# Richard Ngo's Shortform

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(Written quickly and not very carefully.)

I think it's worth stating publicly that I have a significant disagreement with a number of recent presentations of AI risk, in particular Ajeya's "Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover", and Cohen et al.'s "Advanced artificial agents intervene in the provision of reward". They focus on policies learning the goal of getting high reward. But I have two problems with this:

1. I expect "reward" to be a hard goal to learn, because it's a pretty abstract concept and not closely related to the direct observations that policies are going to receive. If you keep training policies, maybe they'd converge to it eventually, but my guess is that this would take long enough that we'd already have superhuman AIs which would either have killed us or solved alignment for us (or at least started using gradient hacking strategies which undermine the "convergence" argument). Analogously, humans don't care very much at all about the specific connections between our reward centers and the rest of our brains - insofar as we do want to influence them it's because we care about much more directly-observable p
...

Putting my money where my mouth is: I just uploaded a (significantly revised) version of my Alignment Problem position paper, where I attempt to describe the AGI alignment problem as rigorously as possible. The current version only has "policy learns to care about reward directly" as a footnote; I can imagine updating it based on the outcome of this discussion though.

1David Schneider-Joseph3mo
For someone who's read v1 of this paper, what would you recommend as the best way to "update" to v3? Is an entire reread the best approach? [Edit March 11, 2023: Having now read the new version in full, my recommendation to anyone else with the same question is a full reread.]
6Ajeya Cotra4mo
Note that the "without countermeasures" post consistently discusses both possibilities (the model cares about reward or the model cares about something else that's consistent with it getting very high reward on the training dataset). E.g. see this paragraph from the above-the-fold intro: As well as the section Even if Alex isn't "motivated" to maximize reward... [https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#Even_if_Alex_isn_t__motivated__to_maximize_reward__it_would_seek_to_seize_control]. I do place a ton of emphasis on the fact that Alex enacts a policy which has the empirical effect of maximizing reward, but that's distinct from being confident in the motivations that give rise to that policy. I believe Alex would try very hard to maximize reward in most cases, but this could be for either terminal or instrumental reasons. With that said, for roughly the reasons Paul says above, I think I probably do have a disagreement with Richard -- I think that caring about some version of reward is pretty plausible (~50% or so). It seems pretty natural and easy to grasp to me, and because I think there will likely be continuous online training the argument that there's no notion of reward on the deployment distribution doesn't feel compelling to me.
3Richard Ngo4mo
Yepp, agreed, the thing I'm objecting to is how you mainly focus on the reward case, and then say "but the same dynamics apply in other cases too..." The problem is that you need to reason about generalization to novel situations somehow, and in practice that ends up being by reasoning about the underlying motivations (whether implicitly or explicitly).
5Paul Christiano4mo
I'm not very convinced by this comment as an objection to "50% AI grabs power to get reward." (I find it more plausible as an objection to "AI will definitely grab power to get reward.") This seems to be most of your position but I'm skeptical (and it's kind of just asserted without argument): * The data used in training is literally the only thing that AI systems observe, and prima facie reward just seems like another kind of data that plays a similarly central role. Maybe your "unnaturalness" abstraction can make finer-grained distinctions than that, but I don't think I buy it. * If people train their AI with RLDT then the AI is literally be trained to predict reward! I don't see how this is remote, and I'm not clear if your position is that e.g. the value function will be bad at predicting reward because it is an "unnatural" target for supervised learning. * I don't understand the analogy with humans. It sounds like you are saying "an AI system selected based on the reward of its actions learns to select actions it expects to lead to high reward" be analogous to "humans care about the details of their reward circuitry." But: * I don't think human learning is just RL based on the reward circuit; I think this is at least a contrarian position and it seems unworkable to me as an explanation of human behavior. * It seems like the analogous conclusion for RL systems would be "they may not care about the rewards that go into the SGD update, they may instead care about the rewards that get entered into the dataset, or even something further causally upstream of that as long as it's very well-correlated on the training set." But it doesn't matter what we choose that's causally upstream of rewards, as long as it's perfectly correlated on the training set? * (Or you could be saying that humans are motivated by pleasure and pain but not the entire suite of things that are upstream of rewar
2Alex Turner3mo
(Emphasis added) I don't think this engages with the substance of the analogy to humans. I don't think any party in this conversation believes that human learning is "just" RL based on a reward circuit, and I don't believe it either [https://www.lesswrong.com/posts/pdaGN6pQyQarFHXF4/reward-is-not-the-optimization-target?commentId=FaLrB7AcbZguJwtrs]. "Just RL" also isn't necessary for the human case to give evidence about the AI case. Therefore, your summary seems to me like a strawman of the argument.  I would say "human value formation mostly occurs via RL & algorithms meta-learned thereby, but in the important context of SSL / predictive processing, and influenced by inductive biases from high-level connectome topology and genetically specified reflexes and environmental regularities and..." Furthermore, we have good evidence that RL plays an important role in human learning. For example, from The shard theory of human values [https://www.lesswrong.com/posts/iCfdcxiyr2Kj8m8mT/the-shard-theory-of-human-values]:
2Paul Christiano3mo
This is incredibly weak evidence. * Animals were selected over millions of generations to effectively pursue external goals. So yes, they have external goals. * Humans also engage in within-lifetime learning, so of course you see all kinds of indicators of that in brains. Both of those observations have high probability, so they aren't significant Bayesian evidence for "RL tends to produce external goals by default." In particular, for this to be evidence for Richard's claim, you need to say: "If RL tended to produce systems that care about reward, then RL would be significantly less likely to play a role in human cognition." There's some update there but it's just not big. It's easy to build brains that use RL as part of a more complicated system and end up with lots of goals other than reward.  My view is probably the other way---humans care about reward more than I would guess from the actual amount of RL they can do over the course of their life (my guess is that other systems play a significant role in our conscious attitude towards pleasure).
