Vol. lix. No. 236.]
[October, 1950
MIND
A QUARTERLY REVIEW
OF
PSYCHOLOGY AND PHILOSOPHY
I.—COMPUTING MACHINERY
AND INTELLIGENCE
By A. M. Turing
1. The Imitation Game.
I propose to consider the question, ‘Can machines think?’
This should begin with definitions of the meaning of the terms
‘machine’ and ‘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. If the meaning of the words
‘machine’ and ‘think’ are to be found by examining how they
are commonly used it is difficult to escape the conclusion
that the meaning and the answer to the question, ‘Can ma-
chines think?’ is to be sought in a statistical survey such as
28
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COMPUTING MACHINERY AND INTELLIGENCE 2
a Gallup poll. But this is absurd. Instead of attempting such
a definition I shall replace the question by another, which is
closely related to it and is expressed in relatively unambigu-
ous words.
The new form of the problem can be described in terms of
a game which we call the ‘imitation game’. It is played with
three people, a man (A), a woman (B), and an interrogator
(C) who may be of either sex. The interrogator stays in a
room apart from the other two. 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 communicat-
ing between the two rooms. Alternatively the question and
answers can be repeated by an intermediary. 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.
She can add such things as “I am the woman, don’t listen to
him!” to her answers, but it will avail nothing as the man
can make similar remarks.
We now ask the question, ‘What will happen when a ma-
chine 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

COMPUTING MACHINERY AND INTELLIGENCE 3
woman? These questions replace our original, ‘Can machines
think?’
2. Critique of the New Problem.
As well as asking, ‘What is the answer to this new form of
the question,‘ one may ask, ‘Is this new question a worthy one
to investigate?’ This latter question we investigate without
further ado, thereby cutting short an infinite regress.
The new problem has the advantage of drawing a fairly
sharp line between the physical and the intellectual capac-
ities of a man. No engineer or chemist claims to be able to
produce a material which is indistinguishable from the hu-
man skin. It is possible that at some time this might be done,
but even supposing this invention available we should feel
there was little point in trying to make a ‘thinking machine’
more human by dressing it up in such artificial flesh. The
form in which we have set the problem reflects this fact in
the condition which prevents the interrogator from seeing or
touching the other competitors, or hearing their voices. Some
other advantages of the proposed criterion may be shown up
by specimen questions and answers. Thus:
Q: Please write me a sonnet on the subject of the Forth Bridge.
A: Count me out on this one. I never could write poetry.
Q: Add 34957 to 70764
A: (Pause about 30 seconds and then give as answer) 105621.
Q: Do you play chess ?
A: Yes.
Q: I have K at my K1, and no other pieces. You have only K
at K6 and R at R1. It is your move. What do you play ?
A: (After a pause of 15 seconds) R-R8 mate.
The question and answer method seems to be suitable for
introducing almost any one of the fields of human endeav-
our that we wish to include. We do not wish to penalise the
machine for its inability to shine in beauty competitions, norCOMPUTING MACHINERY AND INTELLIGENCE 4
to penalise a man for losing in a race against an aeroplane.
The conditions of our game make these disabilities irrele-
vant. The ’witnesses’ can brag, if they consider it advisable,
as much as they please about their charms, strength or hero-
ism, but the interrogator cannot demand practical demon-
strations.
The game may perhaps be criticised on the ground that
the odds are weighted too heavily against the machine. If
the man were to try and pretend to be the machine he would
clearly make a very poor showing. He would be given away at
once by slowness and inaccuracy in arithmetic. May not ma-
chines carry out some-thing which ought to be described as
thinking but which is very different from what a man does?
This objection is a very strong one, but at least we can say
that if, nevertheless, a machine can be constructed to play
the imitation game satisfactorily, we need not be troubled by
this objection.
It might be urged that when playing the ’imitation game’
the best strategy for the machine may possibly be something
other than imitation of the behaviour of a man. This may be,
but I think it is unlikely that there is any great effect of this
kind. In any case there is no intention to investigate here
the theory of the game, and it will be assumed that the best
strategy is to try to provide answers that would naturally be
given by a man.
3. The Machines concerned in the Game.
The question which we put in §1 will not be quite definite
until we have specified what we mean by the word ‘machine’.
It is natural that we should wish to permit every kind of engi-
neering 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 construc-
tors because they have applied a method which is largely ex-
perimental. Finally, we wish to exclude from the machinesCOMPUTING MACHINERY AND INTELLIGENCE 5
men born in the usual manner. It is difficult to frame the
definitions so as to satisfy these three conditions. One might
for instance insist that the team of engineers should be all
of one sex, but this would not really be satisfactory, for it is
probably possible to rear a complete individual from a sin-
gle cell of the skin (say) of a man. To do so would be a feat
of biological technique deserving of the very highest praise,
but we would not be inclined to regard it as a case of ‘con-
structing a thinking machine’. This prompts us to abandon
the requirement that every kind of technique should be per-
mitted. 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.
This restriction appears at first sight to be a very drastic
one. I shall attempt to show that it is not so in reality. To do
this necessitates a short account of the nature and properties
of these computers.
It may also 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.
There are already a number of digital computers in work-
ing order, and it may be asked, ‘Why not try the experiment
straight away? It would be easy to satisfy the conditions of
the game. A number of interrogators could be used, and
statistics compiled to show how often the right identifica-
tion was given.’ The short answer is that we are not asking
whether all digital computers would do well in the game nor
whether the computers at present available would do well,
but whether there are imaginable computers which would
do well. But this is only the short answer. We shall see this
question in a different light later.

