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Chapter 1

King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems . Chapter 1. Introduction to Artificial Intelligence. Objectives. Artificial Intelligence subject to be worthy of study.

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Chapter 1

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  1. King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems Chapter 1 Introduction to Artificial Intelligence

  2. Objectives • Artificial Intelligence subject to be worthy of study. • What is exactly Artificial Intelligence.

  3. Introduction to Artificial Intelligence • Artificial Intelligence is one of the newest sciences which emerged after the world war II. • AI represents a big and open field. • The name Artificial Intelligence was adopted for the first time in 1956. (Computational Intelligence) • Artificial Intelligence can be viewed as a universal field: How to automate intellectual tasks? • Goal of Artificial Intelligence: Not only to understand how does mind work? but also how to build intelligent entities? • Engineering point of view: • Solve real-world problems using knowledge and reasoning • Develop concepts, theory and practice of building intelligent entities • Emphasis on system building • Scientific point of view: • Use computers as a platform for studying intelligence itself • Emphasis on understanding intelligent behavior.

  4. Introduction to Artificial Intelligence • What is artificial Intelligence? • Several definitions are available in the literature. Thinking vs. Behavior Model humans vs. Ideal standard (Rationality) • Rational System = system which does the “right thing” given what it knows. • Definitions fall into four categories:

  5. Introduction to Artificial Intelligence • What is artificial Intelligence? • Keep this one: Science of making machines do things that would require intelligence if done by humans. - Marvin Minsky

  6. Acting Humanly • Turing Test Alan Turing 1950 «Computing Machinery and Intelligence» • Test: The computer passes the “test of intelligence” if a human, after posing some written questions, cannot tell whether the responses were from a person or not. • Provides an operational definition of AI. • Still being used today within the AI community in annual competitions – the Loebner Prize ($100,000 and a true 18K gold medal) B A Is A the human or is B?

  7. Acting Humanly • Turing Test (Cont.) • To give an answer, the computer would need to posses some capabilities: • Natural language processing: To communicate successfully. • Knowledge representation: To store what it knows or hears. • Automated reasoning: to answer questions and draw conclusions using stored information. • Machine learning: To adapt to new circumstances and to detect and extrapolate patterns. • Computer vision: To perceive objects. • Robotics to manipulate objects and move. • Example: ELIZA program.

  8. Acting Humanly • Example: ELIZA program. Some extracts from the test: • Patient: Men are all alike. • ELIZA: In what way? • Patient: They are always bugging about something or the other. • ELIZA: Can you think of a specific example? • Patient: Well, my boyfriend made me come here. • ELIZA: Your boyfriend made you come here? • Patient: He says I’m depressed much of the time. • ELIZA: I am sorry you are depressed.

  9. Thinking humanly • Program think like human → How humans think? • Requires Scientific theories of internal activities of the brain (cognitive science and cognitive neuroscience).

  10. Thinking and acting rationally • Thinking rationally • Based on the Laws of Thought (rules explaining how a human make conclusions from facts) • Example, “Socrates is a man; all men are mortal, therefore Socrates is mortal.” The laws of thought initiated the field of logic. • Acting rationally • Modern AI can be characterized as the engineering of rational agents. • An agent is simply an entity that perceives and acts. A rational agent is an entity that perceives, reasons and acts rationally (correctly).

  11. Introduction to Artificial Intelligence • Foundations: An interdisciplinary subject found on: • Philosophy, • mathematics, • economics, • neuroscience, • psychology, • computer engineering, • linguistics, and so on

  12. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • Ultimately, we are dealing with the question: “What are we (human beings) doing when we are thinking?” • Thought processes in the human mind are computational in nature. There are mechanistic procedures for generating these thoughts. • Such computations can be simulated and implemented by a Turing machine. Therefore, it can be programmed.

  13. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • 1943: first piece of AI work: Warren McCulloch and Walter Pitts • Model of artificial neurons. • Mathematical learnable functions that generate “on/off” depending on inputs (logic gates) • Any computable function can be computed by a network of connected neurons. • Suitably defined networks can learn. • 1949: Hebbian learning • A mechanism for updating the connection strength of a neuron. • Today, neurologists have confirmed that something similar to Hebbian learning indeed is going on in our brain when we are learning. • 1950: Turing test complete vision of AI in “computing machinery and Intelligence” • 1951: first neural network computer • Implemented by M. Minsky and D. Edmonds

  14. History of Artificial Intelligence • Mcculloch and Pitts artificial neuron Human neuron (brain) Artificial neuron

  15. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • 1956: Dartmouth Conference • Organized by John McCarthy and colleagues for starting a new area in studying computation and intelligence. • John McCarthy introduced the term “artificial intelligence” in the conference. • The next 20 years witnessed steady growth of the field led by the pioneers appeared in the Dartmouth conference.

