Scientific discovery

Oct 15, 2026

Revolutionizing Cybersecurity and AI-Driven Science with OpenAI President Greg Brockman

Greg Brockman, OpenAI co-founder and president, discusses OpenAI's recent math results, in which roughly 400 open problems were resolved using AI agents, and why he believes AI can now generate genuinely new knowledge. The conversation covers AI oversight and communication techniques like debate and iterative amplification, the data bottlenecks that separate math from biology, the potential transformation of drug discovery and regulation, the proactive always-on Dots agent, the convergence of consumer and enterprise AI, why the best UI may be no UI, the cybersecurity 'defenders' window,' and why security spending should outstrip software spending while humans stay in control.

Oct 09, 2026

Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin

Misha Laskin, co-founder and CEO of Reflection AI, discusses the company's 500B open-weight reasoning model Beam, the economics of frontier training, the shift from renting to owning intelligence in enterprise, the role of Chinese open models in the global ecosystem, and the case that openness itself is a safety strategy. He also reflects on how AI is accelerating scientific research and reshaping research team sizes.

Sep 18, 2026

The Next Breakthroughs in AI with Yossi Matias | The Google Research Podcast

Yossi Matias, VP and GM of Google Research, discusses how Google prototypes the future by inventing it, emphasizing the current "Golden Age of Research." Key themes include the seamless integration of AI through ambient intelligence and adaptive Generative UI, architectural advancements that push beyond scaling laws with techniques like Speculative Decoding, and multi-agent AI systems such as AI Co-Scientist and Empirical Research Assistance for accelerating scientific discovery. He also details Google Earth AI for crisis resilience and planetary intelligence, breakthroughs in healthcare diagnostics with MedGemma and the Google Health app, and milestones in quantum computing with the Willow chip, ultimately envisioning a future where AI amplifies human ingenuity and solves humanity's biggest challenges.

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.