What makes Watson work? Is it thinking? And what does that mean? Author Stephen Baker provides the answers
Earlier this month, the nation watched as Watson, a computer system designed by IBM, drubbed the two all time champions of Jeopardy. It was a much more difficult challenge than, say, beating a grandmaster at chess. To win, Watson had to navigate the vagaries of human speech, the idioms, the puns, the cultural references — all the things, in short, that make language delightful and deeply machine unfriendly. Journalist Stephen Baker spent a year behind the scenes, as the team of IBM engineers struggled to design and build Watson in time for the show. He tells the story of project Watson, and what it means for the future, in his new book, ” Final Jeopardy: Man vs. Machine and the Quest to Know Everything.” He and Gareth Cook, the editor of Mind Matters, discussed Watson and artificial intelligence.
COOK: For a long time, artificial intelligence was considered a failure. Does Watson represent a new way of thinking about AI?
BAKER: It’s true, the early visions of AI never delivered. It turned out to be a lot harder than many imagined to build systems to handle the complexity and nuance of human communication and thought. You could argue that Watson does not come particularly close, even as it defeats humans in Jeopardy. As long as AI continues to fall short in that area–and it will be a long while–many will view AI as an unfulfilled promise.
However, in the last 15 years or so, there has been tremendous progress in functional aspects of AI. They use statistical approaches to simulate certain aspects of human analysis. This would include everything from Deep Blue, IBM’s chess computer, to the computers at Netflix, Amazon and Google, which study people’s behavioral patterns and automatically calibrate their offerings to them.
What’s new about Watson is the extreme pragmatism of the approach. It combines dozens of different approaches to question answering, from statistical to rules-based, and unleashes them on hunts to solve Jeopardy clues. There is no right or wrong approach. The machine grades them by their results, and in the process “learns” which algorithms to trust, and when. Amid the quasi-theological battles that rage in AI, Watson is a product of agnostics. That’s one new aspect. The other is its comprehension of tricky English. But that, I would say, is the result of steady progress that comes from training machines on massive data sets. The improvement, while impressive, is incremental, not a breakthrough.
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