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ARTIFICIAL INTELLIGENCE: INTRODUCTION. Short presentation Dr. Abdullah Alsheddy د. عبدالله عبدالعزيز الشدي

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Presentation on theme: "ARTIFICIAL INTELLIGENCE: INTRODUCTION. Short presentation Dr. Abdullah Alsheddy د. عبدالله عبدالعزيز الشدي"— Presentation transcript:


2 Short presentation Dr. Abdullah Alsheddy د. عبدالله عبدالعزيز الشدي Office: FR64 Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach, Prentice Hall, 3rd Edition, 2009 Grading: Quizzes/Presentation/Participation (20%) Project (20%) Midterm test (20%) Final Exam: 40% 04/03/1435Artificial Intelligence : Introduction 2

3 Course overview Introduction and Agents (chapters 1,2) Search (chapters 3,4,5,6) Logic (chapters 7,8,9) Planning (chapters 11,12) Uncertainty (chapters 13,14) Learning (chapters 18,20) Robotics (chapter 25,26) 04/03/1435Artificial Intelligence : Introduction 3

4 Chapter1 : Outline What is AI A brief history The state of the art 04/03/1435Artificial Intelligence : Introduction 4

5 What is AI? Intelligence: the capacity to learn and solve problems (Websters dictionary) in particular, the ability to solve novel problems the ability to act rationally the ability to act like humans Artificial Intelligence build and understand intelligent entities or agents 2 main approaches: engineering versus cognitive modeling 04/03/1435Artificial Intelligence : Introduction 5

6 Intelligent behavior Humans Computer 04/03/1435Artificial Intelligence : Introduction 6

7 Why AI? Cognitive Science: As a way to understand how natural minds and mental phenomena work e.g., visual perception, memory, learning, language, etc. Philosophy: As a way to explore some basic and interesting (and important) philosophical questions e.g., the mind body problem, what is consciousness, etc. Engineering: To get machines to do a wider variety of useful things e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc. 04/03/1435Artificial Intelligence : Introduction 7

8 Weak vs. Strong AI Weak AI: Machines can be made to behave as if they were intelligent Strong AI: Machines can have consciousness Subject of fierce debate among philosophers and AI researchers. E.g. Red Herring article and responsesarticleresponses 04/03/1435Artificial Intelligence : Introduction 8

9 04/03/1435Artificial Intelligence : Introduction 9 AI Characterizations

10 AI Characterizations AI Characterizations Discipline that systematizes and automates intellectual tasks to create machines that : Act like humans System passing the Turing Test (1950) Learning from Knowledge (adapt) Representing Knowledge (memorize) Solve Pb (argue) Understanding (communicate) Theoretical Act rationally Rational agent (199X) acts according to his beliefs to reach goals (not only logical) Pragmatic Think like humans Cognitive modeling (GPS (Newel & Simon,61)) Complex Think rationally logical thinking Pascal [ ] (calculating machine) Leibniz [ ] (reasoning machine) Babbage [ ] (Analytical Engine) limited 04/03/1435Artificial Intelligence : Introduction 10

11 Systems that act like humans When does a system behave intelligently? Turing (1950) Computing Machinery and Intelligence "Can machines think?" "Can machines behave intelligently?" Operational test of intelligence: imitation games Test requires the collaboration of major components of AI: knowledge, reasoning, language understanding, learning, … 04/03/1435Artificial Intelligence : Introduction 11 Interrogator interacts with a computer and a person. Computer passes the Turing test if interrogator cannot determine which is which.

