Explore the intersection of machine learning, theoretical physics, and neuroscience in this seminar by Stanford University's Surya Ganguli. Delve into high-dimensional statistics, deep network generalization, and the application of complex systems analysis to neural systems. Discover how optimal convolutional auto-encoders can reveal retinal structure and how recurrent neural networks explain hexagonal firing patterns. Examine the geometry and dynamics of high-dimensional optimization in quantum optimizers, and gain insights into the potential unification of these fields for developing advanced machine learning algorithms.