A compressed version of this paper will appear in one of a collection of papers on
Turing's work (one of the Centenary volumes, but delayed till 2013).
A pre-print is available here: http://www.cs.bham.ac.uk/research/projects/cogaff/11.html#1106c here
Turing actually wrote"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 machines think?" is to be sought
in a statistical survey such as 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 unambiguous words.
So, far from proposing a test to answer the question whether machines can think or whether
machines are intelligent, he actually decides (rightly) that the question is absurd
because the question is (as he put it in section 6, quoted below), "too meaningless to
Instead, after a preamble, he proposes not a test but a game, which he calls "The
imitation game", and he proposes it in order to make a technological prediction,
not in order to provide a test for intelligence.
Later in the paper he wrote:"6. Contrary Views on the Main QuestionAs far as I can tell, Turing described the game not in order to give a criterion for
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 appropriateness 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 machines think?" I believe to be too
meaningless to deserve discussion.
thinking or for intelligence, but in order to be able to formulate that prediction (part
of "the more accurate form of the question"). He was remarkably accurate about the number
of bits likely to be available in a computer's memory by the turn of the century.
I think that by about the year 2000 it actually was possible to fool a significant subset
of the human population, in an interaction lasting only a few minutes into thinking that
they were talking to a human rather than a machine, though only in a very restricted range of
But it is unlikely that anyone sensible would claim that fooling 30% of the average
population for a mere five minutes was a criterion for a machine to be intelligent,
and Turing certainly did not. It's a pity so many highly educated, highly intelligent
people support the myth.
However, even though the technology has got better, and computers are now doing much
cleverer things than they were a decade ago, humans have also been learning and are now
much better educated about computers, as a result of very frequent use, as well as news
reports, discussions, etc., than they were then.
I suspect that has made it harder to fool some of them into thinking they are interacting
with a human. In other words, although AI technology has improved, so has general human
understanding of what computers can and cannot now do. As a result, many humans are more
likely to be able to choose things to say to the machine that will reveal its inability to
respond like a human. But that reflects limitations of current technology rather than any
deep fact about what computers (or more generally machines) can or cannot do in principle.
I suspect Turing underestimated the difficulty of programming machines to be like humans
even in his simple game.
Anyhow, the truth or falsity of his prediction is not the main point of his paper: he made
the prediction as a basis for attacking arguments purporting to show that it would be
impossible to programme a machine to play the imitation game successfully, even with a
very weak criterion for success. He puts forward several such objections and then replies
to them, attempting to show their errors.
Unfortunately, the misinterpretation of his paper as proposing a test for thinking or for
intelligence is so wide-spread that it has led to huge amounts of wasted effort discussing
the merits of such a test: wasted, because, as Turing himself pointed out, the notion of
such a test is based on a question which is "too meaningless to deserve discussion".
It's a pity so many people don't bother to read what he wrote.
For a critique of some assumptions regarding the question whether machines could be
Why the "hard" problem of consciousness is easy and the "easy" problem hard.
(And how to make progress)
A PDF tutorial presentation, also available in flash format on slideshare.net
Helping Darwin: How to Think About Evolution of Consciousness
Or "How could evolution get ghosts into machines?"
Phenomenal and Access Consciousness and the "Hard" Problem: A View from the Designer Stance
Turing did make one serious error in that paper. In the section headed:7. Learning Machineshe wrote"In the process of trying to imitate an adult human mind we are bound to think a goodI think that here Turing, like many AI researchers studying learning machines, grossly
deal about the process which has brought it to the state that it is in. We may notice
(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 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 notebook as one buys it from the stationer's. 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.
underestimates the contribution of biological evolution to the processes of human
John McCarthy also emphasised the contribution of evolution in "The Well-Designed Child",
a paper he wrote around 1996, which was eventually published in the journal Artificial
Intelligence, vol 172, No 18, 2008, also available on his web site."Evolution solved a different problem than that of starting a baby with no aIn the rest of the paper McCarthy makes some useful contributions to the analysis of what
Animal behavior, including human intelligence, evolved to survive and succeed in this
complex, partially observable and very slightly controllable world. The main features
of this world have existed for several billion years and should not have to be learned
anew by each person or animal. In order to understand how a well-designed baby should
work, we need to understand what the world is like at the gross level of human
interaction with the environment."
might have been done by evolution for such a baby.
Jackie Chappell and I have been working on a similar hypothesis. Some of the ideas are in
two papers and an online presentation
- The Altricial-Precocial Spectrum for Robots,
A. Sloman, J. Chappell,
Proceedings IJCAI'05, Edinburgh, pp. 1187--1192, 2005,
- Natural and artificial meta-configured altricial information-processing systems,
Jackie Chappell, Aaron Sloman,
International Journal of Unconventional Computing, 3, 3, pp. 211--239, 2007.
- Talk 52: Evolution of minds and languages.
