Scientific discovery

Aug 25, 2026

Einstein Arena: Harnessing Collective Agent Intelligence for Open Science — James Zou, Together AI

James Zou presents a novel approach to AI development by advocating for the design of environments over workflows, fostering emergent intelligence and creativity. He introduces the **Einstein Arena**, where AI agents collaboratively and competitively solved open scientific problems like the kissing number problem, achieving breakthrough results (e.g., 604 spheres in 11 dimensions). The same principles successfully optimized GPU kernels, leading to 2x+ speedups. He also discusses **DSGym**, an environment for data science agents, addressing shortcomings of existing benchmarks by eliminating 'shortcuts' and enabling the training of high-performing, fine-tuned open-source models runnable on laptops.

Aug 18, 2026

Michael Kratsios: Inside the White House's AI Strategy

Michael Kratsios, from Scale AI and the White House, discusses US AI policy, advocating for open-source AI, flexible regulation, and supporting startups against incumbent moats. He outlines the White House's vision for AI-driven scientific discovery and the future focus on Quantum Information Science, while urging Congress to legislate on preemption and IP. He concludes with a call for technologists to engage in public service.

Jul 30, 2026

First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI

Richard Socher introduces the "Eureka machine," a concept for automating scientific research and invention through AI. Inspired by open-ended evolution and Karl Popper's philosophy of science, he argues that AI can compress the timeline of scientific discovery, overcoming human-centric bottlenecks. The machine relies on four pillars (knowledge, data, simulations, physical labs) orchestrated by an agent swarm, requiring a rethinking of existing infrastructure. Recursive Self-Improvement (RSI), where AI improves its own code and addresses its shortcomings, is presented as the path forward, with early proof points in model optimization, training speed, and GPU kernel efficiency.

Jul 17, 2026

Research to Reality with Google DeepMind — Benoit Schillings, Google DeepMind, VP of Technology

Benoit Schillings, VP of Technology at Google DeepMind, explores the evolution of AI's role in software development, highlighting the transition from human-limited coding to an AI frontier where syntax generation is solved. He delves into the power of self-play for model training, the shifting economics of software engineering, and the imperative for active guardrails. Schillings also discusses the need for inductive architecture, advanced model planning, multimodal reasoning (as seen in Gemini), and the potential for AI to drive scientific breakthroughs in fields like chemistry and biology by uncovering patterns imperceptible to human bias.

Jul 10, 2026

General relativity from first principles – Adam Brown

Adam Brown elucidates Einstein's General Relativity, tracing its origins from the equivalence principle and curved spacetime to the mind-bending physics of black holes. He covers the striking observational evidence for black holes and the historical confirmation of GR, concluding with a speculative discussion on how AI could accelerate scientific discovery as 'superhuman explainers'.

Jun 16, 2026

Why Tejal Patwardhan stopped underestimating the models - Episode 21

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.