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Fei-Fei Li, co-director of Stanford HAI, emphasized the risks of cutting research funding and international student visas to the US as it faces an increasingly competitive global tech race.
Fei-Fei Li, co-director of Stanford HAI, emphasized the risks of cutting research funding and international student visas to the US as it faces an increasingly competitive global tech race.

This brief sheds light on the “regulatory misalignment” problem by considering the technical and institutional feasibility of four commonly proposed AI regulatory regimes.
This brief sheds light on the “regulatory misalignment” problem by considering the technical and institutional feasibility of four commonly proposed AI regulatory regimes.

In this podcast, HAI Executive Director Russell Wald explores how universities, policymakers, and industry must collaborate to keep AI human-centered. Wald shares takeaways from the AI Index, explains how China is narrowing the performance gap, and outlines why academic institutions are vital to ethical AI leadership.
In this podcast, HAI Executive Director Russell Wald explores how universities, policymakers, and industry must collaborate to keep AI human-centered. Wald shares takeaways from the AI Index, explains how China is narrowing the performance gap, and outlines why academic institutions are vital to ethical AI leadership.

This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.
This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.

Vanessa Parli, Stanford HAI Director of Research and AI Index Steering Committee member, notes that the 2025 AI Index reports flourishing and higher-quality academic research in AI.
Vanessa Parli, Stanford HAI Director of Research and AI Index Steering Committee member, notes that the 2025 AI Index reports flourishing and higher-quality academic research in AI.

This brief examines the “regulatory misalignment” of existing regulatory proposals for AI and urges policymakers to first consider feasibility, trade-offs, and unintended consequences before rushing into regulation.
This brief examines the “regulatory misalignment” of existing regulatory proposals for AI and urges policymakers to first consider feasibility, trade-offs, and unintended consequences before rushing into regulation.
