Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Welcome
Sixteenth Lecture for ITEC 1010 3.0 A
Professor G.E. Denzel
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Agenda
Brief discussion of assignment q on changing background colour inline.
Finish Chapter 10 in text, dealing On-Line Analytical Processing (OLAP) and data-mining
Discussion of Artificial Intelligence approaches
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Using Styles
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Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Analytical Processing - the activity of analyzing accumulated data
Online analytical processing (OLAP) An end-user activity Involves large data sets with complex
relationships Uses Decision Support Systems models Is retrospective
What can we do with the stored data?
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Analysis by end users from their desktop, online, using tools like spreadsheets
Analyze the relationships between many types of business elements
Involve aggregated dataCompare aggregated data over hierarchical time
periods (monthly, quarterly, annually)Present data in different perspectivesInvolve complex calculations between data
elementsRespond quickly to users requests
Online Analytical Processing (OLAP)
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Data mining – intelligent search of data stored in data marts or warehouses Find predictive information Discover unknown patterns
End users perform mining tasks with very powerful tools
Mining tools apply advanced computing techniques (learning, intelligence)
What can we do with the stored data?
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Ethical Issues Valuable data-mined information may violate individual
privacy Who is accountable for incorrect decisions that are based
on DSS? Human judgment is fallible Job loss due to automated decision making?
Legal Issues Discrimination based on data mining results Data security from external snooping or sabotage Data ownership of personal data
Data Mining and Analysis Concerns
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Chapter PreviewIn this chapter, we will study:
What is meant by artificial intelligence How expert systems are developed and how they
perform How AI has been applied to other arenas, such as
natural language processing and neural computing
The concept and usefulness of intelligent agents Ethical and legal issues posed by AI
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
‘Intelligent’ Systems? Conventional computer systems do not
possess ‘intelligence.’ They simply follow step-by-step instructions to complete a task
If a computer system had ‘intelligence,’ it would… Deal successfully with complex situations Learn from experience Adapt to new situations quickly
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Why do we want ‘Intelligent’ Systems?
To capture and represent human knowledge permanently
To perform tasks requiring intelligence repetitively, consistently, and capably
To document the performance of a task To conveniently disseminate knowledge
and expertise to others
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Artificial Intelligence Branch of computer science that
Studies human intelligent behavior Attempts to replicate that human intelligent
behavior in a computer system Employs symbolic processing of
knowledge and heuristics Does not really enable computers to ‘think’ Does enable creation of systems with some
human-like behaviors
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Applications of Artificial Intelligence
Expert Systems Natural language
technology Speech
understanding Robotics Computer vision
Intelligent computer-assisted instruction
Machine learning Handwriting
recognition Intelligent agents
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
What is an Expert System? Computer system that solves a problem as
successfully as a human expert Incorporates human expertise Acquires facts about the problem Applies its stored knowledge and expertise
to the problem facts to derive a solution Makes recommendations Can explain its reasoning and logic Successful commercial application of AI
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Key Expert System Terms Knowledge acquisition – the process of
obtaining knowledge and expertise from human experts
Knowledge representation – the method used to represent human knowledge and expertise in the computer system
Knowledge inferencing – the process of applying stored expertise to the facts about the problem to draw conclusions
Knowledge transfer and use – the communication of the problem solution and its justification to the system user
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
More Expert System Terms Knowledge base – stored facts and methods of how to
solve a problem Heuristic – rule of thumb that can be applied in a
problem solution Inference engine – processing logic stored in the system
that correctly applies the stored knowledge to the problem to develop a solution
Domain expert – one or more humans who have achieved a high level of expertise in solving a problem
Knowledge engineer – person who develops expert systems
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
How is an Expert System Created?
Knowledge engineer works with domain expert to extract domain knowledge
Knowledge engineer encodes domain knowledge in knowledge base using appropriate knowledge representation
Knowledge engineer tests system on sample problems and refines system knowledge with help from domain engineer
Refinement continues until system is solving problems with human expert capability
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
How Does an Expert System Perform?
System asks user a series of questions to gather facts about the problem
System uses inference engine to form conclusions from the facts, including a measure of certainty about the conclusions
System displays its recommendation or solution to the problem
If asked, the system can display its reasoning and logic as to how it arrived at the conclusion
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Inferenceengine
Explanationfacility
Knowledgebase
acquisitionfacility
Userinterface
Knowledgebase
Experts User
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
More on Expert Systems Strengths
Rapid, consistent problem solutions
Ability to justify and explain reasoning
Easy to replicate and distribute to non-expert users
Limitations Can only solve
problems in a narrow domain
Can only be applied to certain problem types
Cannot learn from its experience
Hard to acquire knowledge from human expert
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Other Intelligent Systems Natural Language Processing
The ability to communicate with a computer in your natural language• Voice (speech) recognition and speech
understanding – system recognizes spoken words and understands their meaning
• Voice synthesis – computer produces natural language voice output that sounds ‘human’
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Other Intelligent Systems Neural Computing
A computer model that uses architecture that mimics certain brain functions
Performs pattern recognition well Can analyse large data sets and discover
patterns where rules were previously unknown Can ‘learn’ by analysing new cases and
updating itself Many potential business applications
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Figure 11.2 Neural Internet-based optical character recognizer.
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
More Neural Nets
Discussion of using Neural networks to predict the stockmarket --- why not?
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Other Intelligent Systems Case-Based Reasoning
Uses solutions from similar problems and adapts them to new problems
Useful in solving very complex cases Fuzzy Logic
Enables systems to effectively deal with uncertainty
Often use in combination with other technologies to improve productivity
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Rules for a Credit Application (Could be from neural net or expert system)
Mortgage application for a loan for $100,000 to $200,000
If there are no previous credits problems, and
If month net income is greater than 4x monthly loan payment, and
If down payment is 15% of total value of property, and
If net income of borrower is > $25,000, and
If employment is > 3 years at same company
Then accept the applications
Else check other credit rules
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Intelligent Agents Software agent that autonomously performs
tasks on behalf of a user with certain goals or objectives Can tirelessly perform repetitive tasks over a
network Includes knowledge base and ability to learn Can be static (on the client only) or mobile
(move throughout a network) Often used to facilitate search and retrieval on
the Internet and to assist in e-commerce tasks
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Examples of Agents in use today
Search engines (yahoo, alta vista, ask Jeeves, etc.)
Stock trackers http://www.botspot.com
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Virtual Reality Simulation of a physical environment in a
highly realistic way Useful for communication and learning Many potential business applications,
especially marketing
Faculty of ArtsAtkinson College
ITEC 1010 A F 2002
Intelligent Systems Concerns Potential to use the power of intelligent
systems in unethical ways Who will be accountable for decisions
made by intelligent systems? Who ‘owns’ knowledge and expertise?
Can an expert be ‘forced’ to reveal his/her expertise?