From Intuition to Algorithm: Leveraging Machine Intelligence
IN FEBRUARY 2011, IBM made a deep impression on the American public when its super-computer Watson beat human contestants in the popular game show Jeopardy! About 15 million viewers watched live as Watson triumphed over former champions Ken Jennings and Brad Rutter. It was an episode that made clear in the public mind that machine learning could go beyond the single-minded focus of number crunching.
At the end of the two-day Jeopardy! tournament, Watson had amassed $77,147 in prize money — more than three times the amount its human opponents had accumulated. Jennings, who had won more than 50 straight matches previously, came in second, just ahead of Rutter. “Just as factory jobs were eliminated in the 20th century by new assembly-line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking machines’,” said Jennings at the time.
Watson represented a machine that no longer blindly followed instructions. The machine could digest unstructured data in the form of human language and then make judgments on its own, which in turn has profoundly changed the way businesses value managerial expertise. One financial service executive put it succinctly:
“Consider a human who can read essentially an unlimited number of [financial] documents and understand those documents and completely retain all the information. Now imagine you can ask that person a question: ‘Which company is most likely to get acquired in the next three months?’ That’s essentially what [Watson] gives you.”
Wise Counsel in the Making
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