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A Top Mathematician's 9 Lessons for Anyone Who Feels Behind | Ken Ono, Axiom Math

EO37:35Added Aug 31, 2026

Ken Ono, mathematician at Axiom Math and the University of Virginia, on why 2026 shouldn't be the race for more compute. It should be the race for more truth...

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Contributed by 刘嘉琪

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  1. Intro

  2. 00:00Hi, my name is Ken Ono. I'm a mathematician. I work at Axiom Math and the University of Virginia, but in my
  3. 00:07life, I was not a good student in college. In fifth grade, we had a a math contest [music] and I got third. Now,
  4. 00:14third is pretty good out of a fifth grade, but my father was a famous mathematician. They came to the
  5. 00:20competition and son of famous mathematician gets third in Hampton Elementary [music] School. I thought for
  6. 00:2750 years of my life that I had utterly [music] failed. But the reason I bring
  7. 00:32this up is that when my dad passed away in January, we were cleaning up his
  8. 00:38belongings and of all the things that he could have kept and it was just in a closet now was that plaque from my fifth
  9. 00:46grade contest. And I I thought, "Wow, I had misinterpreted
  10. 00:52that event my whole life." It actually meant something to him to keep a plaque
  11. 00:58where I didn't win, but I got third place. What he saw, I'm sure, was that I
  12. 01:03wanted to do well. We live at a time where the world places so much emphasis
  13. 01:10on benchmarks. how these [music] AI firms and the state-of-the-art large language models are competing for these
  14. 01:17crazy scores. There's a lot of anxiety over benchmarks. [music] But when it comes to assessing
  15. 01:22intelligence, do we honestly believe that someone who gets a higher IQ score is somehow smarter? No, of course not.
  16. 01:30It should put people at ease, right? If you have to live up to the standards set by someone else, then you're not living
  17. 01:37for yourself. You're not giving yourself credit. I consider that toxic because
  18. 01:42[music] you know what the brutal truth is? The brutal truth is if you're not LeBron James or Raphael Nadal or a Nobel
  19. 01:52Prizewinning scientist, the reality is you will always be able to find someone that looks better, achieve something
  20. 01:58that you cannot do, and you're not then giving yourself permission to live the life that was meant for you. It's
  21. 02:04important to give yourself permission to live your life.

