Daniel Litt, Assistant Professor of Mathematics at the University of Toronto, describes how frontier AI models fit into his daily mathematical research workflow.
Opinion
Litt: Many AI math breakthroughs are merely 'last mile' completions of human work
“So I think some of the results we've seen have had kind of the flavor of, like, you know, you kind of take some known techniques and apply them in maybe a clever way, or you I don't know, they've kind of been some kind of results I would characterize as, like,…”
Disclosure
Litt: Prompting AI generated three correct algebraic geometry papers in one hour
“Here's an experiment you can do, you can take codecs, you can say, go online, find five recent conjectures in algebraic geometry and prove them, and ok, I've run this experiment, and with some back and forth, I was able to, you know, in an hour, get like three…”
Assertion Supported
Litt: Multiple preprint papers have appeared with identical AI-generated proofs
“Like, sometimes, you know, we've seen examples where, like, three or four or five papers with the exact same proof of the exact same theorem have come out in, within a couple days of each other, which is clearly, you know, some situation where someone's playin…”
Opinion
Litt: Society will still need human mathematicians even if AI becomes superhuman
“So, like, let's suppose the models become, like, really robustly superhuman, like, even, like, we're not even adding, like, meaningful cognitive diversity. Like, I claim, like, still, actually, we still want human mathematicians.”
Opinion
Litt: AI mathematical proofs resemble human reasoning, not alien 'Move 37' leaps
“And I would say that's actually, like, kind of typical of most of the results that I've studied. Like, they don't seem inhuman at all. They seem absolutely like something a human mathematician could produce. And they're, like, typically understandable if, like…”
Opinion
Litt: AI excels at computations but lacks mathematical intuition
“There'll be things that, this isn't surprising, like, there'll be things that rely on the model's strengths, like their ability to, like, grind out a long computation or, like, you know, pull together kind of technical ideas from many areas or, like, maybe man…”