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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-sb 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://lexfridman.com/s/notion-ep472-sb *Shopify:* Sell stuff online. Go to https://lexfridman.com/s/shopify-ep472-sb *NetSuite:* Business management software. Go to https://lexfridman.com/s/netsuite-ep472-sb *LMNT:* Zero-sugar electrolyte drink mix. Go to https://lexfridman.com/s/lmnt-ep472-sb *AG1:* All-in-one daily nutrition drink. Go to https://lexfridman.com/s/ag1-ep472-sb *OUTLINE:* 0:00 - Introduction 0:49 - First hard problem 6:16 - Navier–Stokes singularity 26:26 - Game of life 33:01 - Infinity 38:07 - Math vs Physics 44:26 - Nature of reality 1:07:09 - Theory of everything 1:13:10 - General relativity 1:16:37 - Solving difficult problems 1:20:01 - AI-assisted theorem proving 1:32:51 - Lean programming language 1:42:51 - DeepMind's AlphaProof 1:47:45 - Human mathematicians vs AI 1:57:37 - AI winning the Fields Medal 2:04:47 - Grigori Perelman 2:17:30 - Twin Prime Conjecture 2:34:04 - Collatz conjecture 2:40:50 - P = NP 2:43:43 - Fields Medal 2:51:18 - Andrew Wiles and Fermat's Last Theorem 2:55:16 - Productivity 2:57:55 - Advice for young people 3:06:17 - 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 *SOCIAL LINKS:* - X: https://x.com/lexfridman - Instagram: https://instagram.com/lexfridman - TikTok: https://tiktok.com/@lexfridman - LinkedIn: https://linkedin.com/in/lexfridman - Facebook: https://facebook.com/lexfridman - Patreon: https://patreon.com/lexfridman - Telegram: https://t.me/lexfridman - Reddit: https://reddit.com/r/lexfridman
In this introduction, Lex Fridman introduces Terence Tao, a renowned mathematician celebrated for his groundbreaking contributions across various fields in mathematics and physics. Tao's accolades include the Fields Medal and the Breakthrough Prize, highlighting his status as one of the greatest mathematicians in history.
"The following is a conversation with Terrence 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 Break..."
Terence Tao reflects on the first challenging research-level math problem he encountered during his undergraduate studies. He discusses the nature of problems that lie on the boundary between solvable and unsolvable, emphasizing the significance of problems that can be approached with existing techniques but require a breakthrough for completion.
"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 um education, you learn about the really hard im..."
Tao delves into the CA problem, a mathematical puzzle that involves maneuvering a needle in a plane to execute a U-turn using minimal space. He explains the historical context of the problem and its implications for understanding efficiency in two-dimensional movements, setting the stage for exploring its three-dimensional counterpart.
"from a little puzzle by the Japanese mathematician Soji Kaya uh in like 1918 or so. Um, so the puzzle is that you you you have um a needle um in on the plane. Um think like like a like driving like on..."
Expanding on the CA problem, Tao introduces a three-dimensional scenario involving the Hubble Space Telescope. He discusses the challenge of rotating the telescope to observe every star in the universe while minimizing the volume occupied, drawing connections to broader mathematical concepts and conjectures.
"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 t..."
Tao explains the relationship between wave propagation and singularities in mathematical physics. He illustrates how waves can concentrate energy and potentially lead to singularities, emphasizing the importance of understanding these phenomena in the context of fluid dynamics and the Navier-Stokes equations.
"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, comics. For exam..."
Tao discusses the Navier-Stokes regularity problem, a significant unsolved problem in fluid dynamics. He explains the implications of singularities in fluid flow and the challenges mathematicians face in proving whether singularities can form under certain conditions, highlighting its relevance to real-world fluid behavior.
"Um and so it's possible to do that. Uh and geometrically what's going on is that there's always s of light rays. Um so like if if if this wave represents light for example um you can imagine this wave..."
In this segment, Tao addresses the core question of whether the Navier-Stokes equations can blow up under specific initial conditions. He emphasizes the distinction between practical observations and theoretical possibilities, illustrating the complexities involved in fluid dynamics and the pursuit of mathematical proofs.
"the Navia Stokes regularity problem. So the Navia Stokes equations equations that govern the fluid flow for incompressible fluids like water. The question asks if you start with a smooth velocity fiel..."
Tao introduces the concept of Maxwell's demon as a metaphor for the unpredictability in mathematics. He draws parallels between this idea and the challenges faced in proving the Navier-Stokes problem, emphasizing the role of statistical improbability in mathematical reasoning.
"you speak to the neighbors? So the existence and smoothness like you said millennial prize problem right you've made a lot of progress on this one in 2016 you published a paper finite time blow up for..."
Tao elaborates on the dynamics of energy in fluid motion, discussing how viscosity and turbulence interact. He explains the significance of energy dispersion in preventing singularities and the implications for understanding fluid behavior in practical scenarios.
"clay price problem concerns what's called the incompressible navio stokes which governs things like water. There's something called the compressible navio stokes which governs things like air. And tha..."
In this segment, Tao explores the concept of finite time blow-up in fluid dynamics. He discusses the conditions under which energy can concentrate rapidly, leading to potential singularities, and the mathematical implications of such scenarios for the Navier-Stokes equations.
"statistically it's extremely unlikely but mathematically it's possible that this can happen and we can't rule it out. Um and this is a situation that shows up a lot in mathematics. Um a basic example ..."
Tao shares his innovative approach to understanding blow-up scenarios by modifying the laws of physics in mathematical equations. He explains how this technique can provide insights into the Navier-Stokes problem and help mathematicians rule out ineffective methods.
"and each time it does this uh it takes maybe half as as long as as the previous one and then you you could you could actually uh converge to all the energy concentrating in one point in a finite amoun..."
Tao discusses the concept of supercriticality in partial differential equations, particularly in the context of the Navier-Stokes equations. He explains how the balance between dissipation and transport terms affects the predictability of fluid behavior and the challenges posed by nonlinear dynamics.
"the past there have been many attempts to try to obtain what's called global regularity for Navio Stokes which is the opposite of final time blow up that velocity say smooth and it all failed there wa..."
In this concluding segment, Tao summarizes the tug-of-war between dissipation and transport forces in fluid dynamics. He emphasizes the significance of understanding these competing forces in predicting fluid behavior and the broader implications for mathematical modeling.
