Lex Fridman Podcast - #472 – Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Episode Date: June 15, 2025

Terence Tao is widely considered to be one of the greatest mathematicians in history. He won the Fields Medal and the Breakthrough Prize in Mathematics, and has contributed to a wide range of fields from fluid dynamics with Navier-Stokes equations to mathematical physics & quantum mechanics, prime numbers & analytics number theory, harmonic analysis, compressed sensing, random matrix theory, combinatorics, and progress on many of the hardest problems in the history of mathematics. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep472-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/terence-tao-transcript CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Terence's Blog: https://terrytao.wordpress.com/ Terence's YouTube: https://www.youtube.com/@TerenceTao27 Terence's Books: https://amzn.to/43H9Aiq SPONSORS: To support this podcast, check out our sponsors & get discounts: Notion: Note-taking and team collaboration. Go to https://notion.com/lex Shopify: Sell stuff online. Go to https://shopify.com/lex NetSuite: Business management software. Go to http://netsuite.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex AG1: All-in-one daily nutrition drink. Go to https://drinkag1.com/lex OUTLINE: (00:00) - Introduction (00:36) - Sponsors, Comments, and Reflections (09:49) - First hard problem (15:16) - Navier–Stokes singularity (35:25) - Game of life (42:00) - Infinity (47:07) - Math vs Physics (53:26) - Nature of reality (1:16:08) - Theory of everything (1:22:09) - General relativity (1:25:37) - Solving difficult problems (1:29:00) - AI-assisted theorem proving (1:41:50) - Lean programming language (1:51:50) - DeepMind's AlphaProof (1:56:45) - Human mathematicians vs AI (2:06:37) - AI winning the Fields Medal (2:13:47) - Grigori Perelman (2:26:29) - Twin Prime Conjecture (2:43:04) - Collatz conjecture (2:49:50) - P = NP (2:52:43) - Fields Medal (3:00:18) - Andrew Wiles and Fermat's Last Theorem (3:04:15) - Productivity (3:06:54) - Advice for young people (3:15:17) - The greatest mathematician of all time PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips

The following is a conversation with Terence Tao, widely considered to be one of the greatest mathematicians in history, often referred to as the Mozart of math. He won the Fields Medal and the Breakthrough Prize in Mathematics and has contributed groundbreaking work to a truly astonishing range of fields in mathematics and physics. This was a huge honor for me, for many reasons, including the humility and kindness that Terry showed to me throughout all our interactions. It means the world. And now a quick few second mention of each sponsor. Check them out in the description or at lexfreedman.com slash sponsors. It's the best way to support this podcast. We got Notion for teamwork, Shopify for selling stuff online, NetSuite for your business,

Element for electrolytes, and AG1 for your health. Choose wisely my friends. And now onto the full ad reads. They're all here in one place. I do try to make them interesting by talking about some random things I'm reading or thinking about. But if you skip, please still check out the sponsors. I enjoy their stuff, maybe you will too.

To get in touch with me for whatever reason, go to LexTreamer.com slash contact. Alright, let's go. This episode is brought to you by Notion, a note taking and team collaboration tool. I use Notion for everything, for personal notes, for planning these podcasts, for collaborating with other folks, and for super boosting all of those things with AI because Notion does a great job of integrating AI into the whole thing. You know what's fascinating is the mechanisms of human memory before we had widely adopted technologies and tools for

writing and recording stuff, certainly before the computer. So you can look at medieval monks, for example, that would use the now well-studied memory techniques like the memory palace, the spatial memory techniques to memorize entire books. That is certainly the effect of technology started by Google search and moving to all the other things like Notion that we're offloading more and more and more of the effect of technology started by Google search and moving to all the other things like notion That we're offloading more and more and more of the task of memorization to the computers which I think is

Probably a positive thing because it frees more of our brain to do deep reasoning whether that's deep dive focused specialization or the journalist type of thinking versus memorizing facts Although I do think that there's a kind of background model that's formed when you memorize a lot of things and from there from inspiration arises discovery. So I don't know. It could be a great cost while offloading most of our memorization to the machines. But it is the way of the world.

Try Notion AI for free when you go to Notion.com slash Lex. That's all lowercase Notion.com slash Lex to try the power of Notion AI today. This episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store. Our future friends has a lot of robots in it. Looking into that distant future, you have Amazon warehouses with millions of robots that move packages around. You have Tesla bots everywhere in the factories and in the home and on the streets and the baristas. All of that, that's our future.

Right now you have something like Shopify that connects a lot of humans in the digital space. But more and more, there will be a automated, digitized, AI-fueled connection between humans in the physical space. Like a lot of futures, there's going to be negative things and there's going to be positive things. And like a lot of possible futures, there's little we could do about stopping it all

we can do is steer it in the direction that enables human flourishing instead of hiding in fear or fear-mongering be part of the group of people that are building the best possible trajectory of human civilization. Anyway, sign up for a $1 per month trial period at Shopify.com slash Lex, that's all lower case. Go to Shopify.com slash Lex to take your business to the next level today. This episode is also brought to you by NetSuite, an all-in-one cloud business management system. There's a lot of messy components to running a business and I must ask and I must wonder

at which point there's going to be an AI, AGI like CFO of a company, an AI agent that handles most, if not all of the financial responsibilities or all of the things that NetSuite is doing, at which point will NetSuite increasingly leverage AI for those tasks? I think probably you will integrate AI into its tooling, but I think there's a lot of

edge cases that we need the human wisdom, the human intuition grounded in years of experience in order to make the tricky decision around the edge cases. I suspect that running a company is a lot more difficult than people realize but there's a lot of sort of paperwork type stuff that could be automated, could be digitized, could be summarized, integrated and used as a foundation for the said humans to make decisions. Anyway that's our future. Download the CFO's guide to AI and machine learning at netsuite.com slash Lex. That's netsuite.com slash Lex. This episode is also brought to you by Element, my daily zero sugar

and delicious electrolyte mix. Now I run along the river often and get to meet some really interesting people One of the the people I met was preparing for his first ultra marathon. I believe he said it was a hundred miles and That of course sparked in me the thought that I need for sure to To do one myself some time ago now I was planning to do something with David Goggins and I think that's still on the sort of to-do list between the two of us to do some crazy physical feat

Of course the thing that is crazy for me is a daily activity for Goggins but nevertheless, I think it's important in the physical domain the mental domain and all domains of life to challenge yourself. And athletic endeavors is one of the most sort of crisp, clear, well-structured way of challenging yourself. But there's all kinds of things, writing a book, to be honest, having kids and marriage and relationships and friendships, all of those,

if you take it seriously, if you go all in and do it right, I think that's a serious challenge. Because most of us are not prepared for it. You can learn along the way, and if you have the rigorous feedback loop of improving constantly and growing as a person and really doing a great job of the thing, I think that might as well be an ultramarathon. Anyway, get a sample pack for free with any purchase. Try it at www.drinkelement.com.

And finally, this episode is also brought to you by AG1, an all-in-one daily drink to support better health and peak performance. I drink it every day. I'm preparing for a conversation on drugs in the Third Reich. And funny enough, it's a kind of way to analyze Hitler's biography. It's to look at what he consumed throughout and Norman Ohler does a great job of analyzing all of that and tells the story of Hitler and the Third Reich in a way that hasn't really been touched by historians before It's always nice to look at key moments in history through a perspective. That's not often taken Anyway, I mentioned that because I think

Hitler had a lot of stomach problems. And so that was the motivation for getting a doctor. The doctor that eventually would fill him up with all kinds of drugs but the doctor earned Hitler's trust by giving him probiotics which is a kind of revolutionary thing at the time and so that really helped deal with whatever stomach issues that Hitler was having. All of that is a reminder that war is waged by humans and humans are biological systems and biological systems require fuel and supplements and all of that kind of stuff and depending on what you put in your body will affect your performance in the short term in the long term with meth. That's true with Hitler to his last days in the bunker in Berlin all the cocktail of drugs that he was taking so I

Think I got myself somewhere deep. I'm not sure how to get out of This it deserves a multi-hour conversation versus a few seconds of mention but yeah, all of that was sparked by my thinking of Age you one and how much I love it. I appreciate that you're listening to this and coming along for the wild journey that these ad reads are. Anyway, AG1 will give you a one-month supply of fish oil when you sign up at drinkag1.com slash Lex. This is the Lex Freeman podcast. To support it, please check out our sponsors in the description or at lexfriedman.com slash sponsors.

And now, dear friends, here's Terrence Tao. What was the first really difficult research-level math problem that you encountered? One that gave you pause, maybe. Well, I mean, in your undergraduate education, you learn about the really hard and possible problems, like the Riemann hypothesis, the Trinprimes conjecture. You can make problems arbitrarily difficult. That's not really a problem. In fact, there's even problems that we know to be unsolvable. What's really interesting are the problems just on the boundary between what we can do easily and what are hopeless. But what are problems where existing techniques can do like 90% of the job and then you just need that remaining 10%. I think as a PhD student,

the Kakeya problem certainly caught my eye and it just got solved actually. It's a problem I've worked on a lot in my early research. Historically, it came from a little puzzle by the Japanese mathematician, Suji Kakeya, in like 1918 or so. So the puzzle is that you have a needle on the plane. Think of it like driving on a road. And you want to execute a U-turn. You want to turn the needle around. But you want to

do it in as little space as possible. So you want to use this little area in order to turn it around. But the needle is infinitely maneuverable. So you can imagine just spinning it around, it's the unit needle, you can spin it around its center. And I think that gives you a disc of area, I think pi over four. Or you can do a three point U-turn, which is what we teach people in the driving schools to do.

And that actually takes area pi over eight. So it's a little bit more efficient than a rotation. And so for a while, people thought that was the most efficient way to turn things around. But Bezikovic showed that in fact, you could actually turn the needle around using as little area as you wanted. So 0.001, there was some really fancy multi back and forth U-turn thing that you could do, that you could turn a needle around and in so doing it would pass through every intermediate direction. Is this in the two-dimensional plane?

This is in the two-dimensional plane. So we understand everything in two dimensions. So the next question is what happens in three dimensions? So suppose like the Hubble Space Telescope is tube in space and you want to observe every single star in the universe. So you want to rotate the telescope to reach every single direction. And here's the unrealistic part. Suppose that space is at a premium, which it totally is not. You want to occupy as little volume as possible in order to rotate your needle around in order to see every single star in the sky. How small a volume do you need to do that? And so you can modify a basic curvature's construction. And so if your telescope has zero thickness, then you can use as little volume as you need. That's a simple modification of the two-dimensional construction.

But the question is that if your telescope is not zero thickness, but just very, very thin, some thickness delta, what is the minimum volume needed to be able to see every single direction as a function of delta? So as delta gets smaller, as the needle gets thinner, the volume should go down, but how fast does it go down? And the conjecture was that it goes down very, very slowly, like logarithically, roughly speaking. And that was proved after a lot of work. So this seems like a puzzle. Why is it interesting? So it turns out to be surprisingly connected to a lot of work. So this seems like a puzzle, why is it interesting? So it turns out to be surprisingly connected to a lot of problems in partial differential equations,

in number theory, in geometry, combinatorics. For example, in wave propagation, you splash some water around, you create water waves and they travel in various directions. But waves exhibit both particle and wave type behavior. So you can have what's called a wave packet, which is like a very localized wave that is localized in space and moving a certain direction in time. And so if you plot it in both space and time, it occupies a region which looks like a tube. And so what can happen is that you can have a wave which initially is very dispersed, but it all focuses at a single point later in time. Like you can imagine dropping a pebble into a pond

and the ripple spread out. But then if you time-reverse that scenario and the equations of wave motion are time-reversible, you can imagine ripples that are converging to a single point and then a big splash occurs, maybe even a singularity. And so it's possible to do that. And geometrically what's going on is that there's always light rays. So if this wave represents light, for example, you can imagine this wave as a superposition of photons all traveling at the speed of light. They all travel on these light rays and they're all focusing at this one point. So you can have a very dispersed wave focus into a very concentrated wave at one point in space and time, but then it defocuses again and it separates.

But potentially if the conjecture had a negative solution, so what that means is that there's a very efficient way to pack tubes pointing in different directions to a very, very narrow volume, then you would also be able to create waves that start out, there'll be some arrangement of waves that start out very, very dispersed, but they would concentrate not just at a single point, but there'll be a large, there'll be a lot of concentrations in space and time. And you could create what's called a blow up where these waves, their amplitude becomes so great that the laws of physics that they're governed by are no longer wave equations, but something more complicated and nonlinear. In mathematical physics, we care a lot about whether certain equations and wave equations

are stable or not, whether they can create these singularities. There's a famous unsolved problem called the Navier-Stokes regularity problem. The Navier-Stokes equations are equations that govern the fluid flow or incompressible fluids like water. The question asks, if you start with a smooth velocity field of water, can it ever concentrate so much that the velocity becomes infinite at some point? That's called a singularity. We don't see that in real life.

If you splash around water on the bathtub, it won't explode on you or have water leaving at a speed of light. But potentially it is possible. And in fact, in recent years, the consensus has drifted towards the belief that in fact, for certain very special initial configurations of say water that singularities can form, but people have not yet been able to actually establish this. The Clay Foundation has these seven millennium prize problems, has a million dollar prize for solving one of these problems. This is one of them. Of these seven, only one of them has been solved at the Poincare Conjecture, by the parliament.

So the Kakeya Conjecture is not directly, directly related to the Navier-Stokes problem, but understanding it would help us understand some aspects of things like wave concentration, which would indirectly probably help us understand the Navier-Stokes problem better. Can you speak to the Navier-Stokes? So the existence and smoothness, like you said, millennial prize problem.

You've made a lot of progress on this one. In 2016, you published a paper, Finite Time Blowup, for an average three-dimensional Navier-Stokes equation. Right. So we're trying to figure out if this thing usually doesn't blow up. Right. But can we say for sure it never blows up?

Right. Yeah. So yeah, that is literally the million dollar question. Yeah. So this is what distinguishes mathematicians from pretty much everybody else. Like if something holds 9.99% of the time, that's good enough for most things. But mathematicians are one of the few people who really care about whether 100%, really 100% of all situations are covered by… Yeah, so most fluid, most of the time, water does not blow up, but could you design a very special initial state that does this? And maybe we should say that this is a set of equations that govern in the field of fluid

dynamics. Yes. Trying to understand how fluid behaves and it's actually turns out to be a really complex, you know, fluid is extremely complicated thing to try to model. Yeah, so it has practical importance. So this clay price problem concerns what's called the incompressible Navier-Stokes, which governs things like water. There's something called the compressible Navier-Stokes, which governs things like air.

And that's particularly important for weather prediction. Weather prediction, it has a lot of computational fluid dynamics. A lot of it is actually just trying to solve the Navier-Stokes equations as best they can. Also gathering a lot of data so that they can initialize the equation. There's a lot of moving parts. So it's a very important problem, practically. Why is it difficult to prove general things about the set of equations like it not blowing up?

Short answer is Maxwell's Demon. So, Maxwell's Demon is a concept in thermodynamics. If you have a box of two gases, they're oxygen and nitrogen. Maybe you start with all the oxygen on one side and nitrogen on the other side, but there's no barrier between them. Then they will mix and they should stay mixed. There's no reason why they should unmix. In principle, because of all the collisions between them, there could be some sort of weird conspiracy that maybe there's a microscopic demon called Maxwell's demon that will, every time oxygen and nitrogen atom collide, they will bounce off in such a way that the oxygen drifts onto one side

and then naturally goes to the other. And you could have an extremely improbable configuration emerge, which we never see. And statistically, it's extremely unlikely. But mathematically, it's possible that this can happen and we can't rule it out. This is a situation that shows up a lot in mathematics. A basic example is the digits of pi, 3.14, 1.59, and so forth. The digits look like they have no pattern and we believe they have no pattern. On the long term, you should see as many ones and twos and threes as fours and fives and sixes.

There should be no preference in the digits of pi to favor, let's say, seven over eight. But maybe there is some demon in the digits of pi that every time you compute more and more digits, it sort of biases one digit to another. And this is a conspiracy that should not happen. There's no reason it should happen, but there's no way to prove it with our current technology. Okay, so getting back to Navier-Stokes, a fluid has a certain amount of energy. And because the fluid is in motion, the energy gets transported around and water is also viscous. So if the energy is spread out over many different locations, the natural viscosity of the fluid will just damp out the energy and it will go to zero. And this is what happens when we actually experiment

with water. You splash around, there's some turbulence and waves and so forth, but eventually it settles down and the lower the amplitude, the smaller the velocity, the more calm it gets. But potentially there is some sort of demon that keeps pushing the energy of the fluid into a smaller and smaller scale. And it will move faster and faster. And at faster speeds, the effective viscosity is relatively less. And so it could happen that it creates some sort of what's called a self-similar blowup scenario where the energy of the fluid starts off at some large scale and then it all sort

of transfers its energy into a smaller region of the fluid, which then at a much faster rate moves it into an even smaller region and so forth. And each time it does this, it takes maybe half as long as the previous one. And then you could actually converge to all the energy concentrating in one point in a finite amount of time. And that's now is called finite time blow up. So in practice, this doesn't happen. So water is what's called turbulent. So it is true that if you have a big eddy of water, it will tend to break up into smaller eddies, but it won't transfer all the energy from one big eddy into one smaller eddy, it will transfer into maybe

three or four. And then those ones split up into maybe three or four small eddies of their own. And so the energy gets dispersed to the point where the viscosity can then keep everything under control. But if it can somehow concentrate all the energy, keep it all together, and do it fast enough that the viscous effects don't have enough time to calm everything down, then this blow-up can occur. So there are papers who had claimed that, oh, you just need to take into account conservation of energy and just carefully use the viscosity and you

can keep everything under control for not just the Navier-Stokes but for many, many types of equations like this. And so in the past there have been many attempts to try to obtain what's called global regularity for Navier-Stokes, but for many, many types of equations like this. And so in the past, there have been many attempts to try to obtain what's called global regularity for Navier-Stokes, which is the opposite of final time blow up, that velocity stays smooth. And it all failed. There was always some sign error or some subtle mistake and it couldn't be salvaged. So what I was interested in doing was trying to explain why we were not able to disprove final time blow up. I couldn't do it for the actual equations of fluids which were

too complicated. But if I could average the equations of motion of the Navier-Socs, basically if I could turn off certain types of ways in which water interacts and only keep the ones that I want. So in particular, if there's a fluid and it could transfer its energy from a large eddy into this small eddy or this other small eddy, I would turn off the energy channel that would transfer energy to this one

and direct it only into this smaller eddy while still preserving the law of conservation of energy. So you're trying to make a blow up. Yeah, yeah. So I basically engineer a blow up by changing the laws of physics, which is one thing that mathematicians are allowed to do. We can change the equation.