  22. Q1) Have you ever felt obsolete because of AI?

  23. 02:23Almost exactly one year ago, I was part of a group of mathematicians hired by a
  24. 02:30company called Epoch AI to write very difficult math problems that would serve
  25. 02:36as a benchmark for state-of-the-art large language models. And I thought it
  26. 02:41would be easy money. We were paid. But last year I found it very difficult to
  27. 02:48write some of these problems. Strictly speaking the models would make mistakes but when you studied the reasoning
  28. 02:54traces it was frightening how far these large language models have come. So I
  29. 02:59think the right way to describe it is well as an identity crisis. Maybe it was
  30. 03:04something like being the sharecropper, the farmer in the late 19th century who
  31. 03:10comes face to face with the first combustion engine tractor, recognizing
  32. 03:15that well maybe there's no future for my work as a sharecropper. What to do next?
  33. 03:20What's next for a mathematician? It was pretty devastating honestly seeing these
  34. 03:26models solve problems that were on my research program. Well, came to realize
  35. 03:32that technology has helped mankind over and over again. There was the invention
  36. 03:38of the wheel and later there was the invention of the engine and then calculators and computers and somehow we
  37. 03:46adapted. What's surprising about this particular moment is that many of the techn
  38. 03:52technological advances were about lightning physical work. An elevator meant that you didn't have to climb all
  39. 03:58these stairs. Tractors can do a lot of work that humans shouldn't do. The difference now is the work is mental.
  40. 04:05That is the stuff of identities. And where are we now? We are now at a point where many of those skills can be done
  41. 04:12automatically. And AI companies are talking about what's called selfplay.
  42. 04:18They want their AI systems to play with themselves. And so where does that leave us? Well, mathematics, it's it is
  43. 04:25devastating. It would be dishonest to say that a student who's graduating from
  44. 04:30college now with a bachelor's degree in mathematics or who is in graduate school now isn't deeply worried about all the
  45. 04:37years of effort they put into learning a trade, learning a body of knowledge that now anybody who can type if they have
  46. 04:45access to a large language model. So there's no dancing around that fact.
  47. 04:50This is very disruptive. When I was a graduate student and a young assistant
  48. 04:55professor, I would have said that I was most proud of my works that depended on
  49. 05:02the accumulation of knowledge involving years of effort. I could solve this
  50. 05:07paper because a few years ago I learned this technique and last year I learned this technique and here we are. I've
  51. 05:13written this paper. My view has changed on that and I hope that the viewers here
  52. 05:22think about this. There's actually something quite hollow about how I viewed myself as a mathematician that I
  53. 05:29only recognized recently. If my success as a mathematician relied only on my
  54. 05:35ability to learn techniques that somehow could be put together to prove a
  55. 05:40theorem, well then maybe that was actually automatable. And maybe I
  56. 05:46mistook all of that hard effort for something maybe it wasn't right. As hard
  57. 05:52as it was to master bodies of work, many papers, graduate texts, maybe at the end
  58. 05:58of the day, there is some truth to that being an automated process. Now, make no
  59. 06:04mistake, that's not what we do for a living in mathematics and in most fields. in my work now and this is how I
  60. 06:11think about what we do with AI at Axiom is supported in a number of ways. The the copout would be to say can I ask an
  61. 06:19interesting question that I want an answer to but make no mistake that's not what research is. Research begins with a
  62. 06:25question that you're probably not able to answer. You try to answer and by failing you learn a little bit more
  63. 06:31about that conjecture and the work that you do sheds light on a path that might reveal a long list of questions that you
  64. 06:39one by one try to attack and eventually you might prove a theorem and if you prove that theorem you backtrack and say
  65. 06:45maybe I could have proven this previous question I couldn't answer right this is how you learn it's it's really the
  66. 06:51proverbial two steps forward one back and when you recognize that research is not ask a question, you get an answer.
  67. 06:59You realize that the AI tools are lowering the burden for your ability to
  68. 07:04actually perform discovery. When you were doing all of these homework problems, when I was doing homework problems as a college student, as a
  69. 07:11graduate student, I was learning techniques. But was I really discovering? No. What I was doing though
  70. 07:17was important. I was learning how mathematics fits together to help me become a mathematician who can ask these
  71. 07:23questions and participate in the discovery. So that process I think is is
  72. 07:29changing for students and faculty who want to stick to the traditional ways.
  73. 07:35Well, the reality is in some areas of mathematics, they will be left behind.
  74. 07:41We have computers that can compute 20 million cases overnight while you're
  75. 07:46sleeping and you and it might take you years to do those 20 million cases and you have to decide would you like to
  76. 07:52have that power at your disposal freeing you up to participate in the process of discovery and that's what we have to
  77. 07:58value. So that's what I think science is going to become. So let me give a
  78. 08:03concrete example. Typical person that drove a car today doesn't have the foggiest idea of chemical reactions and
  79. 08:12engineering advances that had to come to fruition before they could actually get in the car and drive. The automobile is
  80. 08:18an incredible invention and it requires mastering chemical processes and engineering challenges. All of that is
  81. 08:25is available to us now for free. But maybe when Henry Ford made his first car, he had to solve all of it. How do I
  82. 08:32make the wheel? What do I make tires out of today? Maybe you only need to know how to pump gas. Now, is that bad? No.
  83. 08:41Because think about all the things that mankind can do now because they can travel great distances very quickly.
  84. 08:46What that opens us up to. And I think that's going to be our future. Is that
  85. 08:53rosé now? No. This year is horrible. If you ask me, I I would rather wake up and
  86. 08:58have it be 2017. Given that that is our future, I think we should do our very
  87. 09:03best to encourage people of all professions, teachers, parents, young
  88. 09:09students to do their best to be prepared to be flexible to seek out those opportunities as they pop up. But I
  89. 09:16don't think the loss of of jobs is anywhere near as significant. And I hope
  90. 09:23that remains to be true. But this is really the time to think very carefully about education, thinking about
  91. 09:30opportunities and being very human.

