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AdvancesinArtificialIntelligenceRequire ProgressAcrossallofComputerScience February2017 GregoryD.Hager,RandalBryant,EricHorvitz,MajaMatarić,andVasantHonavar Overthelastdecade,theconstellationofcomputingtechnologiesreferredtoas artificialintelligence(AI)hasemergedintothepublicviewasanimportantfrontier oftechnologicalinnovationwithpotentialinfluencesinmanyrealms.Advancesin manydisciplinesrelatedtoAI,includingmachinelearning,robotics,computer vision,naturallanguageprocessing,inference,decision-making,andplanning,are contributingtonew-fieldedproducts,services,andexperiences.Offeringssuchas navigationsystems,websearch,speechrecognition,machinetranslation,face recognition,andrecommenderengineshavebecomepartofthedailylifeofmillions ofpeople.Otherapplicationscomingtotheforeincludesemi-autonomousand autonomousgroundandairvehicles,systemsthatharnessplanningandscheduling, intelligenttutoring,robotics.Morebroadly,cyber-physicalandroboticsystems, incorporatingvaryingdegreesofAItechnology,arepoisedtobefieldedinavariety ofreal-worldsettings. AlthoughAIwillbeanengineforprogressinmanyareas,creatingreal-world systemsthatrealizetheseinnovationswillinfactrequiresignificantadvancesin virtuallyallareasofcomputing,includingareasthatarenottraditionallyrecognized asbeingimportanttoAIresearchanddevelopment.Indeed,weexpectfutureAI systemswillnotonlydrawfrommethods,tools,andthemesinotherareasof computerscienceresearch,butwillalsoprovidenewdirectionsforresearchin areassuchasefficiency,trustworthiness,transparency,reliability,andsecurity. Thisisnotnew–thehistoryofAIisinextricablyintertwinedwiththehistoryof advancesinbroadercomputerscience(CS)aswellasapplicationsinrelatedareas suchasspeechandlanguage,computervision,robotics,andothertypesof intelligentsystems. Inwhatfollows,wereviewseveralpromisingareasofinteractionbetweenAIand broadercomputersciencethatproviderichopportunitiesaheadforresearchand development.WeincludeopportunitiesthatseemespeciallyimportantasAI systemsbecomemoreubiquitousandareplayingrolescriticaltoourindividualand combinedhealthandwell-being.Inbrief,weseeparticularlyrichopportunitiesfor supportingadvancesinAIviasynergiesandcollaborationwithresearchand developmentinthefollowingareas,describedbrieflybelow,thenfurtherexpanded upon: 1 Computingsystemsandhardware.Thereareopportunitiesaheadforleveraging innovationsincomputingsystemsandhardware.Directionsincludethe developmentofmethodsforspeedingupcorecomputationalproceduresemployed inAIsystems,suchasthemethodsusedtotrainandtoexecuteclassificationfor perceptualtasksusingconvolutionalneuralnetworks.Opportunitiesincludenew approachestoparallelism,smartcaching,andusesofspecializedhardwarelike FPGAstolowercostsofcomputationandtomeetthedemandsandrobustness neededwithAIapplications. Theoreticalcomputerscience.AIwasbuiltontheoreticalworkbasedonthe mathematicsofcomputabilityintheearly20thcenturybyTuring,Church,and others.AIchallengeshavelongposedandfacedcombinatorialchallengesandhas madeuseofresultsontheperformanceandprecisionofapproximationprocedures. TherearecontinuingopportunitiesfortheformalstudyofhardchallengesinAI withtoolsandtechniquesdevelopedintherealmsofanalysisofalgorithms, includingeffortsincombinatorics,computationalcomplexitytheory,andstudiesof computability. Cybersecurity.AIsystemsarebeingdevelopedforhigh-stakessystemsinsuch areasashealthcareandtransportation.Thesesystemsarealsobringingtothefore newattacksurfacesthatneedtobeunderstoodandprotected.Directionsinclude understandingandhardeningsystemstowholenewcategoriesofattackincluding, “machinelearningattacks,”wherecleveradversarialproceduresareemployedto injectdataintosystemsthatwillconfuseorbiasthemintheirintendedoperation. AIsystemsframenewchallengesthatwillrequireadvancesinsecuritythataddress thenewattacksurfacestoensurethattheyaresafe,reliable,robustandsecure againstmaliciousattacks. Formalmethods.Formalmethodscanplayacriticalroleindefiningand constrainingAIsystems,soastoensurethattheirbehaviorconformsto specifications.Effortsincludemethodsfordoingformalverificationofprogramsand alsotoperformreal-timeverificationofsystemsthroughnewkindsofmonitoring. Formalmethodsarepromisingapproachestoensuringthatprogramsdonottake