Peter Henderson on RL Benchmarking, Climate Impacts of AI, and AI for Law

In episode 14 of The Gradient Podcast, we interview Stanford PhD Candidate Peter HendersonSubscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSPeter 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.Links:* Reproducibility and Reusability in Deep Reinforcement Learning. * Benchmark Environments for Multitask Learning in Continuous Domains* Reproducibility of Bench-marked Deep Reinforcement Learning Tasks for Continuous Control.* Deep Reinforcement Learning that Matters* Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning Methods)* Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning* How blockers can turn into a paper: A retrospective on 'Towards The Systematic Reporting of the Energy and Carbon Footprints of Machine Learning* When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset”* How US law will evaluate artificial intelligence for Covid-19Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music" Get full access to The Gradient at thegradientpub.substack.com/subscribe