orthonormal

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I have to further compliment my past self: this section aged extremely well, prefiguring the Shoggoth-with-a-smiley-face analogies several years in advance.

GPT-3 is trained simply to predict continuations of text. So what would it actually optimize for, if it had a pretty good model of the world including itself and the ability to make plans in that world?

One might hope that because it's learning to imitate humans in an unsupervised way, that it would end up fairly human, or at least act in that way. I very much doubt this, for the following reason:

  • Two humans are fairly similar to each other, because they have very similar architectures and are learning to succeed in the same environment.
  • Two convergently evolved species will be similar in some ways but not others, because they have different architectures but the same environmental pressures.
  • A mimic species will be similar in some ways but not others to the species it mimics, because even if they share recent ancestry, the environmental pressures on the poisonous one are different from the environmental pressures on the mimic.

What we have with the GPTs is the first deep learning architecture we've found that scales this well in the domain (so, probably not that much like our particular architecture), learning to mimic humans rather than growing in an environment with similar pressures. Why should we expect it to be anything but very alien under the hood, or to continue acting human once its actions take us outside of the training distribution?

Moreover, there may be much more going on under the hood than we realize; it may take much more general cognitive power to learn and imitate the patterns of humans, than it requires us to execute those patterns.

The chess example is meant to make specific points about RL*F concealing a capability that remains (or is even amplified); I'm not trying to claim that the "put up a good fight but lose" criterion is analogous to current RL*F criteria. (Though it does rhyme qualitatively with "be helpful and harmless".)

I agree that "helpful-only" RL*F would result in a model that scores higher on capabilities evals than the base model, possibly much higher. I'm frankly a bit worried about even training that model.

Thank you! I'd forgotten about that.

I agree with "When you say 'there's a good chance AGI is near', the general public will hear 'AGI is near'".

However, the general public isn't everyone, and the people who can distinguish between the two claims are the most important to reach (per capita, and possibly in sum).

So we'll do better by saying what we actually believe, while taking into account that some audiences will round probabilities off (and seeking ways to be rounded closer to the truth while still communicating accurately to anyone who does understand probabilistic claims). The marginal gain by rounding ourselves off at the start isn't worth the marginal loss by looking transparently overconfident to those who can tell the difference.

I reached this via Joachim pointing it out as an example of someone urging epistemic defection around AI alignment, and I have to agree with him there. I think the higher difficulty posed by communicating "we think there's a substantial probability that AGI happens in the next 10 years" vs "AGI is near" is worth it even from a PR perspective, because pretending you know the day and the hour smells like bullshit to the most important people who need convincing that AI alignment is nontrivial.

I can imagine this coming from the equivalent of "adapt someone else's StackOverflow code" level capability, which is still pretty impressive. 

In my opinion, the scariest thing I've seen so far is coding Game Of Life Pong, which doesn't seem to resemble any code GPT-4 would have had in its training data. Stitching those things together means coding for real for real.

Kudos for talking about learning empathy in a way that seems meaningfully different and less immediately broken than adjacent proposals.

I think what you should expect from this approach, should it in fact succeed, is not nothing- but still something more alien than the way we empathize with lower animals, let alone higher animals. Consider the empathy we have towards cats... and the way it is complicated by their desire to be a predator, and specifically to enjoy causing fear/suffering. Our empathy with cats doesn't lead us to abandon our empathy for their prey, and so we are inclined to make compromises with that empathy.

Given better technology, we could make non-sentient artificial mice that are indistinguishable by the cats (but their extrapolated volition, to some degree, would feel deceived and betrayed by this), or we could just ensure that cats no longer seek to cause fear/suffering.

I hope that humans' extrapolated volitions aren't cruel (though maybe they are when judged by Superhappy standards). Regardless, an AI that's guaranteed to have empathy for us is not guaranteed, and in general quite unlikely, to have no other conflicts with our volitions; and the kind of compromises it will analogously make will probably be larger and stranger than the cat example.

Better than paperclips, but perhaps missing many dimensions we care about.

Very cool! How does this affect your quest for bounded analogues of Löbian reasoning?

This is honestly some of the most significant alignment work I've seen in recent years (for reasons I plan to post on shortly), thank you for going to all this length!

Typo: "Thoughout this process test loss remains low - even a partial memorising solution still performs extremely badly on unseen data!", 'low' should be 'high' (and 'throughout' is misspelled too).

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