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23min chapter
Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
Exploring Generalist Agents and Autocurricular Learning
The chapter delves into building a generalist agent capable of handling diverse tasks without specific knowledge of rewards, focusing on intrinsic motivation and high entropy search to optimize learning. It discusses autocurricular methods in machine learning, self-supervised objectives, and the balance between self-supervised learning in language models and image learning. The conversation also touches on enhancing productivity through leveraging existing expertise while cautioning against getting locked into suboptimal solutions.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode