 = History of artificial intelligence = 
 
 The history of artificial intelligence ( AI ) began in antiquity , with myths , stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen ; as Pamela McCorduck writes , AI began with " an ancient wish to forge the gods . " 
 The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols . This work culminated in the invention of the programmable digital computer in the 1940s , a machine based on the abstract essence of mathematical reasoning . This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain . 
 The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence , which opens with the words : " I propose to consider the question , ' Can machines think ? ' " The term ' Artificial Intelligence ' was created at a conference held at Dartmouth College in 1956 . Allen Newell , J. C. Shaw , and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine ( 1956 ) , and the General <unk> <unk> in 1957 . In 1958 , John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $ 50 @,@ 000 . John McCarthy also created <unk> in the summer of 1958 , a programming language still important in artificial intelligence research . 
 In 1973 , in response to the criticism of James <unk> and ongoing pressure from <unk> , the U.S. and British Governments stopped funding undirected research into artificial intelligence . Seven years later , a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars , but by the late 80s the investors became disillusioned and withdrew funding again . 
 
 = = <unk> = = 
 
 McCorduck ( 2004 ) writes " artificial intelligence in one form or another is an idea that has <unk> Western intellectual history , a dream in urgent need of being realized , " expressed in humanity 's myths , legends , stories , speculation and clockwork automatons . 
 
 = = = AI in myth , fiction and speculation = = = 
 
 Mechanical men and artificial beings appear in Greek myths , such as the golden robots of <unk> and <unk> 's <unk> . In the Middle Ages , there were rumors of secret mystical or <unk> means of placing mind into matter , such as <unk> ibn <unk> 's <unk> , <unk> ' <unk> and Rabbi Judah <unk> 's <unk> . By the 19th century , ideas about artificial men and thinking machines were developed in fiction , as in Mary Shelley 's <unk> or Karel <unk> 's <unk> ( <unk> 's Universal <unk> ) , and speculation , such as Samuel Butler 's " Darwin among the Machines . " AI has continued to be an important element of science fiction into the present . 
 
 = = = <unk> = = = 
 
 <unk> <unk> automatons were built by craftsman from every civilization , including Yan Shi , Hero of Alexandria , Al @-@ <unk> and Wolfgang von <unk> . The oldest known automatons were the sacred statues of ancient Egypt and Greece . The faithful believed that craftsman had <unk> 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 . " 
 
 = = = 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 . Chinese , Indian and Greek philosophers all developed structured methods of formal deduction in the first millennium BCE . Their ideas were developed over the centuries by philosophers such as <unk> ( who gave a formal analysis of the <unk> ) , Euclid ( whose Elements was a model of formal reasoning ) , Muslim mathematician al @-@ <unk> ( who developed algebra and gave his name to " algorithm " ) and European scholastic philosophers such as William of <unk> and Duns Scotus . 
 <unk> philosopher Ramon Llull ( <unk> – <unk> ) developed several logical machines devoted to the production of knowledge by logical means ; Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations , produced by the machine by mechanical meanings , in such ways as to produce all the possible knowledge . Llull 's work had a great influence on Gottfried Leibniz , who redeveloped his ideas . 
 In the 17th century , Leibniz , Thomas <unk> and René <unk> explored the possibility that all rational thought could be made as systematic as algebra or geometry . <unk> famously wrote in Leviathan : " reason is nothing but reckoning " . Leibniz envisioned a universal language of reasoning ( his <unk> <unk> ) which would reduce <unk> to calculation , so that " there would be no more need of <unk> between two philosophers than between two accountants . For it would <unk> 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 . " These philosophers had begun to articulate the physical symbol system hypothesis that would become the guiding faith of AI research . 
 In the 20th century , the study of mathematical logic provided the essential breakthrough that made artificial intelligence seem plausible . The foundations had been set by such works as <unk> 's The Laws of Thought and <unk> 's <unk> . Building on <unk> 's system , Russell and Whitehead presented a formal treatment of the foundations of mathematics in their masterpiece , the <unk> <unk> in 1913 . Inspired by Russell 's success , David <unk> challenged mathematicians of the 1920s and 30s to answer this fundamental question : " can all of mathematical reasoning be formalized ? " His question was answered by <unk> 's incompleteness proof , Turing 's machine and Church 's <unk> <unk> . 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 , <unk> symbols as simple as 0 and 1 , could imitate any <unk> process of mathematical deduction . The key insight was the Turing machine — a simple theoretical construct that captured the essence of abstract symbol manipulation . This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines . 
 