"had you must use some feature of the true equation which which my artificial equation um does not satisfy. So it it rules out certain um certain approaches. So um the thing about math is is it's not j..."
Terence Tao discusses the complexities of the Navier-Stokes equations, particularly in two dimensions. He explains the critical balance between transport effects and viscosity, highlighting the significance of supercriticality in predicting fluid behavior. Tao emphasizes the challenges posed by nonlinear effects and how they complicate predictions in fluid dynamics, such as weather forecasting.
"So the viscosity are the things that calm things down. Um and so this is um um this is why the problem is hard in two dimensions. So the Soviet mathematician ladish skaya she in the 60s shows in two d..."
In this segment, Tao explores the idea of constructing a liquid computer to address the Navier-Stokes equations. He describes how energy transfer at different scales can be managed to avoid blow-up scenarios in fluid dynamics. Tao draws parallels between fluid dynamics and electronic circuits, suggesting that a fluid-based computation could lead to new insights in solving complex mathematical problems.
"Lots of really strange things are going on at very fine scales. So, whenever there is some huge source of nonlinearity, yeah, that can create a huge problem for predicting what's going to happen. Yeah..."
Tao elaborates on the potential of fluid-based machines for robotics, proposing a self-replicating fluid robot that could colonize other planets. He discusses the theoretical framework for such machines, emphasizing the need for precise engineering to achieve complex behaviors in fluid dynamics. This segment highlights the intersection of mathematics, physics, and engineering in creating innovative solutions.
"way. Can you describe this this Yeah. So this came out of of this work of constructing this this this average equation that that blew up. Um so one um as as part of how I had to do this. So there this..."
In this segment, Tao discusses the theoretical underpinnings of fluid machines and their potential to perform computations. He describes the challenges of creating basic logic gates using fluid dynamics and the implications for future technology. Tao's insights bridge the gap between theoretical mathematics and practical applications in engineering and robotics.
"locks. So um 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 from ..."
Tao draws an analogy between fluid dynamics and cellular automata, particularly Conway's Game of Life. He explains how simple rules can lead to complex behaviors and structures, paralleling the unpredictability of fluid dynamics. This segment emphasizes the mathematical connections between seemingly disparate fields and the potential for new discoveries through interdisciplinary approaches.
"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 um like if you can imagine kind of a steampu..."
In this segment, Tao discusses the engineering of complex mathematical structures and the challenges of proving patterns within random sequences. He highlights the dichotomy between structured and random objects in mathematics, emphasizing the importance of careful initial conditions in generating complex behaviors. Tao's reflections on engineering and mathematics provide insights into the nature of mathematical discovery.
"machine had the ability to mine the planet, create some more materials to smelt them and build more copies of the same machine. Um, then you could colonize a whole planet um over time. Um, so uh if yo..."
Tao explores the concept of structure versus randomness in mathematics, discussing how most mathematical objects appear random. He explains the significance of proving patterns and the challenges associated with identifying structure in random sequences. This segment delves into the philosophical implications of mathematical inquiry and the nature of discovery.
"that this thing is possible. Um there are other groups who are now pursuing ways to make navis blow up which are nowhere near as ridiculously complicated as this. Um um they they actually are pursuing..."
In this thought-provoking segment, Tao discusses the infinite monkey theorem and its implications for randomness in mathematics. He explains how infinite sequences can eventually produce any finite pattern, including arithmetic progressions. This exploration of infinity and randomness highlights the fascinating interplay between mathematical theory and real-world applications.
"game of life. Um 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 at a certain direction a..."
Tao concludes with reflections on how humans conceptualize infinity in mathematics. He discusses the abstraction of infinity and its relevance to finite experiences, emphasizing the philosophical questions it raises. This segment encapsulates the broader themes of the conversation, linking mathematical concepts to human understanding and experience.
"It's so incredible. A lot of this was like community crowdsourced by like amateur mathematicians actually. Um so I knew about that that that work and so that is part of what inspired me to propose the..."
Terence Tao discusses the Infinite Monkey Theorem, illustrating how an infinite number of monkeys typing randomly will eventually produce any finite string of text, including complex patterns like arithmetic progressions. He emphasizes that while randomness can yield structure, understanding the implications of infinity in mathematics requires careful consideration.
"and they contain arithmetic progressions of any length um so in that case it's obvious because the the odd numbers are really really structured I can just take 11 13 15 17 I just I can I can easily fi..."
Tao elaborates on the concept of infinity as an abstraction in mathematics, explaining how it allows for idealization of large or small quantities. He discusses the importance of limits and the potential pitfalls of using infinity in mathematical reasoning, highlighting the need for rigorous methods to avoid mistakes.
"Um have you heard of the infinite monkey theorem? Usually mathematicians give boring names to theorists, but occasionally they they give colorful names. Yes. The popular version of the infinite monkey..."
In this segment, Tao explores the relationship between theoretical mathematics and experimental approaches. He explains how mathematics often starts from axioms to derive conclusions, contrasting it with other disciplines that typically begin with conclusions and work backward to find paths to achieve them.
"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 as as just an abstraction of um a finite number for..."
Tao discusses the role of mathematical models in science, emphasizing that while models are essential for making predictions, they often rely on simplified assumptions. He highlights the importance of refining models through observations and the iterative process of aligning theory with reality.
"work with there. I wonder how often using infinity uh forces us to deviate from um the physics of reality. Yeah. So there's a lot of pitfalls. Um so you know we we spend a lot of time in undergraduate..."
Tao explains the complexities of working with infinite series in mathematics, illustrating how rearranging terms can lead to different convergence values. He emphasizes the necessity of understanding limits and the careful reasoning required when dealing with infinite quantities.
"started taking results that are true in infinite limits and what's called finetizing them. Um so you know that something's true eventually but um you don't know when. Now give me a rate. Okay. Okay, s..."
In this segment, Tao reflects on the interplay between mathematics and physics, discussing how both disciplines contribute to understanding reality. He emphasizes the need for both theoretical and experimental approaches to refine models and enhance our grasp of the universe.
"infinite ones are found first usually like decades earlier and then later on people finize them. So since we mentioned a lot of math and a lot of physics uh what is the difference between mathematics ..."
Tao shares insights on the nature of mathematical discovery, highlighting how mathematicians often start with hypotheses and explore their implications. He contrasts this with the more conclusion-driven approach seen in other fields, emphasizing the unique speculative nature of mathematics.
