Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [87]
Williams’s fantasy is to have a new type of computer in his office. Instead of delivering Kraft’s order to his statisticians, he would simply explain the goals, in English, to the machine. It would pile through mountains of data in a matter of seconds and come back with details about potential macaroni buyers. The language-savvy machine wouldn’t limit its search to traditional data, the neatly organized numbers featuring purchases, dates, and product codes. It might read Twitter or scan social networks to see what people are writing about their appetites and dinner plans. After this analysis, Williams’s dream machine could return with a list of ten recent marketing campaigns that have proven the most effective with the target group. “If I don’t have to go to statisticians and wait while they run the data, that would be huge,” Williams said. Instead of eight hundred campaigns, Catalina might be able to handle eighty thousand, or even a million—and offer them at a fraction of today’s cost. “You’re talking about turning marketing on its head.”
His dream machine, of course, sounds like a version of Watson. Its great potential, in marketing and elsewhere, comes from its ability to automate analysis—to take people, with their time-consuming lunch breaks and vacations, their disagreements and discussions, and drive them right out of the business. The crucial advantage is that Watson—and machines like it—eliminate the detour into the world of numbers. They understand and analyze words. Machines like this—speedy language whizzes—will open many doors for business. The question for IBM is what Watson’s place will be in this drama, assuming it has one.
In the near term, Watson’s job prospects are likely to be in call centers. Enhanced with voice recognition software and trained in specific products and services, the computer could respond to phone calls and answer questions. But more challenging jobs, such as bionic marketing consulting, are further off. For each industry, researchers working with consulting teams will have to outfit Watson with an entirely new set of data and run through batteries of tests and training sets. They’ll have to fine-tune the machine’s judgment—the degree of confidence it generates for each response—and adapt hardware to the job. Will customers want access to mini-Watsons on site or perhaps gain access to a bigger one through a distant data center, a so-called cloud-based service? At this point, no one can say. Jurij Paraszczak, director of Industry Solutions and Emerging Geographies at IBM Research, sees versions of Watson eventually fitting into a number of industries. But such work is hardly around the corner. “Watson’s such a baby,” he said.
The history of innovation is littered with technologies that failed because of bad timing or rotten luck. If that $16,000 Xerox computer, with the e-mail and the mouse, had hit the market a decade later, in 1991, cheaper components would have lowered the cost by a factor of five or ten and a more informed public might have appreciated its features.
Technology breakthroughs can also consign even the most brilliant and ambitious projects to museum pieces or junk. In 1825, the first load of cargo floated from Buffalo to Albany on the new Erie Canal. This major engineering work, the most ambitious to date in the Americas, connected the farms and nascent industries of the Great Lakes to the Hudson River, and on to the Atlantic Ocean. It positioned New York State as a vital thoroughfare for commerce, and New York City as the nation’s premier port. The news could hardly have been worse for the business and government leaders in New York’s neighbor to the south, Pennsylvania, and its international port, Philadelphia. They’d been outmaneuvered. The only way to haul cargo across Pennsylvania was by Conestoga wagon, which often took up to three weeks. So that very year, the Pennsylvanians laid plans