Phase Transitions in Physics and Machine Learning

This lecture explores the concept of phase transitions, comparing the smooth transition in physics to the abrupt jumps in computational problems. The instructor discusses how first-order phase transitions occur in spin glass games and computational problems, leading to easy, hard, and impossible phases. The lecture delves into the relationship between phase transitions in physics and the computational hardness conjecture, highlighting the challenges faced by algorithms in the hard phase. Additionally, the lecture touches on compressing, a signal processing technique that aims to reconstruct images efficiently by capturing sparse features. The discussion extends to the application of physics principles in understanding neural networks, emphasizing the need for theoretical insights to bridge the gap between machine learning and scientific understanding.