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Wrap UpWrap Up
Introduction toIntroduction toArtificial IntelligenceArtificial Intelligence
COS302COS302
Michael L. LittmanMichael L. Littman
Fall 2001Fall 2001
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AdministrationAdministration
Laptop four is busted.Laptop four is busted.
Course evaluations.Course evaluations.
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PlanPlan
LSI and autoassociationLSI and autoassociation
Topic listTopic list
Second half ThemesSecond half Themes
Wrap upWrap up
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Passage AutoassociatorPassage Autoassociator
xx11
hh11
xx22 xx33 xx44
hh22
U (nxk)U (nxk)
UUT T (kxn)(kxn)
xx11 xx22 xx33 xx44
word featuresword features
capture “gist” so it capture “gist” so it can be can be reconstructedreconstructed
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We’ll Make Computers We’ll Make Computers Brighter Brighter (sorry Billy Joel)(sorry Billy Joel)
Gang and Kedar relaxationGang and Kedar relaxation color graphs by propagationcolor graphs by propagation
Neighbor goal state Davis Putnam Neighbor goal state Davis Putnam change to CNFchange to CNF
CSP & satisfactionCSP & satisfaction forward checking giving tractionforward checking giving traction
BFS and DFS andBFS and DFS andA* is the bestA* is the best
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Verse 2Verse 2
Local search save timeLocal search save time optimize hill climboptimize hill climb
Nim Sharks GSATNim Sharks GSAT chess chess evaluation hacksevaluation hacks
Alpha beta game treeAlpha beta game tree prune prune the branch and you’ll seethe branch and you’ll see
Fitness function reproduction phase Fitness function reproduction phase transition min-maxtransition min-max
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ChorusChorus
We’ll make computers brighterWe’ll make computers brighter
With their “and” and “or”ingWith their “and” and “or”ing
They’ll beat Alan Turing.They’ll beat Alan Turing.
We’ll make computers brighterWe’ll make computers brighter
Smarter than a porpoiseSmarter than a porpoise
Train it with a corpusTrain it with a corpus
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Verse 3Verse 3
Markov model Chinese RoomMarkov model Chinese RoomNim-Rand in a matrix formNim-Rand in a matrix form
Labeled data, missing dataLabeled data, missing datalogic of Boolelogic of Boole
Discount rank list Discount rank list Altavista Altavista WordnetWordnet
IR bag-of-words IR bag-of-words flip around with Bayes Ruleflip around with Bayes Rule
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Verse 4Verse 4
Rap song n-queenRap song n-queenSusan swallowed something greenSusan swallowed something green
Language model classifyLanguage model classify take take a max and multiplya max and multiply
Zipf’s law trigramsZipf’s law trigramssmooth it out with bigramssmooth it out with bigrams
Naïve Bayes winning playsNaïve Bayes winning plays write write another programanother program
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ChorusChorus
We’ll make computers brighterWe’ll make computers brighter
With their “and” and “or”ingWith their “and” and “or”ing
They’ll beat Alan Turing.They’ll beat Alan Turing.
We’ll make computers brighterWe’ll make computers brighter
Smarter than a porpoiseSmarter than a porpoise
Train it with a corpusTrain it with a corpus
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Verse 5Verse 5
HMM part-of-speechHMM part-of-speechthey will learn if you will teachthey will learn if you will teach
Baum-Welch if you pleaseBaum-Welch if you please Rush Rush Hour and AnalogiesHour and Analogies
Weighted coins network lagsWeighted coins network lagsViterbi finds most likely tagsViterbi finds most likely tags
Start running watch for tiesStart running watch for tiesexpectation maximizeexpectation maximize
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Verse 6Verse 6
Learning function featureLearning function featuretry learning who’s a girltry learning who’s a girl
overfit PAC boundsoverfit PAC boundsbuild your own decision treebuild your own decision tree
Too big memorize Too big memorize cross validate to generalizecross validate to generalize
supervising pure leafsupervising pure leaf fit fit the function to a ‘t’the function to a ‘t’
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ChorusChorus
We’ll make computers brighterWe’ll make computers brighter
With their “and” and “or”ingWith their “and” and “or”ing
They’ll beat Alan Turing.They’ll beat Alan Turing.
