Final Jeopardy (2 page)

Horn eventually enticed David Ferrucci and his team to pursue his vision. Ferrucci, then in his midforties, wore a dark brown beard wrapped around his mouth and wire-rimmed glasses. An expert in Artificial Intelligence (AI), he had a native New Yorker's gift of the gab and an openness, even about his own life, that was at times jolting. (“I have a growing list of potentially mortal diseases,” he said years later. “People order an MRI a week for me.”) But he also had a wide and ranging intellect. Early in his tenure at IBM he and a friend tried, in their spare time, to teach a machine to write fiction by itself. They trained it for various literary themes, from love to betrayal, and they named it Brutus, for Julius Caesar's traitorous comrade. Ferrucci was comfortable talking about everything from the details of computational linguistics to the evolution of life on earth and the nature of human thought. This made him an ideal ambassador for a
Jeopardy
machine. After all, his project would raise a broad range of issues, and fears, about the role of brainy machines in society. Would they compete for jobs? Could they establish their own agendas, like the infamous computer HAL, in
2001: A Space Odyssey,
and take control? What was the future of knowledge and intelligence, and how would brains and machines divvy up the cognitive work? Ferrucci was always ready with an opinion. At the same time, he could address the strategic questions—how these machines would fit into hundreds of businesses, and why the project he was working on, as he saw it, went far beyond Google.

The Google question was his starting point; until people understood that his machine was not just a souped-up search engine, the project made little sense. For certain types of questions, Ferrucci said, a search engine could come up with answers. These were simple sentences with concrete results, what he and his team called factoids. For example: “What is the tallest mountain in Africa?” A search engine would pick out the three key words from that sentence and in a fraction of a second suggest Kenya's 19,340-foot-high Kilimanjaro. This worked, Ferrucci said, for about 30 percent of
Jeopardy
questions. But performance at that low level would condemn Watson to defeat at the hands of human amateurs.

A
Jeopardy
machine would have to master far thornier questions. Just as important, it would have to judge its level of confidence in an answer. Google's algorithms delivered users to the statistically most likely outposts of the Web and left it to the readers to find the answers. “A search engine doesn't know that it understood the question and that the content is right,” Ferrucci said. But a
Jeopardy
machine would have to find answers and then decide for itself if they were worth betting on. Without this judgment, the machine would never know when to buzz. It would require complex analysis to develop this “confidence.”

Was it worth it? Didn't it make sense for machines to hunt through mountains of data and for people to rely on their exquisitely engineered brains to handle the final judgments? This seemed like a reasonable division of labor. After all, processing language and spotting answers come easily to humans and are so hard for machines.

But what if machines could take the next step? What if they could go beyond locating bits and pieces of information and help us to understand it? “I think there are 1.4 million books on sale online,” Ferrucci said one afternoon. He was sharing a bottle of his own wine, a Shiraz blend that he'd cooked up in the winery attached to his kitchen in the northern suburbs of New York. He was in an expansive mood, which led him to carry out energetic dialogues with himself, asking questions and answering them emphatically. “You can probably fit all the books that are on sale on about two terabytes that you can buy at OfficeMax for a couple hundred dollars. You get every book. Every. Single. Book. Now what do you do? You can't read them all! What I want the computer to do,” he went on, “is to read them for me and tell me what they're about, and answer my questions about them. I want this for
all
information. I want machines to read, understand, summarize, describe the themes, and do the analysis so that I can take advantage of all the knowledge that's out there. We humans need help. I know I do!”

Before building a
Jeopardy
machine, Ferrucci and his team had to carry this vision one step further: They had to make a case that a market existed outside the rarefied world of
Jeopardy
for advanced question-answering technology. IBM's biggest division, after all, was Global Services, which included one of the world's largest consultancies. It sold technical and strategic advice to corporations all over the world. Could the consultants bundle this technology into their offerings? Would this type of machine soon be popping up in offices and answering customers' questions on the phone?

Ferrucci envisioned a
Jeopardy
machine spawning a host of specialized know-it-alls. With the right training, a technology that could understand everyday language and retrieve answers in a matter of seconds could fit just about anywhere. Its first job would likely be in call centers. It could answer tax questions, provide details about bus schedules, ask about the symptoms of a laptop on the fritz and walk a customer through a software update. That stuff was obvious. But there were plenty of other jobs. Consider publicly traded companies, Ferrucci said. They had to comply with a dizzying assortment of rules and regulations, everything from leaks of inside information in e-mails to the timely disclosure of earnings surprises or product failures to regulators and investors. A machine with Watson's skills could stay on top of these compliance matters, pointing to possible infractions and answering questions posed in ordinary English. A law firm could call on such a machine to track down the legal precedent for every imaginable crime, complaint, or trademark.

Perhaps the most intriguing opportunity was in medicine. While IBM was creating the
Jeopardy
machine, one of the top medical shows on television featured a nasty genius named Gregory House. In the beginning of most episodes a character would collapse, tumbling to the ground during a dance performance, a lovers' spat, or a kindergarten class. Each one suffered from a different set of symptoms, many of them gruesome. In the course of the following hour, amid the medical team's social and sexual dramas, House and his colleagues would review the patient's worsening condition. There had to be a pattern. Who could find it and match it to a disease, ideally before the patient died? Drawing from their own experience, the doctors each mastered a diverse set of data. The challenge was to correlate that information to the ever-changing list of symptoms on the white board in House's office. Toward the end of the show, House would often notice some detail—perhaps a lyric in a song or an unlikely bruise. And that would lead his magnificent mind straight to a case he remembered or a research paper he'd read about bee stings or tribal rites in New Guinea. By the end of the show, the patient was headed toward recovery.

