Authors: Stephen Baker
Final Jeopardy
Man vs. Machine and the Quest to Know Everything
Stephen Baker
Table of Contents
1. The Germ of the Jeopardy Machine
2. And Representing the Humans . . .
The Numerati
Final Jeopardy: Man vs. Machine and the Quest to Know Everything
Copyright © 2011 by Stephen Baker
All rights reserved
For information about permission to reproduce selections from this book, write to Permissions, Houghton Mifflin Harcourt Publishing Company, 215 Park Avenue South, New York, New York 10003.
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Library of Congress Cataloging-in-Publication Data is available.
Printed in the United States of America
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To Sally and Jack, my fact-hungry sister and son, who each introduced me to
Jeopardy!
at a different stage of my life
1. The Germ of the
Jeopardy
Machine
2. And Representing the Humans . . .
Watson paused. The closest thing it had to a face, a glowing orb on a flat-panel screen, turned from forest green to a dark shade of blue. Filaments of yellow and red streamed steadily across it, like the paths of jets circumnavigating the globe. This pattern represented a state of quiet anticipation as the supercomputer awaited the next clue.
It was a September morning in 2010 at IBM Research, in the hills north of New York City, and the computer, known as Watson, was annihilating two humans, both champion players, in practice rounds of
Jeopardy!
Within months, it would be playing the game on national television in a million-dollar man vs. machine match against two of
Jeopardy
's all-time greats.
As Todd Crain, an actor and the host of these test games, started to read the next clue, the filaments on Watson's display began to jag and tremble. Watson was thinkingâor coming as close to it as a computer could. The $1,600 clue, in the category The Eyes Have It, read: “This facial ware made Israel's Moshe Dayan instantly recognizable worldwide.”
The three playersâtwo human and one electronicâcould read the words as soon as they appeared on the big
Jeopardy
board. But they had to wait for Crain to read the entire clue before buzzing. That was the rule. As the host pronounced the last word, a light would signal that contestants could buzz. The first to hit the button could win $1,600 with the right answerâor lose the same amount with a wrong one. (In these test matches, they played with funny money.)
This pause for reading gave Watson three or four seconds to hunt down the answer. The first step was to figure out what the clue meant. One of its programs promptly picked apart the grammar of the sentence, identifying the verbs, objects, and key words. In another section, research focused on Moshe Dayan. Was this a person? A place in Israel? Perhaps a holy site? Names like John and Maria would signal a person. But Moshe was more puzzling.
During these seconds, Watson's cognitive apparatusâ 2,208 computer processors working in concertâmounted a massive research operation through thousands of documents around Moshe Dayan and his signature facial ware. After a second or so, different programs, or algorithms, began to suggest hundreds of possible answers. To us, many of them would look like wild guesses. Some were phrases that Dayan had uttered, others were references to his military campaigns and facts about Israel. Still others cited various articles of his clothing. At this point, the computer launched its second stage of analysis, figuring out which response, if any, merited its confidence. It proceeded to check and recheck facts, making sure that Moshe Dayan was indeed a person, an Israeli, and that the answer referred to something he wore on his face.
A person looking at Watson's frantic and repetitive labors might conclude that the player was unsure of itself, laughably short on common sense, and scandalously wasteful of computing resources. This was all true. Watson barked up every tree from every conceivable angle. The pattern on its screen during this process, circles exploding into little stars, provided only a hint of the industrial-scale computing at work. In a room behind the podium, visible through a horizontal window, Watson's computers churned, and the fans cooling them roared. This time, its three seconds of exertion paid off. Watson came up with a response, sending a signal to a mechanical device on the podium. It was the size of a large aspirin bottle with a clear plastic covering. Inside was a
Jeopardy
buzzer. About one one-hundredth of a second later, a metal finger inside this contraption shot downward,
pressing the button.
Justin Bernbach, a thirty-eight-year-old airline lobbyist from Brooklyn, stood to Watson's left. He had pocketed $155,000 while winning seven straight
Jeopardy
matches in 2009. Unlike Watson, Bernbach understood the sentence. He knew precisely who Moshe Dayan was as soon as he saw the clue, and he carried an image of the Israeli leader in his mind. He gripped the buzzer in his fist and frantically pressed it four or five times as the light came on.
But Watson had arrived first.
“Watson?” said Crain.
The computer's amiable male voice arranged the answer, as
Jeopardy
demands, in the form of a question: “What is eye patch?”
“Very good,” Crain said. “An eye patch on his left eye. Choose again, Watson.”
Bernbach slumped at his podium. This match with the machine wasn't going well.
It was going magnificently for David Ferrucci. As the chief scientist of the team developing the
Jeopardy
computer, Ferrucci was feeling vindicated. Only three years earlier, the suggestion that a computer might match wits and word skills with human champions in
Jeopardy
sparked opposition bordering on ridicule in the halls of IBM Research. And the final goal of the venture, a nationally televised match against two
Jeopardy
legends, Ken Jennings and Brad Rutter, seemed risky to some, a bit déclassé to others.
Jeopardy
, a television show, appeared to lack the timeless cachet of chess, which IBM computers had mastered a decade earlier.
Nonetheless, Ferrucci and his team went ahead and built their machine. Months earlier, it had fared well in a set of test matches. But the games revealed flaws in the machine's logic and game strategy. It was a good player, but to beat Jennings and Rutter, who would be jousting for a million-dollar top prize, it would have to be great. So they had worked long hours over the summer to revamp Watson. This September event was the coming-out party for Watson 2.0. It was the first of fifty-six test matches against a higher level of competitor: people, like Justin Bernbach, who had won enough matches to compete in
Jeopardy
's Tournament of Champions.
