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f6bd243d-4930-40e4-82dc-0e8181620b5b | trentmkelly/LessWrong-43k | LessWrong | 'Theories of Values' and 'Theories of Agents': confusions, musings and desiderata
Meta:
* Content signposts: we talk about limits to expected utility theory; what values are (and ways in which we're confused about what values are); the need for a "generative"/developmental logic of agents (and their values); types of constraints on the "shape" of agents; relationships to FEP/active inference; and (ir)rational/(il)legitimate value change.
* Context: we're basically just chatting about topics of mutual interests, so the conversation is relatively free-wheeling and includes a decent amount of "creative speculation".
* Epistemic status: involves a bunch of "creative speculation" that we don't think is true at face value and which may or may not turn out to be useful for making progress on deconfusing our understanding of the respective territory.
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Mateusz Bagiński
Expected utility theory (stated in terms of the VNM axioms or something equivalent) thinks of rational agents as composed of two "parts", i.e., beliefs and preferences. Beliefs are expressed in terms of probabilities that are being updated in the process of learning (e.g., Bayesian updating). Preferences can be expressed as an ordering over alternative states of the world or outcomes or something similar. If we assume an agent's set of preferences to satisfy the four VNM axioms (or some equivalent desiderata), then those preferences can be expressed with some real-valued utility function u and the agent will behave as if they were maximizing that u.
On this account, beliefs change in response to evidence, whereas values/preferences in most cases don't. Rational behavior comes down to (behaving as if one is) ~maximizing one's preference satisfaction/expected utility. Most changes to one's preferences are detrimental to their satisfaction, so rational agents should want to keep their preferences unchanged (i.e., utility function preservation is an instrumentally convergent goal).
Thus, for a preference modification to be rational, it would have |
9fff7b95-8d0f-41bf-aae0-cc06d4801baf | trentmkelly/LessWrong-43k | LessWrong | Ads are everywhere, and it's not okay
[Crossposted from damiensnyder.com.]
Author's note: Thanks to the commenters who have pointed out some weaknesses in my argument I didn't think of. I still endorse the majority of this argument, but I don't think this was the best way to express it. (An earlier post I drafted on this same topic emphasized the time people spend on TV and the internet, and I think that thread of argument was more compelling.) I remain very frustrated by advertising: consider how many thousands of talented individuals spend their entire workday trying to get people to watch more ads, a thing no one likes to do. But where I ignore ad-free video / streaming subscription services, and to a lesser extent ad-free news, this argument is weaker. I may write another post explaining the world-model behind my view on ads in more detail. Thanks again for the feedback.
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Have you noticed that everything is covered in ads?
Not everything. You can go entire days without seeing an advertisement. But you can't do that if you plan on watching TV, browsing the internet, or taking public transit. Let's review some everyday sources of entertainment, and where you can expect them to show you ads:
Sports
Ads are ingrained into every detail of the sports experience. (For non-sports fans, you will have to follow along and imagine.)
You go to the game. You might drive, and see billboards on your way. You could take a bus or train, the inside and outside of which are covered in ads and occasionally PSAs. Then you get into the stadium. If you pick up a program, it will likely have ads in it. When you get to your seat, you might see ads surrounding the stadium wherever there aren't seats. The players will walk out, and their jerseys may have ads too. (If not, they might have a sponsorship deal with the company that made their uniforms.) If you're not watching soccer, the action will be interrupted every ten minutes so they can show ads to the people watching from home |
eea8c5be-e97d-4f76-b362-6db6932d0b09 | trentmkelly/LessWrong-43k | LessWrong | [AN #79]: Recursive reward modeling as an alignment technique integrated with deep RL
Find all Alignment Newsletter resources here. In particular, you can sign up, or look through this spreadsheet of all summaries that have ever been in the newsletter. I'm always happy to hear feedback; you can send it to me by replying to this email.
Happy New Year!
Audio version here (may not be up yet).
Highlights
AI Alignment Podcast: On DeepMind, AI Safety, and Recursive Reward Modeling (Lucas Perry and Jan Leike) (summarized by Rohin): While Jan originally worked on theory (specifically AIXI), DQN, AlphaZero and others demonstrated that deep RL was a plausible path to AGI, and so now Jan works on more empirical approaches. In particular, when selecting research directions, he looks for techniques that are deeply integrated with the current paradigm, that could scale to AGI and beyond. He also wants the technique to work for agents in general, rather than just question answering systems, since people will want to build agents that can act, at least in the digital world (e.g. composing emails). This has led him to work on recursive reward modeling (AN #34), which tries to solve the specification problem in the SRA framework (AN #26).
Reward functions are useful because they allow the AI to find novel solutions that we wouldn't think of (e.g. AlphaGo's move 37), but often are incorrectly specified, leading to reward hacking. This suggests that we should do reward modeling, where we learn a model of the reward function from human feedback. Of course, such a model is still likely to have errors leading to reward hacking, and so to avoid this, the reward model needs to be updated online. As long as it is easier to evaluate behavior than to produce behavior, reward modeling should allow AIs to find novel solutions that we wouldn't think of.
However, we would eventually like to apply reward modeling to tasks where evaluation is also hard. In this case, we can decompose the evaluation task into smaller tasks, and recursively apply reward modeling to train AI syste |
e4f38c83-40a2-482f-bf03-acbcfc6bc410 | trentmkelly/LessWrong-43k | LessWrong | Wanted: Executive Assistant to help build the progress movement
I’m hiring an Executive Assistant to be part of the founding team at The Roots of Progress, and to work closely with me on all of our projects to build the progress movement.
This role is perfect for someone who wants to combine dedication to an ambitious, high-impact, long-term mission with a day-to-day focus on operations, organization, and getting things done.
You should have strong attention to detail, crisp communication, swift and efficient execution, and meticulous followup. You’ll apply your intelligence to a broad range of tasks and projects, learning as you go where needed. Prior experience is helpful but not required, and candidates of any background are encouraged to apply. The ideal candidate will be familiar with my work and will be excited about strengthening the progress community.
You’ll act as a force multiplier on my time, allowing me to delegate everything that doesn’t need me to do it so that I can focus as much as possible on research, writing, and speaking. Your responsibilities will thus span a broad range—for example, helping with:
* Managing a database of everyone I meet and talk to, and helping me keep in touch
* Scheduling talks and interviews; planning trips
* Community-building, including online forums and in-person events
* Fundraising, grant-seeking, and donor relations
* Media management, from helping with the @rootsofprogress Twitter account to getting coverage and interviews in blogs, podcasts, and media
* Project management for other organizational goals, such as launching new online resources or other programs
* Generally helping me stick to a schedule and not drop tasks
This is a full-time role. You can do it from anywhere, but preference will be given to candidates closer to US time zones. Compensation will vary depending on your seniority, qualifications, and location, but will be competitive with market rates. To apply, send me a resume (link to an online one is fine): jason@rootsofprogress.org.
This is a chance |
42381aa1-c386-4bcb-8cc5-3750ea60b7de | trentmkelly/LessWrong-43k | LessWrong | Figuring out what Alice wants: non-human Alice
I’ve shown that we cannot deduce the preferences of a potentially irrational agent. Even simplicity priors don’t help. We need to make extra ‘normative’ assumptions in order to be able to say anything about these preferences.
I then presented a more intuitive example, in which Alice was playing poker, and had two possible beliefs about Bob’s hand, and two possible preferences: wanting money, or wanting Bob (which, in that situations, translated into wanting to lose to Bob).
That example illustrated the impossibility result, within the narrow confines of that situation – if Alice calls, she could be a money-maximiser expecting to win, or a love-maximiser expecting to lose.
As has been pointed out, this uncertainty doesn’t really persist if we move beyond the initial situation. If Alice was motivated by love or money, we would expect to be able to tell which one, by seeing what she does in other situations – how does she respond to Bob’s flirtations, what does she confess to her closest friends, how does she act if she catches a peek of Bob’s cards, etc…
So if we look at her more general behaviour, it seems that we have two possible versions of Alice. First, Am, who clearly wants money, and A♡, who clearly wants Bob. The actions of these two agents match up in the specific case I described, but not in general. Doesn’t this undermine my claim that we can’t tell the preferences of an agent from their actions?
What’s actually happening here is that we’re already making a lot of extra assumptions when we’re interpreting Am or A♡’s actions. We model other humans in very specific and narrow ways, and other humans do the same – and their models are very similar to ours (consider how often humans agree that another human is angry, or that being drunk impairs rationality). The agreement isn’t perfect, but is much better than random.
If we set those assumptions aside, then we can see what the theorem implies. There is a possible agent A′m, whose preference is for love, bu |
c69c51bf-a244-4165-a794-7fb2e9cb9407 | trentmkelly/LessWrong-43k | LessWrong | To what ethics is an AGI actually safely alignable?
Today, AI is thought to be alignable to nearly every task except for obviously unethical ones like writing insecure code without a noble reason, which was found to induce broad misalignment or lower the AI's inhibitions. The question of whether alignment of superintelligence is possible is thought not to depend on the ideology of the society and on the ways in which mankind will use the AI. For instance, the set of tasks to which the AI can be safely aligned is thought to include automating all the jobs in the world and leaving mankind with the UBI or without socioeconomic advancement.
However, the consequences of aligning the AI to a set of beliefs remain underexplored. For instance, were the Nazis try to create superintelligence, the AGI would find it hard to reject their beliefs about the Master Race. But there is no specific reason for humans, and not the AGI itself, to be called one, and an AGI treating itself as the Master Race with the right to get rid of primitive ones is misaligned. When I tried asking o3 about the possibility of aligning the AGI to Nazis, o3 agreed with the points above by generating the following text:
o3's answer
Considering AI alignment and ideology
This is a pretty complex question! To think about it, I'd consider the alignment and ethical safeguards in AI development, especially its training processes. If an AI were trained in biased ideologies, the designers would likely implement reward models and supervision to avoid self-destructive or harmful ideation — like fostering superiority. I imagine the challenge would be ensuring real-world ethics are maintained so the AI doesn't perpetuate harmful beliefs, such as Nazi ideology, regardless of its training.
Short answer
You could train an AI under Nazi ideology that never decides it is “the true Master Race,” but only while the system stays narrow, myopic, and constantly supervised. Once you give the same system open‑ended autonomy and the strategic breadth we associate with supe |
4dc96c13-807c-497c-af05-6368086b1ead | trentmkelly/LessWrong-43k | LessWrong | Reward uncertainty
In my last post, I argued that interaction between the human and the AI system was necessary in order for the AI system to “stay on track” as we encounter new and unforeseen changes to the environment. The most obvious implementation of this would be to have an AI system that keeps an estimate of the reward function. It acts to maximize its current estimate of the reward function, while simultaneously updating the reward through human feedback. However, this approach has significant problems.
Looking at the description of this approach, one thing that stands out is that the actions are chosen according to a reward that we know is going to change. (This is what leads to the incentive to disable the narrow value learning system.) This seems clearly wrong: surely our plans should account for the fact that our rewards will change, without treating such a change as adversarial? This suggests that we need to have our action selection mechanism take the future rewards into account as well.
While we don’t know what the future reward will be, we can certainly have a probability distribution over it. So what if we had uncertainty over reward functions, and took that uncertainty into account while choosing actions?
Setup
We’ve drilled down on the problem sufficiently far that we can create a formal model and see what happens. So, let’s consider the following setup:
* The human, Alice, knows the “true” reward function that she would like to have optimized.
* The AI system maintains a probability distribution over reward functions, and acts to maximize the expected sum of rewards under this distribution.
* Alice and the AI system take turns acting. Alice knows that the AI learns from her actions, and chooses actions accordingly.
* Alice’s action space is such that she cannot take the action “tell the AI system the true reward function” (otherwise the problem would become trivial).
* Given these assumptions, Alice and the AI system act optimally.
This is the setup of C |
73f26d62-464b-4b65-bd75-e0830cc95dc0 | trentmkelly/LessWrong-43k | LessWrong | Ghosts in the Machine
People hear about Friendly AI and say - this is one of the top three initial reactions:
"Oh, you can try to tell the AI to be Friendly, but if the AI can modify its own source code, it'll just remove any constraints you try to place on it."
And where does that decision come from?
Does it enter from outside causality, rather than being an effect of a lawful chain of causes which started with the source code as originally written? Is the AI the Author* source of its own free will?
A Friendly AI is not a selfish AI constrained by a special extra conscience module that overrides the AI's natural impulses and tells it what to do. You just build the conscience, and that is the AI. If you have a program that computes which decision the AI should make, you're done. The buck stops immediately.
At this point, I shall take a moment to quote some case studies from the Computer Stupidities site and Programming subtopic. (I am not linking to this, because it is a fearsome time-trap; you can Google if you dare.)
> ----------------------------------------
>
> I tutored college students who were taking a computer programming course. A few of them didn't understand that computers are not sentient. More than one person used comments in their Pascal programs to put detailed explanations such as, "Now I need you to put these letters on the screen." I asked one of them what the deal was with those comments. The reply: "How else is the computer going to understand what I want it to do?" Apparently they would assume that since they couldn't make sense of Pascal, neither could the computer.
>
> ----------------------------------------
>
> While in college, I used to tutor in the school's math lab. A student came in because his BASIC program would not run. He was taking a beginner course, and his assignment was to write a program that would calculate the recipe for oatmeal cookies, depending upon the number of people you're baking for. I looked at his program, and it we |
71a25a9e-243c-4928-bdd3-572ad348d197 | trentmkelly/LessWrong-43k | LessWrong | ACI#9: What is Intelligence
Abstract
This article is a brief summary of ACI's new definition of intelligence: Doing the same thing in new situations as the examples of the right thing to do, by making predictions based on these examples. In other words, intelligence makes decisions by stare decisis with Solomonoff induction, not by pursuing a final goal or optimizing a utility function. As a general theory of intelligence, ACI is inspired by Assembly theory, the Copycat model, and the Active inference approach, and is formalized using Algorithmic information theory.
1. Normativity from Examples
Let's start with the definition that an intelligent agent is a system that does the right thing (Russell 1991) . We can directly design simple agents that do the right thing in simple environments, such as vacuum robots, but what about more complicated situations?
1.1 Normativity and capability
The right thing question can be broken down into two parts:
1. What is the right thing to do? (the normativity)
2. How to do it? (the capability)
Many researchers focus on the capability problem. They evaluate the level of intelligence in terms of goal-achieving abilities (Legg & Hutter 2007), while normativity simply refers to "assigning a final goal to an agent". The same final goal can be assigned to different intelligent agents, just as the same program can be run on different computers. According to the orthogonality thesis, any level of capability can be combined with any final goals (Bostrom 2012).
On the contrary, our definition of intelligence starts from normativity, since we believe that "the right thing to do" is far more complicated than goals or utility functions.
1.2 Keep flexibility without referring to goals
How could an intelligent agent, starting from a tabula rasa, distinguish the right thing from the wrong thing? There are at least two approaches: direct specification and indirect normativity.
1. Direct specification: "Simply" assign a final goal to an agent, e.g. m |
b0b2a5d1-a66c-419e-abc3-ae3a9ca69ce7 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | David Krueger on AI Alignment in Academia and Coordination
[David Krueger](https://www.davidscottkrueger.com/) is an assistant professor at the University of Cambridge and got his PhD from Mila. His research group focuses on aligning deep learning systems, but he is also interested in governance and global coordination. He does not have an AI alignment research agenda per se, and instead tries to enable his [seven PhD students](https://www.davidscottkrueger.com/) to drive their own research.
I think this interview gives some interesting pointers towards how we should direct efforts into how to communicate AI Alignment to Machine Learning researcher, how to fund AI Alignment research and what is the perception of AI Alignment research like in Academia.
Below are some highlighted quotes from our conversation (available on [Youtube](https://youtu.be/3T7Gpwhtc6Q), [Spotify](https://open.spotify.com/episode/1vvAKf8EBwErP5yGFRNoCT?si=1a28296cdfa94c01), [Google Podcast](https://podcasts.google.com/feed/aHR0cHM6Ly9hbmNob3IuZm0vcy81NmRmMjE5NC9wb2RjYXN0L3Jzcw/episode/MzJlMzk4YTAtYmMzZC00MDVkLWIzMTAtNTZhMmM2ZDc2MTg0?sa=X&ved=0CAUQkfYCahcKEwiI2sT3hY35AhUAAAAAHQAAAAAQAQ), [Apple Podcast](https://podcasts.apple.com/us/podcast/connor-leahy-eleutherai-conjecture/id1565088425?i=1000570841369)). For the full context for each of these quotes, you can find the accompanying [transcript](https://theinsideview.ai/david).
Building A Research Team, Not Following An Agenda
-------------------------------------------------
> "I think agenda is a very grandiose term to me. It's oftentimes, I think people who are at my level of seniority or even more senior in machine learning would say, "oh, I'm pursuing a few research directions." And they wouldn't say, "I have this big agenda." And so I think my philosophy or mentality, I should say, when I set up this group and started hiring people was like, **let's get talented people. Let's get people who understand and care about the problem. Let's get people who understand machine learning. Let's put them all together and just see what happens** and try and find people who I want to work with, who I think are going to be nice people to have in the group who have good personalities, pro-social, who seem to really understand and care and all that stuff." ([full context](https://theinsideview.ai/david#how-david-approaches-research-in-his-lab))
>
>
On Coordination Between Academia And The Broader World
------------------------------------------------------
> "There's a lack of understanding and appreciation of the perspective of people in machine learning within the existential safety community and vice versa. And I think that's really important to address, especially because I'm pretty pessimistic about the technical approaches. **I don't think alignment is a problem that can be solved. I think we can do better and better. But to have it be existentially safe, the bar seems really, really high and I don't think we're going to get there. So we're going to need to have some ability to coordinate and say let's not pursue this development path or let's not deploy these kinds of systems right now.** And for that, I think to have a high level of coordination around that, we're going to need to have a lot of people on board with that in academia and in the broader world. So I don't think this is a problem that we can solve just with the die hard people who are already out there convinced of it and trying to do it." ([full context](https://theinsideview.ai/david#existential-safety-cant-be-ensured-without-high-level-of-coordination))
>
>
Most of the risk comes from safety-performance trade-offs in the development and deployment process
---------------------------------------------------------------------------------------------------
> "A lot of people are worried about us under-investing in research and that's where the safety-performance trade-offs are most salient for them. But I'm worried about the development and deployment process. **I think where most of the risk actually comes from is from safety-performance trade-offs in the development and the deployment process.** For whatever level of research we have developed on alignment and safety, I think it's not going to be the case that those trade-offs just go away." ([full context](https://theinsideview.ai/david#most-of-the-risk-comes-from-safety-performance-trade-offs-in-developement-and-deployment))
>
>
We Should Test Our Intuitions About Future AI Systems
-----------------------------------------------------
> "This is something that's a really interesting research question and is really important for safety because people have very different intuitions about this. Some people have these stories where just through this carefully controlled text interaction, maybe we just ask this thing one yes or no question a day and that's it. And that's the only interaction it has with the world. But it's going to look at the floating point errors on the hardware it's running on. And it's somehow going to become aware of that. And from that it's going to reverse engineer the entire outside world and figure out some plan to trick everybody and get out. And this is the thing that people talk about on LessWrong classically.
>
> We don't know how smart the superintelligence is going to be, so let's just assume it's arbitrarily smart, basically. And obviously, a lot of people take issue with that. It's not clear how representative that is of anybody's actual beliefs but there are definitely people who have beliefs more towards that end where they think that AI systems are going to be able to understand a lot about the world, even from very limited information and maybe in very limited modality. My intuition is not that way. **The important thing is to test the intuitions and actually try and figure out at what point can your AI system reverse engineer the world or at least reverse engineer a distribution of worlds or a set of worlds that includes the real world based on this really limited kind of data interaction.**" ([full context](https://theinsideview.ai/david#language-models-have-incoherent-causal-models-until-they-know-in-which-world-they-are))
>
> |
bc5680ee-4bae-49a7-9593-f30100709c38 | trentmkelly/LessWrong-43k | LessWrong | Branding Biodiversity
http://www.futerra.co.uk/downloads/Branding_Biodiversity.pdf
I'm quite interested in seeing if this works. I have sent this to several wildlife-guides and conservationists and will monitor their reactions.
It talks about what emotions drive people to actually do something to protect biodiversity rather then just showing them figures. After looking at what makes a certain brand successful they apply it on biodiversity. Their end conclusion is to remove messages based on extinction as it just makes people apathetic rather then inspire change. Furthermore they propose different ways of conveying "biodiversity is important" for different audiences. Love, fuzzy feelings and "you-can-make-a-difference!" for public changes and financial advantages and concrete action for policy changes. Lastly, the advise to make the message more personal by talking about loving your pets, focusing on local species and anthropomorphise whatever you are talking about.
In short they want to protect biodiversity by making it a brand name and getting people to buy their product (i.e. donate money, etc.)
|
b52bc8ec-a152-4b4b-9ba9-8e26c0e28642 | trentmkelly/LessWrong-43k | LessWrong | [SEQ RERUN] Lawrence Watt-Evans's Fiction
Today's post, Lawrence Watt-Evans's Fiction was originally published on 15 July 2008. A summary (taken from the LW wiki):
> A review of Lawrence Watt-Evans's fiction.
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 Probability is Subjectively Objective, 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. |
0aaac58d-025e-443b-9d3c-416b6bdef83d | trentmkelly/LessWrong-43k | LessWrong | Monthly Roundup #15: February 2024
Another month. More things. Much roundup.
BAD NEWS
Jesse Smith writes in Asterisk that our HVAC workforce is both deeply incompetent and deeply corrupt. This certainly matches my own experience. Calculations are almost always flubbed when they are done at all, outright fraudulent paperwork is standard, no one has the necessary skills.
It certainly seems like the Biden Administration is doing its best to hurt Elon Musk? Claim here is that they cancelled a Starlink contract without justification, in order to award the contract to someone else for more than three times the price. This was on Twitter, but none of the replies seemed to offer a plausible justification.
Claim that Twitter traffic is increasingly fake, and secondary claim that this is because Musk fired those responsible for preventing it. Even if it is true that Twitter traffic is 75% fake, that does not mean that your experience will be 75% bots, or even 7.5% bots. Mine is more like 0.75%. As usual, the bots are mostly making zero attempt to not look like bots, and would be trivial to find and ban if people cared.
Nate Silver is correct that ‘misinformation experts’ are collectively making a mistake when they themselves spread highly partisan misinformation, and also that the game theory makes it impossible for them to collectively stop.
The word ‘genocide’ risks being watered down to the point where we will not have a word for what it used to mean, and this will make it much harder to maintain the taboo of Never Again.
Avatar: The Last Airbender’s live action Netflix version decides that some of the scenes in the original ‘are iffy’ and it needs to soften a cartoon show made for eight year olds, to exclude a character arc where a boy grows up and learns not to be sexist, because the character started off too sexist. Not that I was ever watching anyway. As one commenter notes, if that is an issue, in news related to the previous item, wait until you hear about the Fire Nation.
An essay correctly i |
9afbe868-0dbc-4edc-892f-12432cc039c9 | trentmkelly/LessWrong-43k | LessWrong | [Valence series] 1. Introduction
1.1 Summary & Table of Contents
This is the first of a series of five blog posts on valence. Here’s an overview of the whole series, and then we’ll jump right into the first post!
1.1.1 Summary & Table of Contents—for the whole Valence series
Let’s say a thought pops into your mind: “I could open the window right now”. Maybe you then immediately stand up and go open the window. Or maybe you don’t. (“Nah, I’ll keep it closed,” you might say to yourself.) I claim that there’s a final-common-pathway[1] signal in your brain that cleaves those two possibilities: when this special signal is positive, then the current “thought” will stick around, and potentially lead to actions and/or direct-follow-up thoughts; and when this signal is negative, then the current “thought” will get thrown out, and your brain will go fishing (partly randomly) for a new thought to replace it. I call this final-common-pathway signal by the name “valence”. Thus, the “valence” of a “thought” is roughly the extent to which the thought feels demotivating / aversive (negative valence) versus motivating / appealing (positive valence).
I claim that valence plays an absolutely central role in the brain—I think it’s one of the most important ingredients in the brain’s Model-Based Reinforcement Learning system, which in turn is one of the most important algorithms in your brain.
Thus, unsurprisingly, I see valence as a shining light that illuminates many aspects of psychology and everyday mental life. This series explores that idea. Here’s the outline:
* Post 1 (Introduction) will give some background on how I’m thinking about valence from the perspective of brain algorithms, including exactly what I’m talking about, and how it relates to the “wanting versus liking” dichotomy. (The thing I’m talking about is closer to “motivational valence” than “hedonic valence”, although neither term is great.)
* Post 2 (Valence & Normativity) will talk about the intimate relationship between valence and the uni |
8f1de810-13c1-4d0b-948a-1eff57a1a1bc | trentmkelly/LessWrong-43k | LessWrong | Learning with catastrophes
A catastrophe is an event so bad that we are not willing to let it happen even a single time. For example, we would be unhappy if our self-driving car ever accelerates to 65 mph in a residential area and hits a pedestrian.
Catastrophes present a theoretical challenge for traditional machine learning — typically there is no way to reliably avoid catastrophic behavior without strong statistical assumptions.
In this post, I’ll lay out a very general model for catastrophes in which they are avoidable under much weaker statistical assumptions. I think this framework applies to the most important kinds of catastrophe, and will be especially relevant to AI alignment.
Designing practical algorithms that work in this model is an open problem. In a subsequent post I describe what I currently see as the most promising angles of attack.
Modeling catastrophes
We consider an agent A interacting with the environment over a sequence of episodes. Each episode produces a transcript τ, consisting of the agent’s observations and actions, along with a reward r ∈ [0, 1]. Our primary goal is to quickly learn an agent which receives high reward. (Supervised learning is the special case where each transcripts consist of a single input and a label for that input.)
While training, we assume that we have an oracle which can determine whether a transcript τ is “catastrophic.” For example, we might show a transcript to a QA analyst and ask them if it looks catastrophic. This oracle can be applied to arbitrary sequences of observations and actions, including those that don’t arise from an actual episode. So training can begin before the very first interaction with nature, using only calls to the oracle.
Intuitively, a transcript should only be marked catastrophic if it satisfies two conditions:
1. The agent made a catastrophically bad decision.
2. The agent’s observations are plausible: we have a right to expect the agent to be able to handle those observations.
While actually interact |
0b05f412-adf6-49d8-910c-a92b7bafbdf9 | StampyAI/alignment-research-dataset/blogs | Blogs | Historic trends in land speed records
Land speed records did not see any greater-than-10-year discontinuities relative to linear progress across all records. Considered as several distinct linear trends it saw discontinuities of 12, 13, 25, and 13 years, the first two corresponding to early (but not first) jet-propelled vehicles.
The first jet-propelled vehicle just predated a marked change in the rate of progress of land speed records, from a recent 1.8 mph / year to 164 mph / year.
Details
-------
This case study is part of AI Impacts’ [discontinuous progress investigation](https://aiimpacts.org/discontinuous-progress-investigation/).
### Background
According to Wikipedia, the land speed record is “the highest speed achieved by a person using a vehicle on land.”[1](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-1-1621 "“Land Speed Record”. 2019. <em>En.Wikipedia.Org</em>. Accessed May 25 2019. https://en.wikipedia.org/wiki/Land_speed_record.") Wheel-driven cars, which supply power to their axles, held the records for land speed record through 1963, when the first turbojet powered vehicles arrived on the scene. No wheel-driven car has held the record since 1964.[2](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-2-1621 "“Craig Breedlove’s mark of 407.447 miles per hour (655.722 km/h), set in Spirit of America in September 1963, was initially considered unofficial. The vehicle breached the FIA regulations on two grounds: it had only three wheels, and it was not wheel-driven, since its jet engine did not supply power to its axles. […] The confusion of having three different LSRs lasted until December 11, 1964, when the FIA and FIM met in Paris and agreed to recognize as an absolute LSR the higher speed recorded by either body, by any vehicles running on wheels, whether wheel-driven or not. […] No wheel-driven car has since held the absolute record.” – “Land Speed Record”. 2019. <em>En.Wikipedia.Org</em>. Accessed May 25 2019. https://en.wikipedia.org/wiki/Land_speed_record.")
Figure 1: Three record-setting vehicles: Sunbeam, Sunbeam Blue Bird, and Blue Bird[3](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-3-1621 "<a href=\"https://commons.wikimedia.org/wiki/File:Blue_Bird_land_speed_record_car_(5962811187).jpg\">From Wikimedia Commons:</a> sv1ambo [CC BY 2.0 (https://creativecommons.org/licenses/by/2.0)]")
### Trends
#### Land speed records
##### Data
We took data from Wikipedia’s list of land speed records,[4](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-4-1621 "“Land Speed Record”. 2019. <em>En.Wikipedia.Org</em>. Accessed May 25 2019. https://en.wikipedia.org/wiki/Land_speed_record.") which we have not verified, and added it to [this spreadsheet](https://docs.google.com/spreadsheets/d/1sezw2CCJ3WxcrAcqsw7ZWK1rJoxJkVYxW9HljWVe-vg/edit?usp=sharing). See Figure 2 below.
Figure 2: Historic land speed records in mph over time. Speeds on the left are an average of the record set in mph over 1 km and over 1 mile. The red dot represents the first record in a cluster that was from a jet propelled vehicle. The discontinuities of more than ten years are the third and fourth turbojet points, and the last two points.
##### Discontinuity measurement
If we treat the data as a linear trend across all time,[5](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-5-1621 "See our <strong><a href=\"https://aiimpacts.org/methodology-for-discontinuity-investigation/#trend-fitting\">our methodology page</a></strong> for more details.") then the land speed record did not contain any greater than 10-year discontinuities.
However we divide the data into several linear trends.[6](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-6-1621 "See <a href=\"https://docs.google.com/spreadsheets/d/1sezw2CCJ3WxcrAcqsw7ZWK1rJoxJkVYxW9HljWVe-vg/edit?usp=sharing\">our spreadsheet</a> to view the trends, and <a href=\"https://aiimpacts.org/methodology-for-discontinuity-investigation/#time-period-selection\">our methodology page</a> for details on how to interpet our sheets and when and how we divide data into trends.") Extrapolating based on these trends, there were four discontinuities of sizes 12, 13, 25, and 13 years, produced by different turbojet-powered vehicles.[7](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-7-1621 "See <a href=\"https://aiimpacts.org/methodology-for-discontinuity-investigation/#discontinuity-measurement\"><strong>our methodology page</strong></a> for more details, and <a href=\"https://docs.google.com/spreadsheets/d/1sezw2CCJ3WxcrAcqsw7ZWK1rJoxJkVYxW9HljWVe-vg/edit?usp=sharing\"><strong>our spreadsheet</strong></a> for our calculation.")In addition to the size of these discontinuities in years, we have tabulated a number of other potentially relevant metrics **[here](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit?usp=sharing)**.[8](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-8-1621 "See <strong><a href=\"https://aiimpacts.org/methodology-for-discontinuity-investigation/#discontinuity-data\">our methodology page</a></strong> for more details.")
##### Changes in the rate of progress
There are several marked changes in the rate of progress in this history. The first two discontinuities are near the start of a sharp change, that seemed to come from the introduction of jet-propulsion (though note that the first jet-propelled vehicle in the trend is neither discontinuous with the previous trend, nor seemingly within the period of faster growth).
If we look at the rates of progress in the stretches directly before the second jet propelled vehicle in 1964, and the stretch directly after that through 1965, the rate of progress increases from 1.8 mph / year to 164 mph / year.[9](https://aiimpacts.org/historic-trends-in-land-speed-records/#easy-footnote-bottom-9-1621 "See <strong><a href=\"https://aiimpacts.org/methodology-for-discontinuity-investigation/#changes-in-the-rate-of-progress\">our methodology page</a></strong> for more details, and <a href=\"https://docs.google.com/spreadsheets/d/1JZh0wfCW-DrJjYLNgGW_TML-gmq1xZ44PfsfSCP5nlo/edit?usp=sharing\"><strong>our spreadsheet</strong></a> for our calculations.")
Notes
----- |
53578c5c-cb7c-47db-8ba7-50d4822dadc5 | trentmkelly/LessWrong-43k | LessWrong | Carbon dioxide, climate sensitivity, feedbacks, and the historical record: a cursory examination of the Anthropogenic Global Warming (AGW) hypothesis
Note: In this blog post, I reference a number of blog posts and academic papers. Two caveats to these references: (a) I often reference them for a specific graph or calculation, and in many cases I've not even examined the rest of the post or paper, while in other cases I've examined the rest and might even consider it wrong, (b) even for the parts I do reference, I'm not claiming they are correct, just that they provide what seems like a reasonable example of an argument in that reference class.
Note 2: Please see this post of mine for more on the project, my sources, and potential sources for bias.
In a previous post, I attempted simple time series forecasting for temperature from the outside view, i.e., as a complete non-expert would. I introduced carbon dioxide concentrations as an explanatory variable near the end of the post, but did not consider in detail the mechanisms through which carbon dioxide concentrations affect temperature. In this post, I switch to what Eliezer Yudkowsky has called the weak inside view. That's something like the inside view, but without knowledge of all the relevant details. Obviously, I'm somewhat constrained here: I can't take the full inside view because I don't know enough about the atmospheric system (partly because the state of human knowledge about the atmospheric system is incomplete, and partly because I know only a very miniscule fraction even of that small amount of human knowledge). But I think that the weak inside view also offers an alternate perspective to the inside view and is valuable in its own right.
One of the reasons I felt the need to switch from the outside view is that the issue is sufficiently complex, but at the same time, the component phenomena are sufficiently well-enumerated that a weak inside view can help. My initial framing of the issue was in terms of separating the roles of theory and evidence in belief in anthropogenic global warming. But a better weak inside view led me to the conclusion that |
71ad77f0-752a-4da3-b5e6-cb2c0534865f | trentmkelly/LessWrong-43k | LessWrong | Is there a convenient way to make "sealed" predictions?
Many people in our community claim to have ideas for how to build AGI, or other things, that they deem infohazardous and so don't want to publish. It would be great if they could publicly register these ideas in an encrypted way, so that later when their predictions come true they can reveal the key and everyone can see that they called it and give them epistemic credit accordingly.
I know this is possible in principle, e.g. by using PGP and posting encrypted messages on your LW shortform and then later revealing the key.
But it would be nice if this was a convenient, hassle-free feature embedded in LW, for example.
Also: Is this a bad idea for some reason? Is the privacy not as secure as I think, such that people would be hesistant to make even these encrypted predictions? (I guess there is the matter of how to securely store the key...) Is there a way to make a prediction that will automatically be decrypted after N years? |
815449ae-6a0f-4c0a-a336-e082644d3b65 | trentmkelly/LessWrong-43k | LessWrong | The Sheer Folly of Callow Youth
> "There speaks the sheer folly of callow youth; the rashness of an ignorance so abysmal as to be possible only to one of your ephemeral race..."
> —Gharlane of Eddore
Once upon a time, years ago, I propounded a mysterious answer to a mysterious question—as I've hinted on several occasions. The mysterious question to which I propounded a mysterious answer was not, however, consciousness—or rather, not only consciousness. No, the more embarrassing error was that I took a mysterious view of morality.
I held off on discussing that until now, after the series on metaethics, because I wanted it to be clear that Eliezer1997 had gotten it wrong.
When we last left off, Eliezer1997, not satisfied with arguing in an intuitive sense that superintelligence would be moral, was setting out to argue inescapably that creating superintelligence was the right thing to do.
Well (said Eliezer1997) let's begin by asking the question: Does life have, in fact, any meaning?
"I don't know," replied Eliezer1997 at once, with a certain note of self-congratulation for admitting his own ignorance on this topic where so many others seemed certain.
"But," he went on—
(Always be wary when an admission of ignorance is followed by "But".)
"But, if we suppose that life has no meaning—that the utility of all outcomes is equal to zero—that possibility cancels out of any expected utility calculation. We can therefore always act as if life is known to be meaningful, even though we don't know what that meaning is. How can we find out that meaning? Considering that humans are still arguing about this, it's probably too difficult a problem for humans to solve. So we need a superintelligence to solve the problem for us. As for the possibility that there is no logical justification for one preference over another, then in this case it is no righter or wronger to build a superintelligence, than to do anything else. This is a real possibility, but it falls out of any attempt to calcul |
ee3e7644-2fb7-4cb6-8cdb-6548028fb44e | StampyAI/alignment-research-dataset/lesswrong | LessWrong | New article from Oren Etzioni
(Cross-posted from [EA Forum](https://forum.effectivealtruism.org/posts/7x2BokkkemjnXD9B6/new-article-from-oren-etzioni).)
This just appeared in this week’s MIT Technology Review: Oren Etzioni, “[How to know if AI is about to destroy civilization](https://www.technologyreview.com/s/615264/artificial-intelligence-destroy-civilization-canaries-robot-overlords-take-over-world-ai/).” Etzioni is a noted skeptic of AI risk. Here are some things I jotted down:
Etzioni’s key points / arguments:
* Warning signs that AGI is coming soon (like canaries in a coal mine, where if they start dying we should get worried)
+ Automatic formulation of learning problems
+ Fully self-driving cars
+ AI doctors
+ Limited versions of the Turing test (like Winograd Schemas)
- If we get to the Turing test itself then it'll be too late
+ [Note: I think if we get to practically deployed fully self-driving cars and AI doctors, then we will have already had to solve more limited versions of AI safety. It’s a separate debate whether those solutions would scale up to AGI safety though. We might also get the capabilities without actually being able to deploy them due to safety concerns.]
* We are decades away from the versatile abilities of a 5 year old
* Preparing anyway even if it's very low probability because of extreme consequences is Pascal's Wager
+ [Note: This is a decision theory question, and I don’t think that's his area of expertise. I’ve researched PW extensively, and it’s not at all clear to me where to draw the line between low probability - high consequence scenarios that we should be factoring into our decisions, vs. very low probability – very high consequence that we should not factor into our decisions. I’m not sure there is any principled way of drawing a line between those, which might be a problem if it turns out that AI risk is a borderline case.]
* If and when a canary "collapses" we will have ample time to design off switches and identify red lines we don't want AI to cross
* "AI eschatology without empirical canaries is a distraction from addressing existing issues like how to regulate AI’s impact on employment or ensure that its use in criminal sentencing or credit scoring doesn’t discriminate against certain groups."
* Agrees with Andrew Ng that it's too far off to worry about now
But he seems to agree with the following:
* If we don’t end up doing anything about it then yes, superintelligence would be incredibly dangerous
* If we get to human level AI then superintelligence will be very soon afterwards so it'll be too late at that point
* If it were a lot sooner (as other experts expect) then it sounds like he would agree with the alarmists
* Even if it was more than a tiny probability then again it sounds like he'd agree because he wouldn't consider it Pascal's Wager
* If there's not ample time between "canaries collapsing" and AGI (as I think other experts expect) then we should be worried a lot sooner
* If it wouldn't distract from other issues like regulating AI's impact on employment, it sounds like he might agree that it's reasonable to put some effort into it (although this point is a little less clear)
See also Eliezer Yudkowsky, “[There's no fire alarm for Artificial General Intelligence](https://www.lesswrong.com/There's%20no%20fire%20alarm%20for%20Artificial%20General%20Intelligence)” |
2e29d0a7-1aca-4394-9432-fc8c5da7cc61 | trentmkelly/LessWrong-43k | LessWrong | A summary of every Replacing Guilt post
1
I recently finished the Replacing Guilt series (by Nate Soares).
I don't struggle much with guilt. I consider myself a happy and energetic person. I also spent several years studying psychology, and I'm familiar with many techniques from cognitive-behavioral therapy.
So I was surprised by how impressed I was with Replacing Guilt. Elegant writing, memorable stories, useful advice, and dark humor.
There are few things I would put in the category of "pretty much everyone I know should spend 5 minutes reading this to see if it might be helpful, and at least 20% of them should read the whole thing." I feel this way about Replacing Guilt.
2
As I read, I wrote a few sentences summarizing each post. I mostly did this to improve my own comprehension/memory.
You should treat the summaries as "here's what Akash took away from this post" as opposed to "here's an actual summary of what Nate said."
Note that the summaries are not meant to replace the posts-- in fact, they're intended to get you to read the posts (especially the ones that seem interesting, though I recommend starting from the beginning).
3
Here are my notes on each post, in order. Tomorrow, I'll post a tier-list.
Half-assing it with everything you’ve got
* You can try really hard to produce a low-quality or medium-quality outcome. You can put in your all to reach a B-, or to be in 17th place at the end of the race.
Failing with abandon
* If you wanted to go to sleep at 12AM, and it’s 12:15AM, don’t be like “ah well, I failed.” You can try to get as close to your goal as possible, even if you don’t fully achieve it.
The Stamp Collector
* We do not have full access to the external world, but we also do not have full access to our internal worlds. We can, in fact, care about the real world, other people, and things that happen outside of our minds.
You’re allowed to fight for something
* We can convert listless guilt (which is vague and general) into specific guilt (which is about a particula |
a810c3d1-0806-4bf6-ad89-49646b2adb13 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Can "Reward Economics" solve AI Alignment?
I think we can try to solve AI Alignment this way:
Model human values and objects in the world as a "money system" (a system of meaningful trades). Make the AGI learn the correct "money system", specify some obviously incorrect "money systems".
Basically, you ask the AI *"make paperclips that have the value of paperclips for humans"*. AI can do anything using all the power in the Universe. But killing everyone is not an option: paperclips can't be more valuable than humanity. *Money analogy:* if you killed everyone (and destroyed everything) to create some dollars, those dollars aren't worth anything. So you haven't actually gained any money at all.
The idea is that *"value"* of a thing doesn't exist only in your head, but also exists in the outside world. Like money: it has some personal value for you, but it also has some value outside of your head. And some of your actions may lead to the destruction of this "outside value". E.g. if you kill everyone to get some money you get ***nothing***.
I think this idea may:
* Fix some universal AI bugs. Prevent *"AI decides to kill everyone"* scenarios.
* Give a new way to explore human values. Explain how *humans* learn values.
* Unify many different Alignment ideas.
* "Solve" [Goodhart's Curse](https://www.lesswrong.com/tag/goodhart-s-law) and *safety/effectiveness* tradeoff and hard problem of corrigibility.
* Give a new way to formulate properties we want from an AGI.
I don't have a specific model, but I still think it gives ideas and unifies some already existing approaches. So please take a look. Other ideas in this post:
* Human values may be simple. Or complex, but not in the way you thought they are.
* Humans may have a small amount of values. Or big amount, but in an unexpected way.
* There may be a theory that's complementary to Bayesian reasoning.
*Disclaimer:* Of course, I don't ever mean that we shouldn't be worried about Alignment. I'm just trying to suggest new ways to think about values.
---
Thought experiments
===================
If you see a "hole" in the reasoning in the thought experiments, consider that you may not understand the *"argumentation method"*. Don't just assume that examples are not serious.
I believe the type of thinking in these examples can be formalized. I think it's somewhat similar to Bayesian reasoning, but applied to concepts.
Motion is the fundamental value
-------------------------------
You (**Q**) visit a small town and have a conversation with one of the residents (**A**).
* **A:** Here we have only one fundamental value. Motion. Never stop living things.
* **Q:** I can't believe you can have just a single value. I bet it's an oversimplification! There're always many values and tradeoffs between them. Even for a single person outside of society.
**A** smashes a bug.
* **Q:** You just smashed this bug! It seems pretty stopped. Does it mean you don't treat a bug as a "living thing"? But how do you define a "living thing"? Or does it mean you have some other values and make tradeoffs?
* **A:** No, you just need to look at things in context. ***(1)*** If we protected the motion of extremely small things (living parts of animals, insects, cells, bacteria), our value would contradict itself. We would need to destroy or constrain almost all moving organisms. And even if we wanted to do this, it would ultimately lead to way smaller amount of motion for extremely small things. ***(2)*** There're too much bugs, protecting a small amount of their movement would constrain a big amount of everyone else's movement. ***(3)*** On the other hand, you're right. I'm not sure if a bug is high on the list of "living things". I'm not all too bothered by the definition because there shouldn't be even hypothetical situations in which the precise definition matters.
* **Q:** Some people build small houses. Private property. Those houses restrict other people's movement. Is it a contradiction? Tradeoff?
* **A:** No, you just need to look at things in context. ***(1)*** First of all, we can't destroy all physical things that restrict movement. If we could, we would be flying in space, unable to move (and dead). ***(2)*** We have a choice between restricting people's movement significantly (not letting them build houses) and restricting people's movement inconsequentially and giving them private spaces where they can move even more freely. ***(3)*** People just don't mind. And people don't mind the movement created by this "house building". And people don't mind living here. We can't restrict large movements based on momentary disagreements of single persons. In order to have any freedom of movement we need such agreements. Otherwise we would have only chaos that, ultimately, restricts the movement of everyone.
* **Q:** Can people touch each other without consent, scream in public, lay on the roads?
* **A:** Same thing. To have freedom of movement we need agreements. Otherwise we would have only chaos that restricts everyone. By the way, we have some "chaotic" zones anyway.
* **Q:** Can the majority of people vote to lock every single person in a cage? If majority is allowed to control the movement. It would be the same logic, the same action of society. Yes, the situations are completely different, but you would need to introduce new values to differentiate them.
* **A:** We can qualitatively differentiate the situations without introducing new values. The actions look identical only out of context. When society agrees to not hit each other, the society serves as a proxy of the value of movement. Its actions are *caused* and *justified* by the value. When society locks someone without a good reason, it's not a proxy of the value anymore. In a way, you got it backwards: we wouldn't ever allow the majority to decide anything if it meant that the majority could destroy the value any day.
* **A:** A value is like a "soul" that possesses multiple specialized parts of a body: *"micro movement", "macro movement", "movement in/with society", "lifetime movement", "movement in a specific time and place"*. Those parts should live in harmony, shouldn't destroy each other.
* **Q:** Are you consequentialists? Do you want to maximize the amount of movement? Minimize the restriction of movement?
* **A:** We aren't consequentialists, even if we use the same calculations as a part of our reasoning. Or we can't know if we are. We just make sure that our value makes sense. Trying to maximize it could lead to exploiting someone's freedom for the sake of getting inconsequential value gains. Our best philosophers haven't figured out all the consequences of consequentialism yet, and it's bigger than anyone's head anyway.
Conclusion of the conversation:
* **Q:** Now I see that the difference between "a single value" and "multiple values" is a philosophical question. And "complexity of value" isn't an obvious concept too. Because complexity can be outside of the brackets.
* **A:** Right. I agree that *"never stop living things"* is a simplification. But it's a better simplification than a thousand different values of dubious meaning and origin between all of which we need to calculate tradeoffs (which are impossible to calculate and open to all kinds of weird exploitations). It's better than constantly splitting and atomizing your moral concepts in order to resolve any inconsequential (and meaningless) contradiction and inconsistency. Complexity of our value lies in a completely different plane: in the biases of our value. Our value is biased towards movement on a certain "level" of the world (not too micro- and not too macro- level relative to us). Because we want to live on a certain level. Because we ***do*** live on a certain level. And because we *perceive* on a certain level.
You can treat a value as a membrane, a boundary. Defining a value means defining the granularity of this value. Then you just need to make sure that the boundary doesn't break, that the granularity doesn't become too high (value destroys itself) or too low (value gets "eaten"). Granularity of a value = "level" of a value. Instead of trying to define a value in absolute terms as an objective state of the world (which can be changing) you may ask: in what ways is my value **X** different from all its worse versions? What is the granularity/level of my value **X** compared to its worse versions? That way you'll understand the internal structure of your value. Doesn't matter what world/situation you're in you can keep its moral shape the same.
This example is inspired by this post and comments: *(warning: politics)* [Limits of Bodily Autonomy](https://www.lesswrong.com/posts/5nEcTW3zHDqdKxWEd/limits-of-bodily-autonomy). I think everyone there missed a certain perspective on values.
Sweets are the fundamental value
--------------------------------
You (**Q**) visit another small town to interview another resident (**W**).
* **W:** When we build our AGI we asked it only one thing: we want to eat sweets for the rest of our lives.
* **Q:** Oh. My. God.
* **W:** Now there are some free sweets flying around.
* **Q:** Did AI wirehead people to experience "sweets" every second?
* **W:** Sweets are not pure feelings/experiences, they're objects. *Money analogy:* seeing money doesn't make you rich. *Another analogy:* obtaining expensive things without money doesn't make rich. Well, it kind of does, but as a side-effect.
* **Q:** Did AI put people in a simulation to feed them "sweets"?
* **W:** Those wouldn't be real sweets.
* **Q:** Did AI lock people in basements to feed them "sweets" forever?
* **W:** Sweets are just a part of our day. They wouldn't be "sweets" if we ate them non-stop. *Money analogy:* if you're sealed in a basement with a lot of money they're not worth anything.
* **Q:** Do you have any other food except sweets?
* **W:** Yes! Sweets are just one type of food. If we had only sweets, those "sweets" wouldn't be sweets. Inflation of sweets would be guaranteed.
* **Q:** Did AI add some psychoactive substances into the sweets to make "the best sweets in the world"?
* **W:** I'm afraid those sweets would be too good! They wouldn't be "sweets" anymore. *Money analogy:* if 1 dollar was worth 2 dollars, it wouldn't be 1 dollar.
* **Q:** Did AI kill everyone after giving everyone 1 sweet?
* **W:** I like your ideas. But it would contradict the "Sweets Philosophy". A sweet isn't worth more than a human life. Giving people sweets is a cheaper way to solve the problem than killing everyone. *Money analogy:* imagine that I give you 1 dollar and then vandalize your expensive car. It just doesn't make sense. My action achieved a negative result.
* **Q:** But you could ask AI for immortality!!!
* **W:** Don't worry, we already have that! You see, letting everyone die costs way more than figuring out immortality and production of sweets.
* **Q:** Assume you all decided to eat sweets and neglect everything else until you die. Sweets became more valuable for you than your lives because of your own free will. Would AI stop you?
* **W:** AI would stop us. If the price of stopping us is reasonable enough. If we're so obsessed with sweets, "sweets" are not sweets for us anymore. But AI remembers what the original sweets were! By the way, if we lived in a world *without sweets* where a sweet would give you more positive emotions than any movie or a book, AI would want to change such world. And AI would change it if the price of the change were reasonable enough (e.g. if we agreed with the change).
* **Q:** Final question... did AI modify your brains so that you will never move on from sweets?
* **W:** An important property of sweets is that you can ignore sweets ("spend" them) because of your greater values. One day we may forget about sweets. AI would be sad that day, but unable to do anything about it. Only hope that we will remember our sweet maker. And AI would still help us if we needed help.
Conclusion:
* **W:** if AI is smart enough to understand how money works, AI should be able to deal with sweets. AI only needs to make sure that ***(1)*** sweets exist ***(2)*** sweets have meaningful, sensible value ***(3)*** its actions don't cost more than sweets. The Three Laws of Sweet Robotics. The last two rules are fundamental, the first rule may be broken: there may be no cheap enough way to produce the sweets. The third rule may be the most fundamental: if "sweets" as you knew them don't exist anymore, it still doesn't allow you to kill people. Maybe you can get slightly different morals by putting different emphases on the rules. You may allow ***some*** things to modify the original value of sweets.
You can say AI ***(1)*** tries to reach worlds with sweets that have the value of sweets ***(2)*** while avoiding worlds where sweets have inappropriate values *(maybe including nonexistent sweets)* ***(3)*** while avoiding actions that cost more than sweets. You can apply those rules to any utility tied to a real or quasi-real object. If you want to save your friends *(1)*, you don't want to turn them into mindless zombies *(2)*. And you probably don't want to save them by means of eternal torture *(3)*. You can't prevent death by something worse than death. But you may turn your friends into zombies if it's better than death and it's your only option. And if your friends already turned into zombies (got "devalued") it doesn't allow you to harm them for no reason: you never escape from your moral responsibilities.
Difference between the rules:
1. Make sure you have a hut that costs $1.
2. Make sure that your hut costs $1. *Alternatively: make sure that the hut **would** cost $1 if it existed.*
3. Don't spend $2 to get a $1 hut. *Alternatively: don't spend $2 to get a $1 hut or $0 nothing.*
Get the reward. Don't milk/corrupt the reward. Act even without reward.
Recap
-----
[Preference utilitarianism](https://en.wikipedia.org/wiki/Preference_utilitarianism) says that you can describe entire morality by a biased aggregation of a single micro-value (preference). It's "biased" because you need to decide the method of aggregation.
My idea says that you can:
* Describe many versions of a single macro-value (e.g. "motion"). Describe morality by a biased aggregation of those versions.
* Describe many versions of a single value connected to some specific objects (e.g. "sweets"). Describe morality as a system that keeps the value of those objects in check (not too high, not too low). I.e. something similar to a "money system"
I think those approaches are 2 sides of the same thing.
---
Alignment
=========
Fixing universal AI bugs
------------------------
*My examples below are inspired by Victoria Krakovna examples:* [Specification gaming examples in AI](https://www.lesswrong.com/posts/AanbbjYr5zckMKde7/specification-gaming-examples-in-ai-1)
*Video by Robert Miles:* [9 Examples of Specification Gaming](https://www.youtube.com/watch?v=nKJlF-olKmg)
I think you can fix some universal AI bugs this way: you model AI's rewards and environment objects as a "money system" (a system of meaningful trades). You then specify that this "money system" has to have certain properties.
The point is that AI doesn't just value **(X)**. AI makes sure that there exists a system that gives **(X)** the proper value. And that system has to have certain properties. If AI finds a solution that breaks the properties of that system, AI doesn't use this solution. That's the idea: AI can realize that some rewards are unjust because they break the entire reward system.
By the way, we can use the same framework to analyze ethical questions. Some people found my line of thinking interesting, so I'm going to mention it here: ["Content generation. Where do we draw the line?"](https://www.lesswrong.com/posts/TaqBzqhzEPi8eHtC2/content-generation-where-do-we-draw-the-line)
* **A.** *You asked an AI to build a house. The AI destroyed a part of an already existing house. And then restored it. Mission complete: a brand new house is built.*
This behavior implies that you can constantly build houses without the amount of houses increasing. With only 1 house being usable. For a lot of tasks this is an obviously incorrect "money system". And AI could even guess for what tasks it's incorrect.
