Explore unsupervised representation learning in this comprehensive lecture by Yann LeCun from New York University. Delve into deep learning concepts, focusing on hierarchical representations and instance segmentation with Mask R-CNN. Examine the nature of common sense and its role in machine learning. Investigate techniques for training actors with optimized action sequences and augmenting neural networks with memory modules. Discover energy-based unsupervised learning strategies, including methods to shape energy functions and maintain constant low-energy volumes. Analyze approaches like sparse coding, auto-encoders, and predictive sparse decomposition. Tackle the challenges of prediction under uncertainty, exploring invariant prediction and video frame forecasting. Gain insights into cutting-edge machine learning research and applications in this hour-long Simons Institute presentation.