Speaker 1: There is so much AI hype out there from the
Speaker 1: doomers and the boomers. People say AI is the future.
Speaker 1: Other people say no, no, it's the beginning of the
Speaker 1: end of humanity. But no, it's gonna make us all rich,
Speaker 1: or it's gonna enslave us, or it's gonna cure cancer,
Speaker 1: or it's going to ruin the environment. But everyone agrees
Speaker 1: that we're at some kind of really fascinating inflection point
Speaker 1: where the future is very hard to predict because the
Speaker 1: present is changing so quickly. So what does that mean
Speaker 1: for us, fellow extraordinaries. Let's zoom in on a more
Speaker 1: specific claim of the AI hypesters. Will AI solve physics?
Speaker 1: What does it mean to solve physics? What is AI
Speaker 1: capable of doing? And is that a good match for
Speaker 1: cracking the mysteries of the universe? Will future humans live
Speaker 1: in universe that is totally understood to them. Will the
Speaker 1: mysteries of the universe fade into the past our descendants
Speaker 1: live in without the sense of wonder about how it
Speaker 1: all works? Or will humans always be curious no matter
Speaker 1: what our AI scientists reveal about the nature of reality.
Speaker 1: Buckle up, because while your future may bring surprises. Daniel's
Speaker 1: prediction for the future of AI and physics may also
Speaker 1: raise your eyebrows. Welcome to Daniel and Kelly's Extraordinary Universe.
Speaker 2: Hello, Kelly Widersmith. I study parasites and space, and I
Speaker 2: have used AI to try to help me tell jokes
Speaker 2: at a conference and they fell pretty flat.
Speaker 1: Was the audience filled with people or AI?
Speaker 2: Oh? No, they were filled with people. And I did
Speaker 2: warn them that they were parasite related jokes that had
Speaker 2: been generated by AI, and we all agreed that I
Speaker 2: could stay president for a few more years because I
Speaker 2: was still funnier than the AI.
Speaker 3: Hi.
Speaker 1: I'm Daniel. I study particles and aliens, and I've never
Speaker 1: relied on AI for any jokes. I think AI would
Speaker 1: be embarrassed to take credit for any of my dad jokes.
Speaker 2: I think the joke was supposed to be that AI's
Speaker 2: jokes were so bad that I am any joke. Yeah,
Speaker 2: And in this case, they were so bad that, like
Speaker 2: the tapeworm related jokes just didn't make any sense at all,
Speaker 2: And so we all had a that's what they think
Speaker 2: tapeworms do, very inside jokes sort of fig.
Speaker 1: But maybe that's a fascinating threshold for AI, not the
Speaker 1: Turing test, like can you sound like a human, but
Speaker 1: can you actually be funny?
Speaker 2: Yeah? Yeah, I mean that was that was sort of
Speaker 2: My point was like, is you know, could this replace
Speaker 2: a president's speech or opening remarks? We decided no.
Speaker 1: Not yet, not yet, work on that nerds.
Speaker 2: That's right, that's right. And so my question, I have
Speaker 2: two questions for you this morning, since we're talking about AI,
Speaker 2: And my first question is when in your life is
Speaker 2: AI most useful? And then what scares you about AI?
Speaker 2: If anything?
Speaker 1: AI is super useful to me every single day and
Speaker 1: was before the rise of chatbots. As you'll hear today,
Speaker 1: AI has taken over every aspect of particle physics, and
Speaker 1: I've been one of the people pushing that. So it's
Speaker 1: AI all day for me. The thing that scares me
Speaker 1: about AI is seeing my daughter ask AI things she
Speaker 1: already knows or things she could just google, you know,
Speaker 1: like you don't need to ask chat GBT what the
Speaker 1: temperature is. You can just look it up, or you
Speaker 1: could type it into Google. If you're too lazy for that,
Speaker 1: you don't need to spin up at GPU for that.
Speaker 1: But people have just become like used to punting everything
Speaker 1: to AI. All thought processes use can just be shunted
Speaker 1: out to AI.
Speaker 2: And what scares you about that is that it's more
Speaker 2: energy expensive, or just that you feel like we're relying
Speaker 2: too much on AI for everything because like it's probably
Speaker 2: going to tell you the right temperature in.
Speaker 1: My yeah, who knows. Yeah, it's all of those things.
Speaker 1: It's unnecessary and it's kind of lazy mentally, right, And
Speaker 1: so the thing that scares me is that it leads
Speaker 1: to people not thinking too much for themselves and just
Speaker 1: relying on AI for everything exactly. It seems like a slippery.
Speaker 2: Slope, got it? You know? I think when books came around,
Speaker 2: there were people arguing that books are going to make
Speaker 2: people lazy because they're not going to have to remember
Speaker 2: stuff anymore. And so I guess what I'm saying is,
Speaker 2: do you think that this is going to be our generations?
Speaker 2: Like the kids are getting lazy and they're going to
Speaker 2: look back and be like, is it actually different? I guess,
Speaker 2: or do you think are we being stagy?
Speaker 1: I know, I think this is a transformational moment, and
Speaker 1: I think it's going to change what it means to
Speaker 1: be human the way books do, right, because now you
Speaker 1: can no longer just tell your friends stuff. You can
Speaker 1: so communicate your thoughts to people who live a thousand
Speaker 1: years later. That's amazing, or two millions of people all
Speaker 1: at once. So that changes what it means to be human.
Speaker 1: It makes it easier in some ways. That allows for laziness,
Speaker 1: but also creates transformational new abilities. And I think we
Speaker 1: got to lean into that.
Speaker 2: Yeah, yeah, yeah, all right, Well let's go ahead and
Speaker 2: jump into the meat of today's discussion. And today we're
Speaker 2: talking about will AI solve physics?
Speaker 3: Oh?
Speaker 1: Even just the phrasing there makes me shutter.
Speaker 2: I mean, that's a pretty big claim. Like, you know,
Speaker 2: I think the first thing we need to talk about
Speaker 2: after we hear what the extraordinaries have to say, is
Speaker 2: what the heck do we even mean by solve physics?
Speaker 2: Because that's, yeah, it's pretty big statements.
Speaker 1: And I choose that because I was on a panel
Speaker 1: at a recent huge AI conference next to a guy
Speaker 1: from Anthropic who was all in on the fact that
Speaker 1: AI was going to quote unquote solve physics. Wow, And
Speaker 1: so this is not just some phrasing I made up, right.
Speaker 2: Okay, Yeah, that is a big claim. And so let's
Speaker 2: see if the extraordinary always think that AI is going
Speaker 2: to solve physics.
Speaker 1: Here's what folks had to say. If you would like
Speaker 1: to join this group of exemplary guessers, please write to
Speaker 1: us two questions at Danielankelly dot org.
Speaker 4: To me, it's more of a question of should not will,
Speaker 4: and as I don't trust AI to handle any human
Speaker 4: safety concerns, I would say absolutely not.
Speaker 1: If AI is chopped up to be intelligent or super intelligent,
Speaker 1: then I believe we could solve any physics riddles or questions.
Speaker 4: Don't think they're able to produce new thoughts, only predictive
Speaker 4: of what already exists.
Speaker 2: I know, I don't think they will solve physics.
Speaker 4: That's already been done.
Speaker 5: That's old news.
Speaker 4: And the answer was forty two. I think that as
Speaker 4: long as scientists treat AI as a tool rather than
Speaker 4: as an oracle, it could be very helpful.
Speaker 2: Yeah, I think the major breakthroughs will come from experimentation
Speaker 2: made by real humans.
Speaker 3: Is, in my opinion, an extremely broad question that needs
Speaker 3: more specificity.
Speaker 5: I think the idea that an a lens might solve
Speaker 5: physics is akin to the idea that if you had
Speaker 5: an infinite number of monkeys typing for an infinite amount
Speaker 5: of time, they would eventually create the works of Shakespeare.
Speaker 4: I think we need to define what solve physics means first.
Speaker 4: AI might solve physics, but I think it's more likely.
Speaker 5: That it a lie and claim that it has.
Speaker 3: Yes, someday, it's going to be able to see relationships
Speaker 3: where humans cannot, and it's going to come up with
Speaker 3: some answers that we're probably right there in front of
Speaker 3: us for a long time, but we just couldn't see it.
