Few organizations need to make sense of as much data as search engine companies do. For example, if a user searches Google for "toy car" and then clicks on a Wal-Mart ad that appears at the top of the results, what's that worth to Wal-Mart, and how much should Google charge for that click? The answers lie in an AI specialty that employs "digital trading agents," which companies like Wal-Mart and Google use in automated online auctions.
Michael Wellman, a University of Michigan professor and an expert in these markets, explains: "There are millions of keywords, and one advertiser may be interested in hundreds or thousands of them. They have to monitor the prices of the keywords and decide how to allocate their budget, and it's too hard for Google or Yahoo to figure out what a certain keyword is worth. They let the market decide that through an auction process."
When the "toy car" query is submitted, in a fraction of a second Google looks up which advertisers are interested in those keywords, then looks at their bids and decides whose ads to display and where to put them on the page. "The problem I'm especially interested in," Wellman says, "is how should an advertiser decide which keywords to bid on, how much to bid and how to learn over time -- based on how effective their ads are -- how much competition there is for each keyword."
The best of these models also incorporate mechanisms for predicting prices in the face of uncertainty, he says. Clearly, none of the parties can hope to optimize the financial result from each transaction, but they can improve their returns over time by applying machine learning to real-time pricing and bidding.
One might expect AI research to start with studies of how the human brain works. But most AI advances have come from computer science, not biology or cognitive science.
These fields have sometimes shared ideas, but their collaboration has been at best a "loose coupling," says Tom Mitchell, a computer scientist and head of the Machine Learning Department at Carnegie Mellon University. "Most of the progress in AI has come from good engineering ideas, not because we see how the brain does it and then mimic that."
But now that's changing, he says. "Suddenly, we have ways of observing what the brain is really doing, via brain imaging methods like functional MRI. It's a way to look into the brain while you are thinking and see, once a second, a movie of your brain's activity with a resolution of 1mm."