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In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.

In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.

This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
In mid-July, a working group focused on AI for the environment convened to outline future directions that would leverage AI to address pressing environmental challenges, ranging from biodiversity and conservation biology to water availability and sustainable communities. The group focused on the concept of building a thrivable planet for all species – not just one that is merely habitable. With the backdrop of the Stanford Educational Farm, we leveraged a human-centered design process to focus on how we might harness AI to uniquely address a range of stakeholder needs. Our objective was to develop an array of prototype projects that lead to insights about future directions for AI in the environmental and sustainability realms. Project prototypes included halting slavery in the seafood industry, intelligent tools for ensuring water and food security, and intelligent approaches for managing species migration. Based on these projects, we identified the following overarching themes that would be exciting to pursue through collaborative research: (1) Predicting, detecting and mitigating or incentivizing environmental transitions, (2) quantifying well-being and compatibility with one’s environment, (3) environmental justice and human rights, (4) opening of new data streams and achieving interoperability of existing data streams.
