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consist of recording the new knowledge by means of new connec- tions that form among neurons.This property of the brain is known as plasticity.

The highly connected architecture of the brain is a model for another approach to AI, the neural net. Unlike a conventional com- puter, such a net more or less simulates real biological brains. Analo- gous to the web of neurons that makes up a natural brain, a neural net consists of many simple processing units interconnected so that they can trade data, with each unit operating on the data it receives. De- pending on how the net is structured, the system can acquire and store knowledge through a learning process that might teach it, for instance, how to identify particular images or sounds.

In 1943,Warren McCulloch and Walter Pitts, at the University of Illinois, laid the groundwork for neural nets through pioneering re- search that depended on viewing the brain as a complex network of neural elements. In order to make a reading machine for the blind that turned printed material into sounded-out words, they intercon- nected light detectors in a way that mimicked neural connections in the brain. In 1951, Marvin Minsky and a collaborator constructed another neural net called the Stochastic Neural-Analog Reinforce- ment Computer (SNARC), which was trained to negotiate a maze as a rat would. Further work in neural nets focused on recognition of visual and aural patterns, but the approach languished in 1968 when Minsky and his colleague Seymour Papert pointed out limitations in the method as then understood. New insights, however, revived the technique in the mid-1980s, along with other approaches that ap- proximate biological styles of thinking.


Initially, AI researchers aimed to produce intelligence within a com- puter, not a robot.A computer interacts with other machines or hu- mans through the abstract medium of data flow but has no direct connection to its physical environment. An operational robot is dif- ferent; it must take in information from its surroundings and respond

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