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

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

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

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.

Deadline extended! First round submissions now due April 15th. See below.

How can a robot self-assess whether it has received enough demonstrations from an expert to ensure a desired level of performance? The authors of this paper examine the problem of determining demonstration sufficiency.

How do we measure the efficacy of language model explainability methods? The authors of this paper present ALMANACS, a language model explainability benchmark that scores explainability methods on simulatability.

In their paper, that was selected for oral presentation at IMOL@NeurIPS2023, the authors compare human and AI agent exploration in a complex, open-ended environment.

The current approach to AI is a reflection of enormous power imbalances between the tech giants and national governments. What happens when a globe-spanning corporation becomes so powerful that even nations must answer to it?”

The authors of this paper find evidence that social dilemmas involving generative agents can be mitigated with contracting and negotiation.