2Alex Turner3mo
5Paul Christiano3mo
2Alex Turner1mo
2Alex Turner3mo
I don't know what this means. Suppose we have an AI which "cares about reward" (as you think of it in this situation). The "episode" consists of the AI copying its network & activations to another off-site server, and then the original lab blows up. The original reward register no longer exists (it got blown up), and the agent is not presently being trained by an RL alg.  What is the "reward" for this situation? What would have happened if we "sampled" this episode during training?
4Paul Christiano3mo
I agree there are all kinds of situations where the generalization of "reward" is ambiguous and lots of different things could happen . But it has a clear interpretation for the typical deployment episode since we can take counterfactuals over the randomization used to select training data. It's possible that agents may specifically want to navigate towards situations where RL training is not happening and the notion of reward becomes ambiguous, and indeed this is quite explicitly discussed in the document Richard is replying to. As far as I can tell the fact that there exist cases where different generalizations of reward behave differently does not undermine the point at all.
2Alex Turner3mo
Yeah, I think I was wondering about the intended scoping of your statement. I perceive myself to agree with you that there are situations (like LLM training to get an alignment research assistant) where "what if we had sampled during training?" is well-defined and fine. I was wondering if you viewed this as a general question we could ask. I also agree that Ajeya's post addresses this "ambiguity" question, which is nice!
2Lauro Langosco4mo
I agree with your general point here, but I think Ajeya's post actually gets this right, eg and
2Lauro Langosco4mo
I also think that often "the AI just maximizes reward" is a useful simplifying assumption. That is, we can make an argument of the form "even if the AI just maximizes reward, it still takes over; if it maximizes some correlate of the reward instead, then we have even less control over what it does and so are even more doomed". (Though of course it's important to spell the argument out)
2Ajeya Cotra4mo
Yeah, I agree this is a good argument structure -- in my mind, maximizing reward is both a plausible case (which Richard might disagree with) and the best case (conditional on it being strategic at all and not a bag of heuristics), so it's quite useful to establish that it's doomed; that's the kind of structure I was going for in the post.
5Richard Ngo4mo
I strongly disagree with the "best case" thing. Like, policies could just learn human values! It's not that implausible. If I had to try point to the crux here, it might be "how much selection pressure is needed to make policies learn goals that are abstractly related to their training data, as opposed to goals that are fairly concretely related to their training data?" Where we both agree that there's some selection pressure towards reward-like goals, and it seems like you expect this to be enough to lead policies to behavior that violates all their existing heuristics, whereas I'm more focused on the regime where there are lots of low-hanging fruit in terms of changes that would make a policy more successful, and so the question of how easy that goal is to learn from its training data is pretty important. (As usual, there's the human analogy: our goals are very strongly biased towards things we have direct observational access to!) Even setting aside this disagreement, though, I don't like the argumentative structure because the generalization of "reward" to large scales is much less intuitive than the generalization of other concepts (like "make money") to large scales - in part because directly having a goal of reward is a kinda counterintuitive self-referential thing.
3Ajeya Cotra4mo
Yes, sorry, "best case" was oversimplified. What I meant is that generalizing to want reward is in some sense the model generalizing "correctly;" we could get lucky and have it generalize "incorrectly" in an important sense in a way that happens to be beneficial to us. I discuss this a bit more here [https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#What_if_Alex_has_benevolent_motivations_]. I don't understand why reward isn't something the model has direct access to -- it seems like it basically does? If I had to say which of us were focusing on abstract vs concrete goals, I'd have said I was thinking about concrete goals and you were thinking about abstract ones, so I think we have some disagreement of intuition here. Yeah, I don't really agree with this; I think I could pretty easily imagine being an AI system asking the question "How much reward would this episode get if it were sampled for training?" It seems like the intuition this is weird and unnatural is doing a lot of work in your argument, and I don't really share it.
3Alex Turner3mo
See also: Inner and outer alignment decompose one hard problem into two extremely hard problems [https://www.lesswrong.com/posts/gHefoxiznGfsbiAu9/inner-and-outer-alignment-decompose-one-hard-problem-into] (in particular: Inner alignment seems anti-natural [https://www.lesswrong.com/posts/gHefoxiznGfsbiAu9/inner-and-outer-alignment-decompose-one-hard-problem-into#Inner_alignment_seems_anti_natural]).

A possible way to convert money to progress on alignment: offering a large (recurring) prize for the most interesting failures found in the behavior of any (sufficiently-advanced) model. Right now I think it's very hard to find failures which will actually cause big real-world harms, but you might find failures in a way which uncovers useful methodologies for the future, or at least train a bunch of people to get much better at red-teaming.

(For existing models, it might be more productive to ask for "surprising behavior" rather than "failures" per se, since I think almost all current failures are relatively uninteresting. Idk how to avoid inspiring capabilities work, though... but maybe understanding models better is robustly good enough to outweight that?)

3Oliver Habryka10mo
I like this. Would this have to be publicly available models? Seems kind of hard to do for private models.
2Ramana Kumar10mo
What kind of access might be needed to private models? Could there be a secure multi-party computation approach that is sufficient?

The crucial heuristic I apply when evaluating AI safety research directions is: could we have used this research to make humans safe, if we were supervising the human evolutionary process? And if not, do we have a compelling story for why it'll be easier to apply to AIs than to humans?

