History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories, and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The study of logic and formal reasoning from antiquity to the present led directly to the invention of the programmable digital computer in the 1940s, a machine based on abstract mathematical reasoning. This device and the ideas behind it inspired scientists to begin discussing the possibility of building an electronic brain. The field of AI research was founded at a workshop held on the campus of Dartmouth College in 1956. 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. The U.S. government provided millions of dollars with the hope of making this vision come true. Eventually, it became obvious that researchers had grossly underestimated the difficulty of this feat. 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 due to the availability of powerful computer hardware, the collection of immense data sets, and the application of solid mathematical methods. Soon after, deep learning proved to be a breakthrough technology, eclipsing all other methods. The transformer architecture debuted in 2017 and was used to produce impressive generative AI applications, amongst other use cases. Investment in AI boomed in the 2020s. The recent AI boom, initiated by the development of transformer architecture, led to the rapid scaling and public releases of large language models (LLMs) like ChatGPT. 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, and fiction ===

Mythology and folklore has depicted of automatons and similar human-like artificial life. In Greek mythology, Talos was a creature made of bronze who acted as a guardian for the island of Crete. Alchemists in the Islamic Golden Age, such as Jabir ibn Hayyan, attempted Takwin, the artificial creation of life, including human life, although this may have been metaphorical. In Jewish folklore during the Middle Ages, a Golem was a clay sculpture that was said to have come to life through the insertion of a piece of paper with any of God's names on it into the mouth. 16th century Swiss alchemist Paracelsus described a procedure he claimed would fabricate a homunculus, or artificial man. Brazen heads were a recurring motif in late medieval and early modern folklore. By the 19th century, ideas about artificial men and thinking machines became a popular theme in fiction. Notable works like Mary Shelley's Frankenstein , Johann Wolfgang von Goethe's, Faust, Part Two, and Karel Čapek's R.U.R. (Rossum's Universal Robots). Speculative essays, such as Samuel Butler's "Darwin among the Machines", and Edgar Allan Poe's "Maelzel's Chess Player" reflected society's growing interest in machines with artificial intelligence.

==== Automata ====

Realistic humanoid automata were built by craftsman 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". English scholar Alexander Neckham asserted that the Ancient Roman poet Virgil had built a palace with automaton statues.

=== Formal reasoning ===

Artificial intelligence is based on the assumption that the process of human thought can be mechanized. Philosophers had developed structured methods of formal deduction by the first millennium BCE. 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. Hobbes wrote in Leviathan: "For reason ... is nothing but reckoning, that is adding and subtracting". 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." The study of mathematical logic, such as Boole's The Laws of Thought and Frege's Begriffsschrift, have allowed for the scientific study of artificial intelligence. Building on Frege's system, Russell and Whitehead presented a formal treatment of the foundations of mathematics in the Principia Mathematica in 1913. Following Russell, David Hilbert challenged mathematicians of the 1920s and 30s to formalize all mathematical reasoning. This question has been addressed by Gödel's incompleteness proof, Turing's machine and Church's Lambda calculus. This work suggested that, within these limits, any form of mathematical reasoning could be mechanized. The Church-Turing thesis implied that a mechanical device, shuffling symbols as simple as 0 and 1, could imitate any conceivable process of mathematical deduction. The key insight was the Turing machine—a simple theoretical construct that captured the essence of abstract symbol manipulation.

=== Neuroscience === In the 18th and 19th centuries Luigi Galvani, Emil du Bois-Reymond, Hermann von Helmholtz and others demonstrated that the nerves carried electrical signals and Robert Bentley Todd correctly speculated in 1828 that the brain was an electrical network. Camillo Golgi's staining techniques enabled Santiago Ramón y Cajal to provide evidence for the neuron theory: "The truly amazing conclusion is that a collection of simple cells can lead to thought, action, and consciousness". Donald Hebb was a Canadian psychologist whose work laid the foundation for modern neuroscience, particularly in understanding learning, memory, and neural plasticity. His most influential book, The Organization of Behavior (1949), introduced the concept of Hebbian learning, often summarized as "cells that fire together wire together." Hebb began formulating the foundational ideas for this book in the early 1940s, particularly during his time at the Yerkes Laboratories of Primate Biology from 1942 to 1947. He made extensive notes between June 1944 and March 1945 and sent a complete draft to his mentor Karl Lashley in 1946. The manuscript for The Organization of Behavior wasn't published until 1949. The delay was due to various factors, including World War II and shifts in academic focus. By the time it was published, several of his peers had already published related ideas, making Hebb's work seem less groundbreaking at first glance. However, his synthesis of psychological and neurophysiological principles became a cornerstone of neuroscience and machine learning.

=== 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. 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. 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. 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. 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. 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.