Speaker 1: I cannot believe that you're asking this question.
Speaker 4: Definitely help to solve physics problems, but who will take
Speaker 4: credit for that? I think this requires an extreme scale
Speaker 4: of creativity, so it will definitely assist, but not solve it.
Speaker 1: That a little.
Speaker 2: I think it's plausible that AI might be able to
Speaker 2: solve physics.
Speaker 4: It's a good plagiarization machine, but I've never heard of
Speaker 4: current generation AI coming up with anything original.
Speaker 2: All Right, quite a range of thoughts here, although I
Speaker 2: feel like I'm seeing quite a bit of like skepticism
Speaker 2: or a bit of concern with AI.
Speaker 1: Yeah, you hear the whole spectrum here of people just
Speaker 1: calling it a lazy plagiarism machine. Do people think absolutely
Speaker 1: it's going to solve everything or it's at least plausible
Speaker 1: that it could to people saying, like, what are even
Speaker 1: talking about? That question means nothing? Yeah, so great job,
Speaker 1: extraordinary is giving us the context to dive into this
Speaker 1: really complicated question.
Speaker 2: So when you were sitting on that panel next to
Speaker 2: the guy from Anthropic, was it Sam Moltman. Is he
Speaker 2: an Anthropic or is he somewhere else he's open AI?
Speaker 1: Yeah, okay, I know it wasn't the head of Anthropic either.
Speaker 2: Okay, all right, what did they mean by soul physics?
Speaker 2: Did they mean like there will be no physics questions
Speaker 2: left to answer when AI is done? What did what
Speaker 2: did they mean?
Speaker 1: Yeah? I don't even know. That was my pushback. I
Speaker 1: said that this doesn't even mean anything. It's nonsense to
Speaker 1: imagine that there's a moment where we have no more
Speaker 1: questions about the nature of the universe, Like there's no
Speaker 1: AI answer that's going to make me say, Okay, we're done.
Speaker 1: I'm retiring, right, Yeah, my curiosity is gone. It just
Speaker 1: doesn't just can't even imagine that scenario. And he didn't
Speaker 1: have a great answer. He just gestured towards the rapid
Speaker 1: increase in AI powers to suggest that it was going
Speaker 1: to dot dot dot to some sort of Einsteinian level
Speaker 1: of intelligence. But we can dig into that a little
Speaker 1: bit more. When we get to that. I thought we
Speaker 1: could start by talking about the role of AI in
Speaker 1: physics today because also a lot of listeners write in
Speaker 1: and ask me, like Hydan, how do you use AI
Speaker 1: in your research? So let's start a little bit more
Speaker 1: grounded and before chatbots took over everything, how we used
Speaker 1: AI and more specifically machine learning in physics.
Speaker 2: Okay, let's do it. But first, can we define what
Speaker 2: is machine learning?
Speaker 1: Yeah, have all of these names floating graphs, not clear
Speaker 1: exactly what means. What The biggest topic is AI? Right?
Speaker 1: That includes all of these things. Within AI, you have
Speaker 1: machine learning. Machine learning is a kind of AI, but
Speaker 1: it's mostly applied statistics. It's like patterned recognition. Think about
Speaker 1: algorithms optimized for one specific task, right, Like, for example,
Speaker 1: you want to tell a cat from a dog in
Speaker 1: an internet video, or you want to know if a
Speaker 1: particle is going to go left or right based on
Speaker 1: some data that you have, right, solving a very specific
Speaker 1: statistical task, and machine learning is helpful when that task
Speaker 1: is too hard to do with pencil and paper, Like
Speaker 1: you couldn't write down an equation that tells you is
Speaker 1: this a cat or a dog an internet video, but
Speaker 1: you could learn to tell the difference if you see
Speaker 1: a bunch of examples. And so that's what machine learning is,
Speaker 1: is like finding patterns iteratively through being exposed to a
Speaker 1: bunch of examples, rather than like knowing how to write
Speaker 1: an equation for the pattern on paper.
Speaker 2: Got it okay? And I feel like I remember lots
Speaker 2: of funny stories about how machine learning could be tricked,
Speaker 2: Like if the cat happened to have a tennis ball
Speaker 2: in its mouth or something, then it would be called
Speaker 2: a dog because dogs always had tennis balls in their
Speaker 2: mouths and stuff like that. Like the machine learning picks
Speaker 2: up on things that maybe you wouldn't have guessed that
Speaker 2: it would have picked up on.
Speaker 1: Yes, and you always want to make sure that what
Speaker 1: it's learning is meaningful to you. Like if you're training
Speaker 1: machine learning to tell wolves from dogs, but all the
Speaker 1: wolves have snow in the background in the picture, the
Speaker 1: machine learning is just going to be like, oh yeah,
Speaker 1: if it has snow in the background, it's a wolf.
Speaker 1: And if it doesn't, it's a dog. But that's not
Speaker 1: what you're interested in, right, ok. And so you have
Speaker 1: to make sure you know what it's doing. And so
Speaker 1: this is a very narrow kind of artificial intelligence. It's
Speaker 1: not general intelligence at all. You can't ask this kind
Speaker 1: of thing, what's the weather today? Or how should I
Speaker 1: write this email back to my brother in law or
Speaker 1: anything like that. Right, it can do what you tell
Speaker 1: it to do. And we use this all the time
Speaker 1: in particle physics because all of our discoveries these days
Speaker 1: are statisic stickle in nature, you know, in the same
Speaker 1: way you can look at a picture and say, like,
Speaker 1: seventy five percent chance this is a dog, twenty five
Speaker 1: percent chance it's a cat. That's what all of particle
Speaker 1: physics is. And it makes me a little sad because
Speaker 1: it didn't used to be like in the glory days
Speaker 1: of particle physics, back when they were discovering the obvious particles,
Speaker 1: you know, and by obvious particles, I mean things that
Speaker 1: were high rate. There was like a lot of examples
Speaker 1: of them and a low background, meaning it was very
Speaker 1: hard to imagine anything else making that kind of signature,
Speaker 1: like the positron discovery. This guy won a Nobel Prize
Speaker 1: for discovering the positron off of one example. Wow, he's
Speaker 1: like a picture of a track of a positron and
Speaker 1: it can't be anything else, and boom, Nobel prize. That's
Speaker 1: an amazing piece of data right there.
Speaker 2: It's a shame we weren't born a few decades earlier.
Speaker 2: It would have been easier then.
Speaker 1: And because most of those are all used up these days,
Speaker 1: particle physics is focused on more rare particles, particles that
Speaker 1: come out of collisions once a trillion times, once a
Speaker 1: quadrillion times, Otherwise they would have been discovered by previous generations,
Speaker 1: and the backgrounds are higher. It's easier to mimic the
Speaker 1: signatures of the particles we're looking for because we never
Speaker 1: see the particle directly. Like when we found the Higgs
Speaker 1: boson fifteen years ago, we didn't say, like, here's one
Speaker 1: Higgs boson, give me a Nobel prize. Because the Higgs
Speaker 1: boson doesn't last long enough. It blows up into other
Speaker 1: particles and we look at the patterns among those particles
Speaker 1: which are not Higgs bosons, and we say, this is
Speaker 1: likely to be a Higgs boson statistically, this is more
Speaker 1: likely to be a Higgs than not. But there are
Speaker 1: other ways to make that same pattern, and so you
Speaker 1: can never know for sure. You can never point to
Speaker 1: one event and say this is my Higgs boson. All
Speaker 1: you can do is say, look is a pile of
Speaker 1: these events, all very very similar and so very likely
Speaker 1: the Higgs boson exists. And because all of our discoveries
Speaker 1: are statistical, machine learning is very very powerful. This is
Speaker 1: exactly what it's good at. If you want to be
Speaker 1: able to tell collisions that lead to Higgs bosons from
Speaker 1: collisions that don't, and they're very similar in your detectors,
Speaker 1: you can give machine learning a bunch of examples and say, look,
Speaker 1: here's a bunch of Higgs bosons, here's a bunch of
Speaker 1: not Higgs bosons. Now I'm going to give you data
Speaker 1: from the real collider. Tell me what's the chance that
Speaker 1: each one is a Higgs boson or not. If you
Speaker 1: can't just like write down an equation to tell you
Speaker 1: the probability of being a Higgs boson, and nobody knows
Speaker 1: how to do that because it's very complicated, then this
Speaker 1: can learn exactly how to do that calculation for you.
Speaker 2: And is machine learning in the end able to give
Speaker 2: you like a statistical output like I'm ninety eight percent sure,
Speaker 2: or does it just say yeah, I think I found it.
Speaker 1: No, it can do exactly the kind of statistics that
Speaker 1: you're talking about. It calculates likelihoods and does likelihood ratios
Speaker 1: for statistic nerves out there. It's learning to approximate the
Speaker 1: likelihood ratio, which if you knew how to write it
Speaker 1: down as a formula, you would write down and it
Speaker 1: can approximate that very very well. So it's really incredible.
Speaker 2: Yeah, that is amazing, okay, And so it's allowing you
Speaker 2: to do things that we wouldn't be able to do
Speaker 2: on our own because the math would just be like,
Speaker 2: way too complicated.
Speaker 1: That's right, And the math is too complicated. And so
Speaker 1: for years we did the math approximately we said, well,
Speaker 1: looks it's complicated, but I can write down a simpler
Speaker 1: version of it, which kind of works. And we were
Speaker 1: giving away information. We were not using all of the
Speaker 1: power of our data. But machine learning just slurps in
Speaker 1: all that data and says, here's the best way to
Speaker 1: discriminate between these two things. It lets us tel cats
Speaker 1: from dogs really really well. Which helps us find rare
Speaker 1: cats in our data much more effectively. And since our
Speaker 1: data is so expensive and so powerful, this is a
Speaker 1: huge boon to particle physics.
Speaker 2: It would be a shame to miss any particle cats exactly.
Speaker 1: And so the lesson here is that it's very powerful,
Speaker 1: but it's applied statistics to very targeted tasks, and it's
Speaker 1: not just limited to labeling collisions and saying this one's Higgs,
Speaker 1: this one's not Higgs, or this one's dark matter, it's
Speaker 1: not dark matter. We also use generative AI. Generative AI
Speaker 1: is the kind that can create new examples you type
Speaker 1: into chat GPT, like give me a picture of a
Speaker 1: kitten riding a horse on the moon or whatever, and
Speaker 1: it makes one for you, right. And we use the
Speaker 1: same kind of technology to very rapidly generate simulated examples.
Speaker 1: Something machine learning needs are lots and lots of examples
Speaker 1: to learn from, and so you have to generate examples.
Speaker 1: Here's what Higgs boson would look like, Here's what a
Speaker 1: not Higgs boson would look like. You need millions or
Speaker 1: billions of those examples for it to really learn what
Speaker 1: you need to do. But generating those examples can be
Speaker 1: very very slow. Like if you generate a collision and
Speaker 1: you have a Higgs boson that leads to two quarks,
Speaker 1: those quarks turn into a shower of particles. To simulate
Speaker 1: what that's going to look like in our detector, you
Speaker 1: have to simulate what all those particles do when they
Speaker 1: hit your detector. What happens when a muon hits a
Speaker 1: block of copper. It creates a bunch of photons, It
Speaker 1: ionizes atoms. Lots of complicated things happen.
Speaker 2: You californiensis Sorry, you just said a lot of physics stuff.
Speaker 2: So I get to say a species name.
Speaker 1: Oh you're right, nice, Okay, sorry, keep going Latin words
Speaker 1: for the wind. So what happens when particles smash into matter?
Speaker 1: You get all sorts of complicated stuff which is hard
Speaker 1: to track. And then you have to track those particles,
Speaker 1: which creates more particles, which creates more particles whose Latin
Speaker 1: names I will not use, and it becomes very very
Speaker 1: computationally expensive. CERN has one of the biggest data centers
Speaker 1: in the world, and a huge fraction of those computers
Speaker 1: spend all of their time just simulating particle collisions and interactions.
Speaker 1: Because we need all of that, but JENNAI can do
Speaker 1: this much faster. You give a bunch of examples to
Speaker 1: GENAI and then say, okay, now make me a new one.
Speaker 1: It's perfectly akin to saying give me a cat on
Speaker 1: a horse on the moon. You can say, give me
Speaker 1: a Higgs Boson with this mass at this angle, and
Speaker 1: you can interpotate between all of its examples and flash
Speaker 1: give you an example without burning all of those CPUs.
Speaker 1: It's much much more efficient. So we're not just using
Speaker 1: AI to separate particles, we're also using it to train
Speaker 1: it how to separate particles all over the place. In
Speaker 1: experimental particle physics, AI has touched every single part of
Speaker 1: our work because almost everything we do is statistical and computational.
Speaker 2: Okay, so it sounds like AI is very helpful if
Speaker 2: you are an experimentalist and you're like tracking particles. But
Speaker 2: what if you are like a pen to paper theoretical physicist.
Speaker 2: Is AI still helpful?
Speaker 1: Yeah? Great question, because when we talk about solving physics,
Speaker 1: we usually mean like figuring out the theory of the universe.
Speaker 1: And there's a lot of talk online in the podos
Speaker 1: sphere about like the crisis in physics, which is mostly
Speaker 1: bs frankly, But it's about theory, right. People want to
Speaker 1: see progress in understanding the universe, and theory has not
Speaker 1: embraced machine learning and AI and nearly as much as
Speaker 1: experimental physics yet. And I think that's because mostly those
Speaker 1: folks are pencil and paper people. They're not as computational.
Speaker 1: The computational people end up in the experimental side mostly,
Speaker 1: and so the theory people don't see their work as
Speaker 1: having like data, but I see their work as having data.
Speaker 1: You know, they are exploring huge parameter spaces of complicated
Speaker 1: particle theories. That's data. That's what machine learning is good at.
Speaker 1: So I've actually done a few projects in particle theory
Speaker 1: using tools from experimental particle physics AI to solve problems
Speaker 1: in theoretical physics, which are super duper fun and I
Speaker 1: think it's a whole flourishing area there. So it's just begun.
Speaker 1: I think it's a little bit behind the experimental side,
Speaker 1: but in the end, all of these things are places
Speaker 1: where humans are asking questions. Right. The AI is not
Speaker 1: doing physics here, it's not solving physics. It's a tool.
Speaker 1: It's an assistant at best. It's helping humans answer questions
Speaker 1: about the universe.
Speaker 2: All right, So we've talked about machine learning MLS. Next
Speaker 2: we're going to talk about l l MS. I like
Speaker 2: that we're focusing on a small subset of letters and
Speaker 2: when we get back we'll find out what that extra
Speaker 2: L means. All right, we are back, and now instead
Speaker 2: of talking about machine learning, we're going to talk about
Speaker 2: large language models and how these help physicists. So, Daniel,
Speaker 2: how are you all using llms?
Speaker 1: So mostly the conversation about AI solving physics is inspired
Speaker 1: by the rise of these chatbots, Claude, chat, GBT, Gemini,
Speaker 1: all these things and their supposed feeling of broader intelligence. Right,
Speaker 1: And everything we talked about in the last segment was
Speaker 1: machine learning. It was statistical learning applied to very specific tasks,
Speaker 1: not the kind of thing that's going to tell you,
Speaker 1: like what the recipe to make a certain kind of
Speaker 1: fancy sandwich or whatever. The things that people use llms
Speaker 1: for LMS are a very different breed of artificial intelligence.
Speaker 1: It's built on natural language. Instead of just like numerical inputs,
Speaker 1: it outputs with a human like language. Right. It feels
Speaker 1: much more natural than mathematical. And these things came on
Speaker 1: the scene a few years ago, and really that's what
Speaker 1: people are interested in. Are llms going to change the
Speaker 1: way we do science? Are they going to crack problems
Speaker 1: that human physicists have not been able to crack? But
Speaker 1: I want to underline that, like these things are still mathematical,
Speaker 1: Like an LLM is not something different from any other
Speaker 1: kind of AI. Underneath the hood, it's the same pieces
Speaker 1: of technology just arranged in a different way to solve
Speaker 1: a different problem, and then just scaled up tremendously, like hugely,
Speaker 1: just like enormous numbers of these nodes and neurons, And
Speaker 1: I think it's worth maybe taking them in to explain
Speaker 1: what is going on underneath the hood of an LM.
