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ARTIFICIAL INTELLIGENCE IS 340 CHANDRA S. AMARAVADI 1 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 2 of 45 INTRODUCTION TO AI 3 of 45 THE HISTORY OF AI (FYI) Major milestones •Alan Turing & test for intelligence •AI as a field of study •Lisp language •Expert Systems •Dendral & Mycin •Small Talk, Prolog •Fifth Generation Project •Honda robot •Stanford driverless car ----- 1950 1956 1958 1965 ----- 1972 1981 1995 2005 4 of 45 EARLY RESEARCH Early research on AI focussed on: Logic Perceptrons Chess Blocks world (a world consisting of only blocks) 5 of 45 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 ? ? ? 6 of 45 DEFINING INTELLIGENCE 7 of 45 DEFINITION Artificial Intelligence (AI) AI is concerned with the principles and mechanisms for achieving intelligent behavior in machines 8 of 45 BRANCHES OF AI Artificial intelligence Expert Systems NLP Robotics Vision Systems Machine Learning 9 of 45 NATURE OF INTELLIGENCE Knowledge + Reasoning power = Intelligence Any other method of achieving intelligence? 10 of 45 Top-down - build logical equivalents, e.g. LOGIC, Expert systems Bottom-up - build physical equivalents, e.g. perceptrons, neural nets 11 of 45 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 12 of 45 THE NATURE OF KNOWLEDGE 13 of 45 KNOWLEDGE Knowledge: information organized for problem solving facts, constraints, problems, goals, procedures. 14 of 45 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) 15 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. 16 of 45 KNOWLEDGE REPRESENTATION 17 of 45 KNOWLEDGE REPRESENTATION Knowledge representation is concerned with how to encode knowledge Logic (Predicate logic) Frames Scripts Semantic nets (Snets) Rules 18 of 45 IDENTIFY THESE AS EXAMPLES OF LOGIC, FRAMES, SCRIPTS… EXAMPLE 1 sister_of(X,Y), bird_of_prey(X), father_of(robin, Y) father_of(robin,_) EXAMPLE 2 EXAMPLE 3 If # of users > 300 then, license fee = $500 If # of users < 300 then, license fee = $300 is_a : dbms software cost : $3,000 License cost : check_with_vendor no of users : 2000 Max # of tables : 10,000 Supports ODBC : Yes 19 of 45 EXAMPLES OF KNOWLEDGE REPRESENTATIONS.. EXAMPLE 5 EXAMPLE 4 Bird-of-prey P PTRANS P to P.O. P ATTEND eyes to counter Is-a P MBUILD line position P PTRANS P to line Eagle P PTRANS M to X X PTRANS Stamps to P Max Wingspan 1.5 m Bird Is-a Max Speed 20 Knots 20 of 45 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 21 of 45 NOTES ON RULES Origins in S-R paradigms Thought to be used by experts Have a IF…THEN… format Note: S-R: stimulus/response 22 of 45 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 23 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? 24 of 45 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. 25 of 45 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 Dendrites The brain nose hair color eyes gait Neural Net (a math model) 26 of 45 BUSINESS APPLICATIONS OF AI Automated voice response Text mining Production applications machine design robotics paper thickness Scheduling of cranes Credit approval 27 of 45 INDUSTRIAL APPLICATIONS OF AI Driverless vehicles Facial recognition Crime prevention Pothole recognition Drones 28 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? 29 of 45 The End! Please note there are only 29 slides 30 of 45