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 428348 BC Aristotle 384322 BC Philosophy Math
Nature and Universe Human knowledge Intelligence *
4. Undrstanding Intelligence Plato 428348 BC Aristotle 384322
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 Booles 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
masters 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 Shannons idea Hardware Neumanns 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. Turings universal machine
concept, along with Neumanns 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 Turings 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 Cs 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 Cs 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