Explore the intricacies of representation learning in artificial intelligence through this 55-minute seminar by Stefano Soatto at New York University. Delve into the Information Knot Tying Sensing & Action and Emergence Theory of Representation Learning. Examine key concepts such as representation sufficiency, information bottleneck, mutual information, and cost functionals. Investigate the representation of past data, disentangling, bias-variance tradeoff, and local entropy solutions. Gain insights into flat minima, limit cycles, eigen values, and the Pocket Planck Equation. Analyze the implications of standard Gaussian relaxation and consider future directions in this field of study.