Transcript Computer - Aberystwyth University Users Site

PH19510 - Chaos, Communication and
Consciousness
From the movie 2001
Lecture #2
“The Brain Versus the
Computer”
Oct 2007
12:10-13:00 in Room B34
11th
Web Site:
http://users.aber.ac.uk/atc/ph19510/lecture2.ppt
Dr Tony Cook
[email protected]
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The Previous Lecture
What is Consciousness?
How do we map the brain?
•EEG - Electrencephalograms
•PET – Positron Emission Tomography
•CAT – Computerized Axial Tomography
•MRI – Magnetic Resonance Imaging
Geography of the Brain
•Cerebrum
•Cererbellum
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Today’s Lecture
1. Comparisons between the Brain and the
Computer
2. What is Artificial Intelligence?
3. Neural Networks
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1. The Computer
Close-up of Fig 5-4 from Tanenbaum (2001)
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1. The brain vs. the computer.
• The human brain has about 100,000,000,000
(100 billion) neurons. (multiprocessor)
• 1 neuron ~ 1/1,000 MIPS (brain ~ 100million
MIPS)
• Neuron state is on/off/ excitable, function to
process and transmit information
• The mind=software and the brain=hardware
• The brain is a eletrochemical piece of wetware,
a computer is a fully electronic(mechanical)
piece of hardware
• Good at visual recognition, multi-tasking
• Bad at arithmetic, remembering
• Cannot replace parts like on a computer
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1. The storage capacity of the human
brain
• How do we make a computer have something
akin to a human brain?
• Well it must have similar processing power –
there are super computers that are
approaching the speeds given on the previous
slide
• They must have similar memory storage
capacity.
• Each neuron is connected to ~5000 other
neurons through synapses.
• Say each synapse can have 256 levels
(voltages) then we have a storage capacity of
about 100,000,000,000*5000 = 500 Terra
bytes
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• But it does not work quite like this
1. The storage capacity of the human
brain
• Another back of the envelope calculation....
• Say someone lived for 90 years and had a
photographic memory capable of recording
640x480 size images every 10 sec – they
could document their whole life!
• If the brain did some data compression then
like a digital camera JPEG image, each image
might be say 100Kbytes
• So 90 years = 90 * 365.25 * 24 * 60 *60 =
2,840,184,000 sec
• Divide by 1 image per 10 sec: 284,018,400
• Get this into bytes: 284018400 * 100Kbytes
= ~27 Terra bytes or 135x200Gbyte drives
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2. Artificial Intelligence
• Artificial Intelligence is the part of computer science concerned
with designing intelligent computer systems, that is, systems
that exhibit the characteristics we associate with intelligence in
human behaivor - understanding language, learning, reasoning,
solving problems, and so on.
• Expert Tasks: Given the necessary knowledge base, AI is very
successful in applications in engineering design, medical diagnosis,
scientific simulation, and financial analysis. In human terms these
tasks are usually regarded as the most sophisticated and intellectual.
• Formal Tasks: Compared to humans, computers excel at solving
numerical problems (which is what they were invented for first) as
well as other logic-based problems like those encountered in Games chess, backgammon etc. Computer algorithms need to be provided
with the rules first.
• Mundane Tasks: Where AI task domains have been directed at basic
perception, simple language utilisation, common-sense reasoning,
robotics etc, these attempts at mimicking human intelligence have
only been modestly successful.
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2. Artificial Intelligence
cont.
• Two schools of thought:
– Conventional A.I. mostly involves methods now classified as machine
learning, characterized by formalism and statistical analysis. AKA
symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial
Intelligence (GOFAI). ;
• Expert systems
• Case based reasoning
• Bayesian networks
• Behaviour based A.I.
– Computational Intelligence involves iterative development or learning
(e.g. parameter tuning e.g. in connectionist systems). Learning is based
on empirical data and is associated with non-symbolic AI, scruffy AI and
soft computing.;
• Neural networks
• Fuzzy systems
• Evolutionary computation
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2. Intellectual Issues
 Mechanism vs Teleolgy (1640-1945)
 Natural Biology vs Vitalism (1800-1920)
 Symbols vs Numbers (1955-1965)
 Power vs Generality (1965-1975)
 Replacing vs Helping Humans (1960-)
 Procedural vs Declarative Representation (1970-1980)
 Psychology vs Neuroscience (1975-)
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2. Uses of A.I.
