Report: Slave and Expert Systems

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  1980s: Artificial Intelligence (AI) - From Lab to Life

Following the commercial success of expert systems, which started in the 1970s, also other AI technologies began to make their way into the marketplace. In 1986, U.S. sales of AI-related hardware and software rose to U.S.$ 425 million. Especially expert systems, because of their efficiency, were still in demand. Yet also other fields of AI turned out to be successful in the corporate world.

Machine vision systems for example were used for the cameras and computers on assembly lines to perform quality control. By 1985 over a hundred companies offered machine vision systems in the U.S., and sales totaled U.S.$ 80 million. Although there was a breakdown in the market for AI-systems in 1986 - 1987, which led to a cut back in funding, the industry slowly recovered.

New technologies were being invented in Japan. Fuzzy logic pioneered in the U.S. and also neural networks were being reconsidered for achieving artificial intelligence. The probably most important development of the 1980s was, that it showed that AI technology had real life uses. AI applications like voice and character recognition systems or steadying camcorders using fuzzy logic were not only made available to business and industry, but also to the average customer.

browse Report:
Slave and Expert Systems
    Introduction: The Substitution of Human Faculties with Technology: Early Tools
-3   1960s - 1970s: Expert Systems Gain Attendance
-2   1970s: Computer-Integrated Manufacturing (CIM)
-1   Late 1970s - Present: Fourth Generation Computers
0   1980s: Artificial Intelligence (AI) - From Lab to Life
Expert system
Expert systems are advanced computer programs that mimic the knowledge and reasoning capabilities of an expert in a particular discipline. Their creators strive to clone the expertise of one or several human specialists to develop a tool that can be used by the layman to solve difficult or ambiguous problems. Expert systems differ from conventional computer programs as they combine facts with rules that state relations between the facts to achieve a crude form of reasoning analogous to artificial intelligence. The three main elements of expert systems are: (1) an interface which allows interaction between the system and the user, (2) a database (also called the knowledge base) which consists of axioms and rules, and (3) the inference engine, a computer program that executes the inference-making process. The disadvantage of rule-based expert systems is that they cannot handle unanticipated events, as every condition that may be encountered must be described by a rule. They also remain limited to narrow problem domains such as troubleshooting malfunctioning equipment or medical image interpretation, but still have the advantage of being much lower in costs compared with paying an expert or a team of specialists.