## 1. Turing (1950) and the Imitation Game



Turing (1950) describes the following kind of game. Suppose that we
have a person, a machine, and an interrogator. The interrogator is in
a room separated from the other person and the machine. The object of
the game is for the interrogator to determine which of the other two
is the person, and which is the machine. The interrogator knows the
other person and the machine by the labels '*X*'
and '*Y*'--but, at least at the beginning of
the game, does not know which of the other person and the machine is
'*X*'--and at the end of the game says either
'*X* is the person and *Y* is the machine'
or '*X* is the machine and *Y* is the
person'. The interrogator is allowed to put questions to the
person and the machine of the following kind: "Will *X*
please tell me whether *X* plays chess?" Whichever of the
machine and the other person is *X* must answer questions that
are addressed to *X*. The object of the machine is to try to
cause the interrogator to mistakenly conclude that the machine is the
other person; the object of the other person is to try to help the
interrogator to correctly identify the machine. About this game,
Turing (1950) says:



> 
> 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 percent chance of making the
> right identification after five minutes of questioning. ... I
> believe that at the end of the century 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.
> 



There are at least two kinds of questions that can be raised about
Turing's predictions concerning his Imitation Game. First, there
are empirical questions, e.g., Is it true that we now--or will
soon--have made computers that can play the imitation game so
well that an average interrogator has no more than a 70 percent chance
of making the right identification after five minutes of questioning?
Second, there are conceptual questions, e.g., Is it true that, if an
average interrogator had no more than a 70 percent chance of making
the right identification after five minutes of questioning, we should
conclude that the machine exhibits some level of thought, or
intelligence, or mentality?



There is little doubt that Turing would have been disappointed by the
state of play at the end of the twentieth century. Participants in the
Loebner Prize Competition--an annual event in which computer
programmes are submitted to the Turing Test-- had come nowhere
near the standard that Turing envisaged. A quick look at the
transcripts of the participants for the preceding decade reveals that
the entered programs were all easily detected by a range of
not-very-subtle lines of questioning. Moreover, major players in the
field regularly claimed that the Loebner Prize Competition was an
embarrassment precisely because we were still so far from having a
computer programme that could carry out a decent conversation for a
period of five minutes--see, for example, Shieber (1994). It was
widely conceded on all sides that the programs entered in the Loebner
Prize Competition were designed solely with the aim of winning the
minor prize of best competitor for the year, with no thought that the
embodied strategies would actually yield something capable of passing
the Turing Test.



At the end of the second decade of the twenty-first century, it is
unclear how much has changed. On the one hand, there have been
interesting developments in language generators. In particular, the
release of Open AI's GPT-3 (Brown, et al. 2020, Other Internet
Resources) has prompted a flurry of excitement. GPT-3 is quite good at
generating fiction, poetry, press releases, code, music, jokes,
technical manuals, and news articles. Perhaps, as Chalmers speculates
(2020, Other Internet Resources), GPT-3 "suggests a potential
mindless path to artificial general intelligence". But, of
course, GPT-3 is not close to passing the Turing Test: GPT-3 neither
perceives nor acts, and it is, at best, highly contentious whether it
is a site of understanding. What remains to be seen is whether, within
the next couple of generations of language generators - GPT-4 or
GPT-5 - we have something that can be linked to perceptual
inputs and behavioural outputs in a way that does produce something
capable of passing the Turing Test. (For further discussion, see
Floridi and Chiriatti (2020).)



On the other hand, as, for example, Floridi (2008) complains, there
are other ways in which progress has been frustratingly slow. In 2014,
claims emerged that, because the computer program *Eugene
Goostman* had fooled 33% of judges in the Turing Test 2014
competition, it had "passed the Turing Test". But there
have been other one-off competitions in which similar results have
been achieved. Back in 1991, *PC Therapist* had 50% of judges
fooled. And, in a 2011 demonstration, *Cleverbot* had an even
higher success rate. In all three of these cases, the size of the
trial was very small, and the result was not reliably projectible: in
no case were there strong grounds for holding that an average
interrogator had no more than a 70% chance of making the right
determination about the relevant program after five minutes of
questioning. Moreover--and much more importantly--we must
distinguish between the test the Turing proposed, and the particular
prediction that he made about how things would be by the end of the
twentieth century. The percentage chance of making the correct
identification, the time interval over which the test takes place, and
the number of conversational exchanges required are all adjustable
parameters in the Test, despite the fact that they are fixed in the
particular prediction that Turing made. Even if Turing was very far
out in the prediction that he made about how things would be by the
end of the twentieth century, it remains possible that the test that
he proposes is a good one. However, before one can endorse the
suggestion that the Turing Test is good, there are various objections
that ought to be addressed.



Some people have suggested that the Turing Test is chauvinistic: it
only recognizes intelligence in things that are able to sustain a
conversation with us. Why couldn't it be the case that there are
intelligent things that are unable to carry on a conversation, or, at
any rate, unable to carry on a conversation with creatures like us?
(See, for example, French (1990).) Perhaps the intuition behind this
question can be granted; perhaps it is unduly chauvinistic to insist
that anything that is intelligent has to be capable of sustaining a
conversation with us. (On the other hand, one might think that, given
the availability of suitably qualified translators, it ought to be
possible for any two intelligent agents that speak different languages
to carry on some kind of conversation.) But, in any case, the charge
of chauvinism is completely beside the point. What Turing claims is
only that, if something can carry out a conversation with us, then we
have good grounds to suppose that that thing has intelligence of the
kind that we possess; he does not claim that only something that can
carry out a conversation with us can possess the kind of intelligence
that we have.



Other people have thought that the Turing Test is not sufficiently
demanding: we already have anecdotal evidence that quite unintelligent
programs (e.g., ELIZA--for details of which, see Weizenbaum
(1966)) can seem to ordinary observers to be loci of intelligence for
quite extended periods of time. Moreover, over a short period of
time--such as the five minutes that Turing mentions in his
prediction about how things will be in the year 2000--it might
well be the case that almost all human observers could be taken in by
cunningly designed but quite unintelligent programs. However, it is
important to recall that, in order to pass Turing's Test, it is
not enough for the computer program to fool "ordinary
observers" in circumstances other than those in which the test
is supposed to take place. What the computer program has to be able to
do is to survive interrogation by someone who knows that one of the
other two participants in the conversation is a machine. Moreover, the
computer program has to be able to survive such interrogation with a
high degree of success over a repeated number of trials. (Turing says
nothing about how many trials he would require. However, we can safely
assume that, in order to get decent evidence that there is no more
than a 70% chance that a machine will be correctly identified as a
machine after five minutes of conversation, there will have to be a
reasonably large number of trials.) If a computer program could do
this quite demanding thing, then it does seem plausible to claim that
we would have at least *prima facie* reason for thinking that
we are in the presence of intelligence. (Perhaps it is worth
emphasizing again that there might be all kinds of intelligent
things--including intelligent machines--that would not pass
this test. It is conceivable, for example, that there might be
machines that, as a result of moral considerations, refused to lie or
to engage in pretence. Since the human participant is supposed to do
everything that he or she can to help the interrogator, the question
"Are you a machine?" would quickly allow the interrogator
to sort such (pathological?) truth-telling machines from humans.)



Another contentious aspect of Turing's paper (1950) concerns his
restriction of the discussion to the case of "digital
computers." On the one hand, it seems clear that this
restriction is really only significant for the prediction that Turing
makes about how things will be in the year 2000, and not for the
details of the test itself. (Indeed, it seems that if the test that
Turing proposes is a good one, then it will be a good test for any
kinds of entities, including, for example, animals, aliens, and analog
computers. That is: if animals, aliens, analog computers, or any other
kinds of things, pass the test that Turing proposes, then there will
be as much reason to think that these things exhibit intelligence as
there is reason to think that digital computers that pass the test
exhibit intelligence.) On the other hand, it is actually a highly
controversial question whether "thinking machines" would
have to be digital computers; and it is also a controversial question
whether Turing himself assumed that this would be the case. In
particular, it is worth noting that the seventh of the objections that
Turing (1950) considers addresses the possibility of continuous state
machines, which Turing explicitly acknowledges to be different from
discrete state machines. Turing appears to claim that, even if we are
continuous state machines, a discrete state machine would be able to
imitate us sufficiently well for the purposes of the Imitation Game.
However, it seems doubtful that the considerations that he gives are
sufficient to establish that, if there are continuous state machines
that pass the Turing Test, then it is possible to make discrete state
machines that pass the test as well. (Turing himself was keen to point
out that some limits had to be set on the notion of
"machine" in order to make the question about
"thinking machines" interesting:



> 
> It is natural that we should wish to permit every kind of engineering
> technique to be used in our machine. We also wish to allow the
> possibility that 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. Finally, we wish to exclude
> from the machines 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 all be of
> one sex, but this would not really be satisfactory, for it is probably
> possible to rear a complete individual from a single 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 'constructing a thinking machine'.
> (435/6)
> 



But, of course, as Turing himself recognized, there is a large class
of possible "machines" that are neither digital nor
biotechnological.) More generally, the crucial point seems to be that,
while Turing recognized that the class of machines is potentially much
larger than the class of discrete state machines, he was himself
*very* confident that properly engineered discrete state
machines could succeed in the Imitation Game (and, moreover, at the
time that he was writing, there were certain discrete state
machines--"electronic computers"--that loomed
very large in the public imagination).


## 2. Turing (1950) and Responses to Objections



Although Turing (1950) is pretty informal, and, in some ways rather
idiosyncratic, there is much to be gained by considering the
discussion that Turing gives of potential objections to his claim that
machines--and, in particular, digital computers--can
"think". Turing gives the following labels to the
objections that he considers: (1) The Theological Objection; (2) The
"Heads in the Sand" Objection; (3) The Mathematical
Objection; (4) The Argument from Consciousness; (5) Arguments from
Various Disabilities; (6) Lady Lovelace's Objection; (7)
Argument from Continuity of the Nervous System; (8) The Argument from
Informality of Behavior; and (9) The Argument from Extra-Sensory
Perception. We shall consider these objections in the corresponding
subsections below. (In some--but not all--cases, the
counter-arguments to these objections that we discuss are also
provided by Turing.)


### 2.1 The Theological Objection



Substance dualists believe that thinking is a function of a
non-material, separately existing, substance that somehow
"combines" with the body to make a person. So--the
argument might go--making a body can never be sufficient to
guarantee the presence of thought: in themselves, digital computers
are no different from any other merely material bodies in being
utterly unable to think. Moreover--to introduce the
"theological" element--it might be further added
that, where a "soul" is suitably combined with a body,
this is always the work of the divine creator of the universe: it is
entirely up to God whether or not a particular kind of body is imbued
with a thinking soul. (There is well known scriptural support for the
proposition that human beings are "made in God's
image". Perhaps there is also theological support for the claim
that only God can make things in God's image.)



There are several different kinds of remarks to make here. First,
there are many serious objections to substance dualism. Second, there
are many serious objections to theism. Third, even if theism and
substance dualism are both allowed to pass, it remains quite unclear
why thinking machines are supposed to be ruled out by this combination
of views. Given that God can unite souls with human bodies, it is hard
to see what reason there is for thinking that God could not unite
souls with digital computers (or rocks, for that matter!). Perhaps, on
this combination of views, there is no especially good reason why,
amongst the things that we can make, certain kinds of digital
computers turn out to be the only ones to which God gives
souls--but it seems pretty clear that there is also no
particularly good reason for ruling out the possibility that God would
choose to give souls to certain kinds of digital computers. Evidence
that God is dead set against the idea of giving souls to certain kinds
of digital computers is not particularly thick on the ground.


### 2.2 The 'Heads in the Sand' Objection



If there were thinking machines, then various consequences would
follow. First, we would lose the best reasons that we have for
thinking that we are superior to everything else in the universe
(since our cherished "reason" would no longer be something
that we alone possess). Second, the possibility that we might be
"supplanted" by machines would become a genuine worry: if
there were thinking machines, then very likely there would be machines
that could think much better than we can. Third, the possibility that
we might be "dominated" by machines would also become a
genuine worry: if there were thinking machines, who's to say
that they would not take over the universe, and either enslave or
exterminate us?



As it stands, what we have here is not an argument against the claim
that machines can think; rather, we have the expression of various
fears about what might follow if there were thinking machines. Someone
who took these worries seriously--and who was persuaded that it
is indeed possible for us to construct thinking machines--might
well think that we have here reasons for giving up on the project of
attempting to construct thinking machines. However, it would be a
major task--which we do not intend to pursue here--to
determine whether there really are any good reasons for taking these
worries seriously.


### 2.3 The Mathematical Objection



Some people have supposed that certain fundamental results in
mathematical logic that were discovered during the 1930s--by
Godel (first incompleteness theorem) and Turing (the halting
problem)--have important consequences for questions about digital
computation and intelligent thought. (See, for example, Lucas (1961)
and Penrose (1989); see, too, Hodges (1983:414) who mentions
Polanyi's discussions with Turing on this matter.) Essentially,
these results show that within a formal system that is strong enough,
there are a class of true statements that can be expressed but not
proven within the system (see the entry on
 Godel's incompleteness theorems).
 Let us say that such a system is "subject to the Lucas-Penrose
constraint" because it is constrained from being able to prove a
class of true statements expressible within the system.



Turing (1950:444) himself observes that these results from
mathematical logic might have implications for the Turing test:



> 
> There are certain things that [any digital computer] cannot do. If it
> is rigged up to give answers to questions as in the imitation 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. (444)
> 



So, in the context of the Turing test, "being subject to the
Lucas-Penrose constraint" implies the existence of a class of
"unanswerable" questions. However Turing noted that in the
context of the Turing test, these "unanswerable" questions
are only a concern if humans can answer them. His "short"
reply was that it is not clear that humans are free from such a
constraint themselves. Turing then goes on to add that he does not
think that the argument can be dismissed "quite so
lightly."



To make the argument more precise, we can write it as follows:


1. Let C be a digital computer.
2. Since C is subject to the Lucas-Penrose constraint, there is an
"unanswerable" question q for C.
3. If an entity, E, is not subject to the Lucas-Penrose constraint,
then there are no "unanswerable" questions for E.
4. The human intellect is not subject to the Lucas-Penrose
constraint.
5. Thus, there are no "unanswerable" questions for the
human intellect.
6. The question q is therefore "answerable" to the human
intellect.
7. By asking question q, a human could determine if the responder is
a computer or a human.
8. Thus C may fail the Turing test.



Once the argument is laid out as above, it becomes clear that premise
(3) should be challenged. Putting that aside, we note that one
interpretation of Turing's "short" reply is that
claim (4) is merely asserted--without any kind of proof. The
"short" reply then leads us to examine whether humans are
free from the Lucas-Penrose constraint.



If humans are subject to the Lucas-Penrose constraint then the
constraint does not provide any basis for distinguishing humans from
digital computers. If humans are free from the Lucas-Penrose
constraint, then (granting premise 3) it follows that digital
computers may fail the Turing test and thus, it seems, cannot
think.



However, there remains a question as to whether being free from the
constraint is necessary for the capacity to think. It may be that the
Turing test is too strict. Since, by hypothesis, we are free from the
Lucas-Penrose constraint, we are, in some sense, too good at asking
and answering questions. Suppose there is a thinking entity that is
subject to the Lucas-Penrose constraint. By an argument analogous to
the one above, it can fail the Turing test. Thus, an entity which can
think would fail the Turing test.



We can respond to this concern by noting that the construction of
questions suggested by the results from mathematical
logic--Godel, Turing, etc.--are extremely complicated,
and require extremely detailed information about the language and
internal programming of the digital computer (which, of course, is not
available to the interrogators in the Imitation Game). At the very
least, much more argument is required to overthrow the view that the
Turing Test could remain a very high quality statistical test for the
presence of mind and intelligence even if digital computers differ
from human beings in being subject to the Lucas-Penrose constraint.
(See Bowie 1982, Dietrich 1994, Feferman 1996, Abramson 2008, and
Section 6.3 of the entry on
 Godel's incompleteness theorems,
 for further discussion.)


### 2.4 The Argument from Consciousness



Turing cites Professor Jefferson's *Lister Oration* for
1949 as a source for the kind of objection that he takes to fall under
this label:



> 
> 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 machine 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
> successes, 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. (445/6)
> 



There are several different ideas that are being run together here,
and that it is profitable to disentangle. One idea--the one upon
which Turing first focuses--is the idea that the only way in
which one could be certain that a machine thinks is to be the machine,
and to feel oneself thinking. A second idea, perhaps, is that the
presence of mind requires the presence of a certain kind of
self-consciousness ("not only write it but know that it had
written it"). A third idea is that it is a mistake to take a
narrow view of the mind, i.e. to suppose that there could be a
believing intellect divorced from the kinds of desires and emotions
that play such a central role in the generation of human behavior
("no mechanism could feel ...").



Against the solipsistic line of thought, Turing makes the effective
reply that he would be satisfied if he could secure agreement on the
claim that we might each have just as much reason to suppose that
machines think as we have reason to suppose that *other* people
think. (The point isn't that Turing thinks that solipsism is a
serious option; rather, the point is that following this line of
argument isn't going to lead to the conclusion that there are
respects in which digital computers could not be our intellectual
equals or superiors.)



Against the other lines of thought, Turing provides a little
"*viva voce*" that is intended to illustrate the
kind of evidence that he supposes one might have that a machine is
intelligent. Given the right kinds of responses from the machine, we
*would* naturally interpret its utterances as evidence of
pleasure, grief, warmth, misery, anger, depression, etc.
Perhaps--though Turing doesn't say this--the only way
to make a machine of this kind would be to equip it with sensors,
affective states, etc., i.e., in effect, to make an artificial
*person*. However, the important point is that if the claims
about self-consciousness, desires, emotions, etc. are right, then
Turing can accept these claims with equanimity: *his* claim is
then that a machine with a digital computing "brain" can
have the full range of mental states that can be enjoyed by adult
human beings.


### 2.5 Arguments from Various Disabilities



Turing considers a list of things that some people have claimed
machines will never be able to do: (1) be kind; (2) be resourceful;
(3) be beautiful; (4) be friendly; (5) have initiative; (6) have a
sense of humor; (7) tell right from wrong; (8) make mistakes; (9) fall
in love; (10) enjoy strawberries and cream; (11) make someone fall in
love with one; (12) learn from experience; (13) use words properly;
(14) be the subject of one's own thoughts; (15) have as much
diversity of behavior as a man; (16) do something really new.



An interesting question to ask, before we address these claims
directly, is whether we should suppose that intelligent creatures from
some other part of the universe would necessarily be able to do these
things. Why, for example, should we suppose that there must be
something deficient about a creature that does not enjoy--or that
is not able to enjoy--strawberries and cream? True enough, we
might suppose that an intelligent creature ought to have the capacity
to enjoy some kinds of things--but it seems unduly chauvinistic
to insist that intelligent creatures must be able to enjoy just the
kinds of things that we do. (No doubt, similar considerations apply to
the claim that an intelligent creature must be the kind of thing that
can make a human being fall in love with it. Yes, perhaps, an
intelligent creature should be the kind of thing that can love and be
loved; but what is so special about us?)



Setting aside those tasks that we deem to be unduly chauvinistic, we
should then ask what grounds there are for supposing that no digital
computing machine *could* do the other things on the list.
Turing suggests that the most likely ground lies in our prior
acquaintance with machines of all kinds: none of the machines that any
of us has hitherto encountered has been able to do these things. In
particular, the digital computers with which we are now familiar
cannot do these things. (Except perhaps for make mistakes: after all,
even digital computers are subject to "errors of
functioning." But this might be set aside as an irrelevant
case.) However, given the limitations of storage capacity and
processing speed of even the most recent digital computers, there are
obvious reasons for being cautious in assessing the merits of this
inductive argument.



(A different question worth asking concerns the progress that has been
made until now in constructing machines that can do the kinds of
things that appear on Turing's list. There is at least room for
debate about the extent to which current computers can: make mistakes,
use words properly, learn from experience, be beautiful, etc.
Moreover, there is also room for debate about the extent to which
recent advances in other areas may be expected to lead to further
advancements in overcoming these alleged disabilities. Perhaps, for
example, recent advances in work on artificial sensors may one day
contribute to the production of machines that can enjoy strawberries
and cream. Of course, if the intended objection is to the notion that
machines can experience any kind of feeling of enjoyment, then it is
not clear that work on particular kinds of artificial sensors is to
the point.)


### 2.6 Lady Lovelace's Objection



One of the most popular objections to the claim that there can be
thinking machines is suggested by a remark made by Lady Lovelace in
her memoir on Babbage's Analytical Engine:



> 
> The Analytical Engine has no pretensions to originate anything. It can
> do whatever we know how to order it to perform (cited by Hartree,
> p. 70)
> 



The key idea is that machines can *only* do what we know how to
order them to do (or that machines can never do anything really new,
or anything that would take us by surprise). As Turing says, one way
to respond to these challenges is to ask whether we can ever do
anything "really new." Suppose, for instance, that the
world is deterministic, so that everything that we do is fully
determined by the laws of nature and the boundary conditions of the
universe. There is a sense in which nothing "really new"
happens in a deterministic universe--though, of course, the
universe's being deterministic would be entirely compatible with
our being surprised by events that occur within it. Moreover--as
Turing goes on to point out--there are many ways in which even
digital computers do things that take us by surprise; more needs to be
said to make clear exactly what the nature of this suggestion is.
(Yes, we might suppose, digital computers are
"constrained" by their programs: they can't do
anything that is not permitted by the programs that they have. But
human beings are "constrained" by their biology and their
genetic inheritance in what might be argued to be just the same kind
of way: they can't do anything that is not permitted by the
biology and genetic inheritance that they have. If a program were
sufficiently complex--and if the processor(s) on which it ran
were sufficiently fast--then it is not easy to say whether the
kinds of "constraints" that would remain would necessarily
differ in kind from the kinds of constraints that are imposed by
biology and genetic inheritance.)



Bringsjord et al. (2001) claim that Turing's response to the
Lovelace Objection is "mysterious" at best, and
"incompetent" at worst (p.4). In their view,
Turing's claim that "computers do take us by
surprise" is only true when "surprise" is given a
very superficial interpretation. For, while it is true that computers
do things that we don't intend them to do--because
we're not smart enough, or because we're not careful
enough, or because there are rare hardware errors, or
whatever--it isn't true that there are any cases in which
we should want to say that a computer has *originated*
something. Whatever merit might be found in this objection, it seems
worth pointing out that, in the relevant sense of
*origination*, human beings "originate something"
on more or less every occasion in which they engage in conversation:
they produce new sentences of natural language that it is appropriate
for them to produce in the circumstances in which they find
themselves. Thus, on the one hand--for all that Bringsjord et al.
have argued--The Turing Test is a perfectly good test for the
presence of "origination" (or "creativity," or
whatever). Moreover, on the other hand, for all that Bringsjord et al.
have argued, it remains an open question whether a digital computing
device is capable of "origination" in this sense (i.e.
capable of producing new sentences that are appropriate to the
circumstances in which the computer finds itself). So we are not
overly inclined to think that Turing's response to the Lovelace
Objection is poor; and we are even less inclined to think that Turing
lacked the resources to provide a satisfactory response on this
point.


### 2.7 Argument from Continuity of the Nervous System



The human brain and nervous system is not much like a digital
computer. In particular, there are reasons for being skeptical of the
claim that the brain is a discrete-state machine. Turing observes that
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. From this, Turing infers that the brain is likely to
be a continuous-state machine; and he then notes that, since
discrete-state machines are not continuous-state machines, there might
be reason here for thinking that no discrete-state machine can be
intelligent.



Turing's response to this kind of argument seems to be that a
continuous-state machine can be imitated by discrete-state machines
with very small levels of error. Just as differential analyzers can be
imitated by digital computers to within quite small margins of error,
so too, the conversation of human beings can be imitated by digital
computers to margins of error that would not be detected by ordinary
interrogators playing the imitation game. It is not clear that this is
the right kind of response for Turing to make. If someone thinks that
real thought (or intelligence, or mind, or whatever) can only be
located in a continuous-state machine, then the fact--if, indeed,
it is a fact--that it is possible for discrete-state machines to
pass the Turing Test shows only that the Turing Test is no good. A
better reply is to ask why one should be so confident that real
thought, etc. can only be located in continuous-state machines (if,
indeed, it is right to suppose that we are not discrete-state
machines). And, before we ask this question, we would do well to
consider whether we really do have such good reason to suppose that,
from the standpoint of our ability to think, we are not essentially
discrete-state machines. (As Block (1981) points out, it seems that
there is nothing in our concept of intelligence that rules out
intelligent beings with quantised sensory devices; and nor is there
anything in our concept of intelligence that rules out intelligent
beings with digital working parts.)


### 2.8 Argument from Informality of Behavior



This argument relies on the assumption that there is no set of rules
that describes what a person ought to do in every possible set of
circumstances, and on the further assumption that there is a set of
rules that describes what a machine will do in every possible set of
circumstances. From these two assumptions, it is supposed to
follow--somehow!--that people are not machines. As Turing
notes, there is some slippage between "ought" and
"will" in this formulation of the argument. However, once
we make the appropriate adjustments, it is not clear that an obvious
difference between people and digital computers emerges.



Suppose, first, that we focus on the question of whether there are
sets of rules that describe what a person and a machine
"will" do in every possible set of circumstances. If the
world is deterministic, then there are such rules for both persons and
machines (though perhaps it is not possible to write down the rules).
If the world is not deterministic, then there are no such rules for
either persons or machines (since both persons and machines can be
subject to non-deterministic processes in the production of their
behavior). Either way, it is hard to see any reason for supposing that
there is a relevant difference between people and machines that bears
on the description of what they will do in all possible sets of
circumstances. (Perhaps it might be said that what the objection
invites us to suppose is that, even though the world is not
deterministic, humans differ from digital machines precisely because
the operations of the latter are indeed deterministic. But, if the
world is non-deterministic, then there is no reason why digital
machines cannot be programmed to behave non-deterministically, by
allowing them to access input from non-deterministic features of the
world.)



Suppose, instead, that we focus on the question of whether there are
sets of rules that describe what a person and a machine
"ought" to do in every possible set of circumstances.
Whether or not we suppose that norms can be codified--and quite
apart from the question of which kinds of norms are in
question--it is hard to see what grounds there could be for this
judgment, other than the question-begging claim that machines are not
the kinds of things whose behavior could be subject to norms. (And, in
that case, the initial argument is badly mis-stated: the claim ought
to be that, whereas there are sets of rules that describe what a
person ought to do in every possible set of circumstances, there are
no sets of rules that describe what machines *ought* to do in
all possible sets of circumstances!)


### 2.9 Argument from Extra-Sensory Perception



The strangest part of Turing's paper is the few paragraphs on
ESP. Perhaps it is intended to be tongue-in-cheek, though, if it is,
this fact is poorly signposted by Turing. Perhaps, instead, Turing was
influenced by the apparently scientifically respectable results of J.
B. Rhine. At any rate, taking the text at face value, Turing seems to
have thought that there was overwhelming empirical evidence for
telepathy (and he was also prepared to take clairvoyance, precognition
and psychokinesis seriously). Moreover, he also seems to have thought
that if the human participant in the game was telepathic, then the
interrogator could exploit this fact in order to determine the
identity of the machine--and, in order to circumvent this
difficulty, Turing proposes that the competitors should be housed in a
"telepathy-proof room." Leaving aside the point that, as a
matter of fact, there is no current statistical support for
telepathy--or clairvoyance, or precognition, or
telekinesis--it is worth asking what kind of theory of the nature
of telepathy would have appealed to Turing. After all, if humans can
be telepathic, why shouldn't digital computers be so as well? If
the capacity for telepathy were a standard feature of any sufficiently
advanced system that is able to carry out human conversation, then
there is no in-principle reason why digital computers could not be the
equals of human beings in this respect as well. (Perhaps this response
assumes that a successful machine participant in the imitation game
will need to be equipped with sensors, etc. However, as we noted
above, this assumption is not terribly controversial. A plausible
conversationalist has to keep up to date with goings-on in the
world.)



After discussing the nine objections mentioned above, Turing goes on
to say that he has "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." (454)
Perhaps Turing sells himself a little short in this self-assessment.
First of all--as his brief discussion of solipsism makes
clear--it is worth asking what grounds we have for attributing
intelligence (thought, mind) to other people. If it is plausible to
suppose that we base our attributions on behavioral tests or
behavioral criteria, then his claim about the appropriate test to
apply in the case of machines seems apt, and his conjecture that
digital computing machines might pass the test seems like a
reasonable--though controversial--empirical conjecture.
Second, subsequent developments in the philosophy of mind--and,
in particular, the fashioning of functionalist theories of the
mind--have provided a more secure theoretical environment in
which to place speculations about the possibility of thinking
machines. If mental states are functional states--and if mental
states are capable of realisation in vastly different kinds of
materials--then there is some reason to think that it is an
empirical question whether minds can be realised in digital computing
machines. Of course, this kind of suggestion is open to challenge; we
shall consider some important philosophical objections in the later
parts of this review.


