国会研究服务部-人工智能：背景、选定问题和政策考虑（英文）-2021.5-47正式版

ArtificialIntelligence:Background,SelectedIssues,andPolicyConsiderations

May19,2021

CongressionalResearchService

R46795

ArtificialIntelligence:Background,SelectedIssues,andPolicyConsiderations

SUMMARY

R46795

May19,2021

Thefieldofartificialintelligence(AI)—atermfirstusedinthe1950s—hasgonethrough

LaurieA.Harris

AnalystinScienceand

multiplewavesofadvancementoverthesubsequentdecades.Today,AIcanbroadlybethought

TechnologyPolicy

ofascomputerizedsystemsthatworkandreactinwayscommonlythoughttorequire

intelligence,suchastheabilitytolearn,solveproblems,andachievegoalsunderuncertainand

varyingconditions.Thefieldencompassesarangeofmethodologiesandapplicationareas,

includingmachinelearning(ML),naturallanguageprocessing,androbotics.

Inthepastdecadeorso,increasedcomputingpower,theaccumulationofbigdata,andadvancesinAItechniqueshaveledtorapidgrowthinAIresearchandapplications.GiventhesedevelopmentsandtheincreasingapplicationofAItechnologiesacrosseconomicsectors,stakeholdersfromacademia,industry,andcivilsocietyhavecalledforthefederalgovernmenttobecomemoreknowledgeableaboutAItechnologiesandmoreproactiveinconsideringpublicpoliciesaroundtheiruse.

FederalactivityaddressingAIacceleratedduringthe115thand116thCongresses.PresidentDonaldTrumpissuedtwoexecutiveorders,establishingtheAmericanAIInitiative(E.O.13859)andpromotingtheuseoftrustworthyAIinthefederalgovernment(E.O.13960).Federalcommittees,workinggroups,andotherentitieshavebeenformedtocoordinateagencyactivities,helpsetpriorities,andproducenationalstrategicplansandreports,includinganupdatedNationalAIResearchandDevelopmentStrategicPlanandaPlanforFederalEngagementinDevelopingTechnicalStandardsandRelatedToolsinAI.InCongress,committeesheldnumeroushearings,andMembersintroducedawidevarietyoflegislationtoaddressfederalAIinvestmentsandtheircoordination;AI-relatedissuessuchasalgorithmicbiasandworkforceimpacts;andAItechnologiessuchasfacialrecognitionanddeepfakes.Atleastfourlawsenactedinthe116thCongressfocusedonAIorincludedAI-focusedprovisions.

TheNationalDefenseAuthorizationActforFY2021(P.L.116-283)includedprovisionsaddressingvariousdefense-andsecurity-relatedAIactivities,aswellastheexpansiveNationalArtificialIntelligenceInitiativeActof2020(DivisionE).

TheConsolidatedAppropriationsAct,2021(P.L.116-260)includedtheAIinGovernmentActof2020(DivisionU,TitleI),whichdirectedtheGeneralServicesAdministrationtocreateanAICenterofExcellencetofacilitatetheadoptionofAItechnologiesinthefederalgovernment.

TheIdentifyingOutputsofGenerativeAdversarialNetworks(IOGAN)Act(P.L.116-258)supportedresearchonGenerativeAdversarialNetworks(GANs),theprimarytechnologyusedtocreatedeepfakes.

P.L.116-94establishedafinancialprogramrelatedtoexportsinAIamongotherareas.

AIholdspotentialbenefitsandopportunities,butalsochallengesandpitfalls.Forexample,AItechnologiescanaccelerateandprovideinsightsintodataprocessing;augmenthumandecisionmaking;optimizeperformanceforcomplextasksandsystems;andimprovesafetyforpeopleindangerousoccupations.Ontheotherhand,AIsystemsmayperpetuateoramplifybias,maynotyetbefullyabletoexplaintheirdecisionmaking,andoftendependonvastdatasetsthatarenotwidelyaccessibletofacilitateresearchanddevelopment(R&D).Further,stakeholdershavequestionedtheadequacyofhumancapitalinboththepublicandprivatesectorstodevelopandworkwithAI,aswellastheadequacyofcurrentlawsandregulationsfordealingwithsocietalandethicalissuesthatmayarise.Together,suchchallengescanleadtoaninabilitytofullyassessandunderstandtheoperationsandoutputsofAIsystems—sometimesreferredtoasthe“blackbox”problem.

