The History of Deep Learning: Top Moments That Shaped the Technology

The origins of deep learning and neural networks date back to the 1950s, when British mathematician and computer scientist Alan Turing predicted the future existence of a supercomputer with human-like intelligence and scientists began trying to rudimentarily simulate the human brain. Here's an excellent summary of how that process worked, courtesy of the very smart MIT Technology Review: A program maps out a set of virtual neurons and then assigns random numerical values, or "weights," to connections between them. These weights determine how each simulated neuron responds--with a mathematical output between 0 and 1--to a digitized feature such as an edge or a shade of blue in an image, or a particular energy level at one frequency in a phoneme, the individual unit of sound in spoken syllables. Programmers would train a neural network to detect an object or phoneme by blitzing the network with digitized versions of images containing those objects or sound waves containing those phonemes. If the network didn't accurately recognize a particular pattern, an algorithm would adjust the weights.