## 3. Some Minor Issues Arising



There are a number of much-debated issues that arise in connection
with the interpretation of various parts of Turing (1950), and that we
have hitherto neglected to discuss. What has been said in the first
two sections of this document amounts to our interpretation of what
Turing has to say (perhaps bolstered with what we take to be further
relevant considerations in those cases where Turing's remarks
can be fairly readily improved upon). But since some of this
interpretation has been contested, it is probably worth noting where
the major points of controversy have been.


### 3.1 Interpreting the Imitation Game



Turing (1950) introduces the imitation game by describing a game in
which the participants are a man, a woman, and a human interrogator.
The interrogator is in a room apart from the other two, and is set the
task of determining which of the other two is a man and which is a
woman. Both the man and the woman are set the task of trying to
convince the interrogator that they are the woman. Turing recommends
that the best strategy for the woman is to answer all questions
truthfully; of course, the best strategy for the man will require some
lying. The participants in this game also use teletypewriter to
communicate with one another--to avoid clues that might be
offered by tone of voice, etc. Turing then says: "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?" (434).



Now, of course, it is *possible* to interpret Turing as here
intending to say what he seems literally to say, namely, that the new
game is one in which the computer must pretend to be a woman, and the
other participant in the game is a woman. (For discussion, see, for
example, Genova (1994) and Traiger (2000).) And it is also
*possible* to interpret Turing as intending to say that the new
game is one in which the computer must pretend to be a woman, and the
other participant in the game is a man who must also pretend to be a
woman. However, as Copeland (2000), Piccinini (2000), and Moor (2001)
convincingly argue, the rest of Turing's article, and material
in other articles that Turing wrote at around the same time, very
strongly support the claim that Turing actually intended the standard
interpretation that we gave above, viz. that the computer is to
pretend to be a human being, and the other participant in the game is
a human being of unspecified gender. Moreover, as Moor (2001) argues,
there is no reason to think that one would get a better test if the
computer must pretend to be a woman and the other participant in the
game is a man pretending to be a woman; and, indeed, there is some
reason to think that one would get a worse test. Perhaps it would make
no difference to the effectiveness of the test if the computer must
pretend to be a woman, and the other participant is a woman (any more
than it would make a difference if the computer must pretend to be an
accountant and the other participant is an accountant); however, this
consideration is simply insufficient to outweigh the strong textual
evidence that supports the standard interpretation of the imitation
game that we gave at the beginning of our discussion of Turing (1950).
(For a dissenting view about many of the matters discussed in this
paragraph, see Sterrett (2000; 2020).)


### 3.2 Turing's Predictions



As we noted earlier, Turing (1950) makes the claim that:



> 
> 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 percent chance of making the
> right identification after five minutes of questioning. ... I
> believe that at the end of the century 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.
> 



Most commentators contend that this claim has been shown to be
mistaken: in the year 2000, *no-one* was able to program
computers to make them play the imitation game so well that an average
interrogator had no more than a 70% chance of making the correct
identification after five minutes of questioning. Copeland (2000)
argues that this contention is seriously mistaken: "about fifty
years" is by no means "exactly fifty years," and it
remains open that we may soon be able to do the required programming.
Against this, it should be noted that Turing (1950) goes on
immediately to refer to how things will be "at the end of the
century," which suggests that not too much can be read into the
qualifying "about." However, as Copeland (2000) points
out, there are other more cautious predictions that Turing makes
elsewhere (e.g., that it would be "at least 100 years"
before a machine was able to pass an unrestricted version of his
test); and there are other predictions that are made in Turing (1950)
that seem to have been vindicated. In particular, it is plausible to
claim that, in the year 2000, educated opinion had altered to the
extent that, in many quarters, one could speak of the possibility of
machines' thinking--and of machines'
learning--without expecting to be contradicted. As Moor (2001)
points out, "machine intelligence" is not the oxymoron
that it might have been taken to be when Turing first started thinking
about these matters.


### 3.3 A Useful Distinction



There are two different theoretical claims that are run together in
many discussions of The Turing Test that can profitably be separated.
One claim holds that the general scheme that is described in
Turing's Imitation Game provides a good test for the presence of
intelligence. (If something can pass itself off as a person under
sufficiently demanding test conditions, then we have very good reason
to suppose that that thing is intelligent.) Another claim holds that
an appropriately programmed computer could pass the kind of test that
is described in the first claim. We might call the first claim
"The Turing Test Claim" and the second claim "The
Thinking Machine Claim". Some objections to the claims made in
Turing (1950) are objections to the Thinking Machine Claim, but not
objections to the Turing Test Claim. (Consider, for example, the
argument of Searle (1982), which we discuss further in Section 6.)
However, other objections are objections to the Turing Test Claim.
Until we get to Section 6, we shall be confining our attention to
discussions of the Turing Test Claim.


### 3.4 A Further Note



In this article, we follow the standard philosophical convention
according to which "a mind" means "at least one
mind". If "passing the Turing Test" implies
intelligence, then "passing the Turing Test" implies the
presence of at least one mind. We cannot here explore recent
discussions of "swarm intelligence", "collective
intelligence", and the like. However, it is surely clear that
two people taking turns could "pass the Turing Test" in
circumstances in which we should be very reluctant to say that there
is a "collective mind" that has the minds of the two as
components.


## 4. Assessment of the Current Standing of The Turing Test



Given the initial distinction that we made between different ways in
which the expression The Turing Test gets interpreted in the
literature, it is probably best to approach the question of the
assessment of the current standing of The Turing Test by dividing
cases. True enough, we think that there is a correct interpretation of
exactly what test it is that is proposed by Turing (1950); but a
complete discussion of the current standing of The Turing Test should
pay at least some attention to the current standing of other tests
that have been mistakenly supposed to be proposed by Turing
(1950).



There are a number of main ideas to be investigated. First, there is
the suggestion that The Turing Test provides logically necessary and
sufficient conditions for the attribution of intelligence. Second,
there is the suggestion that The Turing Test provides logically
sufficient--but not logically necessary--conditions for the
attribution of intelligence. Third, there is the suggestion that The
Turing Test provides "criteria"--defeasible
sufficient conditions--for the attribution of intelligence.
Fourth--and perhaps not importantly distinct from the previous
claim--there is the suggestion that The Turing Test provides
(more or less strong) probabilistic support for the attribution of
intelligence. We shall consider each of these suggestions in turn.


### 4.1 (Logically) Necessary and Sufficient Conditions



It is doubtful whether there are very many examples of people who have
explicitly claimed that The Turing Test is meant to provide conditions
that are both logically necessary and logically sufficient for the
attribution of intelligence. (Perhaps Block (1981) is one such case.)
However, some of the objections that have been proposed against The
Turing Test only make sense under the assumption that The Turing Test
does indeed provide logically necessary and logically sufficient
conditions for the attribution of intelligence; and many more of the
objections that have been proposed against The Turing Test only make
sense under the assumption that The Turing Test provides necessary and
sufficient conditions for the attribution of intelligence, where the
modality in question is weaker than the strictly logical, e.g., nomic
or causal.



Consider, for example, those people who have claimed that The Turing
Test is chauvinistic; and, in particular, those people who have
claimed that it is surely logically possible for there to be something
that possesses considerable intelligence, and yet that is not able to
pass The Turing Test. (Examples: Intelligent creatures might fail to
pass The Turing Test because they do not share our way of life;
intelligent creatures might fail to pass The Turing Test because they
refuse to engage in games of pretence; intelligent creatures might
fail to pass The Turing Test because the pragmatic conventions that
govern the languages that they speak are so very different from the
pragmatic conventions that govern human languages. Etc.) None of this
can constitute objections to The Turing Test unless The Turing Test
delivers *necessary* conditions for the attribution of
intelligence.



French (1990) offers ingenious arguments that are intended to show
that "the Turing Test provides a guarantee not of intelligence,
but of culturally-oriented intelligence." But, of course,
anything that has culturally-oriented intelligence *has*
intelligence; so French's objections cannot be taken to be
directed towards the idea that The Turing Test provides sufficient
conditions for the attribution of intelligence. Rather--as we
shall see later--French supposes that The Turing Test establishes
sufficient conditions that no machine will ever satisfy. That is, in
French's view, what is wrong with The Turing Test is that it
establishes utterly uninteresting sufficient conditions for the
attribution of intelligence.



Floridi and Chiriatti (2020: 683) say that The Turing Test provides
necessary but insufficient conditions for intelligence: not passing
The Turing Test disqualifies an AI from being intelligent, but passing
The Turing Test is not sufficient to qualify an AI as intelligent.
However, they also say that "any reader ... will be well
acquainted with the nature of the test, so we shall not describe
it." The account that they would give of The Turing Test
must be quite different from the account of The Turing Test that we
have been presenting.


### 4.2 Logically Sufficient Conditions



There are many philosophers who have supposed that The Turing Test is
intended to provide logically sufficient conditions for the
attribution of intelligence. That is, there are many philosophers who
have supposed that The Turing Test claims that it is logically
impossible for something that lacks intelligence to pass The Turing
Test. (Often, this supposition goes with an interpretation according
to which passing The Turing Test requires rather a lot, e.g.,
producing behavior that is indistinguishable from human behavior over
an entire lifetime.)