Speaker 1: A lot of people understand that lms are statistically predicting text.
Speaker 1: Like if they are writing an answer to you, they
Speaker 1: are trying to generate for you what you want, and
Speaker 1: they've read a bunch of text, and so if they're
Speaker 1: trying to predict like the next word and the sequence
Speaker 1: of the answer for you, they look at all the
Speaker 1: texts they've read and they say, well, statistically, what is
Speaker 1: the word that usually follows the current word? You know,
Speaker 1: so I'm writing a sentence the sentences Kelly is a
Speaker 1: blank and then you know, I'm trying to generate the
Speaker 1: next word. It's some adjective. And so I'm looking through
Speaker 1: all of my training right all the text on the
Speaker 1: internet that describes Kelly, and I'm trying to statistically predict
Speaker 1: you know, what is the appropriate word to fill in
Speaker 1: the blank. And you know, I'm not going to give
Speaker 1: you that word other than a wonderful co host and
Speaker 1: fantastic scientist.
Speaker 2: Thanks. Thanks, you only had but you said it was
Speaker 2: going to be one word, so I was waiting for it,
Speaker 2: but I'm going to I'll let you off the hot seat,
Speaker 2: although you've really backed yourself into a corner, all right.
Speaker 1: And that's a fair sort of surface level understanding of
Speaker 1: lms and what they're doing, but really misses the transformational
Speaker 1: technology that's underneath them, because lms are doing more than
Speaker 1: just like predicting exactly the next word. They're using something
Speaker 1: called attention, which is really crucial to understand why these
Speaker 1: things are so much better than just like statistical parrots.
Speaker 1: And attention helps you understand which words in a sentence
Speaker 1: are contributing to the context and contributing meaning to the sentence.
Speaker 1: So say, for example, you have a sentence like the
Speaker 1: river bank was steep. Now, bank is a complicated English
Speaker 1: word because it has lots of meanings. Right, Is it
Speaker 1: where you put your money? Is it how you shoot
Speaker 1: a basketball off the backboard? Is it what keeps a
Speaker 1: river in place? Right? In this sentence, the words river
Speaker 1: and steep are important clues that tell you what bank means. Right,
Speaker 1: So when you hear that sentence, you're like, Daniel's not
Speaker 1: talking about depositing money or shooting three pointers, right, He's
Speaker 1: talking about a river. And what the attention mechanism does
Speaker 1: inside these lms is let the chatbot at all of
Speaker 1: the other words, not just the one preceding word, and
Speaker 1: so knows what to pay attention to. It helps it
Speaker 1: understand the context. It's like if you go to a
Speaker 1: science conference and you need to understand some crucial thing
Speaker 1: for your research, you don't just talk to the person
Speaker 1: next to you. You seek out the most relevant person
Speaker 1: to pay attention to, right, the person who's going to
Speaker 1: help you understand the question you're asking. And so that's
Speaker 1: what attention is. And then they couple attention with this
Speaker 1: technology called transformers, which are not a little plastic toys
Speaker 1: that you used to play with, or cartoons, or even
Speaker 1: the things on wires out there that help transform the voltage.
Speaker 1: They're just like stacked layers of these attention modules. So
Speaker 1: the first layer helps you understand the words, and the
Speaker 1: second layer helps you understand the rest of the paragraph.
Speaker 1: And by the time you have multiple stacked layers of attention,
Speaker 1: you can understand. Okay, here's the context of the question
Speaker 1: that's being asked, and therefore here's the answer that I
Speaker 1: should give. And so it's this combination of attention and
Speaker 1: transformers that make LMS so powerful.
Speaker 2: Okay, I didn't know any of that.
Speaker 1: All right, So then what are LMS useful for? Right,
Speaker 1: I'm not really interested in using them to write essays
Speaker 1: about thirteenth century Europe or whatever. Today we're talking about
Speaker 1: using them in physics. So I use lms all the time.
Speaker 1: And here's an example. I find them very useful for
Speaker 1: I need to learn some new software package, right because
Speaker 1: I've started a new research project, and there's a tool
Speaker 1: that simulates the thing I need, for example, and I
Speaker 1: download the tool. It's written terribly. The documentation is garbage.
Speaker 1: There's like hundreds of pages of tables of technical stuff
Speaker 1: that's impenetrable. I try to run the code. It doesn't
Speaker 1: work the first time, Right, I look at the code.
Speaker 1: It's written by physicists without any adult supervision. Right. And
Speaker 1: so in past days, this would be a huge project.
Speaker 1: Make this thing work, validate that it's doing what I
Speaker 1: think it should be doing, Understand the documentation, how it's wrong,
Speaker 1: how the thing actually works, you know. And in practice,
Speaker 1: what I would do is like find somebody else who
Speaker 1: made it work and start from their example. Somebody else's
Speaker 1: invested weeks and weeks to making this thing work. I'm
Speaker 1: going to download their scripts. I'm going to start from
Speaker 1: their code. I'm going to ask them questions when it breaks, right,
Speaker 1: I'm going to lean on some kind of expertise. Otherwise
Speaker 1: I might have to sink weeks into getting this thing
Speaker 1: to work. I no longer have to do that. Now
Speaker 1: I can ask an LLM, hey, read this document and
Speaker 1: write me an example configuration script to make this thing work. Okay,
Speaker 1: it didn't work, debug the issue, and within a few cycles,
Speaker 1: I can usually get a new software package up and
Speaker 1: running and producing useful, validated results in hours, if not minutes.
Speaker 1: Where it used to be either weeks or totally impenetrable.
Speaker 2: It is pretty amazing. Like you know, whenever I use
Speaker 2: a new package in R or something and I know
Speaker 2: inevitably put a comma in the wrong place or something,
Speaker 2: I used to look for that comma for hours and
Speaker 2: now I'm like, jadgbt, where did I put the comma?
Speaker 2: That's right, it doesn't belong and they it finds it immediately,
Speaker 2: saves me hours.
Speaker 1: And it's not magic. Right, Remember what are these lms doing.
Speaker 1: They understand context, they know how to use attention to
Speaker 1: focus the question you're asking to produce the right answer,
Speaker 1: and they're very good at digesting huge volumes of input. Right,
Speaker 1: the things that humans are bad at. For the same
Speaker 1: reason that we're using machine learning to improve on our
Speaker 1: data analysis and the particle physics colliders, because they're good
Speaker 1: at the things we're bad at, like understanding all the
Speaker 1: nuances of some high dimensional statistics problem. Here we're using
Speaker 1: them to do something that we are bad at, and
Speaker 1: that is the key, right, lean on their expertise, find
Speaker 1: the thing that they can do that we can't, and
Speaker 1: then they can contribute and they can go beyond. Just
Speaker 1: like reading documentation for software packages, they can also read
Speaker 1: physics papers. There are so many papers put out every
Speaker 1: single day. If you look at the archive, which is
Speaker 1: where particle physicists put out their papers every day, there's
Speaker 1: a dozen papers every day, all of which are pretty
Speaker 1: good and worth reading. I can't keep up with all
Speaker 1: of those. In addition, I should be reading all the
Speaker 1: machine learning papers. Should be reading statistics papers. I should
Speaker 1: be reading math papers. Some math nerd out there could
Speaker 1: have just invented some incredible new thing that they're into
Speaker 1: just because they're a math nerd. But it might like
Speaker 1: solve exactly the problem we have in particle physics, and
Speaker 1: they don't know, and we don't know, and who knows
Speaker 1: how to put these things together. The reason cross discipline
Speaker 1: or research is often so fruitful and leads to breakthroughs
Speaker 1: and advances is exactly because of this, Because people are
Speaker 1: unaware of solutions in adjacent fields, and when you just
Speaker 1: smush them together, you get peanut butter and chocolate. Right.
