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ModernArtificialIntelligenceandItsImportanceintheFutureWorldZengchangQin(Ph.D.)IntelligentComputingandMachineLearningLabSchoolofAutomationandElectricalEngineeringBeihangUniversityShaheCampusOct272010ModernArtificialIntelligenceThisisScienceThisisScienceGiveabigpictureofmodernArtificialIntelligenceandunderstandwhyitisimportantinthecurrentandthefutureworld.WehavesuchadirectionofresearchintheschoolofASEE.ToclarifythemisunderstandingofA.I.fromthoserobotmoviesandsciencefictions.AboutThisTalkGiveabigpictureofmodernAIhavebeenworkinginA.I.areforthepastdecade.Ienjoymoviesandunboundedthinking.Iamalwaysintriguedbyanykindsfexcellentideasfromhumanintelligence.Feelfreetoaskanyquestionsyouhaveinmind,noguaranteetobeanswered.AboutTheSpeakerAboutTheSpeakerMisunderstandingArtificialIntelligence(A.I.)≠RoboticsJohnMcCarthy(Stanford)MisunderstandingArtificialIntArtificialIntelligence–Wefear?ArtificialIntelligence–WefI,RobotTheThreeLawsofRoboticsbyIssacAsimov

areasthefollows:Arobotmaynotinjureahumanbeingor,throughinaction,allowahumanbeingtocometoharm.Arobotmustobeyanyordersgiventoitbyhumanbeings,exceptwheresuchorderswouldconflictwiththeFirstLaw.ArobotmustprotectitsownexistenceaslongassuchprotectiondoesnotconflictwiththeFirstorSecondLaw.I,RobotTheThreeLawsofRoboMyPhilosophyofModernA.I.ArtificialIntelligenceisamathematical/computingtechnologythatwillmakelifebetter.Ihavebeeninterestedinmakingmachinesintelligentbydesigningalgorithms.Imaynotbelievethatonedaywecanrecreatehumanbrainsusingsiliconchips,butIbelievethatcomputingwillaidourbrainstodomissionsimpossibleinthefuture.MyPhilosophyofModernA.I.ArChineseRoomParadoxChineseRoomParadoxModernA.I.–TheEngineeringApproach:MachineLearningandDataMiningPatternRecognition,ComputervisionandImageProcessingDistributedA.I./multi-agentsystemsBiometricsandcomputerforensicsNaturalLanguageProcessingIntelligentSearchandInformationRetrievalComputationalCognitiveScienceComputationalNeuroscienceandbioinformaticsComputationalCognitiveScienceComputational/BehaviorFinanceBehaviorTargetingandPersonalServicesDigitalAdvertisements/recommendationsystemsModernA.I.–TheEngineeringPhilosophyofMachineLearningMachineLearning–searchinthehypothesisspacetofindtheonesthatmatchthedata.UsingOccam’srazor,wechoosethesimplestone.WilliamofOckham(orOccam)wasa14th-centuryEnglishlogicianandFranciscanfriarwho'snameisgiventotheprinciplethatwhentryingtochoosebetweenmultiplecompetingtheoriesthesimplesttheoryisprobablythebest.ThisprincipleisknownasOckham'srazor.PhilosophyofMachineLearningExampleExampleExample2Example2WhyMachineLearningisimportant?Tofinethetheorythatexplainsthedata,weusuallypreferthesimpleones.Machinelearningandscientificdiscoverysharesimilarities.KarlPopperWhyMachineLearningisimportLogicProgrammingLondonUndergroundExampleLogicProgrammingLondonUndergFuzzyLogicFuzzyLogicMembershipfunction(continuous)Membershipfunction(continuouMembershipFunctionsMembershipFunctionsSomeIntuitionSomeIntuitionProfessorofFuzzyLogicProfessorofFuzzyLogicMulti-agentSystemDistributedA.I.-coordinationMulti-agentSystemDistributedDatamining

istheprocessofextractingpatternsfromdata-Torturethedatauntiltheyconfess.Dataiseverywhereandindifferenttypes.PatternRecognitionandDataMiningPatternRecognitionandDataM

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RE:SLTheadcount=25In-reply-to

