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An Introduction to
Artificial Intelligence
Dr Iman Ardekani
Understanding intelligence
Imitating intelligence
Artificial intelligence - AI
Branches of AI
Content
Undrstanding Intelligence
Many great philosopher over the ages attempted to explain the
process of thought and understanding.
Intelligence
Plato
428–348 BC
Aristotle
384–322 BC
Philosophy
Math
Nature and Universe
Human knowledge Intelligence
*
Undrstanding Intelligence
Plato
428–348 BC
Aristotle
384–322 BC
Copernic
1473-1543
Galileo
1564-1642
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
The real key that started the quest for the
simulation of inteligence did not occure until …
*
Undrstanding Intelligence
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
Thomas Hobbes (English Philosopher) put forth an interesting concept that
thinking consists of symbolic operations and that everything in the life
can be represented mathematically.
Hobbes
1588-1679
*
Undrstanding Intelligence
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
Thomas Hobbes (English Philosopher) put forth an interesting concept that
thinking consists of symbolic operations and that everything in the life
can be represented mathematically.
Hobbes
1588-1679
*
Hobbes (British Philosopher):
Thinking consists of symbolic operations!
Based on this logic, a machine capable of caring out
mathematical operations on symbols could imitate human
thinking.
Undrstanding Intelligence
Hobbes
1588-1679
What is a symbolic operation?
• Numeric operation (2+3)2 = 25
• Symbolic operation (a+b)2 = a2 + b2 + 2ab
Rene Descartes (French Philosopher and Mathematician):
He believed that the mind and the real world are in parallel
planes. The physical word (i.e. machines) cannot imitate the
mind because there is no common reference point.
Undrstanding Intelligence
Descartes
1596-1650
Charles Babbage (British Mathematician):
In Babbage's time, numerical tables were calculated by humans who
were called 'computers’. He saw the high error-rate of this human-
driven process and started work of trying to calculate the tables
mechanically. He created a “difference engine” to compute values
of polynomial functions.
Imitating Inteligence
Babbage
1791-1871
A part of Babbage's difference
engine
He also introduced the idea of “Analytical
Machine”, but he could never realize this
idea.
George Boole (British Mathematician):
Boole formulated the “Laws of Thought” that set up rules of logic for
representing thoughts (symbolic logic). This was the birth of digital
logic, a key component of AI.
In the early 1900s, Alfred Whitehead and Bertrand Russell extended
Boole’s logic to include mathematical operations. This led to the
formulation of digital computers. Also, this made possible one of the
first ties between computers and thought process.
Imitating Inteligence
Boole
1815-1864
Russell
1872-1970
Whitehead
1861-1947
Design a digital computer using logical operations to compute y=x1+x2
where x1 and x2 are 4-digit binary numbers (4-bit adder).
Design a digital computer using logical operations to compute y=x1.x2
where x1 and x2 are 4-digit binary numbers (4-bit multiplier).
Design a digital computer using logical operations to compute y=ex where
x1 and x2 are 4-digit binary numbers (ex=1+x+x2/2+x3/6+…).
Imitating Inteligence
Claude Shannon (American Electrical Engineer):
He wrote his master’s thesis demonstrating that electrical
applications of Boolean algebra could construct and resolve any
logical, numerical relationship.
It has been claimed that this was the most important master's thesis
of all time. His PhD these was on mathematical relationships of
genetics.
He is known as the father of Information Technology.
Imitating Inteligence
Shannon
1916-2001
John Neumann (American Mathematician)
He suggested that the computers
 should be general purpose logic machines.
 could react intelligently to the results of their calculations
 could choose among alternatives, and even play checker and chess
This represented something unheard of at that time: a machine with
built-in intelligence, able to operate on internal instructions.
Before introducing this concept, even the most complex mechanical
devices had always been controlled from the outsides, by knobs and
dials.
He didn't’ invent the computer but what he introduced was equally
significant: computing by use of computer programs.
