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CSNB234 ARTIFICIAL INTELLIGENCE. Chapter: Part I Background & History of AI. Natural Vs. Artificial intelligence. What is Natural Intelligence? Human intelligence The word ‘natural’ is normally omitted What is Artificial Intelligence? Intelligences posses by machines What is IQ?.

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CSNB234ARTIFICIAL INTELLIGENCE

Chapter: Part I

Background & History of AI

COIT, UNITEN

slide2

Natural Vs. Artificial

intelligence

  • What is Natural Intelligence?
    • Human intelligence
    • The word ‘natural’ is normally omitted
  • What is Artificial Intelligence?
    • Intelligences posses by machines
  • What is IQ?

COIT, UNITEN

iq of a person is measured by
IQ of a personis measured by

Mental Age

IQ = -------------------------------- * 100

Chronological Age

This is the simplest formula that works well

E.g. if a 20 years old person undergoes an IQ test and

the examiner determines his mental age as 18,

then his IQ is 90 ------------------> below average!

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ai can be defined as the attempt to get real machines to behave like the ones in the movies
AI can be defined as the attempt to get real machines to behave like the ones in the movies.

First glance at the definition of AI

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ai programs vs traditional programs
AI programs Vs. Traditional programs
  • Traditional Program =____________ + ____________
  • AI Program = _____________ + _____________
  • Main difference
    • Heuristics vs. Algorithmic

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slide6

The AI Theorists

  • Father of “Artificial Intelligence is
    • Alan Turing
  • Other AI Theorists:
    • McDermott, Patrick Winston, Newell, Simon, Rosenblatt
    • & more (perform an internet search)..

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slide7
Warren McCulloch (Columbia University)
    • Human Brain
  • Claude Shannon (Bell Lab)
    • Boolean Algebra
  • Norbert Wiener
  • John McCarthy (Dartmouth College)
  • Marvin Minsky (Harvard U)

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slide8

Alan Turing(1912-1954)

He is the father of AI

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slide9

AI : History

  • 1956: Dartmouth Conference - proposed launch of Joint Research on AI.
    • John McCarthy, Marvin Minsky, Claude Shannon among the attendees.
  • 1960s: Focus on knowledge bases started. Areas of interests are chess games, theorem proving and language translation.
    • Lisp developed by John McCarthy.
  • 1963: Newell & Simon built General Problem Solver (GPS).
  • 1965: DENDRAL developed by Feigenbaum at Stanford University.

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slide10

1970s: MYCIN developed at Stanford University, utilised production rules.

  • 1972: PROLOG developed by Alain Colmerauer at University of Marseilles.
  • 1981: ICOT (Institute of New Generation Computer Technology).

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slide11

Symbolic Processing

It is a branch of Computer Science that deals with symbolic, non-algorithmic methods of problem solving.

Heuristics

It is the branch of Computer Science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb for processing information.

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slide12

Heuristics and

Heuristic programming

  • Heuristics
    • Developed through intuition, experience & judgment.
    • Do not represent (our) knowledge of design, rather, they represent guidelines through which a system may be operated.
    • Often called “Rules of thumb”.
  • Characteristics
      • Screening
      • Filtering
      • Pruning

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slide13

HEURISTIC PROGRAMMING

  • Should not be confused with computer programming.
  • A program is a solution; programming is a procedure for obtaining a solution.
  • Thus, heuristic programming is a procedure for finding the solution to a model consisting of “heuristics”.

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slide14

LANGUAGE LEVELS

FOR AI PROBLEM SOLVING

  • Two Levels of Abstraction:
      • Symbol level
      • Knowledge level
  • Symbol Level:
    • concerns with the particular formalisms used to represent knowledge such as logic or production rules.
    • concerns with the structures used to organize knowledge.

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slide15

Knowledge Level:

    • What queries / questions will be asked?
    • How new knowledge can be added or updated?
    • What objects and relations are necessary?
    • Can the system reasons despite of incompleteness of information?

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slide16

Essential requirements for an AI language

  • Support of Symbolic Computation
    • implementation of a set of operation on symbolic rather than numeric data.
    • predicate calculus is a powerful tool for constructing qualitative descriptions of a domain.

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slide17

Flexibility of Control

    • Rule-based systems being the most important paradigm for building AI programs.
    • AI cannot be achieved through step-by-step execution of a fixed sequence of instructions .
    • Production rules can be fired in virtually any order (i.e. not step-by-step) in response to a given situation.

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slide18

Support of Exploratory Programming Methodologies

    • AI programs seldom respond to standard software approaches such as top-down design, stepwise refinement.
    • This is due to the nature of AI problems that they could be started & tested without having to completely produce the final specification.
    • In other words, most AI programs are initially poorly specified.
    • AI programming is inherently exploratory; the program is the vehicle through which we explore the problem area (domain) and discover solution strategies.

