One of the indications of maturity in "artificial intelligence" research is the emergence of more bounded and tractable sub-fields devoted to specific A.I. problems. Machine learning, computer vision, robotics, etc. The tangible success of these research areas make me wonder if simulating human brains is the most useful approach. Which reminds of this Tom Mitchell quote:
"My late colleague Herb Simon used to talk about how aircraft are artificial birds. In many ways aircraft exceed birds in their ability to fly, but in many other ways they do not. The path to recreating human intelligence may deliver a similar outcome. Just as birds and aircraft are similar but different, we may create artificial intelligence in the future that mimics human brains, yet also differs greatly in its implementation and capability in a variety of arenas."
http://singularityhub.com/2009/04/24/devices-that-read-peopl...
Is simulating a human brain the shortest path to solving "problems in chemistry, biology, physics, economics, engineering, and astronomy?" Machine learning techniques that are very different from what a human brain does, working in concert with the brains of human researchers, might turn out to be the most productive path.