2. INTRODUCTION
• Machine intelligence is popularly known as Artificial
Intelligence – AI
• A.I. is the study of making computers smart – behaviour
oriented view
• A.I. is the study of making computer models of human
intelligence - psychologists point of view
• A.I. is the study concerned with building machines that
simulate human behaviour - robotic approach
• Thinking?...
Television Air Conditioner Electric Cooker
Washing Machine Automatic Iron Box Airplane
Thirsty Crow ??????
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3. KEEP IN MIND?
• Data - a collection of disorganized facts like mutually
unrelated numbers, characters, symbols etc.
– For example frogs, flies, etc.
• Information - an aggregation of data objects forming
a syntactically correct structure.
– For example frog flies.
• Knowledge – a meaningful information. Hence, the
above example is not contributing to knowledge.
• Intelligence - the ability to understand, apply and
acquire the knowledge
• Artificial - Made as a copy of something natural
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4. DEFINITION BY ELIANE RICH
• Artificial Intelligence is the study of how to
make computers do things, at which, at the
moment, people are better
• Some Tasks (numerical computation, information storage,
repetitive tasks, etc) that computers can do better than
human
• Some Tasks (understanding, predicting, common-sense
reasoning , conclusions on incomplete information, etc i.e.
requires parallel processing and simultaneous availability)
that human can do better than computers beings
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5. DEFINITION BY BUCHANIN AND SHORTLIFFE
• AI is the branch of computer science that deals with
symbolic rather than numeric processing and non-
algorithmic methods including the rules of thumb or
heuristics instead of algorithms as techniques for solving
problems
• In numeric processing only a small number of well-defined relations
and operations
• In symbolic processing the relations and operations required to
solve a problem depend upon the problem under consideration
• Non-algorithmic method - rule of thumb that may apply to the
current problem, it may suggest to us how to proceed
• Heuristics experience-based techniques for problem solving,
learning, and discovery
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6. ANOTHER DEFINITION BY ELIANE RICH
• Artificial Intelligence is the study of techniques
for solving exponentially hard problems in
polynomial time exploiting knowledge about
the problem domain
• Polynomial time is a reasonable amount of
time
• Exponential time a impractical or infeasible
amount of time
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7. DEFINITION BY BARR AND FEIGENBAUM
• Artificial Intelligence is the part of computer science
concerned with designing intelligent computer
systems, i.e., systems that exhibit the characteristics
we associate with intelligence in human behaviour
DEFINITION BY SHALKOFF
• Perhaps broadest definition is that AI is a field of
study that seeks to explain and emulate intelligent
behaviour in terms of computational processes
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8. TESTING AI?
• In 1950, Alan Turing
proposed the following
method for determining
whether a machine can
think. Here we used three
rooms A, B & C. In A&B we
can keep a machine and a
human. In room C a human
interrogator is kept.
• If the human interrogator in
room C is not able to
identify who is in room A
and B, then the machine
possesses intelligence
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9. Difference?
Dimension Conventional Computing Intelligent Computing
Processing Algorithmic Includes conceptualizations
Nature of Input Must be complete Can be complete
Search Approach Based on algorithms Based on rules & Heuristics
Explanation Not provided Provided
Focus Data, Information Knowledge
Maintenance &Update Usually Difficult Relatively easy
Reasoning capability No Yes
AI programs Conventional programs
Symbolic processing Numeric processing
It involves large knowledge base Large data base
Modifications are frequent Modifications are rare
Heuristic search technique is used Algorithms search technique is used
Solutions steps are not explicit Solution steps are precise
Knowledge is imprecise Knowledge is precise
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10. APPLICATION AREAS
• Reasoning and decision making(chess, general
games, industrial scheduling, etc)
• Knowledge Representation and Reasoning
(logical, probabilistic)
• Decision Making (search, planning, decision
theory)
• Machine Learning (Google engine)
• Computer vision (face/scene recognition)
• Natural language processing (recognition,
translation)
• Robotics (Mars rover, urban challenge, Robocup)
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