History of artificial intelligence

The history of artificial intelligence traces efforts to build machines that perform tasks associated with intelligence, from early theories and symbolic programs to modern machine learning systems.

Alan Turing: A British mathematician and computer scientist whose work shaped theoretical computing and early discussions of machine intelligence. His 1950 essay proposed an influential behavioral test for machine intelligence.
Symbolic artificial intelligence: An approach to AI that represents knowledge with explicit symbols and manipulates them using formal rules. Early programs used rules and symbols to solve problems and represent knowledge.
AI winter: A period when reduced funding and public interest followed unmet expectations in artificial intelligence. Repeated downturns punctuated early optimism about symbolic AI and expert systems.
Marvin Minsky: An American cognitive scientist and computer scientist who co-founded the MIT Artificial Intelligence Laboratory. His research and institution-building helped shape American AI during its early decades.
Artificial general intelligence: A hypothetical AI system with broad abilities comparable to human intelligence across many kinds of tasks. It contrasts with the narrow, task-specific systems that dominate AI’s actual history.
Turing test: A test of machine intelligence in which a human judge distinguishes a computer from a person through conversation. It turned early debate about thinking machines into a recognizable evaluation proposal.
Expert system: A computer program that applies encoded domain knowledge and rules to make decisions or offer advice. Expert systems became a prominent commercial form of rule-based AI in the 1970s and 1980s.
Deep Blue: IBM’s chess-playing computer that defeated world champion Garry Kasparov in a 1997 match. Its victory made a specialized AI achievement a widely recognized public milestone.
Allen Newell: An American computer scientist and cognitive psychologist who co-created influential early AI programs. His work with Herbert A. Simon established symbolic problem-solving as a major approach.
Connectionism: An approach to cognition that models information processing through networks of simple interconnected units. It competed with symbolic AI as an account of how intelligent behavior could arise.

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