<.0.20040607101523.02623298@imap.eecs.B>To

'RandyKatz'<randy@>Cc

"'GlendaJ.Smith'"<glendajs@>,'GertLanckriet'<gert@>Message-id

<200406081840.i58IegFp007613@relay3.EECS.Berkeley.EDU>MIME-version

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----------------------------------------RobertMiller,AdministrativeSpecialistUniversityofCalifornia,BerkeleyElectronicsResearchLab634SodaHall#1776Berkeley,CA

94720-1776Phone:510-642-6037fax:

510-643-1289<!DOCTYPEHTMLPUBLIC"-//24MedicalImage,handwrittenrecognition24MedicalImage,handwrittenr25Sounds-fingerprints25Sounds-fingerprints26IntelligentSearchandBio-identity26IntelligentSearchandBio-iMirco-arrayDataofGenesMirco-arrayDataofGenesDrugDesignsDrugDesignsComputerHumanInterface–EEGsignalsComputerHumanInterface–EEGStockIndexStockIndexDataTypes–frauddetectionDataTypes–frauddetectionSocialNetworkMiningMonitoringfluthroughtwitter.Monitoringtrafficthroughmobilecalls.SocialNetworkMiningEntityCubeEntityCube34ExperimentalEconomicsVernonL.Smith"forhavingestablishedlaboratoryexperimentsasatoolinempiricaleconomicanalysis,especiallyinthestudyofalternativemarketmechanisms”From/34ExperimentalEconomics"foBehaviorEconomics–IrrationalAgentsNotableforhisworkonthepsychologyofjudgmentsanddecisionmaking,behavioraleconomics.Winning$10or$1000withchanceof1%.Losing$10or$1000withchanceof1%BehaviorEconomics–IrrationaSoftwareAgentsforTradingSoftwareAgentsforTradingWhatisthecapitalofChina?WhatisthepopulationofBeijing?WhatisthepopulationofthecapitalofChina?ReasoningwithNaturalLanguageReasoningwithNaturalLanguEvolutionaryComputingGeneticAlgorithmSirRichardDawkins“TheselfishGenes”EvolutionaryComputingGeneticStochasticOptimizationStochasticOptimizationCellularAutomatonWolframwaseducatedat

Eton.Attheageof15,hepublishedanarticleon

particlephysics[4]

andentered

OxfordUniversity

atage17.Hewroteawidelycitedpaperonheavy

quark

productionatage18.[2]Wolframreceivedhis

Ph.D.

inparticlephysicsfromthe

CaliforniaInstituteofTechnology

atage20[5]

andjoinedthefacultythere.Hebecamehighlyinterestedin

cellularautomata

atage21.[2]

Wolfram'sworkinparticlephysics,cosmologyandcomputerscienceearnedhimoneofthefirst

MacArthurawards.CellularAutomatonWolframwasDecisionTreesDecisionTreesP(h|e)=P(e|h)P(h)/P(e)AProofthateveryonecanunderstandP(h,e)=P(h|e)P(e)P(e,h)=P(e|h)P(h)BayesianStatisticsBayesianStatisticsGraphicalModelofGaussianDistributionandHiearachicalStructurewithLatentVariables

