View source for History of artificial intelligence

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{{See also|Timeline of artificial intelligence|Progress in artificial intelligence}}
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{{Use dmy dates|date=February 2020}}
{{Artificial intelligence|History}}
{{History of computing}}

The '''history of artificial intelligence''' ('''[[Artificial intelligence|AI]]''') began in [[ancient history|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 [[computer|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 [[Dartmouth workshop|workshop]] held on the campus of [[Dartmouth College]] in 1956.{{sfn|Kaplan|Haenlein|2018}} 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 [[DARPA|U.S. government]] provided millions of dollars with the hope of making this vision come true.{{sfn|Newquist|1994|pp=143–156}}

Eventually, it became obvious that researchers had grossly underestimated the difficulty of this feat.{{sfn|Newquist|1994|pp=144–152}} In 1974, criticism from [[James Lighthill]] and pressure from the U.S.A. Congress led the U.S. and [[British Government]]s to stop funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the [[Japanese Government]] and the success of [[expert system]]s  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 (deep learning architecture)|transformer architecture]] debuted in 2017 and was used to produce impressive [[generative AI]] applications, amongst other use cases.

Investment in AI [[AI boom|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 [[ethics of artificial intelligence|ethical implications of advanced AI]] have also emerged, causing debate about the future of AI and its impact on society.

==Precursors==

=== Mythical, fictional, and speculative precursors ===

==== Myth and legend ====
In [[Greek mythology]], [[Talos]] was a creature made of bronze who acted as guardian for the [[Crete|island of Crete]]. He would throw boulders at the ships of invaders and would complete 3 circuits around the island's perimeter daily.{{sfn|Rhodios|2007|loc=Book 4, the Talos episode}} According to [[pseudo-Apollodorus]]' ''[[Bibliotheca (Pseudo-Apollodorus)|Bibliotheke]]'', Hephaestus forged Talos with the aid of a cyclops and presented the [[automaton]] as a gift to [[Minos]].<ref>[[Bibliotheca (Pseudo-Apollodorus)|Bibliotheke]] 1.9.26</ref> In the [[Argonautica]], [[Jason]] and the [[Argonauts]] defeated Talos by removing a plug near his foot, causing the vital [[ichor]] to flow out from his body and rendering him lifeless.{{sfn|Rhodios|2007}}

[[Pygmalion (mythology)|Pygmalion]] was a legendary king and sculptor of Greek mythology, famously represented in [[Ovid]]'s ''[[Metamorphoses]]''. In the 10th book of Ovid's narrative poem, Pygmalion becomes disgusted with women when he witnesses the way in which the [[Propoetides]] prostitute themselves. Despite this, he makes offerings at the temple of Venus asking the goddess to bring to him a woman just like a statue he carved.{{sfn|Morford|2007}}

==== Medieval legends of artificial beings ====
[[File:Homunculus Faust.jpg|thumb|upright|Depiction of a homunculus from Goethe's Faust]]
In ''Of the Nature of Things'', the Swiss alchemist [[Paracelsus]] describes a procedure that he claims can fabricate an "artificial man". By placing the "sperm of a man" in horse dung, and feeding it the "Arcanum of Mans blood" after 40 days, the concoction will become a living infant.{{sfn|Linden|2003}}

The earliest written account regarding golem-making is found in the writings of [[Eleazar of Worms|Eleazar ben Judah of Worms]] in the early 13th century.{{sfn|Kressel|2015}} During the Middle Ages, it was believed that the animation of a [[Golem]] could be achieved by insertion of a piece of paper with any of God's names on it, into the mouth of the clay figure.{{sfn|Jewish Encyclopedia|loc=GOLEM}} Unlike legendary automata like [[Brazen head|Brazen Heads]],{{sfn|Newquist|1994|p=38}} a [[Golem]] was unable to speak.{{sfn|Talmud|loc=Sanhedrin 65b}}

''[[Takwin]]'', the artificial creation of life, was a frequent topic of [[Ismaili]] alchemical manuscripts, especially those attributed to [[Jabir ibn Hayyan]]. Islamic alchemists attempted to create a broad range of life through their work, ranging from plants to animals.{{sfn|O'Connor|1994}}

In [[Faust, Part Two|Faust: The Second Part of the Tragedy]] by [[Johann Wolfgang von Goethe]], an alchemically fabricated [[homunculus]], destined to live forever in the flask in which he was made, endeavors to be born into a full human body. Upon the initiation of this transformation, however, the flask shatters and the homunculus dies.{{sfn|Goethe|1890}}

==== Modern fiction ====
{{Main|Artificial intelligence in fiction}}
By the 19th century, ideas about artificial men and thinking machines became a popular theme in fiction. Notable works like [[Mary Shelley]]'s ''[[Frankenstein]]''  and [[Karel Čapek]]'s ''[[R.U.R. (Rossum's Universal Robots)]]''{{sfn|McCorduck|2004|pp=17–25}}
explored the concept of artificial life. Speculative essays, such as [[Samuel Butler (novelist)|Samuel Butler]]'s "[[Darwin among the Machines]]",{{sfn|Butler|1863}} and [[Edgar Allan Poe|Edgar Allan Poe's]] "[[Maelzel's Chess Player]]"{{sfn|Newquist|1994|p=65}} reflected society's growing interest in machines with artificial intelligence. AI remains a common topic in science fiction today.{{sfn|Cave|Dihal|2019}}

==== Automata ====
{{Main|Automaton}}
[[File:Al-jazari robots.jpg|thumb|right|250px|[[Al-Jazari]]'s programmable automata (1206 CE)]]
Realistic humanoid [[automata]] were built by craftsman from many civilizations, including [[Yan Shi]],{{sfn|Needham|1986|p=53}} [[Hero of Alexandria]],{{sfn|McCorduck|2004|p=6}} [[Al-Jazari]],{{sfn|Nick|2005}} [[Haroun al-Rashid]],{{sfn|McCorduck|2004|p=10}} [[Jacques de Vaucanson]],{{sfn|Newquist|1994|p=40}}{{sfn|McCorduck|2004|p=16}} [[Leonardo Torres y Quevedo]],{{sfn|McCorduck|2004|pp=59–62}} [[Pierre Jaquet-Droz]] and [[Wolfgang von Kempelen]].{{sfn|McCorduck|2004|p=17}}{{sfn|Levitt|2000}}

The oldest known automata were the [[cult image|sacred statues]] of [[ancient Egypt]] and [[ancient Greece|Greece]].{{sfn|Newquist|1994|p=30}}{{sfn|Crevier|1993|p=1}} 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".<ref>Quoted in {{Harvnb|McCorduck|2004|p=8}}.</ref> English scholar [[Alexander Neckham]] asserted that the Ancient Roman poet [[Virgil]] had built a palace with automaton statues.{{sfn|Cave|Dihal|Dillon|2020|p=56}}

During the early modern period, these legendary automata were said to possess the magical ability to answer questions put to them. The late medieval alchemist and proto-Protestant [[Roger Bacon]] was purported to have fabricated a [[brazen head]], having developed a legend of having been a wizard.{{sfn|Butler|1979}}{{sfn|Porterfield|2006|p=136}} These legends were similar to the Norse myth of the Head of [[Mímir]]. According to legend, Mímir was known for his intellect and wisdom, and was beheaded in the [[Æsir-Vanir War]]. [[Odin]] is said to have "embalmed" the head with herbs and spoke incantations over it such that Mímir's head remained able to speak wisdom to Odin. Odin then kept the head near him for counsel.{{sfn|Hollander|1991}}

===Formal reasoning===
Artificial intelligence is based on the assumption that the process of human thought can be mechanized. The study of mechanical—or "formal"—reasoning has a long history. [[Logic in China|Chinese]], [[Indian Logic|Indian]] and [[History of Logic#Logic in the West|Greek]] philosophers all developed structured methods of formal deduction by the first millennium BCE. Their ideas were developed over the centuries by philosophers such as [[Aristotle]] (who gave a formal analysis of the [[syllogism]]),{{sfn|Russell|Norvig|2021|pp=6 & 7}} [[Euclid]] (whose ''[[Euclid's Elements|Elements]]'' was a model of formal reasoning), [[Muhammad ibn Musa al-Khwarizmi|al-Khwārizmī]] (who developed [[algebra]] and gave his name to the word ''[[algorithm]]'') and European [[Scholasticism|scholastic]] philosophers such as [[William of Ockham]] and [[Duns Scotus]].{{sfn|Berlinski|2000}}

Spanish philosopher [[Ramon Llull]] (1232–1315) developed several ''logical machines'' devoted to the production of knowledge by logical means;{{sfn|Carreras y Artau|2018}}{{sfn|Russell|Norvig|2021|p=6}} 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.{{sfn|Bonner|2007}} Llull's work had a great influence on [[Gottfried Leibniz]], who redeveloped his ideas.{{sfn|Bonner|1985|pp=57–71}}

[[File:Gottfried Wilhelm Leibniz, Bernhard Christoph Francke.jpg|left|thumb|upright|[[Gottfried Leibniz]], who speculated that human reason could be reduced to mechanical calculation]]
In the 17th century, [[Gottfried Leibniz|Leibniz]], [[Thomas Hobbes]] and [[René Descartes]] explored the possibility that all rational thought could be made as systematic as algebra or geometry.<ref>
17th century mechanism and AI:
* {{Harvnb|McCorduck|2004|pp=37–46}} 
* {{Harvnb|Russell|Norvig|2021|p=6}} 
* {{Harvnb|Buchanan|2005|p=53}}
</ref> [[Hobbes]] famously wrote in [[Leviathan (Hobbes book)|''Leviathan'']]: "For ''reason'' ... is nothing but ''reckoning'', that is adding and subtracting".<ref>
Hobbes and AI: 
* {{Harvnb|Russell|Norvig|2021|p=6}}
* {{Harvnb|McCorduck|2004|p=42}}
* {{Harvnb|Hobbes|1651|loc=chapter 5}}
</ref> [[Gottfried Leibniz|Leibniz]] envisioned 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 each other (with a friend as witness, if they liked): ''Let us calculate''."<ref>
Leibniz and AI: 
* {{Harvnb|McCorduck|2004|p=41}}
* {{Harvnb|Russell|Norvig|2021|p=6}}} 
* {{Harvnb|Berlinski|2000|p=12}} 
* {{Harvnb|Buchanan|2005|p=53}}
</ref> These philosophers had begun to articulate the [[physical symbol system]] hypothesis that would become the guiding faith of AI research.

The study of [[mathematical logic]] provided the essential breakthrough that made artificial intelligence seem plausible. The foundations had been set by such works as [[George Boole|Boole]]'s ''[[The Laws of Thought]]'' and [[Frege]]'s ''[[Begriffsschrift]]''.{{sfn|Russell|Norvig|2021|p=8}} Building on [[Frege]]'s system, [[Bertrand Russell|Russell]] and [[Alfred North Whitehead|Whitehead]] presented a formal treatment of the foundations of mathematics in their masterpiece, the ''[[Principia Mathematica]]'' in 1913. Inspired by [[Bertrand Russell|Russell]]'s success, [[Hilbert's program|David Hilbert]] challenged mathematicians of the 1920s and 30s to answer this fundamental question: "can all of mathematical reasoning be formalized?"{{sfn|Berlinski|2000}} His question was answered by [[Kurt Gödel|Gödel]]'s [[Gödel's incompleteness theorems|incompleteness proof]],{{sfn|Russell|Norvig|2021|p=9}} [[Alan Turing|Turing]]'s [[Turing machine|machine]]{{sfn|Russell|Norvig|2021|p=9}} and [[Alonzo Church|Church]]'s [[Lambda calculus]].{{efn|The [[Lambda calculus]] was especially important to AI, since it was an inspiration for [[Lisp programming language|Lisp]] (the most important programming language used in 20th century AI).{{sfn|Crevier|1993|pp=190&nbsp;196,61}}}}

[[File:Classic shot of the ENIAC.jpg|right|thumbnail|250px|US Army photo of the ENIAC at the Moore School of Electrical Engineering{{sfn|Rose|1946}}]]
Their answer was surprising in two ways. First, they proved that there were, in fact, limits to what mathematical logic could accomplish. But second (and more important for AI) their 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.{{sfn|Russell|Norvig|2021|p=9}} The key insight was the [[Turing machine]]—a simple theoretical construct that captured the essence of abstract symbol manipulation.<ref>
The [[Turing machine]]: 
* {{Harvnb|Newquist|1994|p=56}}
* {{Harvnb|McCorduck|2004|pp=63–64}}
* {{Harvnb|Crevier|1993|pp=22–24}}
* {{Harvnb|Russell|Norvig|2021|p=9}} 
and see
* {{Harvnb|Turing|1936–1937}}
</ref> This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines.

===Computer science===
{{Main|History of computer hardware|History of computer science}}

Calculating machines were designed or built in antiquity and throughout history by many people, including 
[[Gottfried Leibniz#Information technology|Gottfried Leibniz]],{{sfn|Russell|Norvig|2021|p=6}}{{sfn|Couturat|1901}}
[[Joseph Marie Jacquard]],{{sfn|Russell|Norvig|2021|p=15}} 
[[Charles Babbage]],{{sfn|Russell|Norvig|2021|p=15}}{{sfn|Newquist|1994|p=67}}
[[Percy Ludgate]],<ref>{{Harvtxt|Randall|1982|pp=4–5}}; {{Harvtxt|Byrne|2012}}; {{Harvtxt|Mulvihill|2012}}</ref>
[[Leonardo Torres Quevedo]],<ref>{{Harvtxt|Randall|1982|pp=6, 11–13}}; {{Harvtxt|Quevedo|1914}}; {{Harvtxt|Quevedo|1915}}</ref>
[[Vannevar Bush]],{{sfn|Randall|1982|pp= 13, 16–17}}
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.<ref>Quoted in {{Harvtxt|Russell|Norvig|2021|p=15}}</ref>{{sfn|Menabrea|Lovelace|1843}}

The first modern computers were the massive machines of the [[Second World War]] (such as [[Konrad Zuse]]'s [[Z3 (computer)|Z3]], [[Alan Turing]]'s [[Heath Robinson machine|Heath Robinson]] and [[Colossus computer|Colossus]], [[John Vincent Atanasoff|Atanasoff]] and [[Clifford Berry|Berry]]'s [[Atanasoff–Berry computer|ABC]] and [[ENIAC]] at the [[University of Pennsylvania]]).{{sfn|Russell|Norvig|2021|p=14}} [[ENIAC]] was based on the theoretical foundation laid by [[Alan Turing]] and developed by [[John von Neumann]],{{sfn|McCorduck|2004|pp=76–80}} and proved to be the most influential.{{sfn|Russell|Norvig|2021|p=14}}

==Birth of artificial intelligence (1941-56) ==
[[File:BRL61-IBM 702.jpg|thumb|upright=1.35|The [[IBM 702]]: a computer used by the first generation of AI researchers.]]

