Appears in Bowdlerized form as annotations in: Epstein, Robert & Peters, Grace (Eds.) (2008) Parsing the Turing Test:
Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer.
________________________________________________________________________
Stevan Harnad
I propose to
consider the question, "Can machines think?" (Turing 1950)
Turing starts on an
equivocation. We know now that what he will go on to consider is not
whether or not machines can think, but whether or not machines can do
what thinkers like us can do -- and if so, how. Doing is
performance capacity, empirically observable. Thinking (or cognition)
is an internal state, its correlates empirically
observable as neural activity (if we only knew which neural activity
corresponds
to thinking!) and its associated quality introspectively observable as
our
own mental state when we are thinking. Turing's proposal will turn out
to
have nothing to do with either observing neural states or introspecting
mental
states, but only with generating performance capacity (intelligence?)
indistinguishable
from that of thinkers like us.
This should begin
with definitions of... "machine" and "think"... [A] statistical survey
such as a Gallup poll [would be] absurd. Instead of attempting such a
definition I
shall replace the question by another... in relatively unambiguous
words.
"Machine" will
never be
adequately defined in Turing's paper, although (what will eventually be
known
as) the "Turing Machine," the abstract description of a computer, will
be.
This will introduce a systematic ambiguity between a real physical
system, doing something in the world, and another physical system, a
computer, simulating the first system formally, but not actually doing
what it does: An example would be the difference between a real
airplane -- a machine, flying in the real world -- and a computer
simulation of an airplane, not really flying, but doing something
formally equivalent to flying, in a (likewise simulated) "virtual
world."
A reasonable
definition of "machine," rather than "Turing Machine," might be any dynamical,
causal system. That makes the
universe a machine, a molecule a machine, and also waterfalls,
toasters, oysters and human beings. Whether or not a machine is
man-made is obviously irrelevant. The only relevant property is that it
is "mechanical" -- i.e., behaves in accordance with the cause-effect
laws of physics (Harnad 2003).
"Think" will never
be defined by Turing at all; it will be replaced by an operational
definition to the effect that "thinking is as thinking does." This is
fine, for thinking (cognition, intelligence) cannot be defined in
advance of knowing how thinking systems do it, and we don't yet
know how. But we do know that we thinkers do it, whatever it
is, when we think; and we know when
we are doing it (by introspection). So thinking, a form of
consciousness, is already ostensively defined, by just pointing to that
experience we all have and know.
Taking a
statistical survey
like a Gallup Poll instead, to find out people's opinions of what
thinking
is would indeed be a waste of time, as Turing points out -- but then
later
in the paper he needlessly introduces the equivalent of a statistical
survey
as his criterion for having passed his Turing Test!
The new form of
the problem can be described in terms of a game which we call the
'imitation
game."
Another unfortunate
terminological choice: "Game" implies caprice or trickery, whereas
Turing in fact means serious
empirical business. The game is science (the future science of
cognition --
actually a branch of reverse bioengineering; Harnad 1994a). And
"imitation" has connotations of fakery or deception too, whereas what
Turing will be proposing
is a rigorous empirical methodology for testing theories of human
cognitive
performance capacity (and thereby also theories of the thinking that
presumably
engenders that capacity). Calling this an "imitation game" (instead of
a
methodology for reverse-engineering human cognitive performance
capacity)
has invited generations of needless misunderstanding (Harnad 1992).
The interrogator
stays in a room apart from the other two. The object of the game for
the interrogator is to determine which of the other two is the man and
which is the woman.
The man/woman test
is an intuitive "preparation" for the gist of what will eventually be
the Turing Test, namely, an empirical test of performance capacity. For
this, it is
first necessary that all non-performance data be excluded (hence the
candidates are out of sight). This sets the stage for what will be
Turing's real object of comparison, which is a thinking human being
versus a (nonthinking) machine, a comparison that is to be unbiased by
appearance.
Turing's criteria,
as we all know by now, will turn out to be two (though they are often
confused
or conflated): (1) Do the two candidates have identical performance
capacity?
(2) Is there any way we can distinguish them, based only on their
performance
capacity, so as to be able to detect that one is a thinking human being
and
the other is just a machine? The first is an empirical criterion: Can
they
both do the same things? The second is an intuitive criterion,
drawing
on what decades later came to be called our human "mind-reading"
capacities
(Frith & Frith 1999): Is there anything about the way the
candidates
go about doing what they can both do that cues me to the fact that one
of
them is just a machine?