  92. Q2) What makes a good question?

  93. 09:40So what makes a good question? There's several things I want to say. The first
  94. 09:46thing as a as a teacher, my immediate response is there's no such thing as a bad question. Of course, that's not
  95. 09:52quite true. If you genuinely want to know the answer to a question, then
  96. 09:58that's a great question. You should never ever doubt your interest in a
  97. 10:03subject. Okay? But I don't think that's necessarily what you're asking, right? I could ask, what is the meaning of life?
  98. 10:08That's a great question on the one hand, but on other it's kind of an impossible
  99. 10:14question. Another question is like, I wonder what I have to do to be rich. I want to be rich. How do I do it? Well,
  100. 10:20that is a question, but is it a great question? No. Oh, I think it's a flawed question in many ways. First of all, the
  101. 10:25question is, well, how do I achieve that? So, you need to break that down so that a question becomes maybe a plan,
  102. 10:32something that's actionable, but it's also somewhat hollow. So, questions that don't speak to your humanity somehow,
  103. 10:39whether it's why do you want to be richer? What are you going to do to make the world a better place, makes that
  104. 10:46line of reasoning richer. Now as a scientist you might be facing an open
  105. 10:52problem that your interest in in your field cares. Maybe people outside your
  106. 10:58field might not care so much if I told you about the questions I think about on a daily basis. I'd be very surprised
  107. 11:03that you would care at all. But you know I wouldn't take that personally. I would start by saying here's a math problem
  108. 11:09that I deeply care about. And I would expect that you would respect that. If you're in a situation where you have to
  109. 11:15think about whether the question you're asking has value, I think you should pause and think about who you're asking
  110. 11:21the question for. If you're not asking a question for yourself, well, my question to you would be, well, then who are you
  111. 11:27living for? Are you living a life meant for you, or are you living a life that you think someone should be meant for
  112. 11:33you? And then my question for you would be, why?

  113. Q3) What does 'Superintelligence' mean?

  114. 11:45I'm not honestly comfortable talking about super intelligence because it puts me at unease. Something that is super,
  115. 11:52it means that it's better than others. And I think what we're really talking about here is a future and a present
  116. 12:00honestly where AI is a co-pilot gives us tools that we cohabitate with at our
  117. 12:08service. So to say that a computer could be super intelligent is a bizarre
  118. 12:14thought to me because I would never call my automobile super fast compared to
  119. 12:19people. Right? Obviously it's super fast compared to people. Well, I would I would have never even thought about it for a moment. Reducing the load in
  120. 12:26physical work is super. The only reason we're really worried about super intelligence is that so much of our
  121. 12:32identity is based on thinking skills. Many of the exams I took in college that
  122. 12:38I crammed for, did my best to get a good grade in only to recognize I'd forgotten
  123. 12:43the facts maybe by the middle of summer. Yeah, I did learn something from that the process. But is what I learned the
  124. 12:51information that I'd forgotten? No. So let's not talk about what is super
  125. 12:56intelligence because I don't know what intelligence is. But I do know quite well when I see achievement. We live at
  126. 13:04a time where the world places so much emphasis on benchmarks. In sports, I get
  127. 13:12it. Runner A runs faster than runner B. They're a better runner. Okay, that's academic. But when it comes to assessing
  128. 13:18intelligence, do we honestly believe that someone who gets a higher IQ score is somehow smarter? Do you actually
  129. 13:25believe a school that might be ranked fifth in the college rankings is really better than a school that's ranked
  130. 13:30seventh only to turn around the next year to see that the rankings have changed? And now you think about how
  131. 13:36these AI firms and the state-of-the-art large language models are competing for these crazy scores and we're all caught
  132. 13:43up in that. Is any of that intelligence? No, of course not. But if somebody
  133. 13:49writes a poem that just knocks you off your feet. If someone solves a math
  134. 13:54theorem, even if it's with the help of AI that represents knowledge mankind had never seen before, that is intelligence.
  135. 14:02Is that super intelligence? Absolutely.