actionsbeyondspecifiedgoalsandconstraints. Programminglanguages,tools,andenvironments.Newprogramminglanguages, tools,andprogrammingenvironmentscanhelpengineerstobuild,test,andrefine AIsystems.Higher-levellanguagescanofferengineersandscientistsnewkindsof abstractionsandpowertoweavetogethermultiplecompetencies,suchasavision, speechrecognition,andnaturallanguageunderstandingsoastobeabletodevelop anddebugprogramsthatrelyontheclosecoordinationofmultipleAIanalytical pipelines. Human-computerinteraction.ThekeychallengeswithAIframenumerous opportunitiesinthebroadrealmofresearchinhuman-computerinteraction(HCI), animportantareaofcomputerscienceresearch.Effortsincludemethodsfor 2 explainingtheresultsofAIsystemstopeople,allowingpeopletoworkinteractively withAIsystems(e.g.,interactivemachinelearning),thathelpwiththespecification, encoding,andunderstandingoftheimplicationsofdifferentpolicies,values,and preferencesassumedbyautomatedsystems,andsupportingnewkindsofhuman-AI collaboration,includingmixed-initiativeinteractionandaugmentinghuman cognition. ComputingSystemsandHardware Researchincomputerhardware,operatingsystems,andcomputernetworkinghave been,andcontinuetobe,criticaltocreatingthelarge-scalesystems,includingAI systemsthatusevastamountsofdatatobuildpredictivemodels,optimizecomplex objectivefunctions,performautomatedinferenceoverlargeknowledgebases,or complexprobabilitydistributions.Someofthelargestcomputerfacilitiesinthe worldareoperatedbyindustrialandgovernmentorganizationstocollectdata, buildpredictivemodelsfromdatausingmachinelearningalgorithms,andthenuse theresultingpredictivemodelstoprovideservicesrangingfromdrivingdirections toshoppingrecommendations. AsAIcontinuestogrow,thedemandsonthesesystemswillalsogrowandchange. Forexample,currentusesofcloudcomputingaretypicallynothardreal-time. Whetherasearchqueryisansweredin0.2sor0.4smaynotmattermuchin practice.However,aqueryfromacarmovingat60mphmayneedtobeanswered withinahardreal-timeconstrainttoensurethesafeoperationoftheautomobile. Historically,specialpurposehardwarearchitecturesforspecifictaskse.g.,computer vision,havebeeninandoutoffashion.However,asweapproachtheendofthe Moore’slaw,performancegainsthatarerequiredforsuccessfuldeploymentofAI systemsinreal-worldapplicationsarelikelytorequireinnovationsinhardwareand systemsaswellasco-designofhardwareandsoftwaretoexploitthespecial featuresofspecializedarchitecturesandsystems.Largeserversystemsalreadyuse GPUsextensivelytosupportmodernlearningalgorithms.Atscale,itmaybecome costandenergyefficienttoincludeevenmorecustomizedcapabilitythatmustbe sharedamongonetoamillionusersdependingonthesysteminquestion. Unlikemanycomputingtasks,machinelearningcanoftenmakeuseofapproximate results.Theymakedecisionsusingstatisticalmodelsandtherecanbeatolerance forsmallcomputationalerrorsintroducedbythehardwareorbyimperfect synchronizationacrosscomputingresources.Thisopensupmanypossibilitiesfor makingthesystemmoreenergyefficientorscalableatthehardware,software,and systemlevels.Achievingthesebenefitswillrequireacarefulcharacterizationofthe natureofthecomputationalerrorsthatcanbeintroducedandtheirimpactonthe overallsystemfunctionality. TheoreticalCS:AnalysisofAlgorithms,Combinatorics,andComplexity 3 AIhasinfluenced,andbenefitedfrom,advancesinalgorithmsinanumberofareas includingautomatedreasoning,search,planning,optimization,andlearning.For example,machinelearning,whichliesattheheartofmanymodernAIsystems, enablesGoogle’sAlphaGoprogramtodevisestrategiesforplayingthegameofGoby analyzingmillionsofmovesmadebyhumansinGotournaments;Amazon’s recommendersystemstoanalyzelargedatasetsoftransactionsandthensuggest productstocustomers;automobilelanetrackingsystemstodetectlanemarkers fromvideoimagesandwarndriverswhentheyareveeringoutoftheirlanes; languagetranslationsystemstogeneratemappingsfromonelanguagetoanotherby processinglargecollectionsofhuman-generatedtranslations.Machinelearning requiresalgorithmsthatconstructsophisticatedpredictivemodelstoofferuseful insightsandactionableknowledgefromlargeandcomplexdatasets.Thedemands ofmachinelearninghasledtoadvancesinalgorithms,especiallyforoptimizationof