Oct 28, 2021 • 1HR 29M

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

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Andrey Kurenkov
Interviews with various people who research, build, or use AI, including academics, engineers, artists, entrepreneurs, and more.
Episode details

In episode 14 of The Gradient Podcast, we interview Stanford PhD Candidate Peter Henderson

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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.


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Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"