  136. Q4) What's the biggest AI misconception?

  137. 14:10The easiest way to make a mistake in the era of AI is to confuse what people are
  138. 14:17saying when they're talking about AI. It's important to first understand that AI comes in many different forms. The
  139. 14:23forms of AI that most people encounter these days would be the chat GPT, but
  140. 14:29make no mistake, that's only one form of AI. AI's ability to use machine learning
  141. 14:35techniques to conduct a superhuman search that no person would ever want to do. Right? This is how John Jumper and
  142. 14:42Dennis Hassus won the Nobel Prize in chemistry for solving protein folding.
  143. 14:47It's just smarter and it and it is accelerated. And the third part of AI is where I think there is so much hope. The
  144. 14:53third part of AI is called formalization. And the idea in formalization is to take human natural
  145. 14:59language, transform it into computer code, which is an enhanced or at least
  146. 15:05an exact interpretation of the human language and then have AI study this
  147. 15:12code and look for vulnerabilities. It's called verifiable computer code. We live at a time now where an enormous
  148. 15:19proportion of the computer code that's written and deployed in the world is not the stuff of human programmers. It's
  149. 15:26called vibe coding. But make no mistake, that code is not perfect. And so the
  150. 15:31space that we're in now in terms of formalization is to cut back on those inefficiencies. And when we start
  151. 15:38teaching mathematics or computer science or any field that has been formalized, we've come to learn that our original
  152. 15:44framing of these subjects was somehow incomplete. So I'll give you an example. Our company is partnering with Scott
  153. 15:51Commoners. He's a very distinguished economist at Harvard, a mathematical economist. And in our work, we are
  154. 15:58formalizing, as I described for you before, mathematical theories in economics. And we've discovered that
  155. 16:04some of the foundational theorems in the subject weren't really accurately portrayed or implemented or applied. Let
  156. 16:13me give you an example. 2026 is the 50th anniversary of a very famous theorem by
  157. 16:18the Nobel laurate Robert Alman. And one of his most famous theorems is the theorem that's called we agree to
  158. 16:25disagree or can we agree to disagree where the phenomenon is if you have
  159. 16:31different parties observing and making decisions or indicating their preference
  160. 16:38preferences based on the same common prior knowledge. Is it possible for these parties to disagree? And this is
  161. 16:45the stuff of modern vernacular. You might get in an argument with a friend. you listen to each other and you understand each other's perspective and
  162. 16:52the end is quite satisfying to say well I guess we're just going to have to agree to disagree. Almond's theorem
  163. 16:57doesn't allow for that. It can't be that you can agree to disagree. What really
  164. 17:02happens is you can actually end up understanding each other's perspectives and that's a very big theorem. However,
  165. 17:08there are subtleties, there are hypotheses. What does it mean to say you have the same priors? And that's where
  166. 17:14the formalization came in. And it's become kind of a viral moment in
  167. 17:19mathematical economics. Many economists from around the world are joining our effort recognizing that for the sake of
  168. 17:27getting economics right, it should be formalized. And this is happening across fields. We are even working with
  169. 17:33computer scientists rethinking and formalizing machine learning which underlies all of AI to begin with. And
  170. 17:40so this is our future. So I said, what are the opportunities for AI? Maybe we're worried about the loss of work,
  171. 17:46but there are new opportunities. One is how do we use AI to best guardrail the
  172. 17:53other forms of AI? Cyber security will need legions of computer scientists,
  173. 17:59also ethicists, make no mistake, and lawyers who have to rethink or imagine
  174. 18:05this new world, right? There going to be legal issues that come up. And certainly for the AI experts who are into and
  175. 18:13devoted to formalization, that group will be setting up the guard rails that will keep us safe. A large language
  176. 18:20model is something like the most incredible librarian. A librarian who's read everything. But that doesn't mean
  177. 18:25you want your librarian to be your neurosurgeon. In very high stakes situations, you need taste. You need
  178. 18:32human judgment. And of course, on top of that, you need someone with the emotional intelligence to understand how
  179. 18:39decisions impact people. Well, all of those things can be part of formalization. And I think that's an
  180. 18:45opportunity. And whether you want to help robotic surgeons be accurate or
  181. 18:52whether you're worried about securing the internet or financial networks, any system that can be rewritten or is
  182. 19:00somehow controlled by mathematical language after translation should be
  183. 19:06formalized. So yeah, I think that's a very big future. And for students
  184. 19:11entering college and graduate school, if you want to be a mathematician, start formalizing. You may still prove
  185. 19:18unsolved conjectures along the way, but make no mistake, this is 2026, 2027, I
  186. 19:25don't believe now, is the race for more compute. It really should be the race for more truth. And I think that, and I
  187. 19:32hope I'm right, will be by means of formalization.