"don't yet have the observation we'd like to a prediction. Um and then we have these simplified models sometimes making unrealistic assumptions you know spherical cow type things. Those are the mathema..."
Tao discusses the tension between theoretical predictions and experimental results in physics. He explains how both aspects are crucial for advancing knowledge, with theories guiding experiments and observations informing theoretical refinements.
"the path to get there. Um a lot there there's a lot less sort of speculation about suppose I did this, what would happen? Um you know, planning and and and modeling um uh speculative fiction maybe is ..."
In this segment, Tao reflects on the evolution of mathematical methods, particularly the shift towards experimental mathematics with the advent of computers. He discusses how computational tools have enabled mathematicians to explore previously intractable problems and the implications for future research.
"where to push the other you know. So um if your model is predicting anomalies um that are not picked up by experiment that tells experimenters where to look you know um to to to to find more data to r..."
Tao addresses the limitations of computational power in mathematics, explaining how certain problems become intractable due to combinatorial explosion. He uses examples like chess to illustrate the challenges of exhaustive computation and the potential for AI to assist in exploring complex mathematical landscapes.
"yeah I mean theoretical mathematics was just much more successful I mean because doing complicated mathematical computations is uh was just not not feasible until very recently. Uh and even nowadays, ..."
Tao discusses the potential of AI in mathematics, particularly in exploring complex problems and refining theories. He highlights how AI can assist in experimental mathematics, providing new insights and challenging conventional wisdom.
"computation. Uh chess is another um famous example. The number of chess positions uh we can't get a computer to fully explore. But now we have AI um um we have tools to explore this space not with 100..."
In this segment, Tao reflects on the philosophical implications of mathematics and its relationship to reality. He discusses the distinction between actual reality, observations, and models, emphasizing the ongoing quest to bridge these gaps through scientific inquiry.
"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 that I don't believe it's providing a kind of formal ..."
Tao illustrates how scientific models evolve over time, using astronomy as an example. He explains how initial models often diverge from reality but improve as more observations are made, leading to a more accurate understanding of the universe.
"always be distinct. Um, but they can get closer um over time. Um, you know, so um and the process of getting closer often means that you you have to discard your initial intuitions. Um so um like astr..."
Tao emphasizes the importance of simplicity in scientific models, discussing how effective theories can explain vast amounts of data with minimal parameters. He highlights the balance between model complexity and explanatory power.
"itself is this evidence that this non-constant and uh the explanation behind why that is it's catching up. Um it's catching up. I mean it's still you know the dark matter or dark energy this this kind..."
Tao explores the concept of universality in mathematics, explaining how complex systems can exhibit simple behaviors. He discusses the central limit theorem and its implications for understanding distributions in nature.
"have. Um you can think of of a theory like one way to think about um physical math theory theory is it's a compression of of the universe um and data compression. So you know you have these pabytes of..."
In this segment, Tao warns about the risks of overfitting in mathematical models, particularly in the context of financial predictions. He discusses the importance of understanding systemic risks and the limitations of relying solely on elegant models.
"at the macro scale and normally because of the common form of explosion you would think that uh the macros scale equations must be like infinitely exponentially more complicated than than the uh the m..."
Tao concludes by reiterating the synergy between mathematics and science, emphasizing the need for both theoretical understanding and empirical validation. He highlights how mathematical insights can guide scientific inquiry and vice versa.
"simpler toy models where we do um have a good understanding of why univers universality occurs. Um um most basic one is is the central limit theorem that explains why the bell curve shows up everywher..."
Terence Tao discusses the significance of understanding mathematical models and their validation against reality. He emphasizes that while models may appear elegant, they must accurately reflect real-world conditions. Tao highlights the role of mathematics in identifying weaknesses in models, particularly through concepts like the central limit theorem, which can guide economists in assessing systemic risks.
"that this is systemic risk is actually a much bigger issue and uh just because the model is pretty uh and nice uh it may not match reality. Right. So, so the mathematics of working out what models do ..."
Tao reflects on the interconnectedness of various mathematical disciplines, drawing parallels to historical developments such as the unification of geometry and number theory. He explains how progress in mathematics often involves finding connections between previously unrelated fields, illustrating this with the evolution of analytic geometry and its impact on mathematical thought.
"topic of universality. Mhm. 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 mathematic..."
In this segment, Tao introduces the metaphor of hedgehogs and foxes to describe different styles of mathematicians. Hedgehogs focus deeply on one area, while foxes explore multiple fields. He discusses the value of collaboration between these types, emphasizing how diverse approaches can enhance mathematical discovery and innovation.
"they were not really considered related. Um I mean a little bit like you know you could say that that this length was five times this length because you could take five copies of this length and so fo..."
Tao shares his preference for the 'fox' approach to problem-solving, which involves exploring analogies and narratives across different fields. He describes his process of re-proving results using familiar tools, highlighting the exploratory nature of mathematics and the importance of understanding various techniques to foster deeper insights.
"So I think there's sort of different styles to being a mathematician. I think hedgehogs and fox a fox knows many things a little bit but a hedgehog knows one thing very very well. Um and in mathematic..."
Tao reflects on the artistry involved in mathematical proofs, inspired by John Conway's concept of 'extreme proofs.' He discusses how optimizing proofs for elegance and clarity can enhance their impact and readability, drawing parallels to coding practices that prioritize clean and maintainable code.
"these extra tools. I mean you said that you can be both the hedgehog and and the fox depending on the context depending on the collaboration. So what can you if it's at all possible speak to the diffe..."
In this segment, Tao explores the beauty of Euler's identity, E=eiπ + 1, and its connections to fundamental mathematical concepts. He articulates how this equation unifies various mathematical fields, illustrating the elegance that arises from the interplay of exponential functions, geometry, and complex numbers.
"mean I can do that too but uh there are other people who are extremely good at that. Let's step back and uh uh maybe look at the the a bit of a romanticized version of mathematics. Mhm. So, uh I think..."
Tao discusses the significance of notation in mathematics, emphasizing how the convergence of different mathematical concepts can validate the underlying theories. He reflects on how the evolution of notation can reveal deeper connections and insights within mathematical frameworks.
"mean it may seem like a frivolous exercise but it can generate all these insights which if you didn't have this artificial um objective to to to pursue you might not see. What to you is the most beaut..."
Tao explains the relationship between symmetry and conservation laws in physics, illustrating how these principles manifest in both classical and quantum mechanics. He highlights the Hamiltonian's role in governing dynamics and the importance of recognizing symmetries to understand physical systems.
"that um so the if ei= minus one um so yeah people oh uses all the fundamental constants okay that that's I mean that's cute um but but to me so the exponential function was interested by oil to measur..."
In this segment, Tao shares his belief in the possibility of unifying general relativity and quantum mechanics into a single theory of everything. He discusses the historical context of unification in physics and the challenges posed by the success of current theories, emphasizing the need for innovative mathematical concepts to bridge the gap.
"connects together all these tools mathematics. Yeah. Yeah. dynamic structure and complex and complex and um the complex numbers they all considered almost yeah they were all next door neighbors in mat..."
Tao reflects on the historical interplay between mathematics and physics, noting how mathematicians often provide the foundational concepts that physicists later utilize. He discusses the importance of mathematical frameworks in developing theories that explain the universe, highlighting the collaborative nature of these disciplines.
"know they saw the things they could they could measure they could measure mass and acceleration and force and so forth and so Newtonian mechanics for example F= ma was the famous Newton second law of ..."
Tao addresses the complexities involved in unifying quantum mechanics and general relativity, emphasizing the need for new mathematical frameworks to describe phenomena at extreme scales. He discusses the limitations of current theories and the potential for future breakthroughs in understanding the fundamental nature of reality.
"secretly behind the scenes in classical mechanics also is the key uh object in um um in quantum mechanics that there's there's also an object called Hamiltonian. It's a different type of object. It's ..."
In this concluding segment, Tao expresses optimism about the future of theoretical physics and the ongoing quest for a unified theory. He reflects on the historical patterns of unification in science and the potential for new discoveries that could reshape our understanding of the universe.
"don't know how to what to replace it with um We don't actually have the mathematical um um concepts the analog Hamiltonian that sort of organized everything. Does your gut say that there is a theory o..."
Terence Tao discusses the historical interplay between mathematics and physics, highlighting how mathematicians often lay the groundwork for theories that physicists later utilize. He references Einstein's reliance on Riemannian geometry to develop his theory of curved space, illustrating the 'unreasonable effectiveness of mathematics' in explaining the universe.
"made progress before. There's no reason why we should stop. Do you think it will be a mathematician that develops uh theory of everything? What often happens is that when the physicists need uh um som..."
Tao explores the challenges of understanding complex theories like string theory, emphasizing the difficulty of intuitively grasping higher dimensions. He draws parallels between our limited cognitive abilities and the leaps required to comprehend advanced scientific concepts, stressing the importance of analogies in bridging these gaps.
"Dwick's 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 p..."
In this segment, Tao reflects on the intellectual leap required to move from a flat to a round understanding of the Earth. He discusses how modern science often challenges our intuitions and the necessity of grounding scientific concepts in relatable experiences to foster better understanding.
"analogies are so important, you know. I mean, so yeah, the round earth is not intuitive because we're stuck on it, but you know, but you know, but round objects in general, we have pretty good intuiti..."
Tao emphasizes the importance of science outreach and education, suggesting that many scientific principles can be understood through simple experiments. He recounts how ancient Greeks measured astronomical distances, advocating for accessible methods to engage with complex scientific ideas.
"a lot of evidence for this kind of thing. But you know, we're on a round rock. Yeah. Flying through space. Yeah. Yeah. And it's a big leap and you have to take a chain of those leaps the more and more..."
Tao discusses the concept of 'intellectual travel,' encouraging listeners to immerse themselves in the mindset of historical figures like the ancient Greeks. He highlights the significance of perspective in understanding scientific advancements and the creative processes behind mathematical discoveries.
"Yeah, that's uh I highly recommend that. I believe you give 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 t..."
Tao shares insights into his work on the mathematical understanding of Einstein's field equations. He explains the complexities of wave maps equations and their relevance to understanding fields that exist within the framework of spacetime, showcasing the intricate relationship between mathematics and physics.
"if you propose axioms then the mathematics lets you follow those a to their conclusions and sometimes you can get quite a quite a long way from you know initial hypothesis. If we can stay in the land ..."
In this segment, Tao elaborates on the challenges of nonlinear equations in physics, particularly the wave maps equation. He describes his innovative approach to solving these problems, which involved visualizing mathematical concepts and developing techniques to manage nonlinear effects.
"relatively low in the hierarchy was this thing called the wave maps equation. So it's a wave which at any given point uh is fixed to be like on a sphere. Um so uh I can think of a bunch of arrows in s..."
Tao shares his philosophy on tackling difficult mathematical problems, likening the process to a video game where one can strategically simplify challenges. He emphasizes the importance of isolating difficulties and gradually merging solutions to build a comprehensive understanding.
"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 I couldn't do it with pen and paper. Um and I had not en..."
Tao discusses his preferred methods for visualizing mathematical problems, including the use of blackboards for brainstorming. He highlights the organic nature of this process and how it aids in understanding complex mathematical relationships.
"sort of uh I call it cheating strategically um so u the the beauty of mathematics is that is that you get to change the rule change the problem change the rules as you wish this you don't get to do th..."
Tao introduces the Lean programming language as a tool for formal proofs in mathematics. He explains how Lean differs from traditional programming languages by not only executing code but also producing verifiable proofs, enhancing the rigor of mathematical arguments.
"a lot of these Hong Kong action movies. Um it's from a culture. Um and uh one thing is that every time there was a fight scene, you know, so maybe the the hero will get swarmed by a hundred bad guy go..."
In this segment, Tao contrasts the experience of writing proofs on paper with using Lean. He discusses the challenges of formalizing statements in Lean and how it requires a deeper philosophical understanding of mathematical objects, making the process both rigorous and demanding.
"Let's talk about AI a little bit if we could. So um maybe a good entry point is just talking about computer assisted proofs in general. Can you describe the lean formal proof programming language and ..."
Tao explores the integration of AI in mathematical proofs, discussing how tools like Lean can enhance the proof-writing process. He shares insights on the current state of AI assistance in mathematics and the potential for future developments in this area.
"correct if you trust the compiler of but they made the compiler really small and you can there are several different compilers available for the same for um can you give people some intuition about th..."
Tao concludes by reflecting on the evolving role of AI in mathematics, particularly in formalizing proofs. He discusses the balance between traditional methods and modern technology, emphasizing the need for mathematicians to adapt to new tools while maintaining the essence of mathematical inquiry.
"Oh, I see. Um yeah, so it's it's designed for reliability. So uh 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..."