complexobjectivefunctions,reasoningaboutcomplexprobabilitydistributionse.g., usingfactorizedrepresentations,etc.Withtheemergenceofbigdata,the requirementthatmachinelearningalgorithmsneedtobescalabletomassive amountsofhighdimensionaldata,robustinthepresenceofnoise,etc.,present manychallengesindesignandanalysisofalgorithms. Similarly,AIsystemsforplanning(e.g.,motionplanning,dialogplanning,and activityplanning)callforadvancesindatastructuresandalgorithmsfor representingandreasoningaboutlargestatespaces,copingwithuncertainty,and providingcompactrepresentationsthatsupportefficientaccesstodata.The emergenceoflargeknowledgebasesthatcodifyhumanknowledgeinavarietyof domainscallsforrobustalgorithmsforautomatedinference;forexample,for answeringcomplexqueriesagainstknowledgebases.Manyproblems,including assemblingproductsthatmeetspecifiedrequirementsfromavailablecomponents, canbeformulatedasproblemsinsearchoroptimization,andhence,candrive advancesinconstraintprocessingandoptimization.AIsystemsforcomputervision, naturallanguagedialogue,textprocessing,videoanalytics,andothertaskscan similarlydriveinnovationsinalgorithms. ThesuccessofAIinreducingmoreandmoretasksthatwerethoughttorequire humanintelligenceintoonesthatcanbesolvedalgorithmicallywillstimulatework onestablishingthecorrectness,performancebounds,andthetrade-offsamong them.Thiswillhaveimmediatepracticalconsequences,aswillnegativeresultsthat establishthetheoreticallimitsofalgorithms.Suchinvestigationscanleadto importantinsights,forexamplewhetherataskislearnableinprinciple,inpractice, andunderwhatconditions,suchas,fromobservations,queries,orexperimentation. Ensuringrobustoperationwillrequirefindingboundsonthepotentialdeviationsof anAIsystemwhenpresentedwithunanticipatedinputs. Cybersecurity InadditiontothemanybenefitscomputertechnologyandtheInternetprovide society,ithasprovedtobeapowerfultoolforadversaries,includingmalicious 4 individuals,criminals,andnationstates.Attackersuseanumberofmethodsto extractsensitiveinformationfromorganizationsandtodisableordisruptthe activitiesofindividuals,corporations,andgovernment. AIsystemshavevulnerabilitiesthatincludethoseofbothtraditionalandnew computersystems.Forexample,bycorruptingthetrainingdata,anAIsystemcanbe trickedintoconstructinganinvalidpredictivemodel.Giventhelimitedmechanisms currentlyavailablefortestingthesemodelssuchcorruptionmaybedifficultto detect. AsAIsystemsaredeployedintherealworld,adversarieswillseekwaystotrick themintobehavinginundesirableways.Forexample,imagineattemptingtodeploy anautonomousarmoredtruck.Adversarieswouldbehighlymotivatedtoforceitto stoportoalteritscoursebyputtingthetruck’scontrolsystemoutsidetherangeof conditionsithasbeentrainedtoconsider.Indeed,suchlimitationswillimpedethe deploymentofAIsystemsinsecurity-criticalenvironments. AIcanalsoprovideapowerfultoolforbothcyberattackersandcyberdefenders.On theattackside,anetworkofbotnetscouldusereal-timedataanalyticsto dynamicallyadapttheirbehavior,increasingtheireffectivenessanddiminishing theirdetectability.Conversely,machinelearningisalreadyprovidinganeffective toolfordetectinganomaliesincomputersystemsthatcouldindicateanintrusion. Hence,increasingrelianceonAIsystemsasintegralcomponentsofcomplex systems,callsforadvancesincybersecurity. FormalVerification Computersystemsoftenfailduetoerrorsinthedesignorimplementationofits hardwareandsoftware.Sometimes,suchfailures,suchasthelossofanimportant file,whileannoying,areofnomajorconsequence.Butwhencomputersdirectly controlcriticalsystems,suchasmedicaldevices,civilinfrastructure,anddefense systems,consequencesoffailuresaremuchmoresevere.Hence,weneedtoolsfor ensuringthatsoftware,particularly,complexAIsoftware,complywith specifications. Formalverificationtoolsprovidewaystotestthecorrectnessandhardwareand softwareunderallpossibleoperatingconditions.Theygobeyondthetraditional methodoftestingthesystemonmanydifferentindividualcasestoconsiderall possiblecases.Suchtoolshavehadsignificantsuccesswithpurelydigital computations.Forexample,Inteltookalossof$475millionwhenthefirstversion ofitsPentiumprocessorcould,underveryrarecircumstances,producethewrong resultwhendividingtwonumbers.Theysubsequentlydevelopedasetoftoolsto ensurethecorrectnessoftheirarithmeticcircuitsforallpossibledata.Similarly, othercompanieshavedevelopedanddeployedtoolsthatcandetectmanyclassesof softwareerrors. 