 = = = Computer science = = = 
 
 <unk> machines were built in antiquity and improved throughout history by many mathematicians , including ( once again ) philosopher Gottfried Leibniz . In the early 19th century , Charles <unk> designed a programmable computer ( the Analytical Engine ) , although it was never built . <unk> <unk> speculated that the machine " might compose elaborate and scientific pieces of music of any degree of complexity or extent " . ( She is often credited as the first programmer because of a set of notes she wrote that completely detail a method for <unk> <unk> numbers with the Engine . ) 
 The first modern computers were the massive code breaking machines of the Second World War ( such as <unk> , <unk> and <unk> ) . The latter two of these machines were based on the theoretical foundation laid by Alan Turing and developed by John von Neumann . 
 
 = = The birth of artificial intelligence 1952 – 1956 = = 
 
 In the 1940s and 50s , a handful of scientists from a variety of fields ( mathematics , psychology , engineering , economics and political science ) began to discuss the possibility of creating an artificial brain . The field of artificial intelligence research was founded as an academic discipline in 1956 . 
 
 = = = <unk> and early neural networks = = = 
 
 The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s , 40s and early 50s . Recent research in <unk> had shown that the brain was an electrical network of neurons that fired in all @-@ or @-@ nothing pulses . Norbert <unk> 's <unk> 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 . 
 Examples of work in this vein includes robots such as W. Grey Walter 's <unk> and the Johns Hopkins Beast . These machines did not use computers , digital electronics or symbolic reasoning ; they were controlled entirely by analog <unk> . 
 Walter <unk> and Warren <unk> analyzed networks of <unk> artificial neurons and showed how they might perform simple logical functions . They were the first to describe what later researchers would call a neural network . One of the students inspired by <unk> and <unk> was a young Marvin Minsky , then a 24 @-@ year @-@ old graduate student . In 1951 ( with Dean Edmonds ) he built the first neural net machine , the <unk> . Minsky was to become one of the most important leaders and <unk> in AI for the next 50 years . 
 
 = = = Turing 's test = = = 
 
 In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think . He noted that " thinking " is difficult to define and devised his famous Turing Test . If a machine could carry on a conversation ( over a <unk> ) that was indistinguishable from a conversation with a human being , then it was reasonable to say that the machine was " thinking " . This simplified version of the problem allowed Turing to argue convincingly that a " thinking machine " was at least plausible and the paper answered all the most common objections to the proposition . The Turing Test was the first serious proposal in the philosophy of artificial intelligence . 
 
 = = = Game AI = = = 
 
 In 1951 , using the Ferranti Mark 1 machine of the University of Manchester , Christopher Strachey wrote a checkers program and <unk> <unk> wrote one for chess . Arthur Samuel 's checkers program , developed in the middle 50s and early 60s , eventually achieved sufficient skill to challenge a respectable amateur . Game AI would continue to be used as a measure of progress in AI throughout its history . 
 
 = = = <unk> reasoning and the Logic Theorist = = = 
 
 When access to digital computers became possible in the middle fifties , a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought . This was a new approach to creating thinking machines . 
 In 1955 , Allen Newell and ( future Nobel Laureate ) Herbert A. Simon created the " Logic Theorist " ( with help from J. C. Shaw ) . The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead 's <unk> <unk> , and find new and more elegant proofs for some . Simon said that they had " solved the venerable mind / body problem , explaining how a system composed of matter can have the properties of mind . " ( This was an early statement of the philosophical position John Searle would later call " Strong AI " : that machines can contain minds just as human bodies do . ) 
 
 = = = Dartmouth Conference 1956 : the birth of AI = = = 
 
 The Dartmouth Conference of 1956 was organized by Marvin Minsky , John McCarthy and two senior scientists : Claude Shannon and Nathan Rochester of IBM . The proposal for the conference included this assertion : " every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it " . The participants included Ray <unk> , Oliver <unk> , Trenchard More , Arthur Samuel , Allen Newell and Herbert A. Simon , all of whom would create important programs during the first decades of AI research . At the conference Newell and Simon debuted the " Logic Theorist " and McCarthy persuaded the attendees to accept " Artificial Intelligence " as the name of the field . The 1956 Dartmouth conference was the moment that AI gained its name , its mission , its first success and its major players , and is widely considered the birth of AI . 
 
