Will AI solve physics?

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25:36All right, we are back. And now instead of talking about machine learning, we're going to talk about large language models and how these help physicists. So, Daniel, how are you all using LLMs? So mostly the conversation about AI solving physics is inspired by the rise of these chatbots, Claude, ChatGPT, Gemini, all these things. and their supposed feeling of broader intelligence, right? And everything we talked about in the last segment was machine learning. It was statistical learning applied to very specific tasks, not the kind of thing that's going to tell you like what's a recipe to make a certain kind of fancy sandwich or whatever, the things that people use LLMs for.

26:18LLMs are a very different breed of artificial intelligence. It's built on natural language instead of just like numerical inputs. It outputs with a human-like language, right? It feels much more natural than mathematical. And these things came on the scene a few years ago. And really, that's what people are interested in. Are LLMs going to change the way we do science? Are they going to crack problems that human physicists have not been able to crack? But I want to underline that these things are still mathematical. An LLM is not something different from any other kind of AI underneath the hood.

26:53It's the same pieces of technology just arranged in a different way to solve a different problem and then just scaled up tremendously, like hugely, just like enormous numbers of these nodes and neurons. And I think it's worth maybe taking a minute to explain what is going on underneath the hood of an LLM. A lot of people understand that LLMs are statistically predicting text. Like if they are writing an answer to you, they are trying to generate for you what you want. and they have read a bunch of text. And so if they're trying to predict like the next word in the sequence of the answer for you, they look at all the texts they've read and they say, well, statistically, what is the word that usually follows the current word?

27:35You know, so I'm writing a sentence. The sentence is Kelly is a blank. And then, you know, I'm trying to generate the next word. It's some adjective. And so I'm looking through all of my training, right? All the text on the internet that describes Kelly. And I'm trying to statistically predict, you know, what is the appropriate word to fill in the blank? And, you know, I'm not going to give you that word other than a wonderful co-host and fantastic scientist. There you go. Thanks, thanks. You only had, but you said it was going to be one word. So I was waiting for it. But I'm going to, I'll let you off the hot seat.

28:09Although you really backed yourself into a corner there.

28:14All right. And that's a fair sort of surface level understanding of LLMs and what they're doing. But it really misses the transformational technology that's underneath them. Because LLMs are doing more than just like predicting exactly the next word. They're using something called attention, which is really crucial to understand why these things are so much better than just like statistical parrots. And attention helps you understand which words in a sentence are contributing to the context and contributing meaning to the sentence. So say, for example, you have a sentence like, the river bank was steep.

28:48Now, bank is a complicated English word because it has lots of meanings, right? Is it where you put your money? Is it how you shoot a basketball off the backboard? Is it what keeps a river in place, right? In this sentence, the words river and steep are important clues that tell you what bank means, right? So when you hear that sentence, you're like, Daniel's not talking about depositing money or shooting three-pointers, right? He's talking about a river. And what the attention mechanism does inside these LLMs is let the chatbot look at all of the other words, not just the one preceding word. And so it knows what to pay attention to.

29:26It helps it understand the context. It's like if you go to a science conference and you need to understand some crucial thing for your research, you don't just talk to the person next to you. You seek out the most relevant person to pay attention to, the person who's going to help you understand the question you're asking. And so that's what attention is. And then they coupled attention with this technology called transformers, which are not little plastic toys that you used to play with or cartoons or even the things on wires out there that help transform the voltage. They're just like stacked layers of these attention modules.

30:01So the first layer helps you understand the words and the second layer helps you understand the rest of the paragraph. And by the time you have multiple stacked layers of attention, you can understand, okay, here's the context of the question that's being asked, and therefore here's the answer that I should give. And so it's this combination of attention and transformers that make LLMs so powerful. Okay. I didn't know any of that. All right. So then what are LLMs useful for, right? I'm not really interested in using them to write essays about 13th century Europe or whatever. Today we're talking about using them in physics.

30:35So I use LLMs all the time. And here's an example I find them very useful for. I need to learn some new software package, right? Because I've started a new research project and there's a tool that simulates the thing I need, for example, and I download the tool. It's written terribly. The documentation is garbage. There's like hundreds of pages of tables of technical stuff that's impenetrable. I try to run the code. It doesn't work the first time, right? I look at the code. It's written by physicists without any adult supervision, right? And so in past days, this would be a huge project, make this thing work, validate that it's doing what I think it should be doing, understand the documentation, how it's wrong, how the thing actually works.

