AlphaGo follows in the footsteps of the chess-playing Deep Blue computer that beat Garry Kasporov in 1997. Another IBM computer, Watson, won in 2011 in the Jeopardy quiz show.
The DeepMind program is very different from Deep Blue as the IBM program relied mostly on searching through a very large space of positions, but otherwise contained heuristics derived from human experts, Precup said. AlphaGo also has a powerful search component, but it learns on its own how to play the game, rather than being "told" what people do, she added.
Despite all its engineering ingenuity, Deep Blue was designed to achieve a single purpose: winning a chess game, Yu of IMD said. “All of the time and energy that goes into the program wasn’t useful for solving any other problems,” he added.
Google is planning to test its AI technology in newer applications beyond gaming, such as healthcare and scientific research. “The core deep learning technology is quite good for any time series pattern classification problem,” Hodjat said. His company has used similar technology for its Sentient Aware e-commerce visual intelligence product.
The algorithms in AlphaGo are general purpose and have been deployed in many situations, Precup said. The program relies on two kinds of learning, reinforcement learning, and deep networks, both of which have been used in many applications such as human prosthesis and automated speech recognition. “One may need to tune the algorithms a bit but they are not dependent on the problem domain,” she said.
A general purpose algorithm, capable of self-learning and mimicking reinforcement learning in humans, opens “a future with new possibilities beyond the realm of a human mind,” Yu said.
AlphaGo, however, falls short in the ability to understand human natural languages, an area where IBM scores, according to Yu. “By digesting millions of pages of medical journals and patient data, Watson provides recommendations—from additional blood tests to the latest clinical trails available—to doctors and physicians,” he said.
“If one day, the self-learning property of AlphaGo can be combined with Watson’s understanding of human language and turn them into a general purpose algorithm, the human advantage will sure reach its final limit,” he added.
Concern about the loss of the human advantage has figured in the background during the contest between Lee and AlphaGo with many commenting online that the South Korean was fighting on behalf of mankind in an epic battle with a computer.
But experts think that a victory in Go, a deterministic, perfect information game with set rules, doesn’t mean the time has reached yet when machines will overtake humans. “AI is quite good now in many cognitive applications that used to be the exclusive domain of humans,” Hodjat said. But it is still years away from achieving the broad and general abstracting power of human intelligence, he added.
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