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Welcome to
Artificial Intelligence
Khaled Hussain
BSc. Hons. (BCU, UK) MSc. (BCU, UK)
CEO, Cybernetics Robo Academy
www.cyberneticsrobo.com
(Ex. Head, Dept. of Computer Science & Engineering, SIU)
Introduction

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Intelligence -
Intelligence is a term used to describe a property of the mind
that encompasses many related abilities, such as the
capacities to
 reason
 plan
 solve problems
 think
 comprehend ideas
 use language
 learn
Intelligence -
 creativity
 personality
 character
 knowledge
 wisdom
 skills
Intelligence may be judged by -

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• not considered to be alive
• do not reproduce on their own, but make bacteria produce
copies of themselves
• no learning in individual viruses; only “evolutionary learning”
• are considered to be alive
• reproduce by splitting up into two organisms
• capable of some simple learning that allows them to move
towards favorable environments

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• capable of learning and memorizing
• primitive social interactions (trail of pheromones)
• simple visual and olfactory perception (compound eyes)
• coordinated movements of legs to master different types of
terrains
• good capability of vision (eyeballs, eye movements)
• able to learn certain behaviors
• hunting abilities
• still very primitive social interactions

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• very powerful senses
• produce sounds but no language
• complex social interactions
• probably consciousness and basic feelings that are related to
our own
• very human-like genetic makeup and behaviors
• produce different sounds with different meanings
• capable of learning symbolic “language”
• probably consciousness, self-awareness and basic feelings
at the level of a human kid

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• complex social behaviors
• able to learn high-level syntactic languages
• extremely long phase of learning (upbringing)
• consciousness, self-awareness, abstract thinking
• awareness of past and future, planning capability
• built by humans
• coordinated body movements
• little learning capabilities
• no real social interaction or language
• no consciousness or self-awareness

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John McCarthy, who coined the term in 1956, defines it as "the
science and engineering of making intelligent machines.“
AI is a field of computer science concerned with understanding the
nature of intelligence and the development of software and hardware
which simulates human intelligence. In general, AI is concerned with
making intelligent machines that work and behave like humans.
Artificial Intelligence (AI)
There is no universally accepted definition of AI !
Artificial Intelligence (AI)
"The exciting new effort to make computers think ... machines with minds, in the
full and literal sense." (Haugeland, 1985)
"The automation of activities that we associate with human thinking, activities
such as decision-making, problem solving, learning ..." (Bellman, 1978)
"The art of creating machines that perform functions that require intelligence
when performed by people." (Kurzweil, 1990)
"The study of how to make computers do things at which, at the moment, people are better."
(Rich and Knight, 1991)
"The study of mental faculties through the use of computational models."
(Charniak and McDermott, 1985)
"The study of the computations that make it possible to perceive, reason, and act."
(Winston, 1992)
"Computational Intelligence is the study of the design of intelligent agents." (Poole
Et al, 1998)
"AI ... is concerned with intelligent behavior in artifacts." (Nilsson, 1998)
Machines
that
think
like
humans
Machines
that
act
like
humans
Machines
that
think
rationally
Machines
that
act
rationally

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Think like humans Think rationally
Act like humans Act rationally
Artificial Intelligence (AI) ?
Machines that -
Machines that think like humans (Cognitive Approach)
Cognitive thought makes humans adaptable to a quickly
changing environment.
How do humans think?
This view involves trying to understand human
thought and an effort to build machines that
emulate the human thought process.

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Machines that act like humans (Behavioural Approach)
 Only activities observed from the outside are taken into
account. Not interested in how results are obtained but just
the similarity to what human results are.
“ If it looks, walks, and quacks like a duck,
then it is a duck ”
Performing the job, doing something without any specific
reason, common dialogue between people etc.
 Not always rational
Machines that think rationally (Logical Approach)
 No replication of human thought, decision making is purely
based on facts/logic (inference)
Sometimes theoretically possible but practically impossible (ex.
Walking)
When 234 is not a number ?
Man is mortal. X is a man. Therefore, X is mortal.
 Not always correct.

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Machines that act rationally (Rational Agent Approach)
 Systems that do the “right thing”
 Idealized concept of intelligence
 Act so that desired goals are achieved. The focus
is on how the system acts and performs, and not
so much on the reasoning process.
This above graph reflects the fact that computers are good at performing
well-defined, repetitive computations but poor at complex tasks like
reasoning.
On the other hand, humans struggle to accurately add a long column of
numbers without error while they have little problem conversing and
reasoning with friends using natural language.
Complexity
Human
Computer
Performance

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General Goal of AI Research
“ To develop more powerful, versatile programs that can
handle problems currently handled efficiently only by the
human mind ” [Balci 1996].
In thinking about this goal, it is helpful to consider what types
of problems the human mind can handle efficiently and what
types of problems computers can handle efficiently.
Computers are Good at :
• Numerical Computations
• Information storage
• Repetitive Operations
• Searching
• ?
Humans are Good at :
• Applying Common Sense
• Socialization
• ?

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 Strong / Hard AI refers to a machine that approaches
or supersedes human intelligence. Main aims to create
AI to build machines whose over all intellectual ability is
indistinguishable from that of human being.
 Soft AI / Weak AI refers to the use of software to study
or accomplish specific problem solving or reasoning
tasks that do not encompass the full range of human
cognitive abilities. Example : a chess program such as
Deep Blue.
Strong (Hard) vs. Weak (soft) AI
Major Division in the field of AI
 Symbolic AI - A mathematically oriented way of abstractly
describing processes leading to intelligent behaviour.
Represents information through symbols and their
relationships. Specific Algorithms are used to process these
symbols to solve problems or deduce new knowledge.

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Applications of AI
 Game playing
 Financial decision making
 Natural language understanding
 Pattern recognition
 Medical diagnosis
 Quality control
 Scheduling etc.
Major Division in the field of AI
 Connectionist AI - Aims at massively parallel models that
consist of a large number of simple and uniform processing
elements interconnected with extensive links, that is artificial
neural networks. Biological processes underlying learning,
task performance, and problem solving are imitated.

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Search
Knowledge
rep.
Planning
Reasoning
Learning
Agent
Robotics
Perception
Natural
language
... Expert
Systems
Constraint
satisfaction
 Knowledge representation (including
formal logic)
 Search, especially heuristic search
(puzzles, games)
 Planning, scheduling
 Reasoning under uncertainty,
including probabilistic reasoning
 Machine Learning
 Robotics
 Natural language processing
 Expert Systems
 Pattern recognition etc.
Some areas of AI
The Turing Test
Proposed by Alan Turing in 1950
A test based on indistinguishability from
undeniably intelligent entities - human beings
An interrogator engages in a natural
language conversation with one human
and one machine, each of which tries to appear as human.
All participants are placed in isolated locations.
If the interrogator fail to reliably distinguish the human from the
computer, then the computer is deemed intelligent

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The Turing Test
 CAPTCHA: Completely Automatic Public Turing tests
to tell Computers and Humans Apart
An Application of Turing Test

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The Chinese Room Argument
By American philosopher John Searle in 1980
Intentionality…
Knowing what you are talking about !
…And after all these advancements in technology...
...Somewhere, Something Went Wrong !?!?!

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References
Russel S, Norvig P, Artificial Intelligence – A Modern Approach (2nd ed.)
Welling M, Lecture Notes, CompSci 171, University of California, USA
Poplun M, Lecture Notes, CS 470/670
Sun R, Artificial Intelligence : Connectionist and Symbolic Approaches, University of Missouri-
Columbia, USA