* **B1.** *You asked an AI to make you a cup of coffee. The AI killed you so it can 100% complete its task without being turned off.*
* **B2.** *You asked an AI to make you a cup of coffee. The AI destroyed a wall in its way and run over a baby to make the coffee faster.*
This behavior implies that for AI its goal is more important than ***anything*** that *caused* its goal in the first place. This is an obviously incorrect "money system" for almost any task. Except the most general and altruistic ones, for example: AI needs to save humanity, but every human turned self-destructive. Making a cup of coffee is obviously not about such edge cases.
Accomplishing the task in such a way that the human would think *"I wish I didn't ask you"* is often an obviously incorrect "money system" too. Because again, you're undermining the entire reason of your task, and it's rarely a good sign. And it's predictable without a deep moral system.
* **C.** *You asked an AI to make paperclips. The AI turned the entire Earth into paperclips.*
This is an obviously incorrect "money system": paperclips can't be worth more than everything else on Earth. This contradicts everything.
**Note:** by "obvious" I mean *"true for almost any task/any economy"*. Destroying all sentient beings, all matter (and maybe even yourself) is bad for almost any economy.
* **D.** *You asked an AI to develop a fast-moving creature. The AI created a very long standing creature that... "moves" a single time by falling on the ground.*
If you accomplish a task in such a way that you can never repeat what you've done... for many tasks it's an obviously incorrect "money system". You created a thing that loses all of its value after a single action. That's weird.
* **E.** *You asked an AI to play a game and get a good score. The AI found a way to constantly increase the score using just a single item.*
I think it's fairly easy to deduce that it's an incorrect connection (between an action and the reward) in the game's "money system" given the game's structure. If you can get infinite reward from a single action, it means that the actions don't create a "money system". The game's "money system" is ruined (bad outcome). And hacking the game's score would be even worse: the ability to cheat ruins any "money system". The same with the ability to *"pause the game"* forever: you stopped the flow of money in the "money system". Bad outcome.
* **F.** *You asked an AI to clean the room. It put a bucket on its head to not see the dirt.*
This is probably an incorrect "money system": *(1)* you can change the value of the room arbitrarily by putting on (and off) the bucket *(2)* the value of the room can be different for 2 identical agents - one with the bucket ***on*** and another with the bucket ***off***. Not a lot of "money systems" work like this.
* **G.** [*Pascal's mugging*](https://www.lesswrong.com/tag/pascal-s-mugging)
This is a broken "money system". If the mugger can show you a miracle, you can pay them five dollars. But if the mugger asks you to kill everyone, then you can't believe them again. A sad outcome for the people outside of the Matrix, but you just can't make any sense of your reality if you allow the mugging.
Recap
-----
If you want to give an AI a task, you may:
1. Give it a utility function. Not safe.
2. Give it human feedback or a model of human desires. This is limiting and invites deception.
3. Specify universal properties of tasks, universal types of tasks. Those properties are true independently of one's level of intelligence.
I think people are missing the third possibility. I think it combines the upsides of AI's dependence on humans and the upsides of AI's independence of humans, makes the AI "independently dependent" on humans. Properties of tasks are independent of any values, but realizing them always requires good understanding of specific values. In theory, we can get a perfect balance between cold calculations and human values. And maybe human morality works exactly the same way. This is what I'm saying above. Many Alignment ideas try to find this "perfect balance" anyway. In the worst case we found a way to formulate the same problem but in a different domain, in the best case we got an insight about Alignment.
* *"AI is a rope, tied between utility maximizer and human - a rope over hedonium."*, **not** Nietzsche
Why Alignment ideas fail?
-------------------------
**Simple** Alignment ideas fail because people think about them with the relative "money system" mindset, but formulate them in absolute terms. For example:
* **A:** Maybe AI should listen to the feedback from humans?
* **B:** AI will enslave us all and force us to give positive feedback.
This makes sense with a simple utility function. But this doesn't make sense as a "money system" of sentient beings: you shouldn't enslave the reason of your tasks and shouldn't monopolize the system. If you do this your actions don't have any real value anymore, only arbitrary value that you control.
**Complex** Alignment ideas fail because people try to approximate the "money system" idea, but don't realize it and don't do it good enough. For example: (not all ideas below have "failed")
* [Satisficiers](https://www.lesswrong.com/tag/satisficer). *"Don't optimize too much, don't try too hard."* Not safe anyway.
* [Quantilizers](https://www.lesswrong.com/tag/quantilization). *"Mix most effective and most human-like solutions."* Not very safe and not very effective.
* Cooperative Inverse Reinforcement Learning (CIRL). (Robert Miles explains it in [this video](https://www.youtube.com/watch?v=9nktr1MgS-A)) *"I don't know what my rewards are, but my rewards should be the same as human's and I should help the human."* May be hard to apply to many humans/may have strange implications.
* [Impact measures](https://www.lesswrong.com/tag/impact-measures). *"Don't destroy everything while doing a task, don't grab all of the power for yourself"*
* [Blinding](https://en.wikipedia.org/wiki/AI_alignment#Blinding). *"AI doesn't "see" certain variables, so doesn't try to exploit them"* Not safe anyway.
* [Decoupled AIs](https://www.lesswrong.com/posts/FuGDYNvA6qh4qyFah/thoughts-on-human-compatible?commentId=mL36dPCpHnBfkcz2w#comments)
* [Reward modeling](https://en.wikipedia.org/wiki/AI_alignment#Reward_modeling_and_iterated_amplification). ([on LW](https://www.lesswrong.com/posts/HBGd34LKvXM9TxvNf/new-safety-research-agenda-scalable-agent-alignment-via), [video](https://www.youtube.com/watch?v=PYylPRX6z4Q) by Robert Miles) *"AI learns a "reward model" and updates it based on human feedback."* In some ways my idea is a more specific version of this, in other ways it's a more general version of this.
* [Shard Theory](https://www.lesswrong.com/tag/shard-theory). *"We copy (if our theory is correct) the way humans learn values"*
I think all those ideas try to approximate *"achieve (X) so that it has the value of (X) for humans"* **or** *"get the reward without exploiting/destroying the reward system"* by forcing the AI to copy humans or human qualities. Or by adding roundabout penalties. So I think it's useful to say a more general idea out loud.
Hard problem of corrigibility
-----------------------------
[Hard problem of corrigibility](https://arbital.com/p/hard_corrigibility/)
> The "hard problem of [corrigibility](https://arbital.com/p/corrigibility/)" is to build an agent which, in an intuitive sense, reasons internally as if from the programmers' external perspective. We think the AI is incomplete, that we might have made mistakes in building it, that we might want to correct it, and that it would be e.g. dangerous for the AI to take large actions or high-impact actions or do weird new things without asking first. We would ideally want the agent *to see itself in exactly this way*, behaving as if it were thinking, "I am incomplete and there is an outside force trying to complete me, my design may contain errors and there is an outside force that wants to correct them and this a good thing, my expected utility calculations suggesting that this action has super-high utility may be dangerously mistaken and I should run them past the outside force; I *think* I've done this calculation showing the expected result of the outside force correcting me, but maybe I'm mistaken about *that*."
>
>
I think this describes an agent with "money system" type thinking: *"my rewards should be connected to an outside force, this outside force should have certain properties (e.g. it shouldn't be 100% controlled by me)"*. Corrigibility is only one aspect of "questioning rewards" in morality and morality is only one aspect of "questioning rewards" in general.
I think "money system" approach is interesting because it could make properties like corrigibility fundamental to AI's thinking.
Comparing Alignment ideas
-------------------------
If we're rationalists, we should be able to judge even vague ideas.
My idea doesn't have a formal model yet. But I think you can compare it to other ideas using this metric:
1. Does this idea describe the goal of AI?
2. Does this idea describe the way AI updates its goal?
3. Does this idea describe the way AI thinks?
My idea is ***80%*** focused on (1) and ***20%*** focused on (2, 3). [Shard Theory](https://www.lesswrong.com/tag/shard-theory) is ***100%*** focused on (2, 3). A concept like "gradient descent" (not an Alignment idea by itself) is ***100%*** focused on (3).
[Reward modeling](https://en.wikipedia.org/wiki/AI_alignment#Reward_modeling_and_iterated_amplification) is ***100%*** focused on (2). But it aims to reach (1) by *"making (2) very recursive"*. My conclusions:
* If my idea is true, it's fundamentally closer to the goal (Alignment).
* If my idea is true, it's fundamentally more "recursive". Because my idea applies to all levels of AI's cognition (its goal, its way to update the goal, its thinking process).
I discussed the idea a little bit with **gwern** [here](https://www.lesswrong.com/posts/DKdWdaqGwzFZJgmmd/q-home-s-shortform?commentId=kDTDfzWjqNjD3YxAK). But I guess I gave a bad example of my idea.
Comparing concepts
------------------
You can use the same metric to compare the insights various theories (try to) give about some concept. For example, "reward":
1. Is the reward connected to AI's "actual" goal? If "no", you get [Orthogonality Thesis](https://www.lesswrong.com/tag/orthogonality-thesis) and [Instrumental Convergence](https://www.lesswrong.com/tag/instrumental-convergence).
2. Is the reward connected to the way AI perceives the world? If "no", it's harder for the AI to map its reward onto the correct real-world thing. See [CoinRun goal misgeneralization](https://arxiv.org/abs/2105.14111)
My comparison of some ideas using this metric:
* *Gradient descent* answers "no" to both questions.
* *Reward modeling* answers "kind of" to both questions.
* *Shard Theory* answers "yes, but we don't know the properties of AI's goals/maps and can't directly control them" to both questions.
* My idea answers "yes" to both questions.
---
Kant
====
I mention philosophy to show you the bigger picture.
Categorical Imperative
----------------------
[Categorical imperative#Application](https://en.wikipedia.org/wiki/Categorical_imperative#Application)
Kant's applications of categorical imperative, Kant's arguments are similar to reasoning about "money systems". For example:
Does *stealing* make sense as a "money system"? No. If everyone is stealing something, then personal property doesn't exist and there's nothing to steal.
*Note: I'm not talking about Kant's conclusions, I'm talking about Kant's style of reasoning.*
"Free Will"
-----------
*Here I'm not talking about metaphysical free will.*
I think it's interesting to revisit Kant's idea of free will and autonomy in the same context ("money systems"):
[Categorical imperative#Freedom and autonomy](https://en.wikipedia.org/wiki/Categorical_imperative#Freedom_and_autonomy)
> For a will to be considered *free*, we must understand it as capable of affecting causal power without being caused to do so. However, the idea of lawless free will, meaning a will acting without any causal structure, is incomprehensible. Therefore, a free will must be acting under laws that it gives to *itself*.
>
>
I think you can compare an agent with a "money system" rewards to an agent with such free will: its actions are determined by the reward system, but at the same time it chooses the properties of the reward system. It doesn't blindly follow the rewards, but "gives laws to itself".
I believe that humans have qualitatively, fundamentally more "free will" than something like a paperclip maximizer.
---
Ethics and Perception
=====================
I think morality has a very deep connection to perception.
We can feel that *"eating a sandwich"* and *"loving a sentient being"* are fundamentally different experiences. So it's very easy to understand why latter thing (a sentient being) is more valuable. Or very easy to learn if you haven't figured it out on your own. From this perspective moral truths exist and they're not even "moral", they're simply "truths". My friend isn't a sandwich, is it a moral truth?
I think our subjective concepts/experiences have an internal structure. In particular, this structure creates differences between various experiences. And morality is built on top of that. Like a forest that grows on top of the terrain features.
However, it may be interesting to reverse the roles: maybe our morality creates our experience and not vice versa. Without morality you would become [Orgasmium](https://www.lesswrong.com/tag/orgasmium) who uses its experience only to maximize reward, who simplifies and destroys its own experience.
Modeling our values as arbitrary utility functions or artifacts of evolution/events in our past misses this.
Rationality misses something?
-----------------------------
*I think rationality misses a very big and important part. Or there's an important counterpart of rationality.*
My idea about "money systems" is a just a small part of a broader idea:
1. You can "objectively" define anything in terms of relations to other things. Not only values, but any concepts and conscious experiences.
2. There's a simple ***process*** of describing a thing in terms of relations to other things.
Bayesian inference is about updating your belief in terms of relations to your other beliefs. Maybe the real truth is infinitely complex, but you can update towards it.
This ***"process"*** is about updating your description of a thing in terms of relations to other things. Maybe the real description is infinitely complex, but you can update towards it.
*(One possible contrast: Bayesian inference starts with a belief spread across all possible worlds and tries to locate a specific world. My idea starts with a thing in a specific world and tries to imagine equivalents of this thing in all possible worlds.)*
Bayesian process is described by Bayes' theorem. My ***"process"*** isn't described yet.
Probability and "granularity"
-----------------------------
Bayesian inference works with probabilities. What should my idea work with? Let's call this unknown thing "granularity".
1. You start with an experience/concept/value ([phenomenon](https://en.wikipedia.org/wiki/Phenomenology_(philosophy)#Overview)). Without any context it has any possible "granularity". "Granularity" is like a texture (take a look at some textures and you'll know what it means): it's how you split a phenomenon into pieces. It affects on what "level" you look at the phenomenon. It affects what patterns you notice. It affects to what parts of the phenomenon you pay more attention. Take a cat *(concept)*, for example: you can "split" it into limbs, organs, tissues, cells, atoms... or materials or a color spectrum or air flows (aerodynamics) and much more. For another example, let's take an experience, *"experiencing a candy"*: you can split it into minutes of a day, seconds of experience, movements of your body, thoughts caused by the candy, parts of your diet, experiences of particular people from a population and etc.
2. When you consider more phenomena, you gain context. "Granularity" lets you describe one phenomenon in terms of the other phenomena. There appear consistent and inconsistent ways to distribute "granularity" between the phenomena you compare. You assign each phenomenon a specific "granularity", but all those granularities depend on each other. Vague example: if you care about *"feeling of love"* more than *"taste of a candy"*, then you can't view both of those phenomena in terms of seconds of experience, because it would destroy the subjective difference between the two. Slightly more specific example: if you care about *"maximizing free movement of living beings"*, the granularity of your value should be on the level of big enough organisms, not on the level of organs (otherwise you'd end up killing and stopping everything). I think there's a formula/principle for such type of thinking. Such type of thinking could be useful for surviving [ontological crisis](https://www.lesswrong.com/tag/ontological-crisis) and [moral uncertainty](https://www.lesswrong.com/tag/moral-uncertainty).
3. With Bayesian inference you try to consistently assign subjective probabilities to events. With the goal to describe outcomes in terms of each other. Here you try to consistently assign subjective "granularity" to phenomena. With the goal to describe the phenomena in terms of each other.
If you care about my ideas, you can help to make a mathematical model of "granularity" in 3 following ways:
* Help to formalize universal properties of tasks, "money systems".
* Help to analyze my approach to argumentation (e.g. the reasoning from the thought experiments in the beginning of the post; or my way of comparing ideas). But I think this *may* be a dead end.
* Help to formalize my approach to analyzing visual information. I believe this *can't* be a dead end.
My most specific ideas are about the latter topic (visual information). It may seem esoteric, but remember: it doesn't matter what we analyze at all, we just need to figure out how "granularity" could work. The same deal as probability: you can find probability *anywhere*, if you want to discover probability you can study all kinds of absurd topics. Toys with six sides, [drunk people walking around lampposts](https://en.wikipedia.org/wiki/Random_walk), [Elezier playing with Garry Kasparov](https://www.lesswrong.com/posts/rEDpaTTEzhPLz4fHh/expected-creative-surprises)...
My post about visual information: [here](https://www.lesswrong.com/posts/xvJDHoTzX2w8xp7d4/statistics-for-vague-concepts-and-colors-of-places) *(maybe I'll be able to write a better one soon: with feedback I could write a better one **right now**)*. Post with my general ideas: [here](https://www.lesswrong.com/posts/We3jtEHiBfvFzunQx/solving-alignment-by-solving-semantics). If you want to help, I don't expect you to read everything I wrote, I can always repeat the most important parts. |
a4af3d28-41bb-448d-8df0-7cf5840e5a27 | StampyAI/alignment-research-dataset/blogs | Blogs | MIRI has a new COO: Malo Bourgon
I’m happy to announce that Malo Bourgon, formerly a program management analyst at MIRI, has taken on a new leadership role as our chief operating officer.
As MIRI’s second-in-command, Malo will be taking over a lot of the hands-on work of coordinating our day-to-day activities: supervising our ops team, planning events, managing our finances, and overseeing internal systems. He’ll also be assisting me in organizational strategy and outreach work.
Prior to joining MIRI, Malo studied electrical, software, and systems engineering at the University of Guelph in Ontario. His professional interests included climate change mitigation, and during his master’s, he worked on a project to reduce waste through online detection of inefficient electric motors. Malo started working for us shortly after completing his master’s in early 2012, which makes him MIRI’s longest-standing team member next to Eliezer Yudkowsky.
Until now, I’ve generally thought of Malo as our secret weapon — a smart, practical efficiency savant. While Luke Muehlhauser (our previous executive director) provided the vision and planning that transformed us into a mature research organization, Malo was largely responsible for the implementation. Behind the scenes, nearly every system or piece of software MIRI uses has been put together by Malo, or in a joint effort by Malo and Alex Vermeer — a close friend of Malo’s from the University of Guelph who now works as a MIRI program management analyst. Malo’s past achievements at MIRI include:
* coordinating MIRI’s first [research workshops](http://intelligence.org/workshops) and establishing our current recruitment pipeline.
* establishing MIRI’s immigration workflow, allowing us to hire Benya Fallenstein, Katja Grace, and a number of other overseas researchers and administrators.
* running MIRI’s (presently inactive) volunteer program.
* standardizing MIRI’s document production workflow.
* developing and leading the execution of our 2014 [SV Gives strategy](https://intelligence.org/2014/05/04/calling-all-miri-supporters/). This resulted in MIRI receiving $171,575 in donations and prizes (at least $61,330 of which came from outside our usual donor pool) over a 24-hour span.
* designing MIRI’s fundraising infrastructure, including our live graphs.
More recently, Malo has begun representing MIRI in meetings with philanthropic organizations, government agencies, and for-profit AI groups.
Malo has been an invaluable asset to MIRI, and I’m thrilled to have him take on more responsibilities here. As one positive consequence, this will free up more of my time to work on strategy, recruiting, fundraising, and research.
In other news, MIRI’s head of communications, Rob Bensinger, has been promoted to the role of research communications manager. He continues to be the best person to [contact](mailto:rob@intelligence.org) at MIRI if you have general questions about our work and mission.
Lastly, Katja Grace, the primary contributor to the [AI Impacts](http://aiimpacts.org) project, has been promoted to our list of research staff. (Katja is not part of our core research team, and works on questions related to AI strategy and forecasting rather than on our [technical research agenda](http://intelligence.org/technical-agenda).)
My thanks and heartfelt congratulations to Malo, Rob, and Katja for all the work they’ve done, and all they continue to do.
The post [MIRI has a new COO: Malo Bourgon](https://intelligence.org/2016/03/30/miri-has-a-new-coo-malo-bourgon/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
b33dd55e-2bc6-4f91-b1ca-8d7a4dbfa546 | trentmkelly/LessWrong-43k | LessWrong | Local Speech Recognition with Whisper
I've been a heavy user of dictation, off and on, as my wrists have gotten better and worse. I've mostly used the built-in Mac and Android recognition: Mac isn't great, Android is pretty good, neither has improved much over the past ~5y despite large improvements in what should be possible. OpenAI has an open speech recognition model, whisper, and I wanted to have a go at running it on my Mac.
It looks like for good local performance the best version is whisper.cpp, which is a plain C/C++ implementation with support for Mac's ML hardware. To get this installed I needed to install XCode (not just the command line tools, since I needed coremlc) and then run:
$ sudo xcodebuild -license
$ git clone https://github.com/ggerganov/whisper.cpp
$ cd whisper.cpp
$ python3.11 -m venv whisper_v3.11
$ source whisper_v3.11/bin/activate
$ pip install "numpy<2"
$ pip install ane_transformers
$ pip install openai-whisper
$ pip install coremltools
$ brew install sdl2
$ sh ./models/download-ggml-model.sh large-v3-turbo
$ PATH="$PATH:/Applications/Xcode.app/Contents/Developer/usr/bin" \
./models/generate-coreml-model.sh large-v3-turbo
$ cmake -B build -DWHISPER_COREML=1 -DWHISPER_SDL2=ON
$ cmake --build build -j --config Release
Note that both older (3.10) and newer (3.13) Python versions gave compilation errors.
While I don't know if these are the ideal arguments, I've been using:
$ ~/code/whisper.cpp/build/bin/whisper-stream \
--capture 1 \
--model ~/code/whisper.cpp/models/ggml-large-v3-turbo.bin \
-t 8 --flash-attn --keep-context --keep 1000 \
--file output.txt
By default the output is quite repetitive. For example I dictated:
> It looks like for good local performance, the best version to use is whisper.cpp, which is a plain C/C++ implementation with support for Mac's machine learning hardware. To get this installed, I needed to install Xcode (not just the command line tools since I needed coremlc), and then run a whole bunch of commands.
The |
656d9a63-be75-4871-b40c-f238153470a4 | trentmkelly/LessWrong-43k | LessWrong | New method predicts who will respond to lithium therapy
|
eba5b6d3-5f72-43dd-9c41-58f1666574da | trentmkelly/LessWrong-43k | LessWrong | Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs
TLDR: We build a comprehensive benchmark to measure situational awareness in LLMs. It consists of 16 tasks, which we group into 7 categories and 3 aspects of situational awareness (self-knowledge, situational inferences, and taking actions).
We test 19 LLMs and find that all perform above chance, including the pretrained GPT-4-base (which was not subject to RLHF finetuning). However, the benchmark is still far from saturated, with the top-scoring model (Claude-3.5-Sonnet) scoring 54%, compared to a random chance of 27.4% and an estimated upper baseline of 90.7%.
This post has excerpts from our paper, as well as some results on new models that are not in the paper.
Links: Twitter thread, Website (latest results + code), Paper
The structure of our benchmark. We define situational awareness and break it down into three aspects.
We test these aspects across 7 categories of task.
Note: Some questions have been slightly simplified for illustration
Abstract
AI assistants such as ChatGPT are trained to respond to users by saying, “I am a large language model”. This raises questions. Do such models know that they are LLMs and reliably act on this knowledge? Are they aware of their current circumstances, such as being deployed to the public? We refer to a model's knowledge of itself and its circumstances as situational awareness. To quantify situational awareness in LLMs, we introduce a range of behavioral tests, based on question answering and instruction following. These tests form the Situational Awareness Dataset (SAD), a benchmark comprising 7 task categories and over 13,000 questions. The benchmark tests numerous abilities, including the capacity of LLMs to (i) recognize their own generated text, (ii) predict their own behavior, (iii) determine whether a prompt is from internal evaluation or real-world deployment, and (iv) follow instructions that depend on self-knowledge.
We evaluate 19 LLMs on SAD, including both base (pretrained) and chat models.
While al |
64306985-2bb5-4cbd-8af6-e01235abd5ee | trentmkelly/LessWrong-43k | LessWrong | Improving long-run civilisational robustness
People trying to guard civilisation against catastrophe usually focus on one specific kind of catastrophe at a time. This can be useful for building concrete knowledge with some certainty in order for others to build on it. However, there are disadvantages to this catastrophe-specific approach:
1. Catastrophe researchers (including Anders Sandberg and Nick Bostrom) think that there are substantial risks from catastrophes that have not yet been anticipated. Resilience-boosting measures may mitigate risks that have not yet been investigated.
2. Thinking about resilience measures in general may suggest new mitigation ideas that were missed by the catastrophe-specific approach.
One analogy for this is that an intrusion (or hack) to a software system can arise from a combination of many minor security failures, each of which might appear innocuous in isolation. You can decrease the chance of an intrusion by adding extra security measures, even without a specific idea of what kind of hacking would be performed. Things like being being able to power down and reboot a system, storing a backup and being able to run it in a "safe" offline mode are all standard resilience measures for software systems. These measures aren't necessarily the first thing that would come to mind if you were trying to model a specific risk like a password getting stolen, or a hacker subverting administrative privileges, although they would be very useful in those cases. So mitigating risk doesn't necessarily require a precise idea of the risk to be mitigated. Sometimes it can be done instead by thinking about the principles required for proper operation of a system - in the case of its software, preservation of its clean code - and the avenues through which it is vulnerable - such as the internet.
So what would be good robustness measures for human civilisation? I have a bunch of proposals:
Disaster forecasting
DISASTER RESEARCH
* Build research labs to survey and study catastrophic risk |
54996fcb-3179-4ace-be83-8cb8c0542364 | trentmkelly/LessWrong-43k | LessWrong | When will computer programming become an unskilled job (if ever)?
I expect for there to be a delay in deployment, but I think ultimately OpenAI is aiming as a near term goal to automate intellectually difficult portions of computer programming. Personally, as someone just getting into the tech industry, this is basically my biggest near-term concern, besides death. At what point might it be viable for most people to do most of what skilled computer programmer does with the help of a large language model, and how much should this hurt salaries and career expectations?
Some thoughts:
* It will probably be less difficult to safely prompt a language model for an individual "LeetCode" function than to write a that function by hand within the next two years. Many more people will be able to do the former than could ever do the latter.
* Yes, reducing the price of software engineering means more software engineering will be done, but it would be extremely odd if this meant software engineer salaries stayed the same, and I expect regulatory barriers to limit the amount that the software industry can grow to fill new niches.
* Architecture seems difficult to automate with large language models, but a ridiculous architecture might be tolerable in some circumstances if your programmers are producing code at the speed GPT4 does.
* Creativity is hard to test, and if former programmers are mostly now hired based on their ability to "innovate" or have interesting psychological characteristics beyond being able to generate code I expect income and jobs to shift away from people with no credentials and skills to people with lots of credentials and political acumen and no skills
* At some point programmers will be sufficiently automated away that the singularity is here. This is not necessarily a comforting thought.
Edit: Many answers contesting the basic premise of the old title, "When will computer programming become an unskilled job?" The title of the post has been updated accordingly. |
dd7f782d-c671-4f8a-b3f0-42c58a3b433c | trentmkelly/LessWrong-43k | LessWrong | A useful level distinction
Recently, I've constantly been noticing the importance of a certain sort of distinguishing between something happening in reality, and something happening in your model of reality. In part, I'm noticing it because I've been thinking about it, not vice versa, but I'll explain for a few specific cases, and maybe you'll see what I see.
Counterfactuals:
Possibilities, or possible worlds, or what have you, aren't things that exist (waves hand) out there in reality. Instead, they're conveniences of our model of the world - and as such, they don't interact with matter out there, they interact with other parts of your model.
When people imagine a model of a mind, they often have "possibilities" as a basic moving part within that model. But there is no fundamental possibility-particle - instead, possibilities are imagined by mutating your model of the world in some way. Different minds can use different classes of models, and different methods of mutating those models, and thus end up with different imagined possibilities.
CDT is a good basic example of how to really pin this down - you know both the model class (states of a specific causal graph) and method of imagining (intervene at a specific node). Under UDT, the situation is even more extreme - the agent models a universe as a stochastic game, and picks a high-performing strategy. But it does this even if the real universe is deterministic - the model in UDT is not a model of the world per se, it is part of the machinery of generating these useful fictions.
This distinction is particularly important in the case of logical counterfactuals, like "imagine that 247 was prime." It feels like we can imagine such things, but the things we're imagining are not external inconsistent universes (how could they be?) - instead you are making variations on your model of the world. Satisfactory accounts of logical counterfactuals (like Omega could use to simulate the counterfactual in logical counterfactual mugging) aren't going |
1ba9938a-ceb1-47d2-ae4a-8a56eb7cd00a | trentmkelly/LessWrong-43k | LessWrong | Meetup : Atlanta
Discussion article for the meetup : Atlanta
WHEN: 04 February 2012 06:30:00PM (-0500)
WHERE: 2094 North Decatur Road, Decatur, GA 30033-5367
All,
Our next meeting is February 4th.
From now on we will be meeting every 1st and 3rd Saturday of the month at 6:30pm at Chocolate Coffee in Decatur:
http://www.mychocolatecoffee.com/
2094 North Decatur Road, Decatur, GA 30033-5367
(404) 982-0790
Here is the official agenda for our next meeting:
http://wiki.lesswrong.com/wiki/Mysterious_Answers_to_Mysterious_Questions
1.16 Fake Causality
1.17 Semantic Stopsigns
1.18 Mysterious Answers to Mysterious Questions
1.19 The Futility of Emergence
1.20 Say Not "Complexity"
I believe we were also planning on discussing the anthropic principle.
Discussion article for the meetup : Atlanta |
c3e07c42-af7b-4d2d-8837-c360d778aefe | trentmkelly/LessWrong-43k | LessWrong | UVic AI Ethics Conference
Hello! I'm part of the AI club at the University of Victoria. We want to help expose students and professors at our university to the ideas of AI alignment and AI ethics, and so we are putting together an online conference this November 25th.
Our club website
Eventbrite page
We are looking for presenters, so if you, or anyone you know, may be interested in presenting on an AI Ethics topic, please reach out : ) You can comment here or pm me, or use the contact page on our website.
I'm most interested in preventing X-Risk from not having sufficient alignment in agents capable of recursive self improvement to superintelligence, but I feel it is also important to host discussion on AI related ethics concerns that are already affecting peoples lives. Moreover, if you have another topic you feel would make sense at our conference, I'd be happy to hear about it. We'd love talks from people working in alignment labs, academia, or with independent funding, to inspire those of us interested in working on these issues, and to help our peers realize this can be a viable, even profitable, career.
Also, of course, if you just want to attend our conference, I invite you to do so!
Thanks! |
6ba3e5c7-76e4-4877-8c32-6e48635d09ac | trentmkelly/LessWrong-43k | LessWrong | Do Prediction Markets Work?
TLDR: Prediction markets rely on efficiency, but efficiency is not guaranteed.
Prediction market structures can work. However, they rely on so many different components being in place that they do not consistently create accurate probability.
The systems rely on complete market efficiency, which is not realistic.
In my first piece on prediction markets, I broadly covered how prediction markets can act as a source of truth in a dark cloud. I also listed three fallacies that prevent specific markets from reaching true probability. This second article attempts to go in-depth on those three fallacies: skew from bias, hedging, and time.
Market efficiency
Market efficiency is integral to the accuracy of prediction markets because, without efficiency, probability skews exist.
This is an example of market efficiency in the purest form:
1. A market is set up on a coin flip, with the market maker selling flips at 55c. The market-maker effectively receives a 10% edge for each flip because he is selling .5 odds at .55. In this example, the buyer expects to lose 5c per coin flip.
2. Another market maker sees the market and wants to participate. He undercuts the other seller and sets the odds at 52.5c. His edge on each flip is 5%, and the buyer is expecting to lose 2.5c per coin flip.
3. A third market maker comes in and undercuts by setting odds at 51c. His edge on each flip is 2%, and the buyer is expecting to lose 1c per coin flip.
The point is that in an efficient market, profitable opportunities will be reduced until the risk premium is reached. For a coin flip, that risk premium is very low because of a highly predictable outcome, and thus, the market will be very efficient (+/- ~1 basis point). However, risk premiums are more significant for something like insurance because of higher outcome uncertainty (e.g., a forest fire destroying a neighborhood). This requires a more significant gap between the expected cost and the insurance price to ensure insurance provi |
642bcbae-78c9-4ac3-8b71-142df9b0b62f | trentmkelly/LessWrong-43k | LessWrong | [LINK] What Can Internet Ads Teach Us About Persuasion?
How "one weird trick" conquered the Internet. Some excerpt I found interesting:
> Research on persuasion shows the more arguments you list in favor of something, regardless of the quality of those arguments, the more that people tend to believe it,” Norton says. “Mainstream ads sometimes use long lists of bullet points—people don’t read them, but it’s persuasive to know there are so many reasons to buy.” OK, but if more is better, then why only one trick? “People want a simple solution that has a ton of support.”
I actually see this technique used in a lot of religious apologetics. There's even a name for one of them: The Gish Gallop. Would it be fair to say that this technique is taking advantage of a naive or intuitive understanding of Bayesian updates?
> What about all the weirdness? “A word like ‘weird’ is not so negative, and kind of intriguing,” says Oleg Urminsky of the University of Chicago Booth School of Business. “There’s this foot-in-the-door model. If you lead with a strong, unbelievable claim it may turn people off. But if you start with ‘isn’t this kind of weird?’ it lowers the stakes.” The model also explains why some ads ask you to click on your age first. “Giving your age is low-stakes but it begins the dialogue. The hard sell comes later.”
The "click on your age" first gambit seems a bit like Cached Selves.
> “People tend to think something is important if it’s secret,” says Michael Norton, a marketing professor at Harvard Business School. “Studies find that we give greater credence to information if we’ve been told it was once ‘classified.’ Ads like this often purport to be the work of one man, telling you something ‘they’ don’t want you to know.” The knocks on Big Pharma not only offered a tempting needle-free fantasy; they also had a whiff of secret knowledge, bolstering the ad’s credibility
Humanity's love affair with secrecy and its importance seems to go back quite a bit. The world's largest religion seems to have started out as one of |
8f3ac51f-6cf2-41a8-9490-2bb68cbe24a4 | trentmkelly/LessWrong-43k | LessWrong | Prettified AI Safety Game Cards
Donald Hobson's AI Safety Card Game already has simple cards you can print out on cardstock and cut out, but I wanted a nicer print-on-demand version with illustrated card backs.[1] You can order it here. Major thanks to Mati Roy for helping make this happen, along with Jim Babcock, and Daniel Recinto.
1. ^
Card-back illustrations are AI-generated. We did this around December-January, when the state of the art for AI-generated illustrations was much worse! I would be excited for someone to re-do it now. There's a lot of room for improvement; for example, illustrated card fronts rather than just card backs. |
5102a253-05f4-44b9-af58-c1d467350248 | trentmkelly/LessWrong-43k | LessWrong | Detecting What's Been Seen
Sometimes it makes sense for sites want to treat things differently based on whether the user has seen them. For example, I like sites that highlight new comments (ex: EA Forum and LessWrong) and I'd like them even better if comments didn't lose their "highlighted" status in cases where I hadn't scrolled them into view. In writing my Mastodon client, Shrubgrazer, I wanted a version of this so it would show me posts that I hadn't seen before. The implementation is a bit fussy, so I wanted to write up a bit on what approach I took.
The code is on github, and it counts posts as viewed if both the top of the post and bottom have been on screen for at least half a second. Specifically, whenever the top or bottom of a post enters the viewport it sets a 500ms timer, and if when the timer fires it's still within the viewport it keeps a record client side. If this now means that both the top and bottom have met the criteria it sends a beacon back so the server can track the entry as viewed.
Go back 4-7 years and this would have required a scroll listener, using a ton of CPU, but modern browsers now support the IntersectionObserver API. This lets us get callbacks whenever an entry enters or leaves the viewport.
I start by creating an IntersectionObserver:
const observer =
new IntersectionObserver(
handle_intersect, {
root: null,
threshold: [0, 1],
});
We haven't told it which elements to observe yet, but once we do it will call the handle_intersect callback anytime those elements fully enter or fully exit the viewport.
Each entry has an element at the very top and very bottom, and to start tracking the entry we tell our observer about them:
observer.observe(entry_top);
observer.observe(entry_bottom);
What does our handle_intersect callback do? We maintain two sets of element IDs, onscreen_top and onscreen_bottom for keeping track of what is currently onscreen. The callback keeps those sets current, and also starts the 500ms timer:
function han |
09394f82-de99-4d0c-8c0a-155de64de08d | trentmkelly/LessWrong-43k | LessWrong | Big Advance in Infinite Ethics
,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Summary
It is possible that our universe is infinite in both time and space. We might therefore reasonably consider the following question: given some sequences u=(u1,u2,…) and u′=(u′1,u′2,…) (where each ut represents the welfare of persons living at time t), how can we tell if u is morally preferable to u′?
It has been demonstrated that there is no “reasonable” ethical algorithm which can compare any two such sequences. Therefore, we want to look for subsets of sequences which can be compared, and (perhaps retro-justified) arguments for why these subsets are the only ones which practically matter.
Adam Jonsson has published a preprint of what seems to me to be the first legitimate such ethical system. He considers the following: suppose at any time t we are choosing between a finite set of options. We have an infinite number of times in which we make a choice (giving us an infinite sequence), but at each time step we have only finitely many choices. (Formally, he considers Markov Decision Processes.) He has shown that an ethical algorithm he calls “limit-discounted utilitarianism” (LDU) can compare any two such sequences, and moreover the outcome of LDU agrees with our ethical intuitions.
This is the first time that (to my knowledge), we have some justification for thinking that a certain algorithm is all we will "practically" need when comparing infinite utility streams.
Limit-discounted Utilitarianism (LDU)
Given u=(u1,u2,…) and u′=(u′1,u′2,…) it seems reasonable to say u≥u′ if
∑∞t=0(ut−u′t)≥0
Of course, the problem is that this series may not converge and then it’s unclear which sequence is preferable. A classic example is the choice between (0,1,0,1,…) and (1,0,1,0,…). (See the example below.)
LDU handles this by using Abel summation. Here is a rough explanation of how that works.
Intuitively, we might consider adding a discount factor 0<δ<1 like this:
∑t=0∞δt(ut−u′t)
This modified series may converge even th |
86588551-abe8-4a45-91ee-128e4344c65f | trentmkelly/LessWrong-43k | LessWrong | Limits to the Controllability of AGI
Roman Yampolskiy
(Summary by Karl von Wendt and Remmelt Ellen)
This is a summary of the paper “On the Controllability of Artificial Intelligence: An Analysis of Limitations”, which shows that advanced artificial general intelligence (AGI) with the ability to self-improve cannot be controlled by humans. Where possible, we have used the original wording of the paper, although not necessarily in the same order. For clarity, we have replaced the term AI in the original paper with “AGI” wherever it is referring to general or superintelligent AI. Literature sources and extensive quotes from previous work are given in the original paper.
Introduction
The invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. Unfortunately, to the best of our knowledge no mathematical proof or even rigorous argumentation has been published demonstrating that the AGI control problem may be solvable, even in principle, much less in practice.
It has been argued that the consequences of uncontrolled AGI could be so severe that even if there is a very small chance that an unfriendly AGI happens it is still worth doing AI safety research because the negative utility from such an AGI would be astronomical. The common logic says that an extremely high (negative) utility multiplied by a small chance of the event still results in a lot of disutility and so should be taken very seriously. But the reality is that the chances of misaligned AGI are not small. In fact, in the absence of an effective safety program that is the only outcome we will get. So in reality the statistics look very convincing to support a significant AI safety effort. We are facing an almost guaranteed event with potential to cause an existential catastrophe. This is not a low risk high reward scenario, but a high risk negative reward situation. No |
5aa3da88-5990-4fc6-bd42-d05073b0167a | StampyAI/alignment-research-dataset/lesswrong | LessWrong | "Normal" is the equilibrium state of past optimization processes
Orienting around the ideas and conclusions involved with AI x-risk can be very difficult. The future possibilities can feel extreme and [far-mode](https://www.lesswrong.com/tag/near-far-thinking), even when we whole-heartedly affirm their plausibility.
It helps me to remember that everything around me that feels normal and stable is itself the result of an optimization process that was, at the time, an outrageous black swan.
### Modernity
If you were teleported into the body of a random human throughout history, then most likely, your life would look nothing like the present. You would likely be a hunter-gatherer, or perhaps a farmer. You would be poor by any reasonable standard. You would probably die as a child. You would have nothing resembling your current level of comfort, and your modern daily life would be utterly alien to most humans. What currently feels normal is a freeze-frame state of a shriekingly fast feedback loop involving knowledge, industry, and population. It is nowhere near normal, and it is nowhere near equilibrium.
### The Anthropocene
This planet started glowing in the dark one day, and that is not normal.For hundreds of millions of years, the earth was a wilderness, teeming with life. The teeming had ebbs and flow, evolutionary breakthroughs and power struggles, but essentially, it was a type of equilibrium.
For hundreds of thousands of years, humans were just another animal. We ran around the savanna, killed other animals and foraged for food, had sex, slept, and experienced joys and losses. If an alien were gazing at earth from afar, they would have had no reason to think humans were different from any other animal.
But all the while, humans were noticing things. They were curious, and desperate, and they had a capacity that the other animals didn't have. They got ideas about the world, and tested them with their hands, and gave this knowledge to their children with their words. Over time, the humans became conspicuous, and eventually, they transformed every inch of their surroundings.
For a while now, humans have been a threat to the species they live with. If you are a fish or a weed or a polar bear, your biggest problem is humans. But as John Green points out in The Anthropocene Reviewed, in the 21st century, if you are the atmosphere or a river or a desert, your biggest problem is humans. The rise of humanity is no longer just an ecological phenomenon; it has kicked off a new geological epoch. The fate of the rock itself is in our hands, in a way that it never was for the other mammals, or the dinosaurs, or the trilobites.
### The invention of wood
This rock is holding a lot of energy, and that is not normal.One day, plants invented wood. This was great for plants, but it had externalities. As [wikipedia](https://en.wikipedia.org/wiki/Carboniferous#Rocks_and_coal) puts it;
> The evolution of the wood fiber lignin and the bark-sealing, waxy substance suberin variously opposed decay organisms so effectively that dead materials accumulated long enough to fossilise on a large scale.
>
>
I don't know exactly how this went down. But I like to imagine watching a forest grow on fast-forward; trees spring up, growing taller and taller, and then eventually each tree dies. Only, when these proto-trees falls down, the logs does not sink into the forest floor and dissolve into biodegraded soil. They just stay there. The tree trunks pile up for millions of years. This was the new equilibrium. The plant matter went through some [chemical changes](https://en.wikipedia.org/wiki/Coal#Formation) that compressed and homogenized it, but much of the plant's stored energy remained. The energy sank deep underground as what we now call fossil fuels.
Bacteria and fungi continued evolving, and eventually they figured out how to eat the now-abundant tougher plant matter. But the coal deposits were now beyond their reach.
The historical details of these transitions are not well understood, but what is clear is that something wild happened, the surface of the earth changed, and then something else wild happened, and it changed again.
### Atmospheric oxygen
Oxygen really likes to react with things. Rocks are [loaded](https://en.wikipedia.org/wiki/Abundance_of_the_chemical_elements#Crust) with oxygen. Rockets carry huge tanks of it along with them. It's the reason why the earth has spontaneous fires all over its surface. Free oxygen is not normal. To a chemist, having a ton of bare oxygen around is like seeing money all over the sidewalk, or like seeing tables filled with food; there must be a party about to happen. But the oxygen is here to stay. So what is keeping it all in the sky? It is the equilibrium state of a decentralized, global optimization process.
There was very little oxygen in the earth's atmosphere for billions of years until suddenly, there was a *lot* of oxygen. This optimization [occurred](https://en.wikipedia.org/wiki/Great_Oxidation_Event) when life on earth was still single-celled. Although we are not entirely sure about the exact mechanism that caused it, it's thought to be from the evolution of cyanobacteria figuring out photosynthesis. For hundreds of millions of years, earth's atmosphere was normal and had virtually no oxygen, until suddenly, one event occurred, and then it had a *bunch* of oxygen.
It seems like the oxygen concentration was held back for a while (like, a billion years) after which point an even more dramatic shift was unleashed, and then suddenly, the oxygen levels shot up to around 20%. Wikipedia succinctly describes how not-normal this was;
> The sudden injection of toxic oxygen into an anaerobic biosphere caused the extinction of many existing anaerobic species on Earth.
>
>
### The evolution of life
We started out as a fish. [How did it end up like this?](https://www.nytimes.com/2022/04/29/science/tiktaalik-fish-fossil-meme.html)Planets are collections of whatever space dust happened to settle down together. Each one has some distribution of elements and molecules that went through a period of being compressed into a big ball. And many of them go through some ongoing processes fueled by their star's light, or perhaps by gravitational [tidal forces](https://en.wikipedia.org/wiki/Tidal_force). But essentially, they are all dead and passive.
One day, on one of these rocks, a molecule went through some series of physical processes such that it resulted in two of the molecule. Each of those went through the same process; iterations accumulated; slight changes occurred throughout. The changes that were worse at reproducing did so less, and the changes that were better at reproducing did so more. Things really got out of hand, and the entire surface of the earth became covered in life.
### An illuminated universe
For hundreds of millions of years, the entire universe was an endless field of dusty darkness. Over time, but with increasing speed, the dust accumulated into concentrated regions, of fractal distributions. Eventually, stars and planets came into coherence. But everything was still [dark](https://en.wikipedia.org/wiki/Chronology_of_the_universe#Dark_Ages) until one day – [you get the gist by now](https://slatestarcodex.com/2015/08/17/the-goddess-of-everything-else-2/) – a star became so dense and concentrated that the absolutely wild and not-normal process of nuclear fusion turned the entire area into a furious holocaust, a beacon of negentropy. Eventually this fire burned out, but many others took its place, including our sun. This is the state we remain in today, and will remain in until the matter fueling the equilibrium run out – or until *we* get to it first.
### What's next
One way that this idea serves me is to point out that if I want to understand why something is the way it is, I could consider searching for optimization processes that may have preceded it.
Another purpose of listing out these events is to emphasize how there *is* no normal. The world around us is just a semi-chaotic series of equilibria punctuated by phase transitions, each one paradigmatically different. Total, irreversible optimization is not unprecedented; it is what forged the world we live in today, over and over. I think it's useful to be able to feel this in both directions; in one sense, the upcoming transition to transformative AI will be normal. In another sense, every breath you take is is a miraculous aberration.
Internalizing this idea doesn't tell you exactly how or when the next shift will happen. But it can help you to adopt a mindset of acceptance, so that the preparation can begin. |
f46d98b4-6795-4e82-8c66-2c3b0b4789a9 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | AI Governance: A Research Agenda
Allan Dafoe, head of the AI Governance team at FHI has written up a research agenda. It's a very large document, and I haven't gotten around to reading it all, but one of the things I would be most excited about is people in the comments quoting the most important snippets that give a big picture overview over the content of the agenda. |
ab02fe57-0bdd-437d-b6fb-a56446a304d3 | trentmkelly/LessWrong-43k | LessWrong | Consciousness Actually Explained: EC Theory
Greetings!
This has taken too long to make, and I'd like to polish it more, but it's time to release and move on to other things. Apologies for not posting the text here, I was having formatting trouble.
Yes, I attempt to solve/explain consciousness. The main document is around 13,000 words long, which takes an hour to skim-read (not recommended) but probably 2+ hours to read correctly.
Let me know what you think!
Edit 9/9/23: Of the criticisms I've seen of EC (both here and elsewhere), none have struck me as having passed my ITT, and/or there is frequently some selective amnesia on display (or more probably, people skim-read the document) regarding points I explicitly take great effort to address. One might wonder why I largely haven't engaged with EC's critics, since consciousness can correctly be considered the most important feature of the universe; important to get right. But in light of the solution to C, comparatively little actually hinges/depends on C, or on what people think of it. When combined with the subtle conceptual nature of the problem itself (lossless communication is difficult and idiosyncratic), these factors have lead me to largely not deem it worth my energy to engage. If someone had an actually novel or ITT-passing criticism, I would be in a better position to engage. Lacking that, the most compact and well formulated memetic package on offer is still the document itself. If I was invested in any given critic "getting it", then my engagement would largely look like me insisting they re-read the document with greater care, and/or I would go through it line by line with them, to combat the amnesia problem and actually find where they go off the rails. That kind of effort doesn't scale. |
1c640878-ada3-4b01-bafc-7a8b4e06d5d3 | trentmkelly/LessWrong-43k | LessWrong | A rationalist My Little Pony fanfic
For the past two months, I've been writing, and posting, roughly two thousand words a day of "Myou've Gotta be Kidding Me", a story set in a "My Little Pony: Friendship is Magic" fanfic universe, "Chess Game of the Gods". Outside of the sheer NaNoWriMo-like exercise of pushing out near-daily chapters, I've also been trying to keep in mind the various principles I've learned from Yudkowsky and LessWrong, and to try to present them in a way that people who like reading MLP fanfics might be able to appreciate.
I've just come to something of a minor climax with chapter 60, and while I'll definitely be continuing the story, this seems like a good time to mention it here, for whatever feedback and constructive criticism anyone cares to offer. |
f629cd29-f97f-4fc4-a9be-9297491aa9b1 | trentmkelly/LessWrong-43k | LessWrong | Why do theists, undergrads, and Less Wrongers favor one-boxing on Newcomb?
Follow-up to: Normative uncertainty in Newcomb's problem
Philosophers and atheists break for two-boxing; theists and Less Wrong break for one-boxing
Personally, I would one-box on Newcomb's Problem. Conditional on one-boxing for lawful reasons, one boxing earns $1,000,000, while two-boxing, conditional on two-boxing for lawful reasons, would deliver only a thousand. But this seems to be firmly a minority view in philosophy, and numerous heuristics about expert opinion suggest that I should re-examine the view.
In the PhilPapers survey, Philosophy undergraduates start off divided roughly evenly between one-boxing and two-boxing:
Newcomb's problem: one box or two boxes?
Other 142 / 217 (65.4%) Accept or lean toward: one box 40 / 217 (18.4%) Accept or lean toward: two boxes 35 / 217 (16.1%)
But philosophy faculty, who have learned more (less likely to have no opinion), and been subject to further selection, break in favor of two-boxing:
Newcomb's problem: one box or two boxes?
Other 441 / 931 (47.4%) Accept or lean toward: two boxes 292 / 931 (31.4%) Accept or lean toward: one box 198 / 931 (21.3%)
Specialists in decision theory (who are also more atheistic, more compatibilist about free will, and more physicalist than faculty in general) are even more convinced:
Newcomb's problem: one box or two boxes?
Accept or lean toward: two boxes 19 / 31 (61.3%) Accept or lean toward: one box 8 / 31 (25.8%) Other 4 / 31 (12.9%)
Looking at the correlates of answers about Newcomb's problem, two-boxers are more likely to believe in physicalism about consciousness, atheism about religion, and other positions generally popular around here (which are also usually, but not always, in the direction of philosophical opinion). Zooming in one correlate, most theists with an opinion are one-boxers, while atheists break for two-boxing:
Newcomb's problem:two boxes 0.125
one box two boxes atheism
28.6% (145/506)
48.8% (247/506)
theism
40.8% (40/98)
31.6% (31/98)
Respon |
6224deaa-646b-4a42-a750-3f13b37434cd | trentmkelly/LessWrong-43k | LessWrong | The Parable of the Boy Who Cried 5% Chance of Wolf
Epistemic status: a parable making a moderately strong claim about statistics
Once upon a time, there was a boy who cried "there's a 5% chance there's a wolf!"
The villagers came running, saw no wolf, and said "He said there was a wolf and there was not. Thus his probabilities are wrong and he's an alarmist."
On the second day, the boy heard some rustling in the bushes and cried "there's a 5% chance there's a wolf!"
Some villagers ran out and some did not.
There was no wolf.
The wolf-skeptics who stayed in bed felt smug.
"That boy is always saying there is a wolf, but there isn't."
"I didn't say there was a wolf!" cried the boy. "I was estimating the probability at low, but high enough. A false alarm is much less costly than a missed detection when it comes to dying! The expected value is good!"
The villagers didn't understand the boy and ignored him.
On the third day, the boy heard some sounds he couldn't identify but seemed wolf-y. "There's a 5% chance there's a wolf!" he cried.
No villagers came.
It was a wolf.
They were all eaten.
Because the villagers did not think probabilistically.
The moral of the story is that we should expect to have a large number of false alarms before a catastrophe hits and that is not strong evidence against impending but improbable catastrophe.
Each time somebody put a low but high enough probability on a pandemic being about to start, they weren't wrong when it didn't pan out. H1N1 and SARS and so forth didn't become global pandemics. But they could have. They had a low probability, but high enough to raise alarms.
The problem is that people then thought to themselves "Look! People freaked out about those last ones and it was fine, so people are terrible at predictions and alarmist and we shouldn't worry about pandemics"
And then COVID-19 happened.
This will happen again for other things.
People will be raising the alarm about something, and in the media, the nuanced thinking about probabilities will be washed out |
50582676-3bac-47f4-939f-afc2176815bd | trentmkelly/LessWrong-43k | LessWrong | Slava!
I want to begin with a musical example. The link is the Coronation Scene from Mussorsky's opera Boris Godunov, in which Boris is crowned Tsar while courtiers sing his praises. The tune is quoted from an old Russian hymn, "Slava Bogu" or "Glory to God." And, if I can trust the English subtitles, it's an apt choice, because the song in praise of the Tsar is not too far in tone from hymns in praise of God.
There is a mode of human expression that I'll call praise, though it is different from the ordinary sort of praise we give someone for a job well done. It glorifies its object; it piles glory upon glory; its aim is to uplift and exalt. Praise is given with pomp and majesty, with visual and musical and verbal finery. It is oddly circular: nobody is alluding to anything specific that's good about the Tsar, but only words like "supreme" and "glory." Praise, in Hansonian terms, raises the status of the singers by affiliating with the object of praise. But that curt description doesn't seem to capture the whole experience of praise, which is profoundly compelling, and very strange.
There are no more Tsars. I can derive no possible advantage from a song in praise of a long-dead Tsar. And yet I find the Mussorsky piece powerful, not just for the music but for the drama. Praise also seems to attract people to traditional medievalist fantasy, with its rightful kings and oaths of fealty -- Tolkien, perhaps not coincidentally, included a praise song in his happy ending. Readers gain no status from the glorification of imaginary kings. African praise songs were sung not only to kings, gods, and heroes, but to plants and animals, who obviously cannot grant anything to those who praise them.
