Turing’s Test


1. The Imitation Game

I PROPOSE to consider the question, ‘ Can machines think ? ‘ … The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. …

The new form of the problem can be described in terms of … the ‘ imitation game ‘. It is played with three people, a man (A), a woman (B), and an interrogator … .  The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either ‘ X is A and Y is B ‘ or ‘ X is B and Y is A’. The interrogator is allowed to put questions to A and B thus: C : ‘Will X please tell me the length of his or her hair ?’ Now suppose X is actually A, then A must answer. It is A’s object in the game to try and cause C to make the wrong identification. His answer might therefore be ‘My hair is shingled, and the longest strands are about nine inches long.’ In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. … The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. …

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 ? ‘

3. The Machines concerned in the Game

It is natural that we should wish to permit every kind of engineering technique to be used in our machines. We also wish to allow the possibility than an engineer or team of engineers may construct a machine which works, but whose manner of operation cannot be satisfactorily described by its constructors because they have applied a method which is largely experimental.

But ….

This prompts us to abandon the requirement that every kind of technique should be permitted. We are the more ready to do so in view of the fact that the present interest in ‘ thinking machines’ has been aroused by a particular kind of machine, usually called an ‘electronic computer ‘ or ‘digital computer’. Following this suggestion we only permit digital computers to take part in our game.

It may …  be said that this identification of machines with digital computers, like our criterion for ‘ thinking’, will only be unsatisfactory if (contrary to my belief), it turns out that digital computers are unable to give a good showing in the game.

4. Digital Computers.

The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer. The human computer is supposed to be following fixed rules; he has no authority to deviate from them in any detail. We may suppose that these rules are supplied in a book, which is altered whenever he is put on to a new job. He has also an unlimited supply of paper  … .

An interesting variant on the idea of a digital computer is a ‘ digital computer with a random element’. …  Sometimes such a machine is describe! as having free will (though I would not use this phrase myself). It is not normally possible to determine from observing a machine whether it has a random element, … .

5. Universality of Digital Computer

The digital computers considered in the last section may be classified amongst the ‘discrete state machine’. …. Strictly speaking there are no such machines. …. But there are many kinds of machine which can profitably be thought of as being discrete state machines.

It will seem that given the initial state of the machine and the input signals it is always possible to predict all future states. [But] The system of the ‘ universe as a whole ‘ is such that quite small errors in the initial conditions can have an overwhelming effect at a later time. The displacement of a single electron by a billionth of a centimetre at one moment might make the difference between a man being killed by on avalanche a year later, or escaping. It is an essential property of the mechanical systems which we have called ‘ discrete state machines ‘ that this phenomenon does not occur. Even when we consider the actual physical machines instead of the idealised machines, reasonably accurate knowledge of the state at one moment yields reasonably accurate knowledge any number of steps later.

As we have mentioned, digital computers fall within the class of discrete state machines. But the number of states of which such a machine is capable is usually enormously large.

But in view of the universality property we see that [our question] is equivalent to this, ‘ 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 ?’

6. Contrary Views on the Main Question

… The popular view that scientists proceed inexorably from well established fact to well-established fact, never being influenced by any unproved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research.

(3) The Mathematical Objection. There are a number of results of mathematical logic which can be used to show that there are limitations to the powers of discrete-state machine. … This is the mathematical result: it is argued that it proves a disability of machines to which the human intellect is not subject. …

The short answer to this argument is that although it is established that there are limitations to the powers of any particular machine, it has only been stated, without any sort of proof, that no such limitations apply to the human intellect. But I do not think this view can be dismissed quite so lightly. Whenever one of these machines is asked the appropriate critical question, and gives a definite answer, we know that this answer must be wrong, and this gives us a certain feeling of superiority. Is this feeling illusory ? It is no doubt quite genuine, but I do not think too much importance should be attached to it. We too often give wrong answers to questions ourselves to be justified in being very pleased at such evidence of fallibility on the part of the machines. Further, our superiority can only be felt on such an occasion in relation to the one machine over which we have scored our petty triumph. There T”-uild be no question of triumphing simultaneously over all machines. In short, then, there might be men cleverer than any given machine, but then again there might be other machines cleverer again, and so on.

4) The Argument from Consciousness

… The game (with the player B omitted) is frequently used in practice under the name of viva voce to discover whether some one really understands something or has ‘ learnt it parrot fashion’.