 = = The golden years 1956 – 1974 = = 
 
 The years after the Dartmouth conference were an era of discovery , of sprinting across new ground . The programs that were developed during this time were , to most people , simply " astonishing " : computers were solving algebra word problems , proving theorems in geometry and learning to speak English . Few at the time would have believed that such " intelligent " behavior by machines was possible at all . Researchers expressed an intense optimism in private and in print , predicting that a fully intelligent machine would be built in less than 20 years . Government agencies like <unk> poured money into the new field . 
 
 = = = The work = = = 
 
 There were many successful programs and new directions in the late 50s and 1960s . Among the most influential were these : 
 
 = = = = <unk> 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 , <unk> whenever they reached a dead end . This paradigm was called " reasoning as search " . 
 The principal difficulty was that , for many problems , the number of possible paths through the " maze " was simply astronomical ( a situation known as a " combinatorial explosion " ) . Researchers would reduce the search space by using <unk> or " rules of thumb " that would eliminate those paths that were unlikely to lead to a solution . 
 Newell and Simon tried to capture a general version of this algorithm in a program called the " General <unk> <unk> " . Other " searching " programs were able to accomplish impressive tasks like solving problems in geometry and algebra , such as Herbert <unk> 's <unk> <unk> <unk> ( 1958 ) and <unk> , written by Minsky 's student James <unk> ( 1961 ) . Other programs searched through goals and <unk> to plan actions , like the <unk> system developed at Stanford to control the behavior of their robot Shakey . 
 
 = = = = Natural language = = = = 
 
 An important goal of AI research is to allow computers to communicate in natural languages like English . An early success was Daniel <unk> 's program <unk> , which could solve high school algebra word problems . 
 A semantic net represents concepts ( e.g. " house " , " door " ) as nodes and relations among concepts ( e.g. " has @-@ a " ) as links between the nodes . The first AI program to use a semantic net was written by Ross <unk> and the most successful ( and controversial ) version was Roger Schank 's <unk> dependency theory . 
 Joseph Weizenbaum 's ELIZA could carry out conversations that were so realistic that users occasionally were <unk> into thinking they were communicating with a human being and not a program . But in fact , ELIZA had no idea what she was talking about . She simply gave a canned response or repeated back what was said to her , <unk> her response with a few grammar rules . ELIZA was the first <unk> . 
 
 = = = = <unk> @-@ worlds = = = = 
 
 In the late 60s , Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro @-@ worlds . They pointed out that in successful sciences like physics , basic principles were often best understood using simplified models like <unk> planes or perfectly rigid bodies . Much of the research focused on a " blocks world , " which consists of colored blocks of various shapes and sizes <unk> on a flat surface . 
 This paradigm led to innovative work in machine vision by Gerald Sussman ( who led the team ) , <unk> <unk> , David <unk> ( who invented " <unk> <unk> " ) , 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 . The crowning achievement of the micro @-@ world program was Terry <unk> 's <unk> . It could communicate in ordinary English sentences , plan operations and execute them . 
 
 = = = The 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 . " 
 1965 , H. A. Simon : " machines will be capable , within twenty years , of doing any work a man can do . " 
 1967 , Marvin Minsky : " Within a generation ... the problem of creating ' artificial intelligence ' will substantially be solved . " 
 1970 , Marvin Minsky ( in Life Magazine ) : " In from three to eight years we will have a machine with the general intelligence of an average human being . " 
 
 = = = The money = = = 
 
 In June 1963 , MIT received a $ 2 @.@ 2 million grant from the newly created Advanced Research <unk> Agency ( later known as DARPA ) . The money was used to fund project <unk> which subsumed the " AI Group " founded by Minsky and McCarthy five years earlier . DARPA continued to provide three million dollars a year until the 70s . DARPA made similar grants to Newell and Simon 's program at CMU and to the Stanford AI Project ( founded by John McCarthy in 1963 ) . Another important AI laboratory was established at Edinburgh University by Donald <unk> in 1965 . These four institutions would continue to be the main centers of AI research ( and funding ) in academia for many years . 
 The money was <unk> with few strings attached : J. C. R. <unk> , then the director of <unk> , believed that his organization should " fund people , not projects ! " and allowed researchers to pursue whatever directions might interest them . This created a <unk> atmosphere at MIT that gave birth to the hacker culture , but this " hands off " approach would not last . 
 