Turing introduces
all of this in the form of a party game, rather like 20-Questions. He
never explicitly debriefs the reader to the effect that what is really
at issue is no less than the game of life itself, and that the
"interrogator" is actually the scientist for question (1), and, for
question (2), any of the rest of us, in every one of our daily
interactions with one another. The unfortunate
party-game metaphor again gave rise to needless cycles of
misunderstanding
in later writing and thinking about the Turing Test.
In order that
tones of voice may not help the interrogator, the answers should be
written, or better still, typewritten.
This restriction of
the test exclusively to what we would today call email interactions is,
as noted, a reasonable way of preparing us for its eventual focus on
performance capacity alone, rather than appearance, but it does have
the unintended further effect of ruling out all direct testing of
performance capacities other than verbal ones; and that is potentially
a much more serious equivocation, to which
we will return. For now, we should bear in mind only that if the
criterion
is to be Turing-indistinguishable performance-capacity, we can all do
a lot more than just email!
We now ask the
question, "What will happen when a machine takes the part of A in this
game?" Will the
interrogator decide wrongly as often when the game is played like this
as
he does when the game is played between a man and a woman? These
questions replace our original, "Can machines think?"
Here, with a little
imagination, we can already scale up to the full Turing Test, but again
we are faced with a needless and potentially misleading distraction:
Surely the goal is not merely to design a machine that people mistake
for a human being statistically as often as not! That would reduce the
Turing Test to the Gallup Poll that Turing rightly rejected in raising
the question of what "thinking" is in the
first place! No, if Turing's indistinguishability-criterion is to have
any
empirical substance, the performance of the machine must be totally
indistinguishable from that of a human being -- to anyone and everyone,
for a lifetime (Harnad 1989).
The new problem
has the advantage of drawing a fairly sharp line between the physical
and the intellectual capacities of a man.
It would have had
that advantage, if the line had only been drawn between appearance and
performance, or between structure and function. But if the line is
instead between verbal and nonverbal performance capacities then it is
a very arbitrary line indeed, and a very hard one to defend. As there
is no explicit or even inferrable defense of this arbitrary line in any
of Turing's paper (nor of an equally arbitrary line between those of
our "physical capacities" that do and do not depend on our
"intellectual capacities"), I will take it that Turing simply
did not think this through. Had he done so, the line would have been
drawn
between the candidate's physical appearance and structure on the one
hand,
and its performance capacities, both verbal and nonverbal, on the
other. Just as (in the game) the difference, if
any, between the man and the woman must be detected from what they do,
and not what they look like, so the difference, if any, between human
and
machine must be detected from what they do, and not what they look
like.
This would leave the door open for the robotic version of the Turing
Test
that we will discuss later, and not just for the email version.
But before a reader
calls my own dividing line between structure and function just as
arbitrary, let me quickly agree that Turing has in fact introduced a
hierarchy of Turing Tests here, but not an infinity of them (Harnad
2000). The relevant levels of this hierarchy will turn out to be only
the following 5:
t0: Local
indistinguishability in the capacity to perform some arbitrary task,
such as chess. t0 is not really a
Turing Test at all, because it is so obviously subtotal; hence the
machine candidate is easily distinguished from a human being by seeing
whether it can do anything else, other than play chess. If it
can't, it fails the test.
T2: Total
indistinguishability in email (verbal) performance capacity. This seems like a
self-contained performance module, for one can talk about anything and
everything, and language has the same kind of universality that
computers (Turing Machines) turned out to have. T2 even subsumes
chess-playing. But does it subsume star-gazing, or even food-foraging?
Can the machine go and see and then tell me whether the moon is visible
tonight and can it go and unearth truffles and then let me know how it
went about it? These are things that a machine with email capacity
alone cannot do, yet every (normal) human being can.
T3: Total
indistinguishablity in robotic (sensorimotor) performance capacity. This subsumes T2,
and is (I will argue) the level of Test that Turing really intended (or
should have!).
T4: Total
indistinguishability in external performance capacity as well as in
internal structure/function. This subsumes T3
and adds all data that a neuroscientist
might study. This is no longer strictly a Turing Test, because it goes
beyond performance data, but it correctly embeds the Turing Hierarchy
in a larger empirical hierarchy.
Moreover, the boundary between T3 and T4 really is fuzzy: Is blushing
T3
or T4?