Terence Tao discusses the current state of AI in mathematics, particularly its role in assisting with theorem proving. He explains how AI functions as an advanced autocomplete tool, helping mathematicians formalize proofs more efficiently, while also highlighting the challenges and limitations of this technology.
"10 15% of the time it doesn't quite work but it it's close enough that I can say oh if I just change it here and here it it will work and then like half the time it gives me complete rubbish um so but..."
Tao shares his experience with the Lean programming language in formalizing mathematical proofs. He illustrates how Lean allows for quick updates to proofs, significantly reducing the time and effort required to verify changes, compared to traditional pen-and-paper methods.
"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 p..."
In this segment, Tao explains how Lean facilitates collaboration among mathematicians. He emphasizes the ease of sharing and modifying proofs, allowing for real-time collaboration and problem-solving, which was previously difficult with traditional methods.
"quick process. You um, 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 but the process converges much mo..."
Tao discusses the initial brainstorming process in mathematical research projects. He contrasts it with engineering projects, emphasizing the exploratory nature of mathematics and how collaboration can lead to innovative approaches to solving complex problems.
"and paper um when you want to collaborate with another mathematician um either you do it as a blackboard where you um you can really interact but if you're doing it sort of by email or something um ba..."
Tao recounts his collaboration with Ben Green on the Green-Tao theorem, which involves arithmetic progressions in prime numbers. He describes the dynamics of their collaboration, including the challenges of aligning their approaches and the eventual success they achieved.
"of thing or are you brains are you focusing on a particular part and you're brainstorming? There's always a brainstorming process first. Yeah. So math research projects sort of by their nature when yo..."
In this segment, Tao explains the concept of creating blueprints for mathematical proofs using Lean. He discusses how these blueprints allow for modular contributions from multiple mathematicians, enhancing collaboration and efficiency in proving complex theorems.
"three progressions. They didn't work for longer progressions. Um but I had these techniques coming from agotic theory which is something that I had been playing with and and uh I knew better than Ben ..."
Tao shares insights on how Lean and other software tools can scale up experimental mathematics. He discusses the potential for large-scale collaboration and the ability to tackle numerous mathematical problems simultaneously, which was previously unfeasible.
"right for a problem and then you can really do a divide and conquer with lean where you're working on separate parts right and they're using the computer system proof checker essentially to make sure ..."
Tao introduces the Equation Theories project, which involves generating millions of problems in abstract algebra. He explains the project's goal of exploring the relationships between various algebraic laws and how it leverages Lean for formal verification.
"up um to a much greater degree than we can do now. So right now if you want to um um do any mathematical exploration of some mathematical pattern or something you need some code to write out the patte..."
In this segment, Tao discusses the collaborative nature of the Equation Theories project, highlighting the contributions of around 50 mathematicians. He emphasizes the importance of trust and verification in collaborative mathematical work, facilitated by Lean.
"* Y * Z. Um so um these operations obey some laws that don't obey others. For example, x * x is not always equal to x. So that law is not always true. So given any any operation, it obeys some laws an..."
Tao reflects on the challenges of measuring contributions in collaborative mathematical projects. He discusses the potential for developing metrics to assess contributions while cautioning against the pitfalls of over-reliance on quantitative measures in academia.
"became possible with with lean. Um we were hoping to use a lot of AI as well. Um so the project is almost complete. Um so of these 22 million all but two had been settled. Um wow and uh well actually ..."
Terence Tao discusses the evolution of collaborative mathematics through the Polymath Project, highlighting the challenges of authorship and contribution in large mathematical collaborations. He reflects on the shift from pseudonymous authorship to a more inclusive approach where all contributors are recognized, emphasizing the importance of proper credit in academic settings.
"Yeah. I know. It's rational. So what we've done for this project is is self-report. So um there are actually standard categories um from the sciences of what types of contributions people give. So the..."
Tao explores the integration of AI in mathematics, particularly through DeepMind's AlphaProof, which utilizes reinforcement learning to tackle high-level math problems. He discusses the potential of AI to assist in mathematical proofs and the challenges it faces in scaling to more complex problems, emphasizing the need for human oversight in the verification process.
"actually turned out to be not so great for a couple of reasons. So, so one is that if you actually wanted to be considered for tenure or whatever, you could not use this paper in your uh uh as your su..."
In this segment, Tao elaborates on the exponential difficulty of mathematical proofs as the number of steps increases. He highlights the limitations of large language models in generating valid proofs and the complexities involved in translating natural language into formal mathematical language, which remains a significant challenge for AI.
"of mathematics. Um so I have to ask you here about the integration of AI into this whole process. So deep mind's alpha proof was trained using reinforcement learning on both failed and successful form..."
Tao discusses the performance of AI in mathematical competitions, comparing its capabilities in solving numerical problems versus long-form proofs. He mentions the potential for an AI Math Olympiad and the challenges of grading AI-generated outputs, emphasizing the need for human evaluators to assess the quality of AI's mathematical reasoning.
"is is an unsolved basically unsolved problem. That is fascinating. Okay. So uh but once you have an informal language they're using um their RL train model. So some something akin to alpha zero that t..."
Tao reflects on the unique contributions of human mathematicians, particularly in inventing new theories and conjectures. He discusses the limitations of AI in recognizing when it has made a mistake and the importance of human intuition in navigating complex mathematical problems.
"time period and and uh um but there are smaller competitions um there are competitions where the the answer is a is a number rather than a long form proof um and that's that's um AI are actually a lot..."
In this segment, Tao envisions a future where AI collaborates with mathematicians in a more integrated manner. He discusses the potential for AI to assist in generating proofs and verifying results, while also acknowledging the current challenges in effectively utilizing AI as a mathematical assistant.
"the date of Easter uh and there was really complicated uh calculations you know but it's all automated been automated for centuries we don't need that anymore you know they used to navigate to do sphe..."
Tao speculates on the timeline for AI to contribute significantly to mathematical research, potentially leading to a Fields Medal-winning collaboration. He predicts that by 2026, AI will play a role in research-level mathematics, contributing to published ideas and computations, while still requiring human oversight for validation.
"can you can tell you can tell immediately like, okay, there's signs. But with with a generate code of and then you're right eventually you find an obvious dumb thing that just looks like good code. Ye..."
Tao discusses the capabilities of AI in conducting literature reviews, noting its potential to assist researchers in finding relevant papers. However, he cautions about the current issues with AI hallucinating references and the importance of verifying information.