COMPUTING MACHINERY AND INTELLIGENCE 6
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 al-
tered whenever he is put on to a new job. He has also an
unlimited supply of paper on which he does his calculations.
He may also do his multiplications and additions on a ‘desk
machine’, but this is not important. If we use the above ex-
planation as a definition we shall be in danger of circularity
of argument. We avoid this by giving an outline of the means
by which the desired effect is achieved. A digital computer
can usually be regarded as consisting of three parts:
(i) Store.
(ii) Executive unit.
(iii) Control.
The store is a store of information, and corresponds to the
human computer’s paper, whether this is the paper on which
he does his calculations or that on which his book of rules is
printed. In so far as the human computer does calculations
in his head a part of the store will correspond to his memory.
The executive unit is the part which carries out the various
individual operations involved in a calculation. What these
individual operations are will vary from machine to machine.
Usually fairly lengthy operations can be done such as ‘Mul-
tiply 3540675445 by 7076345687’ but in some machines only
very simple ones such as ‘Write down 0’ are possible. We have
mentioned that the ‘book of rules’ supplied to the computer
is replaced in the machine by a part of the store. It is then
called the ‘table of instructions’. It is the duty of the control
to see that these instructions are obeyed correctly and in the
right order. The control is so constructed that this necessar-

COMPUTING MACHINERY AND INTELLIGENCE 7
ily happens. The information in the store is usually broken
up into packets of moderately small size. In one machine, for
instance, a packet might consist of ten decimal digits. Num-
bers are assigned to the parts of the store in which the var-
ious packets of information are stored, in some systematic
manner. A typical instruction might say- “Add the number
stored in position 6809 to that in 4302 and put the result
back into the latter storage position.”
Needless to say it would not occur in the machine ex-
pressed in English. It would more likely be coded in a form
such as 6809430217. Here 17 says which of various possi-
ble operations is to be performed on the two numbers. In
this case the operation is that described above, viz. “Add the
number....” It will be noticed that the instruction takes up
10 digits and so forms one packet of information, very conve-
niently. The control will normally take the instructions to be
obeyed in the order of the positions in which they are stored,
but occasionally an instruction such as
“Now obey the instruction stored in position 5606, and
continue from there.”
may be encountered, or again “If position 4505 contains 0
obey next the instruction stored in 6707, otherwise continue
straight on.”
Instructions of these latter types are very important be-
cause they make it possible for a sequence of operations to
be repeated over and over again until some condition is ful-
filled, but in doing so to obey, not fresh instructions on each
repetition, but the same ones over and over again. To take
a domestic analogy. Suppose Mother wants Tommy to call
at the cobbler’s every morning on his way to school to see if
her shoes are done, she can ask him afresh every morning.
Alternatively she can stick up a notice once and for all in the
hall which he will see when he leaves for school and whichCOMPUTING MACHINERY AND INTELLIGENCE 8
tells him to call for the shoes, and also to destroy the notice
when he comes back if he has the shoes with him.
The reader must accept it as a fact that digital computers
can be constructed, and indeed have been constructed, ac-
cording to the principles we have described, and that they can
in fact mimic the actions of a human computer very closely.
The book of rules which we have described our human
computer as using is of course a convenient fiction. Actual
human computers really remember what they have got to do.
If one wants to make a machine mimic the behaviour of the
human computer in some complex operation one has to ask
him how it is done, and then translate the answer into the
form of an instruction table. Constructing instruction tables
is usually described as ‘programming’. To ‘programme a ma-
chine to carry out the operation A’ means to put the appro-
priate instruction table into the machine so that it will do
A.
An interesting variant on the idea of a digital computer
is a ‘digital computer with a random element’. These have
instructions involving the throwing of a die or some equiv-
alent electronic process; one such instruction might for in-
stance be, “Throw the die and put the resulting number into
store 1000”.‘ Sometimes such a machine is described as hav-
ing 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, for a similar effect can be
produced by such devices as making the choices depend on
the digits of the decimal for π.
Most actual digital computers have only a finite store.
There is no theoretical difficulty in the idea of a computer
with an unlimited store. Of course only a finite part can have
been used at any one time. Likewise only a finite amount can
have been constructed, but we can imagine more and more
being added as required. Such computers have special theo-
retical interest and will be called infinitive capacity comput-
ers.

COMPUTING MACHINERY AND INTELLIGENCE 9
The idea of a digital computer is an old one. Charles Bab-
bage, Lucasian Professor of Mathematics at Cambridge from
1828 to 1839, planned such a machine, called the Analytical
Engine, but it was never completed. Although Babbage had
all the essential ideas, his machine was not at that time such
a very attractive prospect. The speed which would have been
available would be definitely faster than a human computer
but something like 100 times slower than the Manchester
machine, itself one of the slower of the modern machines.
The storage was to be purely mechanical, using wheels and
cards.
The fact that Babbage’s Analytical Engine was to be en-
tirely mechanical will help us to rid ourselves of a supersti-
tion. Importance is often attached to the fact that modern
digital computers are electrical, and that the nervous sys-
tem also is electrical. Since Babbage’s machine was not elec-
trical, and since all digital computers are in a sense equiva-
lent, we see that this use of electricity cannot be of theoreti-
cal importance. Of course electricity usually comes in where
fast signalling is concerned, so that it is not surprising that
we find it in both these connections. In the nervous system
chemical phenomena are at least as important as electrical.
In certain computers the storage system is mainly acoustic.
The feature of using electricity is thus seen to be only a very
superficial similarity. If we wish to find such similarities we
should look rather for mathematical analogies of function.
5. Universality of Digital Computers.
The digital computers considered in the last section may
be classified amongst the ‘discrete state machines’. These
are the machines which move by sudden jumps or clicks from
one quite definite state to another. These states are suffi-
ciently different for the possibility of confusion between them
to be ignored. Strictly speaking there are no such machines.
Everything really moves continuously. But there are many
kinds of machine which can profitably be thought of as be-

