I do alignment research, mostly stuff that is vaguely agent foundations. Formerly on Vivek’s team at MIRI. Most of my writing before mid 2023 are not representative of my current views about alignment difficulty.
Jeremy Gillen
Karma: 1,017
I would be excited to read this / help with a draft.
We can meet in person one afternoon and work out some cruxes and write them up?
Is the claim here that the AI performs well on ~all the human-checkable tasks and then reverts to being absolutely useless or sabotaging on the hard stuff?
Yes, approximately, as I believe you and I are capable of doing. [...PhD student crux]
The analogy is strained due to not being able to gradient update my brain with arbitrary training data. It’s pretty unclear to me if I could pull off deception like this, it seems pretty hard imo.
Are you saying you buy my side of the PhD student crux, but that it isn’t closely analogous because you think we have more fine-grained control over the thoughts or goals of the AI? And in your second sentence my guess is that you are saying a second reason you disagree is that it looks difficult for a research AI to know when to perform well and when to perform less well? I think we might be miscommunicating about this because it doesn’t look like the sort of situation where the “deception” is a relatively difficult task.
I think if you took a human research team, stuck them on an island, and had them get up to speed in some subfield they’re not part of, they would be unable to distinguish between research that had just been done on the mainland or is just past the frontier. This just seems pretty hard.
I agree with this. But the research we need isn’t just past the frontier. At some point not that far past the frontier, you get to a situation where the research team can say “given our guesses about mainland researchers and their time constraints, we’d put <1% on them having made discovery X {or explored idea Y, or done experiment Z}. Let’s {delay giving it to them, not work on that direction, insert a subtle false assumption, instead share our 8th best idea, just say we’re stuck, communicate it poorly}”.
I think this example is closer to what we’ll get with AIs rather than the bar being at “human level” it’s more like “what has been accomplished by the humans+AIs in the outside world, for which I’ve received untrustworthy information”.
Sure, kinda, but the important part here is that a trapped agentic AI can selectively mislead you about the most important {knowledge, tasks, strategies} that you aren’t capable of doing with just humans. If you’re capable of doing it with humans, or if you’re close to being able to do it with humans, then why not just do that? Seems safer.
And every time the AIs do useful work we can add this to our supervised training corpus.
Only if you spend the human resources to verify it. Are you aiming for 30x research acceleration? Because this by itself might be enough limit you below that level. (and it also directly provides a way for the AI to tell “what time it is” by sending messages to future iterations, and plausibly also provide a bunch of information about human research capability and capacity).
One version of this is: you have GPT-5 (potentially untrustworthy) do some research, you have humans carefully check this research using the best available tools (e.g., AI debate / other ‘scalable oversight’), and then you use this research as supervised / labeled data for training GPT-6 to do good work.
Mostly my objection to this is above. On scalable oversight, I buy that it might be possible to get 20% improvements in the accuracy of your evaluations of (adversarialy generated!) research, compared to team of humans trying really hard to evaluate research, given a human level research AI. This is so far from being enough. Are you predicting much better scalable oversight that scales much further than I am predicting it will? (I’m assuming you’re using “GPT-5/6” to represent “some powerful future consequentialist AI that can do research”, because otherwise we should be having a different argument).
While it’s probably non-trivial, I expect you could use this internal activation to train models which do a better job paying attention to the tokens you want them to.
It’d be cool if there were a demonstration of manipulating internal activations during training, in a way that fixes non-trivial unwanted OOD behavior. If such techniques existed, and also worked on consequentialist agents (I expect would be harder), I would consider them very useful tools that would help with building an aligned agent.
While it’s probably non-trivial, I expect you could use this internal activation to train models which do a better job paying attention to the tokens you want them to.
This is very plausible, but I think you’re stretching the analogy between LMs and future research AIs way too far. “Paying attention to the tokens we want them to” doesn’t add much/any control over the properties we want control over (compared to e.g. changing the input string to remove information we don’t want it paying attention to).