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 [[neuron]]s that fired in all-or-nothing pulses. [[Norbert Wiener]]'s [[cybernetic]]s 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.<ref>
AI's immediate predecessors: 
* {{Harvnb|McCorduck|2004|pp=51–57, 80–107}}
* {{Harvnb|Crevier|1993|pp=27–32}}
* {{Harvnb|Russell|Norvig|2021|pp=9, 11, 15–17, 981–984}}
* {{Harvnb|Moravec|1988|p=3}}
* {{Harvnb|Cordeschi|2002|loc=Chap. 5}}
</ref> Alan Turing was among the first people to seriously investigate the theoretical possibility of "machine intelligence".{{sfn|Copeland|2004}} The field of "[[artificial intelligence|artificial intelligence research]]" was founded as an academic discipline in 1956.<ref name = "Dartmouth workshop"/>

[[File:Turing test diagram.png|thumb|left|Turing test<ref>Image adapted from {{harvnb|Saygin|2000}}</ref>]]

=== Turing Test ===
{{Main|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.<ref>
[[Turing Test]], [[Computing Machinery and Intelligence]]:
* {{Harvnb|McCorduck|2004|pp=70–72}},
* {{Harvnb|Crevier|1993|pp=22−25}},
* {{Harvnb|Russell|Norvig|2021|pp=18, 981–984}},
* {{Harvnb|Haugeland|1985|pp=6–9}},
* {{Harvnb|Cordeschi|2002|pp=170–176}}.
See also
* {{Harvnb|Turing|1950}}
</ref>{{efn|Alan Turing was thinking about machine intelligence at least as early as 1941, when he circulated a paper on machine intelligence which could be the earliest paper in the field of AI — although it is now lost. His 1950 paper was followed by three radio broadcasts on AI by Turing, the two lectures 'Intelligent Machinery, A Heretical Theory' and 'Can Digital Computers Think?' and the panel discussion 'Can Automatic Calculating Machines be Said to Think?'{{sfn|Copeland|2004}}}} 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".{{sfn|Newquist|1994|pp=92–98}} 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.{{sfn|Russell|Norvig|2021|p=981}} The Turing Test was the first serious proposal in the [[philosophy of artificial intelligence]].

=== Neuroscience and Hebbian theory ===
[[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."
<ref>{{cite web | url=https://thedecisionlab.com/thinkers/neuroscience/donald-hebb | title=Donald Hebb }}</ref>

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.
<ref>{{cite journal | pmc=7137474 | date=2020 | title=Donald O. Hebb and the Organization of Behavior: 17 years in the writing | journal=Molecular Brain | volume=13 | issue=1 | page=55 | doi=10.1186/s13041-020-00567-8 | doi-access=free | pmid=32252813 | vauthors = Brown RE }}</ref>
<ref>
https://ojs.library.dal.ca/nsis/article/viewFile/nsis44-1brown/3549
</ref>

=== Artificial neural networks ===
[[Walter Pitts]] and [[Warren Sturgis McCulloch|Warren McCulloch]] analyzed networks of idealized [[artificial neuron]]s and showed how they might perform simple logical functions in 1943. They were the first to describe what later researchers would call a [[Artificial neural network|neural network]].<ref>
Pitts & McCullough:
* {{Harvnb|McCorduck|2004|pp=51–57, 88–94}}
* {{Harvnb|Crevier|1993|p=30}}
* {{Harvnb|Russell|Norvig|2021|p=17}}
* {{Harvnb|Cordeschi|2002|loc=Chap. 5}}
* {{Harvnb|Piccinini|2004}} 
See also: {{Harvnb|McCulloch|Pitts|1943}}
</ref> The paper was influenced by Turing's paper '[[Turing's proof|On Computable Numbers]]' from 1936 using similar two-state boolean 'neurons', but was the first to apply it to neuronal function.{{sfn|Copeland|2004}} 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 [[Stochastic Neural Analog Reinforcement Calculator|SNARC]].<ref>
[[Stochastic Neural Analog Reinforcement Calculator|SNARC]]:
* {{Harvnb|McCorduck|2004|p=102}}
* {{Harvnb|Crevier|1993|pp=34–35}}
* {{Harvnb|Russell|Norvig|2021|p=17}}
</ref> Minsky would later become one of the most important leaders and innovators in AI.

===Cybernetic robots===
Experimental robots such as [[W. Grey Walter]]'s [[Turtle (robot)|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.<ref>
[[Turtle (robot)|Turtle]]s and [[Johns Hopkins Beast]]:
* {{Harvnb|McCorduck|2004|p=98}}
* {{Harvnb|Crevier|1993|pp=27–28}}
* {{Harvnb|Moravec|1988|p=3}}
* {{Harvnb|Cordeschi|2002|loc=Chap. 5}}
</ref>

===Game AI===
In 1951, using the [[Ferranti Mark 1]] machine of the [[University of Manchester]], [[Christopher Strachey]] wrote a checkers program{{sfn|Russell|Norvig|2021|p=17}} and [[Dietrich Prinz]] wrote one for chess.{{sfn|Copeland|1999}} [[Arthur Samuel (computer scientist)|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.{{sfn|Schaeffer|1997|loc=Chapter 6}} Samuel's program was among the first uses of what would later be called [[machine learning]].{{sfn|Russell|Norvig|2021|p=17, p=19}} [[Game AI]] would continue to be used as a measure of progress in AI throughout its history.

===Symbolic reasoning and the Logic Theorist===
{{Main|Logic Theorist}}
[[File:Herbert A. Simon and Allen Newell Chess Match.jpg|thumb|Herbert Simon (left) in a chess match against Allen Newell {{circa|1958}}]]
When access to [[digital computer]]s 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.{{sfn|McCorduck|2004|pp=137–170}}{{sfn|Crevier|1993|pp=44–47}}

In 1955, [[Allen Newell]] and future Nobel Laureate [[Herbert A. Simon]] created the "Logic Theorist", with help from [[Cliff Shaw|J. C. Shaw]]. The program would eventually prove 38 of the first 52 theorems in [[Bertrand Russell|Russell]] and [[Alfred North Whitehead|Whitehead]]'s ''[[Principia Mathematica]]'', and find new and more elegant proofs for some.<ref>
[[Logic Theorist]]:
* {{Harvnb|McCorduck|2004|pp=123–125}}
* {{Harvnb|Crevier|1993|pp=44–46}} 
* {{Harvnb|Russell|Norvig|2021|p=18}}
</ref> Simon said that they had "solved the venerable [[mind/body problem]], explaining how a system composed of matter can have the properties of mind."<ref>Quoted in {{Harvnb|Crevier|1993|p=46}} and {{Harvnb|Russell|Norvig|2021|p=18}}</ref>{{efn|This was an early statement of the philosophical position [[John Searle]] would later call the "[[Strong AI hypothesis]]": that machines can contain minds just as human bodies do.}} The symbolic reasoning paradigm they introduced would dominate AI research and funding until the middle 90s, as well as inspire the [[cognitive revolution]].

===Dartmouth Workshop===
{{Main|Dartmouth workshop}}

The Dartmouth workshop of 1956 was a pivotal event that marked the formal inception of AI as an academic discipline.<ref name="Dartmouth workshop">
[[Dartmouth workshop]]:
* {{Harvnb|McCorduck|2004|pp=111–136}}
* {{Harvnb|Crevier|1993|pp=49–51}}
* {{Harvnb|Russell|Norvig|2021|p=18}}
* {{Harvnb|Newquist|1994|pp=91–112}} 
</ref> It was organized by [[Marvin Minsky]] and [[John McCarthy (computer scientist)|John McCarthy]], with the support of two senior scientists [[Claude Shannon]] and [[Nathaniel Rochester (computer scientist)|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".{{sfn|McCarthy|Minsky|Rochester|Shannon|1955}}{{efn|
[[Daniel Crevier]] wrote "[the proposal] later became known as the 'physical symbol systems hypothesis'".{{sfn|Crevier|1993|p=48}} The [[physical symbol system]] hypothesis was articulated and named by [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]] in their paper on [[General Problem Solver|GPS]].{{sfn|Newell|Simon|1995}} It includes a more specific definition of a "machine" as an agent that manipulates symbols.}} The term "Artificial Intelligence" was introduced by John McCarthy at the workshop.{{efn|
"I won't swear and I hadn't seen it before," McCarthy told [[Pamela McCorduck]] in 1979.{{sfn|McCorduck|2004|p=114}} However, [[John McCarthy (computer scientist)|McCarthy]] also stated unequivocally "I came up with the term" in a [[CNET]] interview.{{sfn|Skillings|2006}} The term was chosen by McCarthy to avoid associations with [[cybernetics]] and the influence of [[Norbert Wiener]]. "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him.".{{sfn|McCarthy|1996|p=73}}}} 
The participants included [[Ray Solomonoff]], [[Oliver Selfridge]], [[Trenchard More]], [[Arthur Samuel (computer scientist)|Arthur Samuel]], [[Allen Newell]] and [[Herbert A. Simon]], all of whom would create important programs during the first decades of AI research.{{sfn|McCorduck|2004|pp=129–130}}{{efn|Pamela McCorduck discusses how the Dartmouth conference alumni dominated the first two decades of AI research, calling them the "invisible college".{{sfn|McCorduck|2004|pp=129–130}}}} At the workshop Newell and Simon debuted the "Logic Theorist".{{sfn|McCorduck|2004|p=125}} 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.{{efn|[[Daniel Crevier]] wrote "the conference is generally recognized as the official birthdate of the new science."{{sfn|Crevier|1993|p=49}}}}

=== Cognitive revolution ===
{{Main|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 Armitage Miller|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."{{sfn|Miller|2003}}{{sfn|Russell|Norvig|2021|p=14}}

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 (philosophy)|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 [[physical symbol system|high level symbols]] in functional networks. These objects had been forbidden as "unobservable" by earlier paradigms such as [[behaviorism]].{{efn|There were a few psychologists who avoided behaviorism and embraced a cognitive approach before it was fashionable, such as [[Frederic Bartlett]] and [[Kenneth Craig]]{{sfn|Russell|Norvig|2021|pp=13-14}}}} 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|Dartmouth Workshop]] were, to most people, simply "astonishing":{{efn|Russell and Norvig wrote "it was astonishing whenever a computer did anything remotely clever."{{sfn|Russell|Norvig|2003|p=18}} AI founder [[John McCarthy (computer scientist)|John McCarthy]] called this the "Look, Ma, no hands!" era.{{sfn|Russell|Norvig|2021|p=18}}}} 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.{{sfn|Crevier|1993|pp=52–107}}{{sfn|Moravec|1988|p=9}}{{sfn|Russell|Norvig|2021|p=18}} Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years.<ref>{{Harvnb|McCorduck|2004|p=218}}; {{Harvnb|Newquist|1994|pp=91–112}}; {{Harvnb|Crevier|1993|pp=108–109}}</ref> Government agencies like the [[Defense Advanced Research Projects Agency]] (DARPA, then known as "ARPA") poured money into the field.<ref>{{Harvnb|Crevier|1993|pp=52–107}}; {{Harvnb|Moravec|1988|p=9}}</ref> Artificial Intelligence laboratories were set up at a number of British and US universities in the latter 1950s and early 1960s.{{sfn|Copeland|2004}}

===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.<ref>[[State space search]] and problem solving:
* {{Harvnb|Russell|Norvig|2021|loc=Chpt: 3-6}}
</ref> 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.{{sfn|McCorduck|2004|p=246}}

[[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]] tried to capture a general version of this algorithm in a program called the "[[General Problem Solver]]".{{sfn|McCorduck|2004|pp=245–250}}{{sfn|Russell|Norvig|2021|pp=19, 106}} 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){{sfn|Russell|Norvig|2021|p=19}} and Symbolic Automatic Integrator (SAINT), written by [[Marvin Minsky|Minsky's]] student James Slagle in 1961.{{sfn|Crevier|1993|pp=51–58,65–66}}{{sfn|Russell|Norvig|2021|p=20}} Other programs searched through goals and subgoals to [[automatic scheduling and planning|plan actions]], like the [[Stanford Research Institute Problem Solver|STRIPS]] system developed at [[Stanford]] to control the behavior of the robot [[Shakey the Robot|Shakey]].<ref>
[[Stanford Research Institute Problem Solver|STRIPS]] and [[Shakey the Robot|Shakey]]:
* {{Harvnb|Russell|Norvig|2021|p=20}}
* {{Harvnb|McCorduck|2004|pp=268–271}}
* {{Harvnb|Crevier|1993|pp=95–96}}
* {{Harvnb|Newquist|1994|pp=148–156}}
* {{Harvnb|Moravec|1988|pp=14–15}}</ref>

====Natural language====
[[File:Semantic Net.svg|thumb|250px|An example of a [[semantic network]]]]
An important goal of AI research is to allow computers to communicate in [[natural language processing|natural languages]] like English. An early success was [[Daniel Bobrow]]'s program [[STUDENT (computer program)|STUDENT]], which could solve high school algebra word problems.<ref>{{Harvnb|McCorduck|2004|p=286}}, {{Harvnb|Crevier|1993|pp=76–79}}, {{Harvnb|Russell|Norvig|2021|p=20}}</ref>

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{{sfn|Crevier|1993|pp=79–83}} and the most successful (and controversial) version was [[Roger Schank]]'s [[Conceptual dependency theory]].{{sfn|Crevier|1993|pp=164–172}}

[[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]].{{sfn|McCorduck|2004|pp=291–296}}{{sfn|Crevier|1993|pp=134–139}}

====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.{{efn|This avoided the commonsense knowledge problem, discussed below.}} 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.<ref name="Blocks world">[[Blocks world]]:
* {{Harvnb|McCorduck|2004|pp=299–305}}
* {{Harvnb|Crevier|1993|pp=83–102}} 
* {{Harvnb|Russell|Norvig|2021|p=20}} 
* {{Harvnb|Copeland|2000}}</ref>

This paradigm led to innovative work in [[machine vision]] by [[Gerald Sussman]], Adolfo Guzman, [[David Waltz]] (who invented "[[constraint propagation]]"), and especially [[Patrick Winston]]. At the same time, Minsky and Papert built a robot arm that could stack blocks, bringing the blocks world to life. [[Terry Winograd]]'s [[SHRDLU]] could communicate in ordinary English sentences about the micro-world, plan operations and execute them.<ref name="Blocks world"/>

==== Perceptrons and early neural networks ====
{{Main|Perceptron}}

In the 1960s funding was primarily directed towards laboratories researching [[symbolic artificial intelligence|symbolic AI]], however several people still pursued research in neural networks.