31:17And in practice, what I would do is find somebody else who's made it work and start from their example. Somebody else has invested weeks and weeks to making this thing work. I'm going to download their scripts. I'm going to start from their code. I'm going to ask them questions when it breaks, right? I'm going to lean on some kind of expertise. Otherwise, I'm going to have to sink weeks into getting this thing to work. I no longer have to do that. Now I can ask an LLM, hey, read this document and write me an example configuration script to make this thing work. Okay, it didn't work. Debug the issue.

31:47And within a few cycles, I can usually get a new software package up and running and producing useful, validated results in hours, if not minutes, where it used to be either weeks or totally impenetrable. It is pretty amazing. Whenever I use a new package in R or something, and I inevitably put a comma in the wrong place or something, I used to look for that comma for hours. And now I'm like, ChatGPT, where did I put the comma that doesn't belong? And it finds it immediately. Saves me hours. And it's not magic, right? Let's remember, what are these LLMs doing? They understand context. They know how to use attention to focus the question you're asking to produce the right answer.

32:29And they're very good at digesting huge volumes of input, right? The things that humans are bad at for the same reason that we're using machine learning to improve on our data analysis and the particle physics colliders, because they're good at the things we're bad at, like understanding all the nuances of some high dimensional statistics problem. Here, we're using them to do something that we are bad at. And that is the key, right? Lean on their expertise, find the thing that they can do that we can't, and then they can contribute And they can go beyond just like reading documentation for software packages.

33:01They can also read physics papers. There are so many papers put out every single day. If you look at the archive, which is where particle physicists put out their papers every day, there's a dozen papers every day, all of which are pretty good and worth reading. I can't keep up with all of those. In addition, I should be reading all the machine learning papers. I should be reading statistics papers. I should be reading math papers. Some math nerd out there could have just invented some incredible new thing that they're into just because they're a math nerd. But it might solve exactly the problem we have in particle physics.

33:34And they don't know, and we don't know, and who knows how to put these things together? The reason cross-disciplinary research is often so fruitful and leads to breakthroughs and advances is exactly because of this. Because people are unaware of solutions in adjacent fields. And when you just smush them together, you get peanut butter and chocolate, right? It's amazing. But you've got to be careful because you might get information in your head that doesn't actually exist from doing that. Like I asked an LLM to summarize my research and it told me that I had done stuff that I had not done and that none of my collaborators have done in the system.

34:08And if I had just asked for the summary, I would have been like, oh, we know a lot of stuff about the system that we don't really know. And so I guess anything that you decide could be vaguely interesting. You need to actually go back and confirm, which is frustrating because you might be like, oh, I didn't know that. I'm not going to use it, but I didn't know that. And you might not necessarily go back and check, but you just will think like, oh, I'll just keep that in the back of my mind. But now you've got a little lie in the back of your mind. Well, I think of it like a superpowered Google search.

34:35I cannot go into Google and say, here's my research. Has there been any recent papers that might be relevant? But I can type that into my LLM and it can find a bunch of papers. And then I can read those instead of reading all of the papers. And I can decide, oh, this is not actually relevant. There's a crucial detail here you missed. Or, hey, that paper doesn't exist. So it's not useful. But it's doing the part that is hard for me, which is just like reading all of those papers. And it might miss something, but it's very, very powerful to tackling the problem that I can't solve myself. which is reading all of those papers.

35:07And of course, there's highly varying quality between the models. Some of them are good at this. Some of them are bad at this. And this is the kind of thing that's improving very rapidly with time. It used to be you type this into an LLM and you got lots of nonsense. These days, I find it's producing higher and higher quality output, insights into papers that I didn't know about, really useful stuff. And you see also in mathematics, an area I'm not an expert in, that it's finding solutions to outstanding problems that already exist in the literature and nobody knew. They're like, oh, this 1957 paper by some Russian, nobody translated it.

35:41So we didn't know this open problem has actually been solved. And I think it's solving Erdős problems that hadn't been solved yet. Exactly. Sometimes by finding techniques in other papers that are relevant already, not inventing those techniques itself, but making those connections because that's what it's good at, scanning through vast quantities of literature for things that are relevant. Yeah. Okay. So, so far it's been working as an assistant, right? It reads the documentation, it gets the software to work, or it goes to find papers. It's like a research tool. What about like more as a role of a physicist?

36:13Can, for example, you give it a bunch of ideas and say, hey, go write a paper about this, or write me a grant proposal on this topic, right? So I've tried this kind of thing. I have access to the top end models from Anthropic, for example, and it can do it, right? It can start from rough ideas. It can flesh it out into full text. It's grammatically perfect. It makes no spelling mistakes. But in my experience, its writing is not very good. I'm a big fan of good writing. When I read a paper, there's sort of two dimensions there. There's like, is the science good? But also, is the writing good? And if the writing is good, it leads you on a story.

36:52It gets you interested. It tells you why this research is compelling. It presents the things logically and crisply. The text is concise and precise. You know, like a well-written scientific paper is a piece of beauty. And there's also like sloppily written good science. Sometimes the science is great and the paper is just like sloppy. And you're like, what do you mean by this exactly? You know, and like, well, you're telling me this, but now we're talking about this. Why is that? It's just not well put together. Claude doesn't produce sloppy stuff, but it produces boring stuff. I find the text that it produces is missing that zhuzh that you get from a really good piece of writing.

37:29And this is the same, I think, kind of skill that it takes to write a good novel. I read a lot. When I start a novel, I can tell instantly, okay, this dude knows how to do it. Or this lady can write. Oh, my gosh. I don't even know what the plot is or what the characters are. But the sentence structure, something about it is just powerful and effective. And that's a joy to read, right? And LLMs cannot produce that, not yet at least. So I'm going to make a book recommendation, but I'm going to say that even though this title sounds like a kid's book, this is not a book for kids. The book is called Benny and the Blue Whale, and it is a fiction writer who collaborated with AI to write a book just to sort of see what the experience was like.

38:12And the experience matches what you were saying. It's not quite as creative, I think. But anyway, it was a fun read. And there's something about the voice and the personality of the author that comes out in their writing. And that's also important for science. If you write a grant proposal and it's fun and it's exciting and it's well-written and it's crisp, the reviewers are going to have fun reading it. They're going to get excited. They're going to like you. They're going to want to fund your work. It's underappreciated, I think, by the public how much of science is writing. Yeah. Not just for folks like you and me who write specifically for the public, but emails, papers, grant proposals, reviews.

38:51All this stuff is writing and so much more powerful when it's done well, when you have a flair and energy, when you can convey excitement and enthusiasm, when you have your own personal voice. Yeah. And I think we should note, though, that a lot of journals and a lot of grant agencies currently have rules for whether or not you're allowed to have AI help you with your writing. I just think it's worth noting that. Yeah, absolutely. And so it can produce sort of like bland, generic writing that seems like suspiciously polished and kind of flat, you know, doesn't really seem to get the point and convey it crisply.

39:25But, you know, this again is a steep gradient. They used to be worse. They're getting better. It's rapidly improving. I don't know that next year these things won't be much better. And, you know, a lot of the way they're improving is just by scaling. They read more. They make these models bigger, more layers of attention, more nodes. You sometimes hear these parameters quoted like 7 billion parameters or whatever. That's the number of connections between neurons, each of which have a number associated with them. It tells you essentially the size of the network. The bigger the network, the more powerful it is.

39:58Also, the bigger the training set it needs to figure out what is the best value for all those nodes. All right. So LLMs have progressed from fancy Google searches to helping you with code to reading and writing. But what about the juice of it, right? What about like doing calculations, actually being a physicist? So there's some fascinating recent examples of top level physicists using AI to contribute to their papers. And we're going to get to that after the break.

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42:06Hey everyone, it's Cal Penn, host of Earsay, the Audible and iHeart Audiobook Club. This week on the podcast, I'm sitting down with divergent author Veronica Roth to talk about her sprawling new novel, Seek the Traitor's Son. It's a sci-fi fantasy epic about two protagonists on opposite sides of a war and a prophecy neither of them wanted. My first book was Divergent. And when that came out, like, because it was so popular, I think it attracted like mostly positivity, but the negativity I sucked in like a sponge. And I think it was like critiques of things I liked when I was like, you know, I was 23 and I wrote this book and it had all my like dorky little cheesy or maybe unrealistic loves in it.

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44:28And we're back and we're talking about how AI is helping scientists do some math. All right. Take it away, Daniel. So there are top level physicists out there at the top of American academic physics who are using and exploring AI to work with them to help them solve problems. And Ed Witten, for example, famous string theorist, maybe one of the smartest dudes alive, put out a paper recently where ChatGPT did an important part of the work. They had worked out a bunch of examples and they had an idea that it could be more generally proven. And ChatGPT came in and found a way to make those connections and say, okay, here's the proof that this really does work.