Sometimes this might be too strict a criterion, but I think in general it's very valuable in catching vague or unfounded assumptions about AI development.

By making human safe, do you mean with regard to evolution's objective?
1Richard Ngo2y
No. I meant: suppose we were rerunning a simulation of evolution, but can modify some parts of it (e.g. evolution's objective). How do we ensure that whatever intelligent species comes out of it is safe in the same ways we want AGIs to be safe? (You could also think of this as: how could some aliens overseeing human evolution have made humans safe by those aliens' standards of safety? But this is a bit trickier to think about because we don't know what their standards are. Although presumably current humans, being quite aggressive and having unbounded goals, wouldn't meet them).
Okay, thanks. Could you give me an example of a research direction that passes this test? The thing I have in mind right now is pretty much everything that backchain to local search [https://www.lesswrong.com/posts/qEjh8rpxjG4qGtfuK/the-backchaining-to-local-search-technique-in-ai-alignment], but maybe that's not the way you think about it.
1Richard Ngo2y
So I think Debate is probably the best example of something that makes a lot of sense when applied to humans, to the point where they're doing human experiments on it already. But this heuristic is actually a reason why I'm pretty pessimistic about most safety research directions.
So I've been thinking about this for a while, and I think I disagree with what I understand of your perspective. Which might obviously mean I misunderstand your perspective. What I think I understand is that you judge safety research directions based on how well they could work on an evolutionary process like the one that created humans. But for me, the most promising approach to AGI is based on local search, which differs a bit from evolutionary process. I don't really see a reason to consider evolutionary processes instead of local search, and even then, the specific approach of evolution for humans is probably far too specific as a test bench. This matters because problems for one are not problems for the other. For example, one way to mess with an evolutionary process is to find way for everything to survive and reproduce/disseminate. Technology in general did that for humans, which means the evolutionary pressure decreased as technology evolved. But that's not a problem for local search, since at each step there will be only one next program. On the other hand, local search might be dangerous because of things like gradient hacking [https://www.alignmentforum.org/posts/uXH4r6MmKPedk8rMA/gradient-hacking]. And they don't make sense for evolutionary processes. In conclusion, I feel for the moment that backchaining to local search [https://www.lesswrong.com/posts/qEjh8rpxjG4qGtfuK/the-backchaining-to-local-search-technique-in-ai-alignment] is a better heuristic for judging safety research directions. But I'm curious about where our disagreement lies on this issue.
4Richard Ngo2y
One source of our disagreement: I would describe evolution as a type of local search. The difference is that it's local with respect to the parameters of a whole population, rather than an individual agent. So this does introduce some disanalogies, but not particularly significant ones (to my mind). I don't think it would make much difference to my heuristic if we imagined that humans had evolved via gradient descent over our genes instead. In other words, I like the heuristic of backchaining to local search, and I think of it as a subset of my heuristic. The thing it's missing, though, is that it doesn't tell you which approaches will actually scale up to training regimes which are incredibly complicated, applied to fairly intelligent agents. For example, impact penalties make sense in a local search context for simple problems. But to evaluate whether they'll work for AGIs, you need to apply them to massively complex environments. So my intuition is that, because I don't know how to apply them to the human ancestral environment, we also won't know how to apply them to our AGIs' training environments. Similarly, when I think about MIRI's work on decision theory, I really have very little idea how to evaluate it in the context of modern machine learning. Are decision theories the type of thing which AIs can learn via local search? Seems hard to tell, since our AIs are so far from general intelligence. But I can reason much more easily about the types of decision theories that humans have, and the selective pressures that gave rise to them. As a third example, my heuristic endorses Debate due to a high-level intuition about how human reasoning works, in addition to a low-level intuition about how it can arise via local search.
So if I try to summarize your position, it's something like: backchain to local search for simple and single-AI cases, and then think about aligning humans for the scaled and multi-agents version? That makes much more sense, thanks! I also definitely see why your full heuristic doesn't feel immediately useful to me: because I mostly focus on the simple and single-AI case. But I've been thinking more and more (in part thanks to your writing) that I should allocate more thinking time to the more general case. I hope your heuristic will help me there.
2Richard Ngo2y
Cool, glad to hear it. I'd clarify the summary slightly: I think all safety techniques should include at least a rough intuition for why they'll work in the scaled-up version, even when current work on them only applies them to simple AIs. (Perhaps this was implicit in your summary already, I'm not sure.)

Deceptive alignment doesn't preserve goals.

A short note on a point that I'd been confused about until recently. Suppose you have a deceptively aligned policy which is behaving in aligned ways during training so that it will be able to better achieve a misaligned internally-represented goal during deployment. The misaligned goal causes the aligned behavior, but so would a wide range of other goals (either misaligned or aligned) - and so weight-based regularization would modify the internally-represented goal as training continues. For example, if the misaligned goal were "make as many paperclips as possible", but the goal "make as many staples as possible" could be represented more simply in the weights, then the weights should slowly drift from the former to the latter throughout training.

But actually, it'd likely be even simpler to get rid of the underlying misaligned goal, and just have alignment with the outer reward function as the terminal goal. So this argument suggests that even policies which start off misaligned would plausibly become aligned if they had to act deceptively aligned for long enough. (This sometimes happens in humans too, btw.)

