Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [84]
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