45:07So it didn't come up with a research question and it didn't even guess at the answer. It came in and filled in a lot of the details. But this is more than just like correcting your grammar or fixing your code. This is like manipulating mathematics and using logic to build a proof from A to B. So it really is pretty impressive. Yeah, it's amazing. I'm sure that saved Dr. Witten a lot of time and allowed him to work on other projects. Exactly. And, you know, it's doing symbolic calculations, not just like, you know, 7.2 plus 12.4 or whatever. This is manipulating symbols, finding equations that capture these things.

45:44So that's very powerful. Then a friend of mine, Matt Schwartz at Harvard, recently put out a paper where he used Claude Opus essentially as a graduate student. He had an idea for a project. He pitched it to Claude. He got Claude to work on it with him. And Claude did all of the coding and making all the plots and simulations and all the actual work involved. And he guided it through it. And it's not like he just said, hey, here's an idea. Go do the work and write me a paper. And then he came back after lunch and it was all done. his experience was that it's sort of like a very naive young graduate student.

46:19It's often getting lost in the weeds. It's not thinking about the bigger picture. It sometimes makes mistakes. It needs a lot of supervision, but that's sort of focusing on the negatives, right? Like it's amazing. This is incredible, right? That you have something which is a capable research assistant, again, not just a search tool anymore, right? But actually doing work and making progress and making it so that a guy who has an idea for a paper no longer has to go out and find somebody to do the work. He can find AI to do the work, right? He still needs to engage his brain. He still came up with a question.

46:52He still needs to follow it and make sure it's reasonable. But this is not something AI could do a year ago or two years ago. So it's a dramatic improvement. That's exciting, but also a bit scary for folks who would like to get positions as graduate students. Yes, exactly. And I saw him give a talk recently, And he said, AI is going to keep getting better and better at this, and we are not. And so his forecast is by 2030, most science becomes AI-assisted. Wow. And that soon we'll see end-to-end AI doing theoretical science. That we won't need this kind of detailed, step-by-step, hand-holding and guidance.

47:31Does end-to-end include AI coming up with its own questions? It's not clear to me what exactly he means by that. And I think that's a very important distinction because being able to say, here's an interesting problem, go solve it, is very different from saying, what's an interesting problem we should work on? Yeah, because I feel like that's when you work with a grad student, you know, you get them to the point where they tell you, I'm familiar with the literature. Here's what the interesting question is. And you're like, oh, that's great. I didn't think I hadn't thought of that question. And now, like, now you're a team.

48:04Will AI become part of the team? So I was on this panel at NeurIPS, one of the premier AI conferences, and I was kind of a wet blanket about anthropics solving physics in the next few years. So folks from the company approached me afterwards and they were like, so Daniel, what's it going to take to convince you? And I said, I don't know, but I'd love to play with your top level models. So they gave me access to their top level models for a few months. And so I played with it and I asked it exactly what you suggested. I said, all right, AI, suggest a project. Here's the kind of thing I'm interested in.

48:34what's an opportunity, right? Because I think a lot of people don't appreciate how much of science is identifying openings. It's a lot like being an entrepreneur, right? You have limited resources, you have limited time, find a place where you can make progress given your resources in a reasonable amount of time that's going to be impactful, right? That's a lot of what being a professional scientist is. It's easy to come up with like big questions about the universe or grandiose plans that would take a thousand years and a zillion dollars. But what what can I actually do today or over the next year to produce some science that's interesting to the community?

49:10What's the current conversation and where can we contribute? So I asked AI, what should we do? And it read my profile and then it suggested a few ideas, all of which were things I had already done in the last few years. Oh, okay. So it's not there yet. So I was kind of disappointed. I was like, hmm, I've already done all of these things. And I didn't know if it's just like not quite there yet and I'm a little bit ahead of it, Or if it's just like I had read my papers and I was like, oh, Daniel's interested in machine learning and particle physics and tracking, so I'll suggest something in that direction.