T5: Total
indistinguishability in physical structure/function. This subsumes T4
and rules out any functionally equivalent but synthetic nervous
systems: The T5
candidate must be indistinguishable from other human beings right down
to
the last molecule.
No engineer or
chemist claims to be able to produce a material which is
indistinguishable from the human skin... [There would be] little point
in trying to make a "thinking machine" more human by dressing it up in
such artificial flesh.
Here Turing
correctly rejects T5 and T4 -- but certainly not T3.
The form in which
we have set the problem reflects this fact in the condition which
prevents the interrogator from seeing or touching the other
competitors, or hearing their voices.
Yes, but using T2
as the
example has inadevertently given the impression that T3 is excluded
too: not only can we not see or touch the candidate, but the candidate
cannot see or touch anything either -- or do anything other than
compute and email.
The question and
answer method seems to be suitable for introducing almost any one of
the fields
of human endeavour that we wish to include.
This correctly
reflects the universal power of natural language (to say and describe
anything in words).
But "almost" does not fit the Turing criterion of identical performance
capacity.
We do not wish to
penalise the machine for its inability to shine in beauty competitions
This is the valid
exclusion of appearance (moreover, most of us could not shine in beauty
competitions either).
nor to penalise a
man for losing in a race against an aeroplane
Most of us could
not beat
Deep Blue at chess, nor even attain ordinary grandmaster level. It is
only
generic human capacities that are at issue, not those of any specific
individual.
On the other hand, just about all of us can walk and run. And even if
we
are handicapped (an anomalous case, and hardly the one on which to
build
one's attempts to generate positive performance capacity), we all have some
sensorimotor capacity. (Neither Helen Keller nor Stephen Hawking is a
disembodied
email-only module.)
The conditions of
our game make these disabilities irrelevant
Disabilities and
appearance are indeed irrelevant. But nonverbal performance capacities
certainly are not. Indeed, our verbal abilities may well be grounded in our nonverbal
abilities (Harnad 1990; Cangelosi & Harnad 2001; Kaplan &
Steels 1999). (Actually, by "disability," Turing means non-ability,
i.e., absence of an ability; he does not really mean being disabled in
the sense of being physically handicapped, although he does mention
Helen Keller later.)
the interrogator
cannot demand practical demonstrations
This would
definitely be a fatal flaw in the Turing Test if Turing had meant it to
exclude T3 -- but I doubt he meant that. He was just arguing that it is
performance capacity that is decisive (for the empirical problem that
future cognitive science would eventually address), not something else
that might depend on irrelevant features of structure or appearance. He
merely used verbal performance as his intuition-priming example,
without meaning to imply that all "thinking" is verbal and only verbal
performance capacity is relevant.
The question...
will not be quite definite until we have specified what we mean by the
word "machine." It is natural that we should wish to permit every kind
of engineering technique to be used in our machines.
This passage (soon
to be contradicted in the subsequent text!) implies that Turing did not
mean only computers: that any dynamical system we build is eligible (as
long
as it delivers the performance capacity). But we do have to build it,
or
at least have a full causal understanding of how it works. A cloned
human
being cannot be entered as the machine candidate (because we didn't
build
it and hence don't know how it works), even though we are all
"machines"
in the sense of being causal systems (Harnad 2000, 2003).
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.
Here is the
beginning of the difference between the field of artificial
intelligence (AI), whose goal is merely to generate a useful
performance tool, and cognitive modeling (CM), whose goal is to explain
how human cognition is generated. A device we built but without knowing
how it works would suffice for AI but not for CM.
Finally, we wish
to exclude from the machines men born in the usual manner.
This does not, of
course, imply that we are not machines, but only that the Turing Test
is about finding out what kind of machine we are, by designing
a machine that can
generate our performance capacity, but by causal/functional means that
we
understand, because we designed them.
[I]t is probably
possible to rear a complete individual from a single cell of the skin
(say) of a man... but we would not be inclined to regard it as a case
of "constructing a thinking machine."
This is because we
want to explain thinking capacity, not merely duplicate it. http://www.ecs.soton.ac.uk/~harnad/Hypermail/Foundations.Cognitive.Science2001/0056.html
This prompts us
to abandon
the requirement that every kind of technique should be permitted. We
[accordingly]
only permit digital computers to take part in our game.
