Whatever happened to artificial intelligence?

The grand promise of intelligent machines underestimated the complexity of reproducing human cognition

Access to tools

Part of offsetting that trend will be better software tools, the type favored by Luger in his book, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Sixth Edition). "Modern languages have roots in AI research, including object oriented design, C++, C#, and Java," Luger says. "The coolest stuff we've done is build a set of exciting tools."

Yet tools and embedded intelligent systems don't answer the "grand challenges" of artificial intelligence, including robots and language processing. Very few projects have captured the public's imagination.

NASA got great public response with their Mars rovers, but little was made of the artificial intelligence components. Artificial intelligence techniques considered pure research 15 years earlier guided rovers Spirit and Opportunity around rocks a world away.

Defense Advanced Research Projects Agency (DARPA) provides money for "grand challenges" including Internet development in their earlier incarnation of ARPA. Now it sponsors a contest to build autonomous vehicles (see Urban Challenge). This forces teams to integrate separate discipline areas such as machine vision, learning systems and problem solving while moving through unfamiliar areas.

One of the most successful artificial intelligence products is literally underfoot. Roomba, the home vacuuming product from iRobot, has sold over 2 million units. One survey showed over half of the deployed Roombas have been given pet names by their owners.

Colin Angle, CEO and co-founder of iRobot, says, "When we started shipping Roomba in 2002, we asked focus groups if it was a robot. They said no, a robot was humanoid and this was an intelligent floor vacuum. Now people are definitely changing to accept robot appliances."

Hollywood again set the bar high. "Since the Jetsons in 1962, they created expectations we failed to meet for over 40 years. Big AI projects have largely gone by the wayside, but you can see effective behavior that solves real world problems," Angle says.

As you might expect from someone making work tools for the real world, Angle takes a practical look at artificial intelligence and robotics. "In general, software is algorithms and code that can be reused across platforms. The more low-level tasks used to handle different situations, such as obstruction avoidance, the more successful. We call it bottom-heavy cognition," he says.

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