CHAI’s mission is to develop the conceptual and technical wherewithal to reorient the general thrust of AI research towards provably beneficial systems.
Highlights
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?”
A Practical Definition of Political Neutrality for AI
It seems a foregone conclusion that intelligent machines will soon be the main way we get information about our world, including our political world. Rather than reading news articles directly, AI systems will summarize exactly what we want to know from a variety of sources. It seems inevitable, then, that these machines will influence our politics. And indeed, recent research shows that they do.
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 progressed from simple reward mechanisms toward care-like relationships, revealing both the potential and limitations of machine caregiving while deepening our understanding of human care itself. The issue is titled “The Social Science of Caregiving” and was co-edited by CHAI Affiliate Alison Gopnik.
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 seek expert assistance. They build an open-source benchmark featuring diverse domains, propose a novel validation approach, and investigate the performance of various learning methods across diverse environments, yielding insights that can guide future research.
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