COMPUTING MACHINERY AND INTELLIGENCE 10
ing discrete state machines. For instance in considering the
switches for a lighting system it is a convenient fiction that
each switch must be definitely on or definitely off. There
must be intermediate positions, but for most purposes we
can forget about them. As an example of a discrete state ma-
chine we might consider a wheel which clicks round through
120° once a second, but may be stopped by a lever which can
be operated from outside; in addition a lamp is to light in
one of the positions of the wheel. This machine could be de-
scribed abstractly as follows. The internal state of the ma-
chine (which is described by the position of the wheel) may
be q1 , q2 or q3 . There is an input signal i0 , or i1 (position of
lever). The internal state at any moment is determined by
the last state and input signal according to the table
Input
i0
i1
Last State
q1 q2 q3
q2 q3 q1
q1 q2 q3
The output signals, the only externally visible indication
of the internal state (the light) are described by the table
State
Output
q1
o0
q2
o0
q3
o1
This example is typical of discrete state machines. They
can be described by such tables provided they have only a
finite number of possible states.
It will seem that given the initial state of the machine
and the input signals it is always possible to predict all fu-
ture states. This is reminiscent of Laplace’s view that from
the complete state of the universe at one moment of time, as
described by the positions and velocities of all particles, it
should be possible to predict all future states. The predic-
tion which we are considering is, however, rather nearer to

COMPUTING MACHINERY AND INTELLIGENCE 11
practicability than that considered by Laplace. 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 bil-
lionth of a centimetre at one moment might make the dif-
ference between a man being killed by an avalanche a year
later, or escaping. It is an essential property of the mechan-
ical systems which we have called ‘discrete state machines”
that this phenomenon does not occur. Even when we con-
sider 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. For instance, the number for the machine now working
at Manchester is about 2165,000 , i.e. about 1050,000 . Compare
this with our example of the clicking wheel described above,
which had three states. It is not difficult to see why the num-
ber of states should be so immense. The computer includes
a store corresponding to the paper used by a human com-
puter. It must be possible to write into the store any one of
the combinations of symbols which might have been written
on the paper. For simplicity suppose that only digits from
0 to 9 are used as symbols. Variations in handwriting are
ignored. Suppose the computer is allowed 100 sheets of pa-
per each containing 50 lines each with room for 30 digits.
Then the number of states is 10100×50×30 , i.e. 10150,000 . This
is about the number of states of three Manchester machines
put together. The logarithm to the base two of the number
of states is usually called the ‘storage capacity’ of the ma-
chine. Thus the Manchester machine has a storage capacity
of about 165,000 and the wheel machine of our example about
1.6. If two machines are put together their capacities must be
added to obtain the capacity of the resultant machine. This

COMPUTING MACHINERY AND INTELLIGENCE 12
leads to the possibility of statements such as “The Manch-
ester machine contains 64 magnetic tracks each with a ca-
pacity of 2560, eight electronic tubes with a capacity of 1280.
Miscellaneous storage amounts to about 300 making a total
of 174,380.”
Given the table corresponding to a discrete state machine
it is possible to predict what it will do. There is no reason why
this calculation should not be carried out by means of a dig-
ital computer. Provided it could be carried out sufficiently
quickly the digital computer could mimic the behaviour of
any discrete state machine. The imitation game could then
be played with the machine in question (as B) and the mim-
icking digital computer (as A) and the interrogator would be
unable to distinguish them. Of course the digital computer
must have an adequate storage capacity as well as working
sufficiently fast. Moreover, it must be programmed afresh for
each new machine which it is desired to mimic.
This special property of digital computers, that they can
mimic any discrete state machine, is described by saying that
they are universal machines. The existence of machines with
this property has the important consequence that, considera-
tions of speed apart, it is unnecessary to design various new
machines to do various computing processes. They can all
be done with one digital computer, suitably programmed for
each case. It will be seen that as a consequence of this all
digital computers are in a sense equivalent.
We may now consider again the point raised at the end
of §3. It was suggested tentatively that the question, ‘Can
machines think?’ should be replaced by ‘Are there imagin-
able digital computers which would do well in the imitation
game?’ If we wish we can make this superficially more gen-
eral and ask ‘Are there discrete state machines which would
do well?’ But in view of the universality property we see
that either of these questions 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

COMPUTING MACHINERY AND INTELLIGENCE 13
storage, suitably increasing its speed of action, and provid-
ing 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.
We may now consider the ground to have been cleared and
we are ready to proceed to the debate on our question, ‘Can
machines think?’ and the variant of it quoted at the end of
the last section. We cannot altogether abandon the original
form of the problem, for opinions will differ as to the appro-
priateness of the substitution and we must at least listen to
what has to be said in this connexion.
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. The original question, ‘Can ma-
chines think?’ I believe to be too meaningless to deserve dis-
cussion. Nevertheless I believe that at the end of the cen-
tury the use of words and general educated opinion will have
altered so much that one will be able to speak of machines
thinking without expecting to be contradicted. I believe fur-
ther that no useful purpose is served by concealing these
beliefs. The popular view that scientists proceed inexorably
from well-established fact to well-established fact, never be-
ing 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.
I now proceed to consider opinions opposed to my own.
(1) The Theological Objection. Thinking is a function of man’s

