Introdcuction of Artificial Intelligence

1. Introdcuction of Artificial Intelligence Artificial Intelligence Department of Industrial Engineering and Management Cheng Shiu University

2. Outline • Definition of Intelligence • Definition of Artificial Intelligence • History of Artificial Intelligence • Application of Artificial Intelligence

3. Definition of Intelligence • Ordinary Human use of data • Common sense • Sound judgment not based on specialized knowledge; native good judgment. • Knowledge • acquaintance with facts, truths, or principles, as from study or investigation • Intelligence………

4. Intelligence (Dictionary..) • capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude in grasping truths, relationships, facts, meanings, etc. • manifestation of a high mental capacity • the faculty of understanding. • knowledge of an event, circumstance, etc., received or imparted; news; information. • the gathering or distribution of information, esp. secret information.

5. Intelligence • Properties: • understanding (awareness) • acting (conclusions) • reasoning • thinking

6. House of intelligence • Head • intelligence connected to mind as opposed to body • Brain • Body • body is a shell that houses mind and hence intelligence How to reconcile the separation between the body and the mind?

7. Why use intelligent systems? • Automation of repetitive tasks • Augmenting limited information processing capability of humans • Easy interaction with machines • Understanding human brain and intelligence • Find out limits of (human) intelligence

8. What is Artificial Intelligence? • AI is the study of agents that exist in an environment and perceive and act • AI is the art of making computers do smart things • AI is a programming style, where programs operate on data according to rules in order to accomplish goals • AI is the activity of providing such machines as computers with behavior that would be regarded as intelligent if it were observed by humans • Branch of computer science that is concerned with the automation of intelligent behavior

9. A Brief History of AI 5th century BC Aristotle invents syllogistic logic, the first formal deductive reasoning system. 16th century AD Rabbi Loew supposedly invents the Golem, an artificial man made out of clay

10. 17th century Descartes proposes animals are machines and founds a scientific paradigm that will dominate for 250 years. Blaise Pascal creates the first mechanical calculator in 1642 18th century Wolfgang von Kempelen “invents” fake chess-playing machine, The Turk.

13. 19th century George Boole creates a binary algebra to represent “laws of thought” Charles Babbage and Lady Lovelace develop sophisticated programmable mechanical computers, precursor to modern electronic computers.

15. 20th century Karel Kapek writes “Rossum’s Universal Robots”, coining the English word “robot” Warren McCulloch and Walter Pitts lay partial groundwork for neural networks Turing writes “Computing Machinery and Intelligence” – proposal of Turing test

16. 1956: John McCarthy coins phrase “artificial intelligence” 1952-62: Arthur Samuel writes the first AI game program to challenge a world champion, in part due to learning. 1950’s-60’s: Masterman et. al at Cambridge create semantic nets that do machine translation.

17. 1961: James Slagle writes first symbolic integrator, SAINT, to solve calculus problems. 1963: Thomas Evan’s writes ANALOGY, which solves analogy problems like the ones on IQ tests. 1965: J. A. Robinson invents Resolution Method using formal logic as its representation language.

18. 1965: Joseph Weizenbaum creates ELIZA, one of the earliest “chatterbots” 1967: Feigenbaum et. al create Dendral, the first useful knowledge-based agent that interpreted mass spectrographs. 1969: Shakey the robot combines movement, perception and problem solving.

19. 1971: Terry Winograd demonstrates a program that can understand English commands in the word of blocks. 1972: Alain Colmerauer writes Prolog 1974: Ted Shortliffe creates MYCIN, the first expert system which showed the effectiveness of rule-based knowledge representation for medical diagnosis.

20. 1978: Herb Simon wins Nobel Prize for theory of bounded rationality 1983: James Allen creates Interval Calculus as a formal representation for events in time. 1980’s: Backpropagation (invented 1974) rediscovered and sees wide use in neural networks

21. 1985: ALVINN, “an autonomous land vehicle in a neural network” navigates across the country (2800 miles). Early 1990’s: Gerry Tesauro creates TD-Gammon, a learning backgammon agent that vies with championship players 1997: Deep Blue defeats Garry Kasparov

22. Models of brain • Mechanistic (Newtonian age) • Complicated (electronic) circuitry (age of electricity) • Large network of simple elements (age of neuroscience) • connectionist (elements serve a common goal) • “tools” for selfish genes