 = = The first AI winter 1974 – 1980 = = 
 
 In the 70s , 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 expectations <unk> high , and when the promised results failed to materialize , funding for AI disappeared . At the same time , the field of connectionism ( or neural nets ) was shut down almost completely for 10 years by Marvin Minsky 's devastating criticism of <unk> . Despite the difficulties with public perception of AI in the late 70s , new ideas were explored in logic programming , commonsense reasoning and many other areas . 
 
 = = = The problems = = = 
 
 In the early <unk> , the capabilities of AI programs were limited . Even the most impressive could only handle trivial versions of the problems they were supposed to solve ; all the programs were , in some sense , " toys " . AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s . Although some of these limits would be conquered in later decades , others still <unk> the field to this day . 
 Limited computer power : There was not enough memory or processing speed to accomplish anything truly useful . For example , Ross <unk> 's successful work on natural language was demonstrated with a vocabulary of only twenty words , because that was all that would fit in memory . 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 increases , eventually it could become easy . With regard to computer vision , Moravec estimated that simply matching the edge and motion detection capabilities of human <unk> in real time would require a general @-@ purpose computer capable of 109 operations / second ( 1000 MIPS ) . As of 2011 , practical computer vision applications require 10 @,@ 000 to 1 @,@ 000 @,@ 000 MIPS . By comparison , the fastest supercomputer in 1976 , <unk> @-@ 1 ( <unk> at $ 5 million to $ 8 million ) , was only capable of around 80 to 130 MIPS , and a typical desktop computer at the time achieved less than 1 MIPS . 
 <unk> and the combinatorial explosion . In 1972 Richard <unk> ( building on Stephen Cook 's 1971 theorem ) showed there are many problems that can probably only be solved in exponential time ( in the size of the inputs ) . Finding optimal solutions to these problems requires <unk> amounts of computer time except when the problems are trivial . This almost certainly meant that many of the " toy " solutions used by AI would probably never scale up into useful systems . 
 <unk> knowledge and reasoning . Many important artificial intelligence applications like vision or natural language require simply 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 truly vast amount of information . No one in 1970 could build a database so large and no one knew how a program might learn so much information . 
 Moravec 's paradox : <unk> theorems and solving geometry problems is comparatively easy for computers , but a supposedly simple task like recognizing a face or crossing a room without <unk> into anything is extremely difficult . This helps explain why research into vision and robotics had made so little progress by the middle 1970s . 
 The frame and qualification problems . AI researchers ( like John McCarthy ) who used logic discovered that they could not represent ordinary <unk> that involved planning or default reasoning without making changes to the structure of logic itself . They developed new logics ( like non @-@ <unk> logics and modal logics ) to try to solve the problems . 
 
 = = = The end of funding = = = 
 
 The agencies which funded AI research ( such as the British government , DARPA and NRC ) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI . The pattern began as early as 1966 when the <unk> report appeared criticizing machine translation efforts . After spending 20 million dollars , the NRC ended all support . In 1973 , the <unk> report on the state of AI research in England criticized the utter failure of AI to achieve its " grandiose objectives " and led to the <unk> of AI research in that country . ( The report specifically mentioned the combinatorial explosion problem as a reason for AI 's failings . ) DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars . By 1974 , funding for AI projects was hard to find . 
 Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues . " Many researchers were caught up in a web of increasing exaggeration . " 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 , <unk> exploration that had gone on in the 60s would not come from DARPA . Instead , the money was directed at specific projects with clear objectives , such as autonomous tanks and battle management systems . 
 
 = = = <unk> from across campus = = = 
 
 Several philosophers had strong objections to the claims being made by AI researchers . One of the earliest was John Lucas , who argued that <unk> '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 . Hubert Dreyfus ridiculed the broken promises of the 60s and critiqued the assumptions of AI , arguing that human reasoning actually involved very little " symbol processing " and a great deal of embodied , <unk> , unconscious " know how " . 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 " <unk> " ) . If the symbols have no meaning for the machine , Searle argued , then the machine can not be described as " thinking " . 
 These critiques were not taken seriously by AI researchers , often because they seemed so far off the point . Problems like <unk> and commonsense knowledge seemed much more immediate and serious . It was unclear what difference " know how " or " <unk> " made to an actual computer program . Minsky said of Dreyfus and Searle " they <unk> , and should be ignored . " Dreyfus , who taught at MIT , was given a cold shoulder : he later said that AI researchers " <unk> not be seen having lunch with me . " Joseph Weizenbaum , the author of ELIZA , felt his colleagues ' treatment of Dreyfus was <unk> and childish . Although he was an outspoken critic of Dreyfus ' positions , he " deliberately made it plain that theirs was not the way to treat a human being . " 
 Weizenbaum began to have serious ethical doubts about AI when Kenneth Colby wrote <unk> , a <unk> therapist . Weizenbaum was disturbed that Colby saw his 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 , Weizenbaum published Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to <unk> human life . 
 