COMPUTING MACHINERY AND INTELLIGENCE 14
immortal soul.1 God has given an immortal soul to every
man and woman, but not to any other animal or to machines.
Hence no animal or machine can think.
I am unable to accept any part of this, but will attempt to
reply in theological terms. I should find the argument more
convincing if animals were classed with men, for there is a
greater difference, to my mind, between the typical animate
and the inanimate than there is between man and the other
animals. The arbitrary character of the orthodox view be-
comes clearer if we consider how it might appear to a mem-
ber of some other religious community. How do Christians
regard the Moslem view that women have no souls? But let
us leave this point aside and return to the main argument. It
appears to me that the argument quoted above implies a se-
rious restriction of the omnipotence of the Almighty. It is ad-
mitted that there are certain things that He cannot do such
as making one equal to two, but should we not believe that He
has freedom to confer a soul on an elephant if He sees fit? We
might expect that He would only exercise this power in con-
junction with a mutation which provided the elephant with
an appropriately improved brain to minister to the needs of
this soul. An argument of exactly similar form may be made
for the case of machines. It may seem different because it is
more difficult to “swallow”. But this really only means that
we think it would be less likely that He would consider the
circumstances suitable for conferring a soul. The circum-
stances in question are discussed in the rest of this paper.
In attempting to construct such machines we should not be
irreverently usurping His power of creating souls, any more
than we are in the procreation of children: rather we are, in
either case, instruments of His will providing mansions for
the souls that He creates.
However, this is mere speculation. I am not very impressed
with theological arguments whatever they may be used to
1
Possibly this view is heretical. St. Thomas Aquinas (Summa Theologica, quoted by Bertrand Russell, p. 480) states
that God cannot make a man to have no soul. But this may not be a real restriction on His powers, but only a result of
the fact that men’s souls are immortal, and therefore indestructible.

COMPUTING MACHINERY AND INTELLIGENCE 15
support. Such arguments have often been found unsatisfac-
tory in the past. In the time of Galileo it was argued that the
texts, “And the sun stood still. . . and hasted not to go down
about a whole day” (Joshua x. 13) and “He laid the founda-
tions of the earth, that it should not move at any time” (Psalm
cv. 5) were an adequate refutation of the Copernican theory.
With our present knowledge such an argument appears fu-
tile. When that knowledge was not available it made a quite
different impression.
(2) The ‘Heads in the Sand’ Objection. “The consequences of
machines thinking would be too dreadful. Let us hope and
believe that they cannot do so.”
This argument is seldom expressed quite so openly as in
the form above. But it affects most of us who think about
it at all. We like to believe that Man is in some subtle way
superior to the rest of creation. It is best if he can be shown
to be necessarily superior, for then there is no danger of him
losing his commanding position. The popularity of the the-
ological argument is clearly connected with this feeling. It
is likely to be quite strong in intellectual people, since they
value the power of thinking more highly than others, and are
more inclined to base their belief in the superiority of Man
on this power.
I do not think that this argument is sufficiently substan-
tial to require refutation. Consolation would be more appro-
priate: perhaps this should be sought in the transmigration
of souls.
(3) The Mathematical Objection. There are a number of re-
sults of mathematical logic which can be used to show that
there are limitations to the powers of discrete-state machines.
The best known of these results is known as Gödel’s the-
orem,2 and shows that in any sufficiently powerful logical
system statements can be formulated which can neither be
proved nor disproved within the system, unless possibly the
2 Author’s names in italics refer to the Bibliography.

COMPUTING MACHINERY AND INTELLIGENCE 16
system itself is inconsistent. There are other, in some re-
spects similar, results due to Church, Kleene, Rosser, and
Turing. The latter result is the most convenient to consider,
since it refers directly to machines, whereas the others can
only be used in a comparatively indirect argument: for in-
stance if Gödel’s theorem is to be used we need in addition
to have some means of describing logical systems in terms
of machines, and machines in terms of logical systems. The
result in question refers to a type of machine which is essen-
tially a digital computer with an infinite capacity. It states
that there are certain things that such a machine cannot do.
If it is rigged up to give answers to questions as in the im-
itation game, there will be some questions to which it will
either give a wrong answer, or fail to give an answer at all
however much time is allowed for a reply. There may, of
course, be many such questions, and questions which cannot
be answered by one machine may be satisfactorily answered
by another. We are of course supposing for the present that
the questions are of the kind to which an answer ‘Yes’ or ‘No’
is appropriate, rather than questions such as ‘What do you
think of Picasso?’ The questions that we know the machines
must fail on are of this type, “Consider the machine speci-
fied as follows. . . . Will this machine ever answer ‘Yes’ to any
question?” The dots are to be replaced by a description of
some machine in a standard form, which could be something
like that used in §5. When the machine described bears a
certain comparatively simple relation to the machine which
is under interrogation, it can be shown that the answer is
either wrong or not forthcoming. 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 intel-
lect. But I do not think this view can be dismissed quite so

