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Artificial Intelligence

Artificial Intelligence. INTRODUCTION to. … Artificial Intelligence ... this course … state-space representation … basic search. The mind beaten by the machine?. Is chess playing a proof of intelligent behaviour?. Today : Watson wins Jeopardy. Requires _very_ much knowledge.

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Artificial Intelligence

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  1. Artificial Intelligence INTRODUCTION to... … Artificial Intelligence ... this course … state-space representation … basic search

  2. The mind beatenbythe machine? • Is chess playing a proof of intelligent behaviour?

  3. Today: Watson winsJeopardy Requires _very_ much knowledge

  4. More recent: deeplearning Solves problems believed to be unsolvable

  5. Other examples of success: Chatbot Alice:

  6. 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.

  7. 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. ...

  8. Is Alice intelligent? • ABSOLUTELY NOT ! • ~ 150 000 fairly trivial input-response rules. • + some pattern matching • + some knowledge • + some randomness • NO reasoning component • BUT: demonstrates ‘human-like’ behaviour. • Won the ‘turing award’

  9. Robot Scientist Eve, an artificially-intelligent ‘robot scientist’, can make drug discovery faster and much cheaper. Decides on research questions. Sets up the experiments. Analyses the results. Reasons on the results. Sets up new questions.

  10. Data mining: • An application of Machine Learning techniques • It solves problems that humans can not solve, because the data involved is too large .. Molecules are graphs Patterns are subgraphs Search for “best”/ most relevant patterns cf. Robot Scientist …

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

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

  13. And :

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

  15. 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?

  16. 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’?

  17. 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

  18. The long term goal: The Turing Test

  19. If the long term goals of AI ever get reached • Then: the computer will be able to improve itself, much faster than humans could improve it, • It would quickly become _much_ more intelligent than humans. The Singularity • Recent debate (Steve Hawking, Bill Gates, …) • A real danger for humanity … ?

  20. 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 !

  21. Artificial Intelligence versus Natural Flight

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

  23. 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.

  24. 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

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

  26. 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 ????

  27. Knowledge Graphs • Automatically constructed by Google and others • Drive many AI systems today

  28. Two types of AI • System 1 — thinking fast —  • can do things like solve “2+2=?” and recognise a car  • System 2 — thinking slow —  • can reason about complex logic problems (IQ tests) and reason about priority in traffic • Alternative terms: learning vs reasoning, data-driven vs knowledge driven, symbolic vs subsymbolic • Real AI needs both reasoning and learning ! 

  29. Answering Exam Questions Learning and reasoning needed (Leuven Example) – cf. Allen AI Institute

  30. 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,...

  31. 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

  32. About this course ...

  33. Choice of thematerial. • Somebooks: • P.Winston ( “Artificial Intelligence’’): • didactically VERY good, but lackstechnicaldepth. Outdated. • Russell & Norvig ( ‘”AI: a modern approach’’): • verygood, a bit encyclopedic, recommended • Poole et. Al (‘ “Computational Intelligence’’): • veryformalandtechnical. Goodfor logic. • Part DDS: Selectionandsynthesis of the best parts of different books. • Part LDR: based on Norvig & Russel

  34. Technically: the contents • - Search techniques in AI • (Including games) • - Machine Learning & Neural Nets • - Automated Reasoning • - Constraint processing • - Probabilistic Reasoning & Planning

  35. Contents:Different (knowledge) representation formalisms ... • State space representation and production rules. • Constraint-based representations. • First-order predicate Logic. • Bayesian networks & Markov Decision Processes • Neural Networks

  36. … 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) • Solvers for crisp computational problems • satisfiability and model counting

  37. Concrete aims: • Provide insight in the basic achievements of AI. • Prepares for more application oriented courses on AI, or on self-study in some application areas • ex.: artificial neural networks, machine learning, computer vision, natural language, etc. • Through case-studies: provide more background in ‘problem solving’. • Mostly algorithmic aspects. • Also techniques for representing and modeling.

  38. Practical info • Exercises: 35 hours: • mainlypractice on themainmethods/algorithmspresented in the course • important preparationforthe examination • Course material: • copies of detailed slides • forsomeparts: supportingtexts • for part LDR: Russell & Norvig • Required background: • understanding of algorithms (andrecursion)

  39. Background Texts The basics, but no complexity IDA*, SMA* Almost complete The essence Complete Ch 13, 15, 17, 18 Introduction: State-space Intro: Basic search,Heuristic search: Optimal search: Advanced search: Games: Version Spaces: Constraints I & II: Automated reasoning: All other parts: No document No document Winston: Ch. Basic search Winston: Ch. Optimal search Russel: Ch. 4 Winston: Ch. Adversary search Winston: Ch. Learning by managing.. Word Document on web page No document Russell & Norvig

  40. AI: Examination • Closed-bookwritten examination • About 1/3 theoryquestions, 2/3 exercises

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