Speaker 2: It's amazing, But you've got to be careful because you
Speaker 2: might get information in your head that doesn't actually exist
Speaker 2: from doing that. Like I asked an LM to summarize
Speaker 2: my research, and it told me that I had done
Speaker 2: stuff that I had not done and that none of
Speaker 2: my collaborators have done in the system. And if I
Speaker 2: had just asked for the summary, I would have been like, Oh,
Speaker 2: we know a lot of stuff about the system that
Speaker 2: we don't really know, and so I guess anything that
Speaker 2: you decide could be vaguely interesting. You need to actually
Speaker 2: go back, oh yes, and confirm aps. It's frustrating because
Speaker 2: you might be like, oh, I didn't know that. I'm
Speaker 2: not going to use it, but I didn't know that,
Speaker 2: and you might not necessarily go back and check, but
Speaker 2: you just will think like, oh, I'll just keep that
Speaker 2: in the back of my mind. But now you've got
Speaker 2: a little lie in the back of your mind.
Speaker 4: Well.
Speaker 1: I think of it like a superpowered Google search. I
Speaker 1: cannot go into Google and say, here's my research. Has
Speaker 1: there been any recent papers that might be relevant? But
Speaker 1: I can't type that into my LM. And you can
Speaker 1: find a bunch of papers, and then I can read
Speaker 1: those instead of reading all of the papers, and I
Speaker 1: can decide, oh, this is not actually irrelevant, this is
Speaker 1: a crucial detail here you missed, or hey, that paper
Speaker 1: doesn't exist, so it's not useful. But it's doing the
Speaker 1: part that is hard for me, which is just like
Speaker 1: reading all of those papers and it might miss something,
Speaker 1: but it's very very powerful to tackling the problem that
Speaker 1: I can't solve myself, which is reading all of those papers.
Speaker 1: And of course there's highly varying quality between the models.
Speaker 1: Some of them are good at this, some of them
Speaker 1: are bad at this. And this is the kind of
Speaker 1: thing that's improving very rapidly with time. It used to
Speaker 1: be you type this into an LLLM and you got
Speaker 1: lots of nonsense. These days, I find it's producing higher
Speaker 1: and higher quality output insights into papers that I didn't
Speaker 1: know about, really useful stuff. And you see also in mathematics,
Speaker 1: an area i'm not an expert in that it's finding
Speaker 1: solutions to outstanding problems that already exist in the literature
Speaker 1: and nobody knew. They're like, oh, this nineteen fifty seven
Speaker 1: paper by some Russian, nobody's translated it, so we didn't
Speaker 1: know this open problem has actually been solved.
Speaker 2: When I think it's solving airdish problems that hadn't been
Speaker 2: solved yet.
Speaker 1: Exactly, sometimes by finding techniques in other papers that are
Speaker 1: relevant already. I'm inventing those techniques itself, but making those connections,
Speaker 1: because that's what it's good at, scanning through vast quantities
Speaker 1: of literature for things that are relevant. Okay, so so
Speaker 1: far it's been working as an assistant. Right, it reads
Speaker 1: a documentation, it gets the software to work, or it
Speaker 1: goes to find papers. It's like a research tool. What
Speaker 1: about like more as the role of a physicist can.
Speaker 1: For example, you give it a bunch of ideas and say, hey,
Speaker 1: go write a paper about this, or write me up
Speaker 1: grant proposal on this topic. Right, So I've tried this
Speaker 1: kind of thing. I have access to the top end
Speaker 1: models from anthropic for example, and it can do it right.
Speaker 1: It can start from rough ideas, it can flesh it
Speaker 1: out into full text. It's grammatically perfect, it makes no
Speaker 1: spelling mistakes. But in my experience, it's writing is not
Speaker 1: very good. I'm a big fan of good writing. Like
Speaker 1: when I read a paper, there's sort of two dimensions there.
Speaker 1: There's the science good, but also like is the writing good?
Speaker 1: And if the writing is good, it leads you on
Speaker 1: a story. It gets you interested, It tells you why
Speaker 1: this research is compelling. It presents the things logically and crisply.
Speaker 1: The text is concise and precise, you know, like a
Speaker 1: well written scientific paper is a piece of beauty. And
Speaker 1: there's also like sloppily written good science. Sometimes the science
Speaker 1: is great and the paper is just like sloppy, and
Speaker 1: you're like, what do you mean by this exactly? You know,
Speaker 1: and like, well you're telling me this, but now we're
Speaker 1: talking about this, why is that's just not well put together?
Speaker 1: KLAW doesn't produce sloppy stuff, but it produces boring stuff.
Speaker 1: I find the text that it produces is missing, like
Speaker 1: you know that juj that you get from like a
Speaker 1: really good piece of writing, And this is the same
Speaker 1: I think kind of skill that it takes to write
Speaker 1: like a good novel. Like I read a lot. When
Speaker 1: I started a novel, I can tell instantly like Okay,
Speaker 1: this dude knows how to do it, or this lady
Speaker 1: can write, Oh my gosh, I don't even know what
Speaker 1: the plot is or the characters are, but the sentence
Speaker 1: structure something about it is just like powerful and effective
Speaker 1: and that's a joy to read, right, And LMS cannot
Speaker 1: produce that, not yet at least.
Speaker 2: So I'm going to make a book recommendation. But I'm
Speaker 2: going to say that even though this title sounds like
Speaker 2: a kid's book, this is not a book for kids.
Speaker 2: The book is called Benny and the Blue Whale, and
Speaker 2: it is a fiction writer who collaborated with AI to
Speaker 2: write a book just to sort of see what the
Speaker 2: experience was like. And the experience matches what you were saying.
Speaker 2: It's not quite as creative, I think, but anyway, it
Speaker 2: was a fun a fun reading.
Speaker 1: And there's something about, you know, the voice and the
Speaker 1: personality of the author that comes out in their writing.
Speaker 1: And that's also important for science. If you write a
Speaker 1: grand proposal and it's fun and it's exciting, and it's
Speaker 1: well written and it's crisp, the reviewers are going to
Speaker 1: have fun reading it. They're going to get excited. They're
Speaker 1: going to like you, they're going to want to fund
Speaker 1: your work. It's underappreciated, I think, by the public. How
Speaker 1: much of science is writing, not just for folks like
Speaker 1: you and me who'd like write specifically for the public,
Speaker 1: but like emails, papers, grand proposals, reviews, all this stuff
Speaker 1: is writing, and so much more powerful when it's done well,
Speaker 1: when you have a flair and energy, when you can
Speaker 1: convey excitement and enthusiasts when you have your own personal voice.
Speaker 2: Yeah. And I think we should note though, that a
Speaker 2: lot of journals and a lot of grant agencies currently
Speaker 2: have rules for whether or not you're allowed to have
Speaker 2: AI help you with your writing. I just think it's
Speaker 2: worth noting that.
Speaker 1: Yeah. Absolutely, And so it can produce sort of like
Speaker 1: bland generic writing that seems like suspiciously polished and kind
Speaker 1: of flat, you know, doesn't really seem to get the
Speaker 1: point and convey it crisply. But you know, this again,
Speaker 1: is a steep gradient. They used to be worse, they're
Speaker 1: getting better. It's rapidly improving. I don't know that next
Speaker 1: year these things won't be much better. And you know
Speaker 1: a lot of the way they're improving is just by scaling.
Speaker 1: They read more. They make these models bigger, more layers
Speaker 1: of attention, more nodes. You sometimes hear these parameters quoted
Speaker 1: like seven billion parameters or whatever. That's the number of
Speaker 1: like connections between neurons, each of which have a number
Speaker 1: associated with them. Tells you essentially the size of the network.
Speaker 1: The bigger the network, the more powerful it is, also
Speaker 1: the bigger the training set it needs to figure out
Speaker 1: what is the best value for all those nodes? All right?
Speaker 1: So lms have progressed from like fancy Google searches to
Speaker 1: helping you with code, to reading and writing. But what
Speaker 1: about the juice of it, right, what about like doing
Speaker 1: calculations actually being a physicist. So there's some fascinating recent
Speaker 1: examples of top level physicists using AI to contribute to
Speaker 1: their papers.
Speaker 2: And we're going to get to that after the break,
Speaker 2: and we're back and we're talking about how AI is
Speaker 2: helping scientists do some math. All right, take it away, Daniel.
Speaker 1: So there are top level physicists out there at the
Speaker 1: top of American academic physics who are using and exploring
Speaker 1: AI to work with them to help them solve problems.
