Ajeya Cotra

Wiki Contributions


my guess is most of that success is attributable to the work on RLHF, since that was really the only substantial difference between Chat-GPT and GPT-3

I don't think this is right -- the main hype effect of chatGPT over previous models feels like it's just because it was in a convenient chat interface that was easy to use and free. My guess is that if you did a head-to-head comparison of RLHF and kludgey random hacks involving imitation and prompt engineering, they'd seem similarly cool to a random journalist / VC, and generate similar excitement.

I strongly disagree with the "best case" thing. Like, policies could just learn human values! It's not that implausible.

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.

But if Alex did initially develop a benevolent goal like “empower humans,” the straightforward and “naive” way of acting on that goal would have been disincentivized early in training. As I argued above, if Alex had behaved in a straightforwardly benevolent way at all times, it would not have been able to maximize reward effectively.

That means even if Alex had developed a benevolent goal, it would have needed to play the training game as well as possible -- including lying and manipulating humans in a way that naively seems in conflict with that goal. If its benevolent goal had caused it to play the training game less ruthlessly, it would’ve had a constant incentive to move away from having that goal or at least from acting on it.[35] If Alex actually retained the benevolent goal through the end of training, then it probably strategically chose to act exactly as if it were maximizing reward.

This means we could have replaced this hypothetical benevolent goal with a wide variety of other goals without changing Alex’s behavior or reward in the lab setting at all -- “help humans” is just one possible goal among many that Alex could have developed which would have all resulted in exactly the same behavior in the lab setting.

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?"...As usual, there's the human analogy: our goals are very strongly biased towards things we have direct observational access to!)

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.

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.

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.

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.

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:

Once this progresses far enough, the best way for Alex to accomplish most possible “goals” no longer looks like “essentially give humans what they want but take opportunities to manipulate them here and there.” It looks more like “seize the power to permanently direct how it uses its time and what rewards it receives -- and defend against humans trying to reassert control over it, including by eliminating them.” This seems like Alex’s best strategy whether it’s trying to get large amounts of reward or has other motives. If it’s trying to maximize reward, this strategy would allow it to force its incoming rewards to be high indefinitely.[6] If it has other motives, this strategy would give it long-term freedom, security, and resources to pursue those motives.

As well as the section Even if Alex isn't "motivated" to maximize reward.... 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.

Yeah I agree more of the value of this kind of exercise (at least within the community) is in revealing more granular disagreements about various things. But I do think there's value in establishing to more external people something high level like "It really could be soon and it's not crazy or sci fi to think so."

Can you say more about what particular applications you had in mind?

Stuff like personal assistants who write emails / do simple shopping, coding assistants that people are more excited about than they seem to be about Codex, etc.

(Like I said in the main post, I'm not totally sure what PONR refers to, but don't think I agree that the first lucrative application marks a PONR -- seems like there are a bunch of things you can do after that point, including but not limited to alignment research.)

I don't see it that way, no. Today's coding models can help automate some parts of the ML researcher workflow a little bit, and I think tomorrow's coding models will automate more and more complex parts, and so on. I think this expansion could be pretty rapid, but I don't think it'll look like "not much going on until something snaps into place."

(Coherence aside, when I now look at that number it does seem a bit too high, and I feel tempted to move it to 2027-2028, but I dunno, that kind of intuition is likely to change quickly from day to day.)

Hm, yeah, I bet if I reflected more things would shift around, but I'm not sure the fact that there's a shortish period where the per-year probability is very elevated followed by a longer period with lower per-year probability is actually a bad sign.

Roughly speaking, right now we're in an AI boom where spending on compute for training big models is going up rapidly, and it's fairly easy to actually increase spending quickly because the current levels are low. There's some chance of transformative AI in the middle of this spending boom -- and because resource inputs are going up a ton each year, the probability of TAI by date X would also be increasing pretty rapidly.

But the current spending boom is pretty unsustainable if it doesn't lead to TAI. At some point in the 2040s or 50s, if we haven't gotten transformative AI by then, we'll have been spending 10s of billions training models, and it won't be that easy to keep ramping up quickly from there. And then because the input growth will have slowed, the increase in probability from one year to the next will also slow. (That said, not sure how this works out exactly.)

Where does the selection come from? Will the designers toss a really impressive AI for not getting reward on that one timestep? I think not.

I was talking about gradient descent here, not designers.

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