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, founder and CEO of Axiom, builds a self-reasoning AI that combines provers, conjecturers, and auto‑formalization. They discuss how that system proves math problems, makes machine proofs inspectable, and where formal verification matters in hardware and safety‑critical software. They also explore auto‑formalization challenges and how these tools could reshape mathematical work.

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.

In this insightful discussion, Carina Hong, Founder and CEO of Axiom, introduces her work on AI that utilizes verified proofs for high-stakes reasoning. Byron Cook, VP at AWS, shares how formal verification secures vast cloud infrastructures. They explore the limitations of current AI models in truth verification, the significance of mathematical proofs in ensuring correctness, and the transformative potential of verified AI in finance, healthcare, and more. Together, they envision a future where AI can reliably assist in critical decision-making.

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.