How AI is accelerating scientific discovery today and what's ahead — the OpenAI Podcast Ep. 10

1. AMAndrew Mayne0:00

   Hello, I'm Andrew Mayne, and this is the OpenAI Podcast. Today, my guests are Kevin Weil, head of OpenAI for Science, and Alex Lupsasca, who is an OpenAI research scientist and professor of physics at Vanderbilt University. We're gonna be discussing how AI is impacting science, an upcoming research paper, and where science may be headed in the next five years.

2. KWKevin Weil0:22

   Maybe the most profound way that people are going to feel AGI in their lives is through science.

3. ALAlex Lupsasca0:29

   With ChatGPT, I can just launch it in that direction, in that direction, that direction.

4. KWKevin Weil0:33

   The acceleration that is going to come from these tools is going to change science.

5. AMAndrew Mayne0:37

   [upbeat music] So you're running the OpenAI for Science initiative. Could you explain what that's about?

6. KWKevin Weil0:45

   Yeah, the, the mission of OpenAI for Science is to accelerate science. So the, the question is, can we help scientists do the next, say, twenty-five years of scientific research and scientific discovery in five years instead? Science underpins so much of, of, you know, what we do and how we live, and if we can make progress go faster by putting our most advanced models into the hands of the best scientists in the world, we should do that, and that's what we're trying to do. The, the-- you could ask, like, why now? Why didn't we do this a year ago? Why aren't we doing this a year from now? One of the big reasons is we're just starting to see our frontier AI models being able to do novel science. So we're starting to see examples where GPT-5 can actually prove new things. Maybe not yet things that humans could not do-

7. AMAndrew Mayne1:33

   Mm-hmm.

8. KWKevin Weil1:33

   -but things that humans have not done. So the- these are, like, these little existence proofs of GPT-5 being able to break out past the frontier of human knowledge and into the unknown. And if there's one thing that I've learned from now, uh, you know, a year and a half or so at OpenAI, it's that you go very quickly from the model can't do something, to the model can just barely do something, and it's not great at it yet, but you see these, these, these early examples, and then, you know, six months later, twelve months later, all of a sudden, you couldn't imagine doing this thing without AI. And I think science is in that initial phase where we're seeing real acceleration for scientists that are using AI, sometimes novel, uh, you know, n- not yet maybe large breakthroughs, call them small breakthroughs, and that just says that there's so much potential in this space.

9. AMAndrew Mayne2:20

   We've seen examples of, let's say, AI helping with mathematical proofs. Could you give me an example of how it might do things in some other areas, like physics or whatever kind of things we might see in the short term?

10. KWKevin Weil2:31

    Yeah, I mean, we're seeing examples every day, and they're across the, the range of, of sort of the scientific frontier. You see examples in mathematics, in physics, uh, astronomy, life sciences, like biology. Uh, Alex, I mean, you've, you've worked on some of these. Maybe, maybe it's a good time to talk about some of the physics stuff that you've seen.

11. ALAlex Lupsasca2:52

    Yeah, I think coming back to, uh, Kevin's point about how this is a special time, that's very much how I feel as well, because I started the year, uh, twenty twenty-five thinking, "Yeah, ChatGPT is cool." Like everybody, I used it when it came out, and I thought it's a great chatbot, but I, I was sure it would take a very long time before it would become really relevant for my own work. Uh, so I started the year, I would say, as an AI skeptic-

12. AMAndrew Mayne3:16

    Mm-hmm.

13. ALAlex Lupsasca3:17

    ... uh, because I like to see evidence before I'm convinced of something. And, um, I saw people using it to help in their writing, and it-- I started to use it for that as well. It's very useful for proofreading, but I thought, "Oh, it's gonna be a while before it gets to do the special stuff that I'm really a specialist at."

14. AMAndrew Mayne3:32

    You're, like, black holes.

15. ALAlex Lupsasca3:33

    Like black hole physics, exactly.

16. AMAndrew Mayne3:35

    Yeah.

