Grant Sanderson – AI and the future of math
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Today, I'm chatting with Drance Anderson, who runs the Blue one Brown and is now working on a new project documenting the progress AI is making in math. And I wanted to talk to you about this because AI has been making the fastest progress in mathematics as of any other field. So whatever is happening here and whatever we were seeing AI progress happen or not happen would tell us about what will happen to the rest of the world as AI gets better and better. So I wanted to start with this question I asked you when I first interviewed you three years ago. And I asked you, once we have AIs that ca
but
there won't be some moment when this happens. First, I'd I think I'd be curious to get your heuristics on why that turned out to be true. And second, I'm curious how long you think this narrowness can continue to continue to be true. So by the point that AI has solved the middle enterprise problem, do you think it's still possible that at that point, there's lots of tasks that humans are doing that AI still can't automate in the economy?
It's an interesting question because it's hard to answer without knowing what the solution looks like ahead of time. I mean, if we take the IMO, that's something where I think the spirit of your question three years ago was in looking at how some of the solutions to these problems really seem to require creativity. Yeah. And the designers of these problems, they'll try to have them come up with things that you can't train for as easily. I think the dirty secret with the IMO is that you really can train for a lot of them, and so with the with the whole AI and math project undergoing, I think, a
Yeah. Or at the very least, even if it couldn't, like, literally do every single thing white collar humans can do
Yeah.
It would just have transformative effects in the way that getting gold in the IMO did not have transformative effects on the world. First of all, I do wanna point out that I'm totally moving the goalpost here because when I interviewed Dario about two, years ago, I asked this question about why haven't they been able to use their vast knowledge to connect ideas together and come up with a new discovery that way. That seems like the kind of thing, even if a moderately intelligent person knew this much information, they'd be able to come up with a medical diagnosis from the fact that this drug c
I mean, you you highlight two pretty good examples there. The for anyone curious about the the unit distance conjecture, there's this really nice video by a math channel called Polylog where where they talk about it. And one of the people in that because all of these discussions, it causes people to reflect on the process of doing math. They're like, oh, this thing can do this impressive stuff. What does that mean for us? Highlights this quote, how good mathematicians prove theorems, great mathematicians come up with conjectures, and the greatest mathematicians come up with definitions. That's
Right. And so it's very interesting. The kinds of things you can't make benchmarks for are also the kinds of things, at least in the current paradigm, you can't easily train for, Because there's really no fundamental difference between a benchmark and a training environment. Yes. I think it's very easy to come up with some dichotomy of like, here's a deep reason why AI can't do a certain thing and then it turns out, well, you're just thinking about it the wrong way and actually I can do it pretty soon thereafter. But I'm gonna come up with
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