Artificial Intelligence: INTRODUCTION - PowerPoint PPT Presentation

artificial intelligence introduction n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Artificial Intelligence: INTRODUCTION PowerPoint Presentation
Download Presentation
Artificial Intelligence: INTRODUCTION

play fullscreen
1 / 32
Artificial Intelligence: INTRODUCTION
162 Views
Download Presentation
alyn
Download Presentation

Artificial Intelligence: INTRODUCTION

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Artificial Intelligence: INTRODUCTION

  2. Short presentation Dr. Abdullah Alsheddy د. عبدالله عبدالعزيز الشدي Email : alsheddy@ccis.imamu.edu.sa 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% Artificial Intelligence : Introduction

  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) Artificial Intelligence : Introduction

  4. Chapter1 : Outline • What is AI • A brief history • The state of the art Artificial Intelligence : Introduction

  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” Artificial Intelligence : Introduction

  6. Intelligent behavior Computer Humans Artificial Intelligence : Introduction

  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. Artificial Intelligence : Introduction

  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 responses • http://groups.yahoo.com/group/webir/message/1002 Artificial Intelligence : Introduction

  9. AI Characterizations Artificial Intelligence : Introduction

  10. AI Characterizations Discipline that systematizes and automates intellectual tasks to create machines that : Artificial Intelligence : Introduction

  11. Systems that actlike 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, … Artificial Intelligence : Introduction

  12. Systems that actlike 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 • Prove a theorem • Play chess • Plan a surgical operation • Diagnose a disease • Navigate in a building Artificial Intelligence : Introduction

  13. Systems that thinklike 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. Artificial Intelligence : Introduction

  14. Systems that thinkrationally • 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? Artificial Intelligence : Introduction

  15. Systems that actrationally • 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. Artificial Intelligence : Introduction

  16. Systems that actrationally • 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. Artificial Intelligence : Introduction

  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 • Connection to economics, operational research, and control theory • But ignores role of consciousness, emotions, fear of dying on intelligence Artificial Intelligence : Introduction

  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. Artificial Intelligence : Introduction

  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. Artificial Intelligence : Introduction

  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 Turing’s “Computing Machinery and Intelligence” • First complete vision of AI. • Idea of Genetic Algorithms Artificial Intelligence : Introduction

  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 (1952-1969) • 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) Artificial Intelligence : Introduction

  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 (1952-1956) : a series of programs for checkers. • Collapse in AI research (1966 - 1973) • Progress was slower than expected. • Unrealistic predictions. • Some systems lacked scalability. • Combinatorial explosion in search. • Fundamental limitations on techniques and representations. Artificial Intelligence : Introduction

  23. A brief history (4) • AI revival through knowledge-based systems (1969-1970) • 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, … Artificial Intelligence : Introduction

  24. A brief history (5) • AI becomes an industry (1980 - 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 1988. Period called “AI winter” in which many companies suffered as they failed to deliver on extravagant promises. • Connectionist revival (1986 - present) • Parallel distributed processing (RumelHart and McClelland, 1986); back-propagation learning (computer science and psychology). Artificial Intelligence : Introduction

  25. A brief history (6) • AI becomes a science (1987 - 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 (1995 - 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”. Artificial Intelligence : Introduction

  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 : IBM’s 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 2003-2007. Artificial Intelligence : Introduction

  27. Stanley RobotStanford Racing Teamwww.stanfordracing.org Artificial Intelligence : Introduction

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

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

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

  32. Quiz • Does a plane fly? • Does a boat swim? • Does a computer think? Artificial Intelligence : Introduction