There are well-known arguments against the claim that passing The
Turing Test--or any other purely behavioral test--provides
logically sufficient conditions for the attribution of intelligence.
*The* standard objection to this kind of analysis of
intelligence (mind, thought) is that a being whose behavior was
produced by "brute force" methods ought not to count as
intelligent (as possessing a mind, as having thoughts).



Consider, for example, Ned Block's *Blockhead*. Blockhead
is a creature that looks just like a human being, but that is
controlled by a "game-of-life look-up tree," i.e. by a
tree that contains a programmed response for every discriminable input
at each stage in the creature's life. If we agree that Blockhead
is logically possible, and if we agree that Blockhead is not
intelligent (does not have a mind, does not think), then Blockhead is
a counterexample to the claim that the Turing Test provides a
logically sufficient condition for the ascription of intelligence.
After all, Blockhead could be programmed with a look-up tree that
produces responses identical with the ones that *you* would
give over the entire course of *your* life (given the same
inputs).



There are perhaps only two ways in which someone who claims that The
Turing Test offers logically sufficient conditions for the attribution
of intelligence can respond to Block's argument. First, it could
be denied that Blockhead is a logical possibility; second, it could be
claimed that Blockhead would be intelligent (have a mind, think).



In order to deny that Blockhead is a logical possibility, it seems
that what needs to be denied is the commonly accepted link between
conceivability and logical possibility: it certainly seems that
Blockhead is *conceivable*, and so, if (properly circumscribed)
conceivability is sufficient for logical possibility, then it seems
that we have good reason to accept that Blockhead is a logical
possibility. Since it would take us too far away from our present
concerns to explore this issue properly, we merely note that it
remains a controversial question whether (properly circumscribed)
conceivability is sufficient for logical possibility. (For further
discussion of this issue, see Crooke (2002).)



The question of whether Blockhead is intelligent (has a mind, thinks)
may seem straightforward, but--despite Block's confident
assertion that Blockhead "has all of the intelligence of a
toaster"--it is not obvious that we should deny that
Blockhead is intelligent. Blockhead may not be a particularly
efficient processor of information; but it is at least a processor of
information, and that--in combination with the behavior that is
produced as a result of the processing of information--might well
be taken to be sufficient grounds for the attribution of *some*
level of intelligence to Blockhead. For further critical discussion of
the argument of Block (1981), see McDermott (2014), and Pautz and
Stoljar (2019).


### 4.3 Criteria



In his *Philosophical Investigations*, Wittgenstein famously
writes: "An 'inner process' stands in need of
outward criteria" (580). Exactly what Wittgenstein meant by this
remark is unclear, but one way in which it might be interpreted is as
follows: in order to be justified in ascribing a "mental
state" to some entity, there must be some true claims about the
observable behavior of that entity that, (perhaps) together with other
true claims about that entity (not themselves couched in
"mentalistic" vocabulary), entail that the entity has the
mental state in question. If no true claims about the observable
behavior of the entity can play any role in the justification of the
ascription of the mental state in question to the entity, then there
are no grounds for attributing that kind of mental state to the
entity.



The claim that, in order to be justified in ascribing a mental state
to an entity, there must be some true claims about the observable
behavior of that entity that alone--i.e. without the addition of
any other true claims about that entity--entail that the entity
has the mental state in question, is a piece of philosophical
behaviorism. It may be--for all that we are able to
argue--that Wittgenstein was a philosophical behaviorist; it may
be--for all that we are able to argue--that Turing was one,
too. However, if we go by the letter of the account given in the
previous paragraph, then all that need follow from the claim that the
Turing Test is criterial for the ascription of intelligence (thought,
mind) is that, when other true claims (not themselves couched in terms
of mentalistic vocabulary) are conjoined with the claim that an entity
has passed the Turing Test, it then follows that the entity in
question has intelligence (thought, mind).



(Note that the parenthetical qualification that the additional true
claims not be couched in terms of mentalistic vocabulary is only one
way in which one might try to avoid the threat of trivialization. The
difficulty is that the addition of the true claim that an entity has a
mind will always produce a set of claims that entails that that entity
has a mind, no matter what other claims belong to the set!)



To see how the claim that the Turing Test is merely criterial for the
ascription of intelligence differs from the logical behaviorist claim
that the Turing Test provides logically sufficient conditions for the
ascription of intelligence, it suffices to consider the question of
whether it is *nomically* possible for there to be a
"hand simulation" of a Turing Test program. Many people
have supposed that there is good reason to deny that Blockhead is a
nomic (or physical) possibility. For example, in *The Physics of
Immortality*, Frank Tipler provides the following argument in
defence of the claim that it is physically impossible to "hand
simulate" a Turing-Test-passing program:



> 
> If my earlier estimate that the human brain can code as much as
> 1015 bits is correct, then since an average book codes
> about 106 bits ... it would require more than 100
> million books to code the human brain. It would take at least thirty
> five-story main university libraries to hold this many books. We know
> from experience that we can access any memory in our brain in about
> 100 seconds, so a hand simulation of a Turing Test-passing program
> would require a human being to be able to take off the shelf, glance
> through, and return to the shelf all of these 100 million books in 100
> seconds. If each book weighs about a pound (0.5 kilograms), and on the
> average the book moves one yard (one meter) in the process of taking
> it off the shelf and returning it, then in 100 seconds the energy
> consumed in just moving the books is 3 x 1019 joules; the
> rate of energy consumption is 3 x 1011 megawatts. Since a
> human uses energy at a normal rate of 100 watts, the power required is
> the bodily power of 3 x 1015 human beings, about a million
> times the current population of the entire earth. A typical large
> nuclear power plant has a power output of 1,000 megawatts, so a hand
> simulation of the human program requires a power output equal to that
> of 300 million large nuclear power plants. As I said, a man can no
> more hand-simulate a Turing Test-passing program than he can jump to
> the Moon. In fact, it is far more difficult. (40)
> 



While there might be ways in which the details of Tipler's
argument could be improved, the general point seems clearly right: the
kind of combinatorial explosion that is required for a look-up tree
for a human being is ruled out by the laws and boundary conditions
that govern the operations of the physical world. But, if this is
right, then, while it may be true that Blockhead is a *logical*
possibility, it follows that Blockhead is not a *nomic* or
*physical* possibility. And then it seems natural to hold that
The Turing Test does indeed provide *nomically* sufficient
conditions for the attribution of intelligence: given everything else
that we already know--or, at any rate, take ourselves to
know--about the universe in which we live, we would be fully
justified in concluding that anything that succeeds in passing The
Turing Test is, indeed, intelligent (possessed of a mind, and so
forth).



There are ways in which the argument in the previous paragraph might
be resisted. At the very least, it is worth noting that there is a
serious gap in the argument that we have just rehearsed. Even if we
can rule out "hand simulation" of intelligence, it does
not follow that we have ruled out all other kinds of mere simulation
of intelligence. Perhaps--for all that has been argued so
far--there are nomically possible ways of producing mere
simulations of intelligence. But, if that's right, then passing
The Turing Test need not be so much as criterial for the possession of
intelligence: it need not be that given everything else that we
already know--or, at any rate, take ourselves to know--about
the universe in which we live, we would be fully justified in
concluding that anything that succeeds in passing The Turing Test is,
indeed, intelligent (possessed of a mind, and so forth).



(McDermott (2014) calculates that a look-up table for a participant
who makes 50 conversational exchanges would have about
1022278 nodes. It is tempting to take this calculation to
establish that it is neither nomically nor physically possible for
there to be a "hand simulation" of a Turing Test program,
on the grounds that the required number of nodes could not be fitted
into a space much much larger than the entire observable
universe.)


### 4.4 Probabilistic Support



When we look at the initial formulation that Turing provides of his
test, it is clear that he thought that the passing of the test would
provide probabilistic support for the hypothesis of intelligence.
There are at least two different points to make here. First, the
*prediction* that Turing makes is itself probabilistic: Turing
predicts that, in about fifty years from the time of his writing, it
will be possible to programme digital computers to make them play the
imitation game so well that an average interrogator will have no more
than a seventy per cent chance of making the right identification
after five minutes of questioning. Second, the probabilistic nature of
Turing's prediction provides good reason to think that the
*test* that Turing proposes is itself of a probabilistic
nature: a given level of success in the imitation game
produces--or, at any rate, should produce--a specifiable
level of increase in confidence that the participant in question is
intelligent (has thoughts, is possessed of a mind). Since Turing
doesn't tell us how he supposes that levels of success in the
imitation game correlate with increases in confidence that the
participant in question is intelligent, there is a sense in which The
Turing Test is greatly underspecified. Relevant variables clearly
include: the length of the period of time over which the questioning
in the game takes place (or, at any rate, the "amount" of
questioning that takes place); the skills and expertise of the
interrogator (this bears, for example, on the "depth" and
"difficulty" of the questioning that takes place); the
skills and expertise of the third player in the game; and the number
of independent sessions of the game that are run (particularly when
the other participants in the game differ from one run to the next).
Clearly, a machine that is very successful in many different runs of
the game that last for quite extended periods of time and that involve
highly skilled participants in the other roles has a much stronger
claim to intelligence than a machine that has been successful in a
single, short run of the game with highly inexpert participants. That
a machine has succeeded in one short run of the game against inexpert
opponents might provide some reason for increase in confidence that
the machine in question is intelligent: but it is clear that results
on subsequent runs of the game could quickly overturn this initial
increase in confidence. That a machine has done much better than
chance over many long runs of the imitation game against a variety of
skilled participants surely provides much stronger evidence that the
machine is intelligent. (Given enough evidence of this kind, it seems
that one could be quite confident indeed that the machine is
intelligent, while still--of course--recognizing that
one's judgment could be overturned by further evidence, such as
a series of short runs in which it does much worse than chance against
participants who use the same strategy over and over to expose the
machine as a machine.)