  188. Q5) What judgement can't AI replace?

  189. 19:40When a scientist says that a fact is formally verified, this statement is
  190. 19:46true. End of story. If there is a mistake, it's because you didn't frame the problem correctly. That's not
  191. 19:52judgment. That's a yes, no binary question. Judgment is how do people when
  192. 19:59given this information choose to act? We have autonomous drones flying all over
  193. 20:05the world doing all sorts of things. Whether it's keeping track of traffic in Los Angeles or Seoul or whether it's
  194. 20:13looking for dangerous people in fields of battle, all of those situations
  195. 20:18require judgment. In some of those low stakes situations, well, you know, maybe
  196. 20:24the drone that's measuring air quality above Los Angeles, maybe the human judgment there isn't so important. But
  197. 20:31if we're talking about whether or not to target a city, how do you know that a
  198. 20:36building that you're targeting actually has a dangerous person in it versus being a school or a hospital? And I
  199. 20:43don't actually think it's very difficult to distinguish situations that really are so high stakes that most rational
  200. 20:50people would not be comfortable with letting an AI decide. I think in most
  201. 20:55cases that we care about the most that are high stakes when you want a person involved. Maybe it's not that easy. We
  202. 21:02have ride share surfaces that are driverless, but people like them. These opinions in these viewpoints can change
  203. 21:08over time, but apart from those strange situations, I think it's very clear when

