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Computerworld - For many people, artificial intelligence evokes the menacing computer Hal from 2001: A Space Odyssey, a machine so intelligent that it could function independently of humans.

Those inflated notions spawned by science fiction writers about the convergence of humans and machines tarnished the image of AI in the 1980s because AI was perceived as failing to live up to its potential.

Still, the field has quietly produced advanced applications such as Google Inc.'s search engine, systems that trade stocks and commodities without human intervention, and software that detects credit card fraud.

There's no precise definition of AI, but broadly, it's a field that attempts to provide machines with humanlike reasoning and language-processing capabilities.

Researchers now are emerging from what has been called an "AI winter" with renewed interest in the biology of the brain and research honed to practical applications in medicine, customer service, manufacturing, education and other areas.

Jeff Hawkins, founder of Palm Computing and chief technology officer at PalmOne Inc. in Milpitas, Calif., created a buzz in the AI world with his book On Intelligence: How a New Understanding of the Brain Will Lead to Truly Intelligent Machines (Times Books, 2004), which asserts that AI research should focus on the parts of the brain associated with intelligence.

"In the past, people thought of the brain as a computer, where I have some input, I write a program to process that, and then I spit it out, and the success is getting the correct output," Hawkins says. "In all these cases, AI kept failing, because brains are not computers; they are memory systems that make a model of the world."

Jeff Hawkins says AI applications should work like the brain, not like a computer.

Hawkins, who founded the Redwood Neuroscience Institute in Redwood, Calif., predicts that once researchers can infuse systems with language, memory and other skills housed in the neocortex, applications will emerge for areas such as drug discovery, robotics, computer vision and remote sensing—tasks that today are hard to automate by conventional techniques.

Fair Isaac Corp. in Minneapolis is automating business decision-making tasks such as approving bank loans and detecting credit card fraud. Robert Hecht-Nielsen, vice president of research and development at Fair Isaac, is building a cognitive system that can understand language and adapt through trial and error—similar to how a child learns to hit a baseball.

The system uses a cognition algorithm modeled on the cerebral cortex of the brain. Hecht-Nielsen's work is based on "confabulation," a mathematical theory that each individual instance of information processing in cognition involves drawing a conclusion based upon a set of assumed facts by applying available knowledge. For example, if a small animal waddles like a duck, quacks like a duck and flies like a duck, one can conclude that it's a duck.