人工智能和機(jī)器學(xué)習(xí)對科學(xué)、技術(shù)和科學(xué)任務(wù)的推進(jìn)帶來的機(jī)遇和挑戰(zhàn)

OpportunitiesandChallengesfromArtificialIntelligenceandMachineLearningfortheAdvancementofScience,

Technology,andtheOfficeofScienceMissions

AreportfortheAdvancedScientificComputingAdvisoryCommitteefromtheSubcommitteeonArtificialIntelligence,MachineLearning,Data-

intensiveScienceandHigh-PerformanceComputing

Chair:TonyHeySeptember2020

Caption:

ThecoverimageshowsthecrystalstructureofDy2Ti2O7inwhichthemagneticionDy3+oftherareearthelementDysprosium(shownincyan)occupiessitesonageometricallyfrustratedcorner-sharingtetrahedralnetwork.AI/MLmethodswereusedtosolveforthecouplingsinthematerialandtoidentifytheoriginofitsfreezingintoaglassystate.Neuralnetworkswereusedtoextractthephaseandcouplingsinthesystemfromdiffuseneutronscatteringdatabysolvingtheinversescatteringproblem.Thestrongsingle-ionicanisotropyofDy3+ionsdefinedbytheirmolecularenvironmentrestrictstheeffectivemagneticmomenttoaligneitherinwardoroutward.Themagneticmomentsimposeanice-rulewherelow-energyspinstatesarerestrictedtoatwo-inandtwo-outconfigurationforeachtetrahedronandthebreakingofthisice-rulecreatestwofractionalizedmagneticmonopoleswithoppositepolarity.

Acknowledgement:

AnjanaSamarakoonandAlanTennant,OakRidgeNationalLaboratory.

TableofContents

ExecutiveSummary 6

Introduction 6

Context 7

KeyFindings 8

RecommendationsforDOE’sOfficeofScience 12

Report 16

IntroductionandBackground 16

ChargeLettertoASCR 17

SubcommitteeInformationGatheringActivities 17

DOEastheleadagencyforAI/MLappliedtoFacilitiesScience 19

OpportunitiesandchallengesfromArtificialIntelligenceandMachineLearningforthe

advancementofscience,technology,andOfficeofSciencemissions 20

StrategiesfortheDOEOfficeofSciencetoaddressthechallengesanddeliveronthe

opportunities 21

Introduction 21

AIApplications 23

AIAlgorithmsandFoundations 31

AISoftwareInfrastructure 37

NewHardwareTechnologiesforAI 40

InstrumenttoEdgeComputing 41

AI/MLWorkforce:Training,Focusing,andRetention 42

UniversityPartnerships 44

CollaborationwithIndustry 45

Inter-AgencyCollaboration 46

InternationalCollaboration 47

ImportanceofASCR’slong-termAppliedMathematicsandComputerScience

ResearchPrograms 49

SummaryofConclusions 49

Figures 51

Figure1:AI,MachineLearning,DeepLearninginaNutshell 51

Figure2:WhatisaDataScientist? 52

Figure3:StructureofSCAIforScience10-yearInitiative 53

ReferencesandURLs 54

AppendixA:ChargeLetter 56

AppendixB:SubcommitteeMembers 58

AppendixD:ListofAcronyms 64

Acknowledgements 65

“AIwon’treplacethescientist,butscientistswhouseAIwillreplace

thosewhodon’t.”

AdaptedfromaMicrosoftreport,“TheFutureComputed”

ExecutiveSummary

Introduction

InFebruary2019,thePresidentsignedExecutiveOrder13859,MaintainingAmericanLeadershipinArtificialIntelligence[1].ThisorderlaunchedtheAmericanArtificialIntelligenceInitiative,aconcertedefforttopromoteandprotectAItechnologyandinnovationintheUnitedStates.TheInitiativeimplementsagovernment-widestrategyincollaborationandengagementwiththeprivatesector,academia,thepublic,andlike-mindedinternationalpartners.

Amongotheractions,keydirectivesintheInitiativecalledforFederalagenciesto:

PrioritizeAIresearchanddevelopmentinvestments,

Enhanceaccesstohigh-qualitycyberinfrastructureanddata,

EnsurethattheUSmaintainsaninternationalleadershiproleinthedevelopmentoftechnicalstandardsforAI,and

ProvideeducationandtrainingopportunitiestopreparetheAmericanworkforcefortheneweraofAI.

ThemissionoftheDepartmentofEnergy(DOE)istoensureAmerica’ssecurityandprosperitybyaddressingitsenergy,environmental,andnuclearchallengesthroughtransformativescienceandtechnologysolutions.IntermsofScienceandInnovation,theDOE’smissionistomaintainavibrantUSeffortinscienceandengineeringasacornerstoneofoureconomicprosperitywithclearleadershipinstrategicareas.

FromJulytoOctoberin2019,theArgonne,OakRidge,andBerkeleyNationalLaboratorieshostedaseriesoffourAIforScienceTownHallmeetingsinChicago,OakRidge,Berkeley,andWashingtonDC.Thefourmeetingswereattendedbyover1300scientistsfromthe17DOELabs,39companies,andover90universities.ThegoaloftheTownHallserieswas‘toexaminescientificopportunitiesintheareasofartificialintelligence,BigData,andhigh-performancecomputing(HPC)inthenextdecade,andtocapturethebigideas,grandchallenges,andnextstepstorealizingthese.’ThediscussionsatthemeetingswerecapturedinthefinalreportoftheAIforScienceTownHallmeetings[2].

InresponsetoachargeletterfromtheDOE’sOfficeofScience(SC),theAdvancedScientificComputingResearch(ASCR)programaskeditsAdvisoryCommittee(ASCAC)toestablishasubcommitteetoexplorethescientificopportunitiesandchallengesarisingfromtheintersectionofArtificialIntelligence(AI)andMachineLearning(ML)withdata-intensivescienceandhighperformancecomputing.Specifically,thisAIforSciencesubcommitteewasaskedto:

AssesstheopportunitiesandchallengesfromArtificialIntelligenceandMachineLearningfortheadvancementofscience,technology,andtheOfficeofSciencemissions.

IdentifystrategiesthatASCRcanuse,incoordinationwiththeotherSCprograms,toaddressthechallengesanddeliverontheopportunities.

ThisreportistheresultoftheSubcommittee’sinvestigationofthesechargequestions.TosetthecontextasummaryofAI,MLandDeepLearningisincludedherealongwithacharacterizationofdifferentrolesfordatascientists.Thisexecutivesummaryreportsthesubcommittee’skeyfindingsandrecommendations.

Context

ThetermArtificialIntelligencewascoinedbyJohnMcCarthyforaworkshopatDartmouthCollegeinNewHampshirein1956.Attheworkshop,McCarthyintroