When artificial intelligence was first being developed, scientists hoped for impressive new achievements, the most prominent and ambitious of which was the creation of computers with the ability to equal or even surpass human thought. These hopes turned out to be wildly optimistic. Today, conventional artificial intelligence has many glaring flaws, including an inability to make decisions in slightly unfamiliar areas and a demanding need for tediously precise instructions to perform required tasks. However, a new type of computer, the neural network, may displace the conventional artificial intelligence program, because neural networks attack problem solving in a very different manner, allowing these network computers to work successfully on problems that regular computers cannot.
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 random problems can be easily demonstrated by trying to describe a tree in a manner a computer could use. Typical definitions we might give would include concepts like leaves and branches. However, consider that the computer also has no concept of what branches or leaves are; these concepts need to be defined as well if they are to be of any use. Even if a computer could understand these ideas, how would you explain to a machine how to understand the difference between a leafless tree in the winte...