Why Vlad Tenev and Tudor Achim of Harmonic Think AI Is About to Change Math—and Why It Matters
Sep 24, 2024
39:45
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Vlad Tenev, co-founder of Robinhood, is now steering his focus on AI's potential to revolutionize mathematics. Alongside Tudor Achim, who tracks the rise of Lean, an esoteric programming language, they delve into the possible integration of AI in solving complex mathematical challenges. They discuss the impact of mathematical superintelligence, the significance of synthetic data, and how Lean is transforming proofs. Their ambitious vision includes tackling Millenium Prize problems, all while emphasizing the importance of mentorship in shaping mathematical minds.
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Quick takeaways
AI's proficiency in mathematical reasoning may lead to breakthroughs in solving complex problems, exemplified by the Riemann hypothesis.
The collaboration between humans and AI in mathematics could redefine the role of mathematicians, transitioning them to guiding forces in problem-solving.
Deep dives
The Role of AI in Mathematics
There is a growing belief that artificial intelligence could play a significant role in solving complex mathematical problems, potentially even claiming prestigious awards like the Millennium Prize. The podcast highlights the perspective that AI's capabilities in math could be underestimated and emphasizes the importance of a direct instructional approach to training AI in mathematical reasoning. By focusing AI development on mathematical understanding, it is argued that these systems could excel in broader fields of science and engineering just as humans do once they gain a strong mathematical foundation. The example given is the possibility of AI solving the Riemann hypothesis, showcasing the potential for profound breakthroughs in mathematics.
Mathematics as a Foundation for Reasoning
Mathematics is presented as foundational in understanding various fields and phenomena, ranging from physics to engineering. The podcast articulates a concept that excelling in math translates to better performance in other quantitative disciplines, thereby underscoring math's unique role in developing critical and logical thinking. This reasoning leads to the hypothesis that, if AI systems become proficient in math, they are likely to transfer that intelligence to tackle complex challenges in other domains. Thus, the focus on teaching AI mathematical reasoning directly is seen as a crucial step towards general intelligence applications.
Challenges and Dynamics in AI Development for Mathematics
The discussion reveals a dual perspective within the mathematics community toward AI, with younger mathematicians tending to embrace its applications while older ones remain skeptical. The trajectory of AI, as discussed, could mirror the evolution seen in chess, where humans first assisted AI before it took on a dominant role. This belief exemplifies how the role of mathematicians may transform to more of a guiding force, directing AI efforts rather than being directly involved in problem-solving. The notion that AI could redefine what it means to be a mathematician is compelling, suggesting that the future of mathematical inquiry may be a collaborative endeavor between humans and machines.
Synthetic Data and Reinforcement Learning in AI
Synthetic data is identified as a critical resource for training AI systems in mathematics, providing a fresh avenue for generating relevant datasets. The framework of reinforcement learning, coupled with verification through languages like Lean, offers an innovative method for AI development, facilitating recursive self-improvement cycles. Emphasis is placed on the lack of readily available math-related data compared to more abundant sources like general internet content, which necessitates generating data synthetically. This approach not only aims to increase the model's proficiency but also promises faster improvement cycles, potentially leading to groundbreaking discoveries in mathematics.
Adding code to LLM training data is a known method of improving a model’s reasoning skills. But wouldn’t math, the basis of all reasoning, be even better? Up until recently, there just wasn’t enough usable data that describes mathematics to make this feasible.
A few years ago, Vlad Tenev (also founder of Robinhood) and Tudor Achim noticed the rise of the community around an esoteric programming language called Lean that was gaining traction among mathematicians. The combination of that and the past decade’s rise of autoregressive models capable of fast, flexible learning made them think the time was now and they founded Harmonic. Their mission is both lofty—mathematical superintelligence—and imminently practical, verifying all safety-critical software.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
IMO and the Millennium Prize: Two significant global competitions Harmonic hopes to win (soon)
Riemann hypothesis: One of the most difficult unsolved math conjectures (and a Millenium Prize problem) most recently in the sights of MIT mathematician Larry Guth
Terry Tao: perhaps the greatest living mathematician and Vlad’s professor at UCLA
Lean: an open source functional language for code verification launched by Leonardo de Moura when at Microsoft Research in 2013 that powers the Lean Theorem Prover
mathlib: the largest math textbook in the world, all written in Lean
Metaculus: online prediction platform that tracks and scores thousands of forecasters
Navier-Stokes equations: another important Millenium Prize math problem. Vlad considers this more tractable that Riemann
John von Neumann: Hungarian mathematician and polymath that made foundational contributions to computing, the Manhattan Project and game theory
Gottfried Wilhelm Leibniz: co-inventor of calculus and (remarkably) creator of the “universal characteristic,” a system for reasoning through a language of symbols and calculations—anticipating Lean and Harmonic by 350 years!
00:00 - Introduction
01:42 - Math is reasoning
06:16 - Studying with the world's greatest living mathematician
10:18 - What does the math community think of AI math?
15:11 - Recursive self-improvement
18:31 - What is Lean?
21:05 - Why now?
22:46 - Synthetic data is the fuel for the model
27:29 - How fast will your model get better?
29:45 - Exploring the frontiers of human knowledge
34:11 - Lightning round
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