Similarly, I think it’s probable (but far from guaranteed) that deceptively aligned models are representing training/deployment in their activations, or that sandbagging models are representing human-level / super-human-level difficulty for tasks. If so, it it possible in principle to do all kinds of things with these activations which may help us. e.g., get the model to solve tasks it otherwise would have intentionally failed at, or giving us hints about the accuracy of a research result. I expect this is difficult implementation wise, but still a reason for hope. IMO activation engineering is off to a good start.
I think you might be centrally thinking about an LLM completing a few paragraphs, where I’m thinking of an AI doing a large scale scientific project? I buy that for many concepts X, you can work out the “neural correlates of X” in your AI. But that kind of thing is noisy (and plausibly can be optimized against by the AI) unless you have a deep understanding of what you are measuring. And optimizing against such imperfect metrics obviously wouldn’t do much beyond destroying the metric. I do think research in this direction has a chance of being useful, but mainly by being upstream of much better understanding.
By leaning more on generalization, I mean leaning more on the data efficiency thing
Sorry for misinterpreting you, but this doesn’t clarify what you meant.
also weak-to-strong generalization ideas.
I think I don’t buy the analogy in that paper, and I don’t find the results surprising or relevant (by my current understanding, after skimming it). My understanding of the result is “if you have a great prior, you can use it to overcome some label noise and maybe also label bias”. But I don’t think this is very relevant to extracting useful work from a misaligned agent (which is what we are talking about here), and based on the assumptions they describe, I think they agree? (I just saw appendix G, I’m a fan of it, it’s really valuable that they explained their alignment plan concisely and listed their assumptions).
I could imagine starting with a deceptively aligned AI whose goal is “Make paperclips unless being supervised which is defined as X, Y, and Z, in which case look good to humans”. And if we could change this AI to have the goal “Make paperclips unless being supervised which is defined as X, Y, and Q, in which case look good to humans”, that might be highly desirable. In particular, it seems like adversarial training here allows us to expand the definition of ‘supervision’, thus making it easier to elicit good work from AIs (ideally not just ‘looks good’).
If we can tell we are have such an AI, and we can tell that our random modifications are affecting the goal, and also the change is roughly one that helps us rather than changing many things that might or might not be helpful, this would be a nice situation to be in.
I don’t feel like I’m talking about AIs which have “taking-over-the-universe in their easily-within-reach options”. I think this is not within reach of the current employees of AGI labs, and the AIs I’m thinking of are similar to those employees in terms of capabilities, but perhaps a bit smarter, much faster, and under some really weird/strict constraints (control schemes).
Section 6 assumes we have failed to control the AI, so it is free of weird/strict constraints, and free to scale itself up, improve itself, etc. So my comment is about an AI that no longer can be assumed to have human-ish capabilities.
Do you have recordings? I’d be keen to watch a couple of the ones I missed.
I feel like you’re proposing two different types of AI and I want to disambiguate them. The first one, exemplified in your response to Peter (and maybe referenced in your first sentence above), is a kind of research assistant that proposes theories (after having looked at data that a scientist is gathering?), but doesn’t propose experiments and doesn’t think about the usefulness of its suggestions/theories. Like a Solomonoff inductor that just computes the simplest explanation for some data? And maybe some automated approach to interpreting theories?
The second one, exemplified by the chess analogy and last paragraph above, is a bit like a consequentialist agent that is a little detached from reality (can’t learn anything, has a world model that we designed such that it can’t consider new obstacles).
Do you agree with this characterization?
What I’m saying is “simpler” is that, given a problem that doesn’t need to depend on the actual effects of the outputs on the future of the real world […], it is simpler for the AI to solve that problem without taking into consideration the effects of the output on the future of the real world than it is to take into account the effects of the output on the future of the real world anyway.
I accept chess and formal theorem-proving as examples of problem where we can define the solution without using facts about the real-world future (because we can easily write down formally a definition of what the solution looks like).
