Machine learning using artificial neural networks, a technology inspired by the structure of the brain, is central to artificial intelligence. The brain's neurons are represented by nodes, and their interactions are represented by connections that can be strengthened or weakened during training. John Hopfield's research, beginning in the 1980s, was crucial for the development of machine learning. His Hopfield network is an associative memory, analogous to a system of atomic spins in physics, that can store and reconstruct patterns, such as images, by adjusting connection strengths between nodes. Building on Hopfield's work, Geoffrey Hinton developed the Boltzmann machine, which can autonomously discover properties in data, leading to applications like image recognition. Today, neural networks enable computers to perform tasks like making predictions, interpreting images, and engaging in human-like conversations.

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https://doi.org/10.1038/s41746-024-01345-9