• Artificial Intelligence
• Daniel Crevier

The 'standard interpretation' of the Turing Test, in which player C, the interrogator, is given the task of trying to determine which player – A or B – is a computer and which is a human. The interrogator is limited to using the responses to written questions to make the determination. The Turing test, developed by in 1950, is a test of a machine's ability to equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a and so the result would not depend on the machine's ability to render words as speech.

If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give.

The test was introduced by Turing in his paper, ', while working at the (Turing, 1950; p. 460). It opens with the words: 'I propose to consider the question, 'Can machines think? ' Because 'thinking' is difficult to define, Turing chooses to 'replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.' Turing's new question is: 'Are there imaginable digital computers which would do well in the imitation game?' This question, Turing believed, is one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that 'machines can think'. Since Turing first introduced his test, it has proven to be both highly influential and widely criticised, and it has become an important concept in the.

How many different automata or moving machines can be made by the industry of man. For we can easily understand a machine's being constituted so that it can utter words, and even emit some responses to action on it of a corporeal kind, which brings about a change in its organs; for instance, if touched in a particular part it may ask what we wish to say to it; if in another part it may exclaim that it is being hurt, and so on.

But it never happens that it arranges its speech in various ways, in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. Here Descartes notes that are capable of responding to human interactions but argues that such automata cannot respond appropriately to things said in their presence in the way that any human can.

Descartes therefore prefigures the Turing Test by defining the insufficiency of appropriate linguistic response as that which separates the human from the automaton. Descartes fails to consider the possibility that future automata might be able to overcome such insufficiency, and so does not propose the Turing Test as such, even if he prefigures its conceptual framework and criterion. Formulates in his a Turing-test criterion: 'If they find a parrot who could answer to everything, I would claim it to be an intelligent being without hesitation.' This does not mean he agrees with this, but that it was already a common argument of materialists at that time. According to dualism, the is (or, at the very least, has ) and, therefore, cannot be explained in purely physical terms.

According to materialism, the mind can be explained physically, which leaves open the possibility of minds that are produced artificially. In 1936, philosopher considered the standard philosophical question of: how do we know that other people have the same conscious experiences that we do? In his book, Ayer suggested a protocol to distinguish between a conscious man and an unconscious machine: 'The only ground I can have for asserting that an object which appears to be conscious is not really a conscious being, but only a dummy or a machine, is that it fails to satisfy one of the empirical tests by which the presence or absence of consciousness is determined.' (This suggestion is very similar to the Turing test, but is concerned with consciousness rather than intelligence. Moreover, it is not certain that Ayer's popular philosophical classic was familiar to Turing.) In other words, a thing is not conscious if it fails the consciousness test. Alan Turing Researchers in the United Kingdom had been exploring 'machine intelligence' for up to ten years prior to the founding of the field of artificial intelligence research in 1956. It was a common topic among the members of the, who were an informal group of British and researchers that included, after whom the test is named.

Turing, in particular, had been tackling the notion of machine intelligence since at least 1941 and one of the earliest-known mentions of 'computer intelligence' was made by him in 1947. In Turing's report, 'Intelligent Machinery', he investigated 'the question of whether or not it is possible for machinery to show intelligent behaviour' and, as part of that investigation, proposed what may be considered the forerunner to his later tests: It is not difficult to devise a paper machine which will play a not very bad game of chess. Now get three men as subjects for the experiment.

A and C are to be rather poor chess players, B is the operator who works the paper machine. Two rooms are used with some arrangement for communicating moves, and a game is played between C and either A or the paper machine. C may find it quite difficult to tell which he is playing. ' was the first published paper by Turing to focus exclusively on machine intelligence. Turing begins the 1950 paper with the claim, 'I propose to consider the question 'Can machines think? ' As he highlights, the traditional approach to such a question is to start with, defining both the terms 'machine' and 'intelligence'. Turing chooses not to do so; instead he replaces the question with a new one, 'which is closely related to it and is expressed in relatively unambiguous words.'

In essence he proposes to change the question from 'Can machines think?' To 'Can machines do what we (as thinking entities) can do?' The advantage of the new question, Turing argues, is that it draws 'a fairly sharp line between the physical and intellectual capacities of a man.' To demonstrate this approach Turing proposes a test inspired by a, known as the 'Imitation Game,' in which a man and a woman go into separate rooms and guests try to tell them apart by writing a series of questions and reading the typewritten answers sent back. In this game both the man and the woman aim to convince the guests that they are the other.

(Huma Shah argues that this two-human version of the game was presented by Turing only to introduce the reader to the machine-human question-answer test. ) Turing described his new version of the game as follows: We now ask the question, 'What will happen when a machine takes the part of A in this game?' Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, 'Can machines think?' Later in the paper Turing suggests an 'equivalent' alternative formulation involving a judge conversing only with a computer and a man. While neither of these formulations precisely matches the version of the Turing Test that is more generally known today, he proposed a third in 1952. In this version, which Turing discussed in a radio broadcast, a jury asks questions of a computer and the role of the computer is to make a significant proportion of the jury believe that it is really a man.

Turing's paper considered nine putative objections, which include all the major arguments against that have been raised in the years since the paper was published (see '). ELIZA and PARRY In 1966, created a program which appeared to pass the Turing test. The program, known as, worked by examining a user's typed comments for keywords. If a keyword is found, a rule that transforms the user's comments is applied, and the resulting sentence is returned. If a keyword is not found, ELIZA responds either with a generic riposte or by repeating one of the earlier comments. In addition, Weizenbaum developed ELIZA to replicate the behaviour of a, allowing ELIZA to be 'free to assume the pose of knowing almost nothing of the real world.'

