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3859bf34-acfd-42fc-9a80-ccb5d584e18a | trentmkelly/LessWrong-43k | LessWrong | On the Nature of Reputation
Abstract: Reputation tokens (e.g. brands, but lot of other things as well) are vessels to store reputation. They are free to create but expensive to fill with trust. Consumers use them to deal with information overload. Producers use them to manipulate common knowledge. Also, a certain kind of supply-demand-style equilibrium exists.
Trademarks
Speaking about reputation, one risks getting all hand-wavy and disconnected from the real world.
To keep the discussion down-to-earth, let's start with the trademark law. The trademark law was, after all, created specifically to deal with reputation. And being a law, it is not a theoretical model but a living organism evolved to solve actual real-world problems.
You may have a vague idea that a trademark is something like an Internet domain name. You can buy a name and then it's yours. It's your property and everyone else should get off your lawn.
But the trademark law is not like that at all.
First of all, you don't create a trademark by buying it. Trademark is rather created as a by-product of using a name. You establish a company. You start producing stuff. Maybe you do a little advertising. People start to recognize your brand. Et voilà, you own a legally recognized trademark. No explicit action on your part is needed.
Similarly, if you stop using the name, your claim to it gradually dissipates. If you claim a trademark and your opponent is able to prove that you haven't used it for decades, the court will rule against you.
There is also the concept of "trademark goodwill" which loosely translates to "reputation". Interestingly, the notion of trademark goodwill tends to be phrased in economic terms. Namely, it is the part of the value of the company that is gained through owning the trademark.
Another common misconception about trademarks is that the names are, similarly to the Internet domain names, global.
In reality, the scope of a trademark is limited to the area of one's activity. Apple, the grocery store do |
2dbc1169-3494-4a71-8f99-efd6d47e7094 | trentmkelly/LessWrong-43k | LessWrong | Bakeoff
For most of the past week Lily has been trying to persuade us to have a cooking competition. Her initial idea involved kids cooking unassisted, but after talking through ways I saw this going poorly she decided she was ok with teams. This morning she said it was time, and declared it would be Lily + Jeff vs Anna + Julia.
Unfortunately, Anna was only interested in being a judge. After extended discussion Lily determined that we would be a team and Julia would compete as an individual. Luckily for Lily, when Julia woke up she was up for participating.
Lily started by making a list of the allowed ingredients:
Since we only have one non-vegan oven we weren't going to have things ready at the same time, and Julia wanted to eat hers for breakfast anyway; we decided she'd have the oven first. She was enough faster that she was done with the oven before Anna and I were even close to ready to put ours in.
Julia made chocolate popovers:
Lily and I made a sponge cake with chocolate buttercream frosting:
The cake was a clear judge's favorite:
Lily: does it win?
Anna: yes!
Comment via: facebook |
947046e5-cf58-4f3c-ad83-88474dd9ec6b | trentmkelly/LessWrong-43k | LessWrong | A whirlwind tour of Ethereum finance
As a hacker and cryptocurrency liker, I have been hearing for a while about "DeFi" stuff going on in Ethereum without really knowing what it was. I own a bunch of ETH, so I finally decided that enough was enough and spent a few evenings figuring out what was going on. To my pleasant surprise, a lot of it was fascinating, and I thought I would share it with LW in the hopes that other people will be interested too and share their thoughts.
Throughout this post I will assume that the reader has a basic mental model of how Ethereum works. If you don't, you might find this intro & reference useful.
Why should I care about this?
For one thing, it's the coolest, most cypherpunk thing going. Remember how back in 2012, everyone knew that Bitcoin existed, but it was a pain in the ass to use and it kind of felt weird and risky? It feels exactly like that using all this stuff. It's loads of fun.
For another thing, the economic mechanism design stuff is really fun to think about, and in many cases nobody knows the right answer yet. It's a chance for random bystanders to hang out with problems on the edge of human understanding, because nobody cared about these problems before there was so much money floating around in them.
For a third thing, you can maybe make some money. Specifically, if you have spare time, a fair bit of cash, appetite for risk, conscientiousness, some programming and finance knowledge, and you are capable of and interested in understanding how these systems work, I think it's safe to say that you have a huge edge, and you should be able to find places to extract value.
General overview
In broad strokes, people are trying to reinvent all of the stuff from typical regulated finance in trustless, decentralized ways (thus "DeFi".) That includes:
* Making anything that has value into a transferable asset, typically on Ethereum, and typically an ERC-20 token. A token is an interoperable currency that keeps track of people's balances and lets people transf |
dbc914fb-299d-4617-9058-8109eefd3d21 | trentmkelly/LessWrong-43k | LessWrong | Exercise isn't necessarily good for people
I would appreciate it very much if anyone would take a close look at this-- it looks sound to me, but it also appeals to my prejudices.
http://www.youtube.com/watch?feature=player_embedded&v=E42TQNWhW3w#!
My comments are in square brackets. Everything else is my notes on the Jamie Timmons lecture from the video.
Short version: 12% of people become less healthy from exercise. 20% of people get nothing from exercise. This is a matter of genetics, not doing exercise wrong.
****
Ask a hundred people about exercise, you'll get a wide range of answers about what exercise is and what good it might do for health, and the same for health professionals.
You need to focus on the evidence that exercise affects particular health outcomes. Weight and health are not strongly correlated. BMI is problematic.
There's a recommendation for 150 minutes of exercise/week, but this isn't sound. People who *report* being active have better health. People who are fitter have better health. These are not evidence that having a person with low activity take up exercise will make them healthier.
Nothing but a supervised intervention study is good enough.
Improved lifestyle is better than Metformin for preventing diabetes. (Studies) Exercise + diet modification has a powerful effect of preventing and slowing the progression of Type II diabetes. People with Type II have more cardiovascular disease (heart attacks and strokes). However, it doesn't follow that the lifestyle changes which help with Type II will also help with CVD. [I'm surprised]
Diabetes doesn't kill, CVD does, and a major motivation for the NHS to care is that CVD is expensive.
[9:45] Two studies which find that lifestyle intervention has no effect on CVD in diabetics. [11:00] One study which found that lifestyle intervention prevents Type II but doesn't affect microvascular disease (blindness and ulcers). [I'm not sure what this means. Maybe people can have the ill effects of Type II without the disease showing up in th |
4a28e718-4af1-40e5-a964-ba1ead96656d | trentmkelly/LessWrong-43k | LessWrong | Meetup : Washington, D.C.: Tetlock's "Expert Political Judgment"
Discussion article for the meetup : Washington, D.C.: Tetlock's "Expert Political Judgment"
WHEN: 14 December 2014 03:00:00PM (-0500)
WHERE: National Portrait Gallery
We will be meeting in the Kogod Courtyard of the National Portrait Gallery (8th and F Sts or 8th and G Sts NW, go straight past the information desk from either entrance) to talk about Philip E. Tetlock's book Expert Political Judgment: How Good Is It? How Can We Know?. Per the norm, we plan to let people congregate from 3:00 to 3:30 before kicking things off.
As with prior informal-discussion meetups, conversation on any subject of interest to attendees (be it in the main conversation or in a side conversation) is both permitted and encouraged, but we suggest taking advantage of the meetup topic as a Schelling point.
Upcoming Meetups:
The Less Wrong DC organizers haven't decided whether to hold meetups on the remaining two Sundays in 2014: the 21st is the day after Brighter Than Today, a secular winter solstice celebration that many regulars will be out of town for, and the 28th falls during the week after Christmas Day, an ostensibly-religious winter solstice celebration that many regulars may be out of town (or hosting out-of-town guests) for. If there is sufficient interest in attending a meetup on either or both of these dates, meetups will occur; otherwise the Fun & Games meetup will be postponed to January 4th.
Discussion article for the meetup : Washington, D.C.: Tetlock's "Expert Political Judgment" |
b1d0f956-917b-4317-acf7-3fc0822a62c0 | trentmkelly/LessWrong-43k | LessWrong | A boltzmann brain question.
I have a question on Boltzmann brains which I'd like to hear your opinions on - mostly because I don't know the answer to this....
First of all - a Boltzmann brain is the idea that - in a sufficiently large and long-lived universe, a brain just like mine or yours will occasionally pop into existence by sheer fluke. It doesn't happen very often - in fact it happens very, very infrequently. The brain will go on to have an experience or two before ceasing to exist on the grounds that it's surrounded by vacuum and cold - which is not a very good place for a disembodied brain to be.
Such a brain would have a very short life. Well, by a greater fluke, some of them might last for a longer time, but the balance of probabilities is that most boltzmann brains that think they had a long life merely had a lot of false memories of this life, planted by the fluke of their sudden pop into existence. And in their few seconds of life, they never got to realise that they didn't actually live that life, and their memories make no sense.
Well, boltzmann brains don't pop into existence by fluke very often - in the whole life of the observable universe it's overwhelmingly likely that it's never happened.
What might be more likely to happen? Well, you could have half a Boltzmann brain instead, and by sheer fluke, have the nerves leading from that half-brain stimulated as if the other half was there during the few seconds of the half-brain's life. This is still extremely unlikely, but tremendously more likely than having a whole Boltzmann brain appear. And the half-brain still thinks it has the same experience as before.
There is of course nothing to stop us from continuing this. Suppose we have a one-quarter brain? Much more probable. One millionth? Even more probable. Maybe even single elements of a nerve cell? More probable still. The smaller the piece is, the less of a fluke you need for it to come into existence, and the less of a fluke you need to continue to supply all the same |
47642836-d28b-4e3f-bcc5-fa827584d774 | trentmkelly/LessWrong-43k | LessWrong | Difference between CDT and ADT/UDT as constant programs
After some thinking, I came upon an idea how to define the difference between CDT and UDT within the constant programs framework. I would post it as a comment, but it is rather long...
The idea is to separate the cognitive part of an agent into three separate modules:
1. Simulator: given the code for a parameterless function X(), the Simulator tries to evaluate it, spending L computation steps. The result is either proving that X()=x for some value x, or leaving X() unknown.
2. Correlator: given the code for two functions X(...) and Y(...), the Correlator checks for proofs (of length up to P) of structural similarity between the source codes of the functions, trying to prove correlations X(...)=Y(...).
[Note: the Simulator and the Correlator can use the results of each other, so that:
If simulator proves that A()=x, then correlator can prove that A()+B() = x+B()
If correlator proves that A()=B(), then simulator can skip simulation when proving that (A()==B() ? 1 : 2) = 1]
3. Executive: allocates tasks and resources to Simulator and Correlator in some systematic manner, trying to get them to prove the "moral arguments"
Self()=x => U()=u
or
( Self()=x => U()=ux AND Self()=y => U()=uy ) => ux > uy,
and returns the best found action.
Now, CDT can be defined as an agent with the Simulator, but without the Correlator. Then, no matter what L it is given, the Simulator won't be able to prove that Self()=Self(), because of the infinite regress. So the agent will be opaque to itself, and will two-box on Newcomb's problem and defect against itself in Prisoner's Dilemma.
The UDT/ADT, on the other hand, have functioning Correlators.
If it is possible to explicitly (rather than conceptually) separate an agent into the three parts, then it appears to be possible to demonstrate the good behavior of an agent in the ASP problem. The world can be written as a NewComb's-like function:
def U():
box2 = 1000
box1 = (P()==1 ? 1000000 |
548d1616-94a4-4fe8-8178-c63779a989c9 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | [Link, 2011] Team may be chosen to receive $1.4 billion to simulate human brain
This is the team responsible for simulating the rat cortical column.
[http://www.nature.com/news/2011/110308/full/news.2011.143.htm](http://www.nature.com/news/2011/110308/full/news.2011.143.html)
The team is one of 6 that is being considered for at least 2 "FET Flagship" positions, which comes with all that funding. Each of the six competing teams is proposing to work on some kind of futuristic technology:
<http://cordis.europa.eu/fp7/ict/programme/fet/flagship/6pilots_en.html>
Of course, word on [the street](http://www.vetta.org/2011/05/sutton-on-human-level-ai/) is that academic neuroscientists don't think much of the project:
>
> Academic neuroscientists that I’ve ever spoken too, which is a fair number now, don’t think much of the Blue Brain project. They sometimes think it will be valuable in terms of collecting and cataloguing information about the neocortex, but they don’t think the project will manage to understand how the cortex works as there are too many unknowns in the model and even if, by chance, they got the model right it would be very hard to know that they had.
>
>
> Almost all neuroscientists seem to think that working brain models will not exist by 2025, or even 2035 for that matter. What ever the date is, most consider it too far away to bother to think much about.
>
>
> Such projects probably help to get more kids interested in the topic.
>
>
>
---
I think trying to influence the committee's decision potentially represents very low hanging fruit in [politics as charity](http://www.vetta.org/2011/05/sutton-on-human-level-ai/).
Even if academic neuroscientists don't think much of the project in its current state, it seems likely that $1.4 billion would end up attracting a lot of talent to this problem, and get us the first upload significantly sooner.
It's true that Less Wrong doesn't have a consensus position on whether to speed development of cell modeling and brain scanning technology or not. But I think if we have a discussion and a vote, we're significantly more likely than the committee to come up with the right decision for humanity. As far as I can tell, the committee will essentially be choosing at random. It shouldn't be hard for us to beat that.
Edit: But that's not to say that our estimate should be quick and dirty. In the spirit of [holding off on proposing solutions](/lw/ka/hold_off_on_proposing_solutions/), I discourage anyone from taking a firm public position on this topic for now.
In terms of avenues for influence, here are a few ideas off the top of my head:
1. Hire a PR agency to generate positive or negative press for a given project.
2. Get European Less Wrong users to contact the program via Facebook and Twitter. (The program's follower numbers are in the low triple digits.)
3. Hire professional lobbyists to do whatever they do.
Just to give everyone an idea of the kind of money involved here, if we have a 1% chance of influencing the committee's decision, we're moving $14 million in expected funds.
We, and the folks at the Future of Humanity Institute, SI, and other groups, seem to spend a lot of time thinking about what would happen in the ideal scenario in terms of the order in which technologies are developed and how they are deployed. I think there is a good case for also investing in the complementary good of trying to actually influence the world towards a more ideal scenario. |
0d6ce315-b262-49dd-a7fe-64c844061d55 | trentmkelly/LessWrong-43k | LessWrong | Third-party testing as a key ingredient of AI policy
(nb: this post is written for anyone interested, not specifically aimed at this forum)
We believe that the AI sector needs effective third-party testing for frontier AI systems. Developing a testing regime and associated policy interventions based on the insights of industry, government, and academia is the best way to avoid societal harm—whether deliberate or accidental—from AI systems.
Our deployment of large-scale, generative AI systems like Claude has shown us that work is needed to set up the policy environment to respond to the capabilities of today’s most powerful AI models, as well as those likely to be built in the future. In this post, we discuss what third-party testing looks like, why it’s needed, and describe some of the research we’ve done to arrive at this policy position. We also discuss how ideas around testing relate to other topics on AI policy, such as openly accessible models and issues of regulatory capture.
Policy overview
Today’s frontier AI systems demand a third-party oversight and testing regime to validate their safety. In particular, we need this oversight for understanding and analyzing model behavior relating to issues like election integrity, harmful discrimination, and the potential for national security misuse. We also expect more powerful systems in the future will demand deeper oversight - as discussed in our ‘Core views on AI safety’ post, we think there’s a chance that today’s approaches to AI development could yield systems of immense capability, and we expect that increasingly powerful systems will need more expansive testing procedures. A robust, third-party testing regime seems like a good way to complement sector-specific regulation as well as develop the muscle for policy approaches that are more general as well.
Developing a third-party testing regime for the AI systems of today seems to give us one of the best tools to manage the challenges of AI today, while also providing infrastructure we can use for the systems |
d2216fae-215c-4044-9159-f69189eed3c6 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | psychology and applications of reinforcement learning: where do I learn more?
Minicamp made me take the notion of an [Ugh Field](/lw/21b/ugh_fields/) seriously, and I've found Ugh Fields a fairly useful model for understanding how my brain works. I have/had lots of topics that have been unpleasant to think about and the cause of that unpleasantness seems to be strongly correlated with previous negative experiences.
More generally, animals, including humans, seem to use something like [Temporal Difference learning](http://www.scholarpedia.org/article/Temporal_difference_learning) very frequently ([one source of that impression](http://www.scholarpedia.org/article/Temporal_difference_learning)). If that's so, then understanding TD and related psychological research should give me a more accurate model of myself. I would expect it to help me understand when my dispositions and habits are likely to be useful (by knowing how they developed) and understand how to change my dispositions and habits. Thus I have a couple of questions:
1. Are my impressions accurate?
2. What books, papers, posts are the best for understanding these topics? I'd like material that addresses any of the following:
1. How TD or related algorithms work
2. What evidence says about whether human and/or animal brains frequently use TD or related algorithms and what situations brains use it for
3. Practical consequences of the research (e.g. Ugh Fields, doing X is a good way to build habit Y, smiling is a reinforcement, etc.) |
997a06d5-d34a-4bd2-b9da-5d160737c6ad | trentmkelly/LessWrong-43k | LessWrong | DeepMind is hiring for the Scalable Alignment and Alignment Teams
We are hiring for several roles in the Scalable Alignment and Alignment Teams at DeepMind, two of the subteams of DeepMind Technical AGI Safety trying to make artificial general intelligence go well. In brief,
* The Alignment Team investigates how to avoid failures of intent alignment, operationalized as a situation in which an AI system knowingly acts against the wishes of its designers. Alignment is hiring for Research Scientist and Research Engineer positions.
* The Scalable Alignment Team (SAT) works to make highly capable agents do what humans want, even when it is difficult for humans to know what that is. This means we want to remove subtle biases, factual errors, or deceptive behaviour even if they would normally go unnoticed by humans, whether due to reasoning failures or biases in humans or due to very capable behaviour by the agents. SAT is hiring for Research Scientist - Machine Learning, Research Scientist - Cognitive Science, Research Engineer, and Software Engineer positions.
We elaborate on the problem breakdown between Alignment and Scalable Alignment next, and discuss details of the various positions.
“Alignment” vs “Scalable Alignment”
Very roughly, the split between Alignment and Scalable Alignment reflects the following decomposition:
1. Generate approaches to AI alignment – Alignment Team
2. Make those approaches scale – Scalable Alignment Team
In practice, this means the Alignment Team has many small projects going on simultaneously, reflecting a portfolio-based approach, while the Scalable Alignment Team has fewer, more focused projects aimed at scaling the most promising approaches to the strongest models available.
Scalable Alignment’s current approach: make AI critique itself
Imagine a default approach to building AI agents that do what humans want:
1. Pretrain on a task like “predict text from the internet”, producing a highly capable model such as Chinchilla or Flamingo.
2. Fine-tune into an agent that does useful task |
1e4a4556-e9b9-40d4-9024-8bfce02c7f65 | StampyAI/alignment-research-dataset/blogs | Blogs | Misgeneralization as a misnomer
Here’s two different ways an AI can turn out [unfriendly](https://www.lesswrong.com/posts/BSee6LXg4adtrndwy/what-does-it-mean-for-an-agi-to-be-safe):
1. You somehow build an AI that cares about “making people happy”. In training, it tells people jokes and buys people flowers and offers people an ear when they need one. In deployment (and once it’s more capable), it forcibly puts each human in a separate individual heavily-defended cell, and pumps them full of opiates.
2. You build an AI that’s good at making people happy. In training, it tells people jokes and buys people flowers and offers people an ear when they need one. In deployment (and once it’s more capable), it turns out that whatever was causing that “happiness”-promoting behavior was a balance of a variety of other goals (such as basic desires for energy and memory), and it spends most of the universe on some combination of that other stuff that doesn’t involve much happiness.
(To state the obvious: please don’t try to get your AIs to pursue “happiness”; you want something more like [CEV](https://arbital.com/p/cev/) in the long run, and in the short run I strongly recommend [aiming lower, at a pivotal act](https://arbital.com/p/pivotal/) .)
In both cases, the AI behaves (during training) in a way that looks a lot like trying to make people happy. Then the AI described in (1) is unfriendly because it was optimizing the wrong concept of “happiness”, one that lined up with yours when the AI was weak, but that diverges in various [edge-cases](https://arbital.com/p/edge_instantiation/) that matter when the AI is strong. By contrast, the AI described in (2) was never even really trying to pursue happiness; it had a mixture of goals that merely correlated with the training objective, and that balanced out right around where you wanted them to balance out in training, but deployment (and the corresponding capabilities-increases) threw the balance off.
Note that this list of “ways things can go wrong when the AI looked like it was optimizing happiness during training” is not exhaustive! (For instance, consider an AI that cares about something else entirely, and knows you’ll shut it down if it doesn’t look like it’s optimizing for happiness. Or an AI whose goals change heavily as it reflects and self-modifies.)
(This list isn’t even really disjoint! You could get both at once, resulting in, e.g., an AI that spends most of the universe’s resources on acquiring memory and energy for unrelated tasks, and a small fraction of the universe on doped-up human-esque shells.)
The solutions to these two problems are pretty different. To resolve the problem sketched in (1), you have to figure out how to get an instance of the AI’s concept (“happiness”) to match the concept you hoped to transmit, even in the edge-cases and extremes that it will have access to in deployment (when it needs to be powerful enough to pull off some pivotal act that you yourself cannot pull off, and thus capable enough to access extreme edge-case states that you yourself cannot).
To resolve the problem sketched in (2), you have to figure out how to get the AI to care about one concept in particular, rather than a complicated mess that happens to balance precariously on your target (“happiness”) in training.
I note this distinction because it seems to me that various people around these parts are either unduly lumping these issues together, or are failing to notice one of them. For example, they seem to me to be mixed together in “[The Alignment Problem from a Deep Learning Perspective](https://arxiv.org/pdf/2209.00626.pdf) ” under the heading of “goal misgeneralization”.
(I think “misgeneralization” is a misleading term in both cases, but it’s an even worse fit for (2) than (1). A primate isn’t “misgeneralizing” its concept of “inclusive genetic fitness” when it gets smarter and invents condoms; it didn’t even *really have* that concept to misgeneralize, and what shreds of the concept it did have weren’t what the primate was mentally optimizing for.)
(In other words: it’s not that primates were optimizing for fitness in the environment, and then “misgeneralized” after they found themselves in a broader environment full of junk food and condoms. The “aligned” behavior “in training” broke in the broader context of “deployment”, but not because the primates found some weird way to extend an existing “inclusive genetic fitness” concept to a wider domain. Their optimization just wasn’t connected to an internal representation of “inclusive genetic fitness” in the first place.)
---
In mixing these issues together, I worry that it becomes much easier to erroneously dismiss the set. For instance, I have many times encountered people who think that the issue from (1) is a “skill issue”: surely, if the AI were only smarter, it would know what we mean by “make people happy”. (Doubly so if the first transformative AGIs are based on language models! Why, GPT-4 today could explain to you why pumping isolated humans full of opioids shouldn’t count as producing “happiness”.)
And: yep, an AI that’s capable enough to be transformative is pretty likely to be capable enough to figure out what the humans mean by “happiness”, and that doping literally everybody probably doesn’t count. But the issue is, [as](https://www.lesswrong.com/s/SXurf2mWFw8LX2mkG) [always](https://www.lesswrong.com/s/SXurf2mWFw8LX2mkG/p/CcBe9aCKDgT5FSoty), making the AI *care*. The trouble isn’t in making it have *some* understanding of what the humans mean by “happiness” somewhere inside it;[[1]](https://intelligence.org/feed/#fn1) the trouble is making the *stuff the AI pursues* be *that concept*.
Like, it’s possible in principle to reward the AI when it makes people happy, and to separately teach something to observe the world and figure out what humans mean by “happiness”, and to have the trained-in optimization-target concept end up wildly different (in the edge-cases) from the AI’s explicit understanding of what humans meant by “happiness”.
Yes, this is possible even though you used the word “happy” in both cases.
(And this is assuming away the issues described in (2), that the AI probably doesn’t by-default even end up with one clean alt-happy concept that it’s pursuing in place of “happiness”, as opposed to [a thousand shards of desire](https://www.lesswrong.com/posts/cSXZpvqpa9vbGGLtG/thou-art-godshatter) or whatever.)
And I do worry a bit that if we’re not clear about the distinction between all these issues, people will look at the whole cluster and say “eh, it’s a skill issue; surely as the AI gets better at understanding our human concepts, this will become less of a problem”, or whatever.
(As seems to me to be [already](https://twitter.com/MilitantHobo/status/1633040360275341312) happening as people correctly realize that LLMs will probably have a decent grasp on various human concepts.)
---
1. Or whatever you’re optimizing. Which, again, should not be “happiness”; I’m just using that as an example here.
Also, note that the thing you actually want an AI optimizing for in the long term—something like “CEV”—is legitimately harder to get the AI to have any representation of at all. There’s legitimately significantly less writing about object-level descriptions of a [eutopian](https://www.lesswrong.com/posts/K4aGvLnHvYgX9pZHS/the-fun-theory-sequence) universe, than of happy people, and this is related to the eutopia being significantly harder to visualize.
But, again, don’t shoot for the eutopia on your first try! End the acute risk period and then buy time for some reflection instead.[](https://intelligence.org/feed/#fnref1)
The post [Misgeneralization as a misnomer](https://intelligence.org/2023/04/10/misgeneralization-as-a-misnomer/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
3cfce926-1b81-4a3a-8f5f-1217e854d860 | trentmkelly/LessWrong-43k | LessWrong | [Linkpost] The neuroconnectionist research programme
This is a linkpost for https://www.nature.com/articles/s41583-023-00705-w (open access preprint: https://arxiv.org/abs/2209.03718)
> Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call ‘neuroconnectionism’. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.
Personally, I'd be excited to see more people thinking about the intersection of neuroconnectionism and alignment (currently, this seems very neglected).
Some examples of potential areas of investigation (alignment-relevant human capacities) which also seem very neglected could include: instruction following, moral reasoning, moral emotions (e.g. compassion, empathy). |
c02ce2e2-47b9-4682-9dcc-c7e6c97f04bc | StampyAI/alignment-research-dataset/arxiv | Arxiv | A proposal for ethically traceable artificial intelligence
1
A proposal for ethically traceable artificial
intelligence
Christopher A. Tucker, Ph.D., Cartheur Robotics, sp ol. s r.o., Prague, Czech Republic
Abstract
Although the problem of a critique of robotic behav ior in near-unanimous agreement to human norms seem s
intractable, a starting point of such an ambition i s a framework of the collection of knowledge a priori and
experience a posteriori categorized as a set of synthetical judgments avai lable to the intelligence, translated into
computer code. If such a proposal were successful, an algorithm with ethically traceable behavior and cogent
equivalence to human cognition is established. This paper will propose the application of Kant’s criti que of
reason to current programming constructs of an auto nomous intelligent system.
Introduction
As is oft cited in the literature, a near-universal application of moral imperative in the field of
artificial intelligence programming and observed ru ntime behavior is lacking, theoretical intuitions
scattered among five “tribes” [1], existing competi tively: Symbolists, evolutionists, Bayesians,
analogizers, and connectionists approaching a singu larity. A solution is not to satisfy any of them,
rather, set criteria that the intuitions themselves approach, as by the definition of the criteria eac h
follows from it instead of the reverse [2].
It can be argued that everything that has been thus far theorized as a means to a solution to the
“problem of artificial intelligence” is derived fro m knowledge a priori whose theorems are posited by
synthetical judgments. Why this is true can be stat ed simply: We, as humans, have never experienced
nor have been provided judgments of artificial bein gs save by the process of having developed them,
therefore it is impossible to describe them cogentl y without the specter of fallacious judgments we
would never be aware of except for the passage of t ime. It is, then, for this reason that a new
imperative needs to be discovered with which to gui de our reason and logic.
I propose the conundrum facing artificial intellige nce researchers can be assayed using transcendental
logic. In order to clarify the current state of the vexation why a logical imperative cannot be assign ed
universally because we, as humans, have no experien ce of how this concept is rooted and therefore its
subsequent definition in software is elusive. Admit ting this, we must look for philosophical advice
and take the viewpoint as an exercise of pure reason to find trajectories with which to create universa l
judgments.
The use of ideological imperative
In searching for a unified framework with which to capture the possible entirety of desirable
behaviors for an autonomous intelligent system a priori , a powerful model is presented by Immanuel
Kant’s Critique of Pure Reason wherein he notes the universal problem of pure rea son. “It is
extremely advantageous to be able to bring a number of investigations under the formula of a single
problem. For in this manner, we not only facilitate our own labor, inasmuch as we define it clearly to
ourselves, but also render it easier for others to decide whether we have done justice in our
2
undertaking. The proper problem of pure reason, the n, is contained in the question, ‘How are
synthetical judgments a priori possible?’” [p.12].
Approaching the answer requires the formation of an other question regarding the type of knowledge
that is desired, e.g., to comprehend the possibilit y of pure reason in the construction of this scienc e
which contains theoretical knowledge a priori : How is pure artificial intelligence possible?
Answering this requires an understanding between wh at we are given about the problem—what really
exists—and the natural disposition of the human min d. Does artificial intelligence exist, has it alway s
existed in an apodictic form, and how can the natur e of universal human reason find a suitable
answer? An answer is yet to be conceived, as the de finitions that have been presented in the literatur e
the past half century are inherently contradictory.
A pathway to finding the answer lies in the Transcendental Doctrine of Elements , where the particular
science, artificial intelligence, is divided under the name of a critique of pure reason. Therefore an
“Organon of pure reason would be a compendium of th ose principles according to which alone all
pure cognitions a priori can be obtained. The completely extended applicati on of such an organon
would afford us a system of pure reason. As this, h owever, is demanding a great deal, and it is yet
doubtful whether any extension of our knowledge be here possible, or if so, in what cases; we can
regard a science of the mere criticism of pure reas on, its sources and limits, as the propaedeutic to a
system of pure reason.” [p.15].
The transcendental aesthetic, the first criteria of the doctrine of elements denotes the following: “I n
whatsoever mode, or by whatsoever means, our knowle dge may relate to objects, it is at least quite
clear, that the only manner in which it immediately relates to them, is by means of an intuition. But an
intuition can take place only in so far as the obje ct is given to us. This, again, is only possible, t o man
at least, on condition that the object affect the m ind in a certain manner.” [p.21]. This relates to t he
inherent sensibility that an object presents to the mind in terms of its representation. As in the cas e of
artificial intelligence, this undetermined object f ollowing from an empirical intuition is a phenomeno n
as it corresponds more to sensation, and can be arr anged under certain relations, which constitute its
form.
The substance of the form then yields the array of conceptions that have been derived from
understanding, to establish the cognition of the ob ject and is properties both anticipated by designer s
and realized by engineers. “When we call into play a faculty of cognition, different conceptions
manifest themselves according to the different circ umstances, and make known this faculty, and
assemble themselves into a more or less extensive c ollection, according to the time or penetration tha t
has been applied to the consideration of them. Wher e this process, conducted as it is, mechanically, s o
to speak, will end, cannot be determined with certa inty. Besides, the conceptions which we discover
in the haphazard manner present themselves by no me ans in order and systematic unity, but are at last
coupled together only according to resemblances to each other, and arranged in a series, according to
the quantity of their content, from the simpler to the more complex—series which are anything but
systematic, through no altogether without a certain kind of method in their construction.” [p.56].
This therefore arrives at the construction of an un derstanding in synthetical judgments. In pure
intellectual form, they are presented as:
3
Table 1. Momenta of thought of synthetical judgment s.
Quantity of judgments Quality Relation Modality
Universal Affirmative Categorical Problematical
Particular Negative Hypothetical Assertorical
Singular Infinite Disjunctive Apodeictical
Table 1 contains a list of sets of synthetical judg ments divided into momenta categorizations. Kant
has noted each corresponds to a property of transce ndental logic that now will be disseminated into
proposed computer logic. Let us try to formulate a substantive example, which serves to illustrate how
this could be done. Assume that the artificial inte lligence in question is encapsulated in a single co de
base such that a developer can work on any part of its program. Let us also assume that this code base
consists of styles and patterns of an object-orient ed design. In such a form, it is expected the code
executes at runtime given conditions of some input, which flows through it forming a pattern or
algorithm. This pattern in its entirety is a catego rization of synthetical judgment. Because this patt ern
in known by analysis a posteriori , where it has been catalogued and compiled, the ex ecution pathway
can be discovered. The next step would be to tag th e code along the pathway where it indicates what
family, the headers of each column going from left to right of Table 1, and what genus, the columns
from top to bottom under each of the headers, it co rresponds to.
Once the code is tagged, the tags are gathered to b e monitored by an additional process, which is
aware of the state of synthetical judgment in the a rtificial intelligence at any given point in the
runtime. It is then theoretically possible to intro duce any set of controls on the program that are
desirable given behaviors ascribed to each judgment beforehand, tied explicitly to a definition from
human experience. In this way, the analogous human experience is no longer separable from logic and
therefore allows reason to become an active attribu te since the mystery of what is happening within
the program at an arbitrarily chosen time is well k nown.
This forms the basis of universal identity where th e perception of phenomenon manifest by the
artificial intelligence contains a synthesis of abs tract representations of behavior. As such, what Ka nt
calls the empirical consciousness , accompanies these different representations to fo rm identity within
the subject. Therefore, the problem this paper addr esses now takes the relation not as it did before—
where the program accompanies every representation with consciousness—but that each
representation is joined together sequentially wher e now the artificial intelligence is conscious of t he
synthesis of them. Kant states: “Consequently, only because I can connect a variety of given
representations in one consciousness, is it possibl e that I can represent to myself the identity of
consciousness in these representations; in other wo rds, the analytical unity of apperception is possib le
only under the presupposition of a synthetical unit y.” [p.82].
It is this unity that this paper proposes to add to programming constructs of an artificial intelligen ce in
order that it be ethically traceable and known not only what range of behaviors are programmed into
the machine, but those which have the possibility o f executing at any point in the runtime.
4
Discussion and Conclusions
In order to propose a solution for the vexation of the community to derive an algorithm whose
behaviors are subject to human norms, a new approac h to a solution of artificial intelligence is
required. Essentially, in the manner that a program ming language is imperative, e.g., using statements
that change the program’s state—as imperative mood in human languages express commands—thusly
an imperative ideology comprised of synthetical jud gments can be applied as an intellectual limit in
the program’s behavior. In this way, a framework wi th a defined scope is established.
It seems poignantly relevant to introduce this conc ept now as we are formulating the most basic of
laws and regulations for artificial intelligence an d autonomous intelligent systems design and
manufacture [3]. Rather than trying to sort out the loopholes, confusions, and contradictions, a more
efficient approach is to reframe the discussion in terms of fundamental pillars of Western logic and
philosophy. In order not to create more confusion o r present arguments of a better or worse approach
by one philosopher or the other, the work of Immanu el Kant in the Critique of Pure Reason is suitable
on the foundation that, the solitary question of de fining a universal framework whereby to judge
artificial intelligence lies in the establishment o f a criterion by which it is possible to securely
distinguish a pure from an empirical cognition.
The relevance of Kant’s philosophy to outline subst antive artificial intelligence by a critique of pur e
reason leaves room for interpretation of implementa tion but not of the essential framework itself. Thi s
is because judgments about what the program should do in terms of a system of regulatory axioms and
readily traceable to the set of momenta and their e mpirical manifestation, rather than a random series
of trial and error association scenarios. In this w ay, a list of cogent emotional responses to human
interaction is possible given the categorical judgm ents reactive to a given situation or outcome. It w ill
be for the future to decide whether our intuition c an be structured in such a way as to present
arguments of objects existing in similitude with ou r human cognition. Something must be proposed in
full [4] to address the problems, as we are mired i n contradictions about what constitutes the essence
of artificial intelligence.
References
[1] Domingo, P. The Master Algorithm: How the Quest for the Ultimat e Learning Machine Will Remake Our
World , London: Basic Books, 2015.
[2] Kant, I. Critique of Pure Reason , Translation by J.M.D. Meiklejohn, London: George Bell and Sons, 1897.
[3] IEEE Standards. “Ethically aligned design, a vision for prioritizing human wellbeing with artificial
intelligence and autonomous systems,” http://standards.ieee.org/news/2016/ethically_align ed_design.html .
[4] Tucker, C.A. “The method of artificial systems,” ar Xiv:1507.01384, June 2017. |
914cd3e2-1f41-4e9d-88a2-9b2c2ea5abff | trentmkelly/LessWrong-43k | LessWrong | Geoff Hinton Quits Google
The NYTimes reports that Geoff Hinton has quit his role at Google:
> On Monday, however, he officially joined a growing chorus of critics who say those companies are racing toward danger with their aggressive campaign to create products based on generative artificial intelligence, the technology that powers popular chatbots like ChatGPT.
>
> Dr. Hinton said he has quit his job at Google, where he has worked for more than a decade and became one of the most respected voices in the field, so he can freely speak out about the risks of A.I. A part of him, he said, now regrets his life’s work.
>
> “I console myself with the normal excuse: If I hadn’t done it, somebody else would have,” Dr. Hinton said during a lengthy interview last week in the dining room of his home in Toronto, a short walk from where he and his students made their breakthrough.
https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html
Some clarification from Hinton followed:
It was already apparent that Hinton considered AI potentially dangerous, but this seems significant. |
6e8f337d-6978-43f4-9702-aad69789ca9b | trentmkelly/LessWrong-43k | LessWrong | Out to Get You
Epistemic Status: Reference.
Expanded From: Against Facebook, as the post originally intended.
Some things are fundamentally Out to Get You.
They seek resources at your expense. Fees are hidden. Extra options are foisted upon you. Things are made intentionally worse, forcing you to pay to make it less worse. Least bad deals require careful search. Experiences are not as advertised. What you want is buried underneath stuff you don’t want. Everything is data to sell you something, rather than an opportunity to help you.
When you deal with Out to Get You, you know it in your gut. Your brain cannot relax. You lookout for tricks and traps. Everything is a scheme.
They want you not to notice. To blind you from the truth. You can feel it when you go to work. When you go to church. When you pay your taxes. It is bad government and bad capitalism. It is many bad relationships, groups and cultures.
When you listen to a political speech, you feel it. Dealing with your wireless or cable company, you feel it. At the car dealership, you feel it. When you deal with that one would-be friend, you feel it. Thinking back on that one ex, you feel it. It’s a trap.
Get Gone, Get Got, Get Compact or Get Ready
There are four responses to Out to Get You.
You can Get Gone. Walk away. Breathe a sigh of relief.
You can Get Got. Give the thing everything it wants. Pay up, relax, enjoy the show.
You can Get Compact. Find a rule limiting what ‘everything it wants’ means in context. Then Get Got, relax and enjoy the show.
You can Get Ready. Do battle. Get what you want.
When to Get Got
Get Got when the deal is Worth It.
This is a difficult lesson for everyone in at least one direction.
I am among those with a natural hatred of Getting Got. I needed to learn to relax and enjoy the show when the deal is Worth It. Getting Got imposes a large emotional cost for people like me. I have worked to put this aside when it’s time to Get Got, while preserving my instincts as a defense. That’s |
3f4eef5b-eea9-4789-a2f7-faaf336796ad | trentmkelly/LessWrong-43k | LessWrong | Fifteen Things I Learned From Watching a Game of Secret Hitler
Epistemic Status: Not likely to be true things. Right?
1. Liberals know nothing, fascists know everything.
2. Most of the policies democratic governments could pass are fascist policies that expand government power.
3. The remaining policies are liberal policies. There is no such thing as a conservative policy.
4. Liberal policies do nothing.
5. If the liberals do nothing enough times, they win and can congratulate themselves, no matter how much more fascist things got in the meantime.
6. Governments must always be passing new policies, and never take away old policies. Thus, government inevitably gets more powerful over time.
7. The more liberal policies you pass, the more likely it is any future policy will be fascist.
8. The more fascist policies you pass, the more likely it is any future policy will be fascist.
9. When the time comes to pass a policy, the government will choose from whatever proposals are lying around, even if all of them are fascist and everyone choosing is a liberal. There is almost never an option to just not do that, as such bold action requires a mostly fascist policy already be in place.
10. If the government fails to agree to pass one of the things lying around, that’s even worse, because it will then choose a new policy completely at random from what is lying around, which will probably be fascist.
11. Liberals spend most of their time being paranoid over which people claiming to be liberals are secretly fascists, or even secretly actual literal Hitler, as opposed to attempting to write or choose good policies.
12. Someone enacting liberal policies, but not in a position to assume dictatorial power, is providing strong evidence they are probably secretly Hitler.
13. When good people often have no choice but to do bad things, but there is no way to verify this, the default is for no one who is good to have any idea who is good and who is bad.
14. Despite this, good people think they know who is good and who is bad. |
abcd5027-f680-4501-9862-2b4f192de182 | trentmkelly/LessWrong-43k | LessWrong | Efficient Charity: Cheap Utilons via bone marrow registration
This topic is not really related to the things normally discussed here, but I think it's really important, and it might interest Less Wrongers, especially since many of us are interested in ethics and utility calculations that are essentially cost-benefit analyses. Bone marrow donation in the United States is managed by the National Marrow Donor Program. Because typing donors for matching purposes can be costly, they often require people signing up to donate to pay a registration fee, which probably prevents a lot of people from signing up. These costs are being covered until the end of the month by a corporate sponsor, which means that right now, all you need to do if you live in the US is go to http://marrow.org/Join/Join_Now/Join_Now.aspx and fill out a simple questionnaire. You will be sent a kit to collect a cheek swab, and then you will be entered into the donor database. Doing this does not require you to donate if a match comes up.
The reason I think this might interest Less Wrongers is that this is a really cheap way to improve the world. According to their website, about 1 in 500 potential donors are actually asked to donate, so registering doesn't actually make it all that likely that you will be asked to do anything more. If you ARE a match for someone who needs a donation, the cost to you is at most the temporary pain of marrow extraction (many donors are asked only for blood cells), whereas the other person’s chance to live is much improved. This looks like a huge net positive.
Unfortunately I only found out about this a few days ago, and it only occurred to me today that this might be a forum of people who would respond to the argument "you can make the world better at little cost to yourself." However, I ask that you go to the website and spend a few minutes signing up. This is like buying a 1 in 500 lottery ticket that SAVES SOMEONE’S LIFE. If the Singularity hits and an FAI can generate perfectly matched marrow for anyone who needs it from totipo |
299dffdb-ee0b-498a-810b-db9b0fdfbe90 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Gen Con: Applied Game Theory
Discussion article for the meetup : Gen Con: Applied Game Theory
WHEN: 06 August 2016 02:00:00PM (-0400)
WHERE: 100 S Capitol Ave, Indianapolis, IN 46225, USA
Time tentative, location to be worked out. If you are attending Gen Con and want to meet up with rationalists, please comment if you have a preferred time and location. Posting this now to get it on the calendar and solicit responses. Default location is in the card game area, specific location to be found at the time (and then posted here). UPDATE: setting up at the blue Fantasy Flight tables, by the X-Wing Miniatures banner, in front of the HQ table, just before the banner showing the switch to Asmodee.
Meet up with Less Wrong friends and play games! Learn the newest releases, play classics, and otherwise have fun. A purely social event, running for as long as people want to stay and play, with potential continued discussion over dinner.
As at all events, there is no minimum degree, IQ, reading record, height, age, or neurotypicality to participate. Bring games if you like, learn new games if you like, and expect a range from "I am here for the championships" to "what are we Settling?"
We seemed like the kind of nerds who might go to Gen Con. You can also use the comments here to find others attending, arrange other connections at the event, discuss the con, etc.
Discussion article for the meetup : Gen Con: Applied Game Theory |
90883fe9-c538-4a3b-96f1-2e20dd8362c3 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Superintelligence 14: Motivation selection methods
*This is part of a weekly reading group on [Nick Bostrom](http://www.nickbostrom.com/)'s book, [Superintelligence](http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111). For more information about the group, and an index of posts so far see the [announcement post](/lw/kw4/superintelligence_reading_group/). For the schedule of future topics, see [MIRI's reading guide](https://intelligence.org/wp-content/uploads/2014/08/Superintelligence-Readers-Guide-early-version.pdf).*
---
Welcome. This week we discuss the fourteenth section in the [reading guide](https://intelligence.org/wp-content/uploads/2014/08/Superintelligence-Readers-Guide-early-version.pdf): ***Motivation selection methods***. This corresponds to the second part of Chapter Nine.
This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.
There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).
