"As mathematicians and programmers tended to be particularly good at chess, they viewed it as a good test of whether a machine could show intelligence." "Back in the 1950s, chess was seen as something that smart humans were good at," Haigh said. " is a departure from the classic AI symbolic tradition of trying to replicate the functioning of human intelligence and understanding by having a machine that can do general-purpose reasoning," Haigh said, hence the effort to make a better chess-playing machine.īut that strategy was based more on computer builders' idea of what was smart than on what intelligence actually might be. What is intelligence?ĭeep Blue also demonstrated that a computer's intelligence might not have much to do with human intelligence. Thomas Haigh, an associate professor at the University of Wisconsin-Milwaukee who has written extensively on the history of computing, said Deep Blue's hardware was a showcase for IBM's engineering at the time the machine combined several custom-made chips with others that were higher-end versions of the PowerPC processors used in personal computers of the day. The hardware to make them wasn't practical in the 1990s, when Deep Blue was built, Campbell said. The learning happened via neural networks, or programs that operate much like the neurons in a human brain. AlphaGo played many board games against itself and used those patterns to learn optimal strategies. The artificially intelligent computer program called AlphaGo, for example, which beat the world's champion player of the board game Go, also works differently from Deep Blue. Machine learning systems that have been developed in the past two decades also make use of huge amounts of data that simply didn't exist in 1997, when the internet was still in its infancy. Watson proved that it could understand and respond to humans by defeating longtime "Jeopardy!" champions in 2011. IBM's next intelligent machine, named Watson, for example, works very differently from Deep Blue, operating more like a search engine. Learning machinesĪt the time Deep Blue was built, the field of machine learning hadn't progressed as far as it has now, and much of the computing power wasn't available yet, Campbell said.
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Building a machine that can tackle different tasks, or that can learn how to do new ones, has proved more difficult, he added. IBM scientists spent years constructing Deep Blue, and all it could do was play chess, Campbell said. "You can look at it the human way, using experience and intuition, or in a more computer-like way." Those methods complement each other, he said.Īlthough Deep Blue's win proved that humans could build a machine that's a great chess player, it underscored the complexity and difficulty of building a computer that could handle a board game. "The more interesting thing we showed was that there's more than one way to look at a complex problem," Campbell told Live Science. "Some doubted that a computer would ever play as well as a top human. "Good as they are, are quite poor at other kinds of decision making," said Murray Campbell, a research scientist at IBM Research.
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