In his new book, Pearl, now 81, elaborates a vision for how truly intelligent machines would think. The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. Once this kind of causal framework is in place, it becomes possible for machines to ask counterfactual questions—to inquire how the causal relationships would change given some kind of intervention—which Pearl views as the cornerstone of scientific thought. Pearl also proposes a formal language in which to make this kind of thinking possible—a 21st-century version of the Bayesian framework that allowed machines to think probabilistically.

Pearl expects that causal reasoning could provide machines with human-level intelligence. They’d be able to communicate with humans more effectively and even, he explains, achieve status as moral entities with a capacity for free will—and for evil. Quanta Magazine sat down with Pearl at a recent conference in San Diego and later held a follow-up interview with him by phone. An edited and condensed version of those conversations follows.

Kevin Hartnett: Why is your new book called “The Book of Why”?

Judea Pearl: It means to be a summary of the work I’ve been doing the past 25 years about cause and effect, what it means in one’s life, its applications, and how we go about coming up with answers to questions that are inherently causal. Oddly, those questions have been abandoned by science. So I’m here to make up for the neglect of science.

Hartnett: That’s a dramatic thing to say, that science has abandoned cause and effect. Isn’t that exactly what all of science is about?

Pearl: Of course, but you cannot see this noble aspiration in scientific equations. The language of algebra is symmetric: If x tells us about y, then y tells us about x. I’m talking about deterministic relationships. There’s no way to write in mathematics a simple fact—for example, that the upcoming storm causes the barometer to go down, and not the other way around.

Mathematics has not developed the asymmetric language required to capture our understanding that if x causes y that does not mean that y causes x. It sounds like a terrible thing to say against science, I know. If I were to say it to my mother, she’d slap me.

But science is more forgiving: Seeing that we lack a calculus for asymmetrical relations, science encourages us to create one. And this is where mathematics comes in. It turned out to be a great thrill for me to see that a simple calculus of causation solves problems that the greatest statisticians of our time deemed to be ill defined or unsolvable. And all this with the ease and fun of finding a proof in high-school geometry.

Hartnett: You made your name in AI a few decades ago by teaching machines how to reason probabilistically. Explain what was going on in AI at the time.