Everyone knows that the cock’s crowing at dawn does not “cause” the sun to rise. Conversely, we have equal confidence “that flipping a switch will cause a light to turn on or off and that a sultry summer afternoon will cause sales to go up at the local ice cream parlor.” Such intuitions are integral to countless practical and moral judgments that fill our daily lives. And yet, as Prof. Judea Pearl and the science writer Dana Mackenzie note in their illuminating new work, “The Book of Why: The New Science of Cause and Effect,” scientists and statisticians lacked a common language until recently to distinguish between these very different kinds of observation. Indeed, within academia, “causal vocabulary was virtually prohibited for more than half a century.”
The subject of causation has preoccupied philosophers at least since Aristotle. Professor Pearl has deftly used the arc of his own career — first at RCA Laboratories and for the last 50 years at the University of California, Los Angeles (initially in the engineering department and since 1970 in computer science) — to chart the recent history of the subject.
This period broadly coincides with what Professor Pearl terms “the causal revolution.” Three ascending rungs of what he calls the “ladder of causation” serve as the central metaphor driving the narrative of “The Book of Why.” The “revolution” charted in the book, and in which Professor Pearl and his disciples played a crucial role, is what has allowed researchers across a vast range of disciplines to move beyond the first rung of the causal ladder, where they had been perennially stuck.
This lowest rung deal simply with observation — basically looking for regularities in past behavior. Professor Pearl places “present-day learning machines squarely on rung one.” While it is true that the explosion of computing power and accessible deep data sets have yielded many surprising and important results, the mechanics still operate “in much the same way that a statistician tries to fit a line to a collection of points.”
“Deep neural networks have added many layers of complexity of the fitted function, but raw data still drives the fitting process,” according to Professor Pearl. The causal revolution is what has enabled researchers to explore the higher rungs of the ladder.
Despite this well-considered skepticism, Professor Pearl is remarkably optimistic about what artificial intelligence can achieve and even whether we can make machines that are capable of distinguishing good and evil. Regardless of whether one agrees with these provocative conclusions, we can all hope that in any counterfactual world in which that is the case, programmers with the humanity of Professor Pearl will be in charge.
The original article can be found here.
Professor Pearl’s book was highly acclaimed with the praise by Dr. Vint Cerf, Chief Internet Evangelist at Google Inc. and World Leader in AI World Society (AIWS) award, “Pearl’s accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence… and they have redefined the term ‘thinking machine.'” In 2011, Professor Pearl also received the Turing award from Association for Computing Machinery (ACM), which is the highest distinction in computer science, “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”. His work will contribute to AI transparency, which is one of important AIWS topics to identify, publish and promote principles for the virtuous application of AI in different domains including healthcare, education, transportation, national security, and other areas. On March 19, Governor Michael Dukakis, Chairman of Michael Dukakis Institute (MDI), Co-founder of the AIWS Innovation (AIWS.net) announced that MDI honors Professor Judea Pearl with the World Leader in AIWS 2020 Award.