This is where
Turing contradicts
what he said earlier, withdrawing the eligibility of all engineering
systems
but one, thereby introducing another arbitrary restriction
-- one that would again rule out T3. Turing earlier said
(correctly)
that any engineering device ought to be eligible. Now he says it can
only
be a computer. His motivation is partly of course the fact that the
computer
(Turing Machine) has turned out to be universal, in that it can
simulate
any other kind of machine. But here we are squarely in the T2/T3
equivocation,
for a simulated robot in a virtual world is neither a real robot, nor
can
it be given a real robotic Turing Test, in the real world. Both T2 and
T3
are tests conducted in the real world. But an email interaction with a
virtual
robot in a virtual world would be T2, not T3.
To put it another
way: With the Turing Test we have accepted, with Turing, that thinking is
as thinking does. But we know that thinkers can and do do more
than just talk. And it remains what thinkers can do that our
candidate must
likewise be able to do, not just what they can do verbally. Hence just
as
flying is something that only a real plane can do, and not a
computer-simulated virtual plane, be it ever so Turing-equivalent to
the real plane -- so passing T3 is something only a real robot can do,
not a simulated robot tested by T2, be it ever so Turing-equivalent to
the real robot. (I also assume it is
clear that Turing Testing is testing in the real world: A
virtual-reality simulation [VR] would be no kind of a Turing Test; it
would merely be fooling our senses in the VR chamber rather than
testing the candidate's real performance capacity in the real world.)
So the restriction
to computer simulation, though perhaps useful for planning, designing
and even pre-testing the T3 robot, is merely a practical methodological
strategy. In principle, any engineered device should be eligible; and
it must be able to deliver T3 performance, not just T2.
It is of interest
that contemporary cognitive robotics has not gotten as much mileage out
of computer-simulation and virtual-worlds as might have been expected,
despite the universality of
computation. "Embodiment" and "situatedness" (in the real world) have
turned
out to be important ingredients in empirical robotics (Brooks 2002,
Kaplan
& Steels 1999), with the watchword being that the real world is
better
used as its own model (rather than roboticists' having to simulate,
hence
second-guess in advance, not only the robot, but the world too).
The impossibility
of second-guessing
the robot's every potential "move" in advance, in response to every
possible
real-world contingency also points to a latent (and I think fatal) flaw
in
T2 itself: Would it not be a dead give-away if one's email T2 pen-pal
proved
incapable of ever commenting on the analog family photos we kept
inserting
with our text? (If he can process the images, he's not just a computer
but
at least a computer plus A/D peripheral sensors, already violating
Turing's arbitrary restriction to computers alone). Or if one's pen-pal
were totally ignorant of contemporaneous real-world events, apart from
those we describe in our letters? Wouldn't even its verbal performance
break down if we questioned it too closely about the qualitative and
practical details of sensorimotor experience? Could all of that really
be second-guessed purely verbally in advance?
This restriction
[to computers] appears at first sight to
be a very drastic one. I shall attempt to show that it is not so in
reality. To do this
necessitates a short account of the nature and properties of these
computers.
The account of
computers that follows is useful and of course correct, but it does not
do anything at all to justify restricting the TT to candidates that are
computers. Hence this arbitrary restriction is best ignored.
It may also be
said that this identification of machines with digital computers, like
our criterion for "thinking," will only be unsatisfactory if (contrary
to my belief), it turns out that digital computers are unable to give a
good showing in the game.
This is the "game"
equivocation again. It is not doubted that computers will give a good
showing, in the Gallup
Poll sense. But empirical science is not just about a good showing: An
experiment
must not just fool most of the experimentalists most of the time! If
the
performance-capacity of the machine must be indistinguishable from that
of
the human being, it must be totally indistinguishable, not just
indististinguishable
more often than not. Moreover, some of the problems that I have raised
for
T2 -- the kinds of verbal exchanges that draw heavily on sensorimotor
experience
-- are not even likely to give a good showing, if the candidate is a
digital
computer only, regardless of how rich a data-base it is given in
advance.
[D]igital
computers... carry out any operations which could be done by a human
computer... following fixed rules...
This goes on to
describe what has since bcome the standard definition of computers as
rule-based symbol-manipulating devices (Turing Machines).
An interesting
variant on the idea of a digital computer is a "digital computer with a
random element"... Sometimes such a machine is described as having free
will (though I would not use this phrase myself)
Nor would I. But
surely an even more important feature for a Turing Test candidate than
a random
element or statistical functions would be autonomy
in the world -- which is something a T3 robot has a good deal more of
than a T2 pen-pal. The ontic side of free will -- namely, whether we
ourselves, real human beings, actually have free will -- rather exceeds
the scope of Turing's paper Harnad 1982b). So too does the question of
whether a TT-passing machine would have any feelings at all (whether
free or otherwise)
(Harnad 1995). What
is clear, though, is that computational rules are not the only ways to
"bind" and determine performance: ordinary physical causality can do so
too.
