The history of artificial intelligence (AI) began in antiquity, with myths, stories, and rumors of artificial beings endowed with intelligence by master craftsmen.Nilsson, Nils J. (2009). The Quest for Artificial Intelligence. Cambridge University Press. Chapter 1
The field of research that eventually evolved into what is widely described as artifical intelligence was founded at a workshop held on the campus of Dartmouth College in 1956. At the workshop, a concept of the first AI program, Logic Theorist, was presented by future Turing Awardee Allen Newell and future Nobel Laureate Herbert A. Simon, with help from J. C. Shaw. The software architecture of this program differed from modern AI systems in that it was a hand-written fixed script. Attendees of the workshop became the leaders of AI research for decades. Many of them predicted that machines as intelligent as humans would exist within a generation. Despite the eventual success of its attendees and the field as a whole, the workshop was considered a failure. "When the workshop failed to deliver, AI was dismissed as a pipe dream and research funding dried up".
It became apparent that researchers had underestimated the complexity of this objective. In 1974, criticism from James Lighthill and pressure from the U.S. Congress led the U.S. and British Governments to stop funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government and the success of expert systems reinvigorated investment in AI, and by the late 1980s, the industry had grown into a billion-dollar enterprise. However, investors' enthusiasm waned in the 1990s, and the field was criticized in the press and avoided by industry (a period known as an "AI winter"). Nevertheless, research and funding continued to grow under other names.
In the early 2000s, machine learning was applied to a wide range of problems in academia and industry. The success was attributable to the availability of powerful computer hardware, the accumulation of expansive data sets, and the application of rigorous mathematical methods. Soon after, deep learning proved to be a breakthrough technology, eclipsing all other methods. The transformer architecture, introduced in 2017, and was utilized to produce generative AI applications, among other implementation.
Investment in AI boomed in the 2020s. The AI boom, initiated by the development of transformer architecture, led to the rapid scaling and public releases of large language models (LLMs) like ChatGPT and Claude (AI). These models exhibit human-like traits of knowledge, attention, and creativity, and have been integrated into various sectors, fueling exponential investment in AI. However, concerns about the potential risks and ethical implications of advanced AI have also emerged, causing debate about the future of AI and its impact on society.
Precursors Myth, folklore, fiction and speculation
Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus, the bronze giant Talos and Pygmalion's Galatea. In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as Jabir ibn Hayyan's Takwin, Paracelsus' homunculus, Rabbi Judah Loew's Golem and Roger Bacon's brazen head. By the 19th century, ideas about artificial men and thinking machines became a popular theme in fiction. Notable works include Mary Shelley's Frankenstein (1818), Johann Wolfgang von Goethe's, Faust, Part Two (1832), and Karel Čapek's R.U.R. (Rossum's Universal Robots) (1921). Stories of these creatures and their fates consider many of the same hopes, fears and ethical concerns that are presented by modern artificial intelligence. Issues relevant to AI were also discussed in speculative essays such as Samuel Butler's "Darwin among the Machines" (1863).
Automata
Realistic humanoid automata were built by craftsmen from many civilizations, including Yan Shi, Hero of Alexandria, Al-Jazari, Haroun al-Rashid, Jacques de Vaucanson, Leonardo Torres y Quevedo, Pierre Jaquet-Droz and Wolfgang von Kempelen. The oldest known automata were sacred statues of ancient Egypt and Greece. The faithful believed that craftsman had imbued these figures with very real minds, capable of wisdom and emotion—Hermes Trismegistus wrote that "by discovering the true nature of the gods, man has been able to reproduce it".Quoted in .
Formal reasoning
Artificial intelligence is based on the assumption that the process of human thought can be mechanized. Chinese, Indian and Greek philosophers developed structured methods of formal reasoning by the first millennium BCE. Formal logic was invented and improved by Greek, Islamic and European philosophers, such as Aristotle, Euclid, Al-Khwarizmi, Duns Scotus and René Descartes.
Spanish philosopher Ramon Llull (1232–1315) developed several logical machines devoted to the production of knowledge by logical means; Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations, produced by the machine by mechanical meanings, in such ways as to produce all the possible knowledge. Llull's work had a great influence on Gottfried Leibniz, who redeveloped his ideas.