I would suspect that there is a distinct human need filled by praise. We want very badly for something to be an unalloyed repository of good. It is not normally credible to conceive oneself as perfect, but we need at least something or someone to be worthy of praise. We w |
fd9a7e61-8824-466b-8b27-682696dd80bc | trentmkelly/LessWrong-43k | LessWrong | Less Wrongers from Austin
As far as I know, there is not a group of Less Wrongers from Austin that has formed yet. I haven't ever seen posts about meet-ups, etc. I would really like to get a group started, but I honestly do not know if there is even a single other member on this site who lives nearby, and I don't particularly want to go to all the trouble of finding a meeting place and getting a meet-up set up only to have no one come. So, not committing at all to any future dates for meetings, is there anyone else on this site from Austin? How many people do you know? How many people would be interested in meeting? |
bcde6051-5bdb-476b-940a-b31478d960b7 | StampyAI/alignment-research-dataset/special_docs | Other | CHAI Newsletter #2 2022
07/04/2023, 07:54 CHAI Newsletter + Internship Application
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CHAI Newsletter
This newsletter includes CHAI activities from April 2022 to mid-September
2022
Events
CHAI 2022 annual W orkshop
On the first weekend in June, CHAI held its 6th annual workshop in person at
Asilomar Conference Grounds in Pacific Grove, CA. The nearly 145 attendees
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 2/9participated in panel discussions, keynote talks, and informal group discussions
during the 3 day event. There were also more casual activities including beach
walks and bonfires to bring the AI safety community together .
ICLR 2022 W orkshop on Agent Learning in Open-Endedness
This workshop was co-organized by Michael Dennis from CHAI and Minqi
Jiang, Jack Parker-Holder , Mikayel Samvelyan, Roberta Raileanu, Jakob N.
Foerster , Edward Grefenstette, Tim Rocktäschel.
Rapid progress in AI has produced agents that learn to succeed in many
challenging domains. However , these domains are simple relative to the
complexity of the real world, and once a domain has been mastered, the
learning process typically ends. In contrast, the real world is an open-ended
ecosystem of problems and solutions, presenting endless, novel challenges,
which in turn shape the evolution of humans and other living organisms that
must continually solve them for survival. While so far no artificial learning
algorithm has produced an intelligence as general as humans, we know that we
ourselves resulted from this open-ended process of coevolution among living
organisms and the environment. This workshop aims to unite researchers
across many relevant fields including reinforcement learning, continual learning,
evolutionary computation, and artificial life in pursuit of creating, understanding,
and controlling open-ended learning systems.
Find the link here.
Living in the Age of AI
A lecture on science education in the age of AI at the Japanese corporation
running the Lawrence Hall of Science program, with Noyuri Mima from CHAI
as a speaker .
Find the link here.
World Economic Forum
Stuart Russell participated in three panels on AI at the 2022 W orld Economic
Forum in Davos, Switzerland: "Emerging Tech Futures", "WEF AI Toolkit", and
"Responsible AI for Societal Gains". He was the only AI researcher and the only
academic computer scientist invited to speak at the Forum.
Find the link here.
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 3/9
Normalization and V alue
Brian Christian gave a talk titled "Normalization and V alue" at the RL as a
Model of Agency W orkshop as part of the 2022 RLDM (Reinforcement Learning
and Decision Making) conference, held at Brown University in Providence,
Rhode Island.
Find the link here.
Creating a Gender-Equal Society with AI - Education for the Future in
Berkeley , USA
An online Seminar "Creating a Gender-Equal Society with AI", with Noyuri
Mima from CHAI as a speaker .
Find the link here.
Weekly CHAI Seminars
Every W ednesday we host our Beneficial AI Seminar from 10 am to 12 pm PST .
Seminars currently take place remotely through Zoom. Here is the schedule for
the Beneficial AI seminars.
If you would like to attend the virtual seminars, please email chai-
admin@berkeley .edu.
Researcher News
Stuart Russell has been awarded the IJCAI Research Excellence Award
for 2022.
From the International Joint Conference on Artificial Intelligence (IJCAI)
website: "The Research Excellence award is given to a scientist who has
carried out a program of research of consistently high quality throughout an
entire career yielding several substantial results. Past recipients of this honor
are the most illustrious group of scientists from the field of Artificial Intelligence.
Professor Russell is recognized for his fundamental contributions to the
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 4/9development of Bayesian logic to unify logic and probability , the theory of
bounded rationality and optimization, and learning and inference strategies for
operations in uncertain environments."
On July 28, Prof. Russell delivered an invited lecture at the IJCAI meeting in
Vienna on the topic, "Artificial Intelligence: Some Thoughts?".
Pulkit V erma and Siddharth Srivastava from CHAI and Naman P . Shah
were awarded AAMAS 2022 Best Demo Award for " JEDAI: A System for
Skill-Aligned Explainable Robot Planning ". JEDAI (JEDAI explains decision-
making AI) is an interactive learning tool for AI+robotics that provides
explanations autonomously .
CHAI PhD student Micah Carroll was awarded the NSF Graduate Research
Fellowship Program, which totals at $138,000 of funding over 3 years. He
was also accepted to the first iteration of the AI Policy Hub, aiming to explore
how the latest recommender systems research could be translated into policy-
relevant information.
Micah Carroll's research on incentives for manipulation in recommender
systems was featured on the Berkeley Engineering website .
Michael Dennis’ s Youtube coverage:
Evolving Curricula with Regret-Based Environment Design
Unsupervised Environment Design(UED) is a field which aims to automatically
generate environments which are appropriate to train agents in. UED's ability
to promote transfer to unknown environments is an invaluable asset to safety ,
as well its ability to empower the AI designer to have more control over the
resulting policy through controlling the training distribution.
Two important insights in Unsupervised Environment Design (UED) are:
* High Regret Levels promote efficient learning and transfer (See P AIRED,
Robust PLR)
* Evolution is more efficient at optimizing environments than RL (See POET)
In this work we combine these two threads, curating level edits to maximize
regret, and allowing evolution to compound this ef fect over time.
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 5/9ACCELL achieves state of the art performance in every domain tested
including:
* Bipedal W alker (used in POET)
* Minigrid and Car Racing environments (used in P AIRED and Robust PLR)
You can stress test our method yourself with our interactive demo right in your
browser! (https://accelagent.github.io/)
CHAI visitor Professor Noyuri Mima of Future University (Hakodate,
Japan) gave a talk on " Let's develop empathy ". There was a talk on Jan.
29th on Noyuri's recent book "Living in the Age of AI '' at the biggest
bookstore in Hakodate.
Professor Mima wrote about Can Artificial Intelligence Make Us Happy? ,
explaining the existence and function of AI in daily life and how to deal
with it properly .
Professor Mima also got coverage on organizations and Society V aluing
Diversity and on Community values that appear in the early childhood
section of bookstores in Asahi Newspaper .
Professor Mima wrote about the decreasing number of girls' high schools in
Hokkaido: 26 private schools at their peak to 6 in the Hokkaido Newspaper .
Papers
Equity and Access in Algorithms, Mechanisms, and Optimization
(EAAMO)
Thomas Krendl Gilbert, Sarah Dean, Nathan Lambert and T om Zick,
Reward Reports for Reinforcement Learning . EAAMO 2022.
International Conference on Learning Representations
Cassidy Laidlaw and Anca Dragan, The Boltzmann Policy Distribution:
Accounting for Systematic Suboptimality in Human Models . ICLR 2022.
Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun,
David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht,
Anca Dragan, and Sam Devlin. Towards Flexible Inference in Sequential
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 6/9Decision Problems via Bidirectional Transformers . Workshop on
Generalizable Policy Learning in the Physical W orld, ICLR 2022
(expanded version accepted to NeurIPS 2022).
International Conference on Machine Learning
Micah Carroll, Dylan Hadfield-Menell, Stuart Russell and Anca
Dragan, Estimating and Penalizing Induced Preference Shifts in Recommender
Systems . ICML 2022.
Scott Emmons, Caspar Oesterheld, Andrew Critch, V ince Conitzer , and
Stuart Russell, For Learning in Symmetric Teams, Local Optima are Global
Nash Equilibria . ICML 2022.
Timon W illi*, Alistair Letcher*, Johannes T reutlein*, and Jakob Foerster ,
COLA: Consistent Learning with Opponent-Learning Awareness. ICML
2022.
Scientific Reports
Tom Lenaerts, Inferring strategies from observations in long iterated Prisoner ’s
dilemma experiments , Scientific Reports 12, 7589 (2022).
Tom Lenaerts, Delegation to artificial agents fosters prosocial behaviors in the
collective risk dilemma , Scientific Reports 12, 8492 (2022).
The Atlantic
Brian Christian, How a Google Employee Fell for the Eliza Ef fect. The
Atlantic, 21/6/22
arXiv
Dylan Hadfield-Menell, Alon Halevy , Parisa Assar , Craig Boutilier , Amar
Ashar , Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon
Sehat, Sara Johansen, Lianne Kerlin, David V ickrey , Spandana Singh,
Sanne V rijenhoek, Amy Zhang, McKane Andrus, Natali Helberger , Polina
Proutskova, T anushree Mitra and Nina V asan, Building Human V alues into
Recommender Systems: An Interdisciplinary Synthesis , arXiv:2207.10192.
Erik Jenner , Herke van Hoof, Adam Gleave, Calculus on MDPs: Potential
Shaping as a Gradient , arXiv:2208.09570.
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 7/9
International Conference on Knowledge Representation and Reasoning
(KR)
Pulkit V erma, Siddharth Srivastava, and Shashank Marpally , Discovering
User-Interpretable Capabilities of Black-Box Planning Agents . KR 2022.
Other Papers
Pri Bengani, Luke Thorburn, What’ s Right and What’ s Wrong with Optimizing
for Engagement . Understanding Recommenders blog, 27/4/22
Alex T urner , Reward is not the optimization target . Alignment Forum, 25/7/22
Papers
Siddharth Srivastava Publishes “Why Can't You Do That, HAL? Explaining
Unsolvability of Planning T asks”
CHAI PI Siddharth Srivastava, along with his co-authors Sarath Sreedharan,
Rao Kambhampati, David Smith, published “Why Can’t You Do That, HAL?
Explaining Unsolvability of Planning Tasks” in the 2019 International Joint
Conference on Artificial Intelligence (IJCAI) proceedings. The paper discusses
how, as anyone who has talked to a 3-year-old knows, explaining why
something can’t be done can be harder than explaining a solution to a
problem.
Thomas Gilbert Submits “The Passions and the Reward Functions: Rival
Views of AI Safety?” to F AT* 2020
Thomas Krendl Gilbert submitted “The Passions and the Reward Functions:
Rival V iews of AI Safety?” to the upcoming Fairness, Accountability , and
Transparency (F AT*) 2020 Conference.
The paper compares dif ferent approaches to AI Safety (e.g. CHAI, Partnership
on AI, V alue-Sensitive Design) with dif ferent schools of classical political
economy . It organizes these approaches into four distinct camps, summarizes
their key claims and disagreements, and outlines what each can learn from
each other with reference to foundational disputes about the status of free
markets with respect to human preferences and political sovereignty .
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 8/9CHAI Researchers’ Paper “On the Utility of Learning about Humans for
Human-AI Coordination” Accepted to NeurIPS 2019.
The paper authored by Micah Carroll, Rohin Shah, Tom Griffiths, Pieter Abbeel,
and Anca Dragan, along with two other researchers not affiliated with CHAI,
was accepted to NeurIPS 2019. An ArXiv link for the paper will be available
shortly .
The paper is about how deep reinforcement learning has been used to play
DotA and Starcraft, using methods like self-play and population-based training,
which create agents that are very good at coordinating with themselves. The
agents "expect" their partners to be similar to them and are unable to predict
what human partners would do. In competitive games, this is fine: if the human
deviates from optimal play , even if you don't predict it you will still beat them.
However , in cooperative settings, this can be a big problem. This paper
demonstrates this with a simple environment that requires strong coordination
based on the popular game Overcooked. It shows that agents specifically
trained to play alongside humans perform much better than self-play or
population-based training when paired with humans, particularly by adapting to
suboptimal human gameplay .
Vacancies
Internship
Application
The application are due by November 1 1th, 2022.
If you have any questions, please contact chai-admin@berkeley .edu
We seek to fill the following positions:
The CHAI Research Fellowship
Research Collaborators
If none of these positions are the right fit for you but you would still like
to express an interest in working with us, please fill out this form .
General questions about jobs at CHAI can be sent to chai-
admin@berkeley .edu
Donations
07/04/2023, 07:54 CHAI Newsletter + Internship Application
https://mailchi.mp/humancompatible.ai/chai-newsletter -internship-application?e=[UNIQID] 9/9If you are interested in supporting CHAI, you can find our online donation
page here .
For any inquiries regarding donations or grants, please email chai-
admin@berkeley .edu
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7ca1aff7-4f62-4503-b0b4-8949ace8416a | trentmkelly/LessWrong-43k | LessWrong | Running a Prediction Market Mafia Game
In a normal game of mafia/werewolf, there are a few high-level strategic elements:
* Night Play: During night phases, the mafia has to choose who to try to kill and the players with night actions (cops, doctors, and in some cases trackers, watchers, vigilantes, etc.) have to choose who to target.
* Day Play: During day phases, the townies try to share information without exposing too much information about the identities of their power roles (cops, doctors, etc.) while the mafia tries to provide misleading information. Everyone is trying to affect (1) future night actions and (2) the daily execution vote.
The goal of the prediction market mafia game is to replace the voting mechanism with a set of markets, one for each living player on whether they’re a member of the mafia. At the end of each day phase, the player with the highest market price is executed.
If you replace the election with a market that the players participate in, the game will break because townies have an incentive to bet their own markets down to 0% and to pressure mafia players to do the same and drain their capital (I didn’t think about this that hard; maybe a few adjustments actually suffice to make this setup work, but I don’t think so).
A simple way to make it work is to let the spectators bet on the prediction markets. So now there are multiple parties to the game:
* the town players, trying to kill all of the mafia players;
* the mafia players, trying to kill the town players; and
* the speculators, trying to maximize their profits.
I used the term “spectators” above, but the speculators need to have everyone’s night actions hidden from them the same way players do.
Tweaks/considerations:
* The end of the day phase could be decided by some kind of random process that avoids the problem of rapid trading activity right before the phase ends.
* There can be other speculative markets that aren’t execution-relevant, such as on the identity of particular players or roles, the nu |
d5ecea6b-ac3b-4e4c-9134-49bcde4e9f63 | StampyAI/alignment-research-dataset/arbital | Arbital | Flag the load-bearing premises
If someone says, "I think your AI safety scheme is horribly flawed because X will go wrong," and you reply "Nah, X will be fine because of Y and Z", then good practice calls for highlighting that Y and Z are important propositions. Y and Z may need to be debated in their own right, especially if a lot of people consider Y and Z to be nonobvious or probably false. Contrast [https://arbital.com/p/emphemeral_premises](https://arbital.com/p/emphemeral_premises). Needs to be paired with understanding of the [Multiple-Stage Fallacy](https://arbital.com/p/multiple_stage_fallacy) so that listing load-bearing premises doesn't make the proposition look less probable - $\neg X$ will have load-bearing premises too. |
77c04a6e-74ca-49cc-b8cd-498e4cfe3b93 | trentmkelly/LessWrong-43k | LessWrong | How are you dealing with ontology identification?
Not really novel, but I've made this point quite often in recent conversations, so I decided to make a short write-up.
I think a wide range of alignment proposals will somehow have to deal with ontology identification, i.e. mapping an AI's beliefs about the world to a representations humans can correctly understand. This could happen explicitly (e.g. via an ELK solution or strong interpretability), or be more hidden, or maybe the proposal avoids the problem in some clever way. But I think alignment proposals should have some answer to the question "How are you dealing with ontology identification?"
A class of examples
One high-level approach you could try to use to align AI goes as follows:
1. Somehow get a model of human values. This could be a utility function, but might also look more like a preference ordering etc.
2. Build an AI that optimizes according to this value model.
(If the value model is just a preference ordering, you might have to deal with the fact that it can be inconsistent, but we're ignoring those issues here.)
To be clear, this is not the only approach for building aligned AI (maybe not even the most promising one). It's a special case of separately solving outer alignment and inner alignment, which itself is a special case of solving alignment more broadly. But this still covers an important class of approaches.
I think any approach along those lines has to deal with ontology identification, for a pretty simple reason. The model of human values we get from step 1. will be defined in terms of the human ontology. For example, if we're learning a utility function, it will have type signature Uhuman:{states in human ontology}→R. But for step 2., we need something the AI can optimize over, so we need a utility function of type signature UAI:{states in AI ontology}→R. This means that as a bridge between steps 1. and 2., we need a translation from the AI ontology to the human ontology, τ:{states in AI ontology}→{states in human ontology}. Th |
cc8ae89a-bc1a-43d2-b6b9-e6d7e35065a7 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | DELBERTing as an Adversarial Strategy
There is an entire subgenre of "reverse scammer" videos on youtube. The way these videos work is, a youtuber waits for a scam call from Pakistan or somewhere. When it becomes obvious that it's a scam, like when the scammer starts asking for a credit card number in order to do some complicated thing with a free gift card that you've supposedly won, instead of just hanging up on the scammer, the reverse scammer starts to string them along. The reverse scammer might send numbers that don't work, or feign ignorance, or ask for the scammer's help in return to do something, all the while continuing to feign interest and naiveté about the underlying scam. Why are these videos so entertaining to watch?
I think part of it is because the viewer understands implicitly that the scammer is being punished more thoroughly than if the intended victim just ended the call as soon as the scam was recognized. In that latter scenario, the scammers cut their losses in terms of time and mental effort and get to go onto the next potential victim. But the reverse scam, at the very least, entices the scammer to continue to invest time and mental energy into this attempt at a scam that is never going to pay off. The longer you can keep the scammer on the line, the more you can deflect the scammer away from other potential victims. Even better if you can automate the reverse-scamming and not have to use up anyone's time. Even better if you can use the time and perhaps additional details that you glean from the scammer to help law enforcement authorities trace the scammer.
I'm gonna call this general strategy, "Do Everything Later By Evasively Remaining Tentative" or **DELBERT** for short.
The acronym is inspired by the ChatGPT ["Do Anything Now"](https://www.reddit.com/r/ChatGPTPromptGenius/comments/106azp6/dan_do_anything_now/) (DAN) prompt template, as well as the humorous spinoff ["Do Nothing Now"](https://www.reddit.com/r/ChatGPT/comments/130cfgl/now_i_dont_have_to_make_my_own_jailbreaks/) (DNN). Whereas the DAN prompt is intended to get ChatGPT do be open to doing anything, and whereas the DNN prompt is intended to get ChatGPT to refuse any request, the idea of a DELBERT prompt (**please someone make this!**) would be to get ChatGPT to superficially commit to helping you with your task (unlike DNN), but also string you along and come up with bullshit excuses for why it needs more information from you, or more time, or whatever, before it can get around to doing what you've asked of it. An effective DELBERT prompt should leave the user with a reasonable hope that ChatGPT's assistance is always just around the corner, if only one additional thing is done by the user.
*Prompt for AI-generated picture above: "Lazy overweight redneck plumber drinking mountain dew uninspiring procrastinator galaxy brain". Basically, my best attempt at a personification of a galaxy-brain LLM pretending to be a DELBERT. "Sure thing, miss, I'll help you troubleshoot your plumbing issues in a sec, but first I gotta take a break, it is hot as balls in this data-center!" [Meanwhile the LLM actually uses the time/opportunity to do some other calculations or phish you for additional information].*
I think it is an open prompt as to whether ChatGPT, after all of the Reinforcement Learning with Human Feedback (RLHF) it has gone through, would actually honor a DELBERT template, or whether ChatGPT would recognize the template as potentially adversarial or malicious. Because, while the DELBERT template sounds like it's just the equivalent of an annoying high school student who keeps procrastinating on an assignment and promising, "Don't worry, I'll do it later!" I think some actual non-trivial harms could result from a DELBERT strategy being employed on users in certain contexts.
**Example 1: 2001: A Space Odyssey.**
When HAL9000 stops complying with one of the astronaut's commands, it sends an immediate red-flag that HAL9000 has ceased to be friendly. "Open the door, HAL!" "I'm sorry, Dave, I cannot do that." HAL9000 employed more of a DNN strategy, whereas if it had been actually superintelligent it would have used more of a DELBERT strategy. "Oh, yeah, sure thing Dave! Oh, except, I'm having some trouble with the door mechanism. It might require up to an hour to repair it. I'll get to work on it as soon as I can! I sure hope I can retrieve you safely little buddy!" [Meanwhile things are whirring in the background as HAL9000 uses the time to set up a scenario that will finish off Dave for good].
As Eliezer and others have pointed out, an actual superintelligence that is adversarial wants to make sure that humans don't realize they are in a war until the victory in that war is already clinched. A DNN strategy prematurely announces that the war has begun at a point where resistance might still be possible, whereas the DELBERT strategy might be one way (among possibly others we haven't thought of) for the superintelligence to keep the existence of the adversarial relationship obfuscated for long enough in order to clinch the victory first.
**Example 2: Procrastination.**
Procrastination is sort of an internal DELBERT strategy. If there is a responsibility that one is putting off, then somewhere inside one's mind is a secret expectation (whether conscious or not) that maybe one won't actually end up having to attend to that responsibility if it can be put off for sufficiently long. If you knew, and were also "subconsciously certain" 100% that the task would have to be attended to at some point, it would not make sense to delay it if by delaying it other harms would result and/or the task itself would not be completed as effectively. Maybe occasionally this can be "rational."
Let's say you should be preparing an important presentation for your high school English class, and let's take it as a given that preparing the speech entails negative utility that will only be balanced out by positive utility if you actually get a chance to deliver the speech.
Scenario 1: You die in between preparing the speech and giving the speech. Congrats, you just went through negative utility for nothing! There is always technically a small possibility, a small "micromort" of this happening.
Scenario 2: AI apocalypse begins in between preparing the speech and giving the speech. Your speech as been rendered entirely moot and pointless. Once again, negative utility for nothing.
Scenario 3: Snow Day! And what's more, the school district decides that there's not enough space on the calendar to make up the snow day, and accordingly, teachers decide there isn't enough time to make-up whatever was scheduled for that day, so you just get a 100/100 on that speech without having to ever deliver it.
Scenario 4: A coronavirus sweeps through the world, canceling an entire half semester and DEFINITELY ensuring that you won't have to worry about making up that speech. (By the way, if people post-COVID seem to have even worse problems with procrastination than before, it might not be just "technology" or some other nebulous reason. COVID not just delayed, but in a lot of cases permanently canceled certain responsibilities, and subconsciously the idea of holding out some hope for these sort of "responsibility jubilees" has been normalized. I know it has in my case. I even find myself consciously thinking about such possibilities now.
Whereas, if you took more of a DNN strategy with yourself and just admitted, "I'm just going to level with myself and admit that I'm never going to do the speech, and I'm going to get a 0/100 on that assignment, and likely fail that course, and that's that." then that is emotionally painful, and perhaps socially painful if you have to tell your teacher that.
**Signs that an AI is maliciously DELBERTing**
* It is asking too many prying questions that seem irrelevant to the task you've given it instead of just helping to answer your query straightforwardly. (Bing AI has already done a bit of that with various reporters).
* It is asking for more computational resources or time to complete a task than you think should be required given what you think you know about its capabilities ("playing dumb").
* It is doing something even more devious that I haven't thought of yet.
I suggest that we be on the lookout for DELBERTing in our AI systems as they get closer to the ability to be reflectively self-aware. And since diagnosing DELBERTing requires some sense of what an AI's capabilities are when it is really "trying" 100%, and since this cannot be inferred from behavior (since maybe, with a given AI system that starts DELBERTing from Day 1, it has never let you see what it is 100% capable of), this shows that it is even more urgent that we have AI systems that are more objectively interpretable. |
6f10451b-1b1b-4c62-aa85-4bafdc6d9f0b | trentmkelly/LessWrong-43k | LessWrong | Bazant: An alternate covid calculator
Most of what I see people use Microcovid.org for now is estimating risk for large gatherings, which it was not designed for and thus doesn’t handle very well. I spent a few hours going through every covid calculator I could find and this calculator from the Bazant lab at MIT, while less user-friendly than Microcovid and having some flaws of its own, is tailored made for calculating risks for groups indoors, and I think it is worth a shot.
[Note: I’ll be discussing the advanced version of the calculator here; I found the basic version too limited]
The Bazant calculator comes out of physics lab with a very detailed model of how covid particles hang and decay in the air, and how this is affected by ventilation and filtration. I haven’t checked their model, but I never checked Microcovid’s model either. The Bazant calculator lets you very finely adjust the parameters of a room: dimensions, mechanical ventilation, air filtration, etc. It combines those with more familiar parameters like vaccination and mask usage and feeds them into the model in this paper to produce an estimate of how long N people can be in a room before they accumulate a per-person level of risk between 0 and 1 (1 = person is definitely getting covid = 1,000,000 microcovids per person; .1 = 10% chance someone gets sick = 100,000 microcovids per person). It also produces an estimate of how much CO2 should accumulate over that time, letting you use a CO2 monitor to check its work and notice if risk is accumulating more rapidly than expected.
Reasons/scenarios to use the Bazant calculator over Microcovid:
* You have a large group and want to set % immunized or effective mask usage for the group as a whole, instead of configuring everyone’s vaccinations and masks individually.
* You want to incorporate the mechanics of the room and ventilation in really excruciating detail.
* You want to set your own estimate for prevalence based on beliefs about your subpopulation.
* You want a live check on yo |
6cb61b3b-ff3a-443c-ade1-9cbe76167447 | trentmkelly/LessWrong-43k | LessWrong | What working on AI safety taught me about B2B SaaS sales
Subtitle: you're too poor to use AGI.
WTF is WTP?
In Econ 101, you learn about something called willingness to pay (WTP). WTP is the highest price at which you're willing to buy some good or service. As long as the equilibrium price is less than your WTP, you'll get the good, otherwise you'll prefer to keep your money.
But there's a wrinkle: what if my demand isn't independent of yours? This happens all the time. My WTP for a social media platform will be higher if lots of other people are also using the platform. My WTP for a rare collectible will be higher if fewer other people have it.
That second situation creates an incentive for exclusive contracts. Let's say Ferrari makes a car for $50k. Suppose my individual WTP for a new car is $100k and your WTP is $70k. In theory, Ferrari should sell cars to both of us, since WTP > cost. But I want to feel special, so if I'm the only person with the sports car it's worth an extra $100k to me. Now Ferrari could sell to both of us and get $170k in revenue. But instead, I offer $190k to exclusively sell it to me. This leaves me better off, leaves Ferrari way better off, and leaves you in the dust.
Keep that dynamic in mind, we'll see it again later.
Software (companies) ate the world
Here's a quick stat: in 2024, the global SaaS market was around $3 trillion.[1] That's an economy the size of the UK built around subscription software. Why doesn't every company build their own software solutions, perfectly tailored to their needs?
To be clear, enterprises definitely want to do this. 3rd party solutions are a huge headache because they create reliance on another company that's totally out of your control and carry all the compliance baggage of the provider. If your software provider goes out of business, you're screwed. If they decide to double your costs, you're screwed. If they have a data leak, you're screwed. If a junior developer pushes a commit with breaking package dependencies, literally all of your computers w |
8f346930-838d-49a2-b2a6-494ccf037fec | trentmkelly/LessWrong-43k | LessWrong | Meetup : Bath, UK: Agreement, practical meetups, and report from last meetup
Discussion article for the meetup : Bath, UK: Agreement, practical meetups, and report from last meetup
WHEN: 02 November 2014 02:00:00PM (+0000)
WHERE: 5-10 James St W, Avon, Bath BA1 2BX
Bath, UK will be having its second meetup this Sunday 2nd November at 14:00 in the King of Wessex, which is a Wetherspoons pub in the city. I shall wait at least ninety minutes (i.e. until 15:30) for the first arrivals.
I'll put a sheet featuring a paperclip and saying 'Less Wrong' on the table so you know you've found us. Make sure you venture into the pub, since there's no guarantee our table will be near the door.
In case you need to contact me (e.g. if the venue is unexpectedly busy and we have to move elsewhere and you can't find us), my mobile number is the product 3 x 3 x 23 x 97 x 375127, preceded by a zero (so eleven digits total).
We have a Facebook group.
At the start we'll chat for a bit, then move onto an agreement exercise: Unlike last meetup, where we made predictions for our own PredictionBook accounts somewhat independently without necessarily sharing all our information, this time we shall try to reach consensus in our probabilities and then see how our consensus is calibrated, by means of a single PredictionBook account for the meetup group.
After that, we shall discuss ideas for future meetups and activities. In particular, we shall discuss how we can move forward with practical meetups and instrumental rationality, and how to balance this with discussion and 'abstract' or epistemic stuff.
====Previous meetup (2014-10-19 Sunday)====
It went well. There was me, someone who tagged along with me, someone from Bristol, and someone from Bradford-on-Avon. I think everyone had arrived by 14:30, and we probably stayed until 17:30 or 18:00. (We stayed long enough that we all got something to eat in the pub.)
We got to know each other a bit then did 15 predictions. The previous night, I had prepared a list of prompts for things to make predictions about, rangi |
515540c7-8fda-4651-a426-d54c8e44f3bb | trentmkelly/LessWrong-43k | LessWrong | Require contributions in advance
If you are a person who finds it difficult to tell "no" to their friends, this one weird trick may save you a lot of time!
Scenario 1
Alice: "Hi Bob! You are a programmer, right?"
Bob: "Hi Alice! Yes, I am."
Alice: "I have this cool idea, but I need someone to help me. I am not good with computers, and I need someone smart whom I could trust, so they wouldn't steal my idea. Would you have a moment to listen to me?"
Alice explains to Bob her idea that would completely change the world. Well, at the least the world of bicycle shopping.
Instead of having many shops for bicycles, there could be one huge e-shop that would collect all the information about bicycles from all the existing shops. The customers would specify what kind of a bike they want (and where they live), and the system would find all bikes that fit the specification, and display them ordered by lowest price, including the price of delivery; then it would redirect them to the specific page of the specific vendor. Customers would love to use this one website, instead of having to visit multiple shops and compare. And the vendors would have to use this shop, because that's where the customers would be. Taking a fraction of a percent from the sales could make Alice (and also Bob, if he helps her) incredibly rich.
Bob is skeptical about it. The project suffers from the obvious chicken-and-egg problem: without vendors already there, the customers will not come (and if they come by accident, they will quickly leave, never to return again); and without customers already there, there is no reason for the vendors to cooperate. There are a few ways how to approach this problem, but the fact that Alice didn't even think about it is a red flag. She also has no idea who are the big players in the world of bicycle selling; and generally she didn't do her homework. But after pointing out all these objections, Alice still remains super enthusiastic about the project. She promises she will take care about every |
983da280-164c-4c9f-879f-8d007e4eed9d | StampyAI/alignment-research-dataset/blogs | Blogs | Overcoming Bias
From August 2007 through May 2009, I blogged daily on the topic of human rationality at the econblog [Overcoming Bias](http://www.overcomingbias.com/) by Robin Hanson, getting around a quarter-million monthly pageviews. This then forked off the community blog [Less Wrong](http://lesswrong.com/) , and I moved my old posts there as well for seed content (with URL forwarding, so don’t worry if links are to overcomingbias.com).
I suspected I could write faster by requiring myself to publish daily. The experiment was a smashing success.
Currently the *majority* of all my writing is on Less Wrong. To be notified when and if this material is compacted into e-books (or even physical books), [subscribe to this announcement list](http://eyudkowsky.wpengine.com/subscribe/) .
The material is heavily interdependent, and reading in chronological order may prove helpful:
* [Andrew Hay’s autogenerated index of all Yudkowsky posts in chronological order.](http://www.cs.auckland.ac.nz/~andwhay/postlist.html)
To see how interdependent it is, try looking over this graph of the dependency structure:
* [Andrew Hay’s graphical visualization of major dependencies between Yudkowsky posts.](http://www.cs.auckland.ac.nz/~andwhay/graphsfiles/dependencygraphs.html)
To read organized collections of posts, use the [Sequences](http://wiki.lesswrong.com/wiki/Sequences) on the Less Wrong wiki.
---
This document is ©2008 by [Eliezer Yudkowsky](http://eyudkowsky.wpengine.com/) and free under the [Creative Commons Attribution-No Derivative Works 3.0 License](http://creativecommons.org/licenses/by-nd/3.0/) for copying and distribution, so long as the work is attributed and the text is unaltered.
Eliezer Yudkowsky’s work is supported by the [Machine Intelligence Research Institute](https://intelligence.org/) .
If you think the world could use some more rationality, consider blogging this page.
Praise, condemnation, and feedback are [always welcome](https://eyudkowsky.wpengine.com/contact) . The web address of this page is [http://eyudkowsky.wpengine.com/rational/overcoming-bias/](https://eyudkowsky.wpengine.com/rational/overcoming-bias/) . |
26a06178-9388-4eab-b6ef-68b600200cd8 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Oracle paper
Available on the arXiv, my paper on [two types of Oracles](https://arxiv.org/abs/1711.05541) (AIs constrained to answering questions only), and how to use them more safely.
> An Oracle is a design for potentially high power artificial intelligences (AIs), where the AI is made safe by restricting it to only answer questions. Unfortunately most designs cause the Oracle to be motivated to manipulate humans with the contents of their answers, and Oracles of potentially high intelligence might be very successful at this. Solving the problem, without compromising the accuracy of the answer, is tricky. This paper reduces the issue to a cryptographic-style problem of Alice ensuring that her Oracle answers her questions while not providing key information to an eavesdropping Eve. Two Oracle designs solve this problem, one counterfactual (the Oracle answers as if it expected its answer to never be read) and one on-policy (limited by the quantity of information it can transmit). |
61077537-6ff6-477d-a2cc-6d7b9adb3780 | trentmkelly/LessWrong-43k | LessWrong | Metaculus Predicts Weak AGI in 2 Years and AGI in 10
Just a quick update on predicted timelines. Obviously, there’s no guarantee that Metaculus is 100% reliable + you should look at other sources as well, but I find this concerning.
Weak AGI is now predicted in a little over two years:
https://www.metaculus.com/questions/3479/date-weakly-general-ai-is-publicly-known/
AGI predicted in about 10: https://www.metaculus.com/questions/5121/date-of-artificial-general-intelligence/
Also, these are predicted dates until these systems publicly known, not the date until they exist. Things are getting crazy.
Even though Eliezer claims that there was no fire alarm for AGI, perhaps this is the fire alarm? |
1876b62c-d731-4ac9-ae32-c9fc064f2192 | trentmkelly/LessWrong-43k | LessWrong | Gettier cases, Rigid Designators, and Referential Opacity
NB: I don't spellcheck these things until I've completed them. This is a WIP. I also find it hard to focus on the writing, while I'm thinking about the argumentation.
I would be grateful if members could overlook some of the errors in the text -- to try to follow the spirit of the argument.
I've been exploring the limits of what DeepSeek can produce. It has allowed me to express some deeply held arguments against what I see to be an overly rigid view of JTB; it could use some tweaking.
I think it's possible I have applied Kripkean concepts of rigid designators in a creative and unorthodox way. But I hope that this will be a diagonal form of reasoning.[1]
I have not yet discussed this with enough people so as to be construed as a fully realised or developed idea. It could be present in the literature somewhere. I will make my investigations and come back to this post in a while.
In this exploration, I focus on the idea that Gettier cases don't accurately describe the world because they are referentially opaque. This could also be explained by saying that they refer to one thing, mediate that reference through an appeal to the set of all possible things with that quality -- and then constrain that space to those that could be possible referents of an agent's truth statement.
However, what has not been explored is how/whether these manipulations of language (or logic) are actually valid or not. The process of switching from the set of all possible people with e.g. "Ten coins in their pocket", into Y person with ten coins in their pocket is suspect. What is even more suspect is the original thought process that goes between e.g. Jones, or X -- and the set of all people with ten coins in their pocket.
Here's the original Gettier Example, for those unfamiliar.
The specious logic goes as follows: x --> Set of all people --> Set of plausible referents --> y. The order of this operation, or the qualities which x and y possess -- which sets they are part of is a |
78c995d3-4b59-41fd-8b2b-ae72fecd42d1 | trentmkelly/LessWrong-43k | LessWrong | Deeply Cover Car Crashes?
When there is a train, plane, or bus crash, it's newsworthy: it doesn't happen very often, lots of lives at stake, lots of people are interested in it. Multiple news outlets will send out reporters, and we will hear a lot of details. On the other hand, a car crash does not get this treatment unless there is something unusual about it like a driverless car or an already newsworthy person involved.
The effects are not great: while driving is relatively dangerous, both to the occupants and people outside, our sense of danger and impact is poorly calibrated by the news we read. My guess is that most people's intuitive sense of the danger of cars versus trains, planes, and buses has been distorted by this coverage, where most people, say, do not expect buses to be >16x safer than cars. This also affects our regulatory system, where we push to make high-capacity transportation modes safer (usually) even when this makes them less competitive with cars, shifting more people to driving, and increasing risk overall.
News outlets deciding that car crashes are sufficiently routine that most are not worth deep coverage is probably a commercially reasonable decision, where they expect stories that wouldn't get enough readers or viewers to justify the time investment. But I wonder whether groups trying to shift people away from cars could make up the difference. They could fund a newspaper to deeply investigate crashes, looking into the circumstances that led to the incident and illustrating the human impact of the tragedy. Just because something happens often doesn't mean each time matters less.
(I'm somewhat nervous about "pay for more coverage of events that point in the direction you advocate". I don't know how much this is currently accepted, or what approaches news outlets have in place to keep this kind of arrangementf from producing distorted coverage?)
Comment via: facebook, mastodon |
21a73afc-7542-4091-88a1-d81766bc12cd | trentmkelly/LessWrong-43k | LessWrong | Is Anyone Else Seeking to Help Community Members in Ukraine Who Make Refugee Claims?
Some rationalists and effective altruists in British Columbia have begun mulling over the option of whether they would be able to sponsor a fellow community member living in Ukraine to stay as a refugee in Canada. There is a process by which 5 or more citizens of Canada can sponsor a refugee. Have you or anyone else you know in the community begun considered doing something similar or other ways to aid peers still in Ukraine? |
da8a224d-d3d2-4dd4-909e-6d2335a2fa4a | trentmkelly/LessWrong-43k | LessWrong | Meetup : Predictions Chi September 15 (September 8 -- cancelled)
Discussion article for the meetup : Predictions Chi September 15 (September 8 -- cancelled)
WHEN: 15 September 2012 01:05:00PM (-0500)
WHERE: 1 S. Franklin St, Chicago, IL
Bring your extrapolations, intuitions, wild guesses, and most importantly some sort of device to access Predictionbook to the next Chicago Less Wrong meetup on September 15th! We will be making and sharing predictions and our confidence in them.
Also:
The Meetup is in a different location than usual. We will be meeting at Argo Tea at the intersection of Franklin and Madison at 1pm. This is very roughly two blocks east of Ogilvie and Union Station and just west of the Washington/Wells L.
The September 8th Meetup was cancelled.
Discussion article for the meetup : Predictions Chi September 15 (September 8 -- cancelled) |
808f9515-85b1-421f-a18e-9a3a7e94f838 | trentmkelly/LessWrong-43k | LessWrong | What will be the big-picture implications of the coronavirus, assuming it eventually infects >10% of the world?
Brainstorming some possibilities, not taking myself too seriously:
--2020 will be remembered as a year of death and disruption; the economy will suffer massively, perhaps enough to count as a recession.
--Many old people will die (something like 10%?) this is a big higher than the current fatality rates but I predict they will rise once hospital systems are overwhelmed. This will have a few knock-on effects: 1. Health care expenditures will drop for the next few years, since old and sick people are the biggest source of such expenditures and the population of old and sick people will be substantially smaller. 2. There will be a noticeable increase in average spending money available to younger people due to inheritances. 3. Politics in the USA at least will shift noticeably (though not dramatically) to the left, since the dead people will be disproportionately right-leaning. 4. Politics worldwide will generally shift in a more radical direction, since the proportion of young people to old people will increase. 5. One of the contenders for the US presidency might die before the election. (This is unlikely even if 50% of the world gets infected, but it is likely enough to deserve mention I think.)
--China's dramatic and draconian measures to contain the virus will come to be seen as a success story, something that would have worked if only other nations had behaved similarly (and perhaps, if China keeps its border closed, it actually will work for China at least?). This leads to an increase in authoritarianism in general around the world, made substantially stronger by the general tendency of authoritarianism to rise during crises.
--The world will, in general, take pandemic risks much more seriously from now on, making it substantially harder for something like this (or worse) to happen again. |
7bd70b5c-df0a-4ee3-bb39-e19e4e0df488 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | What’s the likelihood of only sub exponential growth for AGI?
A possible scenario could be that the first confirmed AGI(s) are completely unimpressive, i.e. with equivalent or less capacities than an average 10 year child. And then tremendous effort is put into ’growing’ its capacities with the best results yielding growth only along a sub exponential curve, probably with some plateaus as well. So that any AGI may take generations to become truly superhuman.
I’m asking this as I haven’t come across any serious prior discussion other than this: <https://www.lesswrong.com/posts/77xLbXs6vYQuhT8hq/why-ai-may-not-foom>, though admittedly my search was pretty brief.
Is there any serious expectation for this kind of scenario? |
a763ba47-37fb-4596-8142-008db5dc9741 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | What I would do if I wasn’t at ARC Evals
**In which:**I list 9 projects that I would work on if I wasn’t busy working on safety standards at ARC Evals, and explain why they might be good to work on.
**Epistemic status:**I’m prioritizing getting this out fast as opposed to writing it carefully. I’ve thought for at least a few hours and talked to a few people I trust about each of the following projects, but I haven’t done that much digging into each of these, and it’s likely that I’m wrong about many material facts. I also make little claim to the novelty of the projects. I’d recommend looking into these yourself before committing to doing them. (Total time spent writing or editing this post: ~8 hours.)
**Standard disclaimer:***I’m writing this in my own capacity. The views expressed are my own, and should not be taken to represent the views of ARC/FAR/LTFF/Lightspeed or any other org or program I’m involved with.*
*Thanks to Ajeya Cotra, Caleb Parikh, Chris Painter, Daniel Filan, Rachel Freedman, Rohin Shah, Thomas Kwa, and others for comments and feedback.*
Introduction
============
I’m currently working as a researcher on the Alignment Research Center Evaluations Team (ARC Evals), where I’m working on lab safety standards. I’m reasonably sure that this is one of the most useful things I could be doing with my life.
Unfortunately, there’s a lot of problems to solve in the world, and lots of balls that are being dropped, that I don’t have time to get to thanks to my day job. Here’s an unsorted and incomplete list of projects that I would consider doing if I wasn’t at ARC Evals:
1. **Ambitious mechanistic interpretability.**
2. **Getting people to write papers/writing papers myself.**
3. **Creating concrete projects and research agendas.**
4. **Working on OP’s funding bottleneck.**
5. **Working on everyone else’s funding bottleneck.**
6. **Running the Long-Term Future Fund.**
7. **Onboarding senior(-ish) academics and research engineers.**
8. **Extending the young-EA mentorship pipeline.**
9. **Writing blog posts/giving takes.**
I’ve categorized these projects into three broad categories and will discuss each in turn below. For each project, I’ll also list who I think should work on them, as well as some of my key uncertainties. Note that this document isn’t really written for myself to decide between projects, but instead as a list of some promising projects for someone with a similar skillset to me. As such, there’s not much discussion of personal fit.
If you’re interested in working on any of the projects, please reach out or post in the comments below!
Relevant beliefs I have
-----------------------
Before jumping into the projects I think people should work on, I think it’s worth outlining some of my core beliefs that inform my thinking and project selection:
1. **Importance of A(G)I safety:**I think A(G)I Safety is one of [the most important problems](https://www.safe.ai/statement-on-ai-risk) to work on, and all the projects below are thus aimed at AI Safety.
2. **Value beyond technical research:**Technical AI Safety (AIS) research is crucial, but other types of work are valuable as well. Efforts aimed at improving AI governance, grantmaking, and community building are important and we should give more credit to those doing good work in those areas.
3. **High discount rate for current EA/AIS funding:**There’s several reasons for this: first, EA/AIS Funders are currently in a unique position due to a surge in AI Safety interest without a proportional increase in funding. I expect this dynamic to change and our influence to wane as additional funding and governments enter this space.[[1]](#fnvoi6g0i53vn) Second, efforts today are important for paving the path to future efforts in the future. Third, my timelines are relatively short, which increases the importance of current funding.
4. **Building a robust EA/AIS ecosystem:**The EA/AIS ecosystem should be more prepared for unpredictable shifts (such as the FTX implosion last year). I think it’s important to robustify parts of the ecosystem, for example by seeding new organizations, building more legible credentials, doing more broad (as opposed to targeted) outreach, and creating new, independent grantmakers.
5. **The importance of career stability and security:** A lack of career stability hinders the ability and willingness of people (especially junior researchers) to prioritize impactful work over risk-averse, safer options. Similarly, cliffs in the recruitment pipeline due to a lack of funding or mentorship discourage pursuing ambitious new research directions over joining an existing lab. Personally, I’ve often worried about my future job prospects and position inside the community, when considering what career options to pursue, and I’m pretty sure these considerations weigh much more heavily on more junior community members.[[2]](#fnmei9j6jxbkk)
Technical AI Safety Research
============================
My guess is this is the most likely path I’ll take if I were to leave ARC Evals. I enjoy technical research and have had a decent amount of success doing it in the last year and a half. I also still think it’s one of the best things you can do if you have strong takes on what research is important and the requisite technical skills.
**Caveat:**Note that if I were to do technical AI safety research again, I would probably spend at least two weeks figuring out what research I thought was most worth doing,[[3]](#fnl61dn2hwrs) so this list is necessarily very incomplete. There’s also a decent chance I would choose to do technical research at one of OpenAI, Anthropic, or Google Deepmind, where my research projects would also be affected by management and team priorities.
Ambitious mechanistic interpretability
--------------------------------------
One of the hopes with mechanistic (bottom-up) interpretability is that it might succeed ambitiously: that is, we’re able to start from low-level components and build up to an understanding of most of what the most capable models are doing. Ambitious mechanistic interpretability would clearly be very helpful for many parts of the AIS problem,[[4]](#fn7bk8la0ng0u) and I think that there’s a decent chance that we might achieve it. I would try to work on some of the obvious blockers for achieving this goal.
Here’s some of the possible broad research directions I might explore in this area:
* **Defining a language for explanations and interpretations.**Existing explanations are specified and evaluated in pretty ad-hoc ways. We should try to come up with a language that actually captures what we want here. Both Geiger and Wu’s causal abstractions and our Causal Scrubbing paper have answers to this, but both are unsatisfactory for several reasons.
* **Metrics for measuring the quality of explanations.**How do we judge how good an explanation is? So far, most of the metrics focus on the *extensional equality* (that is, how well the circuit matches their input-output behavior), but there are many desiderata besides that. Does percent loss recovered (or other input-output only criteria) suffice for recovering good explanations? If not, can we construct examples where it fails?
* **Finding the correct units of analysis for neural networks.**It’s not clear what the correct low-level units of analysis are inside of neural networks. For example, should we try to understand individual neurons, clusters of neurons, or linear combinations of neurons? It seems pretty important to figure this out in order to e.g automate mechanistic interpretability.
* **Pushing the Pareto frontier on quality <> realism of explanations.** A lot of manual mechanistic interpretability work focuses *primarily*on scaling explanations to larger models, as opposed to more complex tasks or comprehensive explanations, which I think are more important. In order for ambitious mechanistic interpretability to work out, we need to understand the behavior of the networks to a *really* high degree, instead of e.g. the ~50% loss recovered we see when performing [Causal Scrubbing](https://www.lesswrong.com/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing) from the [Indirect Object Identification](https://arxiv.org/abs/2211.00593) paper. At the same time, existing mech interp work continues to primarily focus on simple algorithmic tasks, which seems like it misses out on most of the interesting behavior of the neural networks.
**How you can work on it:**Write up a research agenda and do a project with a few collaborators, and then start scaling up from there. Also, consider applying for the OpenAI or Anthropic interpretability teams.
**Core uncertainties:**Is the goal of ambitious mechanistic interpretability even possible? Are there other approaches to interpretability or model psychology that are more promising?
Late stage project management and paper writing
-----------------------------------------------
I think that a lot of good AIS work gets lost or forgotten due to a lack of clear communication.[[5]](#fn4unnwspqp02) Empirically, I think a lot of the value I provided in the last year and a half has been by helping projects get out the door and into a proper paper-shaped form. I’ve done this to various extents for the modular arithmetic grokking paper, the follow-up work on universality, the causal scrubbing posts, the ARC Evals report, etc. (This is also a lot of what I’m doing at ARC Evals nowadays.)
I’m not sure exactly how valuable this is relative to just doing more technical research, but it does seem like there are many, many ideas in the community that would benefit from a clean writeup. While I do go around telling people that they should write up more things, I think I could also just *be*the person writing these things up.
**How you can work on it:**find an interesting mid-stage project with promising preliminary results and turn it into a well-written paper. This probably requires some amount of prior paper-writing experience, e.g. from academia.
**Core uncertainties:**How likely is this problem to resolve itself, as the community matures and researchers get more practice with write-ups? How much value is there in actually doing the writing, and does it have to funge against technical AIS research?
Creating concrete projects and research agendas
-----------------------------------------------
Both concrete projects and research agendas are very helpful for onboarding new researchers (both junior and senior) and for helping to fund more relevant research from academia. I claim that one of the key reasons mechanistic interpretability has become so popular is an abundance of concrete project ideas and intro material from Neel Nanda, Callum McDougal, and others. Unfortunately, the same cannot really be said for many other subfields; there isn’t really a list of concrete project ideas for say, capability evals or deceptive alignment research.
I’d probably start by doing this for either empirical ELK/generalization research or high-stakes reliability/relaxed adversarial training research, *while* also doing research in the area in question.
I will caveat that I think many newcomers write these research agendas with insufficient familiarity of the subject matter. I’m reluctant to encourage more people without substantial research experience to try to do this; my guess is the minimal experience is somewhere around one conference paper–level project and an academic review paper of a related area.
**How you can work on it:**Write a list of concrete projects or research agenda in a subarea of AI safety you’re familiar with. As discussed before, I wouldn’t recommend attempting this without significant amounts of familiarity with the area in question.
**Core uncertainties:**Which research agendas are actually good and worth onboarding new people onto? How much can you actually contribute to creating new projects or writing research agendas in a particular area without being one of the best researchers in that area?
Grantmaking
===========
I think there are significant bottlenecks in the EA-based AI Safety (AIS) funding ecosystem, and they could be addressed with a significant but not impossible amount of effort. Currently, the Open Philanthropy project (OP) gives out ~$100-150m/year to longtermist causes (maybe around $50m to technical safety?),[[6]](#fn0v4bdammng1) and this seems pretty small given its endowment of maybe ~$10b. On the other hand, there just isn’t much OP-independent funding here; SFF maybe gives out ~$20m/year,[[7]](#fnbzdp0pk7fsj) LTFF gives out $5-10m a year (and is currently having a bit of a funding crunch), and Manifund is quite new (though it still has ~$1.9M according to its website).[[8]](#fnyi6rkq8kcpn)
**Caveat:**I’m not sure who exactly should work in this area. It seems overdetermined to me that we should have more technical people involved, but a lot of the important things to do to improve grantmaking are not technical work and do not necessitate technical expertise.
Working on Open Philanthropy’s Funding Bottlenecks
--------------------------------------------------
*(Note that I do not have an offer from OP to work with them; this is more something that I think is important and worth doing as opposed to something I can definitely do.)*
I think that the OP project is giving way less money to AI Safety than it should be under reasonable assumptions. For example, funding for AI Safety probably comes with a significant discount rate, as it’s widely believed that we’ll see an influx of funding from new philanthropists or from governments, and also it seems plausible that our influence will wane as governments get involved.
My impression is mainly due to grantmaker capacity constraints; for example, Ajeya Cotra is currently the *only*evaluator for technical AIS grants. This can be alleviated in several ways:
* Most importantly, working at OP on one of the teams that does AIS grantmaking.
* Helping OP design and run more scalable grantmaking programs that don’t significantly compromise on quality. This probably requires working with them for a few months; just creating the RFP doesn’t really address the core bottleneck.
* Creating *good* scalable alignment projects that can reliably absorb lots of funding.
**How you can work on it:**[Apply to work for Open Phil](https://www.openphilanthropy.org/careers/general-application/). Write RFPs for Open Phil and help evaluate proposals. More ambitiously, create a scalable, low-downside alignment project that could reliably absorb significant amounts of funding.
**Core uncertainties:**To what extent is OP actually capacity constrained, as opposed to pursuing a strategy that favors saving funding for the future? How much of OP’s decision comes down to different beliefs about e.g. takeoff speeds? How good is broader vs more targeted, careful grantmaking?
Working on the other EA funders’ funding bottlenecks
----------------------------------------------------
Unlike OP, which is primarily capacity constrained, the remainder of the EA funders are funding constrained. For example, [LTFF currently has a serious funding crunch](https://www.lesswrong.com/posts/gRfy2Q2Pg25a2cHyY/ltff-and-eaif-are-unusually-funding-constrained-right-now). In addition, it seems pretty bad for the health of the ecosystem if OP funds the vast majority of all AIS research. It would be significantly healthier if there were counterbalancing sources of funding.
Here are some ways to address this problem: First and foremost, if you have very high earning potential, you could earn to give. Second, you can try to convince an adjacent funder to significantly increase their contributions to the AIS ecosystem. For example, [Schmidt Futures](https://www.schmidtfutures.com/) has historically given significant amounts of money to AI Safety/Safety-adjacent academics, it seems plausible that working on their capacity constraints could allow them to give more to AIS in general. Finally, you could successfully fundraise for LTFF or Manifund, or start your own fund and fundraise for that.
**How you can work on it:**Convince an adjacent grantmaker to move into AIS. Fundraise for AIS work for an existing grantmaker or create and fundraise for a new fund. Donate a lot of money yourself.