It will seem that
given the initial state of the machine and the input signals it is
always possible to predict all future states. This is reminiscent of
Laplace's view that from
the complete state of the universe at one moment of time, as described
by
the positions and velocities of all particles, it should be possible to
predict
all future states.
The points about
determinism are probably red herrings. The only relevant property is
performance capacity. Whether either the human or the machine are
completely predictable is irrelevant. (Both many-body physics and
complexity theory suggest that neither causal determinacy nor
rulefulness guarantee predictability in practise -- and this is without
even invoking the arcana of quantum theory.)
Provided it could
be carried out sufficiently quickly the digital computer could mimic
the behavior of any discrete-state machine... they are universal
machines.... [Hence]
considerations of speed apart, it is unnecessary to design various new
machines
to do various computing processes. They can all be done with one
digital
computer, suitably programmed for each case... [A]s a consequence of
this,
all digital computers are in a sense equivalent.
All true, but all
irrelevant to the question of whether a digital computer alone could
pass T2, let alone T3. The fact that eyes and legs can be simulated by
a computer does not mean a computer can see or walk (even when it is
simulating seeing and walking). So much for T3. But even just for T2,
the question is whether simulations alone can give the T2 candidate the
capacity to verbalize and converse about the real world
indistinguishably from a T3 candidate with autonomous sensorimotor
experience in the real world.
(I think yet
another piece
of unnoticed equivocation by Turing -- and many others -- arises from
the
fact that thinking is not observable: That unobservability helps us
imagine that computers think. But even without having to invoke the
other-minds problem (Harnad 1991), one needs to remind oneself that a
universal computer is only formally universal: It can describe
just about any physical system, and simulate it in symbolic
code, but in doing so, it does not capture all of its properties:
Exactly as a computer-simulated airplane cannot really do what a plane
plane does (i.e., fly in the real-world), a computer-simulated robot
cannot really do what a real robot does (act in the real-world) --
hence there is no reason to believe it is really thinking either. A
real robot
may not really be thinking either, but that does require
invoking the
other-minds problem, whereas the virtual robot is already disqualified
for
exactly the same reason as the virtual plane: both fail to meet the TT
criterion
itself, which is real performance capacity, not merely
something formally
equivalent to it!)
I believe that in
about fifty years' time it will be possible, to programme computers...
[to] play the imitation game so well that an average interrogator will
not have more than 70 per cent chance of making the right
identification after five minutes of questioning.
No doubt this
party-game/Gallup-Poll criterion can be met by today's computer
programs -- but that remains as meaningless
a demographic fact today as it was when predicted 50 years ago: Like
any
other science, cognitive science is not the art of fooling most of the
people
for some or most of the time! The candidate must really have the
generic
performance capacity of a real human being -- capacity that is totally
indistinguishable
from that of a real human being to any real human being
(for
a lifetime, if need be!). No tricks: real performance capacity.
The original
question, "Can machines think?" I believe to be too meaningless to
deserve discussion.
It is not
meaningless, it is merely undecidable: What we mean by "think" is, on
the one hand, what thinking creatures can do and how
they can do it, and, on the other hand, what it feels-like to
think. What thinkers can do is captured by the TT. A theory of how they
do it is provided by how our man-made machine does it. (If there are
several different successful machines, it's a matter of normal
inference-to-the-best-theory.) So far, nothing meaningless. Now we ask:
Do the successful candidates really feel, as we do when we think? This
question is not meaningless, it is merely unanswerable -- in any other
way than by being the candidate. It is the familar old
other-minds problem (Harnad 1991).
Nevertheless 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.
Yes, but only at a
cost of demoting "thinking" to meaning only "information processing"
rather than what you or I do when we think, and what that feels-like.
The popular view
that scientists proceed inexorably from well-established fact to
well-established fact, never being influenced by any improved
conjecture, is quite mistaken. Provided it is made clear which are
proved facts and which are conjectures, no harm can result. Conjectures
are of great importance since they suggest useful lines of research.
This is confused.