…  I do not wish to give the impression that I think there is no mystery about consciousness. There is, for instance, something of a paradox connected with any attempt to localise it. But I do not think these mysteries necessarily need to be solved before we can answer the question with which we are concerned in this paper.

5) Arguments from Various Disabilities

No support is usually offered for these statements. I believe they are mostly founded on the principle of scientific induction. A man has seen thousands of machines in his lifetime. From what he sees of them he draws a number of general conclusions. They are ugly, each is designed for a very limited purpose, when required for a minutely different purpose they are useless, the variety of behaviour of any one of them is very small, etc., etc. Naturally he concludes that these are necessary properties of machines in general. … A few years ago, when very little had been heard of digital computers, it was possible to elicit much incredulity concerning them, if one mentioned their properties without describing their construction. That was presumably due to a similar application of the principle of scientific induction. These applications of the principle are of course largely unconscious. When a burnt child fears the fire and shows that he fears it by avoiding it, I should say that he was applying scientific induction. (I could of course also describe his behaviour in many other ways.) The works and customs of mankind do not seem to be very suitable material to which to apply scientific induction. A very large part of space-time most be investigated, if reliable results are to be obtained. Otherwise we may (as most English children do) decide that everybody speaks English, and that it is silly to learn French.

There are, however, special remarks to be made about many of the disabilities that have been mentioned. The inability to enjoy strawberries and cream may have struck the reader as frivolous. Possibly a machine might be made to enjoy this delicious dish, but any -attempt to make one do so would be idiotic. What is important about this disability is that it contributes to some of the other disabilities, e.g. to the difficulty of the same kind of friendliness occurring between man and machine as between white man and white man, or between black man and black man. (sic)

The claim that ” machines cannot make mistakes ” seems a curious one. … It seems to me that this criticism depends on a confusion between two kinds of mistake. We may call them ‘ errors of functioning ‘ and ‘ errors of conclusion’. … To take [an] example, it might have some method for drawing conclusions by scientific induction. We must expect such a method to lead occasionally to erroneous results.

The claim that a machine cannot be the subject of its own thought can of course only be answered if it can be shown that the machine has some thought with some subject matter. … It may be used to help in making up its own programmes, or to predict the effect of alterations in its own structure. By observing the results of its own behaviour it can modify its own programmes so as to achieve some purpose more effectively.

The criticism that a machine cannot have much diversity of behaviour is just a way of saying that it cannot have much storage capacity. …

(6) Lady Lovelace’s Objection. … Lady Lovelace … states, “The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform ” (her italics). This statement is quoted by Hartree (p. 70) who adds: “This does not imply that it may not be possible to construct electronic equipment which will ‘think for itself’, or in which, in biological terms, one could set up a conditioned reflex, which would serve as a basis for ‘learning’. Whether this is possible in principle or not is a stimulating and exciting question, suggested by some of these recent developments. But it did not seem that the machines constructed or projected at the time had this property “.

I am in thorough agreement with Hartree over this. It will be noticed that be does not assert that the machines in question had not got the property, but rather that the evidence available to Lady Lovelace did not encourage her to believe that they had it.

… A better variant of the objection says that a machine can never ‘ take us by surprise ‘. This statement is a more direct challenge and can be met directly. Machines take me by surprise with great frequency. This is largely because I do not do sufficient calculation to decide what to expect them to do, or rather because, although I do a calculation, I do it in a hurried, slipshod fashion, taking risks.

… It is a line of argument we must consider closed, but it is perhaps worth remarking that the appreciation of something as surprising requires as much of a ‘ creative mental act’ whether the surprising event originates from a man, a book, a machine or anything else.

The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences • of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily fo/gets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles.

(7) Argument from Continuity in the Nervous System. The nervous system is certainly not a discrete-state machine. A small error in the information about the size of a nervous impulse impinging on a neuron, may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behaviour of the nervous system with a discrete-state system. It is true that a discrete-state machine must be different from a continuous machine. But if we adhere to the conditions of the imitation game, the interrogator will not be able to take any advantage of this difference.