23. Mechanistic view • Brain is a mechanical machinery that performs operations • Ideas are results of these operations • Energy for operations is provided by water • Separation of ideas of things from things themselves

24. Think Like Human The Cognitive Modeling approach • To develop a program that think like human , the way the human think should be known. • Knowing the precise theory of mind (how human think?)  expressing the theory as a computer program. • GPS (General Problem Solver) [by Newell & Simon, 1961] • Were concerned with comparing the trace of its reasoning steps to traces of human subjects solving the same problem rather that correctly solve problems Cognitive Science Computer models from AI + Experimental techniques from psychology  Construction of human mind working theories

25. Act Like Human • The TURING Test Approach • Alan Turing [1950] designed a test for intelligent behaviour, i.e. • ability to achieve human-level performance in all cognitive tasks • indistinguishable from human being by an human interrogator. • A human (interrogator) interrogates (without seeing) two candidates • A and B (one is a human and the other is a machine). • Computer would need: • Natural Language Processing  Communication. • Knowledge Representationstore info before and during interrogation. • Automated Reasoning answer questions and draw new conclusions. • Machine learning adapt to new circumstances.

26. Turing Test • Turing Imitation Game: Phase 1

27. Turing Test • Turing Imitation Game: Phase 2

28. Think Rationally The Law of Thought Approach • Aristotle and his syllogism (right thinking) : • always gave correct inference given correct premises • Socrates is a Man. %Fact • All men are Mortal. % Rule : if X is a Man then X is Mortal. • Therefore Socrates is Mortal. % Inference These laws of thoughts initiated the field of LOGIC • Two main obstacles: • Not all things can be formally represented in logic notation, particularly • if there is any uncertainty • It is usually the case that even small scale problems can exhaust the • computational power of any computer unless heuristics are used

29. Act Rationally The Rational Agent Approach An agent is something that perceives and acts A rational agent is one that acts so as to achieve the best outcome Making correct inferences is part not all of being a rational agent Act rationally = reason logically to the conclusion and act on that conclusion Correct inference is not always == rationality e.g. reflex actions (acting rationally without involving inference) • Two main advantages • More general than “the laws of thought" a mechanism to achieve rationality) • More amenable to scientific development than approaches based on [human] behavior/thought.

30. Logic • Statement is either true or not • Statement and its complement can not be true at the same time • Statements are represented by symbols • Correct thinking is the process of finding correct conclusions given correct statements • Basis of symbolic AI

31. Symbolic Artificial Intelligence • Logician’s approach to intelligence • Precise mathematical/logical problem formulation andits solution • Sensitive to representation (and hence to errors in therepresentation) • Not robust • User provides the solution or class of solutions • Powerful paradigm for symbol manipulation • Basis of Good-old fashion artificial intelligence (Traditional AI)

32. Different AIs • Connectionist AI • Inspired by the anatomic structure of the brain • Evolutionary AI • Based on a broader definition of intelligence Will be introduced later…

33. Applications of AI? Search engines Science Medicine/ Diagnosis Labor What else? Appliances

34. Typical AI Problems • Mundane tasks which people can do • very easily (understanding language) AI tasks involve both : • Expert tasks that require specialist • knowledge (medical diagnosis)

35. Typical AI Problems Mundane tasks correspond to the following AI problems areas: The ability to decide on a good sequence of actions to achieve our goals • Planning : • Vision : • Robotics: • Natural Language: The ability to make sense of what we see The ability to move and act in the world, possibly responding to new perceptions The ability to communicate with others in any human language

36. Typical AI Problems Experts tasks (require specialized skills and training) include : • Medical diagnosis • Equipment repair • Computer configuration • Financial planning Mundane tasks are generally much harder to automate AI is concerned with automating both mundane and expert tasks.

37. Application Image Processing Pattern (Speech/Vision) Recognition Data Mining Natural Language Processing Robotics Computer/Wireless Networks Others classification prediction optimization 37

38. Why intelligent systems • To recognize computational approaches to intelligence. • To understand the motivation for using intelligent systems. • To master the basic design methodology for intelligent systems. • To use intelligent systems for solving problems in the domain of informatics and engineering. • To understand the motivation for using artificial intelligence systems