5 However,establishingthecorrectnessofcomputersystemsthatoperateinthe physicalworld–eithersystemswhoseperformancedependsonthedatathatthey weretrainedon,asinthecaseofAIsystemsthatrelyonmachinelearning,or systemsthatneedtooperateinopenenvironmentsthatarehard-to-characterize (e.g.,householdrobots,automatedvehicles,etc.)–ismuchmoredifficult.For example,itisimpossibletoanticipateallofthesituationsanautonomousvehicle mightencounter,muchlessguaranteethatitwillhandleeachsuchsituation correctly.Ontheotherhand,itmaybepossibletouseformalverificationtechniques toverifythatAIsystemswillconformtospecificboundsontheirperformancein suchenvironments. ProgrammingLanguages RapiddevelopmentofcomplexAIsystemscallsforadvancesinprogramming languagesandtools.Ofparticularinterestaredomain-specificprogramming languageswithbuilt-inhighlevelabstractionsthatmakeiteasytodesignand programAIsystemsforclassesofapplicationse.g.,naturallanguageprocessing, computervision,multi-robotsystems.Alsoofinterestarenewprogramming languagesthatsupportprobabilisticcomputations,large-scaleautomatedinference, constraintprocessing.,etc. Machinelearningalsoprovidesaninspirationforanewconceptionof programming.Withthesesystems,thehumanprogrammerscreateaframeworkfor howthesystemshouldoperate,buttheactual“program”,suchastheweightsthat specifythedetailedconfigurationofaneuralnetwork,isderivedalgorithmicallyby trainingthesystemoverlargeamountsofdata.Onecanimagineafutureinwhich evenhigherlevelsofautomationareappliedtoconstructmuchofwhatisdoneby humanprogrammerstoday. Human-ComputerInteraction AIwillrevolutionizethetypeofresearchthatcanandmustbedonetoenable peopletoeffectivelyuse,understand,interactandco-existwithAIsystems.Topick oneexample,todaymostvoicequeriesareeitherdictationofamessageororder,or aone-timequery.FutureAIsystemswillneedtoengageinbroaderformsofdialog todealwithambiguity,confusion,ortoimproveengagement.Withtheincreasing adoptionofAIsystems(e.g.,automatedvehicles,robots,softwareassistants,inrealworldenvironments)thereisanincreasingneedforresearchonframeworks, languages,abstraction,andinterfacesthatalloweffectivecommunicationand interactionbetweenhumansandAIsystems. Ofparticularinterestaremixedinitiativesystemsthatallowproductive collaborationsamongAIsystems,humansandAIsystems,aswellasamong humans,withmediationandfacilitationbyhumansorAIsystemsasneeded.AI 6 systemsmustengagewithhumansinacollaborativemanner,allowingpeopleto workinteractivelywithAIsystems(e.g.,interactivemachinelearning),thathelp withthespecification,encoding,andunderstandingoftheimplicationsofdifferent policies,values,andpreferencesassumedbyautomatedsystems,andsupporting newkindsofhuman-AIcollaboration,includingmixed-initiativeinteractionand augmentinghumancognition. Thus,ascapabilitiesinAIgrow,sotoowillthequestionsandopportunitiesto connectAItopeopleinameaningfulandeffectiveway. InConclusion ThereisagrowingandcompellingimperativetoleveragetheadvancesinAIand automationtoimprovehumanlivesinmanyways.Atthesametime,suchsystems willalsobecomefarmorepresentandconsequentialtoeverydaylife,andwill provideservicesandcapabilitiesthatwillexploitlargeamountsofdata(including personaldata),controlphysicaldevicesofvariouskinds,includingdevicesinsafety criticalareas,andbeempoweredtomakeandactondecisionsofvarying importancethatcouldinfluenceindividualsandsocietiesinexplicitandimplicit ways. Thepathtowardabalancedportfolioofcapable,safe,andtransparentAI-based systemswilldrawonabroadspectrumofcomputingideasandprinciples,andis likelytobecomeadriverfornewadvancesincomputing.Byembracingthepromise ofAI,webelievethatmanyareasofcomputersciencewillnotonlybeadvanced,but willalsoallowAItoaddressimportantopportunitiesanddosoinawaythatissafe, reliable,andeffective. ThismaterialisbaseduponworksupportedbytheNationalScienceFoundationunder GrantNo.1136993.Anyopinions,findings,andconclusionsorrecommendations expressedinthismaterialarethoseoftheauthorsanddonotnecessarilyreflectthe viewsoftheNationalScienceFoundation. 7