Deep learning

technology

The family of methods in which many-layered artificial neural networks learn their own representations from data instead of being given hand-designed features. None of the core ideas are recent — backpropagation, convolution and gradient descent were all in the literature by the 1980s — and what changed in the 2010s was not the mathematics but three supplies arriving together: labelled data at a scale nobody had previously assembled, general-purpose graphics processors that made the arithmetic affordable, and training practices that let very deep networks converge at all. The 2012 ImageNet result is the conventional marker for that convergence, and the decade since has been an unusually clean demonstration that a single method family transfers: the same optimisation machinery that classified photographs now predicts protein structure, generates text and forecasts weather. What deep learning does not supply is an account of why any particular trained network behaves as it does, which is why almost every claim about a deep-learning system is empirical rather than derived.

A technology is not a place. Drawing it on a map would assert something about the world that no stored fact supports.