How does that help you get closer to the proof of something? Right. So it provides what's called an obstruction in mathematics. So what I did was that basically if I turned off the certain parts of the equation, which usually when you turn off certain interactions, make it less nonlinear, it makes it more regular and less likely to blow up. But I found that by turning off a very well designed set of interactions, I could force all the energy to blow up in finite time. So what that means is that if you wanted to prove global regularity for Navier-Stokes for the actual equation, you must use some feature of the

true equation, which my artificial equation does not satisfy. So it rules out certain approaches. So the thing about math is it's not just about taking a technique that is going to work and applying it, but you need to not take the techniques that don't work. And for the problems that are really hard, often there are dozens of ways that you might think might apply to solve the problem, but it's only after a lot of experience that you realize there's no way that these methods are going to work. So having these counter examples for nearby problems kind of rules out, it saves you a lot of time because you're not wasting energy on things

that you now know cannot possibly ever work. How deeply connected is it to that specific problem of fluid dynamics or is it some more general intuition you build up about mathematics? Right, yeah. So the key phenomenon that my technique exploits is what's called supercriticality. So in partial differential equations, often these equations are like a tug of war between different forces.

So in Navier-Stokes, there's the dissipation force coming from viscosity and it's very well understood, it's linear, it calms things down. If viscosity was all there was, then nothing bad would ever happen. But there's also transport, that energy from in one location of space can get transported because the fluid is in motion to other locations. And that's a nonlinear effect

and that causes all the problems. So there are these two competing terms in the Navier-Stokes equation, the dissipation term and the transport term. If the dissipation term dominates, if it's large, then basically you get regularity. And if the transport term dominates, then we don't know what's going on. It's a very nonlinear situation. It's unpredictable. It's turbulent. So sometimes these forces are in balance at small scales, but not in balance at large scales or vice versa.

So Navier-Stokes is what's called supercritical. So at smaller and smaller scales, the transport terms are much stronger than the viscosity terms. So the viscosity terms are things that calm things down. And so this is why the problem is hard. In two dimensions, so the Soviet mathematician Ladyshinskaya, she in the 60s shows in two dimensions, there was no blow up. And in two dimensions, the Navier-Sokos equation is what's called critical. The effect of transport and the effect of viscosity about the same strength, even at very, very small scales. And we have a lot of technology to handle critical and also subcritical equations and prove regularity. But for supercritical equations, it was not clear what was going

on. And I did a lot of work and then there's been a lot of follow-up showing that for many other types of supercritical equations, you can create all kinds of blow-up examples. Once the nonlinear effects dominate the linear effects at small scales, you can have all kinds of bad things happen. So this is sort of one of the main insights of this line of work is that supercriticality versus criticality and subcriticality, this makes a big difference. I mean, that's a key qualitative feature that distinguishes some equations for being sort of nice and predictable and you know, like planetary motion.

And I mean, there's certain equations that you can predict for millions of years and or thousands at least. Again, it's not really a problem, but there's a reason why we can't predict the weather past two weeks into the future because it's a super critical equation. Lots of really strange things are going on at very fine scales. So whenever there is some huge source of non-linearity, that can create a huge problem for predicting what's going to happen. Yeah. And if non-linearity is somehow more and more featured and interesting at its more scales.

I mean, there's many equations that are non-linear, but in many equations you can approximate things by the bulk. So for example, planetary motion, you know, if you want to understand the orbit of the moon or Mars or something, you don't really need the microstructure of the seismology of the moon or exactly how the mass distributed. You can almost approximate these planets by point masses. Just the aggregate behavior is important. But if you want to model a fluid like the weather,

you can't just say in Los Angeles, the temperature is this, the wind speed is this. For supercritical equations, the final confirmation is really important. If we can just linger on the Navier-Stokes equations a little bit. So you've suggested, maybe you can describe it, that one of the ways to solve it or to negatively resolve it

would be to sort of to construct a liquid a kind of liquid computer right and then show that the halting problem from computation theory has consequences for fluid dynamics so show it in that way can you describe this right yeah so this came out of this work of constructing this average equation that blew up. So as part of how I had to do this, so this is this naive way to do it. You just keep pushing every time you get energy at one scale, you push it immediately to the next scale as fast as possible. This is sort of the naive way to force blow up. It turns out in five and high dimensions this works.

But in three dimensions, there was this funny phenomenon that I discovered that if you keep, if you change the laws of physics, you just always keep trying to push the energy into smaller and smaller scales. What happens is that the energy starts getting spread out into many scales at once. So you have energy at one scale, you're pushing it into the next scale, and then as soon as

it enters that scale, you also push it to the next scale, but there's still some energy left over from the previous scale. You're trying to do everything at once. And this spreads out the energy too much. And then it turns out that it makes it vulnerable for viscosity to come in and actually just damp out everything. So it turns out this direct abortion doesn't actually work. There was a separate paper by some other authors that actually showed this in three dimensions.

So what I needed was to program a delay, so kind of like airlocks. So I needed an equation which would start with a fluid doing something at one scale. It would push this energy into the next scale, but it would stay there until all the energy from the larger scale got transferred. And only after you pushed all the energy in, then you sort of opened the next gate and then you push that in as well. So by doing that, the energy inches forward scale by scale in such a way that it's always localized at one scale at a time. And then it can resist the effects of viscosity because it's not dispersed. So in order to make that happen, yeah, I had

to construct a rather complicated non-linearity. And it was basically like, you know, it was constructed like an electronic circuit. So I actually thank my wife for this because she was trained as an electrical engineer. And she talked about, she had to design circuits and so forth. And if you want a circuit that does a certain thing, like maybe have a light that flashes on and then turns off and then on and then off, you can build it from more primitive components, capacitors and resistors and so forth. And you have to build a diagram and these diagrams you can sort of follow up your eyeballs and say, oh yeah, the current will build up here and it will stop and then it will do

that. So I knew how to build the analog of basic electronic components, you know, like resistors and capacitors and so forth. And I would stack them together in such a way that I would create something that would open one gate and then there'd be a clock. And then once the clock hits a certain threshold, it would close it. It's kind of a Rube Goldberg type machine, but described mathematically. And this ended up working.

So what I realized is that if you could pull the same thing off for the actual equations, so if the equations of water support a computation, so can imagine a steampunk, but it's really water punk type of thing. Modern computers are electronic. They're powered by electrons passing through very tiny wires and interacting with other electrons and so forth. But instead of electrons, you can imagine these pulses of water moving at a certain velocity and maybe there are two different configurations corresponding to a bit being electrons, you can imagine these pulses of water moving at a certain velocity. Maybe

it's the two different configurations corresponding to a bit being up or down. Probably if you had two of these moving bodies of water collide, they would come out with some new configuration which would be something like an AND gate or OR gate. The output would depend in a very particular way on the inputs. You could chain these together and maybe create a Turing machine. And then you have computers, which are made completely out of water. And if you have computers, then maybe you can do robotics, hydraulics and so forth. And so you could create some machine, which is basically a fluid analog or what's called a von Neumann machine. So von

Neumann proposed if you want to colonize Mars, the sheer cost of transporting people and machines to Mars is just ridiculous. But if you could transport one machine to Mars and this machine had the ability to mine the planet, create some more materials, to smelt them and build more copies of the same machine, then you could colonize the whole planet over time. So if you could build a fluid machine, which, yeah, so it's a fluid robot, and what it would do, its purpose in life, it's programmed so that it would create a smaller version of itself in some sort of cold state.

It wouldn't start just yet. Once it's ready, the big robot, configured water, would transfer all its energy into the smaller configuration and then power down. Then I clean itself up and then what's left is this newest state, which would then turn on and do the same thing, but smaller and faster. Then the equation has a certain scaling symmetry. Once you do that, it can just keep iterating.

This in principle would create a blow up for the actual Navier-Stokes. And this is what I managed to accomplish for this average Navier-Stokes. So it provided this sort of roadmap to solve the problem. Now this is a pipe dream because there are so many things that are missing for this to actually be a reality. So I can't create these basic logic gates. I don't have these special configurations of water. I mean, there's candidates that include vortex rings that might possibly work. But also, you know, analog computing is really nasty compared to digital computing.

I mean, because there's always errors. You have to do a lot of error correction along the way. I don't know how to completely power down the big machine so that it doesn't interfere with the running of the smaller machine. But everything in principle can happen. It doesn't contradict any of the laws of physics. So it's sort of evidence that this thing is possible. There are other groups who are now pursuing ways to make Navier-Stokes blow up, which are nowhere near as ridiculously complicated as this.

They actually are pursuing much closer to the direct self-similar model, which can, it doesn't quite work as is, but there could be some simpler scheme than what I just described to make this work. There is a real leap of genius here to go from Navier-Stokes to this touring machine. So it goes from what the self-similar blob scenario that you're trying to get the smaller and smaller blob to now having a liquid touring machine gets smaller, smaller, smaller, and somehow seeing how that could be used to say something about a blow up. I mean, that's a big leap. So there's precedent.

I mean, so the thing about mathematics is that it's really good at spotting connections between what you might think of as completely different problems. But if the mathematical form is the same, you can draw a connection. So there's a lot of previously on what is called cellular automator, the most famous of which is Conway's Game of Life.

This is infinite discrete grid and at any given time, the grid is either occupied by a cell or it's empty. And there's a very simple rule that tells you how these cells evolve. So sometimes cells live and sometimes they die. And this, you know, when I was a student, it was a very popular screensaver to actually just have these animations going on. And they look very chaotic. In fact, they look a little bit like turbulent flow sometimes.

But at some point, people discovered more and more interesting structures within this game of life. So for example, they discovered this thing called a glider. So a glider is a very tiny configuration of like four or five cells, which evolves and it just moves in a certain direction. And that's like this vortex rings. Yeah, so this is an analogy. The Game of Life is kind of like a discrete equation and the fluid naviosauce is a continuous

equation, but mathematically they have some similar features. And so over time, people discovered more and more interesting things that you could build within the Game of Life. The Game of Life is a very simple system. It only has like three or four rules to do it, but you can design all kinds of interesting configurations inside it. There's something called a glider gun that does nothing to spit out gliders one at a time.

And then after a lot of effort, people managed to create and gates and all gates for gliders. Like there's this massive, ridiculous structure, which if you have a stream of gliders coming in here and a stream of gliders coming in here, then you may produce a stream of gliders coming out. If both of the streams have gliders, then there will be an output stream. But if only one of them does, then nothing comes out. So they could build something like that. And once you could build these basic gates, then just from software engineering, you can build almost anything. You can build a Turing machine. I mean, it's like an enormous steampunk type things. They look ridiculous. But then people also generated self-replicating objects in the game of life, a massive machine,

a bonoma machine, which over a huge period of time, and there were always little gladiagons inside doing these very steampunk calculations, it would create another version of itself which could replicate. It's so incredible. A lot of this was like community crowdsourced by amateur mathematicians actually. So I knew about that work and so that is part of what inspired me to propose the same thing with Navier-Stokes. As I said, analog is much worse than digital.

It's going to be, you can't just directly take the constructions from the game of life and plunk them in. But again, it shows it's possible. You know, there's a kind of emergence that happens with these Setherer automata. Local rules, maybe it's similar to fluids, I don't know, but local rules operating at scale can create these incredibly complex dynamic structures. Do you think any of that is amenable to mathematical analysis? Do we have the tools to say something profound about that? The thing is, you can get this emergent, very complicated structures,

but only with very carefully prepared initial conditions. So these glider guns and gates and software machines, if you just plunk down randomly some cells and you're unlooking at them, you will not see any of these. And that's the analogous situation of Navier-Stokes again, that with typical initial conditions, you will not have any of this weird computation going on. But basically through engineering, by specially designing things in a very special way, you can pick clever constructions.

I wonder if it's possible to prove the sort of the negative of like, basically prove that only through engineering can you ever create something interesting. This is a recurring challenge in mathematics that I call the dichotomy between structure and randomness, that most objects that you can generate in mathematics are random. They look like random, like digits or pi. Well, we believe is a good example. But there's a very small number of things that have patterns. But now, you can prove something as a pattern by just constructing, you know, like if something has a simple pattern and you have a proof that it does something like repeat itself every so often, you can do that. But and you can prove that that, for example, you can do that. And you can prove that most sequences of digits have no pattern.

So if you just pick the digits randomly, there's something called low-large numbers that tells you you're going to get as many ones as twos in the long run. We have a lot fewer tools to, if I give you a specific pattern like the digits of pi, how can I show that this doesn't have some weird pattern to it? Some other work that I spend a lot of time on is to prove what are called structure theorems or inverse theorems that give tests for when something is very structured. So some functions are what's called additive.

Like if you have a function that has natural numbers, the natural numbers, so maybe two maps to four, three maps to six, and so forth. Some functions are also called additive, which means that if you add two inputs together, the output gets added as well. For example, multiply by a constant. If you multiply a number by 10, if you multiply a plus b by 10, that's the same as multiplying a by 10 and b by 10 and then adding them together. So some functions are additive.

Some functions are kind of additive, but not completely additive. So, for example, if I take a number n, I multiply by the square root of 2, and I take the integer part of that. So, 10 by square root of 2 is like 14 point something, so 10 up to 14, 20 up to 28. So in that case, additively is true then, so 10 plus 10 is 20 and 14 plus 14 is 28. But because of this rounding, sometimes there's round off errors and sometimes when you add a plus b, this function doesn't quite give you the sum of the two individual outputs, but the sum plus or minus one.

So it's almost additive, but not quite additive. So there's a lot of useful results in mathematics and I've worked a lot on developing things like this to the effect that if a function exhibits some structure like this, then there's a reason for why it's true. And the reason is because there's some other nearby function which is actually completely structured which is explaining this sort of partial pattern that you have. And so, if you these little inverse theorems, it creates this sort of dichotomy that either the objects that you study either have no structure

at all or they are somehow related to something that is structured. And in either case, you can make progress. A good example of this is that there's this old theorem in mathematics called Szemeredi's theorem proven in the 1970s. It concerns trying to find a certain type of pattern in a set of numbers, the patterns of arithmetic progression, things like three, five, and seven, or 10, 15, and 20. And Szemeredi proved that any set of numbers that are sufficiently big, positive density, has arithmetic progressions in it of any length you wish. So for example, the odd numbers have a density of one half,

and they contain arithmetic progressions of any length. So in that case, it's obvious because the odd numbers are really, really structured. I can just take 11, 13, 15, 17, I can easily find arithmetic progressions in that set. But Zermatt & Simm also applies to random sets. If I take the set of all numbers and I flip a coin for each number and I only keep the numbers for which I got a heads.

So I just flip coins, I just randomly take out half the numbers I keep on half. So that's a set that has no patterns at all. But just from random fluctuations, you will still get a lot of arithmetic progressions in that set. Can you prove that there's arithmetic progressions of arbitrary length within a random? Yes. Have you heard of the infinite monkey theorem? Usually, mathematicians give boring names to theorems, but occasionally they give colorful names. The popular version of the infinite monkey theorem is that if you have an infinite number of monkeys in a room with each typewriter, they type out text randomly. Almost surely one of them is going to generate the entire script of Hamlet or any other finite

string of text. It will just take some time, quite a lot of time actually. But if you have an infinite number, then it happens. So basically the thing is that if you take an infinite string of digits or whatever, eventually any finite pattern you wish will emerge. It may take a long time, but it will eventually happen. In particular, I think progressions of any length will eventually happen, but you need an extremely long random sequence for this to happen. I suppose that's intuitive.

It's just infinity. Yeah, infinity absorbs a lot of sins. Yeah. How are we humans supposed to deal with infinity? Well, you can think of infinity as an abstraction of a finite number for which you do not have a bound for. I mean, so nothing in real life is truly infinite. You can ask yourself questions like,

what if I had as much money as I wanted? Or what if I could go as fast as I wanted? And a way in which mathematicians formalize that is, mathematics has found a formalism to idealize instead of something being extremely large or extremely small to actually be exactly infinite or zero. Often the mathematics becomes a lot cleaner when you do that. In physics, we joke about assuming spherical cows. Real world problems have got all kinds of real world effects, but you can idealize, send something to infinity, send something to zero.

The mathematics becomes a lot simpler to work with it. I wonder how often using infinity forces us to deviate from the physics of reality. Yeah, so there's a lot of pitfalls. So, you know, we spend a lot of time in undergraduate math classes teaching analysis, and analysis is often about how to take limits and whether you know, so for example, A plus B is always B plus A. So when you have a finite number of terms, you add them, you can swap them and there's no problem. But when you have an infinite number of terms, they're these sort of show

games you can play where you can have a series which converges to one value, but you rearrange it and it suddenly converges to another value. You can make mistakes. You have to know what you're doing when you allow infinity. You have to introduce these epsilons and deltas and there's a certain type of way of reasoning that helps you avoid mistakes. In more recent years, people have started taking results that are true in infinite limits and what's called

finite-izing them. So you know that something's true eventually, but you don't know when, now give me a rate. Okay, so if I don't have an infinite number of monkeys, but a large finite number of monkeys, how long do I have to wait for Hamlet to come out? And that's a more quantitative question. And this is something that you can attack by purely finite methods. And you can use your finite intuition. And in this case, it turns out to be exponential in the length of the text that you're trying to generate. And so this is why you never see the monkeys create a hamlet.

You can maybe see them create a four letter word, but nothing that big. And so I personally find once you finite an infinite statement, it does become much more intuitive and it's no longer so weird. So even if you're working with infinity, it's good to finite so that you can have some intuition. Yeah. The downside is that the finite proofs are just much, much messier.

And yeah, so the infinite ones are found first usually, like decades earlier, and then later on people finalize them. So since we mentioned a lot of math and a lot of physics, what is the difference between mathematics and physics as disciplines, as ways of understanding, of seeing the world? Maybe we can throw in engineering in there.

You mentioned your wife is an engineer, give it new perspective on circuits. So this different way of looking at the world, given that you've done mathematical physics. You've worn all the hats. Right. So I think science in general is an interaction between three things. There's the real world. There's what we observe of the real world, our observations, and then our mental models as to how we think the world works. So we can't directly access reality.

All we have are the observations which are incomplete and they have errors. And there are many, many cases where we would, we want to know, for example, what is the weather like tomorrow and we don't yet have the observation and we'd like to make a prediction. And then we have these simplified models, sometimes making unrealistic assumptions, you know, spherical cow type things. Those are

the mathematical models. Mathematics is concerned with the models. Science collects the observations and it proposes the models that might explain these observations. What mathematics does is we stay within the model and we ask what are the consequences of that model? What observations, what predictions would the model make of future observations or past observations? Does it fit observed data? So there's definitely a symbiosis. I guess mathematics is unusual among other disciplines is that we start from hypotheses like the axioms of

a model and ask what conclusions come out from that model. In almost any other discipline, you start with the conclusions, you know, I want to do this, I want to build a bridge, you know, I want to make money, I want to do this, okay, and then you find the paths to get there. There's a lot less sort of speculation about it. Suppose I did this, what would happen? Planning and modeling, speculative fiction maybe is one other place. But that's about it actually. Most of the things we do in life is conclusions driven, including physics and science. I mean, they want to know where is this asteroid going to go? What is the weather going to be tomorrow?