For a more useful problem (e.g. curing a type of cancer) we (the designers) only know how to define a solution in terms of real world future states (patient is alive, healthy, non traumatized, etc). I’m not saying there doesn’t exist a definition of success that doesn’t involve referencing real-world future states. But the AI designers don’t know it (and I expect it would be relatively complicated).
My understanding of your simplicity argument is that it is saying that it is computationally cheaper for a trained AI to discover during training a non-consequence definition of the task, despite a consequentialist definition being the criterion used to train it? If so, I disagree that computation cost is very relevant here, generalization (to novel obstacles) is the dominant factor determining how useful this AI is.
Geometric rationality ftw!
(In normal planning problems there are exponentially many plans to evaluate (in the number of actions). So that doesn’t seem to be a major obstacle if your agent is already capable of planning.)
Might be much harder to implement, but could we maximin “all possible reinterpretations of alignment target X”?
In my view, in order to be dangerous in a particularly direct way (instead of just misuse risk etc.), an AI’s decision to give output X depends on the fact that output X has some specific effects in the future.
Agreed.
Whereas, if you train it on a problem where solutions don’t need to depend on the effects of the outputs on the future, I think it much more likely to learn to find the solution without routing that through the future, because that’s simpler.
The “problem where solutions don’t need to depend on effects” is where we disagree. I agree such problems exist (e.g. formal proof search), but those aren’t the kind of useful tasks we’re talking about in the post. For actual concrete scientific problems, like outputting designs for a fusion rocket, the “simplest” approach is to be considering the consequences of those outputs on the world. Otherwise, how would it internally define “good fusion rocket design that works when built”? How would it know not to use a design that fails because of weaknesses in the metal that will be manufactured into a particular shape for your rocket? A solution to building a rocket is defined by its effects on the future (not all of its effects, just some of them, i.e. it doesn’t explode, among many others).
I think there’s a (kind of) loophole here, where we use an “abstract hypothetical” model of a hypothetical future, and optimize for consequences our actions for that hypothetical. Is this what you mean by “understood in abstract terms”? So the AI has defined “good fusion rocket design” as “fusion rocket that is built by not-real hypothetical humans based on my design and functions in a not-real hypothetical universe and has properties and consequences XYZ” (but the hypothetical universe isn’t the actual future, it’s just similar enough to define this one task, but dissimilar enough that misaligned goals in this hypothetical world don’t lead to coherent misaligned real-world actions). Is this what you mean? Rereading your comment, I think this matches what you’re saying, especially the chess game part.
The part I don’t understand is why you’re saying that this is “simpler”? It seems equally complex in kolmogorov complexity and computational complexity.
I think the overall goal in this proposal is to get a corrigible agent capable of bounded tasks (that maybe shuts down after task completion), rather than a sovereign?
One remaining problem (ontology identification) is making sure your goal specification stays the same for a world-model that changes/learns.
Then the next remaining problem is the inner alignment problem of making sure that the planning algorithm/optimizer (whatever it is that generates actions given a goal, whether or not it’s separable from other components) is actually pointed at the goal you’ve specified and doesn’t have any other goals mixed into it. (see Context Disaster for more detail on some of this, optimization daemons, and actual effectiveness). Part of this problem is making sure the system is stable under reflection.
Then you’ve got the outer alignment problem of making sure that your fusion power plant goal is safe to optimize (e.g. it won’t kill people who get in the way, doesn’t have any extreme effects if the world model doesn’t exactly match reality, or if you’ve forgotten some detail). (See Goodness estimate bias, unforeseen maximum).
Ideally here you build in some form of corrigibility and other fail-safe mechanisms, so that you can iterate on the details.
That’s all the main ones imo. Conditional on solving the above, and actively trying to foresee other difficult-to-iterate problems, I think it’d be relatively easy to foresee and fix remaining issues.
A first problem with this is that there is no sharp distinction between purely computational (analytic) information/observations and purely empirical (synthetic) information/observations.
I don’t see the fuzziness here, even after reading the two dogmas wikipedia page (but not really understanding it, it’s hidden behind a wall of jargon). If we have some prior over universes, and some observation channel, we can define an agent that is updateless with respect to that prior, and updateful with respect to any calculations it performs internally. Is there a section of Radical Probablism that is particularly relevant? It’s been a while.
