Perhaps like many of you, I watched with great interest the recent grand showdown between IBM’s Watson computer and two of the most successful Jeopardy champions, which turned out to be a romp for the aptly-named collection of servers that had been programmed with all kinds of factual information from almanacs, encyclopedias, and a plethora of other human sources.

While that result undoubtedly shocked few, IBM was quick to stress that the purpose of this event was to demonstrate the strides we have made in helping computers to understand natural human speech. If you think of the mythical computer that inhabits the Starship Enterprise, a machine that can answer all questions put to it in colloquial English, you’ll get an idea of the level of communication accuracy that is, or should be, the goal of such research.

Did Watson achieve that level of excellence? Alas, it did not. The television event was made more interesting by the answers (or, since this is Jeopardy, the questions) Watson got wrong, rather than those it gave correctly. The computer had no trouble with straight factual responses. Watson instantly knew the second largest city in New Zealand, while it seemed his human rivals could only ruminate and surmise. When it came to shades and subtlety of meaning, however, Watson was much less sure. The human contestants easily knew that “what is a crook?” was the correct response to “a thief or the bent part of an arm.” Watson’s top two guesses, however, were “knee” and “waist” (“crook was third”)—clearly wrong responses, since neither is related to “thief.”

Still, imperfect as they are at understanding our language, expert knowledge systems like Watson may indeed be useful tools for insurance enterprises in the near future, according to Jamie Bisker, global insurance industry leader, Institute for Business Value, IBM Business Consulting Services. Noting that AI (artificial intelligence) covers a number of disciplines from natural language processing to robotics, Bisker points out that “60 to 80% of underwriting” involves some form of AI.

He adds that expert systems—knowledge-based systems or rule-based systems—are already in use in insurance enterprises. In the future, says Bisker, question answering systems like Watson could contain information on actuarial science, state and federal regulations, insurance processes, and other critical facts in the insurance domain. If, for example, an insurer wanted to determine legality of its products across state lines, a dialog with the computer system would help the actuary to correct or create such a product, or understand why they are getting fined by another state.

“It’s like saying [to a knowledgeable colleague], ‘What do you think?’ about this problem? It can’t answer everything, but it would empower the people who use it to be more efficient,” says Bisker.

Sounds good as far as that goes, but just how far are we willing to go with expert systems, and what effect will that have on humans—and their jobs? I’ll delve into the theoretical side of this a bit more next time.

 

 

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