Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [40]
Once the clue was parsed into a question the machine could understand, the hunt commenced. Each expert algorithm went burrowing through Blue J’s trove of data in search of the answer. The genetic algorithm, following instructions developed for decoding the genome, looked to match strings of words in the clue with similar strings elsewhere, maybe in some stored Wikipedia entry or in articles about diplomacy, the United States, or northern climes. One of the linguists worked on rhyming key words in the clue or finding synonyms. Another algorithm used a Google-like approach and focused on documents that matched the greatest number of key words in the clue, giving special attention to the ones that surfaced the most often.
While they worked, software within Blue J would compare the clue to thousands of others it had encountered. What kind was it—a puzzle, a limerick, a historical factoid? Blue J was learning to recognize more than fifty types of questions, and it was constructing the statistical record of each algorithm for each type of question. This would guide it in evaluating the results when they came back. If the clue turned out to be an anagram, for example, the algorithm that rearranged the letters of words or phrases would be the most trusted source. But that same algorithm would produce gibberish for most other clues.
What kind of clue was this one on Diplomatic Relations? It appeared to require two independent analyses. First, the computer had to come up with the four countries with which the United States had no diplomatic ties. Then it had to figure out which of them was the farthest north. A group of Blue J’s programmers had recently developed an algorithm focused on these so-called nested clues, in which one answer lay inside another. This may sound obscure, but humans ask this type of question all the time. If someone wonders about “cheap pizza joints close to campus,” the person answering has to carry out two mental searches, one for cheap pizza joints and another for those nearby. Blue J’s “nested decomposition” led the computer through a similar process. It broke the clues into two questions, pursued two hunts for answers, and then pieced them together. The new algorithm was proving useful in Jeopardy. One or two of these combination questions came up in nearly every game. They were especially common in the all-important Final Jeopardy, which usually featured more complex clues.
It took Blue J almost an hour for its algorithms to churn through the data and return with their candidate answers. Most were garbage. There were failed anagrams of country names and laughable attempts to rhyme “north” and “diplomatic.” Some suggested the names of documents or titles of articles that had strings of the same words. But the nested algorithm followed the right approach. It found the four countries on the outs with the United States (Bhutan, Cuba, Iran, and North Korea), checked their geographical coordinates, and came up with the answer: “What is North Korea?”
At this point, Blue J had the right answer. It had passed the binary recall test. But it did not yet know that North Korea was correct, nor that it even merited enough confidence for a bet. For this, it needed loads of additional analysis. Since the candidate answer came from an algorithm with a strong record on nested clues, it started out with higher than average confidence in that answer. The machine proceeded to check how many of the answers matched the question type “country.” After ascertaining that North Korea appeared to be a country, confidence in “What is North Korea?” increased. For a further test, it placed “North Korea” into a simple sentence generated from the clue: “North Korea has no diplomatic relations with the United States.” Then it would see if similar sentences showed up in its data trove. If so, confidence climbed higher.
In the end, it chose North Korea as the answer to bet on. In a real game, Blue J would have hit