This 24-Year-Old Founder Raised $64M to Build World’s First AI Mathematician | Axiom, Carina Hong

Intro

00:00Math is full of hardship. Math research is a process of almost a monk praying in the temple [music] day after day. Keep pushing harder and harder. The dopamine hits feeling is a little bit like Quinc Gambit feels natural, effortless. You can look at how far [music] you have come in the competition.

00:17So usually the clock is ticking. I think that feeling is just really amazing. It's exhilarating. If you have a hedge fund being able to afford the quant researchers typically paid at 14 million each year starting but that AI mathematician um at $5 each hour and then you are able to suddenly afford to tackle a market of total trading volume of say 8 million because that market likely has not been deeply studied and previously it was not considered a target.

00:50We are entering an era of mass intelligence and an AI mathematician is a crucial part of this future. Hi, I'm Karina, founder and CEO of Axiom. I was a mathematician for most of my life. I did math and physics double major at MIT where I plunged into the ocean of mathematics.

01:17worked on very interesting research projects that settled some open conjectures and then I was a rose scholar at Oxford pursuing degree of neuroscience and that year I also stumbled into AI at the UCL Gatsby [music] institute which is basically the home of deep mind after that I was at Stanford as a Hennessy scholar pursuing a joint JD PhD then dropped out to Dar axium and we are building an AI mathematician that is the first model that will eventually evolve to be a self-improving super intelligent reasoner To build an AI mathematician, you need three pillars.

01:50AI, programming languages, and mathematics. Because of this vision, we assemble a world-class team of experts from each of these three pillars coming together. So, it's a very interdisciplinary approach. Axiom [music] was fortunate to raise seed round of $64 million.

02:06Investors value us at 300 million valuation. I'm 24 years old. My background is in combinatorics and

Why Math Will Save the World

02:17number theory. It's about patterns. Patterns [music] everywhere in sets and numbers. There are correspondences that are unexpected. I love just everything number theorist. Uh obviously golf. [music] Reading about golf's life was also very inspiring.

02:30All these aha moments, all the long nights when he was pushing for an Eureka moment. Um just fantastic. [music] I think it's the crown draw of mathematics. Um, as GS put it, it's [music] quite beautiful and it feels simless compared to other fields of math.

02:48[music] So, it's a lot of symbolic expressions. The symbols, they actually look beautiful on the sketch paper. I just remember I want to be a number theorist. In the history, every mathematical tool after its invention has [music] led to terrific breakthroughs not just in fundamental science but also in real world applications.

03:07[music] For example, the invention of abacus that has led to the bloom of trade and commerce. Think about integrals and calculus that led to mechanics and thermodynamics. The rest is history. It's the industrial revolution. If you think about babbage engine or the difference engine that is a math tool to calculate log table faster that is the prototype of computer.

03:27So aromathematical tool kind of sparks [music] the flywheel in real world applications and in turn requires more computational tools. So [music] there is this theory of Javon's paradox which is when the price of a tool becomes elastic [music] you will then have unexpected use cases and applications hence requiring more tools and making these tools in turn more valuable by building an AI goals at your fingertip.

03:52[music] We think there will be so many magnitudes of use cases and markets being [music] unlocked and pragmatically if you think about the time span of a mass invention to say the real world application that has in fact spent centuries. AI compress this timeline.

04:10If you think about AI mathematicians working together with applied scientists which by the way human mathematicians seldom work with applied scientists. Now AI mathematicians can go on to these applied [music] fields and solve the complex system that have never been theoretically understood.

04:27And we think that's incredibly powerful and will shorten the century long time span to to much shorter.

Problem Solver to Theory Builder

04:41I grew up loving math. Every time you solve a math problem, you will get that instant reinforcement of um this is the thing that you love doing, you want to continue doing it, you get this little dopamine hit every time you solve an Olympic math question.

04:54I think it's an elementary school at fourth grade. It was about 1,000 really bright kids being assembled into 24 classrooms and try to compete um after each exam. It was a little bit stressful just because you know after each exam you are being sort of ranked and then it's a whole system and only the ones that perform outstandingly can go to um the next level but also it was just eyeopening.

05:20I was able to read the proof of quadratic reciprocity. I was able to look at the names of the mathematicians that I've never known or never heard of. And there are some French and German mathematicians. So you kind of wonder, you know, what it's like to visit the French and German research institute.

05:40It feels like intellectually exploring the world and that felt really motivating to try harder, do more exercises and to do really well in the exams. I remember the elementary school math Olympia problems are made to be quite fun and interesting formulated actually in a real world scenario, right?

05:59like two trucks going across each other and um how long each truck is and how long does it take for them to pass each other. Convert that to mathematical formulation to equations that feels trackable and you can solve without paying much attention to what the real world problem is.

06:17I think that process was quite magical to me. Right? And then you solve it and you plug that back in. What does that tell you in the real world scenario? I think that's very beautiful process and it's mathematical thinking that it's very transferable to other fields.

06:35At one point I do think that I got introduced to research math and that was eye opening. I think that research math is a lot more delay gratification. Research problems is really hard and it takes you a long time to figure it out. You don't have that dopamine hits anymore.