With these techniques, Weizenbaum's program was able to fool some people into believing that they were talking to a real person, with some subjects being 'very hard to convince that ELIZA. is not human.' Thus, ELIZA is claimed by some to be one of the programs (perhaps the first) able to pass the Turing Test, even though this view is highly contentious (see ). Created in 1972, a program described as 'ELIZA with attitude'. It attempted to model the behaviour of a, using a similar (if more advanced) approach to that employed by Weizenbaum.

To validate the work, PARRY was tested in the early 1970s using a variation of the Turing Test. A group of experienced psychiatrists analysed a combination of real patients and computers running PARRY through. Another group of 33 psychiatrists were shown transcripts of the conversations. The two groups were then asked to identify which of the 'patients' were human and which were computer programs.

The psychiatrists were able to make the correct identification only 48 percent of the time – a figure consistent with random guessing. In the 21st century, versions of these programs (now known as ') continue to fool people. 'CyberLover', a program, preys on Internet users by convincing them to 'reveal information about their identities or to lead them to visit a web site that will deliver malicious content to their computers'. The program has emerged as a 'Valentine-risk' flirting with people 'seeking relationships online in order to collect their personal data'. The Chinese room. Main article: 's 1980 paper proposed the ' thought experiment and argued that the Turing test could not be used to determine if a machine can think. Searle noted that software (such as ELIZA) could pass the Turing Test simply by manipulating symbols of which they had no understanding.

Without understanding, they could not be described as 'thinking' in the same sense people do. Therefore, Searle concludes, the Turing Test cannot prove that a machine can think. Much like the Turing test itself, Searle's argument has been both widely criticised and highly endorsed. Arguments such as Searle's and others working on the sparked off a more intense debate about the nature of intelligence, the possibility of intelligent machines and the value of the Turing test that continued through the 1980s and 1990s. Loebner Prize. The Imitation Game, as described by Alan Turing in 'Computing Machinery and Intelligence.'

Player C, through a series of written questions, attempts to determine which of the other two players is a man, and which of the two is the woman. Player A, the man, tries to trick player C into making the wrong decision, while player B tries to help player C. Figure adapted from Saygin, 2000.

Saul Traiger argues that there are at least three primary versions of the Turing test, two of which are offered in 'Computing Machinery and Intelligence' and one that he describes as the 'Standard Interpretation.' While there is some debate regarding whether the 'Standard Interpretation' is that described by Turing or, instead, based on a misreading of his paper, these three versions are not regarded as equivalent, and their strengths and weaknesses are distinct. Huma Shah points out that Turing himself was concerned with whether a machine could think and was providing a simple method to examine this: through human-machine question-answer sessions.

Shah argues there is one imitation game which Turing described could be practicalised in two different ways: a) one-to-one interrogator-machine test, and b) simultaneous comparison of a machine with a human, both questioned in parallel by an interrogator. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalises naturally to all of human performance capacity, verbal as well as nonverbal (robotic). Imitation Game Turing's original article describes a simple party game involving three players. Player A is a man, player B is a woman and player C (who plays the role of the interrogator) is of either sex. In the Imitation Game, player C is unable to see either player A or player B, and can communicate with them only through written notes. By asking questions of player A and player B, player C tries to determine which of the two is the man and which is the woman.

Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one. Turing then asks: What will happen when a machine takes the part of A in this game? Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, 'Can machines think?' The Original Imitation Game Test, in which the player A is replaced with a computer.

The computer is now charged with the role of the man, while player B continues to attempt to assist the interrogator. Figure adapted from Saygin, 2000.

The second version appeared later in Turing's 1950 paper. Similar to the Original Imitation Game Test, the role of player A is performed by a computer. However, the role of player B is performed by a man rather than a woman.

'Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?' In this version, both player A (the computer) and player B are trying to trick the interrogator into making an incorrect decision. Standard interpretation Common understanding has it that the purpose of the Turing Test is not specifically to determine whether a computer is able to fool an interrogator into believing that it is a human, but rather whether a computer could imitate a human. While there is some dispute whether this interpretation was intended by Turing, Sterrett believes that it was and thus conflates the second version with this one, while others, such as Traiger, do not – this has nevertheless led to what can be viewed as the 'standard interpretation.' In this version, player A is a computer and player B a person of either sex.

The role of the interrogator is not to determine which is male and which is female, but which is a computer and which is a human. The fundamental issue with the standard interpretation is that the interrogator cannot differentiate which responder is human, and which is machine. There are issues about duration, but the standard interpretation generally considers this limitation as something that should be reasonable. Imitation Game vs. Standard Turing test Controversy has arisen over which of the alternative formulations of the test Turing intended.

Sterrett argues that two distinct tests can be extracted from his 1950 paper and that, pace Turing's remark, they are not equivalent. The test that employs the party game and compares frequencies of success is referred to as the 'Original Imitation Game Test,' whereas the test consisting of a human judge conversing with a human and a machine is referred to as the 'Standard Turing Test,' noting that Sterrett equates this with the 'standard interpretation' rather than the second version of the imitation game. Sterrett agrees that the Standard Turing Test (STT) has the problems that its critics cite but feels that, in contrast, the Original Imitation Game Test (OIG Test) so defined is immune to many of them, due to a crucial difference: Unlike the STT, it does not make similarity to human performance the criterion, even though it employs human performance in setting a criterion for machine intelligence. A man can fail the OIG Test, but it is argued that it is a virtue of a test of intelligence that failure indicates a lack of resourcefulness: The OIG Test requires the resourcefulness associated with intelligence and not merely 'simulation of human conversational behaviour.' The general structure of the OIG Test could even be used with non-verbal versions of imitation games. Still other writers have interpreted Turing as proposing that the imitation game itself is the test, without specifying how to take into account Turing's statement that the test that he proposed using the party version of the imitation game is based upon a criterion of comparative frequency of success in that imitation game, rather than a capacity to succeed at one round of the game. Saygin has suggested that maybe the original game is a way of proposing a less biased experimental design as it hides the participation of the computer.

