Skip to main content
Icons of Progress

A Computer Called Watson


In an historic event, in February 2011 IBM’s Watson computer competed on Jeopardy! against the TV quiz show’s two biggest all-time champions. Watson is a computer running software called Deep QA, developed by IBM Research. While the grand challenge driving the project was to win on Jeopardy!, the broader goal of Watson was to create a new generation of technology that can find answers in unstructured data more effectively than standard search technology.

Watson does a remarkable job of understanding a tricky question and finding the best answer. IBM’s scientists have been quick to say that Watson does not actually think. “The goal is not to model the human brain,” said David Ferrucci, who spent 15 years working at IBM Research on natural language problems and finding answers amid unstructured information. “The goal is to build a computer that can be more effective in understanding and interacting in natural language, but not necessarily the same way humans do it.”

Computers have never been good at finding answers. Search engines don’t answer a question–they deliver thousands of search results that match keywords. University researchers and company engineers have long worked on question answering software, but the very best could only comprehend and answer simple, straightforward questions (How many Oscars did Elizabeth Taylor win?) and would typically still get them wrong nearly one third of the time. That wasn’t good enough to be useful, much less beat Jeopardy! champions.

The questions on this show are full of subtlety, puns and wordplay—the sorts of things that delight humans but choke computers. “What is The Black Death of a Salesman?” is the correct response to the Jeopardy! clue, “Colorful fourteenth century plague that became a hit play by Arthur Miller.” The only way to get to that answer is to put together pieces of information from various sources, because the exact answer is not likely to be written anywhere.

Watson runs on a cluster of Power 750™ computers—ten racks holding 90 servers, for a total of 2880 processor cores running DeepQA software and storage. It can hold the equivalent of about one million books worth of information. Over a period of years, Watson was fed mountains of information, including text from commercial sources, such as the World Book Encyclopedia, and sources that allow open copying of their content, such as Wikipedia and books from Project Gutenberg.

When a question is put to Watson, more than 100 algorithms analyze the question in different ways, and find many different plausible answers–all at the same time. Yet another set of algorithms ranks the answers and gives them a score. For each possible answer, Watson finds evidence that may support or refute that answer. So for each of hundreds of possible answers it finds hundreds of bits of evidence and then with hundreds of algorithms scores the degree to which the evidence supports the answer. The answer with the best evidence assessment will earn the most confidence. The highest-ranking answer becomes the answer. However, during a Jeopardy! game, if the highest-ranking possible answer wasn’t rated high enough to give Watson enough confidence, Watson decided not to buzz in and risk losing money if it was wrong. The Watson computer does all of this in about three seconds.

By late 2010, in practice games at IBM Research in Yorktown Heights, NY, Watson was good enough at finding the correct answers to win about 70 percent of games against former Jeopardy! champions. Then in February 2011, Watson went up against Jeopardy! superstars Ken Jennings and Brad Rutter and won.

Watson’s question-answering technology is expected to evolve into a commercial product. “I want to create something that I can take into every other retail industry, in the transportation industry, you name it,” John Kelly, who runs IBM Research, told The New York Times. “Any place where time is critical and you need to get advanced state-of-the-art information to the front decision-makers. Computers need to go from just being back-office calculating machines to improving the intelligence of people making decisions.”