Speaker 1: At Witten, for example, you know famous string theorists, maybe
Speaker 1: one of the smartest dudes alive, put out a paper
Speaker 1: recently where chat GPT did an important part of the work.
Speaker 1: They had worked out a bunch of examples and they
Speaker 1: had an idea that it could be more generally proven,
Speaker 1: and chat GPT came in and like found a way
Speaker 1: to make those connections. Say okay, here's the proof that
Speaker 1: this really does work. So you know it didn't come
Speaker 1: up with the research question, and it didn't even guess
Speaker 1: at the answer. It came in and filled in a
Speaker 1: lot of the details. But this is, you know, more
Speaker 1: than just like correcting your grammar or fixing your code.
Speaker 1: This is like manipulating mathematics and using logic to build
Speaker 1: a proof from A to B. So it really is
Speaker 1: pretty impressive.
Speaker 2: Yeah, that's amazing. I'm sure that saved doctor Whitten a
Speaker 2: lot of time. Yeah, and allowed him to work on
Speaker 2: other projects exactly.
Speaker 1: And you know, it's doing symbolic calculations, not just like
Speaker 1: you know, seven point two plus twelve point four or whatever.
Speaker 1: This is manipulating symbols, finding equations that capture these things.
Speaker 1: So that's very powerful. Then a friend of mine, Matt
Speaker 1: Schwartz at Harvard, recently put out of paper where he
Speaker 1: used claude Opus. Essentially, as a graduate student, he had
Speaker 1: an idea for a project, he pitched it to Claude,
Speaker 1: he got claud to work on it with him, and
Speaker 1: Claude did like all of the coding and making all
Speaker 1: the plots and and simulations and all the actual work involved,
Speaker 1: and he guided it through it. And it's not like
Speaker 1: he just said, hey, here's an idea, Go do the
Speaker 1: work and write me a paper. And then it came
Speaker 1: back after lunch and it was all done. His experience
Speaker 1: was that it's sort of like a very naive young
Speaker 1: graduate student. It's often getting lost in the weeds. It's
Speaker 1: not thinking about the bigger picture. It sometimes makes mistakes,
Speaker 1: it needs a lot of supervision. But that's sort of
Speaker 1: focusing on the negatives, right, Like, it's amazing, This is incredible,
Speaker 1: right that you have something which is a capable research assistant. Again,
Speaker 1: not just a search tool anymore, right, but actually doing
Speaker 1: work and making progress and making it so that a
Speaker 1: guy who has an idea for a paper no longer
Speaker 1: has to go out and find somebody to do the work.
Speaker 1: He can find AI to do the work. He still
Speaker 1: needs to engage his brain, he still came up with
Speaker 1: a question, he still needs to follow it and make
Speaker 1: sure it's reasonable. But this is not something AI could
Speaker 1: do a year ago or two years ago. So it's
Speaker 1: a dramatic improvement.
Speaker 2: That's exciting but also h a bit scary for folks
Speaker 2: who would like to get positions as graduate students.
Speaker 1: Yes, exactly. And I saw him go a talk recently
Speaker 1: and he said, AI is going to keep getting better
Speaker 1: and better at this, and we are not. And so
Speaker 1: his forecast is by twenty thirty most science becomes AI assisted. Wow,
Speaker 1: and that soon we'll see end to end AI doing
Speaker 1: theoretical science that we won't need this kind of detailed
Speaker 1: step by step handholding and guidance.
Speaker 2: Does end to end include AI coming up with its
Speaker 2: own questions.
Speaker 1: It's not clear to me what exactly he means by that,
Speaker 1: And I think that's a very important distinction, because being
Speaker 1: able to say, here's an interesting problem, go solve it
Speaker 1: is very different from saying, what's an interesting problem we
Speaker 1: should work on?
Speaker 2: Yeah, because I feel like that's when you work with
Speaker 2: a grad student, you know, there you get them to
Speaker 2: the point where they tell you, I'm familiar with the literature,
Speaker 2: here's what the interesting question is, and you're like, oh,
Speaker 2: that's great. I didn't think I hadn't thought of that question.
Speaker 2: And now like, now you're a team, will AI become
Speaker 2: part of the team.
Speaker 1: So I was on this panel at Neurops, one of
Speaker 1: the premier AI conferences, and I was kind of a
Speaker 1: wet blanket about anthropics solving physics in the next few years.
Speaker 1: So folks from the company approached me afterwards and they
Speaker 1: were like, so, Daniel, what's it going to take to
Speaker 1: convent to you? And I said, I don't know, but
Speaker 1: I'd love to play with your top level models. So
Speaker 1: they give me access to their top level models for
Speaker 1: a few months, and so I played with it, and
Speaker 1: I asked it exactly what you suggested? I said, all right,
Speaker 1: Ai suggest a project. Here's the kind of thing I'm
Speaker 1: interested in. What's an opportunity, right, because I think a
Speaker 1: lot of people don't appreciate how much of science is
Speaker 1: identifying openings. It's a lot like being an entrepreneur. Right,
Speaker 1: you have limited resources, you have limited time. Find a
Speaker 1: place where you can make progress given your resources in
Speaker 1: a reasonable amount of time. That's going to be impactful. Right.
Speaker 1: That's a lot of what being a professional scientist is.
Speaker 1: It's easy to come up with like big questions about
Speaker 1: the universe or grandiose plans that would take a thousand
Speaker 1: year and a zillion dollars. But what can I actually
Speaker 1: do today over the next year to produce some science
Speaker 1: that's interesting to the community. What's the current conversation and
Speaker 1: where can we contribute? So I asked Ai what should
Speaker 1: we do? And it read my profile and then it
Speaker 1: suggested a few ideas, all of which were things I
Speaker 1: had already done in the last few years.
Speaker 2: Okay, so it's not there yet.
Speaker 1: So I was kind of disappointed. I was like, hmm,
Speaker 1: I've already done all of these things, and I didn't
Speaker 1: know if it's just like, not quite there yet and
Speaker 1: I'm a little bit ahead of it, or if it's
Speaker 1: just like had read my papers and I was like, oh,
Speaker 1: Daniel's interested in machine learning and particle physics and tracking,
Speaker 1: so I'll suggest something in that direction. It's not clear
Speaker 1: to me whether it's in the training sample or not.
Speaker 1: And so it's not there yet, right, But that doesn't
Speaker 1: mean that it can't contribute. Right, It's not going to
Speaker 1: be an autonomous physicist just yet. But remember, let's think about
Speaker 1: what it's good at. What can it do. It's good
Speaker 1: at scanning lots of literature, it's good at putting these
Speaker 1: things together. And remember that a lot of advances we
Speaker 1: make in physics are not just like one person has
Speaker 1: a genius idea which leaps us forward one hundred years.
Speaker 1: It's often putting things together from existing tools. Right, And
Speaker 1: so now let's dig into the question that we asked
Speaker 1: at the top of the episode. This claim made by
Speaker 1: these anthropic AI hypesters that it's going to solve physics, right,
Speaker 1: and like again, this is what they were actually saying,
Speaker 1: And this is the kind of thing you see AI
Speaker 1: tech bros saying. You know, Sam Altman said, quote, although
Speaker 1: it will happen incrementally astounding triumphs, fixing the climate, establishing
Speaker 1: a space colony, and the discovery of all of physics
Speaker 1: will eventually become commonplace. So I'm not putting words in
Speaker 1: their mouths, right, these are the claims that are being made.
Speaker 3: Wow.
Speaker 2: Yeah, because of AI, we will have a space colony.
Speaker 1: That's right. We're going to fix the climate, establish a
Speaker 1: space colony, care cancer dot dot dot dot dot. Wow,
Speaker 1: all of this kind of stuff amazing, you know. And
Speaker 1: you see also folks on the other side. One of
Speaker 1: my favorite writers is Corey Doctor Rowe, and he says
Speaker 1: that saying that AI is going to do science and
Speaker 1: gain consciousness is like saying, if we can get a
Speaker 1: horse to run fast enough, it's going to turn into
Speaker 1: a locomotive, you know. And his point is that you
Speaker 1: can't just like extrapolate blindly right, because like, what are
Speaker 1: we even talking about, you know, on that panel and
Speaker 1: lots of times people are talking about how much progress
Speaker 1: AI is making. We have to think about, like what
Speaker 1: is the scale there? What are we measuring its capability
Speaker 1: to do? Is it like percentage of an Einstein or something.