The imitation game also includes a 'social hack' not found in the standard interpretation, as in the game both computer and male human are required to play as pretending to be someone they are not. Should the interrogator know about the computer? A crucial piece of any laboratory test is that there should be a control. Turing never makes clear whether the interrogator in his tests is aware that one of the participants is a computer.

However, if there were a machine that did have the potential to pass a Turing test, it would be safe to assume a double blind control would be necessary. To return to the Original Imitation Game, he states only that player A is to be replaced with a machine, not that player C is to be made aware of this replacement.

When Colby, FD Hilf, S Weber and AD Kramer tested PARRY, they did so by assuming that the interrogators did not need to know that one or more of those being interviewed was a computer during the interrogation. As Ayse Saygin, Peter Swirski, and others have highlighted, this makes a big difference to the implementation and outcome of the test. An experimental study looking at using transcripts of Loebner's one-to-one (interrogator-hidden interlocutor) Prize for AI contests between 1994–1999, Ayse Saygin found significant differences between the responses of participants who knew and did not know about computers being involved.

And, who organised the at which staged simultaneous comparison tests (one judge-two hidden interlocutors), showed that knowing/not knowing did not make a significant difference in some judges' determination. Judges were not explicitly told about the nature of the pairs of hidden interlocutors they would interrogate.

Judges were able to distinguish human from machine, including when they were faced with control pairs of two humans and two machines embedded among the machine-human set-ups. Spelling errors gave away the hidden humans; machines were identified by 'speed of response' and lengthier utterances.

Strengths Tractability and simplicity The power and appeal of the Turing test derives from its simplicity. The, and modern have been unable to provide definitions of 'intelligence' and 'thinking' that are sufficiently precise and general to be applied to machines. Without such definitions, the central questions of the cannot be answered. The Turing test, even if imperfect, at least provides something that can actually be measured. As such, it is a pragmatic attempt to answer a difficult philosophical question. Breadth of subject matter The format of the test allows the interrogator to give the machine a wide variety of intellectual tasks. Turing wrote that 'the question and answer method seems to be suitable for introducing almost any one of the fields of human endeavour that we wish to include.'

Adds that 'understanding the words is not enough; you have to understand the topic as well.' To pass a well-designed Turing test, the machine must use, have and. The test can be extended to include video input, as well as a 'hatch' through which objects can be passed: this would force the machine to demonstrate the skill of and as well.

Together, these represent almost all of the major problems that artificial intelligence research would like to solve. The is designed to take advantage of the broad range of topics available to a Turing test. It is a limited form of Turing's question-answer game which compares the machine against the abilities of experts in specific fields such as literature. 's machine achieved success in a man versus machine television quiz show of human knowledge– Emphasis on emotional and aesthetic intelligence As a Cambridge honours graduate in mathematics, Turing might have been expected to propose a test of computer intelligence requiring expert knowledge in some highly technical field, and thus anticipating. Instead, as already noted, the test which he described in his seminal 1950 paper requires the computer to be able to compete successfully in a common party game, and this by performing as well as the typical man in answering a series of questions so as to pretend convincingly to be the woman contestant.

Given the status of human sexual dimorphism as, it is thus implicit in the above scenario that the questions to be answered will involve neither specialised factual knowledge nor information processing technique. The challenge for the computer, rather, will be to demonstrate empathy for the role of the female, and to demonstrate as well a characteristic aesthetic sensibility—both of which qualities are on display in this snippet of dialogue which Turing has imagined: Interrogator: Will X please tell me the length of his or her hair?

Contestant: My hair is shingled, and the longest strands are about nine inches long. When Turing does introduce some specialised knowledge into one of his imagined dialogues, the subject is not maths or electronics, but poetry: Interrogator: In the first line of your sonnet which reads, 'Shall I compare thee to a summer's day,' would not 'a spring day' do as well or better? Witness: It wouldn't. Interrogator: How about 'a winter's day.' That would scan all right. Witness: Yes, but nobody wants to be compared to a winter's day.

Turing thus once again demonstrates his interest in empathy and aesthetic sensitivity as components of an artificial intelligence; and in light of an increasing awareness of the threat from an AI run amuck, it has been suggested that this focus perhaps represents a critical intuition on Turing's part, i.e., that emotional and aesthetic intelligence will play a key role in the creation of a '. It is further noted, however, that whatever inspiration Turing might be able to lend in this direction depends upon the preservation of his original vision, which is to say, further, that the promulgation of a 'standard interpretation' of the Turing test—i.e., one which focuses on a discursive intelligence only—must be regarded with some caution. Weaknesses Turing did not explicitly state that the Turing test could be used as a measure of intelligence, or any other human quality.

He wanted to provide a clear and understandable alternative to the word 'think', which he could then use to reply to criticisms of the possibility of 'thinking machines' and to suggest ways that research might move forward. Nevertheless, the Turing test has been proposed as a measure of a machine's 'ability to think' or its 'intelligence'. This proposal has received criticism from both philosophers and computer scientists. It assumes that an interrogator can determine if a machine is 'thinking' by comparing its behaviour with human behaviour.

Every element of this assumption has been questioned: the reliability of the interrogator's judgement, the value of comparing only behaviour and the value of comparing the machine with a human. Because of these and other considerations, some AI researchers have questioned the relevance of the test to their field.

Human intelligence vs intelligence in general. The Turing test does not directly test whether the computer behaves intelligently.

It tests only whether the computer behaves like a human being. Since human behaviour and intelligent behaviour are not exactly the same thing, the test can fail to accurately measure intelligence in two ways: Some human behaviour is unintelligent The Turing test requires that the machine be able to execute all human behaviours, regardless of whether they are intelligent. It even tests for behaviours that we may not consider intelligent at all, such as the susceptibility to insults, the temptation to or, simply, a high frequency of. If a machine cannot imitate these unintelligent behaviours in detail it fails the test. This objection was raised by, in an article entitled ' published shortly after the first Loebner Prize competition in 1992. The article noted that the first Loebner winner's victory was due, at least in part, to its ability to 'imitate human typing errors.'

