Transcript 2101INT – Principles of Intelligence Systems

2101INT – Principles of Intelligent Systems
Lecture 2
Last week we covered
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History of automatons, in literature and reality
The birth of AI and the Dartmouth conference
Some applications and domains of AI since then
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Planning
Natural Language Processing
Expert Systems
Neural Networks
Videos demonstrated AIs operating in “microworlds”
Discussion about what we think constitutes intelligent
behaviour
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The Turing Test
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Proposed by Alan Turing in 1950 to be an operational
definition of intelligence
A human “interrogator” asks questions of two entities –
one computer, one human – behind screens and using
a console, to conceal which is which
If the interrogator cannot determine which is which
then the computer is said to be intelligent
An extension of this is the total Turing test which
includes a 1-way video feed and the provision for
haptic interaction
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The Turing Test cont.
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Physical simulation of a human is not considered
necessary for intelligence and is deliberately avoided
by the screen/console/1-way link etc.
The test is purely a behavioural test of intelligence,
relying only on the external behaviour of the entity and
not on its internal mental states.
Many philosophers claim that passing the Turing Test
does not prove that a machine is thinking, just that it
can simulate thinking.
Turing’s response to this objection is simply that in
ordinary life, we never have any direct evidence about
the internal mental states of other humans.
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The Schools of Thought
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At the highest level, AI can be divided into two schools
of thought:
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Weak AI
“the principal value of the computer in the study of the mind is that it
gives us a very powerful tool”
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Strong AI
“the computer is not merely a tool in the study of the mind; rather,
the appropriately programmed computer really is a mind”
[Searle, 1980]
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These relate to the question of consciousness of an
intelligent system
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Weak AI: Can Machines Act Intelligently?
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Weak AI argues that machines are not actually
conscious and only appear to think
Computers can do many things as well as or better
than humans – including tasks considered to require
insight and understanding
But “Weak AI” doesn’t believe that computers actually
use insight and understanding in performing these
tasks
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The mathematical objection
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Certain mathematical questions are unanswerable by
particular formal systems
Gödel’s Incompleteness Theorem (GIT) and the
Halting Problem being two relevant examples
Some philosophers – J. R. Lucas being one – argue
that these demonstrate that machines are mentally
inferior to humans, since machines are formal systems
and can therefore not establish the truth of their own
Gödel sentence
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Arguments against Lucas
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1. Computers are not Turing Machines, they are
approximations only
2. There are statements that humans cannot assert the
truth of
3. It is impossible to prove that humans are not subject
to GIT because human talent is not a formal system
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The argument from informality
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Originally raised by Turing, claims that human
behaviour is far too complex to be captured by simple
rules
Since computers are only able to follow a set of rules,
they cannot therefore generate behaviour as intelligent
as humans
What this argument is really arguing against is GOFAI
– “Good Old Fashioned AI” - that all intelligent
behaviour can be captured by a system that reasons
logically with symbols
Merely acting intelligently isn’t precluded by this
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On the nature of human consciousness
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Descartes considered how the soul (let’s say
consciousness) interacts with the physical body
This has come to be termed the mind-body problem
He concluded that the mind must be distinct from the
body and be two different things – hence dualism
Materialism is a monist theory, doesn’t believe that the
mind and the body are different things, or that there are
such things as immortal souls
It is simply that brains cause minds
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Relationship to AI
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Dualism per se disallows strong AI, since
consciousness is not a consequence of the physical
system
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Materialism on the other hand, does allow strong AI
Explaining away the mind-body problem
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Descartes managed to contradict himself when he
proposed interactionism. In this he said that mind and
body do interact, but this is restricted to the pineal
organ at the base of the brain
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Less contradictory is the theory of parallelism.
According to this, mind and matter are entirely
separate, each obeys its own laws but each keeps time
perfectly with the other - due to the marvellous
planning of a greater being.
Explaining away the mind-body problem
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Similar to the last is the doctrine of occasionalism. So
although mind and matter can’t interact, providence
intervenes on each “occasion” when they need to.
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And finally, epiphenomenonalism. This holds that
minds play no role in the everyday running of the
universe. Consciousness is merely an incidental byproduct of the system. Matter “causes” mind as in
materialism, but thought has no effect on matter. So we
can watch the world go by, but can’t do anything about
it – any impressions to the contrary are simply an
illusion
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Strong AI: Can Machines Actually Think?
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Strong AI argues that a properly programmed
computer is conscious “mind” in the fullest sense
Functionalism
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The influence on functionalism is attributed to influence
of computers on modern society
Functionalism is still Materialism, but:
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Brain states are distinct from Mental states
Behaviour is not directly related to stimulus
It does not matter what the physical cause of the
mental (functional) state is
These functional states are responsible for our outward
behaviour
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Fuctionalism cont.
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Any two systems with isomorphic causal processes will
have the same mental states – even if they don’t have
the same brain states
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Recall from last week
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“The conjecture that every aspect of learning or any other
feature of intelligence can in principle be so precisely
described that a machine can be made to simulate it”
McCarthy et al. (1955) “A Proposal for the Dartmouth Summer
Research Project on Artificial Intelligence”
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Biological Naturalism
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The opposite view is termed biological naturalism
It argues that mental states are emergent features,
caused by low-level neurological processes inside the
neurons
It is the unspecified properties of the neurons that
matter – and equivalent mental states are not
produced by something else with the same functional
behaviour
Of course, this theory doesn’t define why neurons have
this power but the concept of a soul comes to mind
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The Chinese Room Argument
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“No one supposes that a computer simulation of a storm will
leave us all wet… Why one earth would anyone … suppose a
computer simulation of mental processes actually had mental
processes”
Searle (1980) “Minds, Brains, and Programs”
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Searle’s conclusion is that running an appropriate
program is not a sufficient condition for being a mind
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The Chinese Room Argument cont.
