Iterated Amplification

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I think US speed limits are so low in part because of an effort to limit demand for oil in 1973; see Wikipedia.

Even setting aside that ignominious origin, I expect that optimal speeds have significantly increased as technology has improved and so speed limits are probably too low because of inertia.

As a concrete example, suppose that an AI fails to find an error in a piece of code (which will slightly undermine the security of some important computer system). You are unsure if it represents sabotage or a benign error. Relevant evidence for making that distinction:

  • Can the AI identify structurally similar errors in other code? Is the behavior sensitive to evidence about how the code will be used, in a way that doesn't seem relevant to the nature or salience of the error? Is the pattern of sensitivity suggestive of an attempt to disempower humanity?
  • Is the behavior sensitive to the AI's beliefs about the quality of human oversight? For example does training data suggesting more vigilant human oversight in this domain decrease the probability of the mistake? Do other AI systems trained in different ways reproduce a similar pattern of mistakes?
  • If we have one example of a mistake that looks suspicious, we can examine other similar mistakes and build up a detailed pattern of behavior. For example we can hold fix the context and then look at whether the AI misses other kinds of software errors. With multiple mistakes in hand we can run all this analysis on a broader spectrum of mistakes. We can iteratively modify scenarios to explore the landscape of deceptive behavior, gradient descenting our way towards stronger and stronger evidence.

As another example, suppose that an AI system tries to modulate its behavior in any way based on whether it believes it is being closely monitored vs operating in the real world. And suppose that you find an example in the lab where the AI system messes up, and therefore where there is an unusually large gap between its ability to solve a problem in a "realistic-looking" situation vs a situation that's obviously a test. The initial behavior isn't super suspicious (sometimes behavior randomly varies with framing), but it's suspicious enough to get flagged for more thorough review. Subsequent rounds find further evidence of sensitivity, and eventually it's pretty likely (though still not "legal smoking gun") that we've got an example of an AI systems modulating its behavior based on whether it believes it is being tested.

At that point you can start collecting data about what cues the AI system is using to determine whether it is being tested, by varying features of the context and seeing whether you see nicer or less nice behavior. And once you have this understanding in hand you can use it in other contexts to try to get the AI to mess up more. As you find more examples you can build a richer picture, in a virtuous cycle (like normal science).

I think being an AI in this situation is pretty untenable if you are up against human scientists trying hard to figure out what's going on with a typical level of rigor and competence. And so my guess is that typical competent malicious AI systems won't try to modulate their behavior in a significant way based on whether they believe they are being actively tested/monitored. (But it's still worth looking extremely hard, and e.g. it's likely that some AI systems will mess up and try to pull this kind of stunt.) And that's a huge difference with the human case: changing your behavior when you are being closely watched is one of the key things that a human needs to do in order to preserve plausible deniability, and one of the key features of many AI safety horror stories.

(We could tell an identical story about cases where AI systems try to behave badly in ways that are too subtle for humans to notice, but mess up in a single case by underestimating human abilities. In that case we get a clear example of sensitivity to information about human competence, and again can explore the whole frontier of this behavior.)

This kind of possibility forces AI systems to push back bad behavior into cases where they are more and more confident that it's never going to be noticed. But the space of interventions we get to try, if we subject this issue to rigorous scientific scrutiny, includes significantly modifying the AI's training data and limiting information about the world. So "super confident that humans will never notice" is a very high bar.

just got kind of unlucky in how its context shifted it into a more deceptive role-play (but without long-term planning being involved) or was actually doing some more coherent long-term deception plan.

I'm not sure this distinction is particularly important. If ChatGPT is taking actions to undermine human control of AI, and you can establish a pattern of behavior, that's a big deal which will cause scientists and the world to sit up and take interest. I don't think a defense like "it's just trying to undermine human control because it's read a lot of stories about AI systems taking over" or "it lies and manipulates the user because that's what a sociopathic human would do, and RL training put it into a more sociopathic mode" are going to fly very well.

If this is happening at a point in time when the AI system is competent enough to have real consequences, then I think it's going to be an extremely good basis for a policy reaction.

We've seen a tiny example of this with Sydney, in a system where it has almost zero consequences and where it's pretty much random rather than having a pattern of behavior, and that's already had significant effects on public and scientific perception.

Overall I would agree it takes a lot of work to actually do the monitoring (especially if AI developers want to avoid accountability), to establish a pattern of behavior, to amplify a little bit of evidence into a robust scientific picture, to diagnose the issue appropriately and avoid overfitting, etc. I think some people have the view that the evidence is already in and nothing will change, but I think that's very wrong, and it seems to me like it's one of the more harmful features of the AI safety ideological bubble. (If you go out into the broader scientific community, I think "do you have evidence" is really a central hangup, and the standards of evidence being used are overall pretty reasonable IMO.)


One difference is that if you observe a human behaving deceptively you are forced to make educated guesses about their motives and context, but if you catch an AI behaving deceptively you are able to record the exact context in which it acted, examine its behavior in a large number of counterfactual situations,  run other AI systems in exactly the same situation, etc.

