Greg Lindsay's Blog

March 04, 2011  |  permalink

Now It Can Be Told: How I Beat IBM’s Watson (When Jennings and Rutter Couldn’t)

image

It already feels like a million years ago (have the machines taken over yet?), but it’s been barely twelve months since I schlepped to IBM’s Eero Saarinen-designed research campus in Westchester to spar with Watson, it’s then-secret, now-famous Jeopardy!-playing supercomputer. I thrashed him three times only a week after he’d gone undefeated against his human opponents, and I’ve been dining out on it ever since. If you want to hear the story (and it seems everyone does) I wrote about the experience for both Fast Company and McKinsey Quarterly, noting in each story that Watson had me playing his game in no time, hop-scotching all over the board and racing to find Daily Doubles ahead of him. This was not new. As I noted in the McKinsey Quarterly story:

I was hardly the first person to try and beat a computer at its own game rather than stick to a human one. World chess champion Garry Kasparov, in the third game of his match with IBM’s Deep Blue in May 1997, chose to open with the esoteric Mieses Opening1 in a deliberate attempt to drag the computer out of its well-practiced repertoire of openings. It worked, but required Kasparov to abandon his repertoire as well. The game eventually ended in a draw.

Kasparov lost that match three games later in crushing fashion, leading Newsweek to dub his defeat “The brain’s last stand.” Rather than be embittered by his loss and computing’s subsequent hostile takeover of chess, Kasparov has become a proponent of man–machine collaboration. In “freestyle” tournaments, human–computer teams running the most basic commercial software have managed to crush the best chess programs on the market, which in turn had crushed most grand masters. “Having a computer program available during play was as disturbing as it was exciting,” he wrote in the New York Review of Books.

“The machine doesn’t care about style or patterns or hundreds of years of established theory,” he added. “It is entirely free of prejudice and doctrine, and this has contributed to the development of players who are almost as free of dogma as the machines with which they train. Increasingly, a move isn’t good or bad because it looks that way or because it hasn’t been done that way before. It’s simply good if it works and bad if it doesn’t. Although we still require a strong measure of intuition and logic to play well, humans today are starting to play more like computers.”

Jennings tried similar tactics against Watson, but the machine was simply too fast. You could tell Jennings was ticked, but at least he didn’t cry like Kasparov. As I predicted for Fast Company before the match:

So that’s my strategy, and it worked for me. But Jennings and Rutter will be facing a much tougher opponent—you would think Moore’s Law alone would have made Watson that much tougher by now—and he, in turn, will be battling much better players than I. But I had one other advantage they won’t—I felt no pressure to win, let alone on national television with a $1 million first prize on the line. The computer doesn’t feel it either, and this time around, that gives him the edge.

My money’s on Watson (although just how well he buzzes with his new electro-mechanical “hand” is anyone’s guess). I just hope the humans don’t cry this time. That would be so like us.

Posted by Greg Lindsay  |  Categories:  |  Comments


About Greg Lindsay

» Folllow me on Twitter.
» Email me.
» See upcoming events.


Greg Lindsay is a generalist, urbanist, futurist, and speaker. He is a non-resident senior fellow of the Arizona State University Threatcasting Lab, a non-resident senior fellow of MIT’s Future Urban Collectives Lab, and a non-resident senior fellow of the Atlantic Council’s Scowcroft Strategy Initiative. He was the founding chief communications officer of Climate Alpha and remains a senior advisor. Previously, he was an urban tech fellow at Cornell Tech’s Jacobs Institute, where he explored the implications of AI and augmented reality at urban scale.

» More about Greg Lindsay

Blog

January 31, 2024

Unfrozen: Domo Arigatou, “Mike 2.0”

January 22, 2024

The Future of Generative AI in Architecture, Engineering, and Construction

January 18, 2024

The Promise and Perils of the Augmented City

January 13, 2024

Henley & Partners: Generative AI, Human Labor, and Mobility

» More blog posts

Articles by Greg Lindsay

-----  |  January 22, 2024

The Future of Generative AI in Architecture, Engineering, and Construction

-----  |  January 1, 2024

2024 Speaking Topics

-----  |  August 3, 2023

Microtargeting Unmasked

CityLab  |  June 12, 2023

Augmented Reality Is Coming for Cities

CityLab  |  April 25, 2023

The Line Is Blurring Between Remote Workers and Tourists

CityLab  |  December 7, 2021

The Dark Side of 15-Minute Grocery Delivery

Fast Company  |  June 2021

Why the Great Lakes need to be the center of our climate strategy

Fast Company  |  March 2020

How to design a smart city that’s built on empowerment–not corporate surveillance

URBAN-X  |  December 2019

ZINE 03: BETTER

CityLab  |  December 10, 2018

The State of Play: Connected Mobility in San Francisco, Boston, and Detroit

Harvard Business Review  |  September 24, 2018

Why Companies Are Creating Their Own Coworking Spaces

CityLab  |  July 2018

The State of Play: Connected Mobility + U.S. Cities

Medium  |  May 1, 2017

The Engine Room

Fast Company  |  January 19, 2017

The Collaboration Software That’s Rejuvenating The Young Global Leaders Of Davos

The Guardian  |  January 13, 2017

What If Uber Kills Public Transport Instead of Cars

Backchannel  |  January 4, 2017

The Office of the Future Is… an Office

New Cities Foundation  |  October 2016

Now Arriving: A Connected Mobility Roadmap for Public Transport

Inc.  |  October 2016

Why Every Business Should Start in a Co-Working Space

Popular Mechanics  |  May 11, 2016

Can the World’s Worst Traffic Problem Be Solved?

The New Republic  |  January/February 2016

Hacking The City

» See all articles