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= = 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 .
= = = Cybernetics 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 neurology had shown that the brain was an electrical network of neurons that fired in all @-@ or @-@ nothing pulses . Norbert Wiener 's cybernetics described control and stability in electrical networks . Claude Shannon 's information theory described digital signals ( i.e. , all @-@ or @-@ nothing signals ) . Alan Turing 's theory of computation showed that any form of computation could be described digitally . The close relationship between these ideas suggested that it might be possible to construct an electronic brain .
Examples of work in this vein includes robots such as W. Grey Walter 's turtles and the Johns Hopkins Beast . These machines did not use computers , digital electronics or symbolic reasoning ; they were controlled entirely by analog circuitry .
Walter Pitts and Warren McCulloch analyzed networks of idealized 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 Pitts and McCulloch 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 SNARC . Minsky was to become one of the most important leaders and innovators 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 teleprinter ) that was indistinguishable from a conversation with a human being , then it was reasonable to say that the machine was " thinking " . This simplified version of the problem allowed Turing to argue convincingly that a " thinking machine " was at least plausible and the paper answered all the most common objections to the proposition . The Turing Test was the first serious proposal in the philosophy of artificial intelligence .
= = = Game AI = = =
In 1951 , using the Ferranti Mark 1 machine of the University of Manchester , Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess . Arthur Samuel 's checkers program , 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 .
= = = Symbolic 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 Principia Mathematica , and find new and more elegant proofs for some . Simon said that they had " solved the venerable mind / body problem , explaining how a system composed of matter can have the properties of mind . " ( 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 Solomonoff , Oliver Selfridge , Trenchard More , Arthur Samuel , Allen Newell and Herbert A. Simon , all of whom would create important programs during the first decades of AI research . At the 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 ARPA 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 :
= = = = Reasoning 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 . 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 heuristics 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 Problem Solver " . Other " searching " programs were able to accomplish impressive tasks like solving problems in geometry and algebra , such as Herbert Gelernter 's Geometry Theorem Prover ( 1958 ) and SAINT , written by Minsky 's student James Slagle ( 1961 ) . Other programs searched through goals and subgoals to plan actions , like the STRIPS 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 Bobrow 's program STUDENT , which could solve high school algebra word problems .
A semantic net represents concepts ( e.g. " house " , " door " ) as nodes and relations among concepts ( e.g. " has @-@ a " ) as links between the nodes . The first AI program to use a semantic net was written by Ross Quillian and the most successful ( and controversial ) version was Roger Schank 's Conceptual dependency theory .
Joseph Weizenbaum 's ELIZA could carry out conversations that were so realistic that users occasionally were fooled into thinking they were communicating with a human being and not a 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 , rephrasing her response with a few grammar rules . ELIZA was the first chatterbot .
= = = = Micro @-@ worlds = = = =
In the late 60s , Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro @-@ worlds . They pointed out that in successful sciences like physics , basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies . Much of the research focused on a " blocks world , " which consists of colored blocks of various shapes and sizes arrayed on a flat surface .
This paradigm led to innovative work in machine vision by Gerald Sussman ( who led the team ) , 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 . The crowning achievement of the micro @-@ world program was Terry Winograd 's SHRDLU . 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 Projects Agency ( later known as DARPA ) . The money was used to fund project MAC 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 Michie 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 proffered 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 . This created a freewheeling 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 impossibly 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 perceptrons . 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 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 ; 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 stymie the field to this day .
Limited computer power : There was not enough memory or processing speed to accomplish anything truly useful . For example , Ross Quillian '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 retina 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 , Cray @-@ 1 ( retailing 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 .
Intractability and the combinatorial explosion . In 1972 Richard Karp ( 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 unimaginable 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 .
Commonsense 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 : Proving theorems and solving geometry problems is comparatively easy for computers , but a supposedly simple task like recognizing a face or crossing a room without bumping 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 deductions that involved planning or default reasoning without making changes to the structure of logic itself . They developed new logics ( like non @-@ monotonic 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 ALPAC report appeared criticizing machine translation efforts . After spending 20 million dollars , the NRC ended all support . In 1973 , the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its " grandiose objectives " and led to the dismantling 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 , freewheeling 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 .
= = = Critiques 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 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 . 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 , instinctive , 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 " intentionality " ) . 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 intractability and commonsense knowledge seemed much more immediate and serious . It was unclear what difference " know how " or " intentionality " made to an actual computer program . Minsky said of Dreyfus and Searle " they misunderstand , and should be ignored . " Dreyfus , who taught at MIT , was given a cold shoulder : he later said that AI researchers " dared not be seen having lunch with me . " Joseph Weizenbaum , the author of ELIZA , felt his colleagues ' treatment of Dreyfus was unprofessional 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 DOCTOR , a chatterbot 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 devalue human life .
= = = Perceptrons and the dark age of connectionism = = =
A perceptron was a form of neural network introduced in 1958 by Frank Rosenblatt , who had been a schoolmate of Marvin Minsky at the Bronx High School of Science . Like most AI researchers , he was optimistic about their power , predicting that " perceptron 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 Perceptrons . It suggested that there were severe limitations to what perceptrons 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 Advice Taker proposal . In 1963 , J. Alan Robinson had discovered a simple method to implement deduction on computers , the resolution and unification algorithm . However , straightforward implementations , like those attempted by McCarthy and his students in the late 60s , were especially intractable : 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 Colmerauer and Philippe Roussel 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 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 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 psychologists like Peter Wason , Eleanor Rosch , Amos Tversky , Daniel Kahneman 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 scruffies : frames and scripts = = =