"know using the internet um you know um you should in a few years get to the point where you you have a a lema that you need and uh we say has anyone proven this lema before and it will do basically a ..."
Tao elaborates on the challenges AI faces in generating accurate literature reviews and the need for high-quality training data. He compares the current state of AI to self-driving cars, emphasizing the importance of reliability in AI applications.
"it's it's um it's most helpful when you already somewhat know the literature. Um and you just need to be prompted to be reminded of a paper that was already subconsciously in your memory versus helpin..."
Tao shares the dream of physicists for AI to discover new laws of physics from data. He discusses the difficulties AI encounters in both discovering new and even existing laws due to the limitations of current training data.
"um and in fact even worse than good some ways. I mean another way of asking the Fields metal question is what year do you think you'll wake up and be like real surprised? you read the headline, the ne..."
Tao explains the Point Conjecture, a significant problem in mathematics related to the classification of three-dimensional spaces. He describes the journey of Grigori Perelman in solving this conjecture and the mathematical concepts involved.
"mean so um a version of this is um I mean the physicists have a dream of getting the AI to discover new new laws of physics. Um you know the the dream is you just feed it all this data. Okay. and and ..."
Tao reflects on Grigori Perelman's solitary journey in proving the Point Conjecture, emphasizing the dedication and focus required to tackle such complex problems. He highlights the importance of perseverance in mathematical discovery.
"sometimes joke that basically AI has to go through um grad school and actually you know go to grad courses, do the assignments, go to office hours, make mistakes, um get advice on how to correct the m..."
Tao discusses the complexity of mathematical proofs, particularly in higher dimensions. He explains the challenges mathematicians face when trying to visualize and solve problems related to topology and geometry.
"All right. So it's it's a question about curb spaces. Earth is a good example. So you can think of a 2D surface in being round could maybe be a Taurus with a hole in it or it can have many holes and t..."
Tao delves into the concept of singularities in mathematical spaces and how they affect the classification of surfaces. He explains the significance of understanding singularities in the context of the Point Conjecture.
"and and there are also 3D spaces that can't even fit into four dimensions. you need five or six or or higher. But anyway, uh mathematically you can still pose this question that if you have a bounded ..."
Tao introduces the concept of Richie Flow, a mathematical technique used to smooth out surfaces and classify them. He explains how this method was pivotal in Perelman's proof of the Point Conjecture.
"you so you have this object which is so secretly is a sphere but it's given to you in a in a really um in in a weird way. So like like think of a ball that's been kind of crumpled up and twisted and i..."
Tao discusses the challenges posed by nonlinear equations in mathematics, comparing them to the Einstein equations. He highlights the complexity of solving these equations in the context of the Point Conjecture.
"of the two dimensional result but by the way that's a beautiful explanation of reach flow and its application in this context how difficult is the mathematics here like for the 2D case is it yeah thes..."
Tao describes how Perelman transformed a supercritical problem into a critical one, allowing for a clearer analysis of singularities. He emphasizes the innovative steps taken by Perelman in resolving the Point Conjecture.
"quantities kind of like energy that look the same at every single scale and turned the problem into a critical one where the nonlinearities actually suddenly looked a lot less scary than they did befo..."
Tao reflects on the emotional investment mathematicians have in their work. He discusses the balance between dedication to a problem and the ability to move on when faced with challenges.
"in terms of the process he's done similarly difficult things what 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..."
Tao shares insights on how making mistakes can lead to breakthroughs in mathematics. He recounts a personal experience where an initial failure ultimately led to a successful resolution of a complex problem.
"still worth fighting um so yeah you have to do some some sort of forward reconnaissance sometimes to uh you know and that is sometimes productive to assume like okay we'll figure it out oh yeah yeah e..."
Tao discusses the self-doubt and emotional challenges mathematicians face when their work does not yield results. He emphasizes the importance of resilience and the ability to switch focus to overcome obstacles.
"at this we we tried increasingly desperate things and and crazy things um and after two is we found an approach which was actually somewhat different by quite a bit from our initial um strategy which ..."
Tao shares insights into the most challenging problems that linger in the minds of mathematicians, such as the Twin Prime Conjecture and the Riemann Hypothesis. He reflects on the elusive nature of these problems and the breakthroughs needed to tackle them effectively.
"connection. I mean there are cases you know so there are certain problems that are what I call back diseases where where where just latch on to that one problem and they spend years and years thinking..."
In this segment, Tao delves into the structure of prime numbers and their significance in mathematics. He discusses the dual nature of primes in additive and multiplicative contexts, and the challenges that arise when trying to relate these two perspectives.
"need to call our problems in advance. Uh um well uh when we do grant proposals we s say we we will study this set of problems. But even then we don't promise definitely by 5 years I will supply a proo..."
Tao explains the Twin Prime Conjecture, which posits that there are infinitely many pairs of prime numbers that differ by two. He discusses the difficulties in proving this conjecture and the unique characteristics that make twin primes a particularly challenging area of study.
"Yeah. there's no even viable strate like even if I activate all my all the cheats that I know of in this problem like it there's just still no way to get me to be um like it's um I think it needs a br..."
Tao highlights the robustness of arithmetic progressions within the realm of prime numbers, contrasting them with the more fragile nature of twin primes. He explains how certain patterns in prime numbers can withstand significant alterations, showcasing the complexity of mathematical relationships.
"view. Um and separately they're not so bad. Um so like any question about that only was addition is relatively easy to solve and any question that only was multiplication is easy to solve. Um but what..."
In this segment, Tao discusses the challenges faced when attempting to prove the existence of twin primes. He elaborates on the statistical properties of primes and how they can be manipulated, complicating the proof strategies for the Twin Prime Conjecture.
"prime conjecture is just that it posits that there are infinitely many pairs of prime numbers that differ by do. Yes. Now the interesting thing is that you have been very successful at pushing forward..."
Tao draws an analogy between conspiracy theories and mathematical conjectures, explaining how difficult it is to disprove a single theory. He discusses the implications of this analogy for the Twin Prime Conjecture and the challenges of proving or disproving such deep mathematical ideas.
"there's not I mean initially there's quite a few but once you got to the millions the trillions they become rarer and rarer and you could actually just you know if if someone was given access to the d..."
Tao introduces the Pigeonhole Principle as a foundational concept in mathematics and explains its application to prime numbers. He discusses how this principle can help in understanding the distribution of primes and the challenges that arise due to their sparsity.