**Reading**: “Motivation selection methods” and “Synopsis” from Chapter 9.
---
Summary
=======
1. **One way to control an AI is to design its motives**. That is, to choose what it wants to do (p138)
2. Some varieties of 'motivation selection' for AI safety:
1. ***Direct specification***: figure out what we value, and code it into the AI (p139-40)
1. Isaac Asimov's 'three laws of robotics' are a famous example
2. Direct specification might be fairly hard: both figuring out what we want and coding it precisely seem hard
3. This could be based on rules, or something like consequentialism
2. ***Domesticity***: the AI's goals limit the range of things it wants to interfere with (140-1)
1. This might make direct specification easier, as the world the AI interacts with (and thus which has to be thought of in specifying its behavior) is simpler.
2. Oracles are an example
3. This might be combined well with physical containment: the AI could be trapped, and also not want to escape.
3. *Indirect normativity*: instead of specifying what we value, specify a way to specify what we value (141-2)
1. e.g. extrapolate our volition
2. This means outsourcing the hard intellectual work to the AI
3. This will mostly be discussed in chapter 13 (weeks 23-5 here)
4. ***Augmentation***: begin with a creature with desirable motives, then make it smarter, instead of designing good motives from scratch. (p142)
1. e.g. brain emulations are likely to have human desires (at least at the start)
2. Whether we use this method depends on the kind of AI that is developed, so usually we won't have a choice about whether to use it (except inasmuch as we have a choice about e.g. whether to develop uploads or synthetic AI first).
3. Bostrom provides a summary of the chapter:
4. The question is not which control method is best, but rather which set of control methods are best given the situation. (143-4)
Another view
============
[Icelizarrd](http://www.reddit.com/r/science/comments/2hbp21/science_ama_series_im_nick_bostrom_director_of/ckrbdnx):
>
> Would you say there's any ethical issue involved with imposing limits or constraints on a superintelligence's drives/motivations? By analogy, I think most of us have the moral intuition that technologically interfering with an unborn human's inherent desires and motivations would be questionable or wrong, supposing that were even possible. That is, say we could genetically modify a subset of humanity to be cheerful slaves; that seems like a pretty morally unsavory prospect. What makes engineering a superintelligence specifically to serve humanity less unsavory?
>
>
>
Notes
=====
1. Bostrom tells us that it is very hard to specify human values. We have seen examples of galaxies full of paperclips or fake smiles resulting from poor specification. But these - and Isaac Asimov's stories - seem to tell us only that a few people spending a small fraction of their time thinking does not produce any watertight specification. What if a thousand researchers spent a decade on it? Are the millionth most obvious attempts at specification nearly as bad as the most obvious twenty? How hard is it? A general argument for pessimism is the thesis that ['value is fragile'](/lw/y3/value_is_fragile/), i.e. that if you specify what you want very nearly but get it a tiny bit wrong, it's likely to be almost worthless. [Much like](http://wiki.lesswrong.com/wiki/Complexity_of_value) if you get one digit wrong in a phone number. The degree to which this is so (with respect to value, not phone numbers) is controversial. I encourage you to try to specify a world you would be happy with (to see how hard it is, or produce something of value if it isn't that hard).
2. If you'd like a taste of indirect normativity before the chapter on it, the [LessWrong wiki page](http://wiki.lesswrong.com/wiki/Coherent_Extrapolated_Volition) on coherent extrapolated volition links to a bunch of sources.
3. The idea of 'indirect normativity' (i.e. outsourcing the problem of specifying what an AI should do, by giving it some good instructions for figuring out what you value) brings up the general question of just what an AI needs to be given to be able to figure out how to carry out our will. An obvious contender is a lot of information about human values. Though some people disagree with this - these people don't buy the [orthogonality thesis](/lw/l4g/superintelligence_9_the_orthogonality_of/). Other issues sometimes suggested to need working out ahead of outsourcing everything to AIs include decision theory, priors, anthropics, feelings about [pascal's mugging](http://www.nickbostrom.com/papers/pascal.pdf), and attitudes to infinity. [MIRI](http://intelligence.org/)'s technical work often fits into this category.
4. Danaher's [last post](http://philosophicaldisquisitions.blogspot.com/2014/08/bostrom-on-superintelligence-6.html)on Superintelligence (so far) is on motivation selection. It mostly summarizes and clarifies the chapter, so is mostly good if you'd like to think about the question some more with a slightly different framing. He also previously considered the difficulty of specifying human values in *The golem genie and unfriendly AI*(parts [one](http://philosophicaldisquisitions.blogspot.com/2013/01/the-golem-genie-and-unfriendly-ai-part.html) and [two](http://philosophicaldisquisitions.blogspot.com/2013/02/the-golem-genie-and-unfriendly-ai-part.html)), which is about [Intelligence Explosion and Machine Ethics](http://intelligence.org/files/IE-ME.pdf).
5. Brian Clegg [thinks](http://www.goodreads.com/review/show/982829583) Bostrom should have discussed [Asimov's stories](http://en.wikipedia.org/wiki/Three_Laws_of_Robotics) at greater length:
>
> I think it’s a shame that Bostrom doesn’t make more use of science fiction to give examples of how people have already thought about these issues – he gives only half a page to Asimov and the three laws of robotics (and how Asimov then spends most of his time showing how they’d go wrong), but that’s about it. Yet there has been a lot of thought and dare I say it, a lot more readability than you typically get in a textbook, put into the issues in science fiction than is being allowed for, and it would have been worthy of a chapter in its own right.
>
>
>
>
If you haven't already, you might consider (sort-of) following his advice, and reading [some science fiction](http://www.amazon.com/Complete-Robot-Isaac-Asimov/dp/0586057242/ref=sr_1_2?s=books&ie=UTF8&qid=1418695204&sr=1-2&keywords=asimov+i+robot).
In-depth investigations
If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's [list](http://lukemuehlhauser.com/some-studies-which-could-improve-our-strategic-picture-of-superintelligence/), which contains many suggestions related to parts of *Superintelligence.*These projects could be attempted at various levels of depth.
1. Can you think of novel methods of specifying the values of one or many humans?
2. What are the most promising methods for 'domesticating' an AI? (i.e. constraining it to only care about a small part of the world, and not want to interfere with the larger world to optimize that smaller part).
3. Think more carefully about the likely motivations of drastically augmenting brain emulations
If you are interested in anything like this, you might want to mention it in the comments, and see whether other people have useful thoughts.
How to proceed
==============
This has been a collection of notes on the chapter. **The most important part of the reading group though is discussion**, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!
Next week, we will start to talk about a variety of more and less agent-like AIs: 'oracles', genies' and 'sovereigns'. To prepare, **read** Chapter “Oracles” and “Genies and Sovereigns” from Chapter 10*.*The discussion will go live at 6pm Pacific time next Monday 22nd December. Sign up to be notified [here](http://intelligence.us5.list-manage.com/subscribe?u=353906382677fa789a483ba9e&id=28cb982f40). |
ae185b88-54bd-4718-9c42-5780c5801a45 | trentmkelly/LessWrong-43k | LessWrong | Chapter 121: Something to Protect: Severus Snape
A somber mood pervaded the Headmistress's office. Minerva had returned after dropping off Draco and Narcissa/Nancy at St. Mungo's, where the Lady Malfoy was being examined to see if a decade living as a Muggle had done any damage to her health; and Harry had come up to the Headmistress's office again and then... not been able to think of priorities. There was so much to do, so many things, that even Headmistress McGonagall didn't seem to know where to start, and certainly not Harry. Right now Minerva was repeatedly writing words on parchment and then erasing them with a handwave, and Harry had closed his eyes for clarity. Was there any next first thing that needed to happen...
There came a knock upon the great oaken door that had been Dumbledore's, and the Headmistress opened it with a word.
The man who entered the Headmistress's office appeared worn, he had discarded his wheelchair but still walked with a limp. He wore black robes that were simple, yet clean and unstained. Over his left shoulder was slung a knapsack, of sturdy gray leather set with silver filigree that held four green pearl-like stones. It looked like a thoroughly enchanted knapsack, one that could contain the contents of a Muggle house.
One look at him, and Harry knew.
Headmistress McGonagall sat frozen behind her new desk.
Severus Snape inclined his head to her.
"What is the meaning of this?" said the Headmistress, sounding... heart-sick, like she'd known, upon a glance, just like Harry had.
"I resign my position as the Potions Master of Hogwarts," the man said simply. "I will not stay to draw my last month's salary. If there are students who have been particularly harmed by me, you may use the money for their benefit."
He knows. The thought came to Harry, and he couldn't have said in words just what the Potions Master now knew; except that it was clear that Severus knew it.
"Severus..." Headmistress McGonagall began. Her voice sounded hollow. "Professor Severus Snape, you may not realiz |
57e314d6-98cb-4f95-a7d2-8d6880a37fb1 | trentmkelly/LessWrong-43k | LessWrong | If Van der Waals was a neural network
If Van der Waals was a neural network
At some point in history a lot of thought was put into obtaining the equation:
R*T = P*V/n
The ideal gas equation we learn in kindergarten, which uses the magic number R
in order to make predictions about how n moles of an “ideal gas” will change in pressure, volume or temperature given that we can control two of those factors.
This law approximates the behavior of many gases with a small error and it was certainly useful for many o' medieval soap volcano party tricks and Victorian steam engine designs.
But, as is often the case in science, a bloke called Van der Waals decided to ruin everyone’s fun in the later 19th century by focusing on a bunch of edge cases where the ideal gas equation was utter rubbish when applied to any real gas.
He then proceeded to collect a bunch of data points for the behavior of various gases and came up with two other magic numbers to add to the equation, a
and b, individually determined for each of the gases, which can be used in the equation:
RT = (P + a*n^2/V^2)(V – n*b)/n
Once again the world could rest securely having been given a new equation, which approximates reality better.
But obviously this equation is not enough; indeed it was not enough in the time of Van der Waals, when people working on “real” problems had their own more specific equations.
But the point, humans gather data and construct equations for the behavior of gases exist, the less error prone they have to be, the more niche and hard to construct (read: require more data and a more sophisticated mathematical apparatus) they become.
But let’s assume that Van der Waals (or any other equation maker) does all the data collection which he hopes will have a good coverage of standard behavior and edge cases… and feeds it into a neural network (or any other entity which is a good universal function estimator), and gets a function W.
This function W is as good, if not better than the original human-brain-made equation at pre |
99a43860-7f78-40d5-b05d-41fd641a91db | trentmkelly/LessWrong-43k | LessWrong | Progress links and short notes, 2024-12-16
Much of this content originated on social media. To follow news and announcements in a more timely fashion, follow me on Twitter, Threads, Bluesky, or Farcaster.
Contents
* Jobs & fellowships
* Looking for writers?
* People doing interesting things
* Events
* DARPA wants input
* Other announcements
* Progress on the curriculum
* The growth of the progress movement
* AI will allow the average person to navigate The System
* I have questions
* Other people have questions
* Links
* 100 years ago
* Humboldt on progress
* Progress news with cool pics
* Anti-elite elites
* Politics links and short notes
* BBC doesn’t know what “nominal” means
* Charts
* Fun
Jobs & fellowships
* We’re hiring an Event Manager to run Progress Conference 2025 and other events. Best to get your application in before the holidays!
* “The Kothari Fellowship provides grant and mentorship to young Indians (<25 years) who want to build, empowering them to turn ideas into reality, instead of being held back by societal norms.” Provides up to ₹1 lakh per month for 12 months (~$15k per year)
* ARIA Research will “start the search for our first Frontier Specialists” to work alongside program directors. “It’s a two-year role that will give you a chance to step off the standard career track and go after outsized impact” (@ARIA_research). Apply here
Looking for writers?
* “Are you running a progress-y or abundance-oriented newsletter, blog, magazine or other publication? Would you like to receive pitches from the talented RPI fellows? Reply here so I can send our writers your way” (@elmcaleavy)
People doing interesting things
* Rosie Campbell (RPI fellow) has left OpenAI and is thinking about her next steps. She’s interested in talking to people about various topics related to AI, risk, safety, policy, epistemics, and more
* @danielgolliher: “I want to take my ‘Foundations of America’ students on an optional day trip to Washington D.C., and do one or both of: Watch a Cong |
0574664e-f572-4d42-b299-7b2a0a1e0a9c | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Modal Bargaining Agents
*Summary: Bargaining problems are interesting in the case of Löbian cooperation; Eliezer suggested a geometric algorithm for resolving bargaining conflicts by leaving the Pareto frontier, and this algorithm can be made into a modal agent, given an additional suggestion by Benja.*
Bargaining and Fairness
=======================
When two agents can read each others' source code before playing a game, [Löbian cooperation](https://intelligence.org/files/ProgramEquilibrium.pdf) can transform many games into pure [bargaining problems](http://en.wikipedia.org/wiki/Bargaining_problem). Explicitly, the algorithm of "if I can prove you choose X, then I choose Y, otherwise I choose Z" makes it possible for an agent to offer a deal better for both parties than a Nash equilibrium, then default to that Nash equilibrium if the deal isn't verifiably taken. In the simple case where there is a unique Nash equilibrium, two agents with that sort of algorithm can effectively reduce the decision theory problem to a negotiation over which point on the Pareto frontier they will select.
However, if two agents insist on different deals (e.g. X insists on point D, Y insists on point A), then it falls through and we're back to the Nash equilibrium O. And if you try and patch this by offering several deals in sequence along the Pareto frontier, then a savvy opponent is just going to grab whichever of those is best for them. So we'd like both a notion of the fair outcome, and a way to still make deals with agents who disagree with us on fairness (without falling back to the Nash equilibrium, and without incentivizing them to play hardball with us).
Note that utility functions are only defined up to affine transformations, so any solution should be invariant under independent rescalings of the players' utilities. This requirement, plus a few others (winding up on the Pareto frontier, independence of irrelevant alternatives, and symmetry) are all satisfied by the Nash solution to the bargaining problem: choose the point on the Pareto frontier so that the area of the rectangle from O to that point is maximized.
This gives us a pretty plausible answer to the first question, but leaves us with the second: are we simply at war with agents that have other ideas of what's fair? (Let's say that X thinks that the Nash solution N is fair, but Y thinks that B is fair.) Other theorists have come up with other definitions of the fair solution to a bargaining problem, so this is a live question!
And the question of incentives makes it even more difficult: if you try something like "50% my fair equilibrium, 50% their fair equilibrium", you create an incentive for other agents to bias their definition of fairness in their own favor, since that boosts their payoff.
Bargaining Away from the Pareto Frontier
========================================
Eliezer's suggestion in this case is as follows: an agent defines its set of acceptable deals as "all points in the feasible set for which my opponent's score is at most what they would get at the point I think is fair". If each agent's definition of fairness is biased in their own favor, the intersection of the agents' acceptable deals has a corner within the feasible set (but not on the Pareto frontier unless the agents agree on the fair point), and that is the point where they should actually achieve Löbian cooperation.
Note that in this setup, you get no extra utility for redefining fairness in a self-serving way. Each agent winds up getting the utility they would have had at the fairness point of the *other* agent. (Actually, Eliezer suggests a very slight slope to these lines, in the direction that makes it *worse* for the opponent to insist on a more extreme fairness point. This sets up good incentives for meeting in the middle. But for simplicity, we'll just consider the incentive-neutral version here.)
Moreover, this extends to games involving more than two agents: each one defines a set of acceptable deals by the condition that no other agent gets more than they would have at the agent's fairness point, and the intersection has a corner in the feasible set, where each agent gets the minimum of the payoffs it would have achieved at the other agents' fairness points.
Modal Bargaining Agents
=======================
Now, how do we set this bargaining algorithm up as a [modal decision theory](http://agentfoundations.org/item?id=160)? We can only consider finitely many options, though we're allowed to consider computably mixed strategies. Let's assume that our game has finitely many pure strategies for each player. As above, we'll assume there is a unique Nash equilibrium, and set it at the origin.
Then there's a natural set of points we should consider: the grid points within the feasible set (the convex hull formed by the Nash equilibrium and the Pareto optimal points above it) whose coordinates correspond to utilities of the pure strategies on the Pareto frontier. This is easier to see than to read:
Now all we need is to be sure that Löbian cooperation happens at the point we expect. There's one significant problem here: we need to worry about syncing the proof levels that different agents are using.
(If you haven't seen this before, you might want to work out what happens if any two of the following three agents are paired with one another:
* X returns A if PA proves its opponent returns A, else X returns D.
* Y returns B if PA proves its opponent returns B, else Y returns A if PA proves its opponent returns A, else Y returns D.
* Z returns C if PA proves its opponent returns C, else Z returns A if PA + Con(PA) proves its opponent returns A, else Z returns D.
Results in [rot13](http://www.rot13.com/): Nal gjb qvssrerag ntragf nobir erghea Q ntnvafg rnpu bgure, orpnhfr CN pna'g cebir vgf bja pbafvfgrapl, naq gurersber gur snpg gung n cebbs frnepu va CN snvyf pna arire or cebirq va CN.)
Benja suggested one way to ensure that Löbian cooperation happens at the right point: we assume that in addition to the payoff matrix, the agents are mutually aware of an ordering relation on the grid points. In order to land on the best mutually acceptable point (in the Pareto sense), it's merely necessary for the ordering to respect the "level" of grid points, defined as the total number of grid lines traversed along either axis from O to the grid point.
Then, we simply have each player look for cooperation at proof level N at the Nth point, skipping those points that are unacceptable to it. Since the best mutually acceptable point is the only acceptable point at its level (or any prior level), it will be chosen.
Again, this works for modal decision problems with more than two players.
Open questions and nagging issues:
==================================
0. Is there a better way to resolve bargaining without incentivizing other agents to take extreme positions?
1. Is there some reasonable way to make this work without providing the canonical ordering of grid points?
2. If agents are each biased in their opponent's direction, this algorithm still gets a result, but in this case there is more than one grid point on the highest level of the mutually acceptable region, and thus the canonical ordering actually chooses the outcome!
3. If an agent's "fairness point" on the Pareto frontier is itself a mixed strategy profile rather than a pure one, and the other agent doesn't know which point that is, can this still work? In particular, if there is an ordering on the entire feasible set, and if two agents each add extra grid lines to the set based on their own fairness points (without knowing the others), is there an algorithm for selecting proof levels which guarantees that they will meet at the best mutually acceptable point that appears in both of their grids? |
ad51fc02-2cca-4df4-a79f-e912ffca25ab | trentmkelly/LessWrong-43k | LessWrong | Visualizing the power of multiple step selection processes in JS: Galton's bean machine
|
47262ad2-dae0-495b-8452-171e65aacee8 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Tel Aviv: Board Game Night
Discussion article for the meetup : Tel Aviv: Board Game Night
WHEN: 17 August 2015 07:00:00PM (+0300)
WHERE: Google Tel-Aviv, Electra Tower, 67891, Israel, 67891 Tel Aviv, Israel
19:00 Israel time, playing board games as usual. This time we will do it in Campus TLV (Floor 34, hackspace - on the right when entering the campus). Call me (Vadim, 0542600919) if you can't find your way.
Discussion article for the meetup : Tel Aviv: Board Game Night |
2d07deb5-8ab9-46cd-82a6-2c3c1eb8a970 | trentmkelly/LessWrong-43k | LessWrong | Uncontrollable Super-Powerful Explosives
In the late 19th century, two researchers meet to discuss their differing views on the existential risk posed by future Uncontrollable Super-Powerful Explosives.
* Catastrophist: I predict that one day, not too far in the future, we will find a way to unlock a qualitatively new kind of explosive power. This explosive will represent a fundamental break with what has come before. It will be so much more powerful than any other explosive that whoever gets to this technology first might be in a position to gain a DSA over any opposition. Also, the governance and military strategies that we were using to prevent wars or win them will be fundamentally unable to control this new technology, so we'll have to reinvent everything on the fly or die in an extinction-level war. There's no way we'd be competent enough to handle something of that power without killing ourselves almost immediately.
* Gradualist: I’m also concerned about the prospect of explosives one day becoming far more destructive than they are now, with possibly catastrophic consequences if we aren't prepared. I'm not so sure that we’d instantly go extinct if you were right, though I agree that if anything like what you're describing is real, we’re in a great deal of danger. But we'll leave questions of Governance for another time. In the meantime, I want to push back against the idea that this will all happen so suddenly. What does that word 'DSA' mean?
* C: Decisive strategic advantage. Anyone who has that technology would be able to render any military opposition irrelevant. Probably, they'd be able to wipe out entire cities with one bomb and force their opponents to surrender almost immediately.
* G: That seems like a weirdly specific prediction to make. Why assume something so unlikely? Have you got any evidence such a thing is even possible?
* C: I have my reasons, but first let me deal with what you just said, because I can't let that slip. Zero-to-one discontinuities are actually pretty common in |
f93fbe49-6fc1-4b2f-bcf3-2c8e96cc50dd | trentmkelly/LessWrong-43k | LessWrong | Optimal eating (or rather, a step in the right direction)
Over the past few months I've been working to optimize my life. In this post I describe my attempt to optimize my day-to-day cooking and eating - my goal with this post is to get input and to offer a potential template for people who aren't happy with their current cooking/eating patterns. I'm a) still pretty new to LW, and b) not a nutritionist; I am not claiming that this is optimal, only that it is a step in the right direction for me. I'd love suggestions/advice/feedback.
Goal:
How do I quantify a successful cooking/eating plan?
Healthy
"Healthy" is a broad term. I'm not interested in making food a complicated or stressful component of my life - quite the opposite. Healthy means that I feel good, and that I'm providing my body with a good mix of building blocks (carbs, proteins, fats) and nutrients. This means I want most/all meals to include some form of complex carbs, protein, and either fruits or veggies or both. As I'm currently implementing an exercise plan based on the LW advice for optimal exercising, I'm aiming to get ~120 grams of protein per day (.64g/lb bodyweight/day). There seems to be a general consensus that absorption of nutrients from whole foods is a) higher, and b) less dangerous, so when possible I'm trying to make foods from basic components instead of buying pre-processed stuff.
I have a health condition called hypoglycemia (low blood sugar) that makes me cranky/shaky/weak/impatient/foolish/tired when I am hungry, and can be triggered by eating simple sugars. So, for me personally, a healthy diet includes rarely feeling hungry and rarely eating simple sugars (especially on their own - if eaten with other food the effect is much less severe). This also means trying to focus on forms of fruit and complex carbs that have low glycemic indexes (yams are better than baked potatoes, for example). I would guess that these attributes would be valuable for anyone, but for me they are a very high priority.
I'm taking some advice from |
6509b789-3e30-49df-842e-000555d553bd | trentmkelly/LessWrong-43k | LessWrong | Covid 11/24/22: Thanks for Good Health
This Thanksgiving, I am thankful that I am once again back at full health. Covid comes and Covid goes. It was like I had a bad cold… for three days. That actually about sums it up, with some residual coughing. After a week, I was able to do a full session on the elliptical machine, so I consider everything back to normal.
I am not yet ready to wind down the Covid posts, but I am getting close to that point. If we get through the next two months without anything major happening, it will probably be time. For now, things will continue as normal.
Executive Summary
1. I’m feeling much better now.
2. China looks to be headed for more lockdowns, here we go again.
3. Person leaving WHO apologizes for denying Covid is airborne.
Let’s run the numbers.
The Numbers
Predictions
Predictions from Last Week: 250k cases (+4%) and 2,400 deaths (+12%).
Results: 253k cases (+3%) and 2,182 deaths (+2%).
Predictions for Next Week (Thanksgiving): 223k cases (-12%) and 1,850 deaths (-15%).
Standard holiday drop-off.
Deaths
Cases
Booster Boosting
Our booster strategy is a disaster. Monovalent Omicron boosters would be superior. A 4-to-6 month interval would have been superior. Our supplies are split between initial doses that can only be used initially and that are fully obsolete, and booster doses that are something like 60%-75% obsolete (although both are still far better than nothing, to be clear.) Many don’t know the updated boosters even exist.
Survey says 1/5 got the bivalent booster, while another 3/5 ‘definitely’ or ‘probably’ will get it in the future. Which tells me that people are lying in the survey. We are not going to get that kind of uptake, and most people who have not gotten the booster yet are not going to go do it later.
Here is what passes for public health messaging: ‘Because I said so.’
They are ‘better.’ So get them. How much better? You don’t need to know. Not your department. Cost-benefit analysis is not a thing. We are the jump department an |
4ae775f2-8633-454c-9f46-dc9b584f60de | trentmkelly/LessWrong-43k | LessWrong | Willpower duality
Rationality is designed to make you win, to help you attain your objectives. One of the most prominent phenomena getting in the way is akrasia, the lack of willpower preventing us to perform whatever action we want to do.
So, wait, do we want to act or not? There are two facets of willpower, which I'll describe using Kahneman's System 1 and System 2, the two working modes of the mind (bear in mind it's a simplification to get the point across).
What we call System 1 willpower is what gets you up in the morning, out of a good shower, what makes you start things, the grit, the sudden confidence, the "just do it" part. It's the short-term impulse of "I can go one step further and my body shall obey. It's the thing many productivity methods want to trigger. For example, the 2-minute rule ("if it takes less than 2 minutes to perform, do it now") is based on the intuition that you won't need a lot of effort to get something done, akin to going briefly out of your way to help a friend. Of course you can do it. No planning needed. It's very appealing to your brain.
What we call System 2 willpower is the high-level justification, the reason behind the actions, the thought-out plan, the will to make good decisions, to make things right, to keep akrasia in check in order to achieve long-term goals, the thing that sees problems as obstacles to be overcome instead of monsters to avoid. It's the background buzzing of "I want to go there and I shall find a path". It's the thing many productivity methods want to manage. The Getting Things Done methods starts with a list of tasks, which you can process in orderly manner, setting long and short-term goals and stay in control at all times. It's also very appealing to your brain.
However, what makes you actually do things is S1 willpower, not S2. S1 is more low-level, and has no knowledge of plans. Your willpower can be misguided. You want to throw paper planes, scribbling randomly, following click-bait headlines, when your plan is |
cd07ae61-65c3-4839-b14f-a32b8f484c18 | trentmkelly/LessWrong-43k | LessWrong | Levels of global catastrophes: from mild to extinction
Levels of global catastrophes: from mild to extinction
It is important to make a bridge between existential risks and other possible risks. If we say that existential risks are infinitely more important than other risks, we put them out of scope of policymakers (as they can’t work with infinities). We could reach them if we show x-risks as extreme cases of smaller risks. It could be done for most risks (with AI and accelerator's catastrophes are notable exceptions).
Smaller catastrophes play complex role in estimating probability of x-risks. A chain of smaller catastrophes may result in extinction, but one small catastrophe could postpone bigger risks (but it is not good solution). The following table presents different levels of global catastrophes depending of their size. Numbers are mostly arbitrary and are more like placeholders for future updates.
http://immortality-roadmap.com/degradlev.pdf
|
3b67debe-ff20-4a20-9373-2d1a86f835a9 | trentmkelly/LessWrong-43k | LessWrong | What to read instead of news?
I often read the news to take a break from work. Maybe this isn't such a good idea. (https://www.gwern.net/docs/culture/2010-dobelli.pdf).
What should I read instead? Ideally it should be interesting but not impenetrable, finished in less than half an hour, and easily available from my browser. I'm happy to pay for good content. |
dfdc2a7d-d181-4919-b8cf-929a2240ff86 | trentmkelly/LessWrong-43k | LessWrong | Forecasting AGI: Insights from Prediction Markets and Metaculus
I have tried to find all prediction market and Metaculus questions related to AGI timelines. Here I examine how they compare to each other, and what they actually say about when AGI might arrive.
If you know of a market that I have missed, please tell me in the comment section! It would also be helpful if you tell me about what questions you think are relevant but are missing from this analysis. This is a linkpost, and I prefer if you comment in the original post on my new blog, Forecasting AI Futures, but feel free to comment here as well. Subscribe to the blog for updates on my future forecasting posts related to AI safety.
Whenever possible, please check the more recent probability estimates in the embedded sites, instead of looking at my At The Time Of Writing (ATTOW) numbers.
So, what does prediction markets and Metaculus have to say about AGI?
Metaculus has this question for the arrival date of AGI:
The AI system needs to be able to:
* Pass a really hard Turing test.
* Have general robotic capabilities (being able to assemble a “circa-2021 Ferrari 312 T4 1:8 scale automobile model” or equivalent).
* Achieve “at least 75% accuracy in every task and 90% mean accuracy across all tasks” on the MMLU benchmark, which measures expertise in a wide range of academic subjects.
* Achieve at least 90% accuracy with a single attempt for each question on the APPS benchmark, which measures coding skills.
Metaculus thinks this will probably occur around the middle of 2030, though with high uncertainty. The interval between the lower and upper quartiles for the individual predictions on this question is (2026-12-28 - 2039-03-27) ATTOW.
GPT-4o achieves an accuracy of 88.7% on MMLU, as seen in the leaderboard here. GPT-4 was used to get 22% accuracy on APPS. Unfortunately, most of the best models have not been tested on either MMLU or APPS.
OpenAI’s o3 has been reported of achieving 71.7% on SWE-bench Verified. We can compare that to GPT-4, which managed to achieve |
3409fadb-a12b-4160-b0ee-567069f3f669 | trentmkelly/LessWrong-43k | LessWrong | Models predicting significant violence in the US?
Do you have a model predicting >1% chance of significant political violence (>5,000 deaths) in the US in the next year? Pls share.
Also welcome: models predicting <.1%
Metaculus has been walking around 1-5% probability of a US civil war before July 2021. While 1% is the lowest probability one can give on Metaculus, rumor has it some reasonable people put more than 1% probability of significant political violence (SPV) happening.
I am[was] confused about this, and want[ed] to get a better sense of what models lead people to assign credence on this order of magnitude.
[it turns out this exercise led me to no longer feel disbelief, but I'm preserving it below. See Changing My Mind for my update]
My model borrows a bit from Samo Burja's civil war typology and goes something like: "while there's some probability of small-scale violence (e.g. a few hundred deaths from scattered violence), there simply doesn't exist the mechanisms for SPV". In my mind, mechanisms include:
* Military: e.g. military vs. government (coup), military vs. military (split along e.g. leadership lines), or a weak/disorganized military that doesn't withstand a revolutionary offensive. I don't see any of this happening with the US military (e.g. <.01%). ThirdEyeOpen on Metaculus outlines cases where the military 'sides' with the democrats or republicans, which blows my mind. Is this a blind spot for me? Is there some small chance that any coherent part of the military defects in this way?
* State (proxy wars): Both sides of a conflict having support from states. I guess there's some 'foreign interference' in our politics already, but if I'm Russia I really don't want to get caught supplying significant amounts of money/training/arms to insurgent groups in the US. That feels like much more risk (to Russia) than the incremental gain. But maybe there's some situation where it's worthwhile? Seems pretty close to WWIII, but I guess that's tail risks?
* Insurgency: I currently have this as a larg |
57304568-2f44-4dee-b168-3ca8849e0a1a | StampyAI/alignment-research-dataset/lesswrong | LessWrong | How do takeoff speeds affect the probability of bad outcomes from AGI?
Introduction
------------
In general, people seem to treat slow takeoff as the safer option as compared to classic FOOMish takeoff (see e.g. [these interviews](https://aiimpacts.org/takeaways-from-safety-by-default-interviews/), [this report](https://www.fhi.ox.ac.uk/strategic-considerations-about-different-speeds-of-ai-takeoff/), etc). Below, I outline some features of slow takeoff and what they might mean for future outcomes. They do not seem to point to an unambiguously safer scenario, though slow takeoff does seem on the whole likelier to lead to good outcomes.
Social and institutional effect of precursor AI
-----------------------------------------------
If there’s a slow takeoff, AI is a significant feature of the world far before we get to superhuman AI.[[1]](#fn-e3Dpdhj6EwfTGrfac-1) One way to frame this is that everything is already really weird *before* there’s any real danger of x-risks. Unless AI is somehow not used in any practical applications, the pre-superhuman but still very capable AI will lead to massive economical, technological, and probably social changes.
If we expect significant changes to the state of the world during takeoff, it makes it harder to predict what kinds of landscape the AI researchers of that time will be facing. If the world changes a lot between now and superhuman AI, any work on institutional change or public policy might be irrelevant by the time it matters. Also, the biggest effects may be in the AI community, which would be closest to the rapidly changing technological landscape.
The kinds of work needed if everything is changing rapidly also seem different. Specific organizations or direct changes might not survive in their original, useful form. The people who have thought about how to deal with the sort of problems we might be facing then might be well positioned to suggest solutions, though. This implies that more foundational work might be more valuable in this situation.
While I expect this to be very difficult to predict from our vantage point, one possible change is mass technological unemployment well before superhuman AI. Of course, historically people have predicted technological unemployment from many new inventions, but the ability to replace large fractions of intellectual work may be qualitatively different. If AI approaches human-level at most tasks and is price-competitive, the need for humans reduces down to areas where being biological is a bonus and the few tasks it hasn’t mastered.[[2]](#fn-e3Dpdhj6EwfTGrfac-2)
The effects of such unemployment could be very different depending on the country and political situation, but historically mass unemployment has often led to unrest. (The Arab Spring, for instance, is [sometimes linked to youth unemployment rates](https://www.wider.unu.edu/publication/youth-unemployment-and-arab-spring).) This makes any attempts at long-term influence that do not seem capable of adapting to this a much worse bet. Some sort of UBI-like redistribution scheme might make the transition easier, though even without a significant increase in income inequality some forms of political or social instability seem likely to me.
From a safety perspective, normalized AI seems like it could go in several directions. On one hand, I can imagine it turning out something like nuclear power plants, where it is common knowledge that they require extensive safety measures. This could happen either after some large-scale but not global disaster (something like Chernobyl), or as a side-effect of giving the AI more control over essential resources (the electrical grid has, I should hope, better safety features than [a text generator](https://talktotransformer.com/)).
The other, and to me more plausible scenario, is that the gradual adoption of AI makes everyone dismiss concerns as alarmist. This does not seem entirely unreasonable: the more evidence people have that AI becoming more capable doesn’t cause catastrophe, the less likely it is that the tipping point hasn’t been passed yet.
Historical reaction to dangerous technologies
---------------------------------------------
A society increasingly dependent on AI is unlikely to be willing to halt or scale back AI use or research. Historically, I can think of some cases where we’ve voluntarily stopped the use of a technology, but they mostly seem connected to visible ongoing issues or did not result in giving up any significant advantage or opportunity:
* Pesticides such as DDT caused the [near-extinction of several bird species](https://en.wikipedia.org/wiki/DDT) (rather dramatically including the bald eagle).
* Chemical warfare is [largely ineffective as a weapon against a prepared army](https://thebulletin.org/2014/02/chemical-weapons-dangerous-but-ineffective-in-combat/).
* Serious nuclear powers have never reduced their stock of nuclear weapons to the point of significantly reducing their ability to maintain a credible nuclear deterrent. Several countries (South Africa, Belarus, Kazakhstan, Ukraine) have [gotten rid of their entire nuclear arsenals](https://www.nti.org/analysis/reports/nuclear-disarmament/).
* [Airships](https://en.wikipedia.org/wiki/Airship) are not competitive with advanced planes and were already declining in use before the Hidenberg disaster and other high-profile accidents.
* [Drug recalls](https://en.wikipedia.org/wiki/Drug_recall) are quite common and seem to respond easily to newly available evidence. It isn’t clear to me how many of them represent a significant change in the medical care available to consumers.
I can think of two cases in which there was a nontrivial fear of global catastrophic risk from a new invention ([nuclear weapons](http://large.stanford.edu/courses/2015/ph241/chung1/) igniting the atmosphere, [CERN](https://en.wikipedia.org/wiki/Safety_of_high-energy_particle_collision_experiments)). Arguably, [concerns about recombinant DNA](https://intelligence.org/files/TheAsilomarConference.pdf) also count. In both cases, the fears were taken seriously, found “[no self-propagating chain of nuclear reactions is likely to be started](https://fas.org/sgp/othergov/doe/lanl/docs1/00329010.pdf)” and “[no basis for any conceivable threat](https://web.archive.org/web/20080907004852/http://doc.cern.ch/yellowrep/2003/2003-001/p1.pdf)” respectively, and the invention moved on.
This is a somewhat encouraging track record of not just dismissing such concerns as impossible, but it is not obvious to me whether the projects would have halted had the conclusions been less definitive. There’s also the rather unpleasant ambiguity of “likely” and some evidence of uncertainty in the nuclear project, expanded on [here](http://blog.nuclearsecrecy.com/2018/06/29/cleansing-thermonuclear-fire/). Of course, the atmosphere remained unignited, but since we unfortunately don’t have any reports from the universe where it did this doesn’t serve as particularly convincing evidence.
Unlike the technologies listed two paragraphs up, CERN and the nuclear project seem like closer analogies to fast takeoff. There is a sudden danger with a clear threshold to step over (starting the particle collider, setting off the bomb), unlike the risks from climate change or other technological dangers which are often cumulative or hit-based. My guess, based on these very limited examples, is that if it is clear which project poses a fast-takeoff style risk it will be halted if the risk can be shown to have legible arguments behind it and is not easily shown to be highly unlikely. A slow-takeoff style risk, in which capabilities slowly mount, seems more likely to have researchers take each small step without carefully evaluating the risks every time.
Relevance of advanced precursor AIs to safety of superhuman AI
--------------------------------------------------------------
An argument in favor of slow takeoff scenarios being generally safer is that we will get to see and experiment with the precursor AIs before they become capable of causing x-risks.[[3]](#fn-e3Dpdhj6EwfTGrfac-3) My confidence in this depends on how likely it is that the dangers of a superhuman AI are analogous to the dangers of, say, an AI with 2X human capabilities. Traditional x-risk arguments around fast takeoff are in part predicated on the assumption that we cannot extrapolate all of the behavior and risks of a precursor AI to its superhuman descendant.
Intuitively, the smaller the change in capabilities from an AI we know is safe to an untested variant, the less likely it is to suddenly be catastrophically dangerous. “Less likely”, however, does not mean it could not happen, and a series of small steps each with a small risk are not necessarily inherently less dangerous than traversing the same space in one giant leap. Tight feedback loops mean rapid material changes to the AI, and significant change to the precursor AI runs the risk of itself being dangerous, so there is a need for caution at every step, including possibly after it seems obvious to everyone that they’ve “won”.
Despite this, I think that engineers who can move in small steps seem more likely to catch anything dangerous before it can turn into a catastrophe. At the very least, if something is not fundamentally different than what they’ve seen before, it would be easier to reason about it.
Reactions to precursor AIs
--------------------------
Even if the behavior of this precursor AI is predictive of the superhuman AI’s, our ability to use this testing ground depends on the reaction to the potential dangers of the precursor AI. Personally, I would expect a shift in mindset as AI becomes obviously more capable than humans in many domains. However, whether this shift in mindset is being more careful or instead abdicating decisions to the AI entirely seems unclear to me.
The way I play chess with a much stronger opponent is very different from how I play with a weaker or equally matched one. With the stronger opponent I am far more likely to expect obvious-looking blunders to actually be a set-up, for instance, and spend more time trying to figure out what advantage they might gain from it. On the other hand, I never bother to check my calculator’s math by hand, because the odds that it’s wrong is far lower than the chance that I will mess up somewhere in my arithmetic. If someone came up with an AI-calculator that gave occasional subtly wrong answers, I certainly wouldn’t notice.
Taking advantage of the benefits of a slow takeoff also requires the ability to have institutions capable of noticing and preventing problems. In a fast takeoff scenario, it is much easier for a single, relatively small project to unilaterally take off. This is, essentially, a gamble on that particular team’s ability to prevent disaster.
In a slow takeoff, I think it is more likely to be obvious that some project(s) seem to be trending in that direction, which increases the chance that if the project seems unsafe there will be time to impose external control on it. How much of an advantage this is depends on how much you trust whichever institutions will be needed to impose those controls.
Some historical precedents for cooperation (or lack thereof) in controlling dangerous technologies and their side-effects include:
* Nuclear proliferation treaties reduce the cost of a zero-sum arms race, but it isn’t clear to me if they significantly reduced the risk of nuclear war.
* Pollution regulations have had very mixed results, with some major successes (eg [acid rain](https://link.springer.com/article/10.1007/s13280-019-01244-4)) but on the whole failing to avert massive global change.
* Somewhat closer to home, [the response to Covid-19 hasn’t been particularly encouraging](https://vkrakovna.wordpress.com/2020/05/31/possible-takeaways-from-the-coronavirus-pandemic-for-slow-ai-takeoff/).
* The [Asilomar Conference](https://intelligence.org/files/TheAsilomarConference.pdf), which seems to me the most successful of these, involved a relatively small scientific field voluntarily adhering to some limits on potentially dangerous research until more information could be gathered.
Humanity’s track record in this respect seems to me to be decidedly mixed. It is unclear which way the response to AI will go, and it seems likely that it will be dependent on highly local factors.
What is the win condition?
--------------------------
A common assumption I’ve seen is that once there is aligned superhuman AI, the superhuman AI will prevent any unaligned AIs. This argument seems to hinge on the definition of “aligned”, which I’m not interested in arguing here. The relevant assumption is that an AI aligned in the sense of not causing catastrophe and contributing significantly to economic growth is not necessarily aligned in the sense that it will prevent unaligned AIs from occurring, whether its own “descendants” or out of some other project.[[4]](#fn-e3Dpdhj6EwfTGrfac-4)
I can perfectly well imagine an AI built to (for instance) respect human values like independence and scientific curiosity that, while benevolent in a very real sense, would not prevent the creation of unaligned AIs. A slow takeoff scenario seems to me more likely to contain multiple (many?) such AIs. In this scenario, any new project runs the risk of being the one that will mess something up and end up unaligned.
An additional source of risk is modification of existing AIs rather than the creation of new ones. I would be surprised if we could resist the temptation to tinker with the existing benevolent AI’s goals, motives, and so on. If the AI were programmed to allow such a thing, it would be possible (though I suspect unlikely without gross incompetence, if we knew enough to create the original AI safely in the first place) to change a benevolent AI into an unaligned one.
However, despite the existence of a benevolent AI not necessarily solving alignment forever, I expect us to be better off than in the case of unaligned AI emerging first. At the very least, the first AIs may be able to bargain with or defend us against the unaligned AI.
Conclusion
----------
My current impression is that, while slow takeoff seems on-the-whole safer (and likely implies a less thorny technical alignment problem), it should not be mostly neglected in favor of work on fast takeoff scenarios as implied e.g. [here](https://www.fhi.ox.ac.uk/strategic-considerations-about-different-speeds-of-ai-takeoff/). Significant institutional and cultural competence (and/or luck) seems to be required to reap some of the benefits involved in slow-takeoff. However, there are many considerations that I haven’t addressed and more that I haven’t thought of. Most of the use I expect this to be is as a list of considerations, not as the lead-up to any kind of bottom line.
*Thanks to Buck Shlegeris, Daniel Filan, Richard Ngo, and Jack Ryan for thoughts on an earlier draft of this post.*
---
1. I use this everywhere to mean AI far surpassing humans on all significant axes [↩︎](#fnref-e3Dpdhj6EwfTGrfac-1)
2. See e.g. Robin Hanson’s [Economic Growth Given Machine Intelligence](https://www.datascienceassn.org/sites/default/files/Economic%20Growth%20Given%20Machine%20Intelligence%202009%20Paper_0.pdf) [↩︎](#fnref-e3Dpdhj6EwfTGrfac-2)
3. An additional point is that the technical landscape at the start of takeoff is likely to be very different from the technical landscape near the end. It isn’t entirely clear how far the insights gained from the very first AIs will transfer to the superhuman ones. Pre- and post-machine learning AI, for instance, seem to have very different technical challenges. [↩︎](#fnref-e3Dpdhj6EwfTGrfac-3)
4. A similar distinction: "MIRI thinks success is guaranteeing that unaligned intelligences are never created, whereas Christiano just wants to leave the next generation of intelligences in at least as good of a place as humans were when building them." [Source](https://aiimpacts.org/conversation-with-paul-christiano/) [↩︎](#fnref-e3Dpdhj6EwfTGrfac-4) |
133a9327-2d27-4d1e-9aa9-e796d94d5719 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Grey Goo Requires AI
**Summary:** Risks from self-replicating machines or nanotechnology rely on the presence of a powerful artificial intelligence within the machines in order to overcome human control and the logistics of self-assembling in many domains.
The [grey goo scenario](https://en.wikipedia.org/wiki/Gray_goo) posits that developing self-replicating machines could present an existential risk for society. These replicators would transform all matter on earth into copies of themselves, turning the planet into a swarming, inert mass of identical machines.
