 Whereas connectionism’s ambitions seemed to mature and temper towards the end of its Golden Age from 1980–1995, neural network research has recently returned to the spotlight after a combination of technical achievements made it practical to train networks with many layers of nodes between input and output (Krizhevsky, Sutskever, &amp; Hinton 2012; Goodfellow, Bengio, &amp; Courville 2016). Amazon, Facebook, Google, Microsoft, and Uber have all since made substantial investments in these “deep learning” systems. Their many promising applications include recognition of objects and faces in photographs, natural language translation and text generation, prediction of protein folds, medical diagnosis and treatment, and control of autonomous vehicles. The success of the game-playing program AlphaZero (Silver et al. 2018) has brought intense publicity to deep learning in the popular press. What is especially telling about AlphaZero is that essentially the same algorithm was capable of learning to defeat human world champions and other top-performing artificial systems in three different rule-based games (chess, shogi, and Go) “without human knowledge” of strategy, that is, by using only information about the rules of these games and policies it learned from extensive self-play. Its ability to soundly defeat expert-knowledge-based programs at their forte has been touted as the death knell for the traditional symbolic paradigm in artificial intelligence.