Ankur Moitra: I think also students have so much anxiety about, you know, what publications are gonna look like that one of the hardest parts is, you know, when I’m thinking about a project, I’m really trying to think about it in a sort of multi-year time horizon because I have some meta vision that I wanna realize, like how can we use learning theory techniques in quantum many-body systems? And there are a lot of stepping stones on route to that, but some of the most exciting problems are, you know, many years out. But students are so worried about the incoming AI tidal wave, you know, the future of work, universal basic income. Are any of us gonna have jobs? That they’re really thinking in three- and six-month horizons, and then it’s hard to find problems that we both care about just because- You know, we’re thinking about different worlds.
Lex Fridman: You don’t think we should encourage them to continue to think long term? I mean, I understand that-
Ankur Moitra: Oh, I do, but they don’t always listen to you. I mean, I don’t know if I’m right or wrong. You know, maybe AI will solve all of our problems, but it seems like the only ones that are actually worth us working on at this moment in time are the ones we’re still gonna care about in a bunch of years, and you should only be working on future-proof problems. If you’re working on things that, you know, you’re in a rush because you think the next generation of models will definitely solve those problems, then maybe just go on vacation and don’t solve that problem.
From Lex Fridman Podcast #503 (video, from 2:25:46); text from the official transcript.
Ever since the shock the AI wave brought, I seem to have forgotten that I used to be able to think about problems like this. It’s a really good way of thinking, and I should try to pick it back up.