"all yeah on the other hand progressions has turned out to be much more robust. um like you can take the primes and you can eliminate 99% of the primes actually you know and you can take take any 99% y..."
In this segment, Tao discusses the Parity Barrier, a significant obstacle in proving conjectures related to prime numbers. He explains how this barrier limits the density of primes and the implications it has for solving major problems in number theory.
"behave like a random set. Okay. Random. Yeah. Random versions of the primes we know contain twins. Um at least with with 100% probability or probably tending to 100% as you go out further and further...."
Tao shares his thoughts on the future of prime number research, expressing optimism for partial results in the coming years. He discusses the potential for breakthroughs in understanding the Riemann Hypothesis and the Twin Prime Conjecture.
"you know like it would you have to also infiltrate other areas of mathematics to sort of but but like it could be made consistent at least as far as we know but there's a weird phenomenon that you can..."
Tao explores the concept of randomness in the distribution of prime numbers, discussing the Riemann Hypothesis and its implications for understanding prime behavior. He emphasizes the challenges of proving that primes behave randomly and the need for innovative approaches.
"between the prize that okay so um that there's an infinite number of so it's ultimately based on what's called the pigeon hole principle um so the pigeon hole principle uh it's a statement that if you..."
In this concluding segment, Tao reflects on the mysteries surrounding prime numbers and their unpredictable nature. He discusses the balance between conjectural models and the inherent randomness of primes, highlighting the ongoing quest for deeper understanding in mathematics.
"sparse. They make um you can set up a set of almost primes where the primes have density like say 1%. Um and that gives you a shot at proving by applying some sort of original principle that that thos..."
Terence Tao discusses the Riemann Hypothesis, emphasizing its connection to the behavior of prime numbers and randomness. He explains how the hypothesis suggests that as we average more data, the fluctuations in prime number distribution should behave more randomly, yet proving this remains elusive due to the lack of effective mathematical tools.
"primes in a certain multiplicative sense there's a certain type of statistic you can measure and it's called the reman's data function and it fluctuates up and down but in some sense um as you keep av..."
In this segment, Tao reflects on the enigmatic nature of prime numbers, noting their apparent randomness despite some conjectured patterns. He introduces the 'Crema random model' of primes, which suggests that primes behave like a random set after a certain point, yet proving this remains a significant challenge in mathematics.
"question. Um the proof has to come out of left field. Um yeah but uh what that is yeah no one has any serious proposal. Um yeah and and there's there's various ways to sort of as I said you can modify..."
Tao explains the Collatz Conjecture, a simple yet profoundly difficult problem in mathematics. He describes the iterative process of the conjecture and its implications, highlighting its simplicity in formulation but complexity in proving that all natural numbers eventually reach one.
"Another incredibly surprisingly difficult problem is the colots's conjecture. Oh yes. simple to state, beautiful to visualize in its simplicity and yet extremely uh difficult to solve and yet you have..."
In this segment, Tao elaborates on hailstone sequences derived from the Collatz Conjecture, illustrating how they exhibit seemingly random behavior. He draws parallels between these sequences and random walks, discussing the statistical nature of their outcomes and the challenges in proving the conjecture.
"together. It's still reasonably simple. Um, 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 ..."
Tao discusses the role of probability theory in understanding the Collatz Conjecture, emphasizing the existence of exceptional events that can disrupt expected outcomes. He compares this to the Navier-Stokes equations, highlighting the complexities of proving mathematical conjectures amidst unpredictable variables.
"to prove the full conjecture? Well, the problem is that um my I I used arguments from probability theory um and there's always this exceptional event. So you know, so in probability we have this this ..."
Tao reflects on John Conway's work related to the Collatz Conjecture and its implications for complexity in mathematics. He discusses how Conway's explorations into cellular automata and iterative processes have influenced the understanding of mathematical problems and their inherent difficulties.
"undecidable and and do things like this. In fact, he invented a programming language for uh these kind of fractional linear transformations. He called a factrat as a play on forrat. Uh and he showed t..."
In this segment, Tao addresses the broader implications of solving major mathematical conjectures like the Riemann Hypothesis and P vs NP. He explains how breakthroughs in these areas could have significant ripple effects across various fields, including cryptography and number theory.
"Um POS MP is a good one because like uh that's that's a meta problem like if you solve that in the um in the positive sense that you can find a PMP algorithm that potentially this solves a lot of othe..."
Tao discusses the difficulty of proving randomness in prime numbers and the implications of the Riemann Hypothesis. He emphasizes the need for new mathematical tools to demonstrate the randomness of primes and the potential consequences if established beliefs about their behavior are proven incorrect.
"But then how do you then say stuff about the the primes? Yeah. That you're going towards the collect conjecture again. Um because if I I you you want it to be random, right? You want it to be randomly..."
Tao shares his thoughts on the Fields Medal and its impact on a mathematician's career. He reflects on the balance between recognition and the pursuit of mathematical problems, highlighting the importance of maintaining focus on research rather than accolades.
"equal to NP. I mean it seems like it's one of those cases similar similar to reman hypothesis that I think the evidence is le leaning pretty heavily on the no. Certainly more on the no than on on the ..."
Tao discusses Gregori Perelman's decision to decline the Fields Medal and the Millennium Prize, emphasizing Perelman's disinterest in fame and money. He reflects on the broader implications of such choices in the mathematical community and the value of personal principles over public recognition.
"keep I'm going to keep working on them. It's just first of all, it's funny to me that you would answer an email in that context, and second of all, it um it just shows your humility. But anyway, uh ma..."
Terence Tao discusses the importance of humanizing mathematics by connecting it to individuals. He emphasizes that while big problems attract attention, understanding the human aspect behind mathematical concepts is crucial for comprehension. Tao reflects on how our minds can only grasp relationships among a limited number of people, which can lead to oversimplified models that overlook the complexity of human contributions.
"see these people try to only solve like a really big math problems and not work on on on things that are less uh sexy if you wish but but but actually still interesting and instructive as you say like..."
In this segment, Tao shares insights about the varied trajectories in mathematics careers. He highlights that not every mathematician's path is typical and that different styles and approaches are valuable. Tao stresses the importance of recognizing diverse contributions to mathematics, rather than attributing success to a single individual, which can oversimplify the collaborative nature of mathematical progress.
