Centaur Math

In the past, new PhD students would ask how they could succeed when they had to compete with the likes of say, Richard Karp or Avi Wigderson. I would say Karp and Wigderson have limited bandwidth and you can work on problems they don't work on, or think deeper about a problem than Karp or Wigderson has time to.

Now we get the same question but with names like Claude and ChatGPT and it's hard to make the same bandwidth argument. What do we tell them as we get closer to Math AGI?

What even is Math AGI? It's not that every math problem gets solved. I don't expect P vs NP to be solved anytime soon. It would require a completely new approach, and AI doesn't (yet) think outside the box, though it has a very large box.

Math AGI means that with rare exceptions, if AI can't solve a math problem then no human could either. If you need a proof, you'd have to pay for more cycles, or wait for the next new and improved model. Like the Turing test, we'll only truly realize we've reached Math AGI once we've gone well past it.

We haven't reached Math AGI yet and we may never fully get there. We have entered the world of Centaur Math. Mathematicians can still prove theorems AI can't, AI can prove some theorems mathematicians haven't yet proven, but the real strength comes with mathematicians and AI working together. Working with AI today is like having a pretty good PhD student, who has a huge broad base knowledge of mathematics, is a whiz at coding, but still needs direction, encouragement and verification.

Chess had a short centaur moment when humans and AI working together could beat the best human players and AI programs. Now, any human would play worse not following what AI says. Nevertheless, we still enjoy watching two sub-AI humans play chess against each other. I doubt the same would hold for sub-AI mathematicians.

So what do we tell the students? If you love math, do math. Embrace AI, use it to go further, not as a crutch. Challenge yourself and remain agile so you can find success whatever the future might hand us. And remember, math is not ultimately about the theorems we prove but how we understand the principles behind them, and that's a human endeavor not a machine one.