06:50I mean, if you're a mass Olympia student, you can solve a dozen problems each day and feel good about yourself. A dozen months has passed, you have done nothing. That's usually the status of research math if you encounter a bottleneck in the problem.

07:02So, I wanted to be a better mathematician. Um, kind of changing from a problem solver to a theory builder. And for that, I owe um to a lot to my mentors that teach me how to do research, how to be patient, how to look around the corners and look for unexpected connections.

07:18um from another field to the current problem. That was why I got into research and continued um the fruitful collaboration with um professor Ono and other collaborators. [music] The feeling that you can develop new

Why Taste is Important in the AI Era

07:33mathematical theories [music] based on how the flow of past theories go is quite fascinating. You will [music] invent definitions in the natural way. You will link these definitions together to formulate [music] interesting conjectures. And you know what does um natural mean?

07:49What does interesting mean? And then after you formulate that conjecture, you prove it in an elegant way. What does elegance mean? These are questions about taste. And I felt like by learning so many [music] um branches of math, I started to form my taste about math.

08:05And I think in an era where AI is prevalent and can do a lot, taste becomes quite important. It distinguishes between a good scientist and a mediocre one. I [music] think we want to try to understand the problem of taste and intuition [music] better um using modern-day machine learning techniques and that of course will be a very difficult [music] technical challenge.

08:30But it's something that our team is incredibly excited about.

The Hardest Problems Are the Strategy

08:40at the Ross uh mathematics program actually when I was 15. It was a beautiful summer where the professors will teach us how to think deeply about simple things. I remember first day they're like can you prove that zero multiplied by everything is zero.

08:53I'm like well this is obvious like why would you want me to prove that? In fact you are asked to derive that statement strictly from a set of five axioms such as zero plus everything is that thing itself. one multiplied by everything is that thing itself etc.

09:08That way of acimatic and deductive reasoning really was inspiring to me. Thought that was just first of all a bit crazy that a bunch of us stuck for more than 24 hours on um that simple problem set but also it teaches me reason strictly rigorously um to get to the final destination.

09:31Axium the name of our company actually comes with this inspiration. We want to build out the knowledge graph and expand the frontier of mathematics through deductive logic and we are using the programming language of proofs lean for that. I think that there are so many components [music] just like math right one think very deeply about simple things a very short concise mission statement AI mathematician requires various techniques joining together for example my colleague Hugh Leather and his team have been working on applying deep learning to code generation since very early since like 2017

10:13chart have been pioneering the field of AI for mass discovery using transformer to solve symbolic integration in 2019 and they show that it work better than computer algebra. CTO Shoen Gupta have worked in fair for many years and Facebook AI research pioneered work such as large reinforcement learning models like open go.

10:34We believe in solving the hardest problem is the best way to win and I think this mission [music] in itself is incredibly attractive to talents. I love the fast-paced environment of startups. I love executing. I love executing with the team.

10:51I love unblocking others. I love asking for help. You have the sort of very instant reward signal. It's similar to the childhood Karina trying to solve the ma math problems. Just being in the moment, being immersed in how research engineering is done on day-to-day basis is just a once in a-lifetime experience.

Math is the Sandbox of Reality

11:16Mass really is the fundamental of lots of branches of sciences [music] and it's also the sandbox for reality where you can try to put a lot of the real world objects into mathematical variables and then formulate the problem in a purely theoretical way.

11:31So I thought understanding math will allow me to generalize to other domains. And indeed in machine learning we found the transferability of mass reasoning to be quite striking. So a model that does really well on math can likely do better in coding.

11:47We find this sort of surprising phenomena of math concepts [music] being unexpectedly present in a lot of the applied scientific [music] fields. And if you have a real world problem, you can try to understand that theoretically through solving it mathematically.

12:06I think by math is the sandbox of reality. There's another [music] meaning which is if you think about modern machine learning models, they want to gather real world data for them to try to experiment, generate new knowledge from spatial reasoning data.

12:25Math is a digital version of that. We can solve reasoning by being in the digital world without relying on the real world [music] data. So yeah, it is the playground for trying out what we understand about about reality and the universe. Math is full of hardships.

12:46Like I think like mass research is a process of almost like a monk praying in the temple day after day. You just hope that the storm that you are looking at will have a flower grow out of it. I think the feeling of being stuck [music] can feel quite depressing just because you don't really know where [music] your work and the math ends and where you know your life begins.

13:11You kind of blur [snorts] your identity [music] um with your work. I think that's a struggle a lot of mathematicians do face. I think that with AI um hopefully AI mathematicians will make the process a lot more enjoyable. The ideal state is obviously one has a spec a lema that one wants to prove [music] and instead of the human banging the head on the wall um [music] for days and sometimes weeks months the AI mathematician can help prove it and the human mathematician will just guide the collaboration to the next lema makes the journey a lot more exhilarating.

13:46I do think that's part of the vision more like a collaborative endgame that we are imagining rather than say replacing humans. Five or 10 years is hard to imagine but uh we fully believe that this is a fundamental technology that will turn us into a generational company.

14:13Thank you. Thank you.