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slide19

Late Binding & Constraint Propagation

    • Often, the problems addressed by AI program (such as Prolog program) require that the values of certain entities to remain unknown until sufficient information is gathered to determine the assignment.
    • As constraints are accumulated, the set of possible values is reduced, ultimately converging on a solution.

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slide20

Clear and Well-defined Semantics

    • Traditional computer languages are too complex in its programming constructs and semantic definitions. They are not subject to self-proof.
    • This could be achieved by developing new languages that do not (to certain extent) conform to the architecture underlying von Neumann computer and be on the foundation of mathematical formalisms such as logic (Prolog).

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ai systems development
AI Systems Development

Immature but can be used (tested)

Knowledge and expertise slowly building up..

This methodology is called _____________

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slide22

CCSB354ARTIFICIAL INTELLIGENCE

Chapter 1: Part II

Introduction to AI

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can a machine think
Can a machine think?
  • Can be answered by the following “tests” for machine (i.e. the program/software)
  • The Alan Turing Test
      • Alan Turing (father of AI)
  • Revised Turing Test
      • ELIZA (By Joseph Weizenbaum of MIT)

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artificial intelligence
Artificial Intelligence
  • Definition
    • AI is the study of how to make computers do things at which, at the moment, people are better.
  • What computer can do better than people?
    • Numerical computation: Fast & accurate
    • Information storage: Voluminous amounts
    • Repetitive operations : Not getting bored (??)
  • However, these are mechanical mindless activities, and thus cannot be regarded as ‘intelligent’ tasks

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what people can do better than computers
What people can do better than computers?
  • Activities that involve intelligence include:
    • Understanding
    • Common sense reasoning
    • Natural language processing and generation
    • Planning & Design
    • Learning (e.g. from mistakes, by analogy, by experience or examples)
    • Emotions

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what is intelligence
What is “intelligence”?

It has the ability

  • To respond to situation very flexibly
  • To make sense out of ambiguous messages
  • To recognize the relative importance of different elements of a situation

It is the part of Computer Science that concerned with the designing of intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior.

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differences between ai and conventional systems
Conventional Systems

Procedural

Numerical processing

Algorithmic

Rigid syntax

Differences between AI and Conventional Systems
  • AI Systems
    • Declarative
    • Symbolic processing
    • Heuristic programming
    • More natural syntax

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areas of ai research
Areas of AI Research
  • Automated reasoning
  • Expert systems
  • Natural language processing
  • Speech recognition
  • Computer vision
  • Robotics
  • Automatic programming
  • Data mining
  • Optimization

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slide29

Applied Fields of AI

AI

Natural

Language

Processing

Computerized

Speech

Recognition

Expert

Systems

Computer

Vision

Machine

Learning

Robotics

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slide30

Other AI branches:

Intelligent software agents

Machine learning

Neural networks

Evolutionary algorithms

Semantic technology

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class exercise 1
Class Exercise 1

Some characteristics of “intelligence” are:

  • Be able to identify d_________ between situations.
  • Be able to identify w______________ in a situation.
  • Be able to respond to a situation very f________.
  • Be able to l____ from experience.
  • Be able to p__________ and make events cohere.
  • Be able to see s__________ out of complexity.
  • Be able to ad______, j ______, and j________.
  • Be able to handle un___________ of information/data.

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slide32

Class Exercise 2

  • Name some features of “Artificial Intelligence”.
    • The use of large amount of d________- s________ knowledge in its problem solving.
    • Solutions may be just g____- e________ (i.e. neither exact nor optimal).
    • Q_______ and S________ aspects are in concern (not numerical analysis).
    • Non-a____________.
    • H_________ programming is the key to software intelligence.

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the birth of ai i
The Birth of AI (I)
  • The Turing Test
    • This test was invented by Alan Turing (1912-1954)
    • It was first described in his 1950 article Computing machinery and intelligence (Mind, Vol. 59, No. 236, pp. 433-460)
  • An interrogator is connected to one person and one machine via a terminal, and therefore can't see his counterparts.

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the birth of ai ii
The Birth of AI (II)
  • The Turing Test
    • His task is to find out which of the two candidates is the machine, and which is human only by asking them questions.
    • If the interrogator cannot make a decision within a certain time (Turing proposed five minutes, but the exact amount of time is generally considered irrelevant),
      • the machine is considered to be intelligent.

COIT, UNITEN

slide35

Pening aku ni...

Siapa yang

menjawab ini?

If the computer succeeds in fooling the interrogator,

i.e. the interrogator cannot distinguish the machine from the human,

then, Turing argues, the machine may be assumed to be “intelligent”

COIT, UNITEN