Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [39]
Every computing technology Ferrucci had ever touched, from the first computer he saw at Iona to the Brutus machine that spit out story plots, had a clueless side to it. Such machines could follow orders and carry out surprisingly complex jobs. But they were nowhere close to humans. The same was true of expert systems and neural networks: smart in one area, dumb in every other. And it was also the case with the Jeopardy algorithms his team was piecing together in the Hawthorne labs. These sets of finely honed computer commands each had a specialty, whether it was hunting down synonyms, parsing the syntax of a clue, or counting the most common words in a document. Beyond these meticulously programmed tasks, each was helpless.
So how would Blue J concoct broader intelligence—or at least enough of it to win at Jeopardy? Ferrucci considered the human brain. “If I ask you ‘36 plus 43,’ a part of you goes, ‘Oh, I’ll send that question over to the part of my brain that deals with math,’” he said. “And if I ask you a question about literature, you don’t stay in the math part of your brain. You work on that stuff somewhere else.” Now this may be the roughest approximation of how the brain works, but for Ferrucci’s purposes, it didn’t matter. He knew that the brain had different specialties, that people instinctively skipped from one to another, and that Blue J would have to do the same thing.
Unlike a human, however, Blue J wouldn’t know where to start. So with its vast resources, it would start everywhere. Instead of reading a clue and assigning the sleuthing work to specialist algorithms, Blue J would unleash scores of them on a hunt, then see which one came up with the best answer. The algorithms inside Blue J—each following a different set of marching orders—would bring in competing results. This process, a lot less efficient than the human brain, would require an enormous complex of two thousand processors, each handling a different piece of the job.
To see how these algorithms carried out their hunt, consider one of thousands of clues the fledgling system grappled with. In the category Diplomatic Relations, it read: “Of the 4 countries the United States does not have diplomatic relations with, the one that’s farthest north.”
In the first wave of algorithms to assess the clue was a cluster that specialized in grammar. They diagrammed the sentence, much the way a grade school teacher once did, identifying the nouns, verbs, direct objects, and prepositional phrases. This analysis helped to resolve doubts about specific words. The “United States,” in this clue, referred to the country, not the army, the economy, or the Olympic basketball team. Then they pieced together interpretations of the clue. Complicated clues, like this one, might lead to different readings, one more complex, the other simpler, perhaps based solely on words in the text. This duplication was wasteful, but waste was at the heart of the Blue J strategy. Duplicating or quadrupling its effort, or multiplying it by 100, was one way it would compensate for its cognitive shortcomings—and play to its advantage in processing speed. Unlike humans, who instantly understand a question and pursue a single answer, the computer might hedge, launching simultaneous searches for a handful of different possibilities. In this way and many others, Blue J would battle the efficient human mind with spectacular, flamboyant inefficiency. “Massive redundancy” was how Ferrucci described it. Transistors were cheap and plentiful. Blue J would put them to use.
While the machine’s grammar-savvy algorithms were dissecting the clue, one of them searched for its LAT. In this clue about diplomacy, “the one” evidently referred to a country. If this was the case, the universe of Blue J’s possible answers was reduced to a mere 194, the number of countries in the world. (This, of course, assumed that “country” didn’t refer to “Marlboro Country