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Artificial Intelligence
Chapter 12
Definition:
• Artificial Intelligence (AI):
– “The activity of providing such machines as
computers the ability to display behavior that
would be regarded as intelligent if it were
observed in humans.”
AI
ARTIFICIAL INTELLIGENCE
(AI) SYSTEMS:
(Laudon & Laudon Definition)
AI: COMPUTER-BASED SYSTEMS WITH
ABILITIES TO LEARN LANGUAGE,
ACCOMPLISH TASKS, USE
PERCEPTUAL APPARATUS, EMULATE
HUMAN EXPERTISE & DECISION
MAKING
*
History of AI
1950
• Turing Test
– "Can machines think?"
• Loebner Prize
– $100,000 Grand Prize
– Not yet awarded
1950:
• Alan Turing proposes the “Turing Test”
for computers
• Can a computer pass for a human?
1952:
• UNIVAC correctly predicts Dwight
Eisenhower’s election with only 7% of
votes reported
1956: “Artificial Intelligence”
• John McCarthy coins the term in 1956 as
the theme of a conference held at
Dartmouth College.
“Artificial Intelligence”
• Dartmouth, 1956
• 25-year Prediction (1981):
– Prediction: in 25 years (1981) (would be
before George Orwell’s 1984)
– Intelligent machines would be able to do all
the physical and intellectual work for human
beings.
– Leaving people to devote all their time to
recreational activities.
1958:
• John McCarthy:
• If we work really hard, we’ll have an
intelligent system in from four to four
hundred years.
1958:
• Herbert Simon:
• Said that a program would be chess
champion in ten years (by 1968).
Deep Blue
• 1997 IBM’s computer “Deep
Blue” defeats world chess
champion, Gary Kasparov.
• First time a computer had
defeated a top-ranked chess
player
• Not Undisputed
Major Areas of AI:
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Expert Systems
Neural Networks
Perceptive systems
Robotics
AI
EXPERT SYSTEMS
KNOWLEDGE - INTENSIVE
CAPTURES HUMAN EXPERTISE
IN LIMITED DOMAINS OF
KNOWLEDGE
*
Development of
Expert Systems
• What is Expertise?
– Skill and knowledge whose input
into a process results in
performance high above the
norm.
First-to-100-game
• Rules:
– 2 Players alternate by adding a number to the
total.
– Numbers must be within 1-10.
– First player to reach 100 wins
Following a Set of Rules /
Pattern Recognition
• The game can easily be won by anyone who
recognizes the pattern…
• You must be the first to 89 in order to be the
first to 100…
Development of
Expert Systems
 Components
of Expert
Systems
 The
interface or dialog
 The
knowledge base
 The
interface engine
Development of
Expert Systems
Components of an expert system; numbers indicate the order of the
processes
Expert Systems
• The Benefits
–
–
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Longevity
Cost savings
Availability
Replicable
Contribution of
Expert Systems
• Areas where ESs can help in
business
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Planning
Decision making
Monitoring
Diagnosis
Training
Incidental learning
Replication of expertise
Timely response
Consistent solutions
Contribution of Expert Systems
Major reasons for using expert systems
Expert Systems in Action
• Business areas using ESs
–
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–
–
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Telephone network maintenance
Credit evaluation
Tax planning
Detection of insider securities trading
Mineral exploration
– Legal Advice/ Medical Advice
– Visa & M/C: 2 purchases + 1
Gas out of town: call for
verification
Knowledge
Representation Methods
• Factors Justifying the
Acquisition of Expert Systems
AI
•
•
•
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EXPERT SYSTEMS
LIMITATIONS:
Often reduced to problems of classification
Can be large, lengthy, expensive
Maintaining knowledge base critical
Many managers unwilling to trust such
systems
*
Limitations of
Expert Systems
• Three limitations of ESs
– Can handle only narrow domains
– Do not possess common sense
– Have a limited ability to learn
Bobs Cars
• Simple A.I. Application based on weights
+/- w/ each choice you make
• http://www.src-net.com/BobsCars/fbdeal.htm
AI: Neural Networks
Neural Networks
• Biologically inspired flexible
statistical models.
• Function approximations
– Offers not only point estimates but
also converges on the derivatives of
the unknown functions
Neural Networks
• A mathematical model
of the human brain
that simulates the way
that neurons interact to
process data and learn
from experience.
Human Neurons
• Dendrites (input)
• Soma (processor)
• Axons (output)
Biological Neural Network
• Patterns of electrical impulses from cell to cell
form memory.
From Biological to
Artificial Neural Networks
Neural nets simulate the association and inference that take place in a
network of neurons in the human brain. Instead of a network of
neurons, a network of nodes is developed.
Artificial Neuron
• Y is the result of
the weighted
input signals
• Any non-linear
function can be
used, the Sigmoid
function is the
most popular
Multi-Layered Artificial Neural
Network (A.N.N.)
• All possible interactions are considered
• All relationships are considered non-linear
• High inter-correlation is not a problem
Specific Examples of A.N.N.
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Bankruptcy Prediction
Forecasting Stock Prices
Direct Marketing Mail Prediction
Credit Scoring
Real Estate Appraisal
Finding Gold (testing soil samples)
Thoroughbred Horse Racing: 17 wins in 22 races
Weather Forecasting
Beer Testing
Credit Card Fraud Detection
• Mars-Rock testing
Neural Network Simulator
for Character Recognition
http://diwww.epfl.ch/mantra/tutorial/english/ocr/html/index.html
AI: Ethical and Societal Issues
Ethical and Societal Issues
Too Sophisticated Technology
• Increasing dependence on machine intelligence
raises legal and ethical issues.
– Who is legally responsible for advice provided by a
program?
– Is expert judgment needed to interpret program output?
– Does machine expertise replace or complement the
‘real thing’?
– How do we know if the experts behind expert systems
are expert at all?
Ethical and Societal Issues
Too Sophisticated Technology
• Malfunctions of an ES can be
caused by anyone involved in the
development.
– Experts who contribute knowledge
– Knowledge engineer who builds the
system
– Professional who uses the ES
– The person who is affected by the
decision
British Telecom
• “Soul Catcher”
• Implant a microchip in
the human brain
Questions?
Needed Links:
Bobs Cars
• Simple A.I. Application based on weights
+/- w/ each choice you make
• http://www.src-net.com/BobsCars/fbdeal.htm
Neural Network Simulator
for Character Recognition
http://diwww.epfl.ch/mantra/tutorial/english/ocr/html/index.html

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