INTELLIGENCE AND ARTIFICIALINTELLIGENCE

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INTRODUCTION

• Machine intelligence is popularly known as ArtificialIntelligence – AI

• A.I. is the study of making computers smart – behaviouroriented view

• A.I. is the study of making computer models of humanintelligence - psychologists point of view

• A.I. is the study concerned with building machines thatsimulate human behaviour - robotic approachsimulate human behaviour - robotic approach

• Thinking?...

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Television Air Conditioner Electric Cooker

Washing Machine Automatic Iron Box Airplane

Thirsty Crow ??????

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 forminga syntactically correct structure.– For example frog flies.

• Knowledge – a meaningful information. Hence, theabove example is not contributing to knowledge.above example is not contributing to knowledge.

• Intelligence - the ability to understand, apply andacquire the knowledge

• Artificial - Made as a copy of something natural

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DEFINITION BY ELIANE RICH

• Artificial Intelligence is the study of how tomake computers do things, at which, at themoment, people are better

• Some Tasks (numerical computation, information storage,repetitive tasks, etc) that computers can do better thanhumanhuman

• Some Tasks (understanding, predicting, common-sensereasoning , 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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DEFINITION BY BUCHANIN AND SHORTLIFFE

• AI is the branch of computer science that deals withsymbolic rather than numeric processing and non-algorithmic methods including the rules of thumb orheuristics instead of algorithms as techniques for solvingproblems

• In numeric processing only a small number of well-defined relationsand operationsand operations

• In symbolic processing the relations and operations required tosolve a problem depend upon the problem under consideration

• Non-algorithmic method - rule of thumb that may apply to thecurrent problem, it may suggest to us how to proceed

• Heuristics experience-based techniques for problem solving,learning, and discovery

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ANOTHER DEFINITION BY ELIANE RICH

• Artificial Intelligence is the study of techniquesfor solving exponentially hard problems inpolynomial time exploiting knowledge aboutthe problem domain

• Polynomial time is a reasonable amount of• Polynomial time is a reasonable amount oftime

• Exponential time a impractical or infeasibleamount of time

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DEFINITION BY BARR AND FEIGENBAUM

• Artificial Intelligence is the part of computer scienceconcerned with designing intelligent computersystems, i.e., systems that exhibit the characteristicswe associate with intelligence in human behaviour

DEFINITION BY SHALKOFF

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DEFINITION BY SHALKOFF

• Perhaps broadest definition is that AI is a field ofstudy that seeks to explain and emulate intelligentbehaviour in terms of computational processes

TESTING AI?• In 1950, Alan Turing

proposed the followingmethod for determiningwhether a machine canthink. Here we used threerooms A, B & C. In A&B wecan keep a machine and acan keep a machine and ahuman. In room C a humaninterrogator is kept.

• If the human interrogator inroom C is not able toidentify who is in room Aand B, then the machinepossesses intelligence

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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

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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

APPLICATION AREAS• Reasoning and decision making(chess, general

games, industrial scheduling, etc)

• Knowledge Representation and Reasoning(logical, probabilistic)

• Decision Making (search, planning, decisiontheory)

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theory)

• Machine Learning (Google engine)

• Computer vision (face/scene recognition)

• Natural language processing (recognition,translation)

• Robotics (Mars rover, urban challenge, Robocup)

Puzzled?

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