In the 1980s, Judea Pearl introduced a new approach to artificial intelligence called

In the 1980s, Judea Pearl introduced a new approach to artificial intelligence called Bayesian networks. This probability-based model of machine reasoning enabled machines to function—in a complex and uncertain world—as “evidence engines,’ continuously revising their beliefs in light of new evidence. Within a few years, Judea’s Bayesian networks had completely overshadowed the previous rule-based approaches to artificial intelligence. The advent of deep learning— in which computers, in effect, teach themselves to be smarter by observing tons of data, has given him pause, because this method lacks transparency. While recognizing the impressive achievements in deep learning by colleagues such as Michael Jordan and Geoffrey Hinton, he feels uncomfortable with this kind of opacity. He set out to understand the theoretical limitations of deep-learning systems and points out that basic barriers exist that will prevent them from achieving a human kind of intelligence, no matter what we do. Leveraging the computational benefits of Bayesian networks, Judea realized that the combination of simple graphical models and data could also be used to represent and infer cause-effect relationships. The significance of this discovery far transcends its roots in artificial intelligence. His latest book explains causal thinking to the general public; you might say it is a primer on how to think even though human. Judea’s principled, mathematical approach to causality is a profound contribution to the realm of ideas. It has already benefited virtually every field of inquiry, especially the data-intensive health and social sciences. 3 24 HOUSE_OVERSIGHT_016244