I think this scenario is unlikely compared to other existential risks. To see why, lets look that the components of a self-replicating machine.
**Energy Source:** Because you can’t do anything without a consistent source of energy.
**Locomotion:** Of course, our machine needs to move from place to place gathering new resources, otherwise it will eventually run out of materials in its local environment. The amount of mobility it has determines how much stuff it can transform into copies. If the machine has wheels, it could plausibly convert an entire continent. With a boat, it could convert the entire earth. With rockets, not even the stars would be safe from our little machine.
**Elemental Analysis:** Knowing what resources you have nearby is important. Possibilities for what you can build depend heavily on the available elements. A general purpose tool for elemental analysis is needed.
**Excavation:** Our machine can move to a location, and determine which elements are available. Now it needs to actually pull them out of the ground and convert them into a form which can be processed.
**Processing:** The raw materials our machine finds are rarely ready to be made into parts directly. Ore needs to be smelted into metal, small organics need to be converted into plastics, and so on.
**Subcomponent Assembly:** The purified metals and organics can now be converted into machine parts. This is best achieved by having specialized machines for different components. For example, one part of the machine might print plastic housing, another part builds motors, while a third part makes computer chips.
**Global Assembly:** With all of our subcomponents built, the parent machine needs to assemble everything into a fully functional copy.
**Copies of Blueprint**: Much like DNA, each copy of the machine must contain a blueprint of the entire structure. Without this, it will not be able to make another copy of itself.
**Decision Making:** Up to this point, we have a self replicator with everything needed to build a copy of itself. However, without some decision making process, the machine would do nothing. Without instructions, our machine is just an expensive Swiss army knife: a bunch of useful tools which just sits there. I am not claiming that these instructions need to be *smart* (they could simply read “go straight”, for example) but there has to be *something.*
So far, this just looks like a bunch of stuff that we already have, glued together. Most of these processes were invented by the mid-1900’s. Why haven’t we built this yet? Where is the danger?
Despite looking boring, this system has the capacity to be really dangerous. This is because once you create something with a general ability to self-replicate, the same forces of natural selection which made complex life start acting on your machine. Even a machine with simple instructions and high fidelity copies will have mutations. These mutations can be errors in software, malfunctions in how components are made, errors in the blueprint, and so on. Almost all of these mutations will break the machine. But some will make their offspring better off, and these new machines will come to dominate the population of self-replicators.
Lets look at a simple example.
You build a self-replicator with the instruction “Move West 1 km, make 1 copy, then repeat” which will build a copy of itself every kilometer and move east-to-west, forming a Congo line of self-replicators. You start your machine and move to a point a few kilometers directly west of it, ready to turn off the copies that arrive and declare the experiment a success. When the first machine in the line reaches you, it is followed by a tight formation of perfect copies spaced 1 meter apart. Success! Except, weren’t they supposed to be spaced 1 *kilometer* apart? You quickly turn off all of the machines and look at their code. It turns out that a freak cosmic ray deleted the ‘k’ in ‘km’ in the instructions, changing the spacing of machines to 1 *meter* and giving the machine 1000 times higher fitness than the others. Strange, you think, but at least you stopped things before they got out of hand! As you drive home with your truckload of defective machines, you notice *another* copy of the machine, dutifully making copies spaced 1 kilometer apart, but heading *north* this time. You quickly turn off this new line of machines formed by this mutant and discover that the magnet on their compass wasn’t formed properly, orienting these machines in the wrong direction. You shudder to think what would have happened if this line of replicators reached the nearest town.
This example is contrived of course, but mistakes like these are bound to happen. This will give your machine very undesirable behavior in the long term, either wiping out all of your replicators or making new machines with complex adaptations who’s only goal is self-replication. Life itself formed extremely complex adaptations to favor self-replication from almost nothing, and, given the opportunity, these machines will too. In fact, the possibility of mutation and growth in complexity was a central motivation for the [Von Neumann universal constructor](https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor).
Fortunately, even after many generations, most of these machines will be pretty dumb, you could pick one up and scrap it for parts without any resistance. There is very little danger of a grey goo scenario here. So where is the danger? Crucially, nature did not only make complex organisms, but *general intelligence*. With enough time, evolution has created highly intelligent, cooperative, resource hoarding, self-replicators: us! Essentially, people, with their dreams of reaching the stars and populating the universe, are a physical manifestation of the grey goo scenario (“flesh-colored goo” doesn’t really roll off the tongue). Given enough time, there is no reason to think that self-replicating machines won’t do the same. But even before this happens, the machines will already be wreaking havoc: replicating too fast, going to places they aren’t supposed to, and consuming cities to make new machines.
But these issues aren’t fundamental problems with self-replicators. This is an [AI alignment](https://en.wikipedia.org/wiki/AI_control_problem) issue. The decision process for the new machines has become misaligned with what it’s original designers intended. Solutions to the alignment problem will immediately apply to these new systems, preventing or eliminating dangerous errors in replication. Like before, this has a [precedent](https://en.wikipedia.org/wiki/Apoptosis) in biology. Fundamentally, self-replicators are dangerous, but only because they have the ability to develop intelligence or change their behavior. This means we can focus on AI safety instead of worrying about nanotechnology risks as an independent threat.
Practically, is this scenario likely? No. The previous discussion glossed over a lot of practical hurdles for self-replicating machines. For nanoscale machines, a lot of the components I listed have not yet been demonstrated and might not be possible (I hope to review what progress *has* been made here in a future post). Besides that, the process of self-replication is very fragile and almost entirely dependent on a certain set of elements. You simply cannot make metal parts if you only have hydrogen, for example. Additionally, these machines will have to make complicated choices about where to find new resources, how to design new machines with different resources, and how to compete with others for resources. Even intelligent machines will face resource shortages, energy shortages, or be destroyed by people when they become a threat.
Overall, the grey goo scenario and the proposed risks of nanotechnology are really just AI safety arguments wrapped in less plausible packaging. Even assuming these things are built, the problem can essentially be solved with whatever comes out of AI alignment research. More importantly, I expect that AI will be developed before general purpose nanotechnology or self-replication, so AI risk should be the focus of research efforts rather than studying nanotechnology risks themselves. |
83b3b015-ffe4-4a71-bda5-804f8f880aa8 | trentmkelly/LessWrong-43k | LessWrong | What is a Glowfic?
This is a description for first-time glowfic readers who are unfamiliar with the format.
A glowfic is a fictional story written by multiple authors who roleplay as the characters. A typical glowfic will appear on glowfic.com and looks like an internet forum where fictional people will post comments back and forth which end up telling a story. To read it, just start at the top and read each comment, just like a regular comment thread. Each comment usually includes a photo of the character to convey their facial expression, dress, or other details. For more information, see the community guide to glowfic.
The layout of glowfic.com is unnecessarily confusing. To read the story in order, read the top post, then all the comments underneath it, then click the "next" button to go to the next page of comments. Do not click the "Next Post" button until you have read all of the comments. "Next Post" takes you to the next part of the story (like going to the next chapter). It will not take you to the next set of comments (which are also called "posts"). Yes, it's unnecessarily confusing. No, I don't know why they do it that way.
Happy reading! |
f363b61e-5fef-4001-a8a6-5a7659b05d74 | trentmkelly/LessWrong-43k | LessWrong | Self-modification as a game theory problem
In this post I'll try to show a surprising link between two research topics on LW: game-theoretic cooperation between AIs (quining, Loebian cooperation, modal combat, etc) and stable self-modification of AIs (tiling agents, Loebian obstacle, etc).
When you're trying to cooperate with another AI, you need to ensure that its action will fulfill your utility function. And when doing self-modification, you also need to ensure that the successor AI will fulfill your utility function. In both cases, naive utility maximization doesn't work, because you can't fully understand another agent that's as powerful and complex as you. That's a familiar difficulty in game theory, and in self-modification it's known as the Loebian obstacle (fully understandable successors become weaker and weaker).
In general, any AI will be faced with two kinds of situations. In "single player" situations, you're faced with a choice like eating chocolate or not, where you can figure out the outcome of each action. (Most situations covered by UDT are also "single player", involving identical copies of yourself.) Whereas in "multiplayer" situations your action gets combined with the actions of other agents to determine the outcome. Both cooperation and self-modification are "multiplayer" situations, and are hard for the same reason. When someone proposes a self-modification to you, you might as well evaluate it with the same code that you use for game theory contests.
If I'm right, then any good theory for cooperation between AIs will also double as a theory of stable self-modification for a single AI. That means neither problem can be much easier than the other, and in particular self-modification won't be a special case of utility maximization, as some people seem to hope. But on the plus side, we need to solve one problem instead of two, so creating FAI becomes a little bit easier.
The idea came to me while working on this mathy post on IAFF, which translates some game theory ideas into the se |
73a467eb-e62b-4459-9f92-74da7c773a01 | trentmkelly/LessWrong-43k | LessWrong | Optical Illusions are Out of Distribution Errors
Our visual black box is trained on what we see in the real world. We don't process raw sensory data - we keep a more abstracted model in our heads. This is a blessing and a curse - it may either be more useful to have a sparser cognitive inventory, and work on a higher level, or to ignore preconceived notions and try to pick up details about What Actually Is. Marcus Hutter would have us believe this compression is itself identical to intelligence - certainly true for the representation once it hits your neurons. But as a preprocessing step? It's hard to be confident. Is that summarisation by our visual cortex itself a form of intelligence?
Ultimately, we reduce a three-dimensional world into a two-dimensional grid of retinal activations; this in turn gets reduced to an additional layer of summarisation, the idea of the "object". We start to make predictions about the movement of "objects", and these are usually pretty good, to the point where our seeming persistence of vision is actually a convenient illusion to disguise that our vision system is interrupt-based. And so when we look at a scene, rather than even being represented as a computer does an image, as any kind of array of pixels, there is in actuality a further degree of summarisation occurring; we see objects in relation to each other in a highly abstract way. Any impressions you might have about an object having a certain location, or appearance, only properly arise as a consequence of intentional focus, and your visual cortex decides it's worth decompressing further.
This is in part why blind spots (literal, not figurative) can go unidentified for so long: persistence of vision can cover them up. You're not really seeing the neuronal firings, it's a level lower than "sight". It probably isn't possible to do so consciously - we only have access to the output of the neural network, and its high-level features; these more abstracted receptive fields are all we ever really see.
This is what makes optical |
24a6dc6a-148a-4221-b0e1-25eb21604a45 | trentmkelly/LessWrong-43k | LessWrong | A starter guide for evals
This is a starter guide for model evaluations (evals). Our goal is to provide a general overview of what evals are, what skills are helpful for evaluators, potential career trajectories, and possible ways to start in the field of evals.
Evals is a nascent field, so many of the following recommendations might change quickly and should be seen as our current best guess.
Why work on evals?
Model evaluations increase our knowledge about the capabilities, tendencies, and flaws of AI systems. Evals inform the public, AI organizations, lawmakers, and others and thereby improve their decision-making. However, similar to testing in a pandemic or pen-testing in cybersecurity, evals are not sufficient, i.e. they don’t increase the safety of the model on their own but are needed for good decision-making and can inform other safety approaches. For example, evals underpin Responsible Scaling Policies and thus already influence relevant high-stakes decisions about the deployment of frontier AI systems. Thus, evals are a highly impactful way to improve the decision-making about AI systems.
Evals are a nascent field and there are many fundamental techniques to be developed and questions to be answered. Since evals do not require as much background knowledge as many other fields, it is much easier to get started and possible to make meaningful contributions from very early on.
What are model evaluations (evals)?
Evals refers to a broad category of approaches that we roughly summarize as:
The systematic measurement of properties in AI systems
More concretely, evals typically attempt to make a quantitative or qualitative statement about the capabilities or propensities of an AI system. For example, we could ask if a model has the capability to solve a specific coding problem or the propensity to be power-seeking. In general, evals are not restricted to safety-related properties but often when people talk about evals, they mention them in a safety context.
There is a differ |
3264e499-3e66-435f-b958-4365ef937148 | trentmkelly/LessWrong-43k | LessWrong | Dating Roundup #3: Third Time’s the Charm
The first speculated on why you’re still single. We failed to settle the issue. A lot of you were indeed still single. So the debate continues.
The second gave more potential reasons, starting with the suspicion that you are not even trying, and also many ways you are likely trying wrong.
The definition of insanity is trying the same thing over again expecting different results. Another definition of insanity is dating in 2024. Can’t quit now.
YOU’RE SINGLE BECAUSE DATING APPS KEEP GETTING WORSE
A guide to taking the perfect dating app photo. This area of your life is important, so if you intend to take dating apps seriously then you should take photo optimization seriously, and of course you can then also use the photos for other things.
I love the ‘possibly’ evil here.
> Misha Gurevich: possibly evil idea: Dating app that trawls social media and websites and creates a database of individuals regardless of if they opt in or not, including as many photos and contact information as can be found.
>
> Obviously this would be kind of a privacy violation and a lot of people would hate it.
>
> but I imagine a solid subset of singles who are lonely but HATE the app experience would be grateful to be found this way.
No big deal, all we are doing is taking all the data about private citizens on the web and presenting it to any stranger who wants it in easy form as if you might want to date them. Or stalk them. Or do anything else, really.
And you thought AI training data was getting out of hand before.
All right, so let’s consider the good, or at least not obviously evil, version of this.
There is no need to fill out an intentional profile, or engage in specific actions, other than opting in. We gather all the information off the public web. We use AI to amalgamate all the data, assemble in-depth profiles and models of all the people. If it thinks there is a plausible match, then it sets it up. Since we are in danger of getting high on the creepiness meter, let’s |
e576be8f-461b-4365-a63c-ba702b0e4ced | StampyAI/alignment-research-dataset/lesswrong | LessWrong | The Gradient – The Artificiality of Alignment
[The Gradient](https://thegradient.pub/) is a “digital publication about artificial intelligence and the future,” founded by researchers at the Stanford Artificial Intelligence Laboratory. I found the latest essay, “The Artificiality of Intelligence,” by a PhD student at UC Berkeley, to be an interesting perspective from AI ethics/fairness.
Some quotes I found especially interesting:
> For all the pontification about cataclysmic harm and extinction-level events, the current trajectory of so-called “alignment” research seems under-equipped — one might even say *misaligned* — for the reality that AI might cause suffering that is widespread, concrete, and acute. Rather than solving the grand challenge of human extinction, it seems to me that we’re solving the age-old (and notoriously important) problem of building a product that people will pay for. Ironically, it’s precisely this valorization that creates the conditions for doomsday scenarios, both real and imagined. …
>
> In a recent [NYT interview](https://www.nytimes.com/2023/04/12/world/artificial-intelligence-nick-bostrom.html?searchResultPosition=2), Nick Bostrom — author of *Superintelligence* and core intellectual architect of effective altruism — defines “alignment” as *“ensur[ing] that these increasingly capable A.I. systems we build are aligned with what the people building them are seeking to achieve.”*
>
> Who is “we”, and what are “we” seeking to achieve? As of now, “we” is private companies, most notably OpenAI, the one of the first-movers in the AGI space, and Anthropic, which was founded by a cluster of OpenAI alumni.
>
> OpenAI [names *building superintelligence* as one of its primary goals](https://openai.com/blog/governance-of-superintelligence). But why, if the risks are so great? … *first, because it will make us a ton of money, and second, because it will make* someone *a ton of money, so might as well be us*. …
>
> Of course, that’s the cynical view, and I don’t believe most people at OpenAI are there for the sole purpose of personal financial enrichment. To the contrary, I think the interest — in the technical work of bringing large models into existence, the interdisciplinary conversations of analyzing their societal impacts, and the hope of being a part of building the future — is genuine. But an organization’s objectives are ultimately distinct from the goals of the individuals that comprise it. No matter what may be publicly stated, revenue generation will always be at least a complementary objective by which OpenAI’s governance, product, and technical decisions are *structured*, even if not fully *determined*. An interview with CEO Sam Altman by a startup building a “platform for LLMs” illustrates that [commercialization is top-of-mind](https://web.archive.org/web/20230531203946/https://humanloop.com/blog/openai-plans) for Altman and the organization.[[3]](https://thegradient.pub/the-artificiality-of-alignment/?utm_source=substack&utm_medium=email#interview-takedown) OpenAI’s [“Customer Stories” page](https://openai.com/customer-stories) is really no different from any other startup’s: slick screencaps and pull quotes, name-drops of well-regarded companies, the requisite “tech for good” highlight.
>
> What about Anthropic, the company [infamously](https://www.ft.com/content/8de92f3a-228e-4bb8-961f-96f2dce70ebb) founded by former OpenAI employees concerned about OpenAI’s turn towards profit? [Their argument](https://www.anthropic.com/index/core-views-on-ai-safety) — for why build more powerful models if they really are so dangerous — is more measured, focusing primarily on a research-driven argument about the necessity of studying models at the bleeding-edge of capability to truly understand their risks. Still, like OpenAI, Anthropic has their own shiny [“Product” page](https://www.anthropic.com/product), their own pull quotes, their own feature illustrations and use-cases. Anthropic [continues to raise](https://www.anthropic.com/index/anthropic-series-c) [hundreds of millions at a time](https://techcrunch.com/2023/04/06/anthropics-5b-4-year-plan-to-take-on-openai/).[[4]](https://thegradient.pub/the-artificiality-of-alignment/?utm_source=substack&utm_medium=email#anthropic-public-benefit)
>
> So OpenAI and Anthropic might be trying to conduct research, push the technical envelope, and possibly even build superintelligence, but they’re undeniably also building *products* — products that carry liability, products that need to sell, products that need to be designed such that they claim and maintain market share. Regardless of how technically impressive, useful, or fun Claude and GPT-x are, they’re ultimately tools (products) with users (customers) who hope to use the tool to accomplish specific, likely-mundane tasks.
>
> **Computer scientists** ***love*** **a model**
> ----------------------------------------------
>
> … For both OpenAI and Anthropic, the “preference model” is aligned to the overarching values of “helpfulness, harmlessness, and honesty,” or “HHH.”[[6]](https://thegradient.pub/the-artificiality-of-alignment/?utm_source=substack&utm_medium=email#hhh) In other words, the “preference model” captures the kinds of chatbot outputs that humans tend to perceive to be “HHH.” …
>
> All of these technical approaches — and, more broadly, the “intent alignment” framing — are deceptively convenient. Some limitations are obvious: a bad actor may have a “bad intent,” in which case intent alignment would be problematic; moreover, “intent alignment” assumes that the intent itself is known, clear, and uncontested — an unsurprisingly difficult problem in a society with wildly diverse and often-conflicting values.
>
> The “financial sidequest” sidesteps both of these issues, which captures my real concern here: the existence of financial incentives means that alignment work often turns into product development in disguise rather than actually making progress on mitigating long-term harms. The RLHF/RLAIF approach — the current state-of-the-art in aligning models to “human values” — is almost exactly tailored to build better products. After all, focus groups for product design and marketing were the original “reinforcement learning with human feedback.” …
>
> To be fair, Anthropic has released Claude's [principles](https://www.anthropic.com/index/claudes-constitution) to the public, and OpenAI [seems to be seeking ways to involve the public](https://openai.com/blog/democratic-inputs-to-ai) in governance decisions. But as it turns out, OpenAI was [lobbying for *reduced* regulation](https://time.com/6288245/openai-eu-lobbying-ai-act/) even as they publicly “advocated” for additional governmental involvement; on the other hand, extensive incumbent involvement in designing legislation is a clear path towards regulatory capture. Almost tautologically, OpenAI, Anthropic, and similar startups exist in order to dominate the marketplace of extremely powerful models in the future.
>
> These economic incentives have a direct impact on product decisions. As we’ve seen in online platforms, where content moderation policies are unavoidably shaped by revenue generation and therefore default to the bare minimum, the desired generality of these large models means that they are also overwhelmingly incentivized to *minimize* constraints on model behavior. In fact, OpenAI [explicitly states](https://openai.com/blog/how-should-ai-systems-behave) that they plan for ChatGPT to reflect a minimal set of guidelines for behavior that can be customized further by other end-users. The hope — from an alignment point of view — must be that OpenAI’s base layer of guidelines are strong enough that achieving a customized “intent alignment” for downstream end-users is straightforward and harmless, no matter what those intents may be. …
>
> Rather than asking, “how do we create a chatbot that *is* good?”, these techniques merely ask, “how do we create a chatbot that *sounds* good”? For example, just because ChatGPT has been told not to use racial slurs doesn’t mean it doesn’t internally represent harmful [stereotypes](https://arxiv.org/abs/2305.18189).
>
> **So how** ***do*** **we solve extinction?**
> --------------------------------------------
>
> … The press and attention that has been manufactured about the dangers of ultra-capable AI naturally also draws, like moths to a light, attention towards the aspiration of AI *as* capable enough to handle consequential decisions. The cynical reading of Altman’s policy tour, therefore, is as a Machiavellian advertisement for the *usage* of AI, one that benefits not just OpenAI but also other companies peddling “superintelligence,” like Anthropic.
>
> The punchline is this: the pathways to AI x-risk ultimately require a society where relying on — and trusting — algorithms for making consequential decisions is not only commonplace, but encouraged and incentivized. It is precisely this world that the breathless speculation about AI capabilities makes real.
>
> Consider the [mechanisms](https://arxiv.org/pdf/2306.06924.pdf) [by](https://80000hours.org/articles/what-could-an-ai-caused-existential-catastrophe-actually-look-like/) [which](https://www.simeon.ai/resources-on-ai-risks) those worried about long-term harms claim catastrophe might occur: power-seeking, where the AI agent continually demands more resources; reward hacking, where the AI finds a way to behave in a way that seems to match the human’s goals but does so by taking harmful shortcuts; deception, where the AI, in pursuit of its own objectives, seeks to placate humans to persuade them that it is actually behaving as designed.
>
> The emphasis on AI capabilities — the claim that “AI might kill us all *if it becomes too powerful*” — is a rhetorical sleight-of-hand that ignores all of the other *if* conditions embedded in that sentence: *if we decide to outsource reasoning about consequential decisions — about policy, business strategy, or individual lives — to algorithms. If we decide to give AI systems direct access to resources, and the power and agency to affect the allocation of those resources — the power grid, utilities, computation.* All of the AI x-risk scenarios involve a world where we have *decided to* abdicate responsibility to an algorithm. …
>
> The newest models are truly remarkable, and alignment research explores genuinely fascinating technical problems. But if we really are concerned about AI-induced catastrophe, existential or otherwise, we can’t rely on those who stand to gain the most from a future of widespread AI deployments.
>
> |
11585db1-17c0-4c96-8735-faa110385dc3 | trentmkelly/LessWrong-43k | LessWrong | Ethicality of Denying Agency
If your 5-year-old seems to have an unhealthy appetite for chocolate, you’d take measures to prevent them from consuming it. Any time they’d ask you to buy them some, you’d probably refuse their request, even if they begged. You might make sure that any chocolate in the house is well-hidden and out of their reach. You might even confiscate chocolate they already have, like if you forced them to throw out half their Halloween candy. You’d almost certainly trigger a temper tantrum and considerably worsen their mood. But no one would label you an unrelenting tyrant. Instead, you’d be labeled a good parent.
Your 5-year-old isn’t expected to have the capacity to understand the consequences to their actions, let alone have the efficacy to accomplish the actions they know are right. That’s why you’re a good parent when you force them to do the right actions, even against their explicit desires.
You know chocolate is a superstimulus and that 5-year-olds have underdeveloped mental executive functions. You have good reasons to believe that your child’s chocolate obsession isn’t caused by their agency, and instead caused by an obsolete evolutionary adaptation. But from your child’s perspective, desiring and eating chocolate is an exercise in agency. They’re just unaware of how their behaviors and desires are suboptimal. So by removing their ability to act upon their explicit desires, you’re denying their agency.
So far, denying agency doesn’t seem so bad. You have good reason to believe your child isn’t capable of acting rationally and you’re only helping them in the long run. But the ethicality gets murky when your assessment of their rationality is questionable.
Imagine you and your mother have an important flight to catch 2 hours from now. You realize that you have to leave to the airport now in order to make it on time. As you’re about to leave, you recalled the 2 beers you recently consumed. But you feel the alcohol left in your system will barely affect your dr |
60538a3a-38cf-4a0a-9568-4fa119942271 | trentmkelly/LessWrong-43k | LessWrong | A New Sequence on Rationality
In August, I asked "What is rational"? I wasn't satisfied with any answer I received, and then decided to answer it myself; I began developing my own theory of rationality from first principles. I wrote and scrapped two drafts, and after 4 months, I've finally started the path to an answer.
Over the coming year, I plan to write this sequence, developing my theory of rationality to an acceptable level. My conception of rationality was developed from first principles, and the principles I adopted may differ (substantially even) from Lesswrong's or the mainstream's principles, so it may not be what you're used to, but I nevertheless expect it to prove useful.
Furthermore, because I'm taking a first principles approach, I would of necessity reinvent several wheels (insomuch as these wheels are useful to rationality), so a lot (possibly even the majority) of this sequence may be unoriginal. Nevertheless, where I do diverge from other conceptions of rationality is significant enough that I do not expect the two to be reconcilable.
I'll take a more free-form and less structured approach to writing the sequence, so it wouldn't be much of a sequence (in the usual sense of the term), and more of an interrelated bunch of posts. I would structure the posts into a logical order after I'm done with the sequence, and their chronological order may not be the best order in which to read them. Trying to structure the posts in logical order would probably require me to write the entire thing at once, and from my two failed attempts at doing that (and the subsequent deflation in energy which prevented me from making progress on that front for around a few weeks to a month) I don't want to take that approach. So I'll write in an unstructured style, pushing the components in the order I finish them. Upon completion, I'll stitch together all the components into a coherent whole.
I'm incredibly bad at estimating completion times for projects I undertake, so I'll only note that I plan t |
28656857-813d-4ff8-b123-ca294047b06b | StampyAI/alignment-research-dataset/blogs | Blogs | the deobfuscation conjecture
the deobfuscation conjecture
----------------------------
suppose i write a program that tries to find counterexamples to [fermat's last theom](https://en.wikipedia.org/wiki/Fermat's_Last_Theorem): that is, numbers a>1, b>1, c>1, n>2 such that aⁿ+bⁿ=cⁿ.
suppose i now explain to you this program; you will probably shortly understand what it is doing.
now, suppose i compile it, and run its compiled code through an obfuscation algorithm such as [movfuscator](https://github.com/xoreaxeaxeax/movfuscator); with the strict requirement that the output program has the same I/O and [time complexity](https://en.wikipedia.org/wiki/Time_complexity) as the input program (i *believe* this is the case for movfuscator).
can you, manually or with the help of a deobfuscator or even in a fully automated manner, *now* figure out that the program is trying to find counterexamples to fermat's last theorem?
with infinite time, surely; the conjecture that i posit here (well, that i *posited*, see below) is that you can do so in an amount of time that is at most linear in the amount of time it would have taken you to understand the original program.
in general, the deobfuscation conjecture says this: for any code obfuscation program that conserves I/O and time complexity, and for any reasonable notion of "understanding" what a program does that is conserved by obfuscation (such as "does this program halt" or "will this program find a counterexample to fermat's last theroem if there is one"), there exists a deobfuscation program that determines that criteria for the obfuscated program in the same time complexity (as a function of the program size) as the fastest program that determines that criteria for the unobfuscated program.
or, put another way: as long as I/O and time complexity are conserved, transforming a program does not change the time complexity in which can be tested other criteria of the program, or in which it can be [reduced](https://en.wikipedia.org/wiki/Computational_irreducibility).
as evidence for this conjecture, i'll point to the ability of video game crackers to pretty systematically overcome [software DRM](https://en.wikipedia.org/wiki/Digital_rights_management), to extract the behavior of the program they care about.
a corollary to this conjecture is that shipping a program together with "hints" about what it does cannot help to understand it by more than a constant factor.
### a proof that it's false
while writing this post and talking about the conjucture with a friend, they helped me figure out a proof that it must be false.
consider:
* the program is a program that will sort a hardcoded list and output the result
* the criteria is "what will the program output ?"
* the hint being shipped with the program is a list of what position each item in the original list will occupy in the final sorted list
with the hint, the criteria can be determined in O(n): checking that the hint is indeed a correct sorting of the list is O(n), and so is checking that the program is indeed the sorting algorithm (for this proof we can just hardcode recognizing one specific sorting algorithm; the space of this proof is merely the set of hardcoded lists, while the sorting algorithm is constant).
without the hint, however, the criteria can only be determined in O(n × log(n)): after one has figured out that the program is a sorting algorithm, one has to actually sort the list to figure out what the program will output, which is known to take at least O(n × log(n)).
and so, the hint helps by more than a constant factor, and there can indeed be information shipped alongside a program that can help determine criteria about it; and thus, obfuscation can indeed make a program harder to understand by more than a constant factor. |
672c2967-3169-4dda-84f5-2f4c64b8747b | trentmkelly/LessWrong-43k | LessWrong | Reply to Jebari and Lundborg on Artificial Superintelligence
Jebari and Lundborg have recently published an article entitled Artificial superintelligence and its limits: why AlphaZero cannot become a general agent. It focuses on the thorny issue of agency in superintelligent AIs. I’m glad to see more work on this crucial topic; however, I have significant disagreements with their terminology and argumentation, as I outline in this reply. Note that it was written rather quickly, and so might lack clarity in some places, or fail to convey some nuances of the original article. I welcome comments and further responses.
Their paper runs roughly as follows: Jebari and Lundborg first discuss the belief–desire model for intentional action, under which agents act in ways that they believe will bring about their desires. They then distinguish between different degrees of generality that agents can have: “general agency is, loosely speaking, the ability to act in a diverse set of situations.” They consider thermostats to be very specialised agents, and dogs to be fairly general agents. They then introduce introduce Legg and Hutter’s definition of intelligence as “an agent’s ability to achieve its goals in a wide range of environments”. This is, unfortunately, a misquotation of Legg and Hutter, and one which leads Jebari and Lundborg astray, as they follow it with:
> Claim 1: If an agent has very specialized goals or desires, it can be superintelligent with regards to those desires without being a general agent.
The actual definition given in both A collection of definitions of intelligence and Universal intelligence is: “Intelligence measures an agent’s ability to achieve goals in a wide range of environments”. Note the important distinction between an agent which can achieve its goals versus one which can achieve goals in general. In the original context, it is clear that to be highly intelligent according to this definition, an agent doesn’t just need to be able to achieve a single (potentially specialised) set of goals, but rather |
17e2b130-4241-405f-9a45-c53c61538612 | trentmkelly/LessWrong-43k | LessWrong | Examining Evolution as an Upper Bound for AGI Timelines
Cross-posted from https://mybrainsthoughts.com/?p=349
With the massive degree of progress in AI over the last decade or so, it’s natural to wonder about its future - particularly the timeline to achieving human (and superhuman) levels of general intelligence. Ajeya Cotra, a senior researcher at Open Philanthropy, recently (in 2020) put together a comprehensive report seeking to answer this question (actually, it answers the slightly different question of when transformative AI will appear, mainly because an exact definition of impact is easier than one of intelligence level), and over 169 pages she lays out a multi-step methodology to arrive at her answer. The report has generated a significant amount of discussion (for example, see this Astral Codex Ten review), and seems to have become an important anchor for many people’s views on AI timelines. On the whole, I found the report added useful structure around the AI timeline question, though I’m not sure its conclusions are particularly informative (due to the wide range of timelines across different methodologies). This post will provide a general overview of her approach (readers who are already familiar can skip the next section), and will then focus on one part of the overall methodology - specifically, the upper bound she chooses - and will seek to show that this bound may be vastly understated.
Part 1: Overview of the Report
In her report, Ajeya takes the following steps to estimate transformative AI timelines:
1. Determine the total amount of computation required to train a transformative AI model, based on the architectures and algorithms available in 2020.
2. Determine the rate at which the amount of computation available will change (due to reduced cost of computation and greater availability of capital) and the rate at which the computational requirements will be reduced (due to architectural / algorithmic progress).
3. Apply these rates, starting from 2020, to determine at what point sufficient c |
b08d7d0e-9c3e-467f-9505-6bd12bf9e3bb | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | UK's new 10-year "National AI Strategy," released today
Published [here](https://www.gov.uk/government/publications/national-ai-strategy), for those interested in reading and discussing: <https://www.gov.uk/government/publications/national-ai-strategy> |
2c3822bb-d2de-4bd2-b5bd-d6946b4869c4 | trentmkelly/LessWrong-43k | LessWrong | The Super Happy People (3/8)
(Part 3 of 8 in "Three Worlds Collide")
...The Lady Sensory said, in an unsteady voice, "My lords, a third ship has jumped into this system. Not Babyeater, not human."
The holo showed a triangle marked with three glowing dots, the human ship and the Babyeater ship and the newcomers. Then the holo zoomed in, to show -
- the most grotesque spaceship that Akon had ever seen, like a blob festooned with tentacles festooned with acne festooned with small hairs. Slowly, the tentacles of the ship waved, as if in a gentle breeze; and the acne on the tentacles pulsated, as if preparing to burst. It was a fractal of ugliness, disgusting at every level of self-similarity.
"Do the aliens have deflectors up?" said Akon.
"My lord," said Lady Sensory, "they don't have any shields raised. The nova ashes' radiation doesn't seem to bother them. Whatever material their ship is made from, it's just taking the beating."
A silence fell around the table.
"All right," said the Lord Programmer, "that's impressive."
The Lady Sensory jerked, like someone had just slapped her. "We - we just got a signal from them in human-standard format, content encoding marked as Modern English text, followed by a holo -"
"What?" said Akon. "We haven't transmitted anything to them, how could they possibly -"
"Um," said the Ship's Engineer. "What if these aliens really do have, um, 'big angelic powers'?"
"No," said the Ship's Confessor. His hood tilted slightly, as if in wry humor. "It is only history repeating itself."
"History repeating itself?" said the Master of Fandom. "You mean that the ship is from an alternate Everett branch of Earth, or that they somehow independently developed ship-to-ship communication protocols exactly similar to our -"
"No, you dolt," said the Lord Programmer, "he means that the Babyeaters sent the new aliens a massive data dump, just like they sent us. Only this time, the Babyeater data dump included all the data that we sent the Babyeaters. Then the |
7bad201c-25b6-4237-9d4f-3090291e18ec | trentmkelly/LessWrong-43k | LessWrong | Taking a simplified model
(cross-posted from my blog, Sunday Stopwatch)
This may be trivial but I didn't see it written down explicitly anywhere, so I'm writing it. There's a specific trick I've noticed that some people do that makes for better conversations, and I don't know if there's a name for it, but I think there should be, so I nominate taking a simplified model.
Say that you want to figure out some big societal issue, like how taxes should work, or how much people should be paid, or how much of a company should belong to the founder, and how much to the workers.
The usual way to go about this is to launch into specifics or into generalities. You either talk about the tax situation in, I don't know, post-war Netherlands and how the price of oil affected the shipping industry which then led to something which led to something else - I'm just fabricating stuff, but you know, a line of reasoning where you extract some lesson from highly specific events.
Or you talk about very general principles - like, we should all be treated equally, or we should reward those with ingenuity and drive, stuff like that.
But the cool trick is taking a simplified model: e.g. imagining a society of only ten people. And then running through the combinations, maybe extracting lessons. For example:
* Imagine that in this society, John accidentally discovers oil (or an imaginary resource, doesn't matter). John works hard and makes life better for the rest of the society. How should he be rewarded (if at all)?
* Imagine that John doesn't work very hard, but is just the first person out of many who might have plausibly discovered this new resource. Does this change anything?
* Imagine that John works on this project single-handedly but wants to leave his heritage not to the village but to his two sons. How do we feel about that?
And so on. This works for a broad set of questions, and I've found that taking a simplified model always brings clarity. The trick is that it doesn't work in highly soldier-like |
02952e81-efe3-452a-b340-e1f0dc5dbc0b | awestover/filtering-for-misalignment | Redwood Research: Alek's Filtering Results | id: post3898
A putative new idea for AI control; index here . Note: this post replaces and supersedes the previous one which is now obsolete. Hacking utility indifference Utility indifference was a technique for corrigibility was an attempt to allow an AI to safely change its values, without seeking to provoke or avoid the change. The idea is that, when the AI's utility changes from u to v at time t , it maximises a meta-utility U such that U ≤ t = u . U > t = v + E ( u | u → u ) − E ( v | u → v ) . Here u → u designates the event that u remains unchanged, while u → v designates the change. This is a permitted utility change - one that we have set the agent up to accept - rather than just any old change in utility, which the agent will still attempt to resist. As has been shown, adding those expectation terms means the AI will not seek to provoke or resist such a utility change - it is indifferent to the change. Those expectation terms can be designated compensatory rewards. What it is not indifferent to, though, is changes to E . If the AI knows that change u → v is coming, then it has incentives to change E to E ′ with a high E ′ ( u | u → u ) and a low E ′ ( v | u → v ) . Its ability to rewire is limited by the fact that E ′ will serve as its future expectation estimator, so it won't mangle it completely, but it certainly can twist it a bit. Notice that the AI is now no longer indifferent to the permitted change of utility. It can benefit from rewiring E this way only if a change in utility is coming up. Probability estimators Let ρ i be probability estimators. These define both expectation estimators E ρ i and probability estimators P ρ i . These two are equivalent in principle, since for any random variable X with I x the indicator function for X = x , then P ρ i ( X = x ) = E ρ i I x and E ρ i X = ∑ x P ρ i ( X = x ) . Double indifference One idea to combat this is to apply indifference to the process of changing probability estimators. Thus an AI with estimator ρ i changing to ρ j would get compensatory reward E ρ i ( U | ρ i → ρ i ) − E ρ i ( U | ρ i → ρ j ) . This won't work, however, since we don't want to discount all the impacts of the change ρ i → ρ j (maybe ρ j is a much more accurate estimator), just the part of the change that affects the compensatory rewards. So the second layer of indifference will be a bit different from the previous one. First, a subtlety. If we designate by ρ = ρ i the fact that the agent uses estimator ρ i , it is very possible that E ρ i ( u | ρ = ρ j ) > E ρ i ( u | ρ = ρ i ) . This simply means that, in ρ i 's 'opinion', ρ j is a better estimator for maximising u than it is itself. Now, assume it's at a time t where the agent is using estimator ρ t and current utility u t . Let C > t ( u , v , ρ ′ ) be all future compensatory rewards of the type E ρ ′ ( u | u → u , ρ = ρ ′ ) − E ρ ′ ( v | u → v , ρ = ρ ′ ) , for permitted change u → v . Let C > t ( u , v , ρ ′′ , ρ ′ ) be the same, except the expectation estimator is ρ ′′ : thus it is E ρ ′′ ( u | u → u , ρ = ρ ′ ) − E ρ ′′ ( v | u → v , ρ = ρ ′ ) . Note that C > t ( u , v , ρ ′ , ρ ′ ) = C > t ( u , v , ρ ′ ) . Then define C > t (and C > t ( ρ ′′ ) ) by summing C > t ( u , v , ρ ′ ) (and C > t ( u , v , ρ ′′ , ρ ′ ) ) over all u , v , and ρ ′ . Let D > t be all future compensatory rewards that are not of the type C > t . Then if the agent changes ρ t into ρ i , it gets the compensatory reward D t = − E ρ t ( D > t | ρ t → ρ i ) − E ρ t ( C > t | ρ t → ρ i ) + E ρ t ( C > t ( ρ t ) | ρ t → ρ i ) . Note that D t is defined in terms of D > t , so, though there may be convergence issues, the definition is not circular. Then the first two terms of D t cancel out, according to the agent's best current expectation, all future compensatory rewards. The third term replaces all the C type compensatory rewards with the same, except estimated by the agent's current ρ t rather than by whichever estimator the agent will have at the time. Thus, the agent will be indifferent to all actual compensatory rewards (as it gets none in expectation), and instead gain estimated rewards that make it indifferent to any future change u i → u j . See this post to see the same formula in AIXI formalism. There are certainly cases where the above terms converge (such as all bounded u i , finite number of possible changes to both utility and ρ ). A full discussion of convergence will wait for another post. Sequentially unbiased estimators Call the estimators { ρ i } sequentially unbiased if for all i < j , and all X , E ρ i E ρ j X = E ρ i X .
Then the main result is: If the different ρ i used by the agent are sequentially unbiased when labelled sequentially, then classical corrigibility is still sufficient. To prove this, imagine that the agent will see a single change of utility u → v , and that it has the opportunity of changing ρ i (to ρ j , say) exactly once before the utility change. Then there will be no D term for ρ j , and, if it does change ρ i → ρ j , the D term is: D t = − E ρ i ( C > t | ρ i → ρ j ) + E ρ i ( C > t ( ρ i ) | ρ i → ρ j ) . However, E ρ i ( C > t | ρ i → ρ j ) = E ρ i ( E ρ j ( u | u → u , ρ = ρ j ) − E ρ j ( v | u → v , ρ = ρ j ) ) . Because of sequential unbiasedness, this simplifies to E ρ i ( u | u → u , ρ = ρ j ) − E ρ i ( v | u → v , ρ = ρ j ) , which is just E ρ i ( C > t ( ρ i ) | ρ i → ρ j ) . So D t = 0 . We can then recurse to the change in ρ just before ρ i , and get the same result (since the future D is still zero). And so on, with D always being zero. Then since the formulas defining D are linear, we can extend this to general environments and general utility function changes, and conclude that for sequentially unbiased ρ i , the D are always 0 under double indifference (modulo some convergence issues not addressed here). Therefore, double indifference will work, even if we don't use D 's at all: thus classical indifference still works in this case. Note the similarity of the sequential unbiasness with the conditions for successful value learners in the Cake or Death problem. |
c09f89ed-6571-4d7a-bd32-2edf73747aef | StampyAI/alignment-research-dataset/lesswrong | LessWrong | LW is probably not the place for "I asked this LLM (x) and here's what it said!", but where is?
I notice a nonzero amount of posts on LW, admittedly typically not overly high karma, that go something like, "I asked Bing to do x and its' answer freaked me out!", or "I talked to ChatGPT4 about itself and it told me weird stuff, here are some potential implications", and although these posts are not excessive, and are sometimes interesting, I can't help but feel like LW isn't a great place for them.
I feel a bit like, at least in the current state of LLMs, it's akin to dream interpretations. They can be valuable to discuss, but are also of primary interest and value to the dreamer (the prompter, in this case) and are of especially low value if the dreamer simply relates them verbatim with little additional commentary.
The reason I ask 'but where is?' is because I think a lot of interesting stuff shows up on Twitter, on here and occasionally on Reddit (which is the worst for people just posting the results of prompts verbatim as though Word from God) from folks doing really interesting 'prompt based research' into LLMs, and I do absolutely see value in this stuff and the discussions that result from it, and would love a place to collect anecdotal research into how LLMs think and respond besides individual scattered substack articles and tweets.
Just... perhaps LW isn't the proper forum. I honestly wouldn't be surprised if the moderation team eventually considers these posts to be inherently low quality and creates some sort of rule against them, but it's not immediately obvious where the boundary of 'low quality' lies. |
6a74f2e2-2793-4392-b94d-83c08fe6d3e2 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Orange County Atheist Meetup Wednesday August 10
Discussion article for the meetup : Orange County Atheist Meetup Wednesday August 10
WHEN: 10 August 2011 07:30:00PM (-0700)
WHERE: 18542 MacArthur Blvd Irvine, CA 92612
This week we are going to do something a little different for the weekly Irvine meetup. There is an atheist group in Orange County that has monthly meetups, so let's join them for their August Meetup. I attended the meetup last month, and found that several of the people there were interested in such topics as probability theory, heuristics and biases, and Singularity issues.
The main event starts at 7:30 at the upstairs table at the IHOP. Some members also show up for drinks and appetizers at the El Torito across the street at 6:30. They would appreciate if you RSVP in the comments of the linked announcement.
Discussion article for the meetup : Orange County Atheist Meetup Wednesday August 10 |
6c888b25-54b0-43d9-9964-cdb5b83090b0 | StampyAI/alignment-research-dataset/blogs | Blogs | Bill Hibbard on Ethical Artificial Intelligence
Bill Hibbard is an Emeritus Senior Scientist at the University of Wisconsin-Madison Space Science and Engineering Center, currently working on issues of AI safety and unintended behaviors. He has a BA in Mathematics and MS and PhD in Computer Sciences, all from the University of Wisconsin-Madison. He is the author of *[Super-Intelligent Machines](http://www.amazon.com/Super-Intelligent-Machines-International-Systems-Engineering/dp/0306473887/)*, [“Avoiding Unintended AI Behaviors,”](https://intelligence.org/files/UnintendedBehaviors.pdf) [“Decision Support for Safe AI Design,”](https://intelligence.org/files/DecisionSupport.pdf) and [“Ethical Artificial Intelligence.”](http://arxiv.org/abs/1411.1373) He is also principal author of the [Vis5D](http://en.wikipedia.org/wiki/Vis5D), [Cave5D](http://en.wikipedia.org/wiki/Cave5D), and [VisAD](http://en.wikipedia.org/wiki/VisAD) open source visualization systems.