17. ALAlex Lupsasca3:35

    And I had this experience early this year where I was trying to find this magnetic field solution that describes, um, what happens around a pulsar, which is a, a, a rotating star with very powerful magnetic fields, and I was going for this very particular solution. I had to solve a partial differential equation, and I was able to, um, identify that solution as an infinite sum over products of special functions called Legendre polynomials. And if you w- go to physics grad school, this is the kind of thing that you spend a, a lot of time getting, um, familiar with, and I also like these puzzles, and I was playing around with the sum, and I felt like there should be a, a simple formula that it evaluates to. And I thought, "Okay, I have this friend who has ChatGPT o3-Pro-

18. AMAndrew Mayne4:18

    Mm-hmm

19. ALAlex Lupsasca4:18

    ... which I didn't have access to at the time, and I thought, "Okay, I'm just gonna send it to him and see what comes out of it." [lips smack] And [chuckles] he sends me back this output. It thought for eleven minutes, which at the time I'd never seen it do, because I was using the free version, which doesn't think for as long. And it gave this beautiful answer where it was able to understand what the sum was, um, and break it down into pieces that it could tackle, and then it had to go and find this special identity, uh, that was published in one paper from the 1950s in a Norwegian Journal of Mathematics. And so it understood what the problem was, and it knew about this random identity that was just the thing for the job, and it used them, and it gave this beautiful output, and at the end, the answer was wrong because it made this silly typo. It added an extra factor in front-

20. AMAndrew Mayne5:04

    Mm.

21. ALAlex Lupsasca5:04

    -for... It was almost kind of like a human making a, a silly typo at the end. But it was very easy to check the derivation, and I, I went through it, and I realized, "Okay, there's this extra factor, but aside from that, it, it did the work." And that really sent me reeling because I thought, "Okay, [chuckles] I would say that's a uniquely human ability." I, I thought that's something that makes theoretical physicists special. Um, you know, now in twenty twenty-five, clearly, they're capable of doing things that I, I would consider amazing.

22. KWKevin Weil5:30

    Yeah, I think one of the cool things, so you've got examples like Alex's, where it was probably not something that he... Like, he could have done it himself over-

23. ALAlex Lupsasca5:37

    Mm-hmm

24. KWKevin Weil5:37

    ... you know, eventually, but GPT was able to do it faster. That's acceleration on its own. And there's something qualitative about that even as well, because if you can explore-- instead of exploring two paths over the course of a week, if you can explore ten paths in parallel in, you know, an hour, all of a sudden there's a lot more ideas that you can try, and that's also acceleration. We also see examples in, like, literature search, which you don't think of as m- maybe like deep scientific innovation.... but it's really important to be able to understand, you know, h- has somebody worked on this problem before? And if so, is there something I can learn to speed up my own work? So, um, and we've seen interesting examples where, uh, there was one-- I might get the details of this wrong, but we were talking to this researcher, and, uh, he was saying he was exploring this particular idea in, like, high-dimensional optimization. And he was like: Man, you know, this thing I'm working on, it's interesting, but somebody must have worked on this before. I can't be the first person to have had this idea. I just can't. But I can't find any examples. And then he had given it, he, he'd sort of given a description of what he was working on to GPT-5, and GPT-5 found an example from, I think it was like economics or something, a completely different field-

25. ALAlex Lupsasca6:50

    Mm-hmm

26. KWKevin Weil6:51

    ... that used completely different terminology, so no keyword lookup would have ever worked. GPT-5 did sort of a con- a conceptual-level literature search-

27. ALAlex Lupsasca7:00

    Yeah

28. KWKevin Weil7:00

    ... found somebody's PhD thesis in German, so also-

29. ALAlex Lupsasca7:04

    [chuckles]

30. KWKevin Weil7:04

    ... a completely different language. You know, it was like basically lost to time, but this person had done really interesting, sort of related work that helped him in his research. And so, you know, that's another area. So you can talk about, uh, the acceleration that comes from just, like, novel proofs-

Episode duration: 48:12