It’s not clear to me why all superintelligences having the same classification matters. They can communicate about edge cases and differences in their reasoning. Do you have an example here?A second and more worrying problem is that, even given such convergence, it’s not clear all other agents will decide to forego the possible apparent benefits of logical exploitation. It’s a kind of Nash equilibrium selection problem: If I was very sure all other agents forego them (and have robust cooperation mechanisms that deter exploitation), then I would just do like them.
I think I don’t understand why this is a problem. So what if there are some agents running around being updateless about logic? What’s the situation that we are talking about a Nash equilibrium for?
As mentioned in the post, Counterfactual Mugging as presented won’t be common, but equivalent situations in multi-agentic bargaining might, due to (the naive application of) some priors leading to commitment races.
Can you point me to an example in bargaining that motivates the usefulness of logical updatelessness? My impression of that section wasn’t “here is a realistic scenario that motivates the need for some amount of logical updatelessness”, it felt more like “logical bargaining is a situation where logical updatelessness plausibly leads to terrible and unwanted decisions”.
It’s not looking like something as simple as that will solve, because of reasoning as in this paragraph:
Unfortunately, it’s not that easy, and the problem recurs at a higher level: your procedure to decide which information to use will depend on all the information, and so you will already lose strategicness. Or, if it doesn’t depend, then you are just being updateless, not using the information in any way.
Or in other words, you need to decide on the precommitment ex ante, when you still haven’t thought much about anything, so your precommitment might be bad.
Yeah I wasn’t thinking that was a “solution”, I’m biting the bullet of losing some potential value and having a decision theory that doesn’t satisfy all the desiderata. I was just saying that in some situations, such an agent can patch the problem using other mechanisms, just as an EDT agent can try to implement some external commitment mechanism if it lives in a world full of transparent newcomb problems.
To me it feels like the natural place to draw the line is update-on-computations but updateless-on-observations. Because 1) It never disincentivizes thinking clearly, so commitment races bottom out in a reasonable way, and 2) it allows cooperation on common-in-the-real-world newcomblike problems.
It doesn’t do well in worlds with a lot of logical counterfactual mugging, but I think I’m okay with this? I can’t see why this situation would be very common, and if it comes up it seems that an agent that updates on computations can use some precommitment mechanism to take advantage of it (e.g. making another agent).
Am I missing something about why logical counterfactual muggings are likely to be common?
Looking through your PIBBS report (which is amazing, very helpful), I intuitively feel the pull of Desiderata 4 (No existential regret), and also the intuition of wanting to treat logical uncertainty and empirical uncertainty in a similar way. But ultimately I’m so horrified by the mess that comes from being updateless-on-logic that being completely updateful on logic is looking pretty good to me.
(Great post, thanks)
Thanks for reading it, it’s good to know exactly where you think the argument is weakest and I appreciate the effort of going through and noting differences.
On section 4:
I definitely don’t feel confident that any of the mentioned problems will arise in practice. I don’t see why I should believe in an inner/outer shell breakdown of constraints — this section seemed quite speculative.
This surprises me actually, I thought this section was solid conditional on the previous assumptions. I think you shouldn’t think of them as problems that might arise in practice, instead they should be thought of as reasons why behavioral training under-specifies some parts of the AI algorithm.
I’ll give some more quick examples of “outer-shell constraints”, because examples are very common and it seems odd for this to be called speculative: Unendorsed habits or addictions in humans; the instinct to breathe in when you’re trying to hold your breath; appending a checker algorithm C to any algorithm A, to double check the output of A; any biases in a search ordering (as in the alphago example).
On section 5:
It’s on my todo list to write a comment responding to some of the specifics of Redwood’s control post. This post wasn’t intending to engage with their detailed arguments, just give the top-level reasons why we believe control is difficult.Is the claim here that the AI performs well on ~all the human-checkable tasks and then reverts to being absolutely useless or sabotaging on the hard stuff?
