Exploring research trends in modern machine learning through a statistical mechanics lens: An Ising-inspired modeling perspective

Artificial intelligence has become ubiquitous in our society, profoundly influencing various aspects of our daily lives. However, despite its widespread adoption, artificial intelligence remains largely a “black box” with internal mechanisms that are not fully understood. Statistical mechanics offers a potential avenue to unlock this black box, treating neural networks as a complex system composed of many individual components that are too intricate to be directly and explicitly solved at a microscopic level at all times. The Ising spin model can conceptualize how neurons interact and influence each other within the nervous system. We will explore how Ising’s model is the fundamental starting point for investigating neural network models as they have been developed, starting with the pioneering work of McCulloch and Pitts and moving through the Hopfield model to the most recent deep learning architectures.