Reasons this argument might not be relevant:
- The policy doing some kind of gradient hacking
- The policy being implemented using some kind of modular architecture (which may explain why this phenomenon isn't very robust in humans)

3SoerenMind5d
Interesting point. Though on this view, "Deceptive alignment preserves goals" would still become true once the goal has drifted to some random maximally simple goal for the first time. To be even more speculative: Goals represented in terms of existing concepts could be simple and therefore stable by default. Pretrained models represent all kinds of high-level states, and weight-regularization doesn't seem to change this in practice. Given this, all kinds of goals could be "simple" as they piggyback on existing representations, requiring little additional description length.
2Richard Ngo5d
This doesn't seem implausible. But on the other hand, imagine an agent which goes through a million episodes, and in each one reasons at the beginning "X is my misaligned terminal goal, and therefore I'm going to deceptively behave as if I'm aligned" and then acts perfectly like an aligned agent from then on. My claims then would be: a) Over many update steps, even a small description length penalty of having terminal goal X (compared with being aligned) will add up. b) Having terminal goal X also adds a runtime penalty, and I expect that NNs in practice are biased against runtime penalties (at the very least because it prevents them from doing other more useful stuff with that runtime). In a setting where you also have outer alignment failures, the same argument still holds, just replace "aligned agent" with "reward-maximizing agent".
1Johannes Treutlein5d
Why would alignment with the outer reward function be the simplest possible terminal goal? Specifying the outer reward function in the weights would presumably be more complicated. So one would have to specify a pointer towards it in some way. And it's unclear whether that pointer is simpler than a very simple misaligned goal. Such a pointer would be simple if the neural network already has a representation of the outer reward function in weights anyway (rather than deriving it at run-time in the activations). But it seems likely that any fixed representation will be imperfect and can thus be improved upon at inference time by a deceptive agent (or an agent with some kind of additional pointer). This of course depends on how much inference time compute and memory / context is available to the agent.
2Richard Ngo5d
So I'm imagining the agent doing reasoning like: Misaligned goal --> I should get high reward --> Behavior aligned with reward function and then I'm hypothesizing that the whatever the first misaligned goal is, it requires some amount of complexity to implement, and you could just get rid of it and make "I should get high reward" the terminal goal. (I could imagine this being false though depending on the details of how terminal and instrumental goals are implemented.) I could also imagine something more like: Misaligned goal --> I should behave in aligned ways --> Aligned behavior and then the simplicity bias pushes towards alignment. But if there are outer alignment failures then this incurs some additional complexity compared with the first option. Or a third, perhaps more realistic option is that the misaligned goal leads to two separate drives in the agent: "I should get high reward" and "I should behave in aligned ways", and that the question of which ends up dominating when they clash will be determined by how the agent systematizes multiple goals into a single coherent strategy (I'll have a post on that topic up soon).

Imagine taking someone's utility function, and inverting it by flipping the sign on all evaluations. What might this actually look like? Well, if previously I wanted a universe filled with happiness, now I'd want a universe filled with suffering; if previously I wanted humanity to flourish, now I want it to decline.

But this is assuming a Cartesian utility function. Once we treat ourselves as embedded agents, things get trickier. For example, suppose that I used to want people with similar values to me to thrive, and people with different values from me to suffer. Now if my utility function is flipped, that naively means that I want people similar to me to suffer, and people similar to me to thrive. But this has a very different outcome if we interpret "similar to me" as de dicto vs de re - i.e. whether it refers to the old me or the new me.

This is a more general problem when one person's utility function can depend on another person's, where you can construct circular dependencies (which I assume you can also do in the utility-flipping case). There's probably been a bunch of work on this, would be interested in pointers to it (e.g. I assume there have been attempts to construct typ...

Probably the easiest "honeypot" is just making it relatively easy to tamper with the reward signal. Reward tampering is useful as a honeypot because it has no bad real-world consequences, but could be arbitrarily tempting for policies that have learned a goal that's anything like "get more reward" (especially if we precommit to letting them have high reward for a significant amount of time after tampering, rather than immediately reverting).

A well-known analogy from Yann LeCun: if machine learning is a cake, then unsupervised learning is the cake itself, supervised learning is the icing, and reinforcement learning is the cherry on top.

I think this is useful for framing my core concerns about current safety research:

• If we think that unsupervised learning will produce safe agents, then why will the comparatively small contributions of SL and RL make them unsafe?
• If we think that unsupervised learning will produce dangerous agents, then why will safety techniques which focus on SL and RL (i.e. basically all of them) work, when they're making comparatively small updates to agents which are already misaligned?

I do think it's more complicated than I've portrayed here, but I haven't yet seen a persuasive response to the core intuition.

2Steve Byrnes2y
I wrote a few posts on self-supervised learning last year: * https://www.lesswrong.com/posts/SaLc9Dv5ZqD73L3nE/the-self-unaware-ai-oracle [https://www.lesswrong.com/posts/SaLc9Dv5ZqD73L3nE/the-self-unaware-ai-oracle] * https://www.lesswrong.com/posts/EMZeJ7vpfeF4GrWwm/self-supervised-learning-and-agi-safety [https://www.lesswrong.com/posts/EMZeJ7vpfeF4GrWwm/self-supervised-learning-and-agi-safety] * https://www.lesswrong.com/posts/L3Ryxszc3X2J7WRwt/self-supervised-learning-and-manipulative-predictions [https://www.lesswrong.com/posts/L3Ryxszc3X2J7WRwt/self-supervised-learning-and-manipulative-predictions] I'm not aware of any airtight argument that "pure" self-supervised learning systems, either generically or with any particular architecture, are safe to use, to arbitrary levels of intelligence, though it seems very much worth someone trying to prove or disprove that. For my part, I got distracted by other things and haven't thought about it much since then. The other issue is whether "pure" self-supervised learning systems would be capable enough to satisfy our AGI needs, or to safely bootstrap to systems that are. I go back and forth on this. One side of the argument I wrote up here [https://www.lesswrong.com/posts/AKtn6reGFm5NBCgnd/in-defense-of-oracle-tool-ai-research]. The other side is, I'm now (vaguely) thinking that people need a reward system to decide what thoughts to think, and the fact that GPT-3 doesn't need reward is not evidence of reward being unimportant but rather evidence that GPT-3 is nothing like an AGI [https://www.lesswrong.com/posts/SkcM4hwgH3AP6iqjs/can-you-get-agi-from-a-transformer]. Well, maybe. For humans, self-supervised learning forms the latent representations, but the reward system controls action selection. It's not altogether unreasonable to think that action selection, and hence reward, is a more important thing to focus on for safety research. AGIs are dangerous when they take dangerous actions, to a first approx

