Final Jeopardy (32 page)

I remember calling Michael Loughran at IBM on a winter evening and suggesting that this
Jeopardy
machine might make a good book. He was receptive that night, and remained so throughout. He was juggling four or five jobs at the same time and tending to a number of constituencies, from the researchers in the IBM's War Room to the various marketing teams in Manhattan and the television executives in Culver City. Yet he found time for me and made this book possible. Thanks, too, to his colleagues at IBM, including Scott Brooks, Noah Syken, Ed Barbini, and my great friend and former
BusinessWeek
colleague Steve Hamm. I also appreciate the help and insights from the team at Ogilvy & Mather, especially David Korchin and Miles Gilbert, who brought Watson's avatar to life for me.

The indispensable person, of course, was David Ferrucci. If it's not clear in the book how open, articulate, and intelligent he is, I failed as a writer. He was my guide, not only to Watson's brain, but to the broader world of knowledge. He was generous with his time and his team. I'm thankful to all of them for walking me through every aspect of their creation. My questions had to try their patience, yet they never let it show.

Harry Friedman welcomed me to the fascinating world of
Jeopardy
and introduced me to a wonderful cast of characters, including Rocky Schmidt and the unflappable Alex Trebek. Thanks to them all and to Grant Loud, who was always there to answer my calls. I owe a load of New Jersey hospitality to my California hosts, Natalie and Jack Isquith, and my niece Claire Schmidt.

Scores of people, in the tech world and academia, lent me their expertise and their time. I'm especially grateful to my friends at Carnegie Mellon for opening their doors to me, once again, and to MIT. Thanks, too, to Peter Norvig at Google, Prasanna Dhore at HP, Anne Milley at SAS, and the sharpest mind I know in Texas, Neil Iscoe.

And for her love, support, and help in maintaining a sense of balance, I give thanks to my wife, Jalaire. She'd see the forty
Jeopardy
shows stored on TiVo and say, “Let's watch something else.”

[>]
It was a September morning:
Like Yahoo! and a handful of other businesses, the official name of the quiz show in this story ends in an exclamation point:
Jeopardy!
Initially, I tried using that spelling, but I thought it made reading harder. People see a word like this! and they think it ends a sentence. Since I use the name
Jeopardy
more than two hundred times in the book, I decided to eliminate that distraction. My apologies to the
Jeopardy!
faithful, many of whom are sticklers for this kind of detail.

[>]
pressing the button:
A few months before the final match, I was talking to the
Jeopardy
champion Ken Jennings in Los Angeles. Discussing Watson, he suddenly stopped himself. “What do you call it?” he asked. “Him? It?” The question came up all the time, and even among the IBM researchers the treatment wasn't consistent. When they were programming or debugging the machine, they naturally referred to it as a thing. But when Watson was playing, “it” would turn into a “he.” And occasionally David Ferrucci was heard referring to it as “I.” In the end, I opted for calling the machine “it.” That's what it is, after all.

[>]
He was the closest thing:
For narrative purposes, I focused on a handful of researchers in the
Jeopardy
project, including Jennifer Chu-Carroll, James Fan, David Gondek, Eric Brown, and Eddie Epstein. But they worked closely with groups of colleagues too numerous to mention in the telling of the story. Here are the other members of IBM's
Jeopardy
challenge team: Bran Boguraev, Chris Welty, Adam Lally, Anthony (Tony) Levas, Aditya Kalyanpur, James (Bill) Murdock, John Prager, Michael McCord, Jon Lenchner, Gerry Tesauro, Marshall Schor, Tong Fin, Pablo Duboue, Bhavani Iyer, Burn Lewis, Jerry Cwiklik, Roberto Sicconi, Raul Fernandez, Bhuvana Ramabhadran, Andrew Rosenberg, Andy Aaron, Matt Mulholland, Karen Ingraffea, Yuan Ni, Lei Zhang, Hiroshi Kanayama, Kohichi Takeda, David Carmel, Dafna Sheinwald, Jim De Piante, and David Shepler.

[>]
most books had too many words:
For more technical details on the programming of Watson, see
AI Magazine
(vol. 31, no. 3, Fall 2010). The entire issue is devoted to Q-A technology and includes lots of information about the
Jeopardy
project.

[>]
smarter Watson wouldn't have:
One of the reasons the fast version of Watson is so hard to manage and update is its data. In order to speed up the machine's processing of its 75 gigabytes of data, the IBM team processed it all beforehand. This meant that instead of the machine figuring out on the fly the subjects and objects of sentences, this work was done in advance. Watson didn't need to parse a sentence to conclude that the apple fell on Isaac Newton's head and not vice versa. Looking at it from a culinary perspective, the researchers performed for Watson the job that pet food manufacturers like Purina carry out for animals: They converted a rich, varied, and complex diet into the informational equivalent of kibbles. “When we want to run a question,” Ferrucci said, “the evidence is already analyzed. It's already parsed. The people are found, the locations are found.” This multiplied Watson's data load by a factor of 6—to 500 gigabytes. But it also meant that to replicate the speed of Watson in other domains, the data would likely have to be already processed. This makes answering machines less flexible and versatile.

