Mitigating Partial Observability in Decision Processes via the Lambda Discrepancy

28 Jun 2024

This paper was published at “Finding the Frame” workshop at RLC 2024 and “Foundations of RL and Control” workshop at ICML 2024.

8th Annual CHAI Workshop

18 Jun 2024

CHAI held its 8th annual workshop at Asilomar Conference Grounds from June 13th to June 16th in Pacific Grove. The workshop had over 200 attendees which was the highest attendance to date. The workshop featured over 60 speakers and panelists and covered a wide array of topics from Societal Effects of AI to Adversarial Robustness.

When Code Isn’t Law: Rethinking Regulation for Artificial Intelligence

05 Jun 2024

Brian Judge, Mark Nitzberg, and Stuart Russell wrote an article that was featured in Oxford Academic’s Policy and Society.

Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes

13 May 2024

New research from computer scientists Inês Terrucha, Elias Fernández Domingos, Pieter Simoens, and Tom Lenaerts at the Vrije Universiteit Brussel, Université Libre de Bruxelles, and UC Berkeley’s Center for Human-Compatible AI

Reinforcement Learning with Human Feedback and Active Teacher Selection (RLHF and ATS)

30 Apr 2024

CHAI PhD graduate student, Rachel Freedman gave a presentation at Stanford University on critical new developments in AI safety, focusing on problems and potential solutions with Reinforcement Learning from Human Feedback (RLHF).

Reinforcement Learning Safety Workshop (RLSW) @ RLC 2024

15 Apr 2024

Important Dates
Paper submission deadline: May 10, 2024 (AoE)
Paper acceptance notification: May 23, 2024

Regulating Advanced Artificial Agents

06 Apr 2024

“Governance frameworks should address the prospect of AI systems that cannot be safely tested.“

CHAI Policy Internship

02 Apr 2024

Deadline April 17th, 2024. Policy Internship at Center for Human-Compatible Artificial Intelligence

Embracing AI That Reflects Human Values: Insights from Brian Christian’s Journey

28 Mar 2024

Discover how, Brian Christian, an acclaimed author’s quest for deeper understanding could lead to AI systems that truly mirror human values and decisions.

When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning

05 Mar 2024

The researchers at Center for Human-Compatible AI (CHAI) at the University of California, Berkeley, has embarked on a study that brings to light the nuanced challenges encountered when AI systems learn from human feedback, especially under conditions of partial observability.