1. CSNB234 ARTIFICIAL INTELLIGENCE Background & History of AI
Chapter: Part I
Background & History of AI
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2. What is Natural Intelligence?
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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3. IQ of a person is 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 > below average!
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4. First glance at the definition of AI
AI can be defined as the attempt to get real machines to behave like the ones in the movies.
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5. AI programs Vs. Traditional programs
Main difference
Heuristics vs. Algorithmic
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6. 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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7. Warren McCulloch (Columbia University) Claude Shannon (Bell Lab)
Human Brain
Claude Shannon (Bell Lab)
Boolean Algebra
Norbert Wiener
John McCarthy (Dartmouth College)
Marvin Minsky (Harvard U)
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8. Alan Turing( )
He is the father of AI
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9. 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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10. 1981: ICOT (Institute of New Generation Computer Technology).
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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11. Symbolic Processing Heuristics
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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12. Heuristic programming
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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13. 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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14. 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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15. 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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16. 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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17. 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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18. 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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19. 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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20. 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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21. AI Systems Development
Immature but can be used (tested)
Knowledge and expertise slowly building up..
This methodology is called _____________
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22. CCSB354 ARTIFICIAL INTELLIGENCE
Chapter 1: Part II
Introduction to AI
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23. 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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24. 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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25. 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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26. 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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27. Differences between AI and Conventional Systems
Procedural
Numerical processing
Algorithmic
Rigid syntax
AI Systems
Declarative
Symbolic processing
Heuristic programming
More natural syntax
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28. 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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29. Applied Fields of AI AI Natural Language Processing Computer Vision
Computerized
Speech
Recognition
Expert
Systems
Computer
Vision
Machine
Learning
Robotics
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30. Intelligent software agents
Other AI branches:
Intelligent software agents
Machine learning
Neural networks
Evolutionary algorithms
Semantic technology
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31. 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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32. 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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33. The Birth of AI (I) The Turing Test
This test was invented by Alan Turing ( )
It was first described in his 1950 article Computing machinery and intelligence (Mind, Vol. 59, No. 236, pp )
An interrogator is connected to one person and one machine via a terminal, and therefore can't see his counterparts.
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34. 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.
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35. If the computer succeeds in fooling the interrogator,
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”
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