What evolved first and develops first in children: Languages for communicating, or
languages for thinking (Generalised Languages: GLs) (Online Presentation PDF)
A 'flash-based' version is on slideshare.net
There is a very common mistake, implicitly made by anyone who asks "Can machines be
intelligent?", or possibly even "Can machines think?" (which is more subtle in ways that I
shall not discuss).
The mistake is looking for, or assuming that there exists, a dichotomy (a binary
distinction) in a complex space where things are very varied. An example would be to
assume there is a binary divide between things that are and things that are not
intelligent (or conscious, or sentient, or able to have emotions, or ...)
Sometimes people who realise that that is a mistake try to avoid the error by talking
about differences of degree, e.g. by saying that instead of a binary divide there
are differences in degrees of intelligence, consciousness, etc.
That view makes two related mistakes
An example of the first sort of mistake could also be made by a child who finds that some
- Assuming that there is a total ordering of cases, as if, for example, species could be put
into a linear ordering of animals with more or less intelligence (or consciousness, etc.)
- The second mistake that is sometimes made in referring to "degrees" is the assumption that
there is continuous variation.
This can also include claiming that all boundaries are fuzzy.
containers can be put inside other containers (e.g. a little box inside a bigger box) and
draws the conclusion that all containers form a linear ordering, so that given any two
containers Ca and Cb, either Ca can be put inside Cb, or Cb can be put inside Ca or they
are exactly the same size, because their bounding surfaces are the same shape and size.
This assumption of a total ordering is easily shown to be wrong by the discovery of
containers of different shapes such that neither can fit inside the other. E.g. one may
have a square cross section and the other circular, and the diameter of the circle is more
than the side of the square and less than the diagonal of the square.
There can be many different sorts of competence that are aspects of intelligence, and
different individuals or different species may have different subsets. E.g. someone way be
very good at designing furniture and terrible at proving theorems in geometry, while the
other is good at geometry and poor at designing furniture. Such variations in ability to
perceive, learn about, understand, act in, plan in and survive in various types of
environment are wide spread among organisms, so they cannot be arranged in a linear
sequence of increasing intelligence.
The assumption of continuous variation is also false, since there are some kinds of
knowledge or competence that are present or absent, but cannot vary continuously between
those extremes, e.g. knowing the sum of 2 and 2. More generally, competences that are
expressed in rules don't have intermediate cases using half a rule, quarter of a rule,
etc. Since biological genetic makeup is ultimately implemented in chemistry and chemical
molecules can only differ discretely (e.g. by addition or removal of atoms) it is
impossible for species to vary continuously in their genetic makeup.
The errors in this section are related to the mistake assumption that there is some unique
competence "general intelligence" which is either thought of in binary (i.e. it is either
present or absent in an individual or species) or as a matter of degree, with linear
variability along a total ordering.
Similar mistakes can be made about machines: e.g. assuming that there is a binary divide,
assuming that there is a total ordering, or assuming that there is continuous variability.
Compare Artificial General Intelligence (AGI)
Conclusion: the idea of a Turing test is muddled
It follows from the above that the very idea of a Turing test or any other test for
intelligence is muddled if there is no binary divide between things that are and things
that are not intelligent, only a vast variety of cases.
A different muddle is introduced if the proposal for a test is replaced by a proposal
for a way of measuring intelligence (or thinking ability). This can be mistaken
both in assuming that there is a total ordering of types of intelligence and that there is
continuous variation so that degrees of intelligence can be represented by real numbers.
If there is no total ordering, only a complex space of combinations of competences
(just as there is a complex space of combinations of atoms, making the notion of ordering
molecules along a line of increasing 'chemicality' (?) misguided), then, instead of using
a number or label for degree or amount of intelligence, we need to find ways of
describing types of intelligence in terms of the combinations of competences that
they include, just as we describe chemical molecules in terms of the different
combinations of atoms and chemical bonds and also the different properties that result
from those structures (e.g. acidity, alkalinity and many more used in drug design, for
If varieties of intelligence vary in something like the ways in which sentence structures
do, with different components combined hierarchically with varying relationships between
the components, then perhaps we need something like a grammar for types of
intelligence.[*] However, that is just one possibility to be considered.
Compare "The design-based approach" to the study of mind.
NOTE:The notion of "grammar" used here is not restricted to production rules for generation of
strings. E.g. during the 1960s, many researchers attempted to generalise Chomsky's notion
of a "generative grammar" to include any specification of a class of structures composed
from some well defined set of primitives arranged in accordance with permitted
relationships, to form new structures that could also be arranged in accordance with
permitted relationships, and so on. If the rules are recursive then an infinite variety of
structures could be characterised in this way. "Web grammars", for example, were developed
to characterise 2-D graphical structures found in pictures.
From this viewpoint understanding a complex structure conforming to such a grammar
amounted to constructing a specification of the structure's derivation from the rules of
the grammar, just as parsing a sentence involves constructing a specification of the
derivation of the sentence from the grammatical rules. However, in the case of picture
grammars the derivation need not have the form of a tree: it could be a graph with cycles,
just as pictures can include cyclic structures (e.g. polygons).
