A specific software architecture, neural networks, not only takes advantage of the virtually perfect recollection and much faster processing speeds of any software, but also teaches itself and attains skills no human could directly program. We rely on these neural networks for medical diagnoses, financial decisions, weather forecasting, and many other crucial real-world tasks. In 2016, a program named AlphaGo beat the top-rated human player of the game of Go.3 Only a few years ago, this had been considered impossible.4 High-level Go requires remarkable skills, not just of calculation, at which computers obviously excel, but, more critically, of judgment, intuition, pattern recognition, and the weighing of ineffable considerations such as positional balance.5 These skills cannot be directly programmed. Instead, AlphaGo’s neural network6 trained itself with many thousands and, later, millions of games – far more than any individual human could ever play7 – and now routinely beats all human challengers.8 Because it learns and concomitantly modifies itself in response to experience, such a network is termed adaptive.9