Tejal Patwardhan, head of OpenAI's frontier evals team, discusses the critical evolution of AI evaluations. She explains why traditional benchmarks fail as models become more capable, how OpenAI develops realistic, long-horizon tests (including groundbreaking wet lab experiments), and the implications of rapidly advancing multimodal and reasoning models for scientific discovery and the future of human work.
Theoretical physicist Alex Lupsaska shares his journey from AI skeptic to collaborator, detailing how advanced GPT models solved a year-long problem in quantum field theory concerning gluon scattering amplitudes, and then generalized the solution to gravitons, signaling a new era of AI-accelerated scientific discovery.
Terence Tao uses the story of Kepler's discovery of planetary motion as an analogy for AI's role in science. He argues that AI excels at broad, high-temperature idea generation but requires a robust verification process to be useful. The bottleneck in science is shifting from hypothesis generation to verification and curation, a challenge current scientific structures are not equipped to handle. Tao foresees a future of human-AI collaboration where humans provide deep insights and AI explores the vast breadth of possibilities, ultimately making scientific papers richer but not necessarily deeper.
Ten years after the historic match between AlphaGo and Lee Sedol, Google DeepMind's Thore Graepel and Pushmeet Kohli reflect on its legacy. They discuss how AlphaGo's blend of deep learning and tree search conquered the game of Go, the significance of creative breakthroughs like 'Move 37', and how these foundational concepts evolved into systems like AlphaZero, which learns without human data. The conversation bridges the gap from game-playing to solving scientific grand challenges, detailing how the same principles are now used in tools like AlphaTensor to discover novel, more efficient algorithms for fundamental problems like matrix multiplication.
Professor Ellen Zhong discusses the shift from viewing proteins as static objects to dynamic molecular machines. She explores how cryo-electron microscopy (cryo-EM) combined with machine learning creates complex inverse problems to reveal protein motion, moving beyond the "solved" problem of static structure prediction and toward a future of AI-driven scientific discovery.
Sam Altman and Yakob present OpenAI's updated strategy, detailing a concrete research roadmap towards an automated AI researcher by 2028, a vision for an open AI platform, and massive infrastructure plans totaling $1.4 trillion. They also introduce a new corporate structure with a non-profit foundation focused on using AI to cure diseases and build AI resilience.