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Home Mailing List About Mission Partners Privacy Policy People Faculty Staff Researchers Research Fellows Visiting Scholars Alumni Affiliates Students Affiliated Students Interns Former Interns Research Bibliography News Progress Report Blog Work With Us Newsletter Donate Contact CHAI is a multi-institution research group based at UC Berkeley, with academic affiliates at a variety of other universities.
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Learning to Coordinate with Experts
Khanh Nguyen, Benjamin Plaut, Tu Trinh, and Mohamad Danesh introduce a fundamental coordination problem called Learning to Yield and Request Control (YRC), where the objective is to learn a strategy that determines when to act autonomously and when to see
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Computational Frameworks for Human Care
Brian Christian, CHAI Affiliate, has published an article titled "Computational Frameworks for Human Care" in the most recent issue of Daedalus, the journal of the American Academy of Arts and Sciences. In it, Christian traces how AI alignment has progres
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A Practical Definition of Political Neutrality for AI
Jonathan Stray, CHAI Senior Scientist NEW: There's also a video version of this post NEW: Our current research project to build political neutrality evaluations. There is an urgent need for a clear, consistent, and practical definition of political neutra
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RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, PhD student, was an author on "RvS: What is Essential for Offline RL via Supervised Learning?" Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When
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Getting By Goal Misgeneralization With a Little Help From a Mentor
"Tu Trinh, Ben Plaut, Khanh Nguyen, and Mohamad Danesh wrote the paper, "Getting By Goal Misgeneralization With a Little Help From a Mentor." This paper explores whether goal misgeneralization can be mitigated by allowing an agent to ask for help when it
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