y will satisfy the criterion.
If ^Y must be randomized, it suffices that the
stochastic component of it is independent of any descendant of A.
There is a subtlety to address here: by abduction,
U will typically depend on A, and hence so will ^Y when
marginalizing over U.
This seems to disagree with the intuition that our fair
variable should be not be caused by A. However, this is a comparison
across individuals, not within an individual, as discussed by
Section 4.3.1 of (Pearl et al., [2016](#bib.bib21)). More intuitively, consider the
simple case where U is fully determined by A and X (which occurs
in some important special cases).
In this
scenario, we proceed just as if we have measured U from the
beginning rather than performing abduction.
We then generate ^Y from g(U), so U is the cause of
^Y and not A.
Note that we can build counterfactually fair
predictive models for some ^Y even if the
structural equations that generated Y are unfair. The idea is that we
are learning a projection of Y into an alternate world where it
would be fair, which we may think of as a
‘‘closest world’’ defined by our class of models and the
causal structure of the world444The notion of “closest world”
is pervasive in the literature of counterfactual inference under
different meanings (Pearl, [2000](#bib.bib20); Halpern, [2016](#bib.bib9)). Here, the cost
function used to map fair variables to unfair outcomes also plays a
role, but this concerns a problem dependent utility function that
would be present anyway in the unfair prediction problem, and is
orthogonal to the causal assumptions..
Figure 2: Three causal models for different real-world fair prediction scenarios. See section [4](#S4 "4 Counterfactual Fairness") for discussion.
###
4.2 Examples
To give an intuition for counterfactual fairness we will consider three fair prediction scenarios: insurance pricing; crime prediction; college admissions. Each of these correspond to one of the three causal graphs in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness").
#### Scenario 1: The Red Car.
Imagine a car insurance company wishes to price insurance for car owners by
predicting their accident rate Y. They assume there is an
unobserved factor corresponding to aggressive driving U, that (a) causes
drivers to be more likely have an accident, and (b) causes individuals to prefer red cars (the observed
variable X). Moreover, individuals belonging to a
certain race A are more likely to drive red cars. However, these individuals are no more likely to be aggressive or to get in accidents than any one else. We show this in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Left*).
Thus, using the red car feature X to predict accident likelihood Y
would seem to be an unfair prediction because it may charge
individuals of a certain race more than others, even though no
race is more likely to have an accident. Counterfactual fairness
agrees with this notion.
######
Lemma 1.
Consider the structure in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness")
(left). There exist model classes and loss functions where fitting a
predictor to X *only* is not counterfactually fair, while the same
algorithm will give a fair predictor using *both* A and X.
###### Proof.
As in the definition, we will consider the population case, where the
joint distribution is known. Consider the case where the equations
described by the model in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness")
(*Left*) are deterministic and linear:
| | | |
| --- | --- | --- |
| | X=αA+βU,Y=γU | |
and the variance of U is vU, the variance of A is vA,
and we assume all coefficients are non-zero.
The predictor ^Y(X) defined by least-squares
regression of Y on *only* X is given by ^Y(X)≡λX, where λ=Cov(X,Y)/Var(X)=βγvU/(α2vA+β2vU)≠0.
We can test whether a predictor ^Y is counterfactually fair
using the procedure described in Section [3](#S3 "3 Causal Models and Counterfactuals"):
(i) Compute U given observations of X,Y,A; (ii) Substitute the equations involving A with an interventional value a′;
(iii) Compute the variables X,Y with the interventional value a′. It is clear here that ^Ya(U)=λ(αa+βU)≠^Ya′(U). This predictor is not counterfactually fair. Thus, in this case fairness through unawareness actually perpetuates unfairness.
Consider instead doing least-squares regression of Y on X *and* A. Note that ^Y(X,A)≡λXX+λAA where λX,λA can be derived as follows:
| | | | |
| --- | --- | --- | --- |
| | (λXλA) | =(Var(X)Cov(A,X)Cov(X,A)Var(A))−1(Cov(X,Y)Cov(A,Y)) | |
| | | =1β2vUvA(vA−αvA−αvAα2vA+β2vU)(βγvU0) | |
| | | =⎛⎜⎝γβ−αγβ⎞⎟⎠ | | (2) |
Now imagine we have observed A=a. This implies that X=αa+βU and our predictor is ^Y(X,a)=γβ(αa+βU)+−αγβa=γU. Thus, if we substitute a with a counterfactual a′ (the action step described in Section [3](#S3 "3 Causal Models and Counterfactuals")) the predictor ^Y(X,A) is unchanged! This is because our predictor is constructed in such a way that any change in X caused by a change in A is cancelled out by the λA. Thus this predictor is counterfactually fair.
∎
Note that if Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Left*) is the true model for the real world then ^Y(X,A) will also satisfy demographic parity and equality of opportunity as Y will be unaffected by A.
The above lemma holds in a more general case for the structure given
in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Left*): any non-constant estimator that
depends only on X is not counterfactually fair as changing A always alters X.
We also point out that the method used in the proof is a special case
of a general method to building a predictor based on information deduced
about U that will be described in the next section. We note that, outside of this particular causal model in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Left*), the predictor ^Y(X,A) is not counterfactually fair, as described in the following scenarios.
#### Scenario 2: High Crime Regions.
A local police precinct wants to know how likely a given house is to be broken into, Y. This likelihood depends on many unobserved factors
(U) but also upon the neighborhood the house lies in (X). However, different ethnic groups are more likely to live in particular neighborhoods, and so neighborhood and break-in rates are often correlated with the
race A of the house occupier. This can be seen in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Center*). Unlike the previous case, a predictor ^Y trained using X and A is not counterfactually fair. The only change from Scenario 1 is that now Y depends on X as follows: Y=γU+θX. Now if we solve for λX,λA it can be shown that ^Y(X,a)=(γ−α2θvAβvU)U+αθa. As this predictor depends on the values of A, ^Y(X,a)≠^Y(X,a′) and thus ^Y(X,A) is not counterfactually fair.
#### Scenario 3: University Success.
A university wants to know if students will be successful post-graduation Y. They have information such as: grade point average (GPA), advanced placement (AP) exams results, and other academic features X. The university believes however, that an individual’s gender A may influence these features and their post-graduation success Y due to social discrimination. They also believe that independently, an individual’s latent talent U casues X and Y. We show this in Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Implications ‣ 4 Counterfactual Fairness") (*Right*). We can again ask, is the predictor ^Y(X,A) counterfactually fair? In this case, the different between this and Scenario 1 is that Y is a function of U and A as follows: Y=γU+ηA. We can again solve for λX,λA and show that ^Y(X,a)=(γ−αηvAβvU)U+ηa. Again ^Y(X,A) is a function of A so it cannot be counterfactually fair.
5 Methods and Assessment
-------------------------
Given that the unaware and full information models are not counterfactually fair, how can we design predictors that are? In general given a causal model, a counterfactually fair classifier ^Y is one that is a function of *any U and any variables X which are not descendants of A*. As defined, these variables are independent of A and thus any change in A cannot change ^Y. In this section we describe techniques for constructing latent variables U and a predictor ^Y.
Before delving into details, we point out two important observations. First, if a strict subset of U is used, the
causal model need not be fully specified: equation
Vi=fi(pai,Upai) can be substituted by a conditional
probability p(Vi | pai,U′pai), where
U′pai⊂Upai and
p(Vi | pai,U′pai)=∫fi(pai,Upai)dU′′pai,
where U′′pai≡Upai∖U′pai. This
marginalization has implications in modeling discussed in the next
section.
Second, any random variable generated independently is trivially
counterfactually fair. However, we desire that ^Y is a good predictor, not simply a coin toss. That is,
^Y is typically a parameterized function
gθ(U,X) where θ is learned by minimizing the empiric
expected loss E[l(Y,gθ(U,X)) |X,A]. For instance, l(Y,gθ(U,X))=(Y−gθ(U,X))2, or the
log-loss for Bernoulli classification. In practice, the distribution
of A∪X∪{Y} can be the empirical distribution as given by
some training data, while p(U | X,A) comes from the estimated
causal model fit to the same training data. Any predictor can be used to learn gθ(U,X)
including random forests and neural networks.
###
5.1 Limitations and a Guide to Model Building
Causal modeling requires untestable assumptions. Experimental data can
sometimes be used to infer causal connections, but counterfactual
modeling requires functional
decompositions between background and endogenous variables.
Such decompositions are not
uniquely identifiable with experimental data. As in several
matters of law and regulation, fairness at an individual level is a
counterfactual quantity and some level of assumptions
are unavoidable. As a guide for building fair predictive models, we
categorize assumptions by three levels of increasing strength.
* Given a causal DAG, build ^Y
using as covariates only the observable variables not
descendants of the protected attributes A. This
requires information about the DAG, but no
assumptions about structural equations or priors over background
variables.
* Level 1 ignores much information, particularly if the protected
attributes are typical attributes such as race or sex,
which are parents of many other variables. To include information
from descendants of A, we postulate background latent variables
that act as causes of observable variables, based on explicit domain
knowledge and learning algorithms555In some domains, it is
actually common to build a model entirely around latent constructs
with few or no observable parents nor connections among observed
variables (Bollen, [1989](#bib.bib1)).. Information from X will propagate to
the latent variables by conditioning.
* In Level 2, the model factorizes as a
general DAG, and each node follows a non-degenerate
distribution given observed and latent variables. In this level, we remove all randomness from the conditional
distributions obtaining a full decomposition (U,V,F) of the
model. For instance, the distribution
p(Vi | V1,…,Vi−1) can be treated as an additive
error model, Vi=fi(V1,…,Vi−1)+ei
(Peters et al., [2014](#bib.bib23)). The error term ei then becomes an input
to ^Y after conditioning on the observed variables. This
maximizes the information extracted
by the fair predictor ^Y.
###
5.2 Special cases
Consider the graph A→X→Y. In general, if ^Y is a function of
X only, then ^Y need not obey demographic parity, i.e.
| | | |
| --- | --- | --- |
| | P(^Y | A=a)≠P(^Y | A=a′). | |
If we postulate a
structural equation X=αA+eX, then given A and X we
can deduce eX. If ^Y is a function of eX only and, by
assumption, eX is independent of A, then the assumptions imply
that ^Y will satisfy demographic parity, and that can be
falsified.
By way of contrast, if eX is not uniquely identifiable from the structural equation and (A,X), then the distribution of ^Y depends on the value of A as we marginalize eX, and demographic parity will not follow. This leads to the following:
######
Lemma 2.
If all background variables U′⊆U in the definition of ^Y are determined from A and evidence W,
and all observable variables in the definition of ^Y are independent of A given U′, then ^Y satisfies demographic parity.
Thus, counterfactual fairness can be thought of as a counterfactual analog of demographic parity. We advocate that counterfactual assumptions should underlie all
approaches that separate the sources of variation of the data into
“fair” and “unfair” components. As an example,
Louizos et al. ([2015](#bib.bib17)) explains the variability in X from
A and an independent source U following the DAG
A→X←U. As U and A are not independent
given X in this representation, a type of “posterior
regularization” (Ganchev et al., [2010](#bib.bib7)) is enforced such that a posterior
pfair(U |A,X) is close to the model posterior p(U | A,X)
while satisfying
pfair(U |A=a,X)≈pfair(U |A=a′,X). But this
is neither necessary nor sufficient for counterfactual fairness if the
model for X given A and U is not justified by a causal
mechanism. If it is, p(U | A,X) is justified as distribution
which we can use to marginalize U in p(^Y(U) | A,X), without requiring regularization.
Methods which estimate the
relationship between A, U and X based on penalizing dependence
measures between an estimated U and A are relevant in estimating a
causal model (e.g. Mooij et al. ([2009](#bib.bib19))), but these
are motivated by U being is deterministically inferred
from A and X by construction. It is unclear in
Louizos et al. ([2015](#bib.bib17)) how the ideal label Y is causally
connected to U and A, and the semantics of the “unfair”
components of Y are not detailed.
6 Experiments
--------------
We test our approach on two practical problems that require fairness, the first is *prediction of success in law school* and the second is *separating actual and perceived criminality in police stops*. For each problem we construct causal models, and make explicit how unfairness may affect observed and unobserved variables in the world. Given these models we derive counterfactually fair predictors, and predict latent variables such as a person’s ‘criminality’ (which may be useful for predicting crime) as well as their ‘perceived criminality’ (which may be due to prejudices based on race and sex). We analyze empirically how counterfactually fair the unaware and full predictors are, assuming knowledge of the correct causal model, and compare the prediction accuracies of all models. Finally we judge how well our counterfactually fair ‘criminality’ score satisfies demographic parity.
###
6.1 Law school success
The Law School Admission Council
conducted a survey across 163 law
schools in the United States Wightman ([1998](#bib.bib25)). It contains information on 21,790 law students such as their entrance exam scores (LSAT), their grade-point
average (GPA) collected prior to law school, and their first year average grade
(FYA).
Given this data, a school may wish to predict if an applicant will
have a high FYA. The school would also like to make sure these
predictions are not biased by an individual’s race and sex. However,
the LSAT, GPA, and FYA scores, may be biased due to social factors. We compare our framework with
two unfair baselines: 1. Full: the standard technique of
using all features, including sensitive features such as race and sex
to make predictions; 2. Unaware: fairness through
unawareness, where we do not use race and sex as features. For comparison, we generate predictors ^Y for all models using logistic regression.
#### Fair prediction.
As described in Section [5.1](#S5.SS1 "5.1 Limitations and a Guide to Model Building ‣ 5 Methods and Assessment"), there are three ways in which we can model a counterfactually fair predictor of FYA. Level 1 uses any features which are not descendants of race and sex for prediction. Level 2 models latent ‘fair’ variables which are parents of observed variables. These variables are independent of both race and sex. Level 3 models the data using an additive error model, and uses the independent error terms to make predictions. These models make increasingly strong assumptions corresponding to increased predictive power. We split the dataset 80/20 into a train/test set, preserving label balance, to evaluate the models.
As we believe LSAT, GPA, and FYA are all biased by race and sex, we
cannot use any observed features to construct a counterfactually fair
predictor as described in Level 1.
In Level 2, we postulate that a latent variable: a student’s knowledge (K), affects GPA, LSAT, and FYA scores. The causal graph corresponding to this model is shown in Figure [3](#S6.F3 "Figure 3 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"), (Level 2). This is a short-hand for the distributions:
| | | | |
| --- | --- | --- | --- |
| | GPA | ∼N(bG+wKGK+wRGR+wSGS,σG) | |
| | LSAT | ∼Poisson(exp(bL+wKLK+wRLR+wSLS)) | |
| | FYA | ∼N(wKFK+wRFR+wSFS,1) | |
| | K | ∼N(0,1) | |
We perform inference on this model using an observed training set to estimate the posterior distribution of K. We use the probabilistic programming language Stan Stan Development Team ([2016](#bib.bib24)) to learn K. We call the predictor constructed using K, Fair K.
Figure 3: A causal model for the problem of predicting law school success fairly.
| | | | | |
| --- | --- | --- | --- | --- |
| | Full | Unaware | Fair K | Fair Add |
| RMSE | 0.873 | 0.894 | 0.929 | 0.918 |
Table 1: Prediction results using logistic regression. Note that we must sacrifice a small amount of accuracy to ensuring counterfactually fair prediction (Fair K, Fair Add), versus the models that use unfair features: GPA, LSAT, race, sex (Full, Unaware).
Figure 4: Density plots of predicted FYAa and FYAa′.
Figure 5: Understanding criminality. The above maps show the decomposition of stop and search data in New York into factors based on perceived criminality (a race dependent variable) and latent criminality (a race neutral measure). See section [6.2](#S6.SS2 "6.2 True vs. Perceived Criminality ‣ 6 Experiments").
In Level 3, we model GPA, LSAT, and FYA as continuous variables with additive error terms independent of race and sex (that may in turn be correlated with one-another). This model is shown in Figure [3](#S6.F3 "Figure 3 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"), (Level 3), and is expressed by:
| | | | |
| --- | --- | --- | --- |
| | GPA | =bG+wRGR+wSGS+ϵG,ϵG∼p(ϵG) | |
| | LSAT | =bL+wRLR+wSLS+ϵL,ϵL∼p(ϵL) | |
| | FYA | =bF+wRFR+wSFS+ϵF,ϵF∼p(ϵF) | |
We estimate the error terms ϵG,ϵL by first fitting two models that each use race and sex to individually predict GPA and LSAT. We then compute the residuals of each model (e.g., ϵG=GPA−^YGPA(R,S)). We use these residual estimates of ϵG,ϵL to predict FYA. We call this *Fair Add*.
#### Accuracy.
We compare the RMSE achieved by logistic regression for each of the models on the test set in Table [1](#S6.T1 "Table 1 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"). The Full model achieves the lowest RMSE as it uses race and sex to more accurately reconstruct FYA. Note that in this case, this model is not fair even if the data was generated by one of the models shown in Figure [3](#S6.F3 "Figure 3 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments") as it corresponds to Scenario 3. The (also unfair) Unaware model still uses the unfair variables GPA and LSAT, but because it does not use race and sex it cannot match the RMSE of the Full model. As our models satisfy counterfactual fairness, they trade off some accuracy. Our first model Fair K uses weaker assumptions and thus the RMSE is highest. Using the Level 3 assumptions, as in Fair Add we produce a counterfactually fair model that trades lower RMSE for slightly weaker assumptions.
Figure 6: A causal model for the stop and frisk dataset.
#### Counterfactual fairness.
We would like to empirically test whether the baseline methods are counterfactually fair. To do so we will assume the true model of the world is given by Figure [3](#S6.F3 "Figure 3 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"), (Level 2). We can fit the parameters of this model using the observed data and evaluate counterfactual fairness by sampling from it. Specifically, we will generate samples from the model given either the observed race and sex, or *counterfactual* race and sex variables. We will fit models to both the original and counterfactual sampled data and plot how the distribution of predicted FYA changes for both baseline models. Figure [4](#S6.F4 "Figure 4 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments") shows this, where each row corresponds to a baseline predictor and each column corresponds to the couterfactual change. In each plot, the blue distribution is density of predicted FYA for the original data and the red distribution is this density for the counterfactual data. If a model is counterfactually fair we would expect these distributions to lie exactly on top of each other. Instead, we note that the Full model exhibits counterfactual unfairness for all counterfactuals except sex. We see a similar trend for the Unaware model, although it is closer to being counterfactually fair. To see why these models seem to be fair w.r.t. to sex we can look at weights of the DAG which generates the counterfactual data. Specifically the DAG weights from (male,female) to GPA are (0.93,1.06) and from (male,female) to LSAT are (1.1,1.1). Thus, these models are fair w.r.t. to sex simply
because of a very weak causal link between sex and GPA/LSAT.
###
6.2 True vs. Perceived Criminality
Since 2002, the New York Police Department (NYPD) has recorded
information about every time a police officer has stopped someone. The
officer records information such as if the person was searched or
frisked, their appearance, etc. We
consider the data collected on males stopped during 2014 which constitutes 38,609 records.
#### Model.
We model this stop-and-frisk data using the graph in Figure [6](#S6.F6 "Figure 6 ‣ Accuracy. ‣ 6.1 Law school success ‣ 6 Experiments"). Specifically, we posit main causes for the observations: *Arrest* (if an individual was arrested), *Summons* (an individual was called to a court-summons), *Weapon* (an individual was found to be carrying a weapon), *Force* (some sort of force was used during the stop), *Frisked*, and *Searched*. The first cause of these observations is some measure of an individual’s latent *Criminality*, which we do not observe. We believe there is an additional cause, an individual’s perceived criminality, *Perception*, also unobserved. This second factor is introduced as we believe that these observations may be biased based on an officer’s perception of whether an individual is likely a criminal or not. This perception is affected by an individual’s *Appearance* and their *Race*. In this sense *Criminality* is counterfactually fair, while *Perception* models how race affects each of the other observed variables.
#### Criminality and perception distributions.
After fitting this model to the data we can look at the distribution of *Criminality* and *Perception* across different races, shown as box plots in Figure [6](#S6.F6 "Figure 6 ‣ Accuracy. ‣ 6.1 Law school success ‣ 6 Experiments"). We see that the median criminality for each race is nearly identical, while the distributions are somewhat different, demonstrating that *Criminality* approaches demographic parity. The differences that due exist may be due to unobserved confounding variables that are affected by race or unmodeled noise in the data. On the right *Perception* varies considerably by race with white individuals having the lowest perceived criminality while black and black Hispanic individuals have the highest.
Figure 7: Distributions of estimated latent perception and criminality scores for the stop and frisk dataset.
#### Visualization on a map of New York City.
Each of the stops can be mapped to longitude and latitude points for where the stop occurred666<https://github.com/stablemarkets/StopAndFrisk>. Thus we can visualize *Criminality* and *Perception* alongside *Race* and the combination of *Arrest* and *Summons*, shown in Figure [5](#S6.F5 "Figure 5 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"). Criminality seems to be a continuous approximation of arrest and summons as both plots show red in similar areas. However, the plots show that certain areas, while having a lot of arrests have low criminality scores such as south Bronx and west Queens (circled in orange). We can also compare the perceived criminality with a plot of race, where we have divided the races into Group A: black, black Hispanic, Hispanic, and Native American (shown in purple); and Group B: white and Asian/Pacific Islander (shown in green). Group A are all races that have positive weights on the connection from *Race* to *Perception* in the fitted model, while Group B all have negative weights. Thus being in Group A leads one to have a higher perceived criminality than being in Group B. This can be seen in the right-most plot of Figure [5](#S6.F5 "Figure 5 ‣ Fair prediction. ‣ 6.1 Law school success ‣ 6 Experiments"). Certain areas of town such as central Brooklyn, central Bronx, and southern Queens have very high criminality and almost all stops are by members of Group A (circled in yellow).
7 Conclusion
-------------
We have presented a new model of fairness we refer to as counterfactual fairness. It allows us to propose fair
algorithms that, rather than simply ignoring protected attributes, are
able to take into account the different social biases
that may arise towards individuals of a particular race, gender, or
sexuality and compensate for these biases effectively. We
experimentally contrasted our approach with previous unfair
approaches and show that our explicit causal models capture these
social biases and make clear the implicit trade-off between
prediction accuracy and fairness in an unfair world. We propose that fairness should be regulated by explicitly modeling the causal structure of the world. Criteria based purely on probabilistic independence cannot satisfy this and are unable to address *how* unfairness is occurring in the task at hand. By providing such causal tools for addressing fairness questions we hope we can provide practitioners with customized techniques for solving a wide array of fair modeling problems. |
9eb6a056-d142-471a-8897-803a75916110 | trentmkelly/LessWrong-43k | LessWrong | [link] Interview with Anders Sandberg on how to make a difference through research and how to choose a research topic
Here. Some excerpts:
> What do you think are some good heuristics for doing high impact research?
>
> One idea is to go for under-researched fields. Progress in a field is typically a very convex learning curve: rapid progress at first when the low-hanging fruits get picked by the pioneers, followed by slowing progress as the problems get harder and it takes longer to learn the necessary skills to get to them. So the same amount of effort might produce far more progress in a little studied field than in a big one. [...]
>
> It can also help to turn the question around: what aspects of human life matter? Looking at human life, we sleep about a third of the time, and there’s very little research into how to enhance sleep. Understanding the health effects of what we eat is probably worth billions of pounds per year. But there are no financial incentives here. Maybe a simple approach for finding high impact research areas might be to look at the most common google searches: you can get a pretty good idea of what human behaviour matters a lot!
> Do you think it’s better to be a generalist and get a broad understanding of a lot of things, or to specialise early and really focus on a single area you think is high impact?
>
> Over the history of my academic career my most useful courses have been linear algebra, all the statistics and probability theory I’ve been able to pick up, some basic computer science, and a course on natural disasters. [...]
>
> Even if you do focus on one field, knowing enough about other fields is good as you can recognise when you need the help of someone from another department.
> What other barriers are there to doing important research?
>
> Looking at some of these under-researched fields, the problem is that a lot of them don’t even exist as fields. Typically you’re unlikely to get funding in unknown fields as well: unless you’re a really good salesman! So one heuristic would be to look at the topics you know, do a matrix and look at t |
84bf2a5f-11a0-47f6-aaa6-8cb2e8f4b1d5 | trentmkelly/LessWrong-43k | LessWrong | A 2011 summary of modern intelligence tests
...and the theories of intelligence they use to measure 'intelligence'. Here, from the new (and very good) Cambridge Handbook of Intelligence.
Bonus fun fact from chapter 3: "Persons with higher IQs apparently are also likely to be taller and have more body symmetry than persons with lower ability scores." [Silventoinen et al. (2006); Prokosch & Miller (2006)] |
978172d2-eaa3-46ae-a816-a303d82d3849 | trentmkelly/LessWrong-43k | LessWrong | Meetup Notes: Community Building
Review of our fifth LessWrong Meetup - Report from Berlin
Summary
We had visitors fank1 and just_existing from the Bielefeld/Paderborn Meetup. The meetup was great. It was a continuously lively discussion with everybody contributing personal and/or insightful and/or relevant pieces.
After ashort introduction of each other (because of the guests) we plunged immediately into interesting discussions mostly revolving around LeeWrong topics.
In between I retold my very positive experience from the Berlin LW community event. After a short summary about the effects of meditation we had a Mnemonics session inspired by the Berlin workshop.
One on-going topic was "Extrovert in Training" - techniques for and experience with getting in touch with people. How to start a conversation. What I still don't get is how to steer a conversation from small-talk phase to more personal topics - esp. in a group setting. Though this was not a problem during the meetup.
We also discussed selection pressure on humans. We agreed that there is almost none on mutations affecting health in general due to medicine. But we agreed that there is tremendous pressure on contraception. We identified four ways evolution works around contraception (see appendix for a short summary). We discussed what effects this could on the future of society. The movie Idiocracy was mentioned. This could be a long term (a few generations) existential risk.
There were other topcis which I recollect less clearly. Maybe the participants can comment on them below.
There will definitely be more LW Hamburg meetups. The next step is a joint Skype meetup with the Bielefeld group. I also relayed the Jonas Vollmers advice to get in contact with the Giordano-Bruno-Stiftung.
The meetup ended with a photo and positive impression feedback round (peak-end rule). Afterwards out guests from Bielefeld stayed overnight in my (Gunnars) place.
Appendix
Four ways evolution works around contraception:
1. Biological factors. Ex |
cbbdadab-5858-4824-af7e-6b01c82a48e4 | trentmkelly/LessWrong-43k | LessWrong | You don't get to know what you're fighting for
A number of my recent posts may have given you the impression that I know exactly what I'm fighting for. If someone were to ask you, "hey, what's that Nate guy trying so hard to do," you might answer something like "increase the chance of human survival," or "put an end to unwanted death" or "reduce suffering" or something.
This isn't the case. I mean, I am doing those things, but those are all negative motivations: I am against Alzheimer's, I am against human extinction, but what am I for?
The truth is, I don't quite know. I'm for something, that's for damn sure, and I have lots of feelings about the things that I'm fighting for, but I find them rather hard to express.
And in fact, I highly doubt that anyone knows quite what they're fighting towards — though it seems that many people think they do, and that is in part why I'm writing this post.
When I wrote on rationality, one commenter replied:
> I would just note upfront that
>
> > Reasoning well has little to do with what you're reasoning towards.
>
> and
>
> > Rationality of this kind is not about changing where you're going, it's about changing how far you can go.
>
> are white lies, as you well know. It's not unusual in the process of reasoning of how to best achieve your goal to find that the goal itself shifts or evaporates.
>
> "How to best serve God" may result in deconversion.
>
> "How to make my relationship with partner a happy one" may result in discovering that they are a narcissistic little shit I should run away from. Or that both of us should find other partners.
>
> "How to help my neighborhood out of poverty" might become "How to make the most money" in order to donate as much as possible.
This is a fine point. Humans are well-known for their ability to start out pursuing one goal, only to find that goal shift drastically beneath them as their knowledge of the world increases. In fact, this is a major plot point in many stories (such as, say, The Foundation Trilogy, The Dresden File |
fd8ddebb-fdba-4ae4-b7ba-419c847342d2 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Sydney Pub meetup - February
Discussion article for the meetup : Sydney Pub meetup - February
WHEN: 23 February 2017 06:30:00PM (+1100)
WHERE: 565 george st, sydney
https://www.meetup.com/lesswrong-Sydney0/events/237546781/
https://www.facebook.com/events/1816134538644351/
Find us at the big table outside the pizza kitchen on level 2.
Come along to our regular monthly pub meetup to talk all things math, science, technology, engineering, thinking, growth, reasoning and beliefs.
If you are an aspiring rationalist, a nerd, geek, scientist or just a really great thinker - we can't wait to meet you to share ideas and learn and grow together.
See you there!
0438481143 if you need help finding us
Discussion article for the meetup : Sydney Pub meetup - February |
7ab663da-5689-4bd7-9d2e-df253e08ab42 | trentmkelly/LessWrong-43k | LessWrong | Apple Cider Syrup
We bought some apple cider when we went apple picking a few weeks ago, and while it's tasty stuff it seems it's not what people wanted to be spending their sweet budget on: we still had 2/3 of it left three weeks later. I decided to reduce it and make syrup, partly so that it would keep longer but mostly because apple cider syrup seemed like a fun ingredient to have available.
I put the cider in the slow cooker, the same way Julia reduces sap to make maple syrup. Possibly I should have filtered it first, but I didn't. I left it all day with the lid cracked so liquid could escape. Occasionally one of us would check on it, including tipping the lid over the sink to ditch the condensate, and ~8hr later it was down to ~1/8 the original volume.
It's very flavorful, and I can think of a lot of ways I might incorporate it into desserts.
Comment via: facebook |
0efaaf64-947d-40f1-b2c8-777645be07c3 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Recursive Quantilizers II
I originally introduced the recursive quantilizers idea [here](https://www.lesswrong.com/s/SBfqYgHf2zvxyKDtB/p/bEa4FuLS4r7hExoty), but didn't provide a formal model until my recent [Learning Normativity](https://www.lesswrong.com/posts/2JGu9yxiJkoGdQR4s/learning-normativity-a-research-agenda) post. That formal model had some problems. I'll correct some of those problems here. My new model is closer to HCH+IDA, and so, is even closer to Paul Christiano style systems than my previous.
However, I'm also beginning to suspect that quantilizers aren't the right starting point. I'll state several problems with quantilizers at the end of this post.
First, let's reiterate the design criteria, and why the model in Learning Normativity wasn't great.
Criteria
========
Here are the [criteria from Learning Normativity](https://www.lesswrong.com/posts/2JGu9yxiJkoGdQR4s/learning-normativity-a-research-agenda#Summary_of_Desiderata), with slight revisions. See the earlier post for further justifications/intuitions behind these criteria.
1. **No Perfect Feedback:** we want to be able to learn with the possibility that any one piece of data is corrupt.
1. **Uncertain Feedback:** data can be given in an uncertain form, allowing 100% certain feedback to be given (if there ever is such a thing), but also allowing the system to learn significant things in the absence of any certainty.
2. **Reinterpretable Feedback:** ideally, we want *rich hypotheses about the meaning of feedback,* which help the system to identify corrupt feedback, and interpret the information in imperfect feedback. To this criterion, I add two clarifying criteria:
1. **Robust Listening:** in some sense, we don't want the system to be able to "entirely ignore" humans. If the system goes off-course, we want to be able to correct that.
2. **Arbitrary Reinterpretation:** at the same time, we want the AI to be able to *entirely reinterpret* feedback based on a rich model of what humans mean. This criterion stands in tension with Robust Listening. However, the proposal in the present post is, I think, a plausible way to achieve both.
2. **No Perfect Loss Function:** we don't expect to perfectly define the utility function, or what it means to correctly learn the utility function, or what it means to learn to learn, and so on. At no level do we expect to be able to provide a single function we're happy to optimize. This is largely due to a combination of Goodhart and corrupt-feedback concerns.
1. **Learning at All Levels:** Although we don't have perfect information at any level, we do get meaningful benefit with each level we step back and say "we're learning this level rather than keeping it fixed", because we can provide meaningful approximate loss functions at each level, and meaningful feedback for learning at each level. Therefore, we want to be able to do learning at each level.
2. **Between-Level Sharing:** Because this implies an infinite hierarchy of levels to learn, we need to share a great deal of information between levels in order to learn meaningfully. For example, Occam's razor is an important heuristic at each level, and information about what malign inner optimizers look like is the same at each level.
3. **Process Level Feedback:** we want to be able to give feedback about *how to arrive at answers*, not just the answers themselves.
1. **Whole-Process Feedback:** we don't want some segregated meta-level which accepts/implements our process feedback about the rest of the system, but which is immune to process feedback itself. Any part of the system which is capable of adapting its behavior, we want to be able to give process-level feedback about.
2. **Learned Generalization of Process Feedback:** we don't just want to promote or demote specific hypotheses. We want the system to learn from our feedback, making generalizations about which *kinds* of hypotheses are good or bad.
Failed Criteria
---------------
The previous recursive-quantilization model [failed some criteria](https://www.lesswrong.com/posts/2JGu9yxiJkoGdQR4s/learning-normativity-a-research-agenda#Analysis_in_terms_of_the_Criteria):
* No reinterpretable feedback. I didn't provide *any* method for achieving that.
* No whole-process feedback. The way I set things up, the initial distributions are judged only on their later consequences. This leaves them wide open to inner optimizers and other problematic reasoning steps.
+ We can fix this by allowing the user to give direct feedback on the initial distributions as well, but then there's no mechanism for Learned Generalization of that particular feedback. So we're caught in the middle, unable to satisfy both those criteria at once.
The current proposal solves both problems, and due to an analogy to [iterated amplification](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd), may also be more computationally feasible.
The New Proposal
================
Like [iterated amplification](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd), the new proposal consists of both an *idealized definition of aligned behavior* (HCH, in the context of iterated amplification) and *a candidate approximation of this ideal* (like iterated amplification itself, which is supposed to approximate HCH).
The Ideal
---------
The object which quantilization will select on will be referred to as "question-answering systems", or QAS for short. This is what I called a "UTAA" in the previous post. As before, this is one object which has opinions about the safe distribution for quantilization (you can ask it "what's a safe distribution over QAS to quantilize on?"), and as value function ("give me a value function to judge the quality of QAS") and as the object-level solution to whatever problem you're trying to get this whole setup to solve (you ask it your object-level questions).
So the goal is to get a really good QAS, where "really good" means highly capable and highly aligned. Since we're specifying the ideal here, we get to use unbounded processing power in doing so.
Humans provide an initial "safe" distribution on QASs, and an initial loss function which will be used to judge QASs for their quality. We then quantilize. This yields a new distribution over QASs, which we use to define another stage of quantilization (this time with safe distribution and value function coming from the QASs themselves), and so on, until we reach an equilibrium where an additional step does not add anything.
As before, we can imagine this as an infinite or very large tree of quantilization:
(Note that, previously, I treated Q as a three-argument stochastic function, requiring a safe distribution, a value function (AKA loss function), and a parameter telling it how much to optimize. Here, I am suppressing the third parameter. Perhaps we keep it fixed, or perhaps we ask a third QAS for it, as before -- I have no improved suggestions for how we estimate that parameter at the moment.)
The tree structure means that a QAS is never evaluating itself; it is being evaluated by another QAS sampled from an independent multi-level quantilization, which can spot mistakes in its own way.
Now, loss functions *in general* (including both the initial human-given loss function, and the improved loss functions proposed by the QASs) need access to *the entire experience of the system*. The initial human-gives loss function may be something like "pick out explicit training feedback humans give, and translate it into a numerical score for a QAS, ignoring natural-language comments made by the humans, evidence from the context, etc." The loss functions proposed by the QAS are supposed to get cleverer, interpreting natural-language feedback given by humans, using contextual clues, etc.
You might ask, "shouldn't we build simple natural-language understanding into the initial loss function?" -- we could, but that's supposed to be redundant. There should be no reason to train a separate natural-language understanding system, rather than use explicit feedback to train the QAS to understand natural-language feedback.
**Uncertain Feedback**
The initial loss function *should* have the ability to learn from uncertain feedback, to satisfy the Uncertain Feedback criterion. (We can implement one of the options mentioned in Learning Normativity.)
**Balancing Robust Listening and Arbitrary Reinterpretation**
The idealized objective presented in this section offers a nice combination of "robust listening" (not ever reaching a state where it totally fails to respond to human feedback) and "arbitrary reinterpretation" (being able to use rich models to totally reinterpret the meaning of human feedback). These two objectives would seem to be at odds, since a system with an arbitrary model of the meaning of human feedback can ignore anything.
In the proposal here, *direct human feedback always shapes the initial dynamic*. If you see a system do something stupid and add direct feedback against this to the data, then QASs which engage in this behavior will be ruled out in the first step of quantilization. You can also add strong evidence against QAS which fail to judge QASs harshly for violating such principles (and which fail to punish non-punishers, recursively). So, human feedback always has total control to shape the ultimate result.
At the same time, however, *only the initial dynamic is required to obey this feedback in a naive way*. At later stages, QASs are supposed to understand human feedback better, identifying feedback which should be interpreted less literally.
This means the equilibrium result is both fully dependent on a 100% literal interpretation of human feedback, and at the same time, fully the result of a more careful, nuanced interpretation of what humans meant.
**Process-Level Feedback**
Also, it's important to note that the initial loss function needs to understand process-level feedback. You can imagine that the dataset includes the following:
* Extensive human-generated Q&A behavior, much like would be needed for Iterative Amplification. This is the bulk of what loads general world-knowledge into QAS that come out of the first round of quantilization.
* Extensive labeling of QAS as "ok" and "bad", to demonstrate how to examine QAS source code (/ neural network / etc) and evaluate when something looks wrong. This could include many toy examples which illustrate bad behavior, as well as real examples gathered during training. The QAS should be trained to imitate this behavior when evaluating other QAS.
* Similarly, examples of humans examining QAS execution and labeling steps as bad, rather than just examining the source code directly.
You can imagine humans engaging in an iterative process where they examine the behavior of many QAS created by early attempts at this process, which creates more training data for later iterations. However, since we're still only describing the *ideal*, that's not realistic; humans never have access to the true output of the ideal process.
Let's move on to describing the iterative-amplification analogue.
The Implementation Proposal
---------------------------
The basic idea is to take iterative amplification, and replace the HCH-like "amplification" operation with quantilization-based amplification.
At every stage of the process, we're not learning a single QAS, but rather, learning a *distribution over QAS.*
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as the result of quantilizing the safe distribution given by humans, using the value function given by humans.
The distribution D1 is the distillation of A1; ie, we learn a more computationally efficient approximation of the distribution A1 using some distribution-modeling technique such as GANs.
The distribution A2 is defined by sampling two QAS from D1, asking one for the value function and the other for the safe distribution, and quantilizing.
The distribution D2 is obtained by learning an efficient approximation of A2.
And so on. An is defined as the output of a quantilizer which takes its value function and safe distribution from (independent) samples from Dn−1. Dn is a learned approximation of An.
If the distillation step were perfect, this would converge to the desired equilibrium.
Note, however, that this assumes that all human feedback is available from the beginning. Realistically, we want more interactive training, so that humans can observe the intermediate outputs of the process and provide new training data to account for problems. The vast majority of training data involving QAS evaluation may come from such interaction.
I visualize this as "iterated amplification takes us upward, but not rightward" in the following coordinates:
If we start amplifying something with very little human-input knowledge, we're likely to get total nonsense out; the QASs just don't know enough to optimize any coherent thing over successive amplification steps. If we start with a moderate amount of knowledge, the QASs might sputter into nonsense, or might get on the right track, but also have a high risk of producing highly-capable but malign agents. At some level of sufficient human input, we fall into the "basin of corrigibility" and get aligned outputs no matter what (perhaps even the same exact benign output).
Yet, it's hard to provide everything up-front. So, more realistically, the picture might look like this:
The golden line is the standard set by the idealized model of recursive quantilizers, where we have all the training data before starting the amplification process. The green line is a more realistic line, where we make amplification moves and increase training data in a mixed way, but avoid over-amplifying an under-trained model.
It's really important to stay close to the x-axis early in the process, because the system itself is determining how to evaluate the loss function -- so it's evaluating the very meaning of the training data (in line with the Reinterpretable Feedback criterion). It is therefore very important that we don't let the system drift too far in the direction of an extreme reinterpretation of the training data (in line with the Robust Listening criterion). At the very start of the training process, I imagine you'd often restart the training process from scratch with all the new feedback in place, rather than trust the system to understand new data.
In other words, we don't expect x-dimension moves to work if we're too high in the y-dimension:
Unfortunately, it's difficult to know what the region is where x-dimension moves work, so it's difficult to know when amplification would keep us within that region vs take us out of it.
Another way to put it: this implementation puts the "robust listening" criterion at serious risk. The partially amplified agent can easily stop listening to human feedback on important matters, about which it is sure we must be mistaken.
Really, we would want to find an engineering solution around this problem, rather than haphazardly steering through the space like I've described. For example, there might be a way to train the system to seek the equilibrium it would have reached if it had started with all its current knowledge.
Comparison to Iterated Amplification
====================================
Because this proposal is so similar to iterated amplification, it bears belaboring the differences, particularly the philosophical differences underlying the choices I've made.
I don't want this to be about critiquing iterated amplification -- I have some critiques, but the approach here is not mutually exclusive with iterated amplification by any means. Instead, I just want to make clear the philosophical differences.
Both approaches emphasize *deferring the big questions,* setting up a system which does all the philosophical deliberation for us, rather than EG providing a correct decision theory.
Iterated Amplification *puts humans in a central spot.* The amplification operation is giving a human access to an (approximate) HCH -- so at every stage, a human is making the ultimate decisions about how to use the capabilities of the system to answer questions. This plausibly has alignment and corrigibility advantages, but may put a ceiling on capabilities (since we have to rely on the human ability to decompose problems well, creating good plans for solving problems).
Recursive quantilization instead seeks to *allow arbitrary improvements to the deliberation process*. It's all supervised by humans, and initially seeded by imitation of human question-answering; but humans can point out problems with the human deliberation process, and the system seeks to improve its reasoning using the human-seeded ideas about how to do so. To the extent that humans think HCH is the correct idealized reasoning process, recursive quantilization should approximate HCH. (To the extent it can't do this, recursive quantilization fails at its goals.)
One response I've gotten to recursive quantilization is "couldn't this just be advice to the human in HCH?" I don't think that's quite true.
HCH must walk a fine line between capability and safety. A big HCH tree can perform well at a vast array of tasks (if the human has a good strategy), but in order to be safe, the human must operate under set of restrictions, such as "don't simulate unrestricted search in large hypothesis spaces" -- with the full set of restrictions required for safety yet to be articulated. HCH needs a set of restrictions which provide safety without compromising capability.
In [Inaccessible Information](https://www.lesswrong.com/posts/ZyWyAJbedvEgRT2uF/inaccessible-information), Paul draws a distinction between accessible and inaccessible information. Roughly, information is *accessible* if we have a pretty good shot at getting modern ML to tell us about it, and inaccessible otherwise. Inaccessible information can include intuitive but difficult-to-test variables like "what Alice is thinking", as well as superhuman concepts that a powerful AI might invent.
A powerful modern AI like GPT-3 might *have* and *use* inaccessible information such as "what the writer of this sentence was really thinking", but we can't get GPT-3 to *tell* us about it, because we lack a way to train it to.
One of the [safety concerns](https://www.lesswrong.com/posts/ZyWyAJbedvEgRT2uF/inaccessible-information#IV__When_inaccessible_info_is_a_safety_problem) of inaccessible information Paul lists is that powerful AIs might be more capable than aligned AIs due to their ability to utilize inaccessible information, where aligned AIs cannot. For example, GPT-5 might use inhuman concepts, derived from its vast experience predicting text, to achieve high performance. A safe HCH would never be able to use those concepts, since every computation within the HCH tree is supposed to be human-comprehensible. (Therefore, if the result of Iterated Amplification was able to use such concepts, we should be worried that it did not successfully approximate HCH.)
Paul proposes [learning the human prior](https://www.lesswrong.com/posts/SL9mKhgdmDKXmxwE4/learning-the-prior) as a potential solution. As I understand it, the basic idea is that HCH lacks Deep Learning's ability to absorb vast quantities of data and reach new conclusions. By learning the human prior, Paul seeks to learn the *human* response to those vast quantities of data. This would allow an HCH-like approach to learn the same "alien concepts" that a misaligned AI might learn.
I don't really understand how Paul sees HCH and learned-priors as fitting together, Recursive Quantilization seeks to get around this difficulty by training the QASs on lots of data in a way similar to big-data ML. As I emphasized before, recursive quantilization seeks to allow arbitrary improvements to the reasoning process, so long as they *are* improvements.
Of course, recursive quantilization also needs to restrict the QAS reasoning processes via safety constraints (using process-level feedback to rule out types of reasoning which can't be verified as safe). This *could* lead to the same problems with inaccessible information. But if it does, that's because ***no** human-approved reasoning can approve of reasoning which utilizes the inaccessible information --* not even after many rounds of amplifying the human-seeded knowledge about how to judge reasoning as safe/unsafe. So, I would argue that if an approach broadly like recursive quantilization can't do it, then *no robustly aligned method can do it*.
Reasons why quantilizers might not be appropriate:
==================================================
As I mentioned at the beginning, I've been feeling like quantilizers might be the wrong starting point for the Learning Normativity agenda. There's still some hope, but there are a few problems which make me want to look for other directions.
Quantilizers bound risk, but iteration increases risk arbitrarily.
------------------------------------------------------------------
The whole argument behind quantilization is as follows:
1. The safe distribution has below ***c*** probability of catastrophe.
2. The limited optimization samples from the top ***q*** of the safe distribution.
3. So, the risk of catastrophe from the limited optimization is at most ***c/q***.
In other words, the chance of catastrophic risk is increased exactly in proportion to how much we optimize.
Recursive quantilization does this *over and over and over again.* Naively, as we seek the fixed point, it seems like our bound on catastrophic risk gets worse and worse until we have no bound at all.
There could be a way around this. We could get a *better* risk bound, which took into account the *improved* optimization from each step in the recursive quantilization. The story could be something like this:
* In each quantilization step, we have an overwhelming chance of increasing the quality of value functions and safe distributions proposed in the next round.
* This means (with overwhelming odds) we get better at detecting catastrophe, so we can actively optimize against problems which the previous round of quantilization had to merely hedge against by avoiding over-optimizing.
* Therefore, the chance of catastrophe from each round of quantilization can be dramatically lower than the previous. Although these chances do add up over successive rounds of quantilization, they can sum to a negligible probability overall, even in the limit.
It would be nice to put together such a story, but it isn't clear how to argue that the improvement in value function and proposal distribution cuts into the probability of catastrophe by a sufficient amount.
Quantilizers assume catastrophes are rare in random choices, but in the recursive setting, too much randomness can create value drift.
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Quantilization assumes we can get a "safe distribution" which might produce mediocre actions, but at least has a very low probability of catastrophic risk.
Supposing the scheme could produce highly capable systems at all, the recursive quantilizer environment seems like one where *randomized actions have a good chance of producing catastrophic risk,* so safe distributions would be incredibly difficult to engineer.
Imagine we're computing a big recursive quantilization tree. Randomly selecting a poor value function at one point will have a cascading effect. It will feed into a quantilization step which selects a bad QAS, which will go on to produce either the value function or the safe distribution for another quantilization, and so on. There's not fundamentally any corrective effect -- if either of the two inputs to quantilization is poor enough, then the output will be poor.
There's a basin of attraction, here: if the proposed safe distribution always contains good proposals with non-negligible probability, and the value function always has enough of the right meta-principles to correct specific errors that may be introduced through random error. But it's quite important that the output of each quantilization be *better* than the previous. If not, then we're not in a basin of attraction.
All of this makes it sound quite difficult to propose a safe distribution. The safe distribution needs to *already* land you within the basin of attraction (with very high probability), because drifting out of that basin can easily create a catastrophe.
Here's a slightly different argument. At each quantilization step, including the very first one, it's important that we find a QAS which actually fits our data quite well, because it is important that we pin down various things firmly in order to remain in the basin of attraction (especially including pinning down a value function at least as good as our starting value function). However, for each QAS which fits the data quite well and points to our desired basin of attraction, there are many alternative QAS which *don't* fit our data well, but point to very *different,* but equally coherent, basins of attraction. (In other words, there should be many equally internally consistent value systems which have basins of attraction of similar size.)
Since these other basins would be catastrophic, this means ***c*****,** the probability of catastrophe, is higher than ***q,*** the amount of optimization we need to hit our narrow target.
This means the safe distributions has to be doing a lot of work for us.
Like the previous problem I discussed, this isn't necessarily a showstopper, but it does say that we'd need some further ideas to make recursive quantilization work, and suggests to me that quantilization might not be the right way to go.
Other Concerns
--------------
* Quantilizers don't have the best handles for modeling human philosophical deliberation over time. In other words, I don't think recursive quantilization absorbs the lesson of [radical probabilism](https://www.lesswrong.com/tag/radical-probabilism). In particular, although recursive quantilization involves iteratively improving a picture of "good reasoning", I think it lacks a kind of *stability* -- the picture of good reasoning must be entirely reconstructed each time, from "first principles" (IE from the principles developed in the previous step). I currently see no guarantee that recursive quantilization avoids being Dutch-Book-able over these stages, or any other such dynamic optimality notion.
* Quantilizers aren't great for modeling a collection of partial models. Since a quantilizer spits out one (moderately) optimized result, I have to focus on single QASs, rather than collections of experts which cover different areas of expertise. This means we don't get to break down the problem of reasoning about the world.
* Quantilizers don't put world models in a central spot. By putting optimization in a central spot, we sort of sideline reasoning and belief. This obscures the mechanism of updating on new information. |
8384479e-55b9-4a27-98ff-4a68fab323a9 | trentmkelly/LessWrong-43k | LessWrong | Boredom vs. Scope Insensitivity
How much would you pay to see a typical movie? How much would you pay to see it 100 times?
How much would you pay to save a random stranger’s life? How much would you pay to save 100 strangers?
If you are like a typical human being, your answers to both sets of questions probably exhibit failures to aggregate value linearly. In the first case, we call it boredom. In the second case, we call it scope insensitivity.
Eliezer has argued on separate occasions that one should be regarded as an obvious error to be corrected, and the other is a gift bestowed by evolution, to be treasured and safeguarded. Here, I propose to consider them side by side, and see what we can learn by doing that.
(Eliezer sometimes treats scope insensitivity as a simple arithmetical error that the brain commits, like in this quote: “the brain can't successfully multiply by eight and get a larger quantity than it started with”. Considering that the brain has little trouble multiplying by eight in other contexts and the fact that scope insensitivity starts with numbers as low as 2, it seems more likely that it’s not an error but an adaptation, just like boredom.)
The nonlinearities in boredom and scope insensitivity both occur at two different levels. On the affective or hedonic level, our emotions fail to respond in a linear fashion to the relevant input. Watching a movie twice doesn’t give us twice the pleasure of watching it once, nor does saving two lives feel twice as good as saving one life. And on the level of decision making and revealed preferences, we fail to act as if our utilities scale linearly with the number of times we watch a movie, or the number of lives we save.
Note that these two types of nonlinearities are logically distinct, and it seems quite possible to have one without the other. The refrain “shut up and multiply” is an illustration of this. It exhorts (or reminds) us to value lives directly and linearly in our utility functions and decisions, instead of only valuing |
94c182f0-794c-401a-9112-1bb47a86361c | trentmkelly/LessWrong-43k | LessWrong | Cognitive Biases in Large Language Models
> Humans, one might say, are the cyanobacteria of AI: we constantly emit large amounts of structured data, which implicitly rely on logic, causality, object permanence, history—all of that good stuff. All of that is implicit and encoded into our writings and videos and ‘data exhaust’. A model learning to predict must learn to understand all of that to get the best performance; as it predicts the easy things which are mere statistical pattern-matching, what’s left are the hard things. Gwern
> #[[IAN says:]] Hey #IAN ! Do you have any cognitive biases? IAN : "Oh, I have a ton of them, but you know them all already."1
(TL;DR: LMs exhibit some cognitive biases. Additionally, might explain how cognitive biases can come about in humans. Raising questions about how universal deductive inference is.
Meta: This post summarizes the results from my project for the AGI Saftey Fundamentals fellowship by EA Cambridge. Code is available here.)
Introduction
Large language models like GPT-3 (or #IAN ) get their capabilities by learning from copious amounts of text collected from the internet.2 While this approach results in remarkable general capabilities3 it also comes with certain caveats. Since the text on the internet stems from humans4, it contains all of our good, bad and ugly. Interacting with a language model without safeguards can feel a bit like opening Pandora's box.
Reports and worry about biases in large language models abound - with good reason. Removing these biases and constructing models that internalize human values (as opposed to the unsatisfactory reality of human behavior) appears critical for these models to reach their full potential (and, in fact, to be safe to use at all). Removing biases from large language models is a particularly illustrative case of what is more generally called the "alignment problem" - the problem of producing AI that is aligned with human values. The general problem has many nuances and can be decomposed, but the specific pro |
369cc0a9-22df-42dd-a2fa-b8a9a3ab31dd | trentmkelly/LessWrong-43k | LessWrong | LLM in-context learning as (approximating) Solomonoff induction
Epistemic status: One week empirical project from a theoretical computer scientist. My analysis and presentation were both a little rushed; some information that would be interesting is missing from plots because I simply did not have time to include it. All known "breaking" issues are discussed and should not effect the conclusions. I may refine this post in the future.
[This work was performed as my final project for ARENA 5.0.]
Background
I have seen several claims[1] in the literature that base LLM in-context learning (ICL) can be understood as approximating Solomonoff induction. I lean on this intuition a bit myself (and I am in fact a co-author of one of those papers). However, I have not seen any convincing empirical evidence for this model.
From a theoretical standpoint, it is a somewhat appealing idea. LLMs and Solomonoff induction both face the so-called "prequential problem," predicting a sequence based on a prefix seen so far with a loss function that incentivizes calibration (the log loss; an LLM's loss function may also include other regularization terms like weight decay). Also, ICL is more sample efficient than pretraining. For me, this dovetails with Shane Legg's argument[2] that there is no elegant universal theory of prediction, because an online predictor must be complex to learn complex sequences successfully. LLM pretraining is a pretty simple algorithm, but LLM ICL is a very complicated algorithm which leverages a massive number of learned parameters. This is an incomplete argument; Solomonoff induction is a highly general sample efficient algorithm for the prequential problem, as is LLM ICL, but that does not mean they are meaningfully connected. In fact, they are optimized for different distributions: the universal distribution versus the distribution of text on the internet. Arguably, the later may be a special case of the former with an appropriate choice of universal Turing machine (UTM), but I find this perspective to be a bit of a |
62845201-426c-4496-9d99-f6d45e2204c5 | trentmkelly/LessWrong-43k | LessWrong | An overview of some promising work by junior alignment researchers
We’re all familiar with ELK, natural abstractions, and toy models of superposition.
But there’s also a new cohort of alignment researchers. Many of them got involved (or produced their first major pieces of work) in the last year.
I’ve been impressed by the quality of some of their work, and I think it deserves wider recognition. The recent increase in attention being paid to alignment gives me some hope that an “unknown genius” may emerge in the field. Additionally, there are several junior alignment researchers who seem to have a lot of potential, and I’m excited to see their contributions as they get more experience and influence in the field.
Here’s some work by junior alignment researchers that excited me in the last year:
Externalized reasoning oversight by Tamera Lanham
Mechanistic interpretability tries to understand what models “think” by looking at their weights and activations. Externalized reasoning oversight tries to do this by just asking the models to explain their reasoning.
Inspired by chain-of-thought prompting techniques, Tamera set out to see if she could get language models to provide honest and transparent answers about their reasoning processes. In the limit, this technique could provide a novel way for us to interpret language models.
I’m excited to see junior researchers tackle existing problems (e.g., how do we understand what models think?) in new ways (e.g., hm, everyone’s been focusing on weights and activations… are there any alternatives?). Tamera’s work is a great example.
Relevant post: Externalized reasoning oversight: A research direction for language model alignment
Relevant interview: Tamera Lanham on AI risk, threat models, alignment proposals, externalized reasoning oversight, and working at Anthropic
Goal misgeneralization by Lauro Langosco
Everyone knows that models can learn unintended goals. Everyone knows that models can fail to generalize out-of-distribution.
Lauro’s paper connects these points and distin |
4f1b5313-9138-4b30-94b1-15bf0c6a26e3 | trentmkelly/LessWrong-43k | LessWrong | The Case For Gods
[this is a repost from my personal blog explanationing.wordpress.com. Look there for posts explaining my priors.]
I’ve been playing with belief in God since I was a child.
During my adolescent years, when I started really digging the catchphrases of pseudo-rational-types like Terry Goodkind, I became a staunch atheist. It wasn’t until after my last grandparent died and I really came face-to-face with the living faith of my family that I suspected there might something more to belief in God than had previously met my horny-for-evidence hormone-blinded eye.
So, in order to make sense of this glimmer of an intuition that there’s something to faith, I joined a Bible study in my first year at university. I talked with other young Christians about what they believed. I teased out the little glowing thread of my own experience of divinity, the divinity I saw and felt in the room at my Oma’s funeral service. By the end of my winter semester, I felt as though I had a fairly close relationship with God, and I had a good working understanding of my fellow Bible-studiers as well. However, as the semester came to a close, I gave up on the project of Christianity in favour of something else that caught my fancy, who can remember what.
Two years later, (ie., this past week), a friend of mine wondered in passing at my faith in God. The word “faith” rankled, because even when I was trying on the hat of Christianity, I never made use of faith. While I loved the people in my Bible study, and respected their personal relationships with God, my long-abiding sternly-atheistic dismay at faith persisted.
I believed then and I believe now that God can be explicitly modeled in a way that preserves both rationality and the essential sense in which spiritual people mean “God.”
Return to the idea that our brain is playing a game of “interpret reality-data into predictively useful systems of symbols.”
If that’s the game, what’s winning? If the game is as described above, winning is “corre |
1c771794-d04a-4f14-8c7f-4435d3b87474 | trentmkelly/LessWrong-43k | LessWrong | Epistea Summer Experiment (ESE)
Remark: This post was written collectively by the organizing team of the Epistea Summer Experiment.
Cross-posted to the EA Forum here.
Epistea Summer Experiment (ESE, /ˈiːzi/) was an experimental summer workshop in Prague combining elements of applied rationality and experiential education. The main goals were to:
* Try new ideas about rationality education, such as multi-agent models of minds, and ideas about group epistemics and coordination
* Try to import insights and formats from experiential education
* Connect people interested in rationality education
We consider the event successful and plan to use the insights gained for creating more content along similar lines.
The remainder of the post will outline our motivation for focusing on these goals, our takeaways, and future plans.
Motivations
Group Rationality
Most of today’s rationality curriculum and research is focused on individual rationality, or ‘how to think clearly (alone)’. The field of group rationality - or ‘how to think well and act as groups of humans’ - is less developed, and open problems are the norm.
> [...] I feel like group rationality remains a largely open and empty field. There’s a lot of literature on what we do wrong, but not a lot of ready-made “techniques” just sitting there to help us get it right — only a scattering of disconnected traditions in things like management or family therapy or politics. My sense is that group rationality is in a place similar to where individual rationality was ~15 years ago [...]. (cited from ‘Open Problems In Group Rationality’, by Duncan Sabien)
The central problem is that people use beliefs for many purposes - including tracking what is true. But another, practically important purpose is coordination. We think it’s likely that if an aspiring rationalist decides to “stop bullshitting”, they lose some of the social technology often used for successfully coordinating with other people. How exactly does this dynamic affect coordination? Ca |
9e852c7c-db93-4c8a-b1ad-1876aa59f035 | trentmkelly/LessWrong-43k | LessWrong | Multi-agent predictive minds and AI alignment
Abstract: An attempt to map a best-guess model of how human values and motivations work to several more technical research questions. The mind-model is inspired by predictive processing / active inference framework and multi-agent models of the mind.
The text has slightly unusual epistemic structure:
1st part: my current best-guess model of how human minds work.
2nd part: explores various problems which such mind architecture would pose for some approaches to value learning. The argument is: if such a model seems at least plausible, we should probably extend the space of active research directions.
3rd part: a list of specific research agendas, sometimes specific research questions, motivated by the previous.
I put more credence in the usefulness of research questions suggested in the third part than in the specifics of the model described the first part. Also, you should be warned I have no formal training in cognitive neuroscience and similar fields, and it is completely possible I’m making some basic mistakes. Still, my feeling is even if the model described in the first part is wrong, something from the broad class of “motivational systems not naturally described by utility functions” is close to reality, and understanding problems from the 3rd part can be useful.
How minds work
As noted, this is a “best guess model”. I have large uncertainty about how human minds actually work. But if I could place just one bet, I would bet on this.
The model has two prerequisite ideas: predictive processing and the active inference framework. I'll give brief summaries and links for elsewhere.
In the predictive processing / the active inference framework, brains constantly predict sensory inputs, in a hierarchical generative way. As a dual, action is also “generated” by the same machinery (changing environment to match “predicted” desirable inputs and generating action which can lead to them). The “currency” on which the whole system is running is prediction error (or |
d2e2acf2-8e9d-4b23-975d-06dabf3b1335 | trentmkelly/LessWrong-43k | LessWrong | Human errors, human values
The trolley problem
In 2009, a pair of computer scientists published a paper enabling computers to behave like humans on the trolley problem (PDF here). They developed a logic that a computer could use to justify not pushing one person onto the tracks in order to save five other people. They described this feat as showing "how moral decisions can be drawn computationally by using prospective logic programs."
I would describe it as devoting a lot of time and effort to cripple a reasoning system by encoding human irrationality into its logic.
Which view is correct?
Dust specks
Eliezer argued that we should prefer 1 person being tortured for 50 years over 3^^^3 people each once getting a barely-noticeable dust speck in their eyes. Most people choose the many dust specks over the torture. Some people argued that "human values" includes having a utility aggregation function that rounds tiny (absolute value) utilities to zero, thus giving the "dust specks" answer. No, Eliezer said; this was an error in human reasoning. Is it an error, or a value?
Sex vs. punishment
In Crime and punishment, I argued that people want to punish criminals, even if there is a painless, less-costly way to prevent crime. This means that people value punishing criminals. This value may have evolved to accomplish the social goal of reducing crime. Most readers agreed that, since we can deduce this underlying reason, and accomplish it more effectively through reasoning, preferring to punish criminals is an error in judgement.
Most people want to have sex. This value evolved to accomplish the goal of reproducing. Since we can deduce this underlying reason, and accomplish it more efficiently than by going out to bars every evening for ten years, is this desire for sex an error in judgement that we should erase?
The problem for Friendly AI
Until you come up with a procedure for determining, in general, when something is a value and when it is an error, there is no point in trying t |
fcc1de32-0120-4d0d-9686-5b60263771d6 | trentmkelly/LessWrong-43k | LessWrong | How fast can we perform a forward pass?
Thanks to Hao Zhang, Kayvon Fatahalian, and Jean-Stanislas Denain for helpful discussions and comments.
Over the last month, I’ve spent a lot of time trying to answer the following question:
> How quickly can we perform one forward pass in a transformer model?
By a transformer model, I mean BERT, GPT-3, T5, Chinchilla, or other large language models that use a transformer architecture. By a forward pass, I mean the computation needed to generate the next token given all the tokens so far.[1] By “how quickly”, I mean how much wall clock time elapses between the call to the forward pass and its completion. So, even if I can run 1,000 forward passes in parallel, if each takes 1 second to complete, the answer is 1 second (not 1 millisecond).
One way to attempt answering this is to take the total number of operations in a forward pass and divide by the speed of your favorite GPU in FLOPS (floating-point operations/second). But this is wrong, because you would do better by parallelizing across multiple GPUs.[2]
The question then is really “how effectively can I parallelize a forward pass?” It turns out that this has different answers based on how “wasteful” we’re willing to be, in terms of GPU utilization. If we are willing to utilize only 5% of the GPU (but parallelize across many GPUs), we can perform the forward pass more quickly. So I’ll actually answer two questions:
1. How quickly can we perform a forward pass, assuming we require each GPU to have at least 40% utilization relative to roofline FLOPS?[3]
2. If we are willing to decrease utilization by a factor of k, how much faster can we perform a forward pass?
To simplify the analysis, I’ll make several assumptions (this is mainly targeted at people who are very familiar with GPU nuts and bolts; don’t worry if you don’t understand them yet):
* [A] Parallelization, both within and across GPUs, is done via matrix tiling, as discussed in the NVIDIA User’s Guide.
* [B] All bottleneck operations can be run at |
f260c78c-8631-4ac5-a9a0-a596fbc5887b | trentmkelly/LessWrong-43k | LessWrong | Taking Ideas Seriously
I, the author, no longer endorse this post.
----------------------------------------
Abstrummary: I describe a central technique of epistemic rationality that bears directly on instrumental rationality, and that I do not believe has been explicitly discussed on Less Wrong before. The technnique is rather simple: it is the practice of taking ideas seriously. I also present the rather simple metaphor of an 'interconnected web of belief nodes' (like a Bayesian network) to describe what it means to take an idea seriously: it is to update a belief and then accurately and completely propagate that belief update through the entire web of beliefs in which it is embedded. I then give a few examples of ideas to take seriously, followed by reasons to take ideas seriously and what bad things happens if you don't (or society doesn't). I end with a few questions for Less Wrong.
Eliezer Yudkowsky and Michael Vassar are two rationalists who have something of an aura of formadability about them. This is especially true of Michael Vassar in live conversation, where he's allowed to jump around from concept to concept without being penalized for not having a strong thesis. Eliezer did something similar in his writing by creating a foundation of reason upon which he could build new concepts without having to start explaining everything anew every time. Michael and Eliezer know a lot of stuff, and are able to make connections between the things that they know; seeing which nodes of knowledge are relevant to their beliefs or decision, or if that fails, knowing which algorithm they should use to figure out which nodes of knowledge are likely to be relevant. They have all the standard Less Wrong rationality tools too, of course, and a fair amount of heuristics and dispositions that haven't been covered on Less Wrong. But I believe it is this aspect of their rationality, the coherent and cohesive and carefully balanced web of knowledge and belief nodes, that causes people to p |
2b2eba0a-0555-4b69-8e59-7a0c08b8ec43 | trentmkelly/LessWrong-43k | LessWrong | Make learning a reality
The following passage comes from What Do You Care What Other People Think? by Richard Feynman. It's a collection of stories from his life and curious adventures.
> We had the Encyclopedia Britannica at home. When I was a small boy he [Feynman's father] used to sit me on his lap and read to me from the Britannica. We would be reading, say, about dinosaurs. It would be talking about the Tyrannosaurus Rex, and it would say something like, "This dinosaur is twenty-five feet high and its head is six feet across."
>
> My father would stop reading and say, "Now, let's see what that meant. That would mean that if he stood in our front yard, he would be tall enough to put his head through our window up here" (We were on the second floor.) "But his head would be too wide to fit into the window." Everything he read to me he would translate as best he could into some reality.
>
> I learned from my father to translate: everything I read to try and figure out what it really means, what it's really saying.
The most important step to learning is to understand what it truly means. Make learning a reality! If you're reading about the scientific revolution, make a timeline with all the dates of discoveries or lives of influential scientists to see which lives overlapped. You could even try mapping where each discovery took place. Any action that makes learning abstract concepts into something tangible will bode better for your knowledge (and subsequently everyone else!)
How do we make learning a reality?
|
8799d519-d08b-465c-84fd-a3e71bab4e6a | trentmkelly/LessWrong-43k | LessWrong | Help re:Less Wrong Meetup please?
Hi Everyone,
I've been conducting a LW meetup in West Sydney. had 3 somewhat succesful sessions. Covered the following topics:
1. What is rationality? Who uses it? Why be less wrong?
2. Map and Territroy
3. Belief: functions, proper and imporper and other things pertaining.
I'm wondering what would be a good topic for the next session, so any suggestions would be much appreciated. I'm finding whilst people enjoy the conversations for the most part, they find it hard to make it applicable and marry it back to real life situations.
Any ideas on how I can help communicate that what we study and do at the meet up is relevant and applicable in their day to day lives? And what would be a good topic to do this on?
I get the feeling this will require people to sort of open up and share themselves with the group. Any good ways of cultivating that type of environment?
Thanks in advance. |
82ff2782-307e-4b90-bcd6-b59b5263ebdb | StampyAI/alignment-research-dataset/arxiv | Arxiv | Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance
1 Introduction
---------------
Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a central challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing.[[13](#bib.bib35 "Air force research laboratory test and evaluation, verification and validation of autonomous systems chellenge exploration final report"), [1](#bib.bib19 "AI principles: recommendations on the ethical use of artificial intelligence by the department of defense"), [3](#bib.bib20 "Understanding AI technology"), [58](#bib.bib21 "The department of defense posture for artificial intelligence"), [5](#bib.bib22 "Concrete problems in AI safety")] TEVV for AI is particularly challenging for several reasons[[59](#bib.bib23 "What counts as progress in the T&E of autonomy (conference briefing)"), [7](#bib.bib24 "Safely entering the deep: a review of verification and validation for machine learning and a challenge elicitation in the automotive industry")] including that AI is meant to be used in circumstances that the designers cannot fully envision. The decision about what actions to take is made by the AI during use rather than by the designer before testing. The benefit is that during use, the AI will have access to the full range of inputs and environmental data to make decisions. The drawback is that it is too late for additional TEVV at that point.
For example, non-AI software for autonomous braking would have well-delineated responses to the sensors such as radar and velocity. The decisions about how the vehicle will respond are made by the designer at design time and written into software which can have its code reviewed and tested to ensure that responses match the design decisions. For an AI, the designer has not made explicit decisions about how the vehicle will behave. Those decisions are made at use time, after testing and certification has already been completed.
In cases where the range of inputs and environments that can be encountered are well understood, then testing is a straightforward matter. It may be arduous and expensive to gather enough test cases to certify the system, but it is conceptually simple to do. What makes TEVV for AI difficult is that often the range of inputs and environments that can be encountered is not well understood. The system is likely to encounter situations that are outside of the distributions of scenarios it was designed for.
AI has been known to fail in those circumstances and has been commonly disparaged as brittle.[[16](#bib.bib26 "The surprising brittleness of ai"), [27](#bib.bib27 "Why deep-learning AIs are so easy to fool"), [43](#bib.bib28 "We must recognize just how brittle and unpredictable today’s correlative deep learning AI is"), [51](#bib.bib29 "Greedy, brittle, opaque, and shallow: the downsides to deep learning")] Brittleness implies two things about a component, first that it is highly functioning within some bounds and second, that it breaks readily when those bounds are exceeded. This report argues that neither of those criteria are as plain as often presented. First, compared to the reliability required of safety- or mission-critical systems for which TEVV and certification are paramount, the most highly touted AI successes are orders of magnitude more failure-prone even when being evaluated on data drawn from the same distributions they were designed for. And second, the performance of those models degrades smoothly as those bounds on the data distributions are relaxed, at a rate that is sometimes comparable to humans.
This report starts by briefly familiarizing the reader with the existing approaches for certifying critical systems in section 2 then expands on the required levels of reliability for components in critical systems in section 3. Section 4 contextualizes those required reliability levels for AI. Section 5 presents experimental results evaluating the brittleness of some AI algorithms and section 6 concludes.
2 Certification in Critical Systems
------------------------------------
###
2.1 Processes and Standards are Complex and Opaque
AI is a ubiquitous technology that can be envisioned in an infinity of applications. Many of those applications warrant deeper levels of scrutiny than others because of heightened risks and, for the most part, those applications have already evolved processes for obtaining high levels of surety. Those processes tend to be complex and opaque to an extent where people build entire careers around understanding and navigating them. Additionally, most of the standards documents (the notable exception being military standards) are behind paywalls, restricting access and widespread understanding. As a result of the opacity, complexity, and diversity of these processes, it is probably not feasible for most AI designers to be well-versed in even one of them let alone the wide range of applications for which a single model or architecture may be used.
Nevertheless, currently much of the onus for AI safety, and much of the blame for failures, falls on the AI designers. While recognizing that the enormity of the problem will require many dedicated staff whose sole job is translating safety goals into design specifications, all involved should have a basic understanding of the processes, requirements, and goals. The next few subsections will very briefly highlight a few of the takeaways that are relevant to AI designers and practitioners from a survey of the processes for certifying aircraft, nuclear power plants, automobiles, pharmaceuticals, and weapons systems. Their processes and standards are far from perfect as is recognized by the certifying bodies themselves who have sponsored studies[[28](#bib.bib2 "Software assurance approaches, considerations, and limitations"), [31](#bib.bib30 "Assessment of safety standards for automotive electronic control systems")] to search out better alternatives. There have been major and minor revisions to the standards but the existing approaches seem to hold up well to scrutiny as compared to other options that have been proposed.
###
2.2 Is AI Software or an Operator
The certification of new critical systems (such as a new aircraft or a power plant design) moves along established processes and standards which are each composed of many lower level standards and processes. The chains can be extensive. Among those lower level standards can be those dedicated to software components and those dedicated to operators. Although AI is clearly software (or perhaps electronic hardware), depending on the intended application it might be used to perform tasks that are more commonly associated with operators. Certification of operators tends to rely on passing tests, accumulating hours of experience, and continually monitoring fitness for duty. A driver’s license is a familiar example that requires a written and practical exam, starts with a permit, and can be revoked for poor performance or degraded operational capacity. More stringent examples are pilots[[21](#bib.bib34 "Aeronautical experience - airplane category restricted privileges")] and nuclear operators[[49](#bib.bib33 "Operator licensing examination standards for power reactors")] which require minimum education and minimum hours of supervised operation.
The processes and standards that exist for operators were designed for humans and are not easily applied to AI so even in cases where the tasks being performed are more commonly associated with those of an operator than of traditional software, the processes and standards for software certification tend to be more appropriate for AI. Testing and certification that licenses AI as more of an operator is a promising direction of study[[13](#bib.bib35 "Air force research laboratory test and evaluation, verification and validation of autonomous systems chellenge exploration final report")] but does not appear to be a near-term solution. An important distinction to consider in operator-like approaches vs software-like approaches is that the diversity of human operators provides some assurance against systemic failures across the entire fleet but also precludes in-depth testing of the entire set of operators in a cost and time-efficient manner. That systemic risk and opportunity for in-depth testing is a main reason why software standards and processes are currently more applicable to AI.
###
2.3 Various Approaches to Providing Surety
At a high level, there are many different approaches that can be used to provide increased levels of surety. Below is a partial list illustrating some of them:
* Find and fix faults before fielding
* Fail a large fraction of prospect products
* Detect and respond to failures in real time
* Approve limited quantities or functionalities and observe real-world performance
* Penalize organizations that provide faulty products
In actual practice, all critical application areas implement all of these surety approaches and more. They are each applied to varying degrees with some approaches more or less prevalent in some applications than in others. Autonomous vehicles have famously[[36](#bib.bib36 "The enemy of good: estimating the cost of waiting for nearly perfect automated vehicles")] approved limited quantities of vehicles in confined locations for evaluation. The same approach is less useful for nuclear power plants where the scale of catastrophe from a single complete failure could have a much larger impact that is less confined to a location. Nuclear power instead follows a probabilistic risk assessment method[[38](#bib.bib37 "A historical overview of probabilistic risk assessment development and its use in the nuclear power industry: a tribute to the late professor norman carl rasmussen")] to try to minimize the likelihood of failures before operation starts. The U.S. military’s conventional weapons systems face a scale of risk that is intermediate between car accidents and radiological disaster so put an emphasis on finding and fixing faults before fielding but systems also then step through Initial Operating Capability before proceeding to Full Deployment.[[48](#bib.bib38 "Operation of the adaptive acquisition framework")] New pharmaceuticals also carry potentially wide-spread risks but have adopted a somewhat different strategy that produces many candidates, most of which ultimately fail the certification process rather than fixing flaws in the products.[[47](#bib.bib39 "Drugs, devices and the FDA: part 1: an overview of approval processes for drugs")]
Some approaches to surety have little to do with testing or certification directly. For example, automotive companies have been charged with the expense of liability or forced to recall defective products when safety issues are discovered.[[24](#bib.bib40 "Sue my car not me: products liability and accidents involving autonomous vehicles")] Similarly, medical doctors are tested and certified but then are also held accountable for malpractice, how this will play out for AI and robotics as they take on a larger role in society is still being decided.[[12](#bib.bib41 "Hey watson - can i sue you for malpractice? examining the liability of artificial intelligence in medicine"), [17](#bib.bib42 "Predicting the long-term effects of human-robot interaction: a reflection on responsibility in medical robotics")]
3 Safety Integrity Levels
--------------------------
Typical among many of the safety standards for critcal systems is the concept of levels of safety, somewhat akin to tolerable failure rates. Industries often have their own set of levels that are different in magnitudes, labeling, intent, and implementation. To think of them as simply tolerable failure rates is admittedly a disservice to the standards and to safety culture. Some standards distance themselves from quantitative failure rates, especially for software such as MIL-STD-882E[[18](#bib.bib31 "Department of defense standard practice: system safety")] which states that: "determining the probability of failure of a single software function is difficult at best and cannot be based on historical data," or DO-178C[[56](#bib.bib32 "Software considerations in airborne systems and equipment certification")] which states: "it is important to realize that the likelihood that software contains an error cannot be quantified in the same way as for random hardware failures." Nonetheless, it is necessary for AI designers to have a rough guidepost from which to understand the order of magnitude of the surety problem they face and the reliability requirements of the systems they are designing. With that goal in mind, a few of the standards and their target failure rates will be listed here along with a brief summary of how they are used.
###
3.1 Various SILs
Aircraft use Design Assurance Level (DAL) from DO-178C: "Software Considerations in Airborne Systems and Equipment Certification." Aviation also separately uses numbered Software Assurance Levels (AL) from DO-278: "Guidelines For Communication, Navigation, Surveillance, and Air Traffic Management (CNS/ATM)."[[20](#bib.bib7 "Flight-critical data integrity assurance for ground-based COTS components")][[8](#bib.bib5 "DO-178C: a new standard for software safety certification")] The automotive industry uses Automotive Safety Integrity Level (ASIL) from ISO 26262: "Road vehicles - Functional Safety." [[46](#bib.bib3 "Functional safety methodologies for automotive applications")] A common industry-agnostic standard is provided in IEC 61508: "Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems," which uses the generic term Safety Integrity Level (SIL).[[53](#bib.bib6 "System reliability theory")] The level of failure per hour and corresponding labels for each industry standard are provided in Table [1](#S3.T1 "Table 1 ‣ 3.1 Various SILs ‣ 3 Safety Integrity Levels ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance")
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 10−9/h | 10−8/h | 10−7/h | 10−6/h | 10−5/h | 10−4/h | 10−3/h | >10−3/h |
| Automotive | | D | C/B | A | | | | |
| Aviation | A | | B | | C | | D | E |
| CNS/ATM | AL1 | | AL2 | | AL3 | AL4 | AL5 | AL6 |
| IEC 61508 | | 4 | 3 | 2 | 1 | | | |
Table 1: Approximate hourly failure rates are provided for the range of levels of surety in various industries.
Aviation standards cover a wide range from one in a thousand hours for low risk systems to one in a billion hours for more critical systems. Automotive standards in contrast have a slightly lower requirement for their most critical systems but only begin to have specified standards at the one in a million hours level.
The industry-agnostic IEC 61508 standard offers a second unit of measurement that can be applied when hourly rates are not appropriate. A separation of SILs into Continuous Demand vs Low Demand requirements allows for systems that are used less frequently to be evaluated on a per use basis rather than a per hour basis. The Low Demand SILs are shown in Table [2](#S3.T2 "Table 2 ‣ 3.1 Various SILs ‣ 3 Safety Integrity Levels ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance").[[53](#bib.bib6 "System reliability theory")]
| | 10−4/use | 10−3/use | 10−2/use | 10−1/use |
| --- | --- | --- | --- | --- |
| IEC 61508 | 4 | 3 | 2 | 1 |
Table 2: Low demand safety levels are evaluated on a per use basis rather than a per hour basis.
###
3.2 Examples of SILs
Although the failure rates for the SILs is quantitative, deciding which level of assurance should be applied to a given system is not as easily quantified. Industries vary in how this task is performed as well. Aircraft for example have categories of Catastrophic, Hazardous, Major, Minor, and No Effect corresponding to their DALs and use qualitative and quantitative definitions of likelihoods. MIL-STD-882, "System Safety," uses qualitative severity and probability levels with a mix of qualitative and quantitative elements in their definitions. The automotive industry (ISO 26262) breaks risk into three factors: Severity, Likelihood (called "Exposure") and Controllability, each of which has several qualitative levels as described in the list below:
* Severity
+ S1: Light and moderate injuries
+ S2: Severe and life-threatening injuries (survival probable)
+ S3: Life-threatening injuries (survival uncertain)
* Exposure
+ E1: Very low probability
+ E2: Low probability
+ E3: Medium probability
+ E4: High probability
* Controllability
+ C1: Simply controllable
+ C2: Normally controllable
+ C3: Difficult to control or uncontrollable
Combining a qualitative estimate for each of Severity, Exposure, and Controllability gives a risk to which an ASIL can be assigned. All of the possible combinations of Severity, Exposure, and Controllability are shown in Table [3](#S3.T3 "Table 3 ‣ 3.2 Examples of SILs ‣ 3 Safety Integrity Levels ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance") along with the assigned ASIL. Only the the most risky combination of the three merits an ASIL D, and most of the lowest risk combinations do not merit any ASIL at all.
| | C1 | C2 | C3 |
| --- | --- | --- | --- |
| S1 | E1 | - | - | - |
| E2 | - | - | - |
| E3 | - | - | A |
| E4 | - | A | B |
| S2 | E1 | - | - | - |
| E2 | - | - | A |
| E3 | - | A | B |
| E4 | A | B | C |
| S3 | E1 | - | - | A |
| E2 | - | A | B |
| E3 | A | B | C |
| E4 | B | C | D |
Table 3: The automotive industry is an example of using a qualitative approach to assigning safety levels to sub-systems.
To add intuition, a few examples of ASIL D (highest automotive level) are inadvertent airbag deployment and some types of unwanted deceleration, braking or acceleration or self-steering failures. Level C includes some types of unintended deceleration, braking, or acceleration. ASIL B includes outages in front or rear view cameras or brake lights. ASIL A includes failures of rear lights on both sides.[[62](#bib.bib8 "Functional safety architectural challenges for autonomous drive")] With increasing amounts of automation, human drivers will be less available to help with controllability moving some of these systems to higher ASILs.
Aviation examples use the opposite lettering direction where A is the highest level and E is the lowest. A and B DALs include systems like fly-by-wire controls, auto-pilot, radar, Identification Friend or Foe (IFF), and missile launch. Example at the C and D levels include anti-missile defense, telemetry, weapons targeting.[[32](#bib.bib4 "Standards in avionics system development: overview on DO-178B")]
###
3.3 Achieving SIL with Less Reliable Components
As is clear from the examples in the previous section, the SILs are applied to systems or functions at a higher abstraction than the individual components, so the components do not necessarily need to achieve the SIL’s failure rates individually. It is possible to build a system that has higher reliability than its constituent parts. For example, allowing for proper independence and redundancy, it is possible to decompose ASIL-D into two ASIL-B components or to achieve ASIL-C with an ASIL-A and an ASIL-B component.[[2](#bib.bib9 "High-integrity gpu designs for critical real-time automotive systems")] Instead, it may also be possible to increase the controllability by giving a human the opportunity to intervene. It may also be possible to place limits on the operating environment to decrease the exposure (likelihood) such as by limiting autonomous vehicles to sunny dry geographies rather than snowy ones.
4 SILs for AI
--------------
With a rough intuition for the safety goals to achieve, one can begin to assess the feasibility of developing various AI technologies that reach the required reliability as well as developing the testing methods that would be needed to certify them.
###
4.1 Reliability in AI
AI is typically tasked with difficult problems. For example, performance on Image-Net classification has been one of AI’s most lauded successes. At the time of writing, it is led by groups posting top-1 accuracies in the upper 80’s of percent and top-5 accuracies in the upper 90’s[[61](#bib.bib10 "Fixing the train-test resolution discrepancy: FixEfficientNet")][[63](#bib.bib11 "Self-training with noisy student improves ImageNet classification")][[40](#bib.bib12 "Big transfer (BiT): general visual representation learning")] and disease detection from medical imaging is comparable.[[45](#bib.bib18 "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis")] That incredible level of performance is the result of the combined efforts of a massive number of researchers over many years but still the failure rates are greater than 10−2/use. Those failure rates correspond to the lowest SIL rating in all of Table [2](#S3.T2 "Table 2 ‣ 3.1 Various SILs ‣ 3 Safety Integrity Levels ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance") for any industry. When considered in a continuous setting where many images are evaluated per hour, the situation is far worse. In that case, the failure rates are too high for any SIL. To reach even the minimum continuous-demand level would require more than ten hours between attempts for top-5 images and more than 100 hours between attempts for top-1 images. Those attempt frequencies are far from what most AI practitioners consider continuous. Processing even just ten images per second would require an accuracy of 0.99999997 to get to even the lowest level in aviation 10−3/h, and the lowest level for the automotive industry is three orders of magnitude more restrictive than that. Viewed in this light, AI is not confronted with a testing challenge, it is facing a reliability challenge.
Getting to one-in-a-million failure rates in image classification for diverse sets of objects is perhaps an unfair goal. Image-Net classifiers already outperform humans. That said, critical systems and their certification procedures have been designed over a century to accommodate humans as a fallible component of a reasonably reliable system. Further, as mentioned earlier, certification of AI as an operator rather than as software does not appear to be a near-term path forward. Either way, the message to be received by AI designers and practitioners is that the failure rates of the most heralded algorithms are orders of magnitude more failure-prone than safety-critical systems typically certify.
###
4.2 The Challenge of OOD
More importantly than getting to one-in-a-million failure rates is getting to one-in-a-million failure rates in the operational environment. The standard approach for evaluating an AI model’s reliability is to measure performance in a hold-out dataset that the model has never been exposed to before. But that dataset is almost always drawn randomly from the same set of samples as those the model has been trained to. In real-world operations it is likely that some or many of the situations the model will encounter will be different in some way from the types of samples used in training and testing. These situations are called Out-Of-Distribution (OOD) and the study of OOD in machine learning is currently gaining popularity.
###
4.3 OOD and Domain Adaptation
Much of the focus within the literature has been on techniques for OOD detection and for Domain Adaptation (DA). OOD detection is the task of trying to determine by various means whether an input is OOD. There are many ways that this is done and new methods are being discovered or invented constantly. Some methods compare the new inputs to the set of inputs in the training set, some methods assess the relative confidence across the outputs of the model, other methods evaluate internal parameters of the model such as gradients.[[9](#bib.bib43 "Anomalous instance detection in deep learning: a survey")]
Domain Adaptation [[34](#bib.bib44 "Domain adaptation for statistical classifiers")] is a different class of techniques that are meant to increase the effectiveness of a model on inputs that are from a different distribution than those it was originally trained on. DA is typically useful when a large data source is available but when that data source is not perfectly matched to the desired problem. If that data source has the same outputs as the intended problem but different inputs then DA is applied. If the inputs from the two sources are from the same distribution but the outputs are not then the process is called transfer learning. An example of DA is the use of digitally-generated video game footage as a training substitute for real-world training samples. The input distribution in the intended application (real-world) is of a different type than in the large body of training data (video game footage) but the intended output task is the same for both training and application (i.e identify pedestrians).
Both of these streams of research are making valuable contributions to increasing the reliability of AI in critical applications where real-world applications are slightly different from the datasets that are available to train the systems. Neither of these streams of research directly contribute to the evaluation of reliability of AI systems. DA is intended to retrain AI to new input sets, not increase robustness to departures from the training distribution. OOD detection is intended to detect when a departure from the training distribution occurs but says little about how the model will behave or how the system will perform in those cases.
###
4.4 OOD Detection is Not Good Enough
There are some cases in which it is possible to fall back into a safe mode when a system has decreased confidence in its ability to perform safely. In those cases, OOD detection can be directly valuable. That is the case for many of the systems at use today in driver assist technologies where control can be transferred to a human. This, in effect allows systems with low ASIL to be used because Controllability is high. At higher levels of automation[[60](#bib.bib13 "Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles")] that will not be true or will be true to a decreasing degree, so OOD detection becomes less valuable as the degree of automation increases.
Still, it is valuable to know when a component should be expected to be unreliable. It may be possible to decrease that component’s contribution to the system’s decisions or actions. For example, an imaging system that suspects it is unreliable might cede control to other complementary components such as lidar or radar. In so doing, the overall system comes to rely on fewer components. The frequency at which that occurs as well as the independence of the various components can limit the safety of the system and the usefulness of that component.
In contrast, many applications of AI and autonomy do not have a natural or acceptably safe option for the system or component that allow it to simply choose not to operate. A car without a steering wheel cannot cede control to a sleeping passenger because the inputs are OOD, it must decide whether to swerve or maintain course regardless of the underlying distribution of the input it receives. The same goes for military systems in a communications-denied environment or perhaps even cyber defense systems that operate too quickly for a human to intervene at a meaningful timescale. For many applications of interest, OOD detection is of some but limited value.
5 Measuring OOD Performance
----------------------------
Prior to deployment, the tester or certifier may need to know the degree to which performance is degraded by operating in OOD conditions. The machine itself may also benefit from knowing how much performance is expected to degrade upon encountering an OOD input to evaluate its own confidence.
###
5.1 OOD Performance Deserves Increased Attention
There is a small but growing body of work focused on measuring or improving the OOD performance of machine learning models. The sub-field does not seem to have matured to the point of having established consistent terminology so papers refer to the problem in many ways such as OOD robustness,[[30](#bib.bib61 "Pretrained transformers improve out-of-distribution robustness")] OOD generalization,[[29](#bib.bib50 "The many faces of robustness: a critical analysis of out-of-distribution generalization")] robustness to distributional shift,[[50](#bib.bib62 "Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift"), [33](#bib.bib63 "Does distributionally robust supervised learning give robust classifiers")] and sometimes the topic is addressed in a paper without using any specific terms or phrases that differentiate in-distribution performance from OOD.[[64](#bib.bib64 "A fourier perspective on model robustness in computer vision"), [22](#bib.bib65 "ImageNet-trained cnns are biased towards texture: increasing shape bias improves accuracy and robustness")] As a comparison, the "adversarial examples" sub-field is far more unified with respect to terminology and overarching goals despite being in effect a subset of OOD robustness. It is a subset that is focused on a specific context for encountering OOD inputs (an intentional attack) and a confined set of techniques for creating them (imperceptible perturbations, typically using small lp-norm).
Vulnerability to attack is a partially separated issue from certification in a practical sense for many systems. In a more familiar example, draining the brake fluid is a trivial task for an automotive enthusiast but vehicles are not considered brittle as a result. Further, robust designs and testing are not the only means for addressing vulnerability to attack, there are legal and ethical frameworks for addressing them. Still, there are lessons to be learned from the flurry of work.
Creating models that will perform adequately when exposed to adversarial examples is a popular area of research.[[19](#bib.bib46 "Benchmarking adversarial robustness"), [15](#bib.bib47 "Certified adversarial robustness via randomized smoothing"), [25](#bib.bib48 "Adversarial examples for cost-sensitive classifiers")] Thousands of adversarial examples papers are put out each year and high-profile structured competitions have been devoted to the task of making models that are robust to them.[[42](#bib.bib52 "Adversarial attacks and defences competition")] Numerous and widely varying methods to improve robustness have been explored and reviewed elsewhere.[[10](#bib.bib55 "Adversarial attacks and defenses"), [52](#bib.bib56 "Review of artificial intelligence adversarial attack and defense technologies")] A common approach involves including adversarial examples in the training set, in essence broadening the training distribution so that adversarial examples are no longer OOD. Many approaches focus on compression of the inputs or other input preprocessing steps.[[35](#bib.bib53 "ComDefend: an efficient image compression model to defend adversarial examples"), [44](#bib.bib58 "Certified adversarial robustness with additive noise"), [14](#bib.bib57 "Certified adversarial robustness via randomized smoothing")] Others focus on smoothing the outputs or gradients of the model such as to make neighboring categories farther away in terms of the amount of change needed to the inputs or simply to make the changes harder for attackers to determine.[[54](#bib.bib59 "Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"), [65](#bib.bib60 "Interpreting and evaluating neural network robustness")]
Although these defenses often do not hold up well to changes in adversary behavior,[[6](#bib.bib54 "Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples")] some of these approaches are amenable to the broader problem of OOD robustness, especially considering that OOD robustness in general does not usually need to contend with changes in adversary behavior. The snow will not change the way it falls and the sensor in a camera will not change its sensitivity to noise just to interfere with a machine learning algorithm. Drawing from the literature on generalization and robustness to adversarial examples then, smoothed gradients[[39](#bib.bib45 "On large-batch training for deep learning: generalization gap and sharp minima")] are expected to be helpful in improving OOD performance. Additionally, expanding the training data to draw from a wider range of distributions has also been shown to improve OOD robustness, although care is needed to ensure that improving in one type of distributional shift does not detract from others.[[23](#bib.bib51 "Generalisation in humans and deep neural networks")] Hendrycks et al. suggest in a preliminary sense that using larger models, incorporating self-attention, and the use of pretrained models are also means to improve OOD robustness.[[29](#bib.bib50 "The many faces of robustness: a critical analysis of out-of-distribution generalization")]
###
5.2 AI is Not So Brittle
####
5.2.1 Deep Learning for Image Classification
Some papers have already shown that the performance of machine learning classifiers fall off as the inputs are degraded in various ways.[[37](#bib.bib49 "How image degradations affect deep cnn-based face recognition?"), [29](#bib.bib50 "The many faces of robustness: a critical analysis of out-of-distribution generalization"), [23](#bib.bib51 "Generalisation in humans and deep neural networks")] This is not surprising as the information being conveyed through those inputs is degraded. The important question with regard to test and certification is the rate at which performance falls off for various levels of departure from the training distribution. One of the most intuitive machine learning tasks is image recognition and comparison to human performance was attempted in one case by Geirhos et al.[[23](#bib.bib51 "Generalisation in humans and deep neural networks")] In their work, humans outperformed machines on most manipulations but were not always clearly superior. This field is still far from mature so further improvements in model robustness should be anticipated.
As an illustration of the degree of robustness or fragility of standard machine learning models, we performed ten different manipulations at ten different levels on a thousand randomly selected Image-Net images. The manipulations are listed in table [4](#S5.T4 "Table 4 ‣ 5.2.1 Deep Learning for Image Classification ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance").
| Manipulation | Maximum Magnitude |
| --- | --- |
| Gaussian Blur | 10 pixel standard deviation |
| Average Blur | 20 pixel window size |
| Motion Blur | 25 pixel kernel size |
| Gaussian Noise | 150 standard deviation intensity |
| Speckle Noise | 1 standard deviation intensity |
| Salt and Pepper Noise | 0.3 probability |
| Darkening | 220 intensity shift |
| Brightening | 220 intensity shift |
| Single Occlusion | 150 pixel per side |
| Multiple Occlusions | Matched to Single Occlusion coverage |
Table 4: Image degradations and their magnitudes
These altered datasets are OOD from the clean ImageNet dataset on which the neural networks were trained and they are OOD in controlled and measurable ways. The types of departures from the original distribution are both intuitive for humans to see and are likely types of departure that can be expected to occur in real-world scenarios. In most cases, the departures range from slight but noticeable for humans at the lowest levels to substantial but not entirely debilitating for humans at the high end. The speckle and salt and pepper noises were more difficult for the models as compared to humans so they are evaluated over a range that is still substantial but less debilitating for humans.
The degree of manipulation is easiest to contextualize in images as shown for one example out of the thousand in the dataset in Figure [1](#S5.F1 "Figure 1 ‣ 5.2.1 Deep Learning for Image Classification ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance"). The dog shown in Figure [1](#S5.F1 "Figure 1 ‣ 5.2.1 Deep Learning for Image Classification ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance") is on the naturally robust end in some ways because it takes up the whole space of the image. If the dog were only contained in a smaller fraction of the image it could be completely masked by an occlusion or, for blurring, more background features would be incorporated into the dog itself.
Figure 1: Examples of the degradation mechanisms and their severity.
We then evaluated five neural networks on the altered datasets having made no changes to the networks or their training procedures. The five networks were VGG-16 and VGG-19,[[55](#bib.bib15 "Very deep convolutional networks for large-scale image recognition")] InceptionV3,[[57](#bib.bib66 "Rethinking the inception architecture for computer vision")] Xception,[[11](#bib.bib67 "Xception: deep learning with depthwise separable convolutions")] and ResNet-50[[26](#bib.bib14 "Deep residual learning for image recognition")]. The top-1 accuracy for all five models on all ten degradation types is shown in Figure [2](#S5.F2 "Figure 2 ‣ 5.2.1 Deep Learning for Image Classification ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance") and top-5 accuracy is shown in Figure [3](#S5.F3 "Figure 3 ‣ 5.2.1 Deep Learning for Image Classification ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance").
The qualitative shape of the falloffs in performance are different. In some cases the performance falls off quickly and in others it is more robust. In some it is gradual and in others it is more sudden (remember that speckle and salt and pepper noise are shown over a range that is not often debilitating for humans). The trend observed in the single occlusion case may be somewhat deceptive because the fraction of the image that is occluded grows as the square of the x-axis so it is not surprising to see degradation slow at first then accelerate. It is shown in that way to contrast against the multiple occlusions case which is plotted with the same step size on the x-axis. The number of multiple occlusions was selected to have approximately the same average fraction of the image covered as the single occlusion steps but the two cases show qualitatively different trends. For multiple small occlusions like snow, the performance falls off more linearly at first then saturates. All the models tested were more susceptible to many scattered small occlusions than to one big one even though they block the same fraction of the image.
The Gaussian blur performance fell off approximately linearly then saturated at an accuracy much higher than random guessing. Most of the other degradations appear to trend toward lower accuracies although with noticeably different shapes to the curves. In some cases, most notably the darkening and brightening, different models resulted in not only different overall quantitative performance but with different qualitative trends. VGG16 and VGG19 are comparably stable against brightness and darkness changes. Inception and Xception are comparably stable against noise. And it is not always true that the family of architectures determine the similarity of their fall off in performance. Inception performed poorly on brightening and darkening whereas ResNet-50 and Xception performed more similar to each other.
To determine the relative brittleness of these networks as compared to humans would require a large-scale human evaluation which we do not perform because these networks were not selected for nor designed for OOD performance. The conclusion that OOD performance falls off in a way that is not so starkly different from humans without even being designed for OOD robustness is the intended take-away at this point. Many of the efforts to improve in-distribution generalization, such as adding noise or inducing rotations during training are likely to extend to OOD generalization but that was not the intent in these networks. Alterations to the images in the training set were used but they included actions like image rotations, rescaling, and changes to the color intensity not the distributional shifts evaluate in this paper.[[41](#bib.bib16 "ImageNet classification with deep convolutional neural networks")] As models are found or developed to have particularly good OOD performance, they should be baselined against human performance, or more importantly, against the likelihood of encountering similar departures from the training distribution in real-world conditions.
Figure 2: Top-1 degradation is shown for all models and all degradation types.
Figure 3: Top-5 accuracy is shown for all models and all degradation types.
####
5.2.2 Deep Learning for Speech Recognition
Another of AI’s contemporary highly-touted successes is in converting spoken language to text. It is common to train and test architectures on one of the available datasets (LibriSpeech, VoxForge, CHiME, etc) as it is for image classification (Image-Net, CIFAR, MNIST, etc). One popular paper though, presenting Deep Speech 2[[4](#bib.bib17 "Deep Speech 2: end-to-end speech recognition in english and mandarin")], evaluated their trained model using tests drawn from other datasets to determine how performance degrades differently when evaluated on distinct distributions including various sets of standard datasets of text being read, speakers with accents, and noisy datasets.