[[File:330-PSA-80-60 (USN 710739) (20897323365).jpg|thumb|[[Mark I Perceptron|The Mark 1 Perceptron]].]]The [[perceptron]], a single-layer [[Artificial neural network|neural network]] was introduced in 1958 by [[Frank Rosenblatt]]<ref>
[[Perceptron]]s in the 60s: 
* {{Harvnb |Russell |Norvig|2021|p=21}}
* {{Harvnb |Crevier |1993|pp=102–105}}
* {{Harvnb |McCorduck |2004|pp=104–107}}
* {{Harvnb |Schmidhuber|2022}}
</ref> (who had been a schoolmate of [[Marvin Minsky]] at the [[Bronx High School of Science]]).{{sfn|Crevier|1993|p=102}} Like most AI researchers, he was optimistic about their power, predicting that a perceptron "may eventually be able to learn, make decisions, and translate languages."<ref>Quoted in {{Harvnb|Crevier|1993|p=102}}</ref> Rosenblatt was primarily funded by [[Office of Naval Research]].{{sfn|Rosenblatt|1962}}

[[Bernard Widrow]] and his student [[Marcian Hoff|Ted Hoff]] built [[ADALINE]] (1960) and [[ADALINE|MADALINE]] (1962), which had up to 1000 adjustable weights.{{sfn|Russell|Norvig|2021|pp=20-21}}{{sfn|Widrow|Lehr|1990}} A group at [[SRI International|Stanford Research Institute]] led by [[Charles Rosen (scientist)|Charles A. Rosen]] and Alfred E. (Ted) Brain built two neural network machines named MINOS I (1960) and II (1963), mainly funded by [[United States Army Signal Corps|U.S. Army Signal Corps]]. MINOS II{{sfn|Rosen|Nilsson|Adams|1965}} had 6600 adjustable weights,{{sfn|Nilsson|1984}} and was controlled with an [[SDS 9 Series|SDS 910 computer]] in a configuration named MINOS III (1968), which could classify symbols on army maps, and recognize hand-printed characters on [[Fortran]] [[Computer programming in the punched card era|coding sheets]].{{sfn|Hart|Nilsson|Perrault|Mitchell|2003}}{{sfn|Nielson|2005}} Most of neural network research during this early period involved building and using bespoke hardware, rather than simulation on digital computers.{{efn|The hardware diversity was particularly clear in the different technologies used in implementing the adjustable weights. The perceptron machines and the [[Stochastic Neural Analog Reinforcement Calculator|SNARC]] used [[potentiometer]]s moved by electric motors. ADALINE used [[memistor]]s adjusted by [[electroplating]], though they also used simulations on an [[IBM 1620]] computer. The MINOS machines used [[Magnetic-core memory|ferrite cores]] with multiple holes in them that could be individually blocked, with the degree of blockage representing the weights.{{sfn|Olazaran Rodriguez|1991}}}}

However, partly due to lack of results and partly due to competition from [[symbolic AI]] research, the MINOS project ran out of funding in 1966. Rosenblatt failed to secure continued funding in the 1960s.{{sfn|Olazaran Rodriguez|1991}} In 1969, research came to a sudden halt with the publication of [[Marvin Minsky|Minsky]] and [[Seymour Papert|Papert's]] 1969 book ''[[Perceptrons (book)|Perceptrons]]''.{{sfn|Minsky|Papert|1969}} It suggested that there were severe limitations to what perceptrons could do and that Rosenblatt's predictions had been grossly exaggerated. The effect of the book was that virtually no research was funded in [[connectionism]] for 10 years.{{sfn|Russell|Norvig|2021|p=22}} The competition for government funding ended with the victory of symbolic AI approaches over neural networks.{{sfn|Nielson|2005}}{{sfn|Olazaran Rodriguez|1991}}

Minsky (who had worked on [[Stochastic Neural Analog Reinforcement Calculator|SNARC]]) became a staunch objector to pure connectionist AI. [[Bernard Widrow|Widrow]] (who had worked on [[ADALINE]]) turned to adaptive signal processing. The [[SRI International|SRI]] group (which worked on MINOS) turned to symbolic AI and robotics.{{sfn|Nielson|2005}}{{sfn|Olazaran Rodriguez|1991}}

The main problem was the inability to train multilayered networks (versions of [[backpropagation]] had already been used in other fields but it was unknown to these researchers).{{sfn|Schmidhuber|2022}}{{sfn|Russell|Norvig|2021|p=22}} The AI community became aware of backpropogation in the 80s,{{sfn|Russell|Norvig|2021|p=24}} and, in the 21st century, neural networks would become enormously successful, fulfilling all of Rosenblatt's optimistic predictions. Rosenblatt did not live to see this, however, as he died in a boating accident in 1971.{{sfn|Crevier|1993|p=105}}

=== Optimism ===
The first generation of AI researchers made these predictions about their work:
* 1958, [[H. A. Simon]] and [[Allen Newell]]: "within ten years a digital computer will be the world's chess champion" and "within ten years a digital computer will discover and prove an important new mathematical theorem."<ref>{{Harvnb|Simon|Newell|1958|pp=7−8}} quoted in {{Harvnb|Crevier|1993|p=108}}.</ref>
* 1965, H. A. Simon: "machines will be capable, within twenty years, of doing any work a man can do."<ref>{{Harvnb|Simon|1965|p=96}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>
* 1967, [[Marvin Minsky]]: "Within a generation... the problem of creating 'artificial intelligence' will substantially be solved."<ref>{{Harvnb|Minsky|1967|p=2}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>
* 1970, Marvin Minsky (in [[Life magazine|''Life'' magazine]]): "In from three to eight years we will have a machine with the general intelligence of an average human being."{{sfn|Darrach|1970}}{{efn|Minsky strongly believes he was misquoted.{{sfn|McCorduck|2004|pp=272–274}}{{sfn|Crevier|1993|p=96}}}}

=== Financing ===
In June 1963, [[MIT]] received a $2.2 million grant from the newly created Advanced Research Projects Agency (ARPA, later known as [[DARPA]]). The money was used to fund [[project MAC]] which subsumed the "AI Group" founded by [[Marvin Minsky|Minsky]] and [[John McCarthy (computer scientist)|McCarthy]] five years earlier. DARPA continued to provide $3 million each year until the 70s.{{sfn|Crevier|1993|pp=64–65}} DARPA made similar grants to [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon's]] program at [[Carnegie Mellon University]] and to [[Stanford University]]'s [[Stanford AI Lab|AI Lab]], founded by [[John McCarthy (computer scientist)|John McCarthy]] in 1963.{{sfn|Crevier|1993|p=94}} Another important AI laboratory was established at [[Edinburgh University]] by [[Donald Michie]] in 1965.{{sfn|Howe|1994}} These four institutions would continue to be the main centers of AI research and funding in academia for many years.{{sfn|Crevier|1993|p=51}}{{efn|McCorduck also notes that funding was mostly under the direction of alumni of the [[Dartmouth workshop]] of 1956.{{sfn|McCorduck|2004|p=131}}}}

The money was given with few strings attached: [[J. C. R. Licklider]], then the director of ARPA, believed that his organization should "fund people, not projects!" and allowed researchers to pursue whatever directions might interest them.{{sfn|Crevier|1993|p=65}}  This created a freewheeling atmosphere at MIT that gave birth to the [[hacker culture]],<ref>{{Harvnb|Crevier|1993|pp=68–71}}; {{Harvnb|Turkle|1984}}</ref> but this "hands off" approach did not last.

==First AI Winter (1974–1980)==
In the 1970s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised public expectations impossibly high, and when the promised results failed to materialize, funding targeted at AI was severely reduced.{{sfn|Crevier| 1993|pp=163–196}} The lack of success indicated the techniques being used by AI researchers at the time were insufficient to achieve their goals.{{sfn|Dreyfus|1972}}{{sfn|Lighthill|1973}}

These setbacks did not affect the growth and progress of the field, however. The funding cuts only impacted a handful of major laboratories{{sfn|Haigh|2023}} and the critiques were largely ignored.{{sfn|Crevier|1993|p=143}} General public interest in the field continued to grow,{{sfn|Haigh|2023}} the number of researchers increased dramatically,{{sfn|Haigh|2023}} and new ideas were explored in [[logic programming]], [[commonsense reasoning]] and many other areas. Historian Thomas Haigh argued in 2023 that there was no winter,{{sfn|Haigh|2023}} and AI researcher [[Nils John Nilsson|Nils Nilsson]] described this period as the most "exciting" time to work in AI.{{sfn|Nilsson|2009|p=1}}

===Problems===
In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve;{{efn|Russell and Norvig wrote "in almost all cases, these early systems failed on more difficult tasks."{{sfn|Russell|Norvig|2021|p=21}}}} all the programs were, in some sense, "toys".{{sfn|Crevier|1993|p=146}} AI researchers had begun to run into several limits that would be only conquered decades later, and others that still stymie the field in the 2020s:
* '''[[Moore's law|Limited computer power]]''': There was not enough memory or processing speed to accomplish anything truly useful.{{efn|[[Bruce Buchanan]] wrote: "Early programs were necessarily limited in scope by the size and speed of memory"{{sfn|Buchanan|2005|p=56}}}} For example: Ross Quillian's successful work on natural language was demonstrated with a vocabulary of only 20 words, because that was all that would fit in memory.{{sfn|Crevier|1993|pp=146–148}} [[Hans Moravec]] argued in 1976 that computers were still millions of times too weak to exhibit intelligence. He suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require [[horsepower]]. Below a certain threshold, it's impossible, but, as power [[Moore's law|increases]], eventually it could become easy. "With enough horsepower," he wrote, "anything will fly".{{sfn|Moravec|1976}}{{efn|History would prove Moravec right about applications like computer vision. Moravec estimated that simply matching the [[Edge detection|edge]] and [[motion detection]] capabilities of the [[human retina]] in [[Real-time computing|real time]] would require a general-purpose computer capable of 1000 [[million instructions per second]] (MIPS). In 1976, the fastest supercomputer, the $8 million [[Cray-1]] was only capable of 130 MIPS, and a typical desktop computer had 1 MIPS. As of 2011, practical computer vision applications require 10,000 to 1,000,000 MIPS.{{sfn|Moravec|2000}}}}
* '''[[Intractability (complexity)|Intractability]] and the [[combinatorial explosion]]''': In 1972 [[Richard Karp]] (building on [[Stephen Cook]]'s 1971 [[Cook's theorem|theorem]]) showed there are [[Karp's 21 NP-complete problems|many problems]] that can only be solved in [[exponential time]]. Finding optimal solutions to these problems requires extraordinary amounts of computer time, except when the problems are trivial. This limitation applied to all symbolic AI programs that used search trees and meant that many of the "toy" solutions used by AI would never scale to useful systems.{{sfn|Russell|Norvig|2021|p=21}}{{sfn|Lighthill|1973}}
* '''[[Moravec's paradox]]''': Early AI research had been very successful at getting computers to do "intelligent" tasks like proving theorems, solving geometry problems and playing chess. Their success at these intelligent tasks convinced them that the problem of intelligent behavior had been largely solved.{{sfn|McCorduck|2004|p=456}}{{sfn|Brooks|2002}} However, they utterly failed to make progress on "unintelligent" tasks like recognizing a face or crossing a room without bumping into anything.{{sfn|McCorduck|2004|p=456}}{{sfn|Moravec|1988|pp=15–16}} By the 1980s, researchers would realize that symbolic reasoning was utterly unsuited for these perceptual and sensorimotor tasks and that there were limits to this approach.{{sfn|Brooks|2002}}
* '''The breadth of [[commonsense knowledge]]''': Many important artificial intelligence applications like [[computer vision|vision]] or [[natural language]] require enormous amounts of information about the world: the program needs to have some idea of what it might be looking at or what it is talking about. This requires that the program know most of the same things about the world that a child does. Researchers soon discovered that this was a vast amount of information with billions of atomic facts. No one in 1970 could build a database large enough and no one knew how a program might learn so much information.<ref>
Commonsense knowledge:
* {{Harvnb|McCorduck|2004|pp=300 & 421}}
* {{Harvnb|Crevier|1993|pp=113–114}}
* {{Harvnb|Moravec|1988|p=13}}
* {{Harvnb|Lenat|Guha|1989|loc=(Introduction)}}
</ref>
* '''Representing [[commonsense reasoning]]''': A number of related problems{{efn|Such as the [[frame problem|frame]], [[ramification problem|ramification]] and [[qualification problem]]s, as well as the difficulty of [[default reasoning]] and [[word-sense disambiguation]].}} appeared when researchers tried to represent commonsense reasoning using formal logic or symbols.  Descriptions of very ordinary deductions tended to get longer and longer the more one worked on them, as more and more exceptions, clarifications and distinctions were required.{{efn|Russell and Norvig write: "[M]any of the concepts we name in language fail, on closer inspection, to have the logically defined necessary and sufficient conditions that early AI researchers hoped to capture in axiomatic form."{{sfn|Russell|Norvig|2021|p=24}}}} However, when people thought about ordinary concepts they did not rely on precise definitions, rather they seemed to make hundreds of imprecise assumptions, correcting them when necessary using their entire body of commonsense knowledge. [[Gerald Sussman]] observed that "using precise language to describe essentially imprecise concepts doesn't make them any more precise."<ref>Quoted in {{harvnb|Crevier|1993|p=175}}</ref>

===Decrease in funding===
{{See also|AI winter}}

The agencies which funded AI research, such as the [[British government]], [[DARPA]] and the [[United States National Research Council|National Research Council]] (NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected AI research. The pattern began in 1966 when the [[Automatic Language Processing Advisory Committee]] (ALPAC) report criticized machine translation efforts. After spending $20 million, the [[United States National Research Council|NRC]] ended all support.<ref>[[ALPAC]]:
* {{Harvnb|McCorduck|2004|pp=280–281}}
* {{Harvnb|Crevier|1993|p=110}}
* {{Harvnb|Russell|Norvig|2021|p=21}}
* {{Harvnb|NRC|1999|loc = under "Success in Speech Recognition"}}.
</ref> In 1973, the [[Lighthill report]] on the state of AI research in the UK criticized the failure of AI to achieve its "grandiose objectives" and led to the dismantling of AI research in that country.<ref>
[[Lighthill report]]:
* {{Harvnb|Crevier|1993|p=117}}
* {{Harvnb|Howe|1994}}  
* {{Harvnb|Lighthill|1973}}
</ref> (The report specifically mentioned the [[combinatorial explosion]] problem as a reason for AI's failings.){{sfn|Lighthill|1973}}{{sfn|Russell|Norvig|2021|p=21}}{{efn|[[John McCarthy (computer scientist)|John McCarthy]] wrote in response that "the combinatorial explosion problem has been recognized in AI from the beginning"{{sfn|McCarthy|1974}}}} DARPA was deeply disappointed with researchers working on the [[speech recognition|Speech Understanding Research]] program at CMU and canceled an annual grant of $3 million.{{sfn|Crevier|1993|pp=115–116}}{{efn|This account is based on {{Harvnb|Crevier|1993|pp=115–116}}. Other views include {{Harvnb|McCorduck|2004|pp=306–313}} and {{Harvnb|NRC|1999}} under "Success in Speech Recognition".}}

[[Hans Moravec]] blamed the crisis on the unrealistic predictions of his colleagues. "Many researchers were caught up in a web of increasing exaggeration."{{sfn|Crevier|1993|p=115}}{{efn|Moravec explains, "Their initial promises to DARPA had been much too optimistic. Of course, what they delivered stopped considerably short of that. But they felt they couldn't in their next proposal promise less than in the first one, so they promised more."{{sfn|Crevier|1993|p=115}}}} However, there was another issue: since the passage of the [[Mansfield Amendment]] in 1969, [[DARPA]] had been under increasing pressure to fund "mission-oriented direct research, rather than basic undirected research". Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA, which instead directed money at specific projects with clear objectives, such as [[autonomous]] tanks and [[battle management]] systems.{{sfn|NRC|1999|loc = under "Shift to Applied Research Increases Investment."}}{{efn|While the autonomous tank was a failure, the battle management system (called "[[Dynamic Analysis and Replanning Tool|DART]]") proved to be enormously successful, saving billions in the first [[Gulf War]], repaying the investment and justifying the [[DARPA]]'s pragmatic policy, at least as far as [[DARPA]] was concerned.{{sfn|NRC|1999}}
}}

The major laboratories (MIT, Stanford, CMU and Edinburgh) had been receiving generous support from their governments, and when it was withdrawn, these were the only places that were seriously impacted by the budget cuts. The thousands of researchers outside these institutions and the many more thousands that were joining the field were unaffected.{{sfn|Haigh|2023}}

===Philosophical and ethical critiques===
{{See also|Philosophy of artificial intelligence}}

