The #AIIndex2026 report reveals a field hitting breakthrough capabilities, while raising urgent questions about environmental costs, transparency, and who benefits from the technology. Read the main findings here https://
hai.stanford.edu/news/inside-th
e-ai-index-12-takeaways-from-the-2026-report
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@stanfordhai
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AI Index 2026: Breakthrough Capabilities and Critical Challenges Ahead
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AI Index 2026: Comprehensive Analysis of AI Progress and System Gaps
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Introducing the #AIIndex2026: Our most comprehensive, independently sourced data analysis of AI’s trajectory, with a clear-eyed assessment of the critical gaps that remain. As AI advances rapidly, can the systems built around it keep up? Explore the data: https://t.co/WqRGeRZIjA pic.twitter.com/NUsCIIQuBi
— Stanford HAI (@StanfordHAI) 13 avril 2026Introducing the #AIIndex2026: Our most comprehensive, independently sourced data analysis of AI’s trajectory, with a clear-eyed assessment of the critical gaps that remain. As AI advances rapidly, can the systems built around it keep up? Explore the data: https://
hai.stanford.edu/ai-index/2026-
ai-index-report
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AI Index 2026: Evidence-Based Data on Artificial Intelligence Progress
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Over the past year, AI has redefined what's possible and what's at stake. But how do you separate signal from noise?
— Stanford HAI (@StanfordHAI) 10 avril 2026
The #AIIndex2026 delivers unbiased, rigorously vetted data to empower you to make more informed decisions, shape strategy, and ground conversations in evidence.… pic.twitter.com/VFPAqZn5GsOver the past year, AI has redefined what's possible and what's at stake. But how do you separate signal from noise? The #AIIndex2026 delivers unbiased, rigorously vetted data to empower you to make more informed decisions, shape strategy, and ground conversations in evidence.
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Extracting Finite Automata from Generative Sequence Models
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Join @DigEconLab
's seminar with MIT's @asheshrambachan as he presents a framework for extracting finite automata from generative sequence models, creating interpretable summaries of next-token probabilities. For researchers exploring ML and economics: https://
digitaleconomy.stanford.edu/event/ashesh-r
ambachan-del-seminar-series/
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AI Index 2026: Measured Data on AI Research and Adoption
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Everyone has an AI prediction. The #AIIndex2026 has the data. See what changed in AI research, adoption, policy, and public opinion over the past year – measured, not forecasted. Subscribe to the @StanfordHAI newsletter to receive the report on April 13: https://
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Statistics and Machine Learning: Tom Mitchell interviews Michael I. Jordan
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New podcast episode for you! The field of Statistics enters the Machine Learning conversation, as @tommmitchell talks with Michael I. Jordan of @inria_paris! Watch here: piped.video/BfBxS2rEH3k
→ View original post on X — @stanfordhai, 2026-04-06 16:18 UTC
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AI in Science: Breakthroughs, Limits and Risks
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On May 5, join @StanfordHAI and Stanford Data Science as we explore AI's role in science: What are the real breakthroughs, limits, and risks? How do we leverage AI while keeping scientific discovery fundamentally human? Register now to secure your spot: hai.stanford.edu/events/ai-s… [Translated from EN to English]
→ View original post on X — @stanfordhai, 2026-04-03 16:05 UTC
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AI in War: Regulation Challenges and American National Security
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As artificial intelligence becomes central to national security, experts grapple with a technology that remains unpredictable, unregulated, and increasingly powerful. hai.stanford.edu/news/who-de… [Translated from EN to English]
→ View original post on X — @stanfordhai, 2026-04-03 05:01 UTC
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Natural Disasters Amplify Income Inequalities
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Stanford scholars used Google Street View data to study the impact of extreme weather events and found income disparities are amplified after a disaster. hai.stanford.edu/news/how-na… [Translated from EN to English]
→ View original post on X — @stanfordhai, 2026-04-02 14:03 UTC
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Stanford Annual Causal Science Conference Focuses on AI Evaluation
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Stanford HAI and Stanford Data Science are bringing together experts working in evaluation, experimentation, and causal inference for the Annual Causal Science Conference on April 24. This year’s event focuses on AI evaluation. Register here: datascience.stanford.edu/eve…
→ View original post on X — @stanfordhai, 2026-04-01 19:03 UTC