An advanced question-answering machine could serve as a bionic Dr. House. Unlike humans, it could stay on top of the tens of thousands of medical research papers published every year. And, just as in
Jeopardy
, it could come up with lists of potential answers, or diagnoses, for each patient's ills. It could also direct doctors toward the evidence it had considered and provide its reasoning. The machine, lacking common sense, would be far from perfect. Just as the
Jeopardy
computer was certain to botch a fair number of clues, the diagnoses coming from a digital Dr. House would sometimes be silly. So people would still run the show, but they'd be assisted by a powerful analytical tool.

In those early days, only a handful of researchers took part in the
Jeopardy
project at IBM. They could fit easily into Ferrucci's office at the research center in Hawthorne, New York, about thirty-five miles north of New York City (and a fifteen-minute drive from corporate headquarters, in Armonk). But to build a knowledge machine, Ferrucci knew, would require extensive research and development. In a sense, a
Jeopardy
machine would represent an entire section of the human brain. To build it, he would need specialists in many aspects of cognition. Some would be experts in language, others in the retrieval of information. Some would attempt to program the machine with judgment, writing algorithms to steer it toward answers. Others would guide it in so-called machine learning, so that it could train itself to pick the most statistically promising combinations of words and pay more attention to trustworthy sources. Experts in hardware, meanwhile, would have to build a massive computer, or a network of them, to process all of this work. Assembling these efforts on a three-year timetable amounted to a daunting management challenge. The cost of failure would be humiliation, for both the researchers and their company.

Other complications came from the West Coast, specifically the Robert Young building on the Sony lot in Culver City, a neighborhood just south of Hollywood. Unlike chess, a treasure we all share, the
Jeopardy
franchise belonged to Sony Pictures Entertainment, an arm of the Japanese consumer electronics giant. The
Jeopardy
executives, led by a canny negotiator named Harry Friedman, weren't about to let IBM use their golden franchise and their millions of viewers on its own terms. Over the years, the two companies jousted over the terms of the game, the placement of logos, access to stars such as Ken Jennings and Brad Rutter, and the writing of
Jeopardy
clues. They even haggled over the computer's speed on the buzzer and whether IBM should take measures to slow it to a human level. These disagreements echoed until the eve of the match. At one point, only months before the showdown,
Jeopardy
's executives appeared to be on the verge of pulling the plug on the entire venture. That would have left IBM's answering computer, the product of three intense years of research, scrounging for another game to play. This particular disagreement was resolved. But the often conflicting dictates of promotion, branding, science, and entertainment forged a fragile and uneasy alliance.

The
Jeopardy
project also faced harsh critics within IBM's own scientific community. This was to be expected in a field—Artificial Intelligence—where the different beliefs about knowledge, intelligence, and the primacy of the human brain bordered on the theological. How could there be any consensus in a discipline so vast? While researchers in one lab laboriously taught machines the various meanings of the verb “to do,” futurists just down the hall insisted that computers would outrace human intelligence in a couple of decades, controlling the species. Beyond its myriad approaches and outlooks, the field could be divided into two camps, idealists and pragmatists. The idealists debated the nature of intelligence and aspired to build computers that could think conceptually, like human beings, perhaps surpassing us. The pragmatists created machines to carry out tasks. Ferrucci, who had promised to have a television-ready computer by early 2011, fell firmly into the second camp—and his team attracted plenty of barbs for it. The
Jeopardy
machine would sidestep the complex architecture of the brain and contrive to answer questions without truly understanding them. “It's just another gimmick,” said Sajit Rao, a professor in computer science at MIT who's attempting to teach computers to conceptualize forty-eight different verbs. “It's not addressing any fundamental problems.” But as Ferrucci countered, teaching a machine to answer complex questions on a broad range of subjects would represent a notable advance, whatever the method.

IBM's computer would indeed come to answer a dizzying variety of questions—and would raise one of its own. With machines like this in our future, what do we need to store in our own heads? This question, of course, has been recurring since the dawn of the Internet, the arrival of the calculator, and even earlier. With each advance, people have made internal adjustments and assigned ever larger quantities of memory, math, geography, and more to manmade tools. It makes sense. Why not use the resources at hand? In the coming age, it seems, forgoing an effective answering tool will be like volunteering for a lobotomy.

In a sense, many of us living through this information revolution share something with the medieval monks who were ambushed by the last one. They spent years of their lives memorizing sacred texts that would soon be spilling off newfangled printing presses. They could have saved lots of time, and presumably freed up loads of capacity, by archiving those texts on shelves. (No need to discuss here whether the monks were eager for “free time,” a concept dangerously close to Sloth, the fourth of the Seven Deadly Sins.) In the same way, much of the knowledge we have stuffed into our heads over the years has been rendered superfluous by new machinery.

So what does this say about Ken Jennings and Brad Rutter, the humans preparing to wage cognitive war with Watson? Are they relics? Sure, they might win this round. But the long-term prognosis is grim. Garry Kasparov, the chess master who fell to IBM's Deep Blue, recently wrote that the golden age of man-machine battles in chess lasted from 1994 to 2004. Before that decade, machines were too dumb; after it, the roles were reversed. While knowledge tools, including Watson, relentlessly advance, our flesh-and-blood brains, some argue, have stayed more or less the same for forty thousand years, treading evolutionary water from the Cro-Magnon cave painters to Quentin Tarantino.

A few decades ago, know-it-alls like Ken Jennings seemed to be the model of human intelligence. They aced exams. They had dozens of facts at their fingertips. In one quiz show that predated
Jeopardy, College Bowl,
teams of the brainiest students would battle one another for the honor of their universities. Later in life, people turned to them in boardrooms, university halls, and cocktail parties for answers. Public education has been designed, in large part, to equip millions with a ready supply of factual answers. But if Watson can top them, what is this kind of intelligence worth?