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I think that's reasonable, this is the one with the discussion and it has a forward link, would be better to review them as a unit.

I think the dominance principle used in this post is too strong and relatively easy to deny. I think that the Better impossibility results for unbounded utilities are actually significantly better.

I clarified my views here because people kept misunderstanding or misquoting them.

The grandparent describes my probability that humans irreversibly lose control of AI systems, which I'm still guessing at 10-20%. I should probably think harder about this at some point and revise it, I have no idea which direction it will move.

I think the tweet you linked is referring to the probability for "humanity irreversibly messes up our future within 10 years of building human-level AI." (It's presented as "probability of AI killing everyone" which is not really right.)

I generally don't know what people mean when they say p(doom). I think they probably imagine that the vast majority of existential risk from AI comes from loss of control, and that catastrophic loss of control necessarily leads to extinction, both of which seem hard to defend.

If this is what's going on, then I basically can't imagine any context in which I would want someone to read the OP rather a post than showing examples of LM agents achieving goals and saying "it's already the case that LM agents want things, more and more deployments of LMs will be agents, and those agents will become more competent such that it would be increasingly scary if they wanted something at cross-purposes to humans." Is there something I'm missing?

I think your interpretation of Nate is probably wrong, but I'm not sure and happy to drop it.

If you use that definition, I don't understand in what sense LMs don't "want" things---if you prompt them to "take actions to achieve X" then they will do so, and if obstacles appear they will suggest ways around them, and if you connect them to actuators they will frequently achieve X even in the face of obstacles, etc. By your definition isn't that "want" or "desire" like behavior? So what does it mean when Nate says "AI doesn't seem to have all that much "want"- or "desire"-like behavior"?

I'm genuinely unclear what the OP is asserting at that point, and it seems like it's clearly not responsive to actual people in the real world saying "LLMs turned out to be not very want-y, when are the people who expected 'agents' going to update?” People who say that kind of thing mostly aren't saying that LMs can't be prompted to achieve outcomes. They are saying that LMs don't want things in the sense that is relevant to usual arguments about deceptive alignment or reward hacking (e.g. don't seem to have preferences about the training objective, or that are coherent over time).

If your AI system "wants" things in the sense that "when prompted to get X it proposes good strategies for getting X that adapt to obstacles," then you can control what it wants by giving it a different prompt. Arguments about AI risk rely pretty crucially on your inability to control what the AI wants, and your inability to test it. Saying "If you use an AI to achieve a long-horizon task, then the overall system definitionally wanted to achieve that task" + "If your AI wants something, then it will undermine your tests and safety measures" seems like a sleight of hand, most of the oomph is coming from equivocating between definitions of want.

You say:

I definitely don't endorse "it's extremely surprising for there to be any capabilities without 'wantings'" and I expect Nate doesn't either.

But the OP says:

to imagine the AI starting to succeed at those long-horizon tasks without imagining it starting to have more wants/desires (in the "behaviorist sense" expanded upon below) is, I claim, to imagine a contradiction—or at least an extreme surprise

This seems to strongly imply that a particular capability---succeeding at these long horizon tasks---implies the AI has "wants/desires." That's what I'm saying seems wrong.

When the post says:

This observable "it keeps reorienting towards some target no matter what obstacle reality throws in its way" behavior is what I mean when I describe an AI as having wants/desires "in the behaviorist sense".

It seems like it's saying that if you prompt an LM with "Could you suggest a way to get X in light of all the obstacles that reality has thrown in my way," and if it does that reasonably well and if you hook it up to actuators, then it definitionally has wants and desires.

Which is a fine definition to pick. But the point is that in this scenario the LM doesn't want anything in the behaviorist sense, yet is a perfectly adequate tool for solving long-horizon tasks. This is not the form of wanting you need for AI risk arguments.


  • I don't buy the story about long-horizon competence---I don't think there is a compelling argument, and the underlying intuitions seem like they are faring badly. I'd like to see this view turned into some actual predictions, and if it were I expect I'd disagree.
  • Calling it a "contradiction" or "extreme surprise" to have any capability without "wanting" looks really wrong to me.
  • Nate writes: 

This observable "it keeps reorienting towards some target no matter what obstacle reality throws in its way" behavior is what I mean when I describe an AI as having wants/desires "in the behaviorist sense"."