We’ll make computers brighterWe’ll make computers brighter
Smarter than a porpoiseSmarter than a porpoise
Train it with a corpusTrain it with a corpus
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Verse 7Verse 7
Delta rule backpropDelta rule backprophidden unit don’t stophidden unit don’t stop
Learn pairs sum squaresLearn pairs sum squareslinear perceptronlinear perceptron
Error surface is quite bentError surface is quite bentjust descend the gradient and-or just descend the gradient and-or sigmoidsigmoidnonlinear regressionnonlinear regression
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Bridge/ChorusBridge/Chorus
XOR learning rateXOR learning ratesquash the sum and integratesquash the sum and integrate
Neural net split the setNeural net split the setHow much better can it get?How much better can it get?
ChorusChorus: We’ll make computers brighter: We’ll make computers brighterWith their “and” and “or”ingWith their “and” and “or”ingThey’ll beat Alan Turing.They’ll beat Alan Turing.We’ll make computers brighterWe’ll make computers brighterSmarter than a porpoiseSmarter than a porpoiseTrain it with a corpusTrain it with a corpus
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Verse 8Verse 8
LSI max and minLSI max and minlocal search is back againlocal search is back again
TOEFL pick bestTOEFL pick best essay essay grade pass the testgrade pass the test
Matrix Plato SVDMatrix Plato SVD vectors vectors all in high Dall in high D
All the points are in the spaceAll the points are in the spacedon’t you make an Eigenface!don’t you make an Eigenface!
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Verse 9Verse 9
Segmentation Saturn spinSegmentation Saturn spinProbabilities a winProbabilities a win
Q and P iterativelyQ and P iterativelyBayes net CPTBayes net CPT
Autopilots in the skyAutopilots in the skyPlay chess masters for a tiePlay chess masters for a tieAlan Alda robots cryAlan Alda robots cryAll of this is in AI!All of this is in AI!
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ChorusChorus
We’ll make computers brighterWe’ll make computers brighter
With their “and” and “or”ingWith their “and” and “or”ing
They’ll beat Alan Turing.They’ll beat Alan Turing.
We’ll make computers brighterWe’ll make computers brighter
Smarter than a porpoiseSmarter than a porpoise
Train it with a corpusTrain it with a corpus
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Final ChorusFinal Chorus
We’ll make computers brighterWe’ll make computers brighter
Hope you had some funHope you had some fun
Because the class is done, is done, is Because the class is done, is done, is done…done…
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Probability ModelsProbability Models
Compare and contrastCompare and contrast• unigramunigram• bigrambigram• trigramtrigram• HMMHMM• belief netbelief net• Naïve BayesNaïve Bayes
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Classification AlgsClassification Algs
Compare and contrastCompare and contrast• Naïve BayesNaïve Bayes• decision treedecision tree• perceptronperceptron• multilayer neural netmultilayer neural net
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Missing DataMissing Data
Compare and contrastCompare and contrast• EMEM• SVDSVD• gradient descentgradient descent
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Dynamic ProgrammingDynamic Programming
Compare and contrastCompare and contrast• ViterbiViterbi• forward-backwardforward-backward• minimaxminimax• Markov chains (value iteration)Markov chains (value iteration)• segmentationsegmentation
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Big Picture: This is AI?Big Picture: This is AI?
We learned a bunch of programming We learned a bunch of programming tricks: people still do the hard work.tricks: people still do the hard work.
• Yeah, that’s true.Yeah, that’s true.• It’s not such a bad thing: building more It’s not such a bad thing: building more
flexible software (search, learning).flexible software (search, learning).• We’re working towards more We’re working towards more
independent artifacts; not there yetindependent artifacts; not there yet
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Thank You!Thank You!
Thanks for your help!Thanks for your help!• Gang and KedarGang and Kedar• Lisa and the kidsLisa and the kids• AT&TAT&T• Andrew MooreAndrew Moore• Family Feud survey respondentsFamily Feud survey respondents• Peter StonePeter Stone• all of you!all of you!