Final Jeopardy (24 page)

He walked to a shelf by the fireplace and brought back a book,
The Trigger Point Therapy Workbook,
bristling with Post-it notes. On one page was an illustration of the sternocleidomastoid. It's the biggest visible muscle in front of the neck and extends from the sternum to a bony prominence behind the ear. According to the book, trauma within this muscle could cause pain in the head. In a single paragraph on page 53 was a description of Ferrucci's condition. It had references to toothaches in back molars and a “spillover of pain . . . which mimics trigeminal neuralgia.” Ferrucci could have written it himself.

If a computer like Watson, customized for medicine, had access to that trigger point workbook along with thousands of other books and articles related to pain, it could have saved Ferrucci a molar and months of pain and confusion. Such a machine likely would have been able to suggest, with at least some degree of confidence, the connection between Ferrucci's symptoms and his sternocleidomastoid. This muscle was no more obscure than the Asian ornaments or Scandinavian kings that Watson routinely dug up for
Jeopardy
. Such a machine would not have to understand the connections it found. The strength of the diagnostic engine would not be its depth, but its range. That's where humans were weak. Each expert that Ferrucci visited had mastered a limited domain. The dentist knew teeth, the neurologists nerves. But no one person, no matter how smart or dedicated, could stay on top of discoveries across every medical field. Only a machine could do that.

A few months earlier, on an August morning, about a hundred IBM employees filed into the auditorium at the Yorktown labs. They included researchers, writers, marketers, and consulting executives. Their goal was to brainstorm ideas for putting Watson to work outside the
Jeopardy
studio. The time for games was nearly over. Watson, like thousands of other gifted students around the world, had to start earning its keep. It needed a career.

This was an unusual situation for an IBM product, and it indicated that the company had broken one of the cardinal rules of technology development. Instead of focusing first on a market opportunity and then creating the technology for it, IBM was working backward: It built the machine first and was now wondering what in the world to do with it. Other tech companies were notorious for this type of cart-before-the-horse innovation. Motorola, in the 1990s, led the development of a $5 billion satellite phone system, Iridium, before learning that the market for remote communications was tiny and that most people were satisfied with normal cell phones. Within a year of its launch, Iridium went bankrupt. In 1981, Xerox built a new computer, the 6085 Star, featuring a number of startling innovations—a mouse, an ethernet connection, e-mail, and windows that opened and closed. All of this technology would lay the groundwork for personal computers and the networked world. But it would be other companies, notably Apple and Microsoft, that would take it to market. And in 1981, Xerox couldn't find buyers for its $16,000 machines. Would Watson's industry-savvy offspring lead to similar boondoggles?

In fairness to IBM, Grand Challenges, like Watson and the Deep Blue chess machine, boosted the company's brand, even if it came up short in the marketplace. What's more, the technology developed in the
Jeopardy
project, from algorithms that calculated confidence in candidate answers to wizardry in the English language, was likely to work its way into other offerings. But the machine's question-answering potential seemed so compelling that IBM was convinced Watson could thrive in a host of new settings. It was just a question of finding them.

Ferrucci started the session by outlining Watson's skills. The machine, he said, understood questions posed in natural language and could read millions of documents and scour databases at lightning speed. Then it could come up with responses. He cautioned his colleagues not to think of these as answers but hypotheses. Why the distinction? In every domain most of Watson's candidate answers would be wrong. Just as in
Jeopardy
, it would come back with a list of possibilities. People looking to the machine for certainty would be disappointed and perhaps even view it as dumb. Hypotheses initiate a lengthier process. They open up paths of inquiry. If Watson came back from a hunt with ten hypotheses and three of them looked promising, it wouldn't matter much if the other seven were idiotic. The person using the system would focus on the value. And this is where the vision of Watson in the workplace diverged from the game-playing model. In the workplace, Watson would not be on its own. Unlike the
Jeopardy
machine, the Watson Ferrucci was describing would be engineered to supplement the human brain, not supplant it.

The time looked ripe for word-savvy information machines like Watson, thanks to the global explosion of a new type of data. If you analyzed the flow of digital data in, say, 1980, only a smidgen of the world's information had found its way into computers. Back then, the big mainframes and the new microcomputers housed business records, tax returns, real estate transactions, and mountains of scientific data. But much of the world's information existed in the form of words—conversations at the coffee shop, phone calls, books, messages scrawled on Post-its, term papers, the play-by-play of the Super Bowl, the seven o'clock news. Far more than numbers, words spelled out what humans were up to, what they were thinking, what they knew, what they wanted, whom they loved. And most of those words, and the data they contained, vanished quickly. They faded in fallible human memories, they piled up in Dumpsters and moldered in damp basements. Most of these words never reached computers, much less networks.

That has all changed. In the last decade, as billions of people have migrated their work, mail, reading, phone calls, and webs of friendships to digital networks, a giant new species of data has arisen: unstructured data. It's the growing heap of sounds and images that we produce, along with trillions of words. Chaotic by nature, it doesn't fit neatly into an Excel spreadsheet. Yet it describes the minute-by-minute goings-on of much of the planet. This gold mine is doubling in size every year. Of all the data stored in the world's computers and coursing through its networks, the vast majority is unstructured. Hewlett Packard, for example, the biggest computer company on earth, gets a hundred fifty million Web visits a month. That's nearly thirty-five hundred customers and prospects per minute. Those visits produce data. So do notes from the company's call centers, online chat rooms, blog entries, warranty claims, and user reviews. “Ninety percent of our data is unstructured,” said Prasanna Dhore, HP's vice president of customer intelligence. “There's always a gap between what you want to know about the customer and what is knowable.” Analysis of the pile of data helps reduce that gap, bringing the customer into sharper focus.