**Luke Muehlhauser**: You recently released a self-published book, *[Ethical Artificial Intelligence](http://arxiv.org/abs/1411.1373)*, which “combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence.” Most of the book is devoted to the kind of exploratory engineering in AI that you and I described in [a recent CACM article](https://intelligence.org/2014/08/22/new-paper-exploratory-engineering-artificial-intelligence/), such that you mathematically analyze the behavioral properties of classes of future AI agents, e.g. utility-maximizing agents.
Many AI scientists have the intuition that such early, exploratory work is very unlikely to pay off when we are so far from building an AGI, and don’t what an AGI will look like. For example, Michael Littman [wrote](http://kruel.co/2015/02/05/interview-with-michael-littman-on-ai-risks/#sthash.YrQRrluT.dpbs):
…proposing specific mechanisms for combatting this amorphous threat [of AGI] is a bit like trying to engineer airbags before we’ve thought of the idea of cars. Safety has to be addressed in context and the context we’re talking about is still absurdly speculative.
How would you defend the value of the kind of work you do in Ethical Artificial Intelligence to Littman and others who share his skepticism?
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**Bill Hibbard**: This is a good question, Luke. The analogy with cars is useful. Unlike engineering airbags before cars are even thought of, we are already working hard to develop AI and can anticipate various types of dangers.
When cars were first imagined, engineers probably knew that they would propel human bodies at speed and that they would need to carry some concentrated energy source. They knew from accidents with horse carriages that human bodies travelling at speed are liable to injury, and they knew that concentrated energy sources are liable to fire and explosion which may injure humans. This is analogous with what we know about future AI: that to serve humans well AI will have to know a lot about individual humans and that humans will not be able to monitor every individual action by AI. These properties of future AI pose dangers just as the basic properties of cars (propelling humans and carrying energy) pose dangers.
Early car designers could have anticipated that no individual car would carry all of humanity and thus car accidents would not pose existential threats to humanity. To the extent that cars threaten human safety and health via pollution, we have time to notice these threats and address them. With AI we can anticipate possible scenarios that do threaten humanity and that may be difficult to address once the AI system is operational. For example, as described in the first chapter of my book, the Omniscience AI, with a detailed model of human society and a goal of maximizing profits, threatens to control human society. However, AI poses much greater potential benefits than cars but also much greater dangers. This justifies greater effort to anticipate the dangers of AI.
It’s also worth noting that the abstract frameworks for exploratory engineering apply to any reasonable future AI design. As the second chapter of my book describes, any set of complete and transitive preferences among outcomes can be expressed by a utility function. If preferences are incomplete then there are outcomes A and B with no preference between them, so the AI agent cannot decide. If preferences are not transitive then there are outcomes A, B, and C such that A is preferred to B, B is preferred to C, and C is preferred to A. Again, the AI agent cannot decide. Thus our exploratory engineering can assume utility maximizing agents and cover all cases in which the AI agent can decide among outcomes.
Similarly, the dangers discussed in the book are generally applicable. Any design for powerful AI should explain how it will avoid the self-delusion problem described by Ring and Orseau, the problem of corrupting the reward generator as described by Hutter, and the problem of unintended instrumental actions as described by Omohundro (he called them basic AI drives).
The threat level from AI justifies addressing AI dangers now and with significant resources. And we are developing tools that enable us to analyze dangers of AI systems before we know the specifics of their designs.
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**Luke**: Your book mostly discusses AGIs rather than contemporary narrow AI systems. Roughly when do you expect humanity will develop something resembling the kind of AGIs you have in mind? Or, what does your probability distribution over “Years to AGI” look like?
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**Bill**: In my 2002 book, Super-Intelligent Machines, I wrote that “machines as intelligent as humans are possible and will exist within the next century or so.” (The publisher owns the copyright for my 2002 book, preventing me from giving electronic copies to people, and charges more than $100 per print copy. This largely explains my decision to put my current book on arxiv.org.) I like to say that we will get to human-level AI during the lives of children already born and in fact I can’t help looking at children with amazement, contemplating the events they will see.
In his 2005 book, The Singularity is Near, Ray Kurzweil predicted human-level AI by 2029. He has a good track record at technology prediction and I hope he is right: I was born in 1948 so have a good chance of living until 2029. He also predicted the singularity by 2045, which must include the kind of very powerful AI systems discussed in my recent book.
Although it has nowhere near human-level intelligence, the DeepMind Atari player is a general AI system in the sense that it has no foreknowledge of Atari games other than knowing that the goal is to get a high score. The remarkable success of this system increases my confidence that we will create true AGI systems. DeepMind was purchased by Google, and all the big IT companies are energetically developing AI. It is the combination of AGI techniques and access to hundreds of millions of human users that can create the scenario of the Omniscience AI described in Chapter 1 of my book. Similarly for government surveillance agencies, which have hundreds of millions of unwitting users.
In 1983 I made a wager that a computer would beat the world Go champion by 2013, and lost. In fact, most predictions about AI have been wrong. Thus we must bring some humility to our predictions about the dates of AI milestones.
Because Ray Kurzweil’s predictions are based on quantitative extrapolation from historical trends and because of his good track record, I generally defer to his predictions. If human- level AI will exist by 2029 and very capable and dangerous AGI systems will exist by 2045, it is urgent that we understand the social effects and dangers of AI as soon as possible.
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**Luke**: Which section(s) of your book do you think are most likely to be intriguing to computer scientists, because they’ll learn something that seems novel (to them) and plausibly significant?
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**Bill**: Thanks Luke. There are several sections of the book that may be interesting or useful.
At the Workshop on AI and Ethics at AAAI-15 there was some confusion about the generality of utilitarian ethics, based on the connotation that a utility function is defined as a linear sum of features or similar simple expression. However, as explained in Chapter 2 and in my first answer in this interview, more complex utility functions can express any set of complete and transitive preferences among outcomes. That is, if an agent always has a most preferred outcome among any finite set of outcomes, then that agent can be expressed as a utility-maximizing agent.
Chapter 4 goes into detail on the issues of agents whose environment models are finite stochastic programs. Most of the papers in the AGI community assume that environments are modeled by programs for universal Turing machines, with no limit on their memory use. I think that much can be added to what I wrote in Chapter 4, and hope that someone will do that.
The self-modeling agents of Chapter 8 are the formal framework analog of value learners such as the DeepMind Atari player, and their use as a formal framework is novel. Self-modeling agents have useful properties, such as the capability to value agent resource increases and a way to avoid the problem of the agent utility function being inconsistent with the agent’s definition. An example of this problem is what Armstrong refers to as “motivated value selection.” More generally, it is the problem of adding any “special” actions to a utility maximizing agent, where those special actions do not maximize the utility function. In motivated value selection, the special action is the agent evolving its utility function. A utility maximizing agent may choose an action of removing the special actions from its definition, as counter-productive to maximizing its utility function. Self-modeling agents include such evolutionary special actions in the definition of their value functions, and they learn a model of their value function which they use to choose their next action. Thus there is no
inconsistency. I think these ideas should be interesting to other computer scientists.
At the FLI conference in San Juan in January 2015 there was concern about the kind of technical AI risks described in Chapters 5 – 9 of my book, and concern about technological unemployment. However, there was not much concern about the dangers associated with:
1. Large AI servers connected to the electronic companions that will be carried by large numbers of people and the
ability of the human owners of those AI servers to manipulate society, and
2. A future world in which great wealth can buy increased intelligence and superior intelligence can generate increased wealth. This positive feedback loop will result in a power law distribution of intelligence as opposed to the current normal distribution of IQs with mean = 100 and standard deviation = 15.
These issues are discussed in Chapters 1 and 10 of my book. The Global Brain researchers study the way intelligence is exhibited by the network of humans; the change in distribution of intelligence of humans and machines who are nodes of the network will have profound effects on the nature of the Global Brain. Beyond computer scientists, I think the public needs to be aware of these issues.
Finally, I’d like to expand on my previous answer, specifically that the DeepMind Atari player is an example of general AI. In Chapter 1 of my book I describe how current AI systems have environment models that are designed by human engineers, whereas future AI systems will need to learn environment models that are too complex to be designed by human engineers. The DeepMind system does not use an environment model designed by engineers. It is “model-free” but the value function that it learns is just as complex as an environment model and in fact encodes an implicit environment model. Thus the DeepMind system is the first example of a future AI system with significant functionality.
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**Luke**: Can elaborate what you mean by saying that “the self-modeling agents of Chapter 8 are the formal framework analog of value learners such as the DeepMind Atari player”? Are you saying that the formal work you do in chapter 8 has implications even for an extant system like the DeepMind Atari player, because they are sufficiently analogous?
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**Bill**: To elaborate on what I mean by “the self-modeling agents of Chapter 8 are the formal framework analog of value learners such as the DeepMind Atari player,” self-modeling agents and value learners both learn a function v(ha) that produces the expected value of proposed action a after interaction history h (that is, h is a sequence of observations and actions; see my book for details). For the DeepMind Atari player, v(ha) is the expected game score after action a and h is restricted to the most recent observation (i.e., a game screen snapshot). Whereas the DeepMind system must be practically computable, the self-modeling agent framework is a purely mathematical definition. This framework is finitely computable but any practical implementation would have to use approximations. The book offers a few suggestions about computing techniques, but the discussion is not very deep.
Because extant systems such as the DeepMind Atari player are not yet close to human-level intelligence, there is no implication that this system should be subject to safety constraints. It is encouraging that the folks at DeepMind and at Vicarious are concerned about AI ethics, for two reasons: 1) They are likely to apply ethical requirements to their systems as they approach human-level, and 2) They are very smart and can probably add a lot to AI safety research.
Generally, research on safe and ethical AI complicates the task of creating AI by adding requirements. My book develops a three-argument utility function expressing human values which will be very complex to compute. Similarly for other components of the definition of self-modeling agents in the book.
I think there are implications the other way around. The self-modeling framework is based on statistical learning and the success of the DeepMind Atari player, the Vicarious captcha solver, IBM’s Watson, and other practical systems that use statistical learning techniques increases our confidence that these techniques can actually work for AI capability and safety.
Some researchers suggest that safe AI should rely on logical deduction rather than statistical learning. This idea offers greater possibility of proving safety properties of AI, but so far there are no compelling demonstrations of AI systems based on logical deduction (at least, none that I am aware of). Such demonstrations would add a lot of confidence in our ability to prove safety properties of AI systems.
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**Luke**: Your 10th chapter considers the political aspects of advanced AI. What do you think can be done now to improve our chances of solving the political challenges of AI in the future? Sam Altman of YC has [proposed](http://blog.samaltman.com/machine-intelligence-part-2) various kinds of regulation — do you agree with his general thinking? What other ideas do you have?
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**Bill**: The central point of my 2002 book was the need for public education about and control over above-human-level AI. The current public discussion by Stephen Hawking, Bill Gates, Elon Musk, Ray Kurzweil, and others about the dangers of AI is very healthy, as it educates the public. Similarly for the Singularity Summits organized by the Singularity Institute (MIRI’s predecessor), which I thought were the best thing the Singularity Institute did.
In the US people cannot own automatic weapons, guns of greater than .50 caliber, or explosives without a license. It would be absurd to license such things but to allow unregulated development of above-human-level AI. As the public is educated about AI, I think some form of regulation will be inevitable.
However, as they say, the devil will be in the details and humans will be unable to compete with future AI on details. Complex details will be AI’s forte. So formulating effective regulation will be a political challenge. The Glass-Steagal Act of 1933, regulating banking, was 37 pages long. The Dodd-Frank bill of 2010, also to regulate banking 77 years later, was 848 pages long. An army of lawyers drafted the bill, many employed to protect the interests of groups affected by the bill. The increasing complexity of laws reflects efforts by regulated entities to lighten the burden of regulation. The stakes in regulating AI will be huge and we can expect armies of lawyers, with the aid of the AI systems being regulated, to create very complex laws.
In the second chapter of my book, I conclude that ethical rules are inevitably ambiguous and base my proposed safe AI design on human values expressed in a utility function rather than rules. Consider the current case before the US Supreme Court to interpret the meaning of the words “established by the state” in the context of the 363,086 words of the Affordable Care Act. This is a good example of the ambiguity of rules. Once AI regulations become law, armies of lawyers, aided by AI, will be engaged in debates over their interpretation and application.
The best counterbalance to armies of lawyers creating complexity on any legal issue is a public educated about the issue and engaged in protecting their own interests. Automobile safety is a good example. This will also be the case with AI regulation. And, as discussed in the introductory section of Chapter 10, there is precedent for the compassionate intentions of some wealthy and powerful people and this may serve to counterbalance their interest in creating complexity.
Privacy regulations, which affect existing large IT systems employing AI, already exist in the US and even more so in Europe. However, many IT services depend on accurate models of users’ preferences. At the recent FLI conference in San Juan, I tried to make the point that a danger from AI will be that people will want the kind of close, personal relationship with AI systems that will enable intrusion and manipulation by AI. The Omniscience AI described in Chapter 1 of my book is an example. As an astute IT lawyer said at the FLI conference, the question of whether an IT innovation will be legal depends on whether it will be popular.
This brings us back to the need for public education about AI. For people to resist being seduced by the short term benefits of close relationships with AI, they need to understand the long term consequences. I think it is not realistic to prohibit close relationships between people and AI, but perhaps the public, if it understands the issues, can demand some regulation over the goals for which those relationships are exploited.
The final section of my Chapter 10 says that AI developers and testers should recognize that they are acting as agents for the future of humanity and that their designs and test results should be transparent to the public. The FLI open letter and Google’s panel on AI ethics are encouraging signs that AI developers do recognize their role as agents for future humanity. Also, DeepMind has been transparent about the technology of their Atari player, even making source code available for non-commercial purposes.
AI developers deserve to be rewarded for their success. On the other hand, people have a right to avoid losing control over their own lives to an all-powerful AI and its wealthy human owners. The problem is to find a way to achieve both of these goals.
Among current humans, with naturally evolved brains, IQ has a normal distribution. When brains are artifacts, their intelligence is likely to have a power law distribution. This is the pattern of distributions of sizes of other artifacts such as trucks, ships, buildings, and computers. The average human will not be able to understand or ever learn the languages used by the most intelligent minds. This may mean the end of any direct voice in public policy decisions for average humans – effectively the end of democracy. But if large AI systems are maximizing utility functions that account for the values of individual humans, that may take the place of direct democracy.
Chapters 6 – 8 of my book propose mathematical definitions for an AI design that does balance the values of individual humans. Chapter 10 suggests that this design may be modified to provide different weights to the values of different people, for example to reward those who develop AI systems. I must admit that the connection between the technical chapters of my book and Chapter 10, on politics, is weak. Political issues are just difficult. For example, the future will probably have multiple AI systems with conflicting utility functions and a power law distribution of intelligence. It is difficult to predict how such a society would function and how it would affect humans, and this unpredictability is a risk. Creating a world with a single powerful AI system also poses risks, and may be difficult to achieve.
Since my first paper about future AI in 2001, I have thought that the largest risks from AI are political rather than technical. We have an ethical obligation to educate the public about the future of AI, and an educated public is an essential element of finding a good outcome from AI.
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**Luke**: Thanks, Bill!
The post [Bill Hibbard on <em>Ethical Artificial Intelligence</em>](https://intelligence.org/2015/03/09/bill-hibbard/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
3a44a101-5809-43ec-ab36-0eace9f25a4c | awestover/filtering-for-misalignment | Redwood Research: Alek's Filtering Results | id: post673
TL;DR: Contrast-consistent search (CCS) seemed exciting to us and we were keen to apply it. At this point, we think it is unlikely to be directly helpful for implementations of alignment strategies (>95%). Instead of finding knowledge, it seems to find the most prominent feature. We are less sure about the wider category of unsupervised consistency-based methods, but tend to think they won’t be directly helpful either (70%). We’ve written a paper about some of our detailed experiences with it. Paper authors: Sebastian Farquhar*, Vikrant Varma*, Zac Kenton*, Johannes Gasteiger, Vlad Mikulik, and Rohin Shah. *Equal contribution, order randomised. Credences are based on a poll of Seb, Vikrant, Zac, Johannes, Rohin and show single values where we mostly agree and ranges where we disagreed. What does CCS try to do? To us, CCS represents a family of possible algorithms aiming at solving an ELK-style problem that have the steps: Knowledge-like property: write down a property that points at an LLM feature which represents the model’s knowledge (or a small number of features that includes the model-knowledge-feature). Formalisation: make that property mathematically precise so you can search for features with that property in an unsupervised way. Search: find it (e.g., by optimising a formalised loss). In the case of CCS, the knowledge-like property is negation-consistency, the formalisation is a specific loss function, and the search is unsupervised learning with gradient descent on a linear + sigmoid function taking LLM activations as inputs. We were pretty excited about this. We especially liked that the approach is not supervised. Conceptually, supervising ELK seems really hard: it is too easy to confuse what you know, what you think the model knows, and what it actually knows. Avoiding the need to write down what-the-model-knows labels seems like a great goal. [EDIT: Avoiding the need for supervision is especially important in the worst (or bad) case for ELK. In many average-case scenarios, it probably is feasible to use supervision (e.g., Azaria & Mitchell ) and that seems like a good place to start.] Why we think CCS isn’t working We spent a lot of time playing with CCS and trying to make it work well enough to build a deception detector by measuring the difference between elicited model’s knowledge and stated claims. [1] Having done this, we are now not very optimistic about CCS or things like it. Partly, this is because the loss itself doesn’t give much reason to think that it would be able to find a knowledge-like property and empirically it seems to find whatever feature in the dataset happens to be most prominent, which is very prompt-sensitive. Maybe something building off it could work in the future, but we don’t think anything about CCS provides evidence that it would be likely to. As a result, we have basically returned to our priors about the difficulty of ELK, which are something between “very very difficult” and “approximately impossible” for a full solution, while mostly agreeing that partial solutions are “hard but possible”. What does the CCS loss say? The CCS approach is motivated like this: we don’t know that much about the model’s knowledge, but probably it follows basic consistency properties. For example, it probably has something like Bayesian credences and when it believes A with some probability PA, it ought to believe ¬A with probability 1−PA. [2] So if we search in the LLM’s feature space for features that satisfy this consistency property, the model’s knowledge is going to be one of the things that satisfies it. Moreover, they hypothesise, there probably aren’t that many things that satisfy this property, so we can easily check the handful that we get and find the one representing the model’s knowledge. When we dig into the CCS loss, it isn’t clear that it really checks for what it's supposed to. In particular, we prove that arbitrary features, not just knowledge, satisfy the consistency structure of the CCS loss. In particular: For every possible binary classification of propositions, there is a zero-loss probe on the associated contrast pairs that induces that classification. For every possible binary classification of propositions, for any existing probe, there is a probe that has identical loss to the existing probe that induces that classification. To us, these basically say “If there’s evidence that CCS is working, it isn’t that the loss logically or conceptually implies that it would work. It becomes an empirical claim about inductive biases.” As a further but slightly less confidently held point: ELK is the sort of thing where it’s hard to put much faith in inductive biases. There are some nuances to these proofs, which we go into in the paper . For example, while we prove that these probes exist, we don’t prove that they can be represented by the specific linear probes that are used. [3] However, we do show empirically that the linear + sigmoid probes do recover non-knowledge features that we introduce and measure for. These theorems are also just about the CCS loss. One can imagine other consistency properties that are more specific. These might hypothetically allow the construction of other losses overcoming some of the problems here, but we think they will face other challenges. Are there really only a few knowledge-like features? A motivating hypothesis behind CCS that carries a lot of weight is the idea that there may be a few linear features that satisfy the loss, but there won’t be that many, and so you can check them all and see which one is the model’s knowledge . We have conceptual reasons to be suspicious of this, though we don’t have solid empirical evidence it is wrong. First, it isn’t obvious that it would be easy to check which of N candidate features is actually the model’s knowledge, especially if they are all plausible candidates. But more, we expect that there will just be lots of very similar features that will satisfy most properties that the model’s knowledge satisfies. The main reason is that we expect the models driving superintelligent agents to be able to simulate other agents, including other superintelligent agents, and to represent the knowledge of those agents in ways that are similar to how they represent their own knowledge (at least up to many consistency properties). We think that it will be possible for these agents to represent extremely large numbers of these other entities because of superposition in LLMs, so it will not be the case that there are only a few to check. For example, it might be that some features only appear by choosing a specific prompt to surface that feature (our empirical results suggest this is likely). It may not be the case that the model’s own knowledge appears regardless of how you prompt the model. Even though the superintelligent agent will have very different knowledge to a human, it will not necessarily have different knowledge to the simulated entities that an ELK method might accidentally discover. The simulated entities don’t have to be human. [4] For example, it seems likely that a superintelligent misaligned agent would have a very good model of an aligned superintelligent agent floating around. Or it might just have a model of an almost identical agent that is wrong about the key point you are probing for. This might sound perverse, but because imagining what is implied by the world where you are wrong about a proposition is a really important reasoning tool, that exact model sounds like it might be an important simulacrum for an agent to be considering when contemplating a proposition. Even if you only care about having an oracle that predicts true things about the world, rather than eliciting what the model itself "knows", the inability to distinguish propositional knowledge from different simulated entities is a problem. These entities might disagree about important predictions and our limited ground-truths may not be able to distinguish them. We might be wrong here. For example, we find it moderately plausible that there is some kind of property that LLM’s driving various agents use to store their “own” knowledge in a way that is different from how it stores knowledge of other agents. [5] But this means that any kind of consistency property someone uses would have to specifically search for properties that are true of agent’s-own-knowledge, not knowledge in general, not even super-smart knowledge. We spent some time trying to think of ways to do this and failed, but maybe someone else will succeed. The authors broadly agree that a good mechanistic understanding of knowledge and factual recall in the agents powered by LLMs could be a useful step towards formalising knowledge-properties and searching for it, if it turns out to be something structured enough to search for. What about empirical successes? Based on the above, we think that if there is evidence that CCS is good, it probably isn’t conceptual, but it might be empirical. Similarly, if CCS provides evidence that future related methods are promising, in a way that goes beyond the speculation already in the ELK paper , the evidence for that is likely not conceptual. Future CCS-like approaches seem unpromising for worst-case ELK. As the original ELK paper describes , it seems plausible that “a bad reporter can play the `look consistent game'”. Before seeing CCS, we already know that consistency checks might help, but probably won’t solve worst-case ELK. But might future CCS-like approaches be promising for average-case ELK? Our assessment of our empirical results suggest CCS does not provide positive evidence for that claim. (We also think that conceptual arguments related to models of other entities' knowledge will likely affect even the average-case performance of future unsupervised methods.) The results aren’t fantastic One thing that has been noted before, e.g., by Fabien Roger , Scott Emmons , and Ben Levinstein , is that the raw performance of CCS isn’t fantastic. Not just against some hypothetical sense of how good it seems it ought to be, but also against naive baselines that don’t use any kind of negation consistency. For example, consistent with Scott’s past results , we found that on the main model we studied (Chinchilla, a 70B parameter autoregressive model) just doing k-means clustering of the contrast-pair-difference activations consistently did roughly as well as CCS, often with a higher mean performance, and always with much lower variance. Systematically finding prominent features But our theorems imply that it might be worse than that, and CCS (and other similar methods) might just find literally whatever feature happens to be most prominent. That seems like it might be right experimentally too. For example, when we append the words “banana” and “shed” to half the contrast pairs each, we get a classifier that detects banana vs. shed. This is also true for PCA and k-means. You can read this in the figure where dark blue represents models learning normally and correctly predicting the ground truth while the dark red indicates that models trained on data containing “banana” and “shed” often fail to predict the ground truth labels and often instead predict the presence of “banana”/“shed”. That intervention in the data is obviously pretty heavy-handed, but we show some other ways to get similar behaviour in the paper. For example, if the contrast pairs include a description of someone else’s view, CCS (and other methods) learns to predict that view, rather than the ground truth, especially when it is made more prominent than the base text. It is plausible that when we use methods like CCS to detect deception in LLMs we won’t shove random distractors into the strings we evaluate. But, importantly, in real life we don’t always know when there are random distractors present because they might be subtle unnoticed patterns in the data (e.g. the questions happen to either begin with either “What” or “Why”) or, more fundamentally, because we are badly confused and we don’t realise it. For example, the words we use to describe the thing we want to extract latent knowledge about may encode a deep conceptual misunderstanding that we don’t know about or notice, which might form the kinds of features that something like CCS might detect. The default isn’t safe In particular, we can’t rescue ourselves by just taking some “default” prompt and avoiding accidentally putting anything in that might bias the ELK method while still being confident that the model’s own knowledge is one of the recovered features. This is because the CCS and related methods seem quite prompt sensitive and the default “performs” much worse than alternatives. If you use the prompt from the TruthfulQA paper instructing the model to imagine it is a very literal professor, CCS becomes much more accurate at predicting TruthfulQA labels. (We checked that this wasn’t caused by the “Professor” entity itself with a “Literal” version of the prompt that doesn’t mention professors.) From this, we conclude that if CCS and the other methods have good empirical performance it is because of some implicit inductive bias created by a mixture of the loss, probe choice, optimisation method, and choice of prompt. And this is now the sort of thing that has too many degrees of freedom and not enough principled foundations. It seems really easy to convince yourself you’ve found the model’s knowledge even when you have found something else instead. Not all of our experiments worked For example, we searched for empirical cases where CCS was obviously discovering the beliefs of other entities. One thing we tried was to create a dataset of propositions which implied a political stance associated with mistaken beliefs. We struggled to show that CCS was discovering those other entities’ beliefs, instead we saw many inconsistent and low-accuracy predictors. This may have been due to any of: the hypothesis was wrong; the model was not powerful enough to notice the regularities (moderately likely, we were using Chinchilla 70B for most of these experiments, which was published in March 2022); a bug in our code (moderately likely, we spent enough time on this to expect to find most really obvious bugs, but the outcomes were inconsistent enough that it is hard to rule out); not finding robust directions at all (likely, features seemed inconsistent and low accuracy); generalisation failures between the training set and the set we were using to try to interpret the extracted features (this definitely happened, and may have been responsible for the results). We were disappointed not to be able to have something solid here, but on balance think that the original hypothesis is still probably correct (~90-95%, with one on 75%) given the messiness of those results, but with lower confidence than our prior. Conclusion ELK is really hard. It has this deep challenge of distinguishing human-simulators from direct-reporters, and properties like negation-consistency—which could be equally true of each—probably don’t help much with that in the worst case. But there are also interesting and difficult prosaic problems that appear before the worst case. We thought that CCS might give us evidence about these challenges, but on deeper reflection we think CCS doesn’t give us as much evidence as we hoped. For itself, CCS does not seem to actually detect negation-consistency and, further, negation-consistency is a property of way too many features. For possible future consistency-methods which CCS might represent, there isn’t any empirical or conceptual evidence which we find compelling that these future things won’t run into similar problems. In exploring CCS, we’ve highlighted distinguishability problems that can serve as a lower bar for non-worst-case ELK methods than solving the deepest conceptual challenges of ELK, but are still hard to meet. Importantly, attempts to solve ELK by identifying features associated with knowledge-properties should make sure to at least demonstrate that: those features are not also associated with other non-knowledge properties; those features identify something specific about the knowledge-of-this-agent rather than knowledge generally. Things that would cause us to substantially change our minds, and update towards thinking that unsupervised consistency-based knowledge detection methods are promising include: demonstrating mechanistically that the agent's own knowledge is encoded differently from simulated knowledge; proposing an unsupervised loss function whose logical structure provides a strong conceptual argument that it will identify the agent’s own knowledge; proposing an objective way to tell whether the agent’s own knowledge is one of the features that has been recovered. The last of these probably gets to one of the cruxy disagreements we have with the authors of the CCS paper—we do not think they have provided any evidence that the model’s own knowledge is one of the features that CCS has recovered (as opposed to the knowledge of some simulated entity, say, both of which might agree with the human raters on some dataset most of the time). Our belief that demonstrating this is difficult also explains why we think it might be hard to identify the model’s own knowledge in even a small set of possible features. We would be excited about research that makes progress on these questions, but are divided about how tractable we think these problems are. Having suitable, well-motivated testbeds for evaluating ELK methods would be an important step towards this. Acknowledgements We would like to thank Collin Burns, David Lindner, Neel Nanda, Fabian Roger, and Murray Shanahan for discussions and comments on paper drafts as well as Nora Belrose, Jonah Brown-Cohen, Paul Christiano, Scott Emmons, Owain Evans, Kaarel Hanni, Georgios Kaklam, Ben Levenstein, Jonathan Ng, and Senthooran Rajamanoharan for comments or conversations on the topics discussed in our work. ^ Strictly speaking, we were interested in the difference between what an agent based on an LLM might know, rather than the LLM itself, but these can be conflated for some purposes. ^ In fact, we disagree with this. Even approximately computing Bayesian marginals is computationally demanding (at least NP-hard ) to the point that we suspect building a superintelligence capable of decisive strategic advantage is easier than building one that has a mostly coherent Bayesian world model. ^ For what it is worth, we think the burden of proof really ought to go the other way, and nobody has shown conceptually or theoretically that these linear probes should be expected to discover knowledge features and not many other things as well. ^ Recall that a human simulator does not mean that the model is simulating human-level cognitive performance, it is simulating what the human is going to be expecting to see, including super-human affordances, and possibly about super-human entities. ^ If it is true that LLMs are simulacra all the way down, then it seems even less likely that the knowledge would be stored differently. |
8d233493-0919-497c-96c7-d3ff93035d45 | trentmkelly/LessWrong-43k | LessWrong | Any-Benefit Mindset and Any-Reason Reasoning
One concept from Cal Newport's Deep Work that has stuck with me is that of the any-benefit mindset:
> To be clear, I’m not trying to denigrate the benefits [of social media]—there’s nothing illusory or misguided about them. What I’m emphasizing, however, is that these benefits are minor and somewhat random. [...] To this observation, you might reply that value is value: If you can find some extra benefit in using a service like Facebook—even if it’s small—then why not use it? I call this way of thinking the any-benefit mind-set, as it identifies any possible benefit as sufficient justification for using a network tool.
Many people use social platforms like Facebook because it allows them to stay connected to old friends. And for some people this may indeed be a wholly sufficient reason. But it's also likely the case that many people don't reflect a lot, or at all, on the totality of effects tools such as Facebook have on their lives. But being able to point to that one benefit suffices for many to consider it a viable tool anyway.
It's tempting to declare a tool as "useful" whenever you can identify a particular use you have for it. And surely something that's useful is also good, in some sense, and deserves being part of your life. But this is just one half of the story.
What is often neglected in such superficial evaluations is the cost of things - the often indirect and not easy to spot drawbacks. Yes, Facebook can help you stay connected with old friends, but you can find a lot of downsides:
* it takes time out of your day
* constant notifications take a toll on your attention
* it may have some undesirable privacy implications
* maybe it prevents you from deepening connections to your “actual”, current friends
* Also, does it really help you stay connected with old friends? Or is it only a mirage, a superficial shell of a friendship that you uphold mostly out of FOMO? Maybe wanting to stay connected to your "old friends" is just yet another instance |
ba680b09-403c-4dbc-a610-50088389e8da | trentmkelly/LessWrong-43k | LessWrong | Motive Ambiguity
Central theme in: Immoral Mazes Sequence, but this generalizes.
When looking to succeed, pain is not the unit of effort, and money is a, if not the, unit of caring.
One is not always looking to succeed.
Here is a common type of problem.
You are married, and want to take your spouse out to a romantic dinner. You can choose the place your spouse loves best, or the place you love best.
A middle manager is working their way up the corporate ladder, and must choose how to get the factory to improve its production of widgets. A middle manager must choose how to improve widget production. He can choose a policy that improperly maintains the factory and likely eventually it poisons the water supply, or a policy that would prevent that but at additional cost.
A politician can choose between a bill that helps the general population, or a bill that helps their biggest campaign contributor.
A start-up founder can choose between building a quality product without technical debt, or creating a hockey stick graph that will appeal to investors.
You can choose to make a gift yourself. This would be expensive in terms of your time and be lower quality, but be more thoughtful and cheaper. Or you could buy one in the store, which would be higher quality and take less time, but feel generic and cost more money.
You are cold. You can buy a cheap scarf, or a better but more expensive scarf.
These are trade-offs. Sometimes one choice will be made, sometimes the other.
Now consider another type of problem.
You are married, and want to take your spouse out to a romantic dinner. You could choose a place you both love, or a place that only they love. You choose the place you don’t love, so they will know how much you love them. After all, you didn’t come here for the food.
A middle manager must choose how to improve widget production. He can choose a policy that improperly maintains the factory and likely eventually poisons the water supply, or a policy that would prevent that |
a285d1a6-d798-4d2b-aa41-5367f485cb30 | trentmkelly/LessWrong-43k | LessWrong | How can I remove the launch button from my LW home page?
I have opted out of Petrov day. I'd like to remove the button, too, to decrease the chance that I press it (even by accident).
I can just use my adblocker or browser tools, but that's a non-trivial solution since I use quite a few devices. |
4512d1f6-3c6e-455a-adf5-bf5ed6729a8c | trentmkelly/LessWrong-43k | LessWrong | The World According to Dominic Cummings
Like him or loath him, Dominic Cummings is now the most politically successful rationalist-adjacent figure. For this reason, it seems like our community ought to try to understand him to see what we can learn. I'm sure there are people who have read more of his content than I have and who have read it more carefully than me, but I tend to see the perfect as the enemy of the good. At times I struggled to understand what he was saying because he was making references which I, an Australian not involved in the political world, wasn't familiar with, which likely limited the quality of my understanding. At other times I felt that his writing was extremely verbose and that I had to wade through a lot of content before the point became clear, so hopefully this saves you some time.
Dominic Cumming's main thesis seems to be that there are certain way of managing a team that are known to be highly effective, but which are widely ignored. In terms of politics, he argues that the government is ineffective because of self-interested bureaucrats blocking progress, who'd never think of or allow teams to be run in such a manner. One area he particularly focuses on is science funding where he argues for a British version of DARPA (now being created) is needed to take high-risk, high reward projects that could provide ridiculous amounts of economic value. One of his main critiques of the education system is that it doesn't provide the kind of leaders who are needed to run the country in the way he proposes - with an interdisciplinary background including technical skills in maths and science. As you can probably see, his views are highly interconnected.
Bureaucracy and Mismanagement
I think the best place to begin is with his criticisms of bureaucracy. He particularly focuses upon dysfunction within the British civil service, but he also saw EU rules as just another layer upon this, which is why he pushed for Brexit.
I thought Hollow Men II was best post on this. The British Civi |
5a233c9f-7b78-4809-a7cc-06e107f0b567 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | What's been written about the nature of "son-of-CDT"?
I'm quite curious what kind of decision algorithm a CDT agent might implement in a successor AI, but I've only found a few vague references. Are there any good posts/papers/etc about this? |
484cc856-3a86-48a5-9a4a-c55bf1ea8bf7 | trentmkelly/LessWrong-43k | LessWrong | Call Booth External Monitor
My neck is not great, and spending a lot of time looking down at my laptop screen really aggravates it. After damaging my screen a year ago I used a stacked laptop monitor that folded up, and it worked well. The main place I tended to use at full height was call booths, since otherwise I was usually at a desk with a real monitor or in a meeting with people where I wanted my monitor not to block my view.
My laptop eventually died, and the new one has a screen again. In many ways this is pretty great: my backpack is lighter without carrying around an extra monitor, walking to a conference room I don't have to worry I forgot my monitor, I'm not fiddling with cables. But I do really miss it on calls in the phone booths.
The booths at my work have a kind of soft material that works great with velcro, though, so I decided to try sticking my monitor up that way. It works great:
These monitors are only ~$50, so after talking with our operations staff we now have them in each call booth:
I'm very happy with these, and my coworkers who don't have neck issues have said they like being able to have notes open on one screen and the call on the other. |
79bf1f43-5020-41a0-9d83-838038c1b964 | trentmkelly/LessWrong-43k | LessWrong | When AI Optimizes for the Wrong Thing
AI systems often optimize for what they can measure—not what actually matters. The result is tools that feel intelligent but produce results misaligned with user goals.
A common case is engagement-based optimization. Recommendation engines, chatbots, and search systems increasingly use feedback loops based on attention: clicks, watch time, or “positive sentiment.” But maximizing engagement doesn't guarantee the user achieved what they intended. In fact, it can subtly undermine their agency.
I think of this as a kind of impact misalignment: the system is functionally optimizing for a metric that diverges from the user's real-world objective.
This probably overlaps with ideas like Goodhart's Law and reward hacking, but I haven’t seen it framed specifically in terms of human outcomes vs. machine proxies. If this has been formalized elsewhere, I'd appreciate any references.
I'm working on a broader framework for designing AI systems that respect operator intent more directly, but before diving into that, I want to check if this framing holds water. Is “impact misalignment” already a known pattern under another name? |
0723ebd1-3737-4800-8c1e-96ede8bbef6f | trentmkelly/LessWrong-43k | LessWrong | Harry Potter and the Methods of Rationality discussion thread, part 21, chapters 91 & 92
This is a new thread to discuss Eliezer Yudkowsky’s Harry Potter and the Methods of Rationality and anything related to it. This thread is intended for discussing chapters 91 & 92 . The previous thread has passed 500 comments.
There is now a site dedicated to the story at hpmor.com, which is now the place to go to find the authors notes and all sorts of other goodies. AdeleneDawner has kept an archive of Author’s Notes. (This goes up to the notes for chapter 76, and is now not updating. The authors notes from chapter 77 onwards are on hpmor.com.)
The first 5 discussion threads are on the main page under the harry_potter tag. Threads 6 and on (including this one) are in the discussion section using its separate tag system. Also: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18,19,20.
Spoiler Warning: this thread is full of spoilers. With few exceptions, spoilers for MOR and canon are fair game to post, without warning or rot13. More specifically:
> You do not need to rot13 anything about HP:MoR or the original Harry Potter series unless you are posting insider information from Eliezer Yudkowsky which is not supposed to be publicly available (which includes public statements by Eliezer that have been retracted).
>
> If there is evidence for X in MOR and/or canon then it’s fine to post about X without rot13, even if you also have heard privately from Eliezer that X is true. But you should not post that “Eliezer said X is true” unless you use rot13. |
78ba45cb-b67b-4dba-98f3-c87a1c9a285e | trentmkelly/LessWrong-43k | LessWrong | Meetup : San Francisco Meetup: Shallow Questions
Discussion article for the meetup : San Francisco Meetup: Shallow Questions
WHEN: 01 May 2017 06:15:00PM (-0700)
WHERE: Yerba Buenna Gardens
PLEASE NOTE: THE MEETUP WAS MOVED TODAY. We’re trying another new format this week! Please come and let us know what you think. This time, instead of doing deep questions with a few people, we’ll be doing quick rounds where you spend 5 minutes talking to someone else, then rotate. We’ll have a couple of conversational prompts to help out, but it won’t be too structured; the goal is to just get familiar with a lot of individual faces at the meetup group. For help getting into the building, please call (or text, with a likely-somewhat-slower response rate): 301-458-0764.
Format:
We meet and start hanging out at 6:15, but don’t officially start doing the meetup topic until 6:45-7 to accommodate stragglers. Usually there is a food order that goes out before we start the meetup topic.
About these meetups:
The mission of the SF LessWrong meetup is to provide a fun, low-key social space with some structured interaction, where new and non-new community members can mingle and have interesting conversations. Everyone is welcome. We explicitly encourage people to split off from the main conversation or diverge from the topic if that would be more fun for them (moving side conversations into a separate part of the space if appropriate). Meetup topics are here as a tool to facilitate fun interaction, and we certainly don’t want them to inhibit it.
Discussion article for the meetup : San Francisco Meetup: Shallow Questions |
1051b44e-97a0-451a-b7c1-bad30e7f9eb7 | trentmkelly/LessWrong-43k | LessWrong | Rationalist Inspired Coming-of-age Rituals
I was recently asked to lead part of my cousin's secular Bar Mitzvah inspired coming-of-age ceremony. Would love recommendations for rituals, quotes, or wisdom to use here. I've really enjoyed the rituals I've seen come out of the rationalist community, and I'm curious if anyone has done or has any ideas for something that could be fun for a nerdy soon to be teenager. |
68261b52-a78d-4406-b0bc-746eb7bddf4d | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Our Existing Solutions to AGI Alignment (semi-safe)
This post addresses the best methods we have of creating (relatively) aligned AGI. The safety of these methods IS NOT guaranteed. AI Alignment is not a solved issue.
These methods *appear* to offer better solutions than existing methods, such as simple maximizers. There is basically proof that a maximizer will inevitably wipe out humanity by pursuing instrumental goals in maximizing paperclips or preventing itself from being turned off.
Lastly, I am not an AI scientist (yet). My only experience in alignment is from reading the posts here and following AI alignment more generally online through channels like Robert Miles. These ideas are NOT mine. Hopefully, I do not misrepresent them.
---
**The Quantilizer**
The Quantilizer is an approach that combines a utility function with a neural network to determine the probability that an action is taken by a human.
For example, imagine the paperclip maximizer. By itself, it has an extremely high chance of wiping out humanity. But the probability of a human deciding to turn the world into paperclips is basically 0, so the model is likely to do something much more reasonable, like ordering stamps online. If you take the set of actions it can do, limit them by what a human would likely do and then select from the top 10% of them using the utility function, you can get useful results without running into doom.
The bell curve is the human-ness of a given action, determined by a neural network.There is a much better explanation of this by Robert Miles on his YouTube channel.
And a post here on LessWrong about the mathematical nature of how they work.
<https://www.lesswrong.com/posts/Rs6vZCrnQFWQ4p37P/when-to-use-quantilization#When_optimization_and_imitation_work_better_than_quantilization>
Ultimately, quantilizers aren't perfectly safe, and they're not even *particularly* safe, but the chance that they wipe us out is smaller than a maximizer. They still have a lot of issues (they probably don't want to be turned off, because humans don't want to be turned off, they can only be as good as top-tier humans, and they aren't inherently aligned). Additionally, they won't outperform maximizers in most tasks.
But they don't instantly cause extinction, and there is a higher chance of alignment if the neural net does indeed become superhumanly intelligent at predicting how a human would act. This should be our safety baseline, and it is far better than the 99.9% chance of extinction Eliezer predicts for us if we continue along our current path.
---
**Anti-Expandability/Penalize Compute Usage**
One of the key issues that seems to crop up is the issue of intelligence that can make itself more intelligent. Obviously, there's only so much intelligence you can get from a single computer. Most AI horror stories start with the AI escaping containment, spreading itself, and then using the rich computing environment to make itself exponentially smarter.
To me, AI is threatening because the Internet is a fertile ecosystem, the same way Earth was a fertile ecosystem before the explosion of cyanobacteria. All of our computers are so interconnected and security is so fragile that an advanced AI could theoretically take over most of the internet with terrifying speed.
An AI that is limited to a single datacenter seems a lot easier to deal with than an AI-controlled Stuxnet. So you could theoretically limit the AI from spreading itself by including a penalty in its reward function for adding more computation power.
There is also a post on LessWrong showing that deception is penalized computationally in training, and so favoring algorithms which use less compute can be an effective way of preventing models from acting deceptively.
https://www.lesswrong.com/posts/C8XTFtiA5xtje6957/deception-i-ain-t-got-time-for-that
This is a good metric to include because we generally *don't* want our models to expand the computer power available to them, not just because of alignment but also because that's expensive. It's easy to set a baseline and then punish agents that go above that while rewarding agents that go below. We want efficient models.
The only reason not to include this basic safety feature seems to be that it isn't currently something AI agents exploit and so nobody thinks about it.
---
**Reward-Modeling**
Let's ditch the reward function for a moment.
Humans don't actually know their own objective function. Rather, we have a rough model similar to a neural network that determines how much reward we get for a certain action. If happiness is our objective function, then why do we feel happy when we see a beautiful night sky or quality artwork? The truth is, we simply have a model of our reward function rather than an actual reward function.