In the 17th century, Leibniz, Thomas Hobbes and René Descartes explored the possibility that all rational thought could be made as systematic as algebra or geometry.17th-century mechanism and AI:
Hobbes wrote in Leviathan: "For reason ... is nothing but reckoning, that is adding and subtracting".Hobbes and AI:
Leibniz described a universal language of reasoning, the characteristica universalis, which would reduce argumentation to calculation so that "there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in hand, down to their slates, and to say to each other (with a friend as witness, if they liked): Let us calculate."Leibniz and AI:
Boole's The Laws of Thought (1854) and Frege's Begriffsschrift (1879) defined the modern form of symbolic mathematical logic. Building on Frege's system, Russell and Whitehead presented a formal treatment of the foundations of arithmetic in the Principia Mathematica in 1913. Inspired by their success, David Hilbert challenged mathematicians of the 1920s and 30s to formalize all mathematical reasoning. In response, Gödel's incompleteness proof, Turing's machine and Church's Lambda calculus showed that there were, in fact, limits to what formal mathematics could do.
However, within these limits, the Church-Turing thesis implied that a mechanical device, shuffling symbols as simple as 0 and 1, could imitate any conceivable process of mathematical reasoning or problem solving. The key insight was the Turing machine—a simple theoretical construct that captured the essence of abstract symbol manipulation.The Turing machine:
and see This device and the ideas behind it would inspire engineers and mathematicians of the 1940s to build machines that could theoretically carry out any form of formal reasoning or problem solving.
Computer science
Calculating machines were designed or built in antiquity and throughout history by many people, including Gottfried Leibniz, Joseph Marie Jacquard, Charles Babbage, Percy Ludgate,; ; Leonardo Torres Quevedo,; ; Vannevar Bush, and others. Ada Lovelace speculated that Babbage's machine was "a thinking or ... reasoning machine", but warned "It is desirable to guard against the possibility of exaggerated ideas that arise as to the powers" of the machine.Quoted in
The first modern computers were the massive machines of the Second World War (such as Konrad Zuse's Z3, Tommy Flowers' Heath Robinson and Colossus, Atanasoff and Berry's ABC, and ENIAC at the University of Pennsylvania). ENIAC was based on the theoretical foundation laid by Alan Turing and developed by John von Neumann, and proved to be the most influential.
Birth of artificial intelligence (1941–1956)
The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 1930s, 1940s, and early 1950s. Recent research in neurology had shown that the brain was an electrical network of neurons that fired in all-or-nothing pulses. Norbert Wiener's cybernetics described control and stability in electrical networks. Claude Shannon's information theory described digital signals (i.e., all-or-nothing signals). Alan Turing's theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an "electronic brain".
In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) explored several research directions that would be vital to later AI research.AI's immediate predecessors:
Alan Turing was among the first people to seriously investigate the theoretical possibility of "machine intelligence". The field of "artificial intelligence research" was founded as an academic discipline in 1956.
Turing test
In 1950, Turing published a landmark paper, "Computing Machinery and Intelligence", in which he speculated about the possibility of creating machines that think.Turing Test, Computing Machinery and Intelligence: , , , , . See also In the paper, he noted that "thinking" is difficult to define and devised his famous Turing test: If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was "thinking". This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine" was at least plausible and the paper answered all the most common objections to the proposition. The Turing test was the first serious proposal in the philosophy of artificial intelligence.
Artificial neural networks Walter Pitts and Warren McCulloch analyzed networks of idealized artificial neurons and showed how they might perform simple logical functions in 1943. They were the first to describe what later researchers would call a neural network.Pitts & McCullough:
See also: The paper was influenced by Turing's paper "On Computable Numbers" from 1936, using similar two-state boolean 'neurons', but was the first to apply it to neuronal function. One of the students inspired by Pitts and McCulloch was Marvin Minsky who was a 24-year-old graduate student at the time. In 1951, Minsky and Dean Edmonds built the first neural net machine, the SNARC.SNARC:
Minsky would later become one of the most important leaders and innovators in Artificial Intelligence.
Cybernetic robots Experimental robots such as William Grey Walter's turtles and the Johns Hopkins Beast, were built in the 1950s. These machines did not use computers, digital electronics, or symbolic reasoning; they were controlled entirely by analog circuitry.Turtles and Johns Hopkins Beast:
Game AI In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess. Arthur Samuel's checkers program, the subject of his 1959 paper "Some Studies in Machine Learning Using the Game of Checkers", eventually achieved sufficient skill to challenge a respectable amateur. Samuel's program was among the first uses of what would later be called machine learning. Game AI would continue to be used as a measure of progress in AI throughout its history.
Symbolic reasoning and the Logic Theorist