 = = = <unk> and the dark age of connectionism = = = 
 
 A <unk> was a form of neural network introduced in 1958 by Frank Rosenblatt , who had been a <unk> of Marvin Minsky at the Bronx High School of Science . Like most AI researchers , he was optimistic about their power , predicting that " <unk> may eventually be able to learn , make decisions , and translate languages . " An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert 's 1969 book <unk> . It suggested that there were severe limitations to what <unk> could do and that Frank Rosenblatt 's predictions had been grossly exaggerated . The effect of the book was devastating : virtually no research at all was done in connectionism for 10 years . Eventually , a new generation of researchers would revive the field and thereafter it would become a vital and useful part of artificial intelligence . Rosenblatt would not live to see this , as he died in a boating accident shortly after the book was published . 
 
 = = = The neats : logic and symbolic reasoning = = = 
 
 Logic was introduced into AI research as early as 1958 , by John McCarthy in his <unk> <unk> proposal . In 1963 , J. Alan Robinson had discovered a simple method to implement deduction on computers , the resolution and unification algorithm . However , straightforward <unk> , like those attempted by McCarthy and his students in the late 60s , were especially <unk> : the programs required astronomical numbers of steps to prove simple theorems . 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 <unk> and Philippe <unk> who created the successful logic programming language Prolog . Prolog uses a subset of logic ( Horn clauses , closely related to " rules " and " production rules " ) that permit <unk> 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 <unk> and their unified theories of cognition . 
 Critics of the logical approach noted , as Dreyfus had , that human beings rarely used logic when they solved problems . Experiments by <unk> like Peter <unk> , Eleanor <unk> , Amos <unk> , Daniel <unk> and others provided proof . 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 . 
 
 = = = The <unk> : frames and scripts = = = 
 
 Among the critics of 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 required a machine to think like a person . In order to use ordinary concepts like " chair " or " restaurant " they had to make all the same <unk> assumptions that people normally made . Unfortunately , <unk> concepts like these are hard to represent in logic . Gerald Sussman observed that " using precise language to describe essentially <unk> concepts doesn 't make them any more precise . " Schank described their " anti @-@ logic " approaches as " <unk> " , as opposed to the " <unk> " <unk> used by McCarthy , Kowalski , Feigenbaum , Newell and Simon . 
 In 1975 , in a seminal paper , Minsky noted that many of his fellow " <unk> " researchers were using the same kind of tool : a framework that captures all our 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 . We know these facts are not always true and that <unk> using these facts will not be " logical " , but these structured sets of assumptions are part of the context of everything we say and think . He called these structures " frames " . Schank used a version of frames he called " scripts " to successfully answer questions about short stories in English . Many years later object @-@ oriented programming would adopt the essential idea of " inheritance " from AI research on frames . 
 
 = = Boom 1980 – 1987 = = 
 
 In the 1980s a form of AI program called " expert systems " was adopted by corporations around the world and knowledge became the focus of mainstream AI research . In those same years , the Japanese government aggressively funded AI with its fifth generation computer project . Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart . Once again , AI had achieved success . 
 
 = = = The rise of expert systems = = = 
 
 An expert system is a program that answers questions or solves problems about a specific domain of knowledge , using logical rules that are derived from the knowledge of experts . The earliest examples were developed by Edward Feigenbaum and his students . <unk> , begun in 1965 , identified compounds from <unk> readings . <unk> , developed in 1972 , diagnosed infectious blood diseases . They demonstrated the feasibility of the approach . 
 Expert systems restricted themselves to a small domain of specific knowledge ( thus avoiding the commonsense knowledge problem ) 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 . 
 In 1980 , an expert system called <unk> was completed at CMU for the Digital Equipment Corporation . It was an enormous success : it was saving the company 40 million dollars annually by 1986 . 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 . An industry grew up to support them , including hardware companies like <unk> and <unk> Machines and software companies such as <unk> and <unk> . 
 