(8) The Argument from Informality of Behaviour. It is not possible to produce a set of rules purporting to describe what a man should do in every conceivable set of circumstances. One might for instance have a rule that one is to stop when one sees a red traffic light, and to go if one sees a green one, but what if by some fault both appear together ? One may perhaps decide that it is safest to stop. But some further difficulty may well arise from this decision later. To attempt to provide rules of conduct to cover every eventuality, even those arising from traffic lights, appears to be impossible. With all this I agree. …

[We] believe that it is not only true that being regulated by laws of behaviour implies being some sort of machine (though not necessarily a discrete-state machine), but that conversely being such a machine implies being regulated by such laws. However, we cannot so easily convince ourselves of the absence of complete laws of behaviour as of complete rules of conduct. The only way we know of for finding such laws is scientific observation, and we certainly know of no circumstances under which we could say, ‘ We have searched enough. There are no such laws’.

We can demonstrate more forcibly that any such statement would be unjustified. For suppose we could be sure of finding such laws if they existed. Then given a discrete-state machine it should certainly be possible to discover by observation sufficient about it to predict its future behaviour, and this within a reasonable time, say a thousand years. But this does not seem to be the case. I have set up on the Manchester computer a small programme using only 1000 units of storage, whereby the machine supplied with one sixteen figure number replies with another within two seconds. I would defy anyone to learn from these replies sufficient about the programme to be able to predict any replies to untried value.

(9) The Argument from Extra-Sensory Perception. I assume that the reader is familiar with the idea of extra-sensory perception, and the meaning of the four items of it, viz. telepathy, clairvoyance, precognition and psycho-kinesis. These disturbing phenomena seem to deny all our usual scientific ideas. How we should like to discredit them! Unfortunately the statistical evidence, at least for telepathy, is overwhelming. It is very difficult to rearrange one’s ideas so as to fit these new facts in. … The idea that our bodies move simply according to the known laws of physics, together with some others not yet discovered but somewhat similar, would be one of the first to go. This argument is to my mind quite a strong one. One can say in reply that many scientific theories seem to remain workable in practice, in spite of clashing with E.S.P.; that in fact one can get along very nicely if one forgets about it. This is rather cold comfort, and one fears that thinking is just the kind of phenomenon where E.S.P. may be especially, relevant.

… To put the competitors into a ‘ telepathy-proof room’ would satisfy all requirements.

7. Learning Machines.

The reader will have anticipated that I have no very convincing arguments of a positive nature to support my views. If I had I should not have taken such pains to point out the fallacies in contrary views. Such evidence as I have I shall now give.

… One could say that a man can ‘ inject’ an idea into the machine, and that it will respond to a certain extent and then drop into quiescence, like a piano string struck by a hammer. Another simile would be an atomic pile of less than critical size: an injected idea is to correspond to a neutron entering the pile from without. … The majority of them seem to be ‘ sub-critical’, i.e. to correspond in this analogy to piles of subcritical size. An idea presented to such a mind will on average give rise to less than one idea in reply. A smallish proportion are super-critical.’ An idea presented to such a mind may give rise to a whole ‘ theory’ consisting of secondary, tertiary and more remote ideas. … Adhering to this analogy we ask, ‘ Can a machine be made to be super-critical?

The ‘ skin of an onion ‘ analogy is also helpful. In considering the functions of the mind or the brain we find certain operations which we can explain in purely mechanical terms. This we say does not correspond to the real mind : it is a sort of skin which we must strip off if we are to find the real mind. But then in what remains we find a further skin to be stripped off, and so on Proceeding in this way do we ever come to the ‘ real’ mind, or do we eventually come to the akin which has nothing in it ? In the hitter case the whole mind is mechanical. (It would not be a discrete-state machine however. We have discussed this.) These last two paragraphs do not claim to be convincing arguments. They should rather be described as ‘ recitations tending to produce belief ‘

… Our problem then is to find out how to programme these machines to play the game. At my present rate of working I produce about a thousand digits of programme a day, so that about sixty workers, working steadily through the fifty years might accomplish the job, if nothing went into the waste-paper basket. Some more expeditious method seems desirable.

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s ? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child-brain is something like a note-book as one buys it from the stationers. Rather little mechanism, and lots of blank sheets. (Mechanism and writing are from our point of view almost synonymous.) Our hope is that there is so little mechanism in the child-brain that something like it can be easily programmed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child.

We cannot expect to find a good childmachine at the first attempt. One must experiment with teaching one such machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution, by the identifications

Structure of the child machine = Hereditary material
Changes     „       „          = Mutations
Natural selection              = Judgment of the experimenter

One may hope, however, that this process will be more expeditious than evolution. The survival of the fittest is a slow method for measuring advantages. The experimenter, by the exercise of intelligence, should be able to speed it up. Equally important is the fact that he is not restricted to random mutations. If he can trace a cause for some weakness he can probably think of the kind of mutation which will improve it.

