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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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RobertMiller<bmiller@>Subject
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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AcRMtQRp+R26lVFaRiuz4BfImikTRAA0wf3Qtheheadcountisnow32.
----------------------------------------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人工智能详解课件人工智能详解课件人工智能详解课件人工智能详解课件人工智能详解课件Demographics–MSAdCenterLabDemographics–MSAdCenterLabCommercialIntentionsofGivenWebsiteCommercialIntentionsofGiven人工智能详解课件人工智能详解课件人工智能详解课件人工智能详解课件Ifyouwanttosellone,whatisthebestprice?N97(NokiaPhone)N97(NokiaPhone)MinorityGameEIFarolBarMinorityGameModelApplicationInRealworldTherearemorethan100IrishmusicloversbutElFarolhasonly60seats.Theshowisenjoyableonlywhenfewerthan60peopleshowup.Everypeopleshoulddecideweeklywhethergotothebartoenjoylivemusicintheriskofstayinginacrowdplaceorstayathome.Therulesaresimple:afinitenumberofplayershavetochoosebetweentwosides;whoeverendsupintheminoritysideisawinner.SimplifiedfrommarketaimingtoanalyzecomplexfinancialmarketMinorityGameEIFarolBarMinorCollectiveBehaviorDecompositionCollectiveBehaviorDecompositSimulationResults(Li,MaandQin,2010)SimulationResults(Li,Maand人工智能详解课件人工智能详解课件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?人工智能详解课件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
<!DOCTYPEHTMLPUBLIC"-//W3C//DTDHTML4.0Transitional//EN"><html><head> <metahttp-equiv="Content-Type"content="text/html;charset=utf-8"> <title>WelcometoFairmontNET</title></head><STYLEtype="text/css">.stdtext{font-family:Verdana,Arial,Helvetica,sans-serif;font-size:11px;color:#1F3D4E;}.stdtext_wh{font-family:Verdana,Arial,Helvetica,sans-serif;font-size:11px;color:WHITE;}</STYLE><bodyleftmargin="0"topmargin="0"marginwidth="0"marginheight="0"bgcolor="BLACK"><TABLEcellpadding="0"cellspacing="0"width="100%"border="0"><TR><TDwidth=50%background="/TFN/en/CDA/Images/common/labels/decorative_2px_blk.gif"> </TD><TD><imgsrc="/TFN/en/CDA/Images/common/labels/decorative.gif"></td><TDwidth=50%background="/TFN/en/CDA/Images/common/labels/decorative_2px_blk.gif"> </TD></TR></TABLE><tr><tdalign="right"valign="middle"><IMGsrc="/TFN/en/CDA/Images/common/labels/centrino_logo_blk.gif"></td></tr></body></html>HTMLandEmailsReturn-path
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Tue,08Jun200411:40:42-0700From
RobertMiller<bmiller@>Subject
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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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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