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Artificial intelligence (AI) has been one of the most controversial domains of inquiry in computer science since it was first proposed in the 1950s. Defined as the part of computer science concerned with designing systems that exhibit the characteristics associated with human intelligence — understanding language, learning, reasoning, solving problems, and so on (Barr and Feigenbaum, 1981) — the field has attracted researchers because of its ambitious goals and enormous underlying intellectual challenges. The field has been controversial because of its social, ethical, and philosophical implications. Such controversy has affected the funding environment for AI and the objectives of many research programs....
AI research is conducted by a range of scientists and technologists with varying perspectives, interests, and motivations. Scientists tend to be interested in understanding the underlying basis of intelligence and cognition, some with an emphasis on unravelling the mysteries of human thought and others examining intelligence more broadly. Engineering-oriented researchers, by contrast, are interested in building systems that behave intelligently. Some attempt to build systems using techniques analogous to those used by humans, whereas others apply a range of techniques adopted from fields such as information theory, electrical engineering, statistics, and pattern recognition. Those in the latter category often do not necessarily consider themselves AI researchers, but rather fall into a broader category of researchers interested in machine intelligence.
The concept of AI originated in the private sector, but the growth of the field, both intellectually and in the size of the research community, has depended largely on public investments. Public monies have been invested in a range of AI programs, from fundamental, long-term research into cognition to shorter-term efforts to develop operational systems. Most of the federal support has come from the Defense Advanced Research Projects Agency (DARPA, known during certain periods as ARPA) and other units of the Department of Defense (DOD). Other funding agencies have included the National Institutes of Health, National Science Foundation, and National Aeronautics and Space Administration (NASA), which have pursued AI applications of particular relevance to their missions—health care, scientific research, and space exploration.
This chapter highlights key trends in the development of the field of AI and the important role of federal investments. The sections of this chapter, presented in roughly chronological order, cover the launching of the AI field, the government's initial participation, the pivotal role played by DARPA, the success of speech recognition research, the shift from basic to applied research, and AI in the 1990s. The final section summarizes the lessons to be learned from history. This case study is based largely on published accounts, the scientific and technical literature, reports by the major AI research centres, and interviews conducted with several leaders of AI research centres. (Little information was drawn from the records of the participants in the field, funding agencies, editors and publishers, and other primary sources most valued by professional historian.)
The original article is from: nap.edu
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