… The machine has to be so constructed that events which shortly preceded the occurrence of a punishment-signal are unlikely to be repeated, whereas a reward-signal increased the probability of repetition of the events which led up to it.

… Roughly speaking, if the teacher has no other means of communicating to the pupil, the amount of information which can reach him does not exceed the total number of rewards and punishments applied. … It is necessary therefore to have some other ‘ unemotional’ channel of communication. If these are available it is possible to teach a machine by punishments and rewards to obey orders given in some language, e.g. a symbolic language. These orders are to be transmitted through the ‘ unemotional’ channels. The use of this language will diminish greatly the number of punishments and rewards require.

Opinions may vary as to the complexity which is suitable in the child machine. One might try to make it as simple as possible consistently with the general principles. Alternatively one might have a complete system of logical inference ‘ built in ‘.* In the latter case the store would be hugely occupied with definitions and propositions. The propositions would have various kinds of status, e.g. well-established facts, conjectures, mathematically proved theorems, statements given by an authority, expressions having the logical form of proposition but not beliefvalue. Certain propositions may be described as ‘ imperatives’. The machine should be so constructed that as soon as an imperative is classed as * well-established ‘ the appropriate action automatically takes place. … The processes of inference used by the machine need not be such as would satisfy the most exacting logicians. There might for instance be no hierarchy of types. But this need not mean that type fallacies will occur, any more than we are bound to fall over unfenced cliffs. Suitable imperatives (expressed within the systems, not forming part of the rules of the system) such as ‘ Do not use a class unless it is a subclass of one which has been mentioned by teacher ‘ can have a similar effect to ‘ Do not go too near the edge’.

The imperatives that can be obeyed by a machine that has no limbs are bound to be of a rather intellectual character, as in the example (doing homework) given above. Important amongst such imperatives will be ones which regulate the order in which the rules of the logical system concerned are to be applied. For at each stage when one is using a logical system, there is a very large number of alternative steps, any of which one is permitted to apply, so far as obedience to the rules of the logical system is concerned. These choices make the difference between a brilliant and a footling reasoner, not the difference between a sound and a fallacious one. Propositions leading to imperatives of this kind might be ” When Socrates is mentioned, use the syllogism in Barbara ” or ” If one method has been proved to be quicker than another, do not use the slower method “. Some of these may be ‘ given by authority ‘, but others may be produced by the machine itself, e.g. by scientific induction.

Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops.

It is probably wise to include a random element in a learning machine (see p. 438). A random element is rather useful when we are searching for a solution of some, problem.

[The] learning process may be regarded as a search for a form of behaviour which will satisfy the teacher (or some other criterion). Since there is probably a very large number of satisfactory solutions the random method seems to be better than the systematic. It should be noticed that it is used in the analogous process of evolution.

We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with ? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried. We can only see a short distance ahead, but we can see plenty there that needs to be done.

Turing’s Opinion

In the above, I have focussed on Turing’s informed comments and reasons. Others have focused on:

It will simplify matters for the reader if I explain first my own beliefs in the matter. Consider first the more accurate form of the question. I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent, chance of making the right identification after five minutes of questioning.

To translate, is something like the following: ‘By 2000, it will be possible to have bots implemented with 1GB RAM that ‘average’ people will not be able to ‘out’ as bots within 5 minutes.’

My Comments


It seems to me that the above has more or less been achieved, with the added constraint that one doesn’t overtly challenge the bot, in case they are human. It also seems to me that by some notions of ‘average’ Turing may well be correct even without this restriction, but it depends on your notion of ‘average’. Maybe a corollary of Turing’s view is: ‘It is unlikely, for the foreseeable future that a typical education will equip people to be able to distinguish between digital machines and humans.’ If this is correct, I’m not sure its a good thing!

What is ‘Intelligence’?

If we think in terms of I.Q., then one might think of giving the machines a 5 minute I.Q. test. If we set the threshold at much above 85 then we would reject too many humans. Similarly if we sought to test attainment much beyond typical ‘middle school’ (16ish). However we test it, the bar should not be too high.