  • I think this is a semantic motte and bailey that's failing to think about mechanics of the situation. LM agents already have the behavior "reorient towards a target in response to obstacles," but that's not the sense of "wanting" about which people disagree or that is relevant to AI risk (which I tried to clarify in my comment). No one disagrees that an LM asked "how can I achieve X in this situation?" will be able to propose methods to achieve X, and those methods will be responsive to obstacles. But this isn't what you need for AI risk arguments!
  • I think this post is a bad answer to the question "when are the people who expected 'agents' going to update?" I think you should be updating some now and you should be discussing that in an answer. I think the post also fails to engage with the actual disagreements so it's not really advancing the discussion.

Okay, so you know how AI today isn't great at certain... let's say "long-horizon" tasks? Like novel large-scale engineering projects, or writing a long book series with lots of foreshadowing? [...] And you know how the AI doesn't seem to have all that much "want"- or "desire"-like behavior? [...] Well, I claim that these are more-or-less the same fact.

It's pretty unclear if a system that is good at answering the question "Which action would maximize the expected amount of X?" also "wants" X (or anything else) in the behaviorist sense that is relevant to arguments about AI risk. The question is whether if you ask that system "Which action would maximize the expected amount of Y?" whether it will also be wanting the same thing, or whether it will just be using cognitive procedures that are good at figuring out what actions lead to what consequences.

The point seems almost tautological to me, and yet also seems like the correct answer to the people going around saying “LLMs turned out to be not very want-y, when are the people who expected 'agents' going to update?”, so, here we are.

I think that a system may not even be able to "want" things in the behaviorist sense, and this is correlated with being unable to solve long-horizon tasks. So if you think that systems can't want things or solve long horizon tasks at all, then maybe you shouldn't update at all when they don't appear to want things.

But that's not really where we are at---AI systems are able to do an increasingly good job of solving increasingly long-horizon tasks. So it just seems like it should obviously be an update, and the answer to the original question 

Could you give an example of a task you don't think AI systems will be able to do before they are "want"-y? At what point would you update, if ever? What kind of engineering project requires an agent to be want-y to accomplish it? Is it something that individual humans can do? (It feels to me like you will give an example like "go to the moon" and that you will still be writing this kind of post even once AI systems have 10x'd the pace of R&D.) 

(The foreshadowing example doesn't seem very good to me. One way a human or an AI would write a story with foreshadowing is to first decide what will happen, and then write the story and include foreshadowing of the event you've already noted down. Do you think that series of steps is hard? Or that the very idea of taking that approach is hard? Or what?)

Like you, I think that future more powerful AI systems are more likely to want things in the behaviorist sense, but I have a different picture and think that you are overstating the connection between "wanting things" and "ability to solve long horizon tasks" (as well as overstating the overall case). I think a system which gets high reward across a wide variety of contexts is particularly likely to want reward in the behaviorist sense, or to want something which is consistently correlated with reward or for which getting reward is consistently instrumental during training. This seems much closer to a tautology. I think this tendency increases as models get more competent, but that it's not particularly about "ability to solve long-horizon tasks," and we are obviously getting evidence about it each time we train a new language model.


It might be worth making a choice about a single move which is unclear to weak players but where strong players have a consensus. 

Mostly I think it would be faster and I think a lot less noisy per minute. I also think it's a bit unrepresentative to be able to use "how well did this advisor's suggestions work out in hindsight?" to learn which advisors are honest and so it's nice to make the dishonest advisors' job easier.

(In practice I think evaluating what worked well in hindsight is going to be very valuable, and is already enough for crazy research acceleration---e.g. it would be very valuable to just get predictions of which research direction will feel promising to me after spending a day thinking about it. But I think the main open question here is whether some kind of debate or decomposition can add value over and above the obvious big wins.)

For what it's worth I think using chess might be kind of tough---if you provide significant time, the debaters can basically just play out the game.

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