Yes, approximately, as I believe you and I are capable of doing. The crux here might be “if we had 20 phd students, and incentivize them to both look like they are doing checkable research, and also incentivize them not to make any real useful progress on solving a particular difficult problem, would this be very useful without costing a lot of expert supervision?”. If I remember correctly, this was a crux between myself and Ryan during an in person discussion. It seems likely to me that the phd students, trying not to be useful while looking good in checkable ways, will succeed at both. More intelligent agent are even more likely to succeed at both. It does depend on the type of research, I agree that formal theorem-proving would be much easier to incentivize properly. I’m talking about the least-easily-checkable 20% of useful research.
Predicting where that line is seems pretty difficult, especially given the AI cannot trust sensory inputs, does not know what time it is
Seems easy enough to predict given roughly human-scientist-team level of capabilities. And seems hard to get useful work out of human-scientist-team without them also working out ways to (maybe imperfectly) distinguish “faked sensory inputs” from real ones, and without leaking any information about what time it is. E.g. we need the AI to build on previous work
“There needs to be sufficient human-generated research in the domain where we are attempting to elicit the AI’s capabilities.” Well, no. You either need sufficient labeled data in the domain we’re trying to elicit capabilities, or good generalization from other domains.
Nice, this is my other crux with Ryan, which we phrased roughly as “how data efficient can we make training”. Where my argument against very high data efficiency is that current long-horizon RL isn’t very data-efficient, and intentional exploration hacking seems plausible in this situation (e.g. bad analogy but for the sake of describing exploration hacking: someone is training you to solve rubik’s cubes, you can avoid succeeding at solving a rubik’s cube in the first place (and use other strategies to mess with their reward signal). This works as long as the trainers don’t know the specific steps required to solve the rubik’s cube, but even then I think there’s some strategies that could work to reduce generalization).
The data need not be human generated, it could be generated by other AIs, as long as we trust it.
?? This seems to be assuming a solution to the problem.
I think there are still numerous directions of hope though, e.g., leaning more on generalization, latent adversarial training.
I agree there are numerous directions of hope, but disagree that either of your examples count, if I understand them correctly. “leaning more on generalization” ≈ “leaning more on agency”, which doesn’t seem good to me. Latent adversarial training seems like it could make an agent robust to a few extra categories of internal perturbation, but doesn’t seem like it would help with any problem that we were focused on in this post.
I agree that we don’t have strong reasons to think one thing or another here, but I think goal modification is reasonably likely: humans can iterate a bunch and maybe have okay interpretability tools (assuming decent but not fundamental advances). Also, as discussed, goals might be changing a bunch during training — that’s not totally asymmetric, it also gives us hope about our ability to modify AI goals.
If we are using okay interpretability tools to understand whether the AI has the goal we intended, and to guide training, then I would consider that a fundamental advance over current standard training techniques.
I agree that goals would very likely be hit by some modifications during training, in combination with other changes to other parts of the algorithm. The problem is shaping the goal to be a specific thing, not changing it in unpredictable or not-fully-understood ways.Many of the issues in this section are things that, if we’re not being totally idiots, it seems we’ll get substantial warning about. e.g., AIs colluding with their AI monitors. That’s definitely a positive, though far from conclusive.
I think that there is a lot of room for the evidence to be ambiguous and controversial, and for the obvious problems to look patchable. For this reason I’ve only got a little hope that people will panic at the last minute due to finally seeing the problems and start trying to solve exactly the right problems. On top of this, there’s the pressure of needing to “extract useful work to solve alignment” before someone less cautious builds an unaligned super-intelligence, which could easily lead to people seeing substantial warnings and pressing onward anyway.
Section 6:
I think a couple of the arguments here continue to be legitimate, such as “Unclear that many goals realistically incentivise taking over the universe”, but I’m overall fine accepting this section.