Several philosophers had strong objections to the claims being made by AI researchers. One of the earliest was [[John Lucas (philosopher)|John Lucas]], who argued that [[Gödel's incompleteness theorem]] showed that a [[formal system]] (such as a computer program) could never see the truth of certain statements, while a human being could.<ref>Lucas and Penrose' critique of AI: 
* {{Harvnb|Crevier|1993|p=22}}
* {{Harvnb|Russell|Norvig|2021|pp=983–984}} 
* {{Harvnb|Hofstadter|1999|pp=471–477}} 
Lucas original argument:
* {{Harvnb|Lucas|1961}}
</ref> [[Hubert Dreyfus]] ridiculed the broken promises of the 1960s and critiqued the assumptions of AI, arguing that human reasoning actually involved very little "symbol processing" and a great deal of [[embodied cognition|embodied]], [[instinct]]ive, unconscious "[[ready-to-hand|know how]]".{{efn|"Know-how" is Dreyfus' term. Dreyfus makes a distinction between "knowing how" and "knowing that", a modern version of [[Heidegger]]'s distinction of [[ready-to-hand]] and [[present-at-hand]].{{sfn|Dreyfus|Dreyfus|1986}}}}<ref>
[[Dreyfus' critique of artificial intelligence]]: 
* {{Harvnb|McCorduck|2004|pp=211–239}}
* {{Harvnb|Crevier|1993|pp=120–132}}
* {{Harvnb|Russell|Norvig|2021|pp=981–982}} 
Dreyfus' version:
* {{Harvnb|Dreyfus|1965}}
* {{Harvnb|Dreyfus|1972}}
* {{Harvnb|Dreyfus|Dreyfus|1986}}
</ref> [[John Searle]]'s [[Chinese Room]] argument, presented in 1980, attempted to show that a program could not be said to "understand" the symbols that it uses (a quality called "[[intentionality]]"). If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as "thinking".<ref>
Searle's critique of AI: 
* {{Harvnb|McCorduck|2004|pp=443–445}}
* {{Harvnb|Crevier|1993|pp=269–271}}
* {{Harvnb|Russell|Norvig|2021|pp=985–986}}
Searle's version:
* {{Harvnb|Searle|1980}}</ref>

These critiques were not taken seriously by AI researchers. Problems like [[intractability (complexity)|intractability]] and [[Commonsense reasoning|commonsense knowledge]] seemed much more immediate and serious. It was unclear what difference "[[ready-to-hand|know how]]" or "[[intentionality]]" made to an actual computer program. MIT's [[Marvin Minsky|Minsky]] said of Dreyfus and Searle "they misunderstand, and should be ignored."<ref>Quoted in {{Harvnb|Crevier|1993|p=143}}</ref> Dreyfus, who also taught at [[MIT]], was given a cold shoulder: he later said that AI researchers "dared not be seen having lunch with me."<ref>Quoted in {{Harvnb|Crevier|1993|p=122}}</ref> [[Joseph Weizenbaum]], the author of [[ELIZA]], was also an outspoken critic of Dreyfus' positions, but he "deliberately made it plain that [his AI colleagues' treatment of Dreyfus] was not the way to treat a human being,"{{efn|Weizenbaum said: "I became the only member of the AI community to be seen eating lunch with Dreyfus. And I deliberately made it plain that theirs was not the way to treat a human being."{{sfn|Crevier|1993|p=123}}}} and was unprofessional and childish.{{sfn|Newquist|1994|p=276}}

Weizenbaum began to have serious ethical doubts about AI when [[Kenneth Colby]] wrote a "computer program which can conduct [[psychotherapeutic]] dialogue" based on ELIZA.{{sfn|Colby|Watt|Gilbert|1966|p=148}}{{sfn|Weizenbaum|1976|pp=5, 6}}{{efn|Colby and his colleagues later also developed [[chatterbot]]-like "computer simulations of paranoid processes ([[PARRY]])" to "make intelligible paranoid processes in explicit symbol processing terms."{{sfn|Colby|1974|p=6}}}} Weizenbaum was disturbed that Colby saw a mindless program as a serious therapeutic tool. A feud began, and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program. In 1976, [[Joseph Weizenbaum|Weizenbaum]] published ''[[Computer Power and Human Reason]]'' which argued that the misuse of artificial intelligence has the potential to devalue human life.<ref>
Weizenbaum's critique of AI: 
* {{Harvnb|McCorduck|2004|pp=356–373}}
* {{Harvnb|Crevier|1993|pp=132–144}}
* {{Harvnb|Russell|Norvig|2021|p=1001}}
and see 
* {{Harvnb|Weizenbaum|1976}}</ref>

===Logic at Stanford, CMU and Edinburgh ===
Logic was introduced into AI research as early as 1958, by [[John McCarthy (computer scientist)|John McCarthy]] in his [[Advice Taker]] proposal.{{sfn|McCorduck|2004|p=51}}{{sfn|Russell|Norvig|2021|p=19}} In 1963, [[J. Alan Robinson]] had discovered a simple method to implement deduction on computers, the [[resolution (logic)|resolution]] and [[unification (computing)|unification]] algorithm.{{sfn|Russell|Norvig|2021|p=19}} However, straightforward implementations, like those attempted by McCarthy and his students in the late 1960s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.{{sfn|McCorduck|2004|p=51}}{{sfn|Crevier|1993|pp=190–192}} A more fruitful approach to logic was developed in the 1970s by [[Robert Kowalski]] at the [[University of Edinburgh]], and soon this led to the collaboration with French researchers [[Alain Colmerauer]] and {{ill|Philippe Roussel|fr}} who created the successful logic programming language [[Prolog]].{{sfn|Crevier|1993|pp=193–196}} Prolog uses a subset of logic ([[Horn clause]]s, closely related to "[[Rule-based system|rules]]" and "[[Production system (computer science)|production rules]]") that permit tractable computation. Rules would continue to be influential, providing a foundation for [[Edward Feigenbaum]]'s [[expert systems]] and the continuing work by [[Allen Newell]] and [[Herbert A. Simon]] that would lead to [[Soar (cognitive architecture)|Soar]] and their [[unified theories of cognition]].{{sfn|Crevier|1993|pp=145–149,258–63}}

Critics of the logical approach noted, as [[Hubert Dreyfus|Dreyfus]] had, that human beings rarely used logic when they solved problems. Experiments by psychologists like [[Peter Cathcart Wason|Peter Wason]], [[Eleanor Rosch]], [[Amos Tversky]], [[Daniel Kahneman]] and others provided proof.{{efn| {{Harvtxt|Wason|Shapiro|1966}} showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive [[social intelligence]], performance dramatically improves. (See [[Wason selection task]]) {{Harvtxt|Kahneman|Slovic|Tversky|1982}} have shown that people are terrible at elementary problems that involve uncertain reasoning. (See [[list of cognitive biases]] for several examples). [[Eleanor Rosch]]'s work is described in {{Harvnb|Lakoff|1987}}. Kahnmann published a more general theory of symbolic cognition and other kinds of thinking in his book ''[[Thinking Fast and Slow]]'' (2011)}} McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problems—not machines that think as people do.{{efn| An early example of [[John McCarthy (computer scientist)|McCarthy's]] position was in the journal [[Science (journal)|Science]] where he said "This is AI, so we don't care if it's psychologically real" {{Harv|Kolata|1982}}, and he recently reiterated his position at the [[AI@50]] conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" {{Harv|Maker|2006}}.}}

===MIT's "anti-logic" approach===
Among the critics of [[John McCarthy (computer scientist)|McCarthy's]] approach were his colleagues across the country at [[MIT]]. [[Marvin Minsky]], [[Seymour Papert]] and [[Roger Schank]] were trying to solve problems like "story understanding" and "object recognition" that <em>required</em> a machine to think like a person. In order to use ordinary concepts like "chair" or "restaurant" they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. MIT chose instead to focus on writing programs that solved a given task without using high-level abstract definitions or general theories of cognition, and measured performance by iterative testing, rather than arguments from first principles. [[Roger Schank|Schank]] described their "anti-logic" approaches as [[Neats vs. scruffies|''scruffy'']], as opposed to the [[Neats vs. scruffies|''neat'']] paradigm used by [[John McCarthy (computer scientist)|McCarthy]], [[Robert Kowalski|Kowalski]], [[Edward Feigenbaum|Feigenbaum]], [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]].<ref>[[Neats vs. scruffies]]:
* {{Harvnb|McCorduck|2004|pp=421–424}} (who picks up the state of the debate in 1984). 
* {{Harvnb|Crevier|1993|p=168}} (who documents Schank's original use of the term).
* {{Harvnb|Russell|Norvig|2021|pp=19–20}} (who describe MIT's approach as "anti-logic")
</ref>{{efn|Another aspect of the conflict was called "the procedural/declarative distinction" but did not prove to be influential in later AI research.}}

In 1975, in a seminal paper, [[Marvin Minsky|Minsky]] noted that many of his fellow researchers were using the same kind of tool: a framework that captures all our [[commonsense knowledge|common sense assumptions]] about something. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on (none of which are true for all birds). Minsky associated these assumptions with the general category and they could be ''[[inheritance (object-oriented programming)|inherited]]'' by the frames for subcategories and individuals, or over-ridden as necessary. He called these structures ''[[Frame (Artificial intelligence)|frames]]''. [[Roger Schank|Schank]] used a version of frames he called "[[Scripts (artificial intelligence)|scripts]]" to successfully answer questions about short stories in English.<ref>
[[Frame (artificial intelligence)]]: 
* {{Harvnb|McCorduck|2004|pp=305–306}}
* {{Harvnb|Crevier|1993|pp=170–173, 246}} 
* {{Harvnb|Russell|Norvig|2021|p=23}}. 
Minsky's frame paper: 
* {{Harvnb|Minsky|1974}}.</ref> Frames would eventually be widely used in [[software engineering]] under the name [[object-oriented programming]].

The logicians rose to the challenge. [[Pat Hayes]] claimed that "most of 'frames' is just a new syntax for parts of first-order logic." But he noted that "there are one or two apparently minor details which give a lot of trouble, however, especially defaults".{{sfn|Hayes|1981}}

[[Raymond Reiter|Ray Reiter]] admitted that "conventional logics, such as first-order
logic, lack the expressive power to adequately represent the knowledge required for reasoning by default".{{sfn|Reiter|1978}} He proposed augmenting first-order logic with a [[closed world assumption]] that a conclusion holds (by default) if its contrary cannot be shown. He showed how such an assumption corresponds to the common sense assumption made in reasoning with frames. He also showed that it has its "procedural equivalent" as [[negation as failure]] in [[Prolog]]. The closed world assumption, as formulated by Reiter, "is not a first-order notion. (It is a meta notion.)"{{sfn|Reiter|1978}} However, [[Keith Clark (computer scientist)|Keith Clark]] showed that negation as ''finite failure'' can be understood as reasoning implicitly with definitions in first-order logic including a [[unique name assumption]] that different terms denote different individuals.{{sfn|Clark|1977}}

During the late 1970s and throughout the 1980s, a variety of logics and extensions of first-order logic were developed both for negation as failure in [[logic programming]] and for default reasoning more generally. Collectively, these logics have become known as [[non-monotonic logic]]s.

==Boom (1980–1987)==
In the 1980s, a form of AI program called "[[expert system]]s" was adopted by corporations around the world and [[knowledge representation|knowledge]] became the focus of mainstream AI research. Governments provided substantial funding, such as Japan's [[fifth generation computer]] project and the U.S. [[Strategic Computing Initiative]]. "Overall, the AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988."{{sfn|Russell|Norvig|2021|p=24}}

===Expert systems become widely used===
An [[expert system]] is a program that answers questions or solves problems about a specific domain of knowledge, using logical [[Production system (computer science)|rules]] that are derived from the knowledge of experts.<ref>
[[Expert system]]s:
* {{Harvnb|Crevier|1993|pp=148–159}}
* {{Harvnb|Newquist|1994|p=271}}
* {{Harvnb|Russell|Norvig|2021|pp=22–24}}
</ref>
The earliest examples were developed by [[Edward Feigenbaum]] and his students. [[Dendral]], begun in 1965, identified compounds from spectrometer readings.{{sfn|McCorduck|2004|pp=327–335}}{{sfn|Russell|Norvig|2021|p=22}} [[MYCIN]], developed in 1972, diagnosed infectious blood diseases.{{sfn|Russell|Norvig|2021|p=24}} They demonstrated the feasibility of the approach.

Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the [[commonsense knowledge]] problem){{sfn|Russell|Norvig|2021|p=22}} and their simple design made it relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be ''useful'': something that AI had not been able to achieve up to this point.{{sfn|Crevier|1993|pp=158–159}}

In 1980, an expert system called [[R1 (expert system)|R1]] was completed at [[Carnegie Mellon University|CMU]] for the [[Digital Equipment Corporation]]. It was an enormous success: it was saving the company 40 million dollars annually by 1986.{{sfn|Crevier|1993|p=198}} Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments.{{sfn|Newquist|1994|p=259}} An industry grew up to support them, including hardware companies like [[Symbolics]] and [[Lisp Machines]] and software companies such as [[IntelliCorp (Software)|IntelliCorp]] and [[Cleverpath AION Business Rules Expert|Aion]].<ref>
Commercial expert systems:
* {{Harvnb|McCorduck|2004|pp=434–435}}
* {{Harvnb|Crevier|1993|pp=161–162,197–203}} 
* {{Harvnb|Russell|Norvig|20
* {{Harvnb|Newquist|1994|p=275}}</ref>

===Government funding increases===
In 1981, the [[Ministry of International Trade and Industry|Japanese Ministry of International Trade and Industry]] set aside $850 million for the [[Fifth generation computer]] project. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.<ref>
[[Fifth generation computer]]:
* {{Harvnb|McCorduck|2004|pp=436–441}}
* {{Harvnb|Newquist|1994|pp=231–240}}
* {{Harvnb|Crevier|1993|p=211}} 
* {{Harvnb|Russell|Norvig|2021|p=23}}
* {{Harvnb|Feigenbaum|McCorduck|1983}}
</ref> Much to the chagrin of [[neats vs. scruffies|scruffies]], they initially chose [[Prolog]] as the primary computer language for the project.{{sfn|Crevier|1993|p=195}}

Other countries responded with new programs of their own. The UK began the £350 million [[Alvey]] project.{{sfn|Russell|Norvig|2021|p=23}} A consortium of American companies formed the [[Microelectronics and Computer Technology Corporation]] (or "MCC") to fund large scale projects in AI and information technology.{{sfn|Crevier|1993|p=240}}{{sfn|Russell|Norvig|2021|p=23}} [[DARPA]] responded as well, founding the [[Strategic Computing Initiative]] and tripling its investment in AI between 1984 and 1988.{{sfn|McCorduck|2004|pp=426–432}}{{sfn|NRC|1999|loc=under "Shift to Applied Research Increases Investment"}}

===Knowledge revolution===
The power of expert systems came from the expert knowledge they contained. They were part of a new direction in AI research that had been gaining ground throughout the 70s. "AI researchers were beginning to suspect—reluctantly, for it violated the scientific canon of [[Principle of parsimony|parsimony]]—that intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways,"{{sfn|McCorduck|2004|p=299}} writes [[Pamela McCorduck]]. "[T]he great lesson from the 1970s was that intelligent behavior depended very much on dealing with knowledge, sometimes quite detailed knowledge, of a domain where a given task lay".{{sfn|McCorduck|2004|p=421}} [[Knowledge based system]]s and [[knowledge engineering]] became a major focus of AI research in the 1980s.<ref>Knowledge revolution: 
* {{Harvnb|McCorduck|2004|pp=266–276, 298–300, 314, 421}}
* {{Harvnb|Newquist|1994|pp=255–267}}
* {{Harvnb|Russell|Norvig|2021|p=23}}
</ref> It was hoped that vast databases would solve the [[commonsense knowledge]] problem and provide the support that [[commonsense reasoning]] required.