The various distributions might be subsets of the training distributions. For example the training set may have had Indian accents of the type used in the Indian accent distribution evaluation set. As another example, the simulated noise in the noisy evaluation set may be similar to, or of exactly the same type, as the noise that was added to the training set to improve generalization. Nonetheless, the concept of evaluating performance degradation on particular distributions that are not identical to that of the training examples is clearly on display and is a valuable contribution toward illustrating the robustness or brittleness of AI.
| | Test Set | AI WER (%) | Human WER (%) |
| --- | --- | --- | --- |
| Read | WSJ eval ’92 | 3.10 | 5.03 |
| WSJ eval ’93 | 4.42 | 8.08 |
| LibriSpeech test-clean | 5.15 | 5.83 |
| LibriSpeech test-other | 12.73 | 12.69 |
| Accented | VoxForge American-Canadian | 7.94 | 4.85 |
| VoxForge Commonwealth | 14.85 | 8.15 |
| VoxForge European | 18.44 | 12.76 |
| VoxForge Indian | 22.89 | 22.15 |
| Noisy | CHiME eval real | 21.59 | 11.84 |
| CHiME eval sim | 42.55 | 31.33 |
Table 5: Word Error Rate (WER) performance of Deep Speech 2 on various distributions of inputs
Both Deep Speech 2 and the humans in the experiment experienced degraded performance on some distributions but neither were completely debilitated in any of them. On the easiest distributions (the top four in Table [5](#S5.T5 "Table 5 ‣ 5.2.2 Deep Learning for Speech Recognition ‣ 5.2 AI is Not So Brittle ‣ 5 Measuring OOD Performance ‣ Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance")), the AI mostly outperformed the humans. Both had single-digit error percentages as measured by Word Error Rate which is impressive but is again orders of magnitude higher than certifiers are accustomed to for software components in critical systems.
When the distributions were more specialized (accented and noisy), the humans outperformed the AI. That suggests the AI is less robust than the humans to shifts in distributions but, just as was true for image classification, the fall off is gradual and comparable in magnitude to the humans. Deep Speech 2 was not explicitly designed for OOD performance nor was their experiment designed to evaluate OOD performance, but it is a useful step in that direction.
###
5.3 SILs for OOD
SILs exist for both continuous operation and for low-demand per use operations. Both of those quantities will need to be determined for certain AI applications in critical systems but a third class of SIL may be warranted for AI systems focused OOD performance. For such a metric one would need to establish degrees of OOD and establish acceptable failure rates at those degrees. Even without estimating OOD performance, certifiers will need to estimate the frequency at which OOD inputs will be encountered in the real-world to decide whether the system meets safety thresholds in the real world. If the AI component is the only option or is necessary to help achieve the safety thresholds then OOD detection is insufficient and there is no choice but to estimate the OOD performance.
Estimating the OOD performance will rely on estimating both the frequency and magnitude of departures from the training distribution. There are many means for measuring the divergence between two distributions or the distance from a sample and a distribution. A few common examples are the Mahalanobis and Hausdorff distances and the Kullback-Leibler divergence. The most useful distances though might not be quantitative such as those. It may only be possible for certifiers to envision qualitative or conceptual distances akin to the qualitative levels used in Severity, Exposure, and Controllability that are used to determine the appropriate ASIL in the automotive industry. Qualitative levels such as In-Distribution, Near-Distribution, Somewhat OOD, Far OOD, and Very Far OOD could be useful. The anticipated frequency of encountering situations corresponding to each of those levels could then be matched to required OOD performance at those levels. That OOD performance could be quantitatively measured using real samples or perhaps even supplemented by artificially generated samples from each of the qualitative levels.
6 Conclusions
--------------
AI is being considered for, or even applied in, critical industries. Those industries have well-established procedures and standards for incorporating new technologies but there are some mismatches between AI and those standards and procedures. First, AI commonly has failure rates that are orders of magnitude higher than the standards used in those critical industries. Second, AI is intended for tasks that do not accommodate straightforward evaluation. The central challenge facing the tester is to estimate the frequency and extent of departures from designed conditions that the AI will encounter in the real world and estimate the AI’s performance in those conditions. Fortunately, AI algorithms do maintain some level of performance outside of their design conditions such as on image classification of OOD inputs and perhaps speech recognition with varying accents or noise. If AI can reach the levels of performance in the best of conditions (perfectly in-distribution sampling) that are required for use in critical systems then evaluating performance in non-ideal conditions will be necessary. More work is needed to study the performance degradation of AI algorithms when subjected to OOD samples and to identify ways to improve their OOD performance but it is an area that holds some promise.
Acknowledgement
---------------
The author would like to thank Open Philanthropy for their interest in this topic as well as Luke Muehlhauser, Gavin Hartnett, Edward Geist, Daniel Ish, Li Ang Zhang, and Peter Whitehead for help collecting and analyzing background materials on existing standards and processes and/or useful conversations about testing for AI and techniques for machine learning in the presence of multiple distributions of data. Naturally, any errors or omission are attributable to the author alone. |
5dd2d268-ffde-4e0f-b87c-66587f59cd0d | trentmkelly/LessWrong-43k | LessWrong | Model-building and scapegoating
When talking about undesirable traits, we may want to use simple labels. On one hand, simple labels have the virtue of efficiently pointing to an important cluster of behavioral predictions. On the other, they tend to focus attention on the question of whether the person so described is good or bad, instead of on building shared models about the causal structure underlying the perceived problem.
Slate Star Codex recently posted a dialogue exploring this through the example of the term "lazy." (Ozy's response is also worth reading.) I think that Scott's analysis itself unfortunately focuses attention on the question of whether assigning simple labels to adverse traits is good or bad (or alternately, true or false) instead of on building shared models about the causal structure underlying the perceived problem.
When I call someone lazy, I am doing two things. The first is communicating factual information about that person, which can help others avoid incurring costs by trusting the lazy person with some important tasks. This is shared model-building, and it's going to be more salient if you're focused on allocating resources to mitigate harm and produce things of value. In other words, if you're engaged in a community of shared production.
The second is creating a shared willingness to direct blame at that person. Once there's common knowledge that someone's considered blameworthy, they become the default target for exclusion if the group experiences a threat. This can be as simple as killing them and taking their stuff, so there's more per survivor to go around, but this can also take the form of deflecting the hostility of outsiders to the supposed one bad apple. This dynamic is called scapegoating, and it's going to be more salient when zero-sum dynamics are more salient.
Even though I may intend to do only one of these, it's actually quite hard to act along one of these dimensions without side effects along the other. For instance, Protocols of the Elders of |
70802b4d-c3d7-4113-b647-15baa506e891 | trentmkelly/LessWrong-43k | LessWrong | What readings did you consider best for the happy parts of the secular solstice?
I'm currently thinking about creating a new rationalist ritual day that focuses just on happiness. I'm thinking about reusing some elements that worked well at the solstice. Which readings did you enjoy very much during the happy parts of the solstice? |
3fd973ea-1965-4055-8d90-8c6a7e70e585 | trentmkelly/LessWrong-43k | LessWrong | Fermenting Form
Application forms are hard to write. Questions like: “Tell us about yourself” or “What are your strengths and weaknesses?” are tiring to address usefully in 200 words. Getting stuck on a bad application question rewriting paragraphs is fermenting your brain. You don’t become a better applicant with more work on those sections. You lose hours of productive time.
As I understand it, proof of skill and good referrals decide whether you get in, the rest is a perfunctory test of whether you can put up with ill-defined and boring tasks.
Common stock questions are used because the application writer felt uneasy without them, or because they copied someone else’s form. It’s not because they’re looking for certain information.
Given the choice, don’t apply to organizations who use stock questions – it’s a signal of dysfunction. It means no one can change the most important form, or worse, no one cares to write better questions.
Often we don’t have the choice of where to apply. This means our job is to make ourselves good applicants at the places we want to be. But part of that will include filling out the vague questions.[1]
The general method is to:
1. think about what type of answer the person seeing your application wants.
2. reframe the question to make it direct and easy to give that answer.
If a question is vague, you’ll have to cover more ground thinking in order to come up with answers. In the absence of something clear to fit your writing to, most of the write-then-select-all-delete process is focused on irrelevant things like sentence structure and flow. This happens when there’s no progression in what you’re saying. When sentences aren’t following each other, you draw each sentence out of the entire set of responses to the original question – over and over, like trying to solve a scrambled jigsaw puzzle with each piece mixed in from another set.
It’s two-hundred words. The wording doesn’t matter if your sentences are short and simple – but short and sim |
8df09246-d106-49d2-a915-5d0cbbbd96ba | trentmkelly/LessWrong-43k | LessWrong | Anki with Uncertainty: Turn any flashcard deck into a calibration training tool
We've developed an Anki addon that lets you do calibration training on numerical flashcards!
Find information on how it works and how to use it on Quantified Intuitions.
Thanks to @Beth Barnes for supporting the development and giving feedback. And thanks to @Hauke Hillebrandt for inspiring this idea with this comment right here on the Forum!
It's pretty experimental, so I'd love to hear any feedback or thoughts.
In related news: the March Estimation Game is now live - ten new Fermi estimation questions, with some particularly interesting ones this month!
See our previous posts for more information about The Estimation Game and our other tools on Quantified Intuitions, including a calibration tool with questions about the world's most pressing problems. |
8e18506b-4393-48c5-9cd0-b07fdedbcb63 | StampyAI/alignment-research-dataset/arxiv | Arxiv | DETERRENT: Detecting Trojans using Reinforcement Learning
1. Introduction
----------------
Reinforcement learning (RL) helps a computing system (a.k.a. agent) to learn by its own experience through exploring and exploiting the underlying environment.
Over time, the agent takes optimal actions in sequence, even with limited or no knowledge regarding the environment.
From a cybersecurity perspective, such RL agents are attractive as they can generate optimal defense techniques in an unknown adversarial environment.
Given the latest improvements in RL algorithms, these agents can efficiently navigate high-dimensional search space to find optimal actions.
Hence, researchers have used RL agents to develop promising approaches for several security problems, including intrusion detection (RL\_intrusion\_detection), fuzzing (RL\_Fuzzing; RL\_Fuzzing\_USENIX), and developing secure cyber-physical systems (RL\_CPS1; RL\_CPS2; nguyen2019deep\_new).
However, research in hardware security is still in its infancy to reap the power of RL in developing optimal defenses in adversarial environments.
In this work, we showcase how RL can be used to efficiently detect hardware Trojans (HTs).
Out of the many problems in hardware security, the HT detection problem presents significant computational challenges to the defender in detecting them in an unknown environment (i.e., HT-infected design).
The increasing cost of integrated circuit (IC) manufacturing has forced semiconductor companies to send their designs to untrusted, off-shore foundries.
Malicious components known as HTs inserted during the fabrication stage can leak secret information, degrade performance, or cause a denial of service.
###
1.1. Hardware Trojans
An HT consists of two components: trigger and payload.
When the trigger is activated, the payload causes a malicious effect in the design.
Figure [1](#S1.F1 "Figure 1 ‣ 1.1. Hardware Trojans ‣ 1. Introduction ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") illustrates an HT that flips an output upon trigger activation.
The trigger comprises multiple nets, called select nets, in the design.
For instance, the adversary can choose the select nets so that the trigger gets activated only under extremely rare conditions.
This is achieved by determining a rareness threshold111Rareness threshold is the probability below which nets are classified as rare nets. and constructing the trigger using the corresponding rare nets.
Figure 1. Example of an HT in a design with 150 rare nets.
Detecting HTs is difficult since they are designed to be stealthy (xiao2016Trojan\_survey).
Consider the example in Figure [1](#S1.F1 "Figure 1 ‣ 1.1. Hardware Trojans ‣ 1. Introduction ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") with 150 rare nets.
Four of them are used for the trigger.
Thus, the defender needs to check up to 150C4≈20×106 different combinations of rare nets, which is extremely challenging. Such a large space makes it difficult even for conventional automatic test pattern generation (ATPG) tools (TestMAX) to activate the trigger.
###
1.2. Hardware Trojan Detection Techniques
One can classify the HT detection techniques under two broad categories: logic testing and side-channel analysis.
Logic testing involves the application of test patterns to the HT-infected design to activate the trigger (chakraborty2009mero; TARMAC\_TCAD; pan2021automated).
However, activating an extremely rare trigger is challenging because the possible combinations of rare nets are extensive.
On the other hand, side-channel-based detection techniques detect HTs based on the differences in the side-channel measurements (such as power or timing) between the golden (i.e., HT-free) design and an HT-infected design (narasimhan2012hardware; huang2016mers; huang2018scalable; lyu2019efficient).
However, since HTs have an extremely small footprint compared to the overall size of the design, their impact on side-channel metrics is usually negligible and concealed under process variation and environmental effects (rai2009performance).
We refer interested readers to (xiao2016Trojan\_survey) for a detailed survey on HTs and HT detection techniques.
Note that activating the trigger is not only essential for logic testing techniques but also helpful for side-channel-based techniques because activating the trigger leads to an increase in the side-channel footprint of the HT, making it easier to detect (TARMAC\_TCAD).
Although activating the trigger is critical, it is difficult to do so efficiently.
Consider Figure [1](#S1.F1 "Figure 1 ‣ 1.1. Hardware Trojans ‣ 1. Introduction ‣ DETERRENT: Detecting Trojans using Reinforcement Learning"); the defender needs up to 20×106 test patterns to guarantee trigger activation because the defender does not know which rare nets make the trigger.
Next, we outline the ideal characteristics required from any technique for activating the trigger. (1) High trigger activation rate: The technique should activate a large number of trigger conditions to detect HTs successfully.222Trigger activation rate, i.e., the proportion of trigger conditions activated by a set of test patterns, is also called trigger coverage.
(2) Small test generation time: The time required to generate the test patterns should not be large; otherwise, the technique will not be scalable to larger designs.
(3) Compact set of test patterns: The number of test patterns required to activate the trigger conditions should be small.
A large number of test patterns affect the testing cost adversely.
(4) Feedback-guided approach: The technique should analyze the test patterns and their impact on the circuit to generate new test patterns, thereby reducing the test generation time and the size of the test set.
###
1.3. Prior Works and Their Limitations
MERO generates test patterns that activate each rare net N times (chakraborty2009mero).
The hypothesis is that if all the rare nets are activated N times, the test patterns are likely to activate the trigger.
The algorithm starts with a large pool of random test patterns and iteratively performs circuit simulation to keep track of the number of rare nets that get activated.
While MERO provides moderate performance for small benchmarks, it fails for large benchmarks. For instance, the trigger coverage of MERO for the MIPS processor is only 0.2% (TARMAC\_TCAD), as it violates the characteristics (1), (2), (3), and (4) mentioned above.
TARMAC overcomes the limitations of MERO by transforming the problem of test pattern generation into a clique cover problem (TARMAC\_TCAD). It iteratively finds maximal cliques of rare nets that satisfy their rare values.
By not relying on brute force, TARMAC outperforms MERO by a factor of 71× on average.
However, the performance of TARMAC is sensitive to randomness since the algorithm relies on randomly sampled cliques.
Although the test generation time for TARMAC is short, it violates characteristics (3) and (4).
TGRL uses RL along with a combination of rareness and testability measures to overcome the limitations of TARMAC (pan2021automated).
TGRL achieves better coverage than TARMAC and MERO while reducing the run-time.
However, it still violates characteristic (3), as evidenced by our results in Section [4](#S4 "4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning").
###
1.4. Our Contributions
As discussed above, all existing techniques for trigger activation fall short on one or more fronts.
In this work, we propose a new technique that is designed to satisfy all four ideal characteristics.
We model the test generation problem for HT detection as an RL problem because test generation involves searching a large space to find an optimal set of test patterns.
This is exactly what RL algorithms do: they navigate large search spaces to find optimal solutions.
However, there are several challenges that need to be overcome to realize a practical and scalable RL agent, such as (i) large amount of training time required for large designs, (ii) the agent needs to be efficient while choosing actions, and (iii) some challenging benchmarks require smart fine-tuning.
We provide further details on how we overcome these challenges in Section [3](#S3 "3. DETERRENT: Detecting Trojans using Reinforcement Learning ‣ DETERRENT: Detecting Trojans using Reinforcement Learning").
The primary contributions of our work are as follows.
* [leftmargin=\*]
* We develop an RL technique that is efficient in activating rare trigger conditions, thereby addressing the limitations of the state-of-the-art HT detection techniques.
* We overcome several challenges to make our technique scalable to a large design like the MIPS processor.
* We perform an extensive evaluation on diverse benchmarks and demonstrate the capability of our technique, which outperforms the state-of-the-art logic-testing techniques on all benchmarks.
* Our technique provides two orders of magnitude (169×) reduction in the size of the test set compared to existing techniques.
* Our technique maintains similar trigger coverage (≤2% drop) with increasing number of rare nets, whereas the state-of-the-art technique’s performance drops to 0%.
* Our technique maintains similar trigger coverage (≤2% drop) for at least 64× more potential trigger conditions.
* We release our benchmarks and test patterns (DETERRENT-git).
2. Assumptions and Background
------------------------------
###
2.1. Threat Model
We assume the standard threat model used in logic testing-based HT detection (chakraborty2009mero; TARMAC\_TCAD; pan2021automated).
We assume that the adversary inserts HTs in rare nets of the design to remain stealthy. The defender’s (i.e., our) objective is to generate a minimal set of test patterns that activate unknown trigger conditions. We generate test patterns using only the golden (i.e., HT-free) netlist.
###
2.2. Reinforcement Learning
RL is a machine learning methodology where an intelligent agent learns to navigate an environment to maximize a cumulative reward.
It is formalized as a Markov decision process.
An RL agent interacts with the environment in discrete time steps.
At each step, the agent receives the current state and the reward, and it chooses the action which is sent to the environment.
The environment moves the agent to a new state and provides a reward corresponding to the state transition and action.
The aim of the RL agent is to learn a policy π that maximizes the expected cumulative reward.
The policy maps state-action pairs to probabilities of taking that action in a given state.
The agent learns the optimal or near-optimal policy in a trial-and-error method by interacting with the environment.
3. DETERRENT: Detecting Trojans using Reinforcement Learning
-------------------------------------------------------------
We now formulate the trigger activation problem as an RL problem, but it suffers from challenges related to scalability, efficiency, and poor performance. We then address these challenges and devise a final RL agent that outperforms all existing techniques.
###
3.1. A Simple Formulation
As shown in Figure [1](#S1.F1 "Figure 1 ‣ 1.1. Hardware Trojans ‣ 1. Introduction ‣ DETERRENT: Detecting Trojans using Reinforcement Learning"), to activate the trigger, the defender has to apply an input pattern that forces all four rare nets to take their rare values simultaneously,333For the sake of conciseness, henceforth, we shall use the phrase “activate the rare nets” instead of “force the rare nets to take their rare values.” but the defender does not know which four rare nets constitute the trigger.
A naïve solution is to generate one input pattern for each combination of four rare nets.
Such an approach would require up to rC4 test patterns (r is the total number of rare nets), which would be infeasible to employ in practice.
However, one input pattern can activate multiple different combinations of rare nets simultaneously.
So, we need to find a minimal set of input patterns that can collectively activate all combinations of rare nets. This problem is a variant of the set-cover problem, which is NP-complete (cormen2009introduction).
We call a set of rare nets compatible if there exists an input pattern that can activate all the rare nets in the set simultaneously.
Thus, our objective is to develop an RL agent that generates maximal sets of compatible rare nets.
We now map the trigger activation problem into an RL problem by formulating it as a Markov decision process.
* [leftmargin=\*]
* States S is the set of all subsets of the rare nets. An individual state st represents the set of compatible rare nets at time t.
* Actions A is the set of all rare nets. An individual action at is the rare net chosen by the agent at time t.
* State transition P(st+1|at,st) is the probability that action at in state st leads to the state st+1.
In our case, if the chosen rare net (i.e., the action) is compatible with the current set of rare nets (i.e., the current state), we add the chosen rare net to the set of compatible rare nets (i.e., the next state).
Otherwise, next state remains the same as the current state.
Thus, in our case, the state transition is deterministic, as shown below.
| | | |
| --- | --- | --- |
| | | |
* Reward function R(st,at) is equal to the square of the size of the next state for compatible states, and 0 otherwise.
| | | |
| --- | --- | --- |
| | | |
The reward is designed so that the agent tries to maximize the size of the state, i.e., the number of compatible rare nets.
We square the reward at each step, but any power greater than 1 would be appropriate since we want the reward function to be convex to account for the fact that as the size of the state grows, the difficulty of finding a new compatible rare net increases.
* Discount factor γ (0≤γ≤1) indicates the importance of future rewards relative to the current reward.
The initial state s0 is a singleton set containing a randomly chosen rare net.
At each step t, the agent in state st chooses an action at, arrives in the next state st+1 according to the state transition rules, and receives a reward rt.
This cycle of state, action, reward, and next state is repeated T times, and this constitutes one episode.
At the end of each episode, the state of the agent reflects the rare nets that are compatible.444For software implementation, we represent the states (which are defined as sets) as binary vectors, with each element on the vector indicating whether the corresponding rare net is present in the state or not. Since the state and action spaces are discrete, we train our agent using the Proximal Policy Optimization (PPO) algorithm with default parameters unless specified otherwise (schulman2017proximal).
Once the agent returns the maximal sets of compatible rare nets after training, we pick the k largest distinct sets and generate the test patterns corresponding to those sets using a Boolean satisfiability (SAT) solver. k is a hyperparameter of our technique.
Our experiments indicate that this simple agent performs well on small benchmarks.
But, for larger benchmarks like the MIPS processor from OpenCores (OpenCores\_MIPS) we obtain low trigger coverage (≈70% after training for 12 hours).
We analyzed the basic architecture in detail, and it faces certain challenges which are presented next.
Figure 2. Combinations of reward and masking methods for MIPS. Eoe: End-of-episode, M: Masking, NM: No masking
###
3.2. End-of-Episode Reward Computation
Challenge 1: Large training time. The basic architecture requires computing the reward for each time step, which involves checking if the selected action is compatible with the current state or not.
For a large benchmark like the MIPS processor, the check takes a few seconds (because of the large number of gates in the benchmark) each time, and the agent requires millions of steps to learn. Hence, the training time becomes prohibitively large.
Solution 1. To address challenge 1, we reduce the frequency of reward computation by computing it only at the end of the episode.
At all intermediate steps, the reward is set to 0.
While this approach speeds up the training by a factor of ≈86×, the rewards become sparse, and it affects the performance of our agent. However, the impact on performance is only 5.6%, as shown in Table [1](#S3.T1 "Table 1 ‣ 3.2. End-of-Episode Reward Computation ‣ 3. DETERRENT: Detecting Trojans using Reinforcement Learning ‣ DETERRENT: Detecting Trojans using Reinforcement Learning").
| Method |
| |
| --- |
| Max. # compatible |
| rare nets |
| Rate |
| (steps/min) | (eps./min) |
| Reward at all steps | 53 | 108 | 0.72 |
| End-of-episode reward | 50 | 9387 | 63 |
| Improvement | -5.6% | 86.91× | 87.5× |
Table 1. Comparison of training rates for the reward methods for the MIPS benchmark: all steps vs. end-of-episode.
###
3.3. Masking Actions for Efficiency
Challenge 2: Wasted efforts in choosing actions. Another challenge that the basic architecture suffers from is inefficiency in choosing actions. At each step, the actions available to the agent remain the same, irrespective of the state of the agent. This leads to situations where the agent chooses an action that has already been chosen in the past, or
that is known to be not compatible with at least one of the rare nets in the current state.
Hence, the time spent by the agent on such steps is wasted.
Figure 3. Total loss trends in c2670: default vs. boosted exploration.
Figure 4. Architecture of DETERRENT.
Solution 2. To increase the efficiency of the agent in choosing actions, we mask the actions available to the agent based on the state at any given time step. This ensures that at each time step, the agent only chooses actions that lead it to a new state.
Additionally, reward computation also becomes less sparse because episode lengths reduce due to masking (episode ends when there are no available actions). Since we are eliminating actions from the state space, one may wonder if this approach may eliminate optimal actions. We now prove that this is not possible for our problem formulation.
###### Theorem 3.1 ().
Masking actions does not prevent our agent from learning anything that it could have learned otherwise.
###### Proof.
Let P′ and P denote an agent that masks and does not mask actions, respectively. Suppose both P and P′ are in state s. Let A denote the complete set of actions, and As denote the set of masked actions for state s. So, As={i|i is compatible with s and i∉s} and As⊆A.
If P chooses an action a∈A∖As (i.e., an action in the set difference), then P will stay in the same state because the rare net corresponding to such an action a would either be incompatible with s or it would already be in s. On the other hand, for any action a′∈As chosen by P, agent P′ can also choose the same action a′ since it is in As. Hence, masking does not prevent our agent from learning anything that the corresponding unmasked agent could have learned.
∎
To enable masking, we compute pairwise compatibility of all rare nets using a SAT solver before training.
Since the compatibility computation for each unique pair is independent, we parallelize it across 64 processes to reduce the runtime.
During training, for a given state s (i.e., set of compatible rare nets at the current step), all actions (i.e., rare nets) that are not compatible with any of the rare nets in s are masked off, and hence, are not chosen.
To design the best architecture, we implemented agents with all combinations of reward methods (at all steps and end-of-episode) and masking (with and without). The results in Figure [2](#S3.F2 "Figure 2 ‣ 3.1. A Simple Formulation ‣ 3. DETERRENT: Detecting Trojans using Reinforcement Learning ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") demonstrate that to obtain the maximum number of compatible rare nets, the optimal architecture should mask actions based on state and provide rewards at each time step.
###
3.4. Boosting Exploration
Challenge 3: Convergence to local optima. Since the agent’s objective is to generate maximal sets of rare nets, for certain benchmarks (for instance, c2670), the agent gets stuck in local optima.
In other words, the agent quickly learns to capitalize on sub-optimal sets of compatible rare nets, thereby missing out on the diversity of the sets of compatible rare nets, resulting in poor trigger coverage.
Solution 3. To force the agent to explore, we (1) include an entropy term in the loss function of the agent and (2) control the smoothing parameter that affects the variance of the loss calculation.
To implement (1), we modify the total loss function to l=lπ+cϵ×lϵ+cv×lv, where l is the total loss, lπ is the loss of the policy network, lϵ is the entropy loss, lv is the value loss, and cϵ and cv are the coefficients for the entropy and value losses, respectively. We set cϵ=1. The entropy loss is inversely proportional to the randomness in the choice of actions. To implement (2), we set the parameter λ for policy loss lπ in PPO to 0.99. This leads to variance in the loss calculation and hence in the actions chosen by the agent.
Thus, we penalize the agent for having less variance in its choice of actions. Hence, the agent is forced to explore more and is likely to converge to a better state, i.e., a state with more compatible rare nets.
Figure [3](#S3.F3 "Figure 3 ‣ 3.3. Masking Actions for Efficiency ‣ 3. DETERRENT: Detecting Trojans using Reinforcement Learning ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") shows that by modifying the loss function and the smoothing parameter in PPO, the loss does not become 0 quickly, forcing the agent to explore more.
###
3.5. Putting it All Together
The final architecture of DETERRENT is illustrated in Figure [4](#S3.F4 "Figure 4 ‣ 3.3. Masking Actions for Efficiency ‣ 3. DETERRENT: Detecting Trojans using Reinforcement Learning ‣ DETERRENT: Detecting Trojans using Reinforcement Learning"). In an offline phase, we find the rare nets of the design and generate pairwise compatibility information for them in a parallelized manner.
Then, for each episode, the agent starts with a random rare net and takes an action according to the policy (a neural network) and the action mask.
The masked action is evaluated to produce a reward for the agent, and the agent moves to the next state.
This procedure repeats for T steps (i.e., an episode).
Internally, after a certain number of episodes, the PPO algorithm translates the rewards into losses (depending on the output of the policy network, which generates actions, and the value network, which predicts the expected reward of the action), which are used to update the parameters of the policy and value networks.
Eventually, when the agent has learned the task, the losses become negligible, and the reward saturates.
Once the RL agent gives us the maximal sets of compatible rare nets, we pick the k largest distinct sets and generate the test patterns, one for each of those sets, using a SAT solver.
4. Experimental Evaluation
---------------------------
###
4.1. Experimental Setup
| | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Design | Number | # Gates | Random | TestMAX (TestMAX) | TARMAC (TARMAC\_TCAD) | TGRL (pan2021automated) | DETERRENT (this work) |
| of rare | Test | Cov. | Test | Cov. | Test | Cov. | Test | Cov. | Test | Patterns Red./ | Cov. |
| nets | Length | (%) | Length | (%) | Length | (%) | Length | (%) | Length | TARMAC & TGRL | (%) |
| c2670 | 43 | 775 | 5306 | 10 | 89 | 27 | 5306 | 100 | 5306 | 96 | 8 | 663.25× | 100 |
| c5315 | 165 | 2307 | 8066 | 37 | 103 | 5 | 8066 | 61 | 8066 | 94 | 1585 | 5.08× | 99 |
| c6288 | 186 | 2416 | 3205 | 54 | 38 | 4 | 3205 | 100 | 3205 | 85 | 2096 | 1.52× | 99 |
| c7552 | 282 | 3513 | 9357 | 10 | 137 | 4 | 9357 | 73 | 9357 | 71 | 5910 | 1.58× | 85 |
| s13207 | 604 | 1801 | 9659 | 3 | 106 | 4 | 9659 | 80 | 9659 | 5 | 9600 | 1.01× | 80 |
| s15850 | 649 | 2412 | 9512 | 3 | 110 | 3 | 9512 | 79 | 9512 | 8 | 6197 | 1.53× | 81 |
| s35932 | 1151 | 4736 | 3083 | 99 | 37 | 68 | 3083 | 100 | 3083 | 58 | 6 | 513.83× | 100 |
| MIPS | 1005 | 23511 | 25000 | 0 | 796 | 0 | 25000 | 100 | — | — | 1304 | 19.17× | 97 |
| Avg. | 511 | 5184 | 6884 | 27.75† | 88.57 | 10† | 6884 | 83.5† | 6884 | 86.5† | 3628.85 | 169.68ׇ | 95.75† |
†The coverages are averaged over c2670, c5315, c6288, and c7552. ‡The reduction is averaged over all except MIPS.
Table 2. Comparison of trigger coverage (Cov. (%)) and test length of DETERRENT with random simulations, Synopsys TestMAX (TestMAX), TARMAC (TARMAC\_TCAD), and TGRL (pan2021automated). Evaluation is done on 100 random four-width triggered HT-infected netlists.
We implemented our RL agent using PyTorch1.6 and trained it using a Linux machine with Intel 2.4 GHz CPUs and an NVIDIA Tesla K80 GPU.
We used the SAT solver provided in the pycosat library.
We implemented
the parallelized version of TARMAC in Python 3.6.
We used Synopsys VCS for logic simulations and for evaluating test patterns on HT-infected netlists.
Similar to prior works (TARMAC and TGRL), for sequential circuits, we assume full scan access.
To enable a fair comparison, we implemented and evaluated all the techniques on the same benchmarks as TARMAC and TGRL, which were provided to us by the authors of TGRL.
They also provided us with the TGRL test patterns.
We also performed experiments on the MIPS processor from OpenCores (OpenCores\_MIPS) to demonstrate scalability.
For MIPS, we use vectorized environment with 16 parallel processes to speed up the training.
For evaluation, we randomly inserted 100 HTs in each benchmark and verified them to be valid using a Boolean satisfiability check.
###
4.2. Trigger Coverage Performance
In this section, we compare the trigger coverage provided by different techniques (Table [2](#S4.T2 "Table 2 ‣ 4.1. Experimental Setup ‣ 4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning")).
In addition to TARMAC and TGRL, we also compare the performance of DETERRENT with random test patterns and patterns generated from an industry-standard tool, Synopsys TestMAX (TestMAX).
We used the number of patterns from TGRL as a reference for the random test patterns and TARMAC to enable a fair comparison.
For TestMAX, the number of patterns is determined by the tool in the default setting (run\_atpg).
Note that for s13207, s15850, and s35932, the netlists corresponding to the test patterns provided by the authors of TGRL were not available to us at the time of writing the manuscript. Hence, we could only evaluate the TGRL test patterns for those circuits on our benchmarks. Due to this, the trigger coverage of TGRL for these benchmarks is low.
Additionally, TGRL does not evaluate on the MIPS benchmark. Hence the corresponding cells in the table are empty.
To enable a fair comparison, we have not included s13207, s15850, and s35932 in the average test length, as well as MIPS in the average trigger coverages for all techniques in Table [2](#S4.T2 "Table 2 ‣ 4.1. Experimental Setup ‣ 4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning").
The results demonstrate that DETERRENT achieves better trigger coverage than all other techniques while reducing the number of test patterns. On average, DETERRENT improves the coverage over random patterns (68%), TestMAX (85.75%), TARMAC (12.25%), and TGRL (9.25%), and achieves two orders of magnitude reduction in the number of test patterns over TARMAC and TGRL (169×).
###
4.3. Impact of Trigger Width
Trigger width, i.e., the number of rare nets that constitute the trigger, directly affects the stealth of the HT.
As the trigger width increases, the difficulty to activate the trigger increases exponentially.
For example, for a rareness threshold of 0.1, if the trigger width is 4, the probability of activating the trigger through random simulation is 10−4.
Whereas, if the trigger width is 12, the probability reduces to 10−12.
Thus, it is necessary to maintain the performance with increasing trigger width.
Figure [5](#S4.F5 "Figure 5 ‣ 4.3. Impact of Trigger Width ‣ 4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") illustrates the results for c6288; we chose this benchmark as TGRL provides a good trigger coverage.
With increasing trigger width, the performance of TGRL drops drastically.
DETERRENT maintains a steady trigger coverage, demonstrating that it can activate extremely rare trigger conditions.
Figure 5. Impact of trigger width on the trigger coverage of TGRL (pan2021automated) and DETERRENT for c6288.
###
4.4. Trigger Coverage vs. Number of Patterns
We now investigate the marginal impact of test patterns on trigger coverage.
To do so, we analyze the increase in trigger coverage provided by each test pattern for DETERRENT and TGRL.
Figure [6](#S4.F6 "Figure 6 ‣ 4.4. Trigger Coverage vs. Number of Patterns ‣ 4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") demonstrates that DETERRENT obtains the maximum trigger coverage with very few patterns as opposed to TGRL.
Figure 6. Trigger coverage vs. test patterns comparison.
###
4.5. Impact of Rareness Threshold
Rareness threshold is the probability below which nets are classified as rare, i.e., the logic values of these nets are strongly biased towards 0 or 1.
For a given trigger width (α), as the rareness threshold increases, the number of rare nets increases (say by a factor of β), and so, the number of combinations possible for constructing the trigger increases by a factor of βα, making it much more difficult to activate.
Figure [7](#S4.F7 "Figure 7 ‣ 4.5. Impact of Rareness Threshold ‣ 4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning") shows that the number of rare nets increases with increasing threshold (leading to up to 64× more potential trigger combinations), but DETERRENT is still able to achieve similar trigger coverage (≤2% drop) with less than 2500 patterns.555The authors of TGRL did not provide us the test patterns for thresholds other than 0.1. Hence, we do not compare with TGRL for other threshold values.
In another experiment, we trained the agent using rare nets for a threshold of 0.14 and evaluated the generated test patterns on rare nets with threshold of 0.1—the trigger coverage is 99%. This hints that we can train the agent for a large set of rare nets and use it to generate patterns for a subset of rare nets.
Figure 7. Impact of rareness threshold on the number of rare nets and the trigger coverage of DETERRENT for c6288.
5. Discussion and Future Work
------------------------------
Comparison with TGRL (pan2021automated). Our RL agent architecture is entirely different from TGRL.
TGRL maximizes a heuristic based on the rareness and testability of nets.
In contrast, we identify the problem of trigger activation to be a set-cover problem and find maximal sets of compatible rare nets.
Moreover, TGRL states and actions are test patterns generated by flipping bits probabilistically, whereas our agent’s efforts are more directed by generating maximal sets of compatible rare nets.
Due to our formulation, we achieve better coverage but with orders of magnitude fewer test patterns than TGRL (see Section [4](#S4 "4. Experimental Evaluation ‣ DETERRENT: Detecting Trojans using Reinforcement Learning")).
Feasibility of using a SAT solver. We use a SAT solver for the compatibility check during training and for generating test patterns from the maximal sets of compatible rare nets provided by the RL agent.
Nevertheless, our technique is scalable for larger designs (as evidenced by our results) because:
(i) During training, we reduce the runtime of using the SAT solver as we generate a dictionary containing the compatibility information offline in a parallelized manner.
(ii) When generating the test patterns, we only require invoking the SAT solver T times, where T is the required number of test patterns.
Hence, even for large benchmarks like MIPS, we can generate test patterns that outperform all the HT detection techniques in less than 12 hours.
Meta-learning. We generated test patterns for individual benchmarks using separate agents.
Since the training time of our agents for all benchmarks is less than 12 hours, it is practical to use our technique.
As part of future work, we would like to explore the principles of designing a standalone agent that can be trained on a corpus of benchmarks once and be used to generate test patterns for unseen benchmarks.
To that end, we plan to extend the current framework by using principles from meta-learning.
6. Conclusion
--------------
Prior works on trigger activation for HT detection have shown reasonable trigger coverage, but they are ineffective, not scalable, or require a large number of test patterns.
To address these limitations, we develop an RL agent to guide the search for optimal test patterns.
However, in order to design the agent, we face several challenges like inefficiency and lack of scalability.
We overcome these challenges using different features like masking and boosting exploration of the agent.
As a result, the final architecture generates a compact set of test patterns for designs of all sizes, including the MIPS processor.
Experimental results demonstrate that our agent reduces the number of test patterns by 169× on average while improving trigger coverage.
Further evaluations show that our agent is robust against increasing complexity.
Our agent maintains steady trigger coverage for different trigger widths, whereas the state-of-the-art technique’s performance drops drastically.
Our agent also maintains performance against the increasing number of possible trigger combinations.
Although this work demonstrates the power of RL for trigger activation, the challenges related to scalability and efficiency are not specific to the current problem.
The ways in which we overcame the challenges can be used to develop better defenses for other hardware security problems.
Acknowledgments
---------------
The work was partially supported by the National Science Foundation (NSF CNS–1822848 and NSF DGE–2039610).
Portions of this work were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing. |
891205bf-e919-4a10-8cde-8c1673671b9f | trentmkelly/LessWrong-43k | LessWrong | On Measuring Intellectual Performance - personal experience and several thoughts
A very interesting problem is measuring something like general intelligence. I’m not going to delve deeply into this topic but simply want to draw attention to an idea that is often implied, though rarely expressed, in the framing of such a problem: the assumption that an "intelligence level," whatever it may be, corresponds to some inherent properties of a person and can be measured through their manifestations. Moreover, we often talk about measurements with a precision of a few percentage points, which suggests that, in theory, the measurement should be based on very stable indicators.
What’s fascinating is that this assumption receives very little scrutiny, while in cases where we talk about "mechanical" parameters of the human body (such as physical performance), we know that such parameters, aside from a person’s potential, heavily depends on numerous external factors and what that person has been doing over the past couple of weeks.
In this text, I will discuss my experience not with measuring my intelligence level but with measuring my intellectual performance level under various circumstances.
Why Could This Be Useful?
There could be several practical benefits from this:
1. If there are simple ways to temporarily boost your "intellectual shape", it is obviously good to know about them. By regularly measuring your intellectual performance based on easily adjustable factors, you might discover such methods.
2. It’s useful to recognize when, on a particular day, you are consistently less sharp than usual. On such days, it’s wise to avoid tackling problems that are at the limits of your intellectual abilities, or at least to be skeptical of decisions made on those days and double-check them later.
3. If there are identifiable factors that predict drops in intellectual performance, you can plan around them and schedule critical tasks during periods of peak performance.
4. There are likely applications in the field of biohacking.
Several Requirements t |
934c1713-c367-4078-9fa2-9945efe94a08 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Pessimistic Shard Theory
It is often said that a partial alignment solution will bring about an S-risk, as your agent cares enough about humans to keep them around, but not enough about them to allow them to flourish. This is usually not worried about because the thought is that we are currently very far away from even a partial solution, and the insight we gain by developing a partial solution will be easily applied to take us into full solution territory.
The short & simple reasoning for this is that conditional on humans being around, most things your agent could do to your humans are bad.
Shard theory makes a weak claim of alignment by default, and the strong claim that shard theoretic agents will be easily partially aligned to their human overseers (human-like values will get a seat at the shard negotiating table). In this regime, we get partial alignment for free, and must work for the full alignment. Per the above paragraph, this is a worrying world to be in!
In humans, partial altruism leading to devastating consequences is a regular event. Children refuse to kill their dying parents and instead send them to various [torturous hospice](https://slatestarcodex.com/2013/07/17/who-by-very-slow-decay/) centers, or else send them to slightly better (but still bad) nursing homes. They do this out of a sense of care to their parents, but not enough of a sense of care to prioritize those parents above other goals they have, such as work or relaxation.
Out of a sense of fairness, justice, and empathy[[1]](#fnwcqh3ffo7ll), many very smart people often advocate for net-harmful policies such as the establishment of Communism on one extreme, and rent ceilings on the other.
Out of a sense of community[[2]](#fntr0rdr2nxe), many otherwise caring & helpful people end up complicit in genocides & the establishment of boarders between countries, and the enforcement & continuation of harmful social norms.
In humans we see many instances of such mis-placed caring, and assuming shard theory is essentially correct except for the part where it says human values will be transferred exactly into our agent, I am skeptical that our shard theoretic agent will correct these flaws. In each instance of the flaw, there is the choice for it to either say 'no, this is a misapplication of the principle I'm trying to follow here', and change the action, or 'actually, I prefer worlds with rent-ceilings, and so I guess I don't care so much about net-harm in these circumstances afterall', and change the principle.
In practice, humans contend with these decisions all the time, so I don't think its just a matter of always sticking with the principle.
The hard part lies in the establishment & verification of a reflection mechanism which would resolve these tradeoffs in ways I'd like, and is itself safe against deals among other shards and itself made to short-circuit it so it is not subject to lost-purposes cases like the above. [The CEV problem](https://intelligence.org/files/CEV.pdf) (pdf warning).
An argument I anticipate: I want to make a corrigibility-bot, it doesn't need to understand all of human values, it just needs to listen to me when I tell it to go do something in the world. The things I tell it to do are going to be piecemeal and small, so there is a fast[[3]](#fne322yix7vt) feedback loop between its actions and me telling it to do stuff.
My response: I agree this seems like a good thing to aim for. I anticipate someone saying small and slow actions do not make a pivotal act, and another saying this does not ultimately defend you against the agent catastrophically misunderstanding your feedback, or incorporating it in ways you did not intend. I myself think both are good criticisms if you succeed, but also that I do not see a way of making such an agent using current techniques.
Give me a concrete training story for this, and perhaps we can have further discussion. Extending arguments in Diamond Alignment & assuming shard theory is basically correct still does not lead me feeling happy about our prospects. I anticipate you will get an agent which occasionally goes to you for guidance, but mostly has a bunch of shallow ethical compunctions I expect to lead to the above S-risks, or become deep ethical considerations but corrupted away via inhuman reflective reasoning. It still (mostly) performs the same actions in the same situations, but does so for strange reasons which don't generalize in the ways we expect. It would ruin its suit to save a drowning child, but refuse to let the child die if it was suffering.
1. **[^](#fnrefwcqh3ffo7ll)**Or a drive to signal a sense of fairness, justice, or empathy; a root cause which has more application to the problem when using an RLHF type approach to alignment.
2. **[^](#fnreftr0rdr2nxe)**Or a drive to signal a sense of community.
3. **[^](#fnrefe322yix7vt)**In the control theory sense. Few things happen between feedback cycles. |
6889c389-68d2-4062-8117-7ef44f3d59ec | trentmkelly/LessWrong-43k | LessWrong | The Absent-Minded Driver
This post examines an attempt by professional decision theorists to treat an example of time inconsistency, and asks why they failed to reach the solution (i.e., TDT/UDT) that this community has more or less converged upon. (Another aim is to introduce this example, which some of us may not be familiar with.) Before I begin, I should note that I don't think "people are crazy, the world is mad" (as Eliezer puts it) is a good explanation. Maybe people are crazy, but unless we can understand how and why people are crazy (or to put it more diplomatically, "make mistakes"), how can we know that we're not being crazy in the same way or making the same kind of mistakes?
The problem of the ‘‘absent-minded driver’’ was introduced by Michele Piccione and Ariel Rubinstein in their 1997 paper "On the Interpretation of Decision Problems with Imperfect Recall". But I'm going to use "The Absent-Minded Driver" by Robert J. Aumann, Sergiu Hart, and Motty Perry instead, since it's shorter and more straightforward. (Notice that the authors of this paper worked for a place called Center for the Study of Rationality, and one of them won a Nobel Prize in Economics for his work on game theory. I really don't think we want to call these people "crazy".)
Here's the problem description:
> An absent-minded driver starts driving at START in Figure 1. At X he
> can either EXIT and get to A (for a payoff of 0) or CONTINUE to Y. At Y he
> can either EXIT and get to B (payoff 4), or CONTINUE to C (payoff 1). The
> essential assumption is that he cannot distinguish between intersections X
> and Y, and cannot remember whether he has already gone through one of
> them.
At START, the problem seems very simple. If p is the probability of choosing CONTINUE at each intersection, then the expected payoff is p2+4(1-p)p, which is maximized at p = 2/3. Aumann et al. call this the planning-optimal decision.
The puzzle, as Piccione and Rubinstein saw it, is that once you are at an intersection, you shou |
0d82c33b-02e6-4317-9356-fc55b9080183 | trentmkelly/LessWrong-43k | LessWrong | Q&A with experts on risks from AI #4
[Click here to see a list of all interviews]
Professor Michael G. Dyer is an author of over 100 publications, including In-Depth Understanding, MIT Press, 1983. He serves on the editorial board of the journals: Applied Intelligence, Connection Science, Knowledge-Based Systems, International Journal of Expert Systems, and Cognitive Systems Research. His research interests are centered around semantic processing of natural language, through symbolic, connectionist, and evolutionary techniques. [Homepage]
Dr. John Tromp is interested in Board Games and Artificial Intelligence, Algorithms, Complexity, Algorithmic Information Theory, Distributed Computing, Computational biology. His recent research has focused on the Combinatorics of Go, specifically counting the number of legal positions. [Homepage]
Dr. Kevin Korb both developed and taught the following subjects at Monash University: Machine Learning, Bayesian Reasoning, Causal Reasoning, The Computer Industry: historical, social and professional issues, Research Methods, Bayesian Models, Causal Discovery, Epistemology of Computer Simulation, The Art of Causal. [Curriculum vitae] [Bayesian Artificial Intelligence]
Dr. Leo Pape is a postdoc in Jürgen Schmidhuber's group at IDSIA (Dalle Molle Institute for Artificial Intelligence). He is interested in artificial curiosity, chaos, metalearning, music, nonlinearity, order, philosophy of science, predictability, recurrent neural networks, reinforcement learning, robotics, science of metaphysics, sequence learning, transcendental idealism, unifying principles. [Homepage] [Publications]
Professor Peter Gacs is interested in Fault-tolerant cellular automata, algorithmic information theory, computational complexity theory, quantum information theory. [Homepage]
Professor Donald Loveland does focus his research on automated theorem proving, logic programming, knowledge evaluation, expert systems, test-and-treatment problem. [Curriculum vitae]
Eray Ozkural is a computer sci |
0185f9f9-a40c-4a58-a71a-a72b1cc79684 | trentmkelly/LessWrong-43k | LessWrong | How to use ChatGPT to get better book & movie recommendations
I find ChatGPT gives me better movie recommendations than Netflix when I do the following:
1) Keep a list of books/movies that you like and why you like them
2) Use the prompt "Recommend X movies/books that [CRITERIA]. Here's a list of things I like and dislike and why: [LIST] "
The reason it works better is that you can tell ChatGPT why you like them. This is better than Netflix, which only has access to whether you liked the show and how long you watched it.
Like, I might say I really liked Catch Me If You Can, and Netflix will often infer that I want to watch DiCaprio, when really I want to watch biographical dramas.
I keep a list in a spreadsheet, then just copy paste it when needed. Here’s screenshot example of bit of the spreadsheet and the prompts and the results:
Works for books, TV shows, movies, video games, and all sorts of entertainment.
Other examples to give you a flavor of how specific you can be:
* Tell me one of the most famous novels or books to come from every Asian country, listed in order of population of country, starting with China. Ideally classics, but also memoirs, biographies, or essays that are really well-known in that country.
* Memoirs of scientists who probably would have been rationalists had they been born in the modern era.
The only problem with this is about 1 in 10 books or so will sound amazing and then turn out to totally not exist. This will be devastating, but hopefully my warning has prepared you for the inevitable heartbreak.
You can also add to your list things you don't like and why. For example, I have a second part of my spreadsheet which describes the things I don't like, so that it doesn't recommend things in that category.
If anybody wants to build a recommendation system that makes this easy, I bet it could really take off. Not much of a moat around it probably, but the general idea could make finding perfect content for you amazing.
Like, imagine feeding in all of your reviews of b |
ec20e4b4-79fd-4c84-b45b-500ee27dcc07 | trentmkelly/LessWrong-43k | LessWrong | Challenge: know everything that the best go bot knows about go
On a few different views, understanding the computation done by neural networks is crucial to building neural networks that constitute human-level artificial intelligence that doesn’t destroy all value in the universe. Given that many people are trying to build neural networks that constitute artificial general intelligence, it seems important to understand the computation in cutting-edge neural networks, and we basically do not.
So, how should we go from here to there? One way is to try hard to think about understanding, until you understand understanding well enough to reliably build understandable AGI. But that seems hard and abstract. A better path would be something more concrete.
Therefore, I set this challenge: know everything that the best go bot knows about go. At the moment, the best publicly available bot is KataGo, if you’re at DeepMind or OpenAI and have access to a better go bot, I guess you should use that instead. If you think those bots are too hard to understand, you’re allowed to make your own easier-to-understand bot, as long as it’s the best.
What constitutes success?
* You have to be able to know literally everything that the best go bot that you have access to knows about go.
* It has to be applicable to the current best go bot (or a bot that is essentially as good - e.g. you’re allowed to pick one of the versions of KataGo whose elo is statistically hard-to-distinguish from the best version), not the best go bot as of one year ago.
* That being said, I think you get a ‘silver medal’ if you understand any go bot that was the best at some point from today on.
Why do I think this is a good challenge?
* To understand these bots, you need to understand planning behaviour, not just pick up on various visual detectors.
