Alan Turing's 1950 paper, _Computing Machinery and Intelligence_ (Turing 1950) and the Turing test suggested in it are rightly seen as inspirational to the inception and development of AI. However, inspiration can soon become distraction in science, and it is not too early to begin to consider whether or not the Turing test is just such a distraction. What this chapter argues is that this is indeed the case with the Turing test and AI. AI has had an intimate relationship with the Turing test throughout its brief history. The view of this relationship, presented in this paper is that it has developed more or less as follows:
1950 - 1966: A source of inspiration to all concerned with AI.
1966 - 1973: A distraction from some more promising avenues of AI research.
1973 - 1990: By now a source of distraction mainly to philosophers, rather than AI workers.
1990: Consigned to history. 1
One conclusion that is implied by this view of the history of AI and
Turing's 1950 paper is that for most of the period since its publication
it has been a distraction. While not detracting from the brilliance of
the paper and its central role in the philosophy of AI, it can be argued
that Turing's 1950 paper, or perhaps some strong interpretations of it,
has, on occasion, hindered both the practical development of AI and the
philosophical work necessary to facilitate that development.
Thus one can make the claim that, in an important philosophical sense, Computing Machinery and Intelligence has led AI into a blind alley from which it only just beginning to extract itself. It is also an implication of the title of this chapter that the Turing test is not the only blind alley in the progress of AI. Although this chapter makes no examination of this implication, it is one that I am happy to accept.
One main source of this distraction has been the common, yet mistaken reading of Computing Machinery and Intelligence as somehow showing that one can attempt to build an intelligent machine without a prior understanding of the nature of intelligence. If we can, by whatever means, build a computer- based system that deceives a human interrogator for a while into suspecting that it might be human, then we have solved the many philosophical, scientific, and engineering problems of AI! This simplistic reading has, of course, proved both false and misleading in practice. The key to this would seem to be the mistaken view that Turing's paper contains an adequate operational definition of intelligence. A later section of this chapter suggests an interpretation of Computing Machinery and Intelligence and the 'imitation game' in their historical context. This interpretation does not imply the existence of an operational definition of intelligence.
That the paper was almost immediately read as providing an operational definition of intelligence is witnessed by the change from the label, 'imitation game' to 'Turing test' by commentators. Turing himself was always careful to refer to 'the game'. The suggestion that it might be some sort of test involves an important extension of Turing's claims. This is not some small semantic quibble, but an important suggestion that Turing's paper was being interpreted as closer to an operational test than he himself intended. If the Turing test is read as something like an operational definition of intelligence, then two very important defects of such a test must be considered. First, it is all or nothing: it gives no indication as to what a partial success might look like. Second, it gives no direct indications as to how success might be achieved. These two defects in turn have two weak implications. The first is that partial success is impossible, i.e. intelligence in computing machinery is an all-or-nothing phenomenon. The second is that the best route to this is by imitating human beings. Readers will see the flaws in these arguments without difficulty, no doubt, but it is hard to deny that much AI work has been distracted by a view of intelligence as a holistic phenomenon, demonstrated only by human beings, and only to be studied by the direct imitation of human beings.
To avoid the charge of setting up 'straw men', it will be argued in the remainder of this paper that the general misreadings of Turing's 1950 paper have led to the currency of three specific mistaken assertions, namely:
1) Intelligence in computing machinery is (or is nearly, or includes) being able to deceive a human interlocutor.
2) The best approach to the problem of defining intelligence is through some sort of operational test, of which the 'imitation game' is a paradigm example.
3) Work specifically directed at producing a machine that could perform well in the 'imitation game' is genuine (or perhaps even useful) AI research.
This paper does not pursue the falsity of Assertions 1 and 2 in any
great detail. On Assertion 1 it should be sufficient to remark that the
comparative success of ELIZA (Weizenbaum 1966), and programs like it, at
deceiving human interlocutors could not be held to to indicate that they
are closer to achieving intelligence than more sophisticated AI work. What
we should conclude currently about this sort of AI work is that it represents
research into the mechanisms of producing certain sorts of illusion in
human beings rather than anything to do with intelligence, artificial or
Other writers have convincingly attacked Assertion 2 on the grounds that the 'imitation game' does not test for intelligence but rather for other items such as cultural similarity (French 1996 and Michie 1996). Furthermore an all-or-nothing operational definition, such as that provided by the Turing test, is worse than useless for guiding research that is still at an early stage.
Assertion 3 and its effects on the history of AI are clearly the most important for the purposes of this book. A claim already made repeatedly in the previous two chapters is that work directed at success in the Turing test is neither genuine nor useful AI research. In particular, the point will be stressed that, irrespective of whether or not Turing's 1950 paper provided one, the last thing that AI has needed since 1966 is an operational definition of intelligence.
Few, if any, philosophers and AI researchers would assent to Assertion 3 being stated boldly. However, the influence of Alan Turing and his 1950 paper on the history of AI has been so profound that such mistaken claims can have a significant influence at a subconscious or subcultural level.
In any case, the passing of forty years gives sufficient historical perspective to enable us to begin to debate the way in which Computing Machinery and Intelligence has influenced the development of AI. The basic theme of this paper is that the influence of Turing's 1950 paper has been largely unfortunate. This is not through any fault of the paper, but is rather a consequence of the historical circumstances that existed at the time of its writing and some of the pressures that have affected the subsequent development of AI.
In various other places (Yazdani and Whitby 1987, Whitby 1988) an analogy
has been developed between AI and artificial flight. One feature of this
analogy relevant here is the way in which direct imitation of natural flight
proved a relatively fruitless avenue of research. It is true that many
serious aviation pioneers did make detailed study of bird flight, the most
notable being Otto Lilienthal; but it must be stressed that working aircraft
were developed by achieving greater understanding of the principles of
aerodynamics. The Wright brothers were extremely thorough and precise scientists.