But mathematics also has this other direction of going from the axioms. What do you think? There is this tension in physics between theory and experiment. What do you think is the more powerful way of discovering truly novel ideas about reality? Well, you need both, top down and bottom up.

Yeah, it's a really really interaction between all these things. So over time, the observations and the theory and the modeling should both get closer to reality. But initially, and I mean, this is always the case, they're always far apart to begin with. But you need one to figure out where to push the other. So if your model is predicting anomalies that are not picked up by experiment, that tells experimenters where to look to find more data, to refine the models.

So it goes back and forth. Within mathematics itself, there's also a theory and experimental component. It's just that until very recently, theory has dominated almost completely. Like 99% of mathematics is theoretical mathematics. And there's a very tiny amount of experimental mathematics. People do do it. If they want to study prime numbers or whatever, they can just generate large datasets. So once we had computers, we began to do it a little bit.

Although even before, well, like Gauss, for example, he discovered reconjectured the most basic theorem in number theory, it's called the prime number theorem, which predicts how many primes that up to a million, up to a trillion, it's not obvious question. And basically what he did was that he computed, I mean, mostly by himself, but also hired human computers, people whose professional job it was to do arithmetic, to compute the first 100,000 tribes or something and made

tables and made a prediction. That was an early example of experimental mathematics. But until very recently, it was not, yeah, I mean, theoretical mathematics was just much more successful. I mean, because doing complicated mathematical computations was just much more successful because doing complicated mathematical computations was just not feasible until very recently. Even nowadays, even though we have powerful computers, only some mathematical things can be explored numerically.

There's something called the combinatorial explosion. If you want us to study, for example, Zermatt's theorem, you want to study all possible subsets of numbers 1 to 1,000. There's only 1,000 numbers. How bad could it be? It turns out the number of different subsets of 1 to 1,000 is 2 to the power 1,000, which is way bigger than any computer can currently can, in fact, any computer ever will ever enumerate. So you have to be, there are certain math problems that very quickly become just intractable to attack

by direct brute force computation. Chess is another famous example. The number of chess positions we can't get a computer to fully explore. But now we have AI. We have tools to explore this space not with 100% guarantees of success, but with experiment. So we can empirically solve chess now, for example. We have very, very good AIs that can – they don't explore every single position in the game tree, but they have found some very good approximation. And people are using

actually these chess engines to do experimental chess. They're revisiting old chess theories about, oh, you know, this type of opening, this is a good type of move, this is not. And they can use these chess engines to actually refine, and in some cases overturn, conventional wisdom about chess. And I do hope that mathematics will have a larger experimental component in the future,

perhaps powered by AI. We'll of course talk about that, but in the case of chess, and there's a similar thing in mathematics, I don't believe it's providing a kind of formal explanation of the different positions. It's just saying which position is better or not, that you can intuit as a human being.

And then from that, we humans can construct a theory of the matter. You've mentioned the Plato's cave allegory. So in case people don't know, it's where people are observing shadows of reality, not reality itself, and they believe what they're observing to be reality. Is that in some sense what mathematicians

and maybe all humans are doing is looking at shadows of reality? Is it possible for us to truly access reality? Well, there are these three ontological things. There's actual reality, there's observations, and our models. And technically they are distinct and I think they will always be distinct, right? But they can get closer over time. You know, so and the process of getting closer often means that you have to discard your initial intuitions. So astronomy provides great examples, you know, like an initial model of the world is flat because it looks flat, you know, and it's big, you know, and the rest of the universe, the skies is not, you know, like the sun, for example, looks really tiny. And so you start off with a model which is actually really far from reality, but it fits kind of the observations that you have. So things look good, but over time as you make more and more observations, bringing

it closer to reality, the model gets dragged along with it. And so over time we had to realize that the Earth was round, that it spins, it goes around the solar system, the solar system goes around the galaxy, and so on and so forth. And the guys about the universe, the universe is expanding, the expansion is self-expanding, accelerating. And in fact, very recently in this year or so, even the acceleration of the universe itself is this evidence that is non-constant.

And the explanation behind why that is- It's catching up. It's catching up. I mean, it's still the dark matter, dark energy, this kind of thing. Yes, we have a model that sort of explains, that fits the data really well. It just has a few parameters that you have to specify.

So people say, oh, that's fudge factors. With enough fudge factors, you can explain anything. But the mathematical point of the model is that you want to have fewer parameters in your model than data points in your observational set. So if you have a model with 10 parameters that explains 10 observations, that is completely useless model. It's what's called overfitted. But if you have a model with two parameters and it

explains a trillion observations, which is basically. So yeah, the dark matter model, I think it has like 14 parameters and it explains petabytes of data that the astronomers have. You can think of a theory, like one way to think about physical mathematical theory is it's a compression of the universe, a data compression. So you have these petabytes of observations, you'd like to compress it to a model which you can describe in five pages and specify a certain number of parameters.

And if it can fit to reasonable accuracy, almost all of your observations, I mean, the more compression that you make, the better your theory. In fact, one of the great surprises of our universe and of everything in it is that it's compressible at all. That's the unreasonable effectiveness of mathematics. Yeah, Einstein had a quote like that,

the most incomprehensible thing about the universe is that it is comprehensible. Right, and not just comprehensible, you can do an equation like E equals MC squared. There is actually some mathematical possible explanation for that. So there's this phenomenon in mathematics called universality. So many complex systems at the macro scale are coming out of lots of tiny interactions at the macro scale. And normally because of the common form of explosion, you would think that the macroscale equations must be infinitely, exponentially more complicated than the macroscale ones. And they are, if you want to solve them completely exactly.

Like if you want to model all the atoms in a box of air, that's like, Avogadro's number is humongous. There's a huge number of particles. If you actually have to track each one, it'll be ridiculous. But certain laws emerge at the microscopic scale that almost don't depend on what's going on at the macroscale, only depend on a very small number of parameters. If you want to model a gas of

Prentilian particles in a box, you just need to know its temperature and pressure and volume and a few parameters like five or six. It models almost everything just need to know its temperature and pressure and volume and a few parameters, like five or six. And it models almost everything you need to know about these 10 to 23 or whatever particles. So we don't understand universality anywhere near as we would like mathematically, but there are much simpler toy models where we do have a good understanding of why universality occurs. The most basic one is the central limit theorem that explains why the bell curve shows up

everywhere in nature. So many things are distributed by what's called a Gaussian distribution, a famous bell curve. There's not even a meme with this curve. And even the meme applies broadly, the universality to the meme. Yes, you can go meta if you like. But there are many, many processes. For example, you can take lots of independent random variables and average them together in various ways.

You can take a simple average or a more complicated average. And we can prove in various cases that these bell curves, these Gaussians emerge. And it is a satisfying explanation. Sometimes they don't. So if you have many different inputs and they all correlated in some systemic way, then you can get something very far from a bow curve show up. And this is also important to know when a system fails. So universality is not a 100% reliable thing to rely on. The global financial crisis was a famous example of this. People thought that mortgage defaults

had this sort of Gaussian type behavior that if you ask a population of 100,000 Americans with mortgages, ask what proportion of them were defaulting on mortgages. If everything was de-correlated, there would be a nice bell curve and you can manage risk with options and derivatives and so forth. And it is a very beautiful theory. But if there are systemic shocks in the economy that can push everybody to default at the same time, that's very non-Gaussian behavior. And this wasn't fully accounted for in 2008. Now I think there's some more awareness

that this is a systemic risk is actually a much bigger issue. And just because the model is pretty and nice, it may not match reality. So the mathematics of working out what models do is really important, but also the science of validating when the models fit reality and when they don't. You need both.

But mathematics can help because, for example, the central limit theorems, it told you that if you have certain axioms like non-correlation, that if all the inputs were not correlated to each other, then you have this Gaussian behavior, so things are fine. It tells you where to look for weaknesses in the model. So if you have a mathematical understanding of central limit theorem and someone proposes to use these Gaussian copulas or whatever to model default risk, if you're mathematically trained, you would say, okay, but what are the systemic correlation between all your inputs? And so then you can ask the economist, you know, how much a risk is that? And then you can go look for that. So there's always this synergy between science and mathematics. A little bit on the topic of

universality. You're known and celebrated for working across an incredible breadth of mathematics reminiscent of Hilbert a century ago. In fact, the great Fields Medal winning mathematician, Tim Gowers has said that you are the closest thing we get to Hilbert. Ha! He's a colleague of yours. Oh yeah, good friend.

But anyway, so you are known for this ability to go both deep and broad in mathematics. So you're the perfect person to ask, do you think there are threads that connect all the disparate areas of mathematics? Is there a kind of deep underlying structure to all of mathematics? There's certainly a lot of connecting threads and a lot of the progress of mathematics can be represented by taking by stories of two fields of mathematics

that were previously not connected and finding connections. An ancient example is geometry and number theory. So in the times of the ancient Greeks, these were considered different subjects. I mean, mathematicians worked on both. You could work both on geometry most famously, but also on numbers. But they were not really considered related. I mean, a little bit, like, you know, you could say that this length was five times this length because you could take five copies of this length

and so forth. But it wasn't until Descartes who really realized that he developed analytical geometry, that you can parameterize the plane, a geometric object by two real numbers. And so geometric problems can be turned into problems about numbers. And today, this feels almost trivial. There's no content to list.

Of course, a plane is x, x, and y, because that's what we teach and it's internalized. But it was an important development that these two fields were unified. And this process has just gone on throughout mathematics over and over again. Algebra and geometry were separated and now we have this fluid algebraic geometry that connects them over and over again. And that's certainly the type of mathematics that I enjoy the most. So I think there's sort of different styles to being a mathematician. I think hedgehogs and fox. Fox knows many things a little bit, but a hedgehog knows one thing very, very well.

And in mathematics, there's definitely both hedgehogs and foxes. And then there's people who are kind of, who can play both roles. And I think ideal collaboration between mathematicians involves very, you need some diversity. Like a fox working with many hedgehogs or vice versa. But I identify mostly as a fox. I like arbitrage somehow, like learning how one field works, learning the tricks of that wheel and then going to another field which people don't think is related, but I can adapt the tricks. So see the connections between the fields. Yeah, so there are other mathematicians who are far deeper than I am,

like they're really hedgehogs, they know everything about one field, and they're much faster and more effective in that field, but I can give them these extra tools. I mean, you said that you can be both a hedgehog and the fox depending on the context, depending on the collaboration. So what can you, if it's at all possible,

speak to the difference between those two ways of thinking about a problem? Say you're encountering a new problem, searching for the connections versus like very singular focus. I'm much more comfortable with the Fox paradigm. So, yeah, I like looking for analogies, narratives. I spend a lot of time, if there's a result, I see it in one field and I like the result, it's a cool result, but I don't like the proof.

Like it uses types of mathematics that I'm not super familiar with. I often try to re-prove it myself using the tools that I favor. Often my proof is worse, but by the exercise they're doing, I can say, oh, now I can see what the other proof was trying to do. And from that, I can get some understanding of the tools that are used in that field. So it's very exploratory, very doing crazy things in crazy fields and reinventing the wheel a lot. Whereas the hedgehog style is I think much more scholarly, very knowledge-based. You stay up to speed on all the developments in this field, you know all the history,

you have a very good understanding of exactly the strengths and weaknesses of each particular technique. Yeah, I think you'd rely a lot more on sort of calculation than sort of trying to find narratives. So yeah, I mean, I can do that too, but there are other people who are extremely good at that. Let's step back and maybe look at the, the, a bit of a romanticized version of mathematics. So, uh, I think you've said that early on in your life, uh, math was more like a puzzle solving activity when you were, uh, young. When did you first encounter a problem or proof where you realize math can have a kind of elegance and beauty to it? That's a good question. Um, when I came to graduate school, uh, in Princeton, um, so John Conway was

there at the time he passed away a few years ago, but, uh, I remember one of the very first research talks I went to was a talk by Conway on what he called extreme proof. So Conway just had this amazing way of thinking about all kinds of things in a way that you would normally think of. So he thought of proofs themselves as occupying some sort of space. So if you want to prove something, let's say that there's infinitely many primes, okay, there are all different proofs, but you could rank them in different axes. Some proofs are elegant, some proofs

are long, some proofs are elementary and so forth. And so, this is cloud. So, the space of all proofs itself has some sort of shape. And so, he was interested in extreme points of the shape. Out of all these proofs, what is the shortest at the expense of everything else or the most elementary or whatever? And so he gave some examples of well-known theorems and then he would give what he thought was the extreme proof in these different aspects. I just found that really eye-opening. It's not just getting a proof for what was interesting, but once you have that proof, you know, trying to optimize it in various ways, that proofing itself had some craftsmanship to it.

It's something for my writing style that, you know, like when you do your math assignments and as an undergraduate, your homework and so forth, you're sort of encouraged to just write down any proof that works, okay, and the hand is in, as long as it gets a tick mark, you move on. But if you want your results to actually be influential and be read by people, it can't just be correct. It should also be a pleasure to read, you read, motivated, be adaptable to generalize to other things. It's the same in many other disciplines like coding.

There's a lot of analogies between math and coding. I like analogies if you haven't noticed. You can code something spaghettical that works for a certain task and it's quick and dirty and it works. But there's lots of good principles for writing code well so that other people can use it, build upon it, and so on, and it has fewer bugs and whatever. And there's similar things with mathematics. So yeah, first of all, there's so many beautiful things there. And common is one of the great minds in mathematics ever and computer science. Just even considering the space of proofs and saying, okay, what does this space look like

and what are the extremes? Like you mentioned coding is an analogy, it's interesting because there's also this activity called code golf, which I also find beautiful and fun where people use different programming languages to try to write the shortest possible program that accomplishes a particular tasks. Yeah. And I believe there's even competitions on this. Yeah. Yeah.

And, uh, it's also a nice way to stress test, not just the sort of the programs or in this case, the proofs, but also the different languages. Maybe that's a different notation or whatever to use to accomplish a different task. Yeah, you learn a lot. I mean, it may seem like a frivolous exercise, but it can generate all these insights,

which if you didn't have this artificial objective to pursue, you might not see. What to use the most beautiful or elegant equation in mathematics? I mean, one of the things that people often look to in beauty is the simplicity. So if you look at E equals MC squared,

so when a few concepts come together, that's why the Euler identity is often considered the most beautiful equation in mathematics. Do you find beauty in that one, in the oil identity? Yeah. Well, as I said, what I find most appealing is connections between different things. So if you eat the pie, I equals minus one. So yeah, people always use all the fundamental constants.

Okay. That's, I mean, that's cute. But to me, so the exponential function was just to measure exponential growth. So compound interest or decay, or anything which is continuously growing, continuously decreasing growth and decay, or dilation or contraction is modeled by the exponential function. Whereas pi comes around from circles and rotation.

If you want to rotate a needle, for example, 180 degrees, you need to rotate by pi radians. And i, complex numbers, represents the swapping between the root and the imaginary axis of a 90-degree rotation, so a change in direction. So the exponential function represents growth and decay in the direction that you already are. When you stick an i in the exponential,, instead of motion in the same direction as your current position, it's motion as right angles to your current position, so rotation. And then so, e to the pi equals minus one tells you that if you rotate for time pi,

you end up at the other direction. So it unifies geometry through dilation and exponential growth dynamics through this act of complexification, rotation by eye. So it connects together all these two as mathematics. Yeah. Yeah. The dynamics, geometry and complex and complex and the complex numbers, they're all considered almost their own next door neighbors in mathematics because of this identity.

Do you think the thing you mentioned is cute, the, the, the, the collisional notations from these disparate fields, um fields is just a frivolous side effect or do you think there is legitimate value in notation although our old friends come together in the night? Well, it's confirmation that you have the right concepts. So when you first study anything, you have to measure things and give them names.

And initially, sometimes because your your model is, again, too far off from reality, you give the wrong things the best names, and you only find out later what's really important. Physicists can do this sometimes. I mean, but it turns out okay. So actually, with physics, I saw E equals Epsilon squared. Okay, so one of the big things was the E, right?

So when Aristotle first came up with his laws of motion and then Galileo and Newton and so forth, you know, they saw the things they could measure. They could measure mass and acceleration and force and so forth. And so Newtonian mechanics, for example, I think it was MA was the famous Newton's second law of motion. So those were the primary objects.

So they gave them the central building in the theory. It was only later after people started analyzing these equations that there always seemed to be these quantities that were conserved, so in particular momentum and energy. And it's not obvious that things happen in energy. It's not something you can directly measure the same way you can measure mass and velocity, so both. But over time, people realized that this was actually a really fundamental concept., eventually in the 19th century, reformulated Newton's laws of physics into what's called Hamiltonian mechanics, where the energy, which is now called the Hamiltonian, was the dominant object. Once you know how to measure the Hamiltonian of any system,

you can describe completely the dynamics, like what happens to all the states. It really was a central actor, which was not obvious initially. And this change of perspective really helped when quantum mechanics came along because the early physicists who studied quantum mechanics had a lot of trouble trying to adapt their Newtonian thinking because everything was a particle and so forth to quantum mechanics because I think it was a wave. It just looked really, really weird. Like you ask what is the quantum division if it was MA? And it's really, really hard to give an answer to that. But it turns out that the Hamiltonian, which was so secretly behind the scenes in classical mechanics also is the key object in quantum mechanics.

There's also an object called a Hamiltonian. It's a different type of object. It's what's called an operator rather than a function. But again, once you specify it, you specify the entire dynamics. So there's something called Schrodinger's equation that tells you exactly how quantum systems evolve once you have a Hamiltonian. So side by side, they look completely different objects, you know, like one involves particles,

one involves waves and so forth. But with this centrality, you could start actually transferring a lot of intuition and facts from classical mechanics to quantum mechanics. So for example, in classical mechanics, there's this thing called Nutter's theorem. Every time there's a symmetry in a physical system, there was a conservation law.

So the laws of physics are translation invariant. If I move 10 steps to the left, I experience the same laws of physics as if I was here, and that corresponds to conservation momentum. If I turn around by some angle, again, I experience the same laws of physics. This corresponds to conservation of angular momentum. If I wait for 10 minutes, I still have the same laws of physics. So this time transition invariance, this corresponds to the law of conservation of energy. So there's this fundamental connection between symmetry and conservation. And that's also true in quantum mechanics, even though

the equations are completely different, but because they're both coming from the Hamiltonian, the Hamiltonian controls everything. Every time the Hamiltonian has a symmetry, the equations will have a conservation law. So once you have the right language, it actually makes things a lot cleaner. One of the problems is why we can't unify quantum mechanics and general relativity yet. We haven't figured out what the fundamental objects are. For example, we have to give up the notion of space and time being these almost Euclidean-type spaces.

