Late 1950s - Early 1960s: Second Generation Computers An important change in the development of computers occurred in 1948 with the invention of the transistor. It replaced the large, unwieldy vacuum tube and as a result led to a shrinking in size of electronic machinery. The transistor was first applied to a computer in 1956. Combined with the advances in magnetic-core memory, the use of transistors resulted in computers that were smaller, faster, more reliable and more energy-efficient than their predecessors. Stretch by IBM and LARC by Sperry-Rand (1959) were the first large-scale machines to take advantage of the transistor technology (and also used assembly language instead of the difficult machine language). Both developed for atomic energy laboratories could handle enormous amounts of data, but still were costly and too powerful for the business sector's needs. Therefore only two LARC's were ever installed. Throughout the early 1960s there were a number of commercially successful computers (for example the IBM 1401) used in business, universities, and government and by 1965 most large firms routinely processed financial information by using computers. Decisive for the success of computers in business was the stored program concept and the development of sophisticated high-level programming languages like FORTRAN (Formular Translator), 1956, and COBOL (Common Business-Oriented Language), 1960, that gave them the flexibility to be cost effective and productive. The invention of second generation computers also marked the beginning of an entire branch, the software industry, and the birth of a wide range of new types of careers.

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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.

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