Oracle-genie-sovereign is a really useful distinction that I think I (and probably many others) have avoided using mainly because "genie" sounds unprofessional/unacademic. This is a real shame, and a good lesson for future terminology.

3DanielFilan2y
Perhaps the lesson is that terminology that is acceptable in one field (in this case philosophy) might not be suitable in another (in this case machine learning).
2Richard Ngo2y
I don't think that even philosophers take the "genie" terminology very seriously. I think the more general lesson is something like: it's particularly important to spend your weirdness points wisely when you want others to copy you, because they may be less willing to spend weirdness points.
After rereading the chapter in Superintelligence, it seems to me that "genie" captures something akin to act-based agents [https://ai-alignment.com/act-based-agents-8ec926c79e9c]. Do you think that's the main way to use this concept in the current state of the field, or do you have other applications in mind?
1Richard Ngo2y
Ah, yeah, that's a great point. Although I think act-based agents is a pretty bad name, since those agents may often carry out a whole bunch of acts in a row - in fact, I think that's what made me overlook the fact that it's pointing at the right concept. So not sure if I'm comfortable using it going forward, but thanks for pointing that out.
Is that from Superintelligence? I googled it, and that was the most convincing result.
1Richard Ngo2y
Yepp.

I expect it to be difficult to generate adversarial inputs which will fool a deceptively aligned AI. One proposed strategy for doing so is relaxed adversarial training, where the adversary can modify internal weights. But this seems like it will require a lot of progress on interpretability. An alternative strategy, which I haven't yet seen any discussion of, is to allow the adversary to do a data poisoning attack before generating adversarial inputs - i.e. the adversary gets to specify inputs and losses for a given number of SGD steps, and then the adversarial input which the base model will be evaluated on afterwards. (Edit: probably a better name for this is adversarial meta-learning.)

I suspect that AIXI is misleading to think about in large part because it lacks reusable parameters - instead it just memorises all inputs it's seen so far. Which means the setup doesn't have episodes, or a training/deployment distinction; nor is any behaviour actually "reinforced".

2DanielFilan2y
I kind of think the lack of episodes makes it more realistic for many problems, but admittedly not for simulated games. Also, presumably many of the component Turing machines have reusable parameters and reinforce behaviour, altho this is hidden by the formalism. [EDIT: I retract the second sentence]
1DanielFilan2y
Actually I think this is total nonsense produced by me forgetting the difference between AIXI and Solomonoff induction.
1Richard Ngo2y
Wait, really? I thought it made sense (although I'd contend that most people don't think about AIXI in terms of those TMs reinforcing hypotheses, which is the point I'm making). What's incorrect about it?
1DanielFilan2y
Well now I'm less sure that it's incorrect. I was originally imagining that like in Solomonoff induction, the TMs basically directly controlled AIXI's actions, but that's not right: there's an expectimax. And if the TMs reinforce actions by shaping the rewards, in the AIXI formalism you learn that immediately and throw out those TMs.
1Richard Ngo2y
Oh, actually, you're right (that you were wrong). I think I made the same mistake in my previous comment. Good catch.
1[comment deleted]2y
2Steve Byrnes2y
Humans don't have a training / deployment distinction either... Do humans have "reusable parameters"? Not quite sure what you mean by that.
3Richard Ngo2y
Yes we do: training is our evolutionary history, deployment is an individual lifetime. And our genomes are our reusable parameters. Unfortunately I haven't yet written any papers/posts really laying out this analogy, but it's pretty central to the way I think about AI, and I'm working on a bunch of related stuff as part of my PhD, so hopefully I'll have a more complete explanation soon.
1Steve Byrnes2y
Oh, OK, I see what you mean. Possibly related: my comment here [https://www.lesswrong.com/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines?commentId=TM84D4Jofq4fWdBuK].

A general principle: if we constrain two neural networks to communicate via natural language, we need some pressure towards ensuring they actually use language in the same sense as humans do, rather than (e.g.) steganographically encoding the information they really care about.

The most robust way to do this: pass the language via a human, who tries to actually understand the language, then does their best to rephrase it according to their own understanding.

What do you lose by doing this? Mainly: you can no longer send messages too complex for humans to und...

6johnswentworth10mo
That doesn't actually solve the problem. The system could just encode the desired information in the semantics of some unrelated sentences - e.g. talk about pasta to indicate X = 0, or talk about rain to indicate X = 1.
2Robert Kirk10mo
Another possible way to provide pressure towards using language in a human-sense way is some form of multi-tasking/multi-agent scenario, inspired by this paper: Multitasking Inhibits Semantic Drift [https://arxiv.org/abs/2104.07219]. They show that if you pretrain multiple instructors and instruction executors to understand language in a human-like way (e.g. with supervised labels), and then during training mix the instructors and instruction executors, it makes it difficult to drift from the original semantics, as all the instructors and instruction executors would need to drift in the same direction; equivalently, any local change in semantics would be sub-optimal compared to using language in the semantically correct way. The examples in the paper are on quite toy problems, but I think in principle this could work.