In these early matches, Watson was having its way with them. Ferrucci, monitoring the matches from a crowded observation booth, was all smiles. Keen to promote its
Jeopardy
phenom, IBM's advertising agency, Ogilvy & Mather, had hired a film crew to follow Ferrucci's team and capture the drama of this opening round of championship matches. The observation room was packed with cameras. Microphones on long booms recorded the back-and-forth of engineers as they discussed algorithms and Watson's response time, known as latency. Ferrucci, wearing a mike on his lapel, gave a blow-by-blow commentary as Watson, on the other side of the glass, strutted its new and smarter self.
It was almost as if Watson, like a person giddy with hubris, was primed for a fall. The computer certainly had its weaknesses. Even when functioning smoothly, it would make its share of wacky mistakes. Right before the lunch break, one clue asked about “the inspiration for this title object in a novel and a 1957 movie [which] actually spanned the Mae Khlung.” Now, it would be reasonable for a computer to miss “The Bridge over the River Kwai,” especially since the actual river has a different name. Perhaps Watson had trouble understanding the sentence, which was convoluted at best. But how did the computer land on its outlandish response, “What is Kafka?” Ferrucci didn't know. Those things happened, and Watson still won the two morning matches.
It was after lunch that things deteriorated. Bernbach, so frustrated in the morning, started to beat Watson to the buzzer. Meanwhile, the computer was making risky bets and flubbing entire categories of clues. Defeat, which had seemed so remote in the morning, was now just one lost bet away. It came in the fourth match. Watson was winning by $4,000 when it stumbled on this Final Jeopardy clue: “On Feb. 8, 2010, the headline in a major newspaper in this city read: âAmen! After 43 years, our prayers are answered.'” Watson missed the reference to the previous day's Super Bowl, won by the New Orleans Saints. It bet $23,000 on Chicago. Bernbach also botched the clue, guessing New York. But he bet less than Watson, which made him the first person to defeat the revamped machine. He pumped his fist.
In the sixth and last match of the day, Watson trailed Bernbach, $16,200 to $21,000. The computer landed on a Daily Double in the category Colleges and Universities, which meant it could bet everything it had on nailing the clue. A $5,000 bet would have brought it into a tie with Bernbach. A larger bet, while risky, could have catapulted the computer toward victory. “I'll take five,” Watson said.
Five. Not $5,000, not $500. Five measly dollars of funny money. The engineers in the observation booth were stunned. But they kept quieter than usual; the cameras were rolling.
Then Watson crashed. It occurred at some point between placing that lowly bet and attempting to answer a clue about the first Catholic college in Washington, D.C. Watson's “front end,” its voice and avatar, was waiting for its thousands of processors, or “back end,” to deliver an answer. It received nothing. Anticipating such a situation, the engineers had prepared set phrases. “Sorry,” Watson said, reciting one of them, “I'm stumped.” Its avatar displayed a dark blue circle with a single filament orbiting mournfully in the Antarctic latitudes.
What to do? Everyone had ideas. Maybe they should finish the game with an older version of Watson. Or perhaps they could hook it up to another up-to-date version of the program at the company's Hawthorne labs, six miles down the road. But some worried that a remote connection would slow Watson's response time, causing it to lose more often on the buzz. In the end, as often happens with computers, a reboot brought the hulking
Jeopardy
machine back to life. But Ferrucci and his team got an all-too-vivid reminder that their
Jeopardy
player, even as it prepared for a debut on national television, could go haywire or shut down at any moment. When Watson was lifted to the podium, facing banks of cameras and lights, it was anybody's guess how it would perform. Watson, it was clear, had a frighteningly broad repertoire.
Only four years earlier, in 2006, Watson was a prohibitive long shot, not just to win at
Jeopardy
but even to be built. For more than a year, the head of IBM Research, a physicist named Paul Horn, had been pressing a number of teams at the company to pursue a
Jeopardy
machine. The way he saw it, IBM had triumphed in 1997 with its chess challenge. The company's machine, Deep Blue, had defeated the reigning world champion, Garry Kasparov. This burnished IBM's reputation among the global computing elite while demonstrating to the world that computers could rival human beings in certain domains associated with intelligence.
That triumph left IBM's top executives hungry for an encore. Horn felt the pressure. But what could the researchers get a computer to do? Deep Blue had rifled through millions of scenarios per second, calculated probabilities, and made winning moves. It had aced applied math. But it had skipped the far more complex domain of words. This, Horn thought, was where the next challenge would be. Far beyond the sixty-four squares on a chess board, the next computer should charge into the vast expanse of human language and knowledge. For the test, Horn settled on
Jeopardy
, which debuted in 1964 and now attracted some nine million viewers every weeknight. It was the closest thing in the United States to a knowledge franchise. “People associated it with intelligence,” Horn later said.
There was one small problem. For months, he couldn't get any takers.
Jeopardy
, with its puns and strangely phrased clues, seemed too hard for a computer. IBM was already building machines to answer questions, and their performance, in speed and precision, came nowhere close to that of even a moderately informed person. How could the next machine grow so much smarter?
And while researchers regarded the challenge as daunting, many people, Horn knew, saw it precisely the other way. Answering questions? Didn't Google already do that?