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The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced statistical and computational methods.
The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced statistical and computational methods.
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.