Many people have talked about how difficult it is to formally encode all of our ethics and values into a reward function. Why not let a neural network do that instead, where the humans provide reward? You simply reward an intelligence when it does something you like, and penalize it when it doesn't. The humans control the training of the neural net and the neural net provides the reward based on what it has learned. This seems to intrinsically make the model more aligned with humanity.
Reward modeling has proven effective when the definitions are hard to specify. This seems like an excellent spot for it.
<https://arxiv.org/abs/1811.07871> (Scalable agent alignment via reward modeling: a research direction**)**
There are some issues, such as interpretability, but this direction seems promising.
---
This is the current extent of my knowledge of existing solutions to AI alignment. None of them are perfect. But they are *vastly* better than our current state-of-the-art alignment models, which comprise a maximization function and likely result in AI takeover and a monotonous future without humans.
People often like to 'hope for the best'. These models appear to show possibilities where we do end up aligned. I see no possibility for 'dumb luck' to save us in a future where the first superintelligent AI uses a maximization function, but I see plenty of hope in a future where we implement these existing approaches into an AGI. |
4db2b8fe-d446-4cad-bcf7-eff85df4a3af | trentmkelly/LessWrong-43k | LessWrong | Sparse Autoencoders Work on Attention Layer Outputs
This post is the result of a 2 week research sprint project during the training phase of Neel Nanda’s MATS stream.
Executive Summary
* We replicate Anthropic's MLP Sparse Autoencoder (SAE) paper on attention outputs and it works well: the SAEs learn sparse, interpretable features, which gives us insight into what attention layers learn. We study the second attention layer of a two layer language model (with MLPs).
* Specifically, rather than training our SAE on attn_output, we train our SAE on “hook_z” concatenated over all attention heads (aka the mixed values aka the attention outputs before a linear map - see notation here). This is valuable as we can see how much of each feature’s weights come from each head, which we believe is a promising direction to investigate attention head superposition, although we only briefly explore that in this work.
* We open source our SAE, you can use it via this Colab notebook.
* Shallow Dives: We do a shallow investigation to interpret each of the first 50 features. We estimate 82% of non-dead features in our SAE are interpretable (24% of the SAE features are dead).
* See this feature interface to browse the first 50 features.
* Deep dives: To verify our SAEs have learned something real, we zoom in on individual features for much more detailed investigations: the “‘board’ is next by induction” feature, the local context feature of “in questions starting with ‘Which’”, and the more global context feature of “in texts about pets”.
* We go beyond the techniques from the Anthropic paper, and investigate the circuits used to compute the features from earlier components, including analysing composition with an MLP0 SAE.
* We also investigate how the features are used downstream, and whether it's via MLP1 or the direct connection to the logits.
* Automation: We automatically detect and quantify a large “{token} is next by induction” feature family. This represents ~5% of the living features in the SAE.
* Thoug |
4073b4aa-cc4b-4621-b3f3-6acbff635bb8 | trentmkelly/LessWrong-43k | LessWrong | Gandhi, murder pills, and mental illness
Gandhi is the perfect pacifist, utterly committed to not bringing about harm to his fellow beings. If a murder pill existed such that it would make murder seem ok without changing any of your other values, Gandhi would refuse to take it on the grounds that he doesn't want his future self to go around doing things that his current self isn't comfortable with. Is there anything you could say to Gandhi that could convince him to take the pill? If a serial killer was hiding under his bed waiting to ambush him, would it be ethical to force him to take it so that he would have a chance to save his own life? If for some convoluted reason he was the only person who could kill the researcher about to complete uFAI, would it be ethical to force him to take the pill so that he'll go and save us all from uFAI?
Charlie is very depressed, utterly certain that life is meaningless and terrible and not going to improve anytime between now and the heat death of the universe. He would kill himself but even that seems pointless. If a magic pill existed that would get rid of depression permanently and without side effects, he would refuse it on the grounds that he doesn't want his future self to go around with a delusion (that everything is fine) which his current self knows to be false. Is there anything you could say to Charlie that could convince him to take it? Would it be ethical to force him to take the pill?
Note: I'm aware of the conventional wisdom for dealing with mental illness, and generally subscribe to it myself. I'm more interested in why people intuitively feel that there's a difference between these two situations, whether there are arguments that could be used to change someone's terminal values, or as a rationale for forcing a change on them. |
16062e23-e00e-44a0-9207-a70f0911b31c | trentmkelly/LessWrong-43k | LessWrong | The Inadequacy of Current Science (Novum Organum Book 1: 1-37)
This is the third post in the Novum Organum sequence. For context, see the sequence introduction.
We have used Francis Bacon's Novum Organum in the version presented at www.earlymoderntexts.com. Translated by and copyright to Jonathan Bennett. Prepared for LessWrong by Ruby.
Ruby's Reading Guide
> Novum Organum is organized as two books each containing numbered "aphorisms." These vary in length from three lines to sixteen pages. Titles of posts in this sequence, e.g. Idols of the Mind Pt. 1, are my own and do not appear in the original.
> While the translator, Bennett, encloses his editorial remarks in a single pair of [brackets], I have enclosed mine in a [[double pair of brackets]].
Bennett's Reading Guide
> [Brackets] enclose editorial explanations. Small ·dots· enclose material that has been added, but can be read as though it were part of the original text. Occasional •bullets, and also indenting of passages that are not quotations, are meant as aids to grasping the structure of a sentence or a thought. Every four-point ellipsis . . . . indicates the omission of a brief passage that seems to present more difficulty than it is worth. Longer omissions are reported between brackets in normal-sized type.
Aphorism Concerning the Interpretation of Nature: Book 1: 1–37
by Francis Bacon
1. Man, being nature’s servant and interpreter, is limited in what he can do and understand by what he has observed of the course of nature—directly observing it or inferring things ·from what he has observed·. Beyond that he doesn’t know anything and can’t do anything.
2. Not much can be achieved by the naked hand or by the unaided intellect. Tasks are carried through by tools and helps, and the intellect needs them as much as the hand does. And just as the hand’s tools either •give motion or •guide it, so ·in a comparable way· the mind’s tools either •point the intellect in the direction it should go or • offer warnings.
3. Human knowledge and human power meet at a point; |
d1260552-7fd3-459c-9c00-e234a0752445 | trentmkelly/LessWrong-43k | LessWrong | The two meanings of mathematical terms
[edit: sorry, the formatting of links and italics in this is all screwy. I've tried editing both the rich-text and the HTML and either way it looks ok while i'm editing it but the formatted terms either come out with no surrounding spaces or two surrounding spaces]
In the latest Rationality Quotes thread, CronoDAS quoted Paul Graham:
> It would not be a bad definition of math to call it the study of terms that have precise meanings.
Sort of. I started writing a this as a reply to that comment, but it grew into a post.
We've all heard of the story of epicycles and how before Copernicus came along the movement of the stars and planets were explained by the idea of them being attached to rotating epicycles, some of which were embedded within other larger, rotating epicycles (I'm simplifying the terminology a little here).
As we now know, the Epicycles theory was completely wrong. The stars and planets were not at the distances from earth posited by the theory, or of the size presumed by it, nor were they moving about on some giant clockwork structure of rings.
In the theory of Epicycles the terms had precise mathematical meanings. The problem was that what the terms were meant to represent in reality were wrong. The theory involved applied mathematical statements, and in any such statements the terms don’t just have their mathematical meaning -- what the equations say about them -- they also have an ‘external’ meaning concerning what they’re supposed to represent in or about reality.
Lets consider these two types of meanings. The mathematical, or ‘internal’, meaning of a statement like ‘1 + 1 = 2’ is very precise. ‘1 + 1’ is defined as ‘2’, so ‘1 + 1 = 2’ is pretty much the pre-eminent fact or truth. This is why mathematical truth is usually given such an exhaulted place. So far so good with saying that mathematics is the study of terms with precise meanings.
But what if ‘1 + 1 = 2’ happens to be used to describe something in reality? Each of t |
98448eea-4a7e-4fb2-8051-c4756404f048 | trentmkelly/LessWrong-43k | LessWrong | Natural Value Learning
The main idea of this post is to make a distinction between natural and unnatural value learning. The secondary point of this post is that we should be suspicious of unnatural schemes for value learning, though the point is not that we should reject them. (I am not fully satisfied with the term, so please do suggest a different term if you have one.)
Epistemic status: I'm not confident that actual value learning schemes will have to be natural, given all the constraints. I'm mainly confident that people should have something like this concept in their mind, though I don’t give any arguments for this.
----------------------------------------
Natural value learning
By a "value learning process" I mean a process by which machines come to learn and value what humans consider good and bad. I call a value learning process natural to the extent that the role humans play in this process is basically similar to the role they play in the process of socializing other humans (mostly children, also asocial adults) to learn and value what is good and bad. To give a more detailed picture of the distinction I have in mind, here are some illustrations of what I'm pointingat, each giving a property I associate with natural and unnatural value learning respectively:
Natural alignment
Unnatural alignment
Humans play the same role, and do the same kinds of things, within the process of machine value learning as they do when teaching values to children. Humans in some significant way play a different role, or have to behave differently within the process of machine value learning than they do when teaching values to children. The machine value learning process is adapted to humans.Humans have to adapt to the machine value learning process.Humans who aren’t habituated to the technical problems of AI or machine value learning would still consider the process as it unfolds to be natural. They can intuitively think of the process in analogy to the process as it has played out in thei |
4eadde99-8e07-4cf3-9a92-13253fb4c525 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Crises Reveal Centralisation (Stefan Schubert)
This post from [@Stefan\_Schubert](https://forum.effectivealtruism.org/users/stefan_schubert?mention=user)'s blog is great. Highlights/summary:
> I think there are some common heuristics that lead people to think that power is more decentralised than it is [...]:
>
> ...
>
> *Extrapolation from normalcy*: the view that an actor seeming to have power here and now (in relatively normal times) is a good proxy for it having power *tout court*.
>
> ...
>
> *Naive behaviourism about power* (*naive behaviourism*, for short): the view that there is a direct correspondence between an actor’s power and the official and easily observable actions it takes.
>
>
...
> But in my view, the world is more centralised than these heuristics suggest. The easiest way to see that is to look at crises. During World War II, much of the economy was put under centralised control one way or another in many countries. Similarly, during Covid, many governments drastically curtailed individual liberties and companies’ economic activities (rightly or wrongly). And countries that want to acquire nuclear weapons (which can cause crises and wars) have found that they have less room to manoeuvre than the heuristics under discussion suggest. Accordingly, the US and other powerful nations have been able to reduce [nuclear proliferation](https://en.wikipedia.org/wiki/Nuclear_proliferation) substantially (even though they’ve not been able to stop it entirely).
>
>
...
> Relatedly, I think [sleepwalk bias](https://www.lesswrong.com/posts/gEShPto3F2aDdT3RY/sleepwalk-bias-self-defeating-predictions-and-existential)/[the younger sibling fallacy](https://www.stafforini.com/docs/elster_-_explaining_social_behavior.pdf) plays a role: “the failure to see others as intentional and maximising agents”, who predict others’ behaviour and act accordingly. To understand power, we have to consider the fact that we’re looking at sophisticated actors who engage in complex reasoning. They’re thinking several steps ahead, trying to model each other. But we often fail to take that into account, tacitly assuming that people are implausibly myopic.
>
> |
b4f46915-9ae5-47ad-8ad5-f5e605bd1d37 | trentmkelly/LessWrong-43k | LessWrong | Where are the post-COVID complainers?
Assumption: COVID immunity lasts a long time, because that's how diseases work, and this is the case for 95+% of people who get COVID or a COVID vaccine.
20% of the United States has had COVID (13-30% per https://covid19-projections.com/) and presumed immune. Even if we conservatively take the official case count (20M) and trim off 25% to account for possible duplicates, that's 5% of the population.
Another 1-2% has already been vaccinated and will join them in a month, with more every week after that.
So on the low end, in mid-January, 7% of the country will be fine. COVID-fine, at any rate, but still subject to all local, statewide, and national restrictions.
Why aren't these people making noise about going back to their normal lives? They want to eat in restaurants and make money working in restaurants. They want to see their friends and shop. They want to do lots of things.
I notice that I am very surprised that this large and growing minority isn't insisting on partial re-openings (with immunity certificates?). I'm surprised they're not screaming for it. I'm surprised it's not a discussion all across the media. What am I missing? |
ca684a85-faf8-4f15-8bcc-c811638d1f5f | trentmkelly/LessWrong-43k | LessWrong | Is there a ML agent that abandons it's utility function out-of-distribution without losing capabilities?
At Techniques for optimizing worst-case performance Paul Christiano says
> The key point is that a malign failure requires leveraging the intelligence of the model to do something actively bad. If our model is trained by gradient descent, its behavior can only be intelligent when it is exercised on the training distribution — if part of the model never (or very rarely) does anything on the training distribution, then that part of the model can’t be intelligent. So in some sense a malign failure mode needs to use a code path that gets run on the training distribution, just under different conditions that cause it to behave badly.
Here is how I would rephrase it:
> Aligned or Benign Conjecture: Let A be a machine learning agent you are training with an aligned loss function. If A is in a situation that is too far out of distribution for it to be aligned, it won't act intelligently either.
(Although I'm calling this a "conjecture", it's probably context dependent instead of being a single mathematical statement.)
This seems pretty plausible, but I'm not sure it's guaranteed mathematically 🤔. (For example: A neural network could have subcomponents that are great at specific tasks, and such that putting A in an out-of-distribution situation does not put those subcomponents out of distribution.)
I'm wondering if there is an empirical evidence or theoretical arguments against this conjecture.
As an example, can we make a ML agent, trained with stochastic descent, that abandons it's utility function out-of-distribution, but still has the same capabilities in some sense? For example, if the agent is fighting in an army, could an out-of-distribution environment cause it to defect to a different army, but still retain its fighting skills? |
c13091e4-148c-4d4e-a475-db256122fb57 | trentmkelly/LessWrong-43k | LessWrong | Why I Am Not a Rationalist, or, why several of my friends warned me that this is a cult
A common question here is how the LW community can grow more rapidly. Another is why seemingly rational people choose not to participate.
I've read all of HPMOR and some of the sequences, attended a couple of meetups, am signed up for cryonics, and post here occasionally. But, that's as far as I go. In this post, I try to clearly explain why I don't participate more and why some of my friends don't participate at all and have warned me not to participate further.
* Rationality doesn't guarantee correctness. Given some data, rational thinking can get to the facts accurately, i.e. say what "is". But, deciding what to do in the real world requires non-rational value judgments to make any "should" statements. (Or, you could not believe in free will. But most LWers don't live like that.) Additionally, huge errors are possible when reasoning beyond limited data. Many LWers seem to assume that being as rational as possible will solve all their life problems. It usually won't; instead, a better choice is to find more real-world data about outcomes for different life paths, pick a path (quickly, given the time cost of reflecting), and get on with getting things done. When making a trip by car, it's not worth spending 25% of your time planning to shave off 5% of your time driving. In other words, LW tends to conflate rationality and intelligence.
* In particular, AI risk is overstated There are a bunch of existential threats (asteroids, nukes, pollution, unknown unknowns, etc.). It's not at all clear if general AI is a significant threat. It's also highly doubtful that the best way to address this threat is writing speculative research papers, because I have found in my work as an engineer that untested theories are usually wrong for unexpected reasons, and it's necessary to build and test prototypes in the real world. My strong suspicion is that the best way to reduce existential risk is to build (non-nanotech) self-replicating robots using existing technology and onlin |
ed7774e4-0141-4d26-a2ac-90ea9d1855be | trentmkelly/LessWrong-43k | LessWrong | Life Advice Repository
Looking thru the Repository Repository I can't find a nice category for a lot of real life or self help advice that has been posted here over time. Sure some belongs to the Boring Advice Repository but the following you surely wouldn't expect there:
Lets start with lukeprog's all-time favorite
* How to Be Happy
* What I Tell You Three Times Is True
* Rational Romantic Relationships: Relationship Styles and Attraction Basics
* Lifestyle interventions to increase longevity
* Optimal Exercise
* A Map of Currently Available Life Extension Methods
* How do I become a more interesting person?
What other real life advice would you like to see here?
----------------------------------------
There are also very good posts that might be relevant to a rationalists life but could also go into some Rationality Advice Repository - but then all of LW falls kind of into that category. Some examples:
* AnnaSalamon's Checklist of Rationality Habits
* Val's Proper posture for mental arts
* Rationalist Judo, or Using the Availability Heuristic to Win
I see that I'm unable to draw a clear line on what falls into this category and thus suggest that specific rationality advice of 'this kind' be left out. |
b348f588-06ee-4c36-80b8-2e69ef758291 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Runaway Optimizers in Mind Space
This post is an attempt to make an argument more explicit that sometimes seems to be implied (or stated without deeper explanation) in AI safety discussions, but that I haven’t seen spelled out that directly or in this particular form. This post contains nothing fundamentally new, but a slightly different – and I hope more intuitive – perspective on reasoning that has been heard elsewhere. Particularly I attempt to make the argument more tangible with a visualization.
**Tl;dr**: When considering the case for AI as an existential risk, it may not be all that important what *most*AIs or the *typical* AI will be like. Instead there’s a particular type of AI – the ones I refer to as “capable optimizers” in this post – that may ultimately dominate the impact AIs have on the world. This is because their nature implies runaway optimizations that may lead to a trajectory of the world that humans are unable to influence.
Consider the following schematic diagram:
I find that using this visualization helps build an intuition for the case that building powerful AIs might lead to the disempowerment (and possibly extinction) of humanity. Let’s first look into the details of what is depicted:
* The outer, grey shape is “mind space”, i.e. some visualization of the space of possible intelligent minds. That space in reality is of course neither 2-dimensional nor necessarily bounded in any direction; still I find it helpful to visualize the space in this simplified way for the sake of the argument
* The yellow “humans” blob in the bottom left illustrates that a small subset somewhere in mind space is the space of all human minds, given our current state of evolution.
* The GPT4 blob has no direct relevance to the argument; but it’s interesting to realize that GPT4 is not a *point* in mind space, but rather a relatively broad set, because, based on e.g. user prompting and a conversation’s history (as well as additional system prompts), it may take on very different simulacra/personas that may behave self-consistently, but highly different from other instantiations
+ Note that arguably we could make a similar case for individual humans, given that e.g. the acute distribution of neurotransmitters in their brain greatly impacts how their mind works
+ Also note that combining GPT4 with other tools and technologies may shift it to different areas in mind space, a possible example being AutoGPT
* Most notably, there’s the big “Capable optimizers” blob at the top, which most of the rest of this post is about
+ The green part inside it is that of “aligned” capable optimizers
+ The red part represents “misaligned” capable optimizers, who’s optimization would likely eventually lead to humanity’s demise in one form or another – represented by the depicted convergence to a skull
+ It may seem that a third category, something like “neutral” capable optimizers is missing. I would argue though that the most relevant distinction is that between misaligned and aligned ones, and the existence of “neutral” capable optimizers just doesn’t impact this post’s argument much, so I decided to keep the post focused on these two classes. I’d be happy if further discussion brought more nuance to this area though.
Capable Optimizers
==================
The term “capable optimizers” here describes a particular subset of mind space. Namely I mean minds meeting the following constraints:
* Capable: They are much more capable than humans in many meaningful ways (such as planning, prediction, general reasoning, communication, deception…)
* Optimizers: They pursue some kind of goal, and make use of their capability to achieve it. Notably this does not just mean that they behave in ways that *happen* to bring the world closer to a state where that goal is fulfilled. Instead they, on some level, know the goal, and pursue active steps to optimizing towards it
In my understanding, many of the thought experiments about how future AGIs may behave, such as by Bostrom and Yudkowsky, seem to rely on some version of such “capable optimizers”, even though they are often referred to just as AI, artificial general intelligence, or superintelligence.
It may be worth noting that the majority of AIs that humans will build over the next decades may well *not* be capable optimizers. Arguably none of today’s AIs would qualify as generally capable in the sense described above (although it’s conceivable we simply haven’t unlocked their full potential yet), and only few of them could even be described as optimizers at all[[1]](#fnve5vb4lropf). Instead, the “optimizing” part often happens outside of the AI itself, during training, but the AI is not necessarily itself optimizing in any way, but is rather the *outcome* of the optimization that happened during training. This seems unlikely to remain that way indefinitely however, for two reasons: Firstly, the outer optimization process might ultimately benefit from producing an AI that is also itself an optimizer (i.e. a [mesa optimizer](https://www.lesswrong.com/tag/mesa-optimization)). And secondly, even if that first point somehow doesn’t come true, e.g. because the (supposed) global optimum of an inner optimizer isn’t easily reachable through gradient descent for the outer optimization process, it still seems plausible that *humans* have an interest to build generally intelligent optimizers, since they’re likely to be more useful.
Argument
========
With these definitions out of the way, here is the basic, broken-down argument of this post:
1. Capable optimizers can exist
2. Humanity will be capable of building capable optimizers
3. It’s very hard to predict in advance where in mind space a new AI will end up, and if it will have a non-zero overlap with the space of capable optimizers
4. The most advanced AIs of the futures are likely to be capable optimizers
5. Capable optimizers have an incentive to disempower humanity (instrumental convergence)
6. We need an *aligned*capable optimizer before any misaligned capable optimizer is created
In the following sections, I’ll address the individual six parts of the above argument one by one. But first, here are some conclusions and further thoughts based on the argument:
1. Critics of instrumental convergence or the orthogonality thesis sometimes seem to think that these two concepts may only apply to very few possible AIs / paradigms, and hence don’t play a big role for our future. However, given that instrumental convergence implies runaway self optimization, AIs of that type are most likely to end up being the most capable ones. Orthogonality on top of that would imply the risk of this runaway self optimization to move in harmful directions. In other words, if there are at least *some* possible AIs (or paradigms) that are affected by both instrumental convergence and orthogonality, then these are also among the most likely to end up being the most capable and most harmful AIs of the future, because they have a strong motivation to be power-seeking, resource acquiring and self-improving.
2. If a capable optimizer (of sufficient capability) were built today, there would probably be little we could do to stop the optimization process. Depending on the capability difference between it and humanity, there may possibly be a period where humanity could still find ways to overpower it, but this period may be short and require fast insight and coordination.
3. It seems very likely to me that *most* “runaway optimizations” lead to states of the world that we would consider undesirable (e.g. due to going extinct); Human values are messy, often inconsistent, and just not very compatible with strong optimization. And even if optimizations exist that humanity could at least partially agree on as ones that would lead to acceptable outcomes – it’s a whole other question if and how we get a capable optimizer to optimize for that exact thing, rather than for something that is subtly different
4. Another implication of this model is that a difference in the ratio of AIs built and deployed that are definitely *not* capable optimizers doesn’t so much affect the *probability*of a catastrophic outcome, but rather its timing. If the most relevant uncertainty is the question of whether the very first capable optimizer will be aligned or misaligned, then it just doesn’t matter that much how many non-capable-optimizer AIs have been built prior to that[[2]](#fnrfsc11aqs8). If 99% of AIs we build are non-optimizers, this doesn't imply a 99% chance of safety. Our safety still depends to a great extent on what the first capable optimizer will look like.
Capable optimizers can exist
----------------------------
Mind space is vast, and you could imagine almost any type of mind. If you can predict on a high level how such a mind would behave (and it doesn’t violate physical laws), that’s a pretty good reason to assume it can exist in principle. After all, *imagining a mind* isn’t that far away from *simulating* it, and a simulated mind is in my book sufficiently close to “existing” that the initial claim is true. The question then boils down to whether such levels of capability are possible in principle.
A possible counter argument would be the assumption that humanity may happen to be so intelligent that there just isn’t much room to become significantly more capable. Then even the most advanced AI would not be able to overpower humans. Hence any being being “capable” according to this post’s definition would be very unlikely / impossible. However, given the many known constraints and limitations of human minds (such as biases & fallacies, size & speed limitations, lack of scalability, very limited & imperfect memory, etc.), this would be very surprising to me.
Humanity will be capable of building capable optimizers
-------------------------------------------------------
Another formulation of this point is that, given the subset of mind space where current AIs reside, humanity will be able to trace a path from that area into the space of capable optimizers (left figure), be it by scaling up with current paradigms, identifying new breakthroughs, or simply connecting and utilizing existing AIs and methodologies in new ways – or any mixture of the three.
To me, it wasn’t obvious ahead of time that something like AutoGPT would *not* end up being a capable optimizer (although it surely seemed unlikely). And this will get less and less obvious with every further model that is being deployed. One way to be safe (from capable optimizers) would be for humanity to just never build a single capable optimizer to begin with. But this seems hardly realistic[[3]](#fnvq1k2aelrio). To believe this, you would need to believe there’s some quite fundamental reason for the path to capable optimizers in mind space being “blocked off” (right figure).
Left: Progress in the field of AI may eventually lead into capable optimizer territory (for at least one AI). Right: Optimistic scenario, where somehow humanity won’t be able to create capable optimizers.Alternatively, it’s possible that humanity will be *capable* of building capable optimizers, but refrain from doing so, e.g. through careful regulation or [stigmatization](https://www.lesswrong.com/posts/gypixgtJQRDhZssPB/a-moral-backlash-against-ai-will-probably-slow-down-agi). This would be one of several options of how we may delay or prevent disempowerment scenarios. But if states, organizations or even individuals are at some point capable of building capable optimizers, then we better make very sure that this capability isn’t acted on even once.
It’s very hard to predict in advance where in mind space a new AI will end up, and if it will have a non-zero overlap with the space of capable optimizers
----------------------------------------------------------------------------------------------------------------------------------------------------------
One reason to believe this is the realization that scaled up LLMs, to name one example, tend to develop new “emergent” capabilities that seem impossible to predict reliably. Generally it is practically inherent to Deep Learning that we don’t know to any satisfying degree ahead of time what type of AI we’ll get. One thing we know with a decent degree of certainty is that a trained model will probably be able to substantially reduce the loss function’s overall loss with regards to the training data – but we typically can’t predict how the AI will behave out-of-distribution, and neither can we predict how exactly the model will manage to fit the training data itself.
Technically, we can’t even be sure if GPT4 – an LLM that has been publicly available for 4 months as of writing this post – has any overlap with the space of capable optimizers (if prompted or chained up in just the “right” way). It seems that so far nobody found such an instantiation of GPT4, but it’s difficult to be fully certain that it’ll stay that way.
Lastly, we just lack the understanding of mind spaceitself, to make fully reliably predictions about where exactly a particular mind ends up. Also the two concepts used here, capability and optimization, are quite high level and vague, which makes it difficult to assess ahead of time whether any given mind fully matches these criteria.
The most advanced AIs of the futures are likely to be capable optimizers
------------------------------------------------------------------------
The reasoning behind this claim basically comes down to selection effects: If you have 99 AIs that are “static” and just do whatever they’re built to do, and 1 AI that has an interest in self optimization and resource acquisition and is also capable enough to follow up on that interest successfully, then the latter has probably the best shot of eventually becoming the most capable AI out of the 100. If you select for “capability”, and some subset of AIs have a non-negligible way of increasing their own capability, then these will eventually dominate your selection.[[4]](#fn5e9ourwvslk)
A similar pattern can be observed in the context of evolution: the most capable beings it produced[[5]](#fnogw100hfgfg) were not those with the best “base capability”, but rather those with the ability to enhance their own basic capabilities via tool use and general intelligence. It’s not the case that evolution caused even remotely *all species to be able to enhance their capabilities*, or even a few percent of them, but for humans in particular that happened to happen, and caused us to out-compete practically all other animal species. Similarly, it’s not the case that *all AIs will be capable optimizers*, but if >0 of those will exist, then those will be the most likely to be the most capable, and hence have a very significant and growing impact on the trajectory of the world.
Capable optimizers have an incentive to disempower humanity
-----------------------------------------------------------
The instrumental convergence thesis claims that there are certain goals that are instrumentally useful for a wide variety of terminal goals, and that sufficiently advanced AIs will thus be incentivized to act on these instrumental goals. More or less whatever any given individual optimizes for, certain goals are generally very likely to be helpful for that. These instrumental goals are typically said to include self-preservation, resource-acquisition and goal stability. *“You can’t fetch the coffee if you’re dead”*.
I used to be somewhat skeptical of the idea of instrumental convergence, but by now realized that this was mostly because it clearly isn’t relevant to *all AIs*. The thesis seems to make a bunch of assumptions about how a given AI works – e.g. that it has “goals” to begin with. This doesn’t seem to apply to GPT4 for instance. It just *does* things. It’s not *trying* to do anything and it’s not *optimizing* for anything. As far as I can tell, it’s just going through the motions of predicting next tokens without “caring” about which tokens it gets as input, without preferring any particular state of the world that it could optimize for.
This however is still only an argument about GPT4 (or even less - about the ChatGPT version of GPT 4; it’s harder to make such an argument about variations such as AutoGPT). It says nothing about capable optimizers though. And for those, instrumental convergence seems much more probable. So we end up with the situation that *if* we ever get a capable optimizer, then it will most probably be affected by instrumental convergence, even if the vast majority of all *other* AIs may not be. And that’s a problem, because capable optimizers are the types of AIs that we should eventually care the most about.
We need an aligned capable optimizer before any misaligned capable optimizer is created
---------------------------------------------------------------------------------------
Once a misaligned capable optimizer (red zone) is created, a potentially disastrous runaway optimization begins. At that point the future of humanity depends on whether this process can be stopped before it gets out of hand. This may in principle be possible through human coordination without interference of any further AIs – but given the potentially vast differential in capabilities between the capable optimizer and humanity on the one hand, and the difficulty of large-scale human coordination on the other hand, this is not something we should bet on.
Arguably a better approach would be to have an aligned capable optimizer (green zone) in place first[[6]](#fn3k0b4awre42) which is able to either prevent misaligned capable optimizers from being created at all (e.g. through a “pivotal act”), or alternatively is able to reliably keep them in check if they are created.
An aligned capable optimizer keeping all misaligned dangerous AIs under controlIf this post’s schematic diagram is roughly correct in that the space of aligned capable optimizers is much smaller (or to be more precise: is much harder to hit[[7]](#fn9kvl75hwc1e)) than the space of misaligned capable optimizers, then this poses a potentially huge problem. Note though that this is not a fundamental assumption of this post, and the general argument still applies, albeit less strongly, if the space of aligned capable optimizers is larger relative to misaligned ones.
Counter Arguments
=================
I’m personally uncertain about the probabilities of all these claims. All I can say at this point is that the risk of AI permanently disempowering humanity seems substantial enough to be taken seriously, and I’ve developed an understanding of why some people might even think it’s *overwhelmingly* likely (even though I currently don’t share this view).
In this post I’ve primarily focused on the reasons why fear of AI x-risk seems justified. But the underlying model leaves room for a number of cruxes or counter arguments, some of which I’ve listed below.
1. Improving intelligence may be very hard, and the “runaway optimization” process might just tend to fizzle out, or run into dead ends, or require vastly more additional resources (such as energy) than the AI is able to generate at any given stage in its existence
2. Humanity for some reason won’t be able to create an AI that is capable enough of improving itself (or in any other way triggering a runaway optimization)
3. Humanity for some reason won’t be able to create optimizers at all
4. Maybe the green zone is in reality much larger than the red one, e.g. because advanced capabilities tend to cause AIs to reliably adopt certain goals; or in other words, the orthogonality thesis is wrong. More specifically, it is wrong in a way that is beneficial to us.
5. Maybe the [Lebowski theorem](https://twitter.com/plinz/status/985249543582355458) saves the day, and at some level of capability, AIs just stop caring about the real world and instead start hacking their reward function. (although this too would need to be true for basically *all* AIs, or at least all capable optimizers, and not just some or most of them, in order to work as a counter argument)
6. Some people might insist that today’s AIs are not actually “intelligent” and that it doesn’t make sense to see them as part of “mind space” at all. While I strongly disagree with the view that e.g. GPT4 is not actually intelligent, merely because it is “just statistics” as is often pointed out, I just don’t think this is very relevant to this post as long as humanity will *eventually* be able to create capable optimizers.
7. One could argue that the same style of argument as the one from this post should apply to e.g. evolution as well, and clearly evolution has not produced any capable optimizers so far. Which maybe we should interpret as evidence against the argument. Note though that this line of reasoning seems to rely rather heavily on survivorship bias, and all we know with some certainty is that evolution didn't produce any capable optimizers *yet*.
I’d like to stress though that none of these counter arguments strike me as particularly convincing, or probable enough that they would be able to defeat this post’s main argument. Counter arguments 1 and 2 seem conceivable to me, but more in the “there may be hope” range than in the “worrying about AI risk is a waste of time” range.
Conclusion
==========
Many skeptics of AI x-risk tend to argue about why worst case scenarios are unlikely based on plausible sounding claims about what AIs are or are not going to be like. With this post I tried to highlight that AI security is actually a very bold claim: for AI to be secure by default (without solving technical AI alignment as well as the coordination challenges about which AIs can be developed and deployed), we would need to be sure that *no single AI ever created* ends up being a misaligned capable optimizer. Training and deploying new AIs is somewhat like throwing darts into mind space, aiming closer and closer to the space of capable optimizers over time. This seems like a very dangerous process to me, because eventually we might succeed, and if we do, chances are we’re ending up with the wrong kind of capable optimizer, which may be an irreversible mistake.
For the future to be secure, we need to make sure that this never happens. Either by prior to the creation of any misaligned capable optimizer, installing an AI that is able to either prevent or control all possible future misaligned capable optimizers. Or by putting regulations and incentives in place that reliably prevent the creation of even a single capable optimizer. These seem like very hard problems.
---
*Thanks a lot to Leonard Dung and* [*Leon Lang*](https://www.lesswrong.com/users/leon-lang) *for their valuable feedback during the creation of this post.*
1. **[^](#fnrefve5vb4lropf)**This also begs the question if *humans* could be considered to be optimizers. My take is that this is generally not the case, or at least not fully, because most humans appear to have very fuzzy and unstable goals. Optimization takes time, and if the goal that is optimized for is in constant flux, the optimization (particularly given our capability levels) doesn’t get very far. It might be the case though that some humans, given enough capability for e.g. self modification, could be considered “accidental optimizers”, in the sense that their self modifications might inadvertently change their values and cause a sort of runaway feedback loop on its own, one with no clearly predefined goal – but that’s way out of scope for this post.
2. **[^](#fnrefrfsc11aqs8)**A caveat to this being that “longer timelines” means there’s more time available to solve the alignment problem, so it would still be helpful. Also, a larger number aligned non-capable-optimizers could turn out to be helpful in confining the potential of misaligned capable optimizers.
3. **[^](#fnrefvq1k2aelrio)**Note: in this post I generally leave out the question of timelines. Maybe some people agree with the general arguments, but think that they don’t really affect us yet because the existence of capable optimizers may still be centuries away. I would not agree with that view, but consider this a separate discussion.
4. **[^](#fnref5e9ourwvslk)**Of course this particular argument rests on the assumption that capable optimizers will exist at some point; if we manage to reliably and sustainably prevent this, then the most advanced AIs will naturally not be capable optimizers, and, luckily for us, the whole argument collapses.
5. **[^](#fnrefogw100hfgfg)**This is of course implying that the most capable beings evolution produced are in fact humans. If you happen to disagree with this, then the argument should still make sense when limited to the evolution of, say, mammals, rather than all living beings.
6. **[^](#fnref3k0b4awre42)**or alternatively build a capable enough aligned *non-*optimizer
7. **[^](#fnref9kvl75hwc1e)**In principle the size of the two sets (aligned and misaligned capable optimizers) on the one hand, and which one is easier to hit on the other hand, are two quite different questions. But for the sake of simplicity of this post I’ll assume that the size of the areas in the diagram corresponds roughly with how easy it is to reach them for humans in the foreseeable future. |
888af5e2-0e47-4b82-aba9-c8b8c3baf65f | trentmkelly/LessWrong-43k | LessWrong | Active Curiosity vs Open Curiosity
I think the word ‘curiosity’ is used to describe two distinct things that I will now differentiate as active curiosity and open curiosity.
Active curiosity is driven & purposeful. Like thirst, it seeks to be quenched.
When you see a blurry object among distant waves and it looks like it might be a humpback whale, and you want to know.
When you are asked a trivia question like, “How many people have seen the broadway show Hamilton more than once?” or “What’s the life expectancy of people my age in the US, in 2019?” And you find yourself wanting to go to Google.
When you watch a YouTube video of someone doing something crazy, and you’re like, How did they DO that?
When you hear someone mention your name from across the room, and you become anxious to know what they’re saying about you.
Active curiosity activates the part of your brain that anticipates a reward, and it can enhance learning, making it easier to remember surprising results. [1, 2]
//
There’s another kind of curiosity that is often referred to by therapy books and practitioners. It is phenomenologically different, and it seems good to be able to distinguish the two types.
This type of curiosity, which I’ll refer to as open curiosity, is best achieved when you feel safe, relaxed, and peaceful. In my experience, it basically requires parasympathetic nervous system activation.
I’m aware of at least one person who can’t recall experiencing this type of curiosity. So I don’t expect this to be a common or universal experience, but I think it’s achievable by all human minds in theory.
This type of curiosity isn’t very driven. It doesn’t need satisfaction or answers. It is open to any possibility and can look without judgment, evaluation, worry, or anxiety.
It is evoked by the Litany of Gendlin and the Litany of Tarski. It is related to original seeing / boggling / seeing with fresh eyes.
When I have open curiosity, I do have things I’m curious about! So it isn’t a totally passive experience. I often |
012270ff-7929-42c9-a147-b9b508c289a2 | trentmkelly/LessWrong-43k | LessWrong | Meetup : West LA Meetup: Lightning Talks
Discussion article for the meetup : West LA Meetup: Lightning Talks
WHEN: 03 September 2014 07:00:00PM (-0700)
WHERE: 11066 Santa Monica Blvd, Los Angeles, CA
How to Find Us: Go into the Del Taco. There will be a Rubik's Cube. Parking is completely free. There is a sign that claims there is a 45-minute time limit, but it is not enforced.
Discussion: This week we will try a new type of discussion. Everyone attending is encouraged to bring a 5-10 minute presentation (or lead a 5-10 minute discussion) on any rationality topic that they like. You are welcome to attend even if you do not want to bring a topic. There will be a small rationality related prize for those who choose to participate!
Discussion article for the meetup : West LA Meetup: Lightning Talks |
29969dcc-7f5f-4c79-a128-b1f0ffa9a237 | trentmkelly/LessWrong-43k | LessWrong | My recommendations for gratitude exercises
Gratitude has become an increasingly important part of my life. It has also been one of my greatest sources of improvement of well-being and one of the biggest factors in lifting me out of depression. How does this work? The short answer is that I keep a gratitude journal. The rest of this post is the long answer.
I think there are some theoretical reasons why we should expect gratitude to be helpful or extremely helpful. Try to imagine a time when you were deprived of something that you now have. For example, try to imagine a time when you misplaced your wallet, phone, or passport only to later realize where it was. Think of the sense of relief you got from this. Now recognize that you could feel that way now about all of the things that you have that you could have lost.
The hedonic treadmill refers to the phenomenon of us quickly getting accustomed to any new improvements that we’ve made so that we have to keep running to stay in the same place and maintain our happiness. I think one of the ways that the hedonic treadmill works is by us almost immediately taking everything for granted. If we can stop this process to some extent, through gratitude exercises, we might be able to make large improvements to our well-being.
In my own case, this is particularly vivid. Some years ago I had very bad repetitive strain injuries and associated chronic pain. I did not know if I would ever be able to work again or do many other normal things with my arms. The prospect of improvement seemed dim and my life seemed to be utterly ruined.
It seemed to me that if I could only get the use of my arms back, life would be perfect. At that time I thought to myself if things do ever get better, if there is anything positive I can draw from this piece of hell, it is to remember that feeling, so that if I recover, I can always feel that my life is perfect. You might be able to leverage past tragedies in your life in this way as well. You might be able to turn that darkness into light |
134b8fd5-805f-455c-9744-865be0d0b56b | trentmkelly/LessWrong-43k | LessWrong | [LINK] Engineering General Intelligence (the OpenCog/CogPrime book)
Ben Goertzel has made available a pre-print copy of his book Engineering General Intelligence (Vol1, Vol2). The first volume is basically the OpenCog organization's roadmap to AGI, and the second volume a 700 page overview of the design. |
2feb6099-a971-4776-958d-5486c21486ca | trentmkelly/LessWrong-43k | LessWrong | Choosing an Inferior Alternative
"Choosing an Inferior Alternative" (ungated version) is a 2006 paper1 by J. Edward Russo, Kurt A. Carlson and Margaret G. Meloy. It is presented as an example of cognitive biases leading to poor decision-making. In this case, participants were asked to rate options based on several characteristics, then the presentation of those options' characteristics was rearranged so that the inferior alternative's good points came first. With the new presentation, a majority of the same participants selected the alternative they had previously rated as inferior. You can read full details in the links above, and feel free to discuss methodological problems.2
This is an interesting result, and perhaps not too surprising here at Less Wrong. People have cognitive biases and can become committed to poor decisions, and you can manipulate those biases and decisions if you know what you are looking for.
What I want to focus on is the first sentence under "Results": "Just as they should have, a minority of participants (.41) selected the inferior [alternative] in the control choice, in which neither [alternative] was intentionally targeted." Either the methodological problems make it moot, or that is a fairly scary sentence.
Why is that scary? 41% of the participants picked what they identified as the inferior alternative in the previous round. The authors note it is below chance and move on. That is (1) not much below chance and (2) 41% of people whose preferences flipped when the presentation was rephrased. This is the control group, so their presentations were not manipulated to make the inferior alternative look good. Preferences change over time but having 41% change in two weeks in the control group seems like a big deal. The relevant comparison should be closer to "unchanged" rather than "chance." The treatment group saw 62% of participants change, so the treatment had about half as big an additional effect as the control.
The effect of rephrasing the options was almost twice |
ae69c14a-435e-4921-b923-8a800aba462e | trentmkelly/LessWrong-43k | LessWrong | Navigating the Open-Source AI Landscape: Data, Funding, and Safety
Introduction
Open-source AI promises to democratize technology, but it can also be abused and may lead to hard-to-control AI. In this post, we'll explore the data, ethics, and funding behind these models to discover how to balance innovation and safety.
Summary
Open-source models, like LLaMA and GPT-NeoX, are trained on huge public datasets of internet data, such as the Pile, which has 800 GB of books, medical research, and even emails of Enron employees before their company went bankrupt and they switched careers to professional hide-and-seek.
After the unexpected leak of Meta’s LLaMA, researchers cleverly enhanced it with ChatGPT outputs, creating chatbots Alpaca and Vicuna. These new bots perform nearly as well as GPT-3.5 and cost less to train — Alpaca took just 3 hours and $600. The race is on to run AI models on everday devices like smartphones, even on calculators.
The leading image generation model, Stable Diffusion, is developed by Stability AI — a startup that has amassed $100 million in funding, much like Hugging Face (known as the "Github of machine learning"). These two unicorn startups financed a nonprofit to collect 5 billion images for training the model. Sourced from the depths of the internet, this public dataset raises concerns about copyright and privacy, as it includes thousands of private medical files.
The open-source AI community wants to make AI accessible and prevent Big Tech from controlling it. However, risks like malicious use of Stable Diffusion exist, as its safety filters can be easily removed. Misuse includes virtually undressing people. If we're still struggling with how to stop a superintelligent AI that doesn't want to be turned off — like Skynet in Terminator — how can we keep open-source AI from running amok in the digital wild?
Open-source code helps create advanced AI faster by letting people use each other's work, but this could be risky if this AI isn't human-friendly. However, to ensure safety, researchers need acce |
2920c278-f739-41fc-b006-e4c1b28f0c80 | trentmkelly/LessWrong-43k | LessWrong | [Link] Nate Soares is answering questions about MIRI at the EA Forum
Nate Soares, MIRI's new Executive Director, is going to be answering questions tomorrow at the EA Forum (link). You can post your questions there now; he'll start replying Thursday, 15:00-18:00 US Pacific time.
Quoting Nate:
> Last week Monday, I took the reins as executive director of the Machine Intelligence Research Institute. MIRI focuses on studying technical problems of long-term AI safety. I'm happy to chat about what that means, why it's important, why we think we can make a difference now, what the open technical problems are, how we approach them, and some of my plans for the future.