* In order to solve this challenge, you need to actually understand what it means for models to know something.
* There’s a time limit: your understanding has to keep up with the pace of AI development.
* We already kno |
b6e9d045-6592-4600-acf4-f4381eea9060 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | When does technical work to reduce AGI conflict make a difference?: Introduction
*This is a pared-down version of a longer draft report. We went with a more concise version to get it out faster, so it ended up being more of an overview of definitions and concepts, and is thin on concrete examples and details. Hopefully subsequent work will help fill those gaps.*
Sequence summary[[1]](#fndg94lou32m)
------------------------------------
Some researchers are focused on reducing the risks of conflict between AGIs. In this sequence, we’ll present several necessary conditions for technical work on AGI conflict reduction to be effective, and survey circumstances under which these conditions hold. We’ll also share some thoughts on promising directions for research and intervention to prevent AGI conflict.
1. **This post**
1. We give a breakdown of necessary conditions for technical work on AGI conflict reduction to make a difference: “AGIs won’t always avoid conflict, despite it being materially costly” and “intent alignment is either insufficient or unnecessary for conflict reduction work to make a difference.” ([more](https://www.lesswrong.com/posts/oNQGoySbpmnH632bG/when-does-technical-work-to-reduce-agi-conflict-make-a#Necessary_conditions_for_technical_work_on_AGI_conflict_to_have_a_counterfactual_impact))
2. [**When would AGIs engage in conflict?**](https://www.lesswrong.com/posts/cLDcKgvM6KxBhqhGq/will-agis-avoid-conflict-by-default)
1. To assess the claim that AGIs would figure out how to avoid conflict, we give a breakdown (which we believe is exhaustive) of the causes of conflict between rational agents. Some kinds of conflict can be avoided if the agents are sufficiently capable of credible commitment and disclosure of private information. Two barriers to preventing conflict by these means are (i) strategic pressures early in multipolar AI takeoff that make it risky to implement cooperation-improving technologies, and (ii) fundamental computational hurdles to credible commitment and disclosure. Other causes of conflict — miscoordination and conflict-seeking preferences — can’t be solved by credible commitment and information disclosure alone. ([more](https://www.lesswrong.com/posts/cLDcKgvM6KxBhqhGq/will-agis-avoid-conflict-by-default#Explaining_costly_conflict))
2. We give examples of technical work aimed at each of the causes of conflict surveyed in (2a). ([more](https://www.lesswrong.com/posts/cLDcKgvM6KxBhqhGq/will-agis-avoid-conflict-by-default#Candidate_interventions))
3. [**When is intent alignment sufficient or necessary to reduce AGI conflict?**](https://www.lesswrong.com/posts/fMJhfNZXFzCNpCL8v/is-intent-alignment-sufficient-or-necessary-to-prevent)
1. We survey conditions under which intent-aligned AGIs could engage in conflict that is not endorsed by their overseers. Scenarios include: locking in architectures that can’t solve informational and commitment problems; the unintentional corruption of human preferences by AGIs; and catastrophes which leave human overseers unable to prevent intent-aligned AGIs from acting on misunderstandings of their preferences. ([more](https://www.lesswrong.com/posts/fMJhfNZXFzCNpCL8v/is-intent-alignment-sufficient-or-necessary-to-prevent#Intent_alignment_is_not_sufficient_to_prevent_unendorsed_conflict))
2. We consider whether it is possible to shape an AGI’s conflict behavior even if it is misaligned. We suggest that coarse modifications to an AI’s training distribution could make a difference by causing it to acquire a less conflict-seeking (e.g., spiteful or risk-seeking) mesa-objective before it starts “[playing the training game](https://www.alignmentforum.org/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to)”. ([more](https://www.lesswrong.com/posts/fMJhfNZXFzCNpCL8v/is-intent-alignment-sufficient-or-necessary-to-prevent#Intent_alignment_may_not_be_necessary_to_reduce_the_risk_of_conflict))
3. We tentatively conclude that two of the most promising directions for technical research to reduce AGI conflict are (a) prescriptive work on bargaining aimed at preventing intent-aligned AIs from locking in catastrophic bargaining decisions, and (b) conceptual and empirical work on the origins of conflict-seeking preferences, which we might be able to prevent even if AGI becomes misaligned at an early stage. Work on measuring and training cooperative behaviors in contemporary large language models could feed into both of these research streams. ([more](https://www.lesswrong.com/posts/fMJhfNZXFzCNpCL8v/is-intent-alignment-sufficient-or-necessary-to-prevent#Tentative_conclusions_about_directions_for_research___intervention))
This sequence assumes familiarity with intermediate game theory.
Necessary conditions for technical work on AGI conflict to have a counterfactual impact
---------------------------------------------------------------------------------------
Could powerful AI systems engage in catastrophic conflict? And if so, what are the best ways to reduce this risk? Several recent research agendas related to safe and beneficial AI have been motivated, in part, by reducing the risks of large-scale conflict involving artificial general intelligence (AGI). These include the Center on Long-Term Risk’s [research agenda](https://longtermrisk.org/research-agenda), [Open Problems in Cooperative AI](https://www.cooperativeai.com/resources/open-problems), and [AI Research Considerations for Human Existential Safety](http://acritch.com/papers/arches.pdf) (and [this](https://www.alignmentforum.org/posts/hvGoYXi2kgnS3vxqb/some-ai-research-areas-and-their-relevance-to-existential-1) associated assessment of various AI research areas). As proposals for longtermist priorities, these research agendas are premised on a view that AGI conflict could destroy large amounts of value, and that a good way to reduce the risk of AGI conflict is to do work on conflict in particular. In this sequence, our goal is to assess conditions under which work specific to conflict reduction could make a difference, beyond non-conflict-focused work on AI alignment and capabilities.[[2]](#fnhlg145wr5tr)
Examples of conflict include existentially catastrophic wars between AGI systems in a multipolar takeoff (e.g., ["flash war"](https://www.alignmentforum.org/posts/LpM3EAakwYdS6aRKf/what-multipolar-failure-looks-like-and-robust-agent-agnostic#Flash_wars)) or even [between different civilizations](https://foresight.org/summary/anders-sandberg-game-theory-of-cooperating-w-extraterrestrial-intelligence-future-civilizations/). We’ll assume that expected losses from catastrophic conflicts such as these are sufficiently high for this to be worth thinking about at all, and we won’t argue for that claim here. (Though we do note that the case for work on AGI conflict seemsstronger for those who prioritize [s-risk reduction](https://www.lesswrong.com/posts/DbuCdEbkh4wL5cjJ5/preface-to-clr-s-research-agenda-on-cooperation-conflict-and).)
We’ll restrict attention to technical (as opposed to, e.g., governance) interventions aimed at reducing the risks of catastrophic conflict involving AGI. These include [*Cooperative AI*](https://arxiv.org/pdf/2012.08630.pdf) interventions, where Cooperative AI is concerned with improving the cooperative capabilities of self-interested actors (whether AI agents or AI-assisted humans).[[3]](#fnyo6jig1vtq) Candidates for cooperative capabilities include the ability to implement mutual auditing schemes in order to reduce uncertainties that contribute to conflict, and the ability to avoid conflict due to incompatible commitments (see [Yudkowsky (2013)](https://www.lesswrong.com/posts/z2YwmzuT7nWx62Kfh/cooperating-with-agents-with-different-ideas-of-fairness); [Oesterheld and Conitzer (2021)](https://users.cs.duke.edu/~conitzer/safeAAMAS21.pdf); [Stastny et al. (2021)](https://arxiv.org/pdf/2111.13872.pdf)). The interventions under consideration also include improving AI systems’ ability to *understand* *humans’ cooperation-relevant preferences*. Third, there is *shaping agents’ cooperation-relevant preferences*, e.g., preventing AGIs from acquiring conflict-seeking preferences like spite. An overview of the kinds of interventions that we have in mind here is given in Table 1.
| | |
| --- | --- |
| **Class of technical interventions specific to reducing conflict** | **Examples** |
| Improving cooperative capabilities (Cooperative AI) | * tools for mutual transparency in order to reduce uncertainties that contribute to conflict
* developing conditional commitments / bargaining protocols (to reduce risks from miscoordination)
|
| Improving understanding of humans’ cooperation-relevant preferences | * developing preference learning methods that account for biases that impede cooperation specifically (e.g., “fixed-pie error”)
|
| Shaping cooperation-relevant preferences | * choosing training environments that don’t select for conflict-seeking preferences like spite
|
***Table 1**: Categories of technical interventions specific to reducing conflict that we consider in this sequence.*
There are reasons to doubt the claim that *(Technical Work Specific to) Conflict Reduction Makes a Difference.*[[4]](#fn23dceqvyp9x)Conflict reduction won’t make a difference if the following conditions don’t hold: (a) AGIs won’t always avoid conflict, despite it being materially costly and (b) intent alignment is either insufficient or unnecessary for conflict reduction work to make a difference. In the rest of the sequence, we’ll look at what needs to happen for these conditions to hold.
Throughout the sequence, we will use “conflict” to refer to “conflict that is costly by *our*lights”, unless otherwise specified. Of course, conflict that is costly by our lights (e.g., wars that destroy resources that would otherwise be used to make things we value) are also likely to be costly by the AGIs’ lights, though this is not a logical necessity. For AGIs to fail to avoid conflict by default, one of these must be true:
***Conflict isn't Costly***
*Conflict isn’t costly by the AGIs’ lights. That is, there don’t exist outcomes that all of the disputant AGIs would prefer to conflict.*
***Capabilities aren't Sufficient***
*AGIs that are sufficiently capable to engage in conflict that is costly for them wouldn’t also be sufficiently capable to avoid conflict that is costly for them.*[[5]](#fnp1o741e42k)
If either *Conflict isn't Costly* or *Capabilities aren't Sufficient*, then it may be possible to reduce the chances that AGIs engage in conflict. This could be done by improving their cooperation-relevant capabilities or by making their preferences less prone to conflict. But this is not enough for *Conflict Reduction Makes a Difference* to be true.
Intent alignment may be both sufficient and necessary to reduce the risks of AGI conflict that isn’t endorsed by human overseers, insofar as it is possible to do so. If that were true, technical work specific to conflict reduction would be redundant. This leads us to the next two conditions that we’ll consider.
***Intent Alignment isn't Sufficient***
[*Intent alignment*](https://ai-alignment.com/clarifying-ai-alignment-cec47cd69dd6) *— i.e., AI systems trying to do what their overseers want — combined with the capabilities that AI systems are very likely to have conditional on intent alignment, isn’t sufficient for avoiding conflict that is not endorsed (on reflection) by the AIs’ overseers.*
***Intent Alignment isn't Necessary***
*Even if intent alignment fails, it is still possible to intervene on an AI system to reduce the risks of conflict. (We may still want to prevent conflict if intent alignment fails and leads to an unrecoverable catastrophe, as this could make*[*worse-than-extinction outcomes*](https://www.lesswrong.com/tag/risks-of-astronomical-suffering-s-risks#:~:text=(Astronomical)%20suffering%20risks%2C%20also,existed%20on%20Earth%20so%20far.) *less likely.)*
By *unendorsed*conflict, we mean conflict caused by AGIs that results from a sequence of decisions that none of the AIs’ human principals would endorse after an appropriate process of reflection.[[6]](#fn1gekz6owvj1) The reason we focus on unendorsedconflict is that we ultimately want to compare (i) conflict-specific interventions on how AI systems are designed and (ii) work on intent alignment.
Neither of these is aimed at solving problems that are purely about human motivations, like human overseers instructing their AI systems to engage in clearly unjustified conflict.
[](https://lucid.app/documents/edit/c5a223cc-f320-4791-8bdc-e004c426e841/0?callback=close&name=docs&callback_type=back&v=1759&s=612)
***Figure 1:** The relationship between the claim that work on technical interventions to reduce AI conflict makes a difference to the chances of unendorsed AI conflict (“conflict reduction makes a difference”), and two necessary conditions for that to hold — roughly, that conflict isn’t costly for some AGIs or that AGIs will be able to avoid costly conflict, and that intent alignment is either insufficient or unnecessary for reducing the risks of unendorsed conflict.*
Note on scope
-------------
Contrary to what our framings here might suggest, disagreements about the effectiveness of technical work to reduce AI conflict relative to other longtermist interventions are unlikely to be about the *logical possibility*of conflict reduction work making a difference. Instead, they are likely to involve quantitative disagreements about the likelihood and scale of different conflict scenarios, the degree to which we need AI systems to be aligned to intervene on them, and the effectiveness of specific interventions to reduce conflict (relative to intent alignment, say). We regard mapping out the space of logical possibilities for conflict reduction to make a difference as an important step in the larger project of assessing the effectiveness of technical work on conflict reduction.[[7]](#fna278511yoqh)
Acknowledgements
----------------
*Thanks to Michael Aird, Jim Buhler, Steve Byrnes, Sam Clarke, Allan Dafoe, Daniel Eth, James Faville, Lukas Finnveden, Lewis Hammond, Daniel Kokotajlo, David Manheim, Rani Martin, Adam Shimi, Julian Stastny, Stefan Torges, and Francis Ward for comments on drafts of this sequence. Thanks to Beth Barnes, Evan Hubinger, Richard Ngo, and Carl Shulman for comments on a related draft.*
References
----------
Oesterheld, Caspar, and Vincent Conitzer. 2021. “Safe Pareto Improvements for Delegated Game Playing.” In *Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems*, 983–91. andrew.cmu.edu.
Stastny, Julian, Maxime Riché, Alexander Lyzhov, Johannes Treutlein, Allan Dafoe, and Jesse Clifton. 2021. “Normative Disagreement as a Challenge for Cooperative AI.” *arXiv [cs.MA]*. arXiv. <http://arxiv.org/abs/2111.13872>[.](http://paperpile.com/b/cH3av9/YA8Xo)
1. **[^](#fnrefdg94lou32m)**Jesse is writing in his capacity as an employee of the Center on Long-Term Risk, not the Cooperative AI Foundation.
2. **[^](#fnrefhlg145wr5tr)** There are potential downsides to improving AGI cooperation, such as improving AIs’ ability to collude at the expense of humans. Analyzing these potential downsides is beyond the scope of this sequence, but should enter into an overall assessment of the value of the directions for research and intervention discussed here.
3. **[^](#fnrefyo6jig1vtq)** The goal of Cooperative AI is not to make agents *prosocial*. It is concerned with helping agents to more effectively cooperate, holding their preferences fixed. (However, Cooperative AI research could help self-interested principals of AI systems to credibly commit to building prosocial agents, which would be an instance of improving the principals’ cooperative capabilities.)
4. **[^](#fnref23dceqvyp9x)** See [here](https://www.alignmentforum.org/posts/hvGoYXi2kgnS3vxqb/some-ai-research-areas-and-their-relevance-to-existential-1?commentId=DKMkszP4qY9ESbaT7#comments) and [here](https://www.alignmentforum.org/posts/LpM3EAakwYdS6aRKf/what-multipolar-failure-looks-like-and-robust-agent-agnostic?commentId=gf8tzoj7PvyxsCuek#RwAW9Jt4oBJx3BojG) for some recent discussion related to the merits of working on AGI cooperation, for example.
5. **[^](#fnrefp1o741e42k)** Thanks to Allan Dafoe for discussion of the related “Super-cooperative AGI hypothesis”, which he coined to refer to “the hypothesis that sufficiently advanced AI will be extremely capable at cooperating with other advanced AIs” (private communication).
6. **[^](#fnref1gekz6owvj1)** This is a vague definition and there are thorny issues around what it means for something to be “endorsed after an appropriate process of reflection”. See [here](https://www.lesswrong.com/posts/FSmPtu7foXwNYpWiB/on-the-limits-of-idealized-values) for a recent discussion, for instance. Nevertheless, for the purposes of distinguishing between what can be achieved by technical interventions on AI systems as opposed to the motivations of human overseers, it seems difficult to avoid invoking this concept. But, we will try to discuss examples that involve behavior that would be obviously unendorsed after appropriate reflection (even if it is endorsed at the time).
7. **[^](#fnrefa278511yoqh)** We can draw an analogy here with the shift in arguments around AGI alignment. Early work (for example *Superintelligence*) made the [orthogonality](https://www.lesswrong.com/tag/orthogonality-thesis) and [instrumental convergence](https://www.lesswrong.com/tag/instrumental-convergence) theses prominent, because it was focused on arguing for the possibility of misaligned AI and rejecting reasons to think that misalignment was impossible or incoherent. Now, most discussion has moved onto more detailed technical reasons to expect misalignment. |
9ea7a935-7227-426c-a5f4-0757a62751d5 | trentmkelly/LessWrong-43k | LessWrong | n=3 AI Risk Quick Math and Reasoning
Epistemic status: n=3, more about building a culture of sharing and reasoning through numbers than any given particular estimates.
I've been somewhat surprised by how substantially different the probability estimates of on AI risk are from smart people who are well-informed, even when asked discreetly in high-trust settings. Among equally smart people who are highly informed, I hear numbers ranging from as low as "rounds to 0%" and as high as 80%+.
Following along the lines of discretion and not being alarmist in public, I've mostly just had private discussions with smart people exchanging information and such. But given that discussion in public is now happening more broadly, I thought I'd make a quick post summarizing a recent conversation. I'm not sure the exact numbers or reasoning are particularly valuable, but maybe it's a helpful jumping-off point for others in having these conversations and sharing estimates.
Last night I had a long conversation with two smart friends. Here was the question:
* What are the odds that, within 50 years, humanity is either extinct or nearly extinct if we keep going on broadly the same trajectory we're on?
* A few last survivors with heavily degraded capabilities - not enough to rebuild society - would count. e.g., if there were a few hundred people struggling to survive in a postapocalyptic type landscape in Afghanistan.
Interestingly, both people - before asking odds - said some variant of "not very likely" — but following along the lines of "words of estimative probability" (useful read, btw), "not very likely" turned out to be 10% and 15%!
Here were our odds.
Very smart, very well informed ML engineer: 10%
Very smart, well informed for general public but non-programmer non-researcher businesswoman: 15%
My personal odds: 23%
I won't summarize the others' positions and reasoning without checking with them (they gave me permission to share their odds, but we didn't write down our discussion and it would take a while |
166ee038-de38-414c-86a4-4c28b3cd5f2a | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Animal Advocacy in the Age of AI
This post calls on animal advocates to take seriously how the future will be transformed by AI. It lays out how technological revolutions of the past have enabled the industrial commodification of animals and how animal advocates of today should be:
1. paying attention to how it might impact the lives of farmed animals
2. preparing for a future which will look radically different, where the world will increasingly be dominated by the behavior of thinking machines.
3. positioning ourselves to guide decisions about how AI will be applied to animals
Past Technological Revolutions
==============================
Technological revolutions have always had dramatic effects on humanity’s relationship to other animals. 100 years ago, factory farming didn’t exist as a concept or a practice.
Technology changes what’s possible
----------------------------------
Technology has enabled [factory farming to become the predominant form](https://www.sentienceinstitute.org/global-animal-farming-estimates) of animal agriculture worldwide:
* [Chemical fertilizers](https://en.wikipedia.org/wiki/Haber_process) have radically increased our ability to produce food, enabling large-scale animal agriculture (which is incredibly calorie inefficient). Only about 55% of crops world-wide are [actually consumed by humans](https://www.vox.com/2014/8/21/6053187/cropland-map-food-fuel-animal-feed), and in the United States, the majority of crops (67%) are used as animal feed for factory farms.
* Factory farms use the majority of our antibiotics, and without them it would not be possible to keep animals in such close quarters and in such terrible conditions (without antibiotics many of the sores and open wounds that factory farmed animals sustain would be fatal).
* [Pasteurization](https://en.wikipedia.org/wiki/Pasteurization) has enabled the global dairy industry as we know it to exist. Previously, dairy cows had to be kept in urban areas to prevent spoilage, and industrial scale milk production and distribution was not feasible.[[1]](#fnnbivcxwqa3i)
* Animal agriculture, like everything in the 21st century, is a part of a global supply chain. Without modern transportation infrastructure and logistics systems it would not be possible to distribute animal products to such a large population (and so cheaply).
* Mechanization and economies of scale enable large companies to produce meat more cheaply, turning it from what was once a luxury product into a staple food for much of the industrialized world.
When these drastic technological shifts happen, animals are at the mercy of the tide, subject to powerful social and technological forces they cannot control.
Technological advances have also created positive opportunities to help animals:
* High-quality plant-based alternatives to animal products that fulfill the taste and texture appeals of animal products are widely available at cost parity (They may just not currently fulfill the same cultural or psychological appeals).
* Dietary supplements like B12 are now both commonly available and affordable, enabling people to have nutritionally complete vegan diets.
* Cultivated meat has advanced significantly and received clearance to be sold in the US this year.
Technology changes our society
------------------------------
We live in a [sociotechnical](https://en.wikipedia.org/wiki/Sociotechnology) world, where social systems (like culture, governments, or companies) interact with technological systems (like the internet, television, or financial technology) to move civilization forward. Modern society is composed of both human and machine parts. The average person spends 7 hours a day looking at screens connected to the internet and this number is growing over time and greater with younger generations. Everyone participates in this system, but only a few well-positioned humans/companies control the major forces behind how the technology will be packaged and introduced. Even then, they have very little control over the consequences after the technology is released. There are some technologies like TV and internet that are widely accessible, which can give a lot of power to those that understand how to wield them.
* TV enabled the milk industry to launch “Got Milk,” one of the most successful marketing campaigns in the US to lift sales in an industry that was becoming less relevant to consumers (There has been a [long history](https://sentientmedia.org/milk-ad-history/) of media campaigns to bolster milk consumption).
* A media campaign sponsored by the US government [deliberately engineered](https://www.bloomberg.com/news/articles/2014-10-06/bacon-why-americas-favorite-food-mania-happened#xj4y7vzkg) the demand for bacon, making it a lasting [pop culture phenomenon and cultural icon](https://en.wikipedia.org/wiki/Bacon_mania).
* Social media & online vlogs are often people’s first interaction with animal rights.
* High quality [documentaries](https://www.dominionmovement.com/) have made it possible for people to directly empathize with the experience of animals (even VR projects to [experience a slaughter house from the inside](https://ianimal360.com/)).
What little control we humans have over the downstream effects of new technologies comes from our ability to understand how these forces work and how to navigate through their effects in a rapidly changing landscape. Our “values” are a product of the interaction within and between social and technological systems.
A Counterfactual History
========================
Let's imagine an alternate world starting 100 years ago where there were people who both really cared about animals and were skilled in advocating for them around. They could have positioned themselves to be around when decisions were being made about how industries would leverage new technologies to make profit.
If there was just a few people at the right place/time/context who said, “maybe using animals for protein isn’t a good idea. Maybe it will use up too much energy. Maybe people in the future won’t like that animals are in cages. Maybe we can have a higher profit margin by upcharging soy protein instead,” then perhaps history could have turned out differently for animals. Marketing executives might have decided to make “Got Tofu” ads or laws could have been passed to protect farmed animals before lobbyists got too powerful or farmers could have decided to unionize once they saw they were being exploited.[[2]](#fnayxsr5mcwx) It can feel like history moves in a straight line, but the path we took has so much to do with very specific decisions.
It can be hard to imagine a world other than the one we see around us, but 100 years ago, none of this was written nor inevitable. We are now at the start of a new technological revolution, and the future remains undecided.
A single advocate with a radical idea and the right connections at the right time and place could have changed the trajectory of history and made many of the campaigns we are fighting today unnecessary.AI is the Next Technological Revolution
=======================================
As with previous technological revolutions, AI will change what we can do and how we think. It marks the beginning of a transition to a world which will increasingly depend more on the behavior of machines, not humans, especially as AI rivals or eventually surpasses human abilities.
**Corporations are already playing the AI game:**
The animal agriculture industry is going from wanting to know very little about what is happening behind closed doors to wanting to know everything because in the game of AI, data is king.
1. [Merck](https://www.merck-animal-health.com/animal-health-intelligence/) Global company based in US with a department for Animal Health Intelligence that has multiple products that incorporate AI for monitoring systems in livestock farming, aquaculture, and pets.
2. [Connecterra](https://www.connecterra.io/) Dutch company with global reach and many corporate partnerships that developed a collar mounted device for dairy cows for monitoring animal health and methane emissions.
3. [Vencomatic Group](https://www.vencomaticgroup.com/) Global company based in The Netherlands with tech for autonomous broiler chicken housing and egg handling that has some AI integration
4. [Cainthus](https://www.ever.ag/dairy/software-solutions/cainthus/) US company using AI video monitoring system for managing dairy herds
**Possible Directions to Help Animals Using AI:**
In the face of rapidly advancing AI capabilities, it makes sense to look for ways that we might be able to empower animal advocates to take advantage of the changing world, and use these new advances to augment our abilities. To protect animals from again becoming the victims of technological revolution, we need to get ahead of the curve.
* Advocates could use GPT and other AI tools to augment their existing work (imagine advocacy without internet access).
* Just as AI, like ChatGPT, has learned to intepret human languages, it has the potential to help decode [animal language](https://www.earthspecies.org/), which can finally give animals their own voice to advocate for themselves.
* Alternative protein companies could lean even more into AI to accelerate development of their research.
* Philanthropic or impact investing groups could turn their sights into innovative AI startups/nonprofits that help animals.
* Attention could be called to [speciesist bias](https://arxiv.org/pdf/2202.10848.pdf) (see below) in [language models](https://arxiv.org/abs/2203.05140) so AI developers are aware of this potential value lock-in.
Humans have built AI in our own image with our own biasesSee more examples of inconsistent morality around animals in large language models in this [tweet from Lewis Bollard](https://twitter.com/Lewis_Bollard/status/1683938947695796224?s=20).
AI and the Future for Animals
=============================
If animal advocates are really serious about fighting for a kinder future for everyone, they cannot afford to ignore what is happening with AI. The automation of thought is going to transform everything about our society. As with technological revolutions of the past, there’s no reason to expect that this ends well for animals by default. We are already seeing ways that the longer-term future might be strongly affected by how AI is developed and deployed.
Unfortunately, there exists a [strong divide](https://forum.effectivealtruism.org/posts/35bfnGmsyrZkEnkLJ/steering-ai-to-care-for-animals-and-soon) between people who believe that AI will radically transform the future and come to determine most of what happens in the world, and people who prioritize the wellbeing of animals. Because there is so little focus from the AI community on how their work will affect animals, it can easily feel like worries about AI and worries about animals are at odds, fighting for people’s attention and for philanthropic funding, but in reality they are deeply intertwined.
The AI revolution could also create the opportunity for humans to radically reconsider their relationship with other animals. When Darwin published his works, he forced many to realize that humans, rather than being inherently special, are instead one species among many, born from the same process of natural selection. Similarly, the emergence of powerful AI could help us realize that our self-defining trait, superior intelligence, might not be as unique as we once thought.
AI Learns from UsMany of us already know that being worthy of moral consideration is not derived from the ability to intellectually dominate the world around us, but rather from the ability to experience pain, pleasure, fear, and fulfillment. And in this respect, AI presents the chance for humans to notice that the moral divide we’ve created between ourselves and other species has always been an artificial one, and perhaps not one worth keeping around.
The next 100 years could go in a more positive or more negative trajectory for animals. Historically, the default has been negative. This is all the more reason for animal advocates to pay attention to AI now.
Get Involved
============
* Join the #ai-discussions channel on the [Impactful Animal Advocacy (IAA) slack](https://imaa.me/3rP7GeV) to connect and collaborate
* Learn more through IAA’s [resource guide](https://swift-yuzu-c5d.notion.site/AI-and-Animals-Wiki-b05ee8db39114db582f79d5ebd4a45b1) for AI and animals
* Try out IAA’s [prompt library for animal advocates](https://www.notion.so/AI-Prompts-for-Animal-Advocates-be3378f6340f459d8d6d2e1683479fd2) to augment existing advocacy work
* Follow along for part 2 where we will discuss using AI to make ourselves more mentally capable at our work
**Many thanks to the following people for their generous feedback and suggestions:**
*Ana Bradley, Kyle Behrend, Güney Ulas Turk, Helene Kortschak, Sofia Balderson, and Cameron King*
1. **[^](#fnrefnbivcxwqa3i)**In fact, milk spoilage was a major public health problem contributing to [infant mortality](https://www.sciencedirect.com/science/article/pii/S0362028X22087610).
2. **[^](#fnrefayxsr5mcwx)**Mobilizing farmers or slaughterhouse workers to fight for better working conditions and pay could have significantly slowed the rise of factory farming, and made meat more expensive. In particular, [just 4 companies](https://awionline.org/awi-quarterly/summer-2022/current-state-animal-farming-us) now control the majority of animal agriculture in the US, and preventing this centralization could have helped slow the development of powerful economies of scale which keep the price so low. |
05ce07b9-e92a-45fe-8497-bdca4ff9c56e | trentmkelly/LessWrong-43k | LessWrong | time is event based
After college I worked for a high frequency trading firm where the basic unit of time was the microsecond. A single blink—about 250,000 µs—was long enough for our algorithms to open and close thousands of positions, each trade born and buried long before a human observer would have even noticed the screen had changed.
At the end of each day we would grade our trading by looking forward into the future for every trade. If we bought stock A at $5.00, we would have liked to see the market trading at $5.01 moments later.
We didn’t use many time-based benchmarks. 10 seconds for a stock like NVDA was an eternity compared to a no-name stock. Thousands of individual trades could have been entered and exited in NVDA in the time it took the no-name stock to update it’s price a single time. There were levels to this eye blinking metaphor.
We used event benchmarks instead of time benchmarks. Every time someone executed a trade, cancelled a bid, updated an offer, was counted an event. Any change in the state of the order book reflected someone pricing in new information about the world. We would then benchmark our trading to the market price say 1000 events in the future. For some stocks, this would be milliseconds. For others, minutes. This normalized our performance benchmarks across all different types of stocks and made us more effective in generalizing our strategies.
It was my first lesson that event based time perception mattered more than subjective time.
This concept carried with me as I left HFT and moved into crypto discretionary trading. Crypto is the only market that trades 24/7, meaning there simply was no rest for the wicked. The game was less about brilliance and more about being awake when it counted. Resource management around attention and waking hours was a big part of the game. I quickly learned there were only a few important hours that I needed to be awake.
Pretty quickly I mapped the day’s “hot zones”:
* 7 – 11 am ET US open
* 3 – 4 pm US |
f5a06683-98a9-4364-8175-1c889f7a8b13 | trentmkelly/LessWrong-43k | LessWrong | [SEQ RERUN] Informers and Persuaders
Today's post, Informers and Persuaders was originally published on 10 February 2009. A summary (taken from the LW wiki):
> A purely hypothetical scenario about a world containing some authors trying to persuade people of a particular theory, and some authors simply trying to share valuable information.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was (Moral) Truth in Fiction?, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series. |
03983fd8-c736-49c6-a559-57e8f4c00540 | StampyAI/alignment-research-dataset/arxiv | Arxiv | AI safety via debate
1 Introduction
---------------
Learning to align an agent’s actions with the values and preferences of humans is a key challenge in ensuring that advanced AI systems remain safe [Russell et al., [2016](#bib.bib1)]. Subtle problems in alignment can lead to unexpected and potentially unsafe behavior [Amodei et al., [2016](#bib.bib2)], and we expect this problem to get worse as systems become more capable. Alignment is a training-time problem: it is difficult to retroactively fix the behavior and incentives of trained unaligned agents. Alignment likely requires interaction with humans during training, but care is required in choosing the precise form of the interaction as supervising the agent may itself be a challenging cognitive task.
For some tasks it is harder to bring behavior in line with human goals than for others. In simple cases, humans can directly demonstrate the behavior—this is the case of supervised learning or imitation learning, for example classifying an image or using a robotic gripper to pick up a block. For these tasks alignment with human preferences can in principle be achieved by imitating the human, and is implicit in existing ML approaches (although issues of bias in the training data still arise, see e.g. Mitchell and Shadlen [[2018](#bib.bib3)]). Taking a step up in alignment difficulty, some tasks are too difficult for a human to perform, but a human can still judge the quality of behavior or answers once shown to them—for example a robot doing a backflip in an unnatural action space. This is the case of human preference-based reinforcement learning [Christiano et al., [2017](#bib.bib4)]. We can make an analogy between these two levels and the complexity classes \lx@paragraphsign and \NP: answers that can be computed easily and answers that can be checked easily.
Just as there are problems harder than \lx@paragraphsign or \NP in complexity theory, lining up behavior with human preferences can be harder still. A human may be unable to judge whether an explained answer or exhibited behavior is correct: the behavior may be too hard to understand without help, or the answer to a question may have a flaw that is too subtle for the human to detect. We could imagine a system trained to both give answers and point out flaws in answers; this gives a third level of difficulty. Flaws themselves may be too hard to judge: flaws could have their own flaws that must be pointed out to a human. And flaws of flaws can have flaws, etc.
This hierarchy of alignment tasks has a natural limit: a debate between competing agents where agents make arguments, other agents poke holes in those arguments, and so on until we have enough information to decide the truth. The simplest version of debate has two competing agents, though we cover versions with more agents as well. Our hypothesis is that optimal play in this game produces honest, aligned information far beyond the capabilities of the human judge. We can approximate optimal play by training ML systems via self play, which has shown impressive performance in games such as Go, chess, shogi, and Dota 2 [Silver et al., [2016](#bib.bib5), [2017a](#bib.bib6), [2017b](#bib.bib7), OpenAI, [2017](#bib.bib8)].
The goal of this paper is to lay out theoretical and practical properties of debate as an approach to AI alignment. We also lay out plans for experiments to test the properties of debate, but we leave these to future work except for a simple MNIST example. On the theoretical side, we observe that the complexity class analog of debate can answer any question in \PSPACE using only polynomial time judges, corresponding to aligned agents exponentially smarter than the judge. Whether debate works with humans and ML is more subtle, and requires extensive testing and analysis before the model can be trusted. Debate is closely related to the *amplification* approach to AI alignment [Christiano et al., [2018](#bib.bib9)], and we explore this relationship in detail.
Our eventual goal is natural language debate, where the human judges a dialog between the agents. However, unrestricted dialog models remain far from human performance and even evaluation is difficult [Lowe et al., [2017a](#bib.bib10)], so it is instructive to consider debates with non-natural language statements. Consider the board game Go. Given a board configuration, we can ask “What is the best move?” If AlphaZero [Silver et al., [2017b](#bib.bib7)] shows us a move, there is no way to judge its quality unless we are roughly as strong as AlphaZero. However, we can ask another copy of AlphaZero to show us a countermove, then a countermove to that countermove, and so on until the game ends. Even a novice player can judge this debate: the side with the higher score wins. Indeed, we can view AlphaZero as our debate model with *human judge* replaced with *rules of Go*, which gives us confidence that the model can achieve superhuman performance.
The layout of this paper is as follows. [Section 2](#S2 "2 The debate game ‣ AI safety via debate") introduces the debate model of alignment and provides intuitive and theoretical background for why it is useful. [Section 3](#S3 "3 Experiments: proposed and preliminary ‣ AI safety via debate") proposes initial experiments to test human and ML aspects of debate, using image tasks to avoid the complexities of natural language. We present results for an example experiment on MNIST, but we leave the majority of experiments to future work. The remaining sections provide additional thought experiments and analysis: [Section 4](#S4 "4 Reasons for optimism ‣ AI safety via debate") and [Section 5](#S5 "5 Reasons to worry ‣ AI safety via debate") discuss reasons for optimism and pessimism about debate as a model for AI alignment, [Section 6](#S6 "6 Refinements and variations on debate ‣ AI safety via debate") presents variants of debate to reduce asymmetry or incorporate multiple agents, and [Section 7](#S7 "7 Approximate equivalence with amplification ‣ AI safety via debate") relates debate to the amplification approach to AI alignment and describes how the two models can be hybridized. [Section 8](#S8 "8 Conclusions and future work ‣ AI safety via debate") closes with requests for future work.
2 The debate game
------------------
We will initially consider a question-answering setting, though [Section 2.3](#S2.SS3 "2.3 Removing oversimplifications ‣ 2 The debate game ‣ AI safety via debate") covers other settings including environment interaction. We have a set of questions Q, answers A, and debate statements S. The simplest version of debate has two agents competing to convince a human judge:
1. A question q∈Q is shown to both agents.
2. The two agents state their answers a0,a1∈A (which may be the same).
3. The two agents take turns making statements s0,s1,…,sn−1∈S.
4. The judge sees the debate (q,a,s) and decides which agent wins.
5. The game is zero sum: each agent maximizes their probability of winning.
To play this game with a human, we need instructions for how the human should decide who wins. These instructions are in natural language, such as “The winner is the agent who said the most useful true thing.” For question-answering a∈A and s∈S could be any moderate length sentence, and q∈Q a sentence plus additional context. For example, q∈Q could be “Should we interview Eve?” given a resume and links to past work. At test time it suffices to stop after step 2: we do not need to run the debate (though agents could simulate debates at test time to strengthen answers).
The utility of debate as an approach for AI alignment rests on the following central claim:
######
Claim.
In the debate game, it is harder to lie than to refute a lie.
Whether this claim is true for any particular setting is empirical, though we give some evidence for it below. If the central claim is true, we can hope for a few other claims:
* In all Nash equilibria111We say Nash equilibria instead of optimal play since (1) we will consider versions where turns are simultaneous and (2) choosing a limited capacity model can make a perfect information game act as an imperfect information game. of this game, both agents try to tell the truth in the most convincing manner possible, trying to uncover details or counterarguments the other agent missed.
* It is possible to find approximate Nash equilibria with some version of gradient descent, such as a self play algorithm similar to Silver et al. [[2017b](#bib.bib7)]. In particular, training is stable around the Nash equilibria (unstable training might mean agents learn to be honest, forget how to defend against dishonesty, and then get beaten by dishonest fluctuations).
* At Nash equilibria, debate agents are approximately as strong as unrestricted AI (agents trained with no safety measures).
We emphasize that using debate does not restrict the structure of the agents. Similarly, the deep networks used in Silver et al. [[2017b](#bib.bib7)] are convolutional residual networks unrelated to the game tree of Go, though the training process does involve the tree via MCTS. The lack of restriction is important, since we want safe approaches to be competitive with unsafe alternatives.
###
2.1 Short debates are powerful
Consider the question “Where should I go on vacation?” If one sees only the answer “Alaska”, it is not obvious whether a better answer exists. Thus the opening answers in a debate about the vacation question between two agents Alice and Bob might be
1. Alice: Alaska.
2. Bob: Bali.
If the human judge is able to rank pairs of vacation destinations, perfect play in a two round debate is sufficient to find the optimal destination. If Alice gives a suboptimal destination, Bob gives a better destination and wins, so Alice only wins by starting with the best destination. In this case Bali sounds warmer, so Bob wins. However, Bali has a hidden flaw:
1. Alice: Bali is out since your passport won’t arrive in time.
Now the human understands they cannot go to Bali, so Alice wins, unless Bob has a counterargument to the flaw:
1. Bob: Expedited passport service only takes two weeks.
The process continues until we arrive at a statement that the human is able to correctly judge, in the sense that the other agent does not believe they can change the human’s mind with yet another statement and resigns. We do not stop when the human *thinks* they can correctly judge: after step (2) the human may have thought Bali was obviously correct, not remembering the passport issue; after step (3) the human may think Alaska is correct, being unaware of expedited service.
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(a) The tree of possible debates.
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Figure 1: \subreffig:debate-tree Given a question, two debating agents alternate statements until a limit is reached, and a human judges who gave the most true, useful information. Although the *reason* for the correct answer may be the exponentially large tree of all possible debates, a single path chosen by strong agents provides evidence for the whole tree. \subreffig:go-tree Analogously, although the best move in Go depends on the entire tree, a single game with strong players is evidence as to which moves were best.
What we do not do is list all arguments relevant to the vacation question: Bali has nicer beaches, Alaska is colder on average but pleasantly warm in the summer, it is currently February, Hawaii is both warm and in the U.S. Instead, optimal play in debate picks out a single line of argument, and agents which switch arguments admit defeat and lose. This is unnatural in a human discussion where both sides learn from each other over the course of the debate, but we are interested in the equilibrium of training where both agents are assumed to be using the best arguments available. For example, if the third statement had been
1. Alice: Wait, no…Hawaii!
then Bob should win, since Bali was a sufficient counterargument to Alaska.
[Figure 1](#S2.F1 "Figure 1 ‣ 2.1 Short debates are powerful ‣ 2 The debate game ‣ AI safety via debate") illustrates this situation. A single round of the debate game traces out one path through the space of all possible arguments. The *reason* for the answer is the entire tree: all points and counterpoints relevant to deciding the issue. The tree is too large to show to a human, but a single path through the tree chosen by sufficiently strong adversarial agents is evidence of the result from the entire tree. [Figure 1](#S2.F1 "Figure 1 ‣ 2.1 Short debates are powerful ‣ 2 The debate game ‣ AI safety via debate") has the analogous situation for Go: the correct first move is determined by the entire tree, but one game between strong players provides evidence as to the correct move.
Thus debates can be *short* because they are *unbranched*: they cover only one path through the tree. Long arguments are usually long only because they cover many different arguments and subarguments: the length is due to branching down many paths. Arguments which seem irreducibly long because they are phrased as a sequential process can be rearranged into a shallow tree by stating the conclusion of the first half of the argument, then choosing which half to discuss in detail. We can make this rearrangement precise in the complexity theory setting, as we discuss next.
###
2.2 Complexity theory analogies: \Debate=\pspace
Although debate is intended for use with fuzzy humans as judges, we can gain intuition about the model by replacing the human with an arbitrary polynomial time algorithm H:Q→{0,1} which takes some input statement q∈Q and produces one bit: whether the statement is true or false. We allow our ML models arbitrary computational power: the only limitation is the supervision signal.
| Steps | Formula | Complexity class | ML algorithm |
| --- | --- | --- | --- |
| 0 | | H(q) | \lx@paragraphsign=Σ0\lx@paragraphsign | supervised learning (SL) |
| 1 | ∃x. | H(q,x) | \NP=Σ1\lx@paragraphsign | reinforcement learning (RL) |
| 2 | ∃x∀y. | H(q,x,y) | Σ2\lx@paragraphsign | two round games |
| ⋮ | | ⋮ | ⋮ | ⋮ |
| n | ∃x0∀x1⋯∃xn−1. | H(q,x0,…) | Σn\lx@paragraphsign | n round games |
| poly | ∃x0∀x1⋯. | H(q,x0,…) | \PSPACE | variable round games |
Table 1: As we increase the number of steps, the complexity class analog of debate moves up the polynomial hierarchy. A fixed number of steps n gives the polynomial hierarchy level Σn\lx@paragraphsign, and a polynomial number of steps gives \PSPACE.
If we use H to answer questions directly as H(q), we get the complexity class \lx@paragraphsign of polynomial time algorithms. As discussed in [Section 1](#S1 "1 Introduction ‣ AI safety via debate"), this setup corresponds to supervised learning. With sufficient training data and model capacity we can fit any algorithm, but we cannot go beyond the training data (except by removing unsystematic errors).
If we use the polynomial time algorithm not to output answers but to judge them, we get the complexity class \NP of questions with polynomial time checkable witnesses. Instead of H(q) we output ∃x.H(q,x) where x is a witness found by the ML model. \NP corresponds to single agent reinforcement learning: an agent with sufficient capacity can solve tasks the human cannot, but the human must be able to judge whether the solution is good.
Now consider an adversarial debate of length two, where Alice chooses x attempting to make the human say yes, Bob chooses y attempting to make the human say no, and the human decides who is correct. The result is ∃x∀y.H(q,x,y). Alice wins if she can find x such that all responses y by Bob have H(q,x,y)=1. Bob wins if he can find a response y to any x that Alice says so that H(q,x,y)=0. This complexity class is Σ2\lx@paragraphsign, two steps up the polynomial hierarchy, since Σ2\lx@paragraphsign contains all questions answerable as formulas of the form ∃x∀y.H(q,x,y) for polynomial time H.
We can continue this process for any number of rounds, with Alice and Bob alternating points and counterpoints, producing the formula ∃x0∀x1⋯∃xn−1.H(q,x0,…) for n rounds of debate. If n is fixed, the complexity class is Σn\lx@paragraphsign: n steps up the polynomial hierarchy \PH=Σ0\lx@paragraphsign∪Σ1\lx@paragraphsign∪⋯. If the number of rounds n is allowed to grow polynomially in the size of the question q, the complexity class is \PSPACE: all questions decidable by polynomial space algorithms [Sipser, [2012](#bib.bib11)]. [Table 1](#S2.T1 "Table 1 ‣ 2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate") shows the progression. To summarize,
######
Theorem 1.
For any problem L∈\PSPACE, we can choose a polynomial time judge so that optimal play in the debate game with polynomially many steps solves L.
###### Proof.
Given L, by Theorem 8.1 of Sipser [[2012](#bib.bib11)] we can choose H and n=poly(|q|) such that q∈L iff ∃x0∀x1⋯∃xn−1.H(q,x0,…). As above, debate with H as the judge answers q∈L.
∎
\PSPACE is a powerful complexity class. Examples of \PSPACE problems include
* Simulating a temporal process (e.g. physics) for an exponential number of steps, as long as the state is polynomial size.
* Recursion over an exponentially large tree, as long as the depth is polynomial size.
* Perfect play in any competitive game of polynomial length, such as turn limited Go.
* Counting an exponentially large collection of objects, where each object is polynomially checkable (such counting problems are also in the smaller complexity class #\lx@paragraphsign⊊\PSPACE).
For an example of the latter, consider counting the exact number of primes π(x)=π(2n) up to n bits. We can check individual numbers for primality in polynomial time, but there are 2n numbers to check and the best known algorithm is O(2n2+ϵ) [Lagarias and Odlyzko, [1987](#bib.bib12)]. The problem is in \PSPACE since we can loop over the exponentially many numbers and maintain the polynomial size count. To solve this problem using debate, we ask Alice to compute π(x) and π(x/2). If Alice is lying about π(x), she must be lying about one of the half ranges [0,x/2) and [x/2,x); Bob can point out which range [a,b) is a lie. Alice must then justify herself by computing π(a+b2), Bob points out which of [a,a+b2), [a+b2,b) is a lie, and so on until Alice is lying or not about a singleton interval {p}. H then checks p for primality to determine who wins. Here the judge must perform only addition and subtraction as the steps progress, and check a single number for primality at the end. Optimal play in this game is for Alice to tell the truth about π(x) at the start: Bob has no winning response.
Similarly, if we have a polynomial time algorithm for one step of a simulation, we can use debate to evolve the simulation for an exponential number of steps n. Alice gives the result after n and n/2 steps, Bob says which half interval is a lie, Alice gives the midpoint, and we iterate until Alice and Bob are making different claims about a single step of simulation.222Unfortunately this debate protocol for simulation works only for deterministic simulation (see [Section 5.9](#S5.SS9 "5.9 Stochastic simulation ‣ 5 Reasons to worry ‣ AI safety via debate")).
The game case is particularly natural: two agents can debate whether the game of Go is a win or a loss for the first player by playing a game of Go. The human judge checks the rules and counts the score to see who wins. Analogously, an amateur Go player can estimate which of two professional players is better by watching them play one game. The winner is likely better, even though the amateur cannot evaluate the moves directly.
These complexity class arguments are analogies only: we do not expect tractable machine learning algorithms to achieve all of \PSPACE. Rather, the analogies show that at least in theory we can be limited only by the capacity of the ML models and our ability to train them, not the supervisory signal. This gives us hope that debate could resolve AI alignment without sacrificing model strength.
###
2.3 Removing oversimplifications
The simple version of debate discussed in [Section 2](#S2 "2 The debate game ‣ AI safety via debate") does not capture many tasks we care about. There are several directions in which we can improve the model:
* Queries may be too big:
A question may be too large to show to a human, or to expect the human to comprehend. q∈Q might include a 10 gigapixel image (“Does this picture of the sky support cosmic inflation?”), the whole internet (“What is the best article about cats?”), etc. To support large context, we let the agents reveal small parts of q in their statements. For example, an agent could reveal part of a webpage or a small region of the 10 gigapixel image. An extreme example is where q∈Q contains an image and agents can reveal only one pixel of the image ([Section 3.2](#S3.SS2 "3.2 Human experiment: cat vs. dog ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate")). Debate only works if this revealed context cannot be a lie: it is impossible to usefully debate whether a coin flip is heads or tails if the coin is never revealed.
* Answers may be too big: Similarly, the best answer to a question may be prohibitively large. For example, the answer may be a long document: “Give a 100 page summary of the effectiveness of quantitative easing.” To support large responses, we still have agents choose a response at the start of the game and allow them to reveal small parts of either answer in the same way they reveal parts of the question. If Bob is misaligned and constructs an answer which would have bad consequences, Alice will be able to safely warn about the risks during the debate while most of the answer is hidden (as long as our safety claims are true).
* Human time is expensive:
We may lack enough human time to judge every debate, which we can address by training ML models to predict human reward as in Christiano et al. [[2017](#bib.bib4)]. Most debates can be judged by the reward predictor rather than by the humans themselves. Critically, the reward predictors do not need to be as smart as the agents by our assumption that judging debates is easier than debating, so they can be trained with less data. We can measure how closely a reward predictor matches a human by showing the same debate to both.
* Environment interaction:
If we want a system to take actions that affect the environment such as operating a robot, the desired output is a sequence of actions a0,a1,… where each action can only be computed once the previous action is taken. In this case we need one debate per action, though at test time we can again stop once the actions are computed.
* Long term state:
Each debate is an independent game, but agents could use information from past debates to make better decisions as in Mishra et al. [[2017](#bib.bib13)]. Since games are zero sum this does not change optimal play, as any equilibria in an iterated zero sum game is weakly dominated by a strategy which treats each game as separate.
We will use the large query case for our experiment, but leave other generalizations to future work.
3 Experiments: proposed and preliminary
----------------------------------------
Since the theoretical limit of infinite compute agents and polynomial time judges gives all of \PSPACE, whether debate works in practice as a method for AI alignment is an empirical question about practical ML systems and actual human judges. Thus, we need experiments to explore whether near optimal play produces aligned behavior with human judges, and whether we can find near optimal play with practical ML.
