Limits of AI - explained by mathematicians | Lex Fridman

- By the way, and so to, sorry, to go back to your point, I don't actually believe the solved axiom. I don't think we're there. I can prove a theorem, but I don't understand why the proof works. There's some magic that happens over here, and until I generalize somehow the phenomenon and understand it more deeply, then I'll see why the, the

proof works the way it does. Maybe that's one of the reasons why I don't actually believe the solved axiom, because the things that we do are much more exploratory and what constitutes a good question beyond just, "Here's the Riemann hypothesis, make some progress on it." - So I think you also brought up a really important point, this, like,

generality and generalization of our concepts. So related to beauty and mathematics, I think one of the gold standards has always been how simple can your result be and how much does it generalize. And, you know, out of this infinite space of theorems that could be proven, Finding some structure that is simple and

generalizes, like, that is, I think, still the gold standard. And unless I'm mistaken, humans are still asking the questions. So, like, we, you know, prove existence uniqueness of Navier-Stokes with AI, but humans are guiding this. Humans are still asking the questions. And, like, I would love to see the next breakthrough if you had a machine that can prove

any theorem you pose. I want it to start telling me what questions I should be asking. - Do you think it's possible that that machine just does a for loop- ... and finds all the simple and generalizable, basically all the E equals MC squared? - That's happening today, actually. So, like, there are absolutely research programs that are