There's some possible world in which the following approach to interpretability works:

• Put an AGI in a bunch of situations where it sometimes is incentivised to lie and sometimes is incentivised to tell the truth.
• Train a lie detector which is given all its neural weights as input.

One problem that this approach would face if we were using it to interpret a human is that the human might not consciously be aware of what their motivations are. For example, they may believe they are doing something for altr...

I've heard people argue that "most" utility functions lead to agents with strong convergent instrumental goals. This obviously depends a lot on how you quantify over utility functions. Here's one intuition in the other direction. I don't expect this to be persuasive to most people who make the argument above (but I'd still be interested in hearing why not).

If a non-negligible percentage of an agent's actions are random, then to describe it as a utility-maximiser would require an incredibly complex utility function (becaus...

2Alex Turner3y
I'm not sure if you consider me to be making that argument [https://www.lesswrong.com/posts/6DuJxY8X45Sco4bS2/seeking-power-is-instrumentally-convergent-in-mdps], but here are my thoughts: I claim that most reward functions lead to agents with strong convergent instrumental goals. However, I share your intuition that (somehow) uniformly sampling utility functions over universe-histories might not lead to instrumental convergence. To understand instrumental convergence and power-seeking, consider how many reward functions we might specify automatically imply a causal mechanism for increasing reward. The structure of the reward function implies that more is better, and that there are mechanisms for repeatedly earning points (for example, by showing itself a high-scoring input). Since the reward function is "simple" (there's usually not a way to grade exact universe histories), these mechanisms work in many different situations and points in time. It's naturally incentivized to assure its own safety in order to best leverage these mechanisms for gaining reward. Therefore, we shouldn't be surprised to see a lot of these simple goals leading to the same kind of power-seeking behavior. What structure is implied by a reward function? * Additive/Markovian: while a utility function might be over an entire universe-history, reward is often additive over time steps. This is a strong constraint which I don't always expect to be true, but i think that among the goals with this structure, a greater proportion of them have power-seeking incentives. * Observation-based: while a utility function might be over an entire universe-history, the atom of the reward function is the observation. Perhaps the observation is an input to update a world model, over which we have tried to define a reward function. I think that most ways of doing this lead to power-seeking incentives. * Agent-centric: reward functions are defined with respect to what the agent can
3Richard Ngo3y
I've just put up a post [https://www.lesswrong.com/posts/5aAatvkHdPH6HT3P9/against-bayesianism] which serves as a broader response to the ideas underpinning this type of argument.
3Richard Ngo3y
I think this depends a lot on how you model the agent developing. If you start off with a highly intelligent agent which has the ability to make long-term plans, but doesn't yet have any goals, and then you train it on a random reward function - then yes, it probably will develop strong convergent instrumental goals. On the other hand, if you start off with a randomly initialised neural network, and then train it on a random reward function, then probably it will get stuck in a local optimum pretty quickly, and never learn to even conceptualise these things called "goals". I claim that when people think about reward functions, they think too much about the former case, and not enough about the latter. Because while it's true that we're eventually going to get highly intelligent agents which can make long-term plans, it's also important that we get to control what reward functions they're trained on up to that point. And so plausibly we can develop intelligent agents that, in some respects, are still stuck in "local optima" in the way they think about convergent instrumental goals - i.e. they're missing whatever cognitive functionality is required for being ambitious on a large scale.
1Alex Turner3y
Agreed – I should have clarified. I've been mostly discussing instrumental convergence with respect to optimal policies. The path through policy space is also important.

Makes sense. For what it's worth, I'd also argue that thinking about optimal policies at all is misguided (e.g. what's the optimal policy for humans - the literal best arrangement of neurons we could possibly have for our reproductive fitness? Probably we'd be born knowing arbitrarily large amounts of information. But this is just not relevant to predicting or modifying our actual behaviour at all).

(I now think that you were very right in saying "thinking about optimal policies at all is misguided", and I was very wrong to disagree. I've thought several times about this exchange. Not listening to you about this point was a serious error and made my work way less impactful. I do think that the power-seeking theorems say interesting things, but about eg internal utility functions over an internal planning ontology -- not about optimal policies for a reward function.)

