Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [32]
The idea for the program, Ferrucci later said, came when Bringsjord asked him if a machine could create its own story line. Ferrucci took up the challenge. Instead of teaching the machine to dream up plots, he programmed it with about a dozen themes, from betrayal to revenge. For each theme, the machine was first given a series of literary examples and then a program to develop stories along those lines. One of its models for betrayal was Shakespeare’s Julius Caesar (the program was named for Caesar’s confidant-turned-conspirer, Brutus). The program produced serviceable plots, but they were less than riveting. “The one thing it couldn’t do is figure out if something was interesting,” Ferrucci said. “Machines don’t understand that.”
In his day job, Ferrucci was teaching computers more practical lessons. As head of Semantic Analysis and Integration at IBM, he was trying to instruct them to make sense of human communication. On the Internet, records of our words and activities were proliferating as never before. Companies—IBM and its customers alike—needed tools to interpret these new streams of information and put them to work. Ideally, an IBM program would tell a manager what customers or employees were saying or thinking as well as what trends and insights to draw from them and perhaps what decisions to make.
Within IBM itself, some two hundred researchers were developing a host of technologies to mine what humans were writing and saying. But each one operated within its own specialty. Some parsed sentences, analyzing the grammar and vocabulary. Others hunted Google-style for keywords and Web links. Some constructed massive databases and ontologies to organize this knowledge. A number of them continued to hone expert systems and neural networks. Meanwhile, the members of the Q-A team coached their computer for the annual TRec competitions. “We had lots of different pockets of researchers working on these different analytical algorithms,” Ferrucci said. “But any time you wanted to combine them, you had a problem.” There was simply no good way to do it.
In the early 2000s, Ferrucci and his team put together a system to unify these diverse technologies. It was called UIMA, Unstructured Information Management Architecture. It was tempting to think of UIMA as a single brain and all of the different specialties, from semantic analysis to fact-checking, as cognitive regions. But Ferrucci maintained that UIMA had no intelligence of its own. “It was just plumbing,” he said. Idle plumbing, in fact, because for years it went largely unused.
But a Jeopardy project, he realized, could provide a starring role for UIMA. Blue J would be more than a single machine. His team would pull together an entire conglomeration of Q-A approaches. The machine would house dozens, even hundreds of algorithms, each with its own specialty, all of them chasing down answers at the same time. A couple of the jury-rigged algorithms that James Fan had ginned up could do their thing. They would compete with others. Those that delivered good answers for different types of questions would rise in the results—a bit like the best singers in the Handel sing-along. As each one amassed its record, it would gain stature in its specialty and be deemed clueless in others. Loser algorithms—those that failed to produce good results in even a single niche—would be ignored and eventually removed. (Each one would have to prove its worth in at least one area to justify its inclusion.) As the system learned which algorithms to pay attention to, it would grow smarter. Blue J would evolve into an ecosystem in which the key to survival, for each of the algorithms, would be to contribute to correct responses to Jeopardy clues.
While part of his team grappled with Blue J’s architecture, Ferrucci