  204. Q6) How do I get past AI filters?

  205. 21:13you want a human in the room.
  206. 21:24We live at a time where the world makes judgments, snap decisions, snap
  207. 21:31evaluations on very little data. It's crazy. you apply for a job, you're
  208. 21:37probably going to submit your cover letter and your CV or resume to an
  209. 21:42automated system that has an algorithm that has a bunch of check boxes that you have to predict so that you know that
  210. 21:49you're not sd in the first round for no good reason. None of us should be happy with that. Everywhere you look, we have
  211. 21:57adopted a system where we are replaced by numbers. we are replaced by what an
  212. 22:03algorithm seeks. And this is coming from someone who works in AI. How can any of us be happy with that? My children,
  213. 22:09they're 27 and 30. They're beyond the most critical phases of getting their career started. But they knew. And I'll
  214. 22:17be lying to you if I didn't say when they were applying to colleges. As a university professor myself, I knew a
  215. 22:23university college admissions committee is going to be looking for these 10 things. Make sure you check those boxes,
  216. 22:29but then still be absolutely genuine about what you're passionate about. Yeah, I would be lying if I didn't say
  217. 22:35we didn't do that. But let's pause and think about what all of that means. Because if we buy into that 100%,
  218. 22:44then you're forgetting that the quality of someone's character matters. You're
  219. 22:49forgetting that the quality of human judgment and achievement matters. You're saying that what matters is can you
  220. 22:56check every box and imagine what those boxes are. And I'm sorry if you want to find the cure for cancer, it's not going
  221. 23:01to be a bunch of check boxes. If it was, we would have already found the cure for cancer. So the question then becomes, if
  222. 23:08we live in a society, in a community where we are so rigid because the
  223. 23:13computer age allows us to. When I was starting out, you would look for a job.
  224. 23:18You might actually go to a company and drop off your CV and resume and shake the hand of a business owner and try to
  225. 23:24make that human contact. Who does that now? You probably upload your your resume and cover letter to a website and
  226. 23:31you might even apply to like 500 jobs. I mean, what what's human in any of that? My dream for the future has many pieces
  227. 23:38to it. one, what I would give to fight against that so that we could start a
  228. 23:43movement where we could slow down and really evaluate people for who they are,
  229. 23:48where they've come from, what their personal experiences are, the quality of their character, and how they interact
  230. 23:53with others. That would be awesome. Now, how have I been lucky enough to identify
  231. 24:00some of my best students, the ones that maybe other schools wouldn't have never taken a chance on? They were the
  232. 24:05outliers. I had a graduate student, his name was Robert Schneider. He was actually and still is a famous
  233. 24:12independent rock artist. He was um producer for a band called Neutral Milk Hotel and lead singer for a band called
  234. 24:19Apples and Stereo. And he had the most fascinating story. He loved equipment.
  235. 24:25He loved to perform with these old microphones, solidstate old microphones
  236. 24:31and speakers when they went on tour. But because they were old, they were
  237. 24:36constantly breaking and they needed to be repaired. And it became so expensive repairing them that he decided that he
  238. 24:43was going to start learning electronics. So he bought a book and the first formula he saw in this book was Oh Law
  239. 24:50and he said to me the first time I met him and it was the craziest thing. He
  240. 24:55had decided to go back to school. He was a college dropout. He stopped touring.
  241. 25:00He went to college, got his math degree, and found his way into my office. And it begins what I what I just described to
  242. 25:07you. And when he said when I saw M's law, it made me stop and think about
  243. 25:12what is it that I am producing when I'm writing and singing music. Electrical
  244. 25:18circuits populate my brain. That's a creative part. I somehow write down the music on paper and then I perform it on
  245. 25:25my guitar to be picked up by the microphone to go back into my brain. And all of this was modulated by an equation
  246. 25:31called Ohm's law. And I wanted to figure out how does the biology work? How does that equation work? How does the world
  247. 25:38work? 3 hours later, I said, you know, you have to be my student because you made me rethink everything I thought
  248. 25:45about mathematical equations, thinking that I knew how you could find inspiration in math. I never thought I
  249. 25:53would have found that story. So from Robert to some some of the other
  250. 25:58students that I could tell you about. I'm proud of all of my students. I've had 35 PhD students. But if if we were
  251. 26:03to go through them one by one, I could tell you a story. His is just particularly colorful. And what I like
  252. 26:10about the process is when they finish their graduate degrees or when they finish their undergraduate thesis,
  253. 26:16there's a huge moment, undeniable. You know it when it happens. And this is particular for graduate students when
  254. 26:23you can look at the student and say, you know, you're like a professor now and
  255. 26:28they look back at you and they know exactly what you mean. And it's not because they checked some box, they
  256. 26:35fulfilled their thesis that has somehow become irrelevant. It's the other part. So to answer your question, how do I
  257. 26:41recognize that? It circles back to what I was saying earlier. We have no shortage of students who mistakenly
  258. 26:49think, and it's not their fault, who mistakenly think that the path to success is you go to the right schools,
  259. 26:55you get the right grades, you find, you know, you get the right degree and all good things will happen to you. That's a
  260. 27:02mindless way of going about one's life. It's not actually giving yourself
  261. 27:08permission to live the life that was meant for you. It's just saying I'm following a recipe that we think will be
  262. 27:14very successful and the odds of success are very high. That's on us. That's on the universities. That's on us, the
  263. 27:20parents. That's because we have decided that there are benchmarks that will
  264. 27:26evaluate whether you're successful. Go to the number five school instead of the number 10. Get the best test scores. Do
  265. 27:32all of that. We haven't given enough credit where credit should be due. And
  266. 27:38we place so much emphasis on all of this other stuff that we're now paying for it and we have to fix that right away. I
  267. 27:44don't know if this is controversial, but I think that's all true.