Imitating Inteligence
Neumann
1903-1957
John Mauchly (American Electrical Engineer):
John Mauchly designed and built the first general purpose digital
computer in 1946 at the University of Pennsylvania:
ENIAC (Electronic Numerical Integrator and Computer)
Weight = 30 Tons
Floor Space = 1500 Square Feet
Shannon’s idea  Hardware
Neumann’s idea  Software
Imitating Inteligence
Mauchly
1907-1980
Alan Turing (British Mathematician):
He introduced “Universal Machine Concept” that describe a
machine for solving all problems based on variable instructions.
Turing’s universal machine concept, along with Neumann’s concept
of computing using programs led to programmable computers.
Operational machines were now being realized. The question was
“Are they intelligent?” and “in what extend?”. Turing also designed
Turing’s test for determining the intelligence of a system.
Imitating Inteligence
Turing
1912-1954
Turing Test – Step 1 (man/woman)
A is a man and B is a woman and C is of either sex.
C is unable to see either A or B, and can communicate
with them only through online computer chat.
By asking questions of A and B, C tries to determine
which of the two is the man and which is the woman.
A's role is to trick C into making the wrong decision,
while B attempts to assist C in making the right one.
Imitating Inteligence
Turing Test – Step 2 (human/computer)
Substitute a computer for A.
By asking questions of Computer and B, C tries to
determine which of the two is the computer.
Computer's role is to trick C into making the wrong
decision, while B attempts to assist C in making the right
one.
If the C’s success rate in human/computer game is not
better than his success rate in the man/woman game
Imitating Inteligence
Turing Test
If the C’s success rate in human/computer game is not better than his
success rate in the man/woman game, then the computer can be said to be
“thinking”.
Imitating Inteligence
There was now a need for a high-level programming language.
Logic Theorist was written in 1955 by A. Newell, H. A. Simon
and J. C. Shaw. It was the first program deliberately
engineered to mimic the problem solving skills of a human
being and is called "the first artificial intelligence program.” It
would eventually prove 38 of the first 52 theorems of
Whitehead and Russell, and find new and more elegant proofs
for some.[2]
Imitating Inteligence
John McCarthy (American Computer Scientist)
He coined the term “Artificial Intelligence” in the first conference on
machine intelligence, 1956.
He also developed LISP (List Processing) programming language,
which has become a standard tool for AI development.
LISP distinctions:
 Memory organization – in a tree fashion
 Control structure – instead of working from perquisites to a goal, it
starts with the goal and works backward to determine what perquisites
are required to achieve the goal.
Artificial Intelligence
McCarthy
1927-2011
GPS (General Problem Solver) was another AI programming language that
introduced in 1959.
It was capable of solving theorems, playing chess, or doing puzzles.
Its core was based on the use of means-end analysis, which involves
comparing a present state with a goal state. The difference between the two
state is determined and a search is done to find a method to reduce this
difference. This process is continued until there is no difference between the
current state and the goal state.
It was capable of backtracking to an earlier state to correct its mistakes.
It was also able to define sub-goals.
GPS did a good job of imitating the human subjects.
Artificial Intelligence
ELIZA was the first intelligent computer program that
was enable of interacting in a two-way conversation.
It could sustain very realistic conversations by very
smart techniques.
For example, ELIZA used a pattern matching method
that would scan for keywords like “I”, “You”, “Like”
and so on. If one of these words was found, it would
execute rules associated with it. If no match was
found, it would request for more information.
Artificial Intelligence
Link to ELIZA
The various attempts at formally defining the use of machines to simulate
human intelligence let to several AI branches
1. Natural Language Processing (NLP)
2. Computer Vision
3. Robotics
4. Problem-solving and planning
5. Learning
6. Expert Systems
Branches of AI
Branches of AI
NLP
ComputerVision
ExpertSystems
ProblemSolving
Robotics
Learning
Artificial Intelligence
Human-like artificial creatures
Other artificial creatures
Special robots/machines with higher capabilities
How successful we have been in creating human-like artificial creatures?
