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ICLR 2020: Yann LeCun and Energy-Based Models

Machine Learning Street Talk (MLST)

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Self-Supervised Learning and Human Cognition

This chapter investigates self-supervised learning in AI, drawing parallels with human and animal learning processes. It examines how infants develop concepts through observation, questioning the balance between innate knowledge and learning through exposure. The conversation also explores the potential for machines to mimic these learning methods and improve AI capabilities while addressing challenges in reasoning and complex task planning.

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