Becauseofthesequestionsandconcerns,somestakeholdershaveadvocatedforslowingthepaceofAIdevelopmentanduseuntilmoreresearch,policymaking,andpreparationcanoccur.OthershavecounteredthatAIwillmakelivessafer,healthier,andmoreproductive,sothefederalgovernmentshouldnotattempttoslowit,butrathershouldgivebroadsupporttoAItechnologiesandincreasefederalAIfunding.

Inresponsetothisdebate,Congresshasbegundiscussingissuessuchasthetrustworthiness,potentialbias,andethicalusesofAI;possibledisruptiveimpactstotheU.S.workforce;theadequacyofU.S.expertiseandtraininginAI;domesticandinternationaleffortstosettechnologicalstandardsandtestingbenchmarks;andthelevelofU.S.federalinvestmentsinAIresearchanddevelopmentandhowthatimpactsU.S.internationalcompetitiveness.CongressislikelytocontinuegrapplingwiththeseissuesandworkingtocraftpoliciesthatprotectAmericancitizenswhilemaximizingU.S.innovationandleadership.

CongressionalResearchService

ArtificialIntelligence:Background,SelectedIssues,andPolicyConsiderations

Contents

Introduction

1

WhatIsAI?

1

AITerminology

3

AlgorithmsandAI

5

HistoricalContextofAI

5

WavesofAI

5

RecentGrowthintheFieldofAI

6

AIResearchandDevelopment

6

PrivateandPublicFunding

8

SelectedResearchandFocusAreas

11

ExplainableAI

11

DataAccess

12

AITrainingwithSmallandAlternativeDatasets

14

AIHardware

15

FederalActivityinAI

16

ExecutiveBranch

16

ExecutiveOrdersonAI

17

NationalScienceandTechnologyCouncilCommittees

17

SelectAIReportsandDocuments

18

FederalAgencyActivities

19

Congress

22

Legislation

23

Hearings

26

SelectedIssuesforCongressionalConsideration

27

ImplicationsfortheU.S.Workforce

28

JobDisplacementandSkillShifts

28

AIExpertWorkforce

30

InternationalCompetitionandFederalInvestmentinAIR&D

35

StandardsDevelopment

37

Ethics,Bias,Fairness,andTransparency

39

TypesofBias

41

Figures

Figure1.TotalNumberofAI-RelatedPublicationsonarXiv,

byFieldofStudy,2015-2020

8

Figure2.ExamplesofNon-ExplainableandExplainableAISystems

12

Figure3.MentionsofArtificialIntelligenceandMachineLearningintheCongressional

Record,2011-2020

23

Contacts

AuthorInformation

43

CongressionalResearchService

ArtificialIntelligence:Background,SelectedIssues,andPolicyConsiderations

Introduction

Artificialintelligence(AI)—atermfirstusedinthe1950s—canbroadlybethoughtofascomputerizedsystemsthatworkandreactinwayscommonlythoughttorequireintelligence,suchastheabilitytolearn,solveproblems,andachievegoalsunderuncertainandvaryingconditions.1Inthepastdecade,increasesincomputingpower,theavailabilityoflarge-scaledatasets(i.e.,bigdata),andadvancesinthemethodologiesunderlyingAI,haveledtorapidgrowthinthefield.AItechnologiescurrentlyshowpromiseforimprovingthesafety,quality,andefficiencyofworkandforpromotinginnovationandeconomicgrowth.Atthesametime,theapplicationofAItocomplexproblemsolvinginreal-worldsituationsraisesconcernsabouttrustworthiness,bias,andethicsandpotentialdisruptiveeffectsontheU.S.workforce.Inaddition,numerouspolicyquestionsareatissue,includingthoseconcerningtheappropriateU.S.approachtointernationalcompetitioninAIresearchanddevelopment(R&D),technologicalstandardsetting,andthedevelopmentoftestingbenchmarks.

GiventheincreasinguseofAItechnologiesacrosseconomicsectors,stakeholdersfromacademia,industry,andcivilsocietyhavecalledforthefederalgovernmenttobecomemoreknowledgeableaboutAItechnologiesandmoreproactiveinconsideringpublicpoliciesaroundtheiruse.ToassistCongressinitsworkonAI,thisreportprovidesanoverviewofAItechnologiesandtheirdevelopment,recenttrendsinAI,federalAIactivity,andselectedissuesandpolicyconsiderations.

ThisreportdoesnotattempttoaddressallapplicationsofAI.InformationontheapplicationofAItechnologiesintransportation,nationalsecurity,andeducationcanbefoundinseparateCRSproducts.2

WhatIsAI?