49:41It's not clear to me whether it was in the training sample or not. And so it's not there yet, right? But that doesn't mean that it can't contribute, right? It's not going to be an autonomous physicist just yet. But remember, let's think about what it's good at. What can it do? It's good at scanning lots of literature. It's good at putting these things together. And remember that a lot of advances we make in physics are not just like one person has a genius idea which leaps us forward 100 years. It's often putting things together from existing tools, right? And so now let's dig into the question that we asked at the top of the episode, this claim made by these anthropic AI hypesters that it's going to solve physics.

50:22And again, this is what they were actually saying. And this is the kind of thing you see AI tech bros saying. Sam Altman said, quote, quote, although it will happen incrementally, astounding triumphs, fixing the climate, establishing a space colony, and the discovery of all of physics will eventually become commonplace. So I'm not putting words in their mouths, right? These are the claims that are being made. Wow. Because of AI, we will have a space colony? That's right. We're going to fix the climate, establish a space colony, cure cancer, dot, dot, dot, dot, dot. All of this kind of stuff. Amazing.

50:58You know, and you see also folks on the other side, one of my favorite writers is Corey Dr. Rowe. And he says that saying that AI is going to do science and gain consciousness is like saying, if we can get a horse to run fast enough, it's going to turn into a locomotive, you know? And his point is that you can't just like extrapolate blindly. Yeah. Right. Because like, what are we even talking about, you know, on that panel? And lots of times people are talking about how much progress AI is making. We have to think about like, what is the scale there? What are we measuring its capability to do?

51:28Is it like percentage of an Einstein or something? It's not something that we really know how to measure where we can say like, by 2047, it's going to be two Einsteins, right? That's all just nonsense. And so we don't know if there's a barrier there. We don't know if it's going to be able to do this. And what they're talking about it doing is something it has never done, right? It's never suggested a new research topic or solved the whole problem. But that doesn't mean it can't, though. That doesn't mean it can't, right? Maybe these horses will turn into locomotives, right? It certainly is gaining a lot of power every year.

52:02So let's talk about the thing they're claiming, right? What does it mean to solve physics? In my mind, there's two possible answers to that. One is, look, we have a list of problems in physics right now. We don't know what dark matter is. We don't know why the universe is mostly matter. We don't understand dark energy. We can't unify quantum mechanics and general relativity. We don't know why there's so many particles and forces, right? How does it all fit together? There are definitely things we don't understand. Maybe solving physics means answering today's open physics questions. I mean, in the minds of Sam Altman and folks, maybe that's what they mean when they say this, solving all of physics.

52:39To me, that's not solving physics. Even if tomorrow they came out with the answers to these questions, I would not be done. I would not be like, well, I'm bored now. I'm just going to go sit on the beach. I'm going to ask, well, why is this the solution? Could it have been this other way? Can I imagine places this could break down? Could it have been a different way? There's always questions to ask. These questions come from the philosophical context, which is the origin of our curiosity. And that's not going to go away, even if we get beautiful equations to solve today's questions in physics.

53:13Well, I feel like if you've talked to any scientist, you know that as soon as you get one answer, that automatically leads to like 20 new questions. Like it's exponential, the questions you get when you get one answer. And so it's hard for me to imagine that solving those problems would suddenly mean, ah, we're done. I totally agree. And so from that perspective, will it solve physics? Absolutely not. Physics is a human pursuit and it's not going to end even if AI makes our answer finding much more powerful. But I do think, and this might surprise people, that it is going to transform the way we find those answers and that it might actually be capable of Einstein-level contributions.

53:55We don't know how to measure Einsteininess, but when you look back in the history of a lot of the big leaps, what are they doing? They are putting together existing pieces that other people were not aware of. It's easy to say Einstein was a super genius. He came up with this whole thing himself, but that's not really the truth. He was building on generations of careful math done by other nerds, not even interested in general relativity. Grassman numbers and Riemannian manifolds, all of these things are essential for Einstein. He could not solve those problems himself. He put together a bunch of interesting pieces, but sometimes just being lucky by who he knew and who he met, being in the right place at the right time to meet these folks and learn about their progress.

54:36So that's the kind of thing AI is good at, is like, I'm going to read all these papers. I'm going to notice, oh, this tool over here that has been developed for totally other reasons might be able to solve this problem you guys are working on. That's exactly the kind of thing that AI is really good at. It's making these connections, it's searching broadly, it's reading the whole literature, right? And there's lots of examples of this. Group theory invented for totally other reasons by math nerds turns out to be essential for quantum field theory. So I actually am optimistic that there could be big problems out there in physics we don't today have solutions for that AI could help us rapidly solve because of its specific capabilities.