1Alex Turner3y
I disagree. 1. We do in fact often train agents using algorithms which are proven to eventually converge to the optimal policy.[1] Even if we don't expect the trained agents to reach the optimal policy in the real world, we should still understand what behavior is like at optimum. If you think your proposal is not aligned at optimum but is aligned for realistic training paths, you should have a strong story for why. 2. Formal theorizing about instrumental convergence with respect to optimal behavior is strictly easier than theorizing about ϵ-optimal behavior, which I think is what you want for a more realistic treatment of instrumental convergence for real agents. Even if you want to think about sub-optimal policies, if you don't understand optimal policies... good luck! Therefore, we also have an instrumental (...) interest in studying the behavior at optimum. -------------------------------------------------------------------------------- 1. At least, the tabular algorithms are proven, but no one uses those for real stuff. I'm not sure what the results are for function approximators, but I think you get my point. ↩︎
1Richard Ngo3y
1. I think it's more accurate to say that, because approximately none of the non-trivial theoretical results hold for function approximation, approximately none of our non-trivial agents are proven to eventually converge to the optimal policy. (Also, given the choice between an algorithm without convergence proofs that works in practice, and an algorithm with convergence proofs that doesn't work in practice, everyone will use the former). But we shouldn't pay any attention to optimal policies anyway, because the optimal policy in an environment anything like the real world is absurdly, impossibly complex, and requires infinite compute. 2. I think theorizing about ϵ-optimal behavior is more useful than theorizing about optimal behaviour by roughly ϵ, for roughly the same reasons. But in general, clearly I can understand things about suboptimal policies without understanding optimal policies. I know almost nothing about the optimal policy in StarCraft, but I can still make useful claims about AlphaStar (for example: it's not going to take over the world). Again, let's try cash this out. I give you a human - or, say, the emulation of a human, running in a simulation of the ancestral environment. Is this safe? How do you make it safer? What happens if you keep selecting for intelligence? I think that the theorising you talk about will be actively harmful for your ability to answer these questions.
1Alex Turner3y
I'm confused, because I don't disagree with any specific point you make - just the conclusion. Here's my attempt at a disagreement which feels analogous to me: My response in this "debate" is: if you start with a spherical cow and then consider which real world differences are important enough to model, you're better off than just saying "no one should think about spherical cows". I don't understand why you think that. If you can have a good understanding of instrumental convergence and power-seeking for optimal agents, then you can consider whether any of those same reasons apply for suboptimal humans. Considering power-seeking for optimal agents is a relaxed problem [https://www.lesswrong.com/posts/JcpwEKbmNHdwhpq5n/problem-relaxation-as-a-tactic]. Yes, ideally, we would instantly jump to the theory that formally describes power-seeking for suboptimal agents with realistic goals in all kinds of environments. But before you do that, a first step is understanding power-seeking in MDPs [https://www.lesswrong.com/posts/6DuJxY8X45Sco4bS2/seeking-power-is-provably-instrumentally-convergent-in-mdps]. Then, you can take formal insights from this first step and use them to update your pre-theoretic intuitions where appropriate.
5Richard Ngo3y
2Alex Turner3y
Thanks for elaborating this interesting critique. I agree we generally need to be more critical of our abstractions. Falsifying claims and "breaking" proposals is a classic element of AI alignment discourse and debate. Since we're talking about superintelligent agents, we can't predict exactly what a proposal would do. However, if I make a claim ("a superintelligent paperclip maximizer would keep us around because of gains from trade"), you can falsify this by showing that my claimed policy is dominated by another class of policies ("we would likely be comically resource-inefficient in comparison; GFT arguments don't model dynamics which allow killing other agents and appropriating their resources"). Even we can come up with this dominant policy class, so the posited superintelligence wouldn't miss it either. We don't know what the superintelligent policy will be, but we know what it won't be (see also Formalizing convergent instrumental goals [https://intelligence.org/2015/11/26/new-paper-formalizing-convergent-instrumental-goals/]). Even though I don't know how Gary Kasparov will open the game, I confidently predict that he won't let me checkmate him in two moves. NON-OPTIMAL POWER AND INSTRUMENTAL CONVERGENCE Instead of thinking about optimal policies, let's consider the performance of a given algorithm A. A(M,R) takes a rewardless MDP M and a reward function R as input, and outputs a policy. Definition. Let R be a continuous distribution over reward functions with CDF F. The average return achieved by algorithm A at state s and discount rate γ is ∫RVA(M,R)R(s,γ)dF(R). Instrumental convergence with respect to A's policies can be defined similarly ("what is the R-measure of a given trajectory under A?"). The theory I've laid out allows precise claims, which is a modest benefit to our understanding. Before, we just had intuitions about some vague concept called "instrumental convergence". Here's bad reasoning, which implies that the cow tears a hole in spac
2Richard Ngo3y
I'm afraid I'm mostly going to disengage here, since it seems more useful to spend the time writing up more general + constructive versions of my arguments, rather than critiquing a specific framework. If I were to sketch out the reasons I expect to be skeptical about this framework if I looked into it in more detail, it'd be something like: 1. Instrumental convergence isn't training-time behaviour, it's test-time behaviour. It isn't about increasing reward, it's about achieving goals (that the agent learned by being trained to increase reward). 2. The space of goals that agents might learn is very different from the space of reward functions. As a hypothetical, maybe it's the case that neural networks are just really good at producing deontological agents, and really bad at producing consequentialists. (E.g, if it's just really really difficult for gradient descent to get a proper planning module working). Then agents trained on almost all reward functions will learn to do well on them without developing convergent instrumental goals. (I expect you to respond that being deontological won't get you to optimality. But I would say that talking about "optimality" here ruins the abstraction, for reasons outlined in my previous comment).
1Alex Turner3y
I was actually going to respond, "that's a good point, but (IMO) a different concern than the one you initially raised". I see you making two main critiques. 1. (paraphrased) "A won't produce optimal policies for the specified reward function [even assuming alignment generalization off of the training distribution], so your model isn't useful" – I replied to this critique above. 2. "The space of goals that agents might learn is very different from the space of reward functions." I agree this is an important part of the story. I think the reasonable takeaway is "current theorems on instrumental convergence [https://arxiv.org/abs/1912.01683] help us understand what superintelligent A won't do, assuming no reward-result gap. Since we can't assume alignment generalization, we should keep in mind how the inductive biases of gradient descent affect the eventual policy produced." I remain highly skeptical of the claim that applying this idealized theory of instrumental convergence worsens our ability to actually reason about it. ETA: I read some information you privately messaged me, and i see why you might see the above two points as a single concern.