Whilethereisnosingle,commonlyagreedupondefinitionofAI,theNationalInstituteofStandardsandTechnology(NIST)hasdescribedAItechnologiesandsystemsascomprising“softwareand/orhardwarethatcanlearntosolvecomplexproblems,makepredictionsorundertaketasksthatrequirehuman-likesensing(suchasvision,speech,andtouch),perception,cognition,planning,learning,communication,orphysicalaction.”3DefinitionsmayvaryaccordingtothedisciplineinwhichAIisbeingdiscussed.4AIisoftendescribedasafieldthatencompassesarangeofmethodologiesandapplicationareas,suchasmachinelearning(ML),naturallanguageprocessing(NLP),androbotics.

AdaptedfromOfficeofScienceandTechnologyPolicy,PreparingfortheFutureofArtificialIntelligence,October2016,p.6.

SeeCRSReportR44940,IssuesinAutonomousVehicleDeployment,byBillCanis;CRSInFocusIF10737,AutonomousandSemi-autonomousTrucks,byJohnFrittelli;CRSReportR45178,ArtificialIntelligenceandNationalSecurity,byKelleyM.Sayler;andCRSInFocusIF10937,ArtificialIntelligence(AI)andEducation,byJoyceJ.LuandLaurieA.Harris.

NationalInstituteofStandardsandTechnology,U.S.LeadershipinAI:APlanforFederalEngagementinDevelopingTechnicalStandardsandRelatedTools,August9,2019,pp.7-8.

See,forexample,AIdefinitionsinthecategoriesofordinarylanguage,computationaldisciplines,engineering,economicsandsocialsciences,ethicsandphilosophy,andinternationallawandpolicy,inSaraMattingly-Jordanetal.,EthicallyAlignedDesign:FirstEditionGlossary,InstituteofElectricalandElectronicsEngineers(IEEE),January2019,p.8,at/content/dam/ieee-standards/standards/web/documents/other/ead1e_glossary.pdf.

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DefiningAIisnotmerelyanacademicexercise,particularlywhendraftinglegislation.AIresearchandapplicationsareevolvingrapidly.Thus,congressionalconsiderationofwhethertoincludeadefinitionforAIinabill,andifsohowtodefinethetermorrelatedterms,necessarilyincludeattentiontothescopeofthelegislationandthecurrentandfutureapplicabilityofthedefinition.ConsiderationsincraftingadefinitionforuseinlegislationincludewhetheritisexpansiveenoughnottohinderthefutureapplicabilityofalawasAIdevelopsandevolves,whilebeingnarrowenoughtoprovideclarityontheentitiesthelawaffects.Somestakeholders,recognizingthemanychallengesofdefiningAI,haveattemptedtodefineprinciplesthatmighthelpguidepolicymakers.ResearchsuggeststhatdifferencesindefinitionsusedtoidentifyAI-relatedresearchmaycontributetosignificantlydifferentanalysesandoutcomesregardingAIcompetition,investments,technologytransfer,andapplicationforecasts.5

TheJohnS.McCainNationalDefenseAuthorizationActforFiscalYear2019(P.L.115-232)includedthefirstdefinitionofAIinfederalstatute.6Likethoseinotherpreviouslyintroducedbills,thedefinitionincorporatedacommonlycitedframeworkoffourpossiblegoalsthatAIsystemsmaypursue:systemsthatthinklikehumans(e.g.,neuralnetworks),actlikehumans(e.g.,naturallanguageprocessing),thinkrationally(e.g.,logicsolvers),oractrationally(e.g.,intelligentsoftwareagentsembodiedinrobots).7However,AIresearchandapplicationsdonotnecessarilyfallsolelywithinanyoneofthesefourcategories.

InDecember2020,theNationalArtificialIntelligenceActof2020,enactedaspartoftheWilliamM.(Mac)ThornberryNationalDefenseAuthorizationAct(NDAA)forFiscalYear2021(P.L.

