1. Artificial Intelligence Robbie Nakatsu AIMS 2710

2. Artificial Intelligence--AI. The techniques and software that enable computers to mimic human behavior in various ways. A major thrust in this field is to develop computer functions associated with human intelligence. Some Types of AI Expert systems Natural language processing Machine learning Robotics Intelligent Agents Logic reasoning

3. AI is like magic! “Any sufficiently advanced technology is indistinguishable from magic.” --Arthur C. Clark, 1962

4. An Expert System is an AI program that emulates the decision- making ability of a human expert An expert system captures expertise from a human expert and applies it to a problem.

5. An Expert System can perform diagnostic and prescriptive tasks like: Auditing and tax planning Diagnosing illnesses Commercial loan decisions Determining the cause of machine failure What is the difference between a diagnostic and prescriptive task?

6. People In An Expert System Domain Expert - the person who knows how to solve the problem without the aid of IT. Knowledge Engineer - the person who works with domain experts to capture knowledge they possess. The knowledge engineer builds the expert system. End User - the person who uses the expert system to solve a problem.

7. Components of an Expert System

8. Components Defined KNOWLEDGE BASE - stores the domain expertise (e.g., a collection of If-Then rules). INFERENCE ENGINE - processes the domain expertise and your problem facts to reach a conclusion. WORKING MEMORY – short term memory of the expert system; contains all the facts (initial facts as well as new facts). USER INTERFACE – part of the expert system that you use to run a consultation.

9. Representing Expertise as a Collection of Rules IF the light is green THEN Go through the intersection If the light is red THEN STOP If the light is yellow AND there is time to go through intersection before the light turns red THEN Go through the intersection If the light is yellow AND there is not time to go through intersection before the light turns red THEN STOP

10. A more complex example IF 1. The infection that requires therapy is meningitis AND 2. The patient has evidence of serious skin or soft tissue infection AND 3. Organisms were not seen on the stain of the culture AND 4. The type of infection is bacterial THEN There is evidence that the organism that might be causing the infection is Staphylococcus coagpos (0.75) or Streptococcus (0.5)

11. Inference Engine It is the part of the Expert System that processes the problem facts and searches for rules in the knowledge base to reach a final recommendation for a user. Two inferencing strategies : Forward Chaining is a data-driven approach in which you start with the initial problem facts, and then try to draw conclusions from them using the rules of the knowledge base. Backward Chaining is a goal-driven approach in which you start with some kind of expectation of what is to occur, or hypothesis, and then find rules that either support or contradict your hypothesis.

12. Illustrating Forward and Backward Chaining Knowledge Base R1: IF A and C, THEN E R2: IF D and C, THEN F R3: IF B and E, THEN F R4: IF B, THEN C R5: IF F, THEN G Two Problems: 1. Forward Chain: Assume B and D 2. Backward Chain: Prove or disprove G, and assume A and B

13. Expert System Opportunities Any activity where human experts are overburdened, undersupplied, or expensive are good candidates for ES. Expertise might be scarce in some organizations (can propagate the expertise through the use of an ES). An ES might also be used to enhance the role of an expert by providing the necessary assistance.

14. Benefits of Expert Systems Increased output and productivity Reduced costs, including decreased personnel required Fewer errors Better and more consistent decision-making Knowledge transfer to remote locations Formalization of organizational knowledge

15. Questions for thought What are some problems and limitations of expert systems? Can expert systems solve all kinds of problems?

16. Machine Learning Field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959) Example: Checkers playing program that sees tens of thousands of examples of board positions, and learn over time what the good positions are.

17. Learning from Data (see video) Regression problems (predicting continuous-valued outcome) Predicting price of home from its size Predicting price of home based on multiple variables (size, year built, location, condition of the building, etc) Classification problems (predicting discrete-valued outcome) Determining malignancy of a tumor based on its size Determining malignancy of a tumor based on multiple variables (size, age of patient, uniformity of tumor, etc.)

18. Some Examples of Machine Learning A credit card company wants to predict whether a credit card transaction is fraudulent or not. A company that sells ice cream wants to predict how much ice cream to produce over the summer months (June – August). A software company wants to design an email spam filter to predict whether an email is spam or not. A marketing researcher has customer data and wants to predict who among the customers are the most profitable. Which of the above are classification problems and which are regression problems?

19. Housing price prediction. Price ($) in 1000’s Size in feet 2 Regression: Predict continuous valued output (price) Supervised Learning “right answers” given

20. Breast cancer (malignant, benign) Classification Discrete valued output (0 or 1) Malignant? 1(Y) 0(N) Tumor Size

21. Age -Clump Thickness -Uniformity of Cell Size -Uniformity of Cell Shape …

22. A Neural Network is an artificial intelligence system which is capable of learning to recognize patterns and relationships in the data it processes. A neural network simulates the human ability to classify things based on the experience of seeing many examples.

23. A Neural Network can perform pattern recognition tasks like: Detecting anomalies in human tissue that may signify disease Reading handwriting Speech recognition Detecting abnormal patterns in electrocardiographs

24. An Intelligent Agent is an artificial intelligence system that can move around your computer or network performing repetitive tasks independently, adapting itself to your preferences. An intelligent agent is like a travel agent in that it performs tasks that you stipulate.

25. Examples Intelligent search engines Search engines that know who you are, your preferences, where you are, who your friends are, etc. Personal assistants Check and filter your e-mails Search the web and collect important news items for you

26. Intelligent Agent Characteristics Autonomy Adaptivity Sociability

27. Recap and Summary Types of decisions Decision Support Systems OLAP (online analytical processing) Supporting groups with technology Expert Systems Machine Learning Intelligent Agents