=== 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 ===

When access to digital computers became possible in the mid-fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought. This was a new approach to creating thinking machines. In 1955, Allen Newell and future Nobel Laureate Herbert A. Simon created the "Logic Theorist", with help from J. C. Shaw. The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead's Principia Mathematica, and find new and more elegant proofs for some. Simon said that they had "solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind." The symbolic reasoning paradigm they introduced would dominate AI research and funding until the mid-90s, as well as inspire the cognitive revolution.

=== Dartmouth Workshop ===

The Dartmouth workshop of 1956 was a pivotal event that marked the formal inception of AI as an academic discipline. It was organized by Marvin Minsky and John McCarthy, with the support of two senior scientists Claude Shannon and Nathan Rochester of IBM. The proposal for the conference stated they intended to test the assertion that "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it". The term "Artificial Intelligence" was introduced by John McCarthy at the workshop. The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research. At the workshop, Newell and Simon debuted the "Logic Theorist". The workshop was the moment that AI gained its name, its mission, its first major success and its key players, and is widely considered the birth of AI.

=== Cognitive revolution ===

In the autumn of 1956, Newell and Simon also presented the Logic Theorist at a meeting of the Special Interest Group in Information Theory at the Massachusetts Institute of Technology (MIT). At the same meeting, Noam Chomsky discussed his generative grammar, and George Miller described his landmark paper "The Magical Number Seven, Plus or Minus Two". Miller wrote "I left the symposium with a conviction, more intuitive than rational, that experimental psychology, theoretical linguistics, and the computer simulation of cognitive processes were all pieces from a larger whole." This meeting was the beginning of the "cognitive revolution"—an interdisciplinary paradigm shift in psychology, philosophy, computer science and neuroscience. It inspired the creation of the sub-fields of symbolic artificial intelligence, generative linguistics, cognitive science, cognitive psychology, cognitive neuroscience and the philosophical schools of computationalism and functionalism. All these fields used related tools to model the mind and results discovered in one field were relevant to the others. The cognitive approach allowed researchers to consider "mental objects" like thoughts, plans, goals, facts or memories, often analyzed using high level symbols in functional networks. These objects had been forbidden as "unobservable" by earlier paradigms such as behaviorism. Symbolic mental objects would become the major focus of AI research and funding for the next several decades.

== Early successes (1956–1974) == The programs developed in the years after the Dartmouth Workshop were, to most people, simply "astonishing": computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such "intelligent" behavior by machines was possible at all. Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years. Government agencies like the Defense Advanced Research Projects Agency (DARPA, then known as "ARPA") poured money into the field. Artificial Intelligence laboratories were set up at many British and US universities in the latter 1950s and early 1960s.

=== Approaches === There were many successful programs and new directions in the late 50s and 1960s. Among the most influential were these:

==== Reasoning, planning and problem solving as search ==== Many early AI programs used the same basic algorithm. To achieve some goal (like winning a game or proving a theorem), they proceeded step by step towards it (by making a move or a deduction) as if searching through a maze, backtracking whenever they reached a dead end. The principal difficulty was that, for many problems, the number of possible paths through the "maze" was astronomical (a situation known as a "combinatorial explosion"). Researchers would reduce the search space by using heuristics that would eliminate paths that were unlikely to lead to a solution. Newell and Simon tried to capture a general version of this algorithm in a program called the "General Problem Solver". Other "searching" programs were able to accomplish impressive tasks like solving problems in geometry and algebra, such as Herbert Gelernter's Geometry Theorem Prover (1958) and Symbolic Automatic Integrator (SAINT), written by Minsky's student James Slagle in 1961. Other programs searched through goals and subgoals to plan actions, like the STRIPS system developed at Stanford to control the behavior of the robot Shakey.

==== Natural language ====

An important goal of AI research is to allow computers to communicate in natural languages like English. An early success was Daniel Bobrow's program STUDENT, which could solve high school algebra word problems. A semantic net represents concepts (e.g., "house", "door") as nodes, and relations among concepts as links between the nodes (e.g. "has-a"). The first AI program to use a semantic net was written by Ross Quillian and the most successful (and controversial) version was Roger Schank's Conceptual dependency theory. Joseph Weizenbaum's ELIZA could carry out conversations that were so realistic that users occasionally were fooled into thinking they were communicating with a human being and not a computer program (see ELIZA effect). But in fact, ELIZA simply gave a canned response or repeated back what was said to it, rephrasing its response with a few grammar rules. ELIZA was the first chatbot.

==== Micro-worlds ==== In the late 60s, Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro-worlds. They pointed out that in successful sciences like physics, basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies. Much of the research focused on a "blocks world," which consists of colored blocks of various shapes and sizes arrayed on a flat surface. This paradigm led to innovative work in machine vision by Gerald Sussman, Adolfo Guzman, David Waltz (who invented "constraint propagation"), and especially Patrick...

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Artificial Intelligence - Computer Science