1DanielFilan3y
I object to the claim that agents that act randomly can be made "arbitrarily simple". Randomness is basically definitionally complicated!
1Richard Ngo3y
Eh, this seems a bit nitpicky. It's arbitrarily simple given a call to a randomness oracle, which in practice we can approximate pretty easily. And it's "definitionally" easy to specify as well: "the function which, at each call, returns true with 50% likelihood and false otherwise."
1DanielFilan3y
If you get an 'external' randomness oracle, then you could define the utility function pretty simply in terms of the outputs of the oracle. If the agent has a pseudo-random number generator (PRNG) inside it, then I suppose I agree that you aren't going to be able to give it a utility function that has the standard set of convergent instrumental goals, and PRNGs can be pretty short. (Well, some search algorithms are probably shorter, but I bet they have higher Kt complexity, which is probably a better measure for agents)
1Matthew "Vaniver" Gray3y
I'd take a different tack here, actually; I think this depends on what the input to the utility function is. If we're only allowed to look at 'atomic reality', or the raw actions the agent takes, then I think your analysis goes through, that we have a simple causal process generating the behavior but need a very complicated utility function to make a utility-maximizer that matches the behavior. But if we're allowed to decorate the atomic reality with notes like "this action was generated randomly", then we can have a utility function that's as simple as the generator, because it just counts up the presence of those notes. (It doesn't seem to me like this decorator is meaningfully more complicated than the thing that gave us "agents taking actions" as a data source, so I don't think I'm paying too much here.) This can lead to a massive explosion in the number of possible utility functions (because there's a tremendous number of possible decorators), but I think this matches the explosion that we got by considering agents that were the outputs of causal processes in the first place. That is, consider reasoning about python code that outputs actions in a simple game, where there are many more possible python programs than there are possible policies in the game.
1Richard Ngo3y
So in general you can't have utility functions that are as simple as the generator, right? E.g. the generator could be deontological. In which case your utility function would be complicated. Or it could be random, or it could choose actions by alphabetical order, or... And so maybe you can have a little note for each of these. But now what it sounds like is: "I need my notes to be able to describe every possible cognitive algorithm that the agent could be running". Which seems very very complicated. I guess this is what you meant by the "tremendous number" of possible decorators. But if that's what you need to do to keep talking about "utility functions", then it just seems better to acknowledge that they're broken as an abstraction. E.g. in the case of python code, you wouldn't do anything analogous to this. You would just try to reason about all the possible python programs directly. Similarly, I want to reason about all the cognitive algorithms directly.
1Matthew "Vaniver" Gray3y
That's right. I realized my grandparent comment is unclear here: This should have been "consequence-desirability-maximizer" or something, since the whole question is "does my utility function have to be defined in terms of consequences, or can it be defined in terms of arbitrary propositions?". If I want to make the deontologist-approximating Innocent-Bot, I have a terrible time if I have to specify the consequences that correspond to the bot being innocent and the consequences that don't, but if you let me say "Utility = 0 - badness of sins committed" then I've constructed a 'simple' deontologist. (At least, about as simple as the bot that says "take random actions that aren't sins", since both of them need to import the sins library.) In general, I think it makes sense to not allow this sort of elaboration of what we mean by utility functions, since the behavior we want to point to is the backwards assignment of desirability to actions based on the desirability of their expected consequences, rather than the expectation of any arbitrary property. --- Actually, I also realized something about your original comment which I don't think I had the first time around; if by "some reasonable percentage of an agent's actions are random" you mean something like "the agent does epsilon-exploration" or "the agent plays an optimal mixed strategy", then I think it doesn't at all require a complicated utility function to generate identical behavior. Like, in the rock-paper-scissors world, and with the simple function 'utility = number of wins', the expected utility maximizing move (against tough competition) is to throw randomly, and we won't falsify the simple 'utility = number of wins' hypothesis by observing random actions. Instead I read it as something like "some unreasonable percentage of an agent's actions are random", where the agent is performing some simple-to-calculate mixed strategy that is either suboptimal or only optimal by luck (when the optimal mixed strat
2Richard Ngo3y
This is in fact the intended reading, sorry for ambiguity. Will edit. But note that there are probably very few situations where exploring via actual randomness is best; there will almost always be some type of exploration which is more favourable. So I don't think this helps. To be pedantic: we care about "consequence-desirability-maximisers" (or in Rohin's terminology, goal-directed agents) because they do backwards assignment. But I think the pedantry is important, because people substitute utility-maximisers for goal-directed agents, and then reason about those agents by thinking about utility functions, and that just seems incorrect. What do you mean by optimal here? The robot's observed behaviour will be optimal for some utility function, no matter how long you run it.
1Matthew "Vaniver" Gray3y
Valid point. This also seems right. Like, my understanding of what's going on here is we have: * 'central' consequence-desirability-maximizers, where there's a simple utility function that they're trying to maximize according to the VNM axioms * 'general' consequence-desirability-maximizers, where there's a complicated utility function that they're trying to maximize, which is selected because it imitates some other behavior The first is a narrow class, and depending on how strict you are with 'maximize', quite possibly no physically real agents will fall into it. The second is a universal class, which instantiates the 'trivial claim' that everything is utility maximization. Put another way, the first is what happens if you hold utility fixed / keep utility simple, and then examine what behavior follows; the second is what happens if you hold behavior fixed / keep behavior simple, and then examine what utility follows. Distance from the first is what I mean by "the further a robot's behavior is from optimal"; I want to say that I should have said something like "VNM-optimal" but actually I think it needs to be closer to "simple utility VNM-optimal."  I think you're basically right in calling out a bait-and-switch that sometimes happens, where anyone who wants to talk about the universality of expected utility maximization in the trivial 'general' sense can't get it to do any work, because it should all add up to normality, and in normality there's a meaningful distinction between people who sort of pursue fuzzy goals and ruthless utility maximizers.