In the 1980s some researchers attempted to attack the [[commonsense reasoning|commonsense knowledge problem]] directly, by creating a massive database that would contain all the mundane facts that the average person knows. [[Douglas Lenat]], who started a database called [[Cyc]], argued that there is no shortcut ― the only way for machines to know the meaning of human concepts is to teach them, one concept at a time, by hand.<ref>
[[Cyc]] and [[ontological engineering]] 
* {{Harvnb|McCorduck|2004|p=489}}
* {{Harvnb|Crevier|1993|pp=239–243}}
* {{Harvnb|Newquist|1994|pp=431–455}}
* {{Harvnb|Russell|Norvig|2021|pp=314−316}} 
* {{Harvnb|Lenat|Guha|1989}}
</ref>

== New directions in the 1980s ==

Although symbolic [[Knowledge representation and reasoning|knowledge representation]] and [[logical reasoning]] produced useful applications in the 80s and received massive amounts of funding, it was still unable to solve problems in [[perception]], [[robotics]], [[machine learning|learning]] and [[common sense reasoning|common sense]]. A small number of scientists and engineers began to doubt that the symbolic approach would ever be sufficient for these tasks and developed other approaches, such as "[[connectionism]]", [[robotic]]s, [[soft computing|"soft" computing]] and [[reinforcement learning]]. [[Nils John Nilsson|Nils Nilsson]] called these approaches "sub-symbolic".

===Revival of neural networks: "connectionism"===
[[File:Hopfield-net-vector.svg|thumb|A Hopfield net with four nodes]]
In 1982, physicist [[John Hopfield]] was able to prove that a form of neural network (now called a "[[Hopfield net]]") could learn and process information, and provably converges after enough time under any fixed condition. It was a breakthrough, as it was previously thought that nonlinear networks would, in general, evolve chaotically.{{sfn| Sejnowski|2018}} Around the same time, [[Geoffrey Hinton]] and [[David Rumelhart]] popularized a method for training neural networks called "[[backpropagation]]".{{efn|Versions of backpropagation had been developed in several fields, most directly as the reverse mode of [[automatic differentiation]] published by [[Seppo Linnainmaa]] (1970). It was  applied to neural networks in the 1970s by [[Paul Werbos]].{{sfn|Schmidhuber|2022}}}} These two developments helped to revive the exploration of [[artificial neural network]]s.{{sfn|Russell|Norvig|2021|p=24}}{{sfn|Crevier|1993|pp=214–215}}

Neural networks, along with several other similar models, received widespread attention after the 1986 publication of the ''[[Parallel Distributed Processing]]'', a two volume collection of papers edited by [[David Rumelhart|Rumelhart]] and psychologist [[James McClelland (psychologist)|James McClelland]]. The new field was christened "[[connectionism]]" and there was a considerable debate between advocates of [[symbolic AI]] and the "connectionists".{{sfn|Russell|Norvig|2021|p=24}} Hinton called symbols the "[[luminous aether]] of AI" – that is, an unworkable and misleading model of intelligence.{{sfn|Russell|Norvig|2021|p=24}} This was a direct attack on the principles that inspired the [[cognitive revolution]].

Neural networks started to advance state of the art in some specialist areas such as protein structure prediction. Following pioneering work from Terry Sejnowski,<ref name="Qian1988">{{cite journal |last1= Qian|first1=Ning |last2=Sejnovski |first2=Terry J |title=Predicting the secondary structure of globular proteins using neural network models |url= https://www.sciencedirect.com/science/article/pii/0022283688905645|journal= Journal of Molecular Biology|volume= 202|issue= 4|date= 1988|pages=865–884 |doi=10.1016/0022-2836(88)90564-5}}</ref> cascading multilayer perceptrons such as PhD<ref name="Rost1993">{{cite journal |last1= Rost|first1=Burkhard |last2=Sander |first2=Chris |date= 1993|title=Improved prediction of protein secondary structure by use of sequence profiles and neural networks|url= https://www.pnas.org/doi/10.1073/pnas.90.16.7558|journal= Proceedings of the National Academy of Sciences|volume= 90|issue= 16 |pages=7558–7562 |doi=10.1073/pnas.90.16.7558|pmc=47181}}</ref> and PsiPred<ref name="McGuffin2000">{{cite journal |last1= McGuffin|first1=Liam J |last2=Bryson |first2=Kevin|last3=Jones|first3=David T |date= 2000|title=The PSIPRED protein structure prediction server.|url= https://academic.oup.com/bioinformatics/article/16/4/404/187312|journal= Bioinformatics|volume= 16|issue= 4|pages=404–405 |doi=10.1093/bioinformatics/16.4.404}}</ref> reached near-theoretical maximum accuracy in predicting secondary structure.

In 1990, [[Yann LeCun]] at [[Bell Labs]] used [[convolutional neural networks]] to recognize handwritten digits. The system was used widely in 90s, reading zip codes and personal checks. This was the first genuinely useful application of neural networks.{{sfn|Russell|Norvig|2021|p=26}}{{sfn|Christian|2020|pp=21-22}}

===Robotics and embodied reason===
{{Main|Nouvelle AI|behavior-based AI|situated AI|embodied cognitive science}}
[[Rodney Brooks]], [[Hans Moravec]] and others argued that, in order to show real intelligence, a machine needs to have a <em>body</em> — it needs to perceive, move, survive and deal with the world.{{sfn|McCorduck|2004|pp=454–462}} Sensorimotor skills are essential to higher level skills such as [[commonsense reasoning]]. They can't be efficiently implemented using abstract symbolic reasoning, so AI should solve the problems of perception, mobility, manipulation and survival without using symbolic representation at all. These robotics researchers advocated building intelligence "from the bottom up".{{efn|[[Hans Moravec]] wrote: "I am confident that this bottom-up route to artificial intelligence will one date meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical [[golden spike]] is driven uniting the two efforts."{{sfn|Moravec|1988|p=20}}}}

A precursor to this idea was [[David Marr (neuroscientist)|David Marr]], who had come to [[MIT]] in the late 1970s from a successful background in theoretical neuroscience to lead the group studying [[computer vision|vision]]. He rejected all symbolic approaches (<em>both</em> [[John McCarthy (computer scientist)|McCarthy's]] logic and [[Marvin Minsky|Minsky]]'s frames), arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place. (Marr's work would be cut short by leukemia in 1980.){{sfn|Crevier|1993|pp=183–190}}

In his 1990 paper "Elephants Don't Play Chess,"{{sfn|Brooks|1990}} robotics researcher Brooks took direct aim at the [[physical symbol system|physical symbol system hypothesis]], arguing that symbols are not always necessary since "the world is its own best model. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough."{{sfn|Brooks|1990|p=3}}

In the 1980s and 1990s, many [[cognitive science|cognitive scientists]] also rejected the symbol processing model of the mind and argued that the body was essential for reasoning, a theory called the "[[embodied mind]] thesis".<ref>See, for example, {{Harvnb|Lakoff|Johnson|1999}}</ref>

===Soft computing and probabilistic reasoning===

[[Soft computing]] uses methods that work with incomplete and imprecise information. They do not attempt to give precise, logical answers, but give results that are only "probably" correct. This allowed them to solve problems that precise symbolic methods could not handle. Press accounts often claimed these tools could "think like a human".{{sfn|Pollack|1984}}{{sfn|Pollack|1989}}

[[Judea Pearl]]'s ''Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference'', an influential 1988 book{{sfn|Pearl|1988}} brought [[probability]] and [[decision theory]] into AI.{{sfn|Russell|Norvig|2021|p=25}} [[Fuzzy logic]], developed by [[Lofti Zadeh]] in the 60s, began to be more widely used in AI and robotics. [[Evolutionary computation]] and [[artificial neural networks]] also handle imprecise information, and are classified  as "soft". In the 90s and early 2000s many other soft computing tools were developed and put into use, including [[Bayesian networks]],{{sfn|Russell|Norvig|2021|p=25}} [[hidden Markov models]],{{sfn|Russell|Norvig|2021|p=25}} [[information theory]] and [[stochastic modeling]]. These tools in turn depended on advanced mathematical techniques such as classical [[optimization (mathematics)|optimization]]. For a time in the 1990s and early 2000s, these soft tools were studied by a subfield of AI called "[[computational intelligence]]".{{sfn|Poole|Mackworth|Goebel|1998}}

===Reinforcement learning===

[[Reinforcement learning]]{{sfn|Russell|Norvig|2021|loc=Section 23}} gives an agent a reward every time it performs a desired action well, and may give negative rewards (or "punishments") when it performs poorly. It was described in the first half of the twentieth century by psychologists using animal models, such as [[Edward Thorndike|Thorndike]],{{sfn|Christian|2020|pp=120-124}}{{sfn|Russell|Norvig|2021|p=819}} [[Ivan Pavlov|Pavlov]]{{sfn|Christian|2020|p=124}} and [[B.F. Skinner|Skinner]].{{sfn|Christian|2020|pp=152-156}} In the 1950s, [[Alan Turing]]{{sfn|Russell|Norvig|2021|p=819}}{{sfn|Christian|2020|p=125}} and [[Arthur Samuel (computer scientist)|Arthur Samuel]]{{sfn|Russell|Norvig|2021|p=819}} foresaw the role of reinforcement learning in AI.

A successful and influential research program was led by [[Richard S. Sutton|Richard Sutton]] and [[Andrew Barto]] beginning 1972. Their collaboration revolutionized the study of reinforcement learning and decision making over the four decades.{{sfn|Christian|2020|pp=127-129}}{{sfn|Russell|Norvig|2021|pp=25, 820}} In 1988, Sutton described machine learning in terms of [[decision theory]] (i.e., the [[Markov decision process]]). This gave the subject a solid theoretical foundation and access to a large body of theoretical results developed in the field of [[operations research]].{{sfn|Russell|Norvig|2021|pp=25, 820}}

Also in 1988, Sutton and Barto developed the "[[Temporal difference learning|temporal difference]]" (TD) learning algorithm, where the agent is rewarded only when its <em>predictions about the future</em> show improvement. It significantly outperformed previous algorithms.{{sfn|Christian|2020|p=140}} TD-learning was used by Gerald Tesauro in 1992 in the program [[TD-Gammon]], which played backgammon as well as the best human players. The program learned the game by playing against itself with zero prior knowledge.{{sfn|Christian|2020|p=141}} In an interesting case of interdisciplinary convergence, neurologists discovered in 1997 that the [[reward system|dopamine reward system]] in brains also uses a version of the TD-learning algorithm.{{sfn|Christian|2020|p=?}}{{sfn|Russell|Norvig|2021|p=820}}{{sfn|Schultz|Dayan|Montague|1997}} TD learning would be become highly influential in the 21st century, used in both [[AlphaGo]] and [[AlphaZero]].{{sfn|Russell|Norvig|2021|p=822}}

==Second AI winter==
The business community's fascination with AI rose and fell in the 1980s in the classic pattern of an [[economic bubble]]. As dozens of companies failed, the perception in the business world was that the technology was not viable.{{sfn|Newquist|1994|pp=501, 511}} The damage to AI's reputation would last into the 21st century. Inside the field there was little agreement on the reasons for AI's failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s. Together, all these factors helped to fragment AI into competing subfields focused on particular problems or approaches, sometimes even under new names that disguised the tarnished pedigree of "artificial intelligence".{{sfn|McCorduck|2004|p=424}}

Over the next 20 years, AI consistently delivered working solutions to specific isolated problems. By the late 1990s, it was being used throughout the technology industry, although somewhat behind the scenes. The success was due to [[Moore's law|increasing computer power]], by collaboration with other fields (such as [[mathematical optimization]] and [[statistics]]) and using the highest standards of scientific accountability.                                                                            By 2000, AI had achieved some of its oldest goals. The field was both more cautious and more successful than it had ever been.

===AI winter===
The term "[[AI winter]]" was coined by researchers who had survived the funding cuts of 1974 when they became concerned that enthusiasm for expert systems had spiraled out of control and that disappointment would certainly follow.{{efn|[[AI winter]] was first used as the title of a seminar on the subject for the [[Association for the Advancement of Artificial Intelligence]].{{sfn|Crevier|1993|p=203}}}} Their fears were well founded: in the late 1980s and early 1990s, AI suffered a series of financial setbacks.{{sfn|Russell|Norvig|2021|p=24}}

The first indication of a change in weather was the sudden collapse of the market for specialized AI hardware in 1987. Desktop computers from [[Apple Computer|Apple]] and [[IBM]] had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive [[Lisp machines]] made by [[Symbolics]] and others. There was no longer a good reason to buy them. An entire industry worth half a billion dollars was demolished overnight.<ref>
[[Lisp machine]] crisis:
* {{Harvnb|Newquist|1994|pp=359–379}}
* {{Harvnb|McCorduck|2004|p=435}}
* {{Harvnb|Crevier|1993|pp=209–210}}
</ref>

Eventually the earliest successful expert systems, such as [[XCON]], proved too expensive to maintain. They were difficult to update, they could not learn, and they were "[[brittle (software)|brittle]]" (i.e., they could make grotesque mistakes when given unusual inputs). Expert systems proved useful, but only in a few special contexts.<ref>
[[Expert system]]s failure (and the reason for it):
* {{Harvnb|Russell|Norvig|2021|p=24}} (inability to handle uncertain reasoning or to learn)
* {{Harvnb|McCorduck|2004|p=435}} (institutional issues)
* {{Harvnb|Newquist|1994|pp=258–283}} (limited deployment after development)
* {{Harvnb|Crevier|1993|pp=204–208}} (the difficulty of truth maintenance, i.e., learning and updating)
* {{Harvnb|Lenat|Guha|1989|loc=Introduction}} (brittleness and the inability to handle extensive [[qualification problem|qualification]].)
</ref>

In the late 1980s, the [[Strategic Computing Initiative]] cut funding to AI "deeply and brutally". New leadership at [[DARPA]] had decided that AI was not "the next wave" and directed funds towards projects that seemed more likely to produce immediate results.{{sfn|McCorduck|2004|pp=430–431}}

By 1991, the impressive list of goals penned in 1981 for Japan's [[fifth generation computer|Fifth Generation Project]] had not been met. Indeed, some of them, like "carry on a casual conversation" would not be accomplished for another 30 years. As with other AI projects, expectations had run much higher than what was actually possible.<ref>
End of the [[Fifth generation computer]] initiative:
* {{Harvnb|McCorduck|2004|p=441}}
* {{Harvnb|Crevier|1993|p=212}}
* {{Harvnb|Newquist|1994|p=476}}
</ref>{{efn|McCorduck writes "Two and a half decades later, we can see that the Japanese didn't quite meet all of those ambitious goals."{{sfn|McCorduck|2004|p=441}}}}

Over 300 AI companies had shut down, gone bankrupt, or been acquired by the end of 1993, effectively ending the first commercial wave of AI.{{sfn|Newquist|1994|p=440}} In 1994, [[HP Newquist]] stated in ''The Brain Makers'' that "The immediate future of artificial intelligence—in its commercial form—seems to rest in part on the continued success of neural networks."{{sfn|Newquist|1994|p=440}}

=== AI behind the scenes ===
In the 1990s, algorithms originally developed by AI researchers began to appear as parts of larger systems. AI had solved a lot of very difficult problems{{efn|See {{slink|Applications of artificial intelligence|Computer science}}}} and their solutions proved to be useful throughout the technology industry,{{sfn|NRC|1999|loc=Artificial Intelligence in the 90s}}{{sfn|Kurzweil|2005|p=264}} such as [[data mining]], [[industrial robots|industrial robotics]], logistics, [[speech recognition]],{{sfn|The Economist|2007}} banking software,{{sfn|CNN|2006}} medical diagnosis{{sfn|CNN|2006}} and [[Google]]'s search engine.{{sfn|Olsen|2004}}{{sfn|Olsen|2006}}

The field of AI received little or no credit for these successes in the 1990s and early 2000s. Many of AI's greatest innovations have been reduced to the status of just another item in the tool chest of computer science.<ref>[[AI effect]], AI behind the scenes in the 90s & 2000s:
* {{Harvnb|McCorduck|2004|p=423}}
* {{Harvnb|Kurzweil|2005|p=265}} 
* {{Harvnb|Hofstadter|1999|p=601}} 
* {{Harvnb|Newquist|1994|p=445}}
</ref> [[Nick Bostrom]] explains: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."{{sfn|CNN|2006}}

Many researchers in AI in the 1990s deliberately called their work by other names, such as [[Informatics (academic field)|informatics]], [[knowledge-based systems]], "cognitive systems" or [[computational intelligence]]. In part, this may have been because they considered their field to be fundamentally different from AI, but also the new names help to procure funding.{{sfn|The Economist|2007}}{{sfn|Tascarella|2006}}{{sfn|Newquist|1994|p=532}} In the commercial world at least, the failed promises of the [[AI Winter]] continued to haunt AI research into the 2000s, as the ''New York Times'' reported in 2005: "Computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."{{sfn|Markoff|2005}}

===Mathematical rigor, greater collaboration and a narrow focus===
AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past.{{sfn|McCorduck|2004|pp=486–487}}{{sfn|Russell|Norvig|2021|pp=24–25}} Most of the new directions in AI relied heavily on mathematical models, including [[artificial neural networks]], [[probabilistic reasoning]], [[soft computing]] and [[reinforcement learning]]. In the 90s and 2000s, many other highly mathematical tools were adapted for AI. These tools were applied to machine learning, perception and mobility.