COMPUTING MACHINERY AND INTELLIGENCE 17
lightly. Whenever one of these machines is asked the appro-
priate 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 ques-
tions 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 rela-
tion to the one machine over which we have scored our petty
triumph. There would be no question of triumphing simul-
taneously 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.
Those who hold to the mathematical argument would, I
think, mostly be willing to accept the imitation game as a
basis for discussion. Those who believe in the two previous
objections would probably not be interested in any criteria.
(4) The Argument from Consciousness. This argument is very
well expressed in Professor Jefferson’s Lister Oration for 1949,
from which I quote. “Not until a machine can write a sonnet
or compose a concerto because of thoughts and emotions felt,
and not by the chance fall of symbols, could we agree that ma-
chine equals brain—that is, not only write it but know that
it had written it. No mechanism could feel (and not merely
artificially signal, an easy contrivance) pleasure at its suc-
cesses, grief when its valves fuse, be warmed by flattery, be
made miserable by its mistakes, be charmed by sex, be angry
or depressed when it cannot get what it wants.”
This argument appears to be a denial of the validity of
our test. According to the most extreme form of this view
the only way by which one could be sure that machine thinks
is to be the machine and to feel oneself thinking. One could
then describe these feelings to the world, but of course no one
would be justified in taking any notice. Likewise according to

COMPUTING MACHINERY AND INTELLIGENCE 18
this view the only way to know that a man thinks is to be that
particular man. It is in fact the solipsist point of view. It may
be the most logical view to hold but it makes communication
of ideas difficult. A is liable to believe ‘A thinks but B does
not’ whilst B believes ‘B thinks but A does not’. Instead of
arguing continually over this point it is usual to have the
polite convention that everyone thinks.
I am sure that Professor Jefferson does not wish to adopt
the extreme and solipsist point of view. Probably he would
be quite willing to accept the imitation game as a test. The
game (with the player B omitted) is frequently used in prac-
tice under the name of viva voce to discover whether some one
really understands something or has ‘learnt it parrot fash-
ion’. Let us listen in to a part of such a viva voce:
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 scan.
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.
Interrogator: Would you say Mr. Pickwick reminded you of
Christmas?
Witness: In a way.
Interrogator: Yet Christmas is a winter’s day, and I do not
think Mr. Pickwick would mind the comparison.
Witness: I don’t think you’re serious. By a winter’s day one
means a typical winter’s day, rather than a special one like
Christmas.
And so on. What would Professor Jefferson say if the
sonnet-writing machine was able to answer like this in the
viva voce? I do not know whether he would regard the ma-
chine as ‘merely artificially signalling’ these answers, but

COMPUTING MACHINERY AND INTELLIGENCE 19
if the answers were as satisfactory and sustained as in the
above passage I do not think he would describe it as ‘an easy
contrivance’. This phrase is, I think, intended to cover such
devices as the inclusion in the machine of a record of some-
one reading a sonnet, with appropriate switching to turn it
on from time to time.
In short then, I think that most of those who support the
argument from consciousness could be persuaded to abandon
it rather than be forced into the solipsist position. They will
then probably be willing to accept our test.
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 lo-
calise 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. These arguments
take the form, “I grant you that you can make machines do
all the things you have mentioned but you will never be able
to make one to do X”. Numerous features X are suggested in
this connexion. I offer a selection:
Be kind, resourceful, beautiful, friendly (p. 448),
have initiative, have a sense of humour, tell right
from wrong, make mistakes (p. 448), fall in love,
enjoy strawberries and cream (p. 448), make some
one fall in love with it, learn from experience (pp. 456
f.), use words properly, be the subject of its own
thought (p. 449), have as much diversity of be-
haviour as a man, do something really new (p. 450).
(Some of these disabilities are given special con-
sideration as indicated by the page numbers.)
No support is usually offered for these statements. I be-
lieve they are mostly founded on the principle of scientific
induction. A man has seen thousands of machines in his

COMPUTING MACHINERY AND INTELLIGENCE 20
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 differ-
ent 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.
Many of these limitations are associated with the very small
storage capacity of most machines. (I am assuming that the
idea of storage capacity is extended in some way to cover ma-
chines other than discrete-state machines. The exact defini-
tion does not matter as no mathematical accuracy is claimed
in the present discussion.) 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 must 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 in-
ability 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

COMPUTING MACHINERY AND INTELLIGENCE 21
between black man and black man.
The claim that “machines cannot make mistakes” seems a
curious one. One is tempted to retort, “Are they any the worse
for that?” But let us adopt a more sympathetic attitude, and
try to see what is really meant. I think this criticism can be
explained in terms of the imitation game. It is claimed that
the interrogator could distinguish the machine from the man
simply by setting them a number of problems in arithmetic.
The machine would be unmasked because of its deadly accu-
racy. The reply to this is simple. The machine (programmed
for playing the game) would not attempt to give the right an-
swers to the arithmetic problems. It would deliberately in-
troduce mistakes in a manner calculated to confuse the in-
terrogator. A mechanical fault would probably show itself
through an unsuitable decision as to what sort of a mistake to
make in the arithmetic. Even this interpretation of the criti-
cism is not sufficiently sympathetic. But we cannot afford the
space to go into it much further. It seems to me that this crit-
icism depends on a confusion between two kinds of mistake.
We may call them ‘errors of functioning’ and ‘errors of con-
clusion’. Errors of functioning are due to some mechanical
or electrical fault which causes the machine to behave other-
wise than it was designed to do. In philosophical discussions
one likes to ignore the possibility of such errors; one is there-
fore discussing ‘abstract machines’. These abstract machines
are mathematical fictions rather than physical objects. By
definition they are incapable of errors of functioning. In this
sense we can truly say that ‘machines can never make mis-
takes’. Errors of con-clusion can only arise when some mean-
ing is attached to the output signals from the machine. The
machine might, for instance, type out mathematical equa-
tions, or sentences in English. When a false proposition is
typed we say that the machine has committed an error of
conclusion. There is clearly no reason at all for saying that
a machine cannot make this kind of mistake. It might do
nothing but type out repeatedly ‘0=1’. To take a less perverse