• Simple A.I. used in web search
sites such as Ask Jeeves
• Pattern recognition
• Games
• Robotic control
• Used a lot in fiction:
– Computers: HAL9000, Matrix
– Robots: C3PO, Data
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2. A.I. Comprehension
• What A.I. can understand
Case 1 - what AI can readily comprehend
A man went into a restaurant and ordered a steak. When the steak
arrived it was burned to a crisp, and the man stormed out of the
restaurant angrily, without paying the bill or leaving a tip.
A man went into a restaurant and ordered a steak. When the steak
arrived he was very pleased with it and as he left the restaurant
he included a large tip when he paid his bill.
Did the man eat the steak in each case?
Although not explicitly stated, the man did not eat the steak in case
1 but did eat the steak in case 2.
AI can assimilate inferences but it costs in terms of associated
memory.
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2. A.I. Comprehension cont.
• What A.I. can’t understand
Case 2
- what AI cannot comprehend yet
Saturday morning Mary went shopping. Her brother tried to call
her then, but he couldn’t get hold of her.
Why couldn’t Mary’s brother reach her?
The answer “Because she wasn’t at home” requires knowing that
a person cannot be in two places at once and then deducing
that Mary could not have been at home because she was
shopping instead.
The basic dilemma of AI is that in order to handle more than
toy problems the system needs a lot of knowledge. This
is particularly true in the case of language.
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2. Difference Engine No. 2
• Babbage designed it in 1847 - 49
• More elegant than DE1, benefited
from work on AE
• Designs offered to Government in
1852
• Nothing happened till 1985
Science Museum project for
Babbage’s centenary
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G5AHOC – History of Computers and Computing - Lecture 3 – Babbage’s Engines
2. Augusta Ada Lovelace (1815-1852)
• Babbage published little on his Engines
• Lovelace’s Notes on Menebrae
(1842) are the best contemporary
description of the proposed Engine
• The Enchantress of Numbers
• The first programmer, inventor of AI and
computer music: a true visionary making
a significant individual contribution?
• Daughter of Byron – pushed towards
mathematics and science by her mother
(Annabella Millibanke) in an effort to make
sure she did not turn out like him!
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2. Augusta Ada Lovelace
• Ada Lovelace’s Dad – Lord Byron
(1788-1824)
• Poet, writer, bit of a rogue – but very
well known in Britain at the time
• Some one once described him as
“mad, bad and dangerous to know”
• Ended up being buried at the Church
of St Mary Magdalene in Hucknall after
being refused by Westminster Abbey
• Ada is buried next to him
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2. Lovelace’s Notes
 In 1840 Babbage lectured on the Analytical Engine in
Turin, seeking international support for his work
 A “sketch” of the AE was written in French in 1842 by
an engineer called Luigi Menebrae. It was late and
disappointing (to Babbage)
 Babbage and Lovelace had met at one of his soirees in
1834; she was fascinated by his models of DE1 and took
on the job of translating Menebrae’s sketch at the
suggestion of Charles Wheatstone, the publisher
 She and Babbage were close friends and carried on a
lengthy correspondence; he encouraged her to do more
than a translation
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2. Lovelace’s Ideas on AI (1843)
From Note G…. “The Analytical Engine has no
pretensions whatever to originate anything. It can do
whatever we know how to order it to perform. It can
follow analysis; but it has no power of anticipating any
analytical relations or truths. Its province is to assist us
in making available what we are already acquainted
with. This it is calculated to effect primarily and chiefly
of course, through its executive faculties; but it is likely
to exert an indirect and reciprocal influence on science
itself in another manner. For, in so distributing….”
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2. The Turing Test
• Computing Machinery and Intelligence
(1950) asked, can machines think?
• Turing again changed the question: can
a computer hold a sustained
conversation in a manner
indistinguishable from a human being?
• An examiner, a human and a computer
Any program would require human
traits - ability to deceive, emotion
• The publication of Turing’s paper is
seen by many as the birth of Artificial
Intelligence
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•No machine has past the Turing Test!
