In many real-world problems, the learning agent needs to learn a problem’s abstractions and solution simultaneously. However, most such abstractions need to be designed and refined by hand for different problems and domains of application.

It is often crucial to have information about one’s collaborators. However, not every feature of collaborators is strategically relevant.

This paper introduces SMCP3, a new family of sequential Bayesian inference algorithms.

Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives.

To optimally coordinate with others in cooperative games, it is often crucial to have information about one’s collaborators. However, not every feature of collaborators is strategically relevant.

From the 16th to the 18th of June, CHAI held its 7th annual workshop at Asilomar Conference Grounds in Pacific Grove, CA.

CHAI Micah Carroll and former CHAI interns Dmitrii Krasheninnikov, Lauro Langosco, and Yawen Duan published a paper titled Harms from Increasingly Agentic Algorithmic Systems for FAccT 2023.

In their paper published in the AI journal, CHAI Tom Lenaerts and Axel Abels argue that quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives.

On the 1st of April, CHAI’s Brian Christian gave an opening keynote address at “The Value Connection Workshop” in Amsterdam, an interdisciplinary workshop on human values, hosted by TU Delft and sponsored by the Royal Netherlands Academy of Arts and Sciences and the Delft Design for Values Institute.

On the 2nd of April 2023, Stuart Russell published an article in The Guardian making the point that systems with abilities exceeding human capacity have been let loose.