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Artificial intelligence

Artificial intelligence. יישומי בינה מלאכותית בניהול פרופ’ שיזף רפאלי ביה”ס למוסמכים במנהל עסקים אוניברסיטת חיפה. Overview. הנושאים העקריים בסרט הוידאו: האם מכונות יכולות לחשוב? אם כן, איך? (לוגית או ביולוגית?) האם מכונות צריכות לחשוב? איך מכונות כבר חושבות, איך נדע ? מבחן Turing

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Artificial intelligence

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  1. Artificial intelligence יישומי בינה מלאכותית בניהול פרופ’ שיזף רפאלי ביה”ס למוסמכים במנהל עסקים אוניברסיטת חיפה

  2. Overview • הנושאים העקריים בסרט הוידאו: • האם מכונות יכולות לחשוב? • אם כן, איך? (לוגית או ביולוגית?) • האם מכונות צריכות לחשוב? • איך מכונות כבר חושבות, איך נדע? מבחן Turing • סוגי מערכות של AI • מערכות מומחה Expert systems • רשתות נוירונים Neural net • לוגיקה מעורפלת Fuzzy logic

  3. שמות ומושגים מתוך הסרט • Common Sense • Inference Engine • Forward, Backward chaining • Expert Systems, Neural Networks • Understanding Natural Language, SHRDLU • Top-down, Bottom-up • Genetic Algorithms • Weizenbaum • Turing • Feigenbaum • Winograd • Searle • McCarthy • Kurzweil • Lenat

  4. Artificial intelligence • Goal • To develop computers that can act like people in every way • What do humans do? • Inwardly: think, react, emote • Outwardly: move, see, feelcreate, innovate, invent, crack jokes • Communicate among ourselves

  5. AI foundations • פסיכולוגיה, קוגניציה Cognitive science Psychology • רובוטיקה Robotics • ממשקים Interfaces • גנטיקה ואבולוציה (Genetics and evolution) • מדעי המחשב Computer science • מערכות מידע

  6. Developments by AI • Decision support systems • Support decision-making by humans • Expert systems • Make decision in place of an expert; • Acts as an expert assistant to a non-expert user • Neural nets • Fuzzy logic • Also: Understanding natural language and robotics

  7. ARTIFICIAL INTELLIGENCE NATURAL PERCEPTIVE EXPERT INTELLIGENT ROBOTICS LANGUAGE SYSTEMS SYSTEMS MACHINES The AI family

  8. AI chronology

  9. The AI paradox / tragedy • Simon’s prediction • Promise / fulfillment

  10. The Turing test • A computer deserves to be called intelligent if it could deceive a human into believing that it was human. • Yes, but… Searle’s Chinese Room... • See Kurzweil’s “Cybernetic Poet”,at http://www.kurzweiltech.com/

  11. Eliza as symbol of paradox • History of Joseph Weizenbaum, • Computer Power and Human Reason • Try ELIZA out for yourself, at: • http://www.planetary.net/robots/eliza.html • or at http://www.parnasse.com/drwww.shtml

  12. ? למה זה מעניין מנהליםWhy would business be interested ? • PRESERVE EXPERTISE • CREATE KNOWLEDGE BASE • MECHANISM NOT SUBJECT TO FEELINGS, FATIGUE, WORRY, CRISIS • ELIMINATE ROUTINE / UNSATISFYING JOBS • ENHANCE KNOWLEDGE BASE • שימור הנסיון • יצירת בסיס ידע • מכונות אינן “רגישות”, מתעייפות, דואגות, נלחצות, יוצאות למילואים או לחופשת לידה • אפשר לוותר על עבודות משעממות, רוטיניות, לא מספקות • אפשר לשפר את הידע הארגוני על משימותיו

  13. מערכות מומחהExpert systems • Application that acts as an expert CAPTURES HUMAN EXPERTISE IN LIMITED DOMAINS OF KNOWLEDGE • It gives recommendations • about customer credit • engineering problems • legal situations • sifting for the exception

  14. Expert System types (1) • KNOWLEDGE BASE: Model of Human Knowledge • RULE - BASED EXPERT SYSTEM : AI System Based on IF - THEN Statements (Bifurcation); Rule Base: Collection of IF - THEN Knowledge • SEMANTIC NETS: Uses Property of INHERITANCE to Organize, Classify Interrelated Characteristics

  15. Expert System types (2) • AI SHELL: Programming Environment of Expert System • INFERENCE ENGINE: Search Through Rule Base • FORWARD CHAINING: Uses Input; Searches Rules for Answer • BACKWARD CHAINING: Begins with Hypothesis, Seeks Information Until Hypothesis Accepted or Rejected

  16. Expert Systems limitations • Often Reduced to Problems of Classification • Can be Large, Lengthy, Expensive • Maintaining Knowledge Base Critical • Many Managers Unwilling to Trust such Systems

  17. Top-down in ExSys

  18. Simple example • If the animal hops and the object is tall, then the animal is a kangaroo. • If the animal is gray and large, then the animal is an elephant • The animal is tall. The animal is gray. The animal is large. • Solve by back chaining: What is the animal?

  19. Parts of an expert system • Inference engine • Knows which rules to execute • Rule base • List of rules • Fact base • Set of facts we provided

  20. Example Consultation Do you generally prefer dry, medium, or sweet wines? DRYDo you generally prefer red or white wines? WHITEDo you generally prefer light, medium or full bodied wines? MEDIUMIs the flavor of the meal delicate, average or strong? AVERAGEDoes the meal have a sauce on it? YESIs the sauce for the meal spicy, sweet, cream, or tomato? SPICYIs the main component of the meal meat, fish, or poultry? MEAT WINE CERTAINTY==========================Burgundy 90%Zinfandel 64%Chardonnay 64%Cabarnet-Sauvignon 40%Sauvignon-Blanc 40%Gevertzraminer 40% RECOMMENDATION:

  21. Other points to be made (1/2) • Symbolic processing • Forget bits, bytes, and data structures — think of ‘symbols’ • Applicability • Rules are specific • Inference engine is general

  22. Other points to be made (2/2) • Expert system shell • Provides inference engine • Helps create rule and fact base • General tool for a wide variety of problems • Gives user capability to answer: • How did you determine that answer?

  23. What is expertise? • Rules, memory volume, frames? • Simon’s chess experiment

  24. ES Examples online • CLIPS: an ES shell made by NASA. Visit at: • http://www.jsc.nasa.gov/~clips/CLIPS.html • Try JESS monkey-and-bananas online, at : • http://www.scs.ryerson.ca/~dgrimsha/java/c820a198/ • Try the whale expert system, at: • http://www.vvv.com/ai/demos/whale.html • Winning AI financial and manufacturing applications: • http://www.brightware.com/company/awards/index.html

  25. Neural Nets - back to biology

  26. רשתות נויורוניםNeural Nets • Designed to simulate the brain • Neurons, synapses • Useful when • Problem is difficult • An expert can’t say exactly how a decision is arrived at • Past situations (descriptions and decisions) are available • Ideal for massively parallel computers

  27. Neurons, Synapses, Axons, Dendrites

  28. And again...

  29. Credit decisions?

  30. יישומי רשתות נורוניםApplications of neural nets • Character recognition • Credit card fraud detection • Petroleum exploration • Financial forecasting • Loan approval • Missile guidance • Parking a truck

  31. רשתות נוירונים לעומת מערכות מומחה • Neural networks are flexible tools in a dynamic environment. • Neural networks have the capacity to learn rapidly and change quickly • Rule based systems are limited to the specific situation for which they were designed;

  32. רשתות נוירונים לעומת מערכות מומחה • Expert systems are rule-based computer programs. • Expert systems are good for applications with logical separators between decision-influencing factors. • Since ES have explicit rules, it is easy to understand how their decisions are made. • But expert systems are difficult to build because the rules don't always exist and the "expert" information is difficult to acquire.

  33. Example: Understanding spoken English • hominyuwan? • wuhjusay? • Ahluv, ahluv an ahluv, cuz ahluv ahluv ‘em lil greentings. • Time flies like an arrow • The spirit is willing but the flesh is weak • He took his leave and my umbrella • Try the START page, at http://sakharov.ai.mit.edu/Start.html

  34. Fuzzy logic • Regular logic • Answer is false or true (i.e., 0 or 1) • Fuzzy logic • Answer ranges from 0 to 1 but can be anywhere in between (i.e., means maybe yes, maybe no) • Another “rule-based” development • Closer to the way people think

  35. Fuzzy logic: example • Think of “youngness” • Not just “young” or “old”; there are degrees

  36. Applications of fuzzy logic • Elevators • Automobile cruise control • Washing machines • Camera auto-focus באיזו מידה יש יישום ניהולי ל Fuzzy Logic? מומלץ לקרוא ולחשוב על “Virtuality”

  37. סיכום • Common Sense • Inference Engine • Forward, Backward chaining • Expert Systems, Neural Networks • Understanding Natural Language, SHRDLU • Top-down, Bottom-up • Genetic Algorithms • Weizenbaum • Turing • Feigenbaum • Winograd • Searle • McCarthy • Kurzweil • Lenat

  38. References מקורות • Spielberg’s AI film and web site • Buchanan’s brief history of AI: http://www.aaai.org/Pathfinder/bbhist.html • John McCarthy’s “What is AI?”http://www-formal.stanford.edu/jmc/whatisai/whatisai.html • AI Trends and Controversieshttp://www.computer.org/intelligent/articles/AI_controversies.htm

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