Turing himself had suggested that programs add errors into their output, so as to be better 'players' of the game. Some intelligent behaviour is inhuman The Turing test does not test for highly intelligent behaviours, such as the ability to solve difficult problems or come up with original insights. In fact, it specifically requires deception on the part of the machine: if the machine is more intelligent than a human being it must deliberately avoid appearing too intelligent. If it were to solve a computational problem that is practically impossible for a human to solve, then the interrogator would know the program is not human, and the machine would fail the test. Because it cannot measure intelligence that is beyond the ability of humans, the test cannot be used to build or evaluate systems that are more intelligent than humans. Because of this, several test alternatives that would be able to evaluate super-intelligent systems have been proposed.

Consciousness vs. The simulation of consciousness.

Main articles: and A modification of the Turing test wherein the objective of one or more of the roles have been reversed between machines and humans is termed a reverse Turing test. An example is implied in the work of psychoanalyst, who was particularly fascinated by the 'storm' that resulted from the encounter of one mind by another.

In his 2000 book, among several other original points with regard to the Turing test, literary scholar discussed in detail the idea of what he termed the Swirski test—essentially the reverse Turing test. He pointed out that it overcomes most if not all standard objections levelled at the standard version. Carrying this idea forward, described the mind as a 'mind recognizing apparatus.' The challenge would be for the computer to be able to determine if it were interacting with a human or another computer. This is an extension of the original question that Turing attempted to answer but would, perhaps, offer a high enough standard to define a machine that could 'think' in a way that we typically define as characteristically human. Is a form of reverse Turing test. Before being allowed to perform some action on a website, the user is presented with alphanumerical characters in a distorted graphic image and asked to type them out.

This is intended to prevent automated systems from being used to abuse the site. The rationale is that software sufficiently sophisticated to read and reproduce the distorted image accurately does not exist (or is not available to the average user), so any system able to do so is likely to be a human. Software that could reverse CAPTCHA with some accuracy by analysing patterns in the generating engine started being developed soon after the creation of CAPTCHA. In 2013, researchers at announced that they had developed a system to solve CAPTCHA challenges from, and up to 90% of the time. In 2014, Google engineers demonstrated a system that could defeat CAPTCHA challenges with 99.8% accuracy. In 2015, former click fraud czar of Google, stated that there were sites that would defeat CAPTCHA challenges for a fee, to enable various forms of fraud. Subject matter expert Turing test.

Main article: Another variation is described as the Turing test, where a machine's response cannot be distinguished from an expert in a given field. This is also known as a 'Feigenbaum test' and was proposed by in a 2003 paper. Total Turing test The 'Total Turing test' variation of the Turing test, proposed by cognitive scientist, adds two further requirements to the traditional Turing test. The interrogator can also test the perceptual abilities of the subject (requiring ) and the subject's ability to manipulate objects (requiring ). Minimum Intelligent Signal Test.

Main article: The Minimum Intelligent Signal Test was proposed by as 'the maximum abstraction of the Turing test', in which only binary responses (true/false or yes/no) are permitted, to focus only on the capacity for thought. It eliminates text chat problems like, and does not require emulation of, allowing for systems that exceed human intelligence. The questions must each stand on their own, however, making it more like an than an interrogation. It is typically used to gather statistical data against which the performance of artificial intelligence programs may be measured. Hutter Prize The organisers of the believe that compressing natural language text is a hard AI problem, equivalent to passing the Turing test.

The data compression test has some advantages over most versions and variations of a Turing test, including:. It gives a single number that can be directly used to compare which of two machines is 'more intelligent.' . It does not require the computer to lie to the judge The main disadvantages of using data compression as a test are:. It is not possible to test humans this way. It is unknown what particular 'score' on this test—if any—is equivalent to passing a human-level Turing test.

Other tests based on compression or Kolmogorov Complexity A related approach to Hutter's prize which appeared much earlier in the late 1990s is the inclusion of compression problems in an extended Turing Test. Or by tests which are completely derived from. Other related tests in this line are presented by Hernandez-Orallo and Dowe. Algorithmic IQ, or AIQ for short, is an attempt to convert the theoretical Universal Intelligence Measure from Legg and Hutter (based on Solomonoff's ) into a working practical test of machine intelligence.

Two major advantages of some of these tests are their applicability to nonhuman intelligences and their absence of a requirement for human testers. Ebert test The Turing test inspired the proposed in 2011 by film critic which is a test whether a computer-based has sufficient skill in terms of intonations, inflections, timing and so forth, to make people laugh.

Predictions Turing predicted that machines would eventually be able to pass the test; in fact, he estimated that by the year 2000, machines with around 100 of storage would be able to fool 30% of human judges in a five-minute test, and that people would no longer consider the phrase 'thinking machine' contradictory. (In practice, from 2009–2012, the chatterbot contestants only managed to fool a judge once, and that was only due to the human contestant pretending to be a chatbot. ) He further predicted that would be an important part of building powerful machines, a claim considered plausible by contemporary researchers in artificial intelligence. In a 2008 paper submitted to 19th Midwest Artificial Intelligence and Cognitive Science Conference, Dr.

Mueller predicted a modified Turing Test called a 'Cognitive Decathlon' could be accomplished within 5 years. By extrapolating an of technology over several decades, predicted that Turing test-capable computers would be manufactured in the near future. In 1990, he set the year around 2020. By 2005, he had revised his estimate to 2029.

