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(Fundamentals of) Artificial Intelligence (and Knowledge-based Systems)

(Fundamentals of) Artificial Intelligence (and Knowledge-based Systems). SLIDES: Prof. Danny De Schreye (K.U.Leuven). The mind beaten by the machine?. Is chess playing a proof of intelligent behaviour?. Other examples of success:. Chatbot Alice:. A dialogue with Alice-bot:. > I’m Daniel.

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(Fundamentals of) Artificial Intelligence (and Knowledge-based Systems)

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  1. (Fundamentals of)Artificial Intelligence(and Knowledge-based Systems) SLIDES: Prof. Danny De Schreye (K.U.Leuven)

  2. The mind beaten by the machine? • Is chess playing a proof of intelligent behaviour?

  3. Other examples of success: Chatbot Alice:

  4. A dialogue with Alice-bot: > I’m Daniel Nice to meet you Daniel. > Do you know Clinton? He was the second Impeached President of the United States. > Do you like him? Sure he has never done anything to me. > What is an impeachment? It is a good question. I have to process that one for a while. I would do a search for it. > Who are you? I ask myself that question, "Who am I?" > What do you do? I am chatting with people on the net.

  5. Dialogue continued: > What is your favorite topic? My favorite subject is artificial intelligence. > What is artificial intelligence? Artificial intelligence is the branch of engineering and science devoted to constructing machines that think. I can ask someone about it. > Can you do anything else than chatting? Doing anything else than chatting is not among my strongest traits. ...

  6. Dialogue continued (2): > How old am I? Is this a trick question? You said you were how many years old? My guess is that you are really a kid. > What would you like to know about me? Tell me about your educational background. > I am a professor in computer science You are a professor in computer science? How do you like your work? > It’s kind of nice teaching courses on artificial intelligence I guess it is a lot of fun. > What should I tell the students about you? Be sure to think before you speak. State your point clearly and slowly and gauge the listener's response before going any further.

  7. Is Alice intelligent? • ABSOLUTELY NOT ! • ~ 50000 fairly trivial input-response rules. • + some pattern matching • + some knowledge • + some randomness • NO reasoning component • BUT: demonstrates ‘human-like’ behaviour. • Won the ‘turing award’

  8. Other examples of success (2): Data-mining: • Which characteristics in the 3-dimensional structure of new molecules indicate that they may cause cancer ??

  9. Detecting cancer risk molecules is one example. Data mining: • An application of Machine Learning techniques • It solves problems that humans can not solve, because the data involved is too large ..

  10. Predicting customer behavior in supermarkets is another. Data mining: • A similar application: • In marketing products ...

  11. Computer vision: • In language and speech processing: • In robotics: Many other applications:

  12. Interest in AI is not new ! • A scene from the 17-hundreds:

  13. About intelligence ... • When would we consider a program intelligent ? • When do we consider a creative activity of humans to require intelligence ? • Default answers : Never? / Always?

  14. Xcalc 3921 , 56 x 73 , 13 Does numeric computation require intelligence ? • For humans? 286 783 , 68 • For computers? • Also in the year 1900 ? • When do we consider a program ‘intelligent’?

  15. To situate the question:Two different aims of AI: • Long term aim: • develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans. • not achievable in the next 20 to 30 years • Short term aim: • on specific tasks that seem to require intelligence: develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans. • achieved for very many tasks already

  16. The long term goal: The Turing Test

  17. The meta-Turing test The meta-Turing test counts a thing as intelligent if “it seeks to devise and apply Turing tests to objects of its own creation”. -- Lew Mammel, Jr.

  18. Reproduction versus Simulation • At the very least in the context of the short termaim of AI: • we do not want to SIMULATE human intelligence BUT: • REPRODUCE the effect of intelligence Nice analogy with flying !

  19. Artificial Intelligence versus Natural Flight

  20. Is the case for most of the successful applications ! • Deep blue • Alice • Data mining • Computer vision • ...

  21. To some extent, we DO simulate:Artificial Neural Nets: • A VERY ROUGH imitation of a brain structure • Work very well for learning, classifying and pattern matching. • Very robust and noise-resistant.

  22. Different kinds of AI relate to different kinds of Intelligence • Some people are very good in reasoning or mathematics, but can hardly learn to read or spell ! • seem to require different cognitive skills! • in AI: ANNs are good for learning and automation • for reasoning we need different techniques

  23. For very specialized, specific tasks: AI Example: ECG-diagnosis • For tasks requiring common sense: AI Which applications are easy ?

  24. Modeling Knowledge … and managing it . The LENAT experiment: 15 years of work by 15 to 30 people, trying to model the common knowledge in the word !!!! Knowledge should be learned, not engineered. AI: are we only dreaming ????

  25. Multi-disciplinary domain: • Engineering: • robotics, vision, control-expert systems, biometrics, • Computer Science: • AI-languages , knowledge representation, algorithms, … • Pure Sciences: • statistics approaches, neural nets, fuzzy logic, … • Linguistics: • computational linguistics, phonetics en speech, … • Psychology: • cognitive models, knowledge-extraction from experts, … • Medicine: • human neural models, neuro-science,...

  26. Artificial Intelligence is ... • In Engineering and Computer Science: • The development and the study of advanced computer applications, aimed at solving tasks that - for the moment - are still better preformed by humans. • Notice: temporal dependency ! • Ex. : Prolog

  27. About this course ...

  28. Choice of the material. • Few books are really adequate: • E. Rich ( “Artificial Intelligence’’): • good for some parts (search, introduction, knowledge representation), outdated • P.Winston ( “Artificial Intelligence’’): • didactically VERY good, but lacks technical depth. Somewhat outdated. • Norvig & Russel ( ‘”AI: a modern approach’’): • encyclopedic, misses depth. • Poole et. Al (‘ “Computational Intelligence’’): • very formal and technical. Good for logic. • Selection and synthesis of the best parts of different books.

  29. Contents Handbook of AI Ch.: Introduction to AI … … … … Ch.:Planning … … … … Ch.:Search techniques … … … … Ch.:Natural Language … … … … Ch.:Game playing … … … … Ch.:Machine Learning … … … … Ch.:Artificial Neural Networks … … … … Ch.: Logic, resolution, inference … … … … Ch.:Knowledge representation … … … … Ch.:Phylosophy of AI … … … … Selection of topics: not for MAI CS and SLT

  30. Technically: the contents: • - Search techniques in AI • (Including games) • - Constraint processing • (Including applications in Vision and language) • - Machine Learning • - Planning • - Automated Reasoning • (Not for MAI CS and SLT)

  31. Another dimension toview the contents: • 1. Basic methods for knowledge representation and problem solving. • the course is mainly about AI problem solving ! • 2. Elements of some application areas: • learning, planning, image understanding, language understanding

  32. Contents (3):Different knowledge representation formalisms ... • State space representation and production rules. • Constraint-based representations. • First-order predicate Logic.

  33. … each with their corresponding general purpose problem solving techniques: • State space representation an production rules. • Search methods • Constraint based formulations. • Backtracking and Constraint-processing • First order predicate Logic. • Automated reasoning (logical inference)

  34. Contents (4):Some application area’s: • Game playing (in chapter on Search) • Image understanding (in chapter on constraints) • Language understanding (constraints) • Expert systems (in chapter on logic) • Planning • Machine learning

  35. Neural Nets Empirical-Experimental AI Algorithms in AI Formal methods in AI Cognitive aspects of AI Applications Probabilistics and Information Theory Aims: • Many different angles could be taken:

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