Foundation models, trained on broad data and adapted to a wide range of tasks, represent a paradigm shift in AI. But open questions remain: How do we make training much more efficient and accurate through technical advances? How do we improve social responsibility through documentation and thoughtful benchmarking? What are novel applications that can benefit from the power of foundation models? In this talk, I will discuss recent projects that advance each of the three pillars above, and discuss the promise of efforts to connect them.
Dr. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford, with a focus on Human-Centered Artificial Intelligence. Dr. Liang directs the Center for Research on Foundation Models (CRFM) and has more than a decade of experience in machine learning and natural language processing. He is actively leading research around Foundation models and their impact on AI system design and user interaction.
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