The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines." Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.
The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of AI research.
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.
History:
Samuel Butler first raised the possibility of "mechanical consciousness" in an article signed with the nom de plume Cellarius and headed "Darwin among the Machines", which appeared in the Christchurch, New Zealand, newspaper The Press on 13 June 1863. Butler envisioned mechanical consciousness emerging by means of Darwinian Evolution, specifically by Natural selection, as a form of natural, not artificial, intelligence.
McCorduck (2004), however, writes "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." She continues:
"Work toward that end has been a splendid effort, the variety of its form as wondrous as anything humans have conceived; its practitioners as lively a group of poets, dreamers, holy men, rascals, and assorted eccentrics as one could hope to find—not a dullard among them. Its visionaries have lifted our spirits and made us transcend our own species, its poets have told us things about ourselves we never suspected, and its fast talkers have set everybody's teeth on edge."
Beginning with the myth of Pygmalian and Galatea, we have imagined making copies of ourselves, with sacred statues, alchemical beings and charming clockwork automatons. Yet we also have a fear that our creations may turn on us, as in The Golem of Prague and Frankenstein.
In the middle of the 20th century, a handful of scientists explored a new approach to an ancient dream, based on their discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.
In the summer of 1956, at a conference on the campus of Dartmouth College, the field of AI research was born. Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle 60s their research was heavily funded by DARPA and they were optimistic about the future of the new field:
1965, H. A. Simon: "[M]achines will be capable, within twenty years, of doing any work a man can do"
1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, DARPA cut off all undirected, exploratory research in AI. This was the first AI Winter.
In the early 80s, the field was revived by the commercial success of expert systems and by 1985 the market for AI had reached more than a billion dollars. Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow. Minsky was right. Beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.
In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.
The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of AI research.
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.
History:
Samuel Butler first raised the possibility of "mechanical consciousness" in an article signed with the nom de plume Cellarius and headed "Darwin among the Machines", which appeared in the Christchurch, New Zealand, newspaper The Press on 13 June 1863. Butler envisioned mechanical consciousness emerging by means of Darwinian Evolution, specifically by Natural selection, as a form of natural, not artificial, intelligence.
McCorduck (2004), however, writes "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." She continues:
"Work toward that end has been a splendid effort, the variety of its form as wondrous as anything humans have conceived; its practitioners as lively a group of poets, dreamers, holy men, rascals, and assorted eccentrics as one could hope to find—not a dullard among them. Its visionaries have lifted our spirits and made us transcend our own species, its poets have told us things about ourselves we never suspected, and its fast talkers have set everybody's teeth on edge."
Beginning with the myth of Pygmalian and Galatea, we have imagined making copies of ourselves, with sacred statues, alchemical beings and charming clockwork automatons. Yet we also have a fear that our creations may turn on us, as in The Golem of Prague and Frankenstein.
In the middle of the 20th century, a handful of scientists explored a new approach to an ancient dream, based on their discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.
In the summer of 1956, at a conference on the campus of Dartmouth College, the field of AI research was born. Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle 60s their research was heavily funded by DARPA and they were optimistic about the future of the new field:
1965, H. A. Simon: "[M]achines will be capable, within twenty years, of doing any work a man can do"
1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, DARPA cut off all undirected, exploratory research in AI. This was the first AI Winter.
In the early 80s, the field was revived by the commercial success of expert systems and by 1985 the market for AI had reached more than a billion dollars. Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow. Minsky was right. Beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.
In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.
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