In 1986 Geoffrey Hinton, David Rumelhart, and Ronald Williams published in Nature the practical demonstration that the backpropagation algorithm could efficiently train multilayer neural networks. Backpropagation calculates how to adjust a network's weights by propagating the error from the output back to earlier layers via partial derivatives. The article pulled neural networks out of the second AI winter and laid the technical foundation for deep learning. Hinton continued for decades as a marginal figure in a community dominated by other approaches, until AlexNet (2012) proved him right. In 2024 he received the Nobel Prize in Physics together with John Hopfield for their fundamental contributions to machine learning.