Branches of AI
Natural Language Processing (NLP)
NLP understands, and generates languages that humans use naturally so that
eventually you will be able to address your computer as though you were
addressing another person (e.g. ELIZA)
Branches of AI
Speech NLP Knowledge
Natural Language Processing (NLP)
NLP Categories:
1- Phonology: modeling the pronunciation of words (chair, car, cell)
2- Morphology: identifying the structure of words (dog, dogs, hot dogs)
3- Syntax (identifying grammars)
4- Semantics (understanding and representing the meaning)
Applications:
automatic text indexing, grammar and style analyser, automatic text generation,
machine translation, optical character recognition (OCR) and etc.
Branches of AI
Computer Vision
Computer vision is a field that includes methods for acquiring, processing,
analysing, and understanding images and, in general, high-dimensional
data from the real world in order to produce numerical or symbolic
information, e.g., in the forms of decisions.
Branches of AI
Images
Computer
Vision
Knowledge
Branches of AI
Computer Vision
US Deference Advance Research Projects Agency (DARPA)
Computer Vision
Applications:
1. Recognize objects (e.g. people we know and things we own)
2. Locate objects in space (to pick them up?)
3. Track objects in motion (catching a baseball, avoiding collisions
with cars on the road)
4. Recognize actions (e.g. walking, running, pushing)
Branches of AI
Robotics
Robotics involves the control of actuators on robots to move, manipulate or
grasp objects, locomotion of independent machines and use of sensory
input to guide actions.
Branches of AI
Problem-solving and Planning
This technology involves application such s refinement of high-level goals
into lower-level ones, determination of actions to achieve goals, revision of
plans based on intermediate results, and focused search of important
goals. A good example is chess players software.
Branches of AI
Learning
Learning deals with research into various forms of learning including rote learning,
learning through advise, learning by example, learning by task performance, and
learning by following concepts.
Branches of AI
Expert Systems
Expert systems deal with the processing of knowledge as opposed to
processing of data. It involves the development of computer software to
solve complex decision problems. In fact, an expert system is a computer
system that make decisions on behalf of human.
Branches of AI
Link to ANNA Android Doctor

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An Introduction to Artificial Intelligence: Understanding Intelligence and Imitating Human Thinking

  • 1. An Introduction to Artificial Intelligence Dr Iman Ardekani
  • 2. Understanding intelligence Imitating intelligence Artificial intelligence - AI Branches of AI Content
  • 3. Undrstanding Intelligence Many great philosopher over the ages attempted to explain the process of thought and understanding. Intelligence Plato 428–348 BC Aristotle 384–322 BC Philosophy Math Nature and Universe Human knowledge Intelligence *
  • 4. Undrstanding Intelligence Plato 428–348 BC Aristotle 384–322 BC Copernic 1473-1543 Galileo 1564-1642 Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge The real key that started the quest for the simulation of inteligence did not occure until … *
  • 5. Undrstanding Intelligence Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge Thomas Hobbes (English Philosopher) put forth an interesting concept that thinking consists of symbolic operations and that everything in the life can be represented mathematically. Hobbes 1588-1679 *
  • 6. Undrstanding Intelligence Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge Thomas Hobbes (English Philosopher) put forth an interesting concept that thinking consists of symbolic operations and that everything in the life can be represented mathematically. Hobbes 1588-1679 *
  • 7. Hobbes (British Philosopher): Thinking consists of symbolic operations! Based on this logic, a machine capable of caring out mathematical operations on symbols could imitate human thinking. Undrstanding Intelligence Hobbes 1588-1679 What is a symbolic operation? • Numeric operation (2+3)2 = 25 • Symbolic operation (a+b)2 = a2 + b2 + 2ab
  • 8. Rene Descartes (French Philosopher and Mathematician): He believed that the mind and the real world are in parallel planes. The physical word (i.e. machines) cannot imitate the mind because there is no common reference point. Undrstanding Intelligence Descartes 1596-1650
  • 9. Charles Babbage (British Mathematician): In Babbage's time, numerical tables were calculated by humans who were called 'computers’. He saw the high error-rate of this human- driven process and started work of trying to calculate the tables mechanically. He created a “difference engine” to compute values of polynomial functions. Imitating Inteligence Babbage 1791-1871 A part of Babbage's difference engine He also introduced the idea of “Analytical Machine”, but he could never realize this idea.
