Computers and Problem Solving

In order to understand why conventional computers fail at some tasks, we need to know how they work. Conventional computers use precisely detailed mathematical formulas called algorithms to solve problems  a very efficient method for problems that can be described in this exacting manner. Unfortunately, one very large set of problems we encounter every day, random problems, cannot be defined so precisely. In fact, the only way these situations can be described precisely to an intelligence lacking any prior knowledge of the subject, like a computer, would be to list descriptions of all of the possible solutions to the problem. The difficulties in defining these rando

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neurophysiologist at the University of California, Irvine, explains the main advantage of neural networks when he notes that, "When you try to study memories you are almost the memories you're studying, and it almost gets in the way. But if you can make a model in silicon, you can stand away and study" ("Mimicking" 53). More specifically, Lynch and Dr. Oranger have set up a 500 neuron network to model how the brain distinguishes smells. When they first began their experiment, the network responded with a unique pattern to each odor; however, after more odors had been processed, the more active neurons began producing stronger signals, eventually becoming representatives for each basic category of odor. After six samples from each group had been processed, the network had organized itself to the point that it responded with the same signal pattern for new smells of the same category. The real surprise came when smells were analyzed by the network for a second time; instead of responding with the old pattern, the network reconstituted itself, creating a slightly different pattern for each specific odor. Essentially, as the two researchers put it, the network decided on its first sniff that a smell was cheese; on the second sniff
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Approximate Word count = 3141
Approximate Pages = 13 (250 words per page)