Sean Carroll: uncertainty

Transcript context

…I think I have some sort of intellectual humility about the whole thing because I was humbled by several stages in the machine learning development over the past 20 years. And I just would never have predicted that LLMs, the way they’re trained, on the scale of data they’re trained would be as impressive as they are. And that’s where intellectual humility steps in, where my intuition would say something like with Melanie, where you need to be able to have very sort of concrete common sense reasoning, symbolic reasoning type things in a system in order for it to be very intelligent. But here, I’m so impressed by what it’s capable to do, train on the next token prediction essentially … My conception of the nature of intelligence is just completely, not completely, but humbled, I should say. Look, and I think that’s perfectly fair. I also was, I almost say pleasantly, but I don’t know whether it’s pleasantly or unpleasantly, but factually surprised by the recent rate of progress. Clearly some kind of phase transition percolation has happened and the improvement has been remarkable, absolutely amazing. That I have no arguments with. That doesn’t yet tell me the mechanism by which that improvement happened. Constructing a model much like a human being is clearly one possible mechanism, but part of the intellectual humility is to say maybe there are others. I was chatting with the CEO of Anthropic, Dario Amodei, so behind Claude and that company, but a lot of the AI companies are really focused on expanding the scale of compute. If we assume that AI is not data limited, but is compute limited, you can make the system much more intelligent by using more compute. So let me ask you almost on the physics level, do you think physics can help expand the scale of compute and maybe the scale of energy required to make that compute happen?…

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