COMPUTING MACHINERY AND INTELLIGENCE 22
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.
Nevertheless, ‘the subject matter of a machine’s operations’
does seem to mean something, at least to the people who deal
with it. If, for instance, the machine was trying to find a solu-
tion of the equation x2 −40x−11 = 0 one would be tempted to
de-scribe this equation as part of the machine’s subject mat-
ter at that moment. In this sort of sense a machine undoubt-
edly can be its own 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. These are possibili-
ties of the near future, rather than Utopian dreams.
The criticism that a machine cannot have much diversity
of behaviour is just a way of saying that it cannot have much
storage capacity. Until fairly recently a storage capacity of
even a thousand digits was very rare.
The criticisms that we are considering here are often dis-
guised forms of the argument from consciousness. Usually
if one maintains that a machine can do one of these things,
and describes the kind of method that the machine could use,
one will not make much of an impression. It is thought that
the method (whatever it may be, for it must be mechanical)
is really rather base. Compare the parenthesis in Jefferson’s
statement quoted on p. 21.
(6) Lady Lovelace’s Objection. Our most detailed information
of Babbage’s Analytical Engine comes from a memoir by Lady
Lovelace (1842). In it she 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

COMPUTING MACHINERY AND INTELLIGENCE 23
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 he does not assert that the machines in ques-
tion had not got the property, but rather that the evidence
available to Lady Lovelace did not encourage her to believe
that they had it. It is quite possible that the machines in
question had in a sense got this property. For suppose that
some discrete-state machine has the property. The Analyt-
ical Engine was a universal digital computer, so that, if its
storage capacity and speed were adequate, it could by suit-
able programming be made to mimic the machine in ques-
tion. Probably this argument did not occur to the Countess
or to Babbage. In any case there was no obligation on them
to claim all that could be claimed.
This whole question will be considered again under the
heading of learning machines.
A variant of Lady Lovelace’s objection states that a ma-
chine can ‘never do anything really new’. This may be par-
ried for a moment with the saw, ‘There is nothing new under
the sun’. Who can be certain that ‘original work’ that he has
done was not simply the growth of the seed planted in him by
teaching, or the effect of following well-known general prin-
ciples. A better variant of the objection says that a machine
can never ‘take us by surprise’. This statement is a more di-
rect 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

COMPUTING MACHINERY AND INTELLIGENCE 24
a hurried, slipshod fashion, taking risks. Perhaps I say to
myself, ‘I suppose the voltage here ought to be the same as
there: anyway let’s assume it is’.
Naturally I am often wrong, and the result is a surprise
for me for by the time the experiment is done these assump-
tions have been forgotten. These admissions lay me open to
lectures on the subject of my vicious ways, but do not throw
any doubt on my credibility when I testify to the surprises I
experience.
I do not expect this reply to silence my critic. He will prob-
ably say that such surprises are due to some creative mental
act on my part, and reflect no credit on the machine. This
leads us back to the argument from consciousness, and far
from the idea of surprise. It is a line of argument we must
consider closed, but it is perhaps worth remarking that the
appreciation of some-thing as surprising requires as much
of a ‘creative mental act’ whether the surprising event origi-
nates 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 math-
ematicians 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 forgets 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 im-
pulse 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’.

COMPUTING MACHINERY AND INTELLIGENCE 25
It is true that a ‘discrete-state machine’ must be different
from a ‘continuous machine’. But if we adhere to the con-
ditions of the imitation game, the interrogator will not be
able to take any advantage of this difference. The situation
can be made clearer if we consider some other simpler ‘con-
tinuous machine’. A ‘differential analyser’ will do very well.
(A ‘differential analyser’ is a certain kind of machine not of
the ‘discrete-state type’ used for some kinds of calculation.)
Some of these provide their answers in a typed form, and
so are suitable for taking part in the game. It would not be
possible for a digital computer to predict exactly what an-
swers the differential analyser would give to a problem, but
it would be quite capable of giving the right sort of answer.
For instance, if asked to give the value of π (actually about
3.1416) it would be reasonable to choose at random between
the values 3.12, 3.13, 3.14, 3.15, 3.16 with the probabilities of
0.05, 0.15, 0.55, 0.19, 0.06 (say). Under these circumstances
it would be very difficult for the interrogator to distinguish
the differential analyser from the digital computer.
(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.
From this it is argued that we cannot be machines. I shall
try to reproduce the argument, but I fear I shall hardly do it
justice. It seems to run something like this. ‘If each man had
a definite set of ‘rules of conduct’ by which he regulated his
life he would be no better than a machine. But there are no

COMPUTING MACHINERY AND INTELLIGENCE 26
such rules, so men cannot be machines.’ The undistributed
middle is glaring. I do not think the argument is ever put
quite like this, but I believe this is the argument used never-
theless. There may however be a certain confusion between
‘rules of conduct’ and ‘laws of behaviour’ to cloud the issue.
By ‘rules of conduct’ I mean precepts such as ‘Stop if you see
red lights’, on which one can act, and of which one can be con-
scious. By ‘laws of behaviour’ I mean laws of nature as ap-
plied to a man’s body such as ‘if you pinch him he will squeak’.
If we substitute ‘laws of behaviour which regulate his life’ for
‘laws of conduct by which he regulates his life’ in the argu-
ment quoted the undistributed middle is no longer insuper-
able. For we believe that it is not only true that being regu-
lated 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 state-
ment 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 obser-
vation 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 1,000 units of stor-
age, 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 val-
ues.

