Yann LeCun "Mathematical Obstacles on the Way to Human-Level AI"

Bryna Kra introduces Yann LeCun as the Josiah Gibbs Lecturer, highlighting his extensive background and contributions to computer science and AI. LeCun then discusses the current state of AI, particularly the limitations of large language models (LLMs) and autoregressive prediction, which he believes are fundamentally flawed due to their divergent nature and inability to handle uncertainty effectively. He argues for a shift towards systems that can learn world models from observation, plan complex action sequences, and reason, emphasizing the need for inference by optimization and energy-based models. LeCun advocates for Joint Embedding Predictive Architectures (JEPA) over generative models, and discusses techniques like V-CREG for regularizing latent variables, and concludes by recommending focusing on world models, planning algorithms, and the mathematical foundations of energy-based learning for achieving true AI.