Machine learning wins physics Nobel

If you use AlphaFold, ChatGPT, or other artificial intelligence tools, you have this year’s physics Nobel laureates, John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto, to thank. The two men laid the foundation for artificial neural networks, computer algorithms designed to mimic how a brain processes information. Although artificial neural networks were introduced in the 1940s, by the 1960s, interest in using them had decreased. That changed when Hopfield published a paper in 1982 describing a dynamic binary model capable of mimicking associative memory, the ability to remember how two pieces of information are connected—for example, putting a name to a face ( Proc. Natl. Acad. Sci. U.S.A., DOI: 10.1073/pnas.79.8.2554 ). Soon after, Hinton and his coworkers applied statistical physics to Hopfield’s algorithm, calling their creation a Boltzmann machine. Such programs are capable of modifying themselves based on the statistical probability of one