Communications of the ACM
Don’t Trust AI Until We Build Systems That Earn Trust
Gary Marcus says it would be foolish to put too much stock in todays artificial intelligence techniques, since they are so prone to failures and lack the transparency needed to understand how algorithms reach their conclusions.
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To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs—save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus.
From an academic background in psychology and neuroscience—rather than computer science—Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.
Marcus argues that it would be foolish of society to put too much stock in today's AI techniques since they are so prone to failures and lack the transparency that researchers need to understand how algorithms reached their conclusions. In classic statistics, the parameters used are determined by people, yet with AI, the system itself decides. Though the techniques work—say, identifying that a cell biopsy is cancerous—it's unclear why it works. This makes it tricky to deploy AI in areas like medicine, aviation, civil-engineering, the judiciary and so on.
This is a point Marcus makes with verve and pith in his latest book, "Rebooting AI" (Pantheon, 2019), which he co-wrote with Ernest Davis. As part of The Economist's Open Future initiative, we asked Marcus about why AI can't do more, how to regulate it and what teenagers should study to remain relevant in the workplace of the future. The brief interview appears after an excerpt from the book on the need to build trustworthiness into artificial intelligence.
From The Economist
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