2. Turing’s Test
How Turing thought a computer might need to respond in order to simulate
“understanding”.
Interrogator
In the first line of your sonnet which reads “Shall I compare thee to
a summer’s day,” would not “a spring day” do as well or better?
Computer
It wouldn’t scan.
Interrogator
How about “a winter’s day”. That would scan all right.
Computer
Yes, but nobody wants to be compared to a winter’s day.
Interrogator
Would you say Mr. Pickwick reminded you of Christmas?
Computer
In a way.
Interrogator
Yet Christmas is a winter’s day, and I do not think Mr. Pickwick would
mind the comparison.
Computer
I don’t think you’re serious. By a winter’s day one means a typical
winter’s day, rather than a special one like Christmas.
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No computer has yet passed the Turing Test. Some think none ever will.
2. The Dartmouth Conference
 The RAND corporation funded a
summer school at Dartmouth College in
1956
 This is seen by others as the start of AI
“We propose that a 2 month, 10 man study of artificial intelligence be carried
out during the summer of 1956 at Dartmouth College in Hanover, New
Hampshire. The study is to proceed on the basis of 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. An attempt will
be made to find how to make machines use language, form abstractions and
concepts, solve kinds of problems now reserved for humans, and improve
themselves…..”
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2. The Dartmouth Conference
 John McCarthy - coined the term Artificial
Intelligence at the Dartmouth meeting, designed
LISP, started major AI programs at MIT &
Stanford…..
 Herbert Simon & Allen Newell – GPS (General
Problem Solver), Logic theorist, set up Carnegie
Mellon University Laboratory…..
 Marvin Minsky – frames, society of mind,
Director of MIT AI Lab……
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Power vs Generality (1965-75)
 Early AI programs took a single well-defined task (e.g. chess,
IQ analogy tests, symbolic integration) and demonstrated that a
machine could perform it, usually using some kind of search
 In the mid 60s there was a shift towards generality in the form
of systems exhibiting “common sense” reasoning
- small puzzles and artificial problems were used to
demo components of (hopefully) more general abilities
 By 1975 most of these systems had failed to generalise to real
problems and emphasis swung back to powerful systems using
knowledge to solve real problems - Expert Systems.
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What is an Expert System?
 Try to encode human expertise and knowledge into a computer
 This is done mostly by defining an explicit set of rules - extracted
from human experts
 The people who extract the rules from experts are known as
knowledge engineers
 Often use Baysian probability to assign conclusions/decisions
 An early expert system was MYCIN (1970’s) - a medical diagnosis
system, with 65% correct diagnosis rate – better than humans physcians
(who were not experts)
 Microsoft Windows troubleshooting guide is an example (perhaps a
poor one) of an expert system
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2. The Chinese Room
Chinese Room
John Searle (1980), invented the Chinese room to
counter the assertion that an algorithmic computer
passing the Turing Test would have understanding.
He imagines that he takes the place of the computer and
operates from a locked room.
He painstakingly follows the algorithm through required to
communicate intelligently to the satisfaction of the interrogator
compared to the other person.
Only proviso - all communication has to be in Chinese but,
symbolic translation is provided e.g. Card system, one card per
translated word.
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2. The Chinese Room
It has to be that if the AI algorithm is worked
through properly, even in symbolic Chinese, then the
correct answer “yes” or “no” (in Chinese) will result.
Searle, however, does not understand a word of
Chinese, so he passes the Turing Test but without
being conscious of or of ever knowing about his
success.
The mental state of understanding is bound up in the
algorithm
The computer has no intention or purpose.
Whilst it might be artificially conscious it does not possess
human intelligence.
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2. Dualism and a Definition for
Consciousness
Searle considers the algorithm is separated from
the mechanics of running it.
This recalls the philosophical concept of dualism mind and matter - of Descartes (Cogito ergo sum).
Descartes “Discourse on Method” (1687)
While I could pretend that I had no body, that there was no
world....I could not pretend that I was not ....
from the fact that I thought of doubting the truth of other
things .... it followed I existed ....
from this I recognised that I was a substance whose essence
or nature is to think and whose being requires no place and
depends on no material things.