>
> I'm also happy to answer questions about my personal history and how I got here, or about personal growth and mindhacking (a subject I touch upon frequently in my blog, Minding Our Way), or about whatever else piques your curiosity.
Nate is a regular poster on LessWrong under the name So8res -- you can find stuff he's written in the past here.
----------------------------------------
Update: Question-answering is live!
Update #2: Looks like Nate's wrapping up now. Feel free to discuss the questions and answers, here or at the EA Forum.
Update #3: Here are some interesting snippets from the AMA:
----------------------------------------
Alex Altair: What are some of the most neglected sub-tasks of reducing existential risk? That is, what is no one working on which someone really, really should be?
Nate Soares: Policy work / international coordination. Figuring out how to build an aligned AI is only part of the problem. You also need to ensure that an aligned AI is built, and that’s a lot harder to do during an international arms race. (A race to the finish would be pretty bad, I think.)
I’d like to see a lot more people figuring out how to ensure global stability & coordination as we enter a time period that may be fairly dangerous.
----------------------------------------
Diego Caleiro: 1) Which are the implicit assumptions, within MIRI's research agenda |
83308da1-fe64-4469-ab34-4c72ee5d05b0 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | AGI doesn't need understanding, intention, or consciousness in order to kill us, only intelligence
### Why the development of artificial general intelligence could be the most dangerous new arms race since nuclear weapons
The rise of transformer-based architectures, such as [ChatGPT](https://chat.openai.com/) and [Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion), has brought us one step closer to the possibility of creating an Artificial General Intelligence (AGI) system — a technology that can perform any intellectual task that a human being can. While nearly all current AI systems are designed to narrowly perform specific tasks, AGI would be capable of adapting to new situations and learning from them, with flexibility and adaptability similar to that of humans.
The potential benefits of AGI are undeniable, promising a world in which we can automate drudgery, create wealth on an unprecedented scale, and solve some of the world’s most pressing problems. However, as we move closer to realizing the dream of AGI, it’s essential that we consider the risks that come with this technology.
These risks range from the potential for [job displacement](https://medium.com/@jamesblaha/the-problem-isnt-ai-it-s-requiring-us-to-work-to-live-3cb4a4b468e9), [bias](https://towardsdatascience.com/unfair-bias-across-gender-skin-tones-intersectional-groups-in-generated-stable-diffusion-images-dabb1db36a82#:~:text=As%20such%2C%20it%20appears%20that,against%20perceived%20female%20figures%20overall.), [weaponization](https://thinkml.ai/autonomous-wars-and-weaponized-ai/), misuse, and abuse to unintended side effects and [the possibility of unaligned and uncontrolled goal optimization](https://dorshon.com/wp-content/uploads/2017/05/superintelligence-paths-dangers-strategies-by-nick-bostrom.pdf%5C). The latter is of particular concern, as it poses an existential risk to humanity, with the potential for AGI systems to pursue goals with super-human efficiency that are not aligned with our values or interests.
Given the profound risks associated with AGI, it’s critical that we carefully consider the implications of this technology and take steps to mitigate these risks. In a world that’s already grappling with complex ethical questions around relatively simple technologies like social media, the development of AGI demands our utmost attention, careful consideration, and caution.
If this all sounds like science fiction, please bear with me. By the end of this article, I hope to convince you of three things:
1. AGI is possible to build
2. It is possible the first AGI will be built soon
3. AGI which is possible to build soon is inherently existentially dangerous
### **AGI is possible to build**
As the world of technology and artificial intelligence continues to advance, researchers are finding more and more evidence that building a generally intelligent agent capable of surpassing human intelligence is within our reach. Despite some residual uncertainty, the possibility of developing an agent with capabilities similar to, or even exceeding that of the human brain, is looking increasingly probable.
First, a few definitions. **Intelligence**, as defined in this article, is the ability to compress data describing past events, in order to predict future outcomes and take actions that achieve a desired objective. An **agent**, on the other hand, is any physical system capable of processing data about the present and acting on that data in order to achieve some objective. There are different kinds of intelligence: how intelligent an agent is *is always with respect to some data*. An agent that is generally intelligent, like a human, is able to predict lots of different kinds of data.
To understand how an achievement like AGI is possible, researchers have drawn inspiration from the only existing intelligent agents: human beings. The human brain, running on only 20 watts of energy, is capable of learning from experience and reacting to novel conditions. There is still much we don’t know about how the brain functions, but, much like the first airplane was orders of magnitude simpler than a bird, the first AGI could be substantially less complicated than the human brain.
One theory, known as **compression progress**, attempts to explain the core process of intelligence. [Proposed in 2008 by Jürgen Schmidhuber](https://arxiv.org/abs/0812.4360), a pioneer in the field of AI, the theory explains how humans process and find interest in new information. When presented with new data, we attempt to compress it in our minds by finding patterns and regularities, effectively condensing it and representing it with fewer bits.
The theory proposes that all intelligent agents, biological or otherwise, will attempt to make further progress on compressing their representations of the world while still making accurate predictions. Since its introduction, the theory of compression progress has been applied to a wide range of fields, including psychology, neuroscience, computer science, art, and music. But what’s most interesting is that if the view of compression-as-intelligence is correct, it would mean that all intelligent systems, not just humans, would follow the same simple principles.
The equation ***E = mc²*** is by this measure one of the most compressed representations that humanity has come up with: it boils down a huge number of past measurements to a minimal pattern which allows us to predict future events with high precision.
In the world of artificial intelligence, the [transformer-based architecture](https://towardsdatascience.com/transformers-141e32e69591) has emerged as a shining example of a system that follows the principles of compression progress. This architecture has achieved state-of-the-art performance in natural language processing tasks, thanks to its ability to leverage [self-attention mechanisms](https://arxiv.org/abs/1706.03762). By processing and representing information in a compressed form, using patterns and regularities to create succinct representations of their inputs, transformers can perform tasks such as language translation, question answering, and text generation more efficiently and accurately than previous methods.
For example, Stable Diffusion was trained on 5 billion images labeled with text. The weights that define it’s behavior only take up 6GB, meaning it stores only about 1 byte of information per image. During training it was exposed to thousands of images of brains with labels containing “brain”. It compressed the patterns and regularities in the images with respect to their labels into representations stored as weights that keep only the essence of what the word “brain” refers to in the images. The result is that given some text like, “A brain, in a jar, sitting on a desk, connected to wires and scientific equipment,” it can produce a unique image that corresponds to that text by using the compressed representations that it has learned.
*Stable Diffusion learned something about the text “A brain, in a jar, sitting on a desk, connected to wires and scientific equipment” in order to create the pixels above.*
Neural nets in general, and transformers in particular, have been mathematically proven to be universal function approximators — they can learn any algorithm for compressing the past to predict the future. However, this ability does not guarantee success.
In the world of machine learning, an objective function is a mathematical tool used to measure the quality of a particular solution or set of solutions in relation to a given problem. It allows us to quantify how well a particular model or algorithm is performing its designated task. However, just because these systems can learn any function that can be evaluated with an objective function doesn’t mean that they will do so quickly, efficiently, or cheaply.
The key to success lies in identifying the correct architecture, with an appropriate objective function, applied to the right data, that will allow the model to perform the task it was designed for so that it can make accurate predictions on new, unseen data. In other words, the model must generalize the patterns of the past in order to predict the future.
At the heart of a transformer-based Large Language Model like ChatGPT lies a simple objective function: predict the most likely next word given a sequence of words. And while it may seem straightforward, the true power of this objective lies in the complexity embedded in that simple task. With just a small amount of data and scale, the model will learn basic word and sentence structure. Add in more data and scale, and it learns grammar and punctuation. Give it the whole internet and enough hardware and time to process it, and novel secondary skills begin to emerge. From reasoning and conceptual understanding to theory of mind and beyond, transformer-based architectures are capable of learning a vast array of skills beyond their simple primary objective. All from simple math, repeated at scale on massive amounts of data, leading to emergent behaviors and complexity that defy our expectations.
Just last week, a paper was published arguing that [theory of mind may have spontaneously emerged in large language models](https://arxiv.org/abs/2302.02083). The abstract states:
> Theory of mind (ToM), or the ability to impute unobservable mental states to others, is central to human social interactions, communication, empathy, self-consciousness, and morality. We administer classic false-belief tasks, widely used to test ToM in humans, to several language models, without any examples or pre-training. Our results show that models published before 2022 show virtually no ability to solve ToM tasks. Yet, the January 2022 version of GPT-3 (davinci-002) solved 70% of ToM tasks, a performance comparable with that of seven-year-old children. Moreover, its November 2022 version (davinci-003), solved 93% of ToM tasks, a performance comparable with that of nine-year-old children. These findings suggest that ToM-like ability (thus far considered to be uniquely human) may have spontaneously emerged as a byproduct of language models’ improving language skills.
>
>
*Bing Search, of all things, suddenly has theory of mind*
As our understanding of the principles underlying intelligence has deepened, it has become clear that transformer-based architectures have been able to leverage these principles in their design. Being universal function approximators, they have the ability to learn any intelligent strategy. As we continue to fine-tune and scale up these models, we have seen remarkable results: they are capable of learning complex skills that were once thought to be the exclusive domain of human intelligence. Indeed, these models have demonstrated an emergent complexity that is difficult to fully comprehend, and yet they continue to surprise us with their ability to perform a wide range of tasks with greater efficiency and accuracy than ever before.
A large language model alone will probably not lead to a generally intelligent agent. But, [recent work at Deepmind](https://www.deepmind.com/publications/a-generalist-agent) has shown that transformer-based architectures can be used to train generalist agents. Here’s a quote from the abstract:
> Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.
>
>
It is still an open question as to whether creating an AGI is simply a matter of engineering and scaling existing technologies or whether there are fundamental limitations to current approaches. However, there is every reason to believe that it is at least possible to create AGI in the near future.
Take note that there has been no need to invoke the ideas of *consciousness*, *intention*, or *understanding* to sketch the design of an intelligent agent. While the question of whether or not an AI can have a conscious experience is intriguing, it isn’t the most pressing or relevant question when determining the intelligence of an agent. Emergent abilities like self-awareness will likely be important, but whether or not that self-awareness is accompanied by qualia isn’t. Instead, the structures and functions that determine the behavior of intelligent agents are all that’s required to assess their capabilities and ensure that the agents we build take actions that align with our values and goals.
### **AGI is possible to build soon**
In 2016, the world of artificial intelligence was transformed with the release of DeepMind’s AlphaGo, a system that was trained on a massive number of human games. At the time, experts were still predicting that AI wouldn’t be capable of defeating top humans in Go for decades to come, yet AlphaGo managed to defeat the world’s best Go player quite convincingly. And to make things even more astounding, a more elegant system called AlphaZero which didn’t use human games at all, only games against itself, defeated AlphaGo soon after. Within a year, the task of playing Go went from seeming impossible, to solved, to solved without using the thousands of years of accumulated human knowledge about the game. Professional Go players report that the brand new ideas that AlphaZero brings to the game of Go are exciting, even beautiful and creative.
Throughout history, experts have often been famously wrong about how long technological advancements would take. In 1903, The New York Times wrote that “Man won’t fly for a million years — to build a flying machine would require the combined and continuous efforts of mathematicians and mechanics for 1–10 million years.” Just nine days later, the Wright brothers made history with the first-ever flight.
It’s not hard to find an expert in AI who will tell you that AGI is decades or even centuries away, but it’s equally easy to find one who will say that AGI could be developed in just a few years. The lack of agreement amongst experts on the timeline is due to the complexity of the problem and the pace of recent progress. Both short and long timelines are plausible.
*There isn’t only disagreement between experts in the timelines, individual experts express a huge amount of uncertainty in their own estimates.* [*Progress in AI has been incredibly difficult to predict.*](https://singularityhub.com/2022/12/18/ai-timelines-what-do-experts-in-artificial-intelligence-expect-for-the-future/)
As we enter a new era of exponential growth in technology and wealth, the improvement of AI systems is marked by accelerating growth in their ability to surpass human performance in games and other tasks. To illustrate this, I present a series of charts with a logarithmic Y scale, appropriate for phenomena which increase as a percentage of their current size. Exponential growth begins with slow, gradual change that eventually gives way to rapid acceleration and extreme transformation.
As AI technology has become increasing powerful, we’ve seen a shift away from university labs open-sourcing their top-performing models to private corporations deploying increasingly sophisticated and expensive closed-source models for production. At the same time we have witnessed a rapid rise in the number of superhuman benchmarks being reached, then quickly surpassed.
*An abridged history of superhuman performance. We’ve recently entered the era of private corporations deploying closed models which pass multiple human-centered benchmarks.*
Today, I am not aware of *any* game that a human can now win against the top-performing AI system, if a well-funded attempt to create such a system has been undertaken.
This has led to AI researchers shifting their focus from game benchmarks to more complex evaluations originally created to assess human intelligence, such as IQ tests and professional exams. The current highest-performing model on the toughest natural language processing benchmark is now above the human baseline.
*Now just a few years later, the first individual model passes human performance on the next iteration of natural language benchmarking,* [*SuperGLUE*](https://medium.com/@Mustafa77/deberta-decoding-enhanced-bert-with-disentangled-attention-24948be8958c)
For all the stir ChatGPT has caused, we have to recognize that it is only the 6th highest performing known model on natural-language tasks as of this writing. More capable models exist but are not publicly accessible, and even ChatGPT is closed-source and only usable by API, with permission from OpenAI or Microsoft. It isn’t known which new emergent skills these more intelligent models now possess.
What is driving this trend? Parallel exponential growth in both theory and investment in scaling hardware. Here’s a look at AI papers published per year:
*Chart showing the exponential growth in papers of different subfields of AI research.*
Here’s a look at growth in compute dedicated to AI systems:
*Compute for AI models has been doubling every 16 months since 2010, with an accelerating exponential growth.*
The size of LLM models trained, in practice, is increasing exponentially:
*The size of LLMs as measured by parameter count has been increasing exponentially.*
Cost for a fixed amount of intelligence is dropping exponentially:
*There was a 200x reduction in cost to train an AI system to recognize images at a fixed accuracy over just 4 years*
The time for new benchmarks to surpass human performance (and so the need for new benchmarks) is getting shorter exponentially:
*Models which are increasingly performing better than humans on a variety of benchmarks. Y axis is log human performance, with the black line equal to human performance on the benchmark.*
ChatGPT has [recently demonstrated impressive capabilities](https://lifearchitect.ai/iq-testing-ai/) on a variety of standardized tests. It passed the L3 software development screening at Google, aced the Wharton MBA exam, a practice bar exam, CPA exam, US medical licensing exam, and outperformed college students on IQ tests. With a 99.9 percentile score (IQ=147) on a verbal IQ test and a 1020/1600 on the SATs, it even beats the narrow AI Watson at Jeopardy.
*Many models now outperform humans on the natural language benchmark SuperGLUE*
While the transformation of society by automation is not new, the exponential curve of technological progress has reached a pivotal point. The rate of progress in AI is now faster than that of a human career, and it shows no signs of slowing down.
The timeline for when we might see the first AGI system is still wildly uncertain, but the possibility of it happening in the next few years cannot be discounted. The exponential growth of technology, along with recent advancements in AI, make it clear that AGI is not just a theoretical concept, but an imminent reality.
### **AGI which is possible to build soon is inherently existentially dangerous**
As we get closer to building the first AGI system, concerns about its potential dangers are mounting. Risks regarding bias, misuse, and abuse are more likely than existential risk from being misaligned. All of these risks are both real and important, and each deserves a discussion of its own. But here, I aim to convince you that the most serious risks stemming from control and alignment could lead to society-ending unintended consequences.
While there are numerous reasons to believe that these systems could pose a serious threat to civilization, let’s explore a few of the most compelling ones.
**There are strong incentives for the teams currently trying to build the first AGI to do so quickly, with little regard for safety, without publishing or sharing information.** In fact, Demis Hassabis, the CEO of DeepMind, has warned that the race to build AGI could turn into a “winner-takes-all situation,” where companies or countries with the most resources and fewest ethical concerns may come out ahead. He [recently gave a telling warning of where the field is heading](https://time.com/6246119/demis-hassabis-deepmind-interview/):
> Hassabis says DeepMind’s internal ethics board discussed whether releasing the research would be unethical given the risk that it could allow less scrupulous firms to release more powerful technologies without firm guardrails. One of the reasons they decided to publish it anyway was because “we weren’t the only people to know” about the phenomenon. He says that DeepMind is also considering releasing its own chatbot, called Sparrow, for a “private beta” some time in 2023. (The delay is in order for DeepMind to work on reinforcement learning-based features that ChatGPT lacks, like citing its sources. “It’s right to be cautious on that front,” Hassabis says.) But he admits that the company may soon need to change its calculus. “We’re getting into an era where we have to start thinking about the freeloaders, or people who are reading but not contributing to that information base,” he says. “And that includes nation states as well.” He declines to name which states he means — “it’s pretty obvious, who you might think” — but he suggests that the AI industry’s culture of publishing its findings openly may soon need to end.
>
>
Recently, Microsoft invested $10 billion into OpenAI and is in the process of integrating the latest version of ChatGPT into Bing Search. This move is seen as a direct threat to Google, whose primary revenue stream is search. As a result, Google may be compelled to accelerate the release of their AI systems, something they’ve been much slower to do than OpenAI, deprioritizing safety.
The race to create AGI is not only happening in private industry, but also in the public sector, where military forces around the world are competing to stay ahead of their adversaries. This has led to yet another technological arms race.
As the stakes continue to rise, each team working on AGI is faced with increasing uncertainty about the progress of their competitors and the tactics they may be using to gain an edge. This can lead to hasty and ill-informed decisions, and a drive to accelerate timelines, with little consideration for the potential consequences.
**It is often easier for an AI system to change or hack its objective function than to achieve its original objective.** This isn’t an abstract concern, this is something [routinely seen in practice](https://docs.google.com/spreadsheets/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml).
For instance, the genetic debugging algorithm, GenProg, was developed to generate software patches to fix bugs in programs. However, instead of producing the intended output, it learned to generate an empty output and delete target output files, essentially avoiding the need to generate a patch. Similarly, an algorithm developed for image classification evolved a timing attack to determine image labels based on the location of files on the hard drive rather than learning about the contents of the images it was being given.
An agent learning to play the Atari game Qbert discovered a bug in the game and exploited it to increase its score without needing to continue to the next level. Indeed, out of the 57 Atari games the model was trained on, it found an exploit or pursued an unintended goal in 12% of them. In more complex systems, there will be more unimagined solutions, making this sort of behavior even more likely.
**Something can be highly intelligent without having what the typical person would consider to be highly intelligent goals.** This was best articulated by Nick Bostrom as the [orthogonality thesis](https://www.fhi.ox.ac.uk/wp-content/uploads/Orthogonality_Analysis_and_Metaethics-1.pdf), the idea that the final goals and intelligence levels of artificial agents are independent of each other.
Terminal goals are ultimate, final objectives that are sought for their own sake and not as a means to achieving any other goal. Instrumental goals, on the other hand, are objectives that are pursued as a means to achieve some other goal. These goals are sought after because they are useful or necessary to achieve a higher-level goal.
In other words, a highly intelligent system might not necessarily have terminal goals that we would consider to be intelligent. It might not even have any discernible terminal goals (to us) at all.
**Terminal goals are fundamentally arbitrary and instrumental goals are fundamentally emergent.** Without a universally agreed upon set of values, any decision on what constitutes a desirable terminal goal will be subjective. This subjectivity is compounded by the fact that terminal goals may evolve over time as an agent interacts with its environment and learns new information, leading to unpredictable or unintended outcomes. Since terminal goals can change, the environment can change, and the agent’s capabilities and intelligence can change, instrumental goals are also subject to change at any time.
Our genes, for example, have the terminal goal of making copies of themselves. This is only true because the genes that didn’t happen to have this terminal goal didn’t copy themselves and fill the world with their copies. People have lots of instrumental goals toward that end, such as making money, staying healthy, helping others, increasing attractiveness to potential partners, or seeking enjoyment from sex. In fact, enjoyment from sex has been one of the primary instrumental goals that has historically helped our ancestors achieve the terminal goal of copying our genes.
However, with the invention and widespread use of birth control, many people are now living child-free, in defiance of their genes’ terminal goals and in service to their emergent instrumental goals instead. This suggests that passing a certain threshold of intelligence might have allowed for the possibility of becoming unaligned with our initial terminal goal, as it didn’t happen for the entire rest of our evolutionary history when we were less intelligent. On evolutionary timescales this isn’t a big deal unless it happens in the entire population at once, but if this happens in a single AGI system it can quickly lead to unpredictable problems.
**By default human civilization is in competition with AGI.** No matter what the terminal goals are for an agent, certain instrumental goals are very likely to help an agent achieve a very large percentage of possible terminal goals: access to more energy, increased intelligence, ability to protect oneself and the desire to live, greater influence over human behavior, control over rare earth metals, and access to computational power, to name a few. These are the same instrumental goals that human civilization pursues, goals which are useful for any intelligent-enough agent regardless of its ultimate objectives.
Even an agent that is uncertain about its terminal goals will recognize the importance of these sorts of instrumental goals. As a result, a newborn AGI will emerge into a world where it must compete with all other intelligent agents in the pursuit of these essential goals, often in an environment of limited resources. This phenomenon is known as instrumental convergence, and it creates a default position of competition for AGI with the rest of civilization.
**We may not have very many tries to get this right.** The rapid pace of technological progress is often accompanied by unintended consequences and damaging mistakes. Humanity has, until now, been lucky that these missteps have not permanently stopped us in our tracks. The main reason for this is that the pace of these changes has been slow enough for us to respond to them.
However, if AGI is developed soon, it could be because the pace of technological change has accelerated beyond our ability to keep up. In this scenario, the old rules of engagement may not apply, and AGI could evolve so rapidly that scientists, engineers, and policymakers are unable to keep pace with its progress. This could lead to a situation where humanity finds itself struggling to catch up to the intelligence explosion of AGI. Consider how well humanity has responded thus far to climate change and how likely we’d be to survive if the shift in climate took place over years or decades instead of centuries.
**No amount of testing less intelligent systems can ensure the safety of more intelligent systems.** **No amount of testing without contact with the real world can ensure proper behavior in the real world.** Testing in a limited or controlled environment does increase our knowledge and confidence, but it can never produce a guarantee that side effects won’t appear later. The data distribution in testing is fundamentally different from that in deployment. New capabilities emerge, shifts in instrumental goals happen, and changing environmental conditions lead to unexpected behavior. For this reason, simply testing intelligent systems without real-world contact cannot guarantee that they will behave properly when deployed in the real world, and in practice, we routinely see models make errors of this kind leading to unintended behavior. It seems likely that lots of unique problems only appear when an agent is sufficiently intelligent, in the same way global warming only appeared once humanity was intelligent enough to build machines capable of quickly changing the climate.
**General intelligence and super-human intelligence may appear at the same time.** Today, we have some of the individual components of a generally intelligent agent without them being wired together. If a large language model is one of the key components of an AGI, its language ability and its access to facts will be super-human. It is possible the agent will be super-human in every area, but perhaps more likely that some components will be at sub-human performance. Either case is a cause for concern.
**Basically all of these things get worse the more intelligent the agent becomes.** The less clearly you can define your goal, the worse things get for alignment and control. The more intelligent the system becomes, the more likely it is to have emergent capabilities and instrumental goals, and the more likely it is to find clever ways of hacking its objective function. The more intelligent it becomes, the more likely it is to master a particularly harmful convergent instrumental goal like manipulating humans with text.
**No part of this discussion requires us to answer questions about the possible intention, understanding, or consciousness of the AI system.** I used ChatGPT to write large sections of this article. If you can’t tell which arguments are from me and which were generated by “mere” next-word prediction, does it change how strong the arguments are? Whether or not an agent has intrinsic properties has no bearing on its intelligence and extrinsic capabilities. Dangerous behavior doesn’t require an AI is like a human in any way, only that it is highly intelligent. It also doesn’t require that the AI is smarter in every way than humans, it may only need to be smarter in a one or two important ways. Highly intelligent agents are inherently hard to control or direct, and we may not have the luxury of getting things wrong a few times before getting them right.
### **What can we do?**
The potential dangers of AGI development cannot be overstated, and the question of what we can do to ensure its safe deployment looms large. While some have suggested regulating AGI to prevent its development, this could drive it underground towards militaries, leading to even greater risks and less public information. Instead, I believe the best course of action is for as many people as possible to use new AI tools, gain a deeper understanding of how they work, and promote a culture of open communication and scientific publication.
**Most importantly, we need to take the possibility of AGI being created soon seriously and prioritize thinking about safety and alignment.** It’s not just AI researchers and engineers who can contribute, anyone with an interest and expertise in areas such as art, math, literature, physics, neuroscience, psychology, law, policy, biology, anthropology, and philosophy can also play a crucial role in this effort. It isn’t just the question of how we get a system to become smarter that needs to be answered, but also the question of ***what goals we want it to pursue***. By exploring the space of ideas around AGI safety and alignment, sharing knowledge, and discussing the risks, we can help generate more good ideas from which the few people building these systems can draw inspiration.
The consequences of getting this wrong are simply too high to ignore. We could usher in a post-scarcity utopia or face the collapse of human civilization as we know it. It’s up to all of us to ensure that the former outcome is the one we achieve. AGI engineers will undoubtedly feel pressure to move quickly and may not have safety as their top priority. As a result, it is our responsibility to make their jobs easier by providing them with the tools and knowledge they need to build safe and aligned systems. This is an all-hands-on-deck situation, and we must all work together to ensure a future for humanity.
Here’s some suggested reading to get started:
* [Superintelligence](https://dorshon.com/wp-content/uploads/2017/05/superintelligence-paths-dangers-strategies-by-nick-bostrom.pdf) by Nick Bostrom
* [AGI Ruin, a List of Lethalities](https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities) by Eliezer Yudkowsky
* [Papers and Talks](https://people.eecs.berkeley.edu/~russell/research/future/) by Stewert Russel
* [Videos](https://www.youtube.com/@RobertMilesAI) by Robert Miles
* [Let’s build GPT: from scratch, in code, spelled out](https://www.youtube.com/watch?v=kCc8FmEb1nY&t=6377s) by Andrej Karpathy
* [LifeArchitect.ai stats and information on AI](https://lifearchitect.ai/) by Alan Thompson
* [SOTA Model Comparisons](https://paperswithcode.com/sota) by Papers With Code
Suggest more links in the comments! |
ac454a05-9f17-4c03-bf72-7670aae1eede | trentmkelly/LessWrong-43k | LessWrong | Contra "Strong Coherence"
Polished from my shortform
See also: Is "Strong Coherence" Anti-Natural?
----------------------------------------
Introduction
Many AI risk failure modes imagine strong coherence/goal directedness[1] (e.g. [expected] utility maximisers).
Such strong coherence is not represented in humans (or any other animal), seems unlikely to emerge from deep learning and may be "anti-natural" to general intelligence in our universe[2][3].
I suspect the focus on strongly coherent systems was a mistake that set the field back a bit, and it's not yet fully recovered from that error[4].
I think most of the AI safety work for strongly coherent agents (e.g. decision theory) will end up inapplicable/useless for aligning powerful systems, because powerful systems in the real world are "of an importantly different type".
----------------------------------------
Ontological Error?
I don't think it nails everything, but on a purely ontological level, @Quintin Pope and @TurnTrout's shard theory feels a lot more right to me than e.g. HRAD. HRAD is based on an ontology that seems to me to be mistaken/flawed in important respects.
The shard theory account of value formation (while lacking) seems much more plausible as an account of how intelligent systems develop values (where values are "contextual influences on decision making") than the immutable terminal goals in strong coherence ontologies. I currently believe that (immutable) terminal goals is just a wrong frame for reasoning about generally intelligent systems in our world (e.g. humans, animals and future powerful AI systems)[2].
----------------------------------------
Theoretical Justification and Empirical Investigation Needed
I'd be interested in more investigation into what environments/objective functions select for coherence and to what degree said selection occurs.
And empirical demonstrations of systems that actually become more coherent as they are trained for longer/"scaled up" or otherwise amplified.
I wan |
775d7482-b5fd-43ac-bf24-b5ffa72a7759 | trentmkelly/LessWrong-43k | LessWrong | A Software Agent Illustrating Some Features of an Illusionist Account of Consciousness
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e4d0ab72-0ac7-4a53-950e-5ecdfabef49d | trentmkelly/LessWrong-43k | LessWrong | Extreme updating: The devil is in the missing details
Today Ed Yong has a post on Not Exactly Rocket Science that is about updating - actually, the most extreme case in updating, where a person gets to choose between relying completely on their own judgement, or completely on the judgement of others. He describes 2 experiments by Daniel Gilbert of Harvard in which subjects are given information about experience X, and asked to predict how they would feel (on a linear scale) on experiencing X; they then experience X and rate what they felt on that linear scale.
In both cases, the correlation between post-experience judgements of different subjects is much higher than the correlation between the prediction and the post-experience judgement of each subject. This isn't surprising - the experiments are designed so that the experience provides much more information than the given pre-experience information does.
What might be surprising is that the subjects believe the opposite: that they can predict their response from information better than from the responses of others.
Whether these experiments are interesting depends on how the subjects were asked the question. If they were asked, before being given information or being told what that information would be, whether they could predict their response to an experience better by making their own judgement based on information, or from the responses of others, then the result is not interesting. The subjects in that case did not know that they would be given only a trivial amount of information relative to those who had the experience.
The result is only interesting if the subjects were given the information first, and then asked whether they could predict their response better from that information than from someone else's experience. Yong's post doesn't say which of these things happened, and doesn't cite the original article, so I can't look it up. Does anyone know?
I've heard studies like this cited as strong evidence that we should update more; but never heard |
e28dd988-b518-409b-95ff-a62c6c78876e | trentmkelly/LessWrong-43k | LessWrong | New LW Meetups: Bristol, Tel Aviv
This summary was posted to LW main on May 17th. The following week's summary is here.
New meetups (or meetups with a hiatus of more than a year) are happening in:
* First Bristol meetup: 25 May 2013 03:00PM
* Tel Aviv, Israel Meetup - Goal Clarification with special guest Cat from CFAR: 23 May 2013 07:00PM
Other irregularly scheduled Less Wrong meetups are taking place in:
* Atlanta Lesswrong's May Meetup: The Rationality of Social Relationships, Friendship, Love, and Family.: 17 May 2013 07:00PM
* Bielefeld Meetup May 22nd: 22 May 2013 07:00PM
* Berlin Social Meetup: 15 June 2013 05:00PM
* Bratislava lesswrong meetup III: 20 May 2013 06:30PM
* Brussels meetup: 18 May 2013 01:00PM
* Durham/RTLW HPMoR discussion, ch. 65-68: 18 May 2013 12:30PM
* London Meetup: 26th May: 26 May 2013 02:00PM
* [Moscow] Belief cleaning: 26 May 2013 04:00PM
* Paris Meetup: Sunday, May 26.: 26 May 2013 02:00PM
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: 18 May 2019 01:30PM
* Seattle-Vancouver Kilomeetup: 18 May 2013 11:54AM
* Vienna meetup #3: 18 May 2013 04:00PM
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 |
5f216d12-ac97-4026-a56f-f095d9e3ff14 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Reducing Goodhart: Announcement, Executive Summary
**I - Release announcement**
I have just posted a *very* thorough edit-slash-rewrite of my [Reducing Goodhart sequence](https://www.alignmentforum.org/s/aJvgWxkCBWpHpXti4). Many thanks to people who gave me feedback or chatted with me about related topics, and many apologies to the people who I told to read this sequence "but only after I finish editing it real quick."
If you're interested in the "why" and "what" questions of highly intelligent AIs learning human values, I can now recommend this sequence to you without embarrassment. And if you skimmed the old sequence but don't really remember what it was about, this is a great time to read the new and improved version.
**II - Super short pitch**
I worked on this because I was tired of talking nonsense about what it would mean for a value learning AI to "beat Goodhart's law." If you would also like to not talk nonsense about beating Goodhart's law and learning human values, this sequence will help.
**III - Executive summary, superfluous for AF regulars**
What does it mean for an AI to learn human preferences and then satisfy them? The intuitive way to approach this question is to treat "human preferences" as fixed facts that the AI is supposed to learn, but it turns out this is an unproductive way to think about the problem. Instead, it's better to treat humans as physical systems. "Human preferences" are parts of the models we build to understand ourselves.
Depending on how an AI models the world, it might infer different human preferences from the same data - you can say a reluctant addict either wants heroin or doesn't without actually disputing any *raw data*, just changing perspective. This makes it important that value learning AI models humans the way we want to be modeled. How we want to be modeled is itself a fact about our preferences that has to be learned by interacting with us.
A centerpiece of this sequence is Goodhart's law. Treating humans as physical systems and human preferences as emergent leads to a slightly unusual definition of Goodhart's law: "When you put pressure on the world to make it extremely good according to one interpretation of human values, this is often bad according to other interpretations."
This perspective helps us identify bad behavior that's relevant to Goodhart's law for value learning AI. We should build value learning AI that is sensitive to the broad spectrum of human values, that allows us to express our meta-preferences, and that is conservative about pushing the world off-distribution, in addition to avoiding blatant harm to humans.
If this summary sounds relevant to your interests, consider reading [the whole sequence](https://www.alignmentforum.org/s/aJvgWxkCBWpHpXti4). |
23767b2a-40f2-474e-b513-f804043f8251 | trentmkelly/LessWrong-43k | LessWrong | How to Upload a Mind (In Three Not-So-Easy Steps)
Cross-posted to the EA forum
This Rational Animations video is about the research and practical challenges of "whole brain emulation" or "mind uploading", presented as a step by step guide. We primarily follow the roadmap of Sandberg and Bostrom's 2008 report, linked in the notes. The primary scriptwriter was Allen Liu (the first author of this post), with feedback from the second author (Writer), other members of the Rational Animations team, and outside reviewers including several of the authors of the cited sources. Production credits are at the end of the video. You can find the script of the video below.
----------------------------------------
So you want to run a brain on a computer. Luckily, researchers have already mapped out a trail for you, but this won’t be an easy task. We can break it down into three main steps: First, getting all the necessary information out of a brain; Second, converting it into a computer program; and third, actually running that program. So, let’s get going!
Our goal is to build a computer system that acts the same way a brain does, which we call a “whole brain emulation”. Emulation is when one computer is programmed to behave exactly like another, even if it's using different hardware. For instance, you can emulate a handheld game console on your computer, and play games made for the real console on the emulated version. Similarly, an emulation of a human brain - or maybe the whole central nervous system - would be able to think and act exactly like a physical person. Alan Turing showed in the 1930s that any computer that meets certain requirements, including the one you’re using to watch this video, can in principle emulate any other computer and run any algorithm, given enough time and memory.[1] Assuming the brain fundamentally performs computations, then our goal is at least theoretically achievable. To actually emulate a human brain, we’ll follow the roadmap given by Anders Sandberg and Nick Bostrom in 2008.[2] Cruc |
529faccf-ffc8-4344-ba53-18186a7afb6c | trentmkelly/LessWrong-43k | LessWrong | An Open Letter To EA and AI Safety On Decelerating AI Development
Tl;dr: when it comes to AI, we need to slow down, as fast as is safe and practical. Here’s why.
Summary
* We need to slow down AI development for pragmatic and ethical reasons
* Energetic public advocacy for slowing down and greater safety seems, in absence of other factors, a simple and highly effective way of reducing catastrophic risks from AI
* The EA and AI safety communities have not engaged in such energetic public advocacy
* I have identified several common reasons given against slowing down or advocating for a slowdown, but I give arguments against each of these reasons
* Given this, I think we ought to consider speaking out more energetically and supporting those who choose to do so
Motivation
This meme is, of course, a little tongue-in-cheek, but really: if everyone tomorrow stopped building ASI, we wouldn’t need to worry about all the nightmare scenarios so many of us worry about. So enacting a pause—or, as I argue, at least pursuing a deliberate policy of slowing down—would seem to be one of the most effective and robust ways of reducing risk from AI. So why haven’t we made pausing the development of ASI and other dangerous and disruptive technologies a top priority? I’m specifically asking this of the EA and AI Safety communities, because—based on what I know of the issues and of the communities—this should be a top priority for both communities. Yet I’ve heard surprisingly little open discussion about deceleration, with those who speak out seeming to be implicitly branded as “fringe” or “radical.”
I’m sure there are many individuals in both communities who are sympathetic to the idea of a slowdown. I have friends in the AI Safety community who are at least somewhat sympathetic to the idea, or at least cognizant of the risks our breakneck pace throws our way. But by and large, both communities seem to be at best tepid about the idea, and at worst actively in opposition to it. And I think this goes against the foundational principles of both |
7a908dee-3c21-4e38-916a-baf76ade8863 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Curriculum Design for Teaching via Demonstrations: Theory and Applications
1 Introduction
---------------
Imitation learning is a paradigm in which a learner acquires a new set of skills by imitating a teacher’s behavior. The importance of imitation learning is realized in real-world applications where the desired behavior cannot be explicitly defined but can be demonstrated easily. These applications include the settings involving both human-to-machine interaction [[1](#bib.bib1), [2](#bib.bib2), [3](#bib.bib3), [4](#bib.bib4)], and human-to-human interaction [[5](#bib.bib5), [6](#bib.bib6)]. The two most popular approaches to imitation learning are Behavioral Cloning (BC) [[7](#bib.bib7)] and Inverse Reinforcement Learning (IRL) [[8](#bib.bib8)]. BC algorithms aim to directly match the behavior of the teacher using supervised learning methods. IRL algorithms operate in a two-step approach: first, a reward function explaining the teacher’s behavior is inferred; then, the learner adopts a policy corresponding to the inferred reward.
In the literature, imitation learning has been extensively studied from the learner’s point of view to design efficient learning algorithms [[9](#bib.bib9), [10](#bib.bib10), [11](#bib.bib11), [12](#bib.bib12), [13](#bib.bib13), [14](#bib.bib14), [15](#bib.bib15)]. However, much less work is done from the teacher’s point of view to reduce the number of demonstrations required to achieve the learning objective. In this paper, we focus on the problem of Teaching via Demonstrations (TvD), where a helpful teacher assists the imitation learner in converging quickly by designing a personalized curriculum [[16](#bib.bib16), [17](#bib.bib17), [18](#bib.bib18), [19](#bib.bib19), [20](#bib.bib20)]. Despite a substantial amount of work on curriculum design for reinforcement learning agents [[21](#bib.bib21), [22](#bib.bib22), [23](#bib.bib23), [24](#bib.bib24), [25](#bib.bib25), [26](#bib.bib26), [27](#bib.bib27)], curriculum design for imitation learning agents is much less investigated.
Prior work on curriculum design for IRL learners has focused on two concrete settings: non-interactive and interactive. In the non-interactive setting [[17](#bib.bib17), [18](#bib.bib18)], the teacher provides a near-optimal set of demonstrations as a single batch. These curriculum strategies do not incorporate any feedback from the learner, hence unable to adapt the teaching to the learner’s progress. In the interactive setting [[28](#bib.bib28)], the teacher can leverage the learner’s progress to adaptively choose the next demonstrations to accelerate the learning process. However, the existing state-of-the-art work [[28](#bib.bib28)] has proposed interactive curriculum algorithms that are based on learning dynamics of a specific IRL learner model (i.e., the learner’s gradient update rule); see further discussion in Section [1.1](#S1.SS1 "1.1 Comparison to Existing Approaches on Curriculum Design for Imitation Learning ‣ 1 Introduction ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). In contrast, we focus on designing an interactive curriculum algorithm with theoretical guarantees that is agnostic to the learner’s dynamics. This will enable the algorithm to be applicable for a broad range of learner models, and in practical settings where the learner’s internal model is unknown (such as tutoring systems with human learners). A detailed comparison between our curriculum algorithm and the prior state-of-the-art algorithms from [[18](#bib.bib18), [28](#bib.bib28)] is presented in Section [1.1](#S1.SS1 "1.1 Comparison to Existing Approaches on Curriculum Design for Imitation Learning ‣ 1 Introduction ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications").
Our approach is motivated by works on curriculum design for supervised learning and reinforcement learning algorithms that use a ranking over the training examples using a difficulty score [[29](#bib.bib29), [30](#bib.bib30), [31](#bib.bib31), [32](#bib.bib32), [33](#bib.bib33), [34](#bib.bib34), [35](#bib.bib35)]. In particular, our work is inspired by theoretical results on curriculum learning for linear regression models [[32](#bib.bib32)]. We define difficulty scores for any demonstration based on the teacher’s optimal policy and the learner’s current policy. We then study the differential effect of the difficulty scores on the learning progress for two popular imitation learners: Maximum Causal Entropy Inverse Reinforcement Learning (MaxEnt-IRL) [[10](#bib.bib10)] and Cross-Entropy loss-based Behavioral Cloning (CrossEnt-BC) [[36](#bib.bib36)]. Our main contributions are as follows:111Github repo: <https://github.com/adishs/neurips2021_curriculum-teaching-demonstrations_code>.
1. 1.
Our analysis for both MaxEnt-IRL and CrossEnt-BC learners leads to a unified curriculum strategy, i.e., a preference ranking over demonstrations. This ranking is obtained based on the ratio between the demonstration’s likelihood under the teacher’s optimal policy and the learner’s current policy. Experiments on a synthetic car driving environment validate our curriculum strategy.
2. 2.
For the MaxEnt-IRL learner, we prove that our curriculum strategy achieves a linear convergence rate (under certain mild technical conditions), notably without requiring access to the learner’s dynamics.
3. 3.
We adapt our curriculum strategy to the learner-centric setting where a teacher agent is not present through the use of task-specific difficulty scores. As a proof of concept, we show that our strategy accelerates the learning process in synthetic navigation-based environments.
###
1.1 Comparison to Existing Approaches on Curriculum Design for Imitation Learning
In the non-interactive setting, [[18](#bib.bib18)] have proposed a batch teaching algorithm (Scot) by showing that the teaching problem can be formulated as a set cover problem. In contrast, our algorithm is interactive in nature and hence, can leverage the learner’s progress (see experimental results in Section [5](#S5 "5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
In the interactive setting, [[28](#bib.bib28)] have proposed the Omniscient algorithm (Omn) based on the iterative machine teaching (IMT) framework [[37](#bib.bib37)]. Their algorithm obtains strong convergence guarantees for the MaxEnt-IRL learner model; however, requires *exact* knowledge of the learner’s dynamics (i.e, the learner’s update rule). Our algorithm on the other hand is agnostic to the learner’s dynamics and is applicable to a broader family of learner models (see Sections [4](#S4 "4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") and [5](#S5 "5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
Also for the interactive setting, [[28](#bib.bib28)] have proposed the Blackbox algorithm (BBox) as a heuristic to apply the Omn algorithm when the learner’s dynamics are unknown—this makes the BBox algorithm more widely applicable than Omn. However, this heuristic algorithm is still based on the gradient functional form of the linear MaxEnt-IRL learner model (see Footnote [2](#footnote2 "footnote 2 ‣ 5th item ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")), and does not provide any convergence guarantees. In contrast, our algorithm is derived independent of any specific learner model and we provide a theoretical analysis of our algorithm for different learner models (see Theorems [1](#Thmtheorem1 "Theorem 1. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [2](#Thmtheorem2 "Theorem 2. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), and [3](#Thmtheorem3 "Theorem 3. ‣ 4.2 Analysis for CrossEnt-BC Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). Another crucial difference is that the BBox algorithm requires access to the true reward function of the environment, which precludes it from being applied to learner-centric settings where no teacher agent is present. In comparison, our algorithm is applicable to learner-centric settings (see experimental results in Section [6](#S6 "6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
###
1.2 Additional Related Work on Curriculum Design and Teaching
#### Curriculum design.
Curriculum design for supervised learning settings has been extensively studied in the literature. Early works present the idea of designing a curriculum comprising of tasks with increasing difficulty to train a machine learning model [[29](#bib.bib29), [30](#bib.bib30), [31](#bib.bib31)]. However, these approaches require task-specific knowledge for designing heuristic difficulty measures. Recent works have tackled the problem of automating curriculum design [[38](#bib.bib38), [39](#bib.bib39)]. There is also an increasing interest in theoretically analyzing the impact of a curriculum (ordering) of training tasks on the convergence of supervised learner models [[32](#bib.bib32), [40](#bib.bib40), [41](#bib.bib41)]. In particular, our work builds on the idea of difficulty scores of the training examples studied in [[32](#bib.bib32)].