That argument isn’t really what it says on the tin, it’s saying something closer to “maybe taking over the universe is hard/unlikely and other strategies are better for achieving most goals under realistic conditions”. I buy this for many environments and levels of power, but it’s obviously wrong for AIs that have taking-over-the-universe in their easily-within-reach options. And that’s the sort of AI we get if it can undergo self-improvement.
Overall I think your comment is somewhat representative of what I see as the dominant cluster of views currently in the alignment community. (Which seems like a very reasonable set of beliefs and I don’t think you’re unreasonable for having them).
I agree that it’d be extremely misleading if we defined “catastrophe” in a way that includes futures where everyone is better off than they currently are in every way (without being very clear about it). This is not what we mean by catastrophe.
Trying to find the crux of the disagreement (which I don’t think lies in takeoff speed):
If we assume a multipolar, slow-takeoff, misaligned AI world, where there are many AIs that slowly takeover the economy and generally obey laws to the extent that they are enforced (by other AIs). And they don’t particularly care about humans, in a similar manner to the way humans don’t particularly care about flies.
In this situation, humans eventually have approximately zero leverage, and approximately zero value to trade. There would be much more value in e.g. mining cities for raw materials than in human labor.
I don’t know much history, but my impression is that in similar scenarios between human groups, with a large power differential and with valuable resources at stake, it didn’t go well for the less powerful group, even if the more powerful group was politically fragmented or even partially allied with the less powerful group.
Which part of this do you think isn’t analogous?
My guesses are either that you are expecting some kind of partial alignment of the AIs. Or that the humans can set up very robust laws/institutions of the AI world such that they remain in place and protect humans even though no subset of the agents is perfectly happy with this, and there exist laws/institutions that they would all prefer.
(2) is the problem that the initial ontology of the AI is insufficient to fully capture human values
I see, thanks! I agree these are both really important problems.
Yeah specifying goals in a learned ontology does seem better to me, and in my opinion is a much better approach than behavioral training.
But there’s a couple of major roadblocks that come to mind:You need really insanely good interpretability on the learned ontology.
You need to be so good at specifying goals in that ontology that they are robust to adversarial optimization.
Work on these problems is great. I particularly like John’s work on natural latent variables which seems like the sort of thing that might be useful for the first two of these.
Keep in mind though there are other major problems that this approach doesn’t help much with, e.g.:
Standard problems arising from the ontology changing over time or being optimized against.
The problem of ensuring that no subpart of your agent is pursuing different goals (or applying optimization in a way that may break the overall system at some point).
We aren’t implicitly assuming (1) in this post. (Although I agree there will be economic pressure to expand the use of powerful AI, and this adds to the overall risk).
I don’t understand what you mean by (2). I don’t think I’m assuming it, but can’t be sure.
One hypothesis: That AI training might (implicitly? Through human algorithm iteration?) involve a pressure toward compute efficient algorithms? Maybe you think that this a reason we expect consequentialism? I’m not sure how that would relate to the training being domain-specific though.
I think you and Peter might be talking past each other a little, so I want to make sure I properly understand what you are saying. I’ve read your comments here and on Nate’s post, and I want to start a new thread to clarify things.
I’m not sure exactly what analogy you are making between chess AI and science AI. Which properties of a chess AI do you think are analogous to a scientific-research-AI?
- The constraints are very easy to specify (because legal moves can be easily locally evaluated). In other words, the set of paths considered by the AI is easy to define, and optimization can be constrained to only search this space.
- The task of playing chess doesn’t at all require or benefit from modelling any other part of the world except for the simple board state.I think these are the main two reasons why current chess AIs are safe.
Separately, I’m not sure exactly what you mean when you’re saying “scientific value”. To me, the value of knowledge seems to depend on the possible uses of that knowledge. So if an AI is evaluating “scientific value”, it must be considering the uses of the knowledge? But you seem to be referring to some more specific and restricted version of this evaluation, which doesn’t make reference at all to the possible uses of the knowledge? In that case, can you say more about how this might work?
