AI agents

A practical introduction to AI agents for biological research, covering tool and model selection, context design, costs and privacy. Participants work with research data to produce analyses, figures, presentations and interactive omics portals. Further sessions develop reusable skills, project instructions and agents, connect external knowledge through retrieval and MCP, and assemble reproducible research workflows. The course includes critical review of generated code, limitations, responsible use and laboratory AI policies. Sessions run 14:00–17:00 CET on 3 and 4 December 2026. Intended for

Ahmad Al-Dahle, Airbnb’s CTO, discusses Project Everest, an effort to capture institutional knowledge from employee workflows for use by AI agents. The conversation examines stale information, context poisoning and the costs of supplying excessive context, alongside evaluation of whether shared knowledge improves engineering work. It also considers productivity measures beyond token consumption and pull-request counts, limitations of chat interfaces, and the challenges of support agents mediating conflicting interests in a marketplace.

John Platt discusses how Google’s applied-science team chooses problems where AI can make a useful contribution. Topics include satellite-based detection and prediction of aircraft contrails, evaluation of rerouting trials, limitations of physical simulation, and the role of AI agents in scientific modelling and data work. The conversation also examines overfitting, action incentives, gradient-free optimisation, SMO and support-vector machines, the physical constraints on scaling, and opportunities for robotics and manufacturing to support climate technology. It concludes with FireSat’s approa

…scarcity, structure and causality, failures in drug development and the limits of speeding up wet-lab work with AI agents. It also considers foundation models for biology, generalization beyond observed data, human evidence and scientific discovery. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Kunle Olukotun explains reconfigurable dataflow architectures for AI inference. The discussion examines mapping computation onto model dataflow graphs, reducing memory-bandwidth bottlenecks, serving multiple models and switching between them efficiently. It also considers dynamic hardware architectures and using AI agents to develop compilers.