Carina Hong

Carina Hong, founder and CEO of Axiom, discusses her groundbreaking work on creating an AI mathematician. She highlights the convergence of advanced reasoning capabilities, formal proof languages like Lean, and breakthroughs in code generation as key trends revitalizing AI in mathematics. Carina delves into the challenges of translating informal proofs into machine-verifiable formats, the concept of self-improvement through conjecturing and proving, and the potential applications in high-stakes software verification.

Carina Hong, co-founder and CEO of Axiom Math, is on a mission to enhance AI's reasoning through machine-checkable mathematics. She discusses why current AI models struggle with complex math and presents three pillars essential for an AI mathematician. Carina emphasizes the need for a hybrid approach, combining formal verification and neural networks. She explores the limits of intuition in math, critiques existing benchmarks, and advises on practical paths for using AI in mathematics, all while navigating the intriguing landscape between AGI and superintelligence.

Karina Hong, a Stanford dropout and founder of Axiom Math, is on a mission to create a self-improving mathematical reasoning AI. She reveals how her team tackled a 130-year-old problem and disproved a longstanding conjecture, showcasing the power of math as a foundation for fields like coding and finance. Karina discusses the challenges of auto-formalizing proofs and highlights Axiom's vision of an IDE co-pilot that aids researchers and traders alike. The exciting intersection of neuroscience and AI is explored, promising a revolutionary leap in mathematical discovery.