A.I. Isn’t as Advanced as You Think
A computer scientist argues that artificial intelligence is progressing more slowly than the hype suggests
Melanie Mitchell wrote her new book, Artificial Intelligence: A Guide for Thinking Humans, because she was confused about how much progress really is being made in A.I. She wanted “to understand the true state of affairs,” she writes.
It’s a relief to learn of her ambivalence because she is an artificial intelligence researcher herself. She’s a professor of computer science at Portland State University and co-chair of the science board at the Santa Fe Institute, a renowned multidisciplinary research center. If Mitchell could be perplexed about where A.I. stands, forgive the rest of us for being mystified or just plain wrong.
As Mitchell notes, a lot of triumphal A.I. narratives are floating around. In these accounts, recent breakthroughs in computer vision, speech recognition, game playing, and other aspects of machine learning are indications that artificial intelligence might surpass human competence in a wide range of tasks in the coming decades. Some people find that prospect marvelous; others worry that “superhuman” computers might decide they don’t need us around and have the power to do something about it.
“Either we are within spitting distance of ‘true’ A.I., or it is centuries away.”
But as Mitchell also demonstrates, even today’s most capable A.I. systems have crucial limitations. They are good only at narrowly defined tasks and utterly clueless about the world beyond. They find correlations in data without regard for what it means, so their predictions can be dangerously unreliable. They have no common sense.
“Either a huge amount of progress has been made, or almost none at all,” Mitchell writes. “Either we are within spitting distance of ‘true’ A.I., or it is centuries away.”
What makes this book memorable and instructive is how Mitchell resolves the question. Her patient explanations of today’s A.I. techniques give the impression that real machine intelligence remains very far off. Not only do computers need better brains, she suggests, but they probably also need…