The potential value of this information is immense. It explains why Facebook, a company founded in 2004, could have a market value six years later of $50 billion. The company gathers data, most of it unstructured, from about half a billion people. Beyond social networks and search engines, an entire industry has sprung up to mine this data, to predict people's behavior as shoppers, drivers, workers, voters, patients, even potential terrorists. As machines, including Watson, have begun to chomp on unstructured data, a fundamental shift is occurring. While people used to break down their information into symbols a computer could understand, computers are now doing that work by themselves. The machines are mastering human communication.

This has broad implications. Once computers can handle language, every person who can type or even speak becomes a potential programmer, a data miner, and an analyst. This is the march of technology. We used to have typists, clerks, legions of data entry experts. With the development of new tools, these jobs became obsolete. We typed (and spell-checked), laid out documents, kept digital records, and even developed our own pictures. Now, a new generation of computers can understand ordinary English, hunt down answers in vast archives of documents, analyze them, and come up with hypotheses. This has the potential to turn entire industries on their heads.

In the August meeting, Ferrucci told the audience the story of his recent medical odyssey and how a machine like Watson could have helped. Others suggested that Watson could man call centers, function as a brainy research assistant in pharmaceutical labs, or work as a whip-smart paralegal, with nearly instant recall of the precedents, both state and federal, for every case. They briefly explored the idea of Watson as a super question-answering Google. After all, it could carry out a much more detailed analysis of questions and piece together sophisticated responses. But this idea went nowhere. IBM had no experience in the commercial Web or with advertisers. Perhaps most important, Watson was engineered to handle one
Jeopardy
clue at a time. In those same three seconds, a search engine like Google's or Microsoft's Bing handled millions of queries. To even think about competing, the IBM team would have to build an entirely new and hugely expensive computing architecture. It was out of the question.

No, Watson's future was as an IBM consulting tool and there were plenty of rich markets to explore. But before Watson could make a go of it, Big Blue would have to resolve serious questions. First, how much work and expense would it take to adapt Watson to another profession, to curate a new body of data and to educate the machine in each domain? No one could say until they tried. Second, and just as important, how much resistance would these new knowledge engines encounter? New machines, after all, are in the business of replacing people—not something that often generates a warm welcome. The third issue involved competition. Assuming that natural-language, data-snarfing, hypothesis-spouting machines made it into offices and laboratories, who was to say that they'd be the kin of a
Jeopardy
contraption? Other companies, from Google to Silicon Valley startups, were sure to be competing in the same market. The potential for these digital oracles was nearly limitless. But in each industry they faced obstacles, some of them considerable.

Medicine was one of the most promising areas but also among the toughest to crack. The natural job for Watson would be as a diagnostic aid, taking down the symptoms in cases like Ferrucci's and producing lists of possible conditions, along with recommended treatments. Already, many doctors facing puzzling symptoms were consulting software tools known as medical decision trees, which guided them toward the most likely diagnoses and recommended treatments. Some were available as applications on smart phones. A medical Watson, though, would plunge into a much deeper pool of data, much of it unstructured. Conceivably, it would come up with hidden linkages. But even that job, according to Robert Wachter, the chief of hospital medicine at the University of California, San Francisco, was bound to raise serious questions. “Doctors like the idea of having information available,” he said. “Where things get more psychologically fraught is when a damned machine tells them what to do.” What's more, once analysis is automated, he said, the recommendation algorithm is likely to include business analysis. In other words, the medical Watsons might come back not with the statistically most effective treatment but the most
cost-effective
one. Even if this didn't happen, many would remain suspicious. And what if Watson had sky-high confidence in a certain diagnosis—say, 97 percent? Would doctors get in trouble if they turned a deaf ear to it? Would they face lawsuits if they ignored the advice and it later turned out the machine was right?

Then, of course, there was the possibility of disastrous mistakes resulting from a computer's suggestions. Even if a bionic assistant scrupulously labeled all of its findings as hypotheses, some of them—just like Watson's answers in
Jeopardy
—were bound to be nutty, generating ridicule and distrust. Others, perhaps more dangerous, would be wrong while appearing plausible. If a treatment recommended by a machine killed a patient, confidence in bionic assistants could plummet.

The other issue, sure to come up in many industries, boils down to a struggle for power, and even survival, in the workplace. “As every profession embraces systems that take humans out of it,” Wachter said, “the profession gets commoditized.” He noted the example of commercial aviation, where pilots who were once considered stars have ended up spending much of the time in flight simply backing up the machines that are actually flying the planes. The result? “Pilots' pensions have been cut and they're paid less, because they're largely interchangeable,” he said. “Doctors don't want to see that happening to them.”

For IBM, this very scenario promises growth. With more than $4 billion in annual revenue, the health care practice within IBM Global Services has the size of a Fortune 500 company. It runs large data centers for hospitals and insurance companies. It also helps them analyze the data, looking for patterns of symptoms, treatments, and diseases—as well as ways to cut costs. This is part of a trend toward statistical analysis in the industry and the rapid growth of so-called evidence-based medicine. But one of the most valuable streams of data—the doctor's notes—rarely makes it into the picture, said Joseph Jasinski, who heads research for IBM's health care division. This is where the doctor writes down what he or she sees and thinks. Sometimes it is stored in a computer, but only, Jasinski said, “as a blob of text.” In other words, it's unstructured data, Watson's forte. “There's a strong belief in the community that if you could study clinical notes, you could analyze patient similarities,” he said. Neurologists' notes—going back to Ferrucci's case—could have pointed to common symptoms between patients with the suicide disease and others with knots in a muscle just below their shoulder blade. This analysis could expand, comparing symptoms and treatments, and later study the outcomes. What works? What falls flat? Which procedures appear to waste money?