Final Jeopardy (27 page)

When it came to
Jeopardy
's betting models, Craig knew them cold. One standard in the Final Jeopardy repertoire is the two-thirds rule. It establishes that a second-place player with at least two-thirds the leader's score often has a better chance to win by betting that the leader will botch the final clue (which players do almost half the time). Say the leader going into Final Jeopardy has $15,000 and the second-place player has $10,000. To ensure a tie for victory (which counts as a win for both players), the leader must bet at least $5,000. Otherwise, the number two could bet everything, reach $20,000, and win. But missing the clue, and losing that $5,000, will drop the leader into a shared victory with the second-place player—if that player bets nothing. This strategy often makes sense, Craig said, because of the statistical correlation among players. He hadn't run nearly as many numbers as the IBM team, but he knew that if one player missed a Final Jeopardy clue, it was probably a hard one, and the chances were much higher that others would miss it as well.

Craig bolstered his
Jeopardy
studies with readings on evolutionary psychology and behavioral economics, including books by Dan Ariely and Daniel Kahneman. They reinforced what he already knew as a poker player: When it comes to betting, most people are scared of losing and bet too small. (In
Jeopardy
's lingo, which some might consider sexist, timid bets are “venusian,” audacious ones, “martian.”)

Craig would tilt strongly toward Mars. In his first game, he held a slender lead when he landed on a Daily Double in the category Elemental Clues. The previous clues in the group all featured symbols for elements in the periodic table. Craig didn't know all hundred and eight of them, but as a scientist he was confident that he'd know any that would be featured on
Jeopardy
. He said he was “95 percent sure” that he'd come up with the right answer, so he bet almost all of his money, $12,500. It turned out to be the largest bet since one placed by Ken Jennings six years earlier. The clue was “PD. A great place to hear music.” For the scientist, it was a cinch. “Palladium,” Craig said, recalling his golden moment. “Boom. Twenty-four thousand dollars.”

That was when he made what he called his rookie mistake, one he was convinced Watson would avoid. His palladium clue was the first Daily Double of the two in Double Jeopardy. Another one lurked somewhere on the board, and he forgot about it. For the leader in
Jeopardy
, Daily Doubles represent danger, for they can lift a trailing player back into contention. So a leader who controls the board, as he did, should hunt down the remaining Daily Double. They tend to be in higher-dollar rows, where the clues are more difficult. Craig seemed to be on the verge of winning in a romp. With only seconds left in the round, he led his closest competitor, a medievalist from Los Angeles named Scott Wells, by a commanding $33,600 to $11,800. But he lost control of the board with a $400 clue: “On May 9, 1921, this ‘letter-perfect' airline opened its first passenger office in Amsterdam.” Wells beat him to the buzzer and correctly answered “What is KLM?” Then, as time ran out, he proceeded to land on the second Daily Double. Craig was mortified. “I thought I'd die,” he said. Wells bet $10,000, which would put him well within striking distance in Final Jeopardy. The clue: “In 1939 this Russian took the 1st flight of a practical, single-rotor helicopter, & why not? He built the thing!” Craig survived his blunder when Wells failed to come up with “Who is Igor Sikorsky?”

As he left the Culver City studios after his first day on
Jeopardy
, Craig was experiencing a host of human sensations. First, he was euphoric. He had amassed $197,801, a five-game record. As he headed out for a bite with the fellow players he had befriended, he felt a little embarrassed. Here he was, swimming in money, and thanks to him, every one of them had crashed and burned on their once-in-a-lifetime chance to win at
Jeopardy
. Between breakfast and dinner, he had doused the dreams of ten players. Many of them had prepared for years, even decades, watching the show religiously, reading almanacs, studying flash cards, wowing friends and relatives, and envisioning that they'd be the next Ken Jennings—or at the very least stick around for a few games. Now they were heading home with a loser's pay of $1,000 or $2,000, barely enough for the plane ticket. Craig, on the other hand, might turn out to be the next superstar. It was at least a possibility. Ken Jennings had never won as much in a match or a single (five-match) day. No one had. That night, in his room at the Radisson Hotel in Culver City (which offered limo service to the Sony lot), he tossed and turned. The next morning, while a
Jeopardy
staffer was applying makeup to the new champion's face, Craig found himself yawning. This was worrisome. The night before his magical five-game run, he recalled, he had slept soundly for nine hours. Now, he didn't feel nearly as good.