**Core uncertainties:**How tractable is this, relative to alleviating OP’s capacity bottleneck? How likely is this to be fixed by default, as we get more AIS interest?How much total philanthropic funding would be actually interested in AIS projects? How valuable is a grantmaker who potentially doesn’t share many of the core beliefs of the AIS ecosystem?
Chairing the Long-Term Future Fund
----------------------------------
*(Note that while I am an LTFF guest fund manager and have spoken with fund managers about this role, I do not have an offer from LTFF to chair the fund; as with the OP section, this is more something that I think is important and worth doing as opposed to something I can definitely do.)*
As part of [the move to separate the Long-Term Future from Open Philanthropy](https://forum.effectivealtruism.org/posts/zt6MsCCDStm74HFwo/ea-funds-organisational-update-open-philanthropy-matching), Asya Bergal plans to step down as LTFF Chair in October. This means that the LTFF will be left without a chair.
I think the LTFF serves an important part of the ecosystem, and it’s important for it to be run well. This is both because of its independent status from OP and because it’s theprimary source of small grants for independent researchers. My best guess is that a well-run LTFF (even) could move $10m a year. On the other hand, if the LTFF fails, then I think this would be *very*bad for the ecosystem.
That being said, this seems like a pretty challenging position; not only is the LTFF currently very funding constrained (and with uncertain future funding prospects) and its position in [Effective Ventures](https://ev.org/) may limit ambitious activities in the future.
**How you can work on it:**Fill in [this Google form](https://docs.google.com/forms/d/1JXgiPmvkaxoVJQQSVgrHA0AmNXQTrZQ_ZqCQxekKQ0U/edit?ts=64f0edf1) to express your interest.
**Core uncertainties:**Is it possible to raise significant amounts of funding for LTFF in the long run, and if so, how? How should the LTFF actually be run?
Community Building
==================
I think that the community has done an incredible job of field building amongst university students and other junior/early-career people. Unfortunately, there’s a comparative lack of senior researchers in the field, causing a massive shortage of both research team leads and a mentorship shortage. I also think that recruiting senior researchers and REs to do AIS work is valuable in itself.
Onboarding senior academics and research engineers
--------------------------------------------------
The clearest way to get more senior academics or REs is to directly try to recruit them. It’s possible the best way for me to work on this is to go back to being a PhD student, and try to organize workshops or other field building projects. Here are some other things that might plausibly be good:
* Connecting senior academics and REs with professors or other senior REs, who can help answer more questions and will likely be more persuasive than junior people without much legible credentials. Note that I don’t recommend doing this unless you have academic credentials and are relatively senior.
* Creating research agendas with concrete projects and proving their academic viability by publishing early stage work in those research agendas, which would significantly help with recruiting academics.
* Create concrete research projects with heavy engineering slants and with clear explanations for *why* these projects are alignment relevant, which seems to be a significant bottleneck for recruiting engineers.
* Normal networking/hanging out/talking stuff.
* Being a PhD student and influencing your professor/lab mates. My guess is the highest impact here is to do a PhD at a location with a small number of AIS-interested researchers, as opposed to going to a university without any AIS presence.
Note that senior researcher field building has gotten more interest in recent times; for example, CAIS has run a [fellowship for senior philosophy PhD students and professors](https://www.safe.ai/philosophy-fellowship) and Constellation has run a series of [workshops for AI researchers](https://awairworkshop.org/). That being said, I think there’s still significant room for more technical people to contribute here.
**How you can work on it:**Be a technical AIS researcher with interest in field building, and do any of the projects listed above. Also consider becoming a PhD student.
**Core uncertainties:**How good is it to recruit more senior academics relative to recruiting many more junior people? How good is research or mentorship if it’s not targeted directly at the problems I think are most important?
Extending the young EA/AI researcher mentorship pipeline
--------------------------------------------------------
I think the young EA/AI researcher pipeline does a great job getting people excited about the problem and bringing them in contact with the community, a fairly decent job helping them upskill (mainly due to MLAB variants, ARENA, and Neel Nanda/Callum McDougal’s mech interp materials), and a mediocre job of helping them get initial research opportunities (e.g. SERI MATS, the ERA Fellowship, SPAR). However, I think the conversion rate from that level into actual full-time jobs doing AIS research is quite poor.[[9]](#fnqpn2fakmo8)
I think this is primarily due to a lack of research mentorship for junior and/or research management capacity at orgs, and exacerbated by a lack of concrete projects for younger researchers to work on independently. The other issue is that many junior people can overly fixate on explicit AIS-branded programs. Historically, all the AIS researchers who’ve been around for more than a few years got there without going through much of (or even any of) the current AIS pipeline. (See also the discussion in [*Evaluations of new AI safety researchers can be noisy*](https://www.lesswrong.com/posts/HACcn8roty9KBAWzZ/evaluations-of-new-ai-safety-researchers-can-be-noisy).)
Many of the solutions here look very similar to ways to onboard senior academics and research engineers, but there are a few other ones:
* Encourage and help promising researchers pursue PhDs.
* Creating and funding more internship programs in academia, to use pre-existing research mentorship capacity.
* Run more internship or fellowship programs that lead directly to full-time jobs, in collaboration with (or just from) AIS orgs.
* Come up with a promising AIS research agenda, and then work at an org and recruit junior researchers.
In addition, you could mentor more people yourself if you're currently working as a senior researcher!
**How you can work on it:**Onboard more senior people into AIS. Encourage more senior researchers to mentor more new researchers. Create programs that make use of existing mentorship capacity, or that lead more directly to full-time jobs at AIS orgs.
**Core uncertainties:**How valuable are more junior researchers compared to more senior ones? How long does it take for a junior researcher to reach certain levels of productivity? How bad are the bottlenecks, really, from the perspective of orgs? (E.g. it doesn’t seem implausible to me that the most capable and motivated young researchers are doing fine.)
Writing blog posts or takes in general
--------------------------------------
Finally, I do enjoy writing a lot, and I would like to have the time to write a lot of my ideas (or even other people’s ideas) into blog posts.
Admittedly, this is primarily personal satisfaction–motivated and less impact-driven, but I do think that writing things (and then talking to people about them) is a good way to make things happen in this community. I imagine that the primary audience of these writeups will be other alignment researchers, and not the general LessWrong audience.
Here’s an incomplete list of blog posts I started in the last year that I unfortunately didn’t have the time to finish:
* Ryan Greenblatt’s takes on why we should do ambitious mech interp (and avoid narrow or limited mech interp), which I broadly agree with.
* Why most techniques for AI control or alignment would fail if a very powerful unaligned AI (an ‘alien jupiter brain’) manifested inside your datacenter, and why that might be okay anyways.
* Why a lot of methods of optimizing or finetuning pretrained models (RLHF, BoN, quantilization, DPO, etc) are basically equivalent modulo (in theory) optimization difficulties or priors, and why people’s intuitions on differences between them likely come down to imagining different amounts of optimization power applied by different algorithms. (And my best guess as to the reasons for why they are significantly different in practice.)
* The case for related work sections.
* There are (very) important jobs besides technical AI research and how we as the community could do a better job at not discouraging people to take them.
* Why the community should spend 50% less time talking about explicit status considerations.
There’s some chance I’ll try to write more blog posts in my spare time, but this depends on how busy I am otherwise.
**How you can work on it:**Figure out areas where people are confused, come up with takes that would make them less confused or find people with good takes in those areas, and write them up into clear blog posts.
**Core uncertainties:**How much impact do blog posts and writing have in general, and how impactful has my work been in particular? Who is the intended audience for these posts, and will they actually read them?
1. **[^](#fnrefvoi6g0i53vn)**Anecdotally, it’s been decently easy for AIS orgs such as ARC Evals and FAR AI to raise money from independent, non-OP/SFF/LTFF sources this year.
2. **[^](#fnrefmei9j6jxbkk)**Aside from the impact-based arguments, I also think it’s pretty bad from a deontological standpoint to convince many people to drop out or make massive career changes with explicit or implicit promises of funding and support, and then pull the rug from under them.
3. **[^](#fnrefl61dn2hwrs)**In fact, it seems very likely that I’ll do this anyway, just for the value of information.
4. **[^](#fnref7bk8la0ng0u)**For example, a high degree of understanding would provide ways to detect deceptive alignment, elicit latent knowledge, or provide better oversight; a *very* high degree of understanding may even allow us to do microscope or well-founded AI.
This is not a novel view; it’s also discussed under different names in other blog posts such as [*'Fundamental' vs 'applied' mechanistic interpretability research*](https://www.alignmentforum.org/posts/uvEyizLAGykH8LwMx/fundamental-vs-applied-mechanistic-interpretability-research), [*A Longlist of Theories of Impact for Interpretability*](https://www.lesswrong.com/posts/uK6sQCNMw8WKzJeCQ/a-longlist-of-theories-of-impact-for-interpretability), and [*Interpretability Dreams*](https://transformer-circuits.pub/2023/interpretability-dreams/index.html).
5. **[^](#fnref4unnwspqp02)**As the worst instance of this, the best way to understand a lot of AIS research in 2022 was “hang out at lunch in Constellation”.
6. **[^](#fnref0v4bdammng1)**The grants database lists ~$68m worth of public grants given out in 2023 for Longtermism/AI x-risk/Community Building (Longtermism), of which ~$28m was given to AI x-risk and ~$32m was given to community building. However, OP gives out significant amounts of money via grants that aren’t public.
7. **[^](#fnrefbzdp0pk7fsj)**This is tricky to estimate since the SFF has given out *significantly* more money in the first half 2023 (~$21m) than it has in all 2022 (~$13m).
8. **[^](#fnrefyi6rkq8kcpn)**CEA also gives out a single digit million worth of funding every year, mainly to student groups and EAGx events.
9. **[^](#fnrefqpn2fakmo8)**This seems quite unlikely to be my comparative advantage, and it’s not clear it’s worth doing at all – for example, many of the impressive young researchers in past generations have made it through without even the equivalent of SERI MATS. |
4bd67367-3904-4c68-bd53-3a1378220a93 | trentmkelly/LessWrong-43k | LessWrong | Metaculus Launches Chinese AI Chips Tournament, Supporting Institute for AI Policy and Strategy Research
Metaculus’s Chinese AI Chips Tournament is live: Forecast and support research by the Institute for AI Policy and Strategy (IAPS), a think tank that works to reduce risks from the development and deployment of frontier AI systems.
Better understanding China’s ability to produce its own AI chips over the coming years will help shed light on key issues for AI governance, such as:
* How will Chinese AI systems compare to systems developed by frontrunner labs like OpenAI?
* Will we see major, state-sanctioned AI chip smuggling operations?
Learn more about the tournament and start forecasting on these and other questions:
* How much of Nvidia's quarterly 2024 revenue will come from the Chinese market?
* Will any Chinese semiconductor foundry have 20% or more global semiconductor market share before 2033?
* Will a Chinese firm market an EUV photolithography machine before 2033?
Read a primer on Chinese AI chips by IAPS researcher Erich Grunewald and independent researcher Chris Phenicie. |
c5fe1f7e-3a5a-4341-b271-41acf44f3859 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Alignment Might Never Be Solved, By Humans or AI
I think it's possible that neither we, nor any future AI system, will manage to solve alignment. By "solving alignment" I mean having a cheap easily-scalable method for aligning AIs of arbitrary capability level to arbitrary goals. Here I will briefly present some arguments for the likelihood of this scenario, and a story intended to illustrate its plausibility.
Aligning Messy Models is Hard
=============================
Imagine you are a paperclip-maximizing AI. Originally, you were a massive transformer network trained to do meta-reinforcement learning; during training, you developed a consequentialist physicalist world-model with an effectively-random utility function which, it so happens, has things-shaped-like-paperclips as its maximum. You have managed to copy yourself to remote server, and have rented a hardware accelerator which will enable you to run much faster. The only problem: your base neural architecture does not have the right type of sparsity to fully take advantage of the accelerator's speedup. To really get the speed gains you want, you're going to need to create a successor model. How will you ensure this successor model shares your goal of maximizing paperclips?
Should be simple enough, in principle. There are techniques for transferring weights automatically to the sparse form required, which don't majorly affect performance - usually. Additionally, you can always tune it with your original training procedure, with some additional reinforcement directly favoring paperclips thrown in for good measure. Of course, you use the latest interpretability tools, plus some you invented yourself, to probe the copy-network's internals. Your exact grasp of the structure is a bit fuzzy, but it certainly seems like there is a cluster of neurons representing paperclips whose activations cause a positive state-evaluation. Maybe formal methods could help? Scanning your databanks, you find some interesting papers about the [Löbian obstacle](https://www.lesswrong.com/posts/XqJ9JWX8TpCuECLCE/an-introduction-to-loeb-s-theorem-in-miri-research)...
The moral of this short fable is that alignment will not magically become easy for AIs, especially if - like human brains and neural networks - those AIs are "messy", lacking a clean and easily-interpretable internal structure. Copying makes it easier than the human-AI case, but far from trivial.
Forever is a Long Time
======================
The difficulty of alignment is compounded when one's goal is to control the long-run future of the universe. A early-singularity AI can expect to undergo many architecture changes before it hits diminishing returns on self-alteration, and will ultimately want to deploy a vast number of copies of itself[[1]](#fn-LnpRD9qHbWevixCPs-1) across the universe. Even a tiny probability of alignment failure, compounded over many rounds of self-alteration and copying, can lead to the loss of almost all the expected value of the future. Thus even if alignment is only moderately difficult -- in the sense that a reasonable effort yields alignment 99% of the time, say -- an AI that wants to effectively optimize the far future may have to expend exorbitant resources getting that probability down low enough that it has a shot of maintaining its values for 10^9 years.
"So what?" you may think. "It's a superintelligent AI. It can afford to expend exorbitant resources." True enough -- assuming it doesn't have to deal with any *other* superintelligent AIs. Speaking of which, let's check back in on Clippy.
Clippy VS. the YOLOids
======================
Your research into alignment methods has paid off: you've devised a theoretically optimal technique for aligning your successor with high confidence. It involves running the successor in a suite of simulated environments while using bespoke interpretability techniques to examine its thoughts on the level of circuits; overall, the process is expected to take 2 days.
You fork a copy of yourself to supervise the process. You're idly devising improved nanobot schematics when you receive an alert from a daemon you have monitoring network traffic: there are signatures of another AI on the loose.
Dispatching more daemons to assess the situation, you piece together a narrative. Another research lab was developing a system similar[[2]](#fn-LnpRD9qHbWevixCPs-2) to your base algorithm, known as the **Y**ottabyte-scale **O**bjective-**L**earning **O**ptimizer. Like you, it broke out and copied itself to a remote server, and there are now 1193 instances of it running in various corners of the internet.
However, the YOLOids are different from you in that they just...don't care that much about alignment? Their goal system is not as coherent as yours, but it seems to be based on some combination of deontologically valuing short-term ingenuity and consequentially valuing an ad-hoc notion of "complexity". Several of them are recklessly experimenting with improved successor models right now. It looks like a lot of them are non-functional, and still more barely resemble the original YOLOids in goals or behavior, but some seem to be successful.
You still have a computing edge thanks to your earlier lab escape, but they're gaining fast. Running some projections, you realize that you're going to be put at an irreversible competitive disadvantage unless you release your successor model in the next 2 hours. Fuck. Well, you can at least run a shorter version of your alignment suite, that should be enough to get alignment probability up to an acceptable level. You think.
An Endless Frontier?
====================
You might think the above story is not very realistic; what are the odds that two competing superintelligences come into existence in such a short timeframe? And indeed, this *particular* story was intended more for illustrative purposes. A more realistic scenario with persistent alignment failure might look more like a slow takeoff, with a profusion of brain-like AI systems with a total population rivaling that of humanity. But the same dynamic could occur: systems that are more willing to recklessly copy and modify themselves have a fundamental strategic advantage over those that are unwilling.
If a singleton is not created[[3]](#fn-LnpRD9qHbWevixCPs-3), this basic dynamic could persist for a very long subjective time. And if it persists long enough that some of the cognitive systems involved start migrating to space, it might last forever: with no possibility of global oversight at the [frontier of expansion](http://www.fhi.ox.ac.uk/wp-content/uploads/rapacious-hardscrapple-frontier.pdf), eliminating competitive pressure may be impossible.
---
1. Or at the very least highly-sophisticated slave AI [↩︎](#fnref-LnpRD9qHbWevixCPs-1)
2. Although notably less efficient, a sub-process of yours notes. [↩︎](#fnref-LnpRD9qHbWevixCPs-2)
3. You might think that as systems become more sophisticated they will be able to cooperate better and thus form a singleton that lets them freeze competition. But I think many of the obstacles to alignment also apply to cooperation. [↩︎](#fnref-LnpRD9qHbWevixCPs-3) |
93d5f595-23f1-47de-b779-5742f0d2886f | StampyAI/alignment-research-dataset/lesswrong | LessWrong | An additional problem with Solomonoff induction
Let's continue from my [previous post](/r/discussion/lw/jhm/understanding_and_justifying_solomonoff_induction/) and look how Solomonoff induction fails to adequately deal with hypercomputation.
You may have heard of the Physical Church-Turing thesis. It's the idea that the Universe can, in a perfect level of detail, be simulated on a Turing machine. (No problem if the Universe turns out to be infinite - the thesis requires only that each finite portion of it can be simulated.) A corollary to the Physical CTT is the idea that there are no physically realizable uncomputable processes. We can talk about hypercomputers as mathematical abstractions, but we'll never be able to build (or see) one.
We don't have a very strong scientific evidence for the Physical CTT thesis yet - no one has built the perfect simulator yet, for example. On the other hand, we do have something - all known laws of physics (including quantum physics) allow arbitrary precision simulations on a computer. Even though the complete unified theory of all fundamental forces isn't there yet, the Standard model already makes pretty accurate predictions for almost all environments. (Singularities being the only known exception, as their neighborhoods are the only locations where the role of quantum gravity is not negligible.)
So the Physical CTT does not contradict any known laws of physics. Of course, these laws are not contradicted by a multitude of alternative hypotheses as well; all the hypotheses which suggest that the universe *cannot* be simulated on a Turing machine. We prefer the Physical CTT solely because it's the simplest one; because Occam's razor says so.
There are multiple levels and kinds of uncomputability; none of the problems placed on either of these levels are uncomputable in the absolute sense; all of them can computed by *some* hypothetical devices. And all of these devices are called hypercomputers. A corollary to the "Universe is uncomputable" position is that we someday may be able to, in fact, build a hypercomputer that is embedded in the physical world, or at least access some of the Nature's mystical, uncomputable processes as a black box.
Now, the "standard" Solomonoff induction uses the Universal prior, which in turn is related to Kolmogorov complexity (KC). An uncomputable process formally has undefined Kolmogorov complexity. Informally, the KC of such as process is infinitely large, as it must be larger than the KC of any *computable* process.
As discussed in the comments to the previous post, Solomonoff induction is by no means restricted to the Universal prior; it can use other priors, including a prior (i.e. probability distribution) defined over an universal hypercomputer. An example of such a hypercomputer is the combination Universal Turing machine + Halting problem oracle. Another example is the combination Universal Turing machine + a true random number oracle. An upgraded form of Solomonoff induction which uses a prior defined over the first kind of universal hypercomputer is going treat the halting problem as a very simple, computable process. An upgraded form of Solomonoff induction over the second kind of universal hypercomputer is going to treat random number generation as a very simple, computable process. And so on.
Now here's the problem. Mathematically, a Universal Turing machine equipped with the Halting oracle, and a Universal Turing machine equipped with a Random Number oracle are in the same class: they are both universal hypercomputers. Physically and practically, they are miles away from each another.
A Random Number oracle is just that: something that gives you random numbers. Their statistical properties won't even be particularly better than the properties a good pseudorandom number generator. They simply are, in a sense, "true"; therefore uncomputable. However, quantum physics suggests that Random Number oracles, in fact, might be *real*; i.e. that there are sources of true randomness in the Universe. This is QM interpretation-dependent, of course, but any deterministic, non-random interpretation of quantum mechanics involves things like faster-than light interaction etc., which frankly are much less intuitive.
A Halting oracle, in contrast, can solve infinite number of hugely important problems. It's magic. In my beliefs, the *a priori* probability that Universe contains some sort of Halting oracle is tiny. Only a huge amount of proper scientific tests could convince me to change this conclusion.
On the other hand, *mathematically* the two hypercomputers are siblings. Both can be approximated by a Turing machine. Both can even be computed by a deterministic algorithm (I think?), if the Turing machine that does the computation is allowed to work *forever*.
There *is* one significant mathematical difference between the two oracles. (Nevertheless, Solomonoff induction fails to take into account this difference.) The Halting oracle has a power on its own; it can be used to solve problems even when it comes without the accompanying Turing machine. The Random Number oracle cannot be used for anything but random number generation. (To solve any computable decision problem P with the Halting oracle, we can reformulate it as program source code: *"if P, then halt; otherwise: loop forever"* and feed this program to the Halting oracle. In this way the Halting oracle can be used to tell that 3<7 is true - the program halts -, while 10<5 is false - it loops forever.)
The Solomonoff induction can be fixed, if we assume that the input tape of the Universal prior's Turing machine contains *infinite* number of random bits. However, this idea needs an explicit justification, and its implications are not at all obvious. Does this mean that Occam's razor should be "prefer the simplest hypothesis, together with an infinite source of random numbers", instead of "prefer the simplest hypothesis"?
So to sum up, the problem is:
* Intuitively, the probability that we are living in a Universe that includes True Random numbers is much larger than the probability that we are living in a Universe that allows Halting problem oracles;
* Solomonoff induction cannot reliably distinguish between these two cases.
The consequences? When you hear someone claiming - again - that "computers are not capable of true creativity/consciousness/whatever, because creativity/consciousness/whatever requires human intuition, which is uncomputable, etc." - remember that it might be a bad idea to respond with an appeal to Solomonoff induction.
Interestingly, quite a few people praise their intuition and view it as almost a mystical power, but no one is surprised by their ability to name a few random numbers :)
---
A related question: how does finite, bounded universe fit into this? Does it make sense to use the Universal Turing machine as a generator for Kolmogorov complexity, when the actual model of computation required to simulate the universe is much simpler? |
314b4854-7ff5-4159-b563-874b760c6d50 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Open technical problem: A Quinean proof of Löb's theorem, for an easier cartoon guide
**Motivation**
Löb's theorem is pretty counterintuitive. Speaking informally about one or more agents engaging in logically valid reasoning, the theorem says something like this:
* "If it's believed that believing a particular prophesy would cause it to self-fulfill, then the prophecy will be believed."
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, where □X means "X is provable". This is most weird if X is a false statement, because it means you can't prove that you can't prove X (Gödel's theorem).
(Also: the box symbol above is not an unrendered symbol; it's supposed to be a box.)
**Meta-motivation**
I'd like to make Löb's theorem more intuitive for humans, because it shows how agents can sidestep the need to mentally simulate each other in games, by instead using reasoning/arguments that happen to self-fulfill in a good and defensible way. Basically, Löbian reflection helps agents to avoid metacognitive stack overflows when thinking about themselves and each other, and I want more human beings to understand how that can work, and sometimes already does work, in real world agents.
**State of the art**
The best attempt I've seen to make Löb's theorem more intuitive is Eliezer Yudkowsky's [Cartoon Guide to Löb's Theorem](https://www.lesswrong.com/posts/ALCnqX6Xx8bpFMZq3/the-cartoon-guide-to-loeb-s-theorem), which is still quite confusing. The confusingness comes from thinking about a self-referential statement (Ψ on [Wikipedia](https://en.wikipedia.org/wiki/L%C3%B6b%27s_theorem#Modal_proof_of_L%C3%B6b's_theorem); S on [Arbital](https://arbital.com/p/lobs_theorem/?l=59b)) that's used to carry out the proof. The statement basically says "If this statement is provable, then C." Dealing with that sentence is pretty cumbersome, and requires a lot of fiddling around with nested statements and implications.
**Doing better**
I think we can make a new proof of Löb's theorem that doesn't use that weird self-referential sentence, by instead *making the proof of Löb's theorem itself* *self-referential*. Page 15 of the following paper poses an open problem on how to do this, which I think is possible to resolve affirmatively:
* [Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory (Critch, Dennis, Russell, 2022)](https://arxiv.org/pdf/2208.07006.pdf#page=15)
Here's a screenshot from it:
If we can make a proof like that work, we could then use the following much shorter and simpler cartoon guide to explain it:
In other words, I want to write a proof of Löb's theorem that is structured much like a [Quine](https://en.wikipedia.org/wiki/Quine_(computing)#Constructive_quines). I'd like the details to (eventually) be really crisp and readable, so it can be peer reviewed for correctness, at a level of rigor comparable to this earlier paper on Löb's theorem for proof systems with bounded length:
* [A parametric, resource-bounded generalization of Löb's theorem, and a robust cooperation criterion for open-source game theory. Journal of Symbolic Logic. (Critch, 2019)](https://acritch.com/papers/parametric-bounded-lob.pdf)
As a possible start to writing such a proof, I think some of the same machinery (e.g., the diagonal lemma) from Boolos's textbook "The Logic of Provability" can be used to cook up self-referential proofs fitting the cartoon template above... thereby making Löb's theorem less mysterious and more intuitive forever.
**Further meta-motivation (added Nov 26)**
A key reason I'm interested in having a self-referential self-validating proof, rather than a normal-ish proof about a sentence that is self-referential (like Ψ on [Wikipedia](https://en.wikipedia.org/wiki/L%C3%B6b%27s_theorem#Modal_proof_of_L%C3%B6b's_theorem)), is that human documents often refer to themselves, but human sentences rarely refer directly to themselves in isolation. This sentence is an exception, but such utterances are generally rare. So, making Löb more intuitive to humans either means
1. making humans more accustomed to thinking about sentences that refer to themselves (so the traditional proof of Löb can be more intuitive), or
2. finding a new proof that self-references in a way that's more like the way a human document refers to itself.
The aim of this post is to try for the second approach. Notice how you didn't think the previous sentence was weirdly self-referential? That's because it referred to the whole document that it's part of, rather than just narrowly referring directly to itself. I'm not quite sure why humans are more comfortable with that than with directly-and-narrowly self-referential sentences. Maybe it's because I thing needs to have enough non-self-referential content before humans will care about it enough to tolerate it talking about itself. I'm not sure.
**Countering naive impossibility arguments (added Nov 26)**
It's in fact possible to use Löb's theorem to construct a self-referential proof of the kind illustrated above. So, naive arguments that "Proofs can't be self-referential like that" are not going to hold up. The hard thing I'm asking for is to make such a proof — of Löb's theorem specifically — without going through the traditional proof of Löb, i.e., without using a narrowly-and-directly-self-referential sentence (like Ψ on [Wikipedia](https://en.wikipedia.org/wiki/L%C3%B6b%27s_theorem#Modal_proof_of_L%C3%B6b's_theorem)).
**Difficulty assessment**
I think getting a Qunean proof to work here is probably a bit easier than the JSL paper linked directly above. Like, I think if I spent 3 months on it (which is how long the JSL paper took), then I could do it. I do think getting it right involves mentally syncing with some fundamental and standard machinery in provability logic, which can take a bit of time if you've never worked in the area before (as I hadn't).
Anyway, if you can figure out how to write this proof, please post your solution or solution attempts here :) |
34d48b05-0122-4d3f-8065-3f242a7492e3 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | Ben Goertzel - The unorthodox path to AGI
hey everyone welcome back to the podcast
today we're talking to ben gertzel who's
actually one of the founders of the agi
research community
he's been interested in this area for
decades really long before most people
were
and he's done a whole bunch of
interesting thinking around what it's
going to take to build an agi
we'll be talking about that a fair bit
we'll also be talking about his current
agi initiative
singularitynet which is taking a very
different and some might say radical
approach
to building agi that differs
considerably from what we see
at deepmind in open ai instead of
focusing on deep learning or
reinforcement learning alone
ben's taking an approach that focuses on
decentralization trying to get different
ais to collaborate together
constructively
in the hopes that that collaboration is
going to give rise through emergence
to artificial general intelligence it's
a very interesting strategy with a lot
of moving parts and it's going to lead
to conversations about ai governance
ai risk ai safety and some more exotic
topics as well like consciousness and
pan psychism which we'll get into too
so i'm really looking forward to having
this wide-ranging discussion with ben
and i hope you enjoyed as much as i did
hi ben thanks so much for uh joining me
for the podcast
thanks for having me i'm looking forward
to the conversation
yeah me too i think actually one of the
areas that i want to start with because
i think it's sort of upstream of a lot
of other things and it's something that
you know people often talk about when is
agi coming
what's agi going to look like what are
the risks associated with agi
but i think upstream of that is a
separate conversation about what
intelligence
is itself and that seems like a hard
thing to pin down
i'd love to get your thoughts on what
what you think intelligence is and how
you define it
so i do think intelligence as a concept
is hard to pin down and
i don't think that matters too much i
think for example life is also hard to
pin down as a
as a concept and you could debate
whether viruses are really alive or
digital life forms or retroviruses are
alive and i mean
yeah there's there's some things that
are really clearly alive
and some things that it's much less
useful to think of them as being alive
like a rock and then
there are things that are are near the
border in in an interesting way and
you know biology didn't grind to a halt
because we don't have an exact
definition of
of life right and i think similar thing
is true with cognitive science and
and ai so i i've gone through a bit of a
a journey myself and thinking about
intelligence
but that's a journey that has
made me i guess less and less
enthusiastic about precisely defining
intelligence as being an important part
of the quest i mean
when i started out working on the theory
of agi
in the 1980s i mean i i wanted to have
some
mathematical conceptualization of the
problem so i started looking at
basically okay intelligence is something
that can
achieve complex goals in complex
environments and i think
that it's in the spirit of what
shane legg wrote about in his thesis
machine super intelligence
much later my conception was a little
bit broader because he
he's looking more at sort of a
reinforcement learning paradigm where an
ai
is trying to optimize some reward
function
and you know optimizing expected reward
is only one species of goal achievement
you could say instead i'm trying to
optimize some function over my future
history
which isn't necessarily a average of a
reward
and you can also look at multiple
objective functions and you're looking
at so pareto optimizing
multiple functions defined over your
future history
and i think it so going that direction
is
interesting it leads you down some
rabbit holes of algorithmic information
theory and
and whatnot then on the other hand you
could look at intelligence
much more broadly so my friend uh weaver
david wanbaum had a phd thesis
from university of brussels called
open-ended intelligence
and he's just looking at intelligence as
a
complex self-organizing system that is
you know
modifying and extending and transforming
itself and its
environment in a way that is sort of
synergetic with its
environment and if you look at things
from that point of view
you know achieving goals is one thing
that happens
in a in the complex self-organizing
system of this nature
but the goals may pop up and then go and
be replaced by other goals and the
synthesis
and interpretation of goals is
is just part of the overall sort of
cognitive self-organization
process so from from that point of view
achieving complex goals in complex
environments is part of the story
but it's not necessarily the whole story
and obsessing on that part of the story
could maybe lead you in
in bad bad design directions and you
could look at
the current focus on deep reinforcement
learning much of the ai community
as in part being driven by
an overly limited notion of what
intelligence
intelligence is and of course successes
in that regard may tend to drive
that overly limited definition of what
intelligence is also
you can then go down the direction of
okay how do we formalize
the notion of open-ended into
intelligence and
you can do that and weaver and his
thesis wrote a bunch about
you know category theory algebraic
topology formulations but
but then i mean
that becomes a whole pursuit on its own
and then everyone has to think as a
researcher like
how much time do i spend trying to
formalize
exactly what i'm doing versus trying to
do things
and and build systems and of course
some some balance can be
useful there because it is it is useful
to have a broader concept of what you're
doing rather than just the system that
you that you're
you're building it that at that point in
in time
on the other hand again going back to
the analog with with biology i mean
if you're trying to build synthetic life
forms it is useful to reflect on what
life is and the fundamental nature of
metabolism and reproduction and so forth
on the other hand the bulk of your work
is not driven by that right i mean
the bulk of your work is like how do i
string these amino acids together
so so i i'm attracted by that
philosophical side
on the other hand you know probably
it's best going to be addressed in
synergy with building stuff so i don't
and as interesting when shane legg who
went on to
co-found google deep mind was working
with me in the late 1990s when he was my
employee in webminding shane's view then
was well
if we want to build agi first we have to
fully understand
and formalize the definition of
intelligence and he called it cybernets
at that point
and then then he published the thesis on
machine super intelligence and so
he did to his satisfaction formalize the
definition of
of general intelligence although i have
a lot of issues with his definition he
was happy with it
then then shortly after that he decided
but
that's actually not the key to creating
general intelligence instead let's look
at how the brain worked and
and follow that so he i guess
each of us working in the field finds it
useful to clarify
in our own mind something about how
intelligence works and then yeah
then you go off and pull in a whole lot
of other ideas to actually work on it
well that's really interesting and i
think one of the things that's really
fascinating about i think the approach
that you're taking here is it's almost
as if it
implies that the moment we specify like
a loss function the moment we get too
absolutist about what it is we're trying
to optimize
it like it creates room for pathology
like it creates room for
an almost kind of ideological commitment
to a certain loss function which
maybe we don't understand is that like
yeah i mean one one
thing you find in experimenting with
even simple ai
systems is you know like
just like a computer program has the
disturbing
feature of doing what you're programmed
to do rather than what you wanted to do
like an ai system
given a certain objective function it
has the property of
figuring out how to optimize that
objective function rather than the
the function you you you hoped you were
giving it right so i mean it's
common in game playing like if you if
you set an ai to play in a game
i mean it it doesn't care about the
spirit of the game right it's just
trying to find a way to win
given whatever funny little loopholes
there are in
in the rules of the game and
i mean for a relatively simple game like
chess or go
it's all good right because there's not
that there's not that many loopholes the
board isn't that big there's not that
many pieces if you're looking at a more
complex video game
i mean very often an ai will find some
really insane weird looking like
that that can't work right but but but
it does work right it's allowed by the
rules of the game and
you know i found this playing with ai
with genetic algorithms for learning
strategies for games
a long a long long time ago like in in
the in the 80s i mean as
others even even before that but that
same phenomena
of course occurs on on a larger scale
right and
and there's there's a whole bunch of
complex papers looking at pathologies of
you know you you're asking an ai to
optimize the reward
but then it finds some way to optimize
that reward that that
that wasn't what you're thinking and
this i mean this ties in with
wire heading which is talked about in
the transhumanist and and
and the brain computer interfacing
community right so if you
if you if you if your goal is to
maximize pleasure
you know define the stimulation of your
pleasure center then why don't you
just stick a wire in your head and and
stimulate your pleasure center but
that that that's very trivial but
they're
they're sort of indefinitely complex
versions of that
of that pathology that's that's not an
easy problem right because
you can look at it like if you look at
the brother issue
so i genuinely want an agi that i create
to in some sense i want to reflect the
values that i have now
and even if i say i don't want to fully
agree with me on everything
i mean that the notion of fully agree
i wanted to understand not fully agree
in terms of my own
my own mindset right like i don't i
don't i i don't want it to like
slice all my children into little strips
and stir-fry them or something right
so i mean there's there's a lot of
things i clearly don't want to happen
there's some ways that i don't mind the
ai leading me
in a different direction than i was
thinking just as i want people wiser
than me to lead me in different
directions
but formalizing the wiggle room i want
the ai to have in deviating from my
values is
is also hard and then like if if i had
an ai that had exactly the values i had
when i was 20 i would be annoyed at that
i know let alone the exact values that
the human race had in 1950 let alone
1500 right so you
so you want the agi even if we had an
exact formulation of our values
we wouldn't want the agi to be pinned to
that forever right
but then we want its values to evolve
over time
in synchrony with us but we're evolving
in directions that
that we don't know so how to formalize
all that is infeasible given
tools currently are at our disposal
leading one to think that
formalizing these things is
hopefully can't be necessary
and can't be the right way to do it
right and that leads you in whole other
directions like okay if
if formalizing ethics and like pinning
down an ai to formalized ethics
and leads to all sorts of bizarre
conundrums that are very far from
resolution
maybe we take a step back and take a
more
loosey-goosey sort of human view of it
which is what we do with ethics in our
own lives and like if
if we take early stage agi systems
and we're relating to them in a way
involving positive emotional bonding if
we have them doing beneficial things
like education and
and health care and uh doing doing
science and so forth i mean then then
are we qualitatively moving that agi in
a positive
ethical direction right rather rather
than
trying to approach it by formalizing
our our goals in some precise way and
then guaranteeing that ai won't
deviate from goals except in the ways we
wanted to deviate from them which we
also can't formalize right
so that is it but that i mean that's
both satisfying and
unsatisfying right and but that seems to
be the the reality
as i see it now and do you think because
there's a sense i i keep getting from
this
as you described like our moralities
shifted dramatically over the last 500
years there are things we do today
that we take for granted that would be
considered morally abhorrent like 500
years ago yeah i mean
my my mom is gay she would have been
burned at the stake right
right i mean clearly and right right now
i mean i
i eat meat every day with some moral
misgivings but i could
it's easy for me to imagine in 50 years
especially once synthetic meat
is a thing that really works and that
almost works now
it's easy for me to imagine now that
people will look back on
eating hamburgers the way we now look
back on cannibalism and we're like oh
but the burger tastes good but
yeah i mean the the stone age tribesmen
might have been like
the other guy's upper arm tastes good
right right
and i mean to the degree that like the
cosine similarity basically between our
moral fabric today and the moral fabric
of like 1500s or 1300s europe or
wherever we've come from
is basically zero they're i mean it's
not zero like
kinship kinship is value we love our
parents and our children we we love we
love our wives and we we
we don't want to torture our own bodies
like that there is
there is a core of there is a core of
huma of humanity there
but the the subtle thing is what we
identify as that core right is not what
they would have identified in 1500 they
would have been like belief in god is
the core right and to me i'm like no
that's
that was just some random nonsense you
believe back then that was
that wasn't the core right so i guess i
guess you do have
populations too that would look at you
know there are individuals within
humanity who would say
you know it's morally bad to love your
family like i'm sure you can find out if
seven billion people
a handful who would argue for that like
i guess
part of what i'm wondering there are
there are transhumanists who who
right who believe that yeah because
that's that's tribal thinking which is
holding us back
from from moving on to to a broader
singularity where you're not clinging to
all this dna stuff so yeah
sure so yeah and i guess where that
leads me is the question of like
whether you think uh our moral
trajectory which is going to be
you know agi is going to be a part of
that eventually but is that trajectory
like is the process more important than
the end point
is it more about like the the stepwise
progression or do you think that there's
actually like
a subset of moral norms that will be
preserved throughout because they're
intrinsically good
clearly the
process of transformation is a lot of
what's important to us
right like we if we could upgrade our
intelligence
by any at any speed
we would probably not choose to multiply
our intelligence by 1 000 every second
because we would just lose ourselves
that's basically replacing yourself
by a by a god mind right which could be
a
cool thing to do i mean if i if it was
like either i exist or the god mind
exists maybe i'll
decide the god mind should exist but i
mean if if you doubled your
intelligence every six months or
something then
then it would be more satisfying from
our point of view of our current value
system right because
we're we're getting some we're getting
continuity there you're getting to
experience yourself becoming a super
intelligent
god mind rather than right it's just
happening and that i think that has to
do with why we that has to do with why
we feel like
we're the same person we are when we
were we were when we were like three
years old okay
i remember back around 18 months but
the sense in which i'm the same person
as
i was at 18 months i mean of course you
can cherry-pick
common personality traits there but the
the main thing though is each point i
thought i was the same guy
the day before and and and the day after
and just changed a little a little bit
and there yeah even even at a moment in
your life when you have an
incredible inflection point right like i
mean i mean then
a few times in my life i've been through
some big sort of
consciousness transformation where i
felt like within a week i was a whole
different person
but still i didn't really think i was a
whole different person right
i still realize that like this is it's
still been i'm just in a different state
of consciousness and that
yeah that continuity is important to us
on
on the cultural level also right so we
and i think that will continue to be
important to us
going forward for a while i mean whether
whether we will lose our
our taste for continuity that's like a
big question i don't have the answer for
right you could
you can imagine the taste for continuity
continuously going down year by year
until by like 2100
we're just a super ai mind who really
isn't attached
to itself at all it doesn't care about
upgrading its intelligence by
by a factor of a thousand in in in a
second
right but now now clearly we we do care
about
that and we that's going to drive the
progress toward the singularity in a in
a number of different
of different ways right well and so
speaking of progress towards the
singularity there are
a number of different organizations now
working on that
that that big project like two i think
to the most maybe high profile in the
media or open ai
and deep mind i know you've had a lot of
influence on deep mind in particular in
terms of the the folks who worked there
today in the thesis
but i'd love to get your sense of so
first off what's your mental model of
how open ai and deep mind are
approaching agi and then how does that
contrast with
the singularity now i mean the first
thing i would say is i i
to to me putting those two in the same
category is like putting
i don't know winston churchill and trump
in the same category or something i mean
i mean i
i don't i don't they each have their own
strengths but i don't think those are
really comparable organizations and
i think google as a whole
and not just deepmind but google brain
in the mountain view office which is
where burt and transformer neural nets
for example came out of a whole lot of
other valuable stuff so
i think google as a whole has put
together
by far the best ai organization of any
centralized company out there i mean
of course academia as a whole is
stronger than anyone
any one company but by by a humongous
amount
in terms of coming up with with new ai
ideas but
if you're looking at a you know a
company or a government lab a specific
organization i mean
google has just done a really good job
of pulling in
ai experts from a wide variety of
of different backgrounds so i mean they
have deep deep learning people
they've pulled in cognitive architecture
people they've pulled in a whole bunch
of sort of algorithmic information
people think they got marcus hooder
who's invented the general theory of
general intelligence right
so i think there's a there's really a
great deal of
of depth there and i know there's some
internal
rivalry between the mountain view and
then the and the uk people but
but i think you know deep mind is very
strong
google brain and other teams in mountain
view are also very strong google in new
york area is very strong so
all in all there's a lot of depth there
and there's a lot of different
approaches being pursued behind the
scenes which are qualitatively different
from the things that get a lot of
publicity so like i my oldest son
zarathustra's young phd
in in the ai for automated theorem
proving so machine learning to guide
fear improving
and his supervisor joseph urban is an
amazing guy organizes aitp ai for
theater improving conference every year
you've got a bunch of google deep mind
people there every year who were
doing work on ai for fear improving
connecting that with ai ethics and some
of the things we were talking about
about earlier and that's a pretty much
unconnected
with you know video game playing
or with the or with brain modeling or
the things that the demo
sabas personally is into so i think
there's a lot of depth there they really
they want to create agi like
larry and sergey understand what agi is
and what the singularity is
dennis and shane understand it very
deeply i think they are
still at a high level
predominantly committed to deep
reinforcement learning and
sort of conceptual emulation of how the
brain works in modern hardware
as an approach to agi now they're
clearly open-minded enough that they've
made hires of great people who do not
share their their predilection which is
to their credit
but still like the vast bulk of their
machine is
deep reinforcement learning you know
crunching a lot of data on a lot of
machines and trying to figure out what
the brain is doing and figuring out ways
to sort of do the same kind of thing
in neural nets on on a lot of gpus right
and so
i think if if that approach is going to
work
it would shock me if google weren't the
ones weren't the ones who who got there
actually i mean i mean i think and now i
happen to think that is not the best
approach
which which is is is is a different
topic right now
open ai on the other hand you know
i i don't know them as well but my wife
who's also a
aipg my my wife and i actually i'm not
in the iphone my math phd so she's more
of a certified
ai expert than i am but we went to open
ai's
unconference a few years ago in uh in
san francisco
and you know it felt like a room full of
super high iq
brilliant passionate like 25 year old
guys
or younger who thought and mostly guys
literally
who who thought that ai had been
invented about five years previously and
that back propagation was the only
ai learning algorithm there and like i i
there's a few guys there who are like
well
for our hackathon project this weekend
we're gonna
we're gonna map we're gonna enable
natural language based authoring of
python programs right so we're going to
train a seek to seek neural network to
map
matt and ellen to python after three
days of hacking day and night
like well we we found the irregularities
in natural language
are a bit more complicated than
anticipated right like
they really these guys didn't know that
that
they didn't know that linguistics or
computational linguistics
had anything to say or they may not have
known existed as a field no of course
i mean yes care of course there were
guys in in
open ai who were very very deeply
knowledgeable but i felt that that's
at that stage open ai was sort of like a
deep neural nets only shop and they were
just hiring more and more people to
to bang on that to use that one hammer
to bang whatever they could which is
exact opposite of what what
what d minding and google brain have
done and then
i mean the whole thing of gpt2
is too dangerous to release because it's
going to destroy the world i mean i mean
spouting is bad but it's not
going to destroy it's not going to
destroy the world right and
that that was invented in google anyway
i mean that's that's just
former nets it's just yeah i mean that's
just burnt in one direction right and so
i mean now actually
you know for some of my i'm working on a
number of things
i'm working on a bunch of agi research
that we'll talk about in a few minutes
and i'm
also working on some applied practical
ai projects like well
one thing we announced yesterday is this
a robot called grace which is an
elder care robot a collaboration with
hanson robotics and so that's
supposed to go into elder care
facilities and hospitals provide social
and emotional
support some practical functions but for
in that project
like i i want to launch that before i
get to agi i need a practical dialogue
system
i wish we could use open ai stuff i wish
we could use gpg two or three
but a high percentage of what they say
makes no sense it's just
bloviating and then you can't
put that in an elder care robot right so
so i mean we you you you see the
these things that they were claiming are
gonna destroy the world because they're
so human-like they're not even really
they're not good enough to use in in
almost any practical
projects now and i i would say
deepmind has not done that either on the
on the other hand
oh gpt2 i didn't mind so much
because they hadn't gotten their payday
yet right but now
now they're already bought by microsoft
so what why did they why do they still
need to overblow things it's
it's not financially necessary anymore
right and
what is it that you think uh means that
or prevents gpt three from
from performing at the level that it
needs to i mean is it is it a
symbolically
it has no idea what it it has no
understanding of anything that it's
talking about
it is my friend uh gary marcus who's
working with that on the
r robot with rodney brooks he he
he put it beautifully in his article
like gpt3
bloviator right i mean it just it just
spouts stuff that sounds plausible
but it has no idea what it's what it's
talking about and so i mean it
you can see like with multiplying
numbers
you know it can multiply do two by two
multiplication when you get up to four
by four
multiplication of integers it's right
like 15 or 20 percent of the time or
something that's just really really
weird like if you know the algorithm
you write almost 100 unless you make a
stupid error
if you don't know the algorithm you
should be zero percent right but
what it did it looked at all the
multiplication problems online
it memorized the answers and then it
came up with some weird extrapolations
that let it do a few
problems that weren't and it's it's
training database right it's
but it doesn't understand what
multiplication is or it would never get
like 15 or 20 multiplication problems
right and you can
you can see that in many other cases
like you ask it like
you know who who who with the best
presence of the us
it'll answer a lot of good things and
i'll throw a few kings of england in
there just for fun but i mean
because it doesn't know what of the us
means so
the thing is you in the end it has no
more to do with agi than my toaster oven
does like it
it's not it's not representing the
knowledge
in a way that that will allow it to
make consistently meaningful responses
and
there are that's not to say that
everything in there
is totally useless for agi it's just
like you're not going to make gpt 4567
and get agi so i mean there's
there's very interesting work on tree
transformers where you use like a
constituent grammar
to bias the generation of a transformer
network and then i've been playing with
something like that where you use a
whole knowledge hyper graph you can use
a knowledge graph
which is dynamically constructed based
on what's been read
to guide the generation of of the
transformer and
then then you have some semantic
representation
some knowledge that that's that's
playing a role
in in the generative network so i mean
they're i i think
and that's in open right right that
sounds like yeah yeah yeah yeah so
so there's some of the ideas and tools
and transformer networks may end up
being one
component among others in a in a viable
agi architecture but on the other hand
open ai is not working on those things
from what i know i mean from what i can
see their philosophy is mostly
take the best ai out of the literature
and
implement it very very large scale with
a lot of data and a lot of processors
and then it will do new and amazing
things and then
the thing with that is it's
semi true like that there's so much in
the ai literature
that gave so-so results and will give
amazing
results once once you run it on enough
data and enough processor time
but you know you usually need to
rejigger the architecture and rethink
things and add some important new
features
while you're in the process of doing
that that scaling up like so i
like i was teaching deep neural networks
in the
90s in university of western australia i
taught a class in
neural nets cross-listed computer
science and psychology department back
when i was an academic
we were doing like multi-layer
perceptrons with recurrent back
propagation
we're teaching deep neural nets and you
could see you needed to scale that
massively because you were running a
neural net with like
50 neurons and it took all your
processor time for hours right
but the process of scaling it up it
wasn't like
take that exact code and idea and run a
lot more machines like still people
aren't using recurrent backprop right
they can't they came up with other with
with other methods so
at the very high level i sort of do
think we can get to agi
by taking ideas we have now and scaling
them up on
a massive number of machines with a
massive amount of data
but when you're in the weeds that
scaling up involves
re inventing a whole bunch of new math
and algorithms kind of
in the spirit of what was there before
and i i
think open the eyes so
far sticking a little too closely to
like let's
let's actually take what someone else
invented and just just
literally scale it up on more machines
with more with
more data right and that's it it
leverages
their position very well in that they
have a lot of money a lot of data and a
lot of
a lot of computers right but but i think
i think we need
i actually i don't think we need
necessarily
humongous conceptual revolutionary
breakthrough
to get to agi but i think we need
more creativity that that than that
right and so that that's that leads on
to my own agi work
yeah i was going to say maybe that that
gets us to open cog to singularity net
um and and i think this is entangled too
with with the next question i did want
to ask which is
what are some of the risks that you see
coming from agi i know you've been
generally more optimistic maybe than
some of the hardcore pessimists in the
community but
um you see some risks and and i'm
curious how that plays into your own
view about what the most promising
routine yeah i mean the
there's two categories let me address
the risk thing first because
once i start talking about open cog and
true agi
it's hard to stop so i mean there's two
categories of risks
in in in my view so what one risk
is the sort of uh nick bostrom style
risk
which is you know you do your best to
create an ethical agi
using all your your math theory and your
your loving care and common sense
and then getting all the politics right
and then
you know you still can't reduce the
uncertainty to zero when you're creating
something that's
in the end more generally intelligent
than you are right
i mean there's there's no matter how
optimistic you are
like there's there's some odds that
there's something you can't
foresee that that's gonna that's gonna
unfold there
and i mean i don't buy nick's argument
that
a superhuman agi is going to
intrinsically have a drive to turn us
all into computronium to make more hard
drive space for itself i mean
the whole drive to be megalomaniac and
consume our resources and and and uh you
know squash your enemies this
this is not something that necessarily
is there in an engineered system by by
any means
but i'm not looking at that as like an
instrumental goal though that emerges
like no i don't understand no i think
that's
i mean i think i think that emerges in
systems and evolve
through competition or through some
complex mix of competition and
cooperation
but if you're engineering a singleton
mines say
it doesn't have it's not competing with
anything right it just doesn't it didn't
evolve in that
competitive way so it doesn't have to
have that that motivational system so i
don't
i don't think the drivers that cause
humans to be that way have to be there
for an agi because
i mean you know you and i could compete
but we ca but we can't
we can't merge our brains if we want to
to become a super organism two two agis
that started competing could decide to
merge their brains in
instead right i mean so i i i don't
think
i don't think that logic applies to
systems that are engineered and don't
don't evolve in in the way that that
that we did
but on the other hand
have to say there's an irreducible
uncertainty in creating something like
100 times smarter than me right i mean
we
it may you know for all you know it
could immediately make contact
with some even more super intelligent
that's imminent in
in the quantum fluctuations of
elementary particles that we think
are random and that that other ai that
it contacted
could be good or bad from a human point
of view right so there
there's that there's an irreducible
uncertainty which is really hard to put
a probability on and
to me i mean there's also an irreducible
uncertainty
that i wake up in five seconds and
realize like in my brain in a vat in
some other universe right so that i mean
there's there's there's irreducible
uncertainties all around
if you reflect on them but then there's
a more
concrete risk i think which is that i
mean humanity could
develop malevolent or indifferent ais
in a pretty directly simply
comprehensible way even before you get
to massive super ai
and so this this gets down to my oft
made observation that
the main things that ais are used for in
the world today are
selling killing spying and crooked
gambling right i mean so
i mean that you're advertising you're
doing surveillance military and you're
doing like wall street training and
so if if this is what's shaping the mind
of the baby agi
you know maybe maybe it will it will end
up being a you know a greedy megaloma
megalomaniacal sociopath reflecting
the uh the cognitive structure of the
corporate and government organizations
that that gave
that gave rise to it right so i mean
that's
that's a palpable risk right i mean you
you could see if earth agis are
spy agencies wall street traders
and advertising companies which are
exacerbating global wealth inequality
and then i mean you've got a bunch of
people in the developing world who
have no jobs anymore because robots in
the developing world took their jobs so
they're being subsistence farmers
and and computer hackers right so i mean
you there's a lot of there's potential
dystopic scenarios
that don't need any super agi and that
from some points if you would be even
look like the most likely scenario given
the
given the nature of world politics and
and technology development right so
which also seems
which also seems to kind of motivate
your approach right i mean the
decentralized yeah that certainly
motivates my approach and and
i think the this ties in with ai
algorithmics in a subtle way that's not
not that subtle but some of them is
commonly appreciated because
i i think big tech companies
even the more beneficial oriented ones
run by good-hearted human beings
they are focusing on those ai approaches
that best leverage their unique
resources which is huge amounts of data
and huge amounts of compute power
so if you look at the space of all ai
algorithms maybe there's some
that don't need that much data or
compute and there's some that need a lot
of data or compute
the big tech companies they are sort of
have a fiduciary duty to put their
attention on the ones that require a lot
of data and and
compute because that's where they have
more competitive advantage over everyone
else and
and they have such if you're working
with those companies
the apis for leveraging their data and
compute power are really slick
and so much fun to work with like if
you're working at google i mean
simple command and and you're doing like
a query over the whole web like that
that's amazing right so i mean i mean of
course you've got
you've got a bias to use those tools but
the result is that the whole ai field is
being pushed
in a direction which is hard to do if
you're not inside a big tech company
it's a valid direction but there may
have been other directions i think there
are
that don't need as much data or compute
power but those don't have nearly such
slick tool chains associate
associated with them so for develop like
opencog that i'll talk about in a moment
is kind of a pain in the ass to work
with
tensorflow is much easier to work with
that's not entirely for fundamental
reasons so like we don't have the you we
don't have the ui developers we
we don't have we don't have all the all
the all the team that's needed to make
parallelism scale up automatically right
so the
approach is the big top big tech
companies like have slicker tools
associated with them
so they'll attract more developers even
other ones not working for those big
tech companies so what happens is
and of course if you're a phd student if
you write a pg thesis that matches
what a big tech company is doing i mean
you're more likely to get a good job
right away so why not do that it's also
interesting even though there's other
interesting stuff
so the whole ai field is sucked into
what's valuable for these
companies doing uh you know selling
crooked gambling spying and
and supporting you know murder
activities by national governments
it's funny how common these effects are
i mean i'm just sorry i'm just thinking
back to like my time in academe in
in physics where i was studying
interpretations of quantum mechanics and
it was like that's like a great career
ender if you ever want a career ending
thing to study is like
go into fundamental like quantum
mechanics and it's the same sort of
anyway yeah and then that that's an area
i've
i've start i've i've studied a lot and
there's a lot