1 is a wager of 20,000 between (pessimist) and (optimist) about whether a computer will pass a lengthy Turing Test by the year 2029. During the Long Now Turing Test, each of three Turing Test Judges will conduct online interviews of each of the four Turing Test Candidates (i.e., the Computer and the three Turing Test Human Foils) for two hours each for a total of eight hours of interviews. The bet specifies the conditions in some detail.

Conferences Turing Colloquium 1990 marked the fortieth anniversary of the first publication of Turing's 'Computing Machinery and Intelligence' paper, and, thus, saw renewed interest in the test. Two significant events occurred in that year: The first was the Turing Colloquium, which was held at the in April, and brought together academics and researchers from a wide variety of disciplines to discuss the Turing Test in terms of its past, present, and future; the second was the formation of the annual competition. Lists four major turning points in the history of the Turing Test – the publication of 'Computing Machinery and Intelligence' in 1950, the announcement of 's in 1966, 's creation of, which was first described in 1972, and the Turing Colloquium in 1990. 2005 Colloquium on Conversational Systems In November 2005, the hosted an inaugural one-day meeting of artificial conversational entity developers, attended by winners of practical Turing Tests in the Loebner Prize:, and. Invited speakers included, Hugh Loebner (sponsor of the ) and. 2008 AISB Symposium In parallel to the 2008 held at the, the (AISB), hosted a one-day symposium to discuss the Turing Test, organised by, and. The Speakers included Royal Institution's Director, Turing's biographer, and consciousness scientist.

No agreement emerged for a canonical Turing Test, though Bringsjord expressed that a sizeable prize would result in the Turing Test being passed sooner. The Alan Turing Year, and Turing100 in 2012 Throughout 2012, a number of major events took place to celebrate Turing's life and scientific impact. The group supported these events and also, organised a special Turing test event in on 23 June 2012 to celebrate the 100th anniversary of Turing's birth. In popular culture The Turing test has been used in a number of instances in popular media where machines are discussed. The 13th May 2017 comic strip of the popular comic featured the Turing test.

See also. Cohen, Paul R. (2006), 26 (4)., 'Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind', vol. 3 (March 2017), pp. 58–63.

Multiple tests of artificial-intelligence efficacy are needed because, 'just as there is no single test of prowess, there cannot be one ultimate test of.' One such test, a 'Construction Challenge', would test perception and physical action—'two important elements of intelligent behavior that were entirely absent from the original Turing test.' Another proposal has been to give machines the same standardized tests of science and other disciplines that schoolchildren take. A so far insuperable stumbling block to artificial intelligence is an incapacity for reliable.

'Virtually every sentence that people generate is, often in multiple ways.' A prominent example is known as the 'pronoun disambiguation problem': a machine has no way of determining to whom or what a in a sentence—such as 'he', 'she' or 'it'—refers. Moor, James H.

(2001), Minds and Machines, 11 (1): 77–93,:,. and Shah, Huma (2016), 'Turing's Imitation Game: Conversations with the Unknown', Cambridge University Press. External links Wikimedia Commons has media related to.

at Curlie (based on ). How accurate could the turing test really be?. reviews a half-century of work on the Turing Test, from the vantage point of 2000., including detailed justifications of their respective positions. by Blay Witby. An AI that learns from and imitates humans.

New York Times essays on machine intelligence and. of. for the Turing Test. 'Talk:Computer professionals celebrate 10th birthday of A.L.I.C.E.'

This section needs additional citations for. Unsourced material may be challenged and removed.

(March 2007) Date Development 1913 and published, which revolutionized. 1915 built a chess automaton, and published speculation about thinking and automata. 1923 's play opened in London. This is the first use of the word ' in English. 1920s and 1930s and lead into logical analysis of. Develops to investigate computability using recursive functional notation.

1931 showed that sufficiently powerful, if consistent, permit the formulation of true theorems that are unprovable by any theorem-proving machine deriving all possible theorems from the axioms. To do this he had to build a universal, integer-based programming language, which is the reason why he is sometimes called the 'father of '. 1941 built the first working program-controlled computers. 1943 and publish 'A Logical Calculus of the Ideas Immanent in Nervous Activity' (1943), laying foundations for. 1943, and Julian Bigelow coin the term '. Wiener's popular book by that name published in 1948.

1945 which would prove invaluable in the progress of AI was introduced with the 1944 paper, by and. 1945 published (, July 1945) a prescient vision of the future in which computers assist humans in many activities. 1948 (quoted by ) in response to a comment at a lecture that it was impossible for a machine to think: 'You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that!' Von Neumann was presumably alluding to the which states that any effective procedure can be simulated by a (generalized) computer. This section needs additional citations for.

Unsourced material may be challenged and removed. (March 2007) Date Development 1950 proposes the as a measure of machine intelligence. 1950 published a detailed analysis of playing as.

1950 published his. 1951 The first working AI programs were written in 1951 to run on the machine of the: a checkers-playing program written by and a chess-playing program written by Dietrich Prinz. 1952–1962 wrote the first game-playing program, for checkers , to achieve sufficient skill to challenge a respectable amateur. His first checkers-playing program was written in 1952, and in 1955 he created a version that to play.

1956 The first is organized by, of and. 1956 The name artificial intelligence is used for the first time as the topic of the second, organized by 1956 The first demonstration of the (LT) written by, and (, now Carnegie Mellon University or CMU). This is often called the first AI program, though Samuel's checkers program also has a strong claim. 1957 The (GPS) demonstrated by Newell, Shaw and Simon while at CMU. 1958 John McCarthy ( or MIT) invented the.

1958 and (IBM) described a in that exploits a semantic model of the domain in the form of diagrams of 'typical' cases. 1958 on the Mechanization of Thought Processes was held in the UK and among the papers presented were John McCarthy's Programs with Common Sense, 's Pandemonium, and 's Some Methods of Programming and Artificial Intelligence. 1959 and founded the. Late 1950s, early 1960s and colleagues at design for. This section needs additional citations for.

