Com1005 Machines and
Intelligence
Lecturer: Dr Amanda Sharkey
 Last
week: Turing Test
 “rules”
for the Imitation Game
 Interrogator puts questions to A and B –
after 5 minutes of questioning, they must
say which is the machine, and which is the
person.
 Repeated trials, with different questioners
and person.
 Underspecified? Skills of questioner, and
3rd player. Number of sessions?
Turing’s beliefs

“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. The original question “can
machines think?” I believe to be too meaningless to
deserve discussion. 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. “Turing (1950)
Descartes (1637)

If there were machines which bore a resemblance to our bodies and imitated our
actions as closely as possible for all practical purposes, we should still have two very
certain means of recognizing that they were not real men. The first is that they could
never use words, or put together signs, as we do in order to declare our thoughts to
others. For we can certainly conceive of a machine so constructed that it utters
words, and even utters words that correspond to bodily actions causing a change in
its organs. … But it is not conceivable that such a machine should produce different
arrangements of words so as to give an appropriately meaningful answer to whatever
is said in its presence, as the dullest of men can do. Secondly, even though some
machines might do some things as well as we do them, or perhaps even better, they
would inevitably fail in others, which would reveal that they are acting not from
understanding, but only from the disposition of their organs. For whereas reason is a
universal instrument, which can be used in all kinds of situations, these organs need
some particular action; hence it is for all practical purposes impossible for a machine
to have enough different organs to make it act in all the contingencies of life in the
way in which our reason makes us act. (Translation by Robert Stoothoff)

Two kinds of questions about Turing Test

Is it likely that a computer will be able to play the imitation game
so well that it can pass? (empirical question)

If a machine was to pass the Turing Test should we conclude
that it is capable of thought, intelligence or mentality?
(philosophical question)
• Even if a machine were to pass the test, some claim that it would not be
capable of thought or intelligence.
 Hollow
shell criticism
• Computer may pass test, but still not be able to
think
• Philosophers – abstract debate about whether we
could ever create intelligent machines, and
whether TT is an adequate test
• Alternative – just use TT as objective test, and look
at ways of creating intelligence
Class exercise
 Is
the Turing Test a good test for
thinking/intelligence?

Is it too easy or too difficult?

How could it be improved?
Possible objections considered by Turing.
“I now proceed to consider opinions opposed to
my own”
 The theological objection *
 The ‘heads in the sand’ objection*
 The mathematical objection
 The argument from consciousness *
 Arguments from various disabilities
 Lady Lovelace’s objection *
 Argument from continuity in the nervous system
 The argument from informality of behaviour
 The argument from extra-sensory perception
The theological objection
People might suggest: ‘Thinking is a function of
man’s immortal soul. God has given an
immortal soul to every man and woman, but not
to any other animal or to machines. Hence no
animal or machine can think’
 Turing’s dismissal: Why not believe that God
could give a soul to a machine if he wished?


“I should find the argument more convincing if animals
were classed with men, for there is a greater difference,
to my mind, between the typical animate and the
inanimate than there is between man and the other
animals.”
The heads in the sand objection
 People
could say: “The consequences of
machines thinking would be too dreadful.
Let us hope and believe that they cannot
do so."
 Turing’s dismissal: argument not
substantial enough to require refutation,
(objection related totheological objection.)
Argument from consciousness

People might suggest that machines cannot feel
emotions ‘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, and 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’” (Jefferson, 1949)
Turing’s dismissal of argument from
consciousness:
 How could we tell?
 The only way we could be sure that a machine
thinks is to be that machine and to feel oneself
thinking.
 Similarly, the only way to be sure that someone
else thinks is to be that person.
 How do we know that anyone else is conscious?
Solipsism.
 But we assume other people are conscious.
Similarly we could assume that a machine that
passes the Turing test is effectively conscious.
Lady Lovelace’s objection
(memoir from Lady Lovelace about Babbage’s
Analytical Engine)
 Babbage (1792-1871) and Analytical Engine:
general purpose calculator. It was entirely
mechanical, and never actually built.
 “The Analytical Engine has no pretensions to
originate anything. It can do what ever we know
how to order it to perform” (Lady Lovelace
memoir 1842)
 A computer cannot be creative, it cannot
originate anything

 Turing’s
dismissal of Lady Lovelace’s
objection
 Computers can surprise their
programmers – producing answers that
were not expected. Data may have
originally be given to the computer, but
then it may have been able to work out its
consequences and implications.
Further objections to Turing
Test







i) Too easy?
5 minutes of questioning?
Sense organs objection
TT focuses on verbal responses, and computer
can use words without really knowing their
meanings.
Like driving test that only consists of answering
questions
Equip it with sense organs, and you could test
knowledge of the world.
But many words can be investigated without the
need for sense organs.
Further objections to Turing Test
Too difficult .....
ii) Chimpanzees, dolphins, dogs and babies can all think
but would not pass TT.
Computer might fail because answers non-human
But only means that TT is not a litmus test (red=acid,
blue=non-acidic)
-nothing definite follows if computer/animal/baby fails the
test
Only passing the test has meaning.
 Too
difficult....
Subtle ways in which we can detect if
something is human ...
Computer unlikely to be able to answer
questions closely based on human
physical and cultural experience
Similar to questions designed to tell
men/women apart in original parlour
game.
Further objections to the
TuringTest

iii) Simulation objection
 The simulation of X is never an X
 In Victorian parlour game, if man misleads the
interrogator into thinking he is a woman – he
doesn’t become a woman!
 2 kinds of simulation


Simulation1: lacks essential features e.g. simulated
death, person is not dead
Simulation2: exactly like what is being simulated but
not produced in standard way
Further objections
 iv).