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2. Dualism and a Definition for
Consciousness
In the present scenario the mind would be
analogous to software and the material brain to
hardware.
Where might middleware fit in?
Dualism distinguishes the mind from the brain, in
which case consciousness could be the essential linkage.
If the mind encompasses the internal model of reality
constructed by the brain
consciousness could be defined to be the envelope of
capacities of the brain to form subjective
representations of reality.
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3. Neurons, Synapses and Neural
Networks
Sensory information and motor commands are transmitted by neurons. These are cellular
structures which are elongated, branching and fractal in geometry. Neurons can be connector,
sensory or motor in type.
Each comprises a nucleus surrounded by a star-like structure called a soma out of which an axon
protrudes - the end of which splits into a mass of dendrites, each dendrite being terminated by a
synaptic knob.
Once it is fired a neuron transmits electrical signals from the nucleus along the axon to the synaptic
knobs which release neurotransmitter chemicals to the dendrites of adjacent neurons. In
vertebrates sensory and motor neurons are coated in fatty Schwann cells - generating a myelin
sheath.
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3. Neurons, Synapses and Neural
Networks
•Nerve fibres contain a mixed solution of potassium chloride (KCl) with some sodium chloride
(NaCl). When it is inactivated the interior of a nerve fibre is slightly electrically negative.
This is because there is a slight excess of chlorine ions compared to potassium and sodium ions. ie
NCl > NK or NNa
•Electrical impulses are slightly positive and travel down the nerve fibre by transverse
exchange of sodium and potassium ions. The approaching signal causes sodium gates to
open pumping sodium ions into the fibre and converting the negative charge to positive charge,
whilst in the train of the impulse potassium is released through potassium gates restoring the
negative charge. Finally the same pumps regenerate the preponderance of potassium within the
fibre.
•For sensory and motor neurons ion exchange only occurs at gaps in the myelin sheath - Nodes of
Ranvier. Signal transfer is very rapid ~ 120 metres per second.
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3. Neurons, Synapses and Neural
Networks
The neurotransmitters released from the synaptic knobs can be
excitory or inhibitory - acetylcholine, dopamine, serotonin etc. The
next neuron fires if the combination of impulses is above a certain
threshold - summation. This is an all or nothing action which has
attracted the analogy with the digital bit.
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3. Neurons, Synapses and Neural
Networks
Within the brain, the interconnection of neurons is colossal. Each
brain cell has typically 5000 input lines. Feeeback is therefore massive.
Individual neurons are generally firing 3 or 4 times a second, even when
there is no perceptible brain activity in that area. When areas become
activated the activity increases and trains of peaks can be detected which
encouraged the search for a neural code, analogous to computer code.
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3. Neurons, Synapses and Neural
Networks
Is the Brain Hardwired?
GUI
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3. Neurons, Synapses and Neural
Networks
The phenomenon of brain plasticity has emerged which argues against the idea of a hard-wired
computer model.
Whilst the general map of neurons in the brain is laid out at birth, the interconnections between the
synaptic knobs and the dendritic spines on the dendrites of neighbouring neurons is constantly
changing.
The separation is only around 0.0250 millionth of a mm and so connections can be made, lost and
re-made with very little movement offering the prospect of a time dependent neural network.
In this way it is possible for memories to be
laid out by using different synaptic
connections for storing the necessary
information.
Particular correlations between the action of
synapses between different neurons (Hebb
Synapses) would afford the rudiments of a
process of learning.
Further versatility in mental activity could
come through the “leaking” of
neurotransmitters to more distant neurons
- neurochemistry.
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3. Computer Neural Networks
 In 1951 Marvin Minsky built the first neural network
called SNARC
 A kind of neural network, the Perceptron, was also
invented by Frank Rosenblatt at Cornell 1957
 Initially seemed promising, but it had limitations...
could recognize a specific pattern
not good at recognizing many classes of patterns
 Work thus halted for many years on this line of
research
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Lecture Summary
• How the brain compares to a computer
– Information processing
– Pros and cons
• A.I.: Turing test and Chinese Room
– Experimental setup
– What does it demonstrate
– What characteristics are required to pass
• Neural Networks
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