History of AI research

Main articles: history of artificial intelligence and timeline of artificial intelligence

In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent 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.^[28]

The field of modern AI research was founded at conference on the campus of Dartmouth College in the summer of 1956.^[29] 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:^[30] computers were solving word problems in algebra, proving logical theorems and speaking English.^[31] By the middle 60s their research was heavily funded by the U.S. Department of Defense^[32] 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"^[33]
• 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."^[34]

These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced.^[35] In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first AI Winter.^[36]

In the early 80s, AI research was revived by the commercial success of expert systems (a form of AI program that simulated the knowledge and analytical skills of one or more human experts) and by 1985 the market for AI had reached more than a billion dollars.^[37] Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.^[38] 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.^[39]

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.^[40] 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.^[41]

Philosophy of AI

Mind and Brain portal

Can the brain be simulated by a digital computer? If it can, then would the simulation have a mind in the same sense that people do?

In a classic 1950 paper, Alan Turing posed the question "Can Machines Think?" In the years since, the philosophy of artificial intelligence has attempted to answer it.^[42]

• Turing's "polite convention": If a machine acts as intelligently as a human being, then it is as intelligent as a human being. Alan Turing theorized that, ultimately, we can only judge the intelligence of machine based on its behavior. This theory forms the basis of the Turing test.^[43]
• The Dartmouth proposal: Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it. This assertion was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.^[44]
• Newell and Simon's physical symbol system hypothesis: A physical symbol system has the necessary and sufficient means of general intelligent action. This statement claims that the essence of intelligence is symbol manipulation.^[45] Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge.^[46]
• Gödel's incompleteness theorem: A physical symbol system can not prove all true statements. Roger Penrose is among those who claim that Gödel's theorem limits what machines can do.^[47]
• Searle's "strong AI position": A physical symbol system can have a mind and mental states. Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.^[48]
• The artificial brain argument: The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original. This argument combines the idea that a suitably powerful machine can simulate any process, with the materialist idea that the mind is the result of a physical process in the brain.^[49]