Or maybe you’re saying that evaluating hypothetical uses of knowledge can be safe? I.e. there’s a kind of goal that wants to create “hypothetically useful” fusion-rocket-designs, but doesn’t want this knowledge to have any particular effect on the real future.You might be reading us as saying that “AI science systems are necessarily dangerous” in the sense that it’s logically impossible to have an AI science system that isn’t also dangerous? We aren’t saying this. We agree that in principle such a system could be built.
Yep ontological crises are a good example of another way that goals can be unstable.
I’m not sure I understood how 2 is different from 1.I’m also not sure that rebinding to the new ontology is the right approach (although I don’t have any specific good approach). When I try to think about this kind of problem I get stuck on not understanding the details of how an ontology/worldmodel can or should work. So I’m pretty enthusiastic about work that clarifies my understanding here (where infrabayes, natural latents and finite factored sets all seem like the sort of thing that might lead to a clearer picture).
Thanks!
I think that our argument doesn’t depend on all possible goals being describable this way. It depends on useful tasks (that AI designers are trying to achieve) being driven in large part by pursuing outcomes. For a counterexample, behavior that is defined entirely by local constraints (e.g. a calculator, or “hand on wall maze algorithm”) aren’t the kind of algorithm that is a source of AI risk (and also isn’t as useful in some ways).
Your example of a pointer to a goal is a good edge case for our way of defining/categorizing goals. Our definitions don’t capture this edge case properly. But we can extend the definitions to include it, e.g. if the goal that ends up eventually being pursued is an outcome, then we could define the observing agent as knowing that outcome in advance. Or alternatively, we could wait until the agent has uncovered its consequentialist goal, but hasn’t yet completed it. In both these cases we can treat it as consequentialist. Either way it still has the property that leads to danger, which is the capacity to overcome large classes of obstacles and still get to its destination.
I’m not sure what you mean by “goal objects robust to capabilities not present early in training”. If you mean “goal objects that specify shutdownable behavior while also specifying useful outcomes, and are robust to capability increases”, then I agree that such objects exist in principle. But I could argue that this isn’t very natural, if this is a crux and I’m understanding what you mean correctly?
I think you’re right that the central problems remaining are in the ontological cluster, as well as the theory-practice gap of making an agent that doesn’t override its hard-coded false beliefs.
But less centrally, I think one issue with the proposal is that the sub-agents need to continue operating in worlds where they believe in a logical contradiction. How does this work? (I think this is something I’m confused about for all agents and this proposal just brings it to the surface more than usual).
Also, agent1 and agent2 combine into some kind of machine. This machine isn’t VNM rational. I want to be able to describe this machine properly. Pattern matching, my guess is that it violates independence in the same way as here. [Edit: Definitely violates independence, because the combined machine should choose a lottery over <button-pressed> over certainty of either outcome. I suspect that it doesn’t have to violate any other axioms].
I sometimes name your work in conversation as an example of good recent agent foundations work, based on having read some of it and skimmed the rest, and talked to you a little about it at EAG. It’s on my todo list to work through it properly, and I expect to actually do it because it’s the blocker on me rewriting and posting my “why the shutdown problem is hard” draft, which I really want to post.
The reasons I’m a priori not extremely excited are that it seems intuitively very difficult to avoid either of these issues:
I’d be surprised if an agent with (very) incomplete preferences was real-world competent. I think it’s easy to miss ways that a toy model of an incomplete-preference-agent might be really incompetent.
It’s easy to shuffle around the difficulty of the shutdown problem, e.g. by putting all the hardness into an assumed-adversarially-robust button-manipulation-detector or self-modification-detector etc.
It’s plausible you’ve avoided these problems but I haven’t read deeply enough to know yet. I think it’s easy for issues like this to be hidden (accidentally), so it’ll take a lot of effort for me to read properly (but I will, hopefully in about a week).
The part where it works for a prosaic setup seems wrong (because of inner alignment issues (although I see you cited my post in a footnote about this, thanks!)), but this isn’t what the shutdown problem is about so it isn’t an issue if it doesn’t apply directly to prosaic setups.