 = = = The 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 — <unk> , for it violated the scientific canon of <unk> — that intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways , " 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 " . Knowledge based systems and knowledge engineering became a major focus of AI research in the 1980s . 
 The 1980s also saw the birth of <unk> , the first attempt to attack the commonsense knowledge problem directly , by creating a massive database that would contain all the mundane facts that the average person knows . Douglas <unk> , who started and led the project , argued that there is no <unk> <unk> the only way for machines to know the meaning of human concepts is to teach them , one concept at a time , by hand . The project was not expected to be completed for many decades . 
 Chess playing programs <unk> and Deep Thought defeated chess masters in 1989 . Both were developed by Carnegie <unk> University ; Deep Thought development paved the way for the Deep Blue . 
 
 = = = The money returns : the fifth generation project = = = 
 
 In 1981 , the 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 . Much to the <unk> of <unk> , they chose Prolog as the primary computer language for the project . 
 Other countries responded with new programs of their own . The UK began the ₤ 350 million <unk> project . A consortium of American companies formed the <unk> and Computer Technology Corporation ( or " MCC " ) to fund large scale projects in AI and information technology . DARPA responded as well , founding the Strategic Computing Initiative and <unk> its investment in AI between 1984 and 1988 . 
 
 = = = The revival of connectionism = = = 
 
 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 in a completely new way . Around the same time , David Rumelhart popularized a new method for training neural networks called " <unk> " ( discovered years earlier by Paul <unk> ) . These two discoveries revived the field of connectionism which had been largely abandoned since 1970 . 
 The new field was unified and inspired by the appearance of <unk> <unk> <unk> in 1986 — a two volume collection of papers edited by Rumelhart and psychologist James <unk> . <unk> networks would become commercially successful in the 1990s , when they began to be used as the engines driving programs like optical character recognition and speech recognition . 
 
 = = <unk> : the second AI winter 1987 – 1993 = = 
 
 The business community 's fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble . The collapse was in the perception of AI by government agencies and investors – the field continued to make advances despite the criticism . Rodney Brooks and Hans Moravec , researchers from the related field of robotics , argued for an entirely new approach to artificial intelligence . 
 
 = = = 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 <unk> out of control and that disappointment would certainly follow . Their fears were well founded : in the late 80s and early 90s , AI suffered a series of financial setbacks . 
 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 and IBM had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive <unk> machines made by <unk> and others . There was no longer a good reason to buy them . An entire industry worth half a billion dollars was demolished overnight . 
 Eventually the earliest successful expert systems , such as <unk> , proved too expensive to maintain . They were difficult to update , they could not learn , they were " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) that had been identified years earlier . Expert systems proved useful , but only in a few special contexts . 
 In the late 80s , 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 . 
 By 1991 , the impressive list of goals penned in 1981 for Japan 's Fifth Generation Project had not been met . Indeed , some of them , like " carry on a casual conversation " had not been met by 2010 . As with other AI projects , expectations had run much higher than what was actually possible . 
 
 = = = The importance of having a body : <unk> AI and embodied reason = = = 
 
 In the late 80s , several researchers advocated a completely new approach to artificial intelligence , based on robotics . They believed that , to show real intelligence , a machine needs to have a body — it needs to perceive , move , survive and deal with the world . They argued that these <unk> skills are essential to higher level skills like commonsense reasoning and that abstract reasoning was actually the least interesting or important human skill ( see Moravec 's paradox ) . They advocated building intelligence " from the bottom up . " 
 The approach revived ideas from <unk> and control theory that had been unpopular since the sixties . Another precursor was David <unk> , who had come to MIT in the late 70s from a successful background in theoretical <unk> to lead the group studying vision . He rejected all symbolic approaches ( both McCarthy 's logic and Minsky 's frames ) , arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place . ( <unk> 's work would be cut short by leukemia in 1980 . ) 
 In a 1990 paper , " <unk> Don 't Play Chess , " robotics researcher Rodney Brooks took direct aim at the 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 . " In the 80s and 90s , many 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 . 
 
 = = AI 1993 – present = = 
 
 The field of AI , now more than a half a century old , finally achieved some of its oldest goals . It began to be used successfully throughout the technology industry , although somewhat behind the scenes . Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability . Still , the reputation of AI , in the business world at least , was less than pristine . 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 <unk> focused on particular problems or approaches , sometimes even under new names that disguised the <unk> pedigree of " artificial intelligence " . AI was both more cautious and more successful than it had ever been . 
 
