(p. B5) Everything we’re injecting artificial intelligence into—self-driving vehicles, robot doctors, the social-credit scores of more than a billion Chinese citizens and more—hinges on a debate about how to make AI do things it can’t, at present.
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On one side of this debate are the proponents of “deep learning”—an approach that, since a landmark paper in 2012 by a trio of researchers at the University of Toronto, has exploded in popularity.
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On the other side of this debate are researchers such as Gary Marcus, former head of Uber Technologies Inc.’s AI division and currently a New York University professor, who argues that deep learning is woefully insufficient for accomplishing the sorts of things we’ve been promised. It could never, for instance, be able to usurp all white collar jobs and lead us to a glorious future of fully automated luxury communism.
Dr. Marcus says that to get to “general intelligence”—which requires the ability to reason, learn on one’s own and build mental models of the world—will take more than what today’s AI can achieve.
“That they get a lot of mileage out of [deep learning] doesn’t mean that it’s the right tool for theory of mind or abstract reasoning,” says Dr. Marcus.
To go further with AI, “we need to take inspiration from nature,” say Dr. Marcus. That means coming up with other kinds of artificial neural networks, and in some cases giving them innate, pre-programmed knowledge—like the instincts that all living things are born with.
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Until we figure out how to make our AIs more intelligent and robust, we’re going to have to hand-code into them a great deal of existing human knowledge, says Dr. Marcus. That is, a lot of the “intelligence” in artificial intelligence systems like self-driving software isn’t artificial at all. As much as companies need to train their vehicles on as many miles of real roads as possible, for now, making these systems truly capable will still require inputting a great deal of logic that reflects the decisions made by the engineers who build and test them.
For the full commentary, see:
Christopher Mims. “KEYWORDS; Should Artificial Intelligence Copy the Brain?” The Wall Street Journal (Saturday, October 26, 2017): B5.
(Note: ellipses added.)
(Note: the online version of the commentary has the same date as the print version, and has the title “KEYWORDS; Should Artificial Intelligence Copy the Human Brain?”)