Explore energy-based approaches to representation learning in this 40-minute lecture by Yann LeCun from NYU and Facebook AI. Delve into topics such as self-supervised learning, video prediction, energy functions, latent variable models, autoencoders, and sparse modeling. Gain insights on how humans and animals learn, the right framework for building predictors, and the applications of energy-based models in high-dimensional continuous spaces. Discover the connections between sparse coding, sparse autoencoders, and linear decoders in convolutional models. Learn about the implications of these approaches for model predictive control and advanced AI systems.