 = = = <unk> and Moore 's Law = = = 
 
 On 11 May 1997 , Deep Blue became the first computer chess @-@ playing system to beat a reigning world chess champion , Garry Kasparov . The super computer was a specialized version of a framework produced by IBM , and was capable of processing twice as many moves per second as it had during the first match ( which Deep Blue had lost ) , reportedly 200 @,@ 000 @,@ 000 moves per second . The event was broadcast live over the internet and received over 74 million hits . 
 In 2005 , a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an <unk> desert trail . Two years later , a team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic <unk> and all traffic laws . In February 2011 , in a <unk> ! quiz show exhibition match , IBM 's question answering system , Watson , defeated the two greatest <unk> ! champions , Brad <unk> and Ken Jennings , by a significant margin . 
 These successes were not due to some revolutionary new paradigm , but mostly on the tedious application of engineering skill and on the tremendous power of computers today . In fact , Deep Blue 's computer was 10 million times faster than the Ferranti Mark 1 that Christopher Strachey taught to play chess in 1951 . 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 . 
 
 = = = <unk> agents = = = 
 
 A new paradigm called " intelligent agents " became widely accepted during the 90s . Although earlier researchers had proposed modular " divide and conquer " approaches to AI , the intelligent agent did not reach its modern form until Judea Pearl , Allen Newell and others brought concepts from decision theory and economics into the study of AI . When the economist 's definition of a rational agent was married to computer science 's definition of an object or <unk> , the intelligent agent paradigm was complete . 
 An intelligent agent is a system that perceives its environment and takes actions which <unk> 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 firms . The intelligent agent paradigm defines AI research as " the study of intelligent agents " . This is a <unk> 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 find solutions that were both <unk> and useful . It provided a common language to describe problems and share their solutions with each other , and with other fields that also used concepts of abstract agents , like economics and control theory . It was hoped that a complete agent architecture ( like Newell 's <unk> ) would one day allow researchers to build more versatile and intelligent systems out of interacting intelligent agents . 
 
 = = = " Victory of the neats " = = = 
 
 AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past . 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 mathematics , 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 <unk> ; AI had become a more <unk> " scientific " discipline . Russell & <unk> ( 2003 ) describe this as nothing less than a " revolution " and " the victory of the neats " . 
 Judea Pearl 's highly influential 1988 book brought probability and decision theory into AI . Among the many new tools in use were Bayesian networks , hidden <unk> models , information theory , <unk> modeling and classical <unk> . <unk> mathematical descriptions were also developed for " computational intelligence " <unk> like neural networks and evolutionary algorithms . 
 
 = = = AI behind the scenes = = = 
 
 <unk> originally developed by AI researchers began to appear as parts of larger systems . AI had solved a lot of very difficult problems and their solutions proved to be useful throughout the technology industry , such as data mining , industrial robotics , logistics , speech recognition , banking software , medical diagnosis and Google 's search engine . 
 The field of AI receives little or no credit for these successes . Many of AI 's greatest innovations have been reduced to the status of just another item in the tool chest of computer science . Nick <unk> 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 . " 
 Many researchers in AI in 1990s deliberately called their work by other names , such as <unk> , knowledge @-@ based systems , cognitive systems or computational intelligence . In part , this may be because they considered their field to be fundamentally different from AI , but also the new names help to procure funding . In the commercial world at least , the failed promises of the AI Winter continue to haunt AI research , 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 <unk> . " 
 
 = = = Where is HAL 9000 ? = = = 
 
 In 1968 , Arthur C. Clarke and Stanley Kubrick had imagined that by the year 2001 , a machine would exist with an intelligence that matched or exceeded the capability of human beings . The character they created , HAL 9000 , was based on a belief shared by many leading AI researchers that such a machine would exist by the year 2001 . 
 Marvin Minsky asks " So the question is why didn 't we get HAL in 2001 ? " Minsky believes that the answer is that the central problems , like commonsense reasoning , were being neglected , while most researchers pursued things like commercial applications of neural nets or genetic algorithms . John McCarthy , on the other hand , still blames the qualification problem . For Ray <unk> , the issue is computer power and , using Moore 's Law , he predicts that machines with human @-@ level intelligence will appear by <unk> . Jeff Hawkins argues that neural net research ignores the essential properties of the human cortex , preferring simple models that have been successful at solving simple problems . There are many other explanations and for each there is a corresponding research program underway . 
 
 