In computer terms, I started out with a “generative model” that includes abstract concepts like greed and deception and describes the process that produces email scams. That lets me recognize the classic Nigerian email spam, but it also lets me imagine many different kinds of possible spam. When I get the journal email, I can work backward: “This seems like just the kind of mail that would come out of a spam-generating process.” The new excitement about AI comes because AI researchers have recently produced powerful and effective versions of both these learning methods. But there is nothing profoundly new about the methods themselves. Bottom-up Deep Learning In the 1980s, computer scientists devised an ingenious way to get computers to detect patterns in data: connectionist, or neural-network, architecture (the “neural” part was, and still is, metaphorical). The approach fell into the doldrums in the ’90s but has recently been revived with powerful “deep-learning” methods like Google’s DeepMind. For example, you can give a deep-learning program a bunch of Internet images labeled “cat,” others labeled “house,” and so on. The program can detect the patterns differentiating the two sets of images and use that information to label new images correctly. Some kinds of machine learning, called unsupervised learning, can detect patterns in data with no labels at all; they simply look for clusters of features—what scientists call a factor analysis. In the deep-learning machines, these processes are repeated at different levels. Some programs can even discover relevant features from the raw data of pixels or sounds; the computer might begin by detecting the patterns in the raw image that correspond to edges and lines and then find the patterns in those patterns that correspond to faces, and so on. Another bottom-up technique with a long history is reinforcement learning. In the 1950s, B. F. Skinner, building on the work of John Watson, famously programmed pigeons to perf