Comparisons The human nervous system is intricately more complex than that of the computer but is more sluggish in handling messages (Fig. 4). The reasons for this are found in the speeds with which electrical impulses are transmitted. Human nerve pulses last about one thousandth of a second whereas typical computer pulses last only a few billionths of a second. Hence, the human brain can process only 50 billion bits of information within a conscious lifetime, while the computer can process this same number within a couple of hours. Complexity or sophistication of the information processes available Fig. 4 COMPARISON OF MAN AND MACHINE III. ELEMENTS NECESSARY FOR ARTIFICIAL INTELLIGENCE A machine must have facilities for representing and analyzing its own goals and resources. There are three basic elements necessary to achieve true artificial intelligence: memory, pattern recognition, and learning. Memory It is assumed that the long-term storage of information and data in the brain is necessary to learning. Memory is, in actuality, a problem of recognition. This is true because facts are rarely at hand in the form they are needed. Man's pattern recognition of data is largely due to his fabulous memory system and its ability to classify information. If 225,000,000,000 computers (IBM 370/135 or equivalent) were connected together, they still would not achieve the memory capacity of the human brain. Pattern Recognition Many of the problems in artificial intelligence are in pattern recognition such as identifying printed letters (Fig. 5). Pattern recognition techniques are necessary in order to cut down on the possibilities to be considered in solving a problem. Unless this is done, the search for a solution becomes exponential in growth, and soon outstrips the limits of the computer. At present, pattern recognition programs do not even approach the flexibility of human pattern recognition abilities. Until they do, true artificial intelligence will not be possible. Learning Over 2300 years ago the Greek philosopher Aristotle studied the process of associative learning. For centuries man has been fascinated by this learning process. The two most important families of contemporary learning theory are: Stimulus-Response theorist and Cognitive field (or Gestalt) theorist. Sequential-processing program for distinguishing four letters, A, H, V and Y, employs three test features: presence or absence of a concavity above, a crossbar, and a vertical line. The tests are applied in order, with each outcome determining the next step. Fig.5 PATTERN RECOGNITION Stimulus-Response. Under Stimulus-Response, behavior is seen as a transaction between the stimuli that impinge an organism, and the resulting response. The Stimulus-Response theorist sees learning as a permanent relation between stimulus and response. In the early 1 900's American psychologist E.L. Thorndike formulated the Law of Effect-when a person repeatedly does something successfully, the neural pathways become reinforced; when a person repeatedly fails to do something successfully, the neural pathways become inhibited. Ivan Pavlov's famous experiments with the salivation of a dog when a bell rings illustrates this theory quite well. Current computer programs, for the most part, follow the Stimulus-Response theory since the same input usually engenders the same output. Gestalt. Gestalt is a German word which means a configuration has characteristics more broadly based than those of its parts. Gestalt psychology originated in Germany in the early part of the 20th century with four psychologists: Max Wertheimer, Wolfgang Kohler, Kurt Koffka, and Kurt Lewin. Gestalt theorists see learning as goal-oriented with the learner being creatively bent. Researchers in artificial intelligence using Gestalt theories as their guide generally analyze techniques human subjects employ and then incorporate them into a program. Generally this type of research is called Cognitive Simulation. Cognitive Simulation (Gestalt) in artificial intelligence research was marked by early success. Unfortunately, the successes diminished quite rapidly until researchers became disenchanted with this approach. Feedback in the Learning Process. Learning is necessarily a goal-seeking process and feedback is inherent in it. In practice, feedback is the process of regulating a procedure or system by returning information gained from its outputs to its inputs (Fig. 9). In order for a system to obtain feedback information, it must be able to develop associative patterns from which it can determine how to use the feedback information (Fig. 10). In other words, a system must be told something for it to learn something.