Download Artificial Intelligence

Artificial Intelligence
MEM 1713
Course Lecturer
Dr. Mohamad Hafis Izran B Ishak
Control and Mechatronics Engineering Department,
Universiti Teknologi Malaysia.
[email protected]
P08-204
0197339815
Outline
• Introduction to Artificial Intelligence and
Intelligent Systems
• Overall Course Objectives
• Course Structure
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What is Artificial Intelligence?
(John McCarthy, Stanford University)
•
What is artificial intelligence?
It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using
computers to understand human intelligence, but AI does not have to confine
itself to methods that are biologically observable.
•
Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the
world. Varying kinds and degrees of intelligence occur in people, many animals
and some machines.
•
Isn't there a solid definition of intelligence that doesn't depend on relating it
to human intelligence?
Not yet. The problem is that we cannot yet characterize in general what kinds of
computational procedures we want to call intelligent. We understand some of
the mechanisms of intelligence and not others.
•
More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
What Is Artificial
Intelligence?
?
AI is a “tool” that has been developed to imitate
human intelligence and decision-making
functions, providing basic reasoning and other
human characteristics.
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According to the Oxford and Penguin English Dictionaries the
word “intelligence” can be defined as follows:
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ability to understand
reason
perceive
quickness in learning
mental alertness
ability to grasp relationships
clever
information
news
One way to understand “intelligence” is by
looking at our own capabilities, which means
that humans are able to:
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think
understand
recognize
perceive
generalize
adapt
learn
make decisions
solve daily
problems
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History of AI
• 1943: early beginnings
– McCulloch & Pitts: Boolean circuit model of brain
• 1950: Turing
– Turing's "Computing Machinery and Intelligence“
• 1956: birth of AI
– Dartmouth meeting: "Artificial Intelligence“ name adopted
• 1950s: initial promise
– Early AI programs, including
– Samuel's checkers program
– Newell & Simon's Logic Theorist
• 1955-65: “great enthusiasm”
– Newell and Simon: GPS, general problem solver
– Gelertner: Geometry Theorem Prover
– McCarthy: invention of LISP
History of AI
•
1966—73: Reality dawns
– Realization that many AI problems are intractable
– Limitations of existing neural network methods identified
• Neural network research almost disappears
•
1969—85: Adding domain knowledge
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Development of knowledge-based systems
Success of rule-based expert systems,
• E.g., DENDRAL, MYCIN
• But were brittle and did not scale well in practice
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1986-- Rise of machine learning
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Neural networks return to popularity
Major advances in machine learning algorithms and applications
1990-- Role of uncertainty
– Bayesian networks as a knowledge representation framework
•
1995-- AI as Science
– Integration of learning, reasoning, knowledge representation
– AI methods used in vision, language, data mining, etc
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EXAMPLES OF IQ TESTS [1]
Which one of the five choices makes the
best comparison? LIVED is to DEVIL as
6323 is to:
a. 2336
b. 6232
c. 3236
d. 3326
e. 6332
EXAMPLES OF IQ TESTS [2]
Which number should come next?
144 121 100 81 64 ?
a. 17
b. 19
c. 36
d. 49
e. 50
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Several forms of intelligence
of biological systems
Capability to Learn
Babies learn from
the day they were born!
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Several forms of intelligence
of biological systems
Capability
to Learn
Capability to
Generalize/Classify
Generalization and Classification
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Several forms of intelligence of
biological systems
Capability
to Learn
Capability to Generalize/Classify
Capability to Survive
Gathering of Information
Recognizing Patterns
Humans are good at recognizing
patterns
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Other forms of intelligence of biological
systems include:
Self-repair
Self-guidance
Reproduction
Making decisions
Reasoning capability
Predicting/forecasting
Understanding noisy or fuzzy
information
Humans have self-repair mechanisms in
their bodies
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Humans are
good at
understanding
even difficult
handwritings thus human
recognition
capability is
robust
What is the implication of adding
“intelligence” in machines?
 If artificial systems can be made more robust, costly
redesigns can be reduced or eliminated
 If higher level of adaptation can be achieved,
existing systems can perform their functions longer
and better
 If machines can be made to be self-organized then
less operations are needed by humans
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Is there really an intelligent machine or
device?
