If you believe the heady prophecies of the world’s tech companies, artificial-intelligence systems will soon do everything from drive our cars to diagnose our diseases. (Whether they add or take jobs away from humans in the process is another question entirely.) Amidst that evolution, Facebook’s software engineers (nominally tasked with figuring out how to transform machine learning into a digital-concierge service) have developed yet another, more exuberant use for artificial intelligence: Playing Go, the board game, with the skill of a human expert. The team claims that, after months of playing games, its bot is “already on par with the other AI-powered systems that have been published and it’s as good as a very strong human player.” How did the team achieve that noteworthy goal? Combining a pattern-matching system with a “traditional search-based approach” that models possible moves throughout the game. For every mogul such as Elon Musk who fears the rise of an A.I. capable of destroying the world, it seems like there’s another technologist advocating the potential of self-thinking machines. Whatever happens in coming years, though, it’s clear that companies are interested in artificial intelligence as a potential new market, meaning that tech pros with the skill to build machine-learning systems can potentially profit immensely. If you’re interested in exploring machine learning as a career (or even just a hobby), an effective place to start is Stanford University’s online Machine Learning course, which offers a very deep dive into fundamental ideas such as computer vision, smart robots, and database mining. A number of people in the field recommend “The Elements of Statistical Learning” as a good jumping-off point for the conceptual underpinnings of machine learning. Those who want to pursue A.I. as a career will also need to become familiar with concepts such as the difference between practical and theoretical machine learning. With enough knowledge, you too could design the next great game-playing machine.