Speaker 1: It's not something that we really know how to measure
Speaker 1: where we can say like by twenty forty seven it's
Speaker 1: going to be two Einstein's right, that's all just nonsense,
Speaker 1: and so we don't know if there's a barrier there.
Speaker 1: We don't know if it's going to be able to
Speaker 1: do this. And what they're talking about it doing is
Speaker 1: something it has never done. Right, It's never suggested a
Speaker 1: new research topic or solve the whole problem.
Speaker 2: But that doesn't mean it can't.
Speaker 1: Though, that doesn't mean it can't. Right, maybe these horses
Speaker 1: will turn into locomotives. Right. It certainly is gaining a
Speaker 1: lot of power every year. So let's talk about the
Speaker 1: thing they're claiming, right, what does it mean to solve physics?
Speaker 1: In my mind, there's two possible answers to that. One is, look,
Speaker 1: we have a list of problems in physics right now.
Speaker 1: We don't know what dark matter is. We don't know
Speaker 1: why the universes mostly matter. We don't understand dark energy.
Speaker 1: We can't unify quantum mechanics and general relativity. We don't
Speaker 1: know why there's so many particles and forces, right, how
Speaker 1: does it all fit together? There are definitely things we
Speaker 1: don't understand. Maybe solving physics means answering today's open physics questions.
Speaker 1: I mean in the minds of same Altman and folks.
Speaker 1: Maybe that's what they mean when they say this solving
Speaker 1: all of physics. To me, that's not solving physics. Even
Speaker 1: if tomorrow they came out with the answers to these questions, right,
Speaker 1: I would not be done. No, I would not be like,
Speaker 1: well I'm bored now, I'm just going to go sit
Speaker 1: on the beach. Right, I'm going to ask like, well,
Speaker 1: why is this the solution? Could it have been this
Speaker 1: other way? Can I imagine places this could break down?
Speaker 1: Could have been a different way? You know, there's always
Speaker 1: questions to ask. These questions come from the philosophical context,
Speaker 1: which is the origin of our curiosity, and that's not
Speaker 1: going to go away even if we get beautiful equations
Speaker 1: to solve today's questions. In physics when.
Speaker 2: I feel like, if you've talked to any scientist, you
Speaker 2: know that as soon as you get one answer, that
Speaker 2: automatically leads to like twenty new questions. Like it's exponential
Speaker 2: the questions you get when you get one answer, and
Speaker 2: so it's hard for me to imagine that solving those
Speaker 2: problems would suddenly mean ah, we're done.
Speaker 1: I totally agree. And so from that perspective, will it
Speaker 1: solve physics? Absolutely not. Physics is a human pursuit and
Speaker 1: it's not going to end even if AI makes our
Speaker 1: answer finding much more powerful. But I do think, and
Speaker 1: this might surprise people, that it is going to transform
Speaker 1: the way we find those answers, and then it might
Speaker 1: actually be capable of Einstein level contributions. We don't know
Speaker 1: how to measure einsteininess, but when you look back in
Speaker 1: the history of a lot of the big leaps, what
Speaker 1: are they doing. They are putting together existing pieces that
Speaker 1: other people were not aware of. It's easy to say
Speaker 1: Einstein was a super genius he came up with this
Speaker 1: whole thing himself, but that's not really the truth. He
Speaker 1: was building on generations of careful math done by other
Speaker 1: nerds not even interested in general relativity. You know, grassman
Speaker 1: numbers and romani and manifolds. All these things are essential
Speaker 1: for Einstein. He could not solve those problems himself. He
Speaker 1: put together a bunch of interesting pieces, but sometimes just
Speaker 1: being lucky by who he knew and who he met,
Speaker 1: being the right place at the right time to meet
Speaker 1: these folks and learned about their progress. So that's the
Speaker 1: kind of thing AI is good at, is like, I'm
Speaker 1: going to read all these papers, I'm going to notice, Oh,
Speaker 1: this tool over here that there has been developed for
Speaker 1: totally other reasons might be able to solve this problem
Speaker 1: you guys are working on. That's exactly the kind of
Speaker 1: thing that AI is really good at is making these connections.
Speaker 1: It's searching broadly, is reading the whole literature. Right, there's
Speaker 1: lots of examples of this group theory invented for totally
Speaker 1: other reasons by math nerds turns out to be essential
Speaker 1: or quantum field theory. So I actually am optimistic that
Speaker 1: there could be big problems out there in physics we
Speaker 1: don't today have solutions for that AI could help us
Speaker 1: rapidly solve because of its specific capabilities.
Speaker 2: Okay, so you've just been pretty wildly optimistic about how
Speaker 2: un characteristically optimistic? That's right, That's right. You know you
Speaker 2: might be the more optimistic of team what blanket? But
Speaker 2: so do you think, like, well AI ever get like
Speaker 2: a Nobel Prize?
Speaker 1: Yeah, it's hard to know how to answer that. I mean,
Speaker 1: computer scientists building AI to solve problems have won Nobel
Speaker 1: prizes already, right, we saw that last year, Like Google
Speaker 1: deep Mind won a Nobel Prize. Right, I mean Alpha
Speaker 1: fold has done incredible stuff, right, They solved protein folding,
Speaker 1: and so the computer scientists wanted for the tool they
Speaker 1: built to solve the problem. Are we in an era
Speaker 1: where AI could win a Nobel Prize? I mean, like,
Speaker 1: I don't know what the estate lawyers would say after
Speaker 1: reading Nobel's will again, but I think we are on
Speaker 1: the verge of AI solving the current generation of hard
Speaker 1: theory problems. It's still going to be like a human
Speaker 1: asks an AI, like, hey, can you unify quantum mechanics
Speaker 1: and general relativity? But if it goes off and thinks
Speaker 1: hard and finds a piece of math we hadn't explored
Speaker 1: or solved some of the current problems in quantum gravity,
Speaker 1: comes up with a novel theory for it, then certainly
Speaker 1: it's going to have made an enormous contribution to our
Speaker 1: understanding of the universe, to physics. Whether you should give
Speaker 1: it the credit or the credit for the person who
Speaker 1: typed in the prompt I think that's a harder question
Speaker 1: I'm not qualified to answer.
Speaker 2: Wow. Yeah, and so many people contributed to making AI,
Speaker 2: it would be hard to know who to give the
Speaker 2: credit to.
Speaker 1: Yeah, that's always the case though, Like you know, for
Speaker 1: the Higgs boson, they give it to the theorists, not
Speaker 1: to the experimentalists who discovered it, partially because there's just
Speaker 1: too many of us, Right, thousands of people contributed to
Speaker 1: building the accelerator and the detector and making it work,
Speaker 1: so we can't all win the Nobel Prizes limit there.
Speaker 1: But I think it's clear that AI can do the
Speaker 1: thing we need to make big conceptual leaps, construct the
Speaker 1: building blocks, pull those building blocks together to make breakthroughs.
Speaker 1: That's what it can do, and that's very powerful. Right,
Speaker 1: We've seen in history that's how a lot of these
Speaker 1: big leaps are made.
Speaker 2: Let's talk about it's impact on like physicists today. So
Speaker 2: like for example, it can't take over the LHC and
Speaker 2: collect the data for you, like run the day Today stuff.
Speaker 2: So you still need experimentalists to collect the data.
Speaker 1: Right still for a while, Yes, But there's a lot
Speaker 1: of work in autonomous labs. You know, you've given AI
Speaker 1: access to the LIEDC, it could certainly decide, you know,
Speaker 1: how to run it and what data to keep, et cetera.
Speaker 1: And people are building these autonomous labs where an AI can,
Speaker 1: for example, test a bunch of materials really quickly, you know,
Speaker 1: build a bunch of materials, test it, and then close
Speaker 1: the loop come up with a new material to test.
Speaker 1: I was involved in a proposal last year for something
Speaker 1: called text to launch technology, where you type into an
Speaker 1: LM like okay, I want a satellite that can do xyz,
Speaker 1: and then it uses an autonomous lab to try to
Speaker 1: develop materials that could do that, and then far down
Speaker 1: the road, maybe it would even launch it into space.