GraphicalModelofGaussianDiUnderstandingSemanticsUnderstandingSemantics人工智能詳解課件人工智能詳解課件人工智能詳解課件人工智能詳解課件人工智能詳解課件Demographics–MSAdCenterLabDemographics–MSAdCenterLabCommercialIntentionsofGivenWebsiteCommercialIntentionsofGiven人工智能詳解課件人工智能詳解課件人工智能詳解課件人工智能詳解課件Ifyouwanttosellone,whatisthebestprice?N97(NokiaPhone)N97(NokiaPhone)MinorityGameEIFarolBarMinorityGameModelApplicationInRealworldTherearemorethan100IrishmusicloversbutElFarolhasonly60seats.Theshowisenjoyableonlywhenfewerthan60peopleshowup.Everypeopleshoulddecideweeklywhethergotothebartoenjoylivemusicintheriskofstayinginacrowdplaceorstayathome.Therulesaresimple:afinitenumberofplayershavetochoosebetweentwosides;whoeverendsupintheminoritysideisawinner.SimplifiedfrommarketaimingtoanalyzecomplexfinancialmarketMinorityGameEIFarolBarMinorCollectiveBehaviorDecompositionCollectiveBehaviorDecompositSimulationResults(Li,MaandQin,2010)SimulationResults(Li,Maand人工智能詳解課件人工智能詳解課件YingMa,GuanyiLi,YingsaiDongandZengchangQin(2010),Minoritygamedataminingformarketpredictions,forStockMarketPredictions,toappearintheProceedingsofAAMAS2010.GuanyiLi,YingMa,YingsaiDongandZengchangQin(2010),Behaviorlearninginminoritygames,ToappearintheProceedingsofCARE2009.ZengchangQin,MarcusThintandZhihengHuang(2009),Rankinganswersbyhierarchicaltopicmodels,ProceedingsofIEA/AIE2009,LNCS5579,pp.103-112,Springer.ZhihengHuang,MarcusThintandZengchangQin(2008),Questionclassificationusingheadwordsandtheirhypernyms,TheProceedingsofConferenceonEmpiricalMethodsonNaturalLanguageProcessing,pp.927-936,ACL.ReferencesYingMa,GuanyiLi,YingsaiDoNon-academicNon-academicAcademicAIAcademicAIFuzzyLogicandLogicofScienceFuzzyLogicandLogicofScienNLP&ANNNLP&ANNGA,ALIFE&Multi-agentGA,ALIFE&Multi-agentWeb:orGoogle“ZengchangQin”formyLinkedInProfiles.ContactInformationWeb:orGoogThankyouverymuch!Anyquestions?人工智能詳解課件ModernArtificialIntelligenceandItsImportanceintheFutureWorldZengchangQin(Ph.D.)IntelligentComputingandMachineLearningLabSchoolofAutomationandElectricalEngineeringBeihangUniversityShaheCampusOct272010ModernArtificialIntelligenceThisisScienceThisisScienceGiveabigpictureofmodernArtificialIntelligenceandunderstandwhyitisimportantinthecurrentandthefutureworld.WehavesuchadirectionofresearchintheschoolofASEE.ToclarifythemisunderstandingofA.I.fromthoserobotmoviesandsciencefictions.AboutThisTalkGiveabigpictureofmodernAIhavebeenworkinginA.I.areforthepastdecade.Ienjoymoviesandunboundedthinking.Iamalwaysintriguedbyanykindsfexcellentideasfromhumanintelligence.Feelfreetoaskanyquestionsyouhaveinmind,noguaranteetobeanswered.AboutTheSpeakerAboutTheSpeakerMisunderstandingArtificialIntelligence(A.I.)≠RoboticsJohnMcCarthy(Stanford)MisunderstandingArtificialIntArtificialIntelligence–Wefear?ArtificialIntelligence–WefI,RobotTheThreeLawsofRoboticsbyIssacAsimov

areasthefollows:Arobotmaynotinjureahumanbeingor,throughinaction,allowahumanbeingtocometoharm.Arobotmustobeyanyordersgiventoitbyhumanbeings,exceptwheresuchorderswouldconflictwiththeFirstLaw.ArobotmustprotectitsownexistenceaslongassuchprotectiondoesnotconflictwiththeFirstorSecondLaw.I,RobotTheThreeLawsofRoboMyPhilosophyofModernA.I.ArtificialIntelligenceisamathematical/computingtechnologythatwillmakelifebetter.Ihavebeeninterestedinmakingmachinesintelligentbydesigningalgorithms.Imaynotbelievethatonedaywecanrecreatehumanbrainsusingsiliconchips,butIbelievethatcomputingwillaidourbrainstodomissionsimpossibleinthefuture.MyPhilosophyofModernA.I.ArChineseRoomParadoxChineseRoomParadoxModernA.I.–TheEngineeringApproach:MachineLearningandDataMiningPatternRecognition,ComputervisionandImageProcessingDistributedA.I./multi-agentsystemsBiometricsandcomputerforensicsNaturalLanguageProcessingIntelligentSearchandInformationRetrievalComputationalCognitiveScienceComputationalNeuroscienceandbioinformaticsComputationalCognitiveScienceComputational/BehaviorFinanceBehaviorTargetingandPersonalServicesDigitalAdvertisements/recommendationsystemsModernA.I.–TheEngineeringPhilosophyofMachineLearningMachineLearning–searchinthehypothesisspacetofindtheonesthatmatchthedata.UsingOccam’srazor,wechoosethesimplestone.WilliamofOckham(orOccam)wasa14th-centuryEnglishlogicianandFranciscanfriarwho'snameisgiventotheprinciplethatwhentryingtochoosebetweenmultiplecompetingtheoriesthesimplesttheoryisprobablythebest.ThisprincipleisknownasOckham'srazor.PhilosophyofMachineLearningExampleExampleExample2Example2WhyMachineLearningisimportant?Tofinethetheorythatexplainsthedata,weusuallypreferthesimpleones.Machinelearningandscientificdiscoverysharesimilarities.KarlPopperWhyMachineLearningisimportLogicProgrammingLondonUndergroundExampleLogicProgrammingLondonUndergFuzzyLogicFuzzyLogicMembershipfunction(continuous)Membershipfunction(continuouMembershipFunctionsMembershipFunctionsSomeIntuitionSomeIntuitionProfessorofFuzzyLogicProfessorofFuzzyLogicMulti-agentSystemDistributedA.I.-coordinationMulti-agentSystemDistributedDatamining