COMPUTING MACHINERY AND INTELLIGENCE 27
(9) The Argument from Extra-Sensory Perception. I assume
that the reader is familiar with the idea of extra-sensory per-
ception, and the meaning of the four items of it, viz., telepa-
thy, 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 overwhelm-
ing. It is very difficult to rearrange one’s ideas so as to fit
these new facts in. Once one has accepted them it does not
seem a very big step to believe in ghosts and bogies. 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.
A more specific argument based on E.S.P. might run as
follows: “Let us play the imitation game, using as witnesses
a man who is good as a telepathic receiver, and a digital com-
puter. The interrogator can ask such questions as ‘What suit
does the card in my right hand belong to?’ The man by telepa-
thy or clairvoyance gives the right answer 130 times out of
400 cards. The machine can only guess at random, and per-
haps gets 104 right, so the interrogator makes the right iden-
tification.” There is an interesting possibility which opens
here. Suppose the digital computer contains a random num-
ber generator. Then it will be natural to use this to decide
what answer to give. But then the random number generator
will be subject to the psycho-kinetic powers of the interroga-
tor. Perhaps this psycho-kinesis might cause the machine to
guess right more often than would be expected on a probabil-
ity calculation, so that the interrogator might still be unable
to make the right identification. On the other hand, he might

COMPUTING MACHINERY AND INTELLIGENCE 28
be able to guess right without any questioning, by clairvoy-
ance. With E.S.P. anything may happen.
If telepathy is admitted it will be necessary to tighten our
test up. The situation could be regarded as analogous to that
which would occur if the interrogator were talking to himself
and one of the competitors was listening with his ear to the
wall. 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 con-
vincing 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.
Let us return for a moment to Lady Lovelace’s objection,
which stated that the machine can only do what we tell it to
do. One could say that a man can ‘inject’ an idea into the ma-
chine, 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. Each such neutron will cause a certain
disturbance which eventually dies away. If, however, the size
of the pile is sufficiently increased, the disturbance caused
by such an incoming neutron will very likely go on and on
increasing until the whole pile is destroyed. Is there a cor-
responding phenomenon for minds, and is there one for ma-
chines? There does seem to be one for the human mind. The
majority of them seem to be ‘sub-critical’, i.e. to correspond
in this analogy to piles of sub-critical 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. An-
imals minds seem to be very definitely ‘sub-critical’. Adher-

COMPUTING MACHINERY AND INTELLIGENCE 29
ing to this analogy we ask, ‘Can a machine be made to be
super-critical?’
The ‘skin of an onion’ analogy is also helpful. In consid-
ering 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 skin
which has nothing in it ? In the latter case the whole mind is
mechanical. (It would not be a discrete-state machine how-
ever. 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’.
The only really satisfactory support that can be given for
the view expressed at the beginning of §6, will be that pro-
vided by waiting for the end of the century and then doing
the experiment described. But what can we say in the mean-
time ? What steps should be taken now if the experiment is
to be successful?
As I have explained, the problem is mainly one of pro-
gramming. Advances in engineering will have to be made
too, but it seems unlikely that these will not be adequate
for the requirements. Estimates of the storage capacity of
the brain vary from 1010 to 1015 binary digits. I incline to
the lower values and believe that only a very small fraction
is used for the higher types of thinking. Most of it is prob-
ably used for the retention of visual impressions. I should
be surprised if more than 109 was required for satisfactory
playing of the imitation game, at any rate against a blind
man. (Note—The capacity of the Encyclopaedia Britannica,
11th edition, is 2 × 109 ) A storage capacity of 109 would be
a very practicable possibility even by present techniques. It
is probably not necessary to increase the speed of operations

COMPUTING MACHINERY AND INTELLIGENCE 30
of the machines at all. Parts of modern machines which can
be regarded as analogs of nerve cells work about a thousand
times faster than the latter. This should provide a ‘margin
of safety’ which could cover losses of speed arising in many
ways. Our problem then is to find out how to programme
these machines to play the game. At my present rate of work-
ing 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.
In the process of trying to imitate an adult human mind
we are bound to think a good deal about the process which
has brought it to the state that it is in. We may notice three
components.
(a) The initial state of the mind, say at birth,
(b) The education to which it has been subjected,
(c) Other experience, not to be described as education, to
which it has been subjected.
Instead of trying to produce a programme to simulate the
adult mind, why not rather try to produce one which simu-
lates the child’s? If this were then subjected to an appro-
priate 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 ed-
ucation we can assume, as a first approximation, to be much
the same as for the human child.
We have thus divided our problem into two parts. The
child-programme and the education process. These two re-
main very closely connected. We cannot expect to find a good
child-machine at the first attempt. One must experiment