There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like [[statistics]], [[mathematics]], [[electrical engineering]], [[economics]] or [[operations research]]. The shared mathematical language allowed both a higher level of collaboration with more established and successful fields and the achievement of results which were measurable and provable; AI had become a more rigorous "scientific" discipline.

Another key reason for the success in the 90s was that AI researchers focussed on specific problems with verifiable solutions (an approach later derided as ''[[narrow AI]]''). This provided useful tools in the present, rather than speculation about the future.

===Intelligent agents===
A new paradigm called "[[intelligent agent]]s" became widely accepted during the 1990s.{{sfn|McCorduck|2004|pp=471–478}}{{sfn|Russell|Norvig|2021|loc=chpt. 2}}{{efn|Russell and Norvig wrote "The whole-agent view is now widely accepted."{{sfn|Russell|Norvig|2021|p=61}}}} Although earlier researchers had proposed modular "divide and conquer" approaches to AI,{{efn|[[Carl Hewitt]]'s [[Actor model]] anticipated the modern definition of intelligent agents. {{Harv|Hewitt|Bishop|Steiger|1973}} Both John Doyle {{Harv|Doyle|1983}} and [[Marvin Minsky]]'s popular classic ''[[The Society of Mind]]'' {{Harv|Minsky|1986}} used the word "agent". Other "modular" proposals included [[Rodney Brooks|Rodney Brook's]] [[subsumption architecture]], [[object-oriented programming]] and others.}} the intelligent agent did not reach its modern form until [[Judea Pearl]], [[Allen Newell]], [[Leslie P. Kaelbling]], and others brought concepts from [[decision theory]] and economics into the study of AI.{{sfn|Russell|Norvig|2021|p=61}} When the [[economics|economist's]] definition of a [[rational agent]] was married to [[computer science]]'s definition of an [[object-oriented programming|object]] or [[module (programming)|module]], the [[intelligent agent]] paradigm was complete.

An [[intelligent agent]] is a system that perceives its environment and takes actions which maximize its chances of success. By this definition, simple programs that solve specific problems are "intelligent agents", as are human beings and organizations of human beings, such as [[firm]]s. The [[intelligent agent|intelligent agent paradigm]] defines AI research as "the study of intelligent agents".{{efn|This is how the most widely used textbooks of the 21st century define artificial intelligence, such as Russell and Norvig, 2021; Padgham and Winikoff, 2004; Jones, 2007; Poole and Mackworth, 2017.{{sfn|Russell|Norvig|2021|p=61}}}} This is a generalization of some earlier definitions of AI: it goes beyond studying human intelligence; it studies all kinds of intelligence.

The paradigm gave researchers license to study isolated problems and to disagree about methods, but still retain hope that their work could be combined into an [[agent architecture]] that would be capable of general intelligence.{{sfn|McCorduck|2004|p=478}}

===Milestones and Moore's law===
On May 11, 1997, [[IBM Deep Blue|Deep Blue]] became the first computer chess-playing system to beat a reigning world chess champion, [[Garry Kasparov]].{{sfn|McCorduck|2004|pp=480–483}} In 2005, a Stanford robot won the [[DARPA Grand Challenge]] by driving autonomously for 131 miles along an unrehearsed desert trail. Two years later, a team from CMU won the [[DARPA Urban Challenge]] by autonomously navigating 55 miles in an urban environment while responding to traffic hazards and adhering to traffic laws.{{sfn|Russell|Norvig|2021|p=28}}

These successes were not due to some revolutionary new paradigm, but mostly on the tedious application of engineering skill and on the tremendous increase in the speed and capacity of computers by the 90s.{{efn|[[Ray Kurzweil]] wrote
that the improvement in computer chess "is governed only by the brute force expansion of computer hardware."{{sfn|Kurzweil|2005|p=274}}}} In fact, [[IBM Deep Blue|Deep Blue's]] computer was 10 million times faster than the [[Ferranti Mark 1]] that [[Christopher Strachey]] taught to play chess in 1951.{{efn|Cycle time of [[Ferranti Mark 1]] was 1.2&nbsp;milliseconds, which is arguably equivalent to about 833&nbsp;[[flops]]. [[IBM Deep Blue|Deep Blue]] ran at 11.38&nbsp;[[gigaflops]] (and this does not even take into account Deep Blue's special-purpose hardware for chess). ''Very'' approximately, these differ by a factor of 10<sup>7</sup>.{{citation needed|date=August 2024}}}} This dramatic increase is measured by [[Moore's law]], which predicts that the speed and memory capacity of computers doubles every two years. The fundamental problem of "raw computer power" was slowly being overcome.

==Big data, deep learning, AGI (2005–2017)==
In the first decades of the 21st century, access to large amounts of data (known as "[[big data]]"), [[Moore's law|cheaper and faster computers]] and advanced [[machine learning]] techniques were successfully applied to many problems throughout the economy. A turning point was the success of [[deep learning]] around 2012 which improved the performance of machine learning on many tasks, including image and video processing, text analysis, and speech recognition.{{sfn|LeCun|Bengio|Hinton|2015}} Investment in AI increased along with its capabilities, and by 2016, the market for AI-related products, hardware, and software reached more than $8 billion, and the New York Times reported that interest in AI had reached a "frenzy".{{sfn|Lohr|2016}}

In 2002, [[Ben Goertzel]] and others became concerned that AI had largely abandoned its original goal of producing versatile, fully intelligent machines, and argued in favor of more direct research into [[artificial general intelligence]]. By the mid-2010s several companies and institutions had been founded to pursue Artificial General Intelligence (AGI), such as [[OpenAI]] and [[Google]]'s [[DeepMind]]. During the same period, new insights into [[superintelligence]] raised concerns that AI was an [[Existential risk from artificial general intelligence|existential threat]]. The risks and unintended consequences of AI technology became an area of serious academic research after 2016.

===Big data and big machines===
{{See also|List of datasets for machine-learning research}}

The success of machine learning in the 2000s depended on the availability of vast amounts of training data and faster computers.{{sfn|Russell|Norvig|2021|pp=26-27}} Russell and Norvig wrote that the "improvement in performance obtained by increasing the size of the data set by two or three orders of magnitude outweighs any improvement that can be made by tweaking the algorithm."{{sfn|Russell|Norvig|2021|p=26}} [[Geoffrey Hinton]] recalled that back in the 90s, the problem was that "our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow."<ref>Quoted in {{harvnb|Christian|2020|p=22}}</ref> This was no longer true by 2010.

The most useful data in the 2000s came from curated, labeled data sets created specifically for machine learning and AI. In 2007, a group at [[University of Massachusetts Amherst|UMass Amherst]] released [[Labeled Faces in the Wild]], an annotated set of images of faces that was widely used to train and test [[face recognition]] systems for the next several decades.{{sfn|Christian|2020|p=31}} [[Fei-Fei Li]] developed [[ImageNet]], a database of three million images captioned by volunteers using the [[Amazon Mechanical Turk]]. Released in 2009, it was a useful body of training data and a benchmark for testing for the next generation of image processing systems.{{sfn|Christian|2020|pp=22-23}}{{sfn|Russell|Norvig|2021|p=26}} Google released [[word2vec]] in 2013 as an open source resource. It used large amounts of data text scraped from the internet and [[word embedding]] to create a numeric vector to represent each word. Users were surprised at how well it was able to capture word meanings, for example, ordinary vector addition would give equivalences like China + River = Yangtze, London-England+France = Paris.{{sfn|Christian|2020|p=6}} This database in particular would be essential for the development of [[large language model]]s in the late 2010s.

The explosive growth of the internet gave machine learning programs access to billions of pages of text and images that could be [[data scraping|scraped]]. And, for specific problems, large privately held databases contained the relevant data. [[McKinsey Global Institute]] reported that "by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data".{{sfn|McKinsey & Co|2011}} This collection of information was known in the 2000s as ''[[big data]]''.

In a ''[[Jeopardy!]]'' exhibition match in February 2011, [[IBM]]'s [[question answering system]] [[IBM Watson|Watson]] defeated the two best ''Jeopardy!'' champions, [[Brad Rutter]] and [[Ken Jennings]], by a significant margin.{{sfn|Markoff|2011}} Watson's expertise would have been impossible without the information available on the internet.{{sfn|Russell|Norvig|2021|p=26}}

===Deep learning===
{{Main|Deep learning}}

In 2012, [[AlexNet]], a [[deep learning]] model,{{efn|[[AlexNet]] had 650,000 neurons and trained using [[ImageNet]], augmented with reversed, cropped and tinted images. The model also used [[Geoffrey Hinton]]'s [[dropout (neural networks)|dropout]] technique and a [[Rectifier (neural networks)|rectified linear]] output function, both relatively new developments at the time.{{sfn|Christian|2020|pp=23-24}}}} developed by [[Alex Krizhevsky]], won the [[ImageNet Large Scale Visual Recognition Challenge]], with significantly fewer errors than the second-place winner.{{sfn|Christian|2020|p=24}}{{sfn|Russell|Norvig|2021|p=26}} Krizhevsky worked with [[Geoffrey Hinton]] at the [[University of Toronto]].{{efn|Several other laboratories had developed systems that, like AlexNet, used GPU chips and performed nearly as well as AlexNet,{{sfn|Schmidhuber|2022}} but AlexNet proved to be the most influential.}} This was a turning point in machine learning: over the next few years dozens of other approaches to image recognition were abandoned in favor of [[deep learning]].{{sfn|Russell|Norvig|2021|pp=26-27}}

Deep learning uses a multi-layer [[perceptron]]. Although this architecture has been known since the 60s, getting it to work requires powerful hardware and large amounts of training data.{{sfn|Russell|Norvig|2021|p=27}} Before these became 
available, improving performance of image processing systems required hand-crafted ''ad hoc'' features that were difficult to implement.{{sfn|Russell|Norvig|2021|p=27}} Deep learning was simpler and more general.{{efn|See {{section link|History of AI|The problems}} above, where Hans Moravec predicted that raw power would eventually make AI "easy".}}

Deep learning was applied to dozens of problems over the next few years (such as speech recognition, machine translation, medical diagnosis, and game playing). In every case it showed enormous gains in performance.{{sfn|Russell|Norvig|2021|pp=26-27}} Investment and interest in AI boomed as a result.{{sfn|Russell|Norvig|2021|pp=26-27}}

===The alignment problem===

It became fashionable in the 2000s to begin talking about the future of AI again and several popular books considered the possibility of [[superintelligent]] machines and what they might mean for human society. Some of this was optimistic (such as [[Ray Kurzweil]]'s ''[[The Singularity is Near]]''), but others warned that a sufficiently powerful AI was [[existential risk of artificial general intelligence|existential threat]] to humanity, such as [[Nick Bostrom]] and [[Eliezer Yudkowsky]].{{sfn|Russell|Norvig|2021|pp=33, 1004}} The topic became widely covered in the press and many leading intellectuals and politicians commented on the issue.

AI programs in the 21st century are defined by their [[utility function|goals]] – the specific measures that they are designed to optimize. [[Nick Bostrom]]'s influential 2005 book ''[[Superintelligence (book)|Superintelligence]]'' argued that, if one isn't careful about defining these goals, the machine may cause harm to humanity in the process of achieving a goal. [[Stuart J. Russell]] used the example of an intelligent robot that kills its owner to prevent it from being unplugged, reasoning "you can't fetch the coffee if you're dead".{{sfn|Russell|2020}} (This problem is known by the technical term "[[instrumental convergence]]".) The solution is to <em>align</em> the machine's goal function with the goals of its owner and humanity in general. Thus, the problem of mitigating the risks and unintended consequences of AI became known as "the value alignment problem" or [[AI alignment]].{{sfn|Russell|Norvig|2021|pp=5, 33, 1002-1003}}

At the same time, machine learning systems had begun to have disturbing unintended consequences. [[Cathy O'Neil]] explained how statistical algorithms had been among the causes of the [[great recession|2008 economic crash]],{{sfn|O'Neill|2016}} [[Julia Angwin]] of [[ProPublica]] argued that the [[COMPAS (software)|COMPAS]] system used by the criminal justice system exhibited racial bias under some measures,{{sfn|Christian|2020|pp=60-61}}{{efn|Later research showed that there was no way for system to avoid a measurable racist bias -- fixing one form of bias would necessarily introduce another.{{sfn|Christian|2020|pp=67-70}}}} others showed that many machine learning systems exhibited some form of racial [[algorithmic bias|bias]],{{sfn|Christian|2020|pp=6-7, 25}} and there were many other examples of dangerous outcomes that had resulted from machine learning systems.{{efn|A short summary of topics would include [[privacy]], [[surveillance]], [[copyright]], [[misinformation]] and [[deep fakes]], [[filter bubbles]] and [[partisanship]], [[algorithmic bias]], misleading results that go undetected without [[algorithmic transparency]], the [[right to an explanation]], misuse of [[autonomous weapon]]s and [[technological unemployment]]. See {{section link|Artificial intelligence|Ethics}}}}

In 2016, the election of [[Donald Trump]] and the controversy over the COMPAS system illuminated several problems with the current technological infrastructure, including misinformation, social media algorithms designed to maximize engagement, the misuse of personal data and the trustworthiness of predictive models.{{sfn|Christian|2020|p=67}} Issues of [[fairness (machine learning)|fairness]] and unintended consequences became significantly more popular at AI conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The [[AI alignment|value alignment problem]] became a serious field of academic study.{{sfn|Christian|2020|pp=67, 73, 117}}{{efn|[[Brian Christian]] wrote "ProPublica's study [of COMPAS in 2015] legitimated concepts like fairness as valid topics for research"{{sfn|Christian|2020|p=73}}}}

===Artificial general intelligence research===

<!--- NOTE TO THE EDITOR: This section is a SOCIAL history of AGI research 2005-2023. Technical history is covered in sections on Deep learning, transformer architecture, reinforcement learning and neurosymbolic AI ---> 
In the early 2000s, several researchers became concerned that mainstream AI was too focused on "measurable performance in specific applications"{{sfn|Russell|Norvig|2021|p=32}} (known as "[[narrow AI]]") and had abandoned AI's original goal of creating versatile, fully intelligent machines. An early critic was [[Nils John Nilsson|Nils Nilsson]] in 1995, and similar opinions were published by AI elder statesmen John McCarthy, Marvin Minsky, and Patrick Winston in 2007–2009. Minsky organized a symposium on "human-level AI" in 2004.{{sfn|Russell|Norvig|2021|p=32}} [[Ben Goertzel]] adopted the term "[[artificial general intelligence]]" for the new sub-field, founding a journal and holding conferences beginning in 2008.{{sfn|Russell|Norvig|2021|p=33}} The new field grew rapidly, buoyed by the continuing success of artificial neural networks and the hope that it was the key to AGI.