Yes, science proceeds by a series of better approximations, from
empirical theory to theory. But the theory here would be the actual
design of a successful TT candidate, not the conjecture that
computation (or anything else) will eventually do the trick. Turing is
confusing formal conjectures (such as
that the Turing Machine and its equivalents capture all future notions
and
instances of what we mean by "computation" -- the so-called
"Church/Turing
Thesis") and empirical hypotheses, such as that thinking is just
computation.
Surely the Turing Test is not a license for saying that we are
explaining
thinking better and better as our candidates fool more and more people
longer
and longer. On the other hand, something else that sounds
superficiallly
similar to this (but happens to be correct) could be said about scaling
up
to the TT empirically by designing a candidate that can do more and
more
of what we can do. And Turing Testing certainly provides a methdology
for
such cumulative theory-building and theory-testing in cognitive science.
The Theological
Objection: Thinking is a
function of man's immortal soul.
The real
theological objection
is not so much that the soul is immortal but that it is immaterial.
This view also has non-theological support
from
the mind/body problem: No one -- theologian, philosopher or scientist
--
has even the faintest hint of an idea of how
mental states can be material states (or, as I prefer to
put
it, how functional states can be felt states). This
problem
has been dubbed "hard" (Chalmers in Shear 1997). It may be even worse:
it
may be insoluble (Harnad 2001). But this
is no
objection to Turing Testing which, even if it will not explain how
thinkers can feel, does explain how they can do what
they can do.
[T] here is a
greater difference, to my mind, between the typical animate and the
inanimate than there is between man and the other animals.
Yes, and this is
why the
other-minds problem comes into its own in doing Turing-Testing of
machines rather than in doing mind-reading of our own species and other
animals. ("Animate" is a weasel-word, though, for vitalists are
probably also animists; Harnad 1994a.)
The Mathematical
Objection: Godel's theorem[:] [A]lthough it is established that there
are limitations to the powers of any particular machine, it has only
been stated, without any sort of proof, that no such limitations apply
to the human intellect.
Godel's theorem
shows that there are statements in arithmetic that are true, and we
know are true, but their truth cannot be computed. Some have
interpreted this as implying that "knowing" (which is just a species of
"thinking") cannot be just computation. Turing replies that maybe the
human mind has similar limits, but it seems to me it would have been
enough to point out that "knowing" is not the same as "proving." Godel
shows the truth is unprovable, not that it is unknowable. (There are
far better reasons for believing that thinking is not computation.)
The Argument from
Consciousness:
Jefferson (1949):
"Not until a machine can [do X] because of
thoughts and emotions felt, and not by the chance fall of symbols,
could we agree that
machine equals brain"
This standard
argument against the Turing Test (repeated countless times in almost
exactly the same way until the present day) is merely a restatement of
the other-minds problem: There is no way to know whether either humans
or machines do what they does because they feel like it -- or whether
they feel anything at all, for that matter. But there is a lot to be
known from identifying what can and cannot generate the capacity to do
what humans can do. (The limits of symbol-manipulation [computation]
are another matter, and one that can be settled empirically, based on
what sorts of machine can and cannot pass the TT; Harnad
2003.)
According to the
most extreme form of this view the only way by which one could be sure
that machine thinks is to be the machine and to feel oneself
thinking... [This] is in fact
the solipsist point of view. It may be the most logical view to hold
but
it makes communication of ideas difficult.
Turing is dead
wrong here.
This is not solipsism
(i.e., not the
belief that only I exist and all else is my dream). It is merely the
other-minds problem
(Harnad 1991); and
it is correct, but irrelevant -- or rather put into perspective by the
Turing Test: There is no one else we can know has a mind but our own
private selves, yet we are not worried about the minds of our
fellow-human beings, because they behave just like us and we know how
to mind-read their behavior. By the
same token, we have no more or less reason to worry about the minds of
anything
else that behaves just like us -- so much so that we can't tell them
apart
from other human beings. Nor is it relevant what stuff they are made
out
of, since our successful mind-reading of other human beings has nothing
to
do with what stuff they are made out of either. It is based only on
what they
do.
Arguments from
Various [In]abilities:
... "I grant you
that you can make machines do all the things you have mentioned but you
will never be able to make one to do X" : [e.g] Be kind, resourceful,
beautiful, friendly, have initiative, have a sense of humour, tell
right from wrong, make mistakes, fall in love, enjoy strawberries and
cream, make some one fall in love with it, learn from experience, use
words properly, be the subject of its own thought,
have as much diversity of behaviour as a man, do something really new.