Speaker 2: Wow, oh my gosh, that's nuts.
Speaker 1: But the point is that this is very new currently,
Speaker 1: but there's no real barrier there. If you can give
Speaker 1: AI control of these labs, if you build a lab
Speaker 1: that's AI compatible, there's no reason why experiments have to
Speaker 1: be out of reach of AI.
Speaker 2: Okay, and so then how is it impacting funding? So,
Speaker 2: like you mentioned that AI could replace a grad student,
Speaker 2: so maybe now you don't have to ask for funding
Speaker 2: for grad students anymore. But like, could AI replace Daniel Whitson? Never, I imagine,
Speaker 2: But but what do you think?
Speaker 1: AI can now do the work of a green graduate student?
Speaker 1: And that's very powerful. I can get more stuff done
Speaker 1: without my students than I could before. That is helpful.
Speaker 1: I always have one project where I'm doing the actual coding,
Speaker 1: and now those projects move faster, so that is very cool.
Speaker 1: And it's definitely changing the way the government sees particle
Speaker 1: physics being done. In some of the latest budget plans,
Speaker 1: hundreds of millions of dollars that used to go to
Speaker 1: pure particle physics is now being sent to AI for
Speaker 1: science that includes particle physics, and particle physicists are still involved,
Speaker 1: but industry is also very deeply involved. It looks to
Speaker 1: me like the government wants us to make all of
Speaker 1: our data AI friendly so that these big models from
Speaker 1: open AI and from Anthropic can play key roles in
Speaker 1: accelerating the progress in physics. I'm not against that, I
Speaker 1: just wish that it was new money instead of repurposed
Speaker 1: old money, like let's keep doing particle physics and let's
Speaker 1: add an investment on AI instead of cannibalizing particle physics
Speaker 1: for this money and sending a few hundred million dollars
Speaker 1: to these vast corporations. Remember that, like academic physics is
Speaker 1: tiny in terms of research budgets compared to what these
Speaker 1: companies have and what they are doing, and so a
Speaker 1: small amount of money for them is vast. Funding for
Speaker 1: us can have a huge impact. And let's not forget
Speaker 1: that all of the ideas at the foundation of the
Speaker 1: current wave of AI came from academia, Academics nerding around,
Speaker 1: coming up with ideas, attention, deep learning, all these things
Speaker 1: came from academic labs that we're just playing with stuff.
Speaker 1: And so I'm going to beat the drum one more
Speaker 1: time for investing in basic research because it pays off
Speaker 1: huge and when it pays off, we shouldn't then cannibalize
Speaker 1: our basic research funding to dig into AI. We should
Speaker 1: also invest in AI, but we should maintain our investments
Speaker 1: in basic research, right.
Speaker 2: Yeah, yeah. And I'm under the impression that if you
Speaker 2: massively cut a budget for i don't know, say five
Speaker 2: or ten years, you really hurt a generation of scientists.
Speaker 2: You know, like labs are going to hire few regrets ud,
Speaker 2: it's fewer post stocs, you're going to start, fewer physics labs,
Speaker 2: and the labs that do get started aren't going to
Speaker 2: get the grants that they need to convince their university
Speaker 2: that they should get tenures so that they should stick around.
Speaker 2: And now you've got, you know, fewer physicists produced by
Speaker 2: the United States. Is that your sense also? Is that
Speaker 2: not how things work in area? Okay?
Speaker 1: Yeah, no, absolutely, Like the theory community has received massive
Speaker 1: cuts to its funding, and theory is very, very cheap.
Speaker 1: You know, you're just paying a few people and pencil
Speaker 1: and paper, but their budget's been cut like seventy eighty percent,
Speaker 1: which means, you know, many fewer students, which means fewer theorists,
Speaker 1: which means fewer smart people asking those questions. And you
Speaker 1: asked a question I've been answered yet, which is like
Speaker 1: could this replace Daniel? And like, I think that it's
Speaker 1: not hard to imagine that you soon have AIS that
Speaker 1: have the capability of like senior scientists to do research
Speaker 1: to even drive other lower level AIS or supervise humans
Speaker 1: who are doing research. I don't think that's out of
Speaker 1: their capability. But something I don't understand is who's asking
Speaker 1: the questions? Right, Physics is a human endeavor. At the
Speaker 1: heart of it, is AI going to solve? Physics is
Speaker 1: about whether we are ever done asking questions, and the
Speaker 1: answer to that it's always gonna be no. It's just
Speaker 1: going to be no. Physics is about our curiosity about
Speaker 1: the universe. Now, that doesn't mean that machines can't be
Speaker 1: there along with us answering some of the questions. The
Speaker 1: open question to me is will they have their own questions?
Speaker 1: I've got my questions about the universe? You have yours.
Speaker 1: That's why I'm in physics and you're in parasites. And
Speaker 1: somebody else is doing art history, and somebody else is
Speaker 1: doing chemistry, you know, God save them, and somebody else
Speaker 1: is at some company inventing new ways to make white
Speaker 1: chocolate cheap. And you know, everybody's driven by their own
Speaker 1: personal curiosity. Do machines have curiosity? If you give Claude
Speaker 1: all the physics papers and you ask it like, what
Speaker 1: do you think is interesting? What is it interested in?
Speaker 1: And is that really its own an interest? Right? Or
Speaker 1: is it just following up on what humans have begun?
Speaker 1: And so, to me, that's the interesting philosophical question. When
Speaker 1: they get to have that level of capacity, are they
Speaker 1: answering their questions or ours? Will they be similar? Is
Speaker 1: this an example of aliens doing physics?
Speaker 3: Right?
Speaker 1: Will machines do physics? I don't know, because I don't
Speaker 1: know what it means to have an AI at that level,
Speaker 1: And they will never really be divorced from humanity because
Speaker 1: they were birthed from the collective intelligence, right the way
Speaker 1: that like something that's learned to write a great novel
Speaker 1: has learned to write that great novel by reading a
Speaker 1: bunch of great novels. But that's also how we train
Speaker 1: great novelists that are humans. Right, You read good writing,
Speaker 1: you're inspired, It teaches you. So, you know, I don't
Speaker 1: think it's impossible for AI to solve the current generation
Speaker 1: of physics problems, and I don't think it's impossible to
Speaker 1: potentially come up with new questions on its own.
Speaker 2: And if AI came up with a list of ten questions,
Speaker 2: and then you were presented with those ten questions alongside
Speaker 2: of ten questions from some of your favorite collaborators, and
Speaker 2: you thought those questions were equally interesting. What would what
Speaker 2: would you say?
Speaker 1: I'd say, bravo. You know, I welcome our future AI collaborators. Okay,
Speaker 1: bring your curiosity wherever it comes from. I think it
Speaker 1: can just help inform. But I think we should invest
Speaker 1: in this stuff, you know, and we should invest in
Speaker 1: human driven curiosity as well as machine driven curiosity and
Speaker 1: alien driven curiosity and all of it.
Speaker 2: That's right, invest in our future.
Speaker 1: Exactly.
Speaker 2: All right, Well that was fascinating. Thank you Daniel for
Speaker 2: walking us through how you've been working with physics and
Speaker 2: AI over the last What do you think like, I
Speaker 2: guess it's been decades. You've been working with LMS.
Speaker 1: I've been doing machine learning and particle physics since the nineties.
Speaker 1: So yeah, it's been thirty years. Okay before it was
Speaker 1: cool for sure?
Speaker 2: All right, Well, maybe you'll give us a yearly update
Speaker 2: on how this kind of stuff is going.
Speaker 1: All right? Or maybe AI Daniel will replace me.
Speaker 2: I doubt it. I can't imagine that his Joe will
Speaker 2: be anywhere near as good or as bad, depending on
Speaker 2: how you see it as yours.
Speaker 1: The day I stopped making fun of chemistry is the day,
Speaker 1: you know, I've been replaced by a robot a.
Speaker 2: Thanks so much to Daniel, and thanks so much to
Speaker 2: the extraordinaries. And if you have a question about anything
Speaker 2: at all, you can send it to us at questions
Speaker 2: at danieland Kelly dot org.
Speaker 1: You'll get an answer from a real human being.
Speaker 2: Yes you will.
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