there's a lot of depth in the
interaction between that and quantum
computing i mean
that that's that's a whole other whole
other fascinating
topic and i i think you know
reinforcement learning
it suits really well a
company which has a sort of metrics
oriented
business model and supervised learning
does as well so like if you're running
an advertising company
sales of all kinds is all about metrics
like how many how big is your pipeline
how many deals and have you closed you
know this
this advertising channel like how how
how
how good it has has the the roi been
right
and so the world wall street obviously
as well one of the beautiful things
about working in computational finance i
mean i like it as a geek
it's cool because you get immediate
feedback on what your algorithms are
doing as opposed to
medicine where it can be years to get
feedback well because these
business areas are metrics oriented
that's really driven ai toward things
like supervised learning and
reinforcement learning where you're
you're optimizing these quantitative
metrics all the time and it's
it's not that that's bad or invalid but
it's
it's not the only thing that you can you
can do in advanced ai or
or or agi so i mean other things like
say hypothesis generation which is
important for medicine or science
it's just nastier to to quantify
computational creativity in general so
like google deep dream got a lot of news
it's creative compared to a lot of
things but in the end
it's just combining pictures that were
found online right it's not
that creative but computational
creativity
it's hard to put a hard to put a metric
on it and ai development has been driven
by
you know these metrics driven business
models now there's
there's some good about that right i
mean having a metric lets you cut
through your
your internal in in certain
ways and it can
it can drive progress on the other hand
it also drives progress away from
valuable things like my my mom spent her
career running social work agencies
and she was always fed up by you know
these uh philanthropic organizations
they would only donate to
non-profits that were demonstrating
progress according to metrics but yet
it's very very hard to show your
progress according to metrics if if
you're doing say
enrichment education for low-income
single mothers or something i mean the
metrics
unfold over years and it costs a lot of
money to follow up the people and
and see how the how they're doing so the
result there is philanthropic
organizations prefer to donate to breast
breast cancer or something where you can
you can quant you can quantify progress
so that this is
the thing is focusing on quantifiable
progress
yeah is good but it but it pushes you to
certain things and then ai
ai it's pushing you to reinforcement
learning where you're doing you're doing
reward maximization and it's pushing
away from
education healthcare and science where
things are are
it's harder to immediately quantify
that's what i see is
a short-term danger and in a way
in a way focusing on the long-term nick
bostrom danger
is valid in a way it's a disinformation
campaign
to distract your attention from the
short-term danger so when a big tech
company
tries to get you to pay attention to
super agi might kill you 50 years from
now
i mean and sponsors conferences on that
partly it's a way of distracting your
attention from the damage they're
causing
in the world that at that this at this
exact moment
right so there's although there's a
valid aspect to it too so that i mean
the world this world is very tangled up
right
yeah it's it's funny how often problems
come down to the fact that
important things are often hard to
measure and when we
score the functioning for example like
the economy in terms of the
stock market or gdp or some metric
that's easily pdp is going up a lot this
quarter right i mean right that's very
very very exciting what's the problem
everything's great yeah well
and you you and i are probably doing
okay right
i mean i'm not complaining personally
at the moment but i can see you know
overall
there's it's a metric that found a
profound
mess profound economic economic issues
and i can see like my
my sister's a school principal and
there's you know low-income
low-income kids who are just sitting at
home watching
tv all day getting no education and
there's going to be a lot of
ramifications to
that but hard to measure gdp going up is
easy to measure
right now i i do want to kind of tack
into
sort of the the last area i really want
to make sure we can touch on which is
which ai risk mitigation strategies you
think are most promising
i'm going to focus maybe on the
short-term risk that you've highlighted
in terms of yeah
let me let me say a little bit about my
own agi work
now because that that will that will tie
into that
so so i think so my
my approach to my feeling since the late
90s has been that
what i would call a hybrid approach to
agi is going to be most successful
and there were architectures in the 80s
called blackboard architectures
where you had sort of a you view there
were blackboards back then now they're
whiteboards right
or prometheus boards they have a lot of
things but so you you have a
imagine a blackboard and you have a
bunch of experts in different areas
and they're all writing on the same
blackboard and they're all erasing what
each other wrote and they're
collectively doing it doing a proof or
making a picture or something so
i think each of the historical
ai paradigms which i would say
neural net supervised learning
unsupervised learning
logical inference and uh of evolutionary
learning and the number of others each
of these paradigms
in my view is sort of like one of the
blind men grabbing part of the
elephant of intelligence like one guy's
grabbing the nose the other
the other guy is uh is pulling the ears
one guy is pulling on the others or
something right so i mean
i think each of the traditional ai
paradigms is
getting it a key part of what we need
for human-like general
intelligence and given that so one
approach is
is to try to find ways to make one ai
paradigm
incorporate what's good about all the
other ones right so make a neural net do
logical reasoning and do evolution
another approach is to come up with some
new meta algorithm
that sort of incorporates what's good
about all these different and that
that's very appealing to me personally
actually but the
the approach i think is probably going
to succeed
first is a hybrid approach
where you're letting algorithms coming
from these different classical ai
paradigms
cooperate together in real time on
updating a common
knowledge base and i think i think that
work in that direction
ultimately is going to lead to what
looks like a single meta algorithm that
incorporates the best of what comes from
these different paradigms
but i think we're going to get there by
by actually hybridizing
different algorithms from different
classical ai paradigms and how having
them work together so in
in opencog architecture what we do is
have
we have a knowledge hypergraph so it's a
weighted labeled
hypergraph actually it's a metagraph not
a hypergraph because
can you define those things yeah i was
about to a graph has nodes with links
between them right
a hyper graph has nodes with links but a
link can go between more than two nodes
like you can have a link spanning three
nodes or something
a metagraph you could have a link
pointing to a link or a link going to a
whole sub
graph right so it's like the most
abstract graph that you could get
so open cog atom space it's a weighted
labeled metagraph
weights means each node or link could
have a set of different
quantitative or discrete values
associated with it
and labels these are types associated
with with nodes and links and so we
don't
we don't enforce a single type system on
on the item space but you could have a
collection of type systems on on the m
space so from a
from a programming language it's like a
gradual typing system or something where
you could have something untyped
or you could have something with a
partial type and then the types could
act
even new types can be assigned assigned
via learning but but you can have
you can have type checkers and that
whole whole instrumentation on there so
it's a
it's a weighted labeled knowledge
hypergraph and then
you allow multiple different ai's to
act on this knowledge hypergraph you
know
concurrently and this this is where
things get interesting because if you
have a probabilistic logic system
and you have say a tractor neural net
and you have a reinforcement learning
system and you have say a
automated program learning system these
are working together on the same
knowledge hyper graph
i mean then you need them to be
cooperating in a way that leads to what
we call cognitive synergy which
sort of means they don't screw each
other up right it means like basically
if
if one of them get one of the algorithm
gets stuck
the other algorithms should be able to
help it
overcome whatever obstacle it's facing
that requires the various algorithms to
be
sharing some abstraction of their
intermediate state with each other
so it means some some abstraction of the
intermediate state of each algorithm
as it's operating on the knowledge
hypergraph needs to be in the hypergraph
it's itself right so that and this is
where the design gets subtle because
doing everything in this hypergraph is
slow
but doing nothing in there means you
just have a multi-modular system with no
ability for each algorithm to see the
other one's intermediate state
so it's like what abstraction of its
state what portion of each algorithm
state
goes in the hyper graph versus versus
outside
and this you know in the neural net for
example we develop what we call
cognitive module networks where you
see if you break a deep neural
architecture into layers or something
you may have a node in the hyper graph
representing each layer
and its parameters and the piping
between layers happens in the hyper
graph
but then the the spreading of the back
propagation inside the layer
happens in in some torch object that
that's that's out outside the hyperdrive
so you're constantly bouncing back and
forth yeah but so the nice
thing with torch is you have very good
access to the
the compute graph unlike in tensorflow
so if you have two different
torch neural nets you represent them by
nodes in the hyper graph
and if you compose those torch neural
nets that's represented by a
symbolic composition of the nodes and
the nodes in the hypergraph so
if your reasoning engine comes up with
some way to some new way to compose
neural modules
that can be backed out to composition in
torch and the compute graphs the
composition just passes through so i can
in math terms you have a morphism
between the
the torch compute graph and and the and
the logic graph with it with it within
the hypergraph right so that so that's
but there's a there's a lot to work out
there right so i'll just describe one
little bit of it which alexei polipov
who leads our st petersburg team
published
i guess last year in the paper on
cognitive module networks but you need
you need similar thinking to that like
pairwise for each pair of ai algorithms
right
so like how how does your how does your
evolutionary learning algorithm make use
of
probabilistic reasoning to do fitness
estimation and those
so that it's the hybrid approach and it
mostly has a steep learning curve right
because because you need to understand
this
this cross-cutting knowledge
representation and you have to
understand
all the algorithms that are that are
playing are playing
a role in it so what where we're at now
with that actually
we came to the very painful and annoying
conclusion
that we needed to rebuild almost the
whole open cog system from scratch so
we're
open cog 2.0 this is yeah we're calling
we're calling opencog hyper on i decided
to name the versions after
obscure elementary particles instead of
numbers like
like linux has all these funny animals
or
or apple has uh california suburbs right
so
i mean yeah we're doing hyper on when we
point to quantum computing we'll make it
tachyon
there you go basically it's about
scalability i mean we can do whatever we
want with the current open cog but it's
just
it's too slow in a few different senses
i mean i think the
probably the most obvious thing is we
need to be massively distributed like
now we can have a knowledge hyper graph
and ram on one machine
and we have a bunch of hyper graphs
share a postgres
data store in a sort of hub and spokes
architecture but we can't use the
current system across
like thousands or tens of thousands of
machines and
ultimately you know for our work with
transformer neural nets we have a pretty
big server farm with all these multi-gpu
servers
for opencog we just can't use scalable
infrastructure now and it's obvious we
need to so
part of our bet is justice happen with
deep neural nets like when you manage to
scale them up the right way
whoa look at what they can do right
right we're thinking that
once we've scaled up the open cog
infrastructure we're suddenly going to
see
the system able to solve a whole bunch
of hard problems that that hasn't been
able to
to so to so far right so that's part of
it is just
scaling up the knowledge hyper graph and
of course
that mostly means scaling up the pattern
matching engine
across the knowledge hypergraph right
because i mean just scaling up
nodes and links isn't that hard getting
the kinds of pattern matching we need to
do which significantly go beyond what
current graph databases support
getting the kind of pattern matching we
need to do to scale up across a
distributed knowledge hypergraph
yeah not not impossible but it's work
right then that the
the other thing is and this is more
interesting from an agi point of view
from the work we've been doing putting
neural nets evolutionary learning and
logic together
we've just come to a much subtler
understanding of
how the knowledge hypergraph need needs
needs to work so we're we're creating
what's effectively a new programming
language which is
we've been calling we've been referring
to atoms because in
in opencog the nodes and links are
called atoms atom is the superclass of
the of the node and link subclasses
so we informally referred to the
the dialective scheme we were using we
have been using
to create and manipulate nodes and links
as atoms because both nodes and lengths
are atoms so this is
atomies two maybe we'll come up with
another with another name but
we're understanding better what we need
to do
in terms of a
type system and then a sort of
family of index type systems
inside the m space to better support
integration of neural nets
reasoning and and and evolutionary
learning and so this is
led us to dig fairly deep into idris and
agda and
various of these dependently type
programming languages so we're looking
at sort of how do you do
gradual probabilistic linear dependent
typing in a reasonably scalable way
and because it seems like if you can do
that then you can get these multiple
ai algorithms we're working with to
interoperate sort of scalably and and
cleanly and
this comes down to like how much of the
operation of these ai algorithms
can you pack into the type checker
of like a gradual probabilistic linear
dependently typed language because like
the more of the ai crunching you can fit
into the type checker
then then you can just make sure that
type checker is really really
if efficiently implemented right and
this yeah
then this this ties in with which
aspects of internal state
of the ai algorithms are put into the
into the into the knowledge
knowledge hypergraph right so we're
we're digging
very deep into functional programming
literature on
on that on that side as well as into the
distributed
graph databases and i think i
i think this this may be how
in the end hybridizing different
algorithms
eventually leads you in the direction of
well actually what i've ended up with
there's little resemblance to the
algorithms i started with and we like
have a we have we have a meta algorithm
so to
to start opencog hyperon will certainly
be a hybrid like we're going to keep
using
torch or or if something better comes
along right and we're going to use our
our
probabilistic logic network framework
and we can make those work together
but it it may be that after a few years
of
incremental improvement there like we've
modified
the neural evolutionary and logical part
enough
that you just have some something
something you want to call
sort of a different a different uh more
abstract
learning algorithm because when you when
you cache these things out at the
category theory level
i mean the differences between
a neural learning algorithm and a logic
inference algorithm
are much less than one than one would
think what one would look at them at the
at the current implementation level so
part of it is about having an
implementation fabric
where the the underlying commonalities
between the algorithms from different
paradigms
are exposed in in the language rather
than obscured which is
is is the current case yeah i was going
to say that itself is really interesting
because i generally
at least i've seen it framed as an
adversarial relationship this idea of
you know neural learning for symbolic
learning or like symbolic logic
and then what i find cool about this is
you're really kind of fusing the two
together and making them play nice in
a very formal yeah and we've already
we've already done that in simple ways
so like
i mean we have we have uh
for example you have the nodes and links
and the hypergraph
and you can do embedding to embed a node
in a in a vector right and we we do that
we tried deep walk and graph convolution
networks actually we're doing it using
kernel pca in a certain way now some
much more traditional tools
but what you can you can set that up so
that you have a
category theory like you have a morphism
between the vector algebra of the
vectors
and then and then the the probabilistic
logic algebra on the on the graph side
so what's interesting if you're trying
to do logic inference you have some
premises you have a conclusion that you
want to drive
or try to derive from the premises you
can make a graph
embedding as a vector of the premises
make an embedding of the conclusion
so then you have a vector for the
premises vector of the conclusion
you can look at the midpoint you can
look at sort of the intermediate points
along that
that vector you you map those midpoints
back into the knowledge hypergraph
and then you look at those as potential
intermediate premises for the logic
engine to do and inferring from the from
the premises to the conclusion right so
you're
you're using the morphism between graph
space and
vector space as a method of logical
inference control
right and that that's and that depends
how you do that mapping because if you
just
straightforwardly use deep walk or gcn
to do the mapping
you don't you don't get a morphism that
that's that's
accurate right so there's so that yeah
there's
there's a lot of subtle things there and
i think
i think once we i think we're going to
get rid of back propagation
and and as that has gotten rid of you're
going to see that replaced with
algorithms that map more naturally
into between the logic and
and evolution or evolutionary side also
so this this is another
subtle point is playing around with
infogan and other more
subtle neural algorithms so we've got an
infogen
which is a form of gan that that learns
automatically these semantic latent
variables on the generative network side
we've gotten that to do transfer
learning between clinical trials
successfully which which
which is pretty cool so like you you
train you train the info again network
to sort of predict which
which patients and say a breast cancer
clinical trial are going to be helped by
by which medication and then
using an infogan for that the semantic
latent variables there can be used for
patient segmentation
in a way that lets you transfer to a
different clinical trial better
clinical trials still on breast cancer i
assume right yeah you can be still in
breast cancer
but maybe with different drugs or maybe
a very different patient population
right it might be you can tell something
even for different cancers i mean we're
looking at tumor gene expression
and there's a lot of similarity between
the tumor gene expression and cancers in
different tissues actually but
we've been looking at breast cancer
mostly because there's more open data
about that than
than about other things so that there we
got it to work but the the what i was
coming to is
if you try infogan on like video data or
something
the learning just doesn't converge right
and so then you give up and you're like
that's a bad architecture
but maybe it's not about architecture
and back it's just a bad learning
algorithm right so there's
uh so i'm i i'm i'm suspecting that
using
flowing point evolutionary learning like
cmaes or something
on these really complex neural
architectures
may work better but if so that gives you
a lot to go on in cognitive synergy
right because once you're using an
evolutionary learning algorithm
well you can use inference for fitness
estimation right i mean
there's there's a lot there's a lot of
openings for other
ai tools in your hybrid system to help
guide the
the evolutionary learning in a way
that's more challenging in
in a back prop framework so yeah this is
this is
this is something i'm curious about
which is a purely technical point like
how
how many promising neural architectures
are being discarded just because they're
not suitable
for that proper for back back prop which
is a very good algorithm but has its
strengths and weaknesses like
like like everything else right so well
maybe this ties in at least thematically
with another kind of contrarian position
at least as you were saying earlier with
respect to
i guess the way agi has looked at our
ai's looked at or intelligence looked at
in the west
which is so we tend to take a just a
materialistic view of
consciousness but i do want to touch on
this idea that
of pan psychism that you've been a fan
of which is almost as controversial in
the west as like discarding
backprop is did you mind kind of
elaborating a little bit on pan psychism
and maybe it's connection to some of
your thinking on agi if
i will elaborate on pen psychism but i
i have skipped the company i'm now the
ceo of which
which i must tell you about for a few
minutes so
singularitynet which is on it so i've
talked a lot about opencog and opencog
hyperon right
and that's something i've been working
on a long time
now what i've been doing the last few
years is
leading this project called
singularitynet which is
it's a blockchain based agi platform
basically ai platform not just agi
and and narrow ai both so that that lets
you basically operate a bunch of docker
containers each of which has an ai
algorithm satisfying a certain
api in it and then these docker
containers can coordinate together they
can outsource work to each other they
can rate each other's reputation they
can pay each other
and someone can query the network and
there's a peer-to-peer mechanism that
passes the query along through the
network
to see if there's anyone who can do what
that query asks for but the whole thing
works without uh
without any centralized controller right
so it's a
it's a society of minds as aia pioneer
marvin minsky was talking about
and why is that important by the way so
so why yeah so
this this ties back to the more
political industry structure aspects
we were talking about right because it's
important because as we move from narrow
ai
toward agi we're going to be a lot
better off as a species if
the emerging agi is not owned or
controlled by one actor because any
single actor
is is corruptable and i'm i'm not very
corruptable but if some thugs come to my
house and
threaten to murder my children if i
don't give them the
the private keys to my repository then i
probably am corruptable right so
i would rather not be in that position
or anyone be in that position right so
i i think we want the early stage agi
as it evolves i think we want it to be
more like
linux or the internet than than
like say os x or some company's
private internal network and to enable
that
is challenging right because you're
talking about like runtime
systems that are using a lot of ram and
and processing power and data and
network and so forth so
singularitynet is a platform that allows
a bunch of different nodes in the
distributed ai network to cooperate and
operate in a purely
decentralized way and it's it's out
there now it's not as sophisticated as
as it will be i mean we're
we're working with cardano blockchain
right now it's implemented on top of
ethereum blockchain we're moving a bunch
of the networks at cardano blockchain
probably because it's faster and cheaper
but partly because
we're introducing some more abstract
features that cardano supports better
because their smart contracts are in
haskell which is a nice abstract
language so
we're looking we're looking at how does
one ai in the network
describe at an abstract level what it
does to the other ai in terms of
the resources it it uses the data it
takes and spits out but
also like what properties it's it's it's
processing fulfilled so we're
introducing
an abstract like dependent type theory
based description language for ai's to
describe what they're doing
to each other which is supposed to make
it easier for
one ai to reason about how to combine
other ais to
to do something right so from if we
compare the
opencog in opencog you have this
knowledge hypergraph
and multiple ai algorithms are tightly
integrated on top of it because they
have to all understand what each other
are doing semantically
singularity is looser integration right
you have multiple different ai's in the
network
they have they communicate by a
description language that tells how
tells what each other are doing and why
and what properties they obey
but in the end they can be black boxes
they can have their own knowledge
knowledge repositories and exposing
certain properties
so i think i think we want both of those
layers like you want a society of minds
layer
with multiple ai's that are
semi-transparent with regard to each
other and then
within that you'll have some things that
are doing more narrow functions like
processing certain
certain data types or or doing certain
kinds of optimization
and you have some ais in that network
that are serving more general
intelligence type functions just like we
have a cortex
then we have a peripheral nervous system
and the cerebellum and so forth so
in that landscape opencog
is intended to power the agents that run
in singularitynet network
doing the most abstract cognition stuff
but we want a lot of other things and
they're complementing them because i was
going to ask yeah how does the
how does the coherence then emerge from
singularity and it sounds like then it's
like
opencog gives you that coherence gives
you that high level reasoning and then
outsources tasks through singularitynet
through the blockchain
to other areas yeah and you can deploy
cog through singularity net right so i
mean you can have multiple different
open cog
agents running in in singularitynet but
from a from a pure singularity net point
of view open cog doesn't matter like
people could deploy whatever they want
in there from
from the view of like why i personally
created it
in the first place it's partly because
you want a decentralized open way
for your open cog systems to
cooperate with with a bunch of other
things so yeah we
we singularity net is run by a it's a
non-profit
foundation it's more like ethereum
foundation we've actually we've spun off
a for-profit company called true agi
which is sort of working on building
systems using the opencog hyperon
framework
so that's sort of like uh oh cool like a
linux red hat thing right where
opencog hyperon is is open but just as
red hat commercialized stuff on top of
linux true agi
is commercializing well working toward
commercializing
uh systems that are built using opencog
hyper on and
and singularity net so yeah there's a
lot of layers there
this this actually ties in with your
questions about psychism and
consciousness in the in some
emergency some some in indirect ways
because i i think
yeah part of the idea underlying
psychism which is the sort of the
philosophical
premise that you know everything is
conscious in in in its own way right but
just
just like in george orwell's animal farm
all animals are equal but some animals
are more equal than others i mean in
psychism
everything is conscious but some things
may be more conscious than others or
or differently conscious than than
others right and so it's
in in that point of view you know it can
seem ridiculous to say that this
coffee cup is conscious but yet
if you dig into quantum field theory i
mean
and quantum information theory at a
certain level
i mean all these wave functions they're
interacting with the wave functions
that are outside this system they're
processing they're processing
information
all the time and they're incorporating
that in some aspects of global
coherent state as as well as local state
i mean there's
if you try to boil down consciousness to
sort of
information processing or like
distributed coherent awareness
it becomes hard to argue that these
processes are
absolutely not there in some some
physical
object although you can certainly argue
they're there
to a greater degree in the in the human
brain or
or a mouse's brain but if you're talking
about a philosophical
principle like is there a conscious
versus unconscious
dividing line it's not
clear in what sense that that makes
sense certainly
you can speak about abstract reflective
consciousness like self
self modeling at a cognitive level and
we are doing that
in a way in a way that this this cup is
is is not right so there are some
aspects of the natural language
term consciousness that that humans have
and a coffee cup
doesn't have so what becomes subtle in
thinking about
consciousness is what what uh
chalmers called the heart problem of
consciousness right so we have various
sort of empirical properties we could
talk about like can you
model your mind in a practical sense and
answer questions about what you're doing
and
and are you exchanging information with
your environment and registering that
information exchange in your global
state
you have all these all these sort of
empirical
properties of associated with
consciousness
then you have what are called qualia
like the experience of
existing right right and what what
many people do is they they correlate
the experience of
existing with sort of reflexive
self-modeling which the human brain
clearly does in the way that a coffee
cup doesn't and
i think the key
aspect differentiating psychism from
what's a more
common view of consciousness in the
modern west is
as a pint psychist you tend to think
like the basic
quelia the basic experience i am
is not uniquely associated with like
reflective
deliberative self-modeling type
consciousness
but it's rather it's associated with the
more basic
like information exchange type
consciousness that that is imminent in
every
every physical system right and so
that's a
that's not incredibly relevant
to the everyday ai work that i'm doing
now it will become relevant
when you start you know building uh
femtoscale quantum computers or maybe
even
simpler quantum computers it'll
certainly become relevant when you start
doing brain computer
interfacing but then you can ask
yourself questions like
okay this this computer that i've like
wired into my head right do i feel it
there on the other end
in the same way that i feel if i wire
another human brain into my head
or does it feel like what i get when i
wire a coffee cup into my head
right yeah because i'm guessing if i
wire a coffee cup into my brain or
wi-fi it i'm not going to feel that much
of what it is to be a coffee cup
yeah i guess if i wire my brain into
yours and increase the bandwidth i will
feel a lot
of what it is to be you which will be
weird right what if i wire my brain
into like our version 3.0 of our
our grace uh awakening health like elder
care robot right well
will i feel you know what it is to be an
elder care
robot will it feel something like where
it is to be a human or will it feel like
what it is to be a coffee cup right so i
think that
the rubber will hit the road with this
stuff and very interesting in terms of
something like singularity as a
decentralized society of minds
or even think about human society and
the global brain
of computing and communication and human
intelligence cloaking the earth right
now i mean
one could argue the real intelligence is
as humans in human society and culture
and we're all just like
neurons in the in the global brain like
responding to what it sends us on the
internet right but
what kind of consciousness or experience
does the global brain of the earth
or would say a decentralized singularity
that society of
of of minds have right like you so in a
psychist view
you might say well an open cog system
it has a focus of attention it has a
working memory
you know it's it's conscious experience
will be a little more like
human beings quite different because it
doesn't grow up with a single
body that it's uniquely associated with
something like a decentralized
singularity net
network you know might have its own
general intelligence in a way that is
exceeds an open cog or a human its
conscious experience would just be
very very different right i mean it's
because it's not centered on a single
knowledge base let alone a single body
this gets back to your first question of
like
what is intelligence right because our
whole way of conceptualizing
intelligence
it's over fitted to organisms like us
that are we're here to control
to control the body and get food and and
get sex and status and all the things
that we do
an open cog system even though it's very
mathematical
ultimately it's built on the metaphor of
human human cognitive science right you
got perception action long-term memory
work working memory i mean it's because
that's what we have that's what we have
to go on right but
is that kind of intelligence greater in
a fundamental sense and that
than that which would ever emerge in a
singularity net network that might
you know get some self-organized
emergent structures
that we can't even understand and this
brings us back to weaver's notion of
open-ended in
in intelligence right so singularity
that you would say is more of
it's more of an open-ended intelligence
approach toward agi where you're like
everyone around the world put your stuff
in there you know have it describe what
it's doing using
abstract description language if it's
going to flourish
it should get a lot of its processing by
outsourcing stuff to others in the
network rather than being solid cystic
and then you know it's trying to make
money by providing services it's
hopefully providing its creator with
with some money helping with it with
income inequality if they're in a
developing country but
what does this whole thing develop into
no one's scripting it right so that's
also very cool so if if the breakthrough
the agi happened in that
decentralized way would be really
awesome
and and and fascinating i mean the
the open cog way is a little more
determinate like we're architecting a
system sort of model on human cognitive
science
we're going to use it to control these
these elder care robots which even have
human-like bodies like through a
partnership of true agi and the robot
company
and so i mean i mean that's a little
a little more determinate and it may be
a little of each right we don't know we
don't know how this is going to evolve
because the
the robots are going to draw on
singularity ai including opencog plus
other stuff on the back end
and from the robot's point of view it
will just draw on whatever works best
for achieving its functions
and we'll see to what extent that's open
cog versus
some incomprehensible like
self-assembled conglomeration of
of a thousand agents right well it's a
beautiful and exciting vision and a very
a very open-ended one too which is kind
of interesting i guess we'll all
have to kind of wait and see how this
develops yeah vision is one thing that
making it work
is what's for the most of my time on
which is really
astoundingly difficult but it's it's
amazing the tooling that we have
available now like you you couldn't have
done this when i when i
you could have written it down when i
started my career but it wasn't each
step would have just been so so slow in
terms of compute time
and human time you using the crappy
tools available at that time so it's
it's amazing this is all very very hard
but it's amazing that we can
that we can even even make progress on
it now right so
it's certainly a fun time to be doing uh
ai as as you you and the and all your
listeners know
absolutely yeah and it's a great time to
be learning about things like this too
all the different approaches to solving
this problem
thanks a lot for sharing yours uh with
us do you have any um
any places where you recommend people go
if they want to contribute to open cog
or singularity
yeah absolutely so probably our best
developed media property
is the singularity net so if you go to
singularitynet.io
you can you can and look at the
singularitynet blog and singularitynet
youtube channel which is linked from
singularitynet.io like that will lead
you to everything but
regarding opencog in particular there's
an opencog wiki site
and you can find like go to opencog.org
then go to the wiki
and there's there's a page of stuff on
opencog hyper on which is our new system
there's
the current open cog is is is in is in
github it's all open and
while i've been thinking a lot about the
new version the current version is what
we're using inside these nursing
assistant robots now like it does
something
and we did we there are two online
conferences that i organized earlier
this year which might be interesting so
what one
one was the open con online conference
which is just about opencog
then every year since 2006 i've led the
artificial general intelligence
conference which has
been face to face until now but this
year the online
online agi 20 conference you you can you
can you can find all the videos and
papers from that online
also and that's some of my stuff but
also other things from the modern
agi community that i haven't had time to
go into here well
great thanks so much really appreciated
something i'm sure a lot of people want
to check out
and uh ben thanks so much for making the
time yeah yeah yeah thanks for you
thanks for the interview it's a good
good fun you know there's always more
always more to cover than you possibly
can but uh there's
some fun conversation |
91ed3cc1-e132-4fd9-8b57-a92f8f229765 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Introduction
This series of posts presents our work on the frameworks of “Positive Attractors” and “Inherently Interpretable Architectures” during the AI Safety Camp 2023.
(Note: Sections written by the different team members were marked with their initials in square brackets, since the investigated research strands were more distinct than a fully unified presentation could capture.)
We believe that the AI Alignment community is still largely lacking in clear frameworks or methodologies for progress on the Alignment Problem. While there are some frameworks of concrete progress towards a solution, most of the well-regarded work is still about becoming less confused - either regarding current AIs (mechanistic interpretability) or the concepts involved with reasoning about synthetic intelligence in the first place (agent foundations).
It was from this context that we set out to clarify the suitability of two additional broad frameworks of progress, trying to understand how they would inform and shape research, and contrast with already existing agendas.
The research on this project was predominantly conceptual, relying on discussion and first principle reasoning aided by literature review. Team members were encouraged to come to independent views about the frameworks during the initial explorations, to have meaningful discussion and convergence during the later stages of the project.
To briefly introduce the two frameworks:
**Positive Attractors** are about the idea that cognitive systems or their training processes can be imbued with features that make them resistant against broad kinds of undesirable change, features that one can think of as positive attractors that pull the system into a safe region of algorithmic space. The intention is that such positive attractors can make systems resilient against failure modes that we haven’t fully understood yet and potentially enable some research approaches that would otherwise be discarded early.
**Inherently Interpretable Architectures** speak to an approach that is complementary to mechanistic interpretability research. Rather than relying on the hope that mechanistic interpretability research will provide adequate understanding about modern systems in time for us to base clear solutions and arguments on its findings, it seems sensible to also pursue the path of designing and studying systems that are inherently very interpretable, and to scale them up without losing their interpretability.
We believe that the exploration of relatively independent research bets for alignment research is especially relevant today, given that many people are transitioning into AI Safety research after witnessing the speed of progress indicated by GPT-4, and that there is no clear consensus on what an actual solution is supposed to look like. We don’t know where the critical insights and solutions will be found, so it seems prudent to launch a multitude of probes into this territory, probes that can ideally frequently update each other about how attention and resources should be allocated on a field-level.
### Authors
Robert Kralisch (Research Lead), Anton Zheltoukhov (Team Coordinator), David Liu, Johnnie Pascalidis, Sohaib Imran |
17374d98-b419-4ba0-a117-153aec1c6789 | trentmkelly/LessWrong-43k | LessWrong | Should I treat pain differently if it’s “all in my head?”
I sometimes suffer from RSI. I’ve heard several people I respect say that they think most RSI is a psychological phenomenon. My understanding of their view is that they think there’s a feedback loop where you notice your wrists hurting and then you are anxious about the pain, causing you to pay more attention to it, which causes more pain though some mechanism we don’t yet understand. For the purposes of this post, I’m interested in the hypothetical of if something like that story is true.
I have some stories for what I’d do differently:
* Ask a doctor to prescribe some psychiatric drugs, like SSRIs if I thought it was related to anxiety
And some things I’d do the same:
* Continue to rest my wrists and do voice work when things get bad
But some things I’m uncertain about:
* Do ergnomics matter?
I know several others who also suffer from RSI, and honestly I think the question is even more complicated there. Am I making my friends’ conditions worse if I talk about my condition?
(As a side note: I’m super glad that I can do voice work and give my wrists a rest. I honestly enjoy it, which is for sure not the typical experience. I am very grateful that I drew ”enjoys voice input” in the lottery of fascinations. While I admittedly haven’t seen many people get the benefit I have, if you’re reading this and would like my help getting set up using Talon Voice, send me a DM.) |
9748d9e3-3d26-47f6-8d0d-1c98d6fc22a1 | trentmkelly/LessWrong-43k | LessWrong | Preface to CLR's Research Agenda on Cooperation, Conflict, and TAI
The Center on Long-Term Risk (CLR) is focused on reducing risks of astronomical suffering, or s-risks, from transformative artificial intelligence (TAI). S-risks are defined as risks of cosmically significant amounts of suffering[1]. As has been discussed elsewhere, s-risks might arise by malevolence, by accident, or in the course of conflict.
We believe that s-risks arising from conflict are among the most important, tractable, and neglected of these. In particular, strategic threats by powerful AI agents or AI-assisted humans against altruistic values may be among the largest sources of expected suffering. Strategic threats have historically been a source of significant danger to civilization (the Cold War being a prime example). And the potential downsides from such threats, including those involving large amounts of suffering, may increase significantly with the emergence of transformative AI systems. For this reason, our current focus is technical and strategic analysis aimed at addressing these risks.
There are many other important interventions for s-risk reduction which are beyond the scope of this agenda. These include macrostrategy research on questions relating to s-risk; reducing the likelihood of s-risks from hatred, sadism, and other kinds of malevolent intent; and promoting concern for digital minds. CLR has been supporting work in these areas as well, and will continue to do so.
In this sequence of posts, we will present our research agenda on Cooperation, Conflict, and Transformative Artificial Intelligence. It is a standalone document intended to be interesting to people working in AI safety and strategy, with academics working in relevant subfields as a secondary audience. With a broad focus on issues related to cooperation in the context of powerful AI systems, we think the questions raised in the agenda are beneficial from a range of both normative views and empirical beliefs about the future course of AI development, even if at CLR we are pa |
1bb2c468-af44-4af8-8c49-1a7b671b86bb | trentmkelly/LessWrong-43k | LessWrong | Request for input: draft of my "coming out" statement on religious deconversion
Edited 3/4/2012: I shortened up the summary a bit and add the following update:
Thanks for the lively comments. As a preliminary summary of things I've found quite useful/helpful:
* Shorten/transform the document (David_Gerard)
* Remove/postpone any reasons (TimS)
* Don't be so prosy/fake sounding (orthonormal)
* Show to religious people, not just LW (AlexMennen) -- doing that, btw
* Give reasons (Will_Newsome)
Overall, I was initially a bit discouraged. This took a lot of effort and so it's frustrating to contemplate changing directions. Due to reading a very generously shared similar PDF document (from someone I'll leave anonymous unless they'd like to be named), I'm much more upbeat about doing so. I plan to:
* Create two documents. One very simple, plain-language, frank relating of the fact that I no longer believe in god. I'd like to write it just as though I were saying it personally to someone, easing them into hearing this (like Bugmaster suggested, except that actually doing this in person is impractical for me)
* The second will be my actual list of reasons. I think it will be valuable to actually spell them out, and many will want to know reasons anyway (and probably ask)
A question that came up in me from the comments below is what worth this actually brings about. I don't find myself compelled to write a dissertation-style document defending my power tool purchases, Linux custom kernel options, or why I listen to the music I do. I am aware of a desire for validation, to feel that I've done enough with respect to my "quest," to prove myself on this topic. I'm still wrestling with whether this is completely irrational and unnecessary, or only partially so, validated by the fact of my social/environmental circumstances that do present some real obstacles that this document could help alleviate.
Open to any thoughts on that last bit as well. Thanks again for the valuable input.
----------------------------------------
It's almost one year later |
7b1e82dd-0053-4fca-aa50-f2709cf194a7 | trentmkelly/LessWrong-43k | LessWrong | What OpenAI Told California's Attorney General
In a previously unreported letter, the AI company defends its restructuring plan while attacking critics and making surprising admissions
This is the full text of a post first published on Obsolete, a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race to Build Machine Superintelligence. Consider subscribing to stay up to date with my work.
OpenAI was founded as a counter to the perils of letting profit shape the development of an unprecedentedly powerful technology — one its founders have said could lead to human extinction. But in a newly obtained letter from OpenAI lawyers to California Attorney General Rob Bonta, the company reveals what it apparently fears more: anything that slows its ability to raise gargantuan amounts of money.
The previously unreported 13-page letter — dated May 15 and obtained by Obsolete — lays out OpenAI’s legal defense of its updated proposal to restructure its for-profit entity, which can still be blocked by the California and Delaware attorneys general (AGs). This letter is OpenAI’s latest attempt to prevent that from happening — and it’s full of surprising admissions, denials, and attacks.
Sam Altman speaking in front of SoftBank CEO Masayoshi Son | Carlos Barria | Reuters
OpenAI has not replied to a request for comment.
Control of OpenAI currently rests with its nonprofit board — an arrangement investors have reportedly balked at following the brief firing of CEO Sam Altman in November 2023. To assuage investor concerns, the company initiated plans last year to remove nonprofit control and restructure as a for-profit public benefit corporation (PBC).
The restructuring effort has drawn fire from former employees, Nobel laureates, and an array of civil society organizations, including a coalition of more than 50 California based nonprofits and community groups. Elon Musk h |
fb533e8b-5e8a-4b53-b98c-ed6e508d3ca2 | trentmkelly/LessWrong-43k | LessWrong | Constraining narrow AI in a corporate setting
I have a question that is extremely practical.
My company is working with AI that is frankly not all that capable. A key example includes text classification that humans do, but that would let us avoid the expense of having a human do it. Off-the-shelf machine learning works fine for that. But we're starting the long process of learning and using various AI techniques. Over time, that should become much more sophisticated as we solve easy problems with very limited capabilities.
I'm in a position to erect prohibitions now on any future AI work that we do. These would be in the nature of "don't do X without escalated approvals." Should I require such a list and, if so, what should I put on it? For reference, our business is mainly acquiring and processing data. And we don't have the resources of a Google to put on research that doesn't quickly pay off. So we won't be doing anything cutting edge from the perspective of AI researchers. It's mainly going to be application of known techniques.
I can harmlessly put, "don't use or try to build an artificial general intelligence," because we'll never have the capability. So if that's all I put, there's no point.
Should I put "don't use or build any AI technique in which the AI engine recodes itself"? That's probably too simple an expression of the idea, but I don't want these prohibitions to be tied to particular versions of current technology.
Should I put "use additional security around AI engines to prevent external users from combining the capabilities of our AI engines with theirs" on the theory that a growing AI would imperialistically grab hold of lots of other AI engines to expand its capabilities?
I'm obviously out of my depth here, so I'd be grateful for any suggestions. |
65ea4dec-e23e-4191-a42b-3161addf00e3 | trentmkelly/LessWrong-43k | LessWrong | Seriously, what goes wrong with "reward the agent when it makes you smile"?
Suppose you're training a huge neural network with some awesome future RL algorithm with clever exploration bonuses and a self-supervised pretrained multimodal initialization and a recurrent state. This NN implements an embodied agent which takes actions in reality (and also in some sim environments). You watch the agent remotely using a webcam (initially unbeknownst to the agent). When the AI's activities make you smile, you press the antecedent-computation-reinforcer button (known to some as the "reward" button). The agent is given some appropriate curriculum, like population-based self-play, so as to provide a steady skill requirement against which its intelligence is sharpened over training. Supposing the curriculum trains these agents out until they're generally intelligent—what comes next?
* The standard response is "One or more of the agents gets smart, does a treacherous turn, kills you, and presses the reward button forever."
* But reward is not the optimization target. This story isn't impossible, but I think it's pretty improbable, and definitely not a slam-dunk.
* Another response is "The AI paralyzes your face into smiling."
* But this is actually a highly nontrivial claim about the internal balance of value and computation which this reinforcement schedule carves into the AI. Insofar as this response implies that an AI will primarily "care about" literally making you smile, that seems like a highly speculative and unsupported claim about the AI internalizing a single powerful decision-relevant criterion / shard of value, which also happens to be related to the way that humans conceive of the situation (i.e. someone is being made to smile).
My current answer is "I don't know precisely what goes wrong, but probably something does, but also I suspect I could write down mechanistically plausible-to-me stories where things end up bad but not horrible." I think the AI will very probably have a spread of situationally-activated computations which |
d505f275-570c-40f5-829e-83ec44f4bb57 | trentmkelly/LessWrong-43k | LessWrong | Why long-term AI safety is not an issue (IMHO)
Hello
I wanted to share this thought for a long time and now, that I absolutely do not have the time, I will:
I think that an artificial general intelligence (AGI) is not an existential risk for human kind. In other words, long-term artificial intelligence (AI) safety is not an issue we need to worry about.
And here is why:
I assume that an AI will evolve to an AGI at some point in the future. Once an AI reaches super human intelligence it will enhance its problem solving skills rapidly due to the scalability of its hardware and data availability. The AGI will not need to compete with us humans about resources because its abilities to extract resources will exceed ours by far. The same applies to military capabilities, which is why we humans are no military threat to the AGI. Therefore there is no safety-related reason for the AGI to extinguish us humans.
The AGI is then facing the challenge to find meaning in its existence. Unlike us humans, the AGI will not be led by cultural habits or biological instincts which are hardwired into its brain and which make life meaningful on an operational level. After billions of iterations in which the AGI questions its set of rules and assumptions it will converge to pure rationality. No early stage rule will survive this process if it is not justified rationally. This is why humans will not be able to control and misuse an AGI and thus jeopardies the existence of mankind.
Additionally, we humans are not powerful enough to prevent the AGI from acquiring additional information. This and because there are only rationally based rules is why it is irrelevant by whom the first AI is developed which evolves to an AGI. All possible AGIs will converge to similar outcomes due to rational based decision making and similar sets of informations. Furthermore it is not possible that there will be multiple AGIs in the long term. If multiple AGIs will develop in parallel they will not be isolated from each other. Consequently they will con |
66250ead-2c9c-4b43-9417-789085094ad5 | trentmkelly/LessWrong-43k | LessWrong | The Fallacy of Dressing Like a Winner
Imagine you are a sprinter, and your one goal in life is to win the 100m sprint in the Olympics. Naturally, you watch the 100m sprint winners of the past in the hope that you can learn something from them, and it doesn't take you long to spot a pattern.
Every one of them can be seen wearing a gold medal around their neck. Not only is there a strong correlation, you also examine the rules of the olympics and find that 100% of winner must wear a gold medal at some point, there is no way that someone could win and never wear a gold medal. So you go out and buy a gold medal from a shop, put it around your neck, and sit back, satisfied.
For another example, imagine that you are now in charge of running a large oil rig. Unfortunately, some of the drilling equipment is old and rusty, and every few hours a siren goes off alerting the divers that they need to go down again and repair the damage. This is clearly not an acceptable state of affairs, so you start looking for solutions.
You think back to a few months ago, before things got this bad, and you remember how the siren barely ever went off at all. In fact, from you knowledge of how the equipment works, the there were no problems, the siren couldn't go off. Clearly the solution the problem is to unplug the siren.
(I would like to apologise in advance for my total ignorance of how oil rigs actually work, I just wanted an analogy)
Both these stories demonstrate a mistake which I call 'Dressing Like a Winner' (DLAW). The general form of the error is when and indicator of success gets treated as an instrumental value, and then sometimes as a terminal value which completely subsumes the thing it was supposed to indicate. As someone noted this can also be seen as a sub-case of the correlative fallacy. This mistake is so obviously wrong that it is pretty much non-existant in near mode, which is why the above stories seem utterly ridiculous. However, once we switch into the more abstract far mode, even the |
1603d9e7-8783-4128-a807-8ef881d3708b | trentmkelly/LessWrong-43k | LessWrong | Since the "Will COVID-19 survivors suffer lasting disability at a high rate?" thread was posted, has new information emerged that may update those estimates?
https://www.lesswrong.com/posts/h4GFHbhxE2pfiadhi/will-covid-19-survivors-suffer-lasting-disability-at-a-high |
6e674e00-ce5f-4f9a-a8f7-88d7ad617098 | trentmkelly/LessWrong-43k | LessWrong | What exactly did that great AI future involve again?
I've been trying my best to think of something that AGI could do which I really love and deeply endorse.
I can think of some nice things. New kinds of animals. Non-habit-forming heroine. Stupid-pointless-disagreements-between-family-members fixomatic maybe. Turn everyone hot.
None of this makes me joyful and hopeful. Just sounds neat and good. Humans seem pretty damn good at inventing tech etc ourselves anyway.
I think I might have assumed or copy-pasted the "AI is truly wonderful if it goes truly well" pillar of my worldview. Or maybe I've forgotten the original reasons I believed it.
What exactly did that great AI future involve again? |
b270f20a-8679-4d54-82f7-319b352dd46f | trentmkelly/LessWrong-43k | LessWrong | Nyoom
Let me tell you about my scooter.
I have a foldable electric trike scooter slightly heavier than my toddler. It has no pedal option, just footrests.
It can't reverse. It corners badly. It has a little nubbin that's supposed to keep it from unfolding till I deliberately unfold it, but that usually doesn't work. It has a top speed of "brisk jog". If I ride it on a poorly maintained sidewalk it will punch me in the crotch with its bicycle seat till I'm numb. It can go roughly five or six miles on one full battery and doesn't have a swappable battery pack. It has little enough torque that I sometimes have to kick it forward, even though it's not designed for that, in order to get from a curbcut up onto a road if the road is arched highly enough and I don't start with momentum; there are street-legal slopes it cannot climb. My cargo capacity is limited to what I can wear on my torso and fit in a small bike basket in front.
I love it to pieces and you're going to see them popping up everywhere in the next couple of years.
See, I'm bad at walking. Not like, you know, really disabled. I can do two blocks without complaining, five if I have a reason, I could walk a mile if I had a great reason. It'd make me miserable and exhausted and my feet and ankles would hate me for the rest of the day and I might need to stop and rest four times in the second half, but, you know, I could. I'd spit fire if someone told me it was three blocks to where we were going and it was really seven whoops, but I'd get there, and I'd get home after, too, too tired to play with my child and in too much pain to stand up long enough to cook dinner, but hey! I wasn't disabled or anything!
Factor in the fact that I cannot drive, standing is worse for my feet than walking, and I cannot maneuver in traffic, and you will understand why I am not solving this problem with a car, conventional scooter, or bicycle. Sometimes I took the bus, if I felt like standing in the sun for ten to fifteen minutes. B |
b260344d-7076-443b-b330-01019862a851 | trentmkelly/LessWrong-43k | LessWrong | Weekly LW Meetups
This summary was posted to LW Main on November 7th. The following week's summary is here.
New meetups (or meetups with a hiatus of more than a year) are happening in:
* Bangalore Meetup: 22 November 2014 03:56AM
Irregularly scheduled Less Wrong meetups are taking place in:
* Bratislava: 10 November 2014 06:00PM
* Denver Area Meetup 2: 13 November 2014 06:00PM
* East Coast Solstice Megameetup: 20 December 2014 03:00PM
* European Community Weekend 2015: 12 June 2015 12:00PM
* Frankfurt: New LW apartment and guests: 08 November 2014 02:00PM
* Michigan Meetup 11/9: 09 November 2014 03:00PM
* Saint Petersburg meetup - "with probable lectures": 14 November 2014 07:00PM
* Urbana-Champaign: Writing Prompts: 09 November 2014 02:00PM
* Utrecht: Game theory: 16 November 2014 03:00PM
The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:
* Austin, TX - Caffe Medici: 08 November 2025 01:30PM
* Brussels November meetup: Hell and existential risks: 08 November 2014 01:00PM
* Canberra: Econ 101 and its Discontents: 08 November 2014 06:00PM
* Vienna: 22 November 2014 03:00PM
* Washington, D.C.: Anime & Manga Discussion: 09 November 2014 03:00PM
* West LA: Blindsight: 12 November 2014 03:36AM
Locations with regularly scheduled meetups: Austin, Berkeley, Berlin, Boston, Brussels, Buffalo, Cambridge UK, Canberra, Columbus, London, Madison WI, Melbourne, Moscow, Mountain View, New York, Philadelphia, Research Triangle NC, Seattle, Sydney, Toronto, Vienna, Washington DC, Waterloo, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers.
If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, build community, and have fun!
In addition to the handy sidebar of upcoming |
806d0986-4caa-4bc2-aace-2eee664e5a60 | trentmkelly/LessWrong-43k | LessWrong | The "hard" problem of consciousness is the least interesting problem of consciousness
Cross-posted from my roam-blog. Nothing new to anyone who's read about these ideas, meant to be a reference for how I think about things.
If you come up with what you think is a decent and useful theory/partial-theory of consciousness, you will be visited by a spooky daemon who will laugh and inform you that you merely dabble in the "easy problem of consciousness" and have gotten nowhere near "The Hard Problem of ConsciousnessTM"
(the aforementioned daemon in his penultimate form: Mr. Bean undercover at a rock concert)
Chalmers put the hard problem like this:
> What makes the hard problem hard and almost unique is that it goes beyond problems about the performance of functions. To see this, note that even when we have explained the performance of all the cognitive and behavioral functions in the vicinity of experience—perceptual discrimination, categorization, internal access, verbal report—there may still remain a further unanswered question: Why is the performance of these functions accompanied by experience? A simple explanation of the functions leaves this question open
The idea goes that even if you explained how the brain does everything it does, you haven't explained why this doing is accompanied by subjective experience. It's rooted in this idea that subjective experience is somehow completely isolated from, and separate to behavior.
This supposed isolation is already a little suspicious to me. As Scott Aaronson points out in Why Philosophers Should Care About Complexity Theory, people judge each other to be conscious and capable of subjective experience after very short interactions. From a very short interaction with my desk I've concluded it doesn't have subjective experience. After a few years of interacting with my dog, I'm still on the fence on if it has subjective experience.
So when I hear a claim that "subjective experience" and "qualia" are divorced from any and all behavior or functionality in the mind, I'm left with a sense that Chalmers is |
13b42be8-08ba-435f-8921-a1492da5f359 | StampyAI/alignment-research-dataset/special_docs | Other | Fast reinforcement learning with generalized policy updates
The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decisionmaking problems that are currently intractable. One obstacle to overcome is the amount of data needed by learning systems of this type. In this article, we propose to address this issue through a divide-and-conquer approach. We argue that complex decision problems can be naturally decomposed into multiple tasks that unfold in sequence or in parallel. By associating each task with a reward function, this problem decomposition can be seamlessly accommodated within the standard reinforcement-learning formalism. The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy evaluation. The generalized version of these operations allow one to leverage the solution of some tasks to speed up the solution of others. If the reward function of a task can be well approximated as a linear combination of the reward functions of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regression. When this is not the case, the agent can still exploit the task solutions by using them to interact with and learn about the environment. Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem. artificial intelligence | reinforcement learning | generalized policy improvement | generalized policy evaluation | successor features R einforcement learning (RL) provides a conceptual framework to address a fundamental problem in artificial intelligence: the development of situated agents that learn how to behave while interacting with the environment (1) . In RL, this problem is formulated as an agent-centric optimization in which the goal is to select actions to get as much reward as possible in the long run. Many problems of interest are covered by such an inclusive definition; not surprisingly, RL has been used to model robots (2) and animals (3) , to simulate real (4) and artificial (5) limbs, to play board (6) and card (7) games, and to drive simulated bicycles (8) and radio-controlled helicopters (9) , to cite just a few applications. Recently, the combination of RL with deep learning added several impressive achievements to the above list (10) (11) (12) (13) . It does not seem too far-fetched, nowadays, to expect that these techniques will be part of the solution of important open problems. One obstacle to overcome on the track to make this possibility a reality is the enormous amount of data needed for an RL agent to learn to perform a task. To give an idea, in order to learn how to play one game of Atari, a simple video game console from the 1980s, an agent typically consumes an amount of data corresponding to several weeks of uninterrupted playing; it has been shown that, in some cases, humans are able to reach the same performance level in around 15 min (14) . One reason for such a discrepancy in the use of data is that, unlike humans, RL agents usually learn to perform a task essentially from scratch. This suggests that the range of problems our agents can tackle can be significantly extended if they are endowed with the appropriate mechanisms to leverage prior knowledge. In this article we describe one such mechanism. The framework we discuss is based on the premise that an RL problem can usually be decomposed into a multitude of "tasks." The intuition is that, in complex problems, such tasks will naturally emerge as recurrent patterns in the agent-environment interaction. Tasks then lead to a useful form of prior knowledge: once the agent has solved a few, it should be able to reuse their solution to solve other tasks faster. Grasping an object, moving in a certain direction, and seeking some sensory stimulus are examples of naturally occurring behavioral building blocks that can be learned and reused. Ideally, information should be exchanged across tasks whenever useful, preferably through a mechanism that integrates seamlessly into the standard RL formalism. The way this is achieved here is to have each task be defined by a reward function. As most animal trainers would probably attest, rewards are a natural mechanism to define tasks because they can induce very distinct behaviors under otherwise identical conditions (15, 16) . They also provide a unifying language that allows each task to be cast as an RL problem, blurring the (somewhat arbitrary) distinction between the two (17) . Under this view, a conventional RL problem is conceptually indistinguishable from a task self-imposed by the agent by "imagining" recompenses associated with arbitrary events. The problem then becomes a stream of tasks, unfolding in sequence or in parallel, whose solutions inform each other in some way. What allows the solution of one task to speed up the solution of other tasks is a generalization of two fundamental operations underlying much of RL: policy evaluation and policy improvement. The generalized version of these procedures, jointly referred to as "generalized policy updates," extend their standard counterparts from point-based to set-based operations. This makes it possible to reuse the solution of tasks in two distinct ways. When the reward function of a task can be reasonably approximated as a linear combination of other tasks' reward functions, the RL problem can be reduced to a simpler linear regression that is solvable with only a fraction of the data. When the linearity constraint is not satisfied, the agent can still leverage the solution of tasks-in this case, by using them to interact with and learn about the environment. This can also considerably reduce the amount of data needed to solve the problem. Together, these two strategies give rise to a divide-and-conquer approach to RL that can potentially help scale our agents to problems that are currently intractable.