  • 10. George Boole (British Mathematician): Boole formulated the “Laws of Thought” that set up rules of logic for representing thoughts (symbolic logic). This was the birth of digital logic, a key component of AI. In the early 1900s, Alfred Whitehead and Bertrand Russell extended Boole’s logic to include mathematical operations. This led to the formulation of digital computers. Also, this made possible one of the first ties between computers and thought process. Imitating Inteligence Boole 1815-1864 Russell 1872-1970 Whitehead 1861-1947
  • 11. Design a digital computer using logical operations to compute y=x1+x2 where x1 and x2 are 4-digit binary numbers (4-bit adder). Design a digital computer using logical operations to compute y=x1.x2 where x1 and x2 are 4-digit binary numbers (4-bit multiplier). Design a digital computer using logical operations to compute y=ex where x1 and x2 are 4-digit binary numbers (ex=1+x+x2/2+x3/6+…). Imitating Inteligence
  • 12. Claude Shannon (American Electrical Engineer): He wrote his master’s thesis demonstrating that electrical applications of Boolean algebra could construct and resolve any logical, numerical relationship. It has been claimed that this was the most important master's thesis of all time. His PhD these was on mathematical relationships of genetics. He is known as the father of Information Technology. Imitating Inteligence Shannon 1916-2001
  • 13. John Neumann (American Mathematician) He suggested that the computers  should be general purpose logic machines.  could react intelligently to the results of their calculations  could choose among alternatives, and even play checker and chess This represented something unheard of at that time: a machine with built-in intelligence, able to operate on internal instructions. Before introducing this concept, even the most complex mechanical devices had always been controlled from the outsides, by knobs and dials. He didn't’ invent the computer but what he introduced was equally significant: computing by use of computer programs. Imitating Inteligence Neumann 1903-1957
  • 14. John Mauchly (American Electrical Engineer): John Mauchly designed and built the first general purpose digital computer in 1946 at the University of Pennsylvania: ENIAC (Electronic Numerical Integrator and Computer) Weight = 30 Tons Floor Space = 1500 Square Feet Shannon’s idea  Hardware Neumann’s idea  Software Imitating Inteligence Mauchly 1907-1980
  • 15. Alan Turing (British Mathematician): He introduced “Universal Machine Concept” that describe a machine for solving all problems based on variable instructions. Turing’s universal machine concept, along with Neumann’s concept of computing using programs led to programmable computers. Operational machines were now being realized. The question was “Are they intelligent?” and “in what extend?”. Turing also designed Turing’s test for determining the intelligence of a system. Imitating Inteligence Turing 1912-1954
  • 16. Turing Test – Step 1 (man/woman) A is a man and B is a woman and C is of either sex. C is unable to see either A or B, and can communicate with them only through online computer chat. By asking questions of A and B, C tries to determine which of the two is the man and which is the woman. A's role is to trick C into making the wrong decision, while B attempts to assist C in making the right one. Imitating Inteligence
  • 17. Turing Test – Step 2 (human/computer) Substitute a computer for A. By asking questions of Computer and B, C tries to determine which of the two is the computer. Computer's role is to trick C into making the wrong decision, while B attempts to assist C in making the right one. If the C’s success rate in human/computer game is not better than his success rate in the man/woman game Imitating Inteligence
  • 18. Turing Test If the C’s success rate in human/computer game is not better than his success rate in the man/woman game, then the computer can be said to be “thinking”. Imitating Inteligence
  • 19. There was now a need for a high-level programming language. Logic Theorist was written in 1955 by A. Newell, H. A. Simon and J. C. Shaw. It was the first program deliberately engineered to mimic the problem solving skills of a human being and is called "the first artificial intelligence program.” It would eventually prove 38 of the first 52 theorems of Whitehead and Russell, and find new and more elegant proofs for some.[2] Imitating Inteligence
  • 20. John McCarthy (American Computer Scientist) He coined the term “Artificial Intelligence” in the first conference on machine intelligence, 1956. He also developed LISP (List Processing) programming language, which has become a standard tool for AI development. LISP distinctions:  Memory organization – in a tree fashion  Control structure – instead of working from perquisites to a goal, it starts with the goal and works backward to determine what perquisites are required to achieve the goal. Artificial Intelligence McCarthy 1927-2011