Still, Craig blitzed though his first game. His crucial clue was another jumbo bet—$12,000, this time—on a Daily Double, in which he identified “small masses of lymphoid tissue in the nasopharynx” (“What are adenoids?”). He chalked up another $34,399 and appeared to be off and running.

But the next match was his undoing. He faced Matt Martin, a police officer from Arlington, Virginia, and Jelisa Castrodale, a sportswriter from North Carolina. Just a day earlier, his luck with Danish kings and atomic elements made him wonder if he was dreaming. Now his fortunes took a cruel turn. Sleep-deprived, he found himself struggling in a category that seemed to be mocking him: “Pillow talk.” Such fluff was hardly his forte. Castrodale identified the “small scattered pillows also known as scatter cushions” (“What is a throw pillow?”) and the “child carrying the pillow in a wedding procession” (“What is a ring-bearer?”). And when a clue asked about “folks with tailbone injuries” sitting on “pillows in the shape of these sweet baked treats,” Martin buzzed in. It was the cop, as Alex Trebek gleefully noted, who answered, “What are donuts?”

Barely a week before Craig's final show aired, Watson was engaged in a closely fought match with the former champion Justin Bernbach, and they were playing the very same clues. This was the day that Watson, following a dominating morning, later faltered and crashed. Its patterns in this game seemed to mirror those of Roger Craig. Like Craig, Watson appeared largely lost on pillow talk. Both of them, however, swept through the category on the ancient civilization of Ur. (When you have a category like that, Craig later explained, “You almost know the answers before they ask the questions.” He listed a few on the fingers of one hand: Iraq, Sumeria, Cyrus the Great, and Ziggurats (the terraced monuments they built). “What else can they ask about Ur?” Watson, though following a different logic, delivered the same winning results. Watson and Craig also thrived in the category “But what am I?” It featured the Latin names for certain animals, along with helpful hints, alerting players, for example, not to confuse “cyanocitta cristata” with Canadian baseball players (“What are Blue Jays?”). These were easy factoids for computer and computer scientist alike.

As Watson went into Final Jeopardy on that September afternoon, it held a slim lead over Bernbach and a comfortable one over Maxine Levaren, a personal success coach from San Diego. But it lost the game to Bernbach, you might recall, by missing a clue in the category Sports and the Media. It failed to name the city whose newspaper celebrated the previous February 8 with the headline: “Amen! After 43 Years, Our Prayers Are Answered.” The computer had only 13 percent confidence in Chicago, but that was higher than its confidence in its other candidates, including Omaha and two cities associated with prayer, Jerusalem and the Vatican. In retrospect, Watson was scouring its database for events dated February 8. But the machine, raised in the era of instant digital news, ignored the lag at the heart of traditional paper headlines: Most of the events they describe occurred the previous day.

Like Watson, Roger Craig reached Final Jeopardy clinging to a narrow lead, $22,000 to $19,700, over Jelisa Castrodale. The Sports and the Media category looked perfect for the sportswriter. But Craig was a fan as well and a master of sports facts—especially those concerning football. The same clue Watson had botched, featuring forty-three years and answered prayers, popped up on the board and the contestants wrote their responses. After the jingle, Alex Trebek turned to them. Martin, who lagged far behind, incorrectly guessed: “What is Miami?” Castrodale was next: “What is New Orleans?” That was right. She had bet all but one dollar, which lifted her to $39,339. Craig had anticipated her bet and topped it: He would win by $2 if he got it right. But his answer was 840 miles off target. The six-time champion, who had trained himself with the methods and rigor of computer science, came up with the same incorrect response as his electronic role model: “What is Chicago?”

Was the melding of man and machine leading Craig and Watson through the same thought processes and even to the same errors? Weeks later, sitting in IBM's empty
Jeopardy
studio, David Gondek opened his Mac and traced the cognitive route that led Watson to the Windy City. “It really didn't have any idea,” he said, clicking away. The critical document, Gondek found, turned out to be news about a prayer meeting in Chicago on February 8, which featured a prominent swami. When Watson failed to come up with convincing responses, which correlated, statistically and semantically, to the clue, it turned to documents like this one with a few matching words. The machine had negligible confidence in answers from such sources. But in this case, the machine had no better option.

