Chinese room
The Chinese room argument holds that a digital computer executing a program cannot have a "mind", "understanding", or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. The argument was presented by philosopher John Searle in his paper "Minds, Brains, and Programs", published in Behavioral and Brain Sciences in 1980. Similar arguments were presented by Gottfried Leibniz (1714), Anatoly Dneprov (1961), Lawrence Davis (1974) and Ned Block (1978). Searle's version has been widely discussed in the years since. The centerpiece of Searle's argument is a thought experiment known as the Chinese room.The argument is directed against the philosophical positions of functionalism and computationalism which hold that the mind may be viewed as an information-processing system operating on formal symbols, and that simulation of a given mental state is sufficient for its presence. Specifically, the argument is intended to refute a position Searle calls the strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."Although it was originally presented in reaction to the statements of artificial intelligence (AI) researchers, it is not an argument against the goals of mainstream AI research because it does not show a limit in the amount of "intelligent" behavior a machine can display. The argument applies only to digital computers running programs and does not apply to machines in general.
Chinese room - Wikipedia Jump to content From Wikipedia, the free encyclopedia Thought experiment on artificial intelligence For the British video game development studio, see The Chinese Room . Part of a series on Artificial intelligence (AI) Major goals Artificial general intelligence Intelligent agent Recursive self-improvement Planning Computer vision General game playing Knowledge representation Natural language processing Robotics AI safety Approaches Machine learning Data mining Symbolic Deep learning Bayesian networks Evolutionary algorithms Neuro-symbolic AI Systems integration Open-s
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