Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [82]
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