Craig had a different story. In the thirty seconds he had to mull Final Jeopardy, thoughts about a prayer service featuring a swami in Chicago never entered his mind. But his analysis, usually so disciplined, was derailed by an all-too-human foible. He fell to suggestion, one nourished by his environment. Just a short drive north of his home in Delaware, the ice hockey team in Philadelphia, the Flyers, had recently battled to the finals of the Stanley Cup. This awakened hockey fever in the metropolitan area and an onslaught of media coverage, along with endless chatter and speculation. Hockey hadn't been on people's minds to this degree since the glory years of the franchise, when the “Broad Street Bullies” won back-to-back cups in the mid-1970s. The Flyers ultimately lost to the Chicago Black Hawks, a team that hadn't won in forty-nine years (six years longer than the Saints). So even though Craig was a “huge football fan” who hadn't missed watching a Super Bowl since his childhood, he had hockey in his head when he saw the Final Jeopardy clue. Much like the psychology test subjects who mistook Moses for the animal keeper on the ark, Craig focused on a forty-something-year championship drought—and looked right past the crucial February date. The hockey final, after all, had been in June. “I blew it,” he said. So did Watson. But despite their virtuoso talents and similar techniques, in this one example of failure they each remained true to their kind. One was dumb as only a machine can be, the other human to a fault.

During the sparring sessions in the spring, Watson had relied on simple heuristics to guide its strategy. Ferrucci at one point called it brain dead, and David Gondek, who had written the rules, had to agree. You might say that such heuristics are “brain-dead by definition,” he said, since they replace analysis with rules. But what a waste it was to equip Watson, a machine that could carry out billions of calculations per second, with such a rudimentary set of instructions.

There was no reason, of course, for Watson's strategy to be guided by a handful of simple rules. The machine had plenty of processing power, enough to run a trillion-dollar trading portfolio or to manage all of the air traffic in North America or even the world. Figuring out bets for a single game of
Jeopardy
was well within its range. But before the machine could become a strategic whiz, Gondek and his team had to turn thousands of
Jeopardy
games into a crazy quilt of statistical probabilities. Then they had to teach Watson—or help it teach itself—how best to play the game. This took time.

The goal was to have Watson analyze a dizzying assortment of variables, from its track record on anagrams or geography puzzlers to its opponents' ever-changing scores. Then it would come up with the ideal betting strategy for each point of the game and for each clue. This promised to be much simpler for Watson than the rest of its work. English, after all, was foreign to the machine, and
Jeopardy
clues, even after years of work, remained challenging. Game strategy, with its statistical crunching of probabilities, played to Watson's strengths.

To tutor Watson in the art of strategy, Gondek brought in one of IBM's gaming masters, an intense computer scientist named Gerald Tesauro. Short, dark, and neatly dressed, his polo shirt tucked cleanly into dark slacks, Tesauro was one of the more competitive members of the
Jeopardy
team. He took pride, for example, in his ability to beat Watson to the buzzer. Once, in a practice match against the machine, he managed to buzz in twenty-four times, he later said, and got eighteen of the clues right. Like a basketball player who's hitting every shot, he said, he was “in some kind of a zone” (though, to be honest, that 75 percent precision rate would place him in a crowd of
Jeopardy
also-rans). Even when Tesauro was in the audience, he would play along in his mind, jerking an imaginary buzzer in his fist each time he knew the response.

Tesauro gained global renown in the '90s when he developed the computer that mastered the five-thousand-year-old game of backgammon. (Sumerians, as Roger Craig may already know, played a variation of it in the ancient city of Ur.) What distinguished Tesauro's approach was that he didn't teach the machine a thing. Using neural networks, his system, known as TD-Gammon, learned on its own. Following Tesauro's instructions, it played games against itself, millions of them. Each time it won or lost, it drew conclusions. Certain moves in certain situations led more often to victory, others to defeat. Although this was primitive feedback—no more than thumbs up, thumbs down—each game delivered a minuscule improvement, Tesauro said. Over the course of millions of games, the machine developed a repertoire of winning moves for countless scenarios. Tesauro's machine beat champions.