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The Center for Human-Compatible Artificial Intelligence (CHAI) was launched on August 29, 2016, at the University of California, Berkeley, under the leadership of Stuart Russell, who holds the Smith-Zadeh Chair in Engineering. CHAI was established with a founding grant of $5.5 million from the Open Philanthropy Project, with the goal of ensuring that increasingly capable AI systems remain beneficial to humanity.
CHAI is a multi-institution research group headquartered at UC Berkeley with faculty principal investigators at Cornell University, the University of Michigan, and Princeton University. The center's co-principal investigators include computer scientists Pieter Abbeel, Anca Dragan, Bart Selman, Joseph Halpern, Michael Wellman, and Satinder Singh Baveja, as well as cognitive scientists Tom Griffiths and Tania Lombrozo. Mark Nitzberg serves as Executive Director…
CHAI's theory of change centers on the idea that current AI systems are designed to optimize objectives specified by humans, but as these systems become more capable, even small misspecifications can lead to catastrophic outcomes. CHAI proposes a new model of AI in which machines are explicitly uncertain about human preferences, must learn those preferences from human behavior (via inverse reinforcement learning), and defer to humans rather than pursuing fixed objectives. By developing the mathematical and conceptual foundations for provably beneficial AI, training the next generation of AI safety researchers through PhD programs and internships, producing influential research, and shaping public policy through advocacy and public engagement, CHAI aims to redirect the trajectory of AI development toward systems that are fundamentally aligned with human values.
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View on grantmaking.aiThe CHAI Internship Program is a structured research mentorship program run by the Center for Human-Compatible AI (CHAI), a research center founded in 2016 at the University of California, Berkeley by Stuart Russell and colleagues. CHAI's mission is to reorient AI research toward provably beneficial systems, focusing on the problem of ensuring that advanced AI remains aligned with human values and intentions.
The internship program has been running since at least 2017, with the first documented cohort completing work in 2017-2018. It is designed to bring talented students and early-career professionals into the AI safety research field, serving both as a pipeline for future PhD students and researchers and as a way for professionals to explore whether AI safety research aligns with their career goals…
The CHAI Internship builds the AI safety research talent pipeline by training promising individuals in technical AI safety research methods early in their careers. By giving students and professionals hands-on, mentored research experience and publication credits, the program aims to increase the number of skilled researchers working on alignment and safety problems. CHAI's broader theory of change holds that provably beneficial AI requires fundamental advances in value alignment · particularly inverse reinforcement learning and related techniques · and that a larger, better-trained research community will accelerate progress on these problems before advanced AI systems pose existential risks.