Peter Henderson on RL Benchmarking, Climate Impacts of AI, and AI for Law
An interview Stanford JD-PhD candidate Peter Henderson, whose research is on creating robust decision-making systems to create new ML methods for applications that are beneficial to society
In episode 14 of The Gradient Podcast, we interview Stanford PhD Candidate Peter Henderson
Peter is a joint JD-PhD student at Stanford University advised by Dan Jurafsky. He is also an OpenPhilanthropy AI Fellow and a Graduate Student Fellow at the Regulation, Evaluation, and Governance Lab. His research focuses on creating robust decision-making systems, with three main goals: (1) use AI to make governments more efficient and fair; (2) ensure that AI isn’t deployed in ways that can harm people; (3) create new ML methods for applications that are beneficial to society.
Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"