Final Jeopardy Read Free

in the northern suburbs of New York. He was in an expansive mood, which led him to carry out energetic dialogues with himself, asking questions and answering them emphatically. “You can probably fit all the books that are on sale on about two terabytes that you can buy at OfficeMax for a couple hundred dollars. You get every book. Every. Single. Book. Now what do you do? You can’t read them all! What I want the computer to do,” he went on, “is to read them for me and tell me what they’re about, and answer my questions about them. I want this for
all
information. I want machines to read, understand, summarize, describe the themes, and do the analysis so that I can take advantage of all the knowledge that’s out there. We humans need help. I know I do!”
    Before building a
Jeopardy
machine, Ferrucci and his team had to carry this vision one step further: They had to make a case that a market existed outside the rarefied world of
Jeopardy
for advanced question-answering technology. IBM’s biggest division, after all, was Global Services, which included one of the world’s largest consultancies. It sold technical and strategic advice to corporations all over the world. Could the consultants bundle this technology into their offerings? Would this type of machine soon be popping up in offices and answering customers’ questions on the phone?
    Ferrucci envisioned a
Jeopardy
machine spawning a host of specialized know-it-alls. With the right training, a technology that could understand everyday language and retrieve answers in a matter of seconds could fit just about anywhere. Its first job would likely be in call centers. It could answer tax questions, provide details about bus schedules, ask about the symptoms of a laptop on the fritz and walk a customer through a software update. That stuff was obvious. But there were plenty of other jobs. Consider publicly traded companies, Ferrucci said. They had to comply with a dizzying assortment of rules and regulations, everything from leaks of inside information in e-mails to the timely disclosure of earnings surprises or product failures to regulators and investors. A machine with Watson’s skills could stay on top of these compliance matters, pointing to possible infractions and answering questions posed in ordinary English. A law firm could call on such a machine to track down the legal precedent for every imaginable crime, complaint, or trademark.
    Perhaps the most intriguing opportunity was in medicine. While IBM was creating the
Jeopardy
machine, one of the top medical shows on television featured a nasty genius named Gregory House. In the beginning of most episodes a character would collapse, tumbling to the ground during a dance performance, a lovers’ spat, or a kindergarten class. Each one suffered from a different set of symptoms, many of them gruesome. In the course of the following hour, amid the medical team’s social and sexual dramas, House and his colleagues would review the patient’s worsening condition. There had to be a pattern. Who could find it and match it to a disease, ideally before the patient died? Drawing from their own experience, the doctors each mastered a diverse set of data. The challenge was to correlate that information to the ever-changing list of symptoms on the white board in House’s office. Toward the end of the show, House would often notice some detail—perhaps a lyric in a song or an unlikely bruise. And that would lead his magnificent mind straight to a case he remembered or a research paper he’d read about bee stings or tribal rites in New Guinea. By the end of the show, the patient was headed toward recovery.
    An advanced question-answering machine could serve as a bionic Dr. House. Unlike humans, it could stay on top of the tens of thousands of medical research papers published every year. And, just as in
Jeopardy
, it could come up with lists of potential answers, or