Many recent advances in artificial intelligence have focused on outmatching human abilities in specific use cases, for example: image recognition, driving, or language generation. Although such capabilities may increase economic growth, these technologies may also undermine the labor market prospects of humans.
On November 2, Anton Korinek, a David M. Rubenstein fellow at the Center on Regulation and Markets in the Brookings Economic Studies program, held a fireside chat with Erik Brynjolfsson, the Jerry Yang and Akiko Yamazaki Professor and senior fellow at the Stanford Institute for Human Centered AI and director of the Stanford Digital Economy Lab. They discussed the perils of focusing AI development on systems that outmatch human capabilities as opposed to systems that complement humans—a phenomenon that Brynjolfsson calls the “Turing Trap.” The two also discussed how to measure welfare effects of progress in AI more comprehensively than traditional GDP statistics, and the implications of foundation models—the latest class of AI systems—for our economy and society.
This event is part of the Brookings Center on Regulations and Markets’ series, “The economics and regulation of artificial intelligence and emerging technologies,” which focuses on analyzing how AI and other emerging technologies impact the economy, markets, and society, and how they can be regulated most effectively.
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