The probabilistic nature of The Turing Test is often overlooked. True
enough, Moor (1976, 2001)--along with various other
commentators--has noted that The Turing Test is
"inductive," i.e. that "The Turing Test"
provides no more than defeasible evidence of intelligence. However, it
is one thing to say that success in "a rigorous Turing
test" provides no more than defeasible evidence of intelligence;
it is quite another to note the probabilistic features to which we
have drawn attention in the preceding paragraph. Consider, for
example, Moor's observation (Moor 2001:83) that "...
inductive evidence gathered in a Turing test can be outweighed by new
evidence. ... If new evidence shows that a machine passed the
Turing Test by remote control run by a human behind the scenes, then
reassessment is called for." This--and other similar
passages--seems to us to suggest that Moor supposes that a
"rigorous Turing test" is a one-off event in which the
machine either succeeds or fails. But this interpretation of The
Turing Test is vulnerable to the kind of objection lodged by
Bringsjord (1994): even on a moderately long single run with
relatively expert participants, it may not be all that unlikely that
an unintelligent machine serendipitously succeeds in the imitation
game. In our view, given enough sufficiently long runs with different
sufficiently expert participants, the likelihood of serendipitous
success can be made as small as one wishes. Thus, while
Bringsjord's "argument from serendipity" has force
against some versions of The Turing Test, it has no force against the
most plausible interpretation of the test that Turing actually
proposed.



It is worth noting that it is quite easy to construct more
sophisticated versions of "The Imitation Game" that yield
more fine-grained statistical data. For example, rather than getting
the judges to issue Yes/No verdicts about both of the participants in
the game, one could get the judges to provide probabilistic answers.
("I give a 75% probability to the claim that A is the machine,
and only 25% probability to the claim that B is the machine.")
This point is important when one comes to consider criticisms of the
"methodology" implicit in "The Turing Test".
(For further discussion of the probabilistic nature of "The
Turing Test", see Shieber (2007).)


## 5. Alternative Tests



Some of the literature about The Turing Test is concerned with
questions about the framing of a test that can provide a suitable
guide to future research in the area of Artificial Intelligence. The
idea here is very simple. Suppose that we have the ambition to produce
an artificially intelligent entity. What tests should we take as
setting the goals that putatively intelligent artificial systems
should achieve? Should we suppose that The Turing Test provides an
appropriate goal for research in this field? In assessing these
proposals, there are two different questions that need to be borne in
mind. First, there is the question whether it is a useful goal for AI
research to aim to make a machine that can pass the given test
(administered over the specified length of time, at the specified
degree of success). Second, there is the question of the appropriate
conclusion to draw about the mental capacities of a machine that does
manage to pass the test (administered over the specified length of
time, at the specified degree of success).



Opinion on these questions is deeply divided. Some people suppose that
The Turing Test does not provide a useful goal for research in AI
because it is far too difficult to produce a system that can pass the
test. Other people suppose that The Turing Test does not provide a
useful goal for research in AI because it sets a very narrow target
(and thus sets unnecessary restrictions on the kind of research that
gets done). Some people think that The Turing Test provides an
entirely appropriate goal for research in AI; while other people think
that there is a sense in which The Turing Test is not really demanding
enough, and who suppose that The Turing Test needs to be extended in
various ways in order to provide an appropriate goal for AI. We shall
consider some representatives of each of these positions in turn.



There are some people who continue to endorse The Turing Test. For
example, Neufeld and Finnestad (2020a) (2020b) argue that The Turing
Test is no barrier to progress in AI, requires no significant
redefinition, and does not shut down other avenues of investigation.
Maybe we do better just to take The Turing Test to define a watershed
rather than a threshold towards which we might hope to make
incremental progression.


### 5.1 The Turing Test is Too Hard



Some people have claimed that The Turing Test doesn't set an
appropriate goal for current research in AI because we are plainly so
far away from attaining this goal. Amongst these people there are some
who have gone on to offer reasons for thinking that it is doubtful
that we shall ever be able to create a machine that can pass The
Turing Test--or, at any rate, that it is doubtful that we shall
be able to do this at any time in the foreseeable future. Perhaps the
most interesting arguments of this kind are due to French (1990); at
any rate, these are the arguments that we shall go on to consider.
(Cullen (2009) sets out similar considerations.)



According to French, The Turing Test is "virtually
useless" as a real test of intelligence, because nothing without
a "human subcognitive substrate" could pass the test, and
yet the development of an artificial "human cognitive
substrate" is almost impossibly difficult. At the very least,
there are straightforward sets of questions that reveal
"low-level cognitive structure" and that--in
French's view--are almost certain to be successful in
separating human beings from machines.



First, if interrogators are allowed to draw on the results of research
into, say, *associative priming*, then there is data that will
very plausibly separate human beings from machines. For example, there
is research that shows that, if humans are presented with series of
strings of letters, they require less time to recognize that a string
is a word (in a language that they speak) if it is preceded by a
related word (in the language that they speak), rather than by an
unrelated word (in the language that they speak) or a string of
letters that is not a word (in the language that they speak). Provided
that the interrogator has accurate data about average recognition
times for subjects who speak the language in question, the
interrogator can distinguish between the machine and the human simply
by looking at recognition times for appropriate series of strings of
letters. Or so says French. It isn't clear to us that this is
right. After all, the design of The Turing Test makes it hard to see
how the interrogator will get reliable information about response
times to series of strings of symbols. The point of putting the
computer in a separate room and requiring communication by teletype
was precisely to rule out certain irrelevant ways of identifying the
computer. If these requirements don't already rule out
identification of the computer by the application of tests of
associative priming, then the requirements can surely be altered to
bring it about that this is the case. (Perhaps it is also worth noting
that administration of the kind of test that French imagines is not
ordinary conversation; nor is it something that one would expect that
any but a few expert interrogators would happen upon. So, even if the
circumstances of The Turing Test do not rule out the kind of procedure
that French here envisages, it is not clear that The Turing Test will
be impossibly hard for machines to pass.)



Second, at a slightly higher cognitive level, there are certain kinds
of "ratings games" that French supposes will be very
reliable discriminators between humans and machines. For instance, the
"Neologism Ratings Game"--which asks participants to
rank made-up words on their appropriateness as names for given kinds
of entities--and the "Category Rating
Game"--which asks participants to rate things of one
category as things of another category--are both, according to
French, likely to prove highly reliable in discriminating between
humans and machines. For, in the first case, the ratings that humans
make depend upon large numbers of culturally acquired associations
(which it would be well-nigh impossible to identify and describe, and
hence which it would (arguably) be well-nigh impossible to program
into a computer). And, in the second case, the ratings that people
actually make are highly dependent upon particular social and cultural
settings (and upon the particular ways in which human life is
experienced). To take French's examples, there would be
widespread agreement amongst competent English speakers in the
technologically developed Western world that "Flugblogs"
is not an appropriate name for a breakfast cereal, while
"Flugly" is an appropriate name for a child's teddy
bear. And there would also be widespread agreement amongst competent
speakers of English in the developed world that pens rate higher as
weapons than grand pianos rate as wheelbarrows. Again, there are
questions that can be raised about French's argument here. It is
not clear to us that the data upon which the ratings games rely is as
reliable as French would have us suppose. (At least one of us thinks
that "Flugly" would be an entirely inappropriate name for
a child's teddy bear, a response that is due to the similarity
between the made-up word "Flugly" and the word
"Fugly," that had some currency in the primarily
undergraduate University college that we both attended. At least one
of us also thinks that young children would very likely be delighted
to eat a cereal called "Flugblogs," and that a good answer
to the question about ratings pens and grand pianos is that it all
depends upon the pens and grand pianos in question. What if the grand
piano has wheels? What if the opponent has a sword or a sub-machine
gun? It isn't obvious that a refusal to play this kind of
ratings game would necessarily be a give-away that one is a machine.)
Moreover, even if the data is reliable, it is not obvious that any but
a select group of interrogators will hit upon this kind of strategy
for trying to unmask the machine; nor is it obvious that it is
impossibly hard to build a machine that is able to perform in the way
in which typical humans do on these kinds of tests. In particular,
if--as Turing assumes--it is possible to make learning
machines that can be "trained up" to learn how to do
various kinds of tasks, then it is quite unclear why these machines
couldn't acquire just the same kinds of "subcognitive
competencies" that human children acquire when they are
"trained up" in the use of language.



There are other reasons that have been given for thinking that The
Turing Test is too hard (and, for this reason, inappropriate in
setting goals for current research into artificial intelligence). In
general, the idea is that there may well be features of human
cognition that are particularly hard to simulate, but that are not in
any sense essential for intelligence (or thought, or possession of a
mind). The problem here is not merely that The Turing Test really does
test for *human* intelligence; rather, the problem here is the
fact--if indeed it is a fact--that there are quite
inessential features of human intelligence that are extraordinarily
difficult to replicate in a machine. If this complaint is
justified--if, indeed, there are features of human intelligence
that are extraordinarily difficult to replicate in machines,
*and* that could and would be reliably used to unmask machines
in runs of The Turing Test--then there is reason to worry about
the idea that The Turing Test sets an appropriate direction for
research in artificial intelligence. However, as our discussion of
French shows, there may be reason for caution in supposing that the
kinds of considerations discussed in the present section show that we
are already in a position to say that The Turing Test does indeed set
inappropriate goals for research in artificial intelligence.


