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 is the study of how to make computers do things at which, at the moment, people are better.
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
Silk Flowers Flowers
Artificial Snow Snow
More on AI
- Natural Language Processor
- Speech Recognition
- Computer Vision
- Intelligent Computer-Aided Instruction
- Data Mining
- Genetic Algorithms
AI Major Areas
2. AI offers ease of duplication
3. AI can be less expensive than natural intelligenc
4. AI is consistent
5. AI can be documented
2. Natural intelligence uses sensory experience directly,
whereas most AI systems must work with symbolic
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
- Solve the problem fairly quickly
- Explain the solution
- Learn from experience
- Restructure knowledge
- Break rules
- Determine relevance
- Degrade gracefully
Characteristics of a Human Experts
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?
2. Knowledge Base
3. Inference Engine
4. User Interface
5. Explanation Facility
6. Knowledge Refining System
Expert Systems Components
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
- Payroll, Inventory
- Simple Tax Returns
- Database Management
- Mortgage Computation
- Regression Analysis
- Facts are Known
- Expertise is Cheap
Too Easy - Use Conventional Software
- 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
- 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
- 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 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
- 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
- The system can be used for training.
- The ES is more dependable and consistent than
Cost of maintenance
Cash flow analysis
B. Technical Feasibility Interface requirements
Availability of data and knowledge
Security of confidential knowledge
Knowledge representation scheme
Priority compare to other projects
Management and user support
Availability of experts
Availability of knowledge
More on Feasibility Study