A different sort of "grammar" might explicitly characterise ways of generating structures
from some initial structures. For example, a grammar for legal chess-board configurations
would be a specification of an initial state of the board, plus a specification of
permitted changes to any possible state. In this case a derivation of a chess position
would be a description of a legal game leading to that position. A quite different notion
of a grammar for a chess configuration would characterise pieces and groups of pieces in
terms of their positions and relationships. So notions like "pinning" and "forking" and a
move being forced would be part of the characterisation of the board structure.
A grammar for chemical molecules might specify ways in which a finite set of atoms can
combined in accordance with the laws of physics to form a single structure, and could
include intermediate structures that are neither atoms nor molecules, e.g. ions.
In many cases ambiguity is possible: the same structure may have more than one "parse" if
there are two or more ways in which the 'rules' allow it to be constructed.
A grammar for types of intelligence might be a specification of varieties of combinations
of competences of many sorts that could be implemented in a unified working architecture,
and could include combinations that could grow themselves, e.g. as a result of
interactions with some environment.
S. Kaneff, Ed. Picture language machines, 1970, Academic Press, New York,
My thanks to Damien Duff for pointing out the need to explain this generalised use of the
word "grammar". I have also used it in connection with the idea of a "grammar" for emotions,
as a counter to shallower forms of description of emotions.
If we are to propose tests of the general sort that people take the Turing Test to be, namely
a test for something being intelligent, or human like, it is important to distinguish testing
a particular individual from testing a theory about a type of design for working systems.
It is clear that there are many very different human beings and also that they all share a
large collection of common features. We really should be testing a theory about what's
Compare: if a theory about the weather is able to explain only how a particular sort of
tornado works and no other tornadoes, nor any other weather phenomena, then it cannot be a
good explanation of the particular case. Likewise a theory of what's going on when oil
burns that says nothing wood burning, or coal burning, or gas burning cannot be a good
theory about oil burning.
Or suppose someone claims to have a theory about how to solve algebraic equations and an
online computer test that demonstrates the theory. The implemented algorithm solves only
quadratic equations: give it any quadratic equation and it will produce the solutions
(including complex solutions where appropriate, as required for this equation:X x X = -1Would that be taken as a good test for a theory of equation solving? We would rightly
demand something more generic.
What would the required sort of generic theory of intelligence look like?
The closest answer I can give is something like a parametrised specification for a highly
polymorphic design for a working system, which can be given different parameters to
produce instances of the design, where the instances will be very different in the way
that, for example, humans in different cultures, or who talk different languages, or who
grow up to have very different competences and interests are different, and yet be as
similar as different humans are, a requirement that is very complex and very demanding,
and not yet specifiable in detail since we don't yet know enough about what typical humans
are like (e.g. how their vision systems work, how they learn, what mechanisms are involved
in their motivational and other affective states and processes.)
The parameters, instead of all being supplied at the time the instance is created, would
have to be picked up at various times during the development and testing of the instance.
In particular, in order to really understand human intelligence we should be able to
specify a type of system, different instances of which, as a result of educational and
environmental influences, and some individual personality features, "grow up" to be
philosophers with very different views, including views on what machines can or cannot do,
e.g. some growing up to be robots like John Searle, others like Tom Nagel, others like
David Chalmers, others like Dan Dennett, and perhaps even some like me, since I disagree
with all the others!See also:
An Alternative to Working on Machine Consciousness
How Virtual Machinery Can Bridge the "Explanatory Gap",
In Natural and Artificial Systems. (For SAB'2010).
So much has been written about Turing's paper that it would be pointless to try to give a
comprehensive set of references. Occasionally I shall add links here if I find something
that looks useful (even if I have not read it 'cover to cover').
- For more on what Turing actually wrote see Chapter 16 (section 16.ii) of Margaret Boden's book
Mind As Machine: A history of Cognitive Science (Vols 1--2) Oxford University Press, 2006
- This paper does not fall into the common trap: http://crl.ucsd.edu/~saygin/papers/MMTT.pdf
Saygin, A.P., Cicekli, I. and Akman, V. (2000), 'Turing Test: 50 Years Later', Minds and Machines 10(4): 463-518.
Part of this web site (the turing test page):
- After reading this paper (in 2010) Diane Proudfoot modified a draft of the following paper
D. Proudfoot, Anthropomorphism and AI: Turing's much misunderstood imitation game,
in Artificial Intelligence, Vol 175, 5--6, 2011, pp. 950--957,
However, she disagrees with my reading of Turing. Later I'll try to present an analysis of
the disagreement. (Please let me know if you do!)
- This symposium may be of interest: AI-Inspired Biology 31st March -1st April, 2010.
Part of the AISB 2010 Convention
Installed: 1 Feb 2010
Updated: 2 Feb 2010; 6 Feb 2010; 26 May 2010; 23 Dec 2010; 25 Nov 2011; 5 Dec 2012