And it has to be, you know. We kind of know that at very tiny scales there's going to be quantum fluctuations, there's space-time foam, and trying to use Cartesian coordinates XYZ is going to be a non-starter. But we don't know what to replace it with. We don't actually have the mathematical concepts. The animal got a Hamiltonian that sort of organized everything. Does your gut say that there is a theory of everything, so this is even possible to unify to find this language that unifies general relativity and quantum mechanics? I believe so. I mean, the history of physics has been that of unification, much like mathematics over the years. Electricity and magnetism were separate theories and then Maxwell unified them. Newton unified

the motions of heavens with the motions of objects on the Earth and so forth. So it should happen. Again, to go back to this model of the observations and theory, part of our problem is that physics is a victim of someone's success. Two big theories of physics, general relativity and quantum mechanics, are so good now. So together, they cover 99.9% of all the observations we can make. And you have to either go to extremely insane particle accelerations or the early universe or things that are really hard to measure in order to get any deviation from either of these two theories to the point where you can actually figure out how to combine them together. But I have faith that we've been doing this for centuries, we've made

progress before, and there's no reason why we should stop. CB Do you think you will be a mathematician that develops a theory of everything. What often happens is that when the physicists need some theory of mathematics, there's often some precursor that the mathematicians worked out earlier. So when Einstein started realizing that space was curved, he went to some mathematician and asked, is there some theory of curved space that the mathematicians already came up with that could be useful? And he said, oh yeah, I think Riemann came up with something. And so yeah, Riemann had developed Riemannian geometry, which is precisely a theory of spaces that are curved in various general ways, which turned out to be almost exactly what was needed

for Einstein's theory. This is going back to witness unreasonable effectiveness of mathematics. I think the theories that work well to explain the universe tend to also involve the same mathematical objects that work well to solve mathematical problems. Ultimately they're just both ways of organizing data in useful ways. It just feels like you might need to go some weird land that's very hard to to intuit. Like you have like string theory yeah that that's that was that was a leading candidate for many decades it's I think is slowly pulling out of fashion because it's not matching experiment so one of

the big challenges of course like you said is experiment is very tough yes because of the how effective yeah both theories are but the other is like just you know, you're talking about, you're not just deviating from space time, you're going into like, some crazy number of dimensions, you're doing all kinds of weird stuff that, to us, we've gone so far from this flat earth

that we started at, like you mentioned. And now we're just, it's very hard to use our limited ape descendants of cognition to intuit what that reality really is like. This is why analogies are so important. I mean, so yeah, the round earth is not intuitive because we're stuck on it. But round objects in general, we have pretty good intuition over and we have interest about light works and so forth.

And like it's actually a good exercise to actually work out how eclipses and phases of the Sun and the Moon and so forth can be really easily explained by round Earth and round Moon and models. And you can just take a basketball and a golf ball and a light source and actually do these things yourself. So the intuition is there, but you have to transfer it. That is a big leap intellectual for us to go from flat to round earth, because our life is mostly lived in flat land. To load that information, and we're all like,

take it for granted. We take so many things for granted because science has established a lot of evidence for this kind of thing, but we're in a round rock, flying through space. That's a big leap, and you have to take a chain of those leaps, the more and more and more we progress. Right. Yeah.

So modern science is maybe again a victim of its own success, is that in order to be more accurate, it has to move further and further away from your initial intuition. And so for someone who hasn't gone through the whole process of science education, it looks more and more suspicious because of that. So we need more grounding. I mean, there are scientists who do excellent outreach, but there's lots of science things that you can do at home. Lots of YouTube videos.

I did a YouTube video recently of Grant Sanderson, we talked about this earlier, how the ancient Greeks were able to measure things like the distance of the Moon, distance of the Earth, and using techniques that you could also replicate yourself. It doesn't all have to be fancy space telescopes and really intimidating mathematics. Yeah, I highly recommend that. I believe you gave a lecture and you also did an incredible video with Grant. It's a beautiful experience to try to put yourself in the mind of a person from that time, shrouded in mystery.

You're like on this planet, you don't know the shape of it, the size of it. You see some stars, you see some things, and you try to like localize yourself in this world and try to make some kind of general statements about distance to places. Change of perspective is really important. You say travel broadens the mind,

this is intellectual travel. Put yourself in the mind of the ancient Greeks or some other person, some other time period, make hypotheses, spherical cows, whatever, speculate. And this is what mathematicians do and some artists do actually. It's just incredible that given the extreme constraints, you can still say very powerful things. That's why it's inspiring. Looking back in history, how much can be figured out when you don't have much to figure out stuff with?

If you propose axioms, then the mathematics tests you follow those axioms to their conclusions. And sometimes you can get quite a long way from initial hypotheses. If we stay in the land of the weird, you mentioned general general relativity are intriguing to you, challenging to you? I have worked on some equations, something called the wave maps equation, or the sigma field model, which is not quite the equation of space-time gravity itself, but of certain fields that might exist on top of space-time. So, science equations of relativity just describe space and time itself. but of certain fields that might exist on top of space-time. So Einstein's equations of relativity just describe space and time itself.

But then there's other fields that live on top of that. There's the electromagnetic field, there's things called Yang-Mills fields, and there's this whole hierarchy of different equations, of which Einstein is considered one of the most nonlinear and difficult. But relatively low on the hierarchy was this thing called the wavenaps equation. So it's a wave which at any given point is fixed to be like on a sphere. So I can think of a bunch of arrows in space and time and yeah, it's pointing in different directions, but they propagate like waves. If you wiggle an arrow, it will propagate and make all the arrows move kind of like sheaves of wheat in the wheat field.

And I was interested in the global regularity problem again for this question. Is it possible for all the energy here to collect at a point? So the equation I considered was actually what's called a critical equation where it's actually the behavior at all scales is roughly the same. And I was able barely to show that you couldn't actually force a scenario where all the energy concentrated at one point, that the energy had to disperse a little bit and the moment it disperse a little bit, it would stay regular. Yeah, this was back in 2000. That was part of why I got interested in Nereus Oaks afterwards, actually.

Yeah, so I developed some techniques to solve that problem. So part of it is this problem is really nonlinear because of the curvature of the sphere. There was a certain nonlinear effect which was a non-perturbed effect. When you looked at it normally, it looked larger than the linear effects of the wave equation. And so it was hard to keep things under control even when the energy was small. But I developed what's called a gauge transformation. So the equation is kind of like an evolution of heaves of wheat and they're all bending back and forth.

And so there's a lot of motion. But like, if you imagine like stabilizing the flow by attaching little cameras at different points in space which are trying to move in a way that captures most of the motion. And under this sort of stabilized flow, the flow becomes a lot more linear. I discovered a way to transform the equation

to reduce the amount of nonlinear effects. And then I was able to solve the equation. I found this transformation while visiting my aunt in Australia. And I was trying to understand the dynamics of all these fields and I couldn't do a pen and paper. And I had none of the facility of computers to do any computer simulations. So I ended up closing my eyes, being on the floor, I just imagined myself to actually

be this vector field and rolling around to try to see how to change coordinates in such a way that somehow things in all directions would behave in a reasonably linear fashion. And yeah, my aunt walked in on me while I was doing that. And she was asking, what am I doing doing this? It's complicated is the answer. Yeah, yeah. And she said, okay, fine, you're a young man. I don't ask questions.

I have to ask about the, you know, how do you approach solving difficult problems? If it's possible to go inside your mind when you're thinking, are you visualizing in your mind the mathematical objects, symbols maybe, what are you visualizing in your mind usually when you're thinking? A lot of pen and paper. One thing you pick up as a mathematician is sort of, I call it cheating strategically. So the beauty of mathematics is that you get to change the rules,

change the problem, change the rules as you wish. Like this, you don't get to do this for any other field. Like, you know, if you're an engineer and someone says, build a bridge over this river, you can't say, I want to build this bridge over here instead, or I want to build it out of paper instead of steel. But in mathematics, you can do whatever you want. It's like trying to solve a computer game where there's unlimited cheat codes available. So you can set this dimension

that's too large, I'll set it to one. I'd solve the one-dimensional problem first. There's a main term and an error term. I'm going to make a spherical cow assumption. I'll assume the error term is zero. And so the way you should solve these problems is not in sort of this Iron Man mode where you make things maximally difficult. But actually, the way you should approach any reasonable math problem is that if there are 10 things that are making life difficult, find a version of the problem that turns off nine of the difficulties but only keeps one of them and solve that.

And then that just figures. So you install nine cheats. Okay, if you solve 10 cheats, then the game is trivial. But if you solve nine cheats, you solve one problem that teaches you how to deal with that particular difficulty. And then you turn that one off and you turn something else on and then you solve that one. And after you know how to solve the 10 problems, 10 difficulties separately, then you have to start merging them a few at a time. As a kid, I watched a lot of these Hong Kong action movies from a culture. And one thing is that every time there's a fight scene, so maybe the hero gets swarmed by a hundred bad guy goons or whatever, but it'll always be choreographed so that you'd

always be only fighting one person at a time and then you would defeat that person and move on. And because of that, he could defeat all of them. But whereas if they had fought a bit more intelligently and just swarmed the guy at once, it would make for much worse cinema, but they would win. AC Are you usually pen and paper? Are you working with computer and latech? Mostly pen and paper, actually.

So in my office, I have four giant blackboards. And sometimes I just have to write everything I know about the problem on the four blackboards and then sit on my couch and just sort of see the whole thing. Is it all symbols like notation or is there some drawings? Oh, there's a lot of drawing and a lot of bespoke doodles that only make sense to me. I mean, and the beautiful blackboard is erased and it's a very organic thing.

I'm beginning to use more and more computers, partly because AI makes it much easier to do simple coding things. If I wanted to plot a function before, which is moderately complicated, some iteration or something, I'd have to remember how to set up a Python program and how does a for loop work and debug it and it would take two hours and so forth. And now I can do it in 10, 15 minutes. I'm using more and more computers to do simple explorations. Let's talk about AI a little bit if we could. So maybe a good entry point is just talking about computer-assisted proofs in general. Can you describe the Lean formal proof programming language and how it can help as a proof assistant

and maybe how you started using it and how it has helped you. So Lean is a computer language, much like sort of standard languages like Python and C and so forth. Except that in most languages, the focus is on using executable code. Lines of code do things, you know, they flip bits or they make a robot move

or they deliver you text on the internet or something. So Lean is a language that can also do that. It can also be run as a standard traditional language, but it can also produce certificates. So a software language like Python might do a computation and give you the answer is seven. Okay, that it does the sum of three plus four is equal to seven, but Lean can produce not just the answer, but a proof that how it got the answer of 7 as 3 plus 4 and all the steps involved in it. So it creates these more complicated objects, not just statements, but statements with proofs attached to them.

And every line of code is just a way of piecing together previous statements to create new ones. So the idea is not new. These things are called proof assistants. And so they provide languages for which you can create quite complicated, intricate mathematical proofs. And they produce these certificates that give a 100% guarantee that your arguments are correct if you trust the compiler, oftenly. But they made the compiler really small.

And there are several different compilers available for the same level. Can you give people some intuition about the difference between writing on pen and paper versus using lean programming language? How hard is it to formalize a statement? So lean, a lot of mathematicians were involved in the design of lean.

So it's designed so that individual lines of code resemble individual lines of mathematical argument. You might want to introduce a variable, you might want to prove a contradiction. There are various standard things that you can do and it's written so that ideally it's like a one-to-one correspondence. In practice, it isn't because Lean is explaining a proof to an extremely pedantic colleague who will point out, okay, did you really mean this? Like what happens if this is zero?

Okay, how do you justify this? So Lean has a lot of automation in it to try to be less annoying. So for example, every methodical object has to come with a type, like if I talk about X, is X a real number or a natural number or a function or something? If you write things informally, it's often in the context. You say clearly x is equal to, let x be the sum

of y and z and y and z were already real numbers, so x should also be a real number. So Lean can do a lot of that, but every so often it says, wait a minute, can you tell me more about what this object is, what type of object it is? You have to think more at the philosophical level, not just sort of computations that you're doing, but sort of what each object actually is in some sense. Is it using something like LLMs to do the type inference or like you match it with the real-time? It's using much more traditional, what's called good old fashioned AI. You can represent all these things as trees

and there's always algorithm to match one tree to another tree. So it's actually doable to figure out if something is a real number or a natural number. Yeah, every object comes with a history of where it came from and you can kind of trace it. Oh, I see. Yeah, so it's designed for reliability. So modern AIs are not used in, it's a disjoint technology.

People are beginning to use AIs on top of Lean. So when a mathematician tries to program a proof in Lean, often there's a step, okay, now I want to use the fundamental calculus, okay, to do the next step. So the Lean developers have built this massive project called Methlib, a collection of tens of thousands of useful facts about methodical objects. And somewhere in there is the fundamental calculus, but you need to find it.

So a lot of the bottleneck now is actually lemma search. There's a tool that you know is in there somewhere and you need to find it. And so there are various search engines specialized for Metholib that you can do. But there's now these large language models that you can say, I need the fundamental calculus at this point. And it was like, okay, for example, when I code, I have GitHub Copilot installed as a plugin to my IDE. And it scans my text and it sees what I need. It says, you know, I might even type it. Okay, now I need to use the fundamental calculus. Okay. And then it might suggest, okay, try this. And like maybe 25% of the time, it works exactly.

And then another 10, 15% of the time, it doesn't quite work, but it's close enough that I can say, oh, yeah, if I just change it here and here, it will work. And then like half the time, it gives me complete rubbish. So but people are beginning to use AIs a little bit on top, mostly on the level of basically fancy autocomplete, that you can type half of one line of a proof and it will tell you. Yeah, but a fancy, especially fancy with the sort of capital letter F is remove some of the friction a mathematician might feel when they move from pen and paper to

formalizing. Yes, yeah. So right now I estimate that the effort, time and effort taken to formalize a proof is about 10 times the amount taken to write it out. Yeah, so it's doable, but you don't, it's annoying. But doesn't it like kill the whole vibe of being a mathematician? Yeah, so I mean-

Having a pedantic coworker. Right, yeah, if that was the only aspect of it, okay. But okay, there's some things, because that was the only aspect of it. Okay. There's some cases where it's actually more pleasant to do things formally. So there's a theorem I formalized and there's a certain constant 12 that came out in the final statement. And so this 12 had to be carried all through the proof. And like everything had to be checked. All these other numbers had to be consistent with this final number 12. And then so we wrote a paper through this theorem with this number 12. And then a few weeks later, someone said, oh, we can actually improve this 12 to an

11 by reworking some of these steps. And when this happens with pen and paper, every time you change a parameter, you have to check line by line that every single line of your proof still works. And there can be subtle things that you didn't quite realize, some properties of the number 12 that you didn't even realize that you were taking advantage of. So a proof can break down at a subtle place. So we had formalized the proof with this constant 12.

And then when this new paper came out, we said, oh, so that took like three weeks to formalize and like 20 people to formalize this original proof. So now let's update the 12 to 11. And what you can do with Lean, so you just in your headline theorem, you change the 12 to 11, you run the compiler and like off the thousands of lines of code you have, 90% of them still work.

And there's a couple that are lined in red. Now I can't justify these steps, but it immediately isolates which steps you need to change. But you can skip over everything which works just fine. And if you program things correctly with good programming practices, most of your lines will not be read. And there'll just be a few places where you, I mean, if you don't hard code your constants, but you sort of, you use smart tactics and so forth, you can localize the things you need to change to a very small period of time. So it's like within a day or two, we had updated our proof.

Because this is a very quick process. You make a change, there are 10 things now that don't work. For each one, you make a change and now there's five more things that don't work. But the process converges much more smoothly than with pen and paper. So that's for writing. Are you able to read it? Like if somebody else has a proof, are you able to like what's the versus paper? Yeah, so the proofs are longer, but each individual piece is easier to read. So if you take a math paper and you jump to page 27 and you look at paragraph six and you have a

line of text or math, I often can't read it immediately because it assumes various definitions, which I had to go back and maybe on 10 pages earlier, this was defined. And the proof is scattered all over the place and you basically are forced to read fairly sequentially. It's not like say a novel where like, you know, in theory, you could open up a novel halfway through and start reading. There's a lot of context. But when a proof in lean, if you put your cursor on a line of code, every single object there, you can hover over it and it will say what it is, where it came from, where the stuff is justified. You can trace things back much easier than flipping through a math paper.

So one thing that Lean really enables is actually collaborating on proofs at a really atomic scale that you really couldn't do in the past. So traditionally, with pen and paper, when you want to collaborate with another mathematician, either you do it at a blackboard where you can really interact, but if you're doing it sort of by email or something, basically, yeah, you have to segment it. So I'm gonna finish section three, you do section four,

but you can't really sort of work on the same thing, collaborate at the same time. But with Lean, you can be trying to formalize some portion of the proof and say, oh, I got time. But with Lean, you can be trying to formalize some portion of the proof and say, I got stuck at line 67 here, I need to prove this thing, but it doesn't quite work. Here's the three lines of code I'm having trouble with. But because all the context is there, someone else can say, oh, okay, I recognize what you need to do. You need to apply this trick or this tool. And you can do extremely atomic

level conversations. So because of Lean, I can collaborate with dozens of people across the world, most of whom I have never met in person. And I may not know actually even how reliable they are in the proof-taking field, but Lean gives me a certificate of trust. So I can do trustless mathematics. So there's so many interesting questions. So one one you're known for being a great collaborator. So what is the right way to approach solving a difficult problem in mathematics when you're collaborating?

Are you doing a divide and conquer type of thing, or are you focused on a particular part and you're brainstorming? There's always a brainstorming process first. Math research projects by their nature, when you start, you don't really know how to do the problem. It's not like an engineering project where some other theory has been established for decades and its implementation is the main difficulty.

You have to figure out even what is the right path. This is what I said about cheating first. It's like, to go back to the bridge building analogy, first assume you have infinite budget and unlimited amounts of workforce and so forth. Now can you build this bridge? Okay. Now have infinite budget, but only finite workforce. Now can you do that and so forth?

So of course, no engineer can actually do this because they have fixed requirements. Yes, there's this sort of jam sessions always at the beginning where you try all kinds of crazy things and you make all these assumptions that are unrealistic but you plan to fix later. And you try to see if there's even some skeleton of an approach that might work. And then hopefully that breaks up the problem into smaller sub-problems, which you don't know how to do, but then you focus on the sub-problems. And sometimes different collaborators are better at working on certain things. So one of my themes I'm known for is a theme of Ben Green, which is now called the Green

Tau theorem. It's a statement that the primes contain arithmetic progressions of any event. So it was a modification of this thema semiready. And the way we collaborated was that Ben had already proven a similar result for progressions of length three. He showed that sets like the primes contain lots and lots of progressions of length three, and even subsets of the primes, certain subsets do. But his techniques only worked for length three progressions.