Black box objection
A device whose inner workings are unknown.
Turing test only looks at outward behaviour
But need to see how behaviour being
produced
E.g. computer could be just matching
sentences to be output in response to
interrogator’s questions – no thought
Eliza

Men are all alike
 IN WHAT WAY?
 They’re always bugging us about something or
other
 CAN YOU THINK OF A SPECIFIC EXAMPLE?
 Well my boyfriend made me come here
 YOUR BOYFRIEND MADE YOU COME HERE?
Etc etc
How Eliza works
 Transform


input to change point of view
E.g change you to I
You understand me > I understand you
 If
pattern matches transformed input
choose one of responses associated with
that pattern. Or choose general purpose
response



Pattern *mother* [tell me more about your
family]
E.g. Perhaps I could learn to get along with
my mother
TELL ME MORE ABOUT YOUR FAMILY
 Eliza


lacks
Means of recognising grammatical structure
of language
Means of converting user’s query into a
representation of the underlying knowledge
from which inferences can be drawn.
Parry






Even simpler
Looks for known patterns
When pattern detected, output first sentence
from a list associated with that pattern
E.g input contains ‘you’ and expression from
Abnormal List (delusional, paranoid etc)
You are delusional
I THINK I KNOW WHAT YOU DOCTORS ARE
UP TO.
Modified Turing Test
 Check
outward form of Turing Test is
passed
 Look inside the program (black box) to see
whether
 i) the program works in a similar way to
humans OR
 ii) the program is extendible and not just
designed for passing test. E.g. could
motor and sensory functions be added?

Or maybe idea of Turing Test should be
abandoned.
 Reason 1: Unitary notion of ‘intelligence’ too
simplistic
 Better to break down question into smaller
questions
 Assess computers in terms of more specific
abilities – eg. Ability to navigate across a room,
ability to perform logical reasoning,
metaknowledge (knowledge of own limitations)
 Reason
2: too anthropocentric
 Why should program have to work like
humans?
 E.g Dogs capable of cognition, but would
not pass Turing Test.
Does it matter?
 Trusting

machines too much
Dangers from overestimating their abilities
 Computer

rights? Robot rights?
Animal rights?
More recent trends

Fewer attempts to simulate general human
intelligence
 Instead: getting programs to do intelligent things
regardless of how they do them
 Language:
machine translation, and text
processing – good results found by
applying statistical methods to large lexical
corpora
 Also in speech recognition
 Less interest in working on natural
language understanding and
representation
 Currently
less interest in devising
programs to pass Turing Test
 Concentration instead on particular
problem areas (e.g. vision, natural
language, speech recognition).
 Might return to Turing Test one day when
individual problem areas solved?
Alternatives to “Traditional AI”
 Alternatives:
Neural Computing and
Adaptive Behaviour
 Minimal claim for a computer to be a
‘thinking thing’ should be adaptable
 Adaptable more than learning – to adapt
an organism needs to be able to learn to
survive in what ever environment it finds
itself.
 Are chess playing programs adaptable?
 Rodney


Brooks
Traditional (symbolic) AI has looked at the
easy bit – high level reasoning, divorced from
the real world.
More important to work on the lower level
skills (e.g. vision, movement, navigation)
which have taken years to evolve.
Summary
Possible objections to Turing Test – raised and
dismissed by Turing
 Discussion of objections to Turing test
 Further objections to TT




Too hard to pass
Too easy to pass
More recently: greater emphasis on adaptation
and ability to survive in environment:

changing emphasis away from human ‘intelligent’
behaviour, and on to low level abilities: e.g. vision,
movement.
The end of lecture 2........
Argument from continuity with the
nervous system

Nervous system is continuous: digital computer
is discrete state machine.
 i.e. in nervous systems, 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.
 Discrete machines – moves by sudden jumps
and clicks from one state to another.
 Turing’s dismissal: a discrete state machine can
still give answers that are indistinguishable from
a continuous machine.
The mathematical objection

Results of mathematical logic can be used to show that
there are limitations to the powers of discrete-state
machines.
 E.g. halting problem: will the execution of a program P
eventually halt, or will it run for ever. Turing (1936)
proved that for any algorithm H that purports to solve the
halting problem, there will always be a program Pi such
that H will not be able to answer the halting program
correctly.
 i.e. certain questions cannot be answered correctly by
any formal system.
 Turing’s dismissal: similar limitations also apply to the
human intellect.