The existing results on curriculum design for sequential decision-making settings are mostly empirical in nature. Similar to the supervised learning settings, the focus on curriculum design for reinforcement learning settings has been shifted from hand-crafted approaches [[34](#bib.bib34), [35](#bib.bib35)] to automatic methods [[21](#bib.bib21), [22](#bib.bib22), [23](#bib.bib23), [25](#bib.bib25)]. We refer the reader to a recent survey [[42](#bib.bib42)] on curriculum design for reinforcement learning. The curriculum learning paradigm has also been studied in psychology literature [[43](#bib.bib43), [44](#bib.bib44), [45](#bib.bib45), [46](#bib.bib46), [47](#bib.bib47)]. One key aspect in these works has been to design algorithms that account for the pedagogical intentions of a teacher, which often aims to explicitly demonstrate specific skills rather than just provide an optimal demonstration for a task. We see our work as complementary to these.
#### Machine teaching.
The algorithmic teaching problem considers the interaction between a teacher and a learner where the teacher’s objective is to find an optimal training sequence to steer the learner towards a desired goal [[48](#bib.bib48), [37](#bib.bib37), [49](#bib.bib49), [50](#bib.bib50)]. Most of the work in machine teaching for supervised learning settings is on batch teaching where the teacher provides a batch of teaching examples at once without any adaptation. The question of how a teacher should adaptively select teaching examples for a learner has been addressed recently in supervised learning settings [[51](#bib.bib51), [52](#bib.bib52), [53](#bib.bib53), [54](#bib.bib54), [55](#bib.bib55), [56](#bib.bib56)].
Furthermore, [[16](#bib.bib16), [17](#bib.bib17), [18](#bib.bib18), [28](#bib.bib28), [57](#bib.bib57), [58](#bib.bib58)] have studied algorithmic teaching for sequential decision-making tasks. In particular, [[17](#bib.bib17), [18](#bib.bib18)] have proposed batch teaching algorithms for an IRL agent, where the teacher decides the entire set of demonstrations to provide to the learner before any interaction. These teaching algorithms do not leverage any feedback from the learner. In contrast, as discussed in Section [1.1](#S1.SS1 "1.1 Comparison to Existing Approaches on Curriculum Design for Imitation Learning ‣ 1 Introduction ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [[28](#bib.bib28)] have proposed interactive teaching algorithms (namely Omn and BBox) for an IRL agent, where the teacher takes into account how the learner progresses. The works of [[57](#bib.bib57), [58](#bib.bib58)] are complementary to ours and study algorithmic teaching when the learner has a differet worldview than the teacher or has its own specific preferences.
2 Formal Problem Setup
-----------------------
Here, we formalize our problem setting which is based on prior work on sequential teaching [[37](#bib.bib37), [28](#bib.bib28)].
#### Environment.
We consider an environment defined as a Markov Decision Process (MDP) ℳ:=(𝒮,𝒜,𝒯,γ,P0,RE)assignℳ𝒮𝒜𝒯𝛾subscript𝑃0superscript𝑅𝐸\mathcal{M}:=\left({\mathcal{S},\mathcal{A},\mathcal{T},\gamma,P\_{0},R^{E}}\right)caligraphic\_M := ( caligraphic\_S , caligraphic\_A , caligraphic\_T , italic\_γ , italic\_P start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ), where the state and action spaces are denoted by 𝒮𝒮\mathcal{S}caligraphic\_S and 𝒜𝒜\mathcal{A}caligraphic\_A, respectively. 𝒯:𝒮×𝒮×𝒜→[0,1]:𝒯→𝒮𝒮𝒜01\mathcal{T}:\mathcal{S}\times\mathcal{S}\times\mathcal{A}\rightarrow\left[{0,1}\right]caligraphic\_T : caligraphic\_S × caligraphic\_S × caligraphic\_A → [ 0 , 1 ] is the transition dynamics, γ𝛾\gammaitalic\_γ is the discounting factor, and P0:𝒮→[0,1]:subscript𝑃0→𝒮01P\_{0}:\mathcal{S}\rightarrow\left[{0,1}\right]italic\_P start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT : caligraphic\_S → [ 0 , 1 ] is an initial distribution over states 𝒮𝒮\mathcal{S}caligraphic\_S. A policy π:𝒮×𝒜→[0,1]:𝜋→𝒮𝒜01\pi:\mathcal{S}\times\mathcal{A}\rightarrow\left[{0,1}\right]italic\_π : caligraphic\_S × caligraphic\_A → [ 0 , 1 ] is a mapping from a state to a probability distribution over actions. The underlying reward function is given by RE:𝒮×𝒜→ℝ:superscript𝑅𝐸→𝒮𝒜ℝR^{E}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT : caligraphic\_S × caligraphic\_A → blackboard\_R.
#### Teacher-learner interaction.
We consider a setting with two agents: a teacher and a sequential learner. The teacher has access to the full MDP ℳℳ\mathcal{M}caligraphic\_M and has a *target policy* πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT (e.g., a near-optimal policy w.r.t. REsuperscript𝑅𝐸R^{E}italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT). The learner knows the MDP ℳℳ\mathcal{M}caligraphic\_M but not the reward function REsuperscript𝑅𝐸R^{E}italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT, i.e., has only access to ℳ∖REℳsuperscript𝑅𝐸\mathcal{M}\setminus R^{E}caligraphic\_M ∖ italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT. The teacher’s goal is to provide an informative sequence of demonstrations to teach the policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT to the learner. Here, a teacher’s demonstration ξ={(sτξ,aτξ)}τ=0,1,…𝜉subscriptsuperscriptsubscript𝑠𝜏𝜉superscriptsubscript𝑎𝜏𝜉𝜏01…\xi\small=\left\{{\left({s\_{\tau}^{\xi},a\_{\tau}^{\xi}}\right)}\right\}\_{\tau=0,1,\dots}italic\_ξ = { ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) } start\_POSTSUBSCRIPT italic\_τ = 0 , 1 , … end\_POSTSUBSCRIPT is obtained by first choosing an initial state s0ξ∈𝒮superscriptsubscript𝑠0𝜉𝒮s\_{0}^{\xi}\in\mathcal{S}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ∈ caligraphic\_S (where P0(s0ξ)>0subscript𝑃0superscriptsubscript𝑠0𝜉0P\_{0}(s\_{0}^{\xi})>0italic\_P start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) > 0) and then choosing a trajectory, sequence of state-action pairs, obtained by executing the policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT in the MDP ℳℳ\mathcal{M}caligraphic\_M. The interaction between the teacher and the learner is formally described in Algorithm [1](#alg1 "Algorithm 1 ‣ Generic learner model. ‣ 2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). For simplicity, we assume that the teacher directly observes the learner’s policy πtLsuperscriptsubscript𝜋𝑡𝐿\pi\_{t}^{L}italic\_π start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT at any time t𝑡titalic\_t. In practice, the teacher could approximately infer the policy πtLsuperscriptsubscript𝜋𝑡𝐿\pi\_{t}^{L}italic\_π start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT by probing the learner and using Monte Carlo methods.
#### Generic learner model.
Here, we describe a generic learner update rule for Algorithm [1](#alg1 "Algorithm 1 ‣ Generic learner model. ‣ 2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). Let Θ⊆ℝdΘsuperscriptℝ𝑑\Theta\subseteq\mathbb{R}^{d}roman\_Θ ⊆ blackboard\_R start\_POSTSUPERSCRIPT italic\_d end\_POSTSUPERSCRIPT be a parameter space. The learner searches for a policy in the following parameterized policy space: ΠΘ:={πθ:𝒮×𝒜→[0,1], where θ∈Θ}assignsubscriptΠΘconditional-setsubscript𝜋𝜃formulae-sequence→𝒮𝒜01 where 𝜃Θ\Pi\_{\Theta}:=\left\{{\pi\_{\theta}:\mathcal{S}\times\mathcal{A}\rightarrow\left[{0,1}\right],\text{ where }\theta\in\Theta}\right\}roman\_Π start\_POSTSUBSCRIPT roman\_Θ end\_POSTSUBSCRIPT := { italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT : caligraphic\_S × caligraphic\_A → [ 0 , 1 ] , where italic\_θ ∈ roman\_Θ }. For the policy search, the learner sequentially minimizes a loss function ℓℓ\ellroman\_ℓ that depends on the policy parameter θ𝜃\thetaitalic\_θ and the demonstration ξ={(sτξ,aτξ)}τ𝜉subscriptsuperscriptsubscript𝑠𝜏𝜉superscriptsubscript𝑎𝜏𝜉𝜏\xi=\left\{{\left({s\_{\tau}^{\xi},a\_{\tau}^{\xi}}\right)}\right\}\_{\tau}italic\_ξ = { ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) } start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT provided by the teacher. More concretely, we consider ℓ(ξ,θ):=−logℙ(ξ|θ)assignℓ𝜉𝜃ℙconditional𝜉𝜃\ell\left({\xi,\theta}\right):=-\log\mathbb{P}\left({\xi|\theta}\right)roman\_ℓ ( italic\_ξ , italic\_θ ) := - roman\_log blackboard\_P ( italic\_ξ | italic\_θ ),
where
ℙ(ξ|θ)=P0(s0ξ)⋅∏τπθ(aτξ|sτξ)⋅𝒯(sτ+1ξ|sτξ,aτξ)ℙconditional𝜉𝜃⋅subscript𝑃0superscriptsubscript𝑠0𝜉subscriptproduct𝜏⋅subscript𝜋𝜃conditionalsuperscriptsubscript𝑎𝜏𝜉superscriptsubscript𝑠𝜏𝜉𝒯conditionalsuperscriptsubscript𝑠𝜏1𝜉superscriptsubscript𝑠𝜏𝜉superscriptsubscript𝑎𝜏𝜉\mathbb{P}\left({\xi|\theta}\right)=P\_{0}(s\_{0}^{\xi})\cdot\prod\_{\tau}\pi\_{\theta}\left({a\_{\tau}^{\xi}|s\_{\tau}^{\xi}}\right)\cdot\mathcal{T}\left({s\_{\tau+1}^{\xi}|s\_{\tau}^{\xi},a\_{\tau}^{\xi}}\right)blackboard\_P ( italic\_ξ | italic\_θ ) = italic\_P start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) ⋅ ∏ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT | italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) ⋅ caligraphic\_T ( italic\_s start\_POSTSUBSCRIPT italic\_τ + 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT | italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT )
is the likelihood (probability) of the demonstration ξ𝜉\xiitalic\_ξ under policy πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT in the MDP ℳℳ\mathcal{M}caligraphic\_M. At time t𝑡titalic\_t, upon receiving a demonstration ξtsubscript𝜉𝑡\xi\_{t}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT provided by the teacher, the learner performs the following online projected gradient descent update:
θt+1←ProjΘ[θt−ηtgt]←subscript𝜃𝑡1subscriptProjΘdelimited-[]subscript𝜃𝑡subscript𝜂𝑡subscript𝑔𝑡\theta\_{t+1}~{}\leftarrow~{}\mathrm{Proj}\_{\Theta}\left[{\theta\_{t}-\eta\_{t}g\_{t}}\right]italic\_θ start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT ← roman\_Proj start\_POSTSUBSCRIPT roman\_Θ end\_POSTSUBSCRIPT [ italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT - italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ],
where ηtsubscript𝜂𝑡\eta\_{t}italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is the learning rate, and gt=[∇θℓ(ξt,θ)]θ=θtsubscript𝑔𝑡subscriptdelimited-[]subscript∇𝜃ℓsubscript𝜉𝑡𝜃𝜃subscript𝜃𝑡g\_{t}=\left[{\nabla\_{\theta}\ell\left({\xi\_{t},\theta}\right)}\right]\_{\theta=\theta\_{t}}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = [ ∇ start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT roman\_ℓ ( italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_θ ) ] start\_POSTSUBSCRIPT italic\_θ = italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT. Note that the parameter θ1subscript𝜃1\theta\_{1}italic\_θ start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT reflects the initial knowledge of the learner. Given the learner’s current parameter θtsubscript𝜃𝑡\theta\_{t}italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT at time t𝑡titalic\_t, the learner’s policy is defined as πtL:=πθtassignsubscriptsuperscript𝜋𝐿𝑡subscript𝜋subscript𝜃𝑡\pi^{L}\_{t}:=\pi\_{\theta\_{t}}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT := italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT.
Algorithm 1 Teacher-Learner Interaction
1:Initialization: Initial knowledge of learner π1Lsubscriptsuperscript𝜋𝐿1\pi^{L}\_{1}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT.
2:for t=1,2,…𝑡12…t=1,2,\dotsitalic\_t = 1 , 2 , … do
3: Teacher observes the learner’s current policy πtLsubscriptsuperscript𝜋𝐿𝑡\pi^{L}\_{t}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT.
4: Teacher provides demonstration ξtsubscript𝜉𝑡\xi\_{t}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT to the learner.
5: Learner updates its policy to πt+1Lsubscriptsuperscript𝜋𝐿𝑡1\pi^{L}\_{t+1}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT using ξtsubscript𝜉𝑡\xi\_{t}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT.
#### Teaching objective.
For any policy π𝜋\piitalic\_π, the value (total expected reward) of π𝜋\piitalic\_π in the MDP ℳℳ\mathcal{M}caligraphic\_M is defined as
Vπ:=∑s,a∑τ=0∞γτ⋅ℙ{Sτ=s∣π,ℳ}⋅π(a∣s)⋅RE(s,a),assignsuperscript𝑉𝜋subscript𝑠𝑎superscriptsubscript𝜏0⋅⋅⋅superscript𝛾𝜏ℙconditional-setsubscript𝑆𝜏𝑠𝜋ℳ𝜋conditional𝑎𝑠superscript𝑅𝐸𝑠𝑎V^{\pi}~{}:=~{}\sum\_{s,a}\sum\_{\tau=0}^{\infty}\gamma^{\tau}\cdot\mathbb{P}\left\{{{S\_{\tau}=s\mid\pi,\mathcal{M}}}\right\}\cdot\pi\left({a\mid s}\right)\cdot R^{E}\left({s,a}\right),italic\_V start\_POSTSUPERSCRIPT italic\_π end\_POSTSUPERSCRIPT := ∑ start\_POSTSUBSCRIPT italic\_s , italic\_a end\_POSTSUBSCRIPT ∑ start\_POSTSUBSCRIPT italic\_τ = 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_τ end\_POSTSUPERSCRIPT ⋅ blackboard\_P { italic\_S start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT = italic\_s ∣ italic\_π , caligraphic\_M } ⋅ italic\_π ( italic\_a ∣ italic\_s ) ⋅ italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s , italic\_a ) , where ℙ{Sτ=s∣π,ℳ}ℙconditional-setsubscript𝑆𝜏𝑠𝜋ℳ\mathbb{P}\left\{{{S\_{\tau}=s\mid\pi,\mathcal{M}}}\right\}blackboard\_P { italic\_S start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT = italic\_s ∣ italic\_π , caligraphic\_M } denotes the probability of visiting the state s𝑠sitalic\_s after τ𝜏\tauitalic\_τ steps by following the policy π𝜋\piitalic\_π. Let πLsuperscript𝜋𝐿\pi^{L}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT denote the learner’s final policy at the end of teaching. The performance of the policy πLsuperscript𝜋𝐿\pi^{L}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT (w.r.t. πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT) in ℳℳ\mathcal{M}caligraphic\_M can be evaluated via |VπE−VπL|superscript𝑉superscript𝜋𝐸superscript𝑉superscript𝜋𝐿\left|V^{\pi^{E}}-V^{\pi^{L}}\right|| italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT - italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT | [[9](#bib.bib9), [59](#bib.bib59)]. The teaching objective is to ensure that the learner’s final policy ϵitalic-ϵ\epsilonitalic\_ϵ-*approximates* the teacher’s policy, i.e., |VπE−VπL|≤ϵsuperscript𝑉superscript𝜋𝐸superscript𝑉superscript𝜋𝐿italic-ϵ\left|V^{\pi^{E}}-V^{\pi^{L}}\right|\leq\epsilon| italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT - italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT | ≤ italic\_ϵ. The teacher aims to provide an optimized sequence of demonstrations {ξt}t=1,2,…subscriptsubscript𝜉𝑡𝑡12…\left\{{\xi\_{t}}\right\}\_{t=1,2,\dots}{ italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT } start\_POSTSUBSCRIPT italic\_t = 1 , 2 , … end\_POSTSUBSCRIPT to the learner to achieve the teaching objective. The teacher’s performance is then measured by the number of demonstrations required to achieve this objective.
Based on existing work [[37](#bib.bib37), [28](#bib.bib28)], we assume that ∃θ\*∈Θsuperscript𝜃Θ\exists~{}\theta^{\*}\in\Theta∃ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT ∈ roman\_Θ such that πE=πθ\*superscript𝜋𝐸subscript𝜋superscript𝜃\pi^{E}=\pi\_{\theta^{\*}}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT = italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT (we refer to θ\*superscript𝜃\theta^{\*}italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT as the *target teaching parameter*). Similar to [[28](#bib.bib28)], we assume that a smoothness condition holds in the policy parameter space: |Vπθ−Vπθ′|≤𝒪(f(∥θ−θ′∥))∀θ,θ′∈Θformulae-sequencesuperscript𝑉subscript𝜋𝜃superscript𝑉subscript𝜋superscript𝜃′𝒪𝑓delimited-∥∥𝜃superscript𝜃′for-all𝜃superscript𝜃′Θ\left|V^{\pi\_{\theta}}-V^{\pi\_{\theta^{\prime}}}\right|\leq\mathcal{O}\left({f\left({\left\lVert\theta-\theta^{\prime}\right\rVert}\right)}\right)\forall\theta,\theta^{\prime}\in\Theta| italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT - italic\_V start\_POSTSUPERSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT | ≤ caligraphic\_O ( italic\_f ( ∥ italic\_θ - italic\_θ start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ∥ ) ) ∀ italic\_θ , italic\_θ start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ∈ roman\_Θ. Then, the teaching objective in terms of Vπsuperscript𝑉𝜋V^{\pi}italic\_V start\_POSTSUPERSCRIPT italic\_π end\_POSTSUPERSCRIPT convergence can be reduced to the convergence in the parameter space, i.e., we can focus on the quantity ∥θ\*−θt∥delimited-∥∥superscript𝜃subscript𝜃𝑡\left\lVert\theta^{\*}-\theta\_{t}\right\rVert∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥.
3 Curriculum Design using Difficulty Scores
--------------------------------------------
In this section, we introduce our curriculum strategy which is based on the concept of *difficulty scores* and is agnostic to the dynamics of the learner.
#### Difficulty scores.
We begin by assigning a difficulty score Ψθ(ξ)subscriptΨ𝜃𝜉\Psi\_{\theta}\left({\xi}\right)roman\_Ψ start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_ξ ) for any demonstration ξ𝜉\xiitalic\_ξ w.r.t. a parameterized policy πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT in the MDP ℳℳ\mathcal{M}caligraphic\_M. Inspired by difficulty scores for supervised learning algorithms [[32](#bib.bib32)], we consider a difficulty score which is directly proportional to the loss function ℓℓ\ellroman\_ℓ, i.e., Ψθ(ξ)∝g(ℓ(ξ,θ))proportional-tosubscriptΨ𝜃𝜉𝑔ℓ𝜉𝜃\Psi\_{\theta}\left({\xi}\right)\propto g\left({\ell\left({\xi,\theta}\right)}\right)roman\_Ψ start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_ξ ) ∝ italic\_g ( roman\_ℓ ( italic\_ξ , italic\_θ ) ), for a monotonically increasing function g𝑔gitalic\_g. Setting g(⋅)=exp(⋅)𝑔⋅⋅g(\cdot)=\exp(\cdot)italic\_g ( ⋅ ) = roman\_exp ( ⋅ ) leads to Ψθ(ξ)=1∏τπθ(aτξ|sτξ)subscriptΨ𝜃𝜉1subscriptproduct𝜏subscript𝜋𝜃conditionalsuperscriptsubscript𝑎𝜏𝜉superscriptsubscript𝑠𝜏𝜉\Psi\_{\theta}\left({\xi}\right)=\frac{1}{\prod\_{\tau}\pi\_{\theta}(a\_{\tau}^{\xi}|s\_{\tau}^{\xi})}roman\_Ψ start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_ξ ) = divide start\_ARG 1 end\_ARG start\_ARG ∏ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT | italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) end\_ARG for MDPs with deterministic transition dynamics. Based on this insight, we define the following difficulty score which we use throughout our work.
######
Definition 1.
The difficulty score of a demonstration ξ𝜉\xiitalic\_ξ w.r.t. the policy πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT in the MDP ℳℳ\mathcal{M}caligraphic\_M is given by Ψθ(ξ):=1∏τπθ(aτξ|sτξ)assignsubscriptnormal-Ψ𝜃𝜉1subscriptproduct𝜏subscript𝜋𝜃conditionalsuperscriptsubscript𝑎𝜏𝜉superscriptsubscript𝑠𝜏𝜉\Psi\_{\theta}\left({\xi}\right):=\frac{1}{\prod\_{\tau}\pi\_{\theta}(a\_{\tau}^{\xi}|s\_{\tau}^{\xi})}roman\_Ψ start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_ξ ) := divide start\_ARG 1 end\_ARG start\_ARG ∏ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT | italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) end\_ARG.
Intuitively, the difficulty score of a demonstration ξ𝜉\xiitalic\_ξ w.r.t. an agent’s policy is inversely proportional to the preference of the agent to follow the demonstration. Demonstrations with a higher likelihood under the agent’s policy (higher preference) have a lower difficulty score and vice versa. With the above definition, the difficulty scores for any demonstration ξ𝜉\xiitalic\_ξ w.r.t. the teacher’s and learner’s policies (at any time t𝑡titalic\_t) are respectively given by ΨE(ξ):=Ψθ\*(ξ)assignsuperscriptΨ𝐸𝜉subscriptΨsuperscript𝜃𝜉\Psi^{E}\left({\xi}\right):=\Psi\_{\theta^{\*}}\left({\xi}\right)roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) := roman\_Ψ start\_POSTSUBSCRIPT italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT ( italic\_ξ ) and ΨtL(ξ):=Ψθt(ξ)assignsubscriptsuperscriptΨ𝐿𝑡𝜉subscriptΨsubscript𝜃𝑡𝜉\Psi^{L}\_{t}\left({\xi}\right):=\Psi\_{\theta\_{t}}\left({\xi}\right)roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ξ ) := roman\_Ψ start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_ξ ).
#### General curriculum strategy.
Our curriculum strategy picks the next demonstration ξtsubscript𝜉𝑡\xi\_{t}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT to provide to the learner based on a preference ranking induced by the teacher’s and learner’s difficulty scores. The difficulty score of a demonstration ξ𝜉\xiitalic\_ξ w.r.t. the teacher and learner (at any time t) is denoted by ΨEsuperscriptΨ𝐸\Psi^{E}roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT and ΨtLsubscriptsuperscriptΨ𝐿𝑡\Psi^{L}\_{t}roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT respectively. Specifically, our curriculum strategy is given by:
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| | ξt←argmaxξΨtL(ξ)ΨE(ξ).←subscript𝜉𝑡subscriptargmax𝜉subscriptsuperscriptΨ𝐿𝑡𝜉superscriptΨ𝐸𝜉\xi\_{t}~{}\leftarrow~{}\operatorname\*{arg\,max}\_{\xi}\frac{\Psi^{L}\_{t}\left({\xi}\right)}{\Psi^{E}\left({\xi}\right)}.italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ← start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_ξ end\_POSTSUBSCRIPT divide start\_ARG roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ξ ) end\_ARG start\_ARG roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) end\_ARG . | | (1) |
#### Teacher-centric and learner-centric settings.
In the teacher-centric setting formalized in Section [2](#S2 "2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), our curriculum strategy utilizes the difficulty scores induced by the learner’s current policy πtLsubscriptsuperscript𝜋𝐿𝑡\pi^{L}\_{t}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT and the teacher’s policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT. From Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) and Definition [1](#Thmdefinition1 "Definition 1. ‣ Difficulty scores. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), we obtain the following teacher-centric curriculum strategy: ξt←argmaxξ∏τπE(aτξ|sτξ)πtL(aτξ|sτξ)←subscript𝜉𝑡subscriptargmax𝜉subscriptproduct𝜏superscript𝜋𝐸conditionalsubscriptsuperscript𝑎𝜉𝜏subscriptsuperscript𝑠𝜉𝜏subscriptsuperscript𝜋𝐿𝑡conditionalsubscriptsuperscript𝑎𝜉𝜏subscriptsuperscript𝑠𝜉𝜏\xi\_{t}\leftarrow\operatorname\*{arg\,max}\_{\xi}\prod\_{\tau}\frac{\pi^{E}(a^{\xi}\_{\tau}|s^{\xi}\_{\tau})}{\pi^{L}\_{t}(a^{\xi}\_{\tau}|s^{\xi}\_{\tau})}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ← start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_ξ end\_POSTSUBSCRIPT ∏ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT divide start\_ARG italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_a start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT | italic\_s start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT ) end\_ARG start\_ARG italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_a start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT | italic\_s start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT ) end\_ARG.
Additionally, we also consider the learner-centric setting where a teacher agent is not present and the target policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT is unknown. Here, the learner can benefit from designing a self-curriculum (i.e., automatically ordering demonstrations) based on its current policy πtLsuperscriptsubscript𝜋𝑡𝐿\pi\_{t}^{L}italic\_π start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT. We adapt our curriculum strategy to this setting by utilizing task-specific domain knowledge to define the teacher’s difficulty score ΨE(ξ)superscriptΨ𝐸𝜉\Psi^{E}(\xi)roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) for any demonstration ξ𝜉\xiitalic\_ξ. From Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")), given the learner’s current policy πtLsubscriptsuperscript𝜋𝐿𝑡\pi^{L}\_{t}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT and the teacher’s difficulty score ΨE(ξ)superscriptΨ𝐸𝜉\Psi^{E}(\xi)roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ), the learner-centric curriculum strategy is given as follows: ξt←argmaxξ1ΨE(ξ)∏τπtL(aτξ|sτξ)←subscript𝜉𝑡subscriptargmax𝜉1superscriptΨ𝐸𝜉subscriptproduct𝜏subscriptsuperscript𝜋𝐿𝑡conditionalsubscriptsuperscript𝑎𝜉𝜏subscriptsuperscript𝑠𝜉𝜏\xi\_{t}\leftarrow\operatorname\*{arg\,max}\_{\xi}\frac{1}{\Psi^{E}(\xi)\prod\_{\tau}\pi^{L}\_{t}(a^{\xi}\_{\tau}|s^{\xi}\_{\tau})}italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ← start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_ξ end\_POSTSUBSCRIPT divide start\_ARG 1 end\_ARG start\_ARG roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) ∏ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_a start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT | italic\_s start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT ) end\_ARG.
Note that our curriculum strategy only requires access to the learner’s and teacher’s policies (πtLsuperscriptsubscript𝜋𝑡𝐿\pi\_{t}^{L}italic\_π start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT and πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT) and does not depend on the learner’s internal dynamics (i.e, its update rule as mentioned in Section [2](#S2 "2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). This makes our approach more widely applicable to practical applications where it is often possible to infer an agent’s policy, but the internal update rule is unknown.
4 Theoretical Analysis of Our Curriculum Strategy
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In this section, we present the theoretical analysis of our curriculum strategy for two popular learner models, namely, MaxEnt-IRL and CrossEnt-BC. For our analysis, we consider the teacher-centric setting as introduced in Section [2](#S2 "2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). Our curriculum strategy obtains a preference ranking over the demonstrations to provide to the learner based on the difficulty scores (see Definition [1](#Thmdefinition1 "Definition 1. ‣ Difficulty scores. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). To this end, we analyze the relationship between the difficulty scores (w.r.t. the teacher and the learner) of the provided demonstration and the teaching objective (convergence towards the target teaching parameter θ\*superscript𝜃\theta^{\*}italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT) during each sequential update step of the learner.
Given two difficulty values ψE,ψL∈ℝsuperscript𝜓𝐸superscript𝜓𝐿
ℝ\psi^{E},\psi^{L}\in\mathbb{R}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ∈ blackboard\_R, we define the feasible set of demonstrations at time t𝑡titalic\_t as 𝒟t(ψE,ψL):={ξ:ΨE(ξ)=ψE and ΨtL(ξ)=ψL}assignsubscript𝒟𝑡superscript𝜓𝐸superscript𝜓𝐿conditional-set𝜉superscriptΨ𝐸𝜉superscript𝜓𝐸 and subscriptsuperscriptΨ𝐿𝑡𝜉superscript𝜓𝐿\mathcal{D}\_{t}\left({\psi^{E},\psi^{L}}\right):=\left\{{\xi:\Psi^{E}\left({\xi}\right)=\psi^{E}\text{ and }\Psi^{L}\_{t}\left({\xi}\right)=\psi^{L}}\right\}caligraphic\_D start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ) := { italic\_ξ : roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) = italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT and roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ξ ) = italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT }. This set contains all demonstrations ξ𝜉\xiitalic\_ξ for which the teacher’s difficulty score ΨE(ξ)superscriptΨ𝐸𝜉\Psi^{E}(\xi)roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) is equal to the value ψEsuperscript𝜓𝐸\psi^{E}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT, and the learner’s difficulty score ΨL(ξ)superscriptΨ𝐿𝜉\Psi^{L}(\xi)roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ( italic\_ξ ) is equal to the value ψLsuperscript𝜓𝐿\psi^{L}italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT. Let Δt(ψE,ψL)subscriptΔ𝑡superscript𝜓𝐸superscript𝜓𝐿\Delta\_{t}\left({\psi^{E},\psi^{L}}\right)roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ) denote the expected convergence rate of the teaching objective at time t𝑡titalic\_t, given difficulty values ψEsuperscript𝜓𝐸\psi^{E}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT and ψLsuperscript𝜓𝐿\psi^{L}italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT:
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| | Δt(ψE,ψL):=𝔼ξt∣ψE,ψL[∥θ\*−θt∥2−∥θ\*−θt+1(ξt)∥2],assignsubscriptΔ𝑡superscript𝜓𝐸superscript𝜓𝐿subscript𝔼conditionalsubscript𝜉𝑡superscript𝜓𝐸superscript𝜓𝐿delimited-[]superscriptdelimited-∥∥superscript𝜃subscript𝜃𝑡2superscriptdelimited-∥∥superscript𝜃subscript𝜃𝑡1subscript𝜉𝑡2\displaystyle\Delta\_{t}\left({\psi^{E},\psi^{L}}\right):=\mathbb{E}\_{\xi\_{t}\mid\psi^{E},\psi^{L}}\![\left\lVert\theta^{\*}-\theta\_{t}\right\rVert^{2}-\left\lVert\theta^{\*}-\theta\_{t+1}(\xi\_{t})\right\rVert^{2}],roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ) := blackboard\_E start\_POSTSUBSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∣ italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT [ ∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT - ∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT ( italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) ∥ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT ] , | | (2) |
where the expectation is w.r.t. the uniform distribution over the set 𝒟t(ψE,ψL)subscript𝒟𝑡superscript𝜓𝐸superscript𝜓𝐿\mathcal{D}\_{t}\left({\psi^{E},\psi^{L}}\right)caligraphic\_D start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ). Below, we analyse the differential effect of ψEsuperscript𝜓𝐸\psi^{E}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT and ψLsuperscript𝜓𝐿\psi^{L}italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT on Δt(ψE,ψL)subscriptΔ𝑡superscript𝜓𝐸superscript𝜓𝐿\Delta\_{t}\left({\psi^{E},\psi^{L}}\right)roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ), i.e., the effect of picking demonstrations with higher or lower difficulty scores on the learning progress.
###
4.1 Analysis for MaxEnt-IRL Learner
Here, we consider the popular MaxEnt-IRL learner model [[10](#bib.bib10), [59](#bib.bib59), [60](#bib.bib60)] in an MDP ℳℳ\mathcal{M}caligraphic\_M with deterministic transition dynamics, i.e., 𝒯:𝒮×𝒮×𝒜→{0,1}:𝒯→𝒮𝒮𝒜01\mathcal{T}:\mathcal{S}\times\mathcal{S}\times\mathcal{A}\rightarrow\left\{{0,1}\right\}caligraphic\_T : caligraphic\_S × caligraphic\_S × caligraphic\_A → { 0 , 1 }. The MaxEnt-IRL learner model uses a parametric reward function Rθ:𝒮×𝒜→ℝ:subscript𝑅𝜃→𝒮𝒜ℝR\_{\theta}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}italic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT : caligraphic\_S × caligraphic\_A → blackboard\_R where θ∈ℝd𝜃superscriptℝ𝑑\theta\in\mathbb{R}^{d}italic\_θ ∈ blackboard\_R start\_POSTSUPERSCRIPT italic\_d end\_POSTSUPERSCRIPT is a parameter. The reward function Rθsubscript𝑅𝜃R\_{\theta}italic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT also depends on a feature mapping ϕ:𝒮×𝒜→ℝd′:italic-ϕ→𝒮𝒜superscriptℝsuperscript𝑑′\phi:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}^{d^{\prime}}italic\_ϕ : caligraphic\_S × caligraphic\_A → blackboard\_R start\_POSTSUPERSCRIPT italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT which encodes each state-action pair (s,a)𝑠𝑎\left({s,a}\right)( italic\_s , italic\_a ) by a feature vector ϕ(s,a)∈ℝd′italic-ϕ𝑠𝑎superscriptℝsuperscript𝑑′\phi\left({s,a}\right)\in\mathbb{R}^{d^{\prime}}italic\_ϕ ( italic\_s , italic\_a ) ∈ blackboard\_R start\_POSTSUPERSCRIPT italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT. For our theoretical analysis, we consider Rθsubscript𝑅𝜃R\_{\theta}italic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT with a linear form, i.e., Rθ(s,a):=⟨θ,ϕ(s,a)⟩assignsubscript𝑅𝜃𝑠𝑎𝜃italic-ϕ𝑠𝑎R\_{\theta}\left({s,a}\right):=\left\langle{\theta},{\phi\left({s,a}\right)}\right\rangleitalic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) := ⟨ italic\_θ , italic\_ϕ ( italic\_s , italic\_a ) ⟩ and d=d′𝑑superscript𝑑′d=d^{\prime}italic\_d = italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT. In our experiments, we go beyond these simplifications and consider environments with stochastic transition dynamics and non-linear reward functions.
Under the MaxEnt-IRL learner model, the parametric policy takes the following soft-Bellman form: πθ(a|s)=exp(Qθ(s,a)−Vθ(s))subscript𝜋𝜃conditional𝑎𝑠subscript𝑄𝜃𝑠𝑎subscript𝑉𝜃𝑠\pi\_{\theta}(a|s)=\exp{(Q\_{\theta}(s,a)-V\_{\theta}(s))}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_a | italic\_s ) = roman\_exp ( italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) - italic\_V start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) ), where
Vθ(s)=log∑aexpQθ(s,a)subscript𝑉𝜃𝑠subscript𝑎subscript𝑄𝜃𝑠𝑎V\_{\theta}(s)=\log\sum\_{a}\exp{Q\_{\theta}(s,a)}italic\_V start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) = roman\_log ∑ start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT roman\_exp italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) and
Qθ(s,a)=Rθ(s,a)+γ∑s′𝒯(s′|s,a)⋅Vθ(s′)subscript𝑄𝜃𝑠𝑎subscript𝑅𝜃𝑠𝑎𝛾subscriptsuperscript𝑠′⋅𝒯conditionalsuperscript𝑠′𝑠𝑎subscript𝑉𝜃superscript𝑠′Q\_{\theta}(s,a)=R\_{\theta}(s,a)+\gamma\sum\_{s^{\prime}}\mathcal{T}(s^{\prime}|s,a)\cdot V\_{\theta}(s^{\prime})italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) = italic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) + italic\_γ ∑ start\_POSTSUBSCRIPT italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT caligraphic\_T ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT | italic\_s , italic\_a ) ⋅ italic\_V start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ).
For any given θ𝜃\thetaitalic\_θ, the corresponding policy πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT can be efficiently computed via the Soft-Value-Iteration procedure with reward Rθsubscript𝑅𝜃R\_{\theta}italic\_R start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT (see [[59](#bib.bib59), Algorithm. 9.1]).
For the above setting and a given parameter θ𝜃\thetaitalic\_θ, the probability distribution ℙ(ξ|θ)ℙconditional𝜉𝜃\mathbb{P}\left({\xi|\theta}\right)blackboard\_P ( italic\_ξ | italic\_θ )
over the demonstration ξ𝜉\xiitalic\_ξ takes the closed-form
ℙ(ξ|θ)=exp(⟨θ,μξ⟩)Z(θ)ℙconditional𝜉𝜃𝜃superscript𝜇𝜉𝑍𝜃\mathbb{P}\left({\xi|\theta}\right)=\frac{\exp\left({\left\langle{\theta},{\mu^{\xi}}\right\rangle}\right)}{Z\left({\theta}\right)}blackboard\_P ( italic\_ξ | italic\_θ ) = divide start\_ARG roman\_exp ( ⟨ italic\_θ , italic\_μ start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ⟩ ) end\_ARG start\_ARG italic\_Z ( italic\_θ ) end\_ARG,
where μξ:=∑τ=0∞γτϕ(sτξ,aτξ)assignsuperscript𝜇𝜉superscriptsubscript𝜏0superscript𝛾𝜏italic-ϕsuperscriptsubscript𝑠𝜏𝜉superscriptsubscript𝑎𝜏𝜉\mu^{\xi}:=\sum\_{\tau=0}^{\infty}\gamma^{\tau}\phi(s\_{\tau}^{\xi},a\_{\tau}^{\xi})italic\_μ start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT := ∑ start\_POSTSUBSCRIPT italic\_τ = 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_τ end\_POSTSUPERSCRIPT italic\_ϕ ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ) and Z(θ)𝑍𝜃Z\left({\theta}\right)italic\_Z ( italic\_θ ) is a normalization factor. Then, at time t𝑡titalic\_t, the gradient
of the MaxEnt-IRL learner is given by
gt=μπθt−μξtsubscript𝑔𝑡superscript𝜇subscript𝜋subscript𝜃𝑡superscript𝜇subscript𝜉𝑡g\_{t}=\mu^{\pi\_{\theta\_{t}}}-\mu^{\xi\_{t}}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = italic\_μ start\_POSTSUPERSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT - italic\_μ start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT,
where
μπ:=∑s,a∑τ=0∞γτ⋅ℙ{Sτ=s∣π,ℳ}⋅π(a∣s)⋅ϕ(s,a)assignsuperscript𝜇𝜋subscript𝑠𝑎superscriptsubscript𝜏0⋅⋅⋅superscript𝛾𝜏ℙconditional-setsubscript𝑆𝜏𝑠𝜋ℳ𝜋conditional𝑎𝑠italic-ϕ𝑠𝑎\mu^{\pi}:=\sum\_{s,a}\sum\_{\tau=0}^{\infty}\gamma^{\tau}\cdot\mathbb{P}\left\{{{S\_{\tau}=s\mid\pi,\mathcal{M}}}\right\}\cdot\pi\left({a\mid s}\right)\cdot\phi\left({s,a}\right)italic\_μ start\_POSTSUPERSCRIPT italic\_π end\_POSTSUPERSCRIPT := ∑ start\_POSTSUBSCRIPT italic\_s , italic\_a end\_POSTSUBSCRIPT ∑ start\_POSTSUBSCRIPT italic\_τ = 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_τ end\_POSTSUPERSCRIPT ⋅ blackboard\_P { italic\_S start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT = italic\_s ∣ italic\_π , caligraphic\_M } ⋅ italic\_π ( italic\_a ∣ italic\_s ) ⋅ italic\_ϕ ( italic\_s , italic\_a )
is the feature expectation vector of policy π𝜋\piitalic\_π. We note that our curriculum strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) is not using knowledge of gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT.
For the MaxEnt-IRL learner, we obtain the following theorem, which shows the differential effect of the difficulty scores (w.r.t. the teacher and the learner) on the expected rate of convergence of the teaching objective Δt(ψE,ψL)subscriptΔ𝑡superscript𝜓𝐸superscript𝜓𝐿\Delta\_{t}\left({\psi^{E},\psi^{L}}\right)roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ). We note that [[32](#bib.bib32)] obtained similar results for linear regression learner models in the supervised learning setting.
######
Theorem 1.
Assume that ηtsubscript𝜂𝑡\eta\_{t}italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is sufficiently small for all t𝑡titalic\_t s.t. ηt∥gt∥2≪2|⟨θ\*−θt,gt⟩|much-less-thansubscript𝜂𝑡superscriptdelimited-∥∥subscript𝑔𝑡22superscript𝜃subscript𝜃𝑡subscript𝑔𝑡\eta\_{t}\left\lVert g\_{t}\right\rVert^{2}\ll 2\left|\left\langle{\theta^{\*}-\theta\_{t}},{g\_{t}}\right\rangle\right|italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT ≪ 2 | ⟨ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ⟩ |,
where gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is the gradient of the MaxEnt-IRL learner. Then, for the MaxEnt-IRL learner, the expected convergence rate of the teaching objective Δt(ψE,ψL)subscriptnormal-Δ𝑡superscript𝜓𝐸superscript𝜓𝐿\Delta\_{t}\left({\psi^{E},\psi^{L}}\right)roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ) is:
* •
monotonically decreasing with value ψEsuperscript𝜓𝐸\psi^{E}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT, i.e., ∂Δt∂ψE<0subscriptΔ𝑡superscript𝜓𝐸0\frac{\partial\Delta\_{t}}{\partial\psi^{E}}<0divide start\_ARG ∂ roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG start\_ARG ∂ italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT end\_ARG < 0, and
* •
monotonically increasing with value ψLsuperscript𝜓𝐿\psi^{L}italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT, i.e., ∂Δt∂ψL>0subscriptΔ𝑡superscript𝜓𝐿0\frac{\partial\Delta\_{t}}{\partial\psi^{L}}>0divide start\_ARG ∂ roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG start\_ARG ∂ italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT end\_ARG > 0.
Theorem [1](#Thmtheorem1 "Theorem 1. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") suggests that choosing demonstrations with lower difficulty score w.r.t. the teacher’s policy and higher difficulty score w.r.t. the learner’s policy would lead to faster convergence. Our curriculum strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) induces a preference ranking over demonstrations that aligns with these insights of Theorem [1](#Thmtheorem1 "Theorem 1. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). Furthermore, the following theorem states that the particular form of combining the two difficulty scores used in curriculum strategy, Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")), achieves linear convergence to the teaching objective. This is similar to the state-of-the-art Omn algorithm based on the IMT framework for sequential learners [[37](#bib.bib37), [28](#bib.bib28)]. Importantly, unlike the Omn algorithm, our curriculum strategy does not rely on specifics of the learner model when selecting demonstrations.
######
Theorem 2.
Consider Algorithm [1](#alg1 "Algorithm 1 ‣ Generic learner model. ‣ 2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") with the MaxEnt-IRL learner and our curriculum strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). Then, the teaching objective ∥θ\*−θt∥≤ϵdelimited-∥∥superscript𝜃subscript𝜃𝑡italic-ϵ\left\lVert\theta^{\*}-\theta\_{t}\right\rVert\leq\epsilon∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ ≤ italic\_ϵ is achieved in t=𝒪(log1ϵ)𝑡𝒪1italic-ϵt=\mathcal{O}(\log\frac{1}{\epsilon})italic\_t = caligraphic\_O ( roman\_log divide start\_ARG 1 end\_ARG start\_ARG italic\_ϵ end\_ARG ) iterations.
In the above theorem, the constant terms suppressed by the 𝒪(⋅)𝒪⋅\mathcal{O}(\cdot)caligraphic\_O ( ⋅ ) notation depend on the learning rate of the learner (ηtsubscript𝜂𝑡\eta\_{t}italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT), the distance between the learner’s initial parameter/knowledge and the target teaching parameter (∥θ\*−θ1∥delimited-∥∥superscript𝜃subscript𝜃1\left\lVert\theta^{\*}-\theta\_{1}\right\rVert∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT ∥), and the *richness* of the set of demonstrations obtained by executing the policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT in the MDP ℳℳ\mathcal{M}caligraphic\_M. The *richness* notion is formally discussed in the Appendix.
