Motivation, Representation, and Autonomous Agents

The past decade’s series of dramatic AI successes has brought us closer to realizing the dream of the autonomous agent - an artificial system that can learn about its world, make decisions, and set and achieve goals, all with minimal human intervention. Nature has given us an incredible motif for the autonomous agent. Humans are born with core motivations that drive behavior, and as they gain experience, they develop increasingly sophisticated representations of their worlds, which in turn facilitate increasingly complex behaviors. 

How should we attempt to realize this motif for intelligence to create autonomous agents? In this seminar, the speaker will cover a number of efforts aimed at assembling pieces of it - starting with their own research into intrinsic motivation and adaptive “world model” representations, including work inspired by biology and, in particular, human development. They will also describe some of the opportunities language models provide for us to make increasingly autonomous systems.

Speaker: Nick Haber

Attend in person or online (see weblink). Lecture starts at 10:00.