Andrew Gordon Wilson: belief

Transcript context

…Also reminiscent of of the IT surprised everyone that that it did so well compared CNNs. But final question, we were talking about this in the car on the way over, Andrew. The elephant in the room is that GPT 5, you know, we have all these huge overparameterized models. And on the surface, they seem to be doing very well. They're they're bench maxing and they're they're they're brilliant to use. But it feels that there's something missing. I mean, I I think they're not intelligent. I believe you would agree with that statement. What's missing and what's next? So 1 of the things I'm most excited about is developing AI systems that can discover new scientific theories at the level of general relativity or quantum mechanics. And we haven't even really scratched the surface in being able to do this. It's not even clear how data driven that process would be, how much it would look like symbolic logic if you were to think about what Einstein did when he proposed relativity and how we might want to write that down as an algorithm and automate it on a computer. And so I would love to see progress in this direction. I think that some of the ideas that we've discussed around compressibility could play an important role in how we think about selecting for scientific hypotheses. Also, ideas around universality, like what sorts of assumptions might be more universal than others, and at what level of abstraction. But it's something that we haven't really made progress on at all, despite a number of very exciting research projects in AI for science, where neural nets, for example, are being used as black box function approximators in some sort of pipeline targeted at a very specific application. And I think this is extraordinary work and it's really a way in which machine learning is clearly doing a lot of good in the world. But I think it's time to to try to go beyond that paradigm towards really giving us new scientific insights into the data that we didn't have before. And in fact, that's something that I'm I'm really more excited about than anything else in terms of how technology might develop in the future. Like if I were to go a thousand years in the future, 1 of the first questions I would have is well, do we understand something about physics that we didn't before? Do we understand how the brain works? And this is sort of a conventional sort of like approach to science where the theory is really the quantity of primary interest and the applications of course are important, but they're not primarily why like no individual application is primarily why we care about the theory. Like GPS would break within minutes if we weren't accounting for gravitational time dilation in general relativity. But Einstein wasn't thinking about GPS when he proposed relativity. And if you have the theory, then you can sort of suggest all sorts of applications that otherwise wouldn't have been on the horizon. And like we probably could train a neural network to correct for gravitational time dilation without understanding what's going on. But that wouldn't be nearly as exciting or useful as as having the theory of relativity. Mhmm. Yeah. Professor Wilson, thank you so much for joining us today. It's been amazing. Thank you.…

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