Unsourced material may be challenged and removed. (March 2007) Date Development 1960s lays the foundations of a theory of AI, introducing universal for inductive inference and prediction. 1960 by J.C.R.

1961 James Slagle (PhD dissertation, MIT) wrote (in Lisp) the first symbolic program, SAINT, which solved problems at the college freshman level. 1961 In, denied the possibility of machine intelligence on or grounds.

He referred to 's result of 1931: sufficiently powerful formal systems are either inconsistent or allow for formulating true theorems unprovable by any theorem-proving AI deriving all provable theorems from the axioms. Since humans are able to 'see' the truth of such theorems, machines were deemed inferior.

1961 's worked on a. 1963 Thomas Evans' program, ANALOGY, written as part of his PhD work at MIT, demonstrated that computers can solve the same problems as are given on tests.

1963 and published Computers and Thought, the first collection of articles about artificial intelligence. 1963 and Charles Vossler published 'A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators', which described one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of simple perceptrons of 1964 Danny Bobrow's dissertation at MIT (technical report #1 from MIT's AI group, ), shows that computers can understand natural language well enough to solve correctly. 1964 's MIT dissertation on the SIR program demonstrates the power of a logical representation of knowledge for question-answering systems. Alan Robinson invented a mechanical procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language. 1965 (MIT) built, an that carries on a dialogue in on any topic. It was a popular toy at AI centers on the when a version that 'simulated' the dialogue of a was programmed. 1965 initiated, a ten-year effort to develop software to deduce the molecular structure of organic compounds using scientific instrument data.

It was the first. 1966 Ross Quillian (PhD dissertation, Carnegie Inst.

Of Technology, now CMU) demonstrated. 1966 Machine Intelligence workshop at Edinburgh – the first of an influential annual series organized by and others. 1966 Negative report on machine translation kills much work in (NLP) for many years.

1967 program (Edward Feigenbaum, Bruce Buchanan, Georgia Sutherland at ) demonstrated to interpret mass spectra on organic chemical compounds. First successful knowledge-based program for scientific reasoning. 1968 (PhD work at MIT) demonstrated the power of for integration problems in the program. First successful knowledge-based program in. 1968 at MIT built a knowledge-based, that was good enough to achieve a class-C rating in tournament play. 1968 Wallace and Boulton's program, Snob (Comp.J.

11(2) 1968), for unsupervised classification (clustering) uses the Bayesian criterion, a mathematical realisation of. 1969 (SRI):, demonstrated combining, and. 1969 (Stanford) defined dependency model for. Later developed (in PhD dissertations at ) for use in story understanding by and Wendy Lehnert, and for use in understanding memory by Janet Kolodner. 1969 Yorick Wilks (Stanford) developed the semantic coherence view of language called Preference Semantics, embodied in the first semantics-driven machine translation program, and the basis of many PhD dissertations since such as Bran Boguraev and David Carter at Cambridge.

1969 First International Joint Conference on Artificial Intelligence held at Stanford. 1969 Marvin Minsky and publish, demonstrating previously unrecognized limits of this feed-forward two-layered structure. This book is considered by some to mark the beginning of the of the 1970s, a failure of confidence and funding for AI.

Nevertheless, significant progress in the field continued (see below). 1969 McCarthy and Hayes started the discussion about the with their essay, 'Some Philosophical Problems from the Standpoint of Artificial Intelligence'. This section needs additional citations for. Unsourced material may be challenged and removed. (March 2007) Date Development Early 1970s Jane Robinson and Don Walker established an influential group at SRI. 1970 Jaime Carbonell (Sr.) developed SCHOLAR, an interactive program for based on semantic nets as the representation of knowledge. 1970 Bill Woods described Augmented Transition Networks (ATN's) as a representation for natural language understanding.

1970 's PhD program, ARCH, at MIT learned concepts from examples in the world of children's blocks. 1971 's PhD thesis demonstrated the ability of computers to understand English sentences in a restricted world of children's blocks, in a coupling of his language understanding program, with a robot arm that carried out instructions typed in English. 1971 Work on the Boyer-Moore theorem prover started in Edinburgh.

1972 programming language developed. 1972 Earl Sacerdoti developed one of the first hierarchical planning programs, ABSTRIPS. 1973 The Assembly Robotics Group at builds Freddy Robot, capable of using to locate and assemble models. (See: a versatile computer-controlled assembly system.) 1973 The gives a largely negative verdict on AI research in Great Britain and forms the basis for the decision by the British government to discontinue support for AI research in all but two universities. 1974 's PhD dissertation on the program (Stanford) demonstrated a very practical rule-based approach to medical diagnoses, even in the presence of uncertainty. While it borrowed from DENDRAL, its own contributions strongly influenced the future of development, especially commercial systems. 1975 Earl Sacerdoti developed techniques of in his NOAH system, replacing the previous paradigm of search among state space descriptions.

Artificial Intelligence

NOAH was applied at SRI International to interactively diagnose and repair electromechanical systems. 1975 developed the Nonlin hierarchical planning system able to search a space of characterised as alternative approaches to the underlying goal structure of the plan.

1975 Marvin Minsky published his widely read and influential article on as a representation of knowledge, in which many ideas about and are brought together. 1975 The Meta-Dendral learning program produced new results in (some rules of ) the first scientific discoveries by a computer to be published in a refereed journal. Mid-1970s (SRI) established limits to traditional AI approaches to discourse modeling. Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of 'centering', used in establishing focus of and anaphoric references in. Mid-1970s and colleagues describe the 'primal sketch' and its role in. 1976 's (Stanford PhD dissertation) demonstrated the discovery model (loosely guided search for interesting conjectures).

