18 March 2008
In a conversation with Kevin Quinn this week I was reminded of a fascinating lecture given at Google in 2006 by Luis von Ahn, an assistant professor in computer science at Carnegie Mellon. Von Ahn gives a very entertaining and thought-provoking talk on ingenious ways to apply human intelligence and judgment on a large scale to fairly small problems that computers still struggle with.
(Or watch video on Google video.)
Von Ahn devises games that produce data, the best-known example being the ESP Game, which Google acquired and developed as Google Image Labeler. In the game, you are paired with another (anonymous) player and shown an image. Each of you feverishly types in words describing the image (eg, "Spitzer", "politician", "scandal", "prostitution"); you get points and move to the next image when you and your partner agree on a label. The game is fun, even addictive, and of course Google gets a big, free payoff -- a set of validated keywords for each image.
I'm curious about how these approaches can be applied to coding problems in social science. A lot of recent interesting work has involved developing machine learning techniques to teach computers to label text, but there are clearly cases where language is just too subtle and complex to accurately extract meaning, and we need real people to read the text and make judgments. Mostly we hire RAs or do it ourselves; could we devise games instead?