So, cognitive science and computer science are now poised to share ideas as they never could before, he says. For example, certain AI algorithms send a robot a little reward signal when it does the right thing and a penalty signal when it makes a mistake. Over time, these have a cumulative effect, and the robot learns and improves.
Mitchell says researchers have found with functional MRIs that regions of the brain behave exactly as predicted by these "reinforcement learning" algorithms. "AI is actually helping us develop models for understanding what might be happening in our brains," he says.
Mitchell and his colleagues have been examining the neural activity revealed by brain imaging to decipher how the brain represents knowledge. To train their computer model, they presented human subjects with a list of 60 nouns -- such as telephone, house, tomato and arm -- and observed the brain images that each produced. Then, using a trillion-word text database from Google, they determined the verbs that tend to appear with the 60 base words -- ring with telephone , for example -- and they weighted those words according to the frequency of both occurring.
The resulting model was able to accurately predict the brain image that would result from a word for which no image had ever before been observed. Oversimplifying, the model would, for example, predict that the noun airplane would produce a brain image more like that for train than for tomato .
"We were interested in how the brain represents ideas," Mitchell says, "and this experiment could shed light on a question AI has had a lot of trouble with: What is a good, general-purpose representation of knowledge?" There may be other lessons as well. Noting that the brain is also capable of forgetting, he asks, "Is that a feature or a bug?"
Andrew Ng, an assistant professor of computer science at Stanford University, led the development of the multitalented Stair. He says the robot is evidence that many previously separate fields within AI are now mature enough to be integrated "to fulfill the grand AI dream."
And just what is that dream? "Early on, there were famous predictions that within a relatively short time computers would be as intelligent as people," he says. "We still hope that some time in the future computers will be as intelligent as we are, but it's not a problem we'll solve in 10 years. It may take over 100 years."