They didn't work for longer progressions. But I had these techniques coming from my Gothic theory, which is something that I had been playing with and I knew better than Ben at the time. And so if I could justify certain randomness properties of some set relating to the primes, like there's a certain technical condition which if I could have it, if Ben could supply me this fact, I could conclude the theorem. But what I asked was a really difficult question in number theory, which he said, no, there's no way we can prove this. So he said, can you prove your part

of the theorem using a weaker hypothesis that I have a chance to prove it? And he proposed something which he could prove, but it was too weak for me. I can't use this. So there's this conversation going back and forth. which he could prove, but it was too weak for me. I can't use this. So there was this conversation going back and forth. Different cheats too. Yeah, yeah, yeah. I want to cheat more, he wants to cheat less. But eventually we found a property which A, he could prove, and B, I could use, and then we could prove our view. And yeah, so there's all kinds of dynamics. I mean, every collaboration has some story.

No two are the same. And then on the flip side of that, like you mentioned, with Lean programming, now that's almost like a different story because you can do, you can create, I think you've mentioned a kind of a blueprint for a problem, and then you can really do a divide and conquer with lean where you're working on separate parts and they're using

the computer system proof checker essentially to make sure that everything is correct along the way. Yeah, so it makes everything compatible and yeah and trustable. Yeah, so currently only a few mathematical projects can be cut up in this way. At the current state of the art, most of the lean activity is on formalizing books that have already been proven by humans. Math paper basically is a blueprint in a sense. It is taking a difficult statement like big theorem and breaking it up into a hundred

little lemmas, but often not all written with enough detail that each one can be sort of directly formalized. A blueprint is like a really pedantically written version of a paper where every step is explained as much detail as possible and to try to make each step kind of self-contained and depending on only a very specific number of previous statements that have been proven so that each node of this blueprint graph that gets generated can be tackled independently of all the others. You don't even need to know how the whole thing works. It's like a modern supply chain.

If you want to create an iPhone or some other complicated object, no one person can build a single object. But you can have specialists who just, if they're given some widgets from some other company, they can combine them together to form a slightly bigger widget. I think that's a really exciting possibility because you can have, if you can find problems that could be

broken down this way, then you could have, you know, thousands of contributors, right? Yes, yes, yes. To be completely distributed. So I told you before about the split between theoretical and experimental mathematics, and right now, most mathematics is theoretical, and when you type it, it's experimental. I think the platform that Lean and other software tools, so GitHub and things like that, will allow experimental mathematics to

scale up to a much greater degree than we can do now. Right now, if you want to do any mathematical exploration of some mathematical pattern or something. You need some code to write out the pattern. And I mean, sometimes there are some computer algebra packages that can help, but often it's just one mathematician coding lots and lots of Python or whatever. And because coding is such an error-prone activity, it's not practical to allow other people to collaborate with you on writing modules for your code.

Because if one of the modules has a bug in it, the whole thing is unreliable. So you get these bespoke spaghetti code written by non-professional programmers, mathematicians, and they're clunky and slow. And so because of that, it's hard to really mass produce experimental results. But I think with Lean, I'm already starting some projects where we are not just experimenting with data, but experimenting with proofs. So I have this project called the Equational Theories Project. Basically we generated about 22 million little problems in abstract algebra.

Maybe I should back up and tell you what the project is. Okay, so abstract algebra studies operations like multiplication and addition and their abstract properties. So multiplication, for example, is commutative. X times Y is always Y times X, at least for numbers. And it's also associative. X times Y times Z is the same as X times Y times Z. So these operations obey some laws that don't obey others.

For example, X times X is not always equal to X. So that law is not always true. So given any operation, it obeys some laws and not others. And so we generated about 4,000 of these possible laws of algebra that certain operations can satisfy. And our question is, which laws imply which other ones? So for example, does communicativity imply associativity? And the answer is no, because it turns out you can describe an operation which obeys the communicative law, but doesnutativity imply associativity? And the answer is no,

because it turns out you can describe an operation which obeys the commutative law, but doesn't obey the associative law. So by producing an example, you can show that commutativity does not imply associativity, but some other laws do imply other laws by substitution and so forth. And you can write down some algebraic proof.

So we look at all the pairs between these 4,000 laws and this up to 22 million of these pairs. And for each pair we ask, does this law imply this law? If so, give a proof, if not give a count of example. So 22 million problems, each one of which you could give to like an undergraduate algebra student and they had a decent chance of solving the problem. Although there are a few, at least 22 million,

there are like 100 or so that are really quite hard, okay, but a lot are easy. And the project was just to work out to determine the entire graph, like which ones imply which other ones. That's an incredible project, by the way. Such a good idea, such a good test of the very thing we've been talking about

on a scale that's remarkable. Yeah, so it would not have been feasible. I mean, the state of the art in the literature was like 15 equations and sort of how they imply it. That's sort of at the limit of what a human independent paper can do 15 equations and sort of how they apply. That's the limit of what a human independent paper can do. So you need to scale it up. So you need to crowdsource, but you also need to trust all the… I mean, no one person can check 22 million of these proofs. You need to be computerized. And so it only became possible with Lean. We were hoping to use a lot of AI as well.

So the project is almost complete. So at least 20 million, all but two have been settled. Wow. And well, actually, and all those two, we have a pen and paper proof of the two and we were formalizing it. In fact, this morning I was working on finishing it. So we're almost done on this. It's incredible.

Yeah, fantastic. How many people were able to get? About 50, which in mathematics is considered a huge number. It's a huge number. That's crazy. Yeah. So we're going to have a paper of 50 authors and a big appendix of who contributed what. Here's an interesting question, not to maybe speak even more generally about it.

When you have this pool of people, is there a way to organize the contributions by level of expertise of the people, all the contributors? Now, okay, I'm asking a lot of pothead questions here, but I'm imagining a bunch of humans and maybe in the future some AIs. Can there be like an ELO rating type of situation where like a gamification of this? The beauty of these lean projects is that automatically you get all this data.

So like everything's uploaded to this GitHub and GitHub tracks who contributed what. So you could generate statistics from at any later point in time. You can say, oh, this person contributed this many lines of code or whatever. I mean, these are very crude metrics. I would definitely not want this to become like, you know contributed this many lines of code or whatever. These are very crude metrics. I would definitely not want this to become part of your tenure review or something. But I think already in enterprise computing, people do use some of these metrics as part of the assessment of performance of an employee. Again, this is a direction which is a bit scary for academics to go down.

We, we, we don't like metrics so much. And yet academics use metrics. They just use old ones. Number of papers. Yeah. Yeah. It's true. It's true that, I mean, um, it feels like this is a metric while flawed is, is

going in the more in the right direction, right? Yeah. It's interesting. I mean, at more in the right direction, right? Yeah. It's an interesting, at least it's a very interesting metric. Yeah. I think it's interesting to study. I mean, I think you can do studies of whether these are better predictors. Um, there's this problem called good heart's law. If a statistic is actually used to incentivize performance, it becomes

gained, um, and then it is no longer a useful measure. Oh, humans always. Yeah. Yeah. I know. I mean, it's rational. So what we've done for this project is self-report. So there are actually these standard categories

from the sciences of what types of contributions people give. So there's this concept and validation and resources and coding and so forth. So there's a standard list of 12 or so categories. And we just ask each contributor to, there's a big list of 12 or so categories. And we just ask each contributor to this big matrix of all the authors and all the categories just to tick the boxes where they think that they contributed. And just give a rough idea, you know, like, oh, so you did some coding and you provided some compute, but you didn't do any of the pen and paper verification or whatever.

And I think that that works out. Traditionally, mathematicians just order alphabetically by surname. So we don't have this tradition as in the sciences of lead author and second author and so forth, which we're proud of. We make all the authors equal status, but it doesn't quite scale to this size. So a decade ago, I was involved in these things called polymath projects. It was the crowdsourcing mathematics mathematics but without the lean component. So it was limited by, you needed a human moderator

to actually check that all the contributions coming in were actually valid. And this was a huge bottleneck actually. But still we had projects that were 10 authors or so. But we had decided at the time not to try to decide who did what, but to have a single pseudonym. So we created this fictional character called

D.H.J. Polymath in the spirit of Bo Baki. Bo Baki is the pseudonym for a famous group of mathematicians in the 20th century. But, and so the paper was also authored on the pseudonym. So none of us got the author credit. This actually turned out to be not so great for a couple of reasons. So one is that if you actually wanted to be considered for tenure or whatever, you could not use

this paper as you submitted it as one of your publications because you didn't have the formal author credit. The other thing that we've recognized much later is that when people referred to these projects, they naturally referred to the most famous person who was involved in the project. Oh, so this was Tim Gower's point of view project, this was Terence Tao's point of view project, and not mention the other 19 or whatever people

that were involved. So we're trying something different this time around where we have, everyone's an author, but we will have an appendix with this matrix, and we'll see how that works. I mean, so both projects are incredible, just the fact that you're involved in such huge collaborations. But I think I saw a talk from Kevin Buzzard about the linear programming languages a few

years ago, and he was saying that this might be the future of mathematics. And so it's also exciting that you're embracing embracing one of the greatest mathematicians in the world, embracing this, what seems like the paving of the future of mathematics. So I have to ask you here about the integration of AI into this whole process. So DeepMind's alpha proof was trained using reinforcement learning on both failed

and successful formal lean proofs of IMO problems. So this is sort of high level high school. Oh, very high level, yes. Very high level high school level mathematics problems. What do you think about the system and maybe what is the gap between this system that is able to prove the high school level problems

versus gradual level problems. Yeah, the difficulty increases exponentially with the number of steps involved in the proof. It's a commentarial explosion. So the thing with large language models is that they make mistakes. And so if a proof has got 20 steps and your large language model has a 10% failure rate

at each step of going in the wrong direction. It's just extremely unlikely to actually reach the end. Actually, just to take a small tangent here, how hard is the problem of mapping from natural language to the formal program? Oh yeah, it's extremely hard actually. Natural language, it's very fault tolerant. You can make a few minor grammatical errors and a speaker in the second language can get some idea of

what you're saying. Yeah, but formal language, yeah, you know, if you get one little thing wrong, I think that the whole thing is nonsense. Even formal to formal is very hard. There are different incompatible pro-physis languages. There's Lean, but also Coq and Isabelle and so forth. Even converting from a formal language to formal language is an unsolved, basically an unsolved problem. That is fascinating. Okay, so, but once you have an informal language, they're using their RL train model,

so something akin to AlphaZero that they used to go to then try to come up with tools. They also have a model, I believe it's a separate model for geometric problems. So what impresses you about this system and what do you think is the gap? Yeah, we talked earlier about things that are amazing over time become kind of normalized. So now somehow it's, of course, geometry is a solvable problem. Right, that's true, that's true. So now somehow it's, of course geometry is a solvable problem. Right, that's true, that's true. I mean, it's still beautiful. Yeah, yeah, no, it's a great work.

It shows what's possible. I mean, the approach doesn't scale currently. Three days of Google's server is server time to solve one high school math problem. This is not a scalable prospect, especially with the exponential increase as the complexity increases. Which you mentioned that they got a silver medal performance.

The equivalent of. I mean, yeah. The equivalent of a silver medal performance. So first of all, they took way more time than was allotted, and they had this assistance where the humans started helped by formalizing. But also, they're giving us those full marks for the solution, which I guess is formally verified. So I guess that's fair. There will be a proposal at some point to actually have an AI Math Olympiad where at the same time as the human contestants

get the actual Olympiad problems, the AIs will also be given the same contestants get the actual Olympiad problems. AS will also be given the same problems with the same time period. And the outputs will have to be graded by the same judges. Which means that will have to be written in natural language rather than formal language. Oh, I hope that happens. I hope this IMO happens.

I hope next one. It won't happen this IMO. The performance is not good enough in the time period. But there are smaller competitions. There are competitions where the answer is a number rather than a long form proof. And that's, AI is actually a lot better at problems where there's a specific numerical answer because it's easy to do reinforcement learning on it. Yeah, you got the right answer, you got the wrong answer. It's a very clear signal. But a long-form

proof either has to be formal and then the lean can give it a thumbs up, thumbs down, or it's informal. But then you need a human to create it. And if you're trying to do billions of reinforcement learning runs, you can't hire enough humans to grade those. I mean, it's already hard enough for the last language to do reinforcement learning on just the regular text that people get. But now if you hire people, not just give thumbs up, thumbs down, but actually check the output mathematically. Yeah. That's too expensive. So if we just explore this possible future,

what is the thing that humans do that's most special in mathematics? So that you could see AI not cracking for a while. So inventing new theories, so coming up with new conjectures versus proving the conjectures versus proving the conjectures, building new abstractions, new representations, maybe an AI turnstile with seeing new connections between disparate fields. It's a good

question. I think the nature of what mathematicians do over time has changed a lot. So a thousand years ago, mathematicians had to compute the date of Easter and there's really complicated calculations, but it's all automated, been automated centuries. We don't need that anymore. They used to navigate to do spherical navigation, spherical trigonometry to navigate how to get

from the old world to the new. It's like a very complicated calculation. Again, we'd been automated. Even a lot of undergraduate mathematics even before AI, like Wolfram Alpha, for example, is not a language model, but it can solve a lot of undergraduate-level math tasks. So on the computational side, verifying routine things like having a problem and say, here's a problem in partial differential equations. Could you solve it using any of the 20 standard techniques? And they say, yes, I've tried all 20,

and here are the 100 different permutations, and here's my results. And that type of thing, I think, will work very well. Type of scaling to once you solve one problem to make the AI attack 100 adjacent problems. The things that humans do still, so where the AI really struggles right now is knowing when it's made a wrong turn. And it can say, oh, I'm going to solve this problem.

I'm going to split up this problem into these two cases. I'm going to try this technique. And sometimes if you're lucky, it's a simple problem. It's the right technique and you solve the problem. And sometimes it will propose an approach which is just complete nonsense. But it looks like a proof. So this is one annoying thing about LLM-generated mathematics. We've had human-generated mathematics that's very low quality, like submissions from people

who don't have the formal training and so forth. But if a human proof is bad, you can tell it's bad pretty quickly. It makes really basic mistakes. But the AI generative proofs, they can look superficially flawless. And it's partly because that's what the reinforcement learning has actually trained them to do, to produce text that looks like what is correct, which for many applications is good enough. So the errors are often really subtle,

and then when you spot them, they're really stupid. Like no human would have actually made that mistake. Yeah, it's actually really frustrating in the programming context, because I program a lot. And yeah, when a human makes low quality code, there's something called code smell, right? You can tell, you can tell.

Immediately, like, okay, there's signs. But with AI-generated code, and then you're right, eventually you find an obvious dumb thing that just looks like good code. Yeah, so. It's very tricky too, and frustrating for some reason. Yeah, so. To have to work. Yeah.

So the sense of smell, this is one thing that humans have and there's a metaphorical mathematical smell that is not clear how to get the AIS to duplicate that. Eventually, I mean, so the way AlphaZero and so forth that make progress on Go and chess and so forth is in some sense they have developed so forth make progress on Go and chess and so forth is in some sense they have developed a sense of smell for Go and test positions. That this position is good for white, this is good for black.

They can't initiate why, but just having that sense of smell lets them strategize. So if AIs gain that ability to sort of, a sense of viability of certain proof strategies, so you can say, I'm going to try to break up this problem into two small subtasks. And then you can say, oh, this looks good. The two tasks look like they're simpler tasks than your main task.

And they still got a good chance of being true. So this is good to try. Or no, you've made the problem worse because each of the two subproblems is actually harder than your original problem, which is actually what normally happens if you try a random thing to try. It's very easy to transform a problem into an even harder problem. Very rarely do you transform into a simpler problem. So if they can pick up the sense of smell, then they could maybe start competing with

human-level mathematicians. So this is a hard question, but not competing, but collaborating. Yeah. Okay, hypothetical. If I gave you an oracle that was able to do some aspect of what you do and you could just collaborate with it. Yeah, yeah. What would that oracle, what would you like that oracle to be able to do? Would you like it to maybe be a verifier, like check, do the codes, like your, yes, Professor Tao, this is the correct,

this is a promising fruitful direction. Yeah, yeah, yeah. Or would you like it to generate possible proofs and then you see which one is the right one? Or would you like it to maybe generate different representation, totally different ways of seeing this problem? I think all of the above.

A lot of it is, we don't know how to use these tools because it's a paradigm that it's not, yeah, we have not had in the past assistance that are competent enough to understand complex instructions that can work at massive scale, but are also unreliable. It's an interesting, but unreliable in subtle ways, whereas providing sufficiently good output. It's an interesting combination. I mean, you have graduate students that you work with who kind of like this, but not at scale.

And we had previous software tools that can work at scale, but very narrow. So we have to figure out how to use. I mean, so Tim Gowell, you mentioned, he actually foresaw, like in 2000, he was envisioning what mathematics would look like in actually two and a half decades. That's funny. Yeah, he wrote in his article like a hypothetical conversation between a mathematical assistant of the future and himself, you know, trying to solve a problem and they would have a conversation

that sometimes the human would propose an idea and the AI would evaluate it. And sometimes the AI would propose an idea and sometimes a competition was required and the AI would just go and say, okay, I've checked the 100 cases needed here. Or the first you said this is true for all n, I've checked to put n up to 100 and it looks good so far. Or hang on, there's a problem at n equals 46. And so just a free-form conversation where you don't know in advance where things are gonna go,

but just based on, I think ideas are good for both sides, calculations are good for both sides. I've had conversations with AI where I say, okay, we're gonna collaborate to solve this math problem. And it's a problem that I already know a solution to. So I try to prompt it, okay, so here's the problem. I suggest using this tool.

And it'll find this lovely argument using a completely different tool, which eventually goes into the weeds and say, no, no, no, try using this. Okay, and I might start using this and then it'll go back to the tool that I wanted to do before. And you have to keep railroading it onto the path you want. And I could eventually force it to give the proof I wanted,

but it was like herding cats. And the amount of personal effort I had to take to not just to prompt it, but also check its output because a lot of what it looked like it's going to work. I know there's a problem on 917. And basically arguing with it, it was more exhausting than doing it unassisted. But that's the current state of the art. I wonder if there's a phase shift that happens to where it's no longer feels like hurting cats and maybe you'll surprise us how quickly that comes. I believe so. So in formalization, I mentioned before that it takes 10 times longer to formalize a proof and then divide it by hand. With these modern AI tools and also just better tooling,

than to write it by hand. With these modern AI tools, and also just better tooling, the lean developers are doing a great job adding more and more features and making it user-friendly. It's going from nine to eight to seven, okay, no big deal. But one day it will drop all to one, and that's a phase shift. Because suddenly it makes sense when you write a paper to write it in lean first or through a conversation with AI who's generally on the fly with you. And it becomes natural for journals to accept, and maybe they'll offer expedite refereeing. If a paper has already been formalized in lean, they'll just ask the referee to comment

on the significance of the results and how it connects to literature and not worry so much about the correctness because that's been certified. Papers are getting longer and longer in mathematics and it's harder and harder to get good refereeing for the really long ones, unless they're really important. It is actually an issue which in the formalization is coming in just the right time for this to be. And the easier and easier to guess because of the tooling and all the other factors, then you're going to see much more like math label grow potentially exponentially.