  16. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • 1963: Thomas Evan’s program ANALOG • Solved analogy problems in an IQ test. • 1965: ELIZA • Simulates a dialog with a computer in English on any topic. • Became popular when programmed to simulate a psychotherapist (Fedora’s Emacs). • 1967: Dendral program (developed at Stanford) • - First successful program for scientific reasoning – one of the earlier rule based expert systems. • - A program that can infer molecular structures given the information provided by a mass spectrometer

  17. History of Artificial Intelligence AI becomes an industry (1980 – present) Reality Check (1966 – 1973) Birth of AI (1956) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • Series of disappointments and frustrations • AI was poured little buckets of “reality cold water” • Problems: • - Most early systems contain little or no knowledge of their subject matter • Example: Poor performance of earlier machine translation system (Russian ⇔English): “the spirit is willing but the flesh is weak” was translated to “the vodka is good but the meat is rotten”. • - Computational Intractability of AI problems • Theory of computational complexity was not developed. Polynomial solvable problems, NP-completeness, etc • People thought a faster machine could solve any hard problem.

  18. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • 1971: T. Winograd’s Ph.D. thesis (MIT) • demonstrated a system that can understand English in a micro-domain (the block world). • - 1972: PROLOG was developed • 1974: MYCIN was developed by Ted Shortliffe • Expert system for medical diagnosis. Sometimes called the first expert system. • - And many other works…

  19. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • AI started to become industrially and commercially beneficial • - 1982: R1 was deployed at DEC – an expert system that saved the company around $40M / year • - Du Pont had 100 in use and an estimated 500 in development at late 90’s to early 21st century • At an international level, AI was considered a part of a country’s technological developments • - Japan: “First Generation” project (10 year plan to build intelligence machines running in Prolog) • - USA: Microelectronics and Computer Technology Corporation (MCC) was formed in response • - Britain: Funding for AI was reinstated

  20. History of Artificial Intelligence AI becomes an industry (1980 – present) Birth of AI (1956) Reality Check (1966 – 1973) Big Dream Early days (1943-1955) Expectations and Initial enthusiasm (1952 – 1969) Resurgence (1969 – 1979) Renewing with connectionism and AI becomes a science (1986 – present) • Work of the physicist John Hopfield (1982) on using techniques from statistical mechanics. • Connectionist models of intelligent systems competitor to the symbolic models (Newell and Simon) and logicist approach (McCarthy). (complementary approaches in fact). • Several revolutions in many fields: pattern recognition, computer vision, robotics… • Emergence of intelligent agents.

  21. Examples of AI applications (1) • Game Playing • TDGammon • The world champion backgammon player, built by Gerry Tesauroof IBM research (1992) • Deep Blue • Chess program that beat world champion Gary Kasparov (1997)

  22. Examples of AI applications (2) • Natural Language Understanding • Spell/Grammar checkers • AI translators • Alta Vista’s translation of web pages • Text summarization • Question answering • PROVERB (Littman 1999): • Automated crossword solver • Competed in the American Crossword Puzzle Tournament crossword puzzles • START system • Accesses raw data tables, and then can carry on a dialogue (English Conversation)

  23. Examples of AI applications (3) • Expert systems • In geology • prospector expert system carries evaluation of mineral potential of geological site or region • Diagnostic Systems • Pathfinder (medical diagnosis system) developed by Heckerman and other Microsoft research • Microsoft Office Assistant in Office (provides customized help) • MYCIN system (diagnosing bacterial infections of the blood and suggesting treatments) • System Configuration • "XCON" (for custom hardware configuration): a production-rule-based to assist in the ordering of DEC's VAX computer systems by automatically selecting the computer system components based on the customer's requirements.

  24. Examples of AI applications (4) • Robotics • Robotics becoming increasing important in various areas like: • Games • To handle hazardous conditions • To do tedious jobs among other things • Examples: automated cars, ping pong player, mining, construction, robot assistant in microsurgery,…

  25. Examples of AI applications (5) • Google’s Automated Cars (2010) • They use video cameras, radar sensors and a laser range finder to "see" other traffic, detailed maps • The cars have already logged more than 140,000 miles.

  26. Summary • Intelligence is studied from many perspectives: Are you concerned with thinking or behavior? • AI can help us solve difficult, real-world problems, creating new opportunities in business, engineering, and many other application areas • The history of AI has had cycles of success, misplaced optimism, and resulting cutbacks in enthusiasm and funding. There have also been cycles of introducing new creative approaches and systematically refining the best ones • AI has advanced more rapidly in the past decade because of greater use of the scientific method in experimenting with and comparing approaches

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