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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.”

Notes

[>] 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