1976 Randall Davis demonstrated the power of meta-level reasoning in his PhD dissertation at Stanford. 1978, at Stanford, invented the concept of for describing the of a concept formation program. 1978 wins the for his theory of, one of the cornerstones of AI known as '. 1978 The MOLGEN program, written at Stanford by Mark Stefik and Peter Friedland, demonstrated that an representation of knowledge can be used to plan gene- experiments. 1979 Bill VanMelle's PhD dissertation at Stanford demonstrated the generality of 's representation of knowledge and style of reasoning in his program, the model for many commercial expert system 'shells'.

1979 Jack Myers and Harry Pople at developed INTERNIST, a knowledge-based medical diagnosis program based on Dr. Myers' knowledge. 1979 Cordell Green, David Barstow, Elaine Kant and others at Stanford demonstrated the CHI system for. 1979 The Stanford Cart, built by, becomes the first computer-controlled, when it successfully traverses a chair-filled room and circumnavigates the. 1979 BKG, a backgammon program written by at, defeats the reigning world champion. 1979 Drew McDermott and Jon Doyle at, and John McCarthy at Stanford begin publishing work on and formal aspects of truth maintenance. Late 1970s Stanford's SUMEX-AIM resource, headed by Ed Feigenbaum and Joshua Lederberg, demonstrates the power of the ARPAnet for scientific collaboration.

This section needs additional citations for. Unsourced material may be challenged and removed. (March 2007) Date Development 1980s developed and marketed. First shells and commercial applications. 1980 First National Conference of the (AAAI) held at Stanford.

1981 designs the connection machine, which utilizes to bring new power to AI, and to computation in general. (Later founds ) 1982 The (FGCS), an initiative by Japan's Ministry of International Trade and Industry, begun in 1982, to create a 'fifth generation computer' (see history of computing hardware) which was supposed to perform much calculation utilizing massive parallelism. 1983 John Laird and Paul Rosenbloom, working with, complete CMU dissertations on (program). 1983 invents the Interval Calculus, the first widely used formalization of temporal events.

Mid-1980s Neural Networks become widely used with the (first described by in 1974). 1985 The autonomous drawing program, created by, is demonstrated at the AAAI National Conference (based on more than a decade of work, and with subsequent work showing major developments). 1986 The team of at builds the first robot cars, driving up to 55 mph on empty streets. 1986 and Candace Sidner create the first computation model of, establishing the field of research.

1987 Marvin Minsky published, a theoretical description of the mind as a collection of cooperating. He had been lecturing on the idea for years before the book came out (c.f. 1987 Around the same time, introduced the and as a more minimalist modular model of natural intelligence;. 1987 Commercial launch of generation 2.0 of Alacrity by Alacritous Inc./Allstar Advice Inc. Toronto, the first commercial strategic and managerial advisory system. The system was based upon a forward-chaining, self-developed expert system with 3,000 rules about the evolution of markets and competitive strategies and co-authored by Alistair Davidson and Mary Chung, founders of the firm with the underlying engine developed by Paul Tarvydas. The Alacrity system also included a small financial expert system that interpreted financial statements and models.

1989 Dean Pomerleau at CMU creates ALVINN (An Autonomous Land Vehicle in a Neural Network). This section needs additional citations for.

Unsourced material may be challenged and removed. (March 2007) Date Development Early 1990s, a program written by Gerry Tesauro, demonstrates that (learning) is powerful enough to create a championship-level game-playing program by competing favorably with world-class players. 1990s Major advances in all areas of AI, with significant demonstrations in machine learning, case-based reasoning, multi-agent planning, uncertain reasoning, natural language understanding and translation, vision, games, and other topics. 1991 scheduling application deployed in the first paid back investment of 30 years in AI research. 1993 extended by creating, the first robot to navigate using and operate at animal-like speeds (1 meter/second). 1993, and started the widely publicized with numerous collaborators, in an attempt to build a child in just five years. 1993 ISX corporation wins 'DARPA contractor of the year' for the (DART) which reportedly repaid the US government's entire investment in AI research since the 1950s.

1994 With passengers on board, the twin robot cars and VITA-2 of and drive more than one thousand kilometers on a Paris three-lane highway in standard heavy traffic at speeds up to 130 km/h. They demonstrate autonomous driving in free lanes, convoy driving, and lane changes left and right with autonomous passing of other cars. 1994 world champion resigned a match against computer program. Chinook defeated 2nd highest rated player,.

Chinook won the USA National Tournament by the widest margin ever. 1995 'No Hands Across America': A semi-autonomous car drove coast-to-coast across the United States with computer-controlled steering for 2,797 miles (4,501 km) of the 2,849 miles (4,585 km). Throttle and brakes were controlled by a human driver.

1995 One of ' robot cars (with robot-controlled throttle and brakes) drove more than 1000 miles from to and back, in traffic, at up to 120 mph, occasionally executing maneuvers to pass other cars (only in a few critical situations a safety driver took over). Active vision was used to deal with rapidly changing street scenes.

1997 The chess machine defeats the (then) world champion,. 1997 First official football (soccer) match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators. 1997 Computer program defeated the world champion Takeshi Murakami with a score of 6–0. 1998 ' is released, and becomes the first successful attempt at producing a type of A.I to reach a. 1998 published his paper. 1998, and Anthony Cassandra introduce the first method for solving offline, jumpstarting widespread use in robotics and 1999 introduces an improved domestic robot similar to a Furby, the becomes one of the first artificially intelligent 'pets' that is also. Late 1990s and other AI-based information extraction programs become essential in widespread use of the.

Late 1990s Demonstration of an Intelligent room and Emotional Agents at AI Lab. Late 1990s Initiation of work on the, which connects mobile and stationary computers in an adaptive. This section needs additional citations for.

Unsourced material may be challenged and removed. (March 2007) Date Development 2000 Interactive robopets (') become commercially available, realizing the vision of the 18th century novelty toy makers. 2000 at MIT publishes her dissertation on Sociable machines, describing, with a face that expresses.

2000 The explores remote regions of Antarctica looking for meteorite samples. 2002 's autonomously vacuums the floor while navigating and avoiding obstacles. 2004 OWL W3C Recommendation (10 February 2004).

2004 introduces the requiring competitors to produce autonomous vehicles for prize money. 2004 's robotic exploration rovers and autonomously navigate the surface of. 2005 's robot, an artificially intelligent humanoid robot, is able to walk as fast as a human, delivering to customers in restaurant settings.

2005 based on tracking web activity or media usage brings AI to marketing. 2005 is born, a project to simulate the brain at molecular detail.

2006 The Dartmouth Artificial Intelligence Conference: The Next 50 Years (AI@50) (14–16 July 2006) 2007, one of the world's oldest scientific journals, puts out a special issue on using AI to understand biological intelligence, titled Models of Natural 2007 is by a team of researchers at the. 2007 launches the for to obey traffic rules and operate in an urban environment.

2010s Date Development 2010 launched Kinect for Xbox 360, the first gaming device to, using just a 3D camera and infra-red detection, enabling users to play their Xbox 360 wirelessly. The award-winning machine learning for human motion capture technology for this device was developed by the at, Cambridge. 2011 's computer defeated champions and. 2011–2014 's (2011), 's (2012) and 's (2014) are that use to answer questions, make recommendations and perform actions. 2013 HRP-2 built by SCHAFT Inc of, a subsidiary of, defeats 15 teams to win ’s. HRP-2 scored 27 out of 32 points in 8 tasks needed in disaster response.

Tasks are drive a vehicle, walk over debris, climb a ladder, remove debris, walk through doors, cut through a wall, close valves and connect a hose. 2013 NEIL, the Never Ending Image Learner, is released at Carnegie Mellon University to constantly compare and analyze relationships between different images. 2015 An open letter to ban development and use of autonomous weapons signed by, and 3,000 researchers in AI and robotics. 2015 's (version: Fan) defeated 3 time European Go champion 2 dan professional by 5 games to 0. 2016 's (version: Lee) defeated 4–1. Lee Sedol is a 9 dan professional Korean champion who won 27 major tournaments from 2002 to 2016.

Before the match with AlphaGo, Lee Sedol was confident in predicting an easy 5–0 or 4–1 victory. 2017 's (version: Master) won 60–0 rounds on two public websites including 3 wins against world champion. 2017, designed by professor Tuomas Sandholm and his grad student Noam Brown won against four top players at no-limit, a very challenging version of poker. Unlike Go and Chess, Poker is a game in which some information is hidden (the cards of the other player) which makes it much harder to model.

2017 An -machined learned played at tournament in August 2017. It won during a demonstration game against professional Dota 2 player. 2017 Google DeepMind revealed that AlphaGo Zero—an improved version of AlphaGo—displayed significant performance gains while using far fewer (as compared to AlphaGo Lee; it used same amount of TPU's as AlphaGo Master).

Unlike previous versions, which learned the game by observing millions of human moves, AlphaGo Zero learned by playing only against itself. The system then defeated AlphaGo Lee 100 games to zero, and defeated AlphaGo Master 89 to 11. Although unsupervised learning is a step forward, much has yet to be learned about general intelligence.

AlphaZero masters chess in 4 hours, defeating the best chess engine, StockFish 8. AlphaGo won 28 out of 100 games, and the remaining 72 games ended in stalemate.

Daniel Crevier

See also. Notes., pp. 4–5., p. 53., pp. 5–9)., p. 6., p. 366. O'Connor, Kathleen Malone (1994), retrieved 10 January 2007. 19 December 2007 at the., pp. 10–12, 37., pp. 13–14., pp. 14–15, p. 50., pp. 36–40. Please see.,., p. 42.

Please see:., p. 26., pp. 41–42. Quoted in, p. 317., pp. 43., p. 17., pp. 19–25., pp. 26–34., pp. 48–51.

eBook., pp. 59–60., p. 25., pp. 61–62 and see also., pp. 55–56. Crevier 1993:22–25. Samuel 1959. Schaeffer, Jonathan. One Jump Ahead:: Challenging Human Supremacy in Checkers, 1997,2009, Springer,.

Crevier 1993:148–150. Retrieved 24 November 2008. Retrieved 15 March 2015. Grosz, Barbara; Sidner, Candace L. Computational Linguistics. 12 (3): 175–204.

Retrieved 5 May 2017. Harry Henderson (2007). Artificial Intelligence: Mirrors for the Mind. NY: Infobase Publishing. Retrieved 15 March 2015. Archived from on 5 September 2006. Retrieved 15 March 2015.

Jochem, Todd M.; Pomerleau, Dean A. Retrieved 2015-10-20. Jochem, Todd. Robotic Trends. Retrieved 2015-10-20. Retrieved 24 November 2008.

Kaelbling, Leslie Pack; Littman, Michael L; Cassandra, Anthony R. Artificial Intelligence. Retrieved 5 May 2017.

Retrieved 24 November 2008. Fisher, Adam. Popular Science. Bonnier Corporation. Retrieved 10 October 2013. Microsoft Research.

US Defense Advanced Research Projects Agency. Retrieved 25 December 2013.

Tegmark, Max. Future of Life Institute.

Retrieved 25 April 2016. ^; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis;; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian;; Lillicrap, Timothy;; Sifre, Laurent; Driessche, George van den; Graepel, Thore; (19 October 2017). 550 (7676): 354–359.

Retrieved 11 December 2017. Hassabis, Demis. Retrieved 25 April 2016. Ormerod, David. Go Game Guru. Retrieved 25 April 2016. Associated Press.

Retrieved 25 April 2016. Metz, Cade. 11 August 2017. Greenemeier, Larry (18 October 2017). Scientific American.

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