The scale

In 2012 a network called AlexNet, trained on two consumer graphics cards for about a week, cut the error rate in the ImageNet competition by a margin nobody had seen. Nothing in it was new on its own. Convolutional networks were twenty years old, the data was three years old, and the graphics cards had been built for games. What changed was that the ingredients met at the right sizes. Within four years the same recipe read speech, translated text and, in AlphaGo (2016), beat one of the strongest Go players in the world. Each result rested on things that are easy to leave out of the story: the people who labelled the images, the electricity and cooling for the machines, the benchmarks that decided what counted as progress, and the companies that could afford to run the experiment again. Results emerged from several ingredients interacting, and the ingredients had owners.