Turing rightly
dismisses this sort of scepticism
(which I've dubbed
"Granny Objections" http://www.ecs.soton.ac.uk/~harnad/CM302/Granny/sld001.htm)
by pointing out that these are empirical
questions about what computers (and other kinds of machines) will
eventually be shown to be able to do. The performance items on the
list, that is. The mental states
(feelings), on the other hand, are moot, because of the other-minds
problem.
(6) Lady
Lovelace's Objection:
"The Analytical
Engine has no pretensions to originate anything. It can
do whatever
we know how to order it to perform"... a
machine can "never do anything really new."
This is one of the
many Granny objections. The correct reply is that (i) all causal
systems are describable by formal rules (this is the equivalent of the
Church/Turing Thesis), including ourselves; (ii)
we know from complexity theory as well as statistical mechanics that
the
fact that a system's performance is governed by rules does not mean we
can
predict everything it does; (iii) it is not clear that anyone or
anything
has "originated" anything new since the Big Bang.
The view that
machines cannot give rise to surprises is due, I believe, to a fallacy
to which philosophers and mathematicians are particularly subject. This
is the assumption that as
soon as a fact is presented to a mind all consequences of that fact
spring into the mind simultaneously with it. It is a very useful
assumption under many circumstances, but one too easily forgets that it
is false. A natural consequence of doing so is that one then assumes
that there is no virtue in
the mere working out of consequences from data and general principles.
Turing is quite
right to point out that knowing something is true does not mean knowing
everything it entails; this is especially true of mathematical
conjectures, theorems, and axioms.
But I think Lady
Lovelace's preoccupation with freedom from rules and novelty is even
more superficial than this. It takes our introspective ignorance about
the causal basis of our performance capacities at face-value, as if
that ignorance demonstrated that our capacities are actually sui
generis acts of our psychokinetic will -- rather than being merely the
empirical evidence of our functional ignorance, for future
reverse-engineering (cognitive science) to remedy.
Argument from
Continuity in the Nervous System: It may be argued that... one cannot
expect to be able to mimic the behaviour of the nervous system with a
discrete-state system.
According to the
Church/Turing Thesis, there is almost nothing that a computer cannot
simulate, to as close an approximation as desired, including the brain.
But, as noted, there is no reason computers should be the only machines
eligible for Turing Testing. Robots can have analog components too. Any
dynamical causal system is eligible, as long as it delivers the
peformance capacity.
The Argument from
Informality of Behaviour: It is not possible to produce a set of rules
purporting to describe
what a man should do in every conceivable set of circumstances.
First, the
successful TT candidate need not be just computational (rule-based);
all the arguments for T3 robots and their need of real-world
sensorimotor capacities, mechanisms and experience suggest that more is
required in a successful candidate than just computation. The
impossibility of second-guessing a set of rules that predicts every
contingency in advance is probably also behind the so-called "Frame
Problem" in Artificial Intelligence (Harnad 1993). But it will still be
true, because of the Church-Turing Thesis, that the successful hybrid
computational/dynamic T3 robot still be computer-simulable in principle
--
a virtual robot in a virtual world. So the rule-based system can describe
what a T3 robot would do under all contingencies; that simulation would
simply
not be a T3 robot, any more than its virtual world would be the
real
world.
Learning Machines
Turing successfully
anticipates machine learning, developmental modeling and evolutionary
modeling in this prescient section.
The Argument from
Extrasensory Perception:... telepathy, clairvoyance, precognition and
psychokinesis.... [T]he statistical evidence, at least for telepathy,
is overwhelming.
It is a pity that
at the
end Turing reveals his credulousness about these dubious phenomena, for
if
psychokinesis (mind over matter) were genuinely possible, then ordinary
matter/energy
engineering would not be enough to generate a thinking mind; and if
telepathy
(true mind-reading) were genuinely possible, then that would
definitely
trump the Turing Test.
REFERENCES
Brooks, R. A.,
(2002) Flesh and Machines, Pantheon Books.