It's a virtuous cycle. Okay. I mean, one facet of this type that happened in the past was the adoption of LaTeX. So LaTeX is this typesetting language that all mathematicians use now. So in the past, people used all kinds of word processors and typewriters and whatever. But at some point, LaTeX became easier to use than all other competitors. And people would switch within a few years. It was just a dramatic phase shift.

It's a wild out there question, but what year, how far away are we from What year, how far away are we from AI system being a collaborator on a proof that wins the Fields Medal? So that level. Okay. Well, it depends on the level of collaboration. No, like it deserves to get the Fields Medal. So half and half. Okay.

Already, I can imagine if it was a metal wording paper, having some AI systems and writing it, you know, just, you know, like the auto-complete alone is already, I use it, like it speeds up my own writing. Like, you know, you can have a theorem, you have a proof and the proof has three cases. And I write down the proof of the first case and the auto-complete just suggests to me

that now here's how the proof of the case could work. And that was exactly correct. That was great. Saved me like five, 10 minutes of typing. But in that case, the AI system doesn't get the Fields Medal. No. I was talking 20 years, 50 years, 100 years. What do you think? Okay.

So I gave a bit of a print, but it print by 2026, which is now next year. There will be math collaborations with AI. So not fields metal winning, but actual research level math. Like published ideas that are in part generated by AI. Maybe not the ideas, but at least some of the computations, the verifications. Has that already happened? Has it already happened? verifications. Yeah, I mean, there are problems that were solved by a complicated process, conversing with AI to propose things and the human goes and tries it and the contract doesn't work, but it might pose a different idea. It's hard to disentangle exactly. There are certainly math results which could only have been accomplished because there

was a human mathematician and an AI involved. But it's hard to disentangle credit. These tools, they do not replicate all the skills needed to do mathematics, but they can replicate sort of some non-trivial percentage of them, 30, 40%. So they can fill in gaps. Coding is a good example. It's annoying for me to code in Python.

I'm not a native professional programmer. But with AI, the friction cost of doing it is much reduced. So it fills in that gap for me. AI is getting quite good at literature review. I mean, there's still a problem with hallucinating references that don't exist. But this, I think, is a civil war problem. If you train in the right way and so forth, you can verify using the internet. You should in a few years get to the point where you have a lemma that you need and say, has anyone proven this lemma before? And we'll do basically a fancy web search, AI assistant, and say, yeah, there are these

six papers where something similar has happened. And I mean, you can ask it right now and it will give you six papers of which maybe one is legitimate and relevant. One exists, but it's not relevant, and four are hallucinated. It has a non-zero success rate right now, but there's so much garbage,

so much, the signal-to-noise ratio is so poor that it's most helpful when you already somewhat know the relationship and you just need to be prompted to be reminded of a paper that was subconsciously in your memory. Versus helping you discover a new one you were not even aware of, but is the correct citation. Yeah, that it can sometimes do, but when it does, it's buried in a list of options for which the other... That are bad. Yeah.

I mean, being able to automatically generate a related work section that is correct, that's actually a beautiful thing that might be another phase shift because it assigns credit correctly. It breaks you out of the silos of thought. Yeah, yeah, yeah. There's a big hump to overcome right now. I mean, it's like self-driving cars The safety margin it has to be really high. Yeah to be To be feasible. So yeah, so there's a last mile problem with a lot of AI applications That you know, they can do our tools that work 20% 80% of the time, but it's still not good enough

I worked 20%, 80% of the time, but it's still not good enough, and in fact, even worse than good in some ways. I mean, another way of asking the fields medal question is what year do you think you'll wake up and be like real surprised? You read the headline, the news, or something happened that AI did, like, you know, real breakthrough, something.

It doesn't, you know, like fields medal, or even hypothesis, it could be like really just This alpha zero moment would go that right, right? Yeah, this this decade I can I can see it like making a conjecture Between two unrelated tooth tooth things that people would force unrelated. Oh interesting generating a conating a conjecture that's a beautiful conjecture. Yeah, and actually has a real chance of being correct and meaningful. Because that's actually kind of doable, I suppose. But where the data is, yeah.

Yeah. No, that would be truly amazing. The current models struggle a lot. I mean, so a version of this is, I mean, the physicists have a dream of getting the AIs to discover new laws of physics. The dream is you just feed it all this data, okay? And here is a new patent that we didn't see before. But it actually even struggled, the current state of the art even struggles to discover

old laws of physics from the data. Or if it does, there's a big concern of contamination that it did it only because it's somewhere in this training data, it already somehow knew, you know, Boyle's law or whatever you're trying to reconstruct. Part of it is that we don't have the right type of training data for this. Yeah, so the laws of physics, like we don't have like a million different universes with a million different balls of nature. We don't have a million different universes with a million different balls of nature. A lot of what we're missing in math is actually the negative space. We have published things that people have been able to prove and conjectures that ended

up being verified or kind of examples produced. We don't have data on things that were proposed and they're kind of a good thing to try, but then people quickly realized that it was the wrong conjecture and then they said, oh, but we should actually change our claim to modify it in this way to actually make it more plausible. There's a trial and error process, which is a real integral part of human mathematical discovery, which we don't record because it's embarrassing. We make mistakes and we only like to publish our wins. And the AI has no access to this data to train on. I sometimes joke that

basically AI has to go through grad school and actually go to grad courses, do the assignments, go to office hours, make mistakes, get advice on how to correct the mistakes and learn from that. Let me ask you, if I may, about, uh, Gregory Perlman. You mentioned that you try to be careful in your work and not let a problem completely consume you. Just you've really fallen in love with the problem and it really cannot rest until you solve it.

But you also hasted to add that sometimes this approach actually can be very successful. An example you gave is Guguru Perlman who proved the Poincare conjecture and did so by working alone for seven years with basically little contact with the outside world. Can you explain this one millennial prize problem that's been solved, Poincare conjecture, and maybe speak to the journey that Gagora Perlman's been on? All right.

So it's a question about curved spaces. Earth is a good example. So I think it was a 2D surface. In just assuming round, you know, it could maybe be a torus with a hole in it or it could have many holes. And there are many different topologies a priori that a surface could have, even if you assume that it's bounded and smooth and so forth. So we have figured out how to classify surfaces. As a first approximation, everything is determined by something called

the genus, how many holes it has. So the sphere has genus zero, a donut has genus one and so forth. And one way you can tell these surfaces apart, probably the sphere has, which is called simply connected. If you take any closed loop on the sphere, like a big closed loop rope, you can contract it to a point and while staying on the surface. And the sphere has this property, but a torus doesn't. And if you're on a torus and you take a rope that goes around, say, the outer diameter of the torus, it can't get through the hole. There's no way to contract it to a point. So it turns out that the sphere is the only surface with this property of contractability up to continuous deformations of the sphere. So things that I want to call topologically equivalent of the sphere. So Poincare asked the same question in higher dimensions.

So it becomes hard to visualize because surface you can think of as embedded in three dimensions, but as a curved free dimensions, but a curved three space, we don't have good intuition of 4D space to live in. And then there are also 3D spaces that kind of even fit into four dimensions. You need five or six or higher. But anyway, mathematically, you can still pose this question that if you have a bounded three-dimensional space now, which also has this simply connected property that every loop can be contracted, can you turn it into a three-dimensional version of the sphere? And so this is the Poincare conjecture.

Weirdly, in higher dimensions, four and five, it was actually easier. So it was solved first in higher dimensions. There's somehow more room to do the deformation. It's easier to move things around to a sphere. But three was really hard. So people tried many approaches. There's sort of comment commentary approaches where you chop up the surface into little triangles or tetrahedra,

and you just try to argue based on how the faces interact each other. There were algebraic approaches. There's various algebraic objects, like things called the fundamental group that you can attach to these homology and cohomology and all these very fancy tools. They also didn't quite work. But Richard Hamilton's proposed a partial differential equations approach. So you have this object which is sort of secretly a sphere, but it's given to you in a really weird way.

So it's like a ball that's been crumpled up and twisted and it's not obvious that it's a ball. But if you have some sort of surface which is a deformed sphere, you could think of it as the surface of a balloon. You could try to inflate it, blow it up, and naturally as you fill it with air, the wrinkles will sort of smooth out and it will turn into a nice round sphere. Unless, of course, it was a torus or something in which case it would get stuck at some point. Like if you inflate a torus, there would be a point in the middle. When the inner ring shrinks to zero, you get a singularity and you can't blow up any further. You can't flow any further. So he created this flow,

which is now called Ricci flow, which is a way of taking an arbitrary surface or space and smoothing it out to make it rounder and rounder, to make it look like a sphere. And he wanted to show that either this process would give you a sphere or it would create a singularity. I think very much like how PDEs either have global regularity or finite and blow up. Basically, it's almost exactly the same thing. It's all

connected. And he showed that for two dimensions, two dimensional surfaces, if you start to simply connect, no singularities ever form. You never ran into trouble and you could flow and it will give you a sphere. And so he got a new proof of the two dimensional result. But by the way, that's a beautiful explanation where we should flow on its application in this context. How difficult is the mathematics here, like for the 2D case? Yeah, these are quite sophisticated equations on par with the Einstein equations. It's slightly simpler, but yeah, but they were considered hard nonlinear equations to solve.

And there's lots of special tricks in 2D that helped. But in 3D, the problem was that this equation was actually super critical. It's the same problem as Navier-Stokes. As you blow up, maybe the curvature could get concentrated in smaller and smaller regions. It looked more and more nonlinear and things just looked worse and worse. There could be all kinds of singularities that showed up. Some singularities, there's these things called neck pinches where the surface sort of behaves like a barbell and it pinches at a point. Some singularities are simple enough that you can sort of see what to do next. You just make a snip and then you can turn one surface into two and e-bolt them separately. But there was the prospect that

there's some really nasty knotted singularities showed up that you couldn't see how to resolve in any way, that you couldn't do any surgery to. So you need to classify all the singularities, like what are all the possible ways that things can go wrong. So what Kramlman did was, first of all, he made the problem, he turned the problem from a supercritical problem to a critical problem. I said before about how the invention of energy,

the Hamiltonian, that really clarified Newtonian mechanics. So he introduced something which is now called Perlman's reduced volume and Perlman's entropy. He introduced new quantities, kind of like energy, that looked the same at every single scale and turned the problem into a critical one where the non-linearities actually suddenly looked a lot less scary than they did before.

And then he had to solve, he still had to analyze the singularities of this critical problem. And that itself was a problem similar to this wave mapping I worked on actually. So on the level of difficulty of that, so he managed to classify all the singularities of this problem and show how to apply surgery to each of these and through that was able to resolve the Poincare Conjecture. So quite a lot of really ambitious steps and nothing that a large language model today, for example, could. I mean, at best, I could imagine a model proposing this idea as one of hundreds of different things to try. But the other 99 would be

complete dead ends, but you'd only find out after months of work. He must have had some sense that this was the right track to pursue because it takes years to get from A to B. So you've done, like you said, actually, you see even strictly mathematically, but more broadly in terms of the process, you've done similarly difficult things.

What can you infer from the process he was going through because he was doing it alone? What are some low points in a process like that? When you start to like, you've mentioned hardship, like AI doesn't know when it's failing. What happens to you, you're sitting in your office, when you realize the thing you did for the last few days, maybe weeks, is a failure.

Well, for me, I switched to a different problem. So I'm a fox. I'm not a hedgehog. But you legitimately, that is a break that you can take is to step away and look at a different problem. Yeah. You can modify the problem too. I mean, you can ask them, if there's a specific thing that's blocking you that just some bad case keeps showing up

for which your tool doesn't work, you can just assume by fiat this bad case doesn't occur. So you do some magical thinking, but strategically, okay, for the point to see if the rest of the argument goes through. If there's multiple problems with your approach, then maybe you just give up. But if this is the only problem, then everything else checks out, then it's still worth fighting. So yeah, you have to do some forward reconnaissance sometimes.

That is sometimes productive to assume like, okay, we'll figure it out eventually. Oh yeah, yeah, yeah. Sometimes actually it's even productive to make mistakes. So one of the, I mean, there was a project which actually we won some prizes for, over four other people. We worked on this PDE problem, again, actually this blow-off regularity type problem. And it was considered very hard. Jean Bourguin, who was another fields specialist, he worked on a special case of this, but he could not solve the general case. And we worked on this problem for two months and we thought we solved it.

We had this cute argument that everything fit and we were excited. We were planning celebrationaries to all get together and have champagne or something. And we started writing it up. And one of us, not me, but another co-author said, in this lemma here, we have to estimate these 13 terms that show up in this expansion. And we estimate 12 of them, but in our notes, I can't find the estimation of the 13th, can you?

Can someone supply that? And I said, sure, I'll look at this and I actually, yeah, we didn't cover, we completely omitted this term. And this turned out to be worse than the other 12 terms put together. In fact, we could not estimate this term. And we tried for a few more months and all different permutations and there was always this one term that we could not control. And so this was very frustrating. But because we had already invested months and months of effort in this already, we stuck at this.

We tried increasingly desperate things and crazy things. After two years, we found an approach that was somewhat different, quite a bit from our initial strategy, which didn't generate these problematic terms and actually solved the problem. We solved the problem after two years. But if we hadn't had that initial full storm of nearly solving the problem, we would have given up by month two or something and worked on an easier problem. If we had known it would take two years, not sure we would have started the project. Sometimes actually

having the incorrect, it's like Columbus traveling in the New World, he had an incorrect version of the measurement of the size of the earth. He thought he thought he was going to find a new trade route to India. Uh, or at least that was how he sold it in his prospectus. I mean, it could be that he actually secretly knew, but just on the psychological element, do you have like emotional or. Like self doubt that just overwhelms you most like that, you know, cause this stuff, it feels like math. overwhelms you most like that, you know, because this stuff, it feels like math,

it's so engrossing that like it can break you when you like invest so much yourself in the problem and then it turns out wrong, you could start to, similar way chess has broken some people. Yeah, I think different mathematicians have different levels of emotional investment in what they do. I mean, I think for some people it's levels of emotional investment in what they do.

I mean, I think for some people it's as a job. You know, you have a problem and if it doesn't work out, you go on the next one. Yeah, so the fact that you can always move on to another problem, it reduces the emotional connection. I mean, there are cases, you know, so there are certain problems that are what are called mathematical diseases where we just latch on to that one problem and they spend years and years thinking about nothing but that one problem.

Maybe their career suffers and so forth, but how could this big win, once I finish this problem I will make up for all the years of lost opportunity. I mean, occasionally, occasionally it works, but I really don't recommend it for people who have the right fortitude. Yeah, so I've never been super invested in any one problem. One thing that helps is that we don't need to call our problems in advance.

Well, when we do grant proposals, we sort of say, we will study this set of problems. But even though we don't promise, definitely by five years, I will supply a proof of all these things. You promise to make some progress or discover some interesting phenomena. And maybe you don't solve the problem,

but you find some related problem that you can say something new about. And that's a much more feasible task. But I'm sure for you, there's problems like this. You have made so much progress towards the hardest problems in the history of mathematics. So is there a problem that just haunts you? It sits there in the dark corners, you know, twin prime conjecture, Riemann hypothesis, global conjecture. The problems like the Riemann hypothesis,

those are so far out of reach. Yeah, there's just still no way to get made to be. Like, it's, I think it needs a breakthrough in another area of mathematics to happen first. And for someone to recognize that, that would be a useful thing to transport into this problem. So we should maybe step back for a little bit and just talk about prime numbers. Okay.

So they're often referred to as the atoms of mathematics. Can you just speak to the structure that these atoms provide? So the natural numbers have two basic operations, attachment, addition and multiplication. So if you want to generate the natural numbers you can do one of two things. You can start with one and add one to itself over and over again and that generates you the natural numbers. So additively they're very easy to generate, one, two, three, four, five. Or you can take the prime number, if you wanna generate multiplicatively,

you can take all the prime numbers, two, three, five, seven, and multiply them all together. And together, that gives you all the natural numbers, except maybe for one. So there are these two separate ways of thinking about the natural numbers from an additive point of view and a multiplicative point of view.

And separately, they're not so bad. So any question about the natural numbers that only involves addition is relatively easy to solve. And any question that only involves multiplication is relatively easy to solve. But what has been frustrating is that you combine the two together and suddenly you get this extremely rich – I mean, we know that there are statements in number theory that are actually as undecidable. There are certain polynomials in some number of variables, there's a solution in the natural numbers, and the answer depends on an undecidable statement,

like whether the axioms of mathematics are consistent or not. But yeah, but even the simplest problems that combine something more applicative, such as the primes, with something something additive such as shifting by two. Separately, we understand both of them well, but if you ask when you shift the prime by two, how often can you get another prime? It's been amazingly hard to relate the two. And we should say that the twin prime conjecture is just that. It pauses that there are infinitely many pairs of prime

numbers that differ by two. Now, the interesting thing is that you have been very successful at pushing forward the field in answering these complicated questions of this variety. Like you mentioned the Green-Tile Theorem, it proves that prime numbers contain arithmetic progressions of any length. Right. Which is mind-blowing that you could prove something like that. Right. Yeah, so what we've realized because of this type of research is that different patterns have different levels of indestructibility.

So what makes the twin prime problem hard is that if you take all the primes in the world, you know, 3, 5, 7, 11, so forth, there are some twins in there, eleven and thirteen is a twin prime, pair of twin primes, so forth. But you could easily, if you wanted to, um, redact the primes to get rid of, to get rid of the, um, these twins. Like the twins, they'd show up and there are infinitely many of them, but they're actually reasonably sparse. Um, not, there's, there's not, I mean, initially there's quite a few, but once you've got the millions, trillions, they become rarer and rarer. If someone was given access to the database of primers, you just edit out a few primers here and there, they could make the trend pattern conjecture false by just removing like 0.01% of the primers or

something. Just well chosen to do this. you could present a censored database of the primes, which passes all of the statistical tests of the primes. It obeys things like the primal theorem and other texts about the primes, but doesn't contain any true primes anymore. And this is a real obstacle for the true prime conjecture. It means that any proof strategy to actually find trend primes in the actual primes must fail when applied to these slightly edited primes. And so it must be some very subtle, delicate feature

of the primes that you can't just get from like aggregate statistical analysis. Okay, so that's all. Yeah. On the other hand, I think progressions has turned out to be much more robust. Like, you can take the primes and you can eliminate 99% of the primes, actually. You know, and you can take any 90% you want. And it turns out, another thing we proved is that you still get arithmetic progressions. Arithmetic progressions are much, you know, they're like

cockroaches. Of arbitrary length. Yes. Yes. That's crazy. Yeah. So for people who don't know arithmetic progressions is a sequence of numbers that differ by some fixed amount. Yeah. But it's again like an infinite monkey type phenomenon. For any fixed length of your set,

you don't get arbitrary length of progressions. You only get quite short progressions. But you're saying twin prime is not an infinite monkey phenomena. I mean, it's a very subtle one. It's still an infinite monkey. Right. If the primes were really genuinely random, if the primes were generated by monkeys, then yes, in fact, the infinite monkey theorem would.