I think this removes most of the leeway that humans use to maintain plausible deniability, especially in high-stakes situations with thorough investigations. Overall I'm more intuitively worried about cases where humans never flag any suspicious behavior (e.g. because its thoroughly undetectable, or occurs only once an AI takeover is underway) than cases where they flag suspicious behavior but the AI is able to maintain plausible deniability during a thorough investigation.

Taking your numbers at face value, you'd have 1.5 billion passenger hours afflicted by the ban per life saved, or about 3000 lifetimes worth of hours.

Or: if people spent every waking minute of their lives under annoying regulatory requirments about as bad as this one with the same tradeoffs, the benefit would be extending the average lifespan from 77.28 years to 77.29 years.

I expect most people would demand more like +10 years of lifespan in return for that level of restriction, not +0.01 years. So the cost benefit is probably off by ~3 orders of magnitude.

I generally prefer to think about this kind of tradeoff by scaling up the benefits to 1 life and then concentrating the costs in 1 life, and seeing how the tradeoff looks. That might be idiosyncratic, but to me it's very natural to ask my gut how much lifespan I'd like to trade off for a few minutes of pain or inconvenience.

I think the term "existential risk" comes from here, where it is defined as: 

Existential risk – One where an adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.

(I think on a plain english reading "existential risk" doesn't have any clear precise meaning. I would intuitively have included e.g. social collapse, but probably wouldn't have included an outcome where humanity can never expand beyond the solar system, but I think Bostrom's definition is also consistent with the vague plain meaning.)

In general I don't think using "existential risk" with this precise meaning is very helpful in broader discourse and will tend to confuse more than it clarifies. It's also a very gnarly concept. In most cases it seems better to talk directly about human extinction, AI takeover, or whatever other concrete negative outcome is on the table.

It seems fairly unlikely that this specific task will be completed soon for a variety of reasons: it sounds like it technically requires training a new LM that removes all data about zelda games; it involves a fair amount of videogame-specific engineering hassle; and it's far from anything with obvious economic relevance + games are out of fashion (not because they are too hard). I do still think it will be done before 2033.

If we could find a similar task that was less out of the way then I'd probably be willing to bet on it happening much sooner. Presumably this is an analogy to something that would be relevant for AI systems automating R&D and is therefore closer to what people are interested in doing with LMs.

Although we can't bet on it, I do think that if AI developers made a serious engineering effort on the zelda task right now then they would have a reasonable chance of success within 2 years (I'd wildly guess 25%), and this will rise over time. I think GPT-4 with vision will do a reasonable job of identifying the next step needed to complete the game, and models trained with RL to follow instructions in video games across a broad variety of games (including 3d games with similar controls and perspective to Zelda) would likely be competent enough to solve most of the subtasks if you really went all out on it.

I don't have a good sense of what part you think is hard. I'd guess that the most technically uncertain part is training an RL policy that takes a description of a local task (e.g. "throw a bomb so that it explodes next to the monster's eye") and then actually executing it. But my sense is that you might be more concerned about high-level planning.

Do you have any hard things that you are confident LLMs won't do soon? (Short of: "autonomously carry out R&D.") Any tasks you think an LM agent won't be able to achieve?

I can't tell if you think these problems will remain hard for the model, and if so why.

I think 70% that an LM agent can do the 4x4 grid example by EOY 2024 because it seems pretty easy. I'd update if that was wrong. (And I'd be fine replacing that by held out examples of similar complexity.)

Will you be updating your picture if it can do these tasks by EOY? How much  have you updated in the last few years? I feel like 2018 Paul was pretty surprised by how good ChatGPT is now (its turing test ability is maybe ~85th percentile of my forecasts), and that in 2018 you were at least qualitatively trying to argue in the opposite direction.

I don't see how that's a valid interpretation of the rules. Isn't it checking to find that there is at least one 2x repetition and at least one 3x repetition? Whereas the request was exactly two of each.

I'm glad you have held out problems, and I think it would be great if you had a handful (like 3) rather than just one. (If you have 5-10 it would also be cool to plot the success rate going up over time as ChatGPT improves.)

Here is the result of running your prompt with a generic system prompt (asking for an initial answer + refinement). It fails to meet the corner condition (and perplexingly says "The four corners (top left 'A', top right 'A', bottom left 'A', bottom right 'B') are distinct."). When I point out that the four corners aren't distinct it fixes this problem and gets it correct.

I'm happy to call this a failure until the model doesn't need someone to point out problems. But I think that's entirely fine-tuning and prompting and will probably be fixed on GPT-4.

That said, I agree that if you keep making these problems more complicated you will be able to find something that's still pretty easy for a human (<5 minutes for the top 5% of college grads) and stumps the model. E.g. I tried: fill in a 4 x 4 grid such that one column and row have the same letter 4 times, a second column has the same letter 3 times, and all other rows and columns have distinct letters (here's the model's attempt). I'm predicting that this will no longer work by EOY 2024.

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