  268. Q7) Is it bad that I prefer talking to AI over humans?

  269. 27:52I know what you're talking about. I work at an AI company and for the last year I
  270. 27:58work with AI models. I study them. My wife will say, "Ken, you must have a
  271. 28:04relationship with these models." And I don't think she is wrong. It's sometimes quite satisfying when the models start
  272. 28:11thinking like you do because they learn. But I also believe that if it's not cared for and those in charge aren't
  273. 28:18mindful of its use, it could be a train wreck. Do you want to take a trip with your AI? Hey chat GPT, here we are. I'm
  274. 28:26in Rome. What kind of wine would you like with dinner? That's not living. I was in the taxi cab from Inchan airport
  275. 28:34to the my hotel in Gangman yesterday and the traffic was horrible. Monday four
  276. 28:39o'clock you can sit for 10 minutes at a at a block. So I did a little experiment. I started counting the
  277. 28:44people walk by with their phones in their hands like this. And it was something like 70% of the folks here in
  278. 28:51gangmen walking on the street probably going home from work were looking at their phones like this. That's messed
  279. 28:57up. Think about all the opportunities that you are missing because you think your world evolves from that little
  280. 29:04screen. You might be missing the opportunity to make a new best friend. If you find yourself engaging with a
  281. 29:12chatbot as if it was really a person, stop. Put it down. Go for a long walk.
  282. 29:19Put yourself in a position where you see something beautiful or provocative. do
  283. 29:24something that reminds you that the world before AI has a much longer

  284. Q8) AI is smarter than most of us. What should we do?

  285. 29:30history than the world with AI.
  286. 29:39As a 58-year-old mathematician, I want to see some questions answered in my
  287. 29:46lifetime. We're already beginning to see that happen. There are famous examples.
  288. 29:51OpenAI a few weeks ago announced a proof of a theorem called the airish unit distance conjecture which is a problem I
  289. 29:59thought was never going to be solved in my lifetime and post hawk meaning when you go back and look at how this was
  290. 30:04achieved the truth is it was achieved by a little bit of human collaboration the mathematicians at open AAI with their
  291. 30:10system but I don't think it could have been solved by people alone unless you
  292. 30:17had a remarkable collection of experts from different fields who somehow came
  293. 30:22together. I don't think this would have been the stuff of one person and that I think represents some of the strength
  294. 30:28and possibility in AI where think about all the things in science that you would
  295. 30:35like to have solved and maybe the accumulated wisdom of mankind can solve it but when would you ever be in a
  296. 30:41position to put the right people together in a room to discuss it. So what AI offers and promise is the access
  297. 30:48to the accumulation of human knowledge. tirelessly and it lowers the bar for
  298. 30:53solving these problems. Is it the case that some of the ideas and solutions are
  299. 30:59beyond what humans have ever come up with? And this is probably the most provocative point. There are many who
  300. 31:06will argue that yeah, AI is going to come up with ideas, genuinely new ideas
  301. 31:12that people have never thought of before. I don't know that I believe that. I do believe that AI computer
  302. 31:18systems can compute more than people have ever done before. Can find patterns
  303. 31:24in different areas of science that humans are unable to do, but the ideas are somehow already there. Do people
  304. 31:31come up with new ideas all the time? The artwork that you find in Picasso, good
  305. 31:36luck finding evidence of that before Picasso. Do I think AI has that ability
  306. 31:42to come up with those new ideas? I don't know. Do I hope it does? God, I hope never.