[>]
“a huge knowledge base”:
NELL has a human-instructed counterpart. Called Cyc, it's a universal knowledge base painstakingly assembled and organized since 1984 by Cycorp, of Austin, Texas. In its scope, Cyc was as ambitious as the eighteenth-century French encyclopedists, headed by Denis Diderot, who attempted to catalogue all of modern knowledge (which had grown significantly since the days of Aristotle). Cyc, headed by a computer scientist named Douglas Lenat, aspired to fill a similar role for the computer age. It would lay out the relationships of practically everything, from plants to presidents, so that intelligent machines could make inferences. If they knew, for example, that Ukraine produced wheat, that wheat was a plant, and that plants died without water, it could infer that a historic drought in Ukraine would curtail wheat production. By 2010, Cyc has grown to nearly half a million terms, from plants to presidents. It links them together with some fifteen thousand types of relations. A squirrel, just to pick one example, has scores of relationships: trees (climbed upon), rats (cousins of), cars (crushed by), hawks (hunted by), acorns (food), and so on. The Cyc team has now accumulated five million facts, or assertions, relating all of the terms to one another. Cyc represents more than six hundred researcher-years but is still limited in its scope. And in the age of information, the stratospheric growth of knowledge seems sure to outstrip the efforts of humans to catalogue it manually.

[>]
And there were still so many:
Before working on a new algorithm for Watson, team members had to come up with a hypothesis for the goals and effectiveness of the algorithm, then launch it on a Wiki where all the team members could debate the concept, refine it, and follow its progress. Here's an example of one hypothesis: “A Pun-Relation classifier based on a statistical combination of synonymy, ngram associations, substring and sounds like detectors will increase Watson's accuracy and precision at 70 by more than 10 percent on pun questions while not negatively impacting overall performance on non-pun questions.”

Bailey, James,
Afterthought: The Computer Challenge to Human Intelligence,
Basic Books, 1997

Benjafield, John G.,
Cognition,
Oxford University Press, 2007

Bringsjord, Selmer, and David Ferrucci,
Artificial Intelligence and Literary Creativity: Inside the Mind of Brutus, A Storytelling Machine,
Psychology Press, 1999

Dyson, George B.,
Darwin among the Machines: The Evolution of Global Intelligence,
Basic Books, 1997

Harris, Bob,
Prisoner of Trebekistan: A Decade in Jeopardy!,
Crown Publishers, 2006

Hawkins, Jeff, with Sandra Blakeslee,
On Intelligence,
Henry Holt and Co., 2004

Hsu, Feng-Hsiung,
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion,
Princeton University Press, 2002

Jennings, Ken,
Brainiac: Adventures in the Curious, Competitive, Compulsive World of Trivia Buffs,
Villard Books, 2006

Johnson, Steven,
Mind Wide Open: Your Brain and the Neuroscience of Everyday Life,
Scribner, 2004

Kidder, Tracy,
The Soul of a New Machine,
Little, Brown and Co., 1981

Klingberg, Torel,
The Overflowing Brain: Information Overload and the Limits of Working Memory,
Oxford University Press, 2009

Lanier, Jaron,
You Are Not a Gadget: A Manifesto,
Alfred A. Knopf, 2010

Ma, Jeffrey,
The House Advantage: Playing the Odds to Win Big in Business,
Palgrave MacMillan, 2010

McNeely, Ian F., with Lisa Wolverton,
Reinventing Knowledge: From Alexandria to the Internet,
W. W. Norton & Co., 2008

Nass, Clifford, with Corina Yen,
The Man Who Lied to His Laptop: What Machines Teach us about Human Relationships,
Current, 2010

Norretranders, Tor,
The User Illusion: Cutting Consciousness Down to Size,
Penguin, 1999

Pinker, Steven,
How the Mind Works,
W. W. Norton & Co., 1997

Rasskin-Gutman, Diego,
Chess Metaphors: Artificial Intelligence and the Human Mind,
MIT Press, 2009

Richmond, Ray,
This Is Jeopardy!: Celebrating America's Favorite Quiz Show,
Barnes & Noble Books, 2004

Storrs Hall, J.,
Beyond AI: Creating the Conscience of the Machine,
Prometheus Books, 2007

Wright, Alex,
Glut: Mastering Information through the Ages,
Joseph Henry Press, 2007

Stephen Baker was
BusinessWeek
's senior technology writer for a decade, based first in Paris and later New York. He blogs at finaljeopardy.net and is on Twitter @Stevebaker. Roger Lowenstein called his first book,
The Numerati,
“eye-opening and chilling.” Baker is an alumnus of the University of Wisconsin and Columbia University's Graduate School of Journalism.

1. In
Jeopardy,
the answers on the board are called “clues,” and the players' questions—what most viewers perceive as answers—are “responses.”

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