Oh, but you're saying that twin prime is it doesn't, you can't use the same tools like the it doesn't appear random almost. Well, we don't know. Yeah, we Like the, it doesn't appear random almost. Well, we don't know. Yeah. We, we, we, we believe the primes behave like a random set. And so the reason why we care about the trim half conjecture is a test case for whether we can genuinely confidently say with zero percent chance of error that the prime has behaved like a random set.

Okay. Random, yeah, random versions of the primes we know contain twins. Um, at least we've, we've, at least 100% probably, or probably tending to 100% as you go out further and further. The primes we believe are random, the reason why atomic progressions are indestructible is that regardless of whether it looks random or looks structured, like periodic, in both cases, atomic atomic progression appears, but for different reasons.

And this is basically all the ways in which there are many proofs of these sort of atomic progression epitheliums. And they're all proven by some sort of dichotomy where your set is either structured or random, and in both cases, you can say something and then you put the two together. But in twin primes, if the primes are random, then you're happy, you win. If your primes are structured, they can be structured in a specific way that eliminates the twins.

And we can't rule out that one conspiracy. And yet you were able to make a Zandertjian progress on the K-tupo version. Right, yeah. So the one funny thing about conspiracies is that any one conspiracy theory is really hard to disprove. If you believe the word is run by lizards, you say, here's some evidence that it's not run by lizards, well, that episode was planted by the lizards.

You might have encountered this kind of phenomena. There's almost no way to definitively rule out a conspiracy. And the same is true in mathematics. A conspiracy is solely devoted to eliminating twin primes. You have to also infiltrate other areas of mathematics, but it could be made consistent, at least as far as we know. But there's a weird phenomenon that you can make one conspiracy rule out other conspiracies. So if the world is run by this, it can also be run by aliens.

Right. So one unreasonable thing is hard to disprove, but more than one, there are tools. So, yeah, so for example, we know there's infinitely many primes that are, no two of which are, so the infinite pairs of primes which differ by at most, 246 actually is the code.

So there's like a bound on the- Right. So like this trin primes, this thing called cousin primes that differ by four. This thing called sexy primes that differ by six. What are sexy primes? Primes that differ by six. The name is much less exciting than the name suggests. Got it.

So you can make a conspiracy rule out one of these, but once you have like 50 of them, it turns out that you can't rule out all of them at once. It requires too much energy somehow in this conspiracy space. How do you do the bound part? How do you develop a bound for the difference between the pros and cons? There's an infinite number of. So it's ultimately based on what's called the pigeonhole principle. So the pigeonhole principle, it's the statement that if you have a number of pigeons and they

all have to go into pigeonholes and you have more pigeons than pigeonholes, then one of the pigeonholes has to have at least two pigeons in. So there has to be two pigeons that are close together. So for instance, if you have a hundred numbers and they all range from one to a thousand, two of them have to be at most 10 apart because you can divide up the numbers from one to a hundred into 100 pigeon holes. Let's say they are 101 numbers, 101 numbers, then two of them have to be distance less than 10 apart because two of them have to belong to the same pigeon hole. So it's a basic, basic feature of a basic principle in mathematics. So it doesn't quite work with the primes

because the primes get sparser and sparser as you go out. That few and fewer numbers are prime. But it turns out that there's a way to assign weights to numbers. So there are numbers that are almost prime, but they don't have no factors at all other than themselves and one. They have very few factors. It turns out that we understand almost primes a lot better than primes. For example, it was known for a long time that they were twin almost primes.

This has been worked out. Almost primes are things we can't understand. You can actually restrict attention to a suitable set of almost primes. Whereas the primes are very sparse overall, relative to the almost primes, they actually are much less sparse. They may, you can set up a set of almost primes where the primes of density like say 1%. And that gives you a shot at proving

by applying some sort of original principle that there's pairs of primes that are just only 100. But in order to prove the train of prime conjecture, you need to get the density of priors that are almost always up to a threshold of 50%. Once you get up to 50%, you will get Trin priors. But unfortunately, there are barriers. We know that no matter what kind of good set of almost-priors you pick, the density of priors can never get above 50%. It's called the parity barrier. And I would love to find, yeah, so one of my long-term dreams is to find a way to breach that barrier because it would open up not only the twin-prime conjecture, the go-back conjecture, and many other problems in number theory are currently blocked because our current techniques would

require going beyond this theoretical parity barrier. It's like pulling fast at the speed of light. Yeah. So we should say a twin-prime conjecture. One of the biggest problems in the history of mathematics, go back conjecture also. They feel like next door neighbors. Is there been days when you felt you saw the path? Oh yeah. Yeah. Sometimes you try something and it works super well. You, again, again, the sense of methodical smell, we talked about earlier,

you learn from experience when things are going too well. Because there are certain difficulties that you sort of have to encounter. I think the way a colleague might put it is that, if you are on the streets of New York and you put on a blindfold and you put on a car, and after some hours, the blindfold is New York and you put on a blindfold and you put on a car and after some hours, the pipe was off and you're in Beijing.

I mean, that was too easy somehow. There was no ocean being crossed. Even if you don't know exactly what was done, you're suspecting that something wasn't right. But is that still in the back of your head? Do you return to the prime numbers every once in a while to see? Yeah, when I have nothing better to do, which is less and less than 10 times, which is like busy with so many things these days. But yeah, when I have free time and I'm too frustrated to work on my

sort of real research projects, and I also don't want to do my administrative stuff, or I don't want to do some errands for my family. I can play with these things for fun and usually you get nowhere. You have to learn to just say, okay, fine. Once again, nothing happened. I will move on. Very occasionally, one of these problems I actually solved, or sometimes as you say, you think you solved it and then you forward for maybe 15 minutes and then you think I should check this because this is too easy, too good to be true and usually is.

What's your gut say about when these problems will be solved? Twin Prime and GoBot? I think we'll keep getting more partial results. It doesn't need at least one. This parity barrier is the biggest remaining obstacle. There are simpler versions of conjecture where we are getting really close. So I think in 10 years, we will have many more much closer results. We may not have the whole thing. So twin trimes is somewhat close. Riemann hypothesis, I have no, I mean, it has to happen by accident, I think.

So the Riemann hypothesis is a kind of more general conjecture about the distribution of prime numbers, right? Right, yeah. It states that sort of viewed multiplicatively, like for questions only involving multiplication, no addition, the primes really do behave as randomly as you could hope. So there's a phenomenon in probability called square root cancellation that, you know, if you want to poll, say, America up on some issue and you ask one or two voters, you may have sampled a bad sample and then you get a really imprecise measurement of the full

average. But if you sample more and more people, the accuracy gets better and better and the accuracy improves like the square root of the number of people you sample. So if you sample 1,000 people, you can get like a 2, 3% margin of error. So in the same sense, if you measure the primes in a certain multiplicative sense, there's a certain type of statistic you can measure, and it's called the Riemann's data function, and it fluctuates up and down. But in some sense, as you keep averaging more and more, if you sample more and more, the fluctuations should go down as if they were random. And there's a very precise way to quantify that. And the Riemann hypothesis is a very elegant

way that captures this. But as with many others in mathematics, we have very few tools to show that something really genuine behaves like really random. And this is actually not just a little bit random, but it's asking that it behaves as random as it actually random set, this square root cancellation. And we know because of things related to the parity problem, most of us usual techniques cannot hope to settle this question. The proof has to come out of left field. Yeah, but what that is, no one has any serious proposal. And there's various ways to sort of, as I said, you can modify the primes a little bit and you can destroy the Riemann hypothesis.

So, like, it has to be very delicate. You can't apply something that has huge margins of error. It has to just barely work. And like, there's like all these pitfalls they like dodge very adeptly. The prime numbers are just fascinating. What to you is most mysterious about the prime numbers? That's a good question. So like conjecturally, we have a good model of them. I mean, like I said, I mean, they they have certain patterns like the primes are usually odd, for instance, but apart from these obvious patterns, they behave very randomly. And just assuming that they behave, so there's something called the Kramer random model of the primes, that after a certain point, primes just behave

like a random set. And there's various slight modifications as a model, but this has been a very good model. It matches the numerics. It tells us what to predict. I can tell you with complete certainty that the true-matter conjecture is true. The random model gives overwhelming odds that it's true. I just can't prove it. Most of our mathematics is optimized for solving things with patterns in them. The primes have this anti-pattern, as do almost everything really, but we can't prove that. I guess it's not mysterious that the primes are random because there's no reason for them to have any kind of secret pattern. But what is mysterious is what is the mechanism that really forces the randomness to happen.

This is just absent. Another incredibly surprisingly difficult problem is the Kolotts conjecture. Oh yes. Simple to state, beautiful to visualize in its simplicity, and yet extremely difficult to solve. And yet you have been able to make progress. Paul Erdard said about the Kolotts conjecture that mathematics may not be ready for such problems. Others have stated that it is an extraordinarily difficult problem completely out of reach. This is in 2010, out of reach of present-day mathematics and yet you have made some progress. Why is it so

difficult to make? Can you actually even explain what it is? It yeah, so it's something that you can explain. It helps with some visual aids. So you take any natural number, like say 13, and you apply the following procedure to it. So if it's even, you divide it by 2. And if it's odd, you multiply it by 3 and add 1. So even numbers get smaller, odd numbers get bigger. So 13 would become 40, because 13 times 3 is 39, add when you get 40. So it's a simple process. For odd numbers and even numbers, they're both very easy operations. And then you put together, it's still reasonably simple.

But then you ask what happens when you iterate it. You take the output that you just got and feed it back in. So 13 becomes 40, 40 is now even, divide by 2 is 20, 20 is still even, divide by 2, 10, 40 is now even divided by two is 20, 20 is still even divided by two, 10, five, and then five times three plus one is 16, and then eight, four, two, one. So, and then from one it goes one, four, two, one, four, two, one, it cycles forever. So this sequence I just described, you know, 13, 40, 20, 10, so both of these are also called hailstorm sequences because there's an oversimplified model of hailstorm formation, which is not actually quite correct, but it's somehow taught to high school students as a first-box summation,

is that a little nugget of ice gets an ice crystal that forms and cloud it. It goes up and down because of the wind. And sometimes when it's cold, it requires a bit more mass and maybe it melts a little bit. And this process of going up and down creates this sort of partially melted ice, which eventually causes hell stone. And eventually it falls down to the earth. So the conjecture is that no matter how high you start up, like you take a number which is in the millions or billions, this process that goes up if you're odd

and down if you're even, eventually goes down to earth all the time. No matter where you start with this very simple algorithm, you end up at one. And you might climb for a while. Right. Yeah, so if you plotted these sequences, they look like Brownian motion.

They look like the stock market. They just go up and down in a seemingly random pattern. And in fact, usually that's what happens, that if you plug in a random number, you can actually prove, at least initially, that it would look like a random walk. And that's actually a random walk with a downward drift. It's like if you're always gambling on a roulette at the casino with odds slightly weighted against you. So sometimes you win, sometimes you lose, but over in the long run, you lose a bit more

than you win. And so normally your wallet will hit, will go to zero. Um, if you just keep playing over and over again. So statistically, it makes sense. Yes. So, so the result that I, I proved roughly speaking, so that that statistically like 19% of all inputs would, would drift down to maybe not all the way to one, but to be much, much smaller than what you started. So it's, it's like, if I told you that if you go to a casino, most of the time you end up, if you keep playing it for long enough, you end up with a smaller amount in your wallet than when you started.

That's kind of like the result that I proved. So why is that result, like can you continue down that thread to prove the full conjecture? Well, the problem is that I used arguments from probability theory, and there's always this exceptional event. So in probability, we have this low large numbers,

which tells you things like if you play a casino with a game at a casino with a losing expectation, over time you are guaranteed, well, almost surely, with probability as close to 100% as you wish, you're guaranteed to lose money. But there's always this exceptional outlier. It is mathematically possible that even in the game is the odds and not in your favor, you could just keep winning slightly more often than you lose. Very much like how in Navier-Stokes, it could be most of the time your waves can disperse, there could be just one outlier choice of initial conditions

that would lead you to blow up. And there could be one outlier choice of a special number that you stick in that shoots off infinity while all other numbers crash to earth, crash to one. In fact, there's some mathematicians, Alex Kontorovich, who proposed that actually these collapse iterations are like these similar automata. If you look at what happened in binary, they do actually look a little bit like these Game of Life type patterns. And in an analogy to how the Game of Life can create these massive self-replicating objects and so forth.

Possibly you could create some sort of heavier than air flying machine, a number which is actually encoding this machine, whose job it is to encode is to create a version of a cell which is larger. Heavier than air machine, encoded in a number that flies forever. So Conway in fact worked on this problem as well. Oh wow. So Conway, so similar, in fact, that was one of my inspirations

for the Navier-Stokes project. Conway studied generalizations of the collapse problem where instead of multiplying by three and adding one or dividing by two, you have a more complicated branching list. But instead of having two cases, maybe you have 17 cases and then you go up and down. And he showed that once your iteration gets complicated enough, you can actually encode Turing machines

and you can actually make these problems undecidable and do things like this. In fact, he invented a programming language for these fractional linear transformations. He called it Fract-Trat as a play on Fortrat. He showed that you can program if it was too incomplete. You could make a program that if your number you insert in was encoded as a prime, it would sink to zero. It would go down, otherwise it would go up and things like that. So the general cluster problems is really as complicated as all the mathematics.

Some of the mystery of the cellular automata that we talked about, having a mathematical framework to say anything about cellular automata, maybe the same kind of framework is required for the Glocks Injector. Yeah, if you want to do it, not statistically, but you really want 100% of all inputs to follow the Earth. Yeah, so what might be feasible is,

yeah, statistically 99% go to 1, but everything, that looks hard. What would you say is out of these within reach, famous problems is the hardest problem we have today? Is there a Riemann hypothesis? Riemann is up there. Pico's MP is a one because like that's a meta problem. Like if you solve that in the positive sense that you can find a P equals NP algorithm, then potentially this solves a lot of other problems as well. And we should mention some of the conjectures we've been talking about. You know a lot of stuff is built on top of them though. There's ripple effects. P equals NP has

more ripple effects than basically any other. Right. If the Riemann hypothesis is disproven, that'd be a big mental shock to number theorists, but it would have follow-on effects for cryptography. Because a lot of cryptography uses number theory, it uses number theory constructions involving primes and so forth. And it relies very much on the intuition that number theories have built over many, many years of what operations involving primes behave randomly and what ones don't. In particular, encryption methods are designed to turn text with information on it into text which is indistinguishable from random noise. And hence, we believe to be almost impossible to crack, at least mathematically. But if something has caught our beliefs as human hypothesis is wrong, it means that there are

actual patterns of the primes that we're not aware of. And if there's one, there's probably going more. And suddenly a lot of our crypto systems are in doubt. Yeah. But then how do you then say stuff about the primes? Yeah. That you're going towards the colex conjecture again. Because you want it to be random, right? You want it to be random. So more broadly, I'm just looking for more tools, more ways to show that things are random. So more broadly, I'm just looking for more tools, more ways to show that things are random. How do you prove a conspiracy doesn't happen? Is there any chance to you that P equals NP?

Is there some, can you imagine a possible universe? It is possible. I mean, there's various scenarios. I mean, there's one where it is technically possible, but in fact, it's never actually implementable. The evidence is sort of slightly pushing in favor of no, that we'd probably be as not a good NP.

I mean, it seems like it's one of those cases similar to Riemann hypothesis. I think the evidence is leaning pretty heavily on the no. Certainly more on the no than on the yes. The funny thing about P equals NP is that we have also a lot more obstructions than we do for almost any other problem. So while there's evidence, we also have a lot of results ruling out many, many types of approaches to the problem.

This is the one thing that the computer science has actually been very good at. It's actually saying that certain approaches cannot work. No-go theorems. It could be undecidable. We don't, yeah, we don't know. There's a funny story I read that when you won the Fields Medal, somebody from the internet wrote you and asked, what are you going to do now that you've won this prestigious award?

And then you just quickly, very humbly said that this shiny medal is not going to solve any of the problems I'm currently working on. So I'm going to keep working on them. First of all, it's funny to me that you would answer an email in that context, and second of all, it just shows your humility. But anyway, maybe you could speak to the Fields Medal, but it's another way for me to ask about Grigori Perlman. What do you think about him famously declining the Fields Medal and the Millennial Prize,

which came with a $1 million of prize money? He stated that, I'm not interested in money or fame. The prize is completely irrelevant for me if the proof is correct and no other recognition is needed. Yeah. He's somewhat of an outlier, even among mathematicians who tend to have somewhat idealistic views. I've never met him.

I think I'd be interested in meeting one day, but I never had the chance. I know people who met him. He's always had strong views about certain things. I mean, it's not like he was completely isolated from the math community. I mean, he would give talks and write papers and so forth. But at some point, he just decided not to engage with the rest of the community. He was disillusioned or something, I don't know. And he decided to peace out

and collect mushrooms in St. Petersburg or something. And that's fine, you can do that. I mean, that's another sort of flip side. I mean, a lot of problems that we solve, some of them do have practical application, and that's another sort of flip side. I mean, we are not, a lot of problems that we solve, you know, some of them do have practical application. That's great. But like, if you stop thinking about a problem, you know, so he hasn't published since in this field, but that's fine. There's many, many other people who've done so as well.

Yeah, so I guess one thing I didn't realize initially with the Fields Medal is that it sort of makes you part of the establishment. So most mathematicians, there's just career mathematicians, you just focus on publishing your next paper, maybe getting one test to promote one rank and starting a few projects, maybe having some students or something. But then suddenly people want your opinion on things and you have to think a little bit about the things that you might just so foolishly say because you know, no one's going to listen

to you. It's more important now. Is it constraining to you? Are you able to still have fun and be a rebel and try crazy stuff and play with ideas? I have a lot less free time than I had previously. I mean, mostly by choice. I mean, I can always see I have the option to sort of decline.

So I'd decline a lot of things. I could decline even more. Or I could acquire a repetition of things so unreliable that people don't even ask anymore. But this is the- I love the different algorithms here. This is great. This is, it's always an option.

But you know, there are things that are like, I mean, so I mean, I don't spend as much time as I do as a postdoc, just working on one part of the time or fooling around. I still do that a little bit. But yeah, as you advance in your career, the more soft skills, so math somehow front loads all the technical skills to the early stages of a career. So as, so it's as opposed to publish or perish, you're incentivized to basically focus on proving very technical things, so prove yourself as well as proof of theorems. But then as you get more senior, you have to start mentoring and giving interviews and trying to shape direction of the field both research-wise and sometimes you have to do some administrative things. And it's kind of the right social contract because you need to work in the trenches to

see what can help mathematicians. The other side of the establishment, the really positive thing is that you get to be a light that's an inspiration to a lot of young mathematicians or young people that are just interested in mathematics. And it's like, it's just how the human mind works. This is where I would probably say that I like the Fields Medal, that it does inspire a lot of young people somehow, I don't, this is just how human brains work.

