In his new book, The Book of Why: The New Science of Cause and Effect, computer scientist and statistician Judea Pearl argues that AI will need to understand the how and why of relationships to reach human-like intelligence. Pearl, winner of the 2011 Turing Award, the highest honor in computer science, proposes that a reliance on association rule learning is hampering the development of AI. Pearl asserts that truly intelligent machines could handle situations for which they no have data and that machines equipped with causal reasoning tools, such as the algorithmization of counterfactuals, will experience accelerated learning speeds.
The Book of Why: The New Science of Cause and Effect
Michael McLaughlin is a research assistant at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
View all posts by Michael McLaughlin