Several competing companies, laboratories and foundations were founded to develop AGI in the 2010s. [[DeepMind]] was founded in 2010 by three English scientists, [[Demis Hassabis]], [[Shane Legg]] and [[Mustafa Suleyman]], with funding from [[Peter Thiel]] and later [[Elon Musk]]. The founders and financiers were deeply concerned about [[AI safety]] and the [[existential risk of AI]]. DeepMind's founders had a personal connection with Yudkowsky and Musk was among those who was actively raising the alarm.{{sfn|Metz|Weise|Grant|Isaac|2023}} Hassabis was both worried about the dangers of AGI and optimistic about its power; he hoped they could "solve AI, then solve everything else."{{sfn|Russell|Norvig|2021|p=31}} The New York Times wrote in 2023 "At the heart of this competition is a brain-stretching paradox. The people who say they are most worried about AI are among the most determined to create it and enjoy its riches. They have justified their ambition with their strong belief that they alone can keep AI from endangering Earth."{{sfn|Metz|Weise|Grant|Isaac|2023}}

In 2012, [[Geoffrey Hinton]] (who been leading neural network research since the 80s) was approached by [[Baidu]], which wanted to hire him and all his students for an enormous sum. Hinton decided to hold an auction and, at a Lake Tahoe AI conference, they sold themselves to [[Google]] for a price of $44 million. Hassabis took notice and sold DeepMind to Google in 2014, on the condition that it would not accept military contracts and would be overseen by an ethics board.{{sfn|Metz|Weise|Grant|Isaac|2023}}

[[Larry Page]] of Google, unlike Musk and Hassabis, was an optimist about the future of AI. Musk and Paige became embroiled in an argument about the risk of AGI at Musk's 2015 birthday party. They had been friends for decades but stopped speaking to each other shortly afterwards. Musk attended the one and only meeting of the DeepMind's ethics board, where it became clear that Google was uninterested in mitigating the harm of AGI. Frustrated by his lack of influence he founded [[OpenAI]] in 2015, enlisting [[Sam Altman]] to run it and hiring top scientists. OpenAI began as a non-profit, "free from the economic incentives that were driving Google and other corporations."{{sfn|Metz|Weise|Grant|Isaac|2023}} Musk became frustrated again and left the company in 2018. OpenAI turned to Microsoft for continued financial support and Altman and OpenAI formed a for-profit version of the company with more than $1 billion in financing.{{sfn|Metz|Weise|Grant|Isaac|2023}}

In 2021, [[Dario Amodei]]  and 14 other scientists left OpenAI over concerns that the company was putting profits above safety. They formed [[Anthropic]], which soon had $6 billion in financing from Microsoft and Google.{{sfn|Metz|Weise|Grant|Isaac|2023}}

== Large language models, AI boom (2017–present) ==
{{Main|AI boom}}
[[File:2024 AI patents by country - artificial intelligence.svg |thumb|In 2024, AI patents in China and the US numbered more than three-fourths of AI patents worldwide.<ref name=RandDworld_20241103/> Though China had more AI patents, the US had 35% more patents per AI patent-applicant company than China.<ref name=RandDworld_20241103>{{cite web |last1=Buntz |first1=Brian |title=Quality vs. quantity: US and China chart different paths in global AI patent race in 2024 / Geographical breakdown of AI patents in 2024 |url=https://www.rdworldonline.com/quality-vs-quantity-us-and-china-chart-different-paths-in-global-ai-patent-race-in-2024/ |publisher=R&D World |archive-url=https://web.archive.org/web/20241209072113/https://www.rdworldonline.com/quality-vs-quantity-us-and-china-chart-different-paths-in-global-ai-patent-race-in-2024/ |archive-date=9 December 2024 |date=3 November 2024 |url-status=live}}</ref>]]
[[File:20250202 "AI" (search term) on Google Trends.svg|thumb|The number of Google searches for the term "AI" accelerated in 2022.]]
The AI boom started with the initial development of key architectures and algorithms such as the [[transformer architecture]] in 2017, leading to the scaling and development of large language models exhibiting human-like traits of knowledge, attention and creativity. The new AI era began since 2020, with the public release of scaled large language models (LLMs) such as [[ChatGPT]].<ref>
[[AI boom]]:
* {{Harvnb|Marr|2023}}
* {{Harvnb|Clark|2023}}
* {{Harvnb|Gates|2023}}
* {{Harvnb|Lee|2024}}
</ref>

===Transformer architecture and large language models===
{{Main|Large language models}}
In 2017, the [[Transformer (machine learning model)|transformer]] architecture was proposed by Google researchers. It exploits an [[Attention (machine learning)|attention]] mechanism and became widely used in large language models.{{sfn|Murgia|2023}}

[[Large language models]], based on the transformer, were developed by AGI companies: [[OpenAI]] released [[GPT-3]] in 2020, and [[DeepMind]] released [[Gato (DeepMind)|Gato]] in 2022. These are [[foundation models]]: they are trained on vast quantities of unlabeled data and can be adapted to a wide range of downstream tasks.{{citation needed|date=August 2024}}

These models can discuss a huge number of topics and display general knowledge. The question naturally arises: are these models an example of [[artificial general intelligence]]? [[Bill Gates]] was skeptical of the new technology and the hype that surrounded AGI. However, Altman presented him with a live demo of [[GPT-4|ChatGPT4]] passing an advanced biology test. Gates was convinced.{{sfn|Metz|Weise|Grant|Isaac|2023}} In 2023, Microsoft Research tested the model with a large variety of tasks, and concluded that "it could reasonably be viewed as an early (yet still incomplete) version of an [[artificial general intelligence]] (AGI) system".{{sfn|Bubeck|Chandrasekaran|Eldan|Gehrke|2023}}

In 2024, [[OpenAI o3]], a type of advanced reasoning model developed by [[OpenAI]] was announced. On the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) benchmark developed by [[François Chollet]] in 2019, the model achieved an unofficial score of 87.5% on the semi-private test, surpassing the typical human score of 84%. The benchmark is supposed to be a necessary, but not sufficient test for AGI. Speaking of the benchmark, Chollet has said "You’ll know AGI is here when the exercise of creating tasks that are easy for regular humans but hard for AI becomes simply impossible."<ref>{{Cite web|last=Hsu|first=Jeremy|title=OpenAI's o3 model aced a test of AI reasoning – but it's still not AGI|url=https://www.newscientist.com/article/2462000-openais-o3-model-aced-a-test-of-ai-reasoning-but-its-still-not-agi/|access-date=2024-12-29|website=New Scientist|language=en}}</ref>

===AI boom===
{{Main|AI boom}}
Investment in AI grew exponentially after 2020, with venture capital funding for generative AI companies increasing dramatically. Total AI investments rose from $18 billion in 2014 to $119 billion in 2021, with generative AI accounting for approximately 30% of investments by 2023.<ref>{{Cite web |title=1.1 Generative AI stats on fundraising for AI companies by year (Copy) (update) |url=https://datawrapper.dwcdn.net/PgFO7/7/ |access-date=2024-04-23 |website=datawrapper.dwcdn.net |language=en}}</ref> According to metrics from 2017 to 2021, the United States outranked the rest of the world in terms of [[venture capital funding]], number of [[startups]], and AI [[patents]] granted.<ref name=":02">{{Cite web |last=Frank |first=Michael |date=September 22, 2023 |title=US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order |url=https://thediplomat.com/2023/09/us-leadership-in-artificial-intelligence-can-shape-the-21st-century-global-order/ |access-date=2023-12-08 |website=The Diplomat |language=en-US}}</ref> The commercial AI scene became dominated by American [[Big Tech]] companies, whose investments in this area surpassed those from U.S.-based [[venture capitalists]].<ref>{{cite web |last1=Hammond |first1=George |title=Big Tech is spending more than VC firms on AI startups |url=https://arstechnica.com/ai/2023/12/big-tech-is-spending-more-than-vc-firms-on-ai-startups/ |website=Ars Technica |language=en-us |date=27 December 2023}}</ref> [[OpenAI]]'s valuation reached $86 billion by early 2024,<ref>{{Cite news |last1=Metz |first1=Cade |last2=Mickle |first2=Tripp |date=February 16, 2024 |title=OpenAI Completes Deal That Values the Company at $80 Billion |work=The New York Times}}</ref> while [[NVIDIA]]'s market capitalization surpassed $3.3 trillion by mid-2024, making it the world's largest company by [[market capitalization]] as the demand for AI-capable [[GPU]]s surged.<ref>{{cite news |last=Hur |first=Krystal |title=Nvidia surpasses Microsoft to become the largest public company in the world |url=https://edition.cnn.com/2024/06/18/markets/nvidia-largest-public-company/index.html |access-date=2024-06-19 |work=CNN |date=2024-06-19}}</ref>

[[15.ai]], launched in March 2020<ref>{{cite web |last=Ng |first=Andrew |date=April 1, 2020 |title=Voice Cloning for the Masses |url=https://www.deeplearning.ai/the-batch/voice-cloning-for-the-masses/|access-date=December 22, 2024 |website=[[DeepLearning.AI]] |archive-url=https://web.archive.org/web/20241228151726/https://www.deeplearning.ai/the-batch/voice-cloning-for-the-masses/ |archive-date=December 28, 2024 |url-status=live}}</ref> by an anonymous [[MIT]] researcher,<ref>{{cite web |last=Chandraseta |first=Rionaldi |date=January 21, 2021 |title=Generate Your Favourite Characters' Voice Lines using Machine Learning |url=https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6 |url-status=live |access-date=December 18, 2024 |website=Towards Data Science |archive-date=January 21, 2021 |archive-url=https://web.archive.org/web/20210121132456/https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6}}</ref><ref>{{cite web |last=Temitope |first=Yusuf |date=December 10, 2024 |title=15.ai Creator reveals journey from MIT Project to internet phenomenon|url=https://guardian.ng/technology/15-ai-creator-reveals-journey-from-mit-project-to-internet-phenomenon/ |access-date=December 25, 2024 |website=[[The Guardian (Nigeria)|The Guardian]] |archive-url=https://web.archive.org/web/20241228152312/https://guardian.ng/technology/15-ai-creator-reveals-journey-from-mit-project-to-internet-phenomenon/ |archive-date=December 28, 2024|url-status=live}}</ref> was one of the earliest examples of [[generative AI]] gaining widespread public attention during the initial stages of the AI boom.<ref>{{cite web |author=Anirudh VK |date=March 18, 2023 |title=Deepfakes Are Elevating Meme Culture, But At What Cost? |url=https://analyticsindiamag.com/ai-origins-evolution/deepfakes-are-elevating-meme-culture-but-at-what-cost/ |access-date=December 18, 2024 |website=Analytics India Magazine |quote="While AI voice memes have been around in some form since '15.ai' launched in 2020, [...]"|url-status=live|archive-url=https://web.archive.org/web/20241226163953/https://analyticsindiamag.com/ai-origins-evolution/deepfakes-are-elevating-meme-culture-but-at-what-cost/|archive-date=December 26, 2024}}</ref> The free [[web application]] demonstrated the ability to clone character voices using neural networks with minimal training data, requiring as little as 15 seconds of audio to reproduce a voice&mdash;a capability later corroborated by [[OpenAI]] in 2024.<ref>{{cite web |title=Navigating the Challenges and Opportunities of Synthetic Voices |url=https://openai.com/index/navigating-the-challenges-and-opportunities-of-synthetic-voices/ |website=[[OpenAI]] |url-status=live |date=March 9, 2024 |access-date=December 18, 2024 |archive-date=November 25, 2024 |archive-url=https://web.archive.org/web/20241125181327/https://openai.com/index/navigating-the-challenges-and-opportunities-of-synthetic-voices/}}</ref> The service went [[viral phenomenon|viral]] on social media platforms in early 2021,<ref>{{cite web |last=Clayton |first=Natalie |date=January 19, 2021 |title=Make the cast of TF2 recite old memes with this AI text-to-speech tool |url=https://www.pcgamer.com/make-the-cast-of-tf2-recite-old-memes-with-this-ai-text-to-speech-tool |url-status=live |archive-url=https://web.archive.org/web/20210119133726/https://www.pcgamer.com/make-the-cast-of-tf2-recite-old-memes-with-this-ai-text-to-speech-tool/ |archive-date=January 19, 2021 |access-date=December 18, 2024 |website=[[PC Gamer]]}}</ref><ref>{{cite web |last=Kurosawa |first=Yuki |date=January 19, 2021 |title=ゲームキャラ音声読み上げソフト「15.ai」公開中。『Undertale』や『Portal』のキャラに好きなセリフを言ってもらえる |trans-title=Game Character Voice Reading Software "15.ai" Now Available. Get Characters from Undertale and Portal to Say Your Desired Lines |url=https://automaton-media.com/articles/newsjp/20210119-149494/ |url-status=live |archive-url=https://web.archive.org/web/20210119103031/https://automaton-media.com/articles/newsjp/20210119-149494/ |archive-date=January 19, 2021 |access-date=December 18, 2024 |website=[[:ja:AUTOMATON|AUTOMATON]] |language=ja}}</ref> allowing users to generate speech for characters from [[popular media]] franchises, and became particularly notable for its pioneering role in popularizing [[deep learning speech synthesis|AI voice synthesis]] for [[content creation|creative content]] and [[Internet meme|memes]].<ref>{{cite web |last=Wright |first=Steven |date=March 21, 2023 |title=Why Biden, Trump, and Obama Arguing Over Video Games Is YouTube's New Obsession |url=https://www.inverse.com/gaming/youtube-ai-presidential-gaming-debates |url-status=live |access-date=December 18, 2024 |website=[[Inverse (magazine)|Inverse]] |quote="AI voice tools used to create "audio deepfakes" have existed for years in one form or another, with 15.ai being a notable example." |archive-date=December 20, 2024 |archive-url=https://web.archive.org/web/20241220012854/https://www.inverse.com/gaming/youtube-ai-presidential-gaming-debates}}</ref>

{{Quote box
|quote=Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization? Such decisions must not be delegated to unelected tech leaders. '''Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable.''' This confidence must be well justified and increase with the magnitude of a system’s potential effects. OpenAI’s recent statement regarding artificial general intelligence, states that "At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models." We agree. That point is now.