At the same time, I also wanna give respect to somebody like Gagore Perlman, who is critical of awards in his mind. Those are his principles, and any human that's able for their principles to do the thing that most humans would not be able to do. It's beautiful to see. Some recognition is necessary and important, but yeah, it's also important to not let these

things take over your life and only be concerned about getting the next big award or whatever. I mean, yeah, so again, you see these people try to only solve like a really big math problems and not work on things that are less sexy if you wish, but actually still interesting and instructive. As you say, like the way the human mind works, we understand things better when they're attached to humans. And also if they're attached to a small number of humans, the way our human mind is wired, we can comprehend the relationships between 10 or 20 people. But once you get beyond that, like 100 people, there's a limit, I figured there's a name for it, beyond which it just becomes the other.

And so you have to simplify the whole mass of, you know, 99.9% of humanity becomes the other. And often these models are incorrect and this causes all kinds of 99.9% of humanity becomes the other. And often these models are incorrect and this causes all kinds of problems. But so yeah, so to humanize a subject, if you identify a small number of people, these are representative people of the subject, role models, for example, that has some role,

but it can also be, yeah, too much of it can be harmful because I'll be the first to say that my own career path is not that of a typical mathematician. I had a very accelerated education. I skipped a lot of classes. I think I was very fortunate mentoring opportunities and I think I was at the right place at the right time. Just because someone doesn't have my trajectory doesn't mean that they can't be good mathematicians.

I mean, they would be in a very different style and we need people with different style. And sometimes too much focus is given on the person who does the last step to complete a project in mathematics or elsewhere that's really taken centuries or decades with lots and lots of previous work. But that's a story that's difficult to tell if you're not an expert because it's easier to just say one person did this one thing. It makes for a much simpler history. I think on the whole, it is a hugely positive thing to talk about Steve Jobs as a representative of Apple. When I personally know, and of course,

everybody knows the incredible design, the incredible engineering teams, just the individual humans on those teams. They're not a team, they're individual humans on a team, and there's a lot of brilliance there, but it's just a nice shorthand like a very like pie Yeah, Steve jobs. Yeah. Yeah as a starting point, you know As a first approximation and then read some biographies and then look into much deeper first approximation. Yeah, that's right

So you mentioned you were a Princeton to? Andrew Wiles at that time. Oh, yeah professor there It's a funny moment how history is just all interconnected. And at that time, he announced that he proved the Fermat's Last Theorem. What did you think, maybe looking back now with more context about that moment in math history? Yeah, so I was a graduate student at the time.

I mean, I vaguely remember, you know, there was press attention and we all had the same, we all had pigeonholes in the same mail room, so we all checked our mail and like suddenly Andrew Wiles' mailbox exploded to be overflowing. That's a good metric. Yeah. So yeah, we all talked about it at T and so forth.

I mean, we didn't understand, most of us sort of didn't understand the proof. We understand sort of high level details. Like there's an ongoing project to formalize it in Lean. Kevin Buzzer is actually. Yeah, can we take that small tangent? Is it, how difficult is that? Because as I understand the proof for,

from as last theorem has like super complicated objects. Yeah. It's really difficult to formalize though. Yeah, I guess, yeah, you're right. The objects that they use, you can define them. So they've been defined in Lean. Okay, so just defining what they are can be done. That's really not trivial, but it's been done. But there's a lot of really basic facts about these objects that have taken decades to prove that they're in all these different math papers. And so lots of these have to be formalized as well.

Kevin Buzzard's goal actually, he has a five-year grant to formalize film as last year. And his aim is that he doesn't think he will be able to get all the way down to the basic axioms, but he wants to formalize it to the point where the only things that he needs to rely on as black boxes are things that were known by 1980 to number of theaters at the time. And then some other person or some other work would have you done to get from there.

So it's a different area of mathematics than the type of mathematics I'm used to. In analysis, which is kind of my area, the objects we study are kind of much closer to the ground. I study things like prime numbers and functions and things that are within scope of a high school math education to at least define. Yeah, but then this is very advanced algebraic side of number theory where people have been building structures upon structures for quite a while. And it's a very sturdy structure. It's been very, at the base, at least, it's extremely well developed in the textbooks and so forth. But it does get to the point where if you haven't taken these years of study and you want to ask about what is going on at level six of this tower, you have to spend quite a bit of time before they

can even get to the point where you can see, you see something you recognize. What inspires you about his journey? That was similar as we talked about seven years, mostly working in secret. Yeah, that is a romantic, so it kind of fits with sort of the romantic image that I think people have of mathematicians to the extent that they think of them at all as these kind of eccentric wizards or something. So that certainly kind of accentuated that perspective. I mean, it is a great achievement. His style of solving problems is so different from my own. But it's great. I mean, we need people like that. Can you speak to it? Like what, in terms of like, you like the collaborative?

I like moving on from a problem if it's giving too much difficulty. But you need the people who have the tenacity and the fearlessness. I've collaborated with people like that where I want to give up because the first approach that we tried didn't work and the second one didn't approach. They're convinced and they have the third, fourth, and the fifth approach works. And I have to eat my words. Okay, I didn't think this was gonna work but yes you were right all along. And we should say for people who don't know, not only are you known for the brilliance of your work but the incredible productivity, just the number of papers which are all of very

high quality. So there's something to be said about being able to jump from topic to topic. Yeah, it works for me. Yeah, I mean, there are also people who are very productive and they focus very deeply on it. Yeah. I think everyone has to find their own workflow. Like, one thing which is a shame in mathematics is that we have mathematics, there's sort

of a one-size-fits-all approach to teaching mathematics. And so we have a certain curriculum and so forth. I mean, maybe like if you do math competitions or something, you get a slightly different experience. But I think many people, they don't find their native math language until very late or usually too late, so they stop doing mathematics and they have a bad experience with a teacher who's trying to teach them one way to do mathematics

and they don't like it. My theory is that evolution has not given us a math center of a brain directly. We have a vision center and a language center and some other centers which have evolution as honed, but we don't have an innate sense of mathematics. But our other centers are sophisticated enough that we can repurpose other areas of our brain to do mathematics. So some people have figured out how to use the visual center to do mathematics, and so they think very visually when they do mathematics. Some people have repurposed their language center and they think very symbolically. You

know, some people like if they are very competitive and they like gaming, there's a type of this part of your brain that's very good at solving puzzles and games and that can be repurposed. But like when I talk to other mathematicians, they don't quite think that, I can tell that they're using some of the different styles of thinking than I am. I mean, not disjoint, but they may prefer visual. I don't actually prefer visual so much.

I need lots of visual aids myself. Mathematics provides a common language so we can still talk to each other even if we are thinking in different ways. But you can tell there's a different set of subsystems being used in the thinking process. They take different paths. They're very quick at things that I struggle with and vice versa, and yet they still get to the same goal.

That's beautiful. Yeah, but I mean, the way we educate, unless you have like a personalized tutor or something, I mean, education sort of just by nature of skill has to be mass produced. You know, you have to teach the 30 kids, you know, if they have 30 different styles, you can't teach 30 different ways. On that topic, what advice would you give to students,

young students who are struggling with math, and but are interested in it and would like to get better. Is there something in this complicated educational context? What would you? Yeah, it's a tricky problem. One nice thing is that there are now lots of sources for mathematical enrichment outside the classroom. So in my day, there are already math competitions. And you know, they're also like popular math books

in the library, but now you have YouTube, there are forums just devoted to solving math puzzles. And math shows up in other places. For example, there are hobbyists who play poker for fun. And they are, for very specific reasons, interested in very specific probability questions. There's a community of amateur probabilists in poker, in chess, in baseball. There's math all over the place. And I'm hoping actually with these new sort of tools for lean and so forth that actually we can incorporate the broader public into math research projects. This is almost, it doesn't happen at all currently. So in the sciences, there is some scope for citizen science, like astronomers, the amateurs

who would discover comets and there's biologists, there are people who could identify butterflies and so forth. And in math, there are a small number of activities where amateur mathematicians can discover new primes and so forth. But previously, because we have to verify every single contribution, most mathematical research projects, it would not help to have input from the general public. In fact, it would just be time consuming because just error checking and everything. But one thing about these formalization projects is that they are bringing together more, bringing

in more people. So I'm sure there are high school students who've already contributed to some of these formalizing projects who contributed to Mathlib. You don't need to be a PhD holder to just work on one atomic thing. There's something about the formalization here that also as a very first step opens it up to the programming community too. The people who are already comfortable with programming.

It seems like programming is somehow maybe just the feeling, but it feels more accessible to folks than math. Math is seen as this like extreme, especially modern mathematics, seen as this extremely difficult to enter area and programming is not, so that could be just an entry point. You can execute code and you can get results. You can print out the world pretty quickly. Like if programming was taught as an almost entirely theoretical subject,

where you just taught the computer science, the theory of functions and routines and so forth. And outside of some very specialized homework assignments, you don't like to program like on the weekend for fun. Yeah. Yeah. They would be as considered as hard as math. Yeah. So as I said, there are communities of non-mathematicians where they're deploying math for some very

specific purpose, like optimizing their poker game. And for them, then math becomes fun for them. What advice would you give in general to young people how to pick a career, how to find themselves? That's a tough, tough, tough question. So there's a lot of certainty now in the world. I mean, there was this period after the war where, at least in the West, if you came from a good demographic, there was a very stable path through it to a good creator. You go to college, you get an education,

you pick one profession and you stick to it. It's becoming much more a thing of the past. So I think you just have to be adaptable and flexible. I think people will have to get skills that are transferable. Like learning one specific programming language or one specific subject of mathematics or something. That itself is not a super transferable skill, but sort of knowing how to reason with abstract concepts or how to problem solve andve and things go wrong or something like that. These are things which I think we will still need. Even as our tools get better,

you'll be working with AI, sports, so forth. But actually, you're an interesting case study. I mean, you're like one of the great living mathematicians, right? And then you had a way of doing things, and then all of a sudden you start learning. I mean, first of all, you kept learning new fields, but you learn lean. That's a non-trivial thing to learn. Like that's a, for a lot of people,

that's an extremely uncomfortable leap to take, right? A lot of mathematicians. First of all, I've always been interested in new ways to do mathematics. I feel like a lot of new ways to do mathematics. I feel like a lot of the ways we do things right now are inefficient. I mean, my colleagues, we spend a lot of time doing very routine computations or doing things that other mathematicians would instantly know how to do and we don't know how to do

them. And why can't we search and get a quick response and so on? So that's why I've always been interested in exploring new workflows. About four or five years ago, I was on a committee where we had to ask for ideas for interesting workshops to run at a math institute. And at the time, Peter Shortser had just formalized one of his new theorems. And there were some other developments in computer-assisted proof that looked quite interesting.

And I said, oh, we should run a workshop on this. This would be a good idea. And then I was a bit too enthusiastic about this idea, so I got Volantol to actually run it. So I did with a bunch of other people, Kevin Byser and Jordan Ellenberg and a bunch of other people. And it was a nice success. We brought together a bunch of mathematicians, including scientists and other people, and we got up to speed on the state of

the art. And it was really interesting developments that most mathematicians didn't know was going on, that lots of nice proofs of concept, you know, it's just so hints of what was going to happen. This was just before chat GBT, but there was even then, there was one talk about language models and the potential capability of those in the future. So that got me excited about the subject. So I started giving talks about this is something we should, more of us should start looking at. Now that I'd arranged the conference and then chat GBT came out and like suddenly AI was

everywhere. And so I got interviewed a lot about this topic and in particular the interaction between AI and formal proof of systems. And I said, yeah, they should be combined. This is perfect synergy to happen here. And at some point I realized that I have to actually do not just talk the talk, but walk the walk. I don't work in machine learning and I don't work in proof formalization. And there's a limit to how much I can just rely on authority and say, you know, I'm a warner mathematician, just trust me. When I say that this is going to change my phlegm, I'm not doing it any way, and I don't

do any of it myself. So I felt like I had to actually justify it. A lot of what I get into actually, I don't quite see and advise as how much time I'm going to spend on it. A lot of what I get into actually, I don't quite see an advice as how much time I'm going to spend on it. And it's only after I'm sort of waist deep in a project that I realized, but that point I'm committed. Well, that's deeply admirable that you're willing to go into the fray, be in some small way a beginner, right? Or have some of the sort of challenges that a beginner would, right? It's new concepts, new ways of thinking, also, you know, sucking at a thing that others,

I think in that talk, you could be a field-medal winning mathematician and an undergrad knows something better than you. Yeah, I think mathematics inherently, I mean, mathematics is so huge these days that nobody knows all of modern mathematics. And inevitably we make mistakes and, you know, you can't cover up your mistakes with just sort of bravado and I mean, because people will ask for your proofs and if you don't have the proofs, you don't have the proofs.

I don't love math. Yeah, so it does keep us honest. I mean, not, not, I mean, you can still, it's not a perfect, uh, panacea, but I think, uh, we do have more of a culture of admitting error then because we're forced to all the time, big ridiculous question. I'm sorry for it. Once again, who is the greatest mathematician of all time? Maybe one who's no longer with us. Who are the

candidates? Euler, Gauss, Newton, Ramanujan, Hilbert. So first of all, as I mentioned before, there's some time dependence. On the day. Yeah. If you part cumulatively over time, for example, Euclid is one of the leading contenders. And then maybe some unnamed anonymous mathematicians before that, whoever came up with the concept of numbers. Do mathematicians today still feel the impact of Hilbert? Oh yeah. Just directly of everything that's happened in the 20th century?

Yeah, Hilbert spaces. We have lots of things that are named after him, of course. Just the arrangement of mathematics and just the introduction of certain concepts. I mean, 23 problems have been extremely influential. There's some strange power to the declaring which problems are hard to solve, the statement of the open problems. Yeah. I mean, there's this bystander effect everywhere.

If no one says you should do X, everyone just will mill around waiting for somebody else to do something and like nothing gets done. So, and like it's the one thing that actually you have to teach undergraduates in mathematics is that you should always try something. So you see a lot of paralysis

in an undergraduate trying a math problem. If they recognize that there's a certain technique that can be applied, they will try it. But there are problems for which they see none of their standard techniques obviously applies. And the common reaction is then just paralysis. I don't know what to do. I think there's a quote from the Simpsons, I've tried nothing and I'm all out of ideas. Um, so, you know, like the next step then is to try anything, like, no matter how stupid, um, and in fact, almost the stupid of the better, um, which, you know, I'm

one, I think we're just almost guaranteed to fail, but the way it fails is going to be instructive. Um, like it, it fails because you're not at all taking into account this hypothesis. Oh, this hypothesis must be useful. That's the clue. I think you also suggested somewhere this fascinating approach, which really stuck with me as they're using it and it really works.

I think you said it's called structured procrastination. No, yes. It's when you really don't wanna do a thing, then you imagine a thing you don't wanna do more. Yes, yes, yes. That's worse than that. And then in that way, you procrastinate by not doing the thing that's worse want to do more that's worse than that. And then in that way you procrastinate by not doing the thing that's worse. It's a nice hack. It actually works.

Yeah. I mean, with anything, psychology is really important. You talk to athletes like marathon runners and so forth, and they talk about what's the most important thing, is it their training regimen or the diet and so forth. So much of it is like your psychology, just tricking yourself to think that the problem is feasible so that you can motivate to do it. CB Is there something our human mind will never be able to comprehend? RW Well, I guess in math addition, I mean, reduction. There must be some large number that you can't understand. That was the first thing that came to mind.

So that, but even broadly, is there something about our mind that's going to be limited even with the help of mathematics? Well, okay. I mean, how much augmentation are you willing? For example, if I didn't even have pen and paper, if I had no technology whatsoever, okay, so I'm not allowed blackboard, pen and paper. You're already much more limited than you would be. Incredibly limited. Even language, the English language is a technology.

It's one that's been very internalized. So you're right. They're really, the formulation of the problem isn't correct because there really is no longer a just a solo human. We're already augmented in extremely complicated, intricate ways, right? Yeah. So we're already like a collective intelligence. Yes.

Yeah. Yes. So humanity plural has much more intelligence in principle on these good days than the individual humans put together. It can all have less. Okay. But yeah, so yeah, the mathematical community plural is an incredibly super intelligent entity that no single human mathematician can come close to replicating. You see it a little bit on these question analysis sites, so this math overflow, which is the math version of stackable flow.

And sometimes you get this very quick response to very difficult questions from the community. And it's a pleasure to watch, actually, as an expert. I'm a fan spectator of that site, just seeing the brilliance of the different people there, the depth of knowledge that some people have and the willingness to engage in the, in the rigor and the nuance of the particular question. It's pretty cool to watch. It's fun. It's almost like just fun to watch. What gives you hope about this whole thing we have going on

human civilization? I think, yeah, the younger generation is always really creative and enthusiastic and inventive. It's a pleasure working with young students. The progress of science tells us that the problems that used to be really difficult can become extremely trivial to solve. I mean, like navigation, just knowing where you were on the planet was this horrendous problem. People died or lost fortunes because they couldn't navigate.

And we have devices in our pockets that do this automatically for us, like it's a completely solved problem. You know, so things that seem unfeasible for us now could be maybe just sort of homework exercises for me. Yeah, one of the things I find really sad about the finiteness of life is that I won't get to see all the cool things we create as a civilization, you know,

that because in the next 100 years, 200 years, just imagine showing up in 200 years. Yeah well already plenty has happened you know like if you could go back in time and talk to you of your teenage self or something you know I mean yeah yeah just the internet and and our AI I mean that's like again they've been into they're getting to be internalized and says yeah of course an AI can understand our voice and give reasonable, you know, slightly incorrect answers to any question. But yeah, this was mind blowing even two years ago.

And in the moment, it's hilarious to watch on the internet and so on the drama, people take everything for granted very quickly, and then they, we humans seem to entertain ourselves with drama. Well, out of anything that's created, somebody needs to take one opinion, another person needs to take an opposite opinion, argue with each other about it.

But when you look at the arc of things, I mean, just even in progress of robotics, just to take a step back and be like, wow, this is beautiful the way humans are able to create this. Yeah, when the infrastructure and the culture is healthy, the community of humans can be so much more intelligent and mature and rational than the individuals within it.

Well, one place I can always count on rationality is the comment section of your blog, which I'm a big fan of. There's a lot of really smart people there. And thank you, of course, for putting those ideas out on the blog. And I can't tell you how honored I am that you would spend your time with me today.

I was looking forward to this for a long time. Terry, I'm a huge fan. You inspire me, you inspire millions of people. Thank you so much for talking. Thank you, it was a pleasure. Thanks for listening to this conversation with Terrence Tao. To support this podcast,

please check out our sponsors in the description or at lexfreedman.com slash sponsors. And now let me leave you with some words from Galileo Galilei. Mathematics is the language with which God has written the universe. Thank you for listening and hope to see you next time.