Rebooting AI
by Gary Marcus
Gary Marcus and Ernest Davis expose the gap between AI hype and reality, advocating for common sense over statistical learning.
"Since its earliest days, artificial intelligence has been long on promise, short on delivery".
Editorial Summary
Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. Gary Marcus is a scientist, best-selling author, and entrepreneur who is the founder and CEO of Robust.AI and was founder and CEO of Geometric Intelligence, a machine-learning company acquired by Uber in 2016. Ernest Davis is a professor of computer science at the Courant Institute of Mathematical Science, New York University. The book argues that the achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices.
Perspective
"Reading this book feels like getting a briefing from seasoned AI researchers who've watched decades of promises evaporate, leaving you with a sobering understanding of how far current systems are from genuine intelligence. The book's distinctive contribution is its systematic debunking of AI hype through concrete examples while proposing that future progress requires endowing machines with common sense and deep understanding rather than relying solely on statistical pattern matching. AI researchers and technologists who need to separate signal from noise in today's AI discourse will find a clear-eyed assessment that cuts through marketing claims to reveal what current systems can and cannot actually do."
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