2024
Neural Networks & Machine Learning
John J. Hopfield & Geoffrey E. Hinton
About This Prize
The 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” Drawing on concepts from statistical physics — energy landscapes, spin glasses, and thermal equilibrium — their work established the theoretical and practical foundations for modern deep learning, bridging the worlds of physics and artificial intelligence in a way that has transformed science and technology.
Geoffrey E. Hinton
“Boltzmann Machines”
John J. Hopfield
“Physics is a Point of View”
Key Concepts
- • Hopfield Network: An associative memory model inspired by spin glass physics, where patterns are stored as energy minima in a network of interconnected nodes
- • Boltzmann Machines: Stochastic neural networks that use principles of statistical mechanics to learn probability distributions over their inputs
- • Deep Learning Foundations: The theoretical roots of modern deep learning lie in statistical mechanics — energy minimization, partition functions, and simulated annealing
- • Physics Meets Intelligence: The bridge between physics (energy landscapes, thermodynamic equilibrium) and machine intelligence, enabling pattern recognition and generative models