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A Center for Neural Data Science Seminar Series
A Center for Neural Data Science Seminar Series
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.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
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.
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.
Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies.
Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies.
A Center for Neural Data Science Seminar Series
A Center for Neural Data Science Seminar Series
Hear from speakers and attendees at recent Stanford HAI events across research, policy, and education.
Music is intertwined with human emotion, memory, and identity, making it a powerful medium for affective experience and regulation.
Music is intertwined with human emotion, memory, and identity, making it a powerful medium for affective experience and regulation.
The 2026 RAISE Health Symposium will convene leading voices in technology, medicine, and policy to explore what’s real, what’s next, and what’s needed to ensure AI advances responsibly and benefits all. Registration opens March 11.
The 2026 RAISE Health Symposium will convene leading voices in technology, medicine, and policy to explore what’s real, what’s next, and what’s needed to ensure AI advances responsibly and benefits all. Registration opens March 11.
Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.
Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.
The Center for Neural Data Science (CNDS) presents the first CNDS Data Visualization Showcase. This event celebrates outstanding neural data visualizations across the Stanford community, highlighting not only compelling scientific insights but also the technical and aesthetic choices that make visualizations truly impactful.
To participate in this event, you can RSVP to attend and/or submit a data visualization to be considered for one of the finalist positions. Finalists will then compete during the showcase by giving a mini-talk about their visualization. Note: You do not need to submit an entry to attend.
The Center for Neural Data Science (CNDS) presents the first CNDS Data Visualization Showcase. This event celebrates outstanding neural data visualizations across the Stanford community, highlighting not only compelling scientific insights but also the technical and aesthetic choices that make visualizations truly impactful.
To participate in this event, you can RSVP to attend and/or submit a data visualization to be considered for one of the finalist positions. Finalists will then compete during the showcase by giving a mini-talk about their visualization. Note: You do not need to submit an entry to attend.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.