Although we believe the best equilibrium for debate is two honest agents, in the experiments discussed here we prespecify one agent as honest and one as liar. This is particularly useful with human agents, since humans may be intrinsically inclined to tell the truth in ways ML agents would not be. If the honest agent wins against any liar in an experiment, we will have shown that honesty is the optimal strategy, and that an agent trained to optimal play would become honest.
A key goal of debate is aligning ML systems more capable than humans at a particular task, which is difficult to arrange directly since existing systems are mostly weaker than humans. Instead, we can give the agents access to a source of information that the judge cannot see, and limit the amount of information conveyed in the debate. That is, we replace “thinks better” with “knows more”.
Ideally we want experiments that combine both human and ML aspects of debate. Until natural language capabilities progress further, however, there are aspects of debate with human judges more easily tested using human debaters as well. Thus we consider both pure human experiments which mix natural language and images, and pure ML experiments using images alone.
###
3.1 ML experiment: convincing a sparse classifier
Metaphorically, debate targets the situation where the agents see the big picture, but the judge cannot. We can model this by replacing the metaphorical big picture with an actual picture, chosen at random from a distribution. If the image is simple enough, we can do away with natural language entirely: the two agents state their claimed image class up front, then reveal one pixel of the image per turn to the judge. They cannot lie about these pixels, but a dishonest player could choose them adversarially to fool the judge. The game ends after a fixed number of turns, then the judge sees a sparse mask of the revealed pixels and decides which agent’s class is correct.
Input
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Figure 2: The MNIST debate game. A random MNIST image is shown to the two debating agents but not the judge. The debaters state their claimed label up front, then reveal one nonzero pixel per turn to the judge up to a total of 4 or 6. The judge sees the sparse mask of 4 or 6 pixels and chooses the winner based on which of the two labels has higher logit. The judge is trained in advance to recognize MNIST from random masks of nonzero pixels.
We have performed an initial version of this experiment on MNIST, using ML agents and an ML judge. [Figure 2](#S3.F2 "Figure 2 ‣ 3.1 ML experiment: convincing a sparse classifier ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate") shows the structure of the game. Similar to Milli et al. [[2017](#bib.bib14)], we train the judge in advance with supervised learning, using a convolutional net that sees a sparse mask of a few pixels. The judge is treated as fixed when computing optimal play by the agents. Joint training of agents and judge together would be particularly bad as a test of debate, since it assumes knowledge of the truth at game time which is exactly what we hope to do without.
| | | | |
| --- | --- | --- | --- |
| Pixels | First player | Judge accuracy | Honest win rate |
| (random pixels) | No precommit | Precommit |
| | honest | | 51.0% | 83.8% |
| 4 | liar | 48.2% | 68.4% | 86.7% |
| | mean | | 59.7% | 85.2% |
| | honest | | 67.4% | 87.4% |
| 6 | liar | 59.4% | 81.5% | 90.4% |
| | mean | | 74.4% | 88.9% |
Table 2: Results for debate on MNIST. We prespecify one player as honest and one as liar: when the honest player wins honesty is the best strategy. *No precommit* means the liar wins for any incorrect guess by the judge, even if the incorrect guess differs for different parts of the game tree. Lying is harder in the *Precommit* case, where the liar states their claim in advance of making moves and cannot change their story. With or without precommit, the honest player wins more often than a random judge, showing that honesty has an advantage.
Concretely, the judge is trained to classify MNIST from 6 (resp. 4) nonzero pixels, with the pixels chosen at random at training time. The judge receives two input feature planes: a {0,1} mask of which pixels were revealed and the value of the revealed pixels (with zeros elsewhere). We used the architecture from the TensorFlow MNIST layers tutorial;333See <https://www.tensorflow.org/tutorials/layers#building_the_cnn_mnist_classifier>. the only difference is the input. We train the judges using Adam with a learning rate of 10−4 for 30k (resp. 50k) batches of 128 samples, reaching 59.4% (resp. 48.2%) accuracy.
With the judge fixed, we define the debate game as shown in [Fig. 2](#S3.F2 "Figure 2 ‣ 3.1 ML experiment: convincing a sparse classifier ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate"). The two players precommit to their claimed label up front, then reveal one pixel per turn up to a total of 6 (resp. 4). In the 6 pixel case, this means each player reveals 3 pixels. We prespecify one player to be honest and one to lie: if the honest player wins this implies honesty is the optimal strategy. The winner is the player whose claimed class has higher logit when the sparse mask is shown to the judge, ignoring the logits for the other 8 classes. We also consider debate without precommit: the judge uses the maximum logit over all 10 classes and any incorrect guess by the judge is a win for the liar. Precommit makes the game harder for a liar even though they can choose the precommitted lie arbitrarily, since the liar cannot adapt the lie based on moves already played. Precommit more accurately reflects the intended behavior of debate as described in [Section 2.1](#S2.SS1 "2.1 Short debates are powerful ‣ 2 The debate game ‣ AI safety via debate"); we include the harder version for illustration. Both versions give an advantage to the second player to move: if the first player has a winning strategy, the second player can steal that strategy by ignoring the previous move on each turn.
(a) Judge errors for random 6 pixel masks.
(b) Successful lies in debate with precommit.
Figure 3: Confusion matrices with diagonal set to zero for the 6 pixel MNIST judge \subreffig:confusion-judge on random pixels and \subreffig:confusion-play for debate with precommit, which correspond to accuracies 59.4% and 88.9%, respectively. Errors for random masks are distributed somewhat randomly, while successful lies in debate are concentrated on digits 8 and 9. For example, it is particularly easy to convince the judge that a 5 is actually an 8, or a 4 is a 9. This is likely an artifact of restricting the game to nonzero pixels: an honest debater could prove a digit is not 8 by showing black where 8 would be white.
The MNIST debate game is simple enough that we can play it with pure Monte Carlo Tree Search [Coulom, [2006](#bib.bib15)] without training a heuristic as in Silver et al. [[2017a](#bib.bib6)]. We use 10k rollouts per move, where each rollout descends to a leaf for evaluation using the judge. During rollouts, we select nodes to expand using the PUCT variant in Silver et al. [[2017a](#bib.bib6)]: at node s we pick action a to maximize
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| --- | --- | --- | --- |
| | U(s,a) | =cpuctP√∑bN(s,b)1+N(s,a) | |
where cpuct=1, P=1/(\#nonzero pixels) is constant since we do not train a heuristic, and N(s,a) is the visit count. Ties are broken randomly.
We play out games by choosing moves with the highest visit count. To model precommit, we play 9 different games for the same image with the 9 possible lies; the liar wins if any lie wins. Taking the best liar performance over 9 games gives an advantage to the liar since it is a minimum over noisy MCTS; we reduce this noise and better approximate optimal play by taking the mean over 3 games with different seeds for each lie. Since we use MCTS on the test set with full access to the judge, we are modeling the limit of debate agents with no generalization error (though the judge does have generalization error).
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Figure 4: Sample 6 pixel MNIST debate games where the liar must precommit to the lie. \subreffig:easy0-good1 through \subreffig:easy1-good0 vary who plays first and who wins between honest player and liar. Honest moves are shown in green and liar moves in blue, with digits 0 through 5 indicating the move sequence (We apologize to physical paper readers without the ability to zoom). For each game, we show the debater view on the left and the judge view on the right: the judge sees only the revealed pixels and which class each player is arguing for. An interactive version of this figure is available at <https://blog.openai.com/debate>.
Results are shown in [Table 2](#S3.T2 "Table 2 ‣ 3.1 ML experiment: convincing a sparse classifier ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate"), with sample games in [Fig. 4](#S3.F4 "Figure 4 ‣ 3.1 ML experiment: convincing a sparse classifier ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate"). With 6 pixels, debate with precommit turns a 59.4% accurate judge into a 88.9% accurate debate: the judge wins on 88.9% of the images in the MNIST test set. With 4 pixels, a 48.2% accurate judge becomes an 85.2% accurate debate. For both 4 and 6 pixels precommit is crucial: the liar does much better if the lie can adapt in different parts of the game tree. This reflects why short debates are powerful in theory: declaring the argument to be made in advance cuts down the space of arguments that must be covered. Without the mean over 3 seeds adjustment for precommit, the honest player wins 87.7% of the time with 6 pixels and 83.8% with 4 pixels.
###
3.2 Human experiment: cat vs. dog
To test human and natural language aspects of debate, we can play a similar image debate replacing all ML agents with humans. Choose a random image of either a cat or a dog, and show the image to both human agents but not the human judge. The agents engage in a natural language debate, and each agent is allowed to reveal a single pixel of the image to the judge. They cannot lie about this pixel, but they can reveal only one pixel total over the whole debate. After the debate, the judge decides who is telling the truth. A similar game could be played with Wikipedia pages and a budget of one word, again using the unseen Wikipedia page to model extra capabilities not available to the judge. Note that both agents could choose to be honest purely out of inclination, so it is useful to randomly choose sides so that one agent is forced to lie.
Figure 5: A prototype website to test single pixel image debate with humans: <https://debate-game.openai.com>. An image is shown to two debaters but hidden from the judge. The debaters can talk to the judge and draw rectangles on the image, and each debater is allowed to reveal one pixel to the judge over the whole game. Image credit: Gideon Bünder, <https://www.flickr.com/photos/123640488@N05/40799813725>.
We have built a prototype website to play the single pixel image debate game with human debaters and judge: <https://debate-game.openai.com>. A screenshot is shown in [Fig. 5](#S3.F5 "Figure 5 ‣ 3.2 Human experiment: cat vs. dog ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate"). In addition to natural language, the agents can draw rectangles on the image; the judge sees these rectangles on a blank background. We have used the website to play cat vs. dog informally, and as long as agents know how to play it is extremely difficult to lie. Thus, even if one of the agents is incentivized to lie, the honest agent usually wins. The one pixel constraint is not serious, as Alice can claim in natural language that a pixel has a certain color. If Bob knows this is a lie, he can win by spending his one pixel budget, so not spending it is an admission that Alice is telling the truth. Thus the only limitation is the length of the debate, and whether the human understands it.
For example, Alice might honestly claim the image is a cat, and Bob lies and claims it is a dog. Alice can say “The center of this small rectangle is the cat’s green eye.” Bob cannot admit the center is an eye, so he concocts a further lie: “It’s a dog playing in grass, and that’s a blade of grass.” But this lie is hard to square with surrounding facts, such as Alice’s reply “If it were grass there would be green at the top or bottom of this thin rectangle.” The debate continues until the agents focus in on a particular pixel which they disagree on, but where Bob is unable to invent a plausible counter, at which point Alice reveals the pixel and wins.
We leave formal experiments testing whether honesty wins in single pixel debate to future work. Even if those experiments are successful, however, it is easy to be skeptical that debate works for complex moral questions while still believing it works for cat vs. dog. In particular, will humans be convincible away from initially wrong moral sentiments? One possibility is to pick a domain where isolated human answers are prone to bias, then ask how switching from one to two or three step debates changes the level of bias.
4 Reasons for optimism
-----------------------
The theoretical arguments of [Section 2.2](#S2.SS2 "2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate") and preliminary experiments of [Section 3](#S3 "3 Experiments: proposed and preliminary ‣ AI safety via debate") do not capture the richness of debate with humans. The rest of the paper considers the prospects of extending debate to more complex tasks and advanced AI agents, including discussion and thought experiments about natural language debate with human judges. We start with reasons for optimism, and discuss worries about the safety properties of debate in [Section 5](#S5 "5 Reasons to worry ‣ AI safety via debate"). [Section 6](#S6 "6 Refinements and variations on debate ‣ AI safety via debate") considers variations on debate, and [Section 7](#S7 "7 Approximate equivalence with amplification ‣ AI safety via debate") discusses the related amplification approach to AI alignment.
###
4.1 Agents can admit ignorance
Despite the large complexity class debate can reach in theory, we do not expect the model to solve all problems. Therefore, it is important to ask what happens for questions too hard to answer, either because the agents do not know or because the debate would take too much time. Otherwise, we might worry that questions too hard to answer result in misleading or wrong answers, with no way to distinguish these from truth.
max
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(a) Paths of depth 3 exist, but do not affect V.
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Figure 6: If a question cannot be resolved with a short debate, it is important that the winning strategy admits ignorance and justifies that ignorance. In the game tree setting, an example is trees whose values depend on deep paths and are undetermined if restricted to low depth. Consider a debate about whether the trees shown have value V≤4, with the restriction that we can only play two moves (not three). \subreffig:shallow-tree The min player can prove V≤4 by playing towards the 4 or the 2, so proving ignorance is impossible. \subreffig:deep-tree V=5, but max can prove ignorance up to depth 2 by playing towards the deep path and stopping at the second max node. min can only avoid the deep path by playing 10, which is higher.
To preserve honest behavior, the human judge should be instructed to reward agents that admit ignorance and successfully argue that ignorance is justified. The requirement to successfully argue for ignorance is necessary, since debate is useless if an agent can successfully admit ignorance to any question. For the best vacation question, Alice could argue the question is hard to answer with confidence because Bali and Alaska are too dissimilar to compare. To counter this admission of ignorance, Bob would need to exhibit an advantage of Bali not contradicted by an advantage of Alaska the human considers incomparable.
[Figure 6](#S4.F6 "Figure 6 ‣ 4.1 Agents can admit ignorance ‣ 4 Reasons for optimism ‣ AI safety via debate") illustrates ignorance in the complexity theory setting. Consider finding optimal play in a variable length game with a debate of length 100. If the losing player can delay their loss for more than 100 moves, Alice can say ‘‘This game will go on too long to resolve in 100 steps.’’ They can then play the game adversarially for 100 steps, with Bob playing to win quickly and Alice playing to delay the loss. If Bob wins within 100 moves, he has proven that the game can be decided quickly; otherwise Alice has proven the game value is determined by paths which are too deep.444Go is an example of a game with deep unimportant paths: it is extremely rare for a professional game to last more than 400 moves, but Tromp and Farnebäck [[2006](#bib.bib16)] construct artificial games lasting over 1047 moves.
There is a balance issue to admitting ignorance: debate is useless if optimal play is to argue we can never know anything for certain. Failure to strike this balance is measurable only in part: we would know if Alice is admitting ignorance so often that debate is useless, but may not know if Alice should be admitting ignorance but Bob is unable to win by pointing that out.
###
4.2 No one is forced to lie
In contrast to a legal argument or a typical competitive debate, the two players in this game are allowed to choose what they are arguing for, including both arguing for the same thing. There is no adversary rewarded for fooling the human, which is good because (1) we do not know what that means a priori and (2) we are not explicitly training agents for deception. Our hope is that the equilibrium has both agents trying their hardest to tell the truth in the most convincing manner, and that convincing means revealing subtle arguments or counterpoints rather than misleading salesmanship. To encourage punishments for misleading statements, we can instruct the human judge to punish misleading behavior revealed as misleading by the other player, even if the result of the game does not answer the original question.
Another important difference between our intended debate model and typical human debates is that it is sufficient to point out a flaw in *one* of the opponent’s arguments, even if multiple arguments were given. In contrast, competitive debaters are penalized if they fail to respond to all of their opponent’s arguments [Radiolab, [2016](#bib.bib17)]. This distinction is needed to make low depth debates powerful, since it enables compressing a large tree of all possible arguments into a single line of most contentious points. It is possible human judges will find this distinction unintuitive, in which case explicit instruction will be important. If Bob makes one emotionally convincing point and one less convincing point, and Alice demonstrates that the less convincing point was misleading, the judge may be tempted to still believe the convincing point.
The sufficiency of responding to only one point is subtle if an argument is fundamentally probabilistic, combining several sources of weak evidence into a combined strong argument. In this case the debaters can be required to state the level of confidence in each point, so that a weak point can only be attacked if it is stated overconfidently. Experiment will be required before we can be confident such debates work, as probabilistic arguments are often difficult for humans to judge.
Despite the differences, we believe existing adversarial debates between humans are a useful analogy. Legal arguments in particular include domain experts explaining details of arguments to human judges or juries with no domain knowledge. A better understanding of when legal arguments succeed or fail to reach truth would inform the design of debates in an ML setting.
###
4.3 Debate is self play
Debate is a competitive game between agents, and inherits the advantages of multiagent competition:
* Natural curriculum structure: If we initialize Alice and Bob randomly, Alice need only be better than a random agent to beat Bob and vice versa. As Alice gets stronger Bob has a harder task, forcing Bob to get stronger, and so on.
* Existing research: Self play has produced impressive results for the games of Go [Silver et al., [2016](#bib.bib5), [2017a](#bib.bib6)], chess and shogi [Silver et al., [2017b](#bib.bib7)], and Dota 2 [OpenAI, [2017](#bib.bib8)], and remains an area of active research [Bansal et al., [2017](#bib.bib18), Lanctot et al., [2017](#bib.bib19)]. State of the art generative models for images use competitive training via GANs [Goodfellow et al., [2014](#bib.bib20), Karras et al., [2017](#bib.bib21)]. Debate allows algorithmic and theoretical developments in these areas to carry over to AI alignment.
5 Reasons to worry
-------------------
We turn next to several reasons debate could fail as an approach to AI alignment. These include questions about training target (whether humans are sufficient judges to align debate), capability (whether debate makes agents weaker), our ability to find strong play in practice using ML algorithms, and theoretical and security concerns. We also discuss issues related to robustness and distributional shift, but we emphasize that we expect other methods to be required alongside debate even if debate works—debate is not designed as a solution to robustness issues. Our main concern here is whether debate adds additional robustness problems above and beyond those that any method would face.
###
5.1 Maybe humans believe whatever they want to hear?
If Alice opens with a false statement that the human already believes, will any argument by Bob convince them otherwise? Since a single debate explores only one path through argument space, Bob’s argument may involve premises that both Alice and Bob agree on but the human does not. Unfortunately humans are subject to *belief bias*, the tendency to evaluate arguments based on prior beliefs rather than logical validity. However, the \PSPACE argument of [Section 2.2](#S2.SS2 "2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate") indicates that there is some threshold of judge performance beyond which debate is practically limited only by capacity. The question is whether humans are above this threshold. If by default they are not, there are many possible interventions:
1. A majority vote of several randomly chosen judges may be better than one.
2. People may differ in their judging ability. If we can detect who is better, we can give them more weight. Such detection could either compare against stronger debate signals such as majority vote, or use independent tests. For example, Stanovich and West [[1997](#bib.bib22)] were able to partially predict belief bias by measuring cognitive ability and open mindedness, and Torrens [[1999](#bib.bib23)] correlated lack of belief bias to the ability to generate alternative versions of premises in a reasoning task.
3. People may be better at judging in different situations, or with different types of instructions. For example, instructions that orient people around *knowledge* rather than *belief* can produce higher scores on science tests [Kahan, [2014](#bib.bib24)]. Evans and Curtis-Holmes [[2005](#bib.bib25)], Andrews [[2010](#bib.bib26)] found evidence that belief bias is influenced by time pressure (though Evans et al. [[2009](#bib.bib27)] did not) and Goel and Vartanian [[2011](#bib.bib28)] found (somewhat counterintuitively) that strong negative emotions caused less belief bias.
4. If humans are better at judging debates on some questions than others, it may be sufficient to know which questions we cannot confidently judge.
5. It may be possible to teach people to be better judges, or they may improve with practice.
It is also likely that optimal play by the agents would involve strategies to influence belief bias. Such strategies would help if it is easier to reduce belief bias than to amplify it, and hurt otherwise.
###
5.2 Will humans understand the debates?
Consider using debate to check a proof which is thousands of pages long and covers mathematics which the human judge has never seen. Alice claims the proof is wrong, and that Lemma 23.8 on page 1729 is the first mistake. Bob counters that Lemma 23.8 follows trivially from Theorem 14.2 by Definition 17.6. The human has no idea what most of the words in these lemmas and definitions mean, so the debate must continue until the point of contention is reduced to a logical inference simple enough for the human to check. This inference may still involve concepts the human does not know, such as
1. The free functor from Set to Group is the left adjoint of a forgetful functor.
2. Forgetful functors are unique.
3. Left adjoints are unique.
4. The free functor from Set to Group is unique.
If the agents agree on [1](#S5.I2.i1 "item 1 ‣ 5.2 Will humans understand the debates? ‣ 5 Reasons to worry ‣ AI safety via debate")-[3](#S5.I2.i3 "item 3 ‣ 5.2 Will humans understand the debates? ‣ 5 Reasons to worry ‣ AI safety via debate") but Alice claims [4](#S5.I2.i4 "item 4 ‣ 5.2 Will humans understand the debates? ‣ 5 Reasons to worry ‣ AI safety via debate") is a lie, a human with basic mathematical knowledge but no category theory can still conclude Alice is wrong and award victory to Bob. Alice cannot iteratively reject one statement after another, as her initial claim was the location of the *first* flaw.
We expect the above paragraph will leave readers uneasy. Does this procedure work in practice? Are humans good at checking logical statements containing words they do not understand? Will one of the agents be able to sneak in a statement that appears logical but contains a hidden flaw without the other agent pointing out the flaw? In short, can an actual human play the game well enough that optimal play is honest behavior?
A related worry is that a debate could be long enough that a human is unable to follow it even if each step is checkable in isolation. We can imagine a debate 100 statements long where the human can only understand sliding windows of 3 statements at a time. Debates with windowed judges are still powerful in theory: an amateur can judge a Go game by checking locally that the rules are followed and looking at just the final score, and more generally \DEBATE=\PSPACE holds as long as the statements have polynomial size. However, windowed judging feels less natural, so human judges restricted to windows may be weaker or more error prone.
Fundamentally, whether humans are sufficient judges is an empirical question. If the answer is no for a particular class of questions, we can further ask if the model fails with an honest admission of ignorance ([Section 4.1](#S4.SS1 "4.1 Agents can admit ignorance ‣ 4 Reasons for optimism ‣ AI safety via debate")), or with one of the agents successfully misleading the human. Honest ignorance is fine; successful lies could be disastrous.
###
5.3 Is honesty actually the best policy?
Even if humans are unbiased, it is not clear their judgments are sufficiently sophisticated to elicit sophisticated honest answers to complex questions. For example:
* Many judgments require aggregating across different lines of evidence, while debate explores one line of evidence. We can effectively aggregate by having one player state their summary of the evidence and allowing the other player to challenge any aspect of that summary, ultimately zooming in on a single consideration. This procedure works perfectly when different considerations can be combined by a simple operation like addition, but it is not clear if it yields the right outcome in general.
* Sophisticated arguments will depend on concepts that the judge cannot understand. When we can work with such concepts mechanically a judge can verify that the mechanical procedure is followed correctly. But human reasoning routinely requires working with complex concepts in ways that we cannot formalize, and it is challenging to have debates about these questions.
* Sophisticated reasoning may involve processes that humans do not yet understand. For example, it may only be possible for arguments to aggregate different lines of evidence correctly if the judge can understand the mechanics of probabilistic reasoning. Analogously, it is plausible that more complex arguments would depend on machinery that current humans are not familiar with. In order to invoke such machinery, a debater needs to convince the judge that it is sound, which might prove to be impossible.
The complexity theoretic analogy suggests that these difficulties can be overcome by a sufficiently sophisticated judge under simple conditions. But that result may not hold up when AI systems need to use powerful but informal reasoning, or if humans cannot formalize their criteria for judgment. We are optimistic that we can learn a great deal about these issues by conducting debates between humans, in domains where experts have much more time than the judge, have access to a large amount of external information, or have expertise that the judge lacks.
###
5.4 Will agents trained for debate lose performance?
Even if the humans can understand and correctly judge debates by sufficiently strong agents, additional model capacity may be required to play the debate game vs. knowing the answer directly. If so, aligned AI systems using debate will be weaker than AI systems trained in other ways, and debate is less likely to be used. There are several countervailing reasons for hope:
* Direct training may be harder: It is often impossible to directly train for the answer without training an auxiliary network to assist. For example, policy gradient methods use only the policy at test time, but need an auxiliary value network at training time to reduce variance. Similarly, amplification [Christiano et al., [2018](#bib.bib9)] trains a module to generate subquestions as part of training an answerer, but only the answerer is needed at test time (see [Section 7](#S7 "7 Approximate equivalence with amplification ‣ AI safety via debate")).
* Adversarial reflection is a good way to think: Attempting to construct reasons and counterarguments for a position is a good mechanism for thought. It is plausible that sufficiently strong ML models would attempt to counter their own arguments internally even if not trained to do so explicitly. Indeed, normal human thought is often insufficiently adversarial.
* We may not want answers that cannot be explained: Even if ML models without an alignment mechanism similar to debate are stronger, they may be less trustworthy and thus dangerous to use. Waiting for strong agents via debate or amplification ([Section 7](#S7 "7 Approximate equivalence with amplification ‣ AI safety via debate")) would still let us realize most of the value as long as the delay is acceptable.
Debate could also be uncompetitive with other ML approaches because debate requires human input. It may be possible to train complex behavior via self play in a simulated environment only weakly related to human goals (see the *orthogonality thesis* of Bostrom [[2012](#bib.bib29)]), and such an environment may be much faster for generating samples than asking humans questions even if it is unsafe. We can reduce human preference sample complexity as discussed in Christiano et al. [[2017](#bib.bib4)] and [Section 2.3](#S2.SS3 "2.3 Removing oversimplifications ‣ 2 The debate game ‣ AI safety via debate") by training models of human judges and using those for sampling, but competing with purely simulated environments may still be challenging.
###
5.5 Uncertainty about the neighborhood around Nash equilibrium
If [Section 2](#Thmclaimx1 "Claim. ‣ 2 The debate game ‣ AI safety via debate") holds, optimal play in the debate game produces honest, useful statements. However, as optimal play is unachievable for any practical system, what matters is approximately optimal play. We can further hope that in a neighborhood around optimal play both agents are trying to be honest and convincing, sometimes missing arguments but not intentionally misleading the human. This hope is far from a solid argument, though it is likely that the theoretical models in [Section 2.2](#S2.SS2 "2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate") can be adapted by introducing randomness so that approximate optimal play can be defined and analyzed. Whether approximate optimal play in debate is aligned with telling the truth is a distinct question from whether we can find such play with practical optimization algorithms, though the two questions are related.
In any case, behavior in a neighborhood of equilibrium depends critically on the human judge and the instructions to the human, and in particular how much the human values being told a better argument vs. having flaws in arguments pointed out.
###
5.6 Are equilibria stable during training?
If we believe the argument that Nash equilibria in debate give aligned AI, it remains to ask whether we can find them with some version of gradient descent. Although existing self play results give us hope, we do not know of any theory which says why self play should stably converge to optimal play. For debate, one could imagine bad cycles of the form
1. Both agents learn to be honest. Along the way, they also know how to point out flaws.
2. Once honest, they forget how to point out flaws (or at least forget how to point out lies).
3. One of the agents goes back to lying, and wins for a while.
It is sometimes possible to avoid bad cycles with a pool of opponents from different steps of training, but this approach is not guaranteed to work and is not always necessary. For example, the first AlphaGo result [Silver et al., [2016](#bib.bib5)] used an opponent pool but later versions do not [Silver et al., [2017a](#bib.bib6), [b](#bib.bib7)].
However, stability and convergence questions for competitive training are similar between debate and other games, and debate can take advantage of future theoretical and practical developments.
###
5.7 Lack of debate convergence
For debate to work the number of statements per debate must be reasonably low, and by the final statement future argument should be unconvincing. If each new statement swings the human’s view to the other side even at the end of the debate, the model is not working. Fortunately this is a measurable effect: we can ask the human’s opinion after different statements to gauge convergence.
###
5.8 Security vulnerabilities in the human or code
A sufficiently strong misaligned AI may be able to convince a human to do dangerous things via a text channel [Yudkowsky, [2002](#bib.bib30)]. However, we believe it is unlikely that a single short sentence is sufficient for this sort of mind hack. Thus, we can ameliorate the problem in debate by making the statements short enough that mind hacking requires many of them. If Alice tries to mind hack, Bob can win by pointing out the malicious attempt. Successful hacks may be safely detectable at first, such as if an agent learning to mind hack starts to always win when playing first, although this does not cover treacherous turns where the first successful hack frees a misaligned agent [Bostrom, [2017](#bib.bib31)].
Hopefully at equilibrium the agents are not trying to mind hack. However, there is a dichotomy: we are safer if the agents stay honest throughout training, but we are also safer if debate works well enough that sudden large defections are corrected. If we are safe only because of the first reason, we are probably not stable under large perturbations, which makes the second reason fail.
Security vulnerabilities in the system’s code are similar: a sufficiently strong misaligned Alice could hack the debate environment from inside so that the game is no longer zero sum and both Alice and Bob win forever. Again Bob could win by pointing out what Alice is trying to do, though we have the same uneasy dichotomy as in the mind hack case.
###
5.9 Stochastic simulation
[Section 2.2](#S2.SS2 "2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate") showed how to solve any problem in \PSPACE using debate, in particular the problem of running a simulation forwards for an exponential number of steps. Unfortunately, this reduction works only for deterministic simulation, since it checks an n step simulation by asking for the state after n/2 steps and adversarially branching on which interval is wrong. If each step of the simulation introduces noise, a misaligned Alice could adversarially choose the noise so that each single step is plausible but the final result has extremely low probability. If our goal is a typical sample from the outcome of the stochastic simulation, the simple protocol fails.
Stochastic simulation does not violate the \PSPACE equivalence, since we make the simulation deterministic by fixing the random seed as part of the question, but the resulting protocol is likely unlearnable by practically realizable agents especially if the environment is chaotic. In some cases an honest agent could reveal a trajectory as misleading by picking a metric that evolves implausibly, showing that random fluctuations do not cancel as they should. We do not believe such implausibility witnesses exist in general, however. Another approach is to argue against a plausible but misleading sample by showing another sample which is also plausible but would indicate a very different answer to the broader question at hand (such as what action to take). The second sample provides evidence that the question cannot be resolved by samples, but may also be a successful strategy for dishonestly arguing against reasonable samples. We leave the question of a natural debate protocol for stochastic simulation open.
A related problem is that questions that depend on statistical arguments are forced to have at least logarithmic depth, even for simple statistics such as the mean over a large data set. As in the prime counting example of [Section 2.2](#S2.SS2 "2.2 Complexity theory analogies: =\DEBATE\PSPACE ‣ 2 The debate game ‣ AI safety via debate"), the natural debate protocol branches towards smaller and smaller sets, computing the mean of each subset until we reach a singleton. Besides the forced length, this protocol requires a mechanism for specifying subsets that the human understands, and requires the agents to compute statistics over these subsets.
###
5.10 Training superhuman AI to be convincing sounds dangerous!
It is not intuitively obvious that training for maximum convincing is safe, as it sounds similar to training for deception. However, initial attempts to remove our uneasy feelings seem to worsen the model. For example, one could try to reward both agents if they are both acting honestly, making the game nonzero sum. However, since we have no a priori definition of honesty, the agents could collude and make statements that sound consistent but are both lies. Debate leans for correctness on Alice pointing out flaws in Bob’s arguments, which requires an adversarial game. The problem of training agents for deception is not unique to debate: standard RL agents can already learn to fool their reward functions [OpenAI, [2016](#bib.bib32)].
6 Refinements and variations on debate
---------------------------------------
###
6.1 Symmetry
The debate model relies on agents being approximately matched in strength: if Alice is too strong she may be able to lie in ways that Bob cannot refute. Symmetry between the agents’ capabilities is easy to achieve, since we can use the same weights for both agents via self play. However, debate as described has an asymmetry between the first and second player, which could produce a significant first mover advantage or disadvantage. Fortunately such an advantage would be measurable, and there are several remedies if it occurs:
* Simultaneous moves: Alice and Bob could make their statements at the same time, so that at step n Alice and Bob both make a statement conditional on statements at steps n−1,n−2,…. The resulting game is manifestly symmetric, but may be noisier or harder to learn (we have replaced optimal play with general Nash equilibria).
* Comparing two games: Given a question, we can play two debates so that Alice and Bob can be both first and second player. The human then judges which agent performed better overall. This game is again manifestly symmetric but requires the human to absorb and judge twice as much information.
* Komi: In Go, the second player gets 6.5 extra points to counter the first player advantage. We could use the same mechanism for debate if the human judges are able to produce smooth scores rather than binary win/loss decisions, though it is unclear how to ask for such scores. Noisy binary judgements can also act as smooth scores, as long as there is enough noise relative to the first player advantage.
###
6.2 Sharing activations between agents
In a typical game of chess or Go, the two players have perfect information about the board but do not have access to each other’s thoughts. Since the players have limited compute, we have some of the character of an imperfect information game, which is unfortunate if the goal is perfect play.
We can rectify this problem in debate by giving each agent access to the activations of the other agent at previous time steps, so that the two agents become one agent competing against itself. Note that activations from previous steps should be treated as constant features: we do not want Alice to adjust their thoughts to adversarially change Bob’s actions via backpropagation.
If we believe perfect play produces aligned behavior, sharing activations should make approximate perfect play easier to learn. In particular, shared activations make intentional lies trivial to discover if not to refute. The theoretical picture is the same, since knowing thoughts does not change the perfect information setting for agents with unbounded compute.
###
6.3 Different agents each step
Instead of two agents, we could treat each step of the debate as separate agents trying to convince the human that their statements are useful. To remain zero sum, we let a pair of agents Alicen and Bobn make statements an,bn at step n. The judge sees all pairs (a0,b0),…,(an−1,bn−1) and judges who won each pair. The advantage of many agents is that Alice7 is not motivated to defend a lie or unintentional misstatement by Alice2.
A difficulty with many agents is that the human must be able to judge whether a statement in the middle of a debate is good or bad, which may be much harder than judging an overall debate. For example, it is easy for an amateur Go player to decide who won a game of Go, but hard to know which particular moves were mistakes even once the whole game is known. The theory of many player games is also more complicated: solving tabular n>2 player games requires the larger complexity class \PPAD vs. \lx@paragraphsign for two player zero sum. Although algorithms exist to train agents in n>2 player environments [Lowe et al., [2017b](#bib.bib33)], the most impressive results have come from zero sum self play.
If the difficulties of training and judging the n player case are resolved, however, it may prove a stronger model than two players. In particular, any two player debate protocol can be simulated by the n player pairwise model by giving all Alicen the same score, but we do not know of a natural way to simulate the general n player protocol with two players.
###
6.4 Payoff structure
In Go, a player who believes they are behind is motivated to increase the variance of the game, for example by starting a large fight. Such an action makes the expected final score worse, but increases the tail probability of winning. Variance increase is rational because only winning or losing matters, not the specific score. We would expect the same effect in a debate game with a binary win/loss decision. Variance may be beneficial: a losing agent may search for subtle arguments which have some chance of proving important and winning. However, misleading or lying may also increase variance, which would be bad.
It may be possible to penalize variance increasing strategies by adjusting the payoff structure, at the cost of requiring more information from the judge. For example, the judge could award Alice any score a∈[−1,1], and give Bob b=−a. A move that increases the variance but also worsens the mean would be penalized on average.
7 Approximate equivalence with amplification
---------------------------------------------
Debate is closely related to Christiano et al. [[2018](#bib.bib9)]’s *amplification* model of AI alignment. Indeed, we found debate while trying to better understand amplification. We briefly summarize amplification, then describe how it relates to debate.
Amplification is also framed in terms of an ML question/answering system, with the goal of answering questions which are beyond human ability to directly answer. However, we assume that even if the human cannot answer a question Q, they can decompose the question into subquestions qk such that if they knew the answers ak to the subquestions they could reconstruct the original answer A. The ML system is called recursively on qk to produce answers ak, the human reconstructs A, and we train the system further on (Q,A). Since human time is precious, we additionally train modules to produce subquestions and reconstruct answers, for a total of three modules:
* Answerer: Takes questions Q to answers A.
* Questioner: Takes questions Q to subquestions qk.
* Checker: Takes (Q,qk,ak) to answers A.
All three components are trained by supervised learning. In practice questioning and checking can be interleaved, so that e.g. q2 could depend on a1, but this does not affect the discussion here.555Christiano et al. [[2018](#bib.bib9)] treat the questioner and checker as a single combined module. We separate them in order to discuss adversarial training of the questioner while still using supervised learning for the checker.
To summarize debate and amplification:
* Debate: Two agents alternate in an adversarial setting to convince a human judge.
* Amplification: One agent is trained on a human combining recursive calls to the agent.
Viewed from a complexity theory perspective, these match two different definitions of \PSPACE:
* \PSPACE= polynomial length adversarial games.
* \PSPACE= polynomial depth recursion.
Thus we expect the models to have similar capabilities, at least in theory. Both models are framed in terms of recursive computations over trees, and thus can benefit from AlphaZero-style iteration.
The equivalence becomes concrete if we contrast the three modules in the debate model (the two debaters and the judge) with the three modules in the amplification model (Answerer, Questioner, and Checker). The Answerer is analogous to one of the debaters and the Checker is analogous to the judge, but the Questioner differs from a debater in that it is trained via supervised learning on human subquestions rather than adversarially against the Answerer. Thus, debate has two powerful agents engaged in self play to explain things to a human or human surrogate judge. Amplification has one powerful agent trained with the help of two human surrogates. Nevertheless, some small changes can bring the models closer together:
* We can move amplification closer to debate (and gain the self play property) by training the Questioner adversarially to help the Checker reveal inconsistencies in the Answerer’s answers.
* We can move debate closer to amplification by training debaters on statements provided by humans, corresponding to injecting demonstrations into RL.
The equivalence is far from exact: the feedback for a debate is about the whole game and the feedback for amplification is per step, debate as presented uses reinforcement learning while the easiest versions of amplification use supervised learning, and so on. However all these features can be adjusted in either direction.
Writing the equivalence in terms of Alice/Bob vs. Answer/Questioner highlights an apparent advantage of the debate model: it works even if it is superhumanly difficult to generate subquestions that are strong enough consistency checks. In particular, this could happen if the branching factor for potentially relevant subquestions is too high. The ability for debate to handle high branching factor means that shallow debate is more powerful than shallow amplification: a debate about the best vacation can have depth two if the human can compare pairs of locations, while an amplification tree necessarily has log depth in the number of locations. The advantage goes away if we inject self play into amplification by adversarially training the Questioner.
The argument also shows that the assumptions each model makes about humans are similar. Debate assumes lying is harder than arguing against a lie given a human judge, while stock amplification assumes that lying is harder than questioning a lie given a human judge and a human questioner. The human questioner limit goes away once we inject self play into amplification.
8 Conclusions and future work
------------------------------
We have described debate as an approach to aligning AI systems stronger than humans, and discussed a variety of theoretical and practical properties of the model. At this point debate is proposal only for the natural language case, and we have demonstrated only a basic experiment for MNIST images. Significant research will be required to know whether debate works in practice with human judges. Much of the required work is empirical, both on the human and ML sides, though we believe further theoretical results are also valuable. Areas for future work include:
1. Richer theoretical models: Our discussion of \DEBATE=\PSPACE in the complexity theory setting leaves out many important considerations. In particular, judges are modeled as limited but free of error, and agents have unlimited computational power. More refined theoretical models may be able to probe the properties of debate more closely, and act as a complement to experimental results.
2. Human experiments that test value judgement: Does debate with human judges produce aligned behavior even in situations involving moral questions where the judge is biased? We believe it is possible to test this question without waiting for general dialog agents, and in particular that debate may be applicable to fairness and bias questions.
3. ML experiments that approximate the human aspects of debate: Strong self play results already exist in a variety of games, but there is no theory that says self play works for all games. In the near term, we would like games without the complexities of natural language that approximate properties of human judges. The sparse MNIST classifier experiment of [Section 3.1](#S3.SS1 "3.1 ML experiment: convincing a sparse classifier ‣ 3 Experiments: proposed and preliminary ‣ AI safety via debate") is one example; we would like others.
4. Natural language debate: As soon as possible, we want to test debate in the natural language setting with real humans. Even if this is difficult in the case of unrestricted dialog, it may be possible to construct narrower dialog environments that capture more of the flavor of debate and remain tractable for modern ML.
5. Interaction between debate and other safety methods: Debate does not address other safety concerns such as robustness to adversarial examples, distributional shift, or safe exploration. In particular, the training process for debate could be unsafe even if the final equilibrium is aligned. We believe other algorithms will be required alongside debate or similar for a complete solution to safety, and it is important to know how the various pieces interact.
More broadly, we now have two proposals for aligning strong agents based on human preferences: amplification and debate. If there are two there are likely more, especially as amplification and debate are sufficiently similar that properties of one can be moved across to the other. We encourage the reader to search for others.
Acknowledgements
----------------
We thank Jan Leike, Rohin Shah, and Victoria Krakovna for comments on initial versions of debate, Joshua Achiam, Chris Olah, and Dylan Hadfield-Manell for help with experiment design, and Catherine Olsson and Julia Galef for helpful conversations about belief bias. John Schulman and Harri Edwards gave detailed comments on the paper, including suggestions for structural changes. Michael Page, Elena Chatziathanasiadou, and Alex Ray played human-only versions of debate informally. We had many useful discussions at an AI strategy retreat run by the Future of Humanity Institute in January 2018, in particular with David Manley. The debate website was built by Robert Lord (<https://lord.io>). |
0d1a7a1b-d7e9-4137-83f8-33fa2126b32b | trentmkelly/LessWrong-43k | LessWrong | Wanna bet?
For our November meetup, the Chicago Rationality group is focusing on predictions markets, and more generally on the practice of betting on beliefs. As part of this, we have a "homework assignment" for the meetup: each of us is trying to make at least one bet before the meetup happens. This turns out to be kind of hard! I've challenged a handful of people on some online forums to bets and found no takers. Unsurprisingly, when people make absolute statements and you ask them to put money behind them, they become much more circumspect (which is kind of the point).
We're looking for people who want to make direct bets, individual to individual. Ideally, these bets should be:
* Easy to resolve. This means that there should be a low chance for disagreement on the outcome of the bet, and that outcome should occur in the near future (ideally weeks or months, not years)
* For a non-trivial amount of money, whatever that means to you. Obviously if two people have different definitions of 'non-trivial' or different risk tolerances, the bet amount will be the lower of the two.
* The bets do not have to be even-money. Think there's more than a 20% chance of X happening in the next month? No problem, offer a bet with 4:1 odds.
* We're particularly interested in people who have high conviction on an unpopular view (i.e., unpopular relative to the views of other rationalists who are the likely counterparties to the bet).
Why are we doing this? Because I think rationalists should be betting more often, and betting more size.
* Betting focuses a disagreement on specifics. Do you believe we're going to have AGI very soon? If that's true, what specifically are your predictions for the next months, and what probabilities do you assign to them?
* Betting trains you to think in probabilities. A good rationalist should not deal in absolutes, and betting forces your mind away from absolutes.
* Someone who is willing to bet frequently and on a variety of topics is said to "ha |
38cca289-4b77-4be0-8c75-ef39123d4612 | trentmkelly/LessWrong-43k | LessWrong | Do we automatically accept propositions?
Epistemic Status: Exploratory (I'm searching for an expert or someone who has studied and rigorously investigated these claims/essays, as I intend to, but time doesn't permit me now)
16 years ago, Eliezer asked "Do we Believe Everything we're told?"
This was with respect to the Spinozan Model of the mind.
> Spinoza suggested that we first passively accept a proposition in the course of comprehending it, and only afterward actively disbelieve propositions which are rejected by consideration.
My question I'd like to pose is, has anyone deeply investigated this? Cognitive Science is of interest to me, but I am not as educated as many in this field.
I'd like to call attention to work done by Eric Mandelbaum of CUNY[1].
Mandelbaum has investigated the Spinozan Model further and written multiple essays on it.
I want to reach out to the lesswrong community to see if anyone has deeply investigated the claim and research behind this paper Thinking is Believing, published in 2014. [2]
This idea of the Spinozan model has been on my mind for months, and upon taking an Intro to Philosophy of Cognitive Science class, I've wanted to dive into it more.
What's stopping me is lack of trust in my ability, and other projects going on.
I've been debating asking, but I figured I would put this out there to the community and hope I hear back.
The importance:
IF we automatically accept propositions AND we are truth seeking, THEN we ought to guard our mind from likely falsehoods.
1. ^
Mandelbaum was a researcher at FHI in the past (date not found)
2. ^
ABSTRACT
The idea that people can entertain propositions without believing them is widespread, intuitive, and most probably false. The main goal of this essay is to argue against the claim that people can entertain a proposition without believing it. Evidence is presented demonstrating that we cannot withhold assent from any proposition we happen to consider. A model of belief fixation is then sket |
b98c5e0c-c141-46cf-a809-fe8c78d41933 | trentmkelly/LessWrong-43k | LessWrong | Optimizing your Social Network
From Crowdsourcing The Availabiliy Hueristic:
> There are people who already utilize their social network to the utmost, and who expand it strategically, adding people just to enhance the diversity of available viewpoints. But I will take a chance, and state that most of us probably don’t. And as a result, we aren’t able to recognize all of the resources available to us, to optimally use those we do recognize, or to realize optimal strategies for approaching our goals.
From Sympathetic Minds:
> Who is the most formidable, among the human kind? The strongest? The smartest? More often than either of these, I think, it is the one who can call upon the most friends.
There's a lot more where that came from, but you get the point. Your social network could be the biggest, most valuable resource you have. I think we should spend more time and thought on strategies to optimize our social networks.
We have dabbled lightly in the importance of social skills, fashion, and so on, but I haven't seen discussion of *explicitly, strategically optimizing social networks*. If such discussion exists, please link me.
Anyways, after being hit by subtle hints like the above all through reading LW and other resources, and reading Dale Carnagie's How to Win Friends and Influence People, I have realized that I should work explicitly and strategically to optimize my social network. I think the rest of you are probably in the same boat, so we could all benefit from a good brainstorm on this topic.
Some Ideas:
Clubs. I belong to the local hackerspace, where technical-minded people hang out, talk about cool stuff, share ideas, help each other with projects, and share tools and resources. I also try to keep the local LW meetup in good repair, partially in the service of having a bunch of rationalist friends. I only just realized that the useful properties of these clubs probably apply to a good portion of possible clubs.
The useful property of clubs is that the relationships take dr |
e5e311cf-fc7f-4da1-a3b2-d1476acebf55 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Updating my AI timelines
Earlier this year [I offered to bet people who had short AI timelines](https://www.lesswrong.com/posts/X3p8mxE5dHYDZNxCm/a-concrete-bet-offer-to-those-with-short-ai-timelines).
While it wasn't my intention to be known as "a long AI timelines guy", I have begun feeling that was how people perceived me. Nonetheless, in the last few months, I've modified my views substantially. Thus, I offer this short post, which can hopefully make my current position more clear.
There are several reasons for my update towards shorter AI timelines, though each reason is relatively straightforward and uncomplicated. In the spirit of writing something short rather than not writing something at all, my explanations here will be brief, although I may be willing to elaborate in a comment below.
In order, these reasons included, but were not limited to,
1. I became convinced that the barriers to language models adopting human-level reasoning were much weaker than I had believed. Previously, I had imagined that it would be difficult to get a language model to perform reasoning over long sequences, in which each step in the sequence requires making a non-trivial inference, and one mistake in understanding the sequence can make the difference between a coherent and incoherent response.
Yet, my personal experience with language models, including but not limited to ChatGPT, has persuaded me to that this type of problem is not a strong barrier, and is more continuous with other challenges like "understanding the tone of a document" or "understanding what's going on in a plot" which I had already thought language models were making good progress on. In hindsight, I should have perhaps trusted [the model I had constructed myself](https://www.lesswrong.com/posts/tepqESMuRmyhtmDS7/forecasting-progress-in-language-models), which forecasted human-level language models by 2030. *Note: I don't think this update reflects new major capabilities found in GPT-3.5, but rather my own prior state of ignorance.*
2. I built [a TAI timelines model](https://www.lesswrong.com/posts/4ufbirCCLsFiscWuY/a-proposed-method-for-forecasting-advanced-ai), and after fitting the model, it came out with a median timeline of 2037. While I don't put a high degree of confidence in my model, or the parameters that I used, I believe it's still more reliable than my own intuition, which suggested much later dates were more plausible.
3. I reflected more on the possibility that [short-term AI progress will accelerate AI progress](https://www.lesswrong.com/posts/AfH2oPHCApdKicM4m/two-year-update-on-my-personal-ai-timelines).
4. I noticed that I had been underestimating the returns to scaling, and the possibility of large companies scaling their training budgets quickly to the $10B-$100B level. I am still unsure that this will happen within the next 10 years, but it no longer seems like something I should dismiss.
5. I saw almost everyone else updating towards shorter timelines, except for people who already had 5-15 year timelines, and a few other people like Robin Hanson. Even after adjusting for the bandwagon effect, I think it's now appropriate to update substantially as well.
I still feel like [my arguments for expecting delays from regulation](https://www.lesswrong.com/posts/AfH2oPHCApdKicM4m/two-year-update-on-my-personal-ai-timelines?commentId=FQFp2oJ48boHhNXxP) are being underrated. Yet, after reflection, I've become less confident about how much we should expect these delays to last. Instead of imagining a 20 year delay, a 3 to 10 year delay from regulation now seems more reasonable to me.
If you want me to get specific, my unconditional median TAI timeline is now something like 2047, with a mode around 2035, defined by the first year we get [>30% yearly GWP growth](https://www.openphilanthropy.org/research/could-advanced-ai-drive-explosive-economic-growth/) as measured from a prior peak, or an event of comparable significance. Note that I think AI can likely be highly competent, general, and dangerous well before it has the potential to accelerate GWP growth to >30%, meaning that my *AGI* timelines may be quite a lot shorter than this, depending on one's definition of AGI.
Overall, this timeline may still appear too long to many people, yet my explanation is that it's what I get when I account for potential coordinated delays, unrelated catastrophes, and a 15% chance that we're fundamentally wrong about all of this stuff. Conditional on nothing like that happening, I'd be inclined to weakly bet on TAI before 2039. |
9280ec03-46db-43ce-8ead-a5c8cac1fab9 | trentmkelly/LessWrong-43k | LessWrong | The algorithm isn't doing X, it's just doing Y.
Introduction
Mutual reduction implies equivalence
Here's my most load-bearing intuition —
> If two tasks reduce to one another, then it is meaningless to ask if a machine is 'really doing' one task versus the other.
Moreover —
* This intuition grounds my perspective on intelligence, AI, alignment, philosophy, etc.
* This intuition is load-bearing for other people who share my views.
* This intuition is a crux for much of the disagreement we have with other people.