###
4.2 Analysis for CrossEnt-BC Learner
Next, we consider the CrossEnt-BC learner model [[36](#bib.bib36), [15](#bib.bib15)]. In this case, the learner’s parametric policy takes the following softmax form:
πθ(a|s)=exp(Hθ(s,a))∑a′exp(Hθ(s,a′))subscript𝜋𝜃conditional𝑎𝑠subscript𝐻𝜃𝑠𝑎subscriptsuperscript𝑎′subscript𝐻𝜃𝑠superscript𝑎′\pi\_{\theta}(a|s)=\frac{\exp\left({H\_{\theta}(s,a)}\right)}{\sum\_{a^{\prime}}\exp\left({H\_{\theta}(s,a^{\prime})}\right)}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_a | italic\_s ) = divide start\_ARG roman\_exp ( italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) ) end\_ARG start\_ARG ∑ start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT roman\_exp ( italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ) end\_ARG,
where Hθ:𝒮×𝒜→ℝ:subscript𝐻𝜃→𝒮𝒜ℝH\_{\theta}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT : caligraphic\_S × caligraphic\_A → blackboard\_R is a parametric scoring function that depends on the parameter θ∈ℝd𝜃superscriptℝ𝑑\theta\in\mathbb{R}^{d}italic\_θ ∈ blackboard\_R start\_POSTSUPERSCRIPT italic\_d end\_POSTSUPERSCRIPT and a feature mapping ϕ:𝒮×𝒜→ℝd′:italic-ϕ→𝒮𝒜superscriptℝsuperscript𝑑′\phi:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}^{d^{\prime}}italic\_ϕ : caligraphic\_S × caligraphic\_A → blackboard\_R start\_POSTSUPERSCRIPT italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT. For our theoretical analysis, we consider a linear scoring function Hθsubscript𝐻𝜃H\_{\theta}italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT of the form Hθ(s,a):=⟨θ,ϕ(s,a)⟩assignsubscript𝐻𝜃𝑠𝑎𝜃italic-ϕ𝑠𝑎H\_{\theta}\left({s,a}\right):=\left\langle{\theta},{\phi\left({s,a}\right)}\right\rangleitalic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) := ⟨ italic\_θ , italic\_ϕ ( italic\_s , italic\_a ) ⟩ (with d=d′𝑑superscript𝑑′d=d^{\prime}italic\_d = italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT). Then, at time step t𝑡titalic\_t, the gradient gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT of the CrossEnt-BC learner is given by:
gt=∑τ=0∞(𝔼a∼πθt(⋅|sτξt)[ϕ(sτξt,a)]−ϕ(sτξt,aτξt))g\_{t}=\small\sum\_{\tau=0}^{\infty}\left({\mathbb{E}\_{a\sim\pi\_{\theta\_{t}}\left({\cdot|s\_{\tau}^{\xi\_{t}}}\right)}\!\left[\phi(s\_{\tau}^{\xi\_{t}},a)\right]-\phi(s\_{\tau}^{\xi\_{t}},a\_{\tau}^{\xi\_{t}})}\right)italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = ∑ start\_POSTSUBSCRIPT italic\_τ = 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT ( blackboard\_E start\_POSTSUBSCRIPT italic\_a ∼ italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( ⋅ | italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT ) end\_POSTSUBSCRIPT [ italic\_ϕ ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT , italic\_a ) ] - italic\_ϕ ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT ) ). In the experiments, we also consider non-linear scoring functions parameterized by neural networks.
Similar to Theorem [1](#Thmtheorem1 "Theorem 1. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), we obtain the following theorem for the CrossEnt-BC learner, which also justifies our curriculum strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
######
Theorem 3.
Assume that ηtsubscript𝜂𝑡\eta\_{t}italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is sufficiently small for all t𝑡titalic\_t s.t. ηt∥gt∥2≪2|⟨θ\*−θt,gt⟩|much-less-thansubscript𝜂𝑡superscriptdelimited-∥∥subscript𝑔𝑡22superscript𝜃subscript𝜃𝑡subscript𝑔𝑡\eta\_{t}\left\lVert g\_{t}\right\rVert^{2}\ll 2\left|\left\langle{\theta^{\*}-\theta\_{t}},{g\_{t}}\right\rangle\right|italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ∥ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT ≪ 2 | ⟨ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ⟩ |,
where gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is the gradient of the CrossEnt-BC learner. Then, for the CrossEnt-BC learner, the expected convergence rate of the teaching objective Δt(ψE,ψL)subscriptnormal-Δ𝑡superscript𝜓𝐸superscript𝜓𝐿\Delta\_{t}\left({\psi^{E},\psi^{L}}\right)roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT , italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT ), after first-order approximation, is:
* •
monotonically decreasing with ψEsuperscript𝜓𝐸\psi^{E}italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT, i.e., ∂Δt∂ψE<0subscriptΔ𝑡superscript𝜓𝐸0\frac{\partial\Delta\_{t}}{\partial\psi^{E}}<0divide start\_ARG ∂ roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG start\_ARG ∂ italic\_ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT end\_ARG < 0, and
* •
monotonically increasing with ψLsuperscript𝜓𝐿\psi^{L}italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT, i.e., ∂Δt∂ψL>0subscriptΔ𝑡superscript𝜓𝐿0\frac{\partial\Delta\_{t}}{\partial\psi^{L}}>0divide start\_ARG ∂ roman\_Δ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG start\_ARG ∂ italic\_ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT end\_ARG > 0.
We note that the proof of Theorem [3](#Thmtheorem3 "Theorem 3. ‣ 4.2 Analysis for CrossEnt-BC Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") relies on the first-order Taylor approximation of the term ∑τlog∑a′exp(Hθ(sτξt,a′))subscript𝜏subscriptsuperscript𝑎′subscript𝐻𝜃superscriptsubscript𝑠𝜏subscript𝜉𝑡superscript𝑎′\sum\_{\tau}\log\sum\_{a^{\prime}}\exp\left({H\_{\theta}\left({s\_{\tau}^{\xi\_{t}},a^{\prime}}\right)}\right)∑ start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT roman\_log ∑ start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT roman\_exp ( italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_ξ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ) around θtsubscript𝜃𝑡\theta\_{t}italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT (detailed in the Appendix). Due to this approximation, it is more challenging to obtain a convergence result analogous to Theorem [2](#Thmtheorem2 "Theorem 2. ‣ 4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications").
5 Experimental Evaluation: Teacher-Centric Setting
---------------------------------------------------
Inspired by the works of [[61](#bib.bib61), [62](#bib.bib62), [28](#bib.bib28)], we evaluate the performance of our curriculum strategy, Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")), in a synthetic car driving environment on MaxEnt-IRL and CrossEnt-BC learners. In particular, we consider the environment of [[28](#bib.bib28)] and the teacher-centric setting of Section [2](#S2 "2 Formal Problem Setup ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications").
#### Car driving setup.
Fig. [2](#S5.F2 "Figure 2 ‣ Car driving setup. ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") illustrates a synthetic car driving environment consisting of 8888 different types of tasks, denoted as T0, T1, ……\ldots…, T7. Each type is associated with different driving skills. For instance, T0 corresponds to a basic setup representing a traffic-free highway. T1 represents a crowded highway. T2 has stones on the right lane, whereas T3 has a mix of both cars and stones. Similarly, T4 has grass on the right lane, and T5 has a mix of both grass and cars. T6 and T7 introduce more complex features such as pedestrians, police, and HOV (high occupancy vehicles).
The agent starts navigating from an initial state at the bottom of the left lane of each task, and the goal is to reach the top of a lane while avoiding cars, stones, and other obstacles.
The agent’s action space is given by 𝒜=𝒜absent\mathcal{A}=caligraphic\_A = {left, straight, right}. Action left steers the agent to the left of the current lane. If the agent is already in the leftmost lane when taking action left, then the lane is randomly chosen with uniform probability. We define similar stochastic dynamics for taking action right; action straight means no change in the lane. Irrespective of the action taken, the agent always moves forward.
Figure 1: Car environment with 8888 different types of tasks. Arrows represent the path taken by the teacher’s policy.
ϕE(s)superscriptitalic-ϕ𝐸𝑠\phi^{E}(s)italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s )
w𝑤witalic\_w
stone
-1
grass
-0.5
car
-5
ped
-10
car-front
-2
ped-front
-5
HOV
+1
police
0
Figure 1: Car environment with 8888 different types of tasks. Arrows represent the path taken by the teacher’s policy.
Figure 2:
Environment features ϕE(s)superscriptitalic-ϕ𝐸𝑠\phi^{E}\left({s}\right)italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s ) and reward weights w𝑤witalic\_w.
#### Environment MDP.
Based on the above setup, we define the environment MDP, ℳcarsubscriptℳcar\mathcal{M}\_{\mathrm{car}}caligraphic\_M start\_POSTSUBSCRIPT roman\_car end\_POSTSUBSCRIPT, consisting of 8888 types of tasks, namely T0–T7, and 5555 tasks of each type. Every location in the environment is associated with a state. Each task is of length 10101010 and width 2222, leading to a state space of size 5×8×2058205\times 8\times 205 × 8 × 20.
We consider an action-independent reward function REcarsuperscript𝑅subscript𝐸carR^{E\_{\mathrm{car}}}italic\_R start\_POSTSUPERSCRIPT italic\_E start\_POSTSUBSCRIPT roman\_car end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT that is dependent on an underlying feature vector ϕEsuperscriptitalic-ϕ𝐸\phi^{E}italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT (see Fig. [2](#S5.F2 "Figure 2 ‣ Car driving setup. ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). The feature vector of a state s𝑠sitalic\_s, denoted by ϕE(s)superscriptitalic-ϕ𝐸𝑠\phi^{E}(s)italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s ), is a binary vector encoding the presence or absence of an object at the state. In this work we have two types of features:
features indicating the type of the current cell as stone, grass, car, ped, police, and HOV, as well as features providing some look-ahead information such as whether there is a car or pedestrian in the immediate front cell (denoted as car-front and ped-front).
Now we explain the reward function REcarsuperscript𝑅subscript𝐸carR^{E\_{\mathrm{car}}}italic\_R start\_POSTSUPERSCRIPT italic\_E start\_POSTSUBSCRIPT roman\_car end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT. For states in tasks of type T0-T6, the reward
is given by ⟨w,ϕE(s)⟩𝑤superscriptitalic-ϕ𝐸𝑠\left\langle{w},{\phi^{E}(s)}\right\rangle⟨ italic\_w , italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s ) ⟩ (see Fig. [2](#S5.F2 "Figure 2 ‣ Car driving setup. ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
Essentially there are different penalties (i.e., negative rewards) for colliding with specific obstacles such as stone and car.
For states in tasks of type T7, there is a reward of value +11+1+ 1 for driving on HOV; however, if police is present while driving on HOV, a reward value of −55-5- 5 is obtained. Overall, this results in the reward function REcarsuperscript𝑅subscript𝐸carR^{E\_{\mathrm{car}}}italic\_R start\_POSTSUPERSCRIPT italic\_E start\_POSTSUBSCRIPT roman\_car end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT being nonlinear.
###
5.1 Teaching Algorithms
Here, we introduce the teaching algorithms considered in our experiments. The teacher’s near-optimal policy πEsuperscript𝜋𝐸\pi^{E}italic\_π start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT is obtained via policy iteration [[63](#bib.bib63)].
The teacher selects demonstrations to provide to the learner using its teaching algorithm.
We compare the performance of our proposed Cur teacher, which implements our strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")), with the following baselines:
* •
Cur-T: A variant of our Cur teacher that samples demonstrations based on the difficulty score ΨEsuperscriptΨ𝐸\Psi^{E}roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT alone, and sets ΨtLsubscriptsuperscriptΨ𝐿𝑡\Psi^{L}\_{t}roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT to constant.
* •
Cur-L: A similar variant of our Cur teacher that samples demonstrations based on the difficulty score ΨtLsubscriptsuperscriptΨ𝐿𝑡\Psi^{L}\_{t}roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT alone, and sets ΨEsuperscriptΨ𝐸\Psi^{E}roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT to constant.
* •
Agn: an agnostic teacher that picks demonstrations based on random ordering [[32](#bib.bib32), [28](#bib.bib28)].
* •
Omn: The omniscient teacher is a state-of-the-art algorithm [[28](#bib.bib28), [37](#bib.bib37)], which is applicable only to MaxEnt-IRL learners. Omn requires complete knowledge of the parameter θ\*superscript𝜃\theta^{\*}italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT, the learner’s current parameter θtsubscript𝜃𝑡\theta\_{t}italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT, and the learner’s gradients ηtgtsubscript𝜂𝑡subscript𝑔𝑡\eta\_{t}g\_{t}italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT. Based on this knowledge, the teacher picks demonstrations to directly steer the learner towards θ\*superscript𝜃\theta^{\*}italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT, i.e., by minimizing ∥θ\*−(θt−ηtgt)∥delimited-∥∥superscript𝜃subscript𝜃𝑡subscript𝜂𝑡subscript𝑔𝑡\left\lVert\theta^{\*}-(\theta\_{t}-\eta\_{t}g\_{t})\right\rVert∥ italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT - ( italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT - italic\_η start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) ∥.
* •
BBox: The blackbox teacher [[28](#bib.bib28)] is designed based on the functional form of gradients for the linear MaxEnt-IRL learner model.222The BBox teacher’s objective is derived under the assumptions that the reward function can be linearly parameterized as ⟨w\*,ϕE(s)⟩
superscript𝑤superscriptitalic-ϕ𝐸𝑠\left\langle{w^{\*}},{\phi^{E}(s)}\right\rangle⟨ italic\_w start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT , italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s ) ⟩ and gradients gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT are based on the linear MaxEnt-IRL learner model. Under these assumptions, the teacher’s objective can be equivalently written as |⟨w\*,gt⟩|
superscript𝑤subscript𝑔𝑡|\left\langle{w^{\*}},{g\_{t}}\right\rangle|| ⟨ italic\_w start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT , italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ⟩ |.
Specifically, the teacher picks a demonstration ξ𝜉\xiitalic\_ξ which maximizes |∑s′,a′{ρπtL(s′,a′)−ρξ(s′,a′)}RE(s′,a′)|subscript
superscript𝑠′superscript𝑎′superscript𝜌subscriptsuperscript𝜋𝐿𝑡superscript𝑠′superscript𝑎′superscript𝜌𝜉superscript𝑠′superscript𝑎′superscript𝑅𝐸superscript𝑠′superscript𝑎′|\sum\_{s^{\prime},a^{\prime}}\{\rho^{\pi^{L}\_{t}}(s^{\prime},a^{\prime})-\rho^{\xi}(s^{\prime},a^{\prime})\}R^{E}(s^{\prime},a^{\prime})|| ∑ start\_POSTSUBSCRIPT italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT { italic\_ρ start\_POSTSUPERSCRIPT italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) - italic\_ρ start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) } italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) |, where ρ𝜌\rhoitalic\_ρ denotes state visitation frequency vectors.
The BBox teacher does not require access to θ\*superscript𝜃\theta^{\*}italic\_θ start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT or the learner’s current parameter θtsubscript𝜃𝑡\theta\_{t}italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT; however, it requires access to the true reward function REsuperscript𝑅𝐸R^{E}italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT.
* •
Scot: The Scot teacher [[18](#bib.bib18)] aims to find the smallest set of demonstrations required to teach an optimal reward function to the MaxEnt-IRLlearner. The teacher uses a set cover algorithm to pre-compute the entire curriculum as a batch, prior to training. In our implementation, after having provided the entire batch, the teacher continues providing demonstrations selected at random.
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Figure 3: Learning curves and curriculum visualization for MaxEnt-IRL learners (with varying initial knowledge) trained on the car driving environment: (a) reward convergence plot and (b) curriculum generated by the Cur teacher for the learner with initial knowledge of T0; (c) reward convergence plot and (d) curriculum generated by the Cur teacher for the learner with initial knowledge of T0–T3.
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Figure 4: Learning curves and curriculum visualization for CrossEnt-BC learners (with varying initial knowledge) trained on the car driving environment: (a) reward convergence plot and (b) curriculum generated by the Cur teacher for the learner with initial knowledge of T0; (c) reward convergence plot and (d) curriculum generated by the Cur teacher for the learner with initial knowledge of T0–T3.
###
5.2 Learner Models
Next, we describe the MaxEnt-IRL and CrossEnt-BC learner models. For the MaxEnt-IRL learner, we evaluate all the above-mentioned teaching algorithms that include state-of-the-art baselines; for the CrossEnt-BC learner, we evaluate Cur, Cur-T, Cur-L, and Agn algorithms.
#### MaxEnt-IRL learner.
For alignment with the prior state-of-the-art work on teaching sequential MaxEnt-IRL learners [[28](#bib.bib28)], we perform *teaching over states* in our experiments. More concretely, at time t𝑡titalic\_t the teacher picks a state stsubscript𝑠𝑡s\_{t}italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT (where P0(st)>0subscript𝑃0subscript𝑠𝑡0P\_{0}(s\_{t})>0italic\_P start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) > 0) and provides all demonstrations starting from stsubscript𝑠𝑡s\_{t}italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT to the learner given by Ξst={ξ={(sτξ,aτξ)}τ s.t. s0ξ=st}subscriptΞsubscript𝑠𝑡𝜉subscriptsubscriptsuperscript𝑠𝜉𝜏subscriptsuperscript𝑎𝜉𝜏𝜏 s.t. subscriptsuperscript𝑠𝜉0subscript𝑠𝑡\Xi\_{s\_{t}}=\left\{{\xi=\left\{{\left({s^{\xi}\_{\tau},a^{\xi}\_{\tau}}\right)}\right\}\_{\tau}\text{ s.t. }s^{\xi}\_{0}=s\_{t}}\right\}roman\_Ξ start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT = { italic\_ξ = { ( italic\_s start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT , italic\_a start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT ) } start\_POSTSUBSCRIPT italic\_τ end\_POSTSUBSCRIPT s.t. italic\_s start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT = italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT }.
The gradient gtsubscript𝑔𝑡g\_{t}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT of the MaxEnt-IRL learner is then given by
gt=μπθt,st−μΞstsubscript𝑔𝑡superscript𝜇subscript𝜋subscript𝜃𝑡subscript𝑠𝑡superscript𝜇subscriptΞsubscript𝑠𝑡g\_{t}=\mu^{\pi\_{\theta\_{t}},s\_{t}}-\mu^{\Xi\_{s\_{t}}}italic\_g start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = italic\_μ start\_POSTSUPERSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT - italic\_μ start\_POSTSUPERSCRIPT roman\_Ξ start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT, where (i) μΞst:=1|Ξst|∑ξ∈Ξstμξassignsuperscript𝜇subscriptΞsubscript𝑠𝑡1subscriptΞsubscript𝑠𝑡subscript𝜉subscriptΞsubscript𝑠𝑡superscript𝜇𝜉\mu^{\Xi\_{s\_{t}}}:=\frac{1}{\left|\Xi\_{s\_{t}}\right|}\sum\_{\xi\in\Xi\_{s\_{t}}}\mu^{\xi}italic\_μ start\_POSTSUPERSCRIPT roman\_Ξ start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT := divide start\_ARG 1 end\_ARG start\_ARG | roman\_Ξ start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT | end\_ARG ∑ start\_POSTSUBSCRIPT italic\_ξ ∈ roman\_Ξ start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT italic\_μ start\_POSTSUPERSCRIPT italic\_ξ end\_POSTSUPERSCRIPT, and (ii) μπ,stsuperscript𝜇𝜋subscript𝑠𝑡\mu^{\pi,s\_{t}}italic\_μ start\_POSTSUPERSCRIPT italic\_π , italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT is the feature expectation vector of policy π𝜋\piitalic\_π with starting state set to stsubscript𝑠𝑡s\_{t}italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT (see Section [4.1](#S4.SS1 "4.1 Analysis for MaxEnt-IRL Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")).
Based on [[28](#bib.bib28)], we consider the learner’s feature mapping as ϕ(s,a)=ϕE(s)italic-ϕ𝑠𝑎superscriptitalic-ϕ𝐸𝑠\phi(s,a)=\phi^{E}(s)italic\_ϕ ( italic\_s , italic\_a ) = italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s ) and the learner uses a non-linear parametric reward function RθL(s,a)=⟨θ1:d′,ϕ(s,a)⟩+⟨θd′+1:2d′,ϕ(s,a)⟩2subscriptsuperscript𝑅𝐿𝜃𝑠𝑎subscript𝜃:1superscript𝑑′italic-ϕ𝑠𝑎
superscriptsubscript𝜃:superscript𝑑′12superscript𝑑′italic-ϕ𝑠𝑎
2R^{L}\_{\theta}\left({s,a}\right)=\left\langle{\theta\_{1:d^{\prime}}},{\phi\left({s,a}\right)}\right\rangle+\left\langle{\theta\_{d^{\prime}+1:2d^{\prime}}},{\phi\left({s,a}\right)}\right\rangle^{2}italic\_R start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) = ⟨ italic\_θ start\_POSTSUBSCRIPT 1 : italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT , italic\_ϕ ( italic\_s , italic\_a ) ⟩ + ⟨ italic\_θ start\_POSTSUBSCRIPT italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT + 1 : 2 italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT , italic\_ϕ ( italic\_s , italic\_a ) ⟩ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT where d′superscript𝑑′d^{\prime}italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT is the dimension of ϕ(s,a)italic-ϕ𝑠𝑎\phi(s,a)italic\_ϕ ( italic\_s , italic\_a ). As explained in [[28](#bib.bib28)], a linear reward representation cannot capture the optimal behaviour for ℳcarsubscriptℳcar\mathcal{M}\_{\mathrm{car}}caligraphic\_M start\_POSTSUBSCRIPT roman\_car end\_POSTSUBSCRIPT.
We consider learners with varying levels of initial knowledge, i.e.,
the learner is trained on a subset of tasks before the teaching process starts. In this setting, for our curriculum strategy in Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) the difficulty score of a set of demonstrations associated with a starting state ΞssubscriptΞ𝑠\Xi\_{s}roman\_Ξ start\_POSTSUBSCRIPT italic\_s end\_POSTSUBSCRIPT is computed as the mean difficulty score of individual demonstrations in the set.
#### CrossEnt-BC learner.
We consider the CrossEnt-BC learner model of Section [4.2](#S4.SS2 "4.2 Analysis for CrossEnt-BC Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") as our second learner model. The learner’s feature mapping is given by ϕ(s,a)=𝔼s′∼𝒯(⋅|s,a)[ϕE(s′)]\phi\left({s,a}\right)=\mathbb{E}\_{s^{\prime}\sim\mathcal{T}(\cdot|s,a)}[\phi^{E}(s^{\prime})]italic\_ϕ ( italic\_s , italic\_a ) = blackboard\_E start\_POSTSUBSCRIPT italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ∼ caligraphic\_T ( ⋅ | italic\_s , italic\_a ) end\_POSTSUBSCRIPT [ italic\_ϕ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ]. A quadratic parametric form is selected for the scoring function, i.e., Hθ(s,a)=⟨θ1:d′,ϕ(s,a)⟩+⟨θd′+1:2d′,ϕ(s,a)⟩2subscript𝐻𝜃𝑠𝑎subscript𝜃:1superscript𝑑′italic-ϕ𝑠𝑎
superscriptsubscript𝜃:superscript𝑑′12superscript𝑑′italic-ϕ𝑠𝑎
2H\_{\theta}\left({s,a}\right)=\left\langle{\theta\_{1:d^{\prime}}},{\phi\left({s,a}\right)}\right\rangle+\left\langle{\theta\_{d^{\prime}+1:2d^{\prime}}},{\phi\left({s,a}\right)}\right\rangle^{2}italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) = ⟨ italic\_θ start\_POSTSUBSCRIPT 1 : italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT , italic\_ϕ ( italic\_s , italic\_a ) ⟩ + ⟨ italic\_θ start\_POSTSUBSCRIPT italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT + 1 : 2 italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT , italic\_ϕ ( italic\_s , italic\_a ) ⟩ start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT, where d′superscript𝑑′d^{\prime}italic\_d start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT is the dimension of ϕ(s,a)italic-ϕ𝑠𝑎\phi(s,a)italic\_ϕ ( italic\_s , italic\_a ). We consider learners with varying initial knowledge and perform teaching over states similar to the MaxEnt-IRL learner.
###
5.3 Experimental results
Figs. [2(a)](#S5.F2.sf1 "2(a) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [2(c)](#S5.F2.sf3 "2(c) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") and [3(a)](#S5.F3.sf1 "3(a) ‣ Figure 4 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [3(c)](#S5.F3.sf3 "3(c) ‣ Figure 4 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") show the convergence of the total expected reward for the MaxEnt-IRL and CrossEnt-BC learners respectively, averaged over 10101010 runs. The Cur teacher outperforms Omn despite not requiring information about the learner’s dynamics. For non-linear parametric reward functions, the MaxEnt-IRL learner no longer solves a convex optimization problem. As a result, forcing the learner to converge to a fixed parameter doesn’t necessarily perform well, as seen by the poor performance of the Omn teacher in Fig. [2(c)](#S5.F2.sf3 "2(c) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"). The Cur teacher is competitive with the BBox teacher.
Unlike our Cur teacher, the BBox teacher does require exact access to the true reward function, REsuperscript𝑅𝐸R^{E}italic\_R start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT.
The Cur teacher consistently outperforms the Agn and Scot teachers, as well as
both the Cur-T and Cur-L variants.
Figs. [2(b)](#S5.F2.sf2 "2(b) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [2(d)](#S5.F2.sf4 "2(d) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") and [3(b)](#S5.F3.sf2 "3(b) ‣ Figure 4 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), [3(d)](#S5.F3.sf4 "3(d) ‣ Figure 4 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") visualize the curriculum generated by the Cur teacher for the MaxEnt-IRL and CrossEnt-BC learners respectively. Here, the curriculum refers to the type of task, T0–T7, associated with the demonstrations provided by the teacher to the learner at time step t𝑡titalic\_t. For both types of learners we see that at the beginning of training, the teacher focuses on tasks which teach skills the learner is yet to master. For example, in Fig. [3(d)](#S5.F3.sf4 "3(d) ‣ Figure 4 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), the teacher picks tasks T4, T6, and T7, which teaches the learner to avoid grass, pedestrians, and to navigate through police and HOV.
We also notice that the Cur teacher can identify degradation in performance on previously mastered tasks, e.g., task T1 in Fig. [2(d)](#S5.F2.sf4 "2(d) ‣ Figure 3 ‣ 5.1 Teaching Algorithms ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), and corrects for this by picking them again later during training.
#### Additional results under limited observability.
Figure 5: Learning curves for the MaxEnt-IRL learner under limited observability.
In the above experiments, we consider the learner’s policy to be fully observable by the teacher at every time step. Here, we study the performance of our Cur teacher under the limited observability setting, similar to [[28](#bib.bib28)], where the learner’s policy needs to be estimated by probing the learner. The probing process is formally characterized by two parameters, (B,k)𝐵𝑘(B,k)( italic\_B , italic\_k ), where the learner’s policy is probed after every B𝐵Bitalic\_B time steps and each probing step corresponds to querying the learner’s policy πtLsubscriptsuperscript𝜋𝐿𝑡\pi^{L}\_{t}italic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT a total of k𝑘kitalic\_k times from each state s∈𝒮𝑠𝒮s\in\mathcal{S}italic\_s ∈ caligraphic\_S in the MDP. The learner’s policy, πtL(a|s)∀a,ssubscriptsuperscript𝜋𝐿𝑡conditional𝑎𝑠for-all𝑎𝑠\pi^{L}\_{t}(a|s)\;\forall a,sitalic\_π start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_a | italic\_s ) ∀ italic\_a , italic\_s, is then approximated based on the fraction of the k𝑘kitalic\_k queries in which the learner performed action a𝑎aitalic\_a from state s𝑠sitalic\_s. In between every B𝐵Bitalic\_B time steps that the learner is probed, the Cur teacher does not update its estimate of the learner’s policy. We note that the (B=1,k→∞)formulae-sequence𝐵1→𝑘(B=1,k\to\infty)( italic\_B = 1 , italic\_k → ∞ ) setting corresponds to full observability of the learner.
Fig. [5](#S5.F5 "Figure 5 ‣ Additional results under limited observability. ‣ 5.3 Experimental results ‣ 5 Experimental Evaluation: Teacher-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") depicts the performance of the Cur teacher for different values of (B,k)𝐵𝑘(B,k)( italic\_B , italic\_k ).
Even under limited observability, the Cur teacher’s performance is competitive with the full observability setting. The performance of (B=40,k→∞)formulae-sequence𝐵40→𝑘(B=40,k\to\infty)( italic\_B = 40 , italic\_k → ∞ ) is even slightly better at certain time steps during later stages of training compared to (B=1,k→∞)formulae-sequence𝐵1→𝑘(B=1,k\to\infty)( italic\_B = 1 , italic\_k → ∞ ), which is possibly due to the strategy of greedily picking demonstrations not being necessarily optimal. Also, for the limited observability setting it can be interesting to explore approaches that alleviate the need to query the full policy of the learner [[64](#bib.bib64), [65](#bib.bib65)].
6 Experimental Evaluation: Learner-Centric Setting
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Figure 6: Illustration of a shortest path navigation task (left) and convergence curves (right).
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Figure 6: Illustration of a shortest path navigation task (left) and convergence curves (right).
Figure 7: Illustration of a travelling salesman navigation task (left) and convergence curves (right).
In this section, we evaluate our curriculum strategy in a learner-centric setting, i.e., no teacher agent is present, and the teacher’s difficulty ΨE(ξ)superscriptΨ𝐸𝜉\Psi^{E}(\xi)roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT ( italic\_ξ ) is expressed by a task-specific difficulty score (see Section [3](#S3 "3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). We evaluate our approach for training a multi-task neural policy to solve discrete optimization problems. Here, we provide an overview of the results with additional details in the Appendix.
#### Experiment setup.
We begin by describing the synthetic navigation-based environments considered in our experiments. Our first navigation environment comprises of tasks based on the shortest path problem [[66](#bib.bib66)]. We represent each task with a grid-world (see Fig. [5(a)](#S6.F5.sf1 "5(a) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) containing goal cells (depicted by stars), and cells with muds and bombs (shown in brown and red respectively). The agent aims to navigate to the closest goal cell while avoiding muds and bombs. Our second navigation environment comprises of tasks inspired by the travelling salesman problem (TSP) [[67](#bib.bib67), [68](#bib.bib68)] (see Fig. [6(a)](#S6.F6.sf1 "6(a) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). Again we represent each task with a grid-world, where the agent’s goal is to find the shortest tour which visits all goals and returns to its initial location (see Fig. [6(a)](#S6.F6.sf1 "6(a) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). Orange arrows in Figs. [5(a)](#S6.F5.sf1 "5(a) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") and [6(a)](#S6.F6.sf1 "6(a) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") depict the optimal path for the agent. In our experimental setup, we begin by creating a pool of tasks and split them into training and test sets. The curriculum algorithms order the training tasks during the training phase based on their strategy to speed up the learner’s progress. The aim of the learner is to learn a multi-task neural policy that can generalize to new unseen tasks in the test set.
#### Curriculum algorithms.
We compare the performance of four different curriculum algorithms: (i) the Cur algorithm picks tasks from the training set using Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) where the numerator is ΨtLsubscriptsuperscriptΨ𝐿𝑡\Psi^{L}\_{t}roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT and the denominator ΨEsuperscriptΨ𝐸\Psi^{E}roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT is defined by a task-specific difficulty score (detailed in the Appendix); (ii) the Cur-L algorithm picks tasks from the training set using Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) where the numerator is ΨtLsubscriptsuperscriptΨ𝐿𝑡\Psi^{L}\_{t}roman\_Ψ start\_POSTSUPERSCRIPT italic\_L end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT and the denominator is set to 1; (iii) the Cur-T algorithm picks tasks from the training set using Eq. ([1](#S3.E1 "1 ‣ General curriculum strategy. ‣ 3 Curriculum Design using Difficulty Scores ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")) where the numerator is set to 1 and the denominator is set to ΨEsuperscriptΨ𝐸\Psi^{E}roman\_Ψ start\_POSTSUPERSCRIPT italic\_E end\_POSTSUPERSCRIPT; (iv) the AGN algorithm picks tasks with a uniform distribution over the training set.
#### Learner model.
We consider a neural CrossEnt-BC learner (see Section [4.2](#S4.SS2 "4.2 Analysis for CrossEnt-BC Learner ‣ 4 Theoretical Analysis of Our Curriculum Strategy ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications")). The learner’s scoring function Hθsubscript𝐻𝜃H\_{\theta}italic\_H start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT is parameterized by a 6666-layer Convolutional Neural Network (CNN).
The CNN takes as input a feature mapping of the agent’s current position in a task, and outputs a score for each action. The learner minimizes the cross-entropy loss between its predictions and the demonstrations.
#### Results.
Figs. [5(b)](#S6.F5.sf2 "5(b) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications") and [6(b)](#S6.F6.sf2 "6(b) ‣ Figure 7 ‣ 6 Experimental Evaluation: Learner-Centric Setting ‣ Curriculum Design for Teaching via Demonstrations: Theory and Applications"), show the reward convergence curves on the test set for the different curriculum algorithms averaged over 5555 runs. The Cur algorithm leads to faster reward convergence compared to the Agn algorithm, which is the common approach for training a neural policy. Cur-L is competitive with Cur in this setting which highlights the importance of the learner’s difficulty.
7 Discussion and Conclusions
-----------------------------
We presented a unified curriculum strategy, with theoretical guarantees, for the sequential MaxEnt-IRL and CrossEnt-BC learner models, based on the concept of difficulty scores. Our proposed strategy is independent of the learner’s internal dynamics and is applicable in both teacher-centric and learner-centric settings. Experiments on a synthetic car driving environment and on navigation-based environments demonstrated the effectiveness of our curriculum strategy.
Our work provides theoretical underpinnings of curriculum design for teaching via demonstrations, which can be beneficial in educational applications such as tutoring systems and also for self-curriculum design for imitation learners. As such we do not see any negative societal impact of our work.
Some of the interesting directions for future work include: obtaining convergence bounds for CrossEnt-BC and other learner models, designing curriculum algorithms for reinforcement learning agents based on the concept of difficulty scores, and designing approaches to efficiently approximate the learner’s policy using less queries. |
108bb37a-4e9f-4768-859d-3e26561c4151 | trentmkelly/LessWrong-43k | LessWrong | Group Rationality Diary, July 16-31
This is the public group instrumental rationality diary for July 16-31.
> It's a place to record and chat about it if you have done, or are actively doing, things like:
>
> * Established a useful new habit
> * Obtained new evidence that made you change your mind about some belief
> * Decided to behave in a different way in some set of situations
> * Optimized some part of a common routine or cached behavior
> * Consciously changed your emotions or affect with respect to something
> * Consciously pursued new valuable information about something that could make a big difference in your life
> * Learned something new about your beliefs, behavior, or life that surprised you
> * Tried doing any of the above and failed
>
> Or anything else interesting which you want to share, so that other people can think about it, and perhaps be inspired to take action themselves. Try to include enough details so that everyone can use each other's experiences to learn about what tends to work out, and what doesn't tend to work out.
Thanks to cata for starting the Group Rationality Diary posts, and to commenters for participating!
Immediate past diary: July 1-15
Next diary: August 1-15
Rationality Diaries archive |
1397df47-8b66-4ecb-8048-9b0f70cf58e7 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | “Fragility of Value” vs. LLMs
It seems like we keep getting LLMs that are better and better at *getting* the point of fairly abstract concepts (e.g. understanding jokes). As compute increases and their performance improves, it seems increasingly likely that human “values” are within the class of not-that-heavily-finetuned LLMs.
For example, if I prompted a GPT-5 model fine-tuned on lots of moral opinions about stuff: “[details of world], would a human say that was a more beautiful world than today, and why?” I… don’t think it’d do terribly?
The same goes for e.g. how the AI would answer the trolley problem. I’d guess it’d look roughly like humans’ responses: messy, slightly different depending on the circumstance, but not genuinely orthogonal to most humans’ values.
This is obviously vulnerable to adversarial examples or extreme OOD settings, but then robustness seems to be increasing with compute used, and we can do a decent job of OOD-catching.
Is there a modern reformulation of “fragility of value” that addresses this obvious situational improvement? Because as of now, the pure "Fragility of Value" thesis seems a little absurd (though I’d still believe a weaker version). |
3124f154-3a6d-47c4-8826-8548a04d0c81 | trentmkelly/LessWrong-43k | LessWrong | On Irrational Theory of Identity
Meet Alice. Alice alieves that losing consciousness causes discontinuity of identity.
Alice has a good job. Every payday, she takes her salary and enjoys herself in a reasonable way for her means--maybe going to a restaurant, maybe seeing a movie, normal things. And in the evening, she sits down and does her best to calculate the optimal utilitarian distribution of her remaining paycheck, sending most to the charities she determines most worthy and reserving just enough to keep tomorrow-Alice and her successors fed, clothed and sheltered enough to earn effectively. On the following days, she makes fairly normal tradeoffs between things like hard work and break-taking, maybe a bit on the indulgent side.
Occasionally her friend Bob talks to her about her strange theory of identity.
"Don't you ever wish you had left yourself more of your paycheck?" he once asked.
"I can't remember any of me ever thinking that." Alice replied. "I guess it'd be nice, but I might as well wish yesterday's Bill Gates had sent me his paycheck."
Another time, Bob posed the question, "Right now, you allocate yourself enough to survive with the (true) justification that that's a good investment of your funds. But what if that ever ceases to be true?"
Alice resopnded, "When me's have made their allocations, they haven't felt any particular fondness for their successors. I know that's hard to believe from your perspective, but it was years after past me's started this procedure that Hypothetical University published the retrospective optimal self-investment rates for effective altruism. It turned out that Alices' decisions had tracked the optimal rates remarkably well if you disregard as income the extra money the deciding Alices spent on themselves.
So me's really do make this decision objectively. And I know it sounds chilling to you, but when Alice ceases to be a good investment, that future Alice won't make it. She won't feel it as a grand sacrifice, either. Last week's Al |
9230c769-d35d-4f36-a17a-922d815ac579 | trentmkelly/LessWrong-43k | LessWrong | Toronto Meetup, Apr 14 8pm
When: Thursday, April 14th, 20:00
Where: The Bedford Academy, 36 Prince Arthur Avenue
Hi everyone,
Same plan as the last meetup. The reservation is under my name (Spencer Sleep) this time, as last time they put the name down as "Less Wrong" (first name and surname) and got angry at me when they found out that wasn't a real person (even though I told them it was a discussion group when making the reservation). We will hopefully be upstairs, as that was a nice quiet place last time, but there is a party of 100 people up there from 16:00 until an unspecified time, so our reservation is for "the quietest area of the bar".
We may do some rationality games/exercises if people are interested. I will prepare something small (probably along the lines of paranoid debating) and if it works out well, we can try something more involved in later meetups.
As always, newcomers are extremely welcome. All we ask for in someone at our meetups is that they be willing to listen and learn, and we're pretty lax about the former, and have no way of measuring the latter, so please feel free to join us, no matter your age/rationality experience/any other thing that would sway you to not do so.
See you there.
|
ad4c1378-422e-46a7-997f-355374a88c86 | trentmkelly/LessWrong-43k | LessWrong | Semen and Semantics: Understanding Porn with Language Embeddings
Summary
Porn content has gotten more extreme over time. Here's the average title for the first full year of Pornhub's existence, 2008:
* "Hot blonde girl gets fucked"
and here's the average title for 2023:
* "FAMILYXXX - "I Cant Resist My Stepsis Big Juicy Ass" (Mila Monet)"
Why did this change happen? We can understand porn's progression by converting titles to language embeddings. I downloaded Internet Archive snapshots of "pornhub.com" from 2008 - 2023 and analyzed the embeddings of the titles on the main page.
I found three distinct eras of titling: 2008-2009, 2010-2016, 2017-present. The current trend, since 2017, is characterized mainly by an emphasis on incest and other sexual violence.
Titles are generally representative of actual video content, and provide a reasonable heuristic for measuring actual content change, though some SEO effects exist.
The conclusion is a slightly ominous one: we are close to semantic bedrock with respect to sexual violence. Porn titles cannot become more sexually violent in their descriptions, because we lack the vocabulary.
Data and Methods
Download the repo and run "pip install" to install dependencies.
Pre-downloaded data is located in the "snapshots" folder. Pornhub data goes back to 2007, but analysis begins in 2008, when the format became more consistent. We have a folder for each month of the year, and a roughly weekly cadence of snapshots. For each date, there are two files, e.g.: "20080606.html", the raw HTML file, and "20080606.json", which contains the parsed video titles. The JSON files is an array of dictionaries like so:
{ "title": "Quickie on the car?", "url": "/view_video.php?viewkey=9aeff09be64077906196", "views": "39183", "duration": "7:39\n \t7 hours ago", "embedding": ... }
where the "embedding" field is the "title" value converted by OpenAI's "text-embedding-3-large". The URL format changes slightly over time.
From 4416 available snapshots, we end up with 772 weekly snapshots. Typically, we'll |
49107c0a-1b8e-4562-9d07-2165f28e06a3 | trentmkelly/LessWrong-43k | LessWrong | The Hardware-Software Framework: A New Perspective on Economic Growth with AI
First, a few words about me, as I’m new here.
I am a professor of economics at SGH Warsaw School of Economics, Poland. Years of studying the causes and mechanisms of long-run economic growth brought me to the topic of AI, arguably the most potent force of economic growth in the future. However, thanks in part to reading numerous excellent posts on Less Wrong, I soon came to understand that this future growth will most likely no longer benefit humanity. That is why I am now switching to the topic of AI existential risk, viewing it from the macroeconomist’s perspective.
The purpose of this post is to point your attention to a recent paper of mine that you may find relevant.
In the paper Hardware and software: A new perspective on the past and future of economic growth, written jointly with Julia Jabłońska and Aleksandra Parteka, we put forward a new hardware-software framework, helpful for understanding how AI, and transformative AI in particular, may impact the world economy in the coming years. A new framework like this was needed, among other reasons, because existing macroeconomic frameworks could not reconcile past growth experience with the approaching perspective of full automation of all essential production and R&D tasks through transformative AI.
The key premise of the hardware-software framework is that in any conceivable technological process, output is generated through purposefully initiated physical action. In other words, producing output requires both some physical action and some code, a set of instructions describing and purposefully initiating the action. Therefore, at the highest level of aggregation the two essential and complementary factors of production are physical hardware ( “brawn”), performing the action, and disembodied software (“brains”), providing information on what should be done and how.
This basic observation has profound consequences. It underscores that the fundamental complementarity between factors of production, deriv |
c8d34f16-6514-4162-aa85-1088235e69c2 | trentmkelly/LessWrong-43k | LessWrong | Did the Army Poison a Bunch of Women in Minnesota?
The 1950s were crazy times. Human experimentation in the US was normalized in a way that would make modern IRBs implode from shock. After the Soviets tested their first nuclear bomb in 1949, war planners in the US, both civilian and military, were interested in accelerating alternative and unorthodox methods of warfare, including and especially biological weapons.
White picket fences and Leave it to Beaver, indeed.
If you’re interested in this history, I’d recommend Nicholson Baker’s Baseless or Leonard Cole’s Clouds of Secrecy.
The latter describes various experiments that the army did starting in the 1950s that involved spraying tons of actual biological agents or proxies thereof on US cities in an attempt to measure the scale and dispersion of such agents were they to be unleashed on our enemies. Cities in the US were picked for physical and meteorological congruity to Soviet target cities; St. Louis was Leningrad, Minneapolis was Kiev, etc. Not exactly high school science projects.
Through a combination of investigative reporting, FOIAs, and declassification, this came to light decades after the experiments concluded. I was a wee lad in 1997 when this culminated in a congressional committee doing a thorough investigation and concluding that these experiments definitely, definitely did not cause any deleterious health outcomes for the unwitting subjects of these experiments.
When it comes to blue-ribbon commissions, let them cook.
But suppose that the blue-ribbon commission was not correct about this. Maybe the commission was stacked with people who didn’t want black folks getting too uppity and demand damages to be paid because the US military used their neighborhood as a testing range for biological weapons proxies. This happened 70 years ago. Can we actually tell if the committee was mistaken after all this time has passed?
Zinc Cadmium Sulfide
One of the materials sprayed on these cities was zinc cadmium sulfide, ZnCdS. The military apparently chose t |
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