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Artificial Intelligence Definition: Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better. According to this test, a computer could be considered to be thinking only when a human interviewer, conversing with both

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Artificial Intelligence

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Artificial Intelligence


Definition:

Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.


According to this test, a computer could be considered to be

thinking only when a human interviewer, conversing with both

an unseen human being and an unseen computer, could not

determine which is which.

The Turing Test


Artificial Real Items

Airplanes Birds

Silk Flowers Flowers

Artificial Snow Snow

More on AI


- Expert Systems

- Natural Language Processor

- Speech Recognition

- Robotics

- Computer Vision

- Intelligent Computer-Aided Instruction

- Data Mining

- Genetic Algorithms

AI Major Areas


Artificial vs. Natural (Human) Intelligence


1. AI is permanent

2. AI offers ease of duplication

3. AI can be less expensive than natural intelligenc

4. AI is consistent

5. AI can be documented

AI Advantages


1. Natural intelligence is creative.

2. Natural intelligence uses sensory experience directly,

whereas most AI systems must work with symbolic

input.

3. Human reasoning is able to make use at all times of a

very wide context experience and bring that to bear on

individual problems, where as AI systems typically

gain their power by having a very narrow domain.

Natural Intelligence Advantages


- Recognize and formulate the problem

- Solve the problem fairly quickly

- Explain the solution

- Learn from experience

- Restructure knowledge

- Break rules

- Determine relevance

- Degrade gracefully

Characteristics of a Human Experts


It is estimated that a world-class expert, such as a chess

grandmaster, has 50,000 to 100,000 chunks of heuristic

information about his/her specialty. On the average, it

takes at least 10 years to acquire 50,000 rules.

What Do Experts Know?


Expert Systems


1. Knowledge Acquisition

2. Knowledge Base

3. Inference Engine

4. User Interface

5. Explanation Facility

6. Knowledge Refining System

Expert Systems Components


Different Categories of Expert Systems

Category Problem Addressed

Interpretation Inferring situation description from observations

Prediction Inferring likely consequences of given situations

Diagnosis Inferring systems malfunctions from observations

Design Configuring objects under constraints

Planning Developing plans to achieve goals

Monitoring Comparing observations to plan vulnerabilities

Debugging Prescribing remedies for malfunctions

Repair Executing a plan to administer a prescribed remedy

Control Interpreting, predicting, repairing, and monitoring

system behavior


What Tasks Are ES Right For?

- Payroll, Inventory

- Simple Tax Returns

- Database Management

- Mortgage Computation

- Regression Analysis

- Facts are Known

- Expertise is Cheap

Too Easy - Use Conventional Software


What Tasks Are ES Right For?

- Diagnosing and Troubleshooting

- Analyzing Diverse Data

- Production Scheduling

- Equipment Layout

- Advise on Tax Shelter

- Facts are known but not precisely

- Expertise is expensive but available

Just Right


What Tasks Are ES Right For?

- Designing New Tools

- Stock Market Forecast

- Discovering New Principles

- Common Sense Problems

- Requires Innovation or Discovery

- Expertise is not available

Too Hard - Requires Human Intelligence


- Knowledge is not always readily available.

- Expertise is hard to extract from humans.

- ES work well only in a narrow domain.

- The approach of each expert to problem under

consideration may be different, yet correct.

Problems and Limitations

of Expert Systems


- The task does not require common sense.

- The task requires only cognitive, not physical, skills.

- There is an expert who is willing to cooperate.

- The experts involved can articulate their methods

of problem solving.

- The task is not too difficult.

- The task is well understood, and is defined clearly.

- The task definition is fairly stable.

- Problem must be well bounded and narrow.

Necessary Requirements for

ES Development


- The solution to the problem has a high payoff.

- The ES can capture scarce human expertise so it

will not be lost.

- The expertise is needed in many locations.

- The expertise is needed in hostile or hazardous

environment.

- The system can be used for training.

- The ES is more dependable and consistent than

human expert.

Justification for

ES Development


A. Financial Feasibility Cost of system development

Cost of maintenance

Payback period

Cash flow analysis

B. Technical Feasibility Interface requirements

Network issues

Availability of data and knowledge

Security of confidential knowledge

Knowledge representation scheme

Hardware/software availability

Hardware/software compatibility

Feasibility Study


C. Operational Feasibility Availability of human resources

Priority compare to other projects

Implementation issues

Management and user support

Availability of experts

Availability of knowledge

engineers

More on Feasibility Study


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