When an IBM computer program called Deep Blue defeated Garry Kasparov at chess in 1997, wise folk opined that since chess was just a game of logic, this was neither significant nor surprising.
Mastering the subtleties of human language, including similes, puns and humor, would remain far beyond the reach of a computer.
Last year another IBM program, Watson, triumphed at just these challenges by winning “Jeopardy!” (Sample achievement: Watson worked out that a long, tiresome speech delivered by a frothy pie topping was a “meringue harangue.”) So is it time to take seriously the prospect of artificial intelligence emulating human abilities?
Yes, argues the inventor and futurist Ray Kurzweil in his new book “How to Create a Mind.” Mr. Kurzweil reckons that a full understanding and simulation of the human brain is a lot closer than most people think. Since he has a more impressive track record of predicting technological progress than most, he deserves to be heard.
It’s become fashionable to think of the brain as so intricate as to be almost beyond even theoretical comprehension. For example, Paul Allen, the Microsoft co-founder, criticized both IBM’s Watson and Mr. Kurzweil in a recent article, claiming that the former’s knowledge was brittle and domain-specific, while the latter failed to understand that every structure in the brain “has been precisely shaped by…evolution to do a particular thing.” Mr. Allen posits a “complexity brake” that would necessarily limit the understanding and replication of the brain.
Mr. Kurzweil’s reply in his book is persuasive. For a start, the brain is built from a relatively small and simple body of information—the 25 million bytes of the genome. The complexity comes from ordered growth and elaboration. Second, the brain contains massive redundancy, with certain kinds of basic pattern-recognizing circuits repeated maybe 300 million times in different brain regions. Third, as Van Wedeen of Harvard Medical School and colleagues found in a recent study, much of the brain has a horizontal grid of fibers running at right angles, connecting vertically: a bit like the streets and elevators of Manhattan.
Moreover, the design of artificial intelligence systems has been converging with the way brains developed. Using evolutionary algorithms (a fancy form of trial and error), Mr. Kurzweil himself developed some of the successful speech-recognition software that we all take for granted.
Mr. Kurzweil agrees with another innovator turned neuroscientist, Jeff Hawkins (the PalmPilot’s inventor), in believing that the human brain is basically a set of prediction machines that work by forecasting how a pattern of perceptions will develop. As we put together the pieces of, say, a visual image, information is flowing up (by the neural grid’s elevators) from basic pattern recognizers to higher and more abstract integrations, but also back down from the higher levels predicting what patterns will be found in missing parts of the image or as an image changes. Failed predictions—”surprises”—may be passed (via the neural grid’s streets) to higher levels in the neural hierarchy for conscious resolution.
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