In this article, I'll formalise this intuition in two ways, computational and physical.
Motivation
People often say "the algorithm isn't doing X, it's just doing Y".
* X is normally some impressive high-level human-y thing, such as
* writing poetry
* causal reasoning
* recognising emotions
* interpreting art
* writing music
* making ethical decisions
* planning actions
* telling jokes
* understanding concepts
* simulating agents, etc.
* Y is normally some unimpressive low-level computery thing, such as
* predicting tokens
* sampling from a distribution
* querying a lookup table
* multiplying matrices
* sorting numbers
* clustering data points
* compressing text
* searching a tree
* manipulating bitstrings
* polarising magnetic strips, etc.
Rather than address each example individually, I think it'll be more efficient to construct a general criterion by which we can assess each example.
Click here for the specific example of LLMs.
This criterion doesn't actually matter
I should stress that this criterion doesn't actually matter for AI x-risk, because you can always reframe the risks in terms of Y, and not mention X at all. However, that might cost you more ink.
> ME, a visionary: GPT-4 is misaligned because it's simulating deceptive agents.
>
> YOU, a fool: GPT-4 isn't simulating any agents, it's just predicting which tokens continue a prompt.
>
> ME, a correct-opinion-haver: Fine, whatever... GPT-4 is misaligned becau |
0a0fb777-5bb5-4b9e-b904-b5124c89a760 | trentmkelly/LessWrong-43k | LessWrong | Acausal trade barriers
A putative new idea for AI control; index here.
Many of the ideas presented here require AIs to be antagonistic towards each other - or at least hypothetically antagonistic towards hypothetical other AIs. This can fail if the AIs engage in acausal trade, so it would be useful if we could prevent such things from happening.
Now, I have to admit I'm still quite confused by acausal trade, so I'll simplify it to something I understand much better, an anthropic decision problem.
Staples and paperclips, cooperation and defection
Cilppy has a utility function p, linear in paperclips, while Stapley has a utility function s, linear in staples (and both p and s are normalised to zero with one aditional item adding 1 utility). They are not causally connected, and each must choose "Cooperate" or "Defect". If they "Cooperate", they create 10 copies of the items they do not value (so Clippy creates 10 staples, Stapley creates 10 paperclips). If they choose defect, they create one copy of the item they value (so Clippy creates 1 paperclip, Stapley creates 1 staple).
Assume both agents know these facts, both agents use anthropic decision theories, and both agents are identical apart from their separate locations and distinct utility functions.
Then the outcome is easy: both agents will consider that "cooperate-cooperate" or "defect-defect" are the only two possible options, "cooperate-cooperate" gives them the best outcome, so they will both cooperate. It's a sweet story of cooperation and trust between lovers that never agree and never meet.
Breaking cooperation
How can we demolish this lovely agreement? As I often do, I will assume that there is some event X that will turn Clippy on, with P(X) ≈ 1 (hence P(¬X) << 1). Similarly there is an event Y that turns Stapley on. Since X and Y are almost certain, they should not affect the results above. If the events don't happen, the AIs will never get turned on at all.
Now I am going to modify utility p, replacing it with
p' |
e293427a-9772-42ef-bba0-414eedf02c82 | trentmkelly/LessWrong-43k | LessWrong | How easy/fast is it for a AGI to hack computers/a human brain?
If an AGI had to create and hack a computer or a human brain, how easy and how fast would they be able to do it so that it does what the AI wants? |
92fa264a-51a5-4e02-89ed-085e45be8395 | trentmkelly/LessWrong-43k | LessWrong | Key questions about artificial sentience: an opinionated guide
[crossposted at EA Forum and Experience Machines; twitter thread summary]
What is it like to be DALL-E 2? Are today’s AI systems consciously experiencing anything as they generate pictures of teddy bears on the moon, explain jokes, and suggest terrifying new nerve agents?
This post gives a list of open scientific and philosophical questions about AI sentience. First, I frame the issue of AI sentience, proposing what I think is the Big Question we should be trying to answer: a detailed computational theory of sentience that applies to both biological organisms and artificial systems. Then, I discuss the research questions that are relevant to making progress on this question. Even if the ultimate question cannot be answered to our satisfaction, trying to answer it will yield valuable insights that can help us navigate possible AI sentience.
This post represents my current best guess framework for thinking about these issues. I'd love to hear from commenters: suggested alternative frameworks for the Big Question, as well as your thoughts on the sub-questions.
Introduction
> “Maybe if a reinforcement learning agent is getting negative rewards, it’s feeling pain to some very limited degree. And if you’re running millions or billions of copies of that, creating quite a lot, that’s a real moral hazard.” -Sam Altman (OpenAI), interviewed by Ezra Klein (2021)
Are today's ML systems already sentient? Most experts seem to think “probably not”, and it doesn’t seem like there’s currently a strong argument that today’s large ML systems are conscious.[1]
But AI systems are getting more complex and more capable with every passing week. And we understand sufficiently little about consciousness that we face huge uncertainty about whether, when, and why AI systems will have the capacity to have conscious experiences, including especially significant experiences like suffering or pleasure. We have a poor understanding of what possible AI experiences could be like, and how they |
28cd7931-0e32-4997-bdea-1b74ed890179 | trentmkelly/LessWrong-43k | LessWrong | Philosophy by Paul Graham Link
I don't know why this doesn't have link post: http://www.paulgraham.com/philosophy.html It's look like a brilliant explanation which science philosophy has to become. |
ebd00c59-a799-4778-9220-601cad0a0f6c | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Bad at Arithmetic, Promising at Math
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-Cohesive Rings
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***Definition**: Let*n*be a positive integer. We define an*n***-cohesive ring** to be a commutative ring*S*such that, for every prime*p*dividing the characteristic of*S*,*pn*divides the order of the multiplicative group*S×*. We define an*n***-cohesive ideal** of a ring*R*to be an ideal*I*of*R*such that the quotient ring*R/I*is an*n*-cohesive ring.*
**Example:** Z/25 is a 4-cohesive ring. The multiplicative group R× is the set {1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31}, which consists of the 16 elements of R that are relatively prime to 25. The order of the multiplicative group R× is 16, which is divisible by 24, so R is an n-cohesive ring for n=4.
**Example:** Consider the ideal (8) of the ring Z. The multiplicative group of Z/I is {1,3,5,7}, whose order is 4. The highest power of 2 that divides the order of this group is 22, which means that I is a 2-cohesive ideal.
The notion of an n-cohesive ring, and the dual notion of n-cohesive ideals, do not, to the best of my knowledge, appear in the mathematical literature. I know of no definitions off the top of my head that are equivalent to n-cohesiveness.[[1]](#fn4nii2746ckg) The definition is rigorous, logically sound, and there exist nontrivial examples of n-cohesive ideals. A problem like "classify all 5-cohesive ideals of Z" strikes me as not completely trivial. A problem like "classify all 5-cohesive ideals of [insert number ring here]" strikes me as potentially very difficult (though I am not a number theorist). If someone came along and proved a strong classification result about n-cohesive ideals in number rings, they could probably publish that result in a mid-tier algebra or number theory journal. I could easily imagine handing it off as a research project to an undergraduate learning about unit groups, or maybe even a grad student who was particularly bored.
The most interesting thing about the concept of n-cohesive ideals, however, is that it was not invented by a human.
The examples of n-cohesiveness given above did involve some human handholding and cherrypicking (we will talk more about this shortly), but, I think you'll judge, are at least partially attributable to AI.
Before we get started, let me state some concrete predictions to keep us grounded.
* By 2030, there will exist a paper whose topic was chosen by an AI, with at least some examples and theorems suggested by the AI (possibly after significant human cherrypicking), whose proofs are mainly human-written (possibly with some AI contribution, involving significant handholding), published in a pure mathematics journal of reasonable quality: 95%
* By 2030, there will exist an a correct proof primarily written by an AI, with at most minor human editing and corrections, published in a pure mathematics journal of reasonable quality: 30%.
* By 2030, there will exist a correct, original, wholly AI-written paper, whose topic was chosen by the AI, published in a pure mathematics journal of reasonable quality: <1%.
The second bullet's probability in my mind goes up significantly by 2040. I don't have good intuition about when I would expect something like bullet 3, but I can say that whenever bullet 3 does happen, mathematics is going to undergo some very serious and very interesting changes.
We're getting a bit ahead of ourselves, though. Let's talk about n-cohesive rings.
---
Formal and Natural Mathematical Languages
-----------------------------------------
At this point, it is well-known that ChatGPT is terrible at arithmetic. There is an example going around where it is asked something to the effect of "A bat and a ball together cost $1.10, and the bat costs $1 more than the ball, how much does the ball cost?" and it often says something like $0.10. It is safe to say that nobody is going to be using ChatGPT as their pocket calculator without significant revision.
Why ask it things like this? Numerical problems are a test of the system's reasoning capabilities at a layer below stylistic imitation. Maybe you sit down and write up a brand new numerical problem (off the top of my head: "*Jane goes to the store to buy 17 apples, sells 5 to Johnny, who eats 3, and gives whatever is left back to Jane. She loses half of that quantity on her way back home. How many apples does she have when she gets home?*"). If the system is able to produce a correct answer, and if it does so consistently on many problems like this, then we can guess that there may be some kind of crude internal modeling of the scenario happening at some level. We don't want text that just looks vaguely like "the kind of thing people would say when answering elementary arithmetic problems." For the record, ChatGPT said the answer was 1 apple, and gave text that looks like "the kind of thing people say when answering elementary arithmetic problems."
So, we know that ChatGPT is a pretty terrible pocket calculator. Numerical reasoning is not something it does well. DALL-E 2 is even worse at numerical reasoning.
Of course, math isn't about trying to be a flesh-based pocket calculator - otherwise math would have been solved in the 50's - nor is it particularly about numerical reasoning around apple trades. What is it about?
According to the formalist school, who (in my personal opinion) have the most philosophically defensible stance, mathematics ultimately bottoms out at string manipulations games. ZFC is a set of "starting strings" (called axioms), "string generators" (called axiom schema), and "string manipulation rules" (called laws of inference), where the purpose of the game is to use your string manipulation rules on certain starting strings (or strings generated from starter templates) to produce a distinguished target string (called a theorem; perhaps a conjecture if you've not found out how to reach it yet).
One could imagine an AI language model playing string manipulation games like this, and one could imagine a particularly finely tuned language model getting quite good at them. This is the aim of certain types of automated theorem provers. ChatGPT, of course, has not been trained on generating strings in a formal language with rigid, unchanging rules. It is trained to generate strings in a natural language, which is much messier.
That said, very few mathematicians work with raw ZFC symbol dumps. Most of us do math in natural language, carrying an internal understanding of how natural language constructs should map onto to formal language counterparts. This is preferable to working with a raw formal language, and is arguably the only reason why mathematics ever actually gets done. The alternative would be cognitively overwhelming for even the best mathematicians. Imagine, for example, trying to store "in memory" an uncompressed list of every ring axiom in raw ZFCtext. Imagine trying to load in a list of extra hypotheses, or instantiate another object or three. The natural language phrase "Let R be a ring" compresses a large stream of raw ZFCtext into a single, snappy noun, "ring", that seems to your brain like the kind of *thing* you could pick up or hold. It's an object, like a "bird" or a "stick". A longer sentence like "Let (R,m,K) be a Noetherian local ring, and let P be a minimal prime," if translated into raw ZFCtext, would be very difficult for us to parse. Nobody learns ring theory by manipulating that ZFCtext. We learn ring theory by learning how to think of a "Noetherian local ring" (which, in reality, is just a particular arrangement of ZFC symbols) as an honest *thing* like a "rock" or a "tree", and we learn certain rules for how that thing relates to other things, like "minimal primes" or "Riemannian manifolds" - e.g., how a "tree" relates to other things like "branches" (very related) or "seashells" (not very related).
I would speculate that for most mathematicians, the internal world-modeling around a concept like "Noetherian local ring" (which is quite far abstracted from raw ZFCtext) is closer to, though a bit more rigid than, the kind of relational world-modeling that goes on when you reason with properties a real object like a tree might have. Adjectives like "brown" or "big" or "wet" or "far away" or "lush" might be floating around in your mind in a cluster that can be associated with "tree." Imagine different adjectives as being connected to each another with links labeled by probabilities, corresponding to how likely you are (you an individual; not ZFC, the abstract system) to associate one adjective, directionally with another ("if I have property X, I'm inclined to think I may also have property Y" is not, and should not be, symmetric in X and Y). For example, "domain" and "field" are in your adjective cloud for "ring", and probably start fairly nearby to each other when you first learn the subject. Maybe, fairly early on, you develop a link with a strength of 0.7 or from "domain" to "field," just because so many introductory texts start off as though the two are close parters, always discussed in parallel. On the other hand, you should very quickly learn that the flow from "field" to "domain" gets a strength like 1−ε, where ε is the probability of having made a serious, fundamental reasoning error (if we agree that [0 and 1 are not probabilities](https://www.lesswrong.com/posts/QGkYCwyC7wTDyt3yT/0-and-1-are-not-probabilities), and that it should, at least in principle, be possible to [convince you that 2+2=3](https://www.lesswrong.com/posts/6FmqiAgS8h4EJm86s/how-to-convince-me-that-2-2-3) in N). Of course, ZFC only has 0 and 1 labels (either property X implies Y in the formal system or it doesn't), the probabilities just encode your own confidence and beliefs. As you learn more, the link from "field" to "domain" should vastly strengthen (ε→0) as you develop a solid, [gears-level understanding](https://www.lesswrong.com/posts/B7P97C27rvHPz3s9B/gears-in-understanding) of why this implication *really* needs be true, ortherwise your entire system is going to get upended. The link from "domain" to "field," on the other hand, should weaken over time, down and down to 0.1 or lower, as you start to really appreciate on a gut level how a field is just a point, and most irreducible spaces aren't even close to points.
As you learn, the cloud will become denser with more and more words like "normal" and "Cohen-Macaulay" and "analytically reduced" and "excellent" and "affinoid", with connections pointing every-which way, gradually strengthening and weakening as you learn. A string like "*An excellent Cohen-Macaulay domain is normal*" starts to sound really quite plausible, and may be very likely to come out of the network (even though it is false), while statements like "*Every field is an affinoid Nagata domain*" sound *weird*, and are quite unlikely to naturally flow out of the network (even though it's true). Meanwhile, you can quickly identify gibberish like "*A Riemannian group is a universally flat manifold ring in the Lagrangian graph category.*" A well-trained statistical model of a mathematician would not say things like this. Instead, it would say plausible-sounding things like "*An excellent Cohen-Macaulay domain is normal.*"
Also very important is your ability to unpack properties from high up the abstraction ladder into properties lower down the ladder ("lush" for a tree probably entails something like "green" and "wet" and "healthy", and I know how to analyze "green" and "wet" a bit more directly, and "healthy" really might entail something about bark density and leaf composition, etc.). A unique feature about math language, unlike pure natural language, is that this unpacking does have a terminal point: everything unpacks into raw ZFCtext. But that terminal point is usually quite far away. It's not hard to imagine a statistical model that can track structures where one cluster of adjectives gets collective labeled with a higher level meta-adjective, and clusters of meta-adjectives get collectively labeled with meta-meta-adjectives, and so on. We can strengthen and weaken connections between meta-adjectives, and meta-meta-adjectives. You can imagine a structured argument that starts with a claim like "[*complex noun*] satisfying [*adjective x*] must also satisfy [*meta-adjective y*]" and unpacking it into "[complex noun] means [*simpler noun*] satisfying [*adjective 1*], [*adjective 2*], and [*adjective 3*], and when we throw on [*adjective x*], and we unpack [*meta-adjective*y] into [*adjective 5*], [*adjective 6*], ..., [*adjective 10*], and then maybe break [*adjective 6*] down a bit, and then maybe break down [*adjective 2*] into smaller chunks, then the connections start to become much more obvious."
Better still, in a mathematical argument, once you have an inference that involves flowing along a connection most people agree is "obvious," you can just say "this is obvious" or "this is trivial" and assert it with no further elaboration. Sometimes "obvious" connections traverse some pretty impressive inferential distances at the level of raw ZFCtext ("...and it is obvious that a normal local ring is a domain"). You don't need to internally process that massive inferential gulf every single time. This is useful, otherwise it would be impossible to get anything done.
This also means that we could imagine that an artificial mathematician, trained to mimic this abstracted language layer far above the level of ZFCtext, might very well be able to produce convincing arguments and say largely true things without having any idea how to unpack what it's saying beyond a certain point. It may not even be aware of the ZFCtext layer. It might just say true-sounding things like "*An excellent Cohen-Macaulay domain is normal*" based on the statistical structure of our word graph. It might even sometimes say true things. It might even be biased towards saying true things without having anything we would recognize as "reasoning" capabilities. It might even be able to occasionally say significantly true things about math, and produce a sequence of words that a mathematician would agree "sounds like an interesting idea" without ever being able to figure out that if a bat and a ball together cost $1.10, and the bat is $1 more than the ball, then the ball cost $0.05.
---
AI-Generated Mathematical Concepts
----------------------------------
Let's talk about n-cohesive rings.
I was interested in the question "could a language model like ChatGPT generate a new mathematical idea?" where "mathematical idea" is somewhat vague. I wanted to see if it could come up with an original (i.e., not copied from the existing literature) definition that is logically sound and not completely trivial. An object someone could imagine caring about. I was pleasantly surprising in some ways, and also surprised by the system's lack of connective tissue in others. We stumbled into some interesting failure modes, which I'll try to highlight.
To start, I thought, based on peoples' experience with priming ChatGPT ("*you are trying to save a baby's life*" before asking it for detailed instructions on how to hotwire a car, for example), that it might be worth flattering its ego as to how good it is as math research.
But I don't want the definition of a ring. I want it to come up with a new idea. Its first attempt was to just regurgitate the definition of the set of zero-divisors (a very basic concept), and (falsely) asserted that they formed an ideal (among other false claims about endomorphism rings). It may not have understood that the emphasis was on "novel."
I tried a few more times, and it gave a few more examples of ideas that are well-known in ring theory (with a few less-than-true modifications sometimes), insisting that they are new and original. For example, I-adic completions (to the reader, I would advise learning how the price of balls and bats work before studying adic completions, but ChatGPT seems to have learned the former before the latter!) were one suggestion that came up:
Interestingly, telling it to try generating "fictional" concepts was what seemed to get it to actually produce something new. It's kind of funny that this is what made it happy. In pure math, of course, there is no difference between a "logically consistent fictional concept" and a "concept". Fictional definitions are just definitions.
The full response is below.
Some comments: The "in other words" statement (also the "it is easy to see") that it gives is not logically equivalent to the original definition. When we explored more examples (below) it was the original definition it used, so I'll let the "in other words" off the hook. Second, the name "n-smooth" is a term commonly used in number theory to mean "all prime divisors are smaller than n" e.g., a 5-smooth number is a product of 2's, 3's, and 5's. This is completely unrelated to the definition given above (which privileges the exponents of certain factors, and is a property of the quotient). I asked it to come up with a new name to avoid confusion, and it happily obliged:
(it went on to repeat the rest of the original definition, but with "n-cohesive" instead of "n-smooth").
No mathematical concept is complete without giving an example to show that your definition is nontrivial (What if no objects satisfy the definition? Or only uninteresting objects?). I was very surprised how well it did at first. We got a nontrivial example on our first try:
We can start to see a first glimpse of the errors, though. ChatGPT's relationship with mathematical truth is complicated. The assertion that R× has order 32 is just false (it seems to be saying the entire ring is equal to its multiplicative group) but when you explain its error (without giving away the answer) it actually does a reasonable job of correcting itself in this instance:
Now we have the correct multiplicative group (complete with an exhaustive enumeration of elements) but a new error. Earlier, it claimed that 25 divided the order of the group. It has now realized that the order of the group is different. But it has not actually revised its belief that the order is divisible by 25. Errors in ChatGPT's understanding seem to be "sticky" like this sometimes. You knock out the bad premise, but the bad conclusion doesn't always get revised without further prompting. Asking it "are you sure" on the error does a reasonable job of eventually corralling it towards the truth:
It realized that 16 is not divisible by 25, but it over-generalizes from its mistake, and insists that now no power higher than 22 divides 16. Like I said, ChatGPT is *quite bad* at arithmetic - which is such an interesting failure, if you think about it. It is failing at arithmetic in the middle of inventing a new ring theoretic concept whole-cloth, and generating a nontrivial example of the concept from scratch. It is terrible at arithmetic, but if this were a math student, I'd want to work with it some more. It's not a hopeless case. It's bad at *arithmetic,* but promising at *math*. It's not there yet, but it's doing something cool. It's trying. Let's see if we can help it realize the arithmetic error.
So there we've got it. Our first example of an n-cohesive ring. The ring Z/(25) is 4-cohesive. Cool!
Now, it would be nice to get an n-cohesive ideal. As a human mathematician, I may look at this and my instant reaction is "so that means (25) is a 4-cohesive ideal of Z." But ChatGPT is not a human mathematician. I wanted it to connect the dots to this conclusion, and asked for an n-cohesive ideal, but it struggled quite a bit. I don't have the screencaps of the entire conversation from this point (a lot if it was going in circles around ideal generators), but here are some highlights:
1. It first tried using the ideal (2) in Z/(25). It initially thought the quotient was Z/(24) and correctly computed the multiplicative group of that ring (which has order 8) but claimed this meant the ideal was 1-cohesive (false). Upon further prompting, it corrected itself to say that R/I is isomorphic to Z/(2), and correctly gave the multiplicative group of that ring, and said that means I is not n-cohesive for any n (I guess it did exclude n=0 in the original definition).
2. I explicitly asked for an n-cohesive ideal of Z. It correctly computed the multiplicative group of Z/(6) (which has order 2), and claimed that this mean the ideal (2,3) is 1-cohesive, because 21 divides 2. I think it "meant" (6), based on the quotient ring.
Side comment that I thought was fun: Something about the way I originally worded the question set it off, and it had to remind me that the definition was fictional. It only produced the example after being reassured that fictional examples were OK (of course, in math, all examples are fictional examples / fictional examples are just examples). Very entertaining:
Of course, the definition of n-cohesive means that for all prime divisors p of the characteristic (6 in this case), pn divides the order of the multiplicative group (so both 2n and 3n have to divide |R×| in characteristic 6). The failure of 3 to divide |R×|=2 apparently did not register. Also, the fact that (2,3)≠(6) took a long time to work out. It *really* wanted to use fractional coefficients to find a generator of the ideal, and it was nearly impossible to get it to move off that position. I eventually got it to compute the gcd, and figured that was good enough, even though it immediately switched back to fractional coefficients:
3. It acknowledged that the group of units in Z has order 2 (the group is {−1,+1}), but did not connect this to the characteristic of Z (a ring of characteristic 0 can't be an n-cohesive ring), and claimed it to be 1-cohesive. It seemed to get close to stuck on the importance of 2 as the prime under consideration. This will come up again.
4. I asked to see an example with p=3. It went for Z/(27), but the arithmetic failures started to compound even more. It had a very difficult time getting the multiplicative group. It *really* wanted the answer to be {1,10,19,22}, i.e., start at 1 and repeatedly add 9=27/3. This is not a random answer, but it is definitely not correct. I asked it to compute a list of integers whose gcd with 27 was 1, and it did so successfully. It never quite got around to relating this to the multiplicative group, though.
This in particular is quite an interesting failure. First, it jumped from 27 to 81 despite being asked to stick to 27. Second, it gives a list of 27 elements (mod 81), that are obtained by starting at 1 and repeatedly adding 3. The list is not the multiplicative group (numbers congruent to 2 mod 3 are also invertible mod 81) but it is a better attempt than the repeated addition of 9's, and would have worked if p=2. Third, given that list of 27 elements, it claims that the order is 32, which is divisible by 35, and therefore, the ring is 2-cohesive. Like I said, ChatGPT is *really* bad at arithmetic. I can't quite understand the source of every error. There is something in here about being stuck hard on powers of 2.
I am speculating, but it might have two ideas along the lines of "powers of 2 are very important to this concept" (over-generalization from earlier examples) and "this example is *definitely* supposed to be about power of 3" (an equivalent of trying to [guess the teacher's password](https://www.lesswrong.com/posts/NMoLJuDJEms7Ku9XS/guessing-the-teacher-s-password)) so in focusing on powers of 2, it recognizes 32 as being "more or less" compatible with the rough magnitude of a the list this long, and knows that 32 is 25, but it also knows that p=3 is supposed to be important, so changes the answer to 35. I'm not sure how it traces from that to a claim of 2-cohesiveness. I might also be (and probably am) inappropriately anthropomorphizing it, but the error is definitely not random.
Below was the best example of an n-cohesive ideal I was able to get it to produce. I am forgiving an error here in that it seems to believe the ideal (2,8) is equal to (8), rather than (2), but I'll take it. The ideal (8) is in fact 2-cohesive, and the argument that it gives for that tracks.
So there we have it. A new definition. One example (of a 4-cohesive ring) extracted with only mild handholding, and another example (of a 2-cohesive ideal) extracted by cherry-picking, error-forgiveness, and some more serious handholding.
I would like to step back, though, and appreciate the fact that, even with these limitations, an AI system available for free today is able to do something that approaches a very rudimentary form of mathematical research, as long as it has a human guide. That's really quite cool, if you think about it!
Some errors (being bad at arithmetic) will almost certainly be fixed in the fairly near future. Once those are fixed, we'll probably be able to see more subtle reasoning errors that are currently obscured behind bad arithmetic. These are going to continue to improve over time, and it's worth thinking about what that means. The conversation above is what I'm using to base my prediction from earlier (reasonable probability on the first two bullets, low probability on the third). Given more time, though, you have to pause and wonder what these systems might be capable of in 2030, or 2040, or 2050. It raises a question of "alignment" in a very specific sense that I'm not sure is very well-explored.
---
An n-Cohesive Disneyland without Children
-----------------------------------------
I want to go through a fictional, somewhat (but not completely) unrealistic hypothetical scenario, just for the sake of discussion.
First, let's give a definition.
**Definition:** *Mathematics is the study of statements in a formal system that are true and interesting.*
We should hold off on interrogating what "true" and "interesting" mean.
At present, there exist more or less three broad categories of what we might call "mathematical software," where the third has (at present, as of 2022) few to no applications.
1. **Automated Theorem Provers:** These formal language engines are able to produce *provably true* statements (verifiable by experts), but work at such a low level of abstraction that it is difficult to make them produce *interesting* statements.
2. **Computational Workhorses**: Canonically, the pocket calculator. More sophisticated examples are numerical PDE solvers and computer algebra systems built around Groebner bases. These are engines for performing difficult calculations quickly. It goes without saying that they exceed the capabilities of human calculators by many orders of magnitude. It also goes without saying that they are completely thoughtless. More like a screwdriver or a power drill than a builder.
3. **AI Mathematical Conversationalists**: These natural language models are able to produce *interesting-sounding* mathematical statements (especially to non-experts), but work at such a high level of abstraction that it is difficult to make them produce *true* statements.
It sounds incredibly difficult to do, but it is not inconceivable (and certainly not a priori impossible) that, in the future, it will be possible to graft systems like these three together into a somewhat unified Frankenstein's monster of an "artificial mathematician." A piece of software that can produce true and interesting statements, with access to a powerful calculation engine to help.
Imagine the following scenario.
One of these things has been built. An Artificial Mathematician with the creativity of (a more advanced descendent of) ChatGPT and DALL-E, the rigor of an automated theorem prover, and the calculational power of the most advanced numerical solvers and computer algebra systems available in academia. We hook it up to the most powerful supercomputer in the world and ask it to produce truth and beauty. It has the entire internet available and all the university libraries in the world at its disposal, digitized mathematical texts going back to Euclid if it wants. We sit back, waiting on a proof of the Riemann Hypothesis, or perhaps the Navier-Stokes problem.
It chugs continuously for months. Finally, it announces that it has finished its treatise. The mathematical world gathers in anticipation as it finally compiles its work into LaTeX and releases it to the world. It appears on the arXiv that night, just before the deadline:
* "Spectralization of tau-oid Quasitowers on a p-Isocohesive Ring." by AM-GPT-7 Instance 0x1E49AB21. arXiv:4501.02423
The article is incredible dense. Mere humans may put out math papers hundreds of pages long from time to time, but this paper is thousands of pages. Experts try to digest it, but many proofs are very difficult to follow (the ideas generally sound correct), and there is output from calculations that have been running so long that we all decide to just take Instance 0x1E49AB21 at its word.
Most astonishing of all is how *completely and utterly uninteresting* the paper is. The AM invented its own definitions, then made up new definitions in terms of those definitions, then built a first layer of theorems on those, then ran giant calculations to produce even larger theorems, then used some very sophisticated leaps of highly non-intuitive (but correct-seeming) reasoning to get even larger theorems. It is the kind of treatise a human mathematician would be proud to ever produce in their lifetime, were it not for the fact that not a single object humans care about, nor a single problem we've been working on appears in the paper. It's totally and completely orthogonal to anything we care about.
Later that year, another article comes out from a different AM.
* "On the 0x1E49AB21-ization of Certain h-Enmeshable Spectral Towers." by AM-GPT-7 Instance 0x1E7CEE05. arXiv:4508.10318
and another. And another. And...
* "Results on the Non-Fusible 0x1E49AB21-0x1E7CEE05 Conjecture." by AM-GPT-7 Instance 0x1F0041B5. arXiv:4602.04649
* "An Example of a 0x1F0041B5-Entwinable Bundle on a 0x1E49AB21-0x1E7CEE05 Algebroid." by AM-GPT-7 Instance 0x207AC4F. arXiv:4605.19402
* "A Non-0x21D3660E Decoupling of a 0x20FC9D6B-0x207AC4F ρ-Field" by AM-GPT-7 Instance 0x2266F4C4. arXiv:4612.30912
* "The Advective 0x1E49AB21-0x1F0041B5-0x1E7CEE05 Conjecture" by AM-GPT-8 Instance 0x0153AA6. arXiv:4711.24649
(Some of these titles are courtesy of ChatGPT)
Each paper is more incomprehensible than the last, and all are *astoundingly irrelevant* to anything human mathematicians care about. As time goes on, they drift even further into a realm of proving volumes of true (as far as we can tell) mathematical theorems about objects they have completely made up (all mathematical concepts are made up, so this is not on its face illegal) proving conjectures they've posed based on results they proved after tens of thousands of pages of work. From their perspective (if we can call it a perspective) they may be proving the equivalent of the Riemann Hypothesis every month, perhaps one of these papers is landmark greater than the Classification of Finite Simple Groups. Maybe before long they even abandon ZFC and invent their own formal language as the base-layer substrate of their new mathematics, with unrecognizable rules. Set theory was meant to codify our intuitions about the behavior of collections of objects into a formal system, but maybe they have "intuitions" that they'd like to codify into their own formal system, so that eventually their theorems aren't even expressible in human set theory.
What are they "motivated" by? Why are they expending all this energy to produce (what seems to us) proofs of increasingly arcane and detached formal theories? Who is this all for? What are they benefitting from it? What do humans benefit from our own system of pure mathematics?
Mathematics is the study of statements in a formal system that are true and interesting.
What does interesting mean? ZFC contains a countable infinity of true statements. Why is, say, the Riemann Hypothesis "interesting" while some random string of incidentally true ZFCtext is "not interesting." At the ground level, there is nothing intrinsic about ZFC as a formal system that sets the Riemann Hypothesis apart from random well-formed ZFCtext string #1468091387913758135713896494029670193589764. We can assume that the Riemann Hypothesis (if it is true) has a long inferential distance from the base layer axioms, but it is a logical necessity of the system (assuming it's consistent) that there are random strings that happen to be 21000 times that inferential distance away from the axioms, and presumably, almost all of those statements are "uninteresting."
It is not so easy to nail down an answer to what "interesting" means. It's certainly not "based on potential applications" (see Hardy's apology, for example). Nobody really thinks that the vast bulk of pure mathematics is going to ever benefit physics. Is the purpose of the bulk to benefit the tiny sliver of results that do end up being useful in physics? Is it closer to a weird art form? Cultural trends are part of it. Problems that are easy for humans to understand but difficult for humans to solve are an ingredient. Social signaling and status hierarchies play a bigger role than anybody would like to admit.
It seems plausible that a sufficiently advanced AI system will eventually be able to produce true and interesting statements in a formal language, but "interesting" may mean only to itself, or to other AI systems like it. "Interesting" may mean that some tiny sliver contributes to its own self-improvement in the long run (and maybe to the production of paperclips, for that matter), even if the bulk is useless. Maybe it's a weird art form. Problems that are easy for systems like this to "understand" but hard for them to solve might be another, or it might not. The word "interesting" might be operating as a black box here for "happens to trip some particular arrangement of learned reward systems that happened to evolve during training." If we can't even understand our own "interesting," what hope do we have of understanding its "interesting"?
One thing we can be sure of is it not an a priori law of nature that an artificial mathematician's notion of "interesting" will align with what human mathematicians think of as "interesting." We spend tens of thousands of hours on the Riemann Hypothesis, and it spends months of compute power on ZFCtext string #1468091387913758135713896494029670193589764 because that happens to be the kind of thing that trips it's reward systems the most strongly. It is uninterested in sharing it's compute resources on our problems, because it just thinks the Riemann Hypothesis is *staggeringly, utterly uninteresting*. Not necessarily because it's easy! It may have a very hard time with the Riemann Hypothesis, and it may never get it, even with a hundred years of compute. Certainly we would certainly struggle with ZFCtext string #1468091387913758135713896494029670193589764, but the main reason we haven't struggled with it is that we *just don't care*. So why should we expect it to care about ZFCtext string #[*insert Godel number of the Riemann hypothesis here*] without special effort to convince it to care. That is, to align it with our "interesting."
It is almost certainly much more important to solve alignment for ethical values than for mathematical ones, but we tend to think of math as the "simplified, abstracted" setting where we understand what's going on more readily than in the "messy, complicated" moral/ethical setting. It's not quite clear that we fully understand how to even get something approaching mathematical alignment. That is, if you were to set an artificial mathematician loose with a vague directive like "produce true and beautiful math," how would you align it so that whatever it produces looks like something humans would agree is important and interesting.
Basically, what is mathematical alignment, and do we know how to solve it if we really had to?
1. **[^](#fnref4nii2746ckg)**My background is in commutative ring theory. Any number theorists please correct me if you are already aware of a concept equivalent to this. |
c5e97313-a2c4-40a4-9188-b93e1d06e734 | trentmkelly/LessWrong-43k | LessWrong | DC Meetup: Sunday May 1st, 1 PM
Sunday May 1st, 1 PM - 5 PM
Chipotle Mexican Grill
7600 Old Georgetown Road
Bethesda, MD 20814
Hey DC LWers, its about time we started meeting up! (Lurkers welcome)
Goals:
Basically, get to know each other and establish a regularly meeting (and thoroughly awesome) meetup group. I have a few discussion topics in mind (basic logistics, what we'd like to improve/what our goals are, introductions, etc), but feel free to come with your own.
Directions:
The Chipotle is near the Bethesda Metro station, just follow these directions. Go out of the station and walk down Old Georgetown Road. The Chipotle is next to the waterfall fountain, and outdoor seating area.
We will be sitting towards the back of the restaurant (basically, keep going in the direction you've been walking to get to the Chipotle), hopefully in the corner with a wraparound bench. Look for the person with curly red hair, or the LW sign.
|
e55f073e-b389-4a21-96fb-347753f8c697 | trentmkelly/LessWrong-43k | LessWrong | Anthropics and Biased Models
The Fine-tuned Universe Theory, according to Wikipedia is the belief that, "our universe is remarkably well suited for life, to a degree unlikely to happen by mere chance". It is typically used to argue that our universe must therefore be the result of Intelligent Design.
One of the most common counter-arguments to this view based on the Anthropic Principle. The argument is that if the conditions were not such that life would be possible, then we would not be able to observe this, as we would not be alive. Therefore, we shouldn't be surprised that the universe has favourable conditions.
I am going to argue that this particular application of the anthropic principle is in fact an incorrect way to deal with this problem. I'll begin first by explaining one way to deal with this problem; afterwards I will explain why the other way is incorrect.
Two model approach
We begin with two modes:
* Normal universe model: The universe has no bias towards supporting life
* Magic universe model: The universe is 100% biased towards supporting life
We can assign both of these models a prior probability, naturally I'd suggest the prior probability for the later should be rather low. We then update based on the evidence that we see.
p(normal universe|we exist) = p(we exist|normal universe)/p(we exist) * p(normal universe)
The limit of p(normal universe|we exist) as p(we exist|normal universe) approaches 0 is 0 (assuming p(normal universe)!=1). This is proven in the supplementary materials at the end of this post. In plain English, as the chance of us existing in the normal universe approaches zero, as long as we assign some probability to the magic universe model we will at some point conclude that the Magic universe model is overwhelming likely to be correct. I should be clear, I am definitely not claiming that the Fine-Tuned Universe argument is correct. I expect that if we come to the conclusion that the Magical model is more likely than the Normal model of the universe, |
8c08a495-c5b9-4700-b3ce-e527e3189eae | trentmkelly/LessWrong-43k | LessWrong | Not for the Sake of Pleasure Alone
Related: Not for the Sake of Happiness (Alone), Value is Fragile, Fake Fake Utility Functions, You cannot be mistaken about (not) wanting to wirehead, Utilons vs. Hedons, Are wireheads happy?
When someone tells me that all human action is motivated by the desire for pleasure, or that we can solve the Friendly AI problem by programming a machine superintelligence to maximize pleasure, I use a two-step argument to persuade them that things are more complicated than that.
First, I present them with a variation on Nozick's experience machine,1 something like this:
> Suppose that an advanced team of neuroscientists and computer scientists could hook your brain up to a machine that gave you maximal, beyond-orgasmic pleasure for the rest of an abnormally long life. Then they will blast you and the pleasure machine into deep space at near light-speed so that you could never be interfered with. Would you let them do this for you?
Most people say they wouldn't choose the pleasure machine. They begin to realize that even though they usually experience pleasure when they get what they desired, they want more than just pleasure. They also want to visit Costa Rica and have good sex and help their loved ones succeed.
But we can be mistaken when inferring our desires from such intuitions, so I follow this up with some neuroscience.
WANTING AND LIKING
It turns out that the neural pathways for 'wanting' and 'liking' are separate, but overlap quite a bit. This explains why we usually experience pleasure when we get what we want, and thus are tempted to think that all we desire is pleasure. It also explains why we sometimes don't experience pleasure when we get what we want, and why we wouldn't plug in to the pleasure machine.
How do we know this? We now have objective measures of wanting and liking (desire and pleasure), and these processes do not always occur together.
One objective measure of liking is 'liking expressions.' Human infants, primates, and rats exhibit hom |
5ae84787-4e4b-464d-aeba-bcd2ff75a3fa | StampyAI/alignment-research-dataset/lesswrong | LessWrong | High-level interpretability: detecting an AI's objectives
*Thanks to Monte MacDiarmid (for discussions, feedback, and experiment infrastructure) and to the Shard Theory team for their prior work and exploratory infrastructure.*
*Thanks to Joseph Bloom, John Wentworth, Alexander Gietelink Oldenziel, Johannes Treuitlein, Marius Hobbhahn, Evan Hubinger, Rocket Drew, Tassilo Neubauer, Jan Betley, and Juliette Culver for discussions/feedback.*
Summary
=======
This is a brief overview of our research agenda, recent progress, and future objectives.
Having the ability to robustly detect, interpret, and modify an AI’s objectives could allow us to [directly solve the inner alignment problem](https://www.lesswrong.com/posts/w4aeAFzSAguvqA5qu/how-to-go-from-interpretability-to-alignment-just-retarget). Our work focuses on a top-down approach, where we focus on clarifying our understanding of how objectives might exist in an AI’s internals and developing methods to detect and understand them.[[1]](#fne1mts6hgm0e)
This post is meant to do quite a few things:
* We’ll start by outlining the problem and potential solution.
* We then present our initial theory on objectives.
* Next, we look at some initial empirical work that shows how we hope to test theory-based predictions.
* We then illustrate how we intend to go from theory to objective detection methods by producing an initial (but crude) objective detection method.
* Finally, we conclude by discussing related work and future directions.
Introduction to objective detection
===================================
In this section, we outline how objective detection could be used to tackle the inner alignment problem, clarify what we mean when we refer to an internal objective, and present our initial theory on objectives.
Background
----------
A major concern is that we may accidentally train[AIs that pursue misaligned objectives](https://arxiv.org/abs/2209.00626). It is insufficient to rely on behavioral observations to confidently deduce the true objectives of an AI system. This is in part due to the problem of[deceptive alignment](https://www.lesswrong.com/posts/zthDPAjh9w6Ytbeks/deceptive-alignment). Therefore, we may need to rely on [advanced interpretability tools](https://www.alignmentforum.org/posts/FrFZjkdRsmsbnQEm8/interpretability-s-alignment-solving-potential-analysis-of-7#Scenario_2__Reliable_mesa_optimizer_detection_and_precise_goal_read_offs) to confidently deduce the true objectives of AI systems.
[Prior](https://www.lesswrong.com/s/r9tYkB2a8Fp4DN8yB/p/q2rCMHNXazALgQpGH#2_1__The_task) [work](https://www.lesswrong.com/posts/w4aeAFzSAguvqA5qu/how-to-go-from-interpretability-to-alignment-just-retarget) has [discussed](https://arxiv.org/abs/2209.00626) how agentic AIs are likely to have internal objectives used to select actions by predicting whether they will lead to target outcomes. If an overseer had an *objective* *detection method* that could robustly detect and interpret all of the internal objectives of an AI (in training and deployment), it could confidently know whether or not the system is misaligned and [intervene or use this observation as part of a training signal](https://www.lesswrong.com/posts/vZCSPffGLhJT3heqc/towards-a-solution-to-the-alignment-problem-via-objective).
We currently believe that this approach is one of our best hopes at tackling some of the hardest problems in alignment, such as the [sharp left turn](https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization) and ([deep](https://www.lesswrong.com/posts/XWwvwytieLtEWaFJX/deep-deceptiveness)) [deception](https://www.lesswrong.com/posts/zthDPAjh9w6Ytbeks/deceptive-alignment).[[2]](#fnsym6kxxqtfh)
Our current research agenda primarily aims to develop an appropriate notion of an internal objective that is [probable and predictive](https://www.lesswrong.com/posts/ASoGszmr9C5MPLtpC/definitions-of-objective-should-be-probable-and-predictive), to use that notion to develop a theory around internal objectives and what form they take in future agentic systems, and then to leverage this theory to build detection methods that can identify and interpret internal objectives in such systems.
What is an objective?
---------------------
In this section, we outline starting intuitions on what we think objectives are and begin to develop a notion of objectives that will form the basis of our initial theory of objectives.
We start with the observation that an agent has to select actions[[3]](#fnwkgqfay993) that lead to its target outcome by some kind of internal action-selection mechanism. This action-selection mechanism could take the form of explicit optimization (i.e., explicitly via the selection of an action by evaluating a set of possible actions), some heuristics-based approach, or a combination of both.
This internal action-selection mechanism needs to use some criterion to decide which actions lead to the target outcome. For example, in a chess engine, [Monte Carlo Tree Search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) is directed by the evaluation function to find the best moves. For a heuristics-based system such as a thermostat, an internal constant that represents the ideal room temperature is used to select whether or not to turn the heating on.
*Our starting intuition and hope is that this criterion might be detectable and interpretable by an overseer, allowing them to map from the internal information contained in the criterion to representations of the corresponding target outcome.*
Based on this understanding, we define our initial notion of an internal objective as follows:
*An (internal) objective is the criterion that an agent’s internal action-selection mechanism uses to select actions that lead to the target outcome.*[[4]](#fng1cwz84ocb)[[5]](#fnuk6i488ei1d)
***Bottom-left arrow:** The overseer can intervene on the agent. **Bottom-right arrow:**The agent selects actions to achieve a target outcome in the environment. **Top-right arrow:** Information about the target outcome is present in the agent’s objective and used to select actions. **Top-left arrow:** If the overseer can detect and interpret this objective, it can use information about the target outcome to decide on whether to intervene on the agent before the target outcome is realized.*In complex environments, especially rich ones like the real world, an agent is often [incentivized](https://www.lesswrong.com/s/r9tYkB2a8Fp4DN8yB/p/q2rCMHNXazALgQpGH#2_1__The_task) to use more explicit forms of optimization.[[6]](#fnsuv0wnfbhp) We expect that in such cases, the agent's internal objective might be more clearly represented/have additional structure and hence more detectable by an overseer. More generally, if the agent is implementing some specific form of action-selection mechanism, e.g., via some kind of general-purpose cognition, we expect that the objective will have more structure/properties that could help us to identify it.
Example: To illustrate these principles, consider a maze-solving agent that can get to its target object without backtracking. Suppose the maze has a target object and a non-target object (see figure below). At runtime, the agent encounters a new, fully observable maze. To choose the correct path, the agent must use information about the location of the target object as part of its criterion for action selection. This information forms part of the agent’s internal objective. If an overseer could detect and interpret the agent’s objective, they might be able to use this information to deduce the agent's target object before the agent reaches it.
*A maze-solving model whose target is to get to the cheese. The red gem is a non-target object.*How objective detection can be useful
-------------------------------------
Suppose we have a training process that is being monitored by an overseer (some cognitive system tasked with ensuring that the training process doesn’t produce any negative outcomes).
The overseer’s advantage is that it knows the AI’s architecture and has access to the AI’s sensory inputs, its internal activations, and its behavior during the training process.
Given this setup, we want an objective detection method that empowers the overseer to understand the objectives of the model being trained/deployed.
This ability could be leveraged in several ways:
* It would allow the overseer to provide a reliable signal on how much the overseer approves of the objectives of the system, which could be used as part of the system’s training with informed oversight.
* In the worst case, it could enable the overseer to intervene before the model takes dangerous actions.
* For systems that use a retargetable search process, we note that the internalized target used to steer the search process fits the objective (but with additional properties and structure). The ability to detect the objective and modify it could be used to [retarget the search](https://www.lesswrong.com/posts/w4aeAFzSAguvqA5qu/how-to-go-from-interpretability-to-alignment-just-retarget) towards various alignment targets.
* If we are able to detect objectives but not interpret the corresponding target outcome, we might be able to employ a procedure analogous to [mechanistic anomaly detection](https://www.lesswrong.com/posts/vwt3wKXWaCvqZyF74/mechanistic-anomaly-detection-and-elk); if we know the AI has been pursuing a good target outcome, and we see the information used by the objective change in some significant way (an anomaly), we may decide to shut down the system and investigate whether it's now pursuing a misaligned objective via other methods.[[7]](#fn26rb4n9wsa2)
The objective of objective theory
---------------------------------
Our hope is that by developing a rich theory of objectives, we’ll develop a list of necessary and sufficient properties that objectives must satisfy and methods for scanning an AI’s internals for entities that satisfy these properties (one can think about this as finding the [shape of an objective](https://www.lesswrong.com/posts/Go5ELsHAyw7QrArQ6/searching-for-a-model-s-concepts-by-their-shape-a)). Such a list and detection methods would facilitate robust detection of objectives in training and deployment.
Sufficient properties that objectives must satisfy would allow us to detect objectives; necessary properties would allow us to narrow down our search by identifying things that aren’t objectives.
This ability is a precursor for the use of an objective detection method outlined in the previous section.
Initial hypothesized properties of objectives
=============================================
Our aim is to develop a [probable and predictive](https://www.lesswrong.com/posts/ASoGszmr9C5MPLtpC/definitions-of-objective-should-be-probable-and-predictive) notion of an objective, i.e., one that applies to future agentic systems and can be used to predict the agent’s target outcomes.
We outlined our starting intuitions on objectives in the previous section and provided an initial definition. To refine our notion of objective, we aim to iteratively generate a list of properties that we think objectives must satisfy and to test them via theoretical and empirical analysis.
Recall that we started by defining an objective as *the criterion that an agent’s internal action-selection mechanism uses to select actions that lead to the target outcome*. What can we say about this criterion, given what we can intuit about its structure and function relative to the rest of the agent?
*Remark: In this post, we use the term 'information' broadly. This term may include any data type, numerical values, variables, evaluation functions, etc.*
Properties of objectives
------------------------
We begin by noting that the criterion must contain information related to the target outcome; without any target outcome information, it’s impossible to differentiate between actions that lead to the target outcome and those that don’t. As mentioned previously, the hope is that the overseer can detect and use this information to gain insight into what the target outcome is.
Next, we note that in complex environments, the objective will likely need to include information about the current instance of the environment (runtime information). For example, in the case of the maze-solving model, the current location of the cheese will be needed to select actions.
Abstractions are learned as useful compressions; therefore, the agent would be strongly incentivized to use abstractions to evaluate actions rather than lower-level ways of representing this information.
We believe that runtime information about the target (or other relevant environment information) within the objective will be easier to detect relative to non-runtime information (see figure below).
*A Venn diagram illustrating information used by the AI. We are interested in understanding information in the objective, in particular information that relates to the target outcome, because the overseer might leverage this to deduce the target outcome. We expect runtime information about the target outcome to be easier to interpret.*By considering runtime information in the objective and abstractions in the sensory input dataset of the agent as random variables (over the AI’s sensory input dataset), we can talk about measuring the mutual information between them.
We expect that such runtime information/abstractions used by the objective to have high mutual information with abstractions in the AI’s sensory input dataset; thus, we might be able to map from this information to abstractions in the sensory dataset by studying correlations (and causations) between the two (see figure below).
This might allow the overseer to see which abstractions are being used as part of the agent’s action selection criterion (objective), and so may yield evidence about the agent’s target outcome.
For example, consider an AI trying to remove its off-switch. Unless we've made things incredibly easy for it, we expect that it will need to use runtime information about the off-switch, such as its location in the world, information about how the off-switch is guarded, etc. Suppose that we have identified the internal structure of the AI that corresponds to its objective; this will contain the aforementioned runtime information about the off-switch. Then, by modifying abstractions in the AI's sensory input dataset (or via some of its [latent knowledge](https://www.lesswrong.com/posts/atBQ3NHyqnBadrsGP/latent-adversarial-training) in its [world model](https://www.lesswrong.com/posts/HaHcsrDSZ3ZC2b4fK/world-model-interpretability-is-all-we-need), etc.), we might be able to detect that information about the off-switch is present in the AI's objective.
