weight them to reach a certain goal. This method in some sense mimics how we learn as children. The results from these new approaches are amazing. Such a deep-learning program was used to teach a computer to play Go, a game that only a few years ago was thought to be beyond the reach of AI because it was so hard to calculate how well you were doing. It seemed that top Go players relied a great deal on intuition and a feel for position, so proficiency was thought to require a particularly human kind of intelligence. But the AlphaGo program produced by DeepMind, after being trained on thousands of high-level Go games played by humans and then millions of games with itself, was able to beat the top human players in short order. Even more amazingly, the related AlphaGo Zero program, which learned from scratch by playing itself, was stronger than the version trained initially on human games! It was as though the humans had been preventing the computer from reaching its true potential. The same method has recently been generalized: Starting from scratch, within just twenty-four hours, an equivalent AlphaZero chess program was able to beat today’s top “conventional” chess programs, which in turn have beaten the best humans. Progress has not been restricted to games. Computers are significantly better at image and voice recognition and speech synthesis than they used to be. They can detect tumors in radiographs earlier than most humans. Medical diagnostics and personalized medicine will improve substantially. Transportation by self-driving cars will keep us all safer, on average. My grandson may never have to acquire a driver’s license, because driving a car will be like riding a horse today—a hobby for the few. Dangerous activities, such as mining, and tedious repetitive work will be done by computers. Governments will offer better targeted, more personalized and efficient public services. AI could revolutionize education by analyzing an individual pupil’s needs and enab