  307. Q9) As a junior, how can I catch up with seniors in the AI era?

  308. 31:55What worries me about what you just said is this need to compare your personal
  309. 32:02situation now with others. That sounds horrible. If you have to live up to the
  310. 32:09standards set by someone else, then you're not living for yourself. you're not giving yourself credit. Whatever
  311. 32:16pressures [snorts] someone may feel that inspires them to constantly be comparing
  312. 32:22themselves to others, I consider that toxic. Because you know what the brutal
  313. 32:27truth is? The brutal truth is if you're not LeBron James or a Nobel
  314. 32:33Prize-winning scientist, the reality is you will always be able to find someone that looks better, achieve something
  315. 32:40that you cannot do, and you're not then giving yourself permission to live the life that was meant for you. In my life,
  316. 32:46I was not a good student in college. In fifth grade, we had a a math contest and
  317. 32:52I got third. Now, third is pretty good out of a fifth grade, but you probably would have thought Canono was a famous
  318. 32:58mathematician. He probably won easily. No, I got third. In fact, when I was in fifth grade and I got third, I thought I
  319. 33:05let my parents down. My father was a famous mathematician. They came to the competition and son of famous
  320. 33:11mathematician gets third in Hampton Elementary School. And this is one of those defining moments on the drive
  321. 33:17home. It was just silence. My mom didn't talk about it. My dad didn't talk about
  322. 33:22it. I thought for 50 years of my life that I had utterly failed. Now, it's not
  323. 33:31true that this experience weighed on me so much that I thought about it for for
  324. 33:37decades and decades and decades, but it was instances like that where like you,
  325. 33:42I was worried about how I would stack up with others. But the reason I bring this up is that when my dad passed away in
  326. 33:49January, we were cleaning up his belongings. There was very little left because they had already downsized to
  327. 33:55very small apartment in Florida where my parents, we had just moved them. And of
  328. 34:01all the things that he could have kept, and it was just in a closet now, was that plaque from my fifth grade contest.
  329. 34:10And I I thought, "Wow, I had misinterpreted that event my whole life." It actually
  330. 34:18meant something to him to keep a plaque where I didn't win, but I got third place. And although he had passed away,
  331. 34:24I could never ask him about it. It's obvious he saw something else. What he saw, I'm sure, was that I wanted to do
  332. 34:31well. So, I hope that's a lesson for anyone who thinks this way because I thought that way. And if you put
  333. 34:39yourself in a position where you're always comparing with others, you might not actually be right and you might
  334. 34:46actually be completely wrong. So you have to give yourself permission to the
  335. 34:51life that was meant for you. And this might be morbid, but one day you will be on your deathbed. You may only have a
  336. 34:58few days left. And someone might ask you some questions. What are your five
  337. 35:03deepest regrets? And this comes up all the time. I'm not making this up. people. Certainly when you get to my
  338. 35:08age, you start being around these kinds of conversations. And I think the number one regret is I wish I had the chance to
  339. 35:16live the life that was meant for me. I wish I was able to keep in close contact
  340. 35:22with the friends that I lost touch with. So try to imagine what those four or
  341. 35:27five wishes are. And at your age, do your very best to recognize that you
  342. 35:34don't want those to be your regrets. You need some inspiration often. You need a creative idea often. And so encouraging
  343. 35:44students, encouraging all people to wonder about the world that they live in is one giving permission to think that
  344. 35:50way. And wouldn't the world be a much better place if everyone thought about what their talents are, gave themselves
  345. 35:57permission to be creative? Wouldn't the world look a lot more interesting
  346. 36:02instead of yeah, I'm supposed to do this or I'm supposed to do that, so I I do it. So, I hope that is food for thought.
  347. 36:10I think I've said several times today that it's important to give yourself permission to live your life. Now, that
  348. 36:17doesn't mean ignore all the signals of what might help you be successful.
  349. 36:22That's not we don't want to be ignorant. But giving yourself permission to lead the life that was meant for yourself
  350. 36:28also is giving yourself permission to find your passion. And that passion might be something that isn't popular.
  351. 36:34But if you find it, you can draw strength from it. I'm a Japanese kid that grew up in a very white suburb of
  352. 36:42Baltimore, Maryland at a time when it wasn't good to be Japanese. I wore glasses. I was Mr. Four Eyes. But that
  353. 36:49gave me strength. As difficult as that was, being one of the only oriental kids in an all-white school being different,
  354. 36:55I ultimately drew strength from that wasn't easy and it probably took 10 years to overcome that. But whatever
  355. 37:02demons AI or culture or family and friends impose, they don't all have to
  356. 37:09be there. And quite frankly, the moral of this conversation is there's very little you can do about the world that's
  357. 37:16around you. So, how can you choose a life that's meant for you? Be flexible and embrace and chase opportunities that
  358. 37:23were that seem to be destined for you.