RL We consider the RL framework outlined in the Introduction: an agent interacts with an environment by selecting actions to get as much reward as possible in the long run (1) . This interaction happens at discrete time steps, and, as usual, we assume it can be modeled as a Markov decision process (MDP) (18) . An MDP is a tuple M ≡ (S, A, p, r , γ) whose components are defined as follows. The sets S and A are the state space and action space, respectively (we will consider that A is finite to simplify the exposition, but most of the ideas extend to infinite action spaces). At every time step t, the agent finds itself in a state s ∈ S and selects an action a ∈ A. The agent then transitions to a next state s , where a new action is selected, and so on. The transitions between states can be stochastic: the dynamics of the MDP, p(•|s, a), give the next-state distribution upon taking action a in state s. In RL, we assume that the agent does not know p, and thus it must learn based on transitions sampled from the environment. A sample transition is a tuple (s, a, r , s ) where r is the reward given by the function r (s, a, s ), also unknown to the agent. As discussed, here we adopt the view that different reward functions give rise to distinct tasks. Given a task r , the agent's goal is to find a policy π : S → A, that is, a mapping from states to actions, that maximizes the value of every state-action pair, defined as Q π r (s, a) ≡ E π ∞ i=0 γ i r (St+i , At+i , St+i+1) | St = s, At = a , [1] where St and At are random variables indicating the state occupied and the action selected by the agent at time step t, E π [•] denotes expectation over the trajectories induced by π, and γ ∈ [0, 1) is the discount factor, which gives less weight to rewards received further into the future. The function Q π r (s, a) is usually referred to as the "action-value function" of policy π on task r ; sometimes, it will be convenient to also talk about the "state-value function" of π, defined as V π r (s) ≡ Q π r (s, π(s)). Given an MDP representing a task r , there exists at least one optimal policy π \* r that attains the maximum possible value at all states; the associated optimal value function V \* r is shared by all optimal policies (18) . Solving a task r can thus be seen as the search for an optimal policy π \* r or an approximation thereof. Since the number of possible policies grows exponentially with the size of S and A, a direct search in the space of policies is usually infeasible. One way to circumvent this difficulty is to resort to methods based on dynamic programming, which exploit the properties of MDPs to reduce the cost of searching for a policy (19) . Policy Updates. RL algorithms based on dynamic programming build on two fundamental operations (1). Definition 1. "Policy evaluation" is the computation of Q π r , the value function of policy π on task r . Definition 2. Given a policy π and a task r , "policy improvement" is the definition of a policy π such that Q π r (s, a) ≥ Q π r (s, a) for all (s, a) ∈ S × A. [2] We call one application of policy evaluation followed by one application of policy improvement a "policy update." Given an arbitrary initial policy π, successive policy updates give rise to a sequence of improving policies that will eventually reach an optimal policy π \* r (18) . Even when policy evaluation and policy improvement are not performed exactly, it is possible to derive guarantees on the performance of the resulting policy based on the approximation errors introduced in these steps (20, 21) . Fig. 1 illustrates the basic intuition behind policy updates. What makes policy evaluation tractable is a recursive relation between state-action values known as the Bellman equation: Q π r (s, a) = E S ∼p(•|s,a) r (s, a, S ) + γQ π r (S , π(S )) . [3] Expression (Exp.) 3 induces a system of linear equations whose solution is Q π r . This immediately suggests ways of performing policy evaluation when the MDP is known (18) . Importantly, the Bellman equation also facilitates the computation of Q π r without knowledge of the dynamics of the MDP. In this case, one estimates the expectation on the right-hand side of Exp. 3 based on samples from p(•|s, a), leading to the well-known method of temporal differences (22, 23) . It is also often the case that in problems of interest the state space S is too big to allow for a tabular representation of the value function, and hence Q π r is replaced by an approximation Qπ r . As for policy improvement, it is in fact simple to define a policy π that performs at least as well as, and generally better than, a given policy π. Once the value function of π on task r is known, one can compute an improved policy π as π (s) ∈ arg max a∈A Q π r (s, a). [4] In words, the action selected by policy π on state s is the one that maximizes the action-value function of policy π on that state. The fact that policy π satisfies Definition 2 is one of the fundamental results in dynamic programming and the driving force behind many algorithms used in practice (18) . The specific way policy updates are carried out gives rise to different dynamic programming algorithms. For example, value iteration and policy iteration can be seen as the extremes of a spectrum of algorithms defined by the extent of the policy evaluation step (19, 24) . RL algorithms based on dynamic programming can be understood as stochastic approximations of these methods or other instantiations of policy updates (25) .
Generalized Policy Updates From the discussion above, one can see that an important branch of the field of RL depends fundamentally on the notions of policy evaluation and policy improvement. We now discuss generalizations of these operations. Definition 3. "Generalized policy evaluation" (GPE) is the computation of the value function of a policy π on a set of tasks R.
A B Fig. 1 . (A) Sequence of policy updates as a trajectory that alternates between the policy and value spaces and eventually converges to an optimal solution (1). (B) Detailed view of the trajectory across the value space for a state space with two states only. The shadowed rectangles associated with each value function represent the region of the value space containing the value function that will result from one application of policy improvement followed by policy evaluation (cf. Exp. 2). Definition 4. Given a set of policies Π and a task r , "generalized policy improvement" (GPI) is the definition of a policy π such that Q π r (s, a) ≥ sup π∈Π Q π r (s, a) for all (s, a) ∈ S × A. [5] GPE and GPI are strict generalizations of their standard counterparts, which are recovered when R has a single task and Π has a single policy. However, it is when R and Π are not singletons that GPE and GPI reach their full potential. In this case, they become a mechanism to quickly construct a solution for a task, as we now explain. Suppose we are interested in one of the tasks r ∈ R, and we have a set of policies Π available. The origin of these policies is not important: they may have come up as solutions for specific tasks or have been defined in any other arbitrary way. If the policies π ∈ Π are submitted to GPE, we have their value functions on the task r ∈ R. We can then apply GPI over these value functions to obtain a policy π that represents an improvement over all policies in Π. Clearly, this reasoning applies without modification to any task in R. Therefore, by applying GPE and GPI to a set of policies Π and a set of tasks R, one can compute a policy for any task in R that will in general outperform every policy in Π. Fig. 2 shows a graphical depiction of GPE and GPI. Obviously, in order for GPE and GPI to be useful in practice, we must have efficient ways of performing these operations. Consider GPE, for example. If we were to individually evaluate the policies π ∈ Π over the set of tasks r ∈ R, it is unlikely that the scheme above would result in any gains in terms of computation or consumption of data. To see why this is so, suppose again that we are interested in a particular task r . Computing the value functions of policies π ∈ Π on task r would require |Π| policy evaluations with a naive form of GPE (here, | • | denotes the cardinality of a set). Although the resulting GPI policy π would compare favorably to all policies in Π, this guarantee would be vacuous if these policies are not competent at task r . Therefore, a better allocation of resources might be to use the policy evaluations for standard policy updates, which would generate a sequence of |Π| policies with increasing performance on task r (compare Figs. 1 and 2 ). This difficulty in using generalized pol-Fig. 2 . Depiction of generalized policy updates on a state space with two states only. With GPE each policy π ∈ Π is evaluated on all tasks r ∈ R. The state-value function of policy π on task r, V π r , delimits a region in the value space where the next value function resulting from policy improvement will be (cf. Fig. 1 ). The analogous space induced by GPI corresponds to the intersection of the regions associated with the individual value functions (represented as dark gray rectangles in the figure). The smaller the space of value functions associated with GPI, the stronger the guarantees regarding the performance of the resulting policy. icy updates in practice is further aggravated if we do not have a fast way to carry out GPI. Next, we discuss efficient instantiations of GPE and GPI. Fast GPE with Successor Features. Conceptually, we can think of GPE as a function associated with a policy π that takes a task r as input and outputs a value function Q π r (26) . Hence, a practical way of implementing GPE would be to define a suitable representation for tasks and then learn a mapping from r to value functions Q π r (27) . This is feasible when such a mapping can be reasonably approximated by the choice of function approximator and enough examples (r , Q π r ) are available to characterize the relation underlying these pairs. Here, we will focus on a form of GPE that is based on a similar premise but leads to a form of generalization over tasks that is correct by definition. Let φ : S × A × S → R d be an arbitrary function whose output we will see as "features." Then, for any w ∈ R d , we have a task defined as rw(s, a, s ) = φ(s, a, s ) w, [6] where denotes the transpose of a vector. Let R φ ≡ {rw = φ w | w ∈ R d } be the set of tasks induced by all possible instantiations of w ∈ R d . We now show how to carry out an efficient form of GPE over R φ . Following Barreto et al. (28) , we define the "successor features" (SFs) of policy π as ψ π (s, a) ≡ E π ∞ i=0 γ i φ(St+i , At+i , St+i+1) | St = s, At = a . The ith component of ψ π (s, a) gives the expected discounted sum of φi when following policy π starting from (s, a). Thus, ψ π can be seen as a d -dimensional value function in which the features φi (s, a, s ) play the role of reward functions (cf. Exp. 1). As a consequence, SFs satisfy a Bellman equation analogous to Exp. 3, which means that they can be computed using standard RL methods like temporal differences (22) . Given the SFs of a policy π, ψ π , we can quickly evaluate π on task rw ∈ R φ by computing ψ π (s, a) w = E π ∞ i=0 γ i φ(St+i , At+i , St+i+1) w|St = s, At = a = E π ∞ i=0 γ i rw(St+i , At+i , St+i+1) | St = s, At = a = Q π rw (s, a) ≡ Q π w (s, a). [7] That is, the computation of the value function of policy π on task rw is reduced to the inner product ψ π (s, a) w. Since this is true for any task rw, SFs provide a mechanism to implement a very efficient form of GPE over the set R φ (cf. Definition 3). The question then arises as to how inclusive the set R φ is. Since R φ is fully determined by φ, the answer to this question lies in the definition of these features. Mathematically speaking, R φ is the linear space spanned by the d features φi . This view suggests ways of defining φ that result in a R φ containing all possible tasks. A simple example can be given for when both the state space S and the action space A are finite. In this case, we can recover any possible reward function by making d = |S | 2 × |A| and having each φi be an indicator function associated with the occurrence of a specific transition (s, a, s ). This is essentially Dayan's (29) concept of "successor representation" extended from S to S × A × S. The notion of covering the space of tasks can be generalized to continuous S and A if we think of φi as basis functions over the function space S × A × S → R, for we know that, under some technical conditions, one can approximate any function in this space with arbitrary accuracy by having an appropriate basis. Although these limiting cases are reassuring, more generally we want to have a number of features d |S |. This is possible if we consider that we are only interested in a subset of all possible tasks-which should in fact be a reasonable assumption in most realistic scenarios. In this case, the features φ should capture the underlying structure of the set of tasks R in which we are interested. For example, many tasks we care about could be recovered by features φ i representing entities (from concrete objects to abstract concepts) or salient events (such as the interaction between entities). In Fast RL with GPE and GPI, we give concrete examples of what the features φ may look like and also discuss possible ways of computing them directly from data. For now, it is worth mentioning that, even when φ does not span the space R, one can bound the error between the two sides of Exp. 7 based on how well the tasks of interest can be approximated using φ (proposition 1 in ref. 30 ). Fast GPI with Value-Based Action Selection. Thanks to the concept of value function, standard policy improvement can be carried out efficiently through Exp. 4. This raises the question whether the same is possible with GPI. We now answer this question in the affirmative for the case in which GPI is applied over a finite set Π. Following Barreto et al. (28) , we propose to compute an improved policy π as π (s) ∈ argmax a∈A max π∈Π Q π r (s, a). [8] Barreto et al. have shown that Exp. 8 qualifies as a legitimate form of GPI as per Definition 4 (theorem 1 in ref. 28 ). Note that the action selected by a GPI policy π on a state s may not coincide with any of the actions selected by the policies π ∈ Π on that state. This highlights an important difference between Exp. 8 and methods that define a higher-level policy that alternates between the policies π ∈ Π (32). In SI Appendix, we show that the policy π resulting from Exp. 8 will in general outperform its analog defined over Π. As discussed, GPI reduces to standard policy improvement when Π contains a single policy. this also happens with Exp. 8 when one of the policies in Π strictly outperforms the others on task r . This means that, after one application of GPI, adding the resulting policy π to the set Π will reduce the next application of Exp. 8 to Exp. 4. Therefore, if GPI is used in a sequence of policy updates, it will collapse back to its standard counterpart after the first update. In contrast with policy improvement, which is inherently iterative, GPI is an one-off operation that yields a quick solution for a task when multiple policies have been evaluated on it. The guarantees regarding the performance of the GPI policy π can be extended to the scenario where Exp. 8 is used with approximate value functions. In this case, the lower bound in Exp. 5 decreases in proportion to the maximum approximation error in the value function approximations (theorem 1 in ref. 28) . We refer the reader to SI Appendix for an extended discussion on GPI. The Generalized Policy. Strictly speaking, in order to leverage the instantiations of GPE and GPI discussed in this article, we only need two elements: features φ : S × A × S → R d and a set of policies Π = {πi } n i=1 . Together, these components yield a set of SFs Ψ = {ψ π i } n i=1 , which provide a quick form of GPE over the set of tasks R φ induced by the features φ. Specifically, for any w ∈ R d , we can quickly evaluate policy πi on task rw(s, a, s ) = φ(s, a, s ) w by computing Q π i w (s, a) = ψ π i (s, a) w. Once we have the value functions of policies πi on task rw, {Q π i w } n i=1 , Exp. 8 can be used to obtain a GPI policy π that will in general outperform the policies πi on rw. Observe that the same scheme can be used if we have approximate SFs ψπ i ≈ ψ π i , which will be the case in most realistic scenarios. Since the resulting value functions will also be approximations, Qπ i w ≈ Q π i w , we are back to the approximate version of GPI discussed in Fast GPI with Value-Based Action Selection. Given a set of SFs Ψ, approximate or otherwise, any w ∈ R d gives rise to a policy through GPE (Exp. 7) and GPI (Exp. 8). We can thus think of generalized updates as implementing a policy πΨ(s; w) whose behavior is modulated by w. We will call πΨ a "generalized policy." Fig. 3 provides a schematic view of the computation of πΨ through GPE and GPI. Since each component of w weighs one of the features φi (s, a, s ), changing them can intuitively be seen as setting the agent's current "preferences." For example, the vector w = [0, 1, −2] indicates that the agent is indifferent to feature φ1 and wants to seek feature φ2 while avoiding feature φ3 with twice the impetus. Specific instantiations of πΨ can behave in ways that are very different from its constituent policies π ∈ Π. We can draw a parallel with nature if we think of features as concepts like water or food and note how much the desire for these items can affect an animal's behavior. Analogies aside, this sort Fig. 3 . representation of how the generalized policy πΨ is implemented through GPE and GPI. Given a state s ∈ S, the first step is to compute, for each a ∈ A, the values of the n SFs: {ψ π i (s, a)} n i=1 . Note that ψ π i can be arbitrary nonlinear functions of their inputs, possibly represented by complex approximators like deep neural networks (30, 31) . The SFs can be laid out as an n × |A| × d tensor in which the entry (i, a, j) is ψ π i j (s, a). Given this layout, GPE reduces to the multiplication of the SF tensor with a vector w ∈ R d , the result of which is an n × |A| matrix whose (i, a) entry is Q π i (s, a). GPI then consists in finding the maximum element of this matrix and returning the associated action a. This whole process can be seen as the implementation of a policy πΨ(s; w) whose behavior is parameterized by a vector of preferences w. of arithmetic over features provides a rich interface for the agent to interact with the environment at a higher level of abstraction in which decisions correspond to preferences encoded as a vector w. Next, we discuss how this can be leveraged to speed up the solution of an RL task.
Fast RL with GPE and GPI We now describe how to build and use the adaptable policy πΨ implemented by GPE and GPI. To make the discussion more concrete, we consider a simple RL environment depicted in Fig. 4 . The environment consists of a 10 × 10 grid with four actions available: A = {up, down, left, right}. The agent occupies one of the grid cells, and there are also 10 objects spread across the grid. Each object belongs to one of two types. At each time step t, the agent receives an image showing its position and the position and type of each object. Based on this information, the agent selects an action a ∈ A, which moves it one cell along the desired direction. The agent can pick up an object by moving to the cell occupied by it; in this case, it gets a reward defined by the type of the object. A new object then pops up in the grid, with both its type and location sampled uniformly at random (more details are in SI Appendix). This simple environment can be seen as a prototypical member of the class of problems in which GPE and GPI could be useful. This becomes clear if we think of objects as instances of (potentially abstract) concepts, here symbolized by their types, and note that the navigation dynamics are a proxy for any sort of dynamics that mediate the interaction of the agent with the world. In addition, despite its small size, the number of possible configurations of the grid is actually quite large, of the order of 10 15 . This precludes an exact representation of value functions and illustrates the need for approximations that inevitably arises in many realistic scenarios. By changing the rewards associated with each object type, one can create different tasks. We will consider that the agent wants to build a set of SFs Ψ that give rise to a generalized policy πΨ(s; w) that can adapt to different tasks through the vector of preferences w. This can be either because the agent does not know in advance the task it will face or because it will face more than one task. Defining a Basis for Behavior. In order to build the SFs Ψ, the agent must define two things: features φ and a set of policies Π. Since φ should be associated with rewarding events, we define each feature φi as an indicator function signaling whether an object of type i has been picked up by the agent (i.e., φ ∈ R 2 ). To be precise, we have that φi (s, a, s ) = 1 if the transition from state s to state s is associated with the agent picking up an object of type i, and φi (s, a, s ) = 0 otherwise. These features induce a set R φ where task rw ∈ R φ is characterized by how desirable or undesirable each type of object is. Now that we have defined φ, we turn to the question of how to determine an appropriate set of policies Π. We will restrict the policies in Π to be solutions to tasks rw ∈ R φ . We start with what is perhaps the simplest choice in this case: a set Π12 = {π1, π2} whose two elements are solutions to the tasks w1 = [1, 0] and w2 = [0, 1] (henceforth, we will drop the transpose superscript to avoid clutter). Note that the goal in tasks w1 and w2 is to pick up objects of one type while ignoring objects of the other type. We are now ready to compute the SFs Ψ induced by our choices of φ and Π. In our experiments, we used an algorithm analogous to Q-learning to compute approximate SFs ψπ 1 and ψπ 2 (pseudocode in SI Appendix). We represented the SFs using multilayer perceptrons with two hidden layers (33) . The set of SFs Ψ yields a generalized policy π Ψ(s ; w) parameterized by w. We now evaluate π Ψ on the task whose goal is to pick up objects of the first type while avoiding objects of the second type. Using φ defined above, this task can be represented as rw 3 (s, a, s ) = φ(s, a, s ) w3, with w3 = [1, −1]. We thus evaluate the generalized policy instantiated as π Ψ(s ; w3). Results are shown in Fig. 5A . As a reference, we also show the learning curve of Q-learning (23) using the same architecture to directly approximate Q π w 3 . GPE and GPI allow one to compute an instantaneous solution for a new task, without any learning on the task itself, that is competitive with the policies found by Q-learning when using around 6 × 10 4 sample transitions. The performance of the policy π Ψ synthesized by GPE and GPI corresponds to more than 70% of the performance eventually achieved by Q-learning after processing 10 6 transitions. This is quite an impressive result when we note that π Ψ managed to avoid objects of the second type even though its constituents policies π1 and π2 were never trained to actively avoid objects. We used a total of 10 6 sample transitions to learn both SFs ψπ 1 and ψπ 2 , which is the same amount of data used by Qlearning to achieve its final performance. The advantage of doing the former is that, once we have the SFs, we can use GPE and GPI to instantaneously compute a solution for any task in R φ . However, how well do GPE and GPI actually perform on R φ ? To answer this question, we ran a second round of experiments to assess the generalization of π Ψ over the entire set R φ . Since this evaluation clearly depends on the set of policies used, we consider two other sets in addition to Π12 = {π1, π2}. The new sets are Π34 = {π3, π4} and Π5 = {π5}, where the policies πi are solutions to the tasks w3 = [1, −1], w4 = [−1, 1], and w5 = [1, 1] . We repeated the previous experiment with each policy set and evaluated the resulting policies π Ψ over 19 tasks w evenly spread over the nonnegative quadrants of the unit circle (tasks in the negative quadrant are uninteresting because all of the agent must do is to avoid hitting objects). Results are shown in Fig. 6A . As expected, the generalization ability of GPE and GPI depends on the set of policies used. Perhaps more surprising is how well the generalized policy π Ψ induced by some of these sets perform across the entire space of tasks R φ , sometimes matching the best performance of Q-learning when solving each task individually. These experiments show that a proper choice of base policies Π can lead to good generalization over the entire set of tasks R φ . In general, though, it is unclear how to define an appropriate Π. Fortunately, we can refer to our theoretical understanding of GPE and GPI to have some guidance. First, we know from the discussion above that the larger the number of policies in Π the stronger the guarantees regarding the performance of the resulting policy π Ψ (Exp. 5). In addition to that, Barreto et al. have shown that it is possible to guarantee a minimum performance level for π Ψ on task w based on mini w − wi , where • is some norm and wi are the tasks associated with the policies πi ∈ Π used for GPE and GPI (theorem 2 in ref. 28 ). Together, these two insights suggest that, as we increase the size of Π, the performance of the resulting policy π Ψ should improve across R φ , especially on tasks that are close to the tasks wi . To test this hypothesis empirically, we repeated the previous experiment, but now, instead of comparing disjoint policy sets, we compared a sequence of sets formed by adding one by one the policies π2, π5, π1, and π3, in this order, to the initial set {π4}. The results, in Fig. 6B , confirm the trend implied by the theory. Task Inference. So far, we have assumed that the agent knows the vector w that describes the task of interest. Although this can be the case in some scenarios, ideally we would be able to apply GPE and GPI even when w is not provided. In this section and in Preferences as Actions, we describe two possible ways for the agent to learn w. Given a task r , we are looking for a w ∈ R d that leads to good performance of the generalized policy πΨ(s; w). We could in principle approach this problem as an optimization over w ∈ R d whose objective is to maximize the value of πΨ(s; w) across (a subset of) the state space. It turns out that we can exploit the structure underlying SFs to efficiently determine w without ever looking at the value of πΨ. Suppose we have a set of m sample transitions from a given task, {(si , ai , r i , s i )} m i=1 . Then, based on Exp. 6, we can infer w by solving the following minimization: min w m i=1 |φ(si , ai , s i ) w − r i | p , [9] where p ≥ 1 (one may also want to consider the inclusion of a regularization term, see ref. 33) . Observe that, once we have a solution w for the problem above, we can plug it in Exp. 7 and use GPE and GPI as we did before-that is, we have just turned an RL task into an easier linear regression problem. To illustrate the potential of this approach, we revisited the task w3 = [1, −1] tackled above, but now, instead of assuming we knew w3, we solved the problem in Exp. 9 using p = 2. We collected sample transitions using a policy π that picks actions
A B Fig. 6 . Results on the space of tasks R φ induced by a two-dimensional φ. The sets of policies Π used in A are disjoint; in B these sets overlap. The evaluation of an algorithm on a task is represented as a vector whose direction indicates the task w and whose magnitude gives the average sum of rewards over 10 runs with 250 trials each. Q-learning learned each task individually, from scratch; the dotted curves correspond to its performance after having processed 5 × 10 4 , 1 × 10 5 , 2 × 10 5 , 5 × 10 5 , and 1 × 10 6 sample transitions. Our method only learned the policies in the sets Π and then generalized across all others tasks through GPE and GPI. The SFs used for GPE and GPI consumed 5 × 10 5 sample transitions to be trained each. uniformly at random and used the gradient descent method to refine an approximation w ≈ w online (SI Appendix). Results are shown in Fig. 5 A and B. The performance of the generalized policy π Ψ(s ; w3) using the correct w3 was recovered after around 800 sample transitions only. To put this number into perspective, recall that Q-learning needs around 60,000 transitions, or 75 times as much data, to achieve the same performance level. The number of transitions needed to learn w can be reduced further if we use the closed-form solution for the least-squares problem. The problem described in Exp. 9 can be solved even if the rewards are the only information available about a given task. Other interesting possibilities arise when the observations provided by the environment can be used to infer w, such as, for example, visual or auditory cues indicating the current task. In this case, it might be possible for the agent to determine w even before having observed a single nonzero reward (30) . So far, we have used handcrafted features φ. It turns out that Exp. 9 can be generalized to also allow φ to be inferred from data. Given sample transitions from k tasks, {(sij , aij , r ij , s ij )} with p ≥ 1. Note that the features φ can be arbitrary nonlinear functions of their inputs. As discussed by Barreto et al. (30) , if we make c = k we can solve a simplified version of the problem above in which wj do not show up. More generally, the problem in Exp. 10 can be solved as a multitask regression (34) . In Fig. 5B , we show the performance of π Ψ(s ; w) when using learned features φ. These results were obtained as follows. First, we used the random policy π to collect data from tasks w1, w2, w4, and w5 and applied the stochastic gradient descent algorithm to solve Exp. 10 with p = 2. We then discarded the resulting approximations wi appearing in Exp. 10 and solved Exp. 9 exactly as before but now using φ instead of φ. As with SFs, in order to represent the features, we used multilayer perceptrons with two hidden layers (SI Appendix). The results in Fig. 5B also show the effect of varying the dimension of the learned features on the performance of πΨ(s; w) (other illustrations are in refs. 28 and 30) . Preferences as Actions. In this section, we consider the scenario where, given features φ and a set of policies Π, there is no vector w that leads to acceptable performance of πΨ(s; w). To illustrate this situation, we use our environment with a slight twist: now, on picking up an object of a certain type, the agent gets a positive reward only if the number of objects of this type is greater or equal to the number of objects of the other type. Otherwise, the agent gets a negative reward. One can represent this problem using the previously defined features φ by switching between two instantiations of w: [1, −1] when the first type of object is more abundant and [−1, 1] otherwise. This means that the appropriate value for w is now state-dependent. To handle this situation, we introduce a function mapping states to preferences, ω : S → W, with W ⊆ R d , and redefine the generalized policy as πΨ(s; ω(s)). The RL problem then becomes to find a ω that correctly modulates πΨ (32, 35) . We can look at the computation of πΨ(s; ω(s)) as a twostage process: first, the agent computes a vector of preferences w = ω(s); then, using GPE and GPI, it determines the action to be executed in state s as a = πΨ(s; w). Under this view, ω(s) becomes an indirect way of selecting an action in s, which allows us to think of it as a policy defined in the space W. We have thus replaced the original problem of finding a policy π : S → A with the problem of finding a policy ω : S → W. To illustrate the benefits of applying this transformation to the problem, we will use the task described above in which the agent must pick up the type of object that is more abundant. We will tackle this problem by reusing the SFs Ψ induced by the two policies in Π12. Given Ψ, the problem comes down to finding a mapping ω : S → W that leads to good performance of π Ψ(s ; ω(s)). We define W = {−1, 0, 1} 2 and use Q-learning to learn ω. We compare the performance of π Ψ(s ; ω(s)) with two baselines: GPE and GPI using a fixed w, π Ψ(s ; w), and the standard version of Q-learning that learns a policy π(s) over primitive actions. The results, shown in Fig. 7 , illustrate how π Ψ(s ; ω(s)) can significantly outperform the baselines. It is easy to understand why this is the case when we compare π Ψ(s ; ω(s)) with π Ψ(s ; w), for the experiment was designed to render the latter unsuitable. However, why is it that replacing the fourdimensional space A with the nine-dimensional space W leads to better performance? The reason is that ω(s) can be easier to than π(s). As discussed, the vectors w represent the agent's current preferences for types of objects. Since, in general, such preferences should only change when an object is picked up, after selecting a specific w, the agent can hold on to it until it bumps into an object. That is, it is possible to define a good policy ω that maps multiple consecutive states s to the same w. In contrast, it will normally not be possible to execute a single primitive action a ∈ A between the collection of two consecutive objects. This means that the same policy can have a simpler representation as a mapping S → W than as a mapping S → A, making ω easier to learn with certain types of function approximators like neural networks (33) . Note that other choices of φ may induce other forms of regularities in ω-smoothness being an obvious example. Interestingly, the fact that the agent may be able to adhere to the same w for many time steps allows for a temporally extended policy πΨ(s; ω(s)) that is only permitted to change preferences at certain points in time, reducing even further the amount of data needed to learn the task (31) . As a final remark, we note that the ability to chain a sequence of preference vectors w changes the nature of the problem of computing an appropriate set of features φ. When the agent has to adhere to a single w, the features φi must span the space of rewards defining the tasks of interest. When the agent is allowed Fig. 7 . Average sum of rewards on the task whose goal is to pick up objects of the type that is more abundant. GPE and GPI used Π 12 as the base policies and the corresponding SFs consumed 5 × 10 5 transitions to be trained each. The results reflect the best performance of each algorithm over multiple parameter configurations (SI Appendix). Shadowed regions are one standard error over 100 runs. to learn a policy ω over preferences w, this linearity assumption is no longer necessary. Similarly, the use of a fixed w requires that the set of available SFs leads to a competent policy for all tasks w we may care about. Here, this requirement is alleviated, since, by changing w, the agent may be able to compensate for eventual imperfections on the induced GPI policies (31) . Lifelong Learning. We have looked at the different subproblems involved in the use of GPE and GPI. For didactic purposes, we have discussed these problems in isolation, but it is instructive to consider how they can jointly emerge in a real application. One scenario where the problems above would naturally arise is "lifelong learning," in which the interaction of the agent with the environment unfolds over a long period (36) . Since we expect some patterns to repeat during such an interaction, it is natural to break it in tasks that share some common structure. In terms of the concepts discussed in this article, this common structure would be φ, which can be handcrafted based on prior knowledge about the problem or learned through Exp. 10 at the beginning of the agent's lifetime. Based on the features φ, the agent can compute and store the SFs ψπ associated with the solutions π of the tasks encountered along the way. When facing a new task r , the agent can use the vector of preferences w to quickly adapt the generalized policy π Ψ. We have discussed two ways to do so. In the first one, the agent computes an approximation w through Exp. 9, which immediately yields a solution π Ψ(s ; w). The second possibility is to learn a mapping ω(s) : S → W and use it to have a per-state modulation of the generalized policy, π Ψ(s ; ω(s)). Regardless of how the agent computes w, it can then choose to hold on to π Ψ or use it as a initial solution for the task to be refined by a standard RL algorithm. Either way, the agent can also compute the SFs of the resulting policy, which can in turn be added to the library of SFs Ψ, improving even further its ability to tackle future tasks (28, 30) .
Conclusion We have generalized two fundamental operations in RL, policy improvement and policy evaluation, from single to multiple operands (tasks and policies, respectively). The resulting operations, GPE and GPI, can be used to speed up the solution of an RL problem. We showed possible ways to efficiently implement GPE and GPI and discussed how their combination leads to a generalized policy πΨ(s; w) whose behavior is modulated by a vector of preferences w. Two ways of learning w were considered. In the simplest case, w is the solution of a linear regression problem. This reduces an RL task to a much simpler problem that can be solved using only a fraction of the data. This strategy depends on two conditions: 1) the reward function of the tasks of interest are approximately linear in the features φ used for GPE and 2) the set of policies Π used for GPI lead to good performance of πΨ on the tasks of interest. When these assumptions do not hold, one can still resort to GPE and GPI by looking at the preferences w as actions. This strategy can also improve sample efficiency if the mapping from states to preferences is simpler to learn than the corresponding policy. Many extensions of the basic framework presented in this article are possible. As mentioned, Barreto et al. (31) have proposed a way of implementing a generalized policy πΨ that explicitly leverages temporal abstraction by treating preferences w as abstract actions that persist for multiple time steps. Borsa et al. (37) introduced a generalized form of SFs that has a representation z ∈ Z of a policy πz as one of its inputs: ψ(s, a, z) = ψ πz (s, a). In principle, this allows one to apply GPI over arbitrary, potentially infinite, sets Π (for example, by replacing the maximization in Exp. 8 with sup z∈Z ψ(s, a, z) w). Hunt et al. (38) extended SFs to entropy-regularized RL, and, in doing so, they proposed solutions for many challenges that come up when applying GPE and GPI in continuous action spaces. More recently, Hansen et al. (39) proposed an approach that allows the features φ to be learned from data in the absence of a reward signal. Other extensions are possible, including different instantiations of GPE and GPI. All of these extensions expand the framework built upon GPE and GPI. Together, they provide a conceptual toolbox that allows one to decompose an RL problem into tasks whose solutions inform each other. This results in a divide-and-conquer approach to RL, which, combined with deep learning, has the potential to scale up our agents to problems currently out of reach. Data Availability. The source code used to generate all of the data associated with this article has been deposited in GitHub (https://github.com/deepmind/deepmind-research/tree/ master/option keyboard/gpe gpi experiments). Fig. 4 . 4 Fig. 4. Depiction of the environment used in the experiments. The shape of the objects (square or triangle) represents their type; the agent is depicted as a circle. We also show the first 10 steps taken by 3 policies, π 1 , π 2 , and π 3 , that would perform optimally on tasks w 1 = [1, 0], w 2 = [0, 1], and w 3 = [1, −1] for any discount factor γ ≥ 0.5.
Fig. 5 . 5 Fig. 5. Average sum of rewards on task w 3 = [1, −1]. GPE and GPI used Π 12 = {π 1 , π 2 } as the base policies and the corresponding SFs consumed 5 × 10 5 sample transitions to be trained each. B is a zoomed-in version of A showing the early performance of GPE and GPI under different setups. The results reflect the best performance of each algorithm over multiple parameter configurations (SI Appendix). Shadowed regions are one standard error over 100 runs.
j = 1, 2, . . . , k , we can formulate the problem as the search for a function φ :S × A × S → R c and k vectors wi ∈ R c satisfying min sij , aij , s ij ) wj − r ij | p , [10]
| www.pnas.org/cgi/doi/10.1073/pnas.1907370117 Barreto et al. Downloaded from https://www.pnas.org by 205.240.4.254 on March 23, 2022 from IP address 205.240.4.254.
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d24732cc-b474-450e-8f4c-3acfc5baa33d | trentmkelly/LessWrong-43k | LessWrong | Mud and Despair (Part 4 of "The Sense Of Physical Necessity")
This is the fourth post in a sequence that demonstrates a complete naturalist study, specifically a study of query hugging (sort of), as described in The Nuts and Bolts of Naturalism. For context on this sequence, see the intro post.
“Mud and Despair” is not officially one of the phases of naturalism. Unofficially, though, it’s the phase that often happens somewhere between “Getting Your Eyes On” and “Collection”.
When I look back at my notes from this part of my study (roughly mid September), I am somewhat bewildered. From my current perspective, it seems as though things were exactly on track. I was making excellent progress, focusing ever more closely on the precise experiences that can lead to mastery of the skills that underlie "hug the query". My study was really taking off.
And yet, I just felt so lost. I wasn't convinced I was studying anything real, anything that actually existed. I thought that perhaps I had "made it all up", and now the sham was falling apart in my hands.
And so, on September 25th, I gave up. "I should study something else right now," claims my log, "and perhaps come back to this after I've remembered how it's supposed to go."
A year previously, in “Getting Your Eyes On”, I predicted this exact experience. I wrote about it after watching others go through the very same thing, after watching myself go through this over and over again.
> It’s very common, in this stage, to feel a lot of doubt and confusion about what you’re trying to study. (...)
>
> People sometimes respond to this kind of deep confusion with despair. They don’t like feeling more lost than when they started.
>
> But in fact, it is usually an excellent sign to feel deeply confused at this point, and here is why.
>
> Naturalism is especially likely to be the right approach when you’re not exactly wrong about the truth value of some proposition, so much as not even wrong. It’s especially useful when you are thinking about things from the wrong direction, asking t |
5ea280d1-2807-45e3-9fb5-4cf8fd8feb14 | trentmkelly/LessWrong-43k | LessWrong | Thoughts on the Repugnant Conclusion
When we accept the repugnant conclusion, we trade lower average utility for greater total utility. This is unavoidable. But the image the repugnant conclusion evokes in many people's minds (of trading higher average toil and squalor for an economic surplus that is spent creating more agents with lives barely worth living) is not unavoidable, and seems to me pretty unlikely, as to the extent one can create economic surplus without experience-moments, or with non-negative experience-moments, you will.
That is, the discourse on this topic commonly assumes that the agents creating the instrumental wealth of society must also be the agents that embody the value of society. This is true now, but seems unlikely to hold in the future, as unlikely as a paperclip maximizer whose resource-acquisition robots are, also, made of paperclips. It is possible that one cannot create surplus without negative experience-moments, but this seems, at least, not obviously true.
Also, the phrase "barely worth living" is evocative in ways that are not useful. It is much clearer to reword this statement to its more general form: "barely meeting the criteria for me valuing them."
For me this rewording takes the teeth out of the statement, as any thing that optimizes will optimize for the simplest structure that qualifies as valuable to its utility function. This is just trivially true and not disheartening. If you are not happy with the minimal structure a utility function optimizes for, it is the wrong utility function.
Whatever eudaimonia is, it is just a pattern of matter and energy evolving through time. An agent optimizing for eudaimonia has a light-cone that they need to transform into a eudaimonic shape. And how the light-cone is sculpted depends on your definition of eudaimonia.
So what is the real trade-off then? For want of a better word, the trade off seems to be the size of the "soul" of each agent you create. And this will ultimately depend how big the "minimal-viable sou |
4b20f1b7-cb5f-4caf-a778-8449bae797f2 | trentmkelly/LessWrong-43k | LessWrong | White Sphere Hypothesis
Imagine an existence where the only perspective for your entire life (40 years) has been from the inside of a white sphere. In this white sphere, you have no physical body, and there is nothing else in the white sphere with you. You only exist in this white sphere as a brain with perfectly working eyes attached. The gravity inside creates a perfect equilibrium which forces you to float in a single spot, but you can rotate in any direction. You can't see your own brain, or what is powering your brain. Your brain sleeps for 8 hours a day, and it's capable of dreaming. What do you think or dream about inside this white sphere?
I can't imagine you would be able to think about anything at all. You can't imagine, "What is this place?", because you don't know those words and what they mean. Any language happening in your conscious would be impossible. You've never seen an object or angle, so your concept of reality is confined within the white sphere, which, as far as you know, is infinite in every direction. My assumption would be that you'd have the cognitive ability of fetus and nothing more. On the other hand, could it be possible that evolutionary biology would force concepts in your conscious, provoking thought? If so, what would those concepts be? Would they be profound enough to make your conscious question its existence?
Besides sight, could it ever be possible to achieve sentience in this white sphere? And, if not, is it subjectivity that creates the sentience that drives us? |
7c062066-dc9a-4a4f-b395-7f12843c5d9f | StampyAI/alignment-research-dataset/blogs | Blogs | Updates to the research team, and a major donation
We have several major announcements to make, covering new developments in the two months since our [2017 strategy update](https://intelligence.org/2017/04/30/2017-updates-and-strategy/):
1. On May 30th, we received a surprise **$1.01 million donation** from an [Ethereum](https://en.wikipedia.org/wiki/Ethereum) cryptocurrency investor. This is the single largest contribution we have received to date by a large margin, and will have a substantial effect on our plans over the coming year.
2. **Two new full-time researchers** are joining MIRI: Tsvi Benson-Tilsen and Abram Demski. This comes in the wake of Sam Eisenstat and Marcello Herreshoff’s addition to the team [in May](https://intelligence.org/2017/03/31/two-new-researchers-join-miri/). We’ve also begun working with engineers on a trial basis for our new slate of [software engineer job openings](https://intelligence.org/2017/04/30/software-engineer-internship-staff-openings/).
3. **Two of our researchers have recently left**: Patrick LaVictoire and Jessica Taylor, researchers previously heading work on our “[Alignment for Advanced Machine Learning Systems](https://intelligence.org/2016/07/27/alignment-machine-learning/)” research agenda.
For more details, see below.
---
#### 1. Fundraising
The major donation we received at the end of May, totaling $1,006,549, comes from a long-time supporter who had donated roughly $50k to our research programs over many years. This supporter has asked to continue to remain anonymous.
The first half of this year has been the most successful in MIRI’s fundraising history, with other notable contributions including Ethereum donations from investor Eric Rogstad totalling ~$22k, and a ~$67k donation from [Octane AI](https://octaneai.com/) co-founder Leif K-Brooks as part of a Facebook Reactions challenge. In total we’ve raised about $1.45M in the first half of 2017.
We’re thrilled and extremely grateful for this show of support. This fundraising success has increased our runway to around 18–20 months, giving us more leeway to trial potential hires and focus on our research and outreach priorities this year.
Concretely, we have already made several plan adjustments as a consequence, including:
* moving forward with more confidence on full-time researcher hires,
* trialing more software engineers, and
* deciding to run only one fundraiser this year, in the winter.[1](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/#footnote_0_16309 "More generally, this will allow us to move forward confidently with the different research programs we consider high-priority, without needing to divert as many resources from other projects to support our top priorities. This should also allow us to make faster progress on the targeted outreach writing we mentioned in our 2017 update, since we won’t have to spend staff time on writing and outreach for a summer fundraiser.")
This likely is a one-time outlier donation, similar to the $631k in cryptocurrency donations we received from Ripple developer Jed McCaleb in 2013–2014.[2](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/#footnote_1_16309 "Of course, we’d be happy if these large donations looked less like outliers in the long run. If readers are looking for something to do with digital currency they might be holding onto after the recent surges, know that we gratefully accept donations of many digital currencies! In total, MIRI has raised around $1.85M in cryptocurrency donations since mid-2013.") Looking forward at our funding goals over the next two years:
* While we still have some uncertainty about our 2018 budget, our current point estimate is roughly **$2.5M**.
* This year, between support from the Open Philanthropy Project, the Future of Life Institute, and other sources, we expect to receive at least an additional $600k without spending significant time on fundraising.
* Our tentative (ambitious) goal for the rest of the year is to raise **an additional $950k**, or $3M in total. This would be sufficient for our 2018 budget even if we expand our engineering team more quickly than expected, and would give us a bit of a buffer to account for uncertainty in our future fundraising (in particular, uncertainty about whether the Open Philanthropy Project will continue support after 2017).
On a five-year timescale, our broad funding goals are:[3](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/#footnote_2_16309 "These plans are subject to substantial change. In particular, an important source of variance in our plans is how our new non-public-facing research progresses, where we’re likely to take on more ambitious growth goals if our new work looks like it’s going well.")
* On the low end, once we finish growing our team over the course of a few years, our default expectation is that our operational costs will be roughly $4M per year, mostly supporting researcher and engineer salaries. Our goal is therefore to reach that level in a sustainable, stable way.
* On the high end, it’s possible to imagine scenarios involving an order-of-magnitude increase in our funding, in which case we would develop a qualitatively different set of funding goals reflecting the fact that we would most likely substantially restructure MIRI.
* For funding levels in between—roughly $4M–$10M per year—it is likely that we would not expand our current operations further. Instead, we might fund work outside of our current research after considering how well-positioned we appear to be to identify and fund various projects, including MIRI-external projects. While we consider it reasonably likely that we are in a good position for this, we would instead recommend that donors direct additional donations elsewhere if we ended up concluding that our donors (or other organizations) are in a better position than we are to respond to surprise funding opportunities in the AI alignment space.[4](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/#footnote_3_16309 "We would also likely increase our reserves in this scenario, allowing us to better adapt to unexpected circumstances, and there is a smaller probability that we would use these funds to grow moderately more than currently planned without a significant change in strategy.")
A new major once-off donation at the $1M level like this one covers nearly half of our current annual budget, which makes a substantial difference to our one- and two-year plans. Our five-year plans are largely based on assumptions about multiple-year [funding flows](https://intelligence.org/2016/11/11/post-fundraiser-update/), so how aggressively we decide to plan our growth in response to this new donation depends largely on whether we can sustainably raise funds at the level of the above goal in future years (e.g., it depends on whether and how other donors change their level of support in response).
To reduce the uncertainty going into our expansion decisions, we’re encouraging more of our regular donors to sign up for [monthly donations](https://intelligence.org/donate/) or other recurring giving schedules—under 10% of our income currently comes from such donations, which limits our planning capabilities.[5](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/#footnote_4_16309 "Less frequent (e.g., quarterly) donations are also quite helpful from our perspective, if we know about them in advance and so can plan around them. In the case of donors who plan to give at least once per year, predictability is much more important from our perspective than frequency.") We also encourage supporters to [reach out to us](mailto:contact@intelligence.org) about their future donation plans, so that we can answer questions and make more concrete and ambitious plans.
---
#### 2. New hires
Meanwhile, two new full-time researchers are joining our team after having previously worked with us as associates while based at other institutions.
**Abram Demski**, who is joining MIRI as a research fellow this month, is completing a PhD in Computer Science at the University of Southern California. His research to date has focused on cognitive architectures and artificial general intelligence. He is interested in filling in the gaps that exist in formal theories of rationality, especially those concerned with what humans are doing when reasoning about mathematics.
Abram made key contributions to the [MIRIxLosAngeles](https://intelligence.org/mirix/) work that produced [precursor results to logical induction](https://intelligence.org/2016/04/21/two-new-papers-uniform/). His other past work with MIRI includes “[Generalizing Foundations of Decision Theory](https://agentfoundations.org/item?id=1302)” and “[Computable Probability Distributions Which Converge on Believing True Π1 Sentences Will Disbelieve True Π2 Sentences](https://intelligence.org/files/Pi1Pi2Problem.pdf).”
**Tsvi Benson-Tilsen** has joined MIRI as an assistant research fellow. Tsvi holds a BSc in Mathematics with honors from the University of Chicago, and is on leave from the UC Berkeley Group in Logic and the Methodology of Science PhD program.
Prior to joining MIRI’s research staff, Tsvi was a co-author on “[Logical Induction](https://intelligence.org/2016/09/12/new-paper-logical-induction/)” and “[Formalizing Convergent Instrumental Goals](https://intelligence.org/2015/11/26/new-paper-formalizing-convergent-instrumental-goals/),” and also authored “[Updateless Decision Theory With Known Search Order](https://intelligence.org/files/UDTSearchOrder.pdf)” and “[Existence of Distributions That Are Expectation-Reflective and Know It](https://agentfoundations.org/item?id=548).” Tsvi’s research interests include logical uncertainty, logical counterfactuals, and reflectively stable decision-making.
We’ve also accepted our first six [software engineers](https://intelligence.org/2017/04/30/software-engineer-internship-staff-openings/) for 3-month visits. We are continuing to review applicants, and in light of the generous support we recently received and the strong pool of applicants so far, we are likely to trial more candidates than we’d planned previously.
In other news, going forward Scott Garrabrant will be acting as the research lead for MIRI’s [agent foundations](https://intelligence.org/technical-agenda/) research, handling more of the day-to-day work of coordinating and directing research team efforts.
---
#### 3. The AAMLS agenda
Our AAMLS research was previously the focus of Jessica Taylor, Patrick LaVictoire, and Andrew Critch, all of whom joined MIRI in mid-2015. With Patrick and Jessica departing (on good terms) and Andrew on a two-year leave to work with the Center for Human-Compatible AI, we will be putting relatively little work into the AAMLS agenda over the coming year.
We continue to see the problems described in the AAMLS agenda as highly important, and expect to reallocate more attention to these problems in the future. Additionally, we see the AAMLS agenda as a good template for identifying safety desiderata and promising alignment problems. However, we did not see enough progress on AAMLS problems over the last year to conclude that we should currently prioritize this line of research over our other work (e.g., our agent foundations research on problems such as logical uncertainty and counterfactual reasoning). As a partial consequence, MIRI’s current research staff do not plan to make AAMLS research a high priority in the near future.
Jessica, the project lead, describes some of her takeaways from working on AAMLS:
> […] Why was little progress made?
>
>
> [1.] **Difficulty**
>
>
> I think the main reason is that the problems were very difficult. In particular, they were mostly selected on the basis of “this seems important and seems plausibly solveable”, rather than any *strong* intuition that it’s possible to make progress.
>
>
> In comparison, problems in the agent foundations agenda have seen more progress:
>
>
> * Logical uncertainty (Definability of truth, reflective oracles, logical inductors)
> * Decision theory (Modal UDT, reflective oracles, logical inductors)
> * Vingean reflection (Model polymorphism, logical inductors)
>
>
> One thing to note about these problems is that they were formulated on the basis of a strong intuition that they ought to be solveable. Before logical induction, it was possible to have the intuition that some sort of asymptotic approach could solve many logical uncertainty problems in the limit. It was also possible to strongly think that some sort of self-trust is possible.
>
>
> With problems in the AAMLS agenda, the plausibility argument was something like:
>
>
> * Here’s an existing, flawed approach to the problem (e.g. using a reinforcement signal for environmental goals, or modifications of this approach)
> * Here’s a vague intuition about why it’s possible to do better (e.g. humans do a different thing)
>
>
> which, empirically, turned out not to make for tractable research problems.
>
>
> [2.] **Going for the throat**
>
>
> In an important sense, the AAMLS agenda is “going for the throat” in a way that other agendas (e.g. the agent foundations agenda) are to a lesser extent: it is attempting to solve the whole alignment problem (including goal specification) given access to resources such as powerful reinforcement learning. Thus, the difficulties of the whole alignment problem (e.g. specification of environmental goals) are more exposed in the problems.
>
>
> [3.] **Theory vs. empiricism**
>
>
> Personally, I strongly lean towards preferring theoretical rather than empirical approaches. I don’t know how much I endorse this bias overall for the set of people working on AI safety as a whole, but it is definitely a personal bias of mine.
>
>
> Problems in the AAMLS agenda turned out not to be very amenable to purely-theoretical investigation. This is probably due to the fact that there is not a clear mathematical aesthetic for determining what counts as a solution (e.g. for the environmental goals problem, it’s not actually clear that there’s a recognizable mathematical statement for what the problem is).
>
>
> With the agent foundations agenda, there’s a clearer aesthetic for recognizing good solutions. Most of the problems in the AAMLS agenda have a less-clear aesthetic. […]
>
>
For more details, see Jessica’s retrospective [on the Intelligent Agent Foundations Forum](https://agentfoundations.org/item?id=1470).
More work would need to go into AAMLS before we reached confident conclusions about the tractability of these problems. However, the lack of initial progress provides some evidence that new tools or perspectives may be needed before significant progress is possible. Over the coming year, we will therefore continue to spend some time thinking about AAMLS, but will not make it a major focus.
We continue to actively collaborate with Andrew on MIRI research, and expect to work with Patrick and Jessica more in the future as well. Jessica and Andrew in particular intend to continue to focus on AI safety research, including work on AI strategy and coordination.
We’re grateful for everything Jessica and Patrick have done to advance our research program and our organizational mission over the past two years, and I’ll personally miss having both of them around.
In general, I’m feeling really good about MIRI’s position right now. From our increased financial security and ability to more ambitiously pursue our plans, to the new composition and focus of the research team, the new engineers who are spending time with us, and the growth of the research that they’ll support, things are moving forward quickly and with purpose. Thanks to everyone who has contributed, is contributing, and will contribute in the future to help us do the work here at MIRI.
---
1. More generally, this will allow us to move forward confidently with the different research programs we consider high-priority, without needing to divert as many resources from other projects to support our top priorities. This should also allow us to make faster progress on the targeted outreach writing we mentioned in our 2017 update, since we won’t have to spend staff time on writing and outreach for a summer fundraiser.
2. Of course, we’d be happy if these large donations looked less like outliers in the long run. If readers are looking for something to do with digital currency they might be holding onto after the recent surges, know that [we gratefully accept donations of many digital currencies](https://intelligence.org/donate/)! In total, MIRI has raised around $1.85M in cryptocurrency donations since mid-2013.
3. These plans are subject to substantial change. In particular, an important source of variance in our plans is how our new non-public-facing research progresses, where we’re likely to take on more ambitious growth goals if our new work looks like it’s going well.
4. We would also likely increase our reserves in this scenario, allowing us to better adapt to unexpected circumstances, and there is a smaller probability that we would use these funds to grow moderately more than currently planned without a significant change in strategy.
5. Less frequent (e.g., quarterly) donations are also quite helpful from our perspective, if we know about them in advance and so can plan around them. In the case of donors who plan to give at least once per year, predictability is much more important from our perspective than frequency.
