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Book cover of the 1979 paperback edition. Hubert Dreyfus was a critic of artificial intelligence research. In a series of papers and books, including Alchemy and AI, What Computers Can't Do (1972; 1979; 1992) and Mind over Machine, he presented a pessimistic assessment of AI's progress and a critique of the philosophical foundations of the field.
Dreyfus was born on 15 October 1929, in Terre Haute, Indiana, to Stanley S. and Irene (Lederer) Dreyfus. [7] [8]He attended Harvard University from 1947. [9] With a senior honors thesis on Causality and Quantum Theory (for which W. V. O. Quine was the main examiner) [9] he was awarded a B.A. summa cum laude in 1951 [8] and joined Phi Beta Kappa. [10]
[7] [8] For example, the criticisms fail to take into account the notion of the “deliberative rationality” of experts, which is a kind of expert reflection in action, as developed in Dreyfus and Dreyfus, Mind Over Machine [9] and further elaborated by Rousse and Dreyfus in "Revisiting the Six Stages of Skill Acquisition." [3]
[v] [7] Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s. [339] Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. [346]
Artificial intelligence research has succeeded in developing many programs that are capable of intelligently solving particular problems. However, AI research has so far not been able to produce a system with artificial general intelligence -- the ability to solve a variety of novel problems, as humans do.
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The philosophy of artificial intelligence is a branch of the philosophy of mind and the philosophy of computer science [1] that explores artificial intelligence and its implications for knowledge and understanding of intelligence, ethics, consciousness, epistemology, [2] and free will.
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...