Cangelosi, A. &
Harnad, S. (2001) The Adaptive Advantage of Symbolic Theft Over
Sensorimotor Toil: Grounding Language in Perceptual Categories. Evolution of
Communication. 4(1) 117-142 http://cogprints.soton.ac.uk/documents/disk0/00/00/20/36/
Frith Christopher
D. &
Frith, Uta (1999) Interacting minds Ð
a biological
basis. Science 286: 1692Ð1695. http://pubpages.unh.edu/~jel/seminar/Frith_mind.pdf
Harnad, S. (1982a)
Neoconstructivism: A Unifying Constraint for the Cognitive Sciences,
In: Language, mind and brain
(T. Simon & R. Scholes, eds., Hillsdale NJ: Erlbaum), 1 - 11. http://cogprints.soton.ac.uk/documents/disk0/00/00/06/62/
Harnad, S. (1982b)
Consciousness: An afterthought. Cognition and Brain Theory 5: 29 - 47. http://cogprints.soton.ac.uk/documents/disk0/00/00/15/70/
Harnad, S. (1989)
Minds, Machines and Searle. Journal of Theoretical and Experimental
Artificial Intelligence 1: 5-25. http://cogprints.soton.ac.uk/documents/disk0/00/00/15/73/
Harnad, S. (1990)
The Symbol Grounding Problem Physica D 42: 335-346. http://cogprints.soton.ac.uk/documents/disk0/00/00/06/15/
Harnad, S. (1991)
"Other Bodies, Other Minds: A Machine Incarnation of an Old
Philosophical Problem"Minds and Machines 1: 43-54. http://cogprints.soton.ac.uk/documents/disk0/00/00/15/78/
Harnad, S. (1992)
The Turing Test Is Not A Trick: Turing Indistinguishability Is A
Scientific Criterion. SIGART Bulletin 3(4) (October 1992) pp. 9 - 10. http://cogprints.soton.ac.uk/documents/disk0/00/00/15/84/
Harnad, Stevan
(1993) Problems, Problems: the Frame Problem as a Symptom of the Symbol
Grounding Problem, Psycoloquy: 4,#34 Frame Problem (11) http://psycprints.ecs.soton.ac.uk/archive/00000328/
Harnad, S. (1993)
Grounding Symbols in the Analog World with Neural Nets. Think 2(1)
12-78. http://psycprints.ecs.soton.ac.uk/archive/00000163/
Harnad, S. (1994a)
Levels of Functional Equivalence in Reverse Bioengineering: The
Darwinian Turing Test for Artificial Life. Artificial Life 1(3):
293-301. Reprinted in: C.G. Langton (Ed.). Artificial Life: An
Overview. MIT Press 1995. http://cogprints.soton.ac.uk/documents/disk0/00/00/15/91/-
Harnad, S. (1994b)
Computation Is Just Interpretable Symbol Manipulation: Cognition Isn't.
Special Issue on "What Is Computation" Minds and Machines 4:379-390 http://cogprints.soton.ac.uk/documents/disk0/00/00/15/92/
Harnad, Stevan
(1995) "Why and How We Are Not Zombies. Journal of Consciousness
Studies1:164-167.
http://cogprints.soton.ac.uk/documents/disk0/00/00/16/01/
Harnad, S. (2000)
Minds, Machines, and Turing: The Indistinguishability of
Indistinguishables. Journal of Logic, Language, and Information 9(4):
425-445. (special issue on "Alan Turing and Artificial Intelligence") http://cogprints.soton.ac.uk/documents/disk0/00/00/16/16/
Harnad, S. (2001)
No Easy
Way Out. The Sciences 41(2) 36-42. http://cogprints.soton.ac.uk/documents/disk0/00/00/16/24/
Harnad, S. (2002a)
Turing Indistinguishability and the Blind Watchmaker. In: J. Fetzer
(ed.) Evolving Consciousness Amsterdam: John Benjamins. Pp 3-18. http://cogprints.soton.ac.uk/documents/disk0/00/00/16/15/
Harnad, S. (2002b)
Darwin, Skinner, Turing and the Mind. Inaugural Address. Hungarian
Academy of Science. http://www.ecs.soton.ac.uk/~harnad/Temp/darwin.htm
Harnad, S. (2003)
Can a Machine Be Conscious? How? Journal of Consciousness Studies. http://www.ecs.soton.ac.uk/~harnad/Temp/machine.htm
Shear, J. (Ed.)
(1997) Explaining Consciousness: The Hard Problem. MIT/Bradford http://www.u.arizona.edu/~chalmers/book/hp-contents.html
Steels, L. and
Kaplan, F. (1999) Situated grounded word semantics. In Dean, T.,
editor, Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence IJCAI'99, pages 862-867, San Francisco, CA.,
1999. Morgan Kaufmann Publishers. http://arti.vub.ac.be/steels/ijcai99.pdf