Physical Mechanism

Research into the physical mechanisms underlying learning explores how fundamental physical processes give rise to learning-like behaviors in both biological and artificial systems. Current investigations utilize machine learning, particularly neural operators and convolutional neural networks, to analyze data from physical systems and identify underlying invariants and correlations that explain learning dynamics, often drawing parallels between learning and processes like aging in glassy systems. This work aims to bridge the gap between abstract machine learning models and the concrete physical realities they represent, potentially leading to more efficient algorithms and a deeper understanding of intelligence's origins.