Therefore, '''we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4'''. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.
|author=''[[Pause Giant AI Experiments: An Open Letter]]''<ref>{{cite web|url=https://futureoflife.org/open-letter/pause-giant-ai-experiments/|title=Pause Giant AI Experiments: An Open Letter|website=Future of Life}}</ref>
|source=
|align=right
|width=500px
}}

===Advent of AI for public use===
[[ChatGPT]] was launched on November 30, 2022, marking a pivotal moment in artificial intelligence's public adoption. Within days of its release it went viral, gaining over 100 million users in two months and becoming the fastest-growing consumer software application in history.<ref name=":8">{{Cite news |last=Milmo |first=Dan |date=December 2, 2023 |title=ChatGPT reaches 100 million users two months after launch |language=en-GB |work=The Guardian}}</ref> The chatbot's ability to engage in human-like conversations, write code, and generate creative content captured public imagination and led to rapid adoption across various sectors including [[AI in education|education]], [[Artificial intelligence in industry|business]], and research.<ref>{{cite journal |last1=Roumeliotis |first1=Konstantinos I. |last2=Tselikas |first2=Nikolaos D. |title=ChatGPT and Open-AI Models: A Preliminary Review |journal=Future Internet |date=2023 |volume=15 |issue=6 |pages=192|doi=10.3390/fi15060192 |doi-access=free }}</ref> ChatGPT's success prompted unprecedented responses from major technology companies&mdash;[[Google]] declared a "code red" and rapidly launched [[Google Gemini|Gemini]] (formerly known as Google Bard), while [[Microsoft]] incorporated the technology into [[Bing Chat]].<ref>{{Cite news |last1=Weise |first1=Karen |last2=Metz |first2=Cade |last3=Grant |first3=Nico |last4=Isaac |first4=Mike |date=December 5, 2023 |title=Inside the A.I. Arms Race That Changed Silicon Valley Forever |work=The New York Times}}</ref>

The rapid adoption of these AI technologies sparked intense debate about their implications. Notable AI researchers and industry leaders voiced both optimism and concern about the accelerating pace of development. In March 2023, over 20,000 signatories, including [[computer scientist]] [[Yoshua Bengio]], [[Elon Musk]], and [[Apple Inc.|Apple]] co-founder [[Steve Wozniak]], signed [[Pause Giant AI Experiments: An Open Letter|an open letter calling for a pause in advanced AI development]], citing "[[Existential risk from artificial intelligence|profound risks to society and humanity]]."<ref>{{cite news |last=Hurst |first=Luke |date=March 30, 2023 |title='Profound risk to humanity': Tech leaders call for 'pause' on advanced AI development |work=Euronews}}</ref> However, other prominent researchers like [[Juergen Schmidhuber]] took a more optimistic view, emphasizing that the majority of AI research aims to make "human lives longer and healthier and easier."<ref name="guardian2023">{{cite news|url=https://www.theguardian.com/technology/2023/may/07/rise-of-artificial-intelligence-is-inevitable-but-should-not-be-feared-father-of-ai-says|title=Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says|last1=Taylor|first1=Josh|date=May 7, 2023|work=The Guardian}}</ref>

By mid-2024, however, the financial sector began to scrutinize AI companies more closely, particularly questioning their capacity to produce a [[return on investment]] commensurate with their massive valuations. Some prominent investors raised concerns about market expectations becoming disconnected from fundamental business realities. [[Jeremy Grantham]], co-founder of [[GMO LLC]], warned investors to "be quite careful" and drew parallels to previous technology-driven market bubbles.<ref>{{Cite web |last=Wollman Rusoff |first=Jane |date=2024-02-01 |title=Jeremy Grantham Warns Investors to 'Be Quite Careful' as Big Risks Loom |url=https://www.thinkadvisor.com/2024/02/01/jeremy-grantham-where-to-invest-in-2024/}}</ref> Similarly, [[Jeffrey Gundlach]], CEO of [[DoubleLine Capital]], explicitly compared the AI boom to the [[dot-com bubble]] of the late 1990s, suggesting that investor enthusiasm might be outpacing realistic near-term capabilities and revenue potential.<ref>{{Cite web |last=Mohamed |first=Theron |date=2024-03-23 |title=Elite investor Jeffrey Gundlach compares the AI boom in stocks to the dot-com bubble — and warns of economic pain |website=[[Business Insider]] |url=https://www.businessinsider.com/stock-market-bubble-crash-gundlach-ai-fed-economy-gross-hussman-2024-3?op=1}}</ref> These concerns were amplified by the substantial market capitalizations of AI-focused companies, many of which had yet to demonstrate sustainable profitability models.

In March 2024, [[Anthropic]] released the [[Claude (AI)|Claude]] 3 family of large language models, including Claude 3 Haiku, Sonnet, and Opus.<ref>{{cite web |title=Introducing the next generation of Claude |url=https://www.anthropic.com/news/claude-3-family |access-date=4 March 2024}}</ref> The models demonstrated significant improvements in capabilities across various benchmarks, with Claude 3 Opus notably outperforming leading models from OpenAI and Google.<ref>{{cite web |last=Nuñez |first=Michael |date=2024-03-04 |title=Anthropic unveils Claude 3, surpassing GPT-4 and Gemini Ultra in benchmark tests |url=https://venturebeat.com/ai/anthropic-unveils-claude-3-claims-new-standard-for-intelligence/ |access-date=2024-04-09 |website=Venture Beat}}</ref> In June 2024, Anthropic released Claude 3.5 Sonnet, which demonstrated improved performance compared to the larger Claude 3 Opus, particularly in areas such as coding, multistep workflows, and image analysis.<ref>{{cite web |last=Pierce |first=David |date=2024-06-20 |title=Anthropic has a fast new AI model — and a clever new way to interact with chatbots |url=https://www.theverge.com/2024/6/20/24181961/anthropic-claude-35-sonnet-model-ai-launch |access-date=2024-06-22 |website=The Verge |language=en}}</ref>

===2024 Nobel Prizes===

In 2024, the [[Royal Swedish Academy]] of Sciences awarded [[Nobel Prizes]] in recognition of groundbreaking contributions to [[artificial intelligence]]. The recipients included:
* In physics: [[John Hopfield]] for his work on physics-inspired [[Hopfield network]]s, and [[Geoffrey Hinton]] for foundational contributions to [[Boltzmann machine]]s and [[deep learning]].
* In chemistry: [[David Baker (biochemist)|David Baker]], [[Demis Hassabis]], and [[John M. Jumper|John Jumper]] for their advancements in [[Protein structure prediction|protein folding prediction]]s. See [[AlphaFold]].<ref>[https://www.nobelprize.org/all-nobel-prizes-2024/ Nobel Prizes for 2024], official Nobel website.</ref>

===Further Study and development of AI===
In January 2025, OpenAI announced a new AI, ChatGPT-Gov, which would be specifically designed for US government agencies to use securely.<ref name="chatgpt-gov">[https://openai.com/global-affairs/introducing-chatgpt-gov/Introducing ChatGPT Gov], ChatGPT Gov is designed to streamline government agencies’ access to OpenAI’s frontier models, OpnAI website.</ref> Open AI said that agencies could utilize ChatGPT Gov on a Microsoft Azure cloud or Azure Government cloud, "on top of Microsoft’s Azure’s OpenAI Service." OpenAI's announcement stated that "Self-hosting ChatGPT Gov enables agencies to more easily manage their own security, privacy, and compliance requirements, such as stringent cybersecurity frameworks (IL5, CJIS, ITAR, FedRAMP High). Additionally, we believe this infrastructure will expedite internal authorization of OpenAI’s tools for the handling of non-public sensitive data."<ref name="chatgpt-gov"/>

===Robotic Integration and Practical Applications of Artificial Intelligence (2025–present) ===
Advanced artificial intelligence (AI) systems, capable of understanding and responding to human dialogue with high accuracy, have matured to enable seamless integration with [[robotics]], transforming industries such as manufacturing, [[home automation|household automation]], healthcare, public services, and materials research.<ref>{{Cite web |url=https://edition.cnn.com/2025/03/25/tech/china-robots-market-competitiveness-intl-hnk |title=Elon Musk thinks robots are a $10 trillion business. He's got some competition from China |publisher=CNN |date=2025-03-25 |access-date=2025-05-31 |language=en}}</ref> [[Applications of artificial intelligence]] also accelerates scientific research through advanced data analysis and hypothesis generation.<ref>{{Cite web |url=https://www.nature.com/articles/s41586-023-06221-2 |title=Scientific discovery in the age of artificial intelligence|publisher=Nature |date=2023-08-02 |access-date=2025-05-31 |language=en}}</ref> Countries including China, the United States, and Japan have invested significantly in policies and funding to deploy [[Autonomous robot|AI-powered robots]], addressing labor shortages, boosting innovation, and enhancing efficiency, while implementing  [[Regulation of artificial intelligence|regulatory frameworks]] to ensure ethical and safe development.<ref>{{Cite web |url=https://onu.delegfrance.org/ai-action-summit-10-and-11-february-2025 |title=AI Action Summit |publisher=AI Action Summit|date=2025-02-11 |access-date=2025-05-31 |language=en}}</ref>

==== China ====
The year 2025 has been heralded as the "Year of AI Robotics," marking a pivotal moment in the seamless integration of artificial intelligence (AI) and robotics. In 2025, China invested approximately 730 billion yuan (roughly $100 billion USD) to advance AI and robotics in smart manufacturing and healthcare.<ref>{{Cite web |url=https://www.unite.ai/dark-factories-and-the-future-of-work-how-ai-driven-automation-is-reshaping-manufacturing/ |title=Dark Factories and the Future of Work: How AI-Driven Automation is Reshaping Manufacturing |publisher=unite.ai |date=2025-03-26 |access-date=2025-05-30 |language=en}}</ref><ref>{{Cite web |url=https://edition.cnn.com/2025/03/25/tech/china-robots-market-competitiveness-intl-hnk |title=Elon Musk thinks robots are a $10 trillion business. He's got some competition from China |publisher=CNN |date=2025-03-25 |access-date=2025-05-30 |language=en}}</ref> The "14th Five-Year Plan" (2021–2025) prioritized service robots, with AI systems enabling robots to perform complex tasks like assisting in surgeries or automating factory assembly lines.<ref>{{Cite web |url=https://www.china-briefing.com/news/chinese-humanoid-robot-market-opportunities/ |title=The Chinese Humanoid Robot AI Market - Investor Opportunities |publisher=China Briefing |date=2025-04-07 |access-date=2025-05-30 |language=en}}</ref> For example, AI-powered humanoid robots in Chinese hospitals can interpret patient requests, deliver supplies, and assist nurses with routine tasks, demonstrating that existing AI conversational capabilities are robust enough for practical robotic applications. Starting in September 2025, China mandated labeling of AI-generated content to ensure transparency and public trust in these technologies.<ref>{{Cite web |url=https://www.yulelaw.com/law/law/getContentDetail?id=bb2e08f59ade44afa9822334440fb0ba&classify=3 |title=人工智能生成合成内容标识办法 |date=2025-03-17 |access-date=2025-05-30 |language=en}}</ref>

==== United States ====
In January 2025, a significant development in AI infrastructure investment occurred with the formation of [[Stargate LLC]]. The joint venture, created by [[OpenAI]], [[SoftBank Group|SoftBank]], [[Oracle Corporation|Oracle]], and [[MGX Fund Management Limited|MGX]], announced plans to invest US$500 billion in AI infrastructure across the [[United States]] by 2029, starting with US$100 billion, in order to support the re-industrialization of the United States and provide a strategic capability to protect the national security of America and its allies.<ref>{{Cite web |url=https://openai.com/index/announcing-the-stargate-project/ |title=Announcing The Stargate Project |publisher=Open AI |date=2025-01-21 |access-date=2025-05-30 |language=en}}</ref> The venture was formally announced by U.S. President Donald Trump on January 21, 2025, with SoftBank CEO [[Masayoshi Son]] appointed as chairman.<ref name="reuters">{{Cite news |title=OpenAI, SoftBank, Oracle to invest US$500 BILLION in AI, Trump says. |url=https://www.reuters.com/technology/artificial-intelligence/openai-softbank-oracle-invest-500-bln-ai-trump-says-2025-01-21/ |access-date=January 22, 2025 |work=[[Reuters]]}}</ref><ref>{{Cite news |last1=Zakrzewski |first1=Cat |date=January 24, 2025 |title=OpenAI and other tech titans worked on Stargate deal months before Trump won |url=https://www.washingtonpost.com/politics/2025/01/24/stargate-trump-ai-altman-musk/ |newspaper=[[The Washington Post]]}}</ref>

The U.S. government allocated approximately $2 billion to integrate AI and robotics in manufacturing and logistics, leveraging AI's ability to process natural language and execute user instructions in 2025.<ref>{{Cite web |url=https://www.trendforce.com/presscenter/news/20250224-12481.html |title=Humanoid Robots to Become the Next US-China Battleground |publisher=Business Wire |date=2025-02-24 |access-date=2025-05-30 |language=en}}</ref> State governments supplemented this with funding for service robots, such as those deployed in warehouses to fulfill verbal commands for inventory management or in eldercare facilities to respond to residents' requests for assistance.<ref>{{Cite web |url=https://apnews.com/article/united-states-robotics-competition-china-tech-702796f1584fe1920e5fd86f15a99b4f |title=US robotics companies push for national strategy, including a central office, to compete with China |publisher=The Boston Globe |date=2025-03-28 |access-date=2025-05-30 |language=en}}</ref> These applications highlight that merging advanced AI, already proficient in human interaction, with robotic hardware is a practical step forward. Some funds were directed to defense, including [[Lethal autonomous weapon]] and [[Military robot]]. In January 2025, Executive Order 14179 established an "AI Action Plan" to accelerate innovation and deployment of these technologies.<ref>{{Cite web |url=https://www.whitehouse.gov/presidential-actions/2025/01/removing-barriers-to-american-leadership-in-artificial-intelligence/ |title=Removing Barriers to American Leadership in Artificial Intelligence |publisher=The White House |date=2025-01-24 |access-date=2025-05-30 |language=en}}</ref>

==== Impact ====
In the 2020s, increased investments in AI by governments and organizations worldwide have accelerated the advancement of artificial intelligence, driving scientific breakthroughs, boosting workforce productivity, and transforming industries through the automation of complex tasks.<ref>{{Cite web |url=https://www.reuters.com/world/china/chinas-ai-powered-humanoid-robots-aim-transform-manufacturing-2025-05-13/ |title=China's AI-powered humanoid robots aim to transform manufacturing |publisher=Reuters |date=2025-05-13 |access-date=2025-05-30 |language=en}}</ref> By seamlessly integrating advanced AI systems into various sectors, these developments are poised to revolutionize smart manufacturing and service industries, fundamentally transforming everyday life.

== See also ==
* [[History of artificial neural networks]]
* [[History of knowledge representation and reasoning]]
* [[History of natural language processing]]
* [[Outline of artificial intelligence]]
* [[Progress in artificial intelligence]]
* [[Timeline of artificial intelligence]]
* [[Timeline of machine learning]]

==Notes==
{{notelist}}
{{Reflist}}

==References==
{{refbegin}}
{{divcol}}

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{{divcolend}}
{{refend}}

[[Category:History of artificial intelligence| ]]
[[Category:History of computing]]