As previously announced, we recently ran a 22-day Colloquium Series on Robust and Beneficial AI (CSRBAI) at the MIRI office, co-hosted with the Oxford Future of Humanity Institute. The colloquium was aimed at bringing together safety-conscious AI scientists from academia and industry to share their recent work. The event served that purpose well, initiating some new collaborations and a number of new conversations between researchers who hadn’t interacted before or had only talked remotely.
Over 50 people attended from 25 different institutions, with an average of 15 people present on any given talk or workshop day. In all, there were 17 talks and four weekend workshops on the topics of transparency, robustness and error-tolerance, preference specification, and agent models and multi-agent dilemmas. The full schedule and talk slides are available on the event page.
Stuart Russell, professor of computer science at UC Berkeley and co-author of Artificial Intelligence: A Modern Approach, gave the opening keynote. Russell spoke on “AI: The Story So Far” (slides). Abstract:
I will discuss the need for a fundamental reorientation of the field of AI towards provably beneficial systems. This need has been disputed by some, and I will consider their arguments. I will also discuss the technical challenges involved and some promising initial results.
Russell discusses his recent work on cooperative inverse reinforcement learning 36 minutes in. This paper and Dylan Hadfield-Menell’s related talk on corrigibility (slides) inspired lots of interest and discussion at CSRBAI.