Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
00:00
Exploring Uncertainty in Synthetic Data and AI Models
This chapter discusses the generation of synthetic data and its role in developing adaptable generalist agents through intrinsic motivation. Emphasizing the need for exploring complex environments, it critiques traditional curriculum learning by prioritizing data collection based on uncertainty and complexity. The speakers also reflect on the implications of self-supervised learning, the evolution of AI systems, and the balance between productivity and creativity in utilizing shared models.
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