Beyond this, Turing makes a number of interesting asides, reflective of attitudes at the time, that still seem of some relevance. My interpretation is as follows:

Firstly, that  one can’t tell if you are talking to someone with ‘free will’, at least under the conditions of his test. (I wouldn’t argue.)

That one can distinguish between crude ‘levels’ of intelligence as follows:

Level 0: Unable to follow clear instructions (In need of training!)
Level 1: Able to follow clear instructions (Like a computer.)
Level 2: Able to be taught (‘Intelligent’)
Level 3: More intelligent (‘SuperIntelligent?)

Turing seems to assume that most of the population can achieve Level 1 (‘footling reasoner’), with suitable ‘education’. I guess that’s right, but if not it doesn’t really detract from the main thrust of his argument.

Turing uses normal science as a benchmark for level 2, requiring an ability to make conjectures beyond what was possible at level and to proceed by ‘scientific induction’.  Thus, he seems to suppose that ‘average’ people will accept the display of normal scientific ability as evidence of Level 2. Hence, he seems to think university students ‘intelligent’. This seems reasonable. He uses the superlative ‘brilliant’ to describe a capability of reasoning that is beyond even most graduates, beyond level 2. Again, reasonable.

He also notes that (according to physicists of the time), physical entities, including humans, cannot be ‘digital’, and so digital computers cannot really be ‘like’ humans. All he is claiming that ‘average’ people will not be able to detect the difference under test conditions. This begs the interesting question as to whether anyone could tell the difference, whether the difference could be taught as a ‘definite method’ (level 1) or by example (level 2) or would require a higher level. But he doesn’t believe that ‘the average person’ will have learnt it by 2000. I guess he was right on this, too.


Turing uses ‘science’ as a kind of benchmark for intelligence. Conversely, we might use the above levels to distinguish between ‘levels’ of science. For example, a technician might operate at level 1, a ‘normal scientist’ at level 2, and a ‘brilliant scientist’ beyond. Doing this suggests that a ‘normal scientist’ might have  a ‘digital view’ of reality, which Turing regards as false, whereas a ‘brilliant scientist’ may have better ‘insight’, but may be unable to express it in a technical textbook or even mentor a level 2 intelligence enough to bring them up to level 3.


Mathematicians sometimes like to compare themselves with artists. This suggests the following: A craftsperson can operate at level 1, producing things that others with a level 1 art appreciation  will regard as ‘art’. An actual artist will be able to produce novel art, within the genres within which they have been ‘trained’ (formally or otherwise. A ‘brilliant’ artist can develop a whole new genre, and the most ‘arty’ artefacts are those produced during the journey, which perhaps will only really be appreciated by other level 3 intelligences.


Mathematics, science and the arts all have different ‘schools’ or ‘cultures’. Just as Turing urges us to be careful about the concept of ‘intelligence’, so we ought to be careful about ‘culture’. In information theory terms culture provides the context. Level 1 can operate within a context, level 2 can adapt to a context and level 3 can adapt the context (aka ‘culture’).

In practice, tests of intelligence are relative to some culture, which it is unreasonable to expect a subject to pick up in 5 minutes. Thus the key concept is not ‘does this subject pass my test now?’ but ‘is this subject capable of learning to pass my test?’


A major oversight of this paper is that it neglects the role of the media between the interrogator and subject. Mathematicians tend to share common mathematical languages, with relatively straightforward variations. In applying the Turing test people have struggled to establish a ‘natural language’ medium. This is an area that might benefit from further insight: perhaps that provided by some of Turing’s other work. But for now we might simply require that the subject is able to learn a language and communicate, not that it has already learned enough languages to be able to communicate with most people.


I feel confident that the above would have been in Turing’s mind as he struggled to produce an artefact readable at level 1 but suggestive at level 3. But it seems to me that the following is ‘strongly suggested at’ by the paper without my having much citeable evidence for it:

Intelligence beyond the normal level 2 can be further subdivided: can one demonstrate an ability beyond level 2, can one mentor others to a point where they can too, and – importantly – can one produce an artefact (such as Turing’s paper, or a great work of art) that will catalyse or encourage people currently operating at level 2 to make the transition to level 3? And finally, could one sneak such a paper past footslogging level 2 ‘interrogators’ (or ‘peer’ reviewers)?

It seems to me that Turing amply demonstrated these levels of intelligence, except possibly the final two. More work to be done indeed!


Dave Marsay

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