116-283),includedthefollowingdefinition:

Theterm“artificialintelligence”meansamachine-basedsystemthatcan,foragivensetofhuman-definedobjectives,makepredictions,recommendationsordecisionsinfluencingrealorvirtualenvironments.Artificialintelligencesystemsusemachineandhuman-basedinputsto—(A)perceiverealandvirtualenvironments;(B)abstractsuchperceptionsintomodelsthroughanalysisinanautomatedmanner;and(C)usemodelinferencetoformulateoptionsforinformationoraction.8

CurrentAIsystemsareconsideredtobenarrowAI,meaningthattheyaretailoredtoparticular,narrowlydefinedtasks.ExampleapplicationsofAIineverydaylifeincludeemailspamfiltering,voiceassistance(e.g.,Siri,Alexa,Cortana),financiallendingdecisions,andsearchengineresults.AItechnologiesarebeingintegratedinarangeofsectors,includingtransportation,healthcare,education,agriculture,manufacturing,anddefense.SomeAIexpertsusethetermsaugmentedintelligenceorhuman-centeredAItocapturethevariousAIapplicationsinphysicalandconnectedsystems,suchasroboticsandtheInternetofThings,9andtoemphasizetheuseofAItechnologiestoenhancehumanactivitiesratherthantoreplacethem.

MostanalystsbelievethatgeneralAI,meaningsystemsthatdemonstrateintelligentbehavioracrossarangeofcognitivetasks,isunlikelytooccurforadecadeorlonger.SomeAIresearchers

DeweyMurdick,JamesDunham,andJenniferMelot,AIDefinitionsAffectPolicymaking,CenterforSecurityandEmergingTechnology,June2020,at/wp-content/uploads/CSET-AI-Definitions-Affect-Policymaking.pdf.

P.L.115-232,Section238;10U.S.C.§2358note.

StuartRussellandPeterNorvig,ArtificialIntelligence:AModernApproach,3rded.(UpperSaddleRiver,NJ:PrenticeHall,2010),pp.1-5.

P.L.116-283(hereinafterreferredtoastheFY2021NDAA);H.R.6395,DivisionE,Section5002(3).

FormoreinformationontheInternetofThings,seeCRSInFocusIF11239,TheInternetofThings(IoT):AnOverview,byPatriciaMoloneyFigliola;andtoidentifyadditionalCRSexpertswhoworkonIoTandrelatedtopics,seeCRSReportR44225,TheInternetofThings:CRSExperts,coordinatedbyPatriciaMoloneyFigliola.

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believethatgeneralAIcanbeachievedthroughincrementaldevelopmentandrefiningofcurrentAIandmachinelearningtools,whileothersbelieveitwillrequirethediscoveryanddevelopmentofanewbreakthroughtechnique.

JustasthereisdebateoverthedefinitionofAI,thereisdebateoverwhichtechnologiesshouldbeclassifiedasAI.Forexample,roboticprocessautomation(RPA)hasbeendefinedas“theuseofsoftwaretoautomatehighlyrepetitive,routinetasksnormallyperformedbyknowledgeworkers.”10Becauseitautomatesactivitiesperformedbyhumans,itisoftendescribedasanAItechnology.However,somearguethatRPAisnotAIbecauseitdoesnotincludealearningcomponent.OthersdiscussRPAasabasictoolthatcanbecombinedwithAItocreatecomplexprocessautomation(CPA)orintelligentprocessautomation(IPA),alongan“intelligentautomationcontinuum.”11

AITerminology

Somestakeholders,includingindustry,advocacygroups,andpolicymakers,haveraisedquestionsaboutwhetherspecificAItechnologiesandtechniquesrequiretailoredlegislation.Forexample,legislationenactedinthe116thCongressfocusedongenerativeadversarialnetworks(GANs),describedbelow,whicharethemainunderlyingAItechniqueusedingeneratingdeepfakes,12whicharemostcommonlydescribedasrealisticaudio,video,andotherforgeriescreatedusingAItechniques.13ThissectionismeanttoprovideabroadunderstandingofasubsetofcommontermsusedinthefieldofAIandhowtheyrelatetooneanother.Theseincludethesubfieldofmachinelearning(ML);MLtechniquessuchasdeeplearning,neuralnetworks,andGANs;andtrainingmethodssuchassupervised,unsupervised,andreinforcementlearning.However,justastherearevariationsinhowAIisdefined,researchersandpractitionersdescribevariousAI-relatedtermsinslightlydifferentways.Further,thefollowingtermsandtechniquesarenotmutuallyexclusive;AIsystemsmayemploymorethanone.Forexample,AlphaGo—thefirstAIprogramtobeatahumanmasterattheancientChinesegameofGo—combineddeepneuralnetworks,supervisedlearning,andreinforcementlearning.14

Machinelearning(ML),oftenreferredtoasasubfieldofAI,examineshowtobuildcomputerprogramsthatautomaticallyimprovetheirperformanceatsometaskthroughexperiencewithoutrelyingonexplicitrules-basedprogrammingtodoso.15OneofthegoalsofMListoteachalgorithmstosuccessfullyinterpretdatathathavenotpreviouslybeenencountered.MLisoneofthemostcommonAItechniquesinusetoday,andmostMLtasksarenarrowlyspecifiedtooptimize

SeeIBM,“AutomateRepetitiveTasks,”at/automation/rpa.