55:16Okay, so you've just been pretty wildly optimistic about how - And characteristically optimistic. That's right. That's right. You might be the more optimistic of team wet blanket, But so do you think like, will AI ever get like a Nobel Prize? Yeah, it's hard to know how to answer that. I mean, computer scientists building AI to solve problems have won Nobel Prizes already, right? We saw that last year, like Google DeepMind won a Nobel Prize, right? I mean, AlphaFold has done incredible stuff, right? They solved protein folding. And so the computer scientists wanted for the tool they built to solve the problem.

55:54Are we in an era where AI could win a Nobel Prize? I mean, I don't know what the estate lawyers would say after reading Nobel's Will again. But I think that we are on the verge of AI solving the current generation of hard theory problems. It's still going to be like a human asks an AI, like, hey, can you unify quantum mechanics and general relativity? But if it goes off and thinks hard and finds a piece of math we hadn't explored or solved some of the current problems in quantum gravity comes up with a novel theory for it, then certainly it's going to have made an enormous contribution to our understanding of the universe, to physics.

56:32Whether you should give it the credit or the credit for the person who typed in the prompt, I think that's a harder question I'm not qualified to answer. Wow. Yeah. And so many people contributed to making AI. It would be hard to know who to give the credit to. Yeah. That's always the case, though. Like, you know, for the Higgs boson, they gave it to the theorists, not to the experimentalists who discovered it, partially because there's just too many of us. Right. Thousands of people contributed to building the accelerator and the detector and making it work. So we can't all win the Nobel Prize.

57:02There's a limit there. But I think it's clear that AI can do the thing we need to make big conceptual leaps, construct the building blocks, pull those building blocks together to make breakthroughs. That's what it can do. And that's very powerful, right? We've seen in history, that's how a lot of these big leaps are made. Let's talk about its impact on like physicists today. So like, for example, it can't take over the LHC and collect the data for you, like run the day-to-day stuff. So you still need experimentalists to collect the data, right? Right. Still for a while. Yes. But there's a lot of work in autonomous labs.

57:41You know, you've given AI access to the LHC. It could certainly decide, you know, how to run it and what data to keep, et cetera. And people are building these autonomous labs where an AI can, for example, test a bunch of materials really quickly, you know, build a bunch of materials, test it, and then close the loop, come up with a new material to test. I was involved in a proposal the last year for something called text to launch technology, where you type into an LLM like, okay, I want a satellite that can do X, Y, Z. And then it uses an autonomous lab to try to develop materials that could do that.

58:16And then far down the road, maybe it would even launch it into space. Wow. Oh my gosh, that's nuts. But the point is that this is very new currently, but there's no real barrier there. If you can give AI control of these labs, if you build a lab that's AI compatible, there's no reason why experiments have to be out of reach of AI. Okay. And so then how is it impacting funding? So like you mentioned that AI could replace a grad student. So maybe now you don't have to ask for funding for grad students anymore. But like could AI replace Daniel Whiteson? Never, I imagine. But what do you think? AI can now do the work of a green graduate student.

59:00And that's very powerful. I can get more stuff done without my students than I could before. That is helpful. I always have one project where I'm doing the actual coding. And now those projects move faster. So that is very cool. And it's definitely changing the way the government sees particle physics being done. And some of the latest budget plans, hundreds of millions of dollars that used to go to pure particle physics is now being sent to AI for science. That includes particle physics, and particle physicists are still involved, but industry is also very deeply involved. It looks to me like the government wants us to make all of our data AI friendly so that these big models from OpenAI and from Anthropic can play key roles in accelerating the progress in physics.

59:47I'm not against that. I just wish that it was new money instead of repurposed old money. Let's keep doing particle physics and let's add an investment on AI instead of cannibalizing particle physics for this money and sending a few hundred million dollars to these vast corporations. Remember that academic physics is tiny in terms of research budgets compared to what these companies have and what they are doing. And so a small amount of money for them is vast funding for us can have a huge impact. And let's not forget that all of the ideas at the foundation of the current wave of AI came from academia.

1:00:25Academics nerding around, coming up with ideas, attention, deep learning, all these things came from academic labs that were just playing with stuff. And so I'm going to beat the drum one more time for investing in basic research because it pays off huge. And when it pays off, we shouldn't then cannibalize our basic research funding to dig into AI. We should also invest in AI, but we should maintain our investments in basic research, right? Yeah, yeah. And I'm under the impression that if you massively cut a budget for, I don't know, say five or ten years, you really hurt a generation of scientists.