istheprocessofextractingpatternsfromdata-Torturethedatauntiltheyconfess.Dataiseverywhereandindifferenttypes.PatternRecognitionandDataMiningPatternRecognitionandDataM

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RobertMiller<bmiller@>Subject

RE:SLTheadcount=25In-reply-to

<.0.20040607101523.02623298@imap.eecs.B>To

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"'GlendaJ.Smith'"<glendajs@>,'GertLanckriet'<gert@>Message-id

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AcRMtQRp+R26lVFaRiuz4BfImikTRAA0wf3Qtheheadcountisnow32.

----------------------------------------RobertMiller,AdministrativeSpecialistUniversityofCalifornia,BerkeleyElectronicsResearchLab634SodaHall#1776Berkeley,CA

94720-1776Phone:510-642-6037fax:

510-643-1289<!DOCTYPEHTMLPUBLIC"-//93MedicalImage,handwrittenrecognition24MedicalImage,handwrittenr94Sounds-fingerprints25Sounds-fingerprints95IntelligentSearchandBio-identity26IntelligentSearchandBio-iMirco-arrayDataofGenesMirco-arrayDataofGenesDrugDesignsDrugDesignsComputerHumanInterface–EEGsignalsComputerHumanInterface–EEGStockIndexStockIndexDataTypes–frauddetectionDataTypes–frauddetectionSocialNetworkMiningMonitoringfluthroughtwitter.Monitoringtrafficthroughmobilecalls.SocialNetworkMiningEntityCubeEntityCube103ExperimentalEconomicsVernonL.Smith"forhavingestablishedlaboratoryexperimentsasatoolinempiricaleconomicanalysis,especiallyinthestudyofalternativemarketmechanisms”From/34ExperimentalEconomics"foBehaviorEconomics–IrrationalAgentsNotableforhisworkonthepsychologyofjudgmentsanddecisionmaking,behavioraleconomics.Winning$10or$1000withchanceof1%.Losing$10or$1000withchanceof1%BehaviorEconomics–IrrationaSoftwareAgentsforTradingSoftwareAgentsforTradingWhatisthecapitalofChina?WhatisthepopulationofBeijing?WhatisthepopulationofthecapitalofChina?ReasoningwithNaturalLanguageReasoningwithNaturalLanguEvolutionaryComputingGeneticAlgorithmSirRichardDawkins“TheselfishGenes”EvolutionaryComputingGeneticStochasticOptimizationStochasticOptimizationCellularAutomatonWolframwaseducatedat

Eton.Attheageof15,hepublishedanarticleon

particlephysics[4]

andentered

OxfordUniversity

atage17.Hewroteawidelycitedpaperonheavy

quark

productionatage18.[2]Wolframreceivedhis

Ph.D.

inparticlephysicsfromthe

CaliforniaInstituteofTechnology

atage20[5]

andjoinedthefacultythere.Hebecamehighlyinterestedin

cellularautomata

atage21.[2]

Wolfram'sworkinparticlephysics,cosmologyandcomputerscienceearnedhimoneofthefirst

MacArthurawards.CellularAutomatonWolframwasDecisionTreesDecisionTreesP(h|e)=P(e|h)P(h)/P(e)AProofthateveryonecanunderstandP(h,e)=P(h|e)P(e)P(e,h)=P(e|h)P(h)BayesianStatisticsBayesianStatisticsGraphicalModelofGaussianDistributionandHiearachicalStructurewithLatentVariables

GraphicalModelofGaussianD

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