IBMGlobalBusinessServices,“UsingArtificialIntelligencetoOptimizetheValueofRoboticProcessAutomation,”September2017,at/downloads/cas/KDKAAK29.

TheIdentifyingOutputsofGenerativeAdversarialNetworks(IOGAN)Act(P.L.116-258).

Foradditionalinformationondeepfakes,seeCRSInFocusIF11333,DeepFakesandNationalSecurity,byKelleyM.SaylerandLaurieA.Harris.

RichardS.SuttonandAndrewG.Barto,ReinforcementLearning:AnIntroduction,2nded.(Cambridge,MA:MITPress,2018),pp.441-442.

AdaptedfromErikBrynjolfsson,TomMitchell,andDanielRock,“WhatCanMachinesLearn,andWhatDoesIt

MeanforOccupationsandtheEconomy?,”AEAPapersandProceedings,vol.108(May1,2018),pp.43-47,at/~tom/pubs/AEA2018-WhatCanMachinesLearn.pdf.MLisdefinedinP.L.116-293tomean

“anapplicationofartificialintelligencethatischaracterizedbyprovidingsystemstheabilitytoautomaticallylearnandimproveonthebasisofdataorexperience,withoutbeingexplicitlyprogrammed.”

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specificfunctionsusingparticulardatasets.Deeplearning,neuralnetworks,andGANsrepresentafewoftheMLtechniquesfrequentlyusedtoday.

Deeplearning(DL)systemslearnfromlargeamountsofdatatosubsequentlyrecognizeandclassifyrelated,butpreviouslyunobserved,data.Forexample,neuralnetworks,oftendescribedasbeinglooselymodeledafterthehumanbrain,consistofthousandsormillionsofprocessingnodesgenerallyorganizedintolayers.Thestrengthoftheconnectionsamongnodesandlayersarerepeatedlytuned—basedoncharacteristicsofthetrainingdata—tocorrespondtothecorrectoutput.Advancesinhardware,suchasthedevelopmentofgraphicalprocessingunits(GPUs),haveallowedthesenetworkstohavemanylayers,whichiswhatputsthe“deep”indeeplearning.DLapproacheshavebeenusedinsystemsacrossmanyareasofAIresearch,fromautonomousvehiclestovoicerecognitiontechnologies.16

Generativeadversarialnetworks(GANs)consistoftwocompetingneuralnetworks—ageneratornetworkthattriestocreatefakeoutputs(suchaspictures),andadiscriminatornetworkthattriestodeterminewhethertheoutputsarerealorfake.AmajoradvantageofthisstructureisthatGANscanlearnfromlessdatathanotherdeeplearningalgorithms.17AdversarialMLsystemscanbeusedinotherways,aswell;forexample,theycantrytoimprovefairnessinfinancialservicedecisionmakingbyhavingasecondmodeltrytoguesstheprotectedclassofapplicantsbasedonmodelsbuiltbyanothermodel.18

Supervisedlearningalgorithmslearnfromatrainingsetofdatathatislabeledwiththecorrectdescription(e.g.,thecorrectlabelforthispictureis“cat”);thesystemsubsequentlylearnswhichcomponentsofthedataareusefulforclassifyingitcorrectlyandusesthatinformationtocorrectlyclassifydataithasneverencounteredbefore.Incontrast,unsupervisedlearningalgorithmssearchforunderlyingstructuresinunlabeleddata.

Reinforcementlearning(RL)referstogivingcomputerprogramstheabilitytolearnfromexperience,providingthemwithminimalinputsandhumaninterventions.19RLalgorithmslearnbytrialanderror,beingrewardedforreachingspecifiedobjectives—bothforimmediateactionsandlong-termgoals.Theemphasisonsimulatedmotivationandlearningfromdirectinteractionwiththeenvironment,withoutrequiringexplicitexamplesandmodels,areamongthecharacteristicsthatsetRLapartfromotherMLapproaches.20

LarryHardesty,“Explained:NeuralNetworks,”MassachusettsInstituteofTechnology(MIT)News,April14,2017,at/2017/explained-neural-networks-deep-learning-0414.