  • 21. GPS (General Problem Solver) was another AI programming language that introduced in 1959. It was capable of solving theorems, playing chess, or doing puzzles. Its core was based on the use of means-end analysis, which involves comparing a present state with a goal state. The difference between the two state is determined and a search is done to find a method to reduce this difference. This process is continued until there is no difference between the current state and the goal state. It was capable of backtracking to an earlier state to correct its mistakes. It was also able to define sub-goals. GPS did a good job of imitating the human subjects. Artificial Intelligence
  • 22. ELIZA was the first intelligent computer program that was enable of interacting in a two-way conversation. It could sustain very realistic conversations by very smart techniques. For example, ELIZA used a pattern matching method that would scan for keywords like “I”, “You”, “Like” and so on. If one of these words was found, it would execute rules associated with it. If no match was found, it would request for more information. Artificial Intelligence Link to ELIZA
  • 23. The various attempts at formally defining the use of machines to simulate human intelligence let to several AI branches 1. Natural Language Processing (NLP) 2. Computer Vision 3. Robotics 4. Problem-solving and planning 5. Learning 6. Expert Systems Branches of AI
  • 24. Branches of AI NLP ComputerVision ExpertSystems ProblemSolving Robotics Learning Artificial Intelligence Human-like artificial creatures Other artificial creatures Special robots/machines with higher capabilities
  • 25. How successful we have been in creating human-like artificial creatures? Branches of AI
  • 26. Natural Language Processing (NLP) NLP understands, and generates languages that humans use naturally so that eventually you will be able to address your computer as though you were addressing another person (e.g. ELIZA) Branches of AI Speech NLP Knowledge
  • 27. Natural Language Processing (NLP) NLP Categories: 1- Phonology: modeling the pronunciation of words (chair, car, cell) 2- Morphology: identifying the structure of words (dog, dogs, hot dogs) 3- Syntax (identifying grammars) 4- Semantics (understanding and representing the meaning) Applications: automatic text indexing, grammar and style analyser, automatic text generation, machine translation, optical character recognition (OCR) and etc. Branches of AI
  • 28. Computer Vision Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Branches of AI Images Computer Vision Knowledge
  • 29. Branches of AI Computer Vision US Deference Advance Research Projects Agency (DARPA)
  • 30. Computer Vision Applications: 1. Recognize objects (e.g. people we know and things we own) 2. Locate objects in space (to pick them up?) 3. Track objects in motion (catching a baseball, avoiding collisions with cars on the road) 4. Recognize actions (e.g. walking, running, pushing) Branches of AI
  • 31. Robotics Robotics involves the control of actuators on robots to move, manipulate or grasp objects, locomotion of independent machines and use of sensory input to guide actions. Branches of AI
  • 32. Problem-solving and Planning This technology involves application such s refinement of high-level goals into lower-level ones, determination of actions to achieve goals, revision of plans based on intermediate results, and focused search of important goals. A good example is chess players software. Branches of AI
  • 33. Learning Learning deals with research into various forms of learning including rote learning, learning through advise, learning by example, learning by task performance, and learning by following concepts. Branches of AI
  • 34. Expert Systems Expert systems deal with the processing of knowledge as opposed to processing of data. It involves the development of computer software to solve complex decision problems. In fact, an expert system is a computer system that make decisions on behalf of human. Branches of AI Link to ANNA Android Doctor

Notes de l'éditeur

  1. https://www.youtube.com/watch?v=MaTfzYDZG8c
  2. Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…) Morphology: Identification of the structure of words (dog, dogs, hot dog, ….) Syntax: Study of grammars Semantics: Understanding and representing the meaning
  3. Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…) Morphology: Identification of the structure of words (dog, dogs, hot dog, ….) Syntax: Study of grammars Semantics: Understanding and representing the meaning
  4. https://www.youtube.com/watch?v=RU_Ed9mA_jE