### 5.2 The Turing Test is Too Narrow



There are authors who have suggested that The Turing Test does not set
a sufficiently broad goal for research in the area of artificial
intelligence. Amongst these authors, there are many who suppose that
The Turing Test is too easy. (We go on to consider some of these
authors in the next sub-section.) But there are also some authors who
have supposed that, even if the goal that is set by The Turing Test is
very demanding indeed, it is nonetheless too restrictive.



Objection to the notion that the Turing Test provides a logically
sufficient condition for intelligence can be adapted to the goal of
showing that the Turing Test is too restrictive. Consider, for
example, Gunderson (1964). Gunderson has two major complaints to make
against The Turing Test. First, he thinks that success in
Turing's Imitation Game might come for reasons other than the
possession of intelligence. But, second, he thinks that success in the
Imitation Game would be but one example of the kinds of things that
intelligent beings can do and--hence--in itself could not be
taken as a reliable indicator of intelligence. By way of analogy,
Gunderson offers the case of a vacuum cleaner salesman who claims that
his product is "all-purpose" when, in fact, all it does is
to suck up dust. According to Gunderson, Turing is in the same
position as the vacuum cleaner salesman *if* he is prepared to
say that a machine is intelligent merely on the basis of its success
in the Imitation Game. Just as "all purpose" entails the
ability to do a range of things, so, too, "thinking"
entails the possession of a range of abilities (beyond the mere
ability to succeed in the Imitation Game).



There is an obvious reply to the argument that we have here attributed
to Gunderson, viz. that a machine that is capable of success in the
Imitation Game is capable of doing a large range of different kinds of
things. In order to carry out a conversation, one needs to have many
different kinds of cognitive skills, each of which is capable of
application in other areas. Apart from the obvious general cognitive
competencies--memory, perception, etc.--there are many
particular competencies--rudimentary arithmetic abilities,
understanding of the rules of games, rudimentary understanding of
national politics, etc.--which are tested in the course of
repeated runs of the Imitation Game. It is inconceivable that that
there be a machine that is startlingly good at playing the Imitation
Game, and yet unable to do well at *any* other tasks that might
be assigned to it; and it is equally inconceivable that there is a
machine that is startlingly good at the Imitation Game and yet that
does not have a wide range of competencies that can be displayed in a
range of quite disparate areas. To the extent that Gunderson considers
this line of reply, all that he says is that there is no reason to
think that a machine that can succeed in the Imitation Game
*must* have more than a narrow range of abilities; we think
that there is no reason to believe that this reply should be taken
seriously.



More recently, Erion (2001) has defended a position that has some
affinity to that of Gunderson. According to Erion, machines might be
"capable of outperforming human beings in limited tasks in
specific environments, [and yet] still be unable to act skillfully in
the diverse range of situations that a person with common sense
can" (36). On one way of understanding the claim that Erion
makes, he too believes that The Turing Test only identifies one
amongst a range of independent competencies that are possessed by
intelligent human beings, and it is for this reason that he proposes a
more comprehensive "Cartesian Test" that "involves a
more careful examination of a creature's language, [and] also
tests the creature's ability to solve problems in a wide variety
of everyday circumstances" (37). In our view, at least when The
Turing Test is properly understood, it is clear that anything that
passes The Turing Test must have the ability to solve problems in a
wide variety of everyday circumstances (because the interrogators will
use their questions to probe these--and other--kinds of
abilities in those who play the Imitation Game).


### 5.3 The Turing Test is Too Easy



There are authors who have suggested that The Turing Test should be
replaced with a more demanding test of one kind or another. It is not
at all clear that any of these tests actually proposes a better goal
for research in AI than is set by The Turing Test. However, in this
section, we shall not attempt to defend that claim; rather, we shall
simply describe some of the further tests that have been proposed, and
make occasional comments upon them. (One preliminary point upon which
we wish to insist is that Turing's Imitation Game was devised
against the background of the limitations imposed by then current
technology. It is, of course, not essential to the game that tele-text
devices be used to prevent direct access to information about the sex
or genus of participants in the game. We shall not advert to these
relatively mundane kinds of considerations in what follows.)


#### 5.3.1 The Total Turing Test



Harnad (1989, 1991) claims that a better test than The Turing Test
will be one that requires responses to all of our inputs, and not
merely to text-formatted linguistic inputs. That is, according to
Harnad, the appropriate goal for research in AI has to be to construct
a robot with something like human sensorimotor capabilities. Harnad
also considers the suggestion that it might be an appropriate goal for
AI to aim for "neuromolecular indistinguishability," but
rejects this suggestion on the grounds that once we know how to make a
robot that can pass his Total Turing Test, there will be no problems
about mind-modeling that remain unsolved. It is an interesting
question whether the test that Harnad proposes sets a more appropriate
goal for AI research. In particular, it seems worth noting that it is
not clear that there could be a system that was able to pass The
Turing Test and yet that was not able to pass The Total Turing Test.
Since Harnad himself seems to think that it is quite likely that
"full robotic capacities [are] ... necessary to generate
... successful linguistic performance," it is unclear why
there is reason to replace The Turing Test with his extended test.
(This point against Harnad can be found in Hauser (1993:227), and
elsewhere.)


#### 5.3.2 The Lovelace Test



Bringsjord et al. (2001) propose that a more satisfactory aim for AI
is provided by a certain kind of meta-test that they call the Lovelace
Test. They say that an artificial agent *A*, designed by human
H, passes the Lovelace Test just in case three conditions are jointly
satisfied: (1) the artificial agent *A* produces output
*O*; (2) *A*'s outputting *O* is not the
result of a fluke hardware error, but rather the result of processes
that *A* can repeat; and (3) *H*--or someone who
knows what *H* knows and who has *H*'s
resources--cannot explain how *A* produced *O* by
appeal to *A*'s architecture, knowledge-base and core
functions. Against this proposal, it seems worth noting that there are
questions to be raised about the interpretation of the third
condition. If a computer program is long and complex, then no human
agent can explain in *complete* detail how the output was
produced. (Why did the computer output 3.16 rather than 3.17?) But if
we are allowed to give a highly schematic explanation--the
computer took the input, did some internal processing and then
produced an answer--then it seems that it will turn out to be
very hard to support the claim that human agents ever do anything
genuinely creative. (After all, we too take external input, perform
internal processing, and produce outputs.) What is missing from the
account that we are considering is any suggestion about the
appropriate *level* of explanation that is to be provided. It
is quite unclear why we should suppose that there is a relevant
difference between people and machines at any level of explanation;
but, if that's right, then the test in question is trivial. (One
might also worry that the proposed test rules out *by fiat* the
possibility that creativity can be best achieved by using genuine
*randomising* devices.)


#### 5.3.3 The Truly Total Turing Test



Schweizer (1998) claims that a better test than The Turing Test will
advert to the evolutionary history of the subjects of the test. When
we attribute intelligence to human beings, we rely on an extensive
historical record of the intellectual achievements of human beings. On
the basis of this historical record, we are able to claim that human
beings are intelligent; and we can rely upon this claim when we
attribute intelligence to individual human beings on the basis of
their behavior. According to Schweizer, if we are to attribute
intelligence to machines, we need to be able to advert to a comparable
historical record of cognitive achievements. So, it will only be when
machines have developed languages, written scientific treatises,
composed symphonies, invented games, and the like, that we shall be in
a position to attribute intelligence to individual machines on the
basis of their behavior. Of course, we can still use The Turing Test
to determine whether an individual machine is intelligent: but our
answer to the question won't depend merely upon whether or not
the machine is successful in The Turing Test; there is the further
"evolutionary" condition that also must be satisfied.
Against Schweizer, it seems worth noting that it is not at all clear
that our reason for granting intelligence to other humans on the basis
of their behavior is that we have prior knowledge of the collective
cognitive achievements of human beings.



**5.3.4 Further Proposals**



Damassino (2020) suggests that it would be better to require test
subjects to produce an enquiry in which performance is assessed along
three dimensions: (a) comparison with human performance; (b) success
in completing the enquiry; and (c) efficiency in completing the
enquiry (minimisation of the number of questions asked in completing
the enquiry). The motivation given for this proposal is that, because
The Turing Test attracts projects whose primary ambition is to fool
judges, it is concerned with whether or how well test subjects perform
on their allocated tasks. It seems to us that there is nothing here
that impugns The Turing Test. It does not count against The
Turing Test that public competitions based on it with prizes
attached lead to gaming, given that everyone knows that those prizes
are being awarded to entries that clearly do not pass The Turing Test.
If anything is impugned here, it is the public competitions, rather
than The Turing Test.



Kulikov (2020) suggests that there is value in considering
Preferential Engagement Tests or Meaningful Engagement Tests. Even
though computers can now beat the best humans at chess, many people
prefer to play chess with humans rather than with expert chess-playing
computers. Perhaps, even if computers could pass The Turing Test,
people would prefer to carry on conversations with humans rather than
with expert conversational computers. We think that this kind of
speculation relies upon assumptions about what could make for expert
conversational partners. If our conversational partners need to be
able to update information about their surroundings in real
time--for example, while watching a game of football--then
we will not think that there is a direct path from GPT-3 to expert
conversational partners. If only androids can be expert conversational
partners, then it is less clear that Preferential Engagement Tests or
Meaningful Engagement Tests will track anything other than
anthropocentric bias.