12 Systems that act like humans AI is the art of creating machines that perform functions that require intelligence when performed by humans Methodology: Take an intellectual task at which people are better and make a computer do it Turing test 04/03/1435Artificial Intelligence : Introduction 12 Prove a theorem Play chess Plan a surgical operation Diagnose a disease Navigate in a building

13 Systems that think like humans How do humans think? Requires scientific theories of internal brain activities (cognitive model): How to validate? requires : Predicting and testing human behavior Identification from neurological data Brain imaging in action Cognitive Science vs. Cognitive neuroscience vs. Neuroimaging They are now distinct from AI Share that the available theories do not explain anything resembling human intelligence. Three fields share a principal direction. 04/03/1435Artificial Intelligence : Introduction 13

14 Systems that think rationally Capturing the laws of thought Aristotle: What are correct argument and thought processes? Correctness depends on irrefutability of reasoning processes. This study initiated the field of logic. The logicist tradition in AI hopes to create intelligent systems using logic programming. Problems: Not all intelligence is expressed by logic behavior What is the purpose of thinking? What thought should one have? 04/03/1435Artificial Intelligence : Introduction 14

15 Systems that act rationally Rational behavior: doing the right thing The Right thing is that what is expected to maximize goal achievement given the available information. Can include thinking, yet in service of rational action. Action without thinking: e.g. reflexes. 04/03/1435Artificial Intelligence : Introduction 15

16 Systems that act rationally Two advantages over previous approaches: More general than law of thoughts approach More amenable to scientific development. Yet rationality is only applicable in ideal environments. Moreover rationality is not a very good model of reality. 04/03/1435Artificial Intelligence : Introduction 16

17 Think/Act Rationally Always make the best decision given what is available (knowledge, time, resources) Perfect knowledge, unlimited resources logical reasoning Imperfect knowledge, limited resources (limited) rationality 04/03/1435Artificial Intelligence : Introduction 17 Connection to economics, operational research, and control theory But ignores role of consciousness, emotions, fear of dying on intelligence

18 Rational agents An agent is an entity that perceives and acts This course is about designing rational agents An agent is a function from percept histories to actions: For any given class of environments and task we seek the agent (or class of agents) with the best performance. Problem: computational limitations make perfect rationality unachievable. 04/03/1435Artificial Intelligence : Introduction 18

19 Foundations of AI Different fields have contributed to AI in the form of ideas, view points and techniques. Philosophy: Logic, reasoning, mind as a physical system, foundations of learning, language and rationality. Mathematics: Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability. Psychology: adaptation, phenomena of perception and motor control. Economics: formal theory of rational decisions, game theory. Linguistics: knowledge representation, grammar. Neuroscience: physical substrate for mental activities. Control theory: homeostatic systems, stability, optimal agent design. 04/03/1435Artificial Intelligence : Introduction 19

20 A brief history What happened after WWII? 1943: Warren Mc Culloch and Walter Pitts: a model of artificial boolean neurons to perform computations. First steps toward connectionist computation and learning (Hebbian learning). Marvin Minsky and Dann Edmonds (1951) constructed the first neural network computer 1950: Alan Turings Computing Machinery and Intelligence First complete vision of AI. Idea of Genetic Algorithms 04/03/1435Artificial Intelligence : Introduction 20

21 A brief history (2) The birth of (the term) AI (1956) Darmouth Workshop bringing together top minds on automata theory, neural nets and the study of intelligence. Allen Newell and Herbert Simon: The logic theorist (first non-numerical thinking program used for theorem proving). For the next 20 years the field was dominated by these participants. Great expectations ( ) Newell and Simon introduced the General Problem Solver. Imitation of human problem-solving Arthur Samuel (1952-) investigated game playing (checkers ) with great success. John McCarthy(1958-) : Inventor of Lisp (second-oldest high-level language) Logic oriented, Advice Taker (separation between knowledge and reasoning) 04/03/1435Artificial Intelligence : Introduction 21

22 A brief history (3) The birth of AI (1956) Great expectations continued.. Marvin Minsky (1958 -) Introduction of microworlds that appear to require intelligence to solve: e.g. blocks-world. Anti-logic orientation, society of the mind. Herbert Gelernter (1959) : constructed the geometry theorem Prover. Artur Samual ( ) : a series of programs for checkers. Collapse in AI research ( ) Progress was slower than expected. Unrealistic predictions. Some systems lacked scalability. Combinatorial explosion in search. Fundamental limitations on techniques and representations. 04/03/1435Artificial Intelligence : Introduction 22

23 A brief history (4) AI revival through knowledge-based systems ( ) General-purpose vs. domain specific E.g. the DENDRAL chemistry project (Buchanan et al. 1969) First successful knowledge intensive system. Expert systems MYCIN to diagnose blood infections (Feigenbaum et al.) Introduction of uncertainty in reasoning. Increase in knowledge representation research. Logic, frames, semantic nets, … 04/03/1435Artificial Intelligence : Introduction 23

24 A brief history (5) AI becomes an industry ( present) First successful commercial expert system R1 at DEC (McDermott, 1982) Fifth generation project in Japan (1981) : a 10-year plan to build intelligent computer running Prolog. American response …: US formed the microelectronics and Computer Technology Corporation designed to assure national competitiveness (chip design and human-interface research) Puts an end to the AI winter. AI industry boomed from a few million to billion dollars in Period called AI winter in which many companies suffered as they failed to deliver on extravagant promises AI industry boomed from a few million to billion dollars in Period called AI winter in which many companies suffered as they failed to deliver on extravagant promises. Connectionist revival ( present) Parallel distributed processing (RumelHart and McClelland, 1986); back-propagation learning (computer science and psychology). 04/03/1435Artificial Intelligence : Introduction 24

25 A brief history (6) AI becomes a science ( present) In speech recognition: hidden markov models In neural networks In uncertain reasoning and expert systems: Bayesian network formalism Problem solving … The emergence of intelligent agents ( present) The whole agent problem: How does an agent act/behave embedded in real environments with continuous sensory inputs Ideally, an intelligent agent takes the best possible action in a situation : study the problem of building intelligent agents in this sense. 04/03/1435Artificial Intelligence : Introduction 25

26 State of the art : AI today 1/2 Autonomous planning and scheduling : on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonhson et al., 2000). Game playing : IBMs Deep Blue became the first computer program to defeat the world champion in chess match (Goodman and Keene, 1997), Autonomous control : the ALVINN computer vision system was trained to steer a car to keep it following a lane (for 2850 miles ALVINN was in control of steering in 98%, only 2% for human control mostly at exit ramps). Diagnosis : medical diagnosis programs based on probabilistic analysis have been able to perform at level of an expert physician in several areas in medicine (Heckerman 1991). Robot driving: DARPA grand challenge /03/1435Artificial Intelligence : Introduction 26

27 Stanley RobotStanford Racing Team 04/03/1435Artificial Intelligence : Introduction 27

28 Major research areas (Applications) Natural Language Understanding Image, Speech and pattern recognition Uncertainty Modeling Problem solving Knowledge representation ….. 04/03/1435Artificial Intelligence : Introduction 28

29 AI Success Story : Medical expert systems Antibiotics & Infectious Diseases Cancer Chest pain Dentistry Dermatology Drugs & Toxicology Emergency Epilepsy Family Practice Genetics Geriatrics 04/03/1435Artificial Intelligence : Introduction 29 Programs listed by Special Field Gynecology Imaging Analysis Internal Medicine Intensive Care Laboratory Systems Orthopedics Pediatrics Pulmonology & Ventilation Surgery & Post-Operative Care Trauma Management

30 Pattern Recognition Applications 04/03/1435Artificial Intelligence : Introduction 30 Handwriting and document recognition Signature, biometrics (finger, face, iris, etc.) Trafic monitoring, Remote Sensing guided missile, target homing

31 Future of AI 04/03/1435Artificial Intelligence : Introduction 31 Making AI Easy to use Easy-to-use Expert system building tools Web auto translation system Recognition-based Interface Packages Integrated Paradigm Symbolic Processing + Neural Processing AI in everywhere, AI in nowhere AI embedded in all products Ubiquitous Computing, Pervasive Computing

32 Quiz Does a plane fly? Does a boat swim? Does a computer think? 04/03/1435Artificial Intelligence : Introduction 32

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