The project involves analyzing 22 million pairs of algebraic laws to determine if one law implies another, providing proofs or counterexamples as necessary. While many problems are solvable by undergraduate students, a few are quite challenging.

I envision a future where programming will involve defining logic in a more human-readable format, moving away from traditional coding languages.

AlphaProof is a system that learns to prove mathematical theorems through experience, without being told how to prove them, ensuring the correctness of its proofs.

The ability to allow AI models to reason for extended periods, rather than just providing quick answers, can lead to significantly better outcomes for complex questions.

The outcome is better if you structure the reasoning and help iterate with the model on how it should do reasoning.

Lean programming enables collaboration on proofs at an atomic level, allowing mathematicians to work together more effectively than with traditional pen and paper methods.

With Grok, especially Grok 3.5, we're applying physics tools to reasoning, aspiring to truth with minimal error.

One of my early projects involved translating classical projective geometry concepts into geometric algebra, which simplified the mathematical complexity of computer vision tasks.

The shift from merely scaling LLMs to incorporating reasoning techniques has opened new avenues for AI development, allowing models to perform better by thinking longer and more deeply.