Let’s look at a so-called
“intelligent” device that’s already
available in the market
An Intelligent pH sensor
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What the Intelligent
Microprocessor-based pH
Transmitter can do?
 It can tell the user if its glass electrode is
damaged or clogged.
 It can determine if a sensor cable is
disconnected.
 It can determine if the liquid level is too low.
Is there really an Intelligent
Machine/System?
From this point of view it appears
that an intelligent system (or device)
contains a collection of simple
features that jointly make the system
easy to use.
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Can machines be developed to have
“intelligence”?
o Perhaps one way to do this is to
develop algorithms based on
human or animal intelligence
Some Examples of
Artificial Intelligence Techniques
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Expert Systems
Fuzzy Logic
Neural Networks
Genetic Algorithms
Chaos Theory
Rough Sets
Artificial Life, etc.
DNA Computing
Quantum Computing
Our Course Topics
Many AI techniques
have been developed
based on biological
systems/behavior.
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•Neuroscience
•Psychology
•Philosophy
•Biological Science
•Physiology
•……………
•Mathematics
•Control Theory
•Computer Science
•Physics
•Operational Research
•……………
Symbolic AI
New AI
•Symbolic M. L.
•Logic Prog.
•Nat. Lang. Proc.
•Search techniques
•……………
Micro. Bio. Models
•ANN
•GA
•A. Life
•DNA Computing
•……..
•Fuzzy
•Rough Sets
•Chaos
•………..
Fuzzy logic has been developed
from the human reasoning process
Dragon Fly
• 6 legs
• wings
• Body with 3 parts
• Insect
Knowledge
Base
Infer
This is a
dragon fly!
Sensor
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Intelligent Systems Design
Expert systems Expert systems
Fuzzy logic
Fuzzy logic
Neural
Neural
networks
networks
………….
………….
Intelligent
ManMachine
Interface
Course
Objectives
Cognition
TASKS
Algorithms,
computations
Execution
Intelligent machine
Perception
(Sensors)
Example of Products with AI
Genie Fuzzy
Logic Jar
Cookers
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It also has a NeuroFuzzy Logic Systema smart system that
“knows”your
lifestyle and learns
your pattern of
use(like what time
the doors are most
frequently opened
or closed) and
controls the
refrigerator
accordingly either
through quick
cooling, low cool or
defrosting.
This Refrigerator has a Neuro-Fuzzy Control System
(For Door Cooling and Super-Cooling and Freezing)
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Why the need to develop “Intelligent
Systems” and Why Now?
More challenging problems
More complex systems
More powerful computers/hardware
Better/powerful algorithms
Better software tools
Man’s desires
Plants are becoming more complex,
Thus, new techniques are needed for better and tighter control.
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ASIMO
Advanced Step in Innovative MObility
Camera Eyes [AI]
Antenna
Battery (Fuel Cell)
Gyro Sensor Measuring
Body Angle
Actuators and Other Peripheral
Systems Controlling leg
movements [AI]
Load Sensors In Leg
Intelligent Real-time
Flexible Walking [AI]
The Honda Man
Deficiencies:
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Consumes large
amount of power
(large battery pack)
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Walking ability rather
awkward
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Moving ability –
dependent on too many
sensors
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Thinking ability
(almost none)
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Where AI can/should be applied?
[1]
• Data is overwhelming/abundance
• Too many manual operations/procedures
• Optimization is possible
• Parallel/Distributed
procedures/architectures are needed
• Decision making is required
• When current techniques are too
complicated to be used/designed
Where AI can/should be applied?
[2]
• Mathematical models are too
complex/impossible
• To increase efficiency
• To reduce cost
• To improve performance and reliability
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Where AI should not be
applied?
• Lack of Data
• Simpler techniques are available /
sufficient
• Further optimization is not possible
Some Important Facts
that you need to know….
• AI is not the only solution
• AI is only one part of technology
• AI is just a tool for improvement
• You must know your domain/target
application
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What you may get
at the end of this course?
COURSE OBJECTIVES
• To understand the broad concept of artificial
intelligence and its applications in industry.
• To understand the basic principles of fuzzy logic and
neural networks.
• To study how fuzzy logic and neural networks are
applied in some real world applications.
• To have some hands-on experience on using fuzzy
logic and neural networks to solve practical
problems.
What you will not get?
Instant Expertise
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