Peter Henderson on RL Benchmarking, Climate Impacts of AI, and AI for Law
The Gradient: Perspectives on AI · Andrey Kurenkov
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Show Notes
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
* 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"
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