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Artificial Intelligence and interdisciplinarity Bert Kappen Symposium Neuroscience Oktober 2012

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Wat is intelligentie? –Evolutionair: hoe is het ontstaan? –Principieel: hoe kan het bestaan? –Praktisch: hoe maak je het?

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"Een rol van intelligentie is om onze waarnemingen aan te vullen waar deze tekort schieten, en is het gevolg van de beperkingen van onze zintuigen en van de onzekerheid in de wereld om ons heen." 1. Evolutionair perspectief

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Intelligentie is een vorm van patroonherkenning

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Is intelligent gedrag verenigbaar met de wetten van de natuur? 2. Principieel perspectief

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Plenz Chialvo 2010

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Complexe dynamische systemen zijn deterministisch en onvoorspelbaar Menselijk gedrag is mechanisch en onvoorspelbaar

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Intelligent gedrag vereist complexe berekeningen en efficiente algoritmes Patroonherkenning, redeneren, leren, control,….. 3. Praktisch perspectief

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Aristotle Syllogic (Aristotle, 350 BC) 'All men are mortal, and no gods are mortal, therefore no men are gods.'

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From Lull (1274) to Leibniz (1670) "It is unworthy of excellent men to lose hours like slaves in the labor of calculation, which could be safely relegated to anyone else if machines were used."

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Talking heads (Albertus Magnus, 13 th C.)

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Descartes ( ) Dualism: proposed to place the rational mind separate from the body.

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AI as an engineering problem Can we obtain the result of 10 8 years of evolution by design or learning?

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The digital age

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Unification of AI

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Chess playing as search (Shannon, 1950) Logic Theorist (Newell and Simon, 1956) Learning checkers player (Samuel, 1952) Intelligent machines

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General purpose search (brute force) not only faces complexity, but also gives non-sense results: "the spirit is willing but the flesh is weak" "the wodka is good but the meat is rotten" Expert systems

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The brain is a universal Turing Machine (McCulloch & Pitts, 1943) Neural networks Multi-layered networks of the ’80s could learn anything (given sufficient data) Many successful applications

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p(ab)=p(a|b)p(b) p(a)+p(not a)=1 Modern AI uses probability theory Bayes rule: p(b|a)p(a)=p(a|b)p(b)

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disease=yes,no test=yes,no d t Bayes’rule p(d=1)=0.01 p(d=0)=0.99 p(t=1|d=1)=0.95 p(t=0|d=1)=0.05 P(t=1|d=0)=0.05 p(t=0|d=0)=0.05 A disease has prevalence of 1 %. A test has an accuracy of 95 % John does the test and the result is positive. What is the probability that John has the disease? P(d=1|t=1) = p(t=1|d=1) p(d=1)/p(t=1) p(t=1|d=1) p(d=1)=0.95*0.01= p(t=1)=p(t=1|d=0)p(d=0)+p(t=1|d=1)p(d=1)=0.05* *0.01=0.059 p(d=1|t=1)=0.0095/0.059=0.16

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Bayes Rule Learning: –X parameters –Y training data p(x|y)=p(y|x)p(x)/p(y)

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Bayes Rule Inference: –X diagnoses –Y patient findings p(x|y)=p(y|x)p(x)/p(y)

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Bayes Rule Localization: –X locations –Y images database PCA Off-line x y p(x|y)=p(y|x)p(x)/p(y)

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Graphical models What are probabilities given evidence: Intractable for large number of variables: 2 n for binary variables

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Unification of AI

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Most interesting problems are hard 101 sec sec 3015 year year year Complexity

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Methods from physics help out

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Control as inference Probe the system with uncontrolled trajectories Choose the ones that are most successful Steer according to their initial direction Improve probing and iterate

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36 Result after 100 trials of motor babbling No model assumed

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37 Application in robotics 37

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38 Summary Research on intelligence is interdisciplinary: –Computer science, physics, neuroscience, engineering, robotics, statistics, mathematics Real progress is hard: –Hard mono-disciplinary problems Complexity of computation, “what does the brain compute?” –Interdisciplinary paradigm clashes and resolutions Bayesian revolution, Control as Statistical physics 38

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39 Interdisciplinary research Interdisciplinary research profits from low hanging fruit Society wants quick results Mono-disciplinary ‘Deep and slow’ Established research paradigm Inter-disciplinary ‘Shallow and fast’ no established research paradigm

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40 Interdisciplinary research The role of industry –Industry has limited vision of fundamental research (top sectoren beleid) –Participation of companies in publicly funded research sometimes confuses ‘relevance for society’ with ‘relevance for the company’. 40

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