1. 1 of 45 ARTIFICIAL INTELLIGENCE IS 340 CHANDRA S. AMARAVADI

2. 2 of 45 ARTIFICIAL INTELLIGENCE IN THIS PRESENTATION Introduction to AI Milestones & early work Machine Intelligence The Nature of knowledge Knowledge representation Examples Neural nets Business & recent applications

3. 3 of 45 INTRODUCTION TO AI

4. 4 of 45 THE HISTORY OF AI (FYI) Alan Turing & test for intelligence -- 1950 AI as a field of study -- 1956 Lisp language -- 1958 Expert Systems -- 1965 Dendral & Mycin Small Talk, Prolog -- 1972 Fifth Generation Project -- 1981 Honda robot -- 1995 Stanford driverless car -- 2005 Major milestones

5. 5 of 45 Early research on AI focussed on: Logic Perceptrons Chess Blocks world (a world consisting of only blocks) EARLY RESEARCH

6. 6 of 45 Generate and Test Generate a possible solution and test to see if it is the answer n Breadth-first n Depth-first n Heuristic n Hill-climbing SEARCH STRATEGIES ? ? ?

7. 7 of 45 DEFINING INTELLIGENCE

8. 8 of 45 Artificial Intelligence (AI) DEFINITION AI is concerned with the principles and mechanisms for achieving intelligent behavior in machines

9. 9 of 45 Artificial intelligence Robotics NLP Vision Systems Machine Learning Expert Systems BRANCHES OF AI

10. 10 of 45 NATURE OF INTELLIGENCE Knowledge + Reasoning power = Intelligence Any other method of achieving intelligence?

11. 11 of 45 Top-down - build logical equivalents, e.g. LOGIC, Expert systems Bottom-up - build physical equivalents, e.g. perceptrons, neural nets

12. 12 of 45 The Turing test: If a person interacting with an entity from a remote location is unable to judge whether he/she is dealing with a computer or a human, and the entity a machine, it is said to possess intelligence. ? THE TEST FOR MACHINE INTELLIGENCE Questions Responses

13. 13 of 45 THE NATURE OF KNOWLEDGE

14. 14 of 45 KNOWLEDGE facts, constraints, problems, goals, procedures. Knowledge: information organized for problem solving

15. 15 of 45 Two types of knowledge: Declarative – Knowledge about an object (size, shape etc.) Procedural – Knowledge about how to do something. (how to install memory) THE NATURE OF KNOWLEDGE

16. 16 of 45 KNOWLEDGE REPRESENTATION A Sampling of Knowledge How to install a water pump The definition of a “field goal” Painters & styles from the modern era The process of becoming a GSA contractor The architectural differences between AMD & Intel chips The meaning of “Lousiana report” in the context of a faculty committee meeting.

17. 17 of 45 KNOWLEDGE REPRESENTATION

18. 18 of 45 KNOWLEDGE REPRESENTATION Logic (Predicate logic) Frames Scripts Semantic nets (Snets) Rules Knowledge representation is concerned with how to encode knowledge

19. 19 of 45 IDENTIFY THESE AS EXAMPLES OF LOGIC, FRAMES, SCRIPTS… sister_of(X,Y), bird_of_prey(X), father_of(robin, Y) father_of(robin,_) EXAMPLE 1 EXAMPLE 2 is_a : dbms software cost : $3,000 License cost : check_with_vendor no of users : 2000 Max # of tables : 10,000 Supports ODBC : Yes If # of users > 300 then, license fee = $500 If # of users < 300 then, license fee = $300 EXAMPLE 3

20. 20 of 45 EXAMPLES OF KNOWLEDGE REPRESENTATIONS.. P PTRANS P to P.O. P ATTEND eyes to counter P MBUILD line position P PTRANS P to line P PTRANS M to X X PTRANS Stamps to P EXAMPLE 4 Eagle Bird Is-a 1.5 m Max Wingspan 20 Knots Max Speed Bird-of-prey Is-a EXAMPLE 5

21. 21 of 45 t Based on associative memory t “node” + “link” formalism t nodes represent concepts or values t links can be structural or descriptive t represent structure or characteristic NOTES ON SEMANTIC NETS

22. 22 of 45 l Origins in S-R paradigms l Thought to be used by experts l Have a IF…THEN… format Note: S-R: stimulus/response NOTES ON RULES

23. 23 of 45 t A description (conceptual representation) of actions in a pre-defined situation t Originated from film industry t Consists of actors/props t Act in predictable ways NOTES ON SCRIPTS

24. 24 of 45 EXAMPLE OF LOGIC facts: has_qualification(brad,3.2,620). has_qualification(jill,4.0,540). has_qualification(ted,3.5,320). has_qualification(matt,3.8, 600). Predicates: select(X) :- has_qualification(X,GPA,GMAT), GPA>3.2, GMAT>550; Goals: select(brad)? jill? ted? matt?

25. 25 of 45 Identify whether the following types of knowledge are declarative or procedural and identify a suitable representation scheme, give rationale: 1. Admit students to MBA program if they have a gmat score of > 550 2. A description of computing facilities at WIU. 3. A proof of the theorem that any triangle circumscribed by a semi-circle will always be a right angled triangle 4. Instructions for assembling a PC 5. Family relationships -- X and Y are the parents of P & Q; P has a maternal aunt Z. 6. Stages in a software life cycle -- analysis, design, implementation etc. FOR DISCUSSION

26. 26 of 45 The brain Dendrites Neurons Neural Net (a math model) NEURAL NETS Mathematical models to simulate neural models of the brain, Often used in applications requiring pattern recognition e.g. crime, fraud, intrusion detection etc. eyesnose hair colorgait

27. 27 of 45 BUSINESS APPLICATIONS OF AI Automated voice response Text mining Production applications machine design robotics paper thickness Scheduling of cranes Credit approval

28. 28 of 45 INDUSTRIAL APPLICATIONS OF AI Driverless vehicles Facial recognition Crime prevention Pothole recognition Drones

29. 29 of 45  Can a machine ever have the intelligence of a human being?  Has Turing’s test been passed?  Why did early researchers concentrate on Chess?  If we make use of a frog’s brain to process stimuli, is that an example of a Top-Down or a Bottom-up approach?  What branch of AI does the work on perceptrons resemble?  What “hardware” item is essential equipment for vision systems?  Are robots useful in industry? How?  If a machine is taking dictation, is it necessary to understand the text or can it be done mechanically?

30. 30 of 45 The End! Please note there are only 29 slides