1:01:00You know, like labs are going to hire fewer grad students, fewer postdocs. You're going to start fewer physics labs and the labs that do get started aren't going to get the grants that they need to convince their university that they should get tenure so that they should stick around. And now you've got fewer physicists produced by the United States. Is that your sense also or is that not how things work in your company? No, absolutely. Like the theory community has received massive cuts to its funding. And theory is very, very cheap. You're just paying a few people and pencil and paper, but their budget's been cut like 70, 80 percent.

1:01:33which means many fewer students, which means fewer theorists, which means fewer smart people asking those questions. And you asked a question I haven't answered yet, which is like, could this replace Daniel? And I think that it's not hard to imagine that you soon have AIs that have the capability of senior scientists to do research, to even drive other lower level AIs or supervise humans who are doing research. I don't think that's out of their capability, but something I don't understand is who's asking the questions, right? Physics is a human endeavor. At the heart of it, is AI going to solve physics, is about whether we are ever done asking questions.

1:02:15And the answer to that, it's always going to be no. It's just going to be no. Physics is about our curiosity about the universe. Now, that doesn't mean that machines can't be there along with us answering some of the questions. The open question to me is, will they have their own questions? I've got my questions about the universe. You have yours. That's why I'm in physics and you're in parasites. And somebody else is doing art history and somebody else is doing chemistry. You know, God save them. And somebody else is at some company inventing new ways to make white chocolate cheap. And, you know, everybody's driven by their own personal curiosity.

1:02:51Do machines have curiosity? If you give Claude all the physics papers and you ask it like, what do you think is interesting? What is it interested in? And is that really its own interest, right? Or is it just following up on what humans have begun? And so to me, that's the interesting philosophical question. When they get to have that level of capacity, are they answering their questions or ours? Will they be similar? Is this an example of aliens doing physics, right? Will machines do physics? I don't know. because I don't know what it means to have an AI at that level. And they will never really be divorced from humanity because they were birthed from the collective intelligence, right?

1:03:32The way that something that's learned to write a great novel has learned to write that great novel by reading a bunch of great novels. But that's also how we train great novelists that are humans, right? You read good writing, you're inspired, it teaches you. So I don't think it's impossible for AI to solve the current generation of physics problems. And I don't think it's impossible to potentially come up with new questions on its own. And if AI came up with a list of 10 questions, and then you were presented with those 10 questions alongside of 10 questions from some of your favorite collaborators, and you thought those questions were equally interesting, what would you say?

1:04:10I'd say bravo, you know? I welcome our future AI collaborators. Bring your curiosity wherever it comes from. I think it can just help inform. But I think we should invest in this stuff, you know, and we should invest in human driven curiosity as well as machine driven curiosity and alien driven curiosity and all of it. That's right. Invest in our future. Exactly. All right. Well, that was fascinating. Thank you, Daniel, for walking us through how you've been working with physics and AI over the last. What do you think? Like, I guess it's been decades you've been working with LMs. I've been doing machine learning and particle physics since the 90s.

1:04:48So yeah, it's been 30 years. Okay. Before it was cool for sure. All right. Well, maybe you'll give us a yearly update on how this kind of stuff is going. All right. Or maybe AI Daniel will replace me. I doubt it. I can't imagine that his jokes will be anywhere near as good or as bad, depending on how you see it, as yours. The day I stop making fun of chemistry is the day you know I've been replaced by a robot. Thanks so much to Daniel and thanks so much to the Extraordinaries. And if you have a question about anything at all, you can send it to us at questions at DanielAnnKelly.org. You'll get an answer from a real human being.

1:05:26Yes, you will.

1:05:33Thanks, everybody, for listening. Please go and do us a favor and rate the show on whatever podcast app you're using. It really helps people find us. Daniel and Kelly's Extraordinary Universe is edited by the amazing Matt Kesselman. He really is a wizard. You can also find us online on Blue Sky, Instagram, and xDNKUniverse. Come engage with us. You can email us at questions at danielandkelly.org. We really do want to hear from you. And you can find our website, www.danielandkelly.org, where you'll also find an invitation to join our Discord, where everybody comes and talks about the amazing universe.

1:06:12And we also have the most amazing moderators. This is an iHeart podcast. Thanks for joining us.

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