The 2024 Nobel Prize in Physics was awarded today to John J. Hopfield and Geoffrey E. Hinton, two pioneers in the field of artificial neural networks who made machine learning possible.
But what are neural networks? An artificial neural network is a computational model inspired by the structure of the human brain. While the brain is organized into neurons, an artificial neural network is divided into nodes. Similar to neurons, when an input arrives at a node, it "communicates" with some neighboring nodes by activating the connections.
The theorization of artificial neural networks began in the early 1940s thanks to the work of Pitts and McCulloch, but it was in 1982, thanks to Hopfield, that the first machine capable of simulating memories and using associative memory was developed: in practice, Hopfield's neural network has the ability to memorize patterns and, when queried, return a very similar output, even starting from a partial or noisy input.
In parallel, Hinton's work aimed to understand how to simulate the interpretation of information in machines. Based on the work of Ludwig Boltzmann, who through statistical equations linked the macroscopic behavior of gases to their microscopic components, Hinton developed the "Boltzmann Machine", capable of generating output from specific patterns: it was the first neural network to show generative capabilities.
The awarding of the Nobel Prize in Physics to Hopfield and Hinton is an important recognition for the field of artificial intelligence and highlights the impact this technology is having on our society. But it is also an invitation to continue research and explore new frontiers.
Comments