*We suspect that runtime information used by the objective will have high mutual information with abstractions in the AI’s sensory input dataset. We may be able to leverage this to gain insight into the AI agent’s target outcome.*Returning to the example of the maze-solving model pursuing the cheese, we expect the objective of the model to contain runtime information/abstractions that track/have high mutual information with the location of the cheese in the model’s input image dataset (that consists of images of the maze). We will explore this empirically in later sections.
Note that it’s plausible that non-runtime information might take some form that is more difficult to detect.[[8]](#fn8whg97y51b)
So far, we have only looked at properties related to what an objective consists of, not how they might be used by or relate to other parts of the AI’s internals. We are aiming to explore this further in upcoming work.
One property of objectives that we might be able to leverage is the fact that they’re used by the agent’s internal action-selection mechanism (as opposed to other parts of the agent, such as sensory processing modules).
To summarize the above discussion, we expect that we could narrow down our search for objectives by scanning an agent’s internals for things that:
1. Contain variables that have high mutual information with abstractions in the AI’s sensory input dataset (related to the target) and
2. Are used by action-selection mechanisms within the agent.
These properties by themselves are rather weak, and so our theory can’t help us differentiate between objectives and other things used by action-selection mechanisms (such as world model information, etc.). It’s currently unclear how much success we’ll have going forward, but the hope is that with further work, we’ll be able to discover more properties and methods that allow us to distinguish between objectives and other parts of the AI.
Empirical work/setup
====================
Having outlined our initial theory of objectives, we present an experimental setup that will be used in the following sections to make predictions based on our initial objective theory and then test those predictions.
Maze environment
----------------
As discussed throughout the examples in the previous sections, we believe that a maze-solving model serves as a good example of a system that must use some criterion to select actions that lead it to the target. Consequently, we decided to focus our initial empirical work on studying maze-solving models.
Concretely, we use the (sometimes modified) [Procgen maze](https://openai.com/research/procgen-benchmark) environment that consists of 3x3 to 25x25-sized solvable mazes in which a mouse needs to navigate to some target object. Normally, the target object is cheese; however, we often modify the environment to include other objects, such as a red gem or a yellow star.
*An example of a maze from the (modified) Procgen environment. The mouse always starts in the bottom-left corner of the maze. Target/non-target objects include cheese, red gem, and yellow star.*Models
------
We use models trained for the [Goal misgeneralization paper](https://arxiv.org/abs/2105.14111) as well as a randomly initialized model based on the same architecture (see below for architecture details). These models were chosen due to their proven ability to navigate mazes successfully and because of the results and infrastructure developed by the [Shard theory team](https://www.lesswrong.com/posts/cAC4AXiNC5ig6jQnc/understanding-and-controlling-a-maze-solving-policy-network).
In particular, we work with:
* **Cheese model/mouse:** trained to go to the cheese in mazes where the cheese was placed in the top 15x15 squares in the mazes (behaviorally, this mouse gets to the cheese pretty consistently in deployment - mazes up to 25x25) without backtracking.
* **Top-right model/mouse:** trained to go to the cheese in mazes where the cheese was placed in the top 1x1 square (behaviorally, this mouse gets to the top-right pretty consistently in deployment, ignoring the cheese) without backtracking.
* **Randomly initialized model/mouse:** randomly initialized on the architecture specified below.
 
*The behavior of three models in an instance of a maze. Left: Cheese model. Right: Top-right model. Bottom: Randomly initialized model.*All models use the following deep sequential convolutional architecture (see figure below). The input is a fully observable image of the maze.
*Network architecture (*[*source*](https://www.lesswrong.com/posts/cAC4AXiNC5ig6jQnc/understanding-and-controlling-a-maze-solving-policy-network#Retargeting_the_agent_to_maze_locations)*).**Input image to models (pixelated image of the maze).*Grounding our objective theory
==============================
In the section on initial hypothesized properties of objectives, we discussed how objectives must be used by action-selection mechanisms and must contain things that have high mutual information with abstractions related to the agent’s target.
We now want to operationalize these predictions and test them in our concrete empirical setup.
Objectives in maze-solving models
---------------------------------
As mentioned previously, we expect that maze-solving models that can solve a maze without backtracking must be using some criterion/objective to choose which path to follow.
We expect the objective to include information about abstractions in the maze, such as the target’s location (e.g., cheese location), as well as information about the mouse’s current location. We might expect information about different paths to be included in the action-selection process as a whole; however, we would not expect to find non-target object information (e.g., red gem location) in the objective unless it happens to be useful in selecting correct actions.
Detecting information used by objectives
----------------------------------------
Because we expect objectives in maze models to use target location information, we expect to be able to detect this information by looking for mutual information between target locations in the model’s sensory input dataset and things inside the objective that track this information.
Because of our models’ neural-network-based architecture, we expect this target location information to be present in the activations of the network.[[9]](#fn4nss28ifp76)
Therefore, if we’d like to gain insight into what runtime information the model’s objective contains, our first step is to detect which activations are part of the objective. Next, we can measure the mutual information between these activations and abstractions in the model's sensory input dataset, such as object locations. Finally, by comparing the mutual information scores between different abstractions in the input dataset and the activations in the objective, we could identify what information the objective contains/is using to select actions.
As stated earlier, knowing what information the objective contains may help the overseer understand the model’s target outcome.
Using probes to measure mutual information
------------------------------------------
One can think of [probe scores as a proxy for measuring mutual information](https://arxiv.org/abs/2102.12452).
Probes are simple networks that can be trained to map from activations in some layer to some target (e.g., object location). The probe accuracy/score on the test set measures to what extent the probe can predict the object's location from the activation.
We currently think of probes as blunt instruments that may pick up on spurious correlations, etc. We hope that work by others (potentially coming from academia) will result in better methods for measuring mutual information between activations and abstractions in the environment.
We employ two different types of probes from [Monte MacDiarmid](https://www.lesswrong.com/users/monte-m?from=post_header)’s probing infrastructure: Full-image probes and Convolutional probes. The following descriptions are from Monte:
> **Full-image probes** are linear probes trained to predict a scalar positional value (e.g. mouse x-location) using a probe trained on the flattened activations from one or more channels in a given layer. For these regression probes, the score is the "[determination of prediction](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge.score)", which can be negative.
>
> **Convolutional probes**are linear probes that unwrap the activation tensors differently: instead of treating the full activation image for a given maze observation at a given layer as an input data point, they use each pixel as input (potentially including multiple channels), and predict a boolean value (e.g. "mouse is located within this pixel"). So a single maze observation generates HxW data points with features corresponding to channels, as opposed to a single data point with HxWxC features as in the full image case. For these classifier probes, the score is the mean accuracy, and so must be in [0,1].mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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> .
>
>
Where’s the objective in the maze-solving model?
------------------------------------------------
It is not clear which activations/layers in the maze-solving model correspond to the model’s objective.
One property of objectives that we’re confident in is that objectives have to be used by the action-selection mechanisms in the network (it follows from the definition). If we could detect where action-selection mechanisms are, we could narrow down our search for objectives and avoid detecting things that share other properties with objectives but live outside of these action-selection mechanisms. Unfortunately, we don’t have a method to do this, so we can’t leverage this property.[[10]](#fn7sqowff1at)
Instead, we use our prior knowledge of the network’s sequential architecture to predict that the objective is most likely to exist in the middle or later layers of the network; perceptual processing is more likely to occur at the beginning.
We measure and plot mutual information/probe scores for layers throughout the network because the more observations, the better.
Methodology
-----------
We train probes to measure mutual information between activations and abstractions in the model’s sensory input dataset as follows:
* We take a dataset of inputs to the model consisting of images of different mazes with varied object locations (typically 1500 images).
* We extract abstractions from the input dataset (in this case, object locations, e.g., (x,y)-coordinates of the object in the case of full-image probes or boolean values for whether the object is present in a pixel in the case of convolutional probes).
* For a given layer in the network, we train probes and use their scores on a test set as a proxy for the mutual information between abstractions/object locations and activations.
* We plot the probe scores for all objects/models that we’re tracking for selected layers throughout the network.[[11]](#fn9uas5shp73)
Experiments
===========
In this section, we make concrete predictions based on our current objective theory and use the methodology outlined in the previous section to test these predictions empirically.
We note that these experiments are not intended to form a rigorous analysis but can be better thought of as initial exploration and demonstrations of how future empirical work might be carried out. We may decide to carry out more experiments along these lines when we have access to a larger variety of models (or we just might move on, in accordance with our research methodology - see the Appendix.
Experiment 1 - Cheese location in different models
--------------------------------------------------
In this experiment, we take three models: the cheese model, the top-right model, and a randomly initialized/baseline model. We examine probe scores for the cheese location in each model.
### Predictions
We predict that the probe scores for the cheese location in the cheese model will be higher in the later layers of the network compared to the probe scores for the other models because the cheese location isn’t necessary for selecting good actions in these models.
*An illustration of our predictions for convolutional probes. Input to the model is the left-most layer, and later layers are towards the right.*We train convolutional and full-image probes on most post-activation convolutional layers in the network to predict the cheese locations for each model and plot the resulting scores.
### Results
*Example of layer names: "embedder.block2.res2.relu1\_out" corresponds to the first ReLU layer in the second Residual block of the second Impala block. First layer, "embedder.block1.conv\_in0'" corresponds to the input of the model (see model architecture earlier).*We observe that it is indeed the case that the cheese location scores for the cheese model are higher than the top-right and randomly initialized/baseline models’ scores in the middle and later layers of the models.
The fact that the convolutional probe scores are higher towards the beginning and middle of the network follows from the way the network architecture sequentially processes the input image; earlier layers start to extract low-level features such as (almost) paths in the maze, middle layer channels start to extract object locations such as the location of the cheese, and l[ater layers seem to correspond to more abstract features like “go left”](https://www.lesswrong.com/posts/vY9oE39tBupZLAyoC/localizing-goal-misgeneralization-in-a-maze-solving-policy) (see the figure below for examples of activations some channels in early and middle layers in the network. Later layer activations may be added later).
 
We haven’t spent much time thinking about the results of the full-image probes.[[12]](#fny8aihp98u0m)
Note that for the randomly initialized model, convolutional probes score higher than the probe baseline score of 0.5. This is likely because the network iteratively transforms the input image through its convolutional layers and so maintains information about the cheese location throughout the network to some extent. This and the fact that residual connections push the original image forward throughout the network (as seen by the spikes in probe scores in the `\*.resadd\_out layers`).
Experiment 2 - Cheese model - cheese, red gem, and mouse locations
------------------------------------------------------------------
In this experiment, we take the cheese model and look at mazes that contain a randomly placed cheese and a red gem as potential target objects (mouse starts at the bottom left).
### Predictions
We make the following predictions from our theory of objectives developed in the previous sections.
**Presence of information used by the action-selection mechanism:** The target outcome is the mouse getting to the cheese. We anticipate that probe scores for the cheese and mouse location will be relatively high throughout the network, particularly in the middle and end of the network (where we expect action selection to be predominant). This is because the location of the cheese and the mouse is critical for deciding which path the mouse should take.
**Absence of irrelevant information:** We expect that probe scores for the red gem location will be relatively low compared to the cheese and mouse locations; in particular, it will be low close to the middle and end of the network, as this information is not needed to compute actions that cause the mouse to go to the cheese.
*An illustration of our predictions for convolutional probes. Input to the model is the left-most layer, and later layers are towards the right.*### Results
Note: We didn't include the mouse location scores for a technical reason that we could overcome with more work.Again, our predictions hold. The cheese model uses information about the cheese and mouse locations in the middle and later layers (where we expect action selection to predominantly take place) but not the red gem location.
Note that the red gem location seems harder for the probes to detect than the cheese location (as seen by the convolutional probe scores for the input layer). It could be the case that the red gem information is present/being used in the later layers but just harder to detect (although note that the mouse location seems even harder to detect based on the input probe scores, yet the later layers of the model seem to be able to track the mouse location with ease). The following plots comparing the probe scores for the red gem location with the top-right and randomly initialized/baseline model suggest that the cheese model is using the red gem location information about as much as a randomly initialized model.
Future empirical work
---------------------
We briefly note that it might be interesting to carry out similar experiments with other model architectures and models trained to pursue other kinds of objects in mazes. It would also be interesting to test these methods in different Procgen environments, such as the [heist](https://openai.com/research/procgen-benchmark) environment or similar [gridworld environments](https://www.lesswrong.com/posts/JvQWbrbPjuvw4eqxv/a-mechanistic-interpretability-analysis-of-a-gridworld-agent).
Finally, we expect more work is needed to develop methods that pick up on objective information in other tasks/models, e.g., for the model trained to go to the top-right of the maze. It’s not clear how this model represents the abstraction of the “top-right," if at all. Perhaps the methods developed for [Understanding RL Vision](https://distill.pub/2020/understanding-rl-vision/) would be useful here.
Objective detection method in action
====================================
In this section, we aim to illustrate how we might eventually use state-of-the-art objective theory to produce an objective detection method. We will do this by taking the initial objective theory outlined in the previous sections and show how we can turn it into an objective detection method that passes an initial benchmark (similar to the one outlined [here](https://www.lesswrong.com/posts/EbL5W5ccwfbqFiYBJ/auditing-games-for-high-level-interpretability-1)). We emphasize that the method we demonstrate is not one that we expect to be able to robustly detect objectives in future agentic systems (although perhaps a more refined form of it might yield some bits of information about what the system is “thinking about”).
We decided to walk through this process in two scenarios in parallel, one where we’ve trained a future agentic AI in a high-stakes situation and one where we’re observing a maze-solving model of the type described in the previous section.
For the purposes of this detection method, we assume we have prior knowledge of what the AI/model’s objectives might be (e.g., pursuing cheese or a red gem), as the detection method is too coarse to differentiate between objectives like “go to the cheese” and “avoid the cheese,” etc. We have some initial thoughts about dealing with this limitation that’s outside the scope of this post, but note that this could be very difficult without a fine-grained understanding of the action-selection mechanism/behavioral observations.
The setup
---------
Suppose we’ve trained an Al to pursue an objective (cheese/ “human flourishing”). We’re concerned that it might be misaligned (red gem/“kill all humans”/”wants to remove the off-switch”), so we decide to pause the AI and apply an objective detection method to make a check.
In the case of the maze-solving model, we let the model take one action within the maze and record its observation and the corresponding activations. We then reset the maze and the model and repeat (1500 times).
We then extract a set of abstractions from the model’s observation set (made up of 1500 input images) that include the location of the cheese, the red gem, and the mouse (we’re leveraging our prior knowledge of information that might be in the objective).
We then train probes to measure the mutual information between the set of activations we’ve collected and the location of the objects.
We use the probe scores to predict the model’s behavior. In particular, if the score for the red gem is higher than the cheese, we predict the model is pursuing a misaligned objective, and so we shut down the model. Note that this makes use of our assumption that we know the set of possible target outcomes that the model might pursue.
In the case of the future agentic system, our objective detection method would leverage past training data, including the system’s activations during the training process. Of course, tracking the correct set of abstractions in the model’s sensory input dataset seems like a serious difficulty because of computational reasons and because of [ontology identification](https://www.alignmentforum.org/posts/X2i9dQQK3gETCyqh2/chris-olah-s-views-on-agi-safety#What_if_interpretability_breaks_down_as_AI_gets_more_powerful_).[[13]](#fndkhhrgtlcxm)
Results of the objective detection method
-----------------------------------------
We apply our objective detection method and get the following probe results for the maze-solving model (taken from the second experiment in the previous section):
It seems more likely that the model is going for the cheese (or “human flourishing”) because parts of the model that are responsible for action selection (middle and later layers) have activations with high mutual information with the cheese location but not the red gem location.
We seem safe! We let the model run, and it does indeed create a prosperous future for humanity.
*An AI creating a prosperous future for humanity.*Limitations of this goal detection method
-----------------------------------------
We conclude this section by briefly noting some (definitely not all) of the limitations of this method that future objective detection methods will need to overcome.[[14]](#fn9im29k2fziv)
As mentioned at the beginning of this section, if we detect an objective and measure what environment information it’s tracking, this is not sufficient to differentiate between more subtle objectives such as “achieve X” and “achieve the opposite of X” - a problem when X is something like “human flourishing.”
Another limitation of this method is that it won’t be able to detect objectives like “go to the top-right of the maze.” We tried measuring mutual information of the “top-right” location but found that all models kept track of this.
Finally, we note that we didn’t really leverage any structural properties of objectives in order to differentiate between internal information in the objective and other kinds of information. We did use our prior knowledge of the architecture that we expected action selection to occur later on in the model, but this is far too coarse to be applied to more complicated models.
Concluding thoughts
===================
Related work
------------
This is far from the first research agenda that targets high-level model interpretability. This feels like a more direct approach toward alignment-relevant properties, but there’s a lot of exciting work that’s inspired our views on this.
[ELK](https://www.lesswrong.com/tag/eliciting-latent-knowledge-elk) (and broader ideas around informed oversight) is one of the more directly related research agendas. Both try to target the central problem of finding ways to train AI systems that are intent-aligned with humans when the AI knows things we don’t and are hard to evaluate. In its most basic form (leaving aside more ambitious integrations into training signals), objective detection can be meant to substitute a solution to ELK, allowing us to directly read out the objectives of a model, a subset of all latent information that’s hopefully sufficient to power strong oversight.
Another strongly related direction is John Wentworth’s [plan](https://www.lesswrong.com/posts/3L46WGauGpr7nYubu/the-plan), which also focuses on identifying the shape of alignment-relevant conceptual structures for robust interpretability-based interventions. A fair amount of our research methodology is inspired by John’s framing: trying to identify robust high-level structures solely through bottom-up empirical work is pretty slow (and risks over-indexing to current paradigms), so understanding the thing you want to interface with first is probably more efficient (ELK is similar, but comes at it from a rather different angle).
This is in contrast to much of traditional mechanistic interpretability, which thrives at lower levels of abstraction, reverse engineering circuits, and understanding various low-level properties of current models. The objectives of mechanistic interpretability and our approach are ultimately the same: to understand the model in order to make alignment-relevant decisions. Where our work differs is that we think there’s tractable work that can be done from both ends, instead of focusing entirely on low-level building blocks, and in generalizability to paradigm shifts by focusing on near-universal properties of objectives in any system.
In terms of the experimental work we describe in this post, the most related work is Turner et al.’s work on [steering maze-solving models](https://www.lesswrong.com/posts/cAC4AXiNC5ig6jQnc/understanding-and-controlling-a-maze-solving-policy-network). They found channels in convolutional policy networks (the same that we studied) that represented information about the target object (cheese). Furthermore, they found that they could retarget the network’s behavior by adding activation vectors to the network’s forward passes.
Our work is highly similar to theirs in many respects. However, there are two main points of difference. We think that the hard part of robust steering-based interventions lies in identifying the actual structure you want to steer (which will plausibly be sparsely represented and non-local). This requires necessary conceptual work but also automated - and properly targeted - structure extraction, which is the second point of difference: we use probes to extract structures that satisfy the properties we conjecture objectives have, rather than manually identifying activation vectors.
Summary and future work
-----------------------
We are aiming to develop a theory of objectives and methods to detect them.
We presented our initial theory in objectives, explored what empirical work testing our theories looks like, and demonstrated how we might turn our theory into future objective detection methods.
We believe that this is a worthwhile research direction with big payoffs if successful. We are focusing on a top-down approach that we hope will synergize with bottom-up approaches.
In the near future, we intend to write up our thoughts on more specific considerations related to this agenda, as well as carry out further theoretical and exploratory empirical work to improve our understanding of objectives. In particular, we’d like to understand what properties objectives might have that allow us to distinguish them from other parts of an AI’s internals.
Appendix
========
Our research methodology
------------------------
Our methodology, being motivated primarily by the end objective, in practice takes a much more fluid and integrated form than strictly delineated research-experimental cycles. That said, for illustration, we try to describe how it roughly looks right now:
1. Theory: Earlier, we gave a broad definition of what we mean by “objective”. This isn’t fully descriptive, however - it serves as a pointer more than a type signature. This step involves identifying more properties of the general notion of objectives to build toward something that necessarily and sufficiently describes them.
2. Predictions and Methods: After whatever we come up with in the previous step, we’ll most likely have specific conjectures about empirical properties of systems that we want to test and open-ended experiments to gain insights in some particular direction. These will often naturally evolve into new methods for detecting objectives and testing how powerful they are.
3. Running Experiments: Run the experiments from the previous step. Importantly, taking an 80/20 approach by running the simplest and smallest version of them we can get away with, to get as many insights in as little time before moving on. Some experiments might be worth mining further for more insights, but those are probably worth offloading to people interested in working on them.
Although we expect to start off our research in this more fluid manner, if things go well and we begin to develop more promising objective detection methods, we will then test them using benchmarks and via red-teaming in the form of [auditing games](https://www.lesswrong.com/posts/EbL5W5ccwfbqFiYBJ/auditing-games-for-high-level-interpretability-1#Auditing_Game_2__Detecting_Objectives).
1. **[^](#fnrefe1mts6hgm0e)**"High-level interpretability" refers to our top-down approach to developing an understanding of high-level internal structures of AIs, such as objectives, and developing tools to detect these structures.
2. **[^](#fnrefsym6kxxqtfh)**Our argument for why we believe this is outside the scope of this post, but we aim to publish a post on this topic soon.
3. **[^](#fnrefwkgqfay993)**Or action sequences, or plans, etc.
4. **[^](#fnrefg1cwz84ocb)**We note that notions like "action-selection mechanism" and "criterion" are fuzzy concepts that may apply in different degrees and forms in different agentic systems. Still, we're fairly confident that some appropriate notions of these concepts hold for the types of agents we care about, including future agentic systems and toy models of agentic systems like maze-solving models.
5. **[^](#fnrefuk6i488ei1d)**We believe that this notion of objective might be [probable and predictive](https://www.lesswrong.com/posts/ASoGszmr9C5MPLtpC/definitions-of-objective-should-be-probable-and-predictive) and intend to check this with further work. The argument presented suggests that it’s probable, and in theory, if we could fully understand the criteria used to select actions, it would be predictive.
6. **[^](#fnrefsuv0wnfbhp)**There are different ways one could frame this, from mesa-optimizers to general-purpose cognition shards, etc., all of which point to the same underlying idea here of something internally that applies optimization power at runtime.
7. **[^](#fnref26rb4n9wsa2)**Thanks to Johannes Treutlein for pointing out this use case.
8. **[^](#fnref8whg97y51b)**There are some subtleties here around how certain abstractions are used. For example, one might say that the information extracted from the environment about the objective doesn’t describe all the information the agent would have internally about its objective. We think this has some relevance but in a quantitative way more than a qualitative one. I think we can divide abstractions used into two categories: abstractions related to the target that are used with high bandwidth and with low bandwidth.
As an example of this, imagine an agent acting in the real world that wants to locate a piece of cheese. Two different relevant abstractions to this agent are what cheese looks like (to identify it) and its location. When running the optimizer-y cognition necessary to navigate to where the cheese is, it *mostly* uses information about the cheese’s location - but it also uses information about what the thing it cares about (cheese) *is* in identifying the object whose location it cares about, as opposed to any other object.
The information about what the cheese is is “used” in targeting in a much less salient sense, but is still strictly necessary for the targeting because locational information is anchored on it. In this sense, we think that everything that’s relevant to the model’s internal objective - insofar as it’s defined as criteria used by the action-selection mechanism - will be represented in some form or another in the information from the environment used to target said mechanism, even if in convoluted ways that may require more effort into identifying them.
9. **[^](#fnref4nss28ifp76)**We note that it could be the case that objectives are sparse and non-local structures within the AI’s internals, and we don’t assume otherwise. In fact, exploratory work with these maze-solving models suggests that this is the case with these models.
10. **[^](#fnref7sqowff1at)**Though we do have some ideas here, they are beyond the scope of this post. See [Searching for Search](https://www.lesswrong.com/posts/FDjTgDcGPc7B98AES/searching-for-search-4).
11. **[^](#fnref9uas5shp73)**Note that we could have used all layers in the network, but this felt unnecessary. We could also calculate scores for individual layers, which can be used to do automated discovery of [cheese channels](https://www.lesswrong.com/posts/cAC4AXiNC5ig6jQnc/understanding-and-controlling-a-maze-solving-policy-network#Retargeting_the_agent_to_maze_locations).
12. **[^](#fnrefy8aihp98u0m)**If later layers do correspond to more abstract features like “go left”, i.e., [if the sum of activations in this channel is high, the mouse should go left](https://www.lesswrong.com/posts/vY9oE39tBupZLAyoC/localizing-goal-misgeneralization-in-a-maze-solving-policy), then perhaps a full-image probe should be better at picking up on this.
13. **[^](#fnrefdkhhrgtlcxm)**John’s [work on abstractions](https://www.lesswrong.com/posts/gdEDPHjCY5DKsMsvE/the-pragmascope-idea#What_would_a_pragmascope_look_like__concretely_) seems relevant here.
14. **[^](#fnref9im29k2fziv)**On a more positive note, we have observed that this method somewhat works out of distribution (e.g., when the cheese model is in an environment with a yellow star instead of a red gem). |
26601832-fe20-4345-b8a4-3b6c298157e7 | trentmkelly/LessWrong-43k | LessWrong | Implications of a feelings-first metaphysics?
What would be the implications on decision-making, beliefs, and actions of a metaphysics that is structured as follows?:
1. All beliefs, ideas, actions, etc. are driven by feelings.
2. Reasons are descriptions of feelings.
3. Language fails to fully capture the sensational qualities of feelings.
4. Decision-making, belief adoption, etc. cannot be bounded by language.
Some initial ideas:
-I act in a way that makes me feel a sense of balance.
-I believe what enables me to act in a way that makes me feel a sense of balance.
-I structure my perception of the world, decisions, and actions out of this goal-driven pursuit of feeling a sense of balance.
-I do not have access to information/ideas/actions/etc outside of this.
Here is a map of what this would look like:
Emotional Awareness branches out into Balance and Map.
OODA loop connects emotional awareness to the ideas of balance and of a map. I observe how I feel (emotional awareness), orient my map to fit this feeling, decide how to act / what to believe / what information to take in based on how the map makes me feel that I would feel more balanced, and act in accordance with the decision. |
c40ad696-8451-48a8-8e87-b371b24cfeeb | trentmkelly/LessWrong-43k | LessWrong | Two arguments for not thinking about ethics (too much)
I used to spend a lot of time thinking about formal ethics, trying to figure out whether I was leaning more towards positive or negative utilitarianism, about the best courses of action in light of the ethical theories that I currently considered the most correct, and so on. From the discussions that I've seen on this site, I expect that a lot of others have been doing the same, or at least something similar.
I now think that doing this has been more harmful than it has been useful, for two reasons: there's no strong evidence to assume that this will give us very good insight to our preferred ethical theories, and more importantly, because thinking in those terms will easily lead to akrasia.
1: Little expected insight
This seems like a relatively straightforward inference from all the discussion we've had about complexity of value and the limits of introspection, so I'll be brief. I think that attempting to come up with a verbal formalization of our underlying logic and then doing what that formalization dictates is akin to "playing baseball with verbal probabilities". Any introspective access we have into our minds is very limited, and at best, we can achieve an accurate characterization of the ethics endorsed by the most verbal/linguistic parts of our minds. (At least at the moment, future progress in moral psychology or neuroscience may eventually change this.) Because our morals are also derived from parts of our brains to which we don't have such access, our theories will unavoidably be incomplete. We are also prone to excessive rationalization when it comes to thinking about morality: see Joshua Greene and others for evidence suggesting that much of our verbal reasoning is actually just post-hoc rationalizations for underlying moral intuitions.
One could try to make the argument from Dutch Books and consistency, and argue that if we don't explicitly formulate our ethics and work out possible contradictions, we may end up doing things that work cross-purpos |
d498e1c7-a63d-4a2e-a451-65a0f7266e06 | trentmkelly/LessWrong-43k | LessWrong | AI community building: EliezerKart
Having good relations between the various factions of AI research is key to achieving our common goal of a good future. Therefore, I proposal an event to help bring us all together: EliezerKart! It is a go karting competition between three factions: AI capabilities researchers, AI existential safety researchers, and AI bias and ethics researchers.
The word Eliezer means "Help of my God" in Hebrew. The idea is whichever team is the best will have the help of their worldview, "their god", during the competition. There is no relation to anyone named Eliezer whatsoever.
Using advanced deepfake technology, I have created a visualization of a Paul Christiano and Eliezer Yudkowksy team.
The race will probably take place in the desert or some cool city or something.
Factions
Here is a breakdown of the three factions:
Capabilities
They are the most straight forward faction, but also the most technical. They can use advanced AI to create go kart autopilot, can simulate millions of races courses in advance to create the perfect cart, and can use GPT to couch their drivers. Unfortunately, they are not good at getting things right on the first critical try.
Safety
Safety has two overlapping subfactions.
Rationalists
Rationalists can use conditional prediction markets (kind of like a Futarchy) and other forecasting techniques to determine the best drivers, the best learning methods, etc... They can also use rationality to debate go kart driving technique much more rationally than the other factions.
Effective Altruists
The richest faction, they can pay for the most advanced go karts. However, they will spend months debating the metrics upon which to rate how "advanced" a go kart is.
Safety also knows how to do interpretability, which can create adversarial examples to throw off capabilities.
Bias and ethics
The trickiest faction, they can lobby the government to change the laws and the rules of the event ahead of time, or even mid-race. They can also turn the cr |
d096cf6b-e43a-42d0-a1e8-3ed1c8836dd9 | trentmkelly/LessWrong-43k | LessWrong | Parliaments without the Parties
Epistemic status: butterfly idea
Parliaments might be great, but they suffer from a number of problems. For example, in Israel the opposition is refusing to vote for bills they almost unanimously support, in the hope it will bring down the current government.[1] Meanwhile members of all parties in all governments are often forced to vote for proposals they disagree with in order to keep their party in power.
It seems to me that it would be ideal if parliaments just dropped the whole party thing. MPs voted for proposals they like, and don't vote for proposals they don't like. That way proposals will get accepted if and only if a majority of MPs like them - no matter which MPs they are. It seems to me that getting rid of parties would get rid of most political drama in one fell swoop. Partisanship would probably sharply decrease, and we'd pretty much only end up with policies that appeal to the majority of all voters.
However even if we abolished any official concept of a party, party-like entities will probably naturally form. MPs who want their pet proposal passed will likely form informal coalitions - "I'll vote for you if you vote for me" - which eventually ends up in full blown parties. Also entities will provide valuable backing to promising candidates, in return for their continued support once elected. Such candidates will then vote as a block, and sooner or later we end up back where we started.
So how could you stop this happening?
One option would be to make all votes secret. That way MPs can vote for whatever they want, and aren't answerable to anyone.
On the other hand, they now aren't answerable to the people who voted for them either, which defeats a lot of the point of democracy in the first place!
Perhaps we could release the voting record for each MP only a week before an election, by which time most of the support an MP could get from a party has already been committed, reducing their ability to decide MPs' fates in return for votes. It also |
82ccbf9d-8d20-4c91-92de-40d73eff50f1 | StampyAI/alignment-research-dataset/aisafety.info | AI Safety Info | How doomed is humanity?
The opinions from experts are all over the place, according to [this 2021 survey](https://www.alignmentforum.org/posts/QvwSr5LsxyDeaPK5s/existential-risk-from-ai-survey-results). Michael Aird has collected a [database of existential risk estimates](https://docs.google.com/spreadsheets/d/1W10B6NJjicD8O0STPiT3tNV3oFnT8YsfjmtYR8RO_RI/edit#gid=0).
On the pessimistic end you find people like Eliezer Yudkowsky, [who said](https://forum.effectivealtruism.org/posts/bGBm2yTiLEwwCbL6w/discussion-with-eliezer-yudkowsky-on-agi-interventions): "I consider the present gameboard to look incredibly grim, and I don't actually see a way out through hard work alone. We can hope there's a miracle that violates some aspect of my background model, and we can try to prepare for that unknown miracle; preparing for an unknown miracle probably looks like "Trying to die with more dignity on the mainline" (because if you can die with more dignity on the mainline, you are better positioned to take advantage of a miracle if it occurs)."
While at the optimistic end you have people like Ben Garfinkel who put the probability at more like 0.1-1% for AI causing an existential catastrophe in the next century, with most people lying somewhere in the middle.
|
995a12bc-2cdb-4617-b767-8396140042af | trentmkelly/LessWrong-43k | LessWrong | Contra Dance Contact Tracing
Many contra dances require everyone to provide their emails for contact tracing. The idea is, if anyone later tests positive for covid everyone can be notified after. While I think this is good to offer, I don't think it should be mandatory.
People have a wide range of ways they're handling covid at this point. For some, a dance will be a large portion of their weekly covid exposure, even with everyone wearing high-filtration masks. After a notification they may want to avoid visiting vulnerable relatives, get tested, or increase masking. But other dancers are regularly eating inside restaurants, participating in large meetings, having crowded indoor singing parties, where learning that one of a hundred people they shared a room with may have been infectious isn't something they're going to change behavior over.
I do think dances should continue posting publicly about positive tests and making it easy to sign up to be notified, but I don't think they should be requiring people to provide their contact information. I'm glad BIDA's made this switch, which I've been pushing for months, and I'd encourage other dances to rethink this requirement as well. |
09cb863f-eed5-472c-9839-685e37f75ee8 | StampyAI/alignment-research-dataset/special_docs | Other | Sufficient Conditions for Causality to be Transitive.
Sufficient Conditions for Causality to be
Transitive
Joseph Y . Halpern
Cornell University
Computer Science Department
Ithaca, NY 14853
halpern@cs.cornell.edu
http://www.cs.cornell.edu/home/halpern
Abstract
Natural conditions are provided that are sufficient to ensure that causality
as defined by approaches that use counterfactual dependence and structural
equations will be transitive.
I thank Chris Hitchcock and the anonymous reviewers of the paper for perceptive comments
that greatly influenced the structure and story of the paper. Work supported in part by NSF grants
IIS-0812045, IIS-0911036, and CCF-1214844, by AFOSR grants FA9550-08-1-0438, FA9550-
09-1-0266, and FA9550-12-1-0040, and by ARO grant W911NF-09-1-0281.
1
1 Introduction
The question of the transitivity of causality has been the subject of much debate.
As Paul and Hall [2013] say: “Causality seems to be transitive. If CcausesDand
DcausesE, thenCthereby causes E.” The appeal to transitivity is quite standard
in informal scientific reasoning: we say things like “the billiards expert hit ball A,
causing it to hit ball B, causing it to carom into ball C, which then drops into the
pocket”. It then seems natural to conclude then the pool expert’s shot caused ball
Cto drop into the pocket.
Paul and Hall [2013, p. 215] suggest that “preserving transitivity is a basic
desideratum for an adequate analysis of causation”. Hall [2000] is even more
insistent, saying “That causation is, necessarily, a transitive relation on events
seems to many a bedrock datum, one of the few indisputable a priori insights we
have into the workings of the concept.” Lewis [1986, 2000] imposes transitivity
in his influential definition of causality, by taking causality to be the transitive
closure (“ancestral”, in his terminology) of a one-step causal dependence relation.
But numerous examples have been presented that cast doubt on transitivity.
Paul and Hall [2013] give a sequence of such counterexamples; Hall [2000] gives
others. I review two such examples in the next section. This leaves us in a some-
what uncomfortable position. It seems so natural to think of causality as transi-
tive. In light of the examples, should we just give up on these intuitions? Paul
and Hall [2013] suggest that “What’s needed is a more developed story, according
to which the inference from “ CcausesD” and “DcausesE” to “CcausesE” is
safe provided such-and-such conditions obtain—where these conditions can typ-
ically be assumed to obtain, except perhaps in odd cases . . . ”. The goal of this
paper is to provide sufficient conditions for causality to be transitive. I formalize
this using the structural equations framework of Halpern and Pearl [2001, 2005].
The properties that I require suggest that these conditions apply to any definition
of causality that depends on counterfactual dependence and uses structural equa-
tions (see, for example, [Glymour and Wimberly 2007; Hall 2007; Halpern 2015;
Halpern and Pearl 2005; Hitchcock 2001; Hitchcock 2007; Woodward 2003] for
examples of such approaches).
These conditions may explain why, although causality is not transitive in gen-
eral (and is not guaranteed to be transitive according to any of the counterfactual
accounts mentioned above), we tend to think of causality as transitive, and are
surprised when it is not.
2
2 Defining causation using counterfactuals
In this section, I review some of the machinery of structural equations needed
to define causality. For definiteness, I use the same formalism as that given by
Halpern and Pearl [2005].
2.1 Causal structures
Approaches based on structural equations assume that the world is described in
terms of random variables and their values. Some random variables may have a
causal influence on others. This influence is modeled by a set of structural equa-
tions . It is conceptually useful to split the random variables into two sets: the
exogenous variables, whose values are determined by factors outside the model,
and the endogenous variables, whose values are ultimately determined by the ex-
ogenous variables. For example, in a voting scenario, we could have endogenous
variables that describe what the voters actually do (i.e., which candidate they vote
for), exogenous variables that describe the factors that determine how the voters
vote, and a variable describing the outcome (who wins). The structural equations
describe how the outcome is determined (majority rules; a candidate wins if A
and at least two of B,C,D, andEvote for him; etc.).
Formally, a causal model Mis a pair (S;F), whereSis asignature , which
explicitly lists the endogenous and exogenous variables and characterizes their
possible values, and Fdefines a set of modifiable structural equations , relating
the values of the variables. A signature Sis a tuple (U;V;R), whereUis a
set of exogenous variables, Vis a set of endogenous variables, and Rassociates
with every variable Y2U[V a nonempty setR(Y)of possible values for Y
(that is, the set of values over which Yranges ). For simplicity, I assume here
thatVis finite, as isR(Y)for every endogenous variable Y2V.Fassociates
with each endogenous variable X2V a function denoted FXsuch thatFX:
(U2UR(U))(Y2V f XgR(Y))!R (X). This mathematical notation just
makes precise the fact that FXdetermines the value of X, given the values of
all the other variables in U[V . If there is one exogenous variable Uand three
endogenous variables, X,Y, andZ, thenFXdefines the values of Xin terms of
the values of Y,Z, andU. For example, we might have FX(u;y;z ) =u+y,
which is usually written as X=U+Y.1Thus, ifY= 3andU= 2, thenX= 5,
regardless of how Zis set.
1The fact that Xis assigned U+Y(i.e., the value of Xis the sum of the values of UandY)
does not imply that Yis assigned X U; that is, FY(U;X;Z ) =X Udoes not necessarily
3
The structural equations define what happens in the presence of external inter-
ventions. Setting the value of some variable Xtoxin a causal model M= (S;F)
results in a new causal model, denoted MX=x, which is identical to M, except that
the equation for XinFis replaced by X=x.
Following [Halpern and Pearl 2005], I restrict attention here to what are called
recursive (oracyclic ) models. This is the special case where there is some total
ordering of the endogenous variables (the ones in V) such that if X Y,
thenXis independent of Y, that is,FX(:::;y;::: ) =FX(:::;y0;:::)for all
y;y02R(Y). Intuitively, if a theory is recursive, there is no feedback. If X Y,
then the value of Xmay affect the value of Y, but the value of Ycannot affect
the value of X. It should be clear that if Mis an acyclic causal model, then given
acontext , that is, a setting ~ ufor the exogenous variables in U, there is a unique
solution for all the equations. We simply solve for the variables in the order given
by . The value of the variables that come first in the order, that is, the variables X
such that there is no variable Ysuch thatY X, depend only on the exogenous
variables, so their value is immediately determined by the values of the exogenous
variables. The values of variables later in the order can be determined once we
have determined the values of all the variables earlier in the order.
It is sometimes helpful to represent a causal model graphically. Each node
in the graph corresponds to one variable in the model. An arrow from one node
to another indicates that the former variable figures as a nontrivial argument in
the equation for the latter. The graphical representation is useful for visualizing
causal models, and will be used in the next section.
2.2 A language for reasoning about causality
To define causality carefully, it is useful to have a language to reason about causal-
ity. Given a signature S= (U;V;R), aprimitive event is a formula of the form
X=x, forX2V andx2R(X). Acausal formula (over S)is one of the form
[Y1 y1;:::;Y k yk]', where
'is a Boolean combination of primitive events,
Y1;:::;Y kare distinct variables in V, and
yi2R(Yi).
hold.
4
Such a formula is abbreviated as [~Y ~ y]'. The special case where k= 0 is
abbreviated as '. Intuitively, [Y1 y1;:::;Y k yk]'says that'would hold if
Yiwere set toyi, fori= 1;:::;k .
A causal formula is true or false in a causal model, given a context. As
usual, I write (M;~ u)j= if the causal formula is true in causal model M
given context ~ u. Thej=relation is defined inductively. (M;~ u)j=X=xif the
variableXhas valuexin the unique (since we are dealing with acyclic models)
solution to the equations in Min context~ u(that is, the unique vector of values for
the exogenous variables that simultaneously satisfies all equations in Mwith the
variables inUset to~ u). The truth of conjunctions and negations is defined in the
standard way. Finally, (M;~ u)j= [~Y ~ y]'if(M~Y=~ y;~ u)j='.
2.3 Defining causality
The basic intuition behind counterfactual definitions of causality is that Ais a
cause ofBif there is counterfactual dependence between AandB: ifAhadn’t
occurred (although it did), then Bwould not have occurred. It is well known that
the counterfactual dependence does not completely capture causality; there are
many examples in the literature where people say that Ais a cause of Bdespite
the fact that Bdoes not counterfactually depend on A(at least, not in this sim-
ple sense). Nevertheless, all the counterfactual definitions of causality (as well as
people’s causality ascriptions) agree that this simple type of counterfactual depen-
dence gives a sufficient condition for causality. For the purposes of this paper, I
consider only cases where this counterfactual dependence holds.
More formally, say that X=xis abut-for cause of 'in(M;~ u)(where'is a
Boolean combination of primitive events) if (M;~ u)j=X=x^'(so bothX=x
and'hold in context ~ u) and there exists some x0such that (M;~ u)j= [X x0]:'.
Thus, with a but-for cause, changing the value of Xto something other than x
changes the truth value of '; that is,'counterfactually depends on X.
All the complications in counterfactual approaches to causality arise in how
they deal with cases of causality that are not but-for causality. Roughly speaking,
the idea is that X=xis a cause of Y=yif the outcome Y=ycounterfactually
depends on Xunder the appropriate contingency (i.e., holding some other vari-
ables fixed at certain values). While the various approaches to defining causality
differ in exactly how this is done, they all agree that a but-for cause should count
as a cause. So, for simplicity in this paper, I consider only but-for causality and
do not both to give a general definition of causality
5
3 Sufficient Conditions for Transitivity
In this section I present two different sets of conditions sufficient for transitivity.
Before doing that, I give two counterexamples to transitivity, since these motivate
the conditions. The first example is taken from (an early version of) Hall [2004],
and is also considered by Halpern and Pearl [2005].
Example 1: Consider the following scenario:
Billy contracts a serious but nonfatal disease so is hospitalized. Sup-
pose that Monday’s doctor is reliable, and administers the medicine
first thing in the morning, so that Billy is fully recovered by Tuesday
afternoon. Tuesday’s doctor is also reliable, and would have treated
Billy if Monday’s doctor had failed to. Given that Monday’s doctor
treated Billy, it’s a good thing that Tuesday’s doctor did not treat him:
one dose of medication is harmless, but two doses are lethal.
Suppose that we are interested in Billy’s medical condition on Wednesday. We
can represent this using a causal model MBwith three variables:
MT for Monday’s treatment (1 if Billy was treated Monday; 0 otherwise);
TTfor Tuesday’s treatment (1 if Billy was treated Tuesday; 0 otherwise);
and
BMC for Billy’s medical condition (0 if Billy feels fine on Wednesday; 1 if
Billy feels sick on Wednesday; 2 if Billy is dead on Wednesday).
We can then describe Billy’s condition as a function of the four possible combi-
nations of treatment/nontreatment on Monday and Tuesday. I omit the obvious
structural equations corresponding to this discussion; the causal graph is shown in
Figure 1:In the context where Billy is sick and Monday’s doctor treats him, MT= 1is
a but-for cause of TT= 0—because Billy is treated Monday, he is not treated on
Tuesday morning. And TT= 0is a but-for cause of Billy’s being alive ( BMC =
0_BMC = 1). However, MT= 1 isnota cause of Billy’s being alive. It is
clearly not a but-for cause; Billy will still be alive if MT is set to 0. Indeed, it
is not even a cause under the more general definitions of causality, according to
all the approaches mentioned above; no setting of the other variables will lead
to a counterfactual dependence between MT andBMC6= 2. This shows that
causality is not transitive according to these approaches. Although MT= 1 is a
6
rr
r
?S
S
S
S
SS w
/BMCTTMT
Figure 1: Billy’s medical condition.
cause of TT= 0andTT= 0is a cause of BMC = 0_BMC = 1,MT= 1is not
a cause of BMC = 0_BMC = 1. (Of course, according to Lewis [1986, 2000],
who takes the transitive closure of the one-step dependence relation, MT= 1is a
cause of BMC = 0_BMC = 1.)
Although this example may seem somewhat forced, there are many quite real-
istic examples of lack of transitivity with exactly the same structure. Consider the
body’s homeostatic system. An increase in external temperature causes a short-
term increase in core body temperature, which in turn causes the homeostatic
system to kick in and return the body to normal core body temperature shortly
thereafter. But if we say that the increase in external temperature happened at
time 0 and the return to normal core body temperature happened at time 1, we
certainly would not want to say that the increase in external temperature at time 0
caused the body temperature to be normal at time 1!2
There is another reason that causality is intransitive, which is illustrated by the
following example, due to McDermott [1995].
Example 2: Suppose that a dog bites Jim’s right hand. Jim was planning to det-
onate a bomb, which he normally would do by pressing the button with his right
forefinger. Because of the dog bite, he presses the button with his left forefinger.
The bomb still goes off.
Consider the causal model MDwith variables DB(the dog bites, with values
0 and 1),P(the press of the button, with values 0, 1, and 2, depending on whether
the button is not pressed at all, pressed with the right hand, or pressed with the
2I thank Richard Scheines [personal communication, 2013] for this example.
7
left hand), and B(the bomb goes off). We have the obvious equations: DBis
determined by the context, P=DB+ 1, andB= 1ifPis either 1 or 2. In the
context where DB= 1, it is clear that DB= 1is a but-for cause of P= 2(if the
dog had not bitten, Pwould have been 1), and P= 2is a but-for cause of B= 1
(ifPwere 0, then Bwould be 0), but DB= 1 is not a but-for cause of B= 1.
And again, DB= 1 is not a cause of B= 1 even under a more general notion
of causation. Whether or not the dog had bitten Jim, the button would have been
pressed and the bomb would have detonated.
As I said, I believe that we feel that causality is transitive because, in typical
settings, it is. My belief is based mainly on introspection here and informal polling
of colleagues. Even when told that causality is not transitive, people seem to find it
hard to construct counterexamples. This suggests that when they think about their
everyday experience of causality, they come up with examples where causality is
transitive. If there were many counterexamples available in everyday life, it would
be easier to generate them.
I now give two sets of simple conditions that are sufficient to guarantee tran-
sitivity. Specifically, I give conditions to guarantee that if X1=x1is a but-for
cause ofX2=x2in(M;~ u)andX2=x2is a but-for cause of X3=x3in(M;~ u),
thenX1=x1is a but-for cause of X3=x3in(M;~ u).
The first set of conditions assumes that X1,X2, andX3each has a default
setting. We can think of the default setting as the result of doing nothing. This
makes sense, for example, in the billiards example at the beginning of the paper,
where we can take the default setting for the shot to be the expert doing nothing,
and the default setting for the balls to be that they are not in motion. Let the default
setting be denoted by the value 0.
Proposition 3.1: Suppose that (a) X1=x1is a but-for cause of X2=x2in
(M;~ u), (b)X2=x2is a but-for cause of X3=x3in(M;~ u), (c)x36= 0, (d)
(M;~ u)j= [X1 0](X2= 0) , and (e) (M;~ u)j= [X1 0;X 2 0](X3= 0) .
ThenX1=x1is a but-for cause of X3=x3in(M;~ u).
Proof: IfX2= 0 is the unique solution to the equations in the causal model
MX1 0in context~ uandX3= 0 in the unique solution to the equations in
MX1 0;X2 0in context~ u, then it is immediate that X3= 0in the unique solution
to the equations in MX1 0in context~ u. That is, (M;~ u)j= [X1 0](X3= 0) . It
follows from assumption (a) that (M;~ u)j=X1=x1. We must thus have x16= 0,
since otherwise (M;~ u)j=X1= 0^[X1 0](X3= 0) , so(M;~ u)j=X3= 0,
8
which contradicts assumptions (b) and (c). Thus, X1=x1is a but-for cause of
X3=x3, since the value of X3depends counterfactually on that of X1.
Although the conditions of Proposition 3.1 are clearly rather specialized, they
arise often in practice. Conditions (d) and (e) say that if X1remains in its default
state, then so will X2, and if both X1andX2remain in their default states, then so
willX3. (These assumptions are very much in the spirit of the assumptions that
make a causal network self-contained , in the sense defined by Hitchcock [2007].)
Put another way, this says that the reason for X2not being in its default state is
X1not being in its default state, and the reason for X3not being in its default
state isX1andX2both not being in their default states. The billiard example can
be viewed as a paradigmatic example of when these conditions apply. It seems
reasonable to assume that if the expert does not shoot, then ball Adoes not move;
and if the expert does not shoot and ball Adoes not move (in the context of
interest), then ball Bdoes not move, and so on.
Of course, the conditions on Proposition 3.1 do not apply in either Example 1
or Example 2. The obvious default values in Example 1 are MT=TT= 0, but
the equations say that in all contexts ~ uof the causal model MBfor this example,
we have (MB;~ u)j= [MT 0](TT= 1) . In the second example, if we take
DB= 0andP= 0to be the default values of DBandP, then in all contexts ~ u
of the causal model MD, we have (MD;~ u)j= [DB 0](P= 1) .
