Published Jan 27, 2022

Percy Liang on Machine Learning Robustness, Foundation Models, and Reproducibility

Percy Liang delves into the intricacies of machine learning robustness and foundation models, emphasizing the importance of strategic approaches to adapt and improve performance, while also advocating for ethical and reproducible AI development. His pioneering work on semantic parsing is also explored, highlighting the potential of language to enhance human-computer interaction.

Episode Highlights

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Episode Highlights

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Percy Liang on Machine Learning Robustness, Foundation Models, and Reproducibility

Topics covered

Popular Clips

The Power of Language

Reproducible Research Platform

AI Innovations Unveiled

AI Research Journey

Foundation Model Adaptation

AI Foundation Models

Shaping AI Ethics

Semantic Parsing Evolution

Evolving Research Interests

Reproducible AI Experiments

Foundation Models Revolution

Foundation Models Evolution

Unsupervised Learning Exploration

Foundation Model Risks

Episode Highlights

Machine Learning Robustness

Semantic Parsing

Foundation Models

Related Episodes

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Stuart Russell: The Foundations of Artificial Intelligence

Hugo Larochelle: Deep Learning as Science

Sergey Levine on Robot Learning & Offline RL

Rishi Bommasani on Foundation Models

Rosanne Liu: Paths in AI Research and ML Collective

Connor Leahy on EleutherAI, Replicating GPT-2/GPT-3, AI Risk and Alignment

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

Laurence Liew: AI Singapore

Peter Lee: Computing Theory and Practice, and GPT-4's Impact

Linus Lee: At the Boundary of Machine and Mind

Kyunghyun Cho: Neural Machine Translation, Language, and Doing Good Science

Luis Voloch: AI and Biology

Chelsea Finn on Meta Learning & Model Based Reinforcement Learning

Subbarao Kambhampati: Planning, Reasoning, and Interpretability in the Age of LLMs

Percy Liang on Machine Learning Robustness, Foundation Models, and Reproducibility

Topics covered

Popular Clips

Episode Highlights

Machine Learning Robustness

Semantic ParsingPercy Liang, an Associate Professor of Computer Science at Stanford, discusses his pioneering work in semantic parsing and its evolution. He shares insights on how language can be harnessed by computers to improve human-computer interaction.

Semantic Parsing

Foundation ModelsPercy Liang explores the evolution and adaptation of foundation models, highlighting their transformative impact on AI. He addresses the risks these models pose, advocating for ethical development and robust research to harness their potential responsibly.

Foundation Models

Related Episodes