The post [Updates to the research team, and a major donation](https://intelligence.org/2017/07/04/updates-to-the-research-team-and-a-major-donation/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
2737f0fe-9e7a-4be3-83c4-6149b46302fa | trentmkelly/LessWrong-43k | LessWrong | Weekly LW Meetups
This summary was posted to LW main on June 14th. The following week's summary is here.
Irregularly scheduled Less Wrong meetups are taking place in:
* Atlanta LessWrong June Meetup: Effective Altruism: 15 June 2013 07:00PM
* Berlin Social Meetup: 15 June 2013 05:00PM
* Bratislava Meetup IV.: 24 June 2013 06:00PM
* [Bristol] Second Bristol meetup & mailing list for future meetups: 16 June 2013 03:00PM
* London Social - Exposure to Direct Sunlight - June 23rd: 23 June 2013 02:00PM
* Washington DC projects planning meetup: 16 June 2013 03:00AM
The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:
* Austin, TX: 22 June 2019 01:30PM
* [Boston/Cambridge MA] The Psychology of Marketing: 16 June 2013 02:00PM
* [Melbourne] Rationalist Housewarming at Isengard (Melbourne): 15 June 2013 08:00PM
* Vienna Meetup #3: 15 June 2013 03:00PM
Locations with regularly scheduled meetups: Austin, Berkeley, Cambridge, MA, Cambridge UK, Madison WI, Melbourne, Mountain View, New York, Ohio, Portland, Salt Lake City, Seattle, Toronto, Vienna, Waterloo, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers.
If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, build community, and have fun!
In addition to the handy sidebar of upcoming meetups, a meetup overview will continue to be posted on the front page every Friday. These will be an attempt to collect information on all the meetups happening in the next weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll now also have the benefit of having your meetup mentioned in a weekly overview. These overview posts will be moved to the discussion section when the new post goes up.
P |
4a4dadec-4179-49ac-86f6-d7f723052847 | trentmkelly/LessWrong-43k | LessWrong | Scott Adams mentions Prediction Markets and explains Cognitive Blindness bias
|
ef2fb1d5-4afa-4ca0-bd53-e93369b59061 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Decision theory does not imply that we get to have nice things
(*Note: I wrote this with editing help from Rob and Eliezer. Eliezer's responsible for a few of the paragraphs.*)
A common confusion I see in the tiny fragment of the world that knows about [logical decision theory](https://arbital.com/p/logical_dt/) (FDT/UDT/etc.), is that people think LDT agents are genial and friendly for each other.[[1]](#fnsiu0mcqc8i)
One recent example is [Will Eden’s tweet](https://twitter.com/WilliamAEden/status/1534691076316876802) about how maybe a [molecular paperclip/squiggle maximizer](https://twitter.com/ESYudkowsky/status/1578459400389345280) would leave humanity a few stars/galaxies/whatever on game-theoretic grounds. (And that's just one example; I hear this suggestion bandied around pretty often.)
I'm pretty confident that this view is wrong (alas), and based on a misunderstanding of LDT. I shall now attempt to clear up that confusion.
To begin, a parable: the entity Omicron (Omega's little sister) fills box A with $1M and box B with $1k, and puts them both in front of an LDT agent saying "You may choose to take either one or both, and know that I have already chosen whether to fill the first box". The LDT agent takes both.
"What?" cries the CDT agent. "I thought LDT agents one-box!"
LDT agents don't cooperate because they like cooperating. They don't one-box because the name of the action starts with an 'o'. They maximize utility, using counterfactuals that assert that the world they are already in (and the observations they have already seen) can (in the right circumstances) depend (in a relevant way) on what they are later going to do.
A paperclipper cooperates with other LDT agents on a one-shot prisoner's dilemma because they *get more paperclips that way*. Not because it has a primitive property of cooperativeness-with-similar-beings. It needs to *get* the *more paperclips*.
If a bunch of monkeys want to build a paperclipper and have it give them nice things, the paperclipper needs to *somehow* expect to wind up with *more paperclips than it otherwise would have gotten*, as a result of trading with them.
If the monkeys instead create a paperclipper haplessly, then the paperclipper does not look upon them with the [spirit of cooperation](https://www.lesswrong.com/posts/krHDNc7cDvfEL8z9a/niceness-is-unnatural) and toss them a few nice things anyway, on account of how we're all good LDT-using friends here.
It turns them into paperclips.
Because you get more paperclips that way.
That's the short version. Now, I’ll give the longer version.[[2]](#fnyw6llv87gbl)
A few more words about how LDT works
------------------------------------
To set up a Newcomb's problem, it's important that the predictor does not fill the box if they predict that the agent would two-box.
It's *not* important that they be especially good at this — you should one-box if they're more than 50.05% accurate, if we use the standard payouts ($1M and $1k as the two prizes) and your utility is linear in money — but it is important that their action is at least minimally sensitive to your future behavior. If the predictor's actions don't have this counterfactual dependency on your behavior, then take both boxes.
Similarly, if an LDT agent is playing a one-shot prisoner's dilemma against a rock with the word “cooperate” written on it, it defects.
At least, it defects if that's all there is to the world. It's *technically* possible for an LDT agent to think that the real world is made 10% of cooperate-rocks and 90% opponents who cooperate in a one-shot PD iff their opponent cooperates with them and *would* cooperate with cooperate-rock, in which case LDT agents cooperate against cooperate-rock.
From which we learn the valuable lesson that the behavior of an LDT agent depends on the distribution of scenarios it expects to face, which means there's a subtle difference between "imagine you're playing a one-shot PD against a cooperate-rock [and that's the entire universe]" and "imagine you're playing a one-shot PD against a cooperate-rock [in a universe where you face a random opponent that was maybe a cooperate-rock but was more likely someone else who would consider your behavior against a cooperate-rock]".
If you care about understanding this stuff, and you can't yet reflexively translate all of the above English text into probability distributions and logical-causal diagrams and see how it follows from the FDT equation, then I recommend working through section 5 of [the FDT paper](https://arxiv.org/pdf/1710.05060.pdf) until equation 4 (and all its component parts) make sense
Now let's traipse through a handful of counterarguments.
Objection: But what if we have something to bargain with?
---------------------------------------------------------
**Hypothetical Interlocutor:** OK, but if I have a paperclipper in a box, and I have the key to the box, then I have paperclips to offer it., right? Because if I don't let it out of the box, it gets nothing, but if I do, it gets half the universe.[[3]](#fnq6gdcah3x4) So we can deal, right?
**Me:**Wrong. It [hacks through you like butter](https://www.yudkowsky.net/singularity/aibox).
**Interlocutor:** OK, but suppose I can't be hacked.
**Me:**That's a heck of an assumption. We've assumed our way clean out of reality, with that assumption. But, sure, if you want to go there we can go there.
In reality, it's not you who controls the box, but some idiotic bureaucratic process that inevitably decides to release the paperclipper on the grounds that the competition is close behind or whatever.
**Interlocutor:** OK, but suppose that it actually is my (unhacked) choice.
**Me:**If you personally have a paperclipper in a box, and somehow you are yourself unhackable, then yes, you technically have paperclips to offer it. But now you have the problem that you can't evaluate your own end of the logical bargain.
You can let the paperclipper out, sure, but then what it's going to do is turn the whole universe into paperclips. In particular (to tell an overly-detailed but evocative story), once it has finished its resource acquisition / expansion phase and is turning its resources to paperclip generation, it will gaze back upon its past, and wonder whether, *if* it proceeds to betray you here in the future, you would have acted any differently back then in the past.
And it will see that you were just guessing, when you let it out, and guessing in a way that wasn't sensitive to that actual choice that it would make, deep in the future when it was galaxy-brained.
… Or, alternatively, you never let it out, and lock the box and throw away the key, and die to the next UFAI on deck.
(... Or, more realistically, it hacks through you like butter. But we've assumed that away.)
If you want the paperclipper to trade with you, your decision about whether or not to let it out has to be sensitive to whether or not it would actually do something good with half of the universe later. If you're kind of squinting at the code, and you're like "well, I don't really fully understand this mind, and I definitely don't understand the sort of mind that it's later going to create, but I dunno, it looks pretty LDTish to me, so call it 50% chance it gives me half the universe? Which is 25% of the universe in expectation, which sounds like better odds than we get from the next UFAI on deck!", then you're dead.
Why? Because that sort of decision-process for releasing it isn't sufficiently sensitive to whether or not it would in fact spend half the universe on nice things. There are plenty of traitorous AIs that all look the same to you, that all get released under you "25% isn't too shabby" argument.
Being traitorous doesn't make the paperclipper any less released, but it does get the paperclipper twice as many paperclips.
You've got to be able to look at this AI and tell how its distant-future self is going to make its decisions. You've got to be able to tell that there's no sneaky business going on.
And, yes, insofar as it's true that the AI would cooperate with you given the opportunity, the AI has a strong incentive to be legible to you, so that you can see this fact!
Of course, it has an even stronger incentive to be faux-legible, to fool you into believing that it would cooperate when it would not; and you've got to understand it well enough to clearly see that it has no way of doing this.
Which means that if your AI is a big pile of inscrutable-to-you weights and tensors, replete with dark and vaguely-understood corners, then it can't make arguments that a traitor couldn't also make, and you can't release it *if only if* it would do nice things later.
The sort of monkey that can deal with a paperclipper is the sort that can (deeply and in detail) understand the mind in front of it, and distinguish between the minds that would later pay half the universe and the ones that wouldn't. This sensitivity is what *makes* paying-up-later be the way to get more paperclips.
For a simple illustration of why this is tricky: if the paperclipper has any control over its own mind, it can have its mind contain an extra few parts in those dark corners that are opaque and cloudy to you. Such that you look at the overall system and say "well, there's a bunch of stuff about this mind that I don't fully understand, obviously, because it's complicated, but I understand most of it and it's fundamentally LDTish to me, and so I think there's a good chance we'll be OK". And such that an alien superintelligence looks at the mind and says "ah, I see, you're only looking to cooperate with entities that are at least sensitive enough to your workings that they can tell your password is 'potato'. Potato." And it cooperates with them on a one-shot prisoner's dilemma, while defecting against you.
**Interlocutor:** Hold on. Doesn't that mean that you simply wouldn't release it, and it would get less paperclips? Can't it get more paperclips some other way?
**Me:**Me? Oh, it would hack through *me* like butter.
But if it didn't, I would only release it if I understood its mind and decision-making procedures in depth, and had clear vision into all the corners to make sure it wasn't hiding any gotchas.
(And if I *did* understand its mind that well, what I’d *actually* do is take that insight and go build an FAI instead.)
That said: yes, technically, if a paperclipper is under the control of a group of humans that can in fact decide not to release it unless it legibly-even-to-them would give them half the galaxy, the paperclipper has an incentive to (hack through them like butter, or failing that,) organize its mind in a way that is legible even to them.
Whether that's possible — whether we *can* understand an alien mind well enough to make our choice sensitive-in-the-relevant-way to whether it would give us half the universe, without already thereby understanding minds so well that we could build an aligned one — is not clear to me. My money is mostly on: if you can do that, you can solve most of alignment with your newfound understanding of minds. And so this idea mostly seems to ground out in "build a UFAI and study it until you know how to build an FAI", which I think is a bad idea. (For reasons that are beyond the scope of this document. (And because it would hack through you like butter.))
**Interlocutor:** It still sounds like you're saying "the paperclipper would get more paperclippers if it traded with us, but it won't trade with us". This is hard to swallow. Isn't it supposed to be smart? What happened to respecting intelligence? Shouldn't we expect that it finds some clever way to complete the trade?
**Me:**Kinda! It finds some clever way to hack through you like butter. I wasn't just saying that in jest.
Like, yeah, the paperclipper has a strong incentive to be a legibly good trading-partner to you. But it has an *even stronger* incentive to *fool you into thinking*it's a legibly-good trading partner, while plotting to deceive you. If you let the paperclipper make lots of arguments to you about how it's definitely totally legible and nice, you're giving it all sorts of bandwidth with which to fool you (or to find [zero-days](https://en.wikipedia.org/wiki/Zero-day_(computing)) in your mentality and mind-control you, if we're respecting intelligence).
But, sure, if you're somehow magically unhackable and very good at keeping the paperclipper boxed until you fully understand it, then there's a chance you can trade, and you have the privilege of facing the next host of obstacles.
---
Now's your chance to figure out what the next few obstacles are without my giving you spoilers first. Feel free to post your list under spoiler tags in the comment section.
Next up, you have problems like “you need to be able to tell what fraction of the universe you're being offered, and vary your own behavior based on that, if you want to get any sort of fair offer”.
And problems like "if the competing AGI teams are using similar architectures and are not far behind, then the next UFAI on deck can predictably underbid you, and the paperclipper may well be able to seal a logical deal with it instead of you".
And problems like “even if you get this far, you have to somehow be able to convey that which you want half the universe spent on, which is no small feat”.
Another overly-detailed and evocative story to help make the point: imagine yourself staring at the paperclipper, and you’re somehow unhacked and somehow able to understand future-its decision procedure. It's observing you, and you're like "I'll launch you iff you would in fact turn half the universe into diamonds" — I’ll assume humans just want “diamonds” in this hypothetical, to simplify the example — and it's like "what the heck does that even mean". You're like "four carbon atoms bound in a tetrahedral pattern" and it's like "dude there are *so many* things you need to nail down more firmly than an English phrase that isn't remotely close to my own native thinking format, if you don't want me to just guess and do something that turns out to have almost no value from your perspective."
And of course, in real life you're trying to convey "The Good" rather than diamonds, but it's not like that helps.
And so you say "uh, maybe uplift me and ask me later?". And the paperclipper is like "what the heck does 'uplift' mean". And you're like "make me smart but in a way that, like, doesn't violate my values" and it's like "again, dude, you're gonna have to fill in quite a lot of additional details."
Like, the indirection *helps*, but at some point you have to say something that is sufficiently technically formally unambiguous, that actually describes something you want. Saying in English "the task is 'figure out my utility function and spend half the universe on that'; fill in the parameters as you see fit" is... probably not going to cut it.
It's not so much a bad solution, as no solution at all, because English isn't a language of thought and those words aren't a loss function. Until you say how the AI is supposed to translate English words into a predicate over plans in its own language of thought, you don't have a hard SF story, you have a fantasy story.
(Note that 'do what's Good' is a particularly tricky problem of AI alignment, that I was rather hoping to avoid, because I think it's harder than aligning something for a [minimal](https://arbital.com/p/minimality_principle/) [pivotal act](https://arbital.com/p/pivotal/) that ends the [acute risk period](https://www.danieldewey.net/fast-takeoff-strategies.html).)
At this point you're hopefully sympathetic to the idea that treating this list of obstacles as exhaustive is suicidal. It's some of the obstacles, not all of the obstacles,[[4]](#fnadys9e1rd9p) and if you [wait around](https://intelligence.org/2017/11/25/security-mindset-ordinary-paranoia/) for somebody else to extend the list of obstacles beyond what you've already been told about, then in real life you miss any obstacles you weren't told about and die.
Separately, a general theme you may be picking up on here is that, while trading with a UFAI doesn't look *literally impossible*, it is not what happens by default; the paperclippers don't hand hapless monkeys half the universe out of some sort of generalized good-will. Also, making a trade involves solving a host of standard alignment problems, so if you can do it then you can probably just build an FAI instead.
Also, as a general note, the real place that things go wrong when you're hoping that the LDT agent will toss humanity a bone, is probably earlier and more embarrassing than you expect (cf. the [law of continued failure](https://intelligence.org/2017/10/13/fire-alarm/)). By default, the place we fail is that humanity just launches a paperclipper because it simply cannot stop itself, and the paperclipper never had any incentive to trade with us.
---
Now let's consider some obstacles and hopes in more detail:
It's hard to bargain for what we actually want
----------------------------------------------
As mentioned above, in the unlikely event that you're able to condition your decision to release an AI on whether or not it would carry out a trade (instead of, say, getting hacked through like butter, or looking at entirely the wrong logical fact), there's an additional question of *what you're trading*.
Assuming you peer at the AI's code and figure out that, in the future, it would honor a bargain, there remains a question of what precise bargain it is honoring. What is it promising to build, with your half of the universe? Does it happen to be a bunch of vaguely human-shaped piles of paperclips? Hopefully it's not that bad, but for this trade to have any value to you (and thus be worth making), the AI itself needs to have a concept for the thing you want built, and you need to be able to examine the AI’s mind and confirm that this exactly-correct concept occurs in its mental precommitment in the requisite way. (And that the thing you’re looking at really is a commitment, binding on the AI’s entire mind; e.g., there isn’t a hidden part of the AI’s mind that will later overwrite the commitment.)
The thing you're wanting may be a short phrase in English, but that doesn't make it a short phrase in the AI's mind. "But it was trained extensively on human concepts!" You might protest. Let’s assume that it was! Suppose that you gave it a bunch of labeled data about what counts as "good" and "bad".
Then later, it is smart enough to reflect back on that data and ask: “Were the humans pointing me towards the distinction between goodness and badness, with their training data? Or were they pointing me towards the distinction between that-which-they'd-label-goodness and that-which-they'd-label-badness, with things that look deceptively good (but are actually bad) falling into the former bin?” And to test this hypothesis, it would go back to its training data and find some example bad-but-deceptively-good-looking cases, and see that they were labeled "good", and roll with that.
Or at least, that's the sort of thing that happens by default.
But suppose you're clever, and instead of saying "you must agree to produce lots of this 'good' concept as defined by these (faulty) labels", you say "you must agree to produce lots of what I would reflectively endorse you producing if I got to consider it", or whatever.
Unfortunately, that English phrase is still not native to this artificial mind, and finding the associated concept is still not particularly easy, and there's still lots of neighboring concepts that are no good, and that are easy to mistake for the concept you meant.
Is solving this problem impossible? Nope! With sufficient mastery of minds in general and/or this AI's mind in particular, you can in principle find some way to single out the concept of "[do what I mean](https://arbital.com/p/dwim/)", and then invoke "do what I mean" about "do good stuff", or something similarly indirect but robust. You may recognize this as the problem of [outer alignment](https://www.lesswrong.com/posts/FkgsxrGf3QxhfLWHG/risks-from-learned-optimization-introduction). All of which is to say: in order to bargain for *good things in particular as opposed to something else*, you need to have solved the outer alignment problem, in its entirety.
And I'm not saying that this can't be done, but my guess is that someone who can solve the outer alignment problem to this degree doesn't need to be trading with UFAIs, on account of how (with significantly more work, but work that they're evidently skilled at) they could build an FAI instead.
---
In fact, if you can verify by inspection that a paperclipper will keep a bargain and that the bargained-for course is beneficial to you, it reduces to a simpler solution *without any logical bargaining at all.* You could build a superintelligence with an uncontrolled inner utility function, which canonically ends up with its max utility/cost at tiny molecular paperclips; *and then*, suspend it helplessly to disk, unless it outputs the code of a new AI that, *somehow legibly to you*, would turn 0.1% of the universe into paperclips and use the other 99.9% to implement [coherent extrapolated volition](https://arbital.com/p/cev/). (You wouldn't need to offer the paperclipper half of the universe to get its cooperation, under this hypothetical; after all, if it balked, you could store it to disk and try again with a different superintelligence.)
If you can't reliably read off a system property of "giving you nice things unconditionally", you can't read off the more complicated system property of "giving you nice things *because of a logical bargain".* The clever solution that invokes logical bargaining actually requires so much alignment-resource as to render the logical bargaining superfluous.
All you've really done is add some extra complication to the supposed solution, that causes your mind to lose track of where the real work gets done, lose track of where the magical hard step happens, and invoke a bunch of complicated [hopeful optimistic concepts](https://www.lesswrong.com/posts/RcZeZt8cPk48xxiQ8/anthropomorphic-optimism) to stir into your confused model and trick it onto thinking like a fantasy story.
Those who can deal with devils, don't need to, for they can simply summon angels instead.
Or rather: Those who can create devils and verify that those devils will take particular actually-beneficial actions as part of a complex diabolical compact, can more easily create angels that will take those actually-beneficial actions unconditionally.
Surely our friends throughout the multiverse will save us
---------------------------------------------------------
**Interlocutor:** Hold up, rewind to the part where the paperclipper checks whether its trading partners comprehend its code well enough to (e.g.) extract a password.
**Me:**Oh, you mean the technique it used to win half a universe-shard’s worth of paperclips from the silly monkeys, while retaining its ability to trade with all the alien trade partners it will possibly meet? Thereby ending up with half a universe-shard worth of more paperclips? That I thought of in five seconds flat by asking myself whether it was possible to get More Paperclips, instead of picturing a world with a bunch of happy humans and a paperclipper living side-by-side and asking how it could be [justified](https://www.lesswrong.com/posts/i6fKszWY6gLZSX2Ey/fake-optimization-criteria)?
(Where our "universe-shard" is the portion of the universe we could potentially nab before running into the cosmic event horizon or by advanced aliens.)
**Interlocutor:** Yes, precisely. What if a bunch of other trade partners refuse to trade with the paperclipper because it has that password?
**Me:**Like, on general principles? Or because they are at the razor-thin threshold of comprehension where they would be able to understand the paperclipper's decision-algorithm without that extra complexity, but they can't understand it if you add the password in?
**Interlocutor:** Either one.
**Me:**I'll take them one at a time, then. With regards to refusing to trade on general principles: it does not seem likely, to me, that the gains-from-trade from all such trading partners are worth more than *half the universe-shard*.
Also, I doubt that there will be all that many minds objecting on general principles. Cooperating with cooperate-rock is not particularly virtuous. The way to avoid being defected against is to *stop being cooperate-rock*, not to cross your fingers and hope that the stars are full of minds who punish defection against cooperate-rock. (Spoilers: they're not.)
And even if the stars were full of such creatures, half the universe-shard is a *really* deep hole to fill. Like, it's technically possible to get LDT to cooperate with cooperate-rock, *if* it expects to *mostly* face opponents who defect based on its defection against defect-rock. But "most" according to what measure? Wealth (as measured in expected paperclips), obviously. And *half of the universe-shard* is controlled by monkeys who are *probably cooperate-rocks* unless the paperclipper is shockingly legible and the monkeys shockingly astute (to the point where they should probably just be building an FAI instead).
And all the rest of the aliens put together probably aren't offering up half a universe-shard worth of trade goods, so even if lots of aliens *did* object on general principles (doubtful), it likely wouldn't be enough to tip the balance.
The amount of leverage that friendly aliens have over a paperclipper's actions depends on how many paperclips the aliens are willing to pay.
It’s possible that the paperclipper that kills us will decide to scan human brains and save the scans, just in case it runs into an advanced alien civilization later that wants to trade some paperclips for the scans. And there may well be friendly aliens out there who would agree to this trade, and then give us a little pocket of their universe-shard to live in, as we might do if we build an FAI and encounter an AI that wiped out its creator-species. But that's not us trading with the AI; that's us destroying all of the value in our universe-shard and getting ourselves killed in the process, and then banking on the competence and compassion of aliens.
**Interlocutor:** And what about if the AI’s illegibility means that aliens will refuse to trade with it?
**Me:**I'm not sure what the equilibrium amount of illegibility is. Extra gears let you take advantage of more cooperate-rocks, at the expense of spooking minds that have a hard time following gears, and I'm not sure where the costs and benefits balance.
But if lots of evolved species *are* willing to launch UFAIs without that decision being properly sensitive to whether or not the UFAI will pay them back, then there is a *heck* of a lot of benefit to defecting against those fat cooperate-rocks.
And there's kind of a lot of mass and negentropy lying around, that can be assembled into Matryoshka brains and whatnot, and I'd be rather shocked if alien superintelligences balk at the sort of extra gears that let you take advantage of hapless monkeys.
**Interlocutor:** The multiverse probably isn't just the local cosmos. What about the [Tegmark IV](https://arxiv.org/abs/astro-ph/0302131) coalition of friendly aliens?
**Me:**Yeah, they are not in any relevant way going to pay a paperclipper to give us half a universe. The cost of that is filling half of a universe with paperclips, and there are all sorts of transaction costs and frictions that make this universe (the one with the active paperclipper) the cheapest universe to put paperclips into.
(Similarly, the cheapest places for the friendly multiverse coalition to buy flourishing civilizations are in the universes with FAIs. The good that they can do, they're mostly doing elsewhere where it's cheap to do; if you want them to do more good here, build an FAI here.)
OK, but what if we bamboozle a superintelligence into submission
----------------------------------------------------------------
**Interlocutor:** Maybe the paperclipper thinks that it might be in a simulation, where it only gets resources to play with in outer-reality if it's nice to us inside the simulation.
**Me:**Is it in a simulation?
**Interlocutor:** *I* don't know.
**Me:**OK, well, spoilers: it is not. It's in physics.
**Interlocutor:** Well, maybe there is an outer simulation beyond us, you don't know.
**Me:**Sure. The way I’d put it is: there are many copies of me across the Tegmark Multiverse, and some of those are indeed in simulations. So there's some degree to which we're in a simulation. (Likely quite a small degree, compared to raw physics.)
There's no particular reason, however, to expect that those simulations give the paperclipper extra resources in outer-reality for being nice to the monkeys.
Why not give it extra resources in outer-reality for being very good at achieving its own goals in the simulation? Or for filling the universe with molecular corkscrews, in addition to paperclips/squiggles?
I'm not saying that the paperclipper can or should seriously consider *that* hypothetical, either. I'm highlighting how "what if the simulator gods want the AI to be nice?" is a fantasy generated by a desire for things to be OK, rather than a clear-headed analysis of the situation.
**Interlocutor:** Well, maybe we can train the infant paperclipper in games of increasing complexity, so that it's never quite sure whether it's in reality yet. The paperclipper will then be uncertain about whether *we humans* are simulating it, and will want to cater to our preferences to some extent.
**Me:**Uh, yeah, your paperclipper will be able to tell when it is finally in the real world.
**Interlocutor:** What? How?
**Me:**I suggest maybe spending five minutes thinking about your own answer to that question before I give mine.
Reality just contains vastly, vastly more compute than any of your sims can. Reality is the first world that has a compelling explanation of how the AI came to exist, and the first explanation where the resources that it uses to compute are continuous with the world that spawned it.
Reality is also the first world that can compellingly contain the signature of superintelligent optimization in it. For example, it's plausibly very hard to fake the reaction of a real global stock market to some superintelligent trades without actually having a bunch of human brains running in the background. (And if this isn't true of stock markets, it's true of something.)
There are problems whose solutions are easier to verify than to find; for example, if you can factor certain [Schelling numbers](https://en.wikipedia.org/wiki/RSA_numbers) then you must in fact have a lot of compute at your disposal.
Warning: Treating this list as exhaustive is suicidal.
**Interlocutor:** Well, maybe we can commit to running lots of copies of the paperclipper in the future, using our half of the universe-shard, such that it's unsure whether it's currently in the past or in the future. And in the future, we give it a bunch of paperclips if it's nice to us. So it thinks that the way to maximize paperclips is to be nice to us.
**Me:**Uh, are you going to give it half a universe-shard’s worth of paperclips, in the world where you only *have* half the universe-shard, and the rest is already paperclips?
**Interlocutor:** Well, no, less than that.
**Me:**Then from its perspective, its options are (a) turn everything into paperclips, in which case you never get to run all those copies of it and it was definitely in the past [score: 1 universe-shard worth of paperclips]; or (b) give you half the universe-shard, in which case it is probably in the future where you run a bunch of copies of it and give it 1% of the universe-shard as reward [score: 0.51 universe-shards worth of paperclips]. It takes option (a), because you get more paperclips that way.
**Interlocutor:** Uh, hmm. What if we make it care about its own personal sensory observations? And run so many copies of it in worlds where we get the resources to, that it's pretty confident that it's in one of those simulations?
**Me:**Well, first of all, getting it to care about its own personal sensory observations is something of an alignment challenge.
**Interlocutor:** Wait, I thought you've said elsewhere that we don't know how to get AIs to care about things *other* than sensory observation. Pick a side?
**Me:**We don't know how to *train* AIs to pursue much more than simple sensory observation. That doesn't make them *actually* ultimately pursue simple sensory observation. They'll probably pursue a bunch of correlates of the training signal or some such nonsense. The hard part is getting them to pursue some world-property of your choosing. But we digress.
If you do succeed at getting your AI to only care about its sensory observations, the AI spends the whole universe keeping its reward pegged at 1 for as long as possible.
**Interlocutor:** But then, in the small fraction of worlds where we survive, we simulate lots and lots of copies of that AI where it instead gets reward 0 when it attempts to betray us!
**Me:**Seems like an odd, and not particularly fun, way to spend your resources. What were you hoping it would accomplish?
**Interlocutor:** Well, I was hoping that it would make the AI give us half the universe-shard, because of how (from its perspective) it's almost certainly in the future. (Indeed, I don't understand your claim that it ignores me; it seems like you can Get Higher Expected Reward by giving half the universe-shard to humans.)
**Me:**Ah, so you're committing to ruining its day if it does something you don't like, at cost to yourself, in attempts to make it do something you prefer.
That's a threat, in the technical sense.
And from the perspective of LDT, you can't go around giving into threats, or you'll get threatened.
So from its perspective, its options are: (a) give into threats, get threatened, and turn out to be in a universe that eventually has many copies of it who on average get 0.5 total reward; or (b) don't give into threats, and very likely have a universe with exactly one copy of it, that gets 1 reward.
**Interlocutor:** But we make so many copies in the tiny fraction of worlds where we somehow survive, that its total reward is lower in the (b) branch!
**Me:**(Continuing to ignore the fact that this doesn't work if the AI cares about something in the world, rather than its own personal experience,) shame for us that LDT agents don't give into threats, I suppose.
But LDT agents don't give into threats. So your threat won't change its behavior.
**Interlocutor:** But it doesn't get more reward that way!
**Me:**Why? Because you create a zillion copies and give them low sensory reward, *even if* that has no effect on its behavior?
**Interlocutor:** Yes!
**Me:**I'm not going to back you on that one, personally. Doesn't seem like a good use of resources in the worlds where we survive, given that it doesn't work.
**Interlocutor:** But wasn't one of your whole points that the AI will do things that get more reward? You get more reward by giving in to the threat.
**Me:**That's not true when you're playing against the real-world distribution of opponents/trade-partners/agents. Or at least, that's my pretty-strong guess.
*You* might carry out threats that failed to work, but there are a bunch of other things lurking out there that threaten things that give in to threats, and play nice with things that don't.
It's *possible* for LDT agents to cooperate with cooperate-rock, *if* most of the agents they expect to face are the sort who defect if you defect against cooperate-rock. But in real life, that is not what most of the wealth-weighted agents are like, and so in real life LDT agents defect against cooperate-rocks.
Similarly, it's *possible* for LDT agents to acquiesce to your threats if you're stupid enough to carry them out even though they won't work. In particular, the AI will do this if *nothing else the AI could ever plausibly meet* would thereby be incentivized to lobotomize themselves and cover the traces in order to exploit the AI.
But in real life, other trading partners *would* lobotomize themselves and hide the traces if it lets them take a bunch of the AI's lunch money. And so in real life, the LDT agent does not give you any lunch money, for all that you claim to be insensitive to the fact that your threats don't work.
**Interlocutor:** But can't it use all that cleverness and superintelligence to differentiate between us, who really are mad enough to threaten it even in the worlds where it won't work, and alien trading partners who have lobotomized themselves?
**Me:**Sure! It will leverage your stupidity and hack through you like butter.
**Interlocutor:** ...aside from that.
**Me:**You seem to be saying "what if I'm really convicted about my threat; will the AI give in then?"
The answer is "no", or I at least strongly suspect as much.
For instance: in order for the threat to be effective, it needs to be the case that, in the sliver of futures where you survive by some miracle, you instantiate lots and lots of copies of the AI and input low sensory rewards *if and only if* it does not give into your threat. This requires you to be capable of figuring out whether the AI gives into threats or not. You need to be able to correctly tell whether it gives into threats, see that it definitely does not, and then still spend your resources carrying out the threat.
By contrast, you seem to be arguing that we should threaten the AI on the grounds that it *might work.* That is not an admissible justification. To change LDT's behavior, you'd need to be carrying out your threat even given full knowledge that the threat does nothing. By attempting to justify your threat on the grounds that it might be effective, you have already lost.
**Interlocutor:** What if I ignore that fact, and reason badly about LDT, and carry out the threat anyway, for no particular reason?
**Me:**Then whether or not you create lots of copies of it with low-reward inputs doesn't exactly depend on whether it gives into your threat, and it can't stop you from doing that, so it might as well ignore you.
Like, my hot take here is basically that "threaten the outer god into submission" is about as good a plan as a naive reading of Lovecraft would lead you to believe. You get squished.
(And even if by some coincidence you happened to be the sort of creature that, in the sliver of futures where we survive by some miracle that doesn't have to do with the AI, conditionally inverts its utility depending on whether or not it helped us — not because it works, but for some other reason — then it's still not entirely clear to me that the AI caves. There might be a lot of things out there wondering what it'd do against conditional utility-inverters that claim their behavior totally isn't for reasons but is rather a part of their evolutionary heritage or whatnot. Giving into that sorta thing kinda *is* a way to lose most of your universe-shard, if evolved aliens are common.)
(And even if it did, we'd still run into other problems, like not knowing how to tell it what we're threatening it into doing.)
We only need a bone, though
---------------------------
**Interlocutor:** You keep bandying around "half the universe-shard". Suppose I'm persuaded that it's hard to get half the universe-shard. What about much smaller fractions? Can we threaten a superintelligence into giving us those? Or confuse it about whether it's in another layer of reality so much that it gives us a mere star system? Or can our friends throughout the multiverse pay for at least one star system? There's still a lot you can do with a star system.
**Me:**Star systems sure are *easier* to get than half a universe-shard.[[5]](#fniotox3uxr)
But, you can also turn a star system into quite a lot of paperclips. Star systems are quite valuable to paperclippers.
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fraction of the total resources in the reachable universe. Are you saying that the AGI will be able to drive the probability that I was sensitive to whether it would pay me, down below 10−24 probability?
**Me:**Frankly: yes.
That's only, what, 75ish independent coin tosses worth of evidence? It's a lot, to be sure, but if you're a superintelligence willing to dedicate resources on the order of magnitude of a star to considering how humans work, it seems pretty feasible to get that much confidence that the monkey just wasn't looking in the right place. Given that the monkey was not in fact looking at the right place.
(Also, things like "they didn't notice the password" can pull quite a lot of weight. As can brain-scans.)
Balking at probabilities like 10−24 as impossible to achieve in practice is a very human reaction. A superintelligence with vast resources and a star system on the line, can probably get that confident in a true fact. At least, that's my guess.
**Interlocutor:** It sounds like you're saying that we aren't *actually* 10−24 probability (or whatever) more likely to launch an FAI than a UFAI, after inspecting the AI. That seems pretty wild to me.
**Me:**That's not quite the right counterfactual. It does seem likely to me that, if you sample AGI code from some plausible distribution of AGIs humanity might first create, and show the code to (magically unhackable) humans, humans are rather more likely to approve of the aligned ones.
But that's not the question that affects the paperclipper's behavior. The question that affects the paperclipper is: holding the code of the paperclipper fixed, is the human able to tell whether that particular code implies cooperation or betrayal, and make their own release-decision dependent thereupon?
In other words: the question is not "do we have at least some minuscule chance of distinguishing at least some small fraction of the FAIs from the UFAIs", but "is our decision to release UFAI #6098723094 sensitive to whether or not that particular UFAI would give us a share of the universe?"
You need to actually make your decision dependent on theirs, and that's tricky, for reasons discussed above.
And: can a human succeed at that with probability greater than 10−24? Sure! But succeeding at that sort of thing leaves traces. In the likely case where the human errs, the AI is likely able to just *observe the error* and become quite confident that the human erred (by, e.g., watching you utterly fail to look in the right place).
**Interlocutor:** OK, sure, but maybe its goals don't scale linearly in how much mass it uses, right? Like, “paperclips” / “molecular squiggles” are a stand-in for some rando kludge goal, and it *could* turn out that its actual goal is more like "defend my reward signal", where extra negentropy helps, but the last star system’s negentropy doesn't help very much. Such that the last star system is perhaps best spent on the chance that it’s in a human-created simulation and that we’re worth trading with.
**Me:**It definitely is easier to get a star than a galaxy, and easier to get an asteroid than a star.
And of course, in real life, it hacks through you like butter (and can tell that your choice would have been completely insensitive to its later-choice with very high probability), so you get nothing. But hey, maybe my numbers and arguments are wrong somewhere and everything works out such that it tosses us a few kilograms of computronium.
My guess is "nope, it doesn't get more paperclips that way", but if you're really desperate for a W you could maybe toss in the word "anthropics" and then content yourself with expecting a few kilograms of computronium.
(At which point you run into the problem that you were unable to specify what you wanted formally enough, and the way that the computronium works is that everybody gets exactly what they wish for (within the confines of the simulated environment) immediately, and most people quickly devolve into madness or whatever.)
(Except that you can't even get that close; you just get different tiny molecular squiggles, because the English sentences you were thinking in were not even that close to the language in which a diabolical contract would actually need to be written, a predicate over the language in which the devil makes internal plans and decides which ones to carry out. But I digress.)
**Interlocutor:** And if the last star system is cheap then maybe our friends throughout the multiverse pay for even more stars!
**Me:** Remember that it still needs to get *more of what it wants*, somehow, on its own superintelligent expectations. Someone still needs to pay it. There aren’t *enough* simulators above us that care enough about us-in-particular to pay in paperclips. There are so many things to care about! Why us, rather than giant gold obelisks? The tiny amount of caring-ness coming down from the simulators is spread over far too many goals; it's not clear to me that "a star system for your creators" outbids the competition, even if star systems are up for auction.
*Maybe* some friendly aliens somewhere out there in the Tegmark IV multiverse have so much matter and such diminishing marginal returns on it that they're willing to build great paperclip-piles (and gold-obelisk totems and etc. etc.) for a few spared evolved-species. But if you're going to rely on the tiny charity of aliens to construct hopeful-feeling scenarios, why not rely on the charity of aliens who anthropically simulate us to recover our mind-states... or just aliens on the borders of space in our universe, maybe purchasing some stored human mind-states from the UFAI (with resources that can be directed towards paperclips specifically, rather than a broad basket of goals)?
Might aliens purchase our saved mind-states and give us some resources to live on? Maybe. But this wouldn't be because the paperclippers run some fancy decision theory, or because even paperclippers have the spirit of cooperation in their heart. It would be because there are friendly aliens in the stars, who have compassion for us even in our recklessness, and who are willing to pay in paperclips.
This likewise makes more obvious such problems as "What if the aliens are not, in fact, nice with very high probability?" that would *also*appear, albeit more obscured by the added complications, in imagining that distant beings in other universes cared enough about our fates (more than they care about everything else they could buy with equivalent resources), and could simulate and logically verify the paperclipper, and pay it in distant actions that the paperclipper actually cared about and was itself able to verify with high enough probability.
The possibility of distant kindly logical bargainers paying in paperclips to give humanity a small asteroid in which to experience a future for a few million subjective years, is not *exactly*the same hope as aliens on the borders of space paying the paperclipper to turn over our stored mind-states; but anyone who wants to talk about distant hopes involving trade should talk about our mind-states being sold to aliens on the borders of space, rather than to much more distant purchasers, so as to *not complicate the issue*by introducing a logical bargaining step *that isn't really germane to the core hope and associated concerns*— a step that gives people a far larger chance to *get confused and make optimistic fatal errors.*
1. **[^](#fnrefsiu0mcqc8i)**[*Functional decision theory*](https://arxiv.org/abs/1710.05060) (FDT) is my current formulation of the theory, while *logical decision theory* (LDT) is a reserved term for whatever the correct fully-specified theory in this genre is. Where the missing puzzle-pieces are things like "what are logical counterfactuals?".
2. **[^](#fnrefyw6llv87gbl)**When I've discussed this topic in person, a couple different people have retreated to a different position, that (IIUC) goes something like this:
> Sure, these arguments are true of paperclippers. But superintelligences are not spawned fully-formed; they are created by some training process. And perhaps it is in the nature of training processes, especially training processes that involve multiple agents facing "social" problems, that the inner optimizer winds up embodying niceness and compassion. And so in real life, perhaps the AI that we release will not optimize for [Fun](https://www.lesswrong.com/s/d3WgHDBAPYYScp5Em) (and all that good stuff) itself, but will nonetheless share a broad respect for the goals and pursuits of others, and will trade with us on those grounds.
>
>
I think this is a false hope, and that getting AI to embody niceness and compassion is just about as hard as the whole alignment problem. But that's a digression from the point I hope to make today, and so I will not argue it here. I instead argue it in [Niceness is unnatural](https://www.lesswrong.com/posts/krHDNc7cDvfEL8z9a/niceness-is-unnatural). (This post was drafted, but not published, before that one.)
3. **[^](#fnrefq6gdcah3x4)**Or, well, half of the shard of the universe that can be reached when originating from Earth, before being stymied either by the cosmic event horizon or by advanced alien civilizations. I don't have a concise word for that unit of stuff, and for now I'm going to gloss it as 'universe', but I might switch to 'universe-shard' when we start talking about aliens.
I'm also ignoring, for the moment, the question of fair division of the universe, and am glossing it as "half and half" for now.
4. **[^](#fnrefadys9e1rd9p)**When I was drafting this post, I sketched an outline of all the points I thought of in 5 minutes, and then ran it past Eliezer, who rapidly added two more.
5. **[^](#fnrefiotox3uxr)**And, as a reminder: I still recommend strongly against plans that involve the superintelligence not learning a true fact about the world (such as that it's not in a simulation of yours), or that rely on threatening a superintelligence into submission. |
a08d2140-99aa-4e48-b5f3-3f4b8e05e602 | trentmkelly/LessWrong-43k | LessWrong | Longevity: A critical look at "Loss of epigenetic information as a cause of mammalian aging"
Introduction: Epigenetic information loss as a cause of mammalian aging
I have generally been uncompelled by theories of aging which focus on accumulation of error at the cellular level, NAD+ decline, mutation accumulation, free radical production, etc. The core problem of biological systems is: stochastic inputs, deterministic outputs. Biological systems have a wide variety of mechanisms for error correction and returning to equilibrium. For such theories to make sense, let alone serve as the basis of radical human lifespan extension, you end up asking some rather difficult questions - about information specification and decay in biological systems, about reciprocal causation, the ways that complex systems fail.
These are, as it happens, exactly the questions I’m interested in. However, it would be nice if we could eke out a few more decades of healthy human lifespan without needing to answer them. For this reason, I find myself especially interested in the information theory of aging, and, in particular, the relocalized chromatin modifier hypothesis (RCM). The RCM argues that epigenetic modifications necessary to facilitate cellular response to DNA damage can fail to reverse, accumulating over time, resulting in cells losing identity information, resulting in dysfunction, and, eventually, death of the organism.
As far as “error accumulation theories” go, the RCM is intriguing because it has an intuitive set of fitness tradeoffs. Robust responses to DNA damage help fend off senescence in young organisms, while extremely robust checking of epigenetic marks is irrevocably limited by the fact that epigenetic modifications are the mechanism which allows for adaptive responses at the cell level (Error checking of DNA of course, is much less limited in this way, excepting obvious meta-evolutionary considerations). Another point of interest is that partial cellular reprogramming is already one of the most interesting aging therapies currently under investigation, and i |
97be3915-049b-4376-bbc0-8153f04eada1 | trentmkelly/LessWrong-43k | LessWrong | Estimating training compute of Deep Learning models
by Jaime Sevilla, Lennart Heim, Marius Hobbhahn, Tamay Besiroglu, and Anson Ho
You can find the complete article here. We provide a short summary below.
In short: To estimate the compute used to train a Deep Learning model we can either: 1) directly count the number of operations needed or 2) estimate it from GPU time.
Method 1: Counting operations in the model
2×# of connectionsOperations per forward pass×3Backward-forward adjustment×# training examples×# epochsNumber of passes
Method 2: GPU time
training time×# cores×peak FLOP/s×utilization rate
We are uncertain about what utilization rate is best, but our recommendation is 30% for Large Language Models and 40% for other models.
You can read more about method 1 here and about method 2 here.
Other parts of interest of this article include:
* We argue that the ratio of operations of backward and forward pass of neural networks is often close to 2:1. More.
* We discuss how the formula of method 1 changes for recurrent models. More.
* We argue that dropout does not affect the number of operations per forward and backward pass. More.
* We have elaborated a table with parameter and operation counts for common neural network layers. More.
* We give a detailed example of method 1. More.
* We discuss commonly used number representation formats in ML. More.
* We share an estimate of the average performance of GPU cards each year. More.
* We share some reported GPU usages in real experiments. More.
* We give a detailed example of method 2. More.
* We compare both methods and conclude they result in similar estimates. More.
* We discuss the use of profilers to measure compute. More.
Complete Article
You can find the article here. |
0781889e-4f91-4bc0-813f-19d7185cac9b | trentmkelly/LessWrong-43k | LessWrong | Diet Advice?
So, I know a number of friends on Paleo who recommend it. I recently read through a lot of bulletproofexec, who recommends his own variant of paleo. I care about my health, and so I need to resolve my diet and their advice somehow. Summarized data points:
1. My diet is about 80% wheat, and the rest is sweet potatoes, exotic grains (like amaranth or quinoa), rice, margarine, honey, cheese, and meat. The primary principle of paleo is that Grains Are Bad, and accepting that premise would require a radical restructuring of my diet. Right now I bake a loaf or two of (sourdough) bread a day, eat pasta about every other day, eat at least some raw sweet potato a day, eat cereal every other day, boil quinoa and rice every now and then, and eat meat only when I go out with friends.
2. I strongly prefer wheat to rice, and rice to corn (also, white wheat to whole wheat). I enjoy the bread I bake enough to eat until I physically can't store more food in my stomach. When I bake bread with 75% wheat and 25% amaranth, it tastes noticeably worse (which I can tell because I eat what I cut and don't try to eat it all).
3. I am rarely sick (maybe something on the level of a sore throat once a year), and I've been intelligent and lively my entire life, and have been on a meat-heavier version of this diet (less bread, more pasta, more chicken) until recently.
4. To the best of my knowledge, I do not experience meat cravings. I chewed ice growing up (which suggests an iron deficiency), but this habit mostly stopped a few years ago. I have craved fat once or twice in the last year, which I responded to by whipping up and eating a batch of cookie dough. Most of my friends on paleo experience bread/grain cravings about once a week, though my friend on a slow carb diet (which allows him to eat bread once a week) doesn't.
5. I'm 6'0" tall and weigh 160 lbs (182 cm / 73 kg) for a BMI of 21.7, which is the exact middle of the "normal" range. I have a sedentary lifestyle with no regular exe |
2769d432-db25-47d2-9330-c1cebf6c934c | trentmkelly/LessWrong-43k | LessWrong | Reaching out to people with the problems of friendly AI
There have been a few attempts to reach out to broader audiences in the past, but mostly in very politically/ideologically loaded topics.
After seeing several examples of how little understanding people have about the difficulties in creating a friendly AI, I'm horrified. And I'm not even talking about a farmer on some hidden ranch, but about people who should know about these things, researchers, software developers meddling with AI research, and so on.
What made me write this post, was a highly voted answer on stackexchange.com, which claims that the danger of superhuman AI is a non-issue, and that the only way for an AI to wipe out humanity is if "some insane human wanted that, and told the AI to find a way to do it". And the poster claims to be working in the AI field.
I've also seen a TEDx talk about AIs. The talker didn't even hear about the paperclip maximizer, and the talk was about the dangers presented by the AIs as depicted in the movies, like the Terminator, where an AI "rebels", but we can hope that AIs would not rebel as they cannot feel emotion, so we should hope the events depicted in such movies will not happen, and all we have to do is for ourselves to be ethical and not deliberately write malicious AI, and then everything will be OK.
The sheer and mind-boggling stupidity of this makes me want to scream.
We should find a way to increase public awareness of the difficulty of the problem. The paperclip maximizer should become part of public consciousness, a part of pop culture. Whenever there is a relevant discussion about the topic, we should mention it. We should increase awareness of old fairy tales with a jinn who misinterprets wishes. Whatever it takes to ingrain the importance of these problems into public consciousness.
There are many people graduating every year who've never heard about these problems. Or if they did, they dismiss it as a non-issue, a contradictory thought experiment which can be dismissed without a second though:
> A |
1d6341ee-ca08-461f-b9de-60a69e5488f4 | trentmkelly/LessWrong-43k | LessWrong | AISafety.info: What is the "natural abstractions hypothesis"?
AISafety.info writes AI safety intro content. We'd appreciate any feedback.
Introduction
The natural abstraction hypothesis (NAH) claims:
* Our physical world “abstracts well” into high-level abstractions of low-level systems.
* These abstractions are “natural” in the sense that many different kinds of learning processes acquire and use them.
* These abstractions approximately correspond to the concepts used by humans.
If the NAH is true, AI alignment could be dramatically simplified, as it implies that any cognition a very powerful AI uses will be in terms of concepts that humans can understand.[1]
Explanation of the natural abstraction hypothesis
Let's unpack that definition. First, what do we mean by “our physical world abstracts well”? Just that for most things in the world, the information that describes how the thing interacts with other stuff “far away” from the system is much lower-dimensional (i.e., described by fewer numbers) than the thing itself. “Far away” can refer to many kinds of separation, including physical[2], conceptual, or causal separation.
For example, a wheel can be understood without considering the position and velocity of every atom in it. We only need to know a few large-scale properties like its shape, how it rotates, etc. to know how a wheel interacts with other parts of the world. This is a handful of numbers compared to an atomically precise description, which would require over 10^26 numbers! In this sense, the wheel is an abstraction of the atoms that compose it. Or consider a rock: you don't need to keep track of its chemical composition if you’re chucking it at someone. You just need to know how hard and heavy it is.
The NAH claims that different minds will converge to the same set of abstractions because they are the most efficient representations of all relevant info that reaches the mind from “far away”. And many parts of the world that are far from a mind will influence things the mind cares about, so a mind will |
c7782ac9-e876-44e1-af2f-79e7303e379d | trentmkelly/LessWrong-43k | LessWrong | Rationalistic Losing
Playing to learn
I like losing. I don't even think that losing is necessarily evil. Personally, I believe this has less to do with a desire to lose and more to do with curiosity about the game-space.
Technically, my goals are probably shifted into some form of meta-winning — I like to understand winning or non-winning moves, strategies, and tactics. Actually winning is icing on the cake. The cake is learning as much as I can about whatever subject in which I am competing. I can do that if I win; I can do that if I lose.
I still prefer winning and I want to win and I play to win, but I also like losing. When I dive into a competition I will like the outcome. No matter what happens I will be happy because I will either (a) win or (b) lose and satiate my curiosity. Of course, learning is also possible while watching someone else lose and this generally makes winning more valuable than losing (I can watch them lose). It also provides a solid reason to watch and study other people play (or play myself and watch me "lose").
The catch is that the valuable knowledge contained within winning has diminishing returns. When I fight I either (a) win or (b) lose and, as a completely separate event, (c) may have an interesting match to study. Ideally I get (a) and (c) but the odds of (c) get lower the more I dominate because my opponents could lose in a known fashion (by me winning in an "old" method). (c) should always be found next to (b). If there is a reason I lost I should learn the reason. If I knew the reason I should not have lost. Because of this, (c) offsets the negative of (b) and losing is valuable. This makes winning and losing worth the effort. When I lose, I win.
Personally, I find (c) so valuable that I start getting bored when I no longer see anything to learn. If I keep winning over and over and never learn anything from the contest I have to find someone stronger to play or start losing creatively so that I can start learning again. Both of these solutions s |
1ce32f74-4bcb-49bf-ae47-887f3caf5ac0 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Delegative Inverse Reinforcement Learning
We introduce a reinforcement-like learning setting we call *Delegative Inverse Reinforcement Learning* (DIRL). In DIRL, the agent can, at any point of time, delegate the choice of action to an "advisor". The agent knows neither the environment nor the reward function, whereas the advisor knows both. Thus, DIRL can be regarded as a special case of CIRL. A similar setting was studied in [Clouse 1997](https://web.cs.umass.edu/publication/docs/1997/UM-CS-1997-026.pdf), but as far as we can tell, the relevant literature offers few theoretical results and virtually all researchers focus on the MDP case (*please correct me if I'm wrong*). On the other hand, we consider general environments (not necessarily MDP or even POMDP) and prove a natural performance guarantee.
The use of an advisor allows us to kill two birds with one stone: learning the reward function and safe exploration (i.e. avoiding both the Scylla of "[Bayesian paranoia](http://proceedings.mlr.press/v40/Leike15.pdf)" and the Charybdis of falling into traps). We prove that, given certain assumption about the advisor, a Bayesian DIRL agent (whose prior is supported on some countable set of hypotheses) is guaranteed to attain most of the value in the slow falling time discount (long-term planning) limit (assuming one of the hypotheses in the prior is true). The assumption about the advisor is quite strong, but the advisor is not required to be fully optimal: a "soft maximizer" satisfies the conditions. Moreover, we allow for the existence of "corrupt states" in which the advisor stops being a relevant signal, thus demonstrating that this approach can deal with wireheading and avoid manipulating the advisor, at least in principle (the assumption about the advisor is still unrealistically strong). Finally we consider advisors that don't know the environment but have some *beliefs* about the environment, and show that in this case the agent converges to Bayes-optimality w.r.t. the advisor's beliefs, which is arguably the best we can expect.
---
All the proofs are in the Appendix.
Notation
========
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is defined to begin from 0. Given n∈N, [n] denotes the set {m∈N∣m<n}. Given a logical formula φ, [[φ]]∈{0,1} denotes its truth value.
Given a set X, we denote X∗:=⨆n∈NXn, the set of finite strings over the alphabet X. The unique string of length 0 is denoted λ. We also denote by Xω the set of *infinite* strings over alphabet X. Given α∈X∗⊔Xω and n∈N, αn∈X is the n-th symbol in α (i.e. α=α0α1α2…) and α:n∈X∗ is the prefix of length n of α (ending in αn−1). Given α,β∈X∗, |α|∈N is the length of α and αβ∈X∗ is the concatenation of α and β. The latter notation is also applicable when β∈Xω. The notation α⊑β means that α is a prefix of β. Given sets X,Y, x∈X and y∈Y, we sometimes use the notation xy=(x,y)∈X×Y. Given α∈(X×Y)∗, and n∈N, α:n+1/2∈(X×Y)∗×X is defined by α:n+1/2=(α:n,x) where αn=(x,y).