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Consider that you have a system consisting of a
human who only understands English
He is placed inside a box with a rule book and some
blank pieces of paper
The box has an opening, through which appear slips of
paper with indecipherable symbols written on them
The instructions in the rulebook direct the human to
write slips of the blank pieces of paper, look at bits of
paper previously used and even write some of the
indecipherable symbols on paper and pass them back
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The Analogy of the Chinese room
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The human is playing the role of the CPU
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The rule book is his program
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The pieces of paper are his memory
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The Chinese Room Argument cont.
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What does this system look like from the outside?
The indecipherable symbols are really questions and
with the help of the rulebook the symbols written out
are the appropriate answers
So it appears that the system can “understand”
Chinese, giving as it does answers appropriate to the
questions asked
It therefore passes the Turing test
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The Chinese Room Argument cont.
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Does the person in the room understand Chinese?
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No.
Do the rule book and the stacks of paper understand
Chinese?
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No.
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So if none of the components of the system understand
Chinese, how can the system understand Chinese?
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Therefore, running the right program does not
necessarily generate understanding.
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The Systems Reply
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"While it is true that the individual person who is locked in the
room does not understand the story, the fact is that he is
merely part of a whole system, and the system does
understand the story. The person has a large ledger in front of
him in which are written the rules, he has a lot of scratch paper
and pencils for doing calculations, he has 'data banks' of sets
of Chinese symbols. Now, understanding is not being ascribed
to the mere individual; rather it is being ascribed to this whole
system of which he is a part.“
Searle citing ‘Berkeley’ (1980) “Minds, Brains, and Programs”
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The Systems Reply - Response
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Although none of the components understand Chinese,
the system as a whole does.
It could be said to be an emergent property of the
system.
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After all, if you ask it “Do you understand Chinese?” it
will of course response (in Chinese) that it does
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Searle’s counter-argument is that if the human were to
memorise the rule book and not use pieces of paper he
still wouldn’t understand Chinese.
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The Robot Reply
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"Suppose we wrote a different kind of program. Suppose we
put a computer inside a robot, and this computer would not just
take in formal symbols as input and give out formal symbols as
output, but rather would actually operate the robot in such a
way that the robot does something very much like perceiving,
walking, moving about, hammering nails, eating drinking -anything you like. The robot would, for example have a
television camera attached to it that enabled it to 'see,' it would
have arms and legs that enabled it to 'act,' and all of this would
be controlled by its computer 'brain.' Such a robot would have
genuine understanding and other mental states."
Searle citing ‘Yale’ (1980) “Minds, Brains, and Programs”
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The Robot Reply - Response
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Searle notes that this argument concedes that
intelligence is more than symbol manipulation and
must relate to the physical world
But the addition of perceptual and motor capabilities do
not add understanding
Suppose you leave the human inside, but now with
additional symbols (still in Chinese) that come from the
camera. Some additional symbols (also in Chinese) will
be given to the motors.
The human still knows nothing about what any of these
mean, and still does not understand Chinese.
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The Brain Simulator Reply
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"Suppose we design a program that doesn't represent information
that we have about the world, but simulates the actual sequence of
neuron firings at the synapses of the brain of a native Chinese
speaker when he understands stories in Chinese and gives answers
to them. The machine takes in Chinese stories and questions about
them as input, it simulates the formal structure of actual Chinese
brains in processing these stories, and it gives out Chinese answers
as outputs. We can even imagine that the machine operates, not with
a single serial program, but with a whole set of programs operating in
parallel, in the manner that actual human brains presumably operate
when they process natural language. Now surely in such a case we
would have to say that the machine understood the stories; and if we
refuse to say that, wouldn't we also have to deny that native Chinese
speakers understood the stories? At the level of the synapses, what
would or could be different about the program of the computer and
the program of the Chinese brain?"
Searle citing ‘Berkeley and MIT’ (1980) “Minds, Brains, and Programs”
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The Brain Simulator Reply - Response
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Searle notes (once again) that intelligence must
therefore be more than symbol manipulation.
Even getting this close to the operation of the brain is
not sufficient to produce understanding
Imagine that the neurons are simulated by water pipes
and that a little man runs around turning valves on and
off according to rules in the book.
After all the right “neural firings” the Chinese answer
pops out
Still the man doesn’t understand, the water pipes don’t
understand and we would return to the Systems Reply
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Summary of the Chinese Room
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Strong AI claims that instantiating a formal program
with the right input is a sufficient condition of
intelligence/understanding/intentionality
Attributing these things to the Chinese Room are
based on the assumption that if it looks and behaves
sufficiently like something else with the same function,
then it must have corresponding mental states
If we knew what to attribute its behaviour to (little man,
pipes, formal program) we would not make this
assumption and would not attribute intentionality to it.
References
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Haugeland, John (1985) “Artificial Intelligence – The Very Idea”, MIT Press.
“Philosophy of Mind – Functionalism”
http://www.philosophyonline.co.uk/pom/pom_functionalism_introduction.htm
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Searle, (1980) “Minds, Brains, and Programs”
http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html