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 10, 2026

Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub

A panel with Google DeepMind's Pushmeet Kohli and Biohub's Sal Candido, moderated by Brandon Anderson, examines how AI should be built for biology. Topics include scaling laws and data quality, the bitter lesson applied to data, AlphaFold's handcrafted design and its limits, protein dynamics, cryo-EM as a richer data source, trust and calibration versus interpretability, and why curing disease requires 10x breakthroughs.

Sep 23, 2026

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

Eric Nguyen, CEO of Radical Numerics, details the journey from early genome language models to Omni, a system that excels in DNA reading, writing, and reasoning. Omni outperforms specialized models in disease-variant prediction, particularly in previously challenging non-coding regions. The discussion also covers the critical need for AI-native biosecurity defenses, addressing how models can design biological sequences that evade current detection systems, and the broader vision for General Biological Intelligence.

Sep 02, 2026

AI agents for therapeutic reasoning across biological contexts

Medea is an AI agent for therapeutic reasoning designed to overcome limitations of existing AI in preserving biological context, verifying computational steps, and reconciling conflicting evidence. It employs a modular architecture with robust verification mechanisms for research planning, analysis, literature reasoning, and multi-round discussion, demonstrating superior performance in cell type-specific target nomination, synthetic lethality prediction, and immunotherapy response prediction across various biological contexts and data types.

Aug 27, 2026

AI models can now help run physical science experiments

The Model Hardware Standard (MHS) is a pioneering framework developed by Anthropic and HHMI Janelia to enable AI, specifically Claude, to safely and intelligently operate diverse scientific and manufacturing hardware. By abstracting device-specific communication, MHS dramatically accelerates scientific discovery, from automating complex microscopy tasks and real-time tracking to optimizing high-throughput drug screening, empowering researchers to focus on core scientific questions.

Aug 04, 2026

Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

Chai Discovery is revolutionizing drug discovery by treating biology as an engineering problem, leveraging AI—particularly diffusion models and the "bitter lesson" of scaling—to design molecules rather than merely discover them. Their approach has boosted antibody design hit rates from 0.1% to 16%, aiming for a "Molecular CAD" suite that collapses discovery timelines from months to days. They partner with pharma, building infrastructure and creating a data flywheel to develop higher-quality, more targeted medicines for previously undruggable diseases.