Artificial Intelligence and interdisciplinarity

Bert Kappen

Symposium Neuroscience

Oktober 2012

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Wat is intelligentie?

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

"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

Is intelligent gedrag verenigbaar met de wetten van de natuur?

2. Principieel perspectief

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Plenz Chialvo 2010

Complexe dynamische systemen zijn deterministischen onvoorspelbaar

Menselijk gedrag is mechanisch en onvoorspelbaar

Intelligent gedrag vereist complexe berekeningen en efficiente algoritmes

Patroonherkenning, redeneren, leren, control,…..

3. Praktisch perspectief

The digital age

Unification of AI

Chess playing as search (Shannon, 1950)

Logic Theorist (Newell and Simon, 1956)

Learning checkers player (Samuel, 1952)

Intelligent machines

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

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)

disease=yes,no test=yes,no

d

t

Bayes’rule

p(d=1)=0.01 p(d=0)=0.99p(t=1|d=1)=0.95 p(t=0|d=1)=0.05P(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=0.0095p(t=1)=p(t=1|d=0)p(d=0)+p(t=1|d=1)p(d=1)=0.05*0.99+0.95*0.01=0.059p(d=1|t=1)=0.0095/0.059=0.16

Bayes Rule

•Learning:

–X parameters

–Y training data

p(x|y)=p(y|x)p(x)/p(y)

Bayes Rule

•Inference:

–X diagnoses

–Y patient findings

p(x|y)=p(y|x)p(x)/p(y)

Bayes Rule

•Localization:

–X locations

–Y images

database

PCA

Off-line

x

y

p(x|y)=p(y|x)p(x)/p(y)

Graphical models

What are probabilities given evidence:

Intractable for large number of variables: 2n for binary variables

Unification of AI

Most interesting problems are hard

10 1 sec

20 20.000 sec

30 15 year

40 300.000 year

50 1010 year

Complexity

Methods from physics help out

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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Result after 100 trials of motor babblingNo model assumed

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Application in robotics

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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 physics38

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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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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’.

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