"to humanize a subject you know if you identify a small number of people and say you know these representative people of the subject role models for example um that has some role um but it can also be ..."
Tao reflects on the narrative surrounding mathematical achievements, particularly the tendency to credit a single individual for monumental discoveries. He argues that many breakthroughs are the result of decades of collaborative work, making it essential to acknowledge the collective effort involved in mathematical advancements. This perspective encourages a deeper understanding of the history of mathematics.
"people of a different style. Um and you know even if and sometimes too much focus is given on the on the person who does the last step to complete um a project in mathematics or elsewhere that's that'..."
Tao reminisces about the moment Andrew Wiles proved Fermat's Last Theorem during his time as a graduate student. He describes the excitement and press attention surrounding the announcement, while acknowledging that many in the mathematical community did not fully grasp the proof's complexity. This segment highlights the significance of Wiles' achievement in the context of mathematical history.
"shorthand like a very like pi. Yeah. Steve Jobs. Yeah. Yeah. As as a starting point, you know, as a first approximation that's how you and then read some biographies and then look into much deeper. Fi..."
In this segment, Tao discusses the ongoing efforts to formalize Andrew Wiles' proof of Fermat's Last Theorem using the Lean programming language. He explains the challenges involved in defining complex mathematical objects and the importance of formalization in making advanced mathematics more accessible. Tao emphasizes the collaborative nature of this project and its potential to involve more mathematicians in formal proof assistance.
"understand sort of high level details um fact there's an ongoing project to formalize it in lean right Kevin puzzly yeah can can we take that small tangent is it is it how difficult does that cuz as a..."
Tao explores the different styles of mathematical thinking among mathematicians, noting that individuals may utilize various cognitive approaches to solve problems. He discusses how education often fails to accommodate these diverse styles, leading to a disconnect for many students. This segment emphasizes the need for personalized approaches in mathematics education to foster a deeper understanding.
"things that he needs to rely on as black boxes are things that were known by 1980 to um to number theorist at the time. Um and then some other person some other work would have to done to to to get fr..."
In this segment, Tao offers advice to young students who are struggling with mathematics but have an interest in the subject. He highlights the availability of resources outside traditional classrooms, such as math competitions and online communities, that can provide enrichment and support. Tao encourages students to explore mathematics in various contexts to find their passion and improve their skills.
"point where you can see you see something you recognize. What uh inspires you about his journey that we similar as we talked about seven years mostly working in secret? Yeah. Uh that is a romantic uh ..."
Tao discusses the intersection of programming and mathematics, suggesting that programming can serve as an accessible entry point for many individuals. He notes that programming allows for immediate feedback and results, making it a more approachable field compared to advanced mathematics. This segment highlights the potential for programming to engage a broader audience in mathematical concepts.
"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 ..."
Tao reflects on the changing landscape of career paths and the need for adaptability in today's world. He emphasizes the importance of developing transferable skills, such as problem-solving and abstract reasoning, that remain relevant despite evolving technologies. This segment encourages young people to embrace flexibility in their careers and to cultivate a diverse skill set.
"Um but our other centers are sophisticated enough that different people we we we can repurpose other areas of our brain to do mathematics. So some people have figured out how to use the visual center ..."
In this segment, Tao shares his personal journey of learning new methodologies and tools in mathematics, including the Lean programming language. He discusses the challenges and discomfort that come with stepping outside one's expertise, emphasizing the value of being open to new ideas and approaches. Tao's willingness to embrace new challenges serves as an inspiration for others in the field.
"same goal. Um, that's beautiful. And yeah, but I mean the way we educate unless you have like a personalized tutor or something. I mean education sort of just by natural scale has to be mass-produced ..."
Tao concludes by discussing the vastness of modern mathematics and the inevitability of making mistakes. He highlights the culture of accountability within the mathematical community, where proofs must be substantiated. This segment underscores the importance of humility and continuous learning in the pursuit of mathematical knowledge.
"to just work on one atomic thing. There's something about the formalization here that also at as a very first step opens it up to the programming community too. The people who are already comfortable ..."
In a thought-provoking discussion, Tao considers the candidates for the title of the greatest mathematician of all time. He reflects on historical figures such as Euclid, Gauss, and Hilbert, while acknowledging the subjective nature of such a title. This segment invites viewers to contemplate the impact of various mathematicians throughout history and their contributions to the field.
"I said, you know, there are communities of non- mathematicians where they're deploying math for some very specific purpose, you know, like like optimizing their poker game and and for them then math b..."
Tao shares insights on the common paralysis faced by students when encountering difficult math problems. He encourages a mindset of experimentation, suggesting that even seemingly foolish attempts can lead to valuable learning experiences.
"you know like the next step then is to try anything like no matter how stupid um and in fact almost as stupid of the better um which you know and one a technique which is almost guaranteed to fail but..."
Tao introduces the concept of structured procrastination, where one avoids a daunting task by engaging in less undesirable activities. He explains how this psychological trick can help maintain productivity and motivation.
"you're motivated to do it. Is there something our human mind will never be able to comprehend? Well I sort of as a mathematician I mean you there must be some suffer that you can't understand. That wa..."
Tao discusses the idea of collective intelligence within the mathematical community, highlighting platforms like Math Overflow where collaborative problem-solving showcases the brilliance of diverse mathematicians.
"mathematical community plural is is is incredibly super intelligent uh entity um that uh no single human mathematician can can come closer to to replicating. You see it a little bit on these like ques..."
Reflecting on the potential of future generations, Tao expresses optimism about the creativity and inventiveness of young mathematicians. He draws parallels between past challenges and modern technological advancements.
"be really difficult can become extremely you know can become like trivial to solve. you know, I mean, like it was like navigation, you know, just just knowing where you were on the planet was this hor..."
Tao contemplates the rapid advancements in technology and science, expressing a sense of wonder at humanity's ability to solve complex problems. He acknowledges the bittersweet nature of not being able to witness future innovations.
"know that cuz in the next 100 years, 200 years, just imagine showing showing up in 200 years. Yeah. Well, already plenty has happened, you know, like if if you could go back in time and and talk to yo..."
In the closing moments, Tao reflects on the importance of community and rational discourse in mathematics. He expresses gratitude for the conversation and leaves the audience with a quote from Galileo, emphasizing the profound connection between mathematics and the universe.
"healthy, you know, the community of humans can be so much more intelligent and mature and and and rational than the individuals within it. Well, one place I can always count on rationality is the comm..."