### 5.4 Should the Turing Test be Considered Harmful?



Perhaps the best known attack on the suggestion that The Turing Test
provides an appropriate research goal for AI is due to Hayes and Ford
(1995). Among the controversial claims that Hayes and Ford make, there
are at least the following:


1. Turing suggested the imitation game as a definite goal for program
of research.
2. Turing intended The Turing Test to be a gender test rather than a
species test.
3. The task of trying to make a machine that is successful in The
Turing Test is so extremely difficult that no one could seriously
adopt the creation of such a machine as a research goal.
4. The Turing Test suffers from the basic design flaw that it sets
out to confirm a "null hypothesis", viz. that there is no
difference in behavior between certain machines and humans.
5. No null effect experiment can provide an adequate criterion for
intelligence, since the question can always arise that the judges did
not look hard enough (and did not raise the right kinds of questions).
But, if this question is left open, then there is no stable endpoint
of enquiry.
6. Null effect experiments cannot measure anything: The Turing Test
can only test for complete success. ("A man who failed to seem
feminine in 10% of what he said would almost always fail the Imitation
game.")
7. The Turing Test is really a test of the ability of the human
species to discriminate its members from human imposters. ("The
gender test ... is a test of making a mechanical
transvestite.")
8. The Turing Test is circular: what it fails to detect cannot be
"intelligence" or"humanity", since many humans
would fail The Turing Test. Indeed, "since one of the players
must be judged to be a machine, half the human population would fail
the species test".
9. The perspective of The Turing Test is arrogant and parochial: it
mistakenly assumes that we can understand human cognition without
first obtaining a firm grasp of the basic principles of
cognition.
10. The Turing Test does not admit of weaker, different, or even
stronger forms of intelligence than those deemed human.



Some of these claims seem straightforwardly incorrect. Consider (h),
for example. In what sense can it be claimed that 50% of the human
population would fail "the species test"? If "the
species test" requires the interrogator to decide which of two
people is a machine, why should it be thought that the verdict of the
interrogator has any consequences for the assessment of the
intelligence of the person who is judged to be a machine? (Remember,
too, that one of the conditions for "the species
test"--as it is originally described by Hayes and
Ford--is that one of the contestants *is* a machine. While
the machine can "demonstrate" its intelligence by winning
the imitation game, a person cannot "demonstrate" their
lack of intelligence by failing to win.)



It seems wrong to say that The Turing Test is defective because it is
a "null effect experiment". True enough, there is a sense
in which The Turing Test does look for a "null result": if
ordinary judges in the specified circumstances fail to identify the
machine (at a given level of success), then there is a given
likelihood that the machine is intelligent. But the point of insisting
on "ordinary judges" in the specified circumstances is
precisely to rule out irrelevant ways of identifying the machine (i.e.
ways of identifying the machine that are not relevant to the question
whether it is intelligent). There might be all kinds of irrelevant
differences between a given kind of machine and a human
being--not all of them rendered undetectable by the experimental
set-up that Turing describes--but The Turing Test will remain a
good test provided that it is able to ignore these irrelevant
differences.



It also seems doubtful that it is a serious failing of The Turing Test
that it can only test for "complete success". On the one
hand, if a man has a one in ten chance of producing a claim that is
plainly not feminine, then we can compute the chance that he will be
discovered in a game in which he answers *N*
questions--and, if *N* is sufficiently small, then it
won't turn out that "he would almost always fail to
win". On the other hand, as we noted at the end of Section 4.4
above, if one were worried about the "YES/NO" nature of
"The Turing Test", then one could always get the judges to
produce probabilistic verdicts instead. This change preserves the
character of The Turing Test, but gives it scope for greater
statistical sophistication.



While there are (many) other criticisms that can be made of the claims
defended by Hayes and Ford (1995), it should be acknowledged that they
are right to worry about the suggestion that The Turing Test provides
the defining goal for research in AI. There are various reasons why
one should be loathe to accept the proposition that the one central
ambition of AI research is to produce artificial people. However it is
worth pointing out that there is no reason to think that Turing
supposed that The Turing Test defined the field of AI research (and
there is not much evidence that any other serious thinkers have
thought so either). Turing himself was well aware that there might be
non-human forms of intelligence--cf. (j) above. However, all of
this remains consistent with the suggestion that it is quite
appropriate to suppose that The Turing Test sets *one* long
term goal for AI research: one thing that we might well aim to do
*eventually* is to produce artificial people. If--as Hayes
and Ford claim--that task is almost impossibly difficult, then
there is no harm in supposing that the goal is merely an
*ambit* goal to which few resources should be committed; but we
might still have good reason to allow that it is *a* goal.



Others who have argued that we need to "move beyond" The
Turing Test include Hernandez-Orallo (2000) (2020) and
Marcus (2020).


## 6. The Chinese Room



There are many different objections to The Turing Test which have
surfaced in the literature during the past fifty years, but which we
have not yet discussed. We cannot hope to canvass all of these
objections here. However, there is one argument--Searle's
"Chinese Room" argument--that is mentioned so often
in connection with the Turing Test that we feel obliged to end with
some discussion of it.



In *Minds, Brains and Programs* and elsewhere, John Searle
argues against the claim that "appropriately programmed
computers literally have cognitive states" (64). Clearly enough,
Searle is here disagreeing with Turing's claim that an
appropriately programmed computer could think. There is much that is
controversial about Searle's argument; we shall just consider
*one* way of understanding what it is that he is arguing
for.



The basic structure of Searle's argument is very well known. We
can imagine a "hand simulation" of an intelligent
agent--in the case described, a speaker of a Chinese
language--in circumstances in which we might well be very
reluctant to allow that there is any appropriate intelligence lying
behind the simulated behavior. (Thus, what we are invited to suppose
is a logical possibility is not so very different from what Block
invites us to suppose is a logical possibility. However, the argument
that Searle goes on to develop is rather different from the argument
that Block defends.) *Moreover*--and this is really the
key point for Searle's argument--the "hand
simulation" in question is, in all relevant respects, simply a
special kind of digital computation. So, there is a possible
world--doubtless one quite remote from the actual world--in
which a digital computer simulates intelligence but in which the
digital computer does not itself possess intelligence. But, if we
consider any digital computer in the actual world, it will not differ
from the computer in that remote possible world in any way which could
make it the case that the computer in the actual world is more
intelligent than the computer in that remote possible world. Given
that we agree that the "hand simulating" computer in the
Chinese Room is not intelligent, we have no option but to conclude
that digital computers are simply not the kinds of things that
*can* be intelligent.



So far, the argument that we have described arrives at the conclusion
that no appropriately programmed computer can think. While this
conclusion is not one that Turing accepted, it is important to note
that it is compatible with the claim that The Turing Test is a good
test for intelligence. This is because, for all that has been argued,
it may be that it is not *nomically* possible to provide any
"hand simulation" of intelligence (and, in particular,
that it is not possible to simulate intelligence using any kind of
computer). In order to turn Searle's argument--at least in
the way in which we have developed it--into an objection to The
Turing Test, we need to have some reason for thinking that it is at
least *nomically* possible to simulate intelligence using
computers. (If it is nomically impossible to simulate intelligence
using computers, then the alleged fact that digital computers cannot
genuinely possess intelligence casts no doubt at all on the usefulness
of the Turing Test, since digital computers are nomically disqualified
from the range of cases in which there is mere simulation of
intelligence.) In the absence of reason to believe this, the most that
Searle's argument yields is an objection to Turing's
confidently held belief that digital computing machines will one day
pass The Turing Test. (Here, as elsewhere, we are supposing that, for
any kind of creature C, there is a version of The Turing Test in which
C takes the role of the machine in the specific test that Turing
describes. This general format for testing for the presence of
intelligence would not necessarily be undermined by the success of
Searle's Chinese Room argument.)



There are various responses that might be made to the argument that we
have attributed to Searle. One kind of response is to dispute the
claim that there is no intelligence present in the case of the Chinese
Room. (Suppose that the "hand simulation" is embedded in a
robot that is equipped with appropriate sensors, etc. Suppose,
further, that the "hand simulation" involves updating the
process of "hand simulation," etc. If enough details of
this kind are added, then it becomes quite unclear whether we do want
to say that we still haven't described an intelligent system.)
Another kind of response is to dispute the claim that digital
computers in the actual world could not be relevantly different from
the system that operates in the Chinese Room in that remote possible
world. (If we suppose that the core of the Chinese Room is a kind of
giant look-up table, then it may well be important to note that
digital computers in the actual world do not work with look-up tables
in that kind of way.) Doubtless there are other possible lines of
response as well. However, it would take us out of our way to try to
take this discussion further. (One good place to look for further
discussion of these matters is Braddon-Mitchell and Jackson
(1996).)


## 7. Brief Notes on Intelligence



There are radically different views about the measurement of
intelligence that have not been canvassed in this article. Our concern
has been to discuss Turing (1950) and its legacy. But, of course, a
more wide-ranging discussion would also consider, for example,
research on the measurement of intelligence using the mathematical and
computational resources of Algorithmic Information Theory, Kolmogorov
Complexity Theory, Minimum Message Length (MML) Theory, and so forth.
(For an introduction to this literature, see Hernandez-Orallo and Dowe
(2010), and the list of references contained therein. For a more
general introduction to research into AI, see Marquis et al.
(2020).)



More broadly, there are radically different views about our
concept--or concepts--of intelligence that have not been canvassed in
this article. There is a dispute, for example, about whether Turing is
best interpreted as working with a response-dependent concept of
intelligence. (Pro: Proudfoot (2013) (2020); contra: Wheeler (2020).)
Relatedly, there is a dispute about whether intelligence bears some
kind of necessary relationship to symmetrical relations of recognition
between agents, as suggested in Mallory (2020) There is also a
broader dispute about whether we should think that useful notions of
intelligence are always domain specific, or whether we should rather
suppose that there is something important in the idea of general,
domain independent intelligence.



And there are radically different views about the most likely paths to
building general intelligence (assuming that there is such a thing as
general intelligence). For example, Crosby (2020) suggests that the
best way forwards may be to try to make machines that can pass animal
cognition tests, i.e. that can create predictive models of their
environment from sensory input. (There are clear precusors to this
line of thought in, for example, Brooks (1990).)