1. Artificial Intelligence

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

3. The Turing Test
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.

4. More on AI Artificial Real Items Airplanes Birds Silk Flowers Flowers
Artificial Snow Snow

5. AI Major Areas - Expert Systems - Natural Language Processor
- Speech Recognition
- Robotics
- Computer Vision
- Intelligent Computer-Aided Instruction
- Data Mining
- Genetic Algorithms

6. Artificial vs. Natural (Human) Intelligence

7. AI Advantages 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

8. Natural Intelligence 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.

9. Characteristics of a Human Experts
- Recognize and formulate the problem
- Solve the problem fairly quickly
- Explain the solution
- Learn from experience
- Restructure knowledge
- Break rules
- Determine relevance
- Degrade gracefully

10. What Do Experts Know?
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.

11. Expert Systems

12. Expert Systems Components
1. Knowledge Acquisition
2. Knowledge Base
3. Inference Engine
4. User Interface
5. Explanation Facility
6. Knowledge Refining System

13. 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

14. 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

15. 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

16. 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

17. Problems and Limitations of Expert Systems
- 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.

18. Necessary Requirements for ES Development
- 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.

19. Justification 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.

20. Feasibility Study 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

21. More on 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