The AlphaZero Paradox

Last December, the developers of AlphaZero published their explanation of the process by which the program mastered chess—a process, it turns out, that ignored human chess strategies developed over centuries and classic games from the past. Having been taught the rules of the game, AlphaZero trained itself entirely by self-play and, in less than 24 hours, became the best chess player in the world—better than grand masters and, until then, the most sophisticated chess-playing computer program in the world. It did so by playing like neither a grand master nor a preexisting program. It conceived and executed moves that both humans and human-trained machines found counterintuitive, if not simply wrong. The founder of the company that created AlphaZero called its performance “chess from another dimension” and proof that sophisticated AI “is no longer constrained by the limits of human knowledge.”

Now established chess experts are studying AlphaZero’s moves, hoping to incorporate its knowledge into their own play. These studies are practical, but larger philosophical questions also emerge. Among those that are currently unanswerable: How can we explain AlphaZero’s capacity to invent a new approach to chess on the basis of a very brief learning period? What was the reality it explored? Will AI lead to an as-yet-unimaginable expansion of familiar reality?

We can expect comparable discoveries by AI in other fields. Some will upend conventional wisdom and standard practices; others will merely tweak them. Nearly all will leave us struggling to understand. Consider the conduct of driverless cars stopped at a traffic light. When cars driven by people inch forward to try to beat the traffic, some driverless cars occasionally join them, though nothing in the rules of driving given to them suggests that they should do so. If this inching-forward has been learned, how and for what purpose? How is it different from what people are taught and learn about waiting at traffic lights? What else might AI learn that it is not “telling” us (because AI does not or cannot explain)? By enabling a process of self-learning for inanimate objects, we do not yet know what we are starting, but we need to find out.

The Nature of the Revolution

Heretofore, digital evolution has relied on human beings to create the software and analyze the data that are so profoundly affecting our lives. Recent advances have recast this process. AI has made it possible to automate an extraordinary range of tasks, and has done so by enabling machines to play a role—an increasingly decisive role—in drawing conclusions from data and then taking action. AI draws lessons from its own experience, unlike traditional software, which can only support human reasoning. The growing transfer of judgment from human beings to machines denotes the revolutionary aspect of AI, as described last year in these pages (“How the Enlightenment Ends,” June 2018).