JamieBeckett,“What’saGenerativeAdversarialNetwork?LeadingResearcherExplains,”NVIDIA,May17,2017,at/blog/2017/05/17/generative-adversarial-network/.

SallyWard-Foxton,“ReducingBiasinAIModelsforCreditandLoanDecision,”EETimes,April30,2019,at/reducing-bias-in-ai-models-for-credit-and-loan-decisions/#.

SeanGarrish,HowSmartMachinesThink(Cambridge,MA:MITPress,2018),p.91.

AdaptedfromRichardS.SuttonandAndrewG.Barto,ReinforcementLearning:AnIntroduction,2nded.(Cambridge,MA:MITPress,2018).

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AlgorithmsandAI

AsinterestinAIcontinuestogrow,someanalystsassertthatgeneraldataanalyticsandspecializedalgorithmsareincreasinglybeingreferredto,erroneously,asAI.Itcanbechallengingtomakesuchdistinctionsclearly,giventhevariabilityindefinitionsofAIandrelatedtermsandbecausethesedistinctionshavearguablyshiftedovertime.Forexample,analgorithmisbasicallyaprocedureorsetofinstructionsdesignedtoperformaspecifictaskorsolveamathematicalproblem.SomeearlyproductsofAIresearch,suchasrule-basedexpertsystems,arealgorithmsencodedwithexpertknowledgebutlackingalearningcomponent.Somefeelthatrule-basedsystemsareasimpleformofAIbecausetheysimulateintelligence,whileothersthinkthatwithoutalearningcomponentasystemshouldnotbeconsideredAI.21Generally,however,thegoalsofAI—automatingorreplicatingintelligentbehavior—haveremainedconsistent.22

HistoricalContextofAI

TheideasunderlyingAIanditsconceptualframeworkhavebeenresearchedsinceatleastthe1940sandinitiallyformalizedinthe1950s.IdeasaboutintelligentmachineswerediscussedandpopularizedbyscientistsandauthorssuchasAlanTuringandIsaacAsimov,23andtheterm“artificialintelligence”wascoinedattheDartmouthSummerResearchProjectonArtificialIntelligence,proposedin1955andheldthefollowingyear.24

Sincethattime,thefieldofAIhasgonethroughwhathavebeentermedbysomeassummersandwinters—periodsofmuchresearchandadvancement,followedbylullsinactivityandprogress.ThereasonsfortheAIwintershaveincludedafocusontheoryoverpracticalapplications,researchproblemsbeingmoredifficultthananticipated,andlimitationsofthetechnologiesofthetime.MuchofthecurrentprogressandresearchinAI,whichbeganaround2010,hasbeenattributedtotheavailabilityofbigdata,improvedMLapproachesandalgorithms,andmorepowerfulcomputers.25

WavesofAI

TheDefenseAdvancedResearchProjectsAgency(DARPA),whichhasfundedAIR&Dsincethe1960s,hasdescribedthedevelopmentofAItechnologiesintermsofthreewaves.26Thesewavesaredescribedbythevaryingabilitiesoftechnologiesineachtoperceiverich,complex,andsubtle

Forabriefdiscussionsee,forexample,Tricentis,“AIApproachesCompared:Rule-BasedTestingvs.Learning,”at/artificial-intelligence-software-testing/ai-approaches-rule-based-testing-vs-learning/.

OfficeofScienceandTechnologyPolicy,PreparingfortheFutureofArtificialIntelligence,October2016,pp.5-6.

AlanM.Turing,“ComputingMachineryandIntelligence,”Mind,vol.49(1950),pp.433-460,at/courses/471/papers/turing.pdf;andIsaacAsimov,I,Robot(GardenCity,NY:Doubleday,1950).

SeeJ.McCarthyetal.,“AProposalfortheDartmouthSummerResearchProjectonArtificialIntelligence,”August

31,1955,at/jmc/history/dartmouth/dartmouth.html.

ExecutiveOfficeofthePresident,NationalScienceandTechnologyCouncil,CommitteeonTechnology,PreparingfortheFutureofArtificialIntelligence,October2016,pp.5-6.;foradditionalinformationonthesefactorsandashorthistoryofAI,seealsotheappendixofPeterStoneetal.,“ArtificialIntelligenceandLifein2030,”OneHundredYearStudyonArtificialIntelligence:Reportofthe2015-2016StudyPanel,Stanford