Faculty of Arts Atkinson College ITEC 1010 A F 2002 Welcome Sixteenth Lecture for ITEC A Professor G.E. Denzel

Faculty of Arts Atkinson 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 Arts Atkinson College ITEC 1010 A F 2002 Using Styles  Different browsers work differently! View the following with IE 5, IE 6, NS 4.79, NS 6.2  estbody.html estbody.html  estbody2.html estbody2.html

Faculty of Arts Atkinson 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 Arts Atkinson 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 data  Compare aggregated data over hierarchical time periods (monthly, quarterly, annually)  Present data in different perspectives  Involve complex calculations between data elements  Respond quickly to users requests Online Analytical Processing (OLAP)

Faculty of Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson College ITEC 1010 A F 2002 Chapter Preview  In 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson College ITEC 1010 A F 2002 Inference engine Explanation facility Knowledge base acquisition facility User interface Knowledge base ExpertsUser

Faculty of Arts Atkinson College ITEC 1010 A F 2002 Expert System Structure

Faculty of Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson College ITEC 1010 A F 2002 Figure 11.2 Neural Internet-based optical character recognizer.

Faculty of Arts Atkinson College ITEC 1010 A F 2002 More Neural Nets Discussion of using Neural networks to predict the stockmarket --- why not?

Faculty of Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson 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 Arts Atkinson College ITEC 1010 A F 2002 Examples of Agents in use today  Search engines (yahoo, alta vista, ask Jeeves, etc.)  Stock trackers 

Faculty of Arts Atkinson 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 Arts Atkinson 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?