IBM’s Watson is the powerful computer that defeated its human rivals in a Jeopardy match against Jeopardy’s two grand champions. In case you missed it, check out Watson here.
Watson represents a startling achievement of computer science from many standpoints. The most important thing it demonstrates, however, is the machine’s ability to respond to natural language. Alex Trebek, the host of Jeopardy, asked regular Jeopardy questions in the normal way. OK, Watson would get the questions simultaneously as text, but they contained all the convoluted syntax, puns, double entendres, and tricks that characterize Jeopardy game questions.
To make Watson competitive, it had to understand the question and come up with the right answer with sufficient confidence in less than three seconds. Watson didn’t have time to go out to the Internet searching for answers. IBM loaded it with all the information it might possible need right in memory—15TB worth, which drew on another 20TB of clustered disk storage. Then it had to pack in all the deep analytics and natural language parsing algorithms Watson needed to be competitive.
The resulting system consisted of 90 tightly integrated IBM Power 750 servers running Linux and containing 2880 POWER7 processor cores as well as the 15TB of onboard memory. The POWER7 ran at 3.55 GHz and had 500 GB per sec. of on-chip bandwidth. Although the scale of Watson’s technology is impressive, even more impressive is the fact that Watson is assembled from off-the-shelf commodity components. The same commercially available off-the-shelf components are available to you today should you want to whip together Watson yourself.
The commodity components are key to Watson. IBM didn’t go to all this trouble just to win a one-off trivia contest. It expects to sell configurations of Watson’s technology to solve real problems, including business problems. To do that, IBM needs Watson to be able to work its magic using commodity components.
Already IBM has targeted its first commercial application for Watson—medical diagnosis. In this case Watson replaces Dr. House, the scruffy, gruff curmudgeon genius from the popular TV show. Watson may lack the sex appeal of actor Hugh Laurie, but its boasts impressive technology specs.
The plan is to optimize a version of Watson for various industries. For the medical industry that means loading Watson with vast and comprehensive medical knowledge from books, archives of medical journals, and the very latest research to respond to queries from doctors. This actually is tame compared to Jeopardy, which required IBM to load up Watson with information on a seemingly endless array of subjects. The medical queries, presumably, will consist of lists of complicated symptoms, conditions, test results, and monitor readings that Watson will analyze and correlate to its medical knowledge and return an ordered list of the best diagnoses.
Another slam dunk for Watson should be customer support. Watson could be optimized and configured to respond to questions from a company’s existing and prospective customers or from its support agents, maybe assisted with speech-to-text translation if necessary. Watson certainly couldn’t do worse than some of the offshore support agents today. Of course, Watson would have to be loaded with customer, product, and company data and maybe industry and regulatory data. Compared to Jeopardy that should be trivial.
Other business areas also are promising: Procurement and global supply and logistics come to mind. IBM already has targeted fraud prevention and the parsing vast tracts of legal documents as key opportunities. Litigation can involve massive amounts of documents, often as email; put Watson to work on ediscovery too
IBM has been selling some of the commodity technology that makes up Watson for a few years. For example, the New York State Department of Taxation and Finance is using Watson-like analytics to transform its approach to refunds from pay-and-chase to next-best-case. In its five years of operation, the system has preserved more than $889 million against fraudulent requests through Watson-like analytics.
The version of Watson that won Jeopardy was far bigger and more expensive than any single organization probably needs. Scaled back to the size of your organization, Watson might be a downright bargain.