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 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.

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-19

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