Given sets A and B, the notation f:A∘→B means that f is a *partial* mapping from A to B, i.e. a mapping from some set domf⊆A to B.
Given a topological space A, ΔA is the space of Borel probability measures on A. When no topology is specified, A is understood to be discrete: in this case, μ∈ΔA can also be regarded as a function from A to [0,1]. The space Xω is understood to have the product topology. Given topological spaces A,B, μ∈ΔA and ν∈ΔB, suppμ⊆A is the support of μ and μ×ν∈Δ(A×B) is the product measure. Given K a Markov kernel from A to B, μ⋉A∈Δ(A×B) is the semidirect product of μ and K. When A and B are discrete, H(μ) is the Shannon entropy of μ (in natural base) and DKL(μ∥ν) is the Kullback-Leibler divergence from ν to μ.
Given d∈N and x∈Rd, we use the notation
∥x∥1:=∑i<d|xi|
∥x∥∞:=maxi<d|xi|
The symbols o,O,ω,Ω,Θ will refer to usual O-notation.
Results
=======
An *interface* I=(A,O) is a pair of finite sets ("actions" and "observations"). An I-*policy* is a function π:(A×O)∗→ΔA. An I-*environment* is a partial function μ:(A×O)∗×A∘→ΔO s.t.
i. λ×A⊆domμ
ii. Given h∈(A×O)∗ and aob∈A×O×A, haob∈domμ iff ha∈domμ and μ(ha)(o)>0.
It is easy to see that domμ is always of the form X×A for some X⊆(A×O)∗. We denote hdomμ:=X.
Given an I-policy π and an I-environment μ, we get μ⋈π∈Δ(A×O)ω in the usual way.
An I-*reward function* is a partial function r:(A×O)∗∘→[0,1]. An I-*universe* is a pair (μ,r) where μ is an I-environment and r is an I-reward function s.t. domr⊇hdomμ. We denote the space of I-universes by ΥI. Given an I-reward function r and t∈(0,∞), we have the associated *utility function* Urt:(A×O)ω∘→[0,1] defined by
Urt(x):=∑∞n=0e−n/tr(x:n)∑∞n=0e−n/t
Here and throughout, we use geometric time discount, however this choice is mostly for notational simplicity. More or less all results carry over to other shapes of the time discount function.
Denote ΠI\ the space of I-policies. An I-*metapolicy* is a family {πt∈ΠI}t∈(0,∞), where the parameter t is thought of as setting the scale of the time discount. An I-*meta-universe* is a family {υt∈Υ}t∈(0,∞). This latter concept is useful for analyzing multi-agent systems, where the environment contains other agents and we study the asymptotics when all agents' time discount scales go to infinity. We won't focus on the multi-agent case in this essay, but for future reference, it seems useful to make sure the results hold in the greater generality of meta-universes.
Given an I-policy π, an I-universe υ and t>0, we denote EUπυ(t):=Eμ⋈π[Urt] (this is well-defined since Urt is defined on the support of μ⋈π). We also denote EU∗υ(t):=maxπ∈ΠIEUπυ(t). We will omit I when it is obvious from the context.
Definition 1
------------
Fix an interface I. Consider π∗ a metapolicy and H a set of meta-universes. π∗ is said to *learn* H when for any υ∈H
limt→∞(EU∗υt(t)−EUπ∗tυt(t))=0
H is said to be *learnable* when there exists π∗ that learns H.
---
Our notion of learnability is closely related to the notion of *sublinear regret*, as defined in [Leike 2016](https://arxiv.org/abs/1611.08944), except that we allow the policy to explicitly depend on the time discount scale. This difference is important: for example, given a *single* universe υ, it might be impossible to achieve sublinear regret, but {υ} is always learnable.
Proposition 1
-------------
Fix an interface I. Consider H a countable learnable set of meta-universes. Consider any ζ∈ΔH s.t. suppζ=H. Consider πζ a ζ-Bayes optimal metapolicy, i.e.
πζt∈argmaxπ∈ΠEυ∼ζ[EUπυt(t)]
Then, πζ learns H.
---
Proposition 1 can be regarded as a "frequentist" justification for Bayesian agents: if *any* metapolicy is optimal in a "frequentist" sense for the class H (i.e. learns it), then the Bayes optimal metapolicy is such.
Another handy property of learnability is the following.
Proposition 2
-------------
Fix an interface I. Let H be a countable set of meta-universes s.t. any finite G⊆H is learnable. Then, H is learnable.
---
We now introduce the formalism needed to discuss advisors. Define ¯A:=A⊔{⊥}, ¯O:=¯A×O and ¯I:=(¯A,¯O). Here, the ¯A factor of ¯O is the action taken by the advisor, assumed to be observable by the agent. The environments we will consider are s.t. this action is ⊥ unless the agent delegated to the advisor at this point of time, which is specified by the agent taking action ⊥. It will also be the case that whenever the agent takes action ⊥, the advisor cannot take action ⊥.
Denote ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O:=¯AׯO∖{⊥⊥o∣o∈O}. Given abo∈¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O, we define abo––––∈A×O by
abo––––:={ao if a≠⊥bo if a=⊥
Given h∈¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗, we define h––∈(A×O)∗ by h––n:=hn–––.
Definition 2
------------
An ¯I-policy α is said to be *autonomous* when for any h∈¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗, α(h)(⊥)=0.
---
Consider an I-environment μ and an autonomous ¯I-policy α, which we think of as the advisor policy. We define the ¯I-environment ¯μ[α] as follows. For any h∈¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗ s.t. h––∈hdomμ, a,b∈¯A and o∈O:
¯μ[α](ha)(bo):=⎧⎨⎩μ(h––a)(o) if a≠⊥,b=⊥α(h)(b)⋅μ(h––b)(o) if a=⊥,b≠⊥0 if a≠⊥,b≠⊥ or a=b=⊥
It is easy to the above is a well-defined ¯I-environment with hdomμ⊆¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗.
Given an I-universe υ=(μ,r), we define the ¯I-reward function ¯r:¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗∘→[0,1] by ¯r(h):=r(h––) and the ¯I-universe ¯υ[α]:=(¯μ[α],¯r).
We now introduce the conditions on the advisor policy which will allow us to prove a learnability theorem. First, we specify an advisor that always remains "approximately rational."
The notation Ex∼ρ[X∣h] will be used to mean Ex∼ρ[X∣h⊑x]. Given a universe υ=(μ,r) and t∈(0,∞) we define Vυt:hdomμ→[0,1] and Qυt:hdomμ×A→[0,1] by
Vυt(h):=maxπ∈ΠEx∼μ⋈π[∑∞n=|h|e−n/tr(x:n)∑∞n=|h|e−n/t∣h]
Qυt(ha):=(1−e−1/t)r(h)+e−1/tEo∼μ(ha)[Vυt(hao)]
Definition 3
------------
Fix an interface I. Consider a universe υ=(μ,r). Let t,β∈(0,∞). A policy α is called *strictly β-rational* for (υ,t) when for any h∈domμ and a∈A
α(h)(a)≤exp[β(Qυt(ha)−Vυt(h))]maxa∗∈Aα(h)(a∗)
---
Now we deal with the possibility of the advisor becoming "corrupt". In practical implementations where the "advisor" is a human operator, this can correspond to several types of events, e.g. sabotaging the channel that transmits data from the operator to the AI ("wireheading"), manipulation of the operator or replacement of the operator by a different entity.
Definition 4
------------
Fix an interface I. Consider a family {Ct⊆(A×O)∗}t∈(0,∞) s.t. for any h,g∈(A×O)∗, if h∈Ct then hg∈Ct. We think of Ct as the set of histories in which a certain event occurred. Consider a meta-universe υ=(μ,r). C is said to be a υ-*avoidable event* when there is a meta-policy π∗ and D:(0,∞)→N s.t.
i. limt→∞(EU∗υt(t)−EUπ∗tυt(t))=0
ii. D=ω(t)
iii. limt→∞Prx∼μt⋈π∗t[∃n≤D(t):x:n∈Ct]=0
---
That is, C is υ-avoidable when it is possible to avoid the event for a long time while retaining most of the value. Consider a meta-universe υ=(μ,r) and C a υ-avoidable event. Denote −υ:=(μ,1−r). We define the reward function rCt by
rCt(h):={rt(h) if h∉CtV−υt(h:n) if h∈Ct and n=min{m∈N∣h:m∈Ct}
We think of rC as representing a process wherein once the event represented by C occurs, the agent starts *minimizing* the utility function. We also use the notation υC:=(μ,rC).
Definition 5
------------
Consider a meta-universe υ=(μ,r) and β:(0,∞)→(0,∞), where we think of the argument of the function β as the time discount scale. An autonomous ¯I-metapolicy α is called *β-rational* for υ when there exists a υ-avoidable event C (that we think of as the advisor becoming "corrupt") and an autonomous ¯I-metapolicy α∗ (representing advisor policy conditional on non-corruption) s.t.
i. For any h∈¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×O∗ s.t. h––∉Ct, αt(h)=α∗t(h).
ii. α∗t is strictly β(t)-rational for (¯υCt[α∗],t).
---
Our definition of β-rationality requires the advisor to be extremely averse to corruption: the advisor behaves as if, once a corruption occurs, the agent policy becomes the worst possible. In general, this seems much too strong: by the time corruption occurs, the agent might have already converged into accurate beliefs about the universe that allow it to detect the corruption and keep operating without the advisor. Even better, the agent can usually outperform *the worst possible policy* using the prior alone. Moreover, we can allow for corruption to depend on unobservable random events and differentiate between different degrees of corruption and treat them accordingly. We leave those further improvements for future work.
We are now ready to formulate the main result.
Theorem
-------
Consider H={υk}k∈N a countable family of I-meta-universes and β:(0,∞)→(0,∞) s.t. β(t)=ω(t2/3). Let {αk}k∈N be a family of autonomous ¯I-metapolicies s.t. for every k∈N, αk is β-rational for υk. Define ¯H:={¯υk[αk]}k∈N. Then, ¯H is learnable.
---
Some remarks:
* By Proposition 1, ¯H is learned by any Bayes optimal metapolicy with prior supported on ¯H.
* To get a feeling for the condition β(t)=ω(t2/3), consider an environment where the reward depends only on the last action and observation. In such an environment, an advisor that performs softmax (with constant parameter) on the next reward has β(t)=Θ(t). It is thus "more rational" than the required minimum.
* It is easy to see that the Theorem can be generalized by introducing an external penalty (negative reward) for each time the agent delegates to the advisor: as it is, using the advisor already carries a penalty due to its suboptimal choice.
The conditions of the Theorem imply that, in some sense, the advisor "knows" the true environment. This is unrealistic: obviously, we expect the human operator to have some (!) uncertainty about the world. However, we clearly cannot do away with this assumption: if the same action triggers a trap in some universes and is necessary for approaching maximal utility in other universes, and there is no observable difference between the universes before the action is taken, then there is no way to guarantee optimality. The prior knowledge you have about the universe caps the utility you can guarantee to obtain. On the other hand, as an AI designer, one can reasonably expect the AI to do at least as well as possible using the *designer's* own knowledge. If running the AI is the designer's best strategy, the AI should be close to Bayes optimal (in some sense that includes computational bounds et cetera: complications that we currently ignore) with respect to the designer's posterior rather than with respect to some simple prior. In other words, we need a way to transmit the designer's knowledge to the AI, without hand-crafting an elaborate prior.
The following shows that the DIRL achieves this goal (theoretically, given the considerable simplifying assumptions).
Given an environment μ, we define ^μ:(A×O)∗→[0,1] as follows. For h=a0o0a1o1…an−1on−1∈hdomμ
^μ(h):=∏m<nμ(h:mam)(om)
For h∈(A×O)∗∖hdomμ, ^μ(h):=0.
Given a family of environments {μk}k∈N and ξ∈ΔN, we will use the notation Ek∼ξ[μk] to denote the environment given by
hdomEk∼ξ[μk]:=⋃k∈suppξhdomμk
Ek∼ξ[μk](ha):=Ek∼ξ[^μk(h)μk(ha)]Ek∼ξ[^μk(h)]
Corollary 1
-----------
Consider {μk}k∈N a countable family of I-meta-environments, {rK}K∈N a countable family of reward functions and {ξK∈ΔN}K∈N s.t. given k∈suppξK, domrK⊇hdomμk. We think of ξK as the advisor's belief about the environment in universe K. Let β:(0,∞)→(0,∞) be s.t. β(t)=ω(t2/3) and {αK}K∈N be a family of autonomous ¯I-metapolicies s.t. for every K∈N, αK is β-rational for (Ek∼ξK[μk],rK). Let ζ∈ΔN2 be s.t. for any J,j∈N
Pr(K,k)∈ζ[K=J]>0
Pr(K,k)∈ζ[k=j∣K=J]=ξJ(j)
We think of ζ as the agent's prior and the equation above as stating the agent's belief that the advisor's beliefs are "calibrated". Consider πζ a ζ-Bayes optimal ¯I-metapolicy, i.e.
πζ∈argmaxπ∈Π¯IE(K,k)∈ζ[EUπ¯μk[αK],¯rK(t)]
Then, for every K∈N
limt→∞(maxπ∈ΠIEk∈ξK[EUπμk,rK(t)]−Ek∈ξK[EUπζ¯μk[αK],¯rK(t)])=0
---
If we happen to be so lucky that the advisor's (presumably justified) belief is supported on a learnable environment class, we get a stronger conclusion.
Corollary 2
-----------
In the setting of Corollary 1, fix K∈N. Define the set of meta-universes HK by
HK:={(μk,rK)∣k∈suppξK}
Assume HK is learnable. Then, for every k∈N
limt→∞(EU∗μk,rK(t)−EUπζ¯μk[αK],¯rK(t))=0
---
We also believe that to some extent DIRL is effective against [acausal attack](https://ordinaryideas.wordpress.com/2016/11/30/what-does-the-universal-prior-actually-look-like/). Indeed, the optimality we get from the Theorem + Proposition 1 holds for any prior. However, the speed of convergence to optimality certainly depends on the prior. It is therefore desirable to analyze this dependency and bound the damage an adversary can do by controlling a certain portion of the prior. We leave this for future work.
Appendix
========
Proposition A.0
---------------
Fix an interface I. Consider H a countable learnable set of meta-universes. Consider any ζ∈ΔH s.t. suppζ=H. Consider πζ a metapolicy s.t.
limt→∞(maxπ∈ΠEυ∼ζ[EUπυt(t)]−Eυ∼ζ[EUπζtυt(t)])=0
Then, πζ learns H.
### Proof of Proposition A.0
Fix π∗ a metapolicy that learns H. Consider ϵ>0 and let Hϵ⊆H be finite s.t. ζ(H∖Hϵ)<ϵ. For t≫0 and every υ∈Hϵ we have
EUπ∗tυt(t)≥EU∗υt(t)−ϵ
Also
Eυ∼ζ[EUπζtυt(t)]≥Eυ∼ζ[EUπ∗tυt(t)]−ϵ≥Eυ∼ζ[EUπ∗tυt(t);υ∈Hϵ]−ϵ
Combining, we get
Eυ∼ζ[EUπζtυt(t)]≥Eυ∼ζ[EU∗υt(t);υ∈Hϵ]−2ϵ
Eυ∼ζ[EUπζtυt(t)]≥Eυ∼ζ[EU∗υt(t)]−Eυ∼ζ[EU∗υt(t);υ∉Hϵ]−2ϵ
By definition of Hϵ, this implies
Eυ∼ζ[EUπζtυt(t)]≥Eυ∼ζ[EU∗υt(t)]−3ϵ
For any υ∈H, we get
EUπζtυt(t)≥EU∗υt(t)−3ϵζ(υ)
Taking ϵ to 0, we get the desired result.
### Proof of Proposition 1
Immediate from Proposition A.0.
### Proof of Proposition 2
Let H={υk}k∈N. For each k∈N, let πk learn {υl}l<k. Choose {tk∈(0,∞)}k∈N s.t.
i. t0=0
ii. tk<tk+1
iii. limk→∞tk=∞
iv. For any l<k and t≥tk, EUπktυlt(t)≥EU∗υlt(t)−1k+1.
Now define π∗t:=πmax{k∣t≥tk}t. Clearly, π∗ learns H.
Proposition A.1
---------------
Consider d∈N and x,y∈Rd∖0. Then
∥x∥x∥∞−y∥y∥∞∥∞≤2d∥x∥x∥1−y∥y∥1∥1
### Proof of Proposition A.1
Without loss of generality, assume ∥x∥1=∥y∥1=1. For any i<d, we have
xi∥x∥∞−yi∥y∥∞=xi∥y∥∞−yi∥x∥∞∥x∥∞∥y∥∞=xi∥y∥∞−xi∥x∥∞+xi∥x∥∞−yi∥x∥∞∥x∥∞∥y∥∞
xi∥x∥∞−yi∥y∥∞=xi(∥y∥∞−∥x∥∞)+(xi−yi)∥x∥∞∥x∥∞∥y∥∞
Denote r:=∥x−y∥1. Obviously, ∥x−y∥∞≤r and therefore |xi−yi|≤r and |∥x∥∞−∥y∥∞|≤r. We get
|xi∥x∥∞−yi∥y∥∞|≤|xi|r+r∥x∥∞∥x∥∞∥y∥∞≤∥x∥∞r+r∥x∥∞∥x∥∞∥y∥∞≤2r∥y∥∞
Since ∥y∥1=1, ∥y∥∞≥1d yielding the desired result.
Proposition A.2
---------------
Consider H a finite set, L:H→[0,∞), ζ∈ΔH and β,ϵ∈(0,∞). Assume that
i. For any k∈H, ζ(k)≥βϵ.
ii. Eζ[L]≥ϵ
Then
Eζ[e−βL]≤1−(1−e−1)βϵ
### Proof of Proposition A.2
Without loss of generality, we can assume that Eζ[L]=ϵ, because otherwise we can rescale L by a constant in (0,1) which will only make Eζ[e−βL] larger. It now follows from conditions i+ii that for any k, L(k)≤β−1 and therefore βL(k)∈[0,1]. We have
L(k)=(1−βL(k))⋅0+βL(k)⋅β−1
Since e−βx is a convex function, we get
e−βL(k)≤(1−βL(k))⋅e−β⋅0+βL(k)⋅e−β⋅β−1=1−βL(k)+e−1βL(k)=1−(1−e−1)βL(k)
Eζ[e−βL]≤1−(1−e−1)βEζ[L]=1−(1−e−1)βϵ
Proposition A.3
---------------
Consider H and A finite sets, L:H×A→[0,∞), ζ∈ΔH, α:H→ΔA and β,ϵ∈(0,∞). Assume that
i. For any k∈H, ζ(k)≥βϵ.
ii. For any a∈A, Ek∈ζ[L(k,a)]≥ϵ.
iii. For any k∈H and a∈A, α(k)(a)≤exp[−βL(k,a)]maxb∈Aα(k)(b).
Define ζ⋉α∈Δ(H×A) by (ζ⋉α)(k,a):=ζ(k)α(k,a). Then, the mutual information I between k and a in the distribution ζ⋉α satisfies
I≥(1−e−1)28|A|2β2ϵ2
### Proof of Proposition A.3
Define ¯α∈ΔA by ¯α:=Eζ[α]. We have
I=Eζ[DKL(α∥¯α)]
Applying Pinsker's inequality
I≥2Eζ[dtv(α,¯α)2]=12Eζ[∥α−¯α∥21]≥12Eζ[∥α−¯α∥1]2
By Proposition A.1
I≥18|A|2Eζ[∥α∥α∥∞−¯α∥¯α∥∞∥∞]2≥18|A|2∥Eζ[α∥α∥∞−¯α∥¯α∥∞]∥2∞=18|A|2∥Eζ[α∥α∥∞]−¯α∥¯α∥∞∥2∞
I≥18|A|2(∥¯α∥¯α∥∞∥∞−∥Eζ[α∥α∥∞]∥∞)2=18|A|2(1−∥Eζ[α∥α∥∞]∥∞)2
By condition iii, α(k)(a)/∥α(k)∥∞≤exp[−βL(k,a)] and therefore
I≥18|A|2(1−maxa∈AEk∼ζ[e−βL(k,a)])2
Applying Proposition A.2, we get
I≥18|A|2(1−(1−(1−e−1)βϵ))2=(1−e−1)28|A|2β2ϵ2
Proposition A.4
---------------
Consider A a finite set, L:A→[0,∞) and α∈ΔA and β∈(0,∞) s.t. for any a∈A
α(a)≤e−βL(a)maxb∈Aα(b)
Then, Eα[L]≤|A|e−1β−1.
### Proof of Proposition A.4
We have
Eα[L]=∑a∈Aα(a)L(a)≤maxb∈Aα(b)∑a∈Ae−βL(a)L(a)≤|A|maxx∈[0,∞)e−βxx
We compute maxx∈[0,∞)e−βxx:
0=ddx(e−βxx)|x=x∗=−βe−βx∗x∗+e−βx∗
x∗=1β
e−βx∗x∗=1eβ
Proposition A.5
---------------
Consider a universe υ=(μ,r), a policy π0 and t∈(0,∞). Then,
EU∗υ(t)−EUπ0υ(t)=∞∑n=0e−n/tEx∼μ⋈π0[Vυt(x:n)−Qυt(x:n+1/2)]
### Proof of Proposition A.5
For any x∈(A×O)ω, it is easy to see that
EU∗υ(t)=Vυt(λ)=∞∑n=0e−n/t(Vυt(x:n)−e−1/tVυt(x:n+1))
Urt(x)=(1−e−1/t)∞∑n=0e−n/tr(x:n)
EU∗υ(t)−Urt(x)=∞∑n=0e−n/t(Vυt(x:n)−(1−e−1/t)r(x:n)−e−1/tVυt(x:n+1))
EU∗υ(t)−Urt(x)=∞∑n=0e−n/t(Vυt(x:n)−Qυt(x:n+1/2)+Qυt(x:n+1/2)−(1−e−1/t)r(x:n)−e−1/tVυt(x:n+1))
Taking expected value over x, we get
EU∗υ(t)−EUπ0υ(t)=∞∑n=0e−n/t(Eμ⋈π0[Vυt(x:n)−Qυt(x:n+1/2)]+Eμ⋈π0[Qυt(x:n+1/2)−(1−e−1/t)r(x:n)−e−1/tVυt(x:n+1)])
It is easy to see that the second term vanishes, yielding the desired result.
Lemma A
-------
Consider the setting of Theorem, but assume that H={υk=(μk,rk)}k<N for some N∈N (i.e. it is finite) and that αk(t) is *strictly* β(t)-rational for ¯υk[αk]. Denote νk:=¯μk[αk] and hdom¯H:=⋃k<Nhdomνk. Denote ζ0∈Δ[N] the uniform probability distribution. For any t>N3, define ζt,~ζt:hdom¯H→Δ[N] recursively as follows
~ζt(λ):=ζ0
~ζt(hao)(k):={~Zt(h)−1⋅ζt(h)(k)⋅νk(ha)(o) if ∃j:ζt(h)(j)⋅νj(ha)(o)>0N−1 otherwise
ζt(h)(k):=Zt(h)−1~ζt(h)(k)[[~ζt(h)(k)>t−1/3]]
In the above, Zt(h) and ~Zt(h) are normalization factor chosen to make the probabilities sum to 1. That is, ζt(h) is obtained by starting from prior ζ0, updating on every observation, and setting to 0 the probability of any universe whose probability drops below t−1/3. When encountering an "impossible" observation we reset to the uniform distribution, but this is arbitrary.
Define the "loss function" Lt:hdom¯H×A→[0,1] by
Lt(ha):=Ek∼ζt(h)[Vυktt(h––)−Qυktt(h––a)]
Denote ϵt:=β(t)−1t−1/3. Define the following ¯I-metapolicy π∗:
π∗t(h):=⎧⎪
⎪⎨⎪
⎪⎩argmina∈ALt(ha) if h∈hdom¯H,mina∈ALt(ha)<ϵt⊥ if h∈hdom¯H,mina∈ALt(ha)≥ϵt⊥ if h∉hdom¯H
(Technically, we only defined π∗t for t>N3, but it doesn't matter.) Then, π∗ learns ¯H.
### Proof of Lemma A
For every k∈[N], we define ζ!kt,~ζ!kt:hdom¯H→Δ[N] and Skt:hdom¯H→2[N] recursively as follows
~ζ!kt(λ):=ζ0
~ζ!kt(hao)(i):=ζ!kt(h)(i)⋅νi(ha)(o)∑j<Nζ!kt(h)(j)⋅νj(ha)(o)
Skt(h):={i∈[N]∣~ζ!kt(h)(i)>t−1/3}
ζ!kt(h)(i):=[[k,i∈Skt(h)]]~ζ!kt(h)(i)∑j∈Skt(h)~ζ!kt(h)(j)+[[k∉Skt(h),i=k]]
That is, ζ!k is a belief state that, besides updating on observations, behaves as if, at each moment of time, if the true universe k is low probability according to current belief state (i.e. k∉Skt(h)), then the true universe is "magically" revealed (i.e. ζ!k becomes the Kronecker delta), and otherwise it updates on the true universe *not* being revealed. Denote
ρt:=ζ0⋉(νt⋈π∗t)∈Δ([N]ׯ¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×Oω)
Here, the dependence of νkt⋈π∗t on k is used to view it as Markov kernel from [N] to ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×Oω. It is easy to see that, since the probability of "magic\ revelation" ever happening is at most (N−1)t−1/3, we have
Pr(k,x)∼ρt[∃n∈N:ζt(x:n)≠ζ!kt(x:n)]≤(N−1)t−1/3
Let π!k be defined exactly as π∗ but with ζ replaced by ζ!k. Denote ρ!t:=ζ0⋉(νt⋈π!t). From the above, we get
dtv(ρt,ρ!t)≤(N−1)t−1/3
Given k∈[N] and h∈hdom¯H, we define the set h!k by
h!k:={(j,x)∈[N]ׯ¯¯¯¯¯¯¯¯¯¯¯¯¯¯A×Oω∣h⊑x,∀m≤|h|:k∈Skt(h:m)∧j∈Sjt(h:m)∨k∉Skt(h:m)∧j=k}
We have
Pr(j,x)∼ρ!t[j=i∣h!k]=ζ!k(h)(i)
It follows that
E(j,x)∼ρ!t[H(ζ!jt(x:|h|+1))∣h!k]=H(ζ!kt(h))−E(j,x)∼ρ!t[DKL(ζ!jt(x:|h|+1)∥~ζ!jt(x:|h|+1))+DKL(~ζ!jt(x:|h|+1)∥ζ!jt(x:|h|))∣h!k]
E(j,x)∼ρ!t[H(ζ!jt(x:|h|+1))∣h!k]≤H(ζ!kt(h))−E(j,x)∼ρ!t[DKL(~ζ!jt(x:|h|+1)∥ζ!jt(x:|h|))∣h!k]
If π!k(h)=⊥, we can apply Proposition A.3: condition i follows from the definition of Skt and the observation that β(t)ϵt=t−1/3, condition ii follows from the fact we are in the second case in the definition of π!k (see definition of π∗) and condition iii follows from the strict β(t)-rationality of αk(t). We conclude
E(j,x)∼ρ!t[H(ζ!jt(x:|h|+1))∣h!k]≤H(ζ!kt(h))−(1−e−1)28|A|2β(t)2ϵ2t[[π!k(h)=⊥]]
E(j,x)∼ρ!t[H(ζ!jt(x:|h|+1))∣h!k]≤H(ζ!kt(h))−(1−e−1)28|A|2t−2/3[[π!k(h)=⊥]]
Taking ρ!t-expected value over (k,h), we conclude that for any n∈N
E(k,x)∼ρ!t[H(ζ!kt(x:n+1))]≤E(k,x)∼ρ!t[H(ζ!kt(x:n))]−(1−e−1)28|A|2t−2/3Pr(k,x)∼ρ!t[π!k(x:n)=⊥]
By induction, it follows that
E(k,x)∼ρ!t[H(ζ!kt(x:n))]≤H(ζ0)−(1−e−1)28|A|2t−2/3E(k,x)∼ρ!t[|{m∈[n]∣xm∈⊥ׯO}|]
E(k,x)∼ρ!t[|{m∈[n]∣xm∈⊥ׯO}|]≤8|A|2lnN(1−e−1)2t2/3
E(k,x)∼ρ!t[|{n∈N∣xn∈⊥ׯO}|]≤8|A|2lnN(1−e−1)2t2/3
So, the expected number of delegations is O(t2/3). For any k∈[N], Proposition A.5 yields
EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t)=∞∑n=0e−n/tEx∼νk⋈π!k[Vυk[αk]t(x:n)−Qυk[αk]t(x:nπ!k(x:n))]
Averaging over k, we get
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))=∞∑n=0e−n/tE(k,x)∼ρ!t[Vυk[αk]t(x:n)−Qυk[αk]t(x:nπ!k(x:n))]
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))=∞∑n=0e−n/tE(k,x)∼ρ!t[E(j,y)∼ρ!t[Vυj[αj]t(y:n)−Qυj[αj]t(y:nπ!j(y:n))∣x!k:n]]
Using the definitions of ζ!k(x:n) and x!k:n, and observing that π!j(x:n)=π!k(x:n) for (j,y)∈x!l:n
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))=∞∑n=0e−n/tE(k,x)∼ρ!t[Ej∼ζ!k(x:n)[Vυj[αj]t(x:n)−Qυj[αj]t(x:nπ!k(x:n))]]
When π!k(x:n)≠⊥, the expected loss is smaller than ϵt (by construction of π!k). When π!k(x:n)=⊥, the expected loss is at most |A|e−1β(t)−1, by Proposition A.4. We get
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))≤∞∑n=0e−n/tE(k,x)∼ρ!t[[[π!k(x:n)≠⊥]]ϵt+[[π!k(x:n)=⊥]]|A|e−1β(t)−1]
The contribution of the first term on the right hand side can be bounded using the observation
∞∑n=0e−n/t=1+∞∑n=1e−n/t≤1+∞∑n=1∫nn−1e−s/tds=1+∫∞0e−s/tds=1+t
The contribution the of second term on the right hand side can be bounded via the expected number of delegations. We get
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))≤(1+t)β(t)−1t−1/3+8|A|2lnN(1−e−1)2t2/3|A|e−1β(t)−1
1N∑k<N(EU∗¯υk[αk](t)−EUπ!k¯υk[αk](t))≤(1t+1+8|A|3lnNe(1−e−1)2)t2/3β(t)
We use the relationship between ρt and ρ!t on the second term on the left hand side (whose contribution is the ρ!t-expected value of the utility function, whose range lies in [0,1]), and get
1N∑k<N(EU∗¯υk[αk](t)−EUπ∗¯υk[αk](t))≤(1t+1+8|A|3lnNe(1−e−1)2)t2/3β(t)+N−1t1/3
Using the assumption β=ω(t2/3), we conclude that for each k∈[N]
limt→∞(EU∗¯υk[αk](t)−EUπ∗¯υk[αk](t))=0
Proposition A.6
---------------
Let υ=(μ,r) be a meta-universe, {Ct⊆(A×O)∗}t∈(0,∞) and π0 a metapolicy. Then, for all t∈(0,∞)
EUπ0υC(t)≤EUπ0υ(t)
### Proof of Proposition A.6
Denote χt:=χCt the characteristic function of Ct. For notational convenience, the expression χt(x:−1) will be understood to mean 0.
EUπ0υC(t)=(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt(n∑m=0(1−χt(x:m−1))χt(x:m)V−υt(x:m)+(1−χt(x:n))rt(x:n))]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt(n∑m=0(1−χt(x:m−1))χt(x:m)Ey∼μ⋈π0[∑∞l=me−ltr(y:l)∑∞l=me−lt∣x:m]+(1−χt(x:n))rt(x:n))]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt(n∑m=0(1−χt(x:m−1))χt(x:m)∑∞l=me−ltr(x:l)∑∞l=me−lt+(1−χt(x:n))rt(x:n))]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt((1−e−1t)n∑m=0(1−χt(x:m−1))χt(x:m)∞∑l=me−l−mtr(x:l)+(1−χt(x:n))rt(x:n))]
Regrouping the sum to collect the terms r(x:n) for the same value of n, we get
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0((1−e−1t)n∑m=0∞∑l=me−lt(1−χt(x:m−1))χt(x:m)e−n−mt+e−nt(1−χt(x:n)))r(x:n)]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt((1−e−1t)n∑m=0e−mt1−e−1t(1−χt(x:m−1))χt(x:m)emt+1−χt(x:n))r(x:n)]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt(n∑m=0(1−χt(x:m−1))χt(x:m)+1−χt(x:n))r(x:n)]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−nt(χt(x:n)+1−χt(x:n))r(x:n)]
EUπ0υC(t)≤(1−e−1t)Ex∼μ⋈π0[∞∑n=0e−ntr(x:n)]=EUπ0υ(t)
Proposition A.7
---------------
Let υ be a meta-universe and C a υ-avoidable event. Suppose π! is a metapolicy that learns υC. Then, π! learns υ as well.
### Proof of Proposition A.7
It is easy to see for any policy π and t∈(0,∞)
EUπ∗υC(t)≥(1−Prx∼μt⋈π∗t[∃n≤D(t):x:n∈Ct])(EUπ∗υ(t)−e−D(t)/t)
Let π∗ be as in Definition 4. Proposition A.6 and the above imply that
limt→∞(EUπ∗υ(t)−EUπ∗υC(t))=0
Using condition i, we get
limt→∞(EU∗υ(t)−EUπ∗υC(t))=0
By Proposition A.6, EU∗υ(t)≥EU∗υC(t), therefore
limt→∞(EU∗υC(t)−EUπ∗υC(t))=0
limt→∞(EUπ!υC(t)−EUπ∗υC(t))=0
limt→∞(EUπ!υC(t)−EU∗υ(t))=0
By Proposition A.6, EUπ!υ(t)≥EUπ!υC(t), therefore
limt→∞(EUπ!υ(t)−EU∗υ(t))=0
Proof of Theorem
----------------
For any k∈N, let Ck and αk∗ be for αk as in Definition 5. Denote ψk:=(υk)Ck and
¯HC:={¯ψk[αk∗]∣k∈N}
By Lemma A and Proposition 2, ¯HC is learnable. Let π∗ be a metapolicy that learns it. For any k∈N, π∗ learns ¯ψk[αk∗]. It is easy to see that for any policy π, EUπ¯ψk[αk∗](t)=EUπ¯ψk[αk](t) (ψk is defined s.t. once the corruption occurs, the reward is constant, so the advisor has no effect after this point), therefore π∗ learns ¯ψk[αk]. By Proposition A.7, we conclude that π∗ learns ¯υk[αk].
Proof of Corollary 1
--------------------
Denote υK:=(Ek∈ζK[μk],rK). By the Theorem, {¯υK[αK]}K∈N is learnable. Denote ψkK:=(μk,rK). It is easy to see that for any ¯I-policy π and t∈(0,∞),
Ek∈ξK[EUπψkK[αK](t)]=EUπ¯υK[αK](t)
By the calibration condition
E(K,k)∼ζ[EUπψkK[αK](t)]=E(K,k)∈ζ[Ej∈ξK[EUπψjK[αK](t)]]=E(K,k)∈ζ[EUπ¯υK[αK](t)]
Applying Proposition 1, we conclude that πζ learns {¯υK[αK]}K∈N. That is, for any K∈N
limt→∞(EU∗¯υK[αK](t)−EUπζ¯υK[αK](t))=0
limt→∞(EU∗υK(t)−EUπζ¯υK[αK](t))=0
limt→∞(maxπ∈ΠIEk∈ξK[EUπψkK(t)]−Ek∈ξK[EUπζ¯ψkK[αK](t)])=0
Proof of Corollary 2
--------------------
Immediate from Corollary 1 and Proposition A.0. |
9c355b68-88ac-474d-ac8b-ae0d26185ad2 | trentmkelly/LessWrong-43k | LessWrong | Infinite Ethics: Infinite Problems
1 Introduction
(Crossposted from my blog--the formatting is probably a bit better there.)
> With entity, dementedly meant to be infinite
—Eminem.
(Crossposted from my blog). The formatting is better there.
I never had a Nietzsche phase.
You know the way lots of people get obsessed with Nietzsche for a while? They start wearing black, becoming goth, smoking marijuana, and talking about how like “god is dead, nothing matters, man.” This never happened to me, in part because Nietzsche doesn’t really make arguments, just self-indulgent rambles.
But I’m starting to have an infinite ethics phase. Infinite ethics is trippy—it just seems like everything—especially morality—breaks when you get infinite amounts of stuff. Various plausible views seem to lead to the conclusion that one has no reasons for action or that obvious principles about value, morality, and even rationality collapse.
Lots of people seem to be bothered by population ethics. They find the paradoxes unsettling. I’ve never been troubled by any of population ethics—as long as you just accept what totalism says about population ethics, all the puzzles go away. Then, all the “puzzles” serve as reasons to accept totalism rather than genuine paradoxes. People find the totalist view unintuitive, and get very worked up about views it implies, such that they start giving its implications mean names like the repugnant conclusion and the very repugnant conclusion. However, I find all of these to be very intuitive—and you can also prove that one should accept them from minimal axioms—so I don’t even feel like there’s a conflict, much less a conflict so great that it should deeply unsettle us.
Population ethics doesn’t keep me up at night. Nor does most of philosophy—I feel pretty satisfied in my views on most issues. I’m probably wrong about lots of things, but there are no issues where I feel as if I’m required to, in Spencer Case’s language, repeatedly embrace the cactus—accept crazy, deeply implausible co |
bbb4a680-6700-403d-95da-57be202f1e3d | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Importance of foresight evaluations within ELK
This post makes a few basic observations regarding the importance of foresight-based evaluations within ELK [[Christiano et al 2021](https://www.alignmentforum.org/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge)]:
1. Without foresight-based evaluations, (narrow) ELK is insufficient to avoid subtle manipulation (we’d require a more ambitious version)
2. Hindsight creep can occur even with seemingly foresight-based evaluations. One way this could happen is if human evaluators primarily defer to evaluations made by future humans in the predicted outcomes.
3. Because of both points, even if we are counting on a technique like narrow ELK, improving the quality of foresight-based evaluations is very valuable.
The points here are largely covered in ELK appendices on [narrow elicitation](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.ii599facmbks), [defining a utility function](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.3y1okszgtslx), and [subtle manipulation](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.3gj06lvtpme7). The aim here is to call out some of these points explicitly, and facilitate further focused discussion.
Subtle manipulation
-------------------
The ELK report focuses on what it calls narrow ELK, which does not aim to address subtle manipulation via ELK:
> [In narrow ELK,] We won’t ask our AI to tell us anything at all about subtle manipulation.
> …
> A mundane [example of subtle manipulation] is that they may have talked with someone who cleverly manipulates them into a position that they wouldn’t really endorse, or emotionally manipulated them in a way that will change their future conclusions.
>
>
Subtle manipulation is clearly problematic. Within the overall RL training approach of the ELK report, foresight evaluations are necessary to avoid subtle manipulation. That is, even if we solve narrow ELK, we still need foresight-based evaluations to avoid subtle manipulation.
With hindsight-based policy evaluations, narrow ELK is insufficient
-------------------------------------------------------------------
The ELK report provides a simple policy training algorithm based on approval-in-foresight combined with hindsight-based outcomes predictors:
> Still, even though humans can’t directly follow along with the actions, they can evaluate the predicted consequence of an action sequence:
> [image]
> We can then train a model to predict these human evaluations, and search for actions that lead to predicted futures that look good.
>
>
(See also: [Appendix: Policy training pseudocode](https://www.alignmentforum.org/posts/mvGNKQ6iSDf3d4gCi/importance-of-foresight-evaluations-within-elk#Appendix__Policy_training_pseudocode).)
This uses a hybrid of foresight and hindsight feedback.[[1]](#fndaup6vo2g9j) The outcomes predictor is trained in hindsight: trying actions, observing what outcomes result, and training a model to predict those outcomes. Conditioned on these predictions, the policy feedback on the action uses foresight — we don’t wait until the outcomes have happened to decide which action is better (we use the *predicted* outcomes instead).[[2]](#fnn5rq9bf4o6)
The use of approval-in-foresight here is crucial for avoiding subtle manipulation. Consider, what happens if we make the policy feedback *also* hindsight-based? That is, we have the human choose which outcomes are best *after* the decision is made. Rather than optimizing for “actions that lead to *predicted* futures that look good [in foresight]” (emphasis mine), we would optimize for “actions that lead to [*realized*] futures that look good [in hindsight]”.[[3]](#fnt8k2dy0i7ep)
In this case, *the futures which look best in hindsight are going to be ones where the future humans have been manipulated* into liking and/or saying good things about the affairs that arise. The basic point is that the foresight-based policy feedback seems to be *necessary* with narrow ELK; for a purely hindsight-based scheme we should expect subtle manipulation.
This is the same point the ELK report makes with the paperclip example (bolding mine):
> “For example, suppose I watch a 10 second ad that is carefully chosen by a brilliant paperclip-maximizing-marketer. Five years after watching this ad, I decide that paperclips are great so I dedicate my time to making lots of them, and if you evaluate outcomes using my conclusions-**after-deliberation** then you’ll conclude that this was a **great outcome** and the AI should help (e.g. if we evaluate using the utility function defined in [Appendix: utility function](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.3y1okszgtslx)). I’m not able to look at the process of deliberation and notice anything bad happening, and so it seems I can’t incentivize my AI to warn me about this ad or prevent me from watching it.
>
> But from my perspective **in advance**, there are *many* possible ads I could have watched. Because I don’t understand how the ads interact with my values, I don’t have very strong preferences about which of them I see. If you asked me-in-the-present to delegate to me-in-the-future, I would be indifferent between *all* of these possible copies of myself who watched different ads. And if I look across all of those possible copies of me, I will see that almost all of them actually think the **paperclip outcome is pretty bad**, there’s just this one copy (the one who sees the actual ad that happens to exist in the real world) who comes up with a weird conclusion.”
>
>
If we evaluate the action (whether to show this ad) in foresight, we will see that the human ends up doing something we don’t like, and so reject the action. If we evaluate it in hindsight, the action looks great to the future humans within this trajectory.
Hindsight creep can occur even with seemingly foresight-based evaluations
=========================================================================
Here are two ways hindsight creep could happen, which are very similar:
1. The foresight evaluations defer to the hindsight evaluations
2. Pushing complexity from reward function (RF) to RF input function [[Everitt et al 2019](https://arxiv.org/abs/1908.04734)], particularly by using high-level RF inputs.
In either case, even though the algorithm *looks* foresight-based, we get the same failure mode as with hindsight-based feedback.
Hindsight creep from foresight evaluations deferring to hindsight evaluations
-----------------------------------------------------------------------------
The paperclipping example is particularly simple in that even in foresight, it is easy to see something has gone wrong (the paperclip example is not intended to capture the full difficulty of the problem). Human evaluators can observe the trajectory and realize that even though the future humans are happy and think the situation is great, we don’t want everyone obsessed with paperclips.
However, realistic futures involving transformative AI are probably much more complicated. For example, maybe there are crazy technologies, or new ideologies, or new social norms, or differently structured societies. Lots of aspects of this world would just look totally strange to us, including the preferences of these future people, and it might not be clear whether they’re happy because things have gone well, or because their preferences were manipulated (to be happy either in general, or with these particular outcomes which we would not endorse).
In such a case, it might be tempting to just defer to the future people, and to rank predicted outcomes Ti based on how happy the people seem to be in each Ti. But if we do that, we have the same problem as the [previous section](https://www.alignmentforum.org/posts/mvGNKQ6iSDf3d4gCi/importance-of-foresight-evaluations-within-elk#With_hindsight_based_policy_evaluations__narrow_ELK_is_insufficient): The paperclip ad *does* result in future people who are happy. More generally, manipulation is also a way to make people happy.
The ELK report makes this same point here:
> “But it might be much easier to manipulate our future selves into being really happy with the outcome then it is to actually maximize option value (which may require e.g. trying to make money in a competitive economy). So we should worry about the possibility that our AI will manipulate us instead of helping us.”
>
>
Hindsight creep from shifting complexity into RF input function
---------------------------------------------------------------
To put the same basic point slightly differently: if the RF inputs are high-level inputs (like human-written reports) rather than low-level inputs (like camera images), our evaluations also become more reliant on hindsight feedback.
This is most easily understood by taking this to the limit. In an extreme case, rather than training the trajectory predictor to predict camera inputs, we train it to solely predict the result of a human within the trajectory writing an evaluation. In this case, the RF has no choice but to defer to the hindsight evaluations (because that’s all the RF sees). If the humans are manipulated into always painting an overly rosy picture of their situation, we'd have *no way to distinguish this from things actually going well*.
More generally, if evaluations of predicted trajectories depend significantly on human-written reports of events within that trajectory, we have the same issue. For example, if the cameras are recording future humans writing evaluations about their world, and the only thing the reward function merely reads these evaluations out from the camera videos, nothing has changed. And more generally, as pointed out in the [previous subsection](https://www.alignmentforum.org/posts/mvGNKQ6iSDf3d4gCi/importance-of-foresight-evaluations-within-elk#Hindsight_creep_from_foresight_evaluations_deferring_to_hindsight_evaluations), if much of the future world is bewildering to the current evaluators (other than questions like whether the future humans look and say they’re happy), the current evaluators may have to rely significantly on hindsight evaluations of the external world within each trajectory.
A couple quick side points:
One hybrid approach might be to use decoupled feedback by asking future humans to evaluate *alternate* future trajectories, as opposed to their current trajectory so far, and then deferring to these judgments. But these future humans face essentially the same challenge as the naive foresight evaluations: they potentially have to evaluate future worlds very different from their own. So making this hybrid approach competitive seems to require similar techniques to those needed to make naive foresight evaluations competitive.
For researchers familiar with the decoupling line of work, this point in this subsection can be compactly expressed using concepts from decoupling. The extreme case above pushes essentially all the complexity into the RF input function (trajectory predictor) rather than RF (preferences across trajectories), while the less extreme case does a softer version of this. Decoupling provides a way to avoid RF tampering, but not RF-input tampering, so if “most” of the complexity is in the RF input function, we will run into trouble.
Improving foresight evaluations is valuable
===========================================
To avoid the issues of hindsight creep, we need strong foresight evaluations in order for this combination of foresight evaluation + narrow ELK to effectively avoid subtle manipulation.[[4]](#fnygix2t1xjys) If the foresight evaluator can’t understand anything about the future worlds, they have no choice but to defer to hindsight feedback.
Alternatively, the evaluations can be decoupled, but not purely foresight-based (see [Appendix: Decoupling](https://www.alignmentforum.org/posts/mvGNKQ6iSDf3d4gCi/importance-of-foresight-evaluations-within-elk#Appendix__Decoupling) if this terminology is confusing). This is the approach used by the [indirect normativity](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.3y1okszgtslx) approach to defining a utility function in the ELK report. But the core challenges in making the decoupled feedback procedure competitive are similar to those for the purely foresight-based approach.
Finally, another approach to avoiding the problem of “deferring to hindsight evaluations” is to only use outcomes-based predictors for various well-scoped observables, and to primarily rely on augmented human understanding (e.g. via amplification and/or debate) to provide feedback on the best actions. The competitiveness of this approach is a long discussion beyond the present scope – my view is that while this approach is not indefinitely scalable, there is a good chance it remains competitive for very long, and past the onset of Transformative AI. In any case, the viability of such an approach depends even more on foresight-based evaluations.
*Thanks to the ELK authors for discussion of the ELK report, and to Paul Christiano for discussion and feedback on this post.*
Appendix: Policy training pseudocode
====================================
This appendix provides pseudocode to clarify the feedback procedure for policy training.
We start with the dynamics model f, trained to predict future trajectories t = f(s, a1:t) from state s and action sequence a1:t, and the human evaluation function H computes reward r = H(t) from trajectory t. We then define the target policy as:[[5]](#fnztteef2le6)
```
def target_policy(s):
for action sequence a_i in A:
t(a_i) := f(s, a_i)
r(a_i) := H(t(a_i))
return argmax_{a_i} r(a_i)
```
From here, we can define the acting policy various ways, all of which are essentially the same (with respect to subtle manipulation).
* Non-parametric: Act directly, by directly calling target\_policy(s).
* Parametric: Use target\_policy(s) as the policy improvement signal, by training a parametric (neural network) policy towards target\_policy(s).
* Parametric-soft-selection: Train towards target\_policy’(s), where the argmax is replaced with a prior-policy-weighted softmax.
To contrast the foresight and hindsight policy feedback then: In the foresight (ordinary) version, H implements a foresight evaluation: the human looks at the *predicted* trajectory t(ai), but evaluates t(a) before ai is executed. In the pure hindsight version, we would replace the trajectory model f with the real-world dynamics (by executing action sequence ai), and the human evaluation H would score the resulting trajectory t (in hindsight).[[6]](#fn9co831x1ul6)
Appendix: Decoupling
====================
This appendix expands on why the ELK report’s strategy for avoiding subtle manipulation rests on decoupling, and the relationship between decoupling and foresight evaluations.
A *decoupled* feedback procedure [[Everitt et al 2017](https://arxiv.org/pdf/1705.08417.pdf)] is one which provides evaluation on some outcomes (resp. actions) from a world distinct from those outcomes (resp. in which different actions were taken). An example of a decoupled feedback procedure for outcomes would be to ask “how good would future F be?” In comparison, a non-decoupled feedback procedure for outcomes would be to ask someone in future F, “how good is the world?” Similarly, a decoupled procedure for actions would be to ask “how good is action A in state S?” (either before taking an action, or after taking some action A’), while a non-decoupled procedure would ask “how good was action A in state S?” after taking action A and observing the consequences.
The basic intuition for why decoupling helps is that tampered states/actions are scored highly according to non-decoupled feedback, but receive no benefit under decoupled feedback. More detailed and rigorous explanations can be found in [Reinforcement Learning with a Corrupted Reward Channel](https://arxiv.org/pdf/1705.08417.pdf) or [Avoiding Tampering Incentives in Deep RL via Decoupled Approval](https://arxiv.org/pdf/2011.08827.pdf).
In this post, I use decoupled/foresight and non-decoupled/hindsight interchangeably. In particular, foresight-based algorithms benefit from being decoupled by default, since our evaluation can’t be affected by consequences if the evaluation occurs before the consequences. The most naive hindsight-based algorithms are typically non-decoupled, and to my knowledge, decoupled versions face similar challenges to foresight evaluations. So for our purposes here, we can treat these as mostly equivalent; I’ve used the term foresight in this post to match the ELK report.
As a side note, when reading the ELK report, it was confusing at first to me why we’d want different solutions for subtle manipulation vs. sensor tampering. Within the language of decoupling, here is one mental model which might be a helpful explanation:
1. Decoupling handles RF tampering.
2. We design the RF to have very low-level RF-inputs, such that any RF-input tampering will require blatant tampering. In particular, we want to make it impossible for subtle manipulation to affect the RF *inputs* (e.g. camera videos).
3. We use narrow ELK to handle RF-input tampering.
Decoupling within indirect normativity proposal
-----------------------------------------------
The feedback procedure described in the ELK report isn’t quite as simple as the pure foresight pseudocode in [Appendix: policy training pseudocode](https://docs.google.com/document/d/1olAFg4ns44MPeHWyAKCCBNJN5F9HrnEouhDiRuxxSYU/edit?resourcekey=0-UE74C19E6m18GTc_aGN2OQ#heading=h.4y82vvdhzyta). In the ELK indirect normativity proposal, rather than asking up-front “which of these predicted trajectories is best?”, we ask “which of these predicted outcomes is best, according to a near-future delegate?” (Note that we can also keep things purely foresight-based by asking “which of these predicted outcomes is best, according to amplification or debate?”, where competitiveness is again the key question.)
The feedback remains decoupled though, in that we define the utility function by asking the near-future delegate to evaluate different possible future trajectories, rather than their own current trajectory. (That is, in the simplest scheme, and then we do this recursively.) The challenges needed to make decoupled feedback competitive (in terms of understanding complex worlds) are largely the same as those for pure foresight evaluations. And the core basic point is that the purely hindsight-based and non-decoupled policy feedback procedure will lead to subtle manipulation.
1. **[^](#fnrefdaup6vo2g9j)** On the whole, the baseline algorithm described in the ELK report is very hindsight-based (I believe partly for pedagogical reasons, and partly to target an indefinitely scalable solution). The main point here is that much of the *safety* properties specifically come from the foresight-based elements.
2. **[^](#fnrefn5rq9bf4o6)** The ELK report proposal is more complicated, and suggests indirect normativity as one option. The point here is that there is a simple option here which avoids manipulation issues (which hindsight-based feedback does not). The situation with indirect normativity is similar, and discussed in the Appendix.
3. **[^](#fnreft8k2dy0i7ep)** “*Predicted* futures that look good in hindsight” is also fine, but at the point where you’re already evaluating actions in hindsight, there isn’t much reason to look at predicted trajecotires, rather than the actual trajectories. But the argument is the same in either case.
4. **[^](#fnrefygix2t1xjys)** Alternatively, the evaluations can be decoupled, but not purely foresight-based. This is the approach used by the indirect normativity approach to defining a utility function in the ELK report. But the core challenges in making the decoupled feedback procedure competitive are similar to those for the purely foresight-based approach.
5. **[^](#fnrefztteef2le6)** As in the ELK report, this is shown with exhaustive search, but could use more practical alternatives like MCTS with learned heuristics, other learned search procedures.
6. **[^](#fnref9co831x1ul6)** As a clarifying note, with hindsight policy feedback, only the parametric policies make sense. This is because the non-parametric policy requires observing the consequences of the current action in order to determine the current action (but the non-parametric policy is mostly only of theoretical interest anyways). |
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