In May 11, 1997, Garry Kasparov fidgeted in his plush leather chair in the Equitable Center in Manhattan, anxiously running his hands through his hair. It was the final game of his match against IBM’s Deep Blue supercomputer—a crucial tiebreaker in the showdown between human and silicon—and things were not going well. Aquiver with self-recrimination after making a deep blunder early in the game, Kasparov was boxed into a corner.
A high-level chess game usually takes at least four hours, but Kasparov realized he was doomed before an hour was up. He announced he was resigning—and leaned over the chessboard to stiffly shake the hand of Joseph Hoane, an IBM engineer who helped develop Deep Blue and had been moving the computer’s pieces around the board.
Then Kasparov lurched out of his chair to walk toward the audience. He shrugged haplessly. At its finest moment, he later said, the machine “played like a god.”
For anyone interested in artificial intelligence, the grand master’s defeat rang like a bell. Newsweek called the match “The Brain’s Last Stand”; another headline dubbed Kasparov “the defender of humanity.” If AI could beat the world’s sharpest chess mind, it seemed that computers would soon trounce humans at everything—with IBM leading the way.
That isn’t what happened, of course. Indeed, when we look back now, 25 years later, we can see that Deep Blue’s victory wasn’t so much a triumph of AI but a kind of death knell. It was a high-water mark for old-school computer intelligence, the laborious handcrafting of endless lines of code, which would soon be eclipsed by a rival form of AI: the neural net—in particular, the technique known as “deep learning.” For all the weight it threw around, Deep Blue was the lumbering dinosaur about to be killed by an asteroid; neural nets were the little mammals that would survive and transform the planet. Yet even today, deep into a world chock-full of everyday AI, computer scientists are still arguing whether machines will ever truly “think.” And when it comes to answering that question, Deep Blue may get the last laugh.
When IBM began work to create Deep Blue in 1989, AI was in a funk. The field had been through multiple roller-coaster cycles of giddy hype and humiliating collapse. The pioneers of the ’50s had claimed that AI would soon see huge advances; mathematician Claude Shannon predicted that “within a matter of ten or fifteen years, something will emerge from the laboratories which is not too far from the robot of science fiction.” This didn’t happen. And each time inventors failed to deliver, investors felt burned and stopped funding new projects, creating an “AI winter” in the ’70s and again in the ’80s.
The reason they failed—we now know—is that AI creators were trying to handle the messiness of everyday life using pure logic. That’s how they imagined humans did it. And so engineers would patiently write out a rule for every decision their AI needed to make.
The problem is, the real world is far too fuzzy and nuanced to be managed this way. Engineers carefully crafted their clockwork masterpieces—or “expert systems,” as they were called—and they’d work reasonably well until reality threw them a curveball. A credit card company, say, might make a system to automatically approve credit applications, only to discover they’d issued cards to dogs or 13-year-olds. The programmers never imagined that minors or pets would apply for a card, so they’d never written rules to accommodate those edge cases. Such systems couldn’t learn a new rule on their own.
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AI built via handcrafted rules was “brittle”: when it encountered a weird situation, it broke. By the early ’90s, troubles with expert systems had brought on another AI winter.
“A lot of the conversation around AI was like, ‘Come on. This is just hype,’” says Oren Etzioni, CEO of the Allen Institute for AI in Seattle, who back then was a young professor of computer science beginning a career in AI.
In that landscape of cynicism, Deep Blue arrived like a weirdly ambitious moonshot.
The project grew out of work on Deep Thought, a chess-playing computer built at Carnegie Mellon by Murray Campbell, Feng-hsiung Hsu, and others. Deep Thought was awfully good; in 1988, it became the first chess AI to beat a grand master, Bent Larsen. The Carnegie Mellon team had figured out better algorithms for assessing chess moves, and they’d also created custom hardware that speedily crunched through them. (The name “Deep Thought” came from the laughably delphic AI in The Hitchhiker’s Guide to the Galaxy—which, when asked the meaning of life, arrived at the answer “42.”)
IBM got wind of Deep Thought and decided it would mount a “grand challenge,” building a computer so good it could beat any human. In 1989 it hired Hsu
By: Clive Thompson
Title: What the history of AI tells us about its future
Sourced From: www.technologyreview.com/2022/02/18/1044709/ibm-deep-blue-ai-history/
Published Date: Fri, 18 Feb 2022 10:00:00 +0000
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