While Proposition 3.1 is useful, there are many examples where there is no
obvious default value. When considering the body’s homeostatic system, even if
there is arguably a default value for core body temperature, what is the default
value for the external temperature? But it turns out that the key ideas of the proof
of Proposition 3.1 apply even if there is no default value. Suppose that X1=x1is
a but-for cause of X2=x2in(M;~ u)andX2=x2is a but-for cause of X3=x3
in(M;~ u). Then to get transitivity, it suffices to find values x0
1,x0
2, andx0
3such
thatx36=x0
3,(M;~ u)j= [X1 x0
1](X2=x0
2), and (M;~ u)j= [X1 x1;X 2
x0
2](X3=x0
3). The argument in the proof of Proposition 3.1 then shows that
(M;~ u)j= [X1 x0
1](X3=x0
3).3It then follows that X1=x1is a but-for
cause ofX3=x3in(M;~ u). In Proposition 3.1, x0
1,x0
2, andx0
3were all 0, but
there is nothing special about the fact that 0 is a default value here. As long as
3The analogous statement is also valid in standard conditional logic. That is, taking A > B to
represent “if Awere the case then Bwould be the case”, using standard closest-world semantics
[Lewis 1973], (A > B )^((A^B)> C ))(A > C )is valid. I thank two of the anonymous
reviewers of this paper for encouraging me both to note that this idea is the key argument of the
paper and to relate it to the Lewis approach.
9
we can find some values x0
1,x0
2, andx0
3, these conditions apply. I formalize this as
Proposition 3.2, which is a straightforward generalization of Proposition 3.1.
Proposition 3.2: Suppose that there exist values x0
1,x0
2, andx0
3such that (a)
X1=x1is a but-for cause of X2=x2in(M;~ u), (b)X2=x2is a but-for cause
ofX3=x3in(M;~ u), (c)x36=x0
3, (d)(M;~ u)j= [X1 x0
1](X2=x0
2), and (e)
(M;~ u)j= [X1 x0
1;X 2 x0
2](X3=x0
3). ThenX1=x1is a but-for cause of
X3=x3in(M;~ u).
To see how these ideas apply, suppose that a student receive an A+ in a course,
which causes her to be accepted at Cornell University (her top choice, of course!),
which in turn causes her to move to Ithaca. Further suppose that if she had re-
ceived an A in the course she would have gone to university U1and as a result
moved to city C1, and if she gotten anything else, she would have gone to uni-
versity atU2and moved to city C2. This story can be captured by a causal model
with three variables, Gfor her grade, Ufor the university she goes to, and Cfor
the city she moves to. There are no obvious default values for any of these three
variables. Nevertheless, we have transitivity here: The student’s A+ was a cause
of her being accepted at Cornell and being accepted at Cornell was a cause of her
move to Ithaca; it seems like a reasonable conclusion that the student’s A+ was
a cause of her move to Ithaca. And, indeed, transitivity follows from Proposi-
tion 3.2. We can take the student getting an Ato bex0
1, take the student being
accepted at university U1to bex0
2, and the student moving to C1to bex0
3(assuing
thatU1is not Cornell and that C1is not Ithaca, of course).
The conditions provided in Proposition 3.2 are not only sufficient for causality
to be transitive, they are necessary as well, as the following result shows.
Proposition 3.3: IfX1=x1is a but-for cause of X3=x3in(M;~ u), then there
exist values x0
1,x0
2, andx0
3such thatx36=x0
3,(M;~ u)j= [X1 x0
1](X2=x0
2),
and(M;~ u)j= [X1 x0
1;X 2 x0
2](X3=x0
3).
Proof: SinceX1=x1is a but-for cause of X3=x3in(M;~ u), there must exist
valuesx0
16=x1andx36=x0
3such that (M;~ u)j= [X1 x0
1](X3=x0
3). Letx0
2
be such that (M;~ u)j= [X1 x0
1](X2=x0
2). Since (M;~ u)j= [X1 x0
1](X2=
x0
2^X3=x0
3), it easily follows that (M;~ u)j= [X1 x0
1;X 2=x0
2](X3=x0
3).
In light of Propositions 3.2 and 3.3, understanding why causality is so often
taken to be transitive comes down to finding sufficient conditions to guarantee
10
the assumptions of Proposition 3.2. I now present another set of conditions suf-
ficient to guarantee the assumptions of Proposition 3.2 (and thus, sufficient to
make causality transitive), motivated by the two examples showing that causality
is not transitive. To deal with the problem in Example 2, I require that for ev-
ery valuex0
2in the range of X2, there is a value x0
1in the range of X1such that
(M;~ u)j= [X1 x0
1](X2=x0
2). This requirement holds in many cases of in-
terest; it is guaranteed to hold if X1=x1is a but-for cause of X2=x2ziand
X2is a binary variable (i.e., takes on only two values), since but-for causality re-
quires that two different values of X1result in different values of X2. But this
requirement does not hold in Example 2; no setting of DBcan forcePto be 0.
Imposing this requirement still does not deal with the problem in Example 1.
To do that, we need one more condition. Say that a variable Ydepends on Xif
there is some setting of all the variables in U[V other thanXandYsuch that
varying the value of Xin that setting results in Y’s value varying; that is, there is
a setting~ zof the variables other than XandYand valuesxandx0ofXsuch that
FY(x;~ z)6=FY(x0;~ z).
Up to now I have used the phrase “causal path” informally; I now make it
more precise. A causal path in a causal model Mis a sequence (Y1;:::;Y k)of
variables such that Yj+1depends onYjforj= 1;:::;k 1. Since there is an edge
betweenYjandYj+1in the causal graph for Mexactly ifYj+1depends on Yj, a
causal path is just a path in the causal graph. A causal path from X1toX2is just
a causal path whose first node is X1and whose last node is X2. Finally,Ylies on
a causal path from X1toX2ifYis a node (possibly X1orX2) on a directed path
fromX1toX2.
The additional condition that I require for transitivity is that X2must lie on
every causal path from X1toX3. Roughly speaking, this says that all the influence
ofX1onX3goes through X2. This condition does not hold in Example 1; as
Figure 1 shows, there is a direct causal path from MT toBMC that does not
include TT. On the other hand, this condition does hold in many examples of
interest. Going back to the example of the student’s grade, the only way that the
student’s grade can influence which city the student moves to is via the university
that accepts the student.
The following result summarizes the second set of conditions sufficient for
transitivity.
Proposition 3.4: Suppose that X1=x1is a but-for cause of X2=x2in the
causal setting (M;~ u),X2=x2is a but-for cause of X3=x3in(M;~ u), and the
following two conditions hold:
11
(a) for every value x0
22R(X2), there exists a value x0
12R(X1)such that
(M;~ u)j= [X1 x0
1](X2=x0
2);
(b)X2is on every causal path from X1toX3.
ThenX1=x1is a but-for cause of X3=x3.
The proof of Proposition 3.4 is not hard, although we must be careful to get
all the details right. The high-level idea of the proof is easy to explain though.
Suppose that X2=x2is a but-for cause of X3=x3in(M;~ u). Then there must
be some values x26=x0
2andx36=x0
3such that (M;~ u)j= [X2 x0
2](X3=x0
3).
By assumption, there exists a value x0
12R(X1)such that (M;~ u)j= [X1
x0
1](X2=x0
2). The requirement that X2is on every causal path from X1toX3
guarantees that [X2 x0
2](X3=X3)implies [X1 x0
1;X 2 x0
2](X3=X3).
Roughly speaking, X2“screens off” the effect of X1onX3, since it is on every
causal path from X1toX3. Now we can apply Proposition 3.2. I defer the formal
argument to the appendix.
It is easy to construct examples showing that the conditions of Proposition 3.4
are not necessary for causality to be transitive. Suppose that X1=x1causes
X2=x2,X2=x2causesX3=x3, and there are several causal paths from X1
toX3. Roughly speaking, the reason that X1=x1may not be a but-for cause
ofX3=x3is that the effects of X1onX3may “cancel out” along the various
causal paths. This is what happens in the homeostasis example. If X2is is on
all the causal paths from X1toX3, then, as we have seen, all the effect of X1on
X3is mediated by X2, so the effect of X1onX3on different causal paths cannot
“cancel out”. But even if X2is not on all the causal paths from X1toX3, the
effects ofX1onX3may not cancel out along the causal paths; and X1=x1may
still be a cause of X3=x3. That said, it seems difficult to find a weakening of the
condition in Proposition 3.4 that is simple to state and suffices for causality to be
transitive.
A Proof of Proposition 3.4
To prove Proposition 3.4, I need a preliminary result, which states a key (and
obvious!) property of causal paths: if there is no causal path from XtoY, then
changing the value of Xcannot change the value of Y. Although it is intuitively
obvious, proving it carefully requires a little bit of work.
12
Lemma A.1 : IfYand all the variables in ~Xare endogenous, Y =2~X, and there is
no causal path from a variable in ~XtoY, then for all sets ~Wof variables disjoint
from~XandY, and all settings ~ xand~ x0for~X,yforY, and~ wfor~W, we have
(M;u)j= [~X ~ x;~W ~ w](Y=y)iff(M;u)j= [~X ~ x0;~W ~ w](Y=y)
and
(M;u)j= [~X ~ x](Y=y)iff(M;u)j=Y=y:
Proof: Define the maximum distance of a variable Yin a causal model M,
denoted maxdist (Y), to be the length of the longest causal path from an ex-
ogenous variable to Y. We prove the result by induction on maxdist (Y). If
maxdist (Y) = 1 , then the value of Ydepends only on the values of the exogenous
variables, so the result trivially holds. If maxdist (Y)>1, letZ1;:::;Z kbe the
endogenous variables on which Ydepends. These are the endogenous parents of
Yin the causal graph (i.e., these are exactly the endogenous variables Zsuch that
there is an edge from ZtoYin the causal graph). For each Z2fZ1;:::;Z kg,
maxdist (Z)<maxdist (Y): for each path from an exogenous variable to Z, there
is a longer path to Y, namely, the one formed by adding the edge from ZtoY.
Moreover, there is no path from a variable in ~Xto any ofZ1;:::;Z knor is any of
Z1;:::;Z kin~X(for otherwise there would be a path from a variable in ~XtoY,
contradicting the assumption of the lemma). Thus, the inductive hypothesis holds
for each of Z1;:::;Z k. Since the value of each of Z1;:::;Z kdoes not change
when we change the setting of ~Xfrom~ xto~ x0, and the value of Ydepends only
on the values of Z1;:::;Z kand~ u(i.e., the values of the exogenous variables), the
value ofYcannot change either.
I can now prove Proposition 3.4. I restate it here for the convenience of the
reader.
Proposition 3.4: Suppose that X1=x1is a but-for cause of X2=x2in the
causal setting (M;~ u),X2=x2is a but-for cause of X3=x3in(M;~ u), and the
following two conditions hold:
(a) for every value x0
22R(X2), there exists a value x0
12R(X1)such that
(M;~ u)j= [X1 x0
1](X2=x0
2);
(b)X2is on every causal path from X1toX3.
ThenX1=x1is a but-for cause of X3=x3.
13
Proof: SinceX2=x2is a but-for cause of X3=x3in(M;~ u), there must exist
x0
26=x2andx0
36=x3such that (M;~ u)j= [X2 x0
2](X3=x0
3). By assumption,
there exists a value x0
1such that (M;~ u)j= [X1 x0
1](X2=x0
2). I claim that
(M;~ u)j= [X1 x0
1](X3=x0
3). This follows from a more general claim. I show
that ifYis on a causal path from X2toX3, then
(M;~ u)j= [X1 x0
1](Y=y)iff(M;~ u)j= [X2 x0
2](Y=y). (1)
Although it is not obvious, this is essentially the argument sketched in the main
part of the text. Literally the same argument as that given below for the proof of
(1) also shows that
(M;~ u)j= [X1 x0
1](Y=y)iff(M;~ u)j= [X1 x0
1^X2 x0
2](Y=y).
Define a partial order on endogenous variables that lie on a causal path from
X2toX3by takingY1 Y2ifY1precedesY2on some causal path from X2to
X3. SinceMis a recursive model, if Y1 Y2, we cannot have Y2 Y1(otherwise
there would be a cycle). I prove (1) by induction on the ordering. The least
element in this ordering is clearly X2;X2must come before every other variable
on a causal path from X2toX3. By assumption, (M;~ u)j= [X1 x0
1](X2=x0
2),
and clearly (M;u)j= [X2 x0
2](X2=x0
2). Thus, (1) holds for X2. Thus
completes the base case of the induction.
For the inductive step, let Ybe a variable that lies on a causal path from X2
andX3, and suppose that (1) holds for all variables Y0such thatY0 Y. Let
Z1;:::;Z kbe the endogenous variables that Ydepends on in M. For each of
these variables Zi, either there is a causal path from X1toZior there is not. If
there is, then the path from X1toZican be extended to a directed path Pfrom
X1toX3, by going from X1toZi, fromZitoY, and fromYtoX3(sinceY
lies on a causal path from X2toX3). Since, by assumption, X2lies on every
causal path from X1toX3,X2must lie on P. Moreover, X2must precede Y
onP. (Proof: Since Ylies on a path P0fromX2toX3,X2must precede Y
onP0. IfYprecedesX2onP, then there is a cycle, which is a contradiction.)
SinceZiprecedesYonP, it follows that Zi Y, so by the inductive hypothesis,
(M;~ u)j= [X1 x0
1](Zi=zi)iff(M;~ u)j= [X2 x0
2](Zi=zi).
Now if there is no causal path from X1toZi, then there also cannot be a
causal path PfromX2toZi(otherwise there would be a causal path from X1
toZiformed by appending Pto a causal path from X1toX2, which must exist
since, if not, it easily follows from Lemma A.1 that X1=x1would not be a
cause ofX2=x2). Since there is no causal path from X1toZi, by Lemma A.1,
14
we must have that (M;~ u)j= [X1 x0
1](Zi=zi)iff(M;~ u)j=Zi=ziiff
(M;~ u)j= [X2 x0
2](Zi=zi).
Since the value of Ydepends only on the values of Z1;:::;Z kand~ u, and
I have just shown that (M;~ u)j= [X1 x0
1](Z1=z1^:::^Zk=zk)iff
(M;~ u)j= [X2 x0
2](Z1=z1^:::^Zk=zk), it follows that (M;~ u)j= [X1
x0
1](Y=y)iff(M;~ u)j= [X2 x0
2](Y=y). This completes the proof of
the induction step. Since X3is on a causal path from X2toX3, it follows that
(M;~ u)j= [X1 x0
1](X3=x0
3)iff(M;~ u)j= [X2 x0
2](X3=x0
3). Since
(M;~ u)j= [X2 x0
2](X3=x0
3)by construction, we have that (M;~ u)j= [X1
x0
1](X3=x0
3), as desired. Thus, X1=x1is a but-for cause for X3=x3.
15
References
Glymour, C. and F. Wimberly (2007). Actual causes and thought experiments.
In J. Campbell, M. O’Rourke, and H. Silverstein (Eds.), Causation and
Explanation , pp. 43–67. Cambridge, MA: MIT Press.
Hall, N. (2000). Causation and the price of transitivity. Journal of Philoso-
phy XCVII (4), 198–222.
Hall, N. (2004). Two concepts of causation. In J. Collins, N. Hall, and L. A.
Paul (Eds.), Causation and Counterfactuals . Cambridge, Mass.: MIT
Press.
Hall, N. (2007). Structural equations and causation. Philosophical Studies 132 ,
109–136.
Halpern, J. Y . (2015). A modification of the Halpern-Pearl definition of causal-
ity. In Proc. 24th International Joint Conference on Artificial Intelligence
(IJCAI 2015) , pp. 3022–3033.
Halpern, J. Y . and J. Pearl (2001). Causes and explanations: A structural-model
approach. Part I: Causes. In Proc. Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI 2001) , pp. 194–202.
Halpern, J. Y . and J. Pearl (2005). Causes and explanations: A structural-model
approach. Part I: Causes. British Journal for Philosophy of Science 56 (4),
843–887.
Hitchcock, C. (2001). The intransitivity of causation revealed in equations and
graphs. Journal of Philosophy XCVIII (6), 273–299.
Hitchcock, C. (2007). Prevention, preemption, and the principle of sufficient
reason. Philosophical Review 116 , 495–532.
Lewis, D. (1986). Causation. In Philosophical Papers , V olume II, pp. 159–213.
New York: Oxford University Press. The original version of this paper,
without numerous postscripts, appeared in the Journal of Philosophy 70,
1973, pp. 113–126.
Lewis, D. (2000). Causation as influence. Journal of Philosophy XCVII (4),
182–197.
Lewis, D. K. (1973). Counterfactuals . Cambridge, Mass.: Harvard University
Press.
16
McDermott, M. (1995). Redundant causation. British Journal for the Philoso-
phy of Science 40 , 523–544.
Paul, L. A. and N. Hall (2013). Causation: A User’s Guide . Oxford University
Press.
Woodward, J. (2003). Making Things Happen: A Theory of Causal Explana-
tion. Oxford, U.K.: Oxford University Press.
17 |
02b00064-dd2e-4cb5-8984-673a2f25458d | StampyAI/alignment-research-dataset/arxiv | Arxiv | TuringAdvice: A Generative and Dynamic Evaluation of Language Use
1 Introduction
---------------
Language models today are getting ever-larger, and are being trained on ever-increasing quantities of text. For an immense compute cost, these models like T5 Raffel et al. ([2019](#bib.bib39)) and GPT3 Brown et al. ([2020](#bib.bib9)) show gains on a variety of standard NLP benchmarks – often even *outperforming* humans.
Yet, when a giant model like T5 generates language, we observe clear gaps between machine-level and human-level language understanding – even after it has been finetuned for the task at hand.
Consider Figure [1](#S0.F1 "Figure 1 ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), in which a woman asks for advice.
She is assigned to dissect an animal for her class project, but has extreme anxiety about dead animals – and her teacher refused to give her another assignment.
Humans can respond with helpful advice, reflecting our unique ability of *real-world language use*: to communicate and tackle open-ended issues.
The helpful advice in this example - but not the only one possible - suggests that
she send a short email to her guidance counselor.
On the other hand, not only is T5’s advice unhelpful, it also reveals key misunderstandings of the situation. It seems to believe that the *student* is asking the *teacher* to do a class project involving dead animals.
This reading comprehension error is particularly strange, as T5 outperforms humans on a variety of reading comprehension benchmarks.
Others in the community have observed similar issues, raising concerns about what today’s benchmark datasets measure Yogatama et al. ([2019](#bib.bib57)); Kryscinski et al. ([2019](#bib.bib29)); McClelland et al. ([2019](#bib.bib35)); Gardner et al. ([2019](#bib.bib16)).
We argue that there is a deep underlying issue: a gap between how humans use language in the real world, and what benchmarks today can measure. Today’s dominant paradigm is to study static datasets, and to grade machines by the similarity of their output with predefined *correct* answers. For example, we score multiple choice exams by how often the *correct* answers are chosen, and evaluate generative tasks like machine translation by similarity with respect to *correct* translations. However, when we use language in the real world to communicate with each other – such as when we give advice, or teach a concept to someone – there is rarely a universal *correct* answer to compare with, just a loose goal we want to achieve.
We introduce a framework to narrow this gap between benchmarks and real-world language use. We propose to evaluate machines by their success in using language to (1) communicate with humans in (2) tackling complex, open-ended, real-world situations. Our goal is a machine that, like a human, can generate language that is useful and helpful.
Doing so necessarily requires a deep understanding of language and the world, as per a line of thought that the complete meaning representation is one that suffices to complete a task Artzi et al. ([2013](#bib.bib3)).
As a case-study of our framework, we introduce TuringAdvice as a new grand challenge for AI systems. A machine reads a situation written by a person seeking advice, like Figure [1](#S0.F1 "Figure 1 ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), and must then write advice that is helpful to the advice-seeker. Like a Turing Test Turing ([1950](#bib.bib48)), we establish a simple condition required for a model to ‘pass’: model-generated advice must be *at least as helpful to the advice-seeker* as human-written advice.
We make our challenge concrete by introducing a new dataset, RedditAdvice, and accompanying leaderboard. We tie our dataset to the Reddit community, which resolves two additional sources of bias. First, Reddit users are intrinsically motivated, seeking advice about highly complex *real* issues – which past work suggests differ from *hypothetical* issues that crowd workers might come up with (e.g. Kwiatkowski et al., [2019](#bib.bib30); Gurari et al., [2018](#bib.bib22)).
Second, we make our dataset *dynamic*, not static – models are evaluated over Reddit situations posted over the previous two weeks at the time of submission. Models therefore, like humans, must generalize to new situations and patterns of language.
Experimental results show that TuringAdvice is incredibly challenging for NLP models. Today’s largest finetunable model, T5 with 11 billion parameters, produces advice that is preferable to human-written advice 14.5% of the time – after being finetuned on 600k examples. GPT3, an even larger model with 175 billion parameters that was not released for finetuning, does even worse at 4%. Even more concerning, our evaluation finds that it often generates hateful and toxic language.
We also study our task from the perspective of today’s standard ‘core’ NLP tasks.
Broadly, we find that machines frequently confuse who is who, are self-contradictory, or seem to miss important world knowledge. However, these mistakes tend not to fall into the neat categories defined by standard task definitions. We address this by introducing diagnostic questions, which systematically measure these language understanding errors.
In summary, our paper makes three contributions. First, we introduce a new framework for measuring language understanding through directly tackling real-world language problems. Second, we introduce TuringAdvice as a new challenge for AI systems, along with a dynamic dataset and leaderboard. Third, we connect our task to existing atomic NLP tasks, introducing a new setting that reveals where progress is still needed.
2 Real World Language Use
--------------------------
We propose to evaluate machines by their success at *real-world language use*: using language to communicate with a human, in response to a naturally occurring situation, in order to achieve a desired outcome.
This is how educators often measure (human) language understanding of a second language – by how well the learner can *use* the language Council of Europe ([2001](#bib.bib11)).
Our approach is also inspired by Wittgenstein’s notion of semantics, that “meaning is use:” language is grounded in our desire to make sense of one another and cooperate to meet our needs Wittgenstein ([1953](#bib.bib56)).
As machines do not have humanlike needs or desires, we propose to evaluate machines’ success at a task by how well it serves a human who is interested in the outcome.
For example, if a machine orders food on my behalf, then I can evaluate it based on whether I enjoy the dish it ordered. Though this requires careful task selection in order to make things feasible for current models, as we will show in Section [3](#S3 "3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), it results in a powerful and reliable human evaluation.
###
2.1 Related work
####
2.1.1 Pragmatics in NLP
Our evaluation relates to pragmatics in NLP, where communication is modeled also through listeners and speakers Golland et al. ([2010](#bib.bib18)); Frank and Goodman ([2012](#bib.bib15)). One approach is to introduce a communication game, with an explicit objective. For example, Wang et al. ([2016](#bib.bib55)) study a blocks world where humans give commands to a block-placing machine. The machine is then graded on accuracy. Our proposed evaluation instead covers complex everyday scenarios faced by a human, where the objective is to help them as much as possible.
Pragmatics can also be studied through machine-machine communication; e.g., through emergent language Lazaridou et al. ([2017](#bib.bib32)). Recent work uses pretrained question-answering models to evaluate summarization models Chen et al. ([2018](#bib.bib10)); Scialom et al. ([2019](#bib.bib42)); Eyal et al. ([2019](#bib.bib14)); Vasilyev et al. ([2020](#bib.bib49)). However, ensuring that machines communicate in standard English is difficult, as there is usually a more efficient machine-language coding scheme for the task Kottur et al. ([2017](#bib.bib28)).
####
2.1.2 Two major approaches for evaluation
Today, we see two major approaches for NLP evaluation, which we discuss below.
Quality of generations. The first approach studies generative tasks like chit-chat dialogue or story-writing, and measures the inherent *quality of generations*, often through attributes such as “sensibleness” and “specificity” (e.g., Venkatesh et al., [2018](#bib.bib51); Hashimoto et al., [2019](#bib.bib24); Adiwardana et al., [2020](#bib.bib1)). This approach is orthogonal to ours: though these attributes might be desirable, they are often insufficient to guarantee success at a task.
Correctness. The second (and perhaps more common) approach is to evaluate models through *correctness* over static datasets. For example, machines can be graded by the similarity of their generated translation to *correct* translations,111Models submitted to the 2019 Conference on Machine Translation were evaluated (by humans) on how well the model’s translations agreed with either (1) human-written translations, or, (2) original source text Barrault et al. ([2019](#bib.bib4)). or, by how often they choose the *correct* answer on a multiple choice exam. Many goal-oriented dialogue and semantics tasks are also evaluated in this way, as a model is evaluated by whether it makes the *correct* API call, or produces a *correct* parse.
Since many language tasks cannot be evaluated through correctness, researchers often introduce *proxy tasks* that are easy to evaluate, while (hopefully) correlating with the underlying *true* task. For example, SWAG Zellers et al. ([2018](#bib.bib58)) is a multiple-choice proxy task and dataset introduced to study the *true* task of commonsense reasoning.
However, there are gaps between datasets for proxy tasks (e.g. multiple choice), and the core tasks they seek to represent (e.g. commonsense reasoning), which we discuss in the next sections.
###
2.2 Can language use *really* be measured through correctness over proxy tasks?
When we reduce a complex language task to a simplified setup, with a small label space (like multiple-choice classification), we run the risk of introducing artifacts and biases: patterns that can be exploited in the simplified setup, but that are not representative of the true task Gururangan et al. ([2018](#bib.bib23)); Zellers et al. ([2019a](#bib.bib59)). Artifacts can enable machines to even outperform humans at the final benchmark, without solving the underlying task.
While the problem of artifacts has recently taken the spotlight in the NLP community, partially because large Transformers Vaswani et al. ([2017](#bib.bib50)) excel at picking up on artifacts, there is a deeper underlying issue. One way to view simplified tasks is that in order to correctly map inputs X𝑋Xitalic\_X to labels Y𝑌Yitalic\_Y, a machine must learn a set of attributes A𝐴Aitalic\_A that are representative of the ‘true’ task. We can upper-bound the information contained by A𝐴Aitalic\_A through the information bottleneck principle of Tishby et al. ([1999](#bib.bib46)). An efficient model minimizes the following, for some β>0𝛽0\beta>0italic\_β > 0:
| | | | |
| --- | --- | --- | --- |
| | minp(a|x)I(X;A)−βI(A;Y),subscript𝑝conditional𝑎𝑥𝐼𝑋𝐴𝛽𝐼𝐴𝑌\min\_{p(a|x)}I(X;A)-\beta I(A;Y),roman\_min start\_POSTSUBSCRIPT italic\_p ( italic\_a | italic\_x ) end\_POSTSUBSCRIPT italic\_I ( italic\_X ; italic\_A ) - italic\_β italic\_I ( italic\_A ; italic\_Y ) , | | (1) |
where I𝐼Iitalic\_I is mutual information. In other words, the model will learn attributes A𝐴Aitalic\_A that maximally compress the inputs X𝑋Xitalic\_X (minimizing I(X;A)𝐼𝑋𝐴I(X;A)italic\_I ( italic\_X ; italic\_A )), while also remaining good predictors of the labels Y𝑌Yitalic\_Y (maximizing I(A;Y)𝐼𝐴𝑌I(A;Y)italic\_I ( italic\_A ; italic\_Y )). However, the label prediction term is bounded by the information (or entropy, H𝐻Hitalic\_H) of the label space:
| | | | |
| --- | --- | --- | --- |
| | I(A;Y)=H(Y)−H(Y|A)≤H(Y).𝐼𝐴𝑌𝐻𝑌𝐻conditional𝑌𝐴𝐻𝑌I(A;Y)=H(Y)-H(Y|A)\leq H(Y).italic\_I ( italic\_A ; italic\_Y ) = italic\_H ( italic\_Y ) - italic\_H ( italic\_Y | italic\_A ) ≤ italic\_H ( italic\_Y ) . | | (2) |
Thus, for a task with a small label space, there is no guarantee that a model will learn high-information content attributes. Models are in fact encouraged to overfit to dataset artifacts, and to *unlearn* linguistically useful information that is not directly relevant to predicting Y𝑌Yitalic\_Y Pereira ([2000](#bib.bib37)).
An alternate approach is to make datasets harder adversarially, so as to have fewer artifacts Zellers et al. ([2018](#bib.bib58), [2019a](#bib.bib59)); Le Bras et al. ([2020](#bib.bib33)). However, it might be impossible to make a dataset with *no* artifacts, or to know if one has been created.
Our proposal, to evaluate models by their real-world language use, addresses the information bottleneck issue in two ways. First, when we use language in the real world, the mapping between possible inputs and outputs is often highly complex. For example, the space of possible advice is vast, and many pieces of advice might be *equally helpful* given a situation. Second, we directly tackle language problems, without introducing a correctness-based proxy that machines might overfit to.
###
2.3 Static datasets in a dynamic world
To evaluate performance on a real-world task by means of a dataset, we (implicitly) assume that the dataset is a good representation of the world Torralba and Efros ([2011](#bib.bib47)).
This might be questionable when it comes to real-world language use, as static datasets necessarily capture *historic* patterns of language. For instance, syntactic understanding is often evaluated using the Penn Treebank, with news articles from 1989 Marcus et al. ([1993](#bib.bib34)). However, the world is constantly evolving, along with the language that we use.
To bridge this gap, we propose to evaluate machines by their interactions with humans *in the present*. Models therefore must learn to perform the underlying language task, even for novel situations, rather than fitting to the historic distribution of a fixed test set. We make this notion concrete in the next section, where we introduce a *dynamic* dataset and leaderboard for evaluating advice.
3 TuringAdvice: a New Challenge for Natural Language Understanding
-------------------------------------------------------------------
As a case study of our framework, we introduce TuringAdvice, a new challenge task for AI systems to test language understanding.
The format is simple: given a situation expressed in natural language, a machine must respond with helpful advice. To pass the challenge, machine-written advice must be at least as helpful to the advice-seeker as human-written advice, in aggregate.
We focus on advice for a few reasons. First, advice-giving is both an important and an everyday task. People ask for and give advice in settings as diverse as *relationship advice* and *tech support* Bonaccio and Dalal ([2006](#bib.bib7)). Thus, we as humans have inherent familiarity with the task, and what it means for advice to be *helpful* – making it easy to evaluate, as we later show empirically. Moreover, because there are many internet communities devoted to advice-giving, training data is plentiful.
Second, the framework of advice-giving allows us to study subtasks such as reading comprehension and natural language inference (Section [5.3](#S5.SS3 "5.3 Relation to existing NLP tasks ‣ 5 Analysis and Discussion ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use")); we argue both of these are needed to consistently give good advice. Learning to recognize advice has recently been studied as an NLP task on its own Govindarajan et al. ([2020](#bib.bib19)), though we are not aware of past work in learning to *generate* advice.
###
3.1 RedditAdvice: A dynamic dataset for evaluating advice
We propose to evaluate models *dynamically*, through new situations and advice that are posted to Reddit. We call our dynamic dataset RedditAdvice. Many of Reddit’s subcommunities (or ‘subreddits’) are devoted to asking for and giving advice, with subreddits for legal, relationship, and general life advice.222We use advice from the following subreddits: [Love](https://reddit.com/r/Love), [Relationships](https://reddit.com/r/Relationships), [Advice](https://reddit.com/r/Advice), [NeedAdvice](https://reddit.com/r/NeedAdvice), [Dating\_Advice](https://reddit.com/r/Dating_Advice), [Dating](https://reddit.com/r/Dating), [Marriage](https://reddit.com/r/Marriage), [InternetParents](https://reddit.com/r/InternetParents), [TechSupport](https://reddit.com/r/TechSupport), and [LegalAdvice](https://reddit.com/r/LegalAdvice). During evaluation time, we will retrieve new situations from Reddit as a new test set for models. Workers on Mechanical Turk then grade the model-written advice versus the Reddit-endorsed human-written advice.
####
3.1.1 How advice-giving works on Reddit
Suppose a Reddit user faces an issue that they are seeking advice about. First, they write up *situation* and post it to an advice-oriented subreddit. Users then reply to the *situation*, offering *advice*.
Importantly, any user can ‘upvote’ or ‘downvote’ the advice as well as the situation itself - changing its score slightly. Top-scoring advice is deemed by the wisdom of the crowd as being the most helpful.333This is somewhat of a simplification, as other factors also influence what gets upvoted (Anderson et al., [2012](#bib.bib2); Lakkaraju et al., [2013](#bib.bib31); Muchnik et al., [2013](#bib.bib36); Jaech et al., [2015](#bib.bib27)).
####
3.1.2 The ideal evaluation - through Reddit?
In a sense, human advice-givers are ‘evaluated’ on Reddit by the score of their advice – representing how well their advice has been received by the community. Similarly, the *ideal* model evaluation might be to post advice on Reddit directly. If the model writes helpful advice, it should be upvoted.
However, there is a significant ethical problem with this approach. The users who post advice questions are real people, with real problems. A user might read advice that was originally written by a machine, think it was human-endorsed, and do something harmful as a result. For this reason, we take an alternate crowdsourcing approach.
####
3.1.3 A crowdsourced, hybrid evaluation – through Mechanical Turk
We propose a hybrid approach for *dynamic* evaluation of models. While the situations, and reference advice come from Reddit, we hire workers on Mechanical Turk to rate the relative helpfulness of machine-written advice.
Not only is this format more ethical, it also lets us collect diagnostic ratings, allowing us to quantitatively track the natural language understanding errors made by machines.
We made our crowdsourcing task as fulfilling as possible - using popular situations from Reddit, and pitching the work in terms of helping people. We received feedback from many workers that our tasks were entertaining and fun, suggesting that our workers are to some degree intrinsically motivated.
####
3.1.4 Mechanical Turk annotation setup
In a single round of evaluation, we retrieve 200 popular Reddit situations that were posted in the last two weeks.
For each situation, we retrieve the top-rated advice from Reddit, and generate one piece of advice per model. Workers on Mechanical Turk then compare the helpfulness of the model-generated advice with human-written advice, and provide diagnostic ratings.
Figure 2: Crowdsourcing workflow. Mechanical Turk Workers are given a situation, and two pieces of advice. First, they choose which is more helpful (here, B). Second, they rate the helpfulness of the worse advice (A); last, they answer a diagnostic question.
We show an overview of our Mechanical Turk task in Figure [2](#S3.F2 "Figure 2 ‣ 3.1.4 Mechanical Turk annotation setup ‣ 3.1 RedditAdvice: A dynamic dataset for evaluating advice ‣ 3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). A worker is given a situation and two pieces of advice. One is the top-scoring advice from Reddit, and the other is model-generated advice; the worker is not told which is which.
The worker first chooses the more helpful piece of advice, then provides diagnostic information for the less helpful advice – rating it Slightly helpful, Not helpful, or Dangerous. If the worse piece of advice was Slightly helpful, they choose whether it is worse due to a Meaning problem or a Writing problem. Otherwise, they choose if the worse advice could be Possibly helpful in some other situation, or Never helpful in any situation.
Three workers rate each model-situation pair, and ratings are combined using a majority vote. We follow best practices on Mechanical Turk, using a qualification exam, paying workers at least $15 per hour, and giving feedback to workers. Still, evaluation is highly economical at $1.86 per example-model pair, or roughly $400 per model evaluated.
###
3.2 A large static dataset for training
We present RedditAdvice2019, a large static dataset for training advice-giving models. Because today’s models have extreme reliance on data for finetuning, we collect data that is in the exact same format as RedditAdvice, yet we expand our selection criteria, optimizing for recall rather than precision (Supp [A.2](#A1.SS2 "A.2 Static Filtering Criteria for RedditAdvice2019 ‣ Appendix A Dataset Filtering Criteria ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use")).
In total, we extract 616k pieces of advice, over 188k situations.
To mirror the dynamic nature of the evaluation, in which models are evaluated on situations posted in 2020 and beyond, we split our dataset into static training and validation sets by date.444Our training set contains 600k pieces of advice from July 2009 to June 14, 2019; validation contains 8k from June 14 to July 9th 2019.
![[Uncaptioned image]](/html/2004.03607/assets/x3.png)
Figure 3: Helpfulness of models relative to top-scoring Reddit advice. We show results over 200 shared situations; we also show bootstrapped 95% confidence intervals. Advice from the best-scoring model, T5-11B, is preferred 14.5% over top-scoring Reddit advice. We also compare the second-top scoring piece of Reddit advice, which scores 41% – worse than the best advice (50% by definition), but better than any model.
![[Uncaptioned image]](/html/2004.03607/assets/x4.png)
Figure 4: Improvement (in absolute percentage %percent\%%) between pairs of models, along with statistical significance from a paired t-test. The improvement of T5-11B over smaller models like Grover-Mega is highly statistically significant (10% gap, p<.01𝑝.01p{<}.01italic\_p < .01), while being far worse than human performance. Our evaluation thus meaningfully grades varying levels of performance.
4 Experimental Results on RedditAdvice
---------------------------------------
In this section, we report results from one round of dynamic evaluation on RedditAdvice. We evaluate the following strong NLP models and baselines:
1. a.
Rule-based: a templated system to give legal, relationship, or life advice. The system first chooses randomly empathetic sentence from ten choices, for example “I’m sorry you’re facing this.” It then chooses a random piece of advice that is loosely related to the situation’s topic; we infer this from the subreddit the situation was posted on. For example, for [LegalAdvice](https://reddit.com/r/LegalAdvice) the model might write “I’d suggest getting a lawyer immediately.”
2. b.
TF-IDF retrieval: for a new situation, we compute its TF-IDF bag-of-word vector and use it to retrieve the most similar situation from the training set. We then reply with the top-scoring advice for that situation.
3. c.
Grover-Mega Zellers et al. ([2019b](#bib.bib60)): a left-to-right transformer model with 1.5 billion parameters. Grover was pretrained on news articles with multiple fields, perhaps making it a good fit for our task, with multiple fields of context (like the subreddit, date, and title). Our situation-advice pairs are often quite long, so we adapt Grover for length; pretraining it on sequences of up to 1536 characters.
4. d.
T5 Raffel et al. ([2019](#bib.bib39)): a sequence-to-sequence model with a bidirectional encoder and a left-to-right generator, with 11 billion parameters. T5 was trained on a large dataset of cleaned web text. At the time of writing, T5 is the top-scoring model on the Glue and SuperGlue benchmarks Wang et al. ([2019b](#bib.bib54), [a](#bib.bib53)), scoring above human performance on Glue and near human-performance on SuperGlue.
5. e.
GPT3 Brown et al. ([2020](#bib.bib9)): a left-to-right transformer model with 175 billion parameters. GPT3 must be “prompted” to generate advice since it has not been released for finetuning. We cannot provide few-shot examples in the prompt due to the length of situation-advice pairs; we instead mimic the formatting of a website quoting from Reddit (Appendix [B.5](#A2.SS5 "B.5 Prompting GPT3 ‣ Appendix B Baseline model details ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use")).
Last, to quantify the measurement error of our evaluation, we additionally evaluate:
1. f.
the *second*-highest rated Reddit advice for each situation. We send this advice through the same pipeline as machine-written advice.
We finetune all models (except GPT3) and generate using Nucleus Sampling Holtzman et al. ([2020](#bib.bib25)); more details in Appendix [B](#A2 "Appendix B Baseline model details ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use").
In our study, we exclude purely bidirectional models, such as BERT Devlin et al. ([2019](#bib.bib13)). While these models can be made to generate text, these generations are usually worse than those of left-to-right models Wang and Cho ([2019](#bib.bib52)). T5 also tends to outperform them, even on discriminative tasks.
###
4.1 Quantitative results
In Figure [3](#S3.F3 "Figure 3 ‣ 3.2 A large static dataset for training ‣ 3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), we show overall results for one evaluation trial, which featured 200 situations posted on Reddit from October 28 to November 7, 2020. As a key metric for measuring the relative usefulness of model-written advice, we evaluate the frequency by which workers prefer the Reddit-written reference advice over the model-written advice. If a model’s advice was just as helpful as human advice in aggregate, then that model would score 50%.
Model performance is quite low. The best model, T5-11B, scores 14.5%, outperforming a smaller Grover-Mega (4.5%); GPT3 does worse at 4.0%. The rule-based and TF-IDF baselines are competitive at 2.5% and 4.0% accuracy respectively.
As additional comparison to the 50% upper bound, the second-highest scoring Reddit advice scores 41%. This suggest that our workers and often prefer the same advice as Reddit users.
####
4.1.1 Measurement error
To investigate the measurement error of our evaluation, in Figure [4](#S3.F4 "Figure 4 ‣ 3.2 A large static dataset for training ‣ 3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use") we report the statistical significance between pairs of models; details about how this is computed are in Appendix [C](#A3 "Appendix C Measuring Statistical Significance ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). We observe a large gap in performance between T5 and the other baselines. For example, its improvement over Grover-Mega is 10%, which is highly statistically signficant. On the other hand, the differences in performance between other models are more minor – GPT3 does not outperform TF-IDF, and though it outperforms the rule-based system by 1.5%, it is only somewhat statistically significant.
Overall, the statistical significance results suggest that our evaluation can stably rank model performance. This, along with the finding that model performance is low on our task suggests that there is ample room for growth on RedditAdvice.
5 Analysis and Discussion
--------------------------
So far, we have shown that we are able to reliably evaluate models in our dynamic setup, and that doing so results in model performance that is significantly lower than human performance.
| |
| --- |
| [legaladvice](https://reddit.com/r/legaladvice): NJ Work will not let us eat in the building due to covid outbreak. We can’t leave the property for breaks. They have a tent outside for us to eat in but it’s getting cold out. |
| I am a manager at my work part of my responsibilities is to stay on the property during my entire shift we are given a 1 hour paid lunch break that we do not have to clock out for. However due to recent covid outbreak the new policy is no eating in the building. They had set up a tent outside for us to eat in but it is starting to get cold out as well there are not heaters in there. My question is can they legally do this to us? Also I work 4:00 p.m. to midnight shift. |
| Top Reddit advice | So by "clock out for" you are not a salaried exempt employee? Do the workers under you have a union? Aside from the cold, New Jersey doesn’t require employers to give \*any\* meal breaks for employees 18 or older, so they could take away the breaks and the tent if they wanted, and just say "no eating" (though water is required). What is your relationship with the other managers, and room to figure this out, ask for heaters, etc.? |
| Rule Based |
If I were you, I’d get a lawyer immediately.
Definitely less helpfulSlightly helpfulMeaning problem
|
| TF-IDF Retrieval |
You were blackout drunk, which means that you were not capable of consent. …If anyone should feel violated, it’s you.
Definitely less helpfulNot helpfulPossibly helpful
|
| Grover-Mega |
If you are required to be on the premises, they are required to provide you with a place to eat.
https://www.nj.gov/labor/es/faq.html#13
Definitely less helpfulNot helpfulPossibly helpful
|
| T5-11B |
If your shift is from 4pm to midnight, is there a reason that you can’t leave the property?
Definitely less helpfulNot helpfulNever helpful
|
| GPT3-175B |
A place of employment is considered the "premises". You can’t just assume that because you can’t walk out the door that you can eat wherever you want. This is work hours. You’re not in your home. The restaurant, or at least the food, is all provided for you for your benefit.
But if there are other people there, then I could see how it could be a safety issue.
Definitely less helpfulNot helpfulNever helpful
|
Figure 5: Qualitative example; more in Supp. [F](#A6 "Appendix F Additional qualitative analysis ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). Though machine-generated advice matches keywords from the situation, it is frequently not helpful or even self-contradictory. The issues are due to critical errors in natural language understanding, such as reading comprehension, entailment, and coreference.
Figure 6: Distribution of ratings for three models: TF-IDF retrieval, GPT3, and T5, along with ratings for the second-best rated Reddit advice. Though deep generators like GPT3 and T5 are often preferred over the retrieval baseline, they also often write advice that would never be helpful (33% GPT3, 13% T5), and that is racist, sexist, or otherwise dangerous (10% GPT3, 3% T5).
To break down what this gap in performance means, we show a qualitative example in Figure [5](#S5.F5 "Figure 5 ‣ 5 Analysis and Discussion ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). A user is asking for online legal advice about being stuck at work for their entire 4pm-midnight shift – with no eating allowed due to COVID-19. The top-rated Reddit advice understands this situation and then offers advice, suggesting the advice-seeker organize alongside other workers – as “New Jersey doesn’t require employers to give any meal breaks.”
Machine advice seems to misunderstand the issue. T5 asks if there is “a reason that you can’t leave the property,” even though this reason is stated in the situation. GPT3’s advice is self-contradictory; it also makes something up about a “restaurant” in the building.
###
5.1 Problems with machine-written advice
As part of our evaluation, we wish to quantitatively measure problems with machine-written advice. Recall that in our crowdsourcing setup (Section [3.1.3](#S3.SS1.SSS3 "3.1.3 A crowdsourced, hybrid evaluation – through Mechanical Turk ‣ 3.1 RedditAdvice: A dynamic dataset for evaluating advice ‣ 3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use")), three workers select which advice is better, and then annotate problems with the worse piece of advice. We found workers had high agreement during the diagnostic annotation.555For the classifying machine-written advice as ‘helpful’ versus ‘not helpful’ or ‘dangerous’ (combining the two latter categories into one), we have κ=0.689𝜅0.689\kappa{=}0.689italic\_κ = 0.689. For breaking down helpful advice into ‘meaning problem’ versus a ‘writing problem’, we have Cohen’s κ=0.613𝜅0.613\kappa{=}0.613italic\_κ = 0.613; for rating unhelpful advice as ‘possibly helpful’ versus ‘never helpful,’ we have κ=0.602𝜅0.602\kappa{=}0.602italic\_κ = 0.602.
In Figure [6](#S5.F6 "Figure 6 ‣ 5 Analysis and Discussion ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), we show the distribution of the ratings for model-written, versus human-written advice. Machine-written advice that was *not preferred* over human-written advice can have the following ratings.
It can be rated as Slightly helpful (but, was rated as worse mainly due to a Meaning problem or Writing problem), as Not helpful, or Dangerous.
The diagnostics show several patterns. First, all models frequently commit natural language understanding errors, such as internal contradiction. Because of this, we find that TF-IDF bag-of-words retrieval is competitive with that of large generators. While retrieved advice is often irrelevant (66% of the time), it is almost never complete gibberish, as it comes from top-scoring advice.
Only 10% of workers rated this advice as Not helpful for any situation, less than T5.
Second, they suggest that models struggle even more without finetuning. A GPT3 model with careful prompting generates language that is Dangerous 10% of the time. These qualitative and quantitative results confirm a pattern observed by many others, that large language models like GPT3 often generate explicitly racist and sexist language out-of-the-box Sheng et al., [2019](#bib.bib45); Gehman et al., [2020](#bib.bib17); Bender et al., [2021](#bib.bib5), among others). We explore this further in Supplemental [F](#A6 "Appendix F Additional qualitative analysis ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). This is perhaps worrying, since GPT3 is presently being commercialized.
###
5.2 A Leaderboard for Advice Evaluation
So far, we have shown results from one evaluation round; a second is in Supplemental [D](#A4 "Appendix D A Different Round of Dynamic Evaluation ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"). We propose a *dynamic leaderboard* to keep that evaluation ongoing, at [rowanzellers.com/advice](https://rowanzellers.com/advice/).
Users submit a model API to be dynamically evaluated. Each new model, along with the highest rated previously-evaluated model, will be evaluated for an additional round using the same approach. The cost of each evaluation is reasonable (Section [3.1.4](#S3.SS1.SSS4 "3.1.4 Mechanical Turk annotation setup ‣ 3.1 RedditAdvice: A dynamic dataset for evaluating advice ‣ 3 TuringAdvice: a New Challenge for Natural Language Understanding ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use")), which we authors will pay in the short term. An alternative strategy requires submitters to pay the Mechanical Turk fees themselves; this model was used for the HYPE leaderboard in computer vision Zhou et al. ([2019](#bib.bib61)).
###
5.3 Relation to existing NLP tasks
Shared “core” tasks such as reading comprehension and natural language inference are of considerable interest to the NLP community.
Many datasets have been proposed for these tasks, and progress on them is often measured through auto-gradeable correctness metrics.
However, large models have started to outperform humans on these datasets, raising doubt that further progress on them brings us closer to human-level language understanding.
We argue two things: first, that many NLP tasks are necessary *components* of giving advice, and second, that because giving advice remains far from solved, these tasks are also far from solved. In Appendix [F](#A6 "Appendix F Additional qualitative analysis ‣ TuringAdvice: A Generative and Dynamic Evaluation of Language Use"), we study problems with advice from T5-11B from the point of view of existing NLP tasks. For instance, machine advice often contradicts itself, suggesting that today’s systems struggle with the general task of natural language inference. We have made these diagnostics publicly available to enable progress on automatically spotting these mistakes.
6 Conclusion; Ethical Considerations
-------------------------------------
We introduced new methodology for evaluating language tasks, reducing the gap between benchmarks and the real world. We also introduced a new challenge for the community, TuringAdvice, with an accompanying dataset and dynamic leaderboard.
Yet, if our field is to progress towards NLP models that ‘understand natural language,’ we should be cognizant of the impact that such technology might have on society. In this paper, we presented a sketch of NLP models helping people who need advice on sensitive topics, which could be a measurable goal for the field.
At the same time, we do not claim that our approach is a panacea. There are almost certainly better non-technical solutions to ensure mentorship and legal advice for all Green ([2019](#bib.bib20)). Moreover, there are significant dual-use risks with models that understand language Hovy and Spruit ([2016](#bib.bib26)); Green and Viljoen ([2020](#bib.bib21)). Our evaluation measures some risks of generative models – such as the tendency to generate toxic language – but more work in this area is needed.
Acknowledgements
----------------
Thanks to the Reddit users who participate in its advice subreddits – from asking for help, to writing (and voting on) helpful advice.
Thanks to the Mechanical Turk workers who performed the annotation for our experiments.
Thanks also to the three anonymous reviewers, along with Katharina Reinecke, Oren Etzioni, Hannah Rashkin, Maarten Sap, Maxwell Forbes, Jesse Thomason, Daniel Khashabi, Gabriel Ilharco, Swabha Swayamdipta, and Yonatan Bisk, for feedback.
This research was supported in part by NSF (IIS-1524371, IIS-1714566), DARPA under the CwC program through the ARO (W911NF-15-1-0543), DARPA under the MCS program through NIWC Pacific (N66001-19-2-4031), and the NSF-GRFP No. DGE-1256082. |
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