Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [78]
Think of a billionaire selecting his outfit for a black-tie event. He can assign some tasks to his minions. One can buy socks while others track down shoes, pants, and a shirt. Those jobs, in computer lingo, run in parallel. But when it comes to getting dressed, the work becomes sequential. The man must place one leg in his pants, then the other. Maybe a few butlers could help with his socks simultaneously and hold out the arms of his shirt for him, but such opportunities are limited. This sequence, to the last snap of the cuff links, takes time.
Inside Watson, some of the sequential algorithms gobbled up a quarter of a second, half a second, even more. And they could not be shared among many machines. Watson, in all likelihood, would need the same two to five seconds by the date of the final match. At this point, the only path to greater speed was to come up with simpler commands—smarter algorithms that led Watson through fewer steps. But Ferrucci didn’t expect advances of more than a few milliseconds in the coming months. Nonetheless, he found it hard to make his case to the Jeopardy team. From their perspective, Watson had risen from a slow-witted assortment of software into a champion-caliber player in two years. Who was to say it wouldn’t keep improving?
In this jittery home stretch, it was becoming clear, the two sides shared parallel fears. While Hollywood worried that the computer would grow too smart, the IBM team focused on its vulnerabilities and fretted that it would fail. Watson’s weekly blunders in the sparring sessions added to the long lists of bugs to eliminate, mauled pronunciations to remedy, potential gaffes to program around. There wasn’t enough time to address them all. In the same pragmatic spirit that had marked the entire enterprise, they carried out time-benefit analyses on their list of items and focused on the ones at the top. “This is triage,” said Jennifer Chu-Carroll.
One small but vital job was to equip Watson with a profanity filter. The machine had already demonstrated, by dropping the F-bomb on its answer panel, how heedless it could be to basic norms of etiquette and decency. The simplest approach would be to prohibit it from even considering the seven forbidden words that George Carlin made famous in his comedy routines, plus a handful of others, including ethnic and racial slurs. It would be easy to draw up a set of rules—heuristics—to override the machine’s statistically generated candidate answers. But what about words that included no-no’s? Consider this 2006 clue in the category T Birds: “In North America this term is properly applied to only 4 species that are crested, including the tufted.” Would a list of forbidden vulgarities impede Watson from answering, “What is a titmouse?” Researchers, said David Gondek, would have to come up with “loose filters,” leaving room for such exceptions. But they were sure to miss some.
Then there was the matter of pronunciation. Watson could turn an everyday word into a profanity with just a slip of its mathematically programmed tongue. This was even more likely with foreign words. How would it fare, for example, answering this 2007 clue in the Plane Crazy category? “In 1912 this Dutch plane builder set up a plant near Berlin; later, his fighter planes were flown by the Red Baron.” This would likely be a slam-dunk for Watson, but leading it to correctly enunciate “What is Fokker?” would involve meticulous calibration of its vowel pronunciation. Surely, some would say, Jeopardy would not include a Fokker clue in a match