COMPUTING MACHINERY AND INTELLIGENCE 31
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 evo-
lution, by the identifications
Structure of the child machine =
Changes
"
"
=
Natural selection
=
hereditary material
Mutations
Judgment of the experimenter
One may hope, however, that this process will be more ex-
peditious 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 mu-
tations. If he can trace a cause for some weakness he can
probably think of the kind of mutation which will improve it.
It will not be possible to apply exactly the same teach-
ing process to the machine as to a normal child. It will not,
for instance, be provided with legs, so that it could not be
asked to go out and fill the coal scuttle. Possibly it might
not have eyes. But however well these deficiencies might be
overcome by clever engineering, one could not send the crea-
ture to school without the other children making excessive
fun of it. It must be given some tuition. We need not be too
concerned about the legs, eyes, etc. The example of Miss He-
len Keller shows that education can take place provided that
communication in both directions between teacher and pupil
can take place by some means or other.
We normally associate punishments and rewards with the
teaching process. Some simple child machines can be con-
structed or programmed on this sort of principle. The ma-
chine has to be so constructed that events which shortly pre-
ceded the occurrence of a punishment signal are unlikely to
be repeated, whereas a reward signal increased the proba-
bility of repetition of the events which led up to it. These
definitions do not presuppose any feelings on the part of the

COMPUTING MACHINERY AND INTELLIGENCE 32
machine, I have done some experiments with one such child
machine, and succeeded in teaching it a few things, but the
teaching method was too unorthodox for the experiment to
be considered really successful.
The use of punishments and rewards can at best be a part
of the teaching process. 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. By the time
a child has learnt to repeat ‘Casabianca’ he would probably
feel very sore indeed, if the text could only be discovered by a
‘Twenty Questions’ technique, every ‘NO’ taking the form of
a blow. It is necessary therefore to have some other ‘unemo-
tional’ channels 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 lan-
guage. These orders are to be transmitted through the ‘un-
emotional’ channels. The use of this language will diminish
greatly the number of punishments and rewards required.
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. Alter-
natively one might have a complete system of logical infer-
ence ‘built in’.3 In the latter case the store would be largely
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 propo-
sition but not belief-value. Certain propositions may be de-
scribed 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. To illus-
trate this, suppose the teacher says to the machine, ‘Do your
homework now.‘ This may cause “Teacher says ‘Do your home-
3
Or rather ‘programmed in’ for our child-machine will be programmed in a digital computer. But the logical system
will not have to be learnt.

COMPUTING MACHINERY AND INTELLIGENCE 33
work now’ ” to be included amongst the well-established facts.
Another such fact might be, “Everything that teacher says is
true.” Combining these may eventually lead to the imper-
ative, ‘Do your homework now,‘ being included amongst the
well-established facts, and this, by the construction of the
machine, will mean that the homework actually gets started,
but the effect is very satisfactory. 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 hi-
erarchy 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 men-
tioned 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 or-
der in which the rules of the logical system concerned are to
be applied, For at each stage when one is using a logical sys-
tem, 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. Propo-
sitions 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.
The idea of a learning machine may appear paradoxical to
some readers. How can the rules of operation of the machine
change? They should describe completely how the machine

COMPUTING MACHINERY AND INTELLIGENCE 34
will react whatever its history might be, whatever changes
it might undergo. The rules are thus quite time-invariant.
This is quite true. The explanation of the paradox is that
the rules which get changed in the learning process are of a
rather less pretentious kind, claiming only an ephemeral va-
lidity. The reader may draw a parallel with the Constitution
of the United States.
An important feature of a learning machine is that its
teacher will often be very largely ignorant of quite what is
going on inside, although he may still be able to some ex-
tent to predict his pupil’s behaviour. This should apply most
strongly to the later education of a machine arising from a
child machine of well-tried design (or programme). This is
in clear contrast with normal procedure when using a ma-
chine to do computations one’s object is then to have a clear
mental picture of the state of the machine at each moment
in the computation. This object can only be achieved with
a struggle. The view that ‘the machine can only do what
we know how to order it to do’,4 appears strange in face of
this. Most of the programmes which we can put into the ma-
chine will result in its doing something that we cannot make
sense (if at all, or which we regard as completely random be-
haviour. Intelligent behaviour presumably consists in a de-
parture 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. An-
other important result of preparing our machine for its part
in the imitation game by a process of teaching and learning is
that ‘human fallibility’ is likely to be omitted in a rather nat-
ural way, i.e., without special ‘coaching’. (The reader should
reconcile this with the point of view on pp. 24, 25.) Processes
that are learnt do not produce a hundred per cent certainty
of result; if they did they could not be unlearnt.
It is probably wise to include a random element in a learn-
ing machine. A random element is rather useful when we
4 Compare Lady Lovelace’s statement (p.450), which does not contain the word ‘only’.

COMPUTING MACHINERY AND INTELLIGENCE 35
are searching for a solution of some problem. Suppose for
instance we wanted to find a number between 50 and 200
which was equal to the square of the sum of its digits, we
might start at 51 then try 52 and go on until we got a num-
ber that worked. Alternatively we might choose numbers at
random until we got a good one. This method has the advan-
tage that it is unnecessary to keep track of the values that
have been tried, but the disadvantage that one may try the
same one twice, but this is not very important if there are
several solutions. The systematic method has the disadvan-
tage that there may be an enormous block without any so-
lutions in the region which has to be investigated first, Now
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. But there the systematic
method is not possible. How could one keep track of the dif-
ferent genetical combinations that had been tried, so as to
avoid trying them again?
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 ap-
proaches should be tried.
We can only see a short distance ahead, but we can see
plenty there that needs to be done.

COMPUTING MACHINERY AND INTELLIGENCE 36
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