ARTIFICIAL INTELLIGENCE IS 340 CHANDRA S. AMARAVADI

1. ARTIFICIAL INTELLIGENCE IS 340 CHANDRA S. AMARAVADI

2. 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. INTRODUCTION TO AI

4. THE HISTORY OF AI (FYI) Major milestones • 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

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

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

7. DEFINING INTELLIGENCE

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

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

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

11. HOW CAN WE ACHIEVE INTELLIGENCE? Top-down - build logical equivalents, e.g. LOGIC, Expert systems Bottom-up - build physical equivalents, e.g. perceptrons, neural nets

12. THE TEST FOR MACHINE INTELLIGENCE 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. Questions ? Responses

13. THE NATURE OF KNOWLEDGE

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

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

16. 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. KNOWLEDGE REPRESENTATION

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

19. IDENTIFY THESE AS EXAMPLES OF LOGIC, FRAMES, SCRIPTS… EXAMPLE 1 EXAMPLE 3 sister_of(X,Y), bird_of_prey(X), father_of(robin, Y) father_of(robin,_) If # of users > 300 then, license fee = $500 If # of users < 300 then, license fee = $300 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

20. EXAMPLES OF KNOWLEDGE REPRESENTATIONS.. EXAMPLE 5 EXAMPLE 4 Bird Bird-of-prey 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 Is-a Is-a Eagle Max Speed Max Wingspan 20 Knots 1.5 m

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

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

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

24. 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. FOR DISCUSSION 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.

26. 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. Neurons nose eyes Dendrites hair color gait Neural Net (a math model) The brain

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

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

29. DISCUSSION QUESTIONS • 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. The End! Please note there are only 29 slides