They succeeded because they were thorough in their experimental methods,
whereas others had failed because they were too hasty to build aircraft
based upon incomplete theoretical work. There may be some important lessons
for AI research in the methodology of the Wrights 2. It is also true that
our understanding of bird flight has stemmed from our knowledge of aerodynamics
and not the reverse 3. If there were an imitation game type of test for
flight we would probably still not be able to build a machine that could
pass it. Some aircraft can imitate some features of bird flight such as
a glider when soaring in a thermal, but totally convincing imitation does
not exist. We do not know how to build a practical ornithopter (an aircraft
that employs a birdlike wing-flapping motion), but this is not of any real
importance. Some of the purposes for which we use artificial flight, such
as the speedy crossing of large distances, are similar to the purposes
for which natural flight has evolved, but others, such as controlling the
re- entry of spacecraft, are radically different. It is clear that AI,
if it is to be a useful technology, should undergo a similar development.
Many of the most useful applications for AI are, and will continue to be,
in areas that in no way replace or imitate natural intelligence. It is
quite probable that we will never build a machine which could pass the
Turing test in its entirety, but this may well be because we can see little
use for such a machine, indeed it could have dangerous side-effects.
What is needed is a definition of intelligence that does not draw on our assumed intuitive knowledge of our own abilities. Such knowledge is at best vague and unreliable, and there may be Godel-like reasons for believing that we do not fully understand our own capabilities (Lucas 1961). Some writers have made tentative approaches to such a definition (Schank 1986, Yazdani 1990) and perhaps some common features are beginning to emerge, among which are that any intelligent entity must be able to form clearly discernible goals. This is a feature that is not suggested by the Turing test nor possessed by programs that have reputedly done well in the imitation game, such as: ELIZA, DOCTOR (Michie 1986), and PARRY (Colby et. al. 1972).
In considering AI as an engineering enterprise - concerned with the development of useful products - the effects of the imitation game are different but equally misleading. If we focus future work in AI on the imitation of human abilities, such as might be required to succeed in the imitation game, we are in effect building intellectual statue' when what we need are intellectual tools. This may prove to be an expensive piece of vanity. In other words, there is no reason why successful AI products should relate to us as if they were humans. They may instead resemble a workbench that enables a human operator to achieve much more than he could without it, or perhaps be largely invisible to humans in that they operate automatically and autonomously.
The remaining portion of a useful interpretation of Computing Machinery
and Intelligence is more relevant today. It also explains the continuing
appeal of the Turing test to present day writers. This is because the paper
clearly illustrates the importance of human attitudes in determining the
answers to questions such as 'Could a machine think?'.
Ascribing the label 'intelligent' is not a purely technical exercise; it involves a number of moral and social dimensions. Human beings consider themselves obligated to behave in certain ways toward intelligent items.
To claim that the ascription of intelligence has moral and social dimensions is not merely to claim that it has moral and social consequences. It may well be the case that certain moral and social criteria must be satisfied before such an ascription can be made 5. In a sense it is true that we feel more at ease ascribing intelligence (and sometimes even the ability to think) to those entities with which we can have an interesting conversation than to radically different entities. This feature of intelligence ascription makes the use of any sort of operational test of intelligence with human beings very unattractive.
These moral and social dimensions to the ascription of intelligence are also covered by Computing Machinery and Intelligence. Turing wanted to ask (although he obviously could not answer), 'What would be the human reaction to the sort of machine that could succeed in the imitation game?'. If, as Turing clearly believed, digital computers could, by the end of the century, succeed in deceiving an interrogator 30 percent of the time, how would we describe such a feat? This is not primarily a technical or philosophical question, but rather a question about human attitudes. As Turing himself observed, the meaning of words such 'thinking' can change with changing patterns of usage. Although sampling human attitudes is rejected as a method of answering the question 'Can a machine think?' in the first paragraph of Computing Machinery and Intelligence, we can read the entire paper as primarily concerned with human attitudes. The contrivance of the imitation game was intended to show the importance of human attitudes, not to be an operational definition of intelligence.
Given this interpretation of the paper, it is not surprising that Turing tolerated a certain amount of misinterpretation of the role of the imitation game. The paper itself was partly an exercise in testing and changing human attitudes. Turing fully expected it to provoke a certain amount of controversy. However, in the fourth decade of research in AI this sort of controversy is no longer productive.
1] The (somewhat arbitrary) dates in this history are derived from the
first publications describing ELIZA (Weizenbaum 1966) and PARRY (Colby
et al 1972) and the Turing 1990 Colloquium.
2] There are two features of the Wright's methodology which contrast sharply with other contemporary experimenters and which may have relevance to contemporary AI. First they spent a good deal of time looking at the work, both successful and unsuccessful of previous aviation experimenters. They developed a coherent account of these successes and failures. Second and remarkably, unlike most of their contemporaries they had a full appreciation of the need to control any aircraft in flight addition to simply getting it airborne. (Mondey 1977)
3] In 1928 the first successful soaring flight was made in a glider; the aircraft climbing in the rising air of a thermal. Birds had been observed doing so for centuries, but at this time many biologists maintained that they could do so by virtue of the air trapped within the hollow bones of their wings being expanded by the heat of the sun. In this case a young pilot was able to overturn biological theory.
4] Eventually published (though perhaps incorrectly dated) in Meltzer, B. & Michie, D. (eds.) Machine Intelligence 5, Edinburgh University Press.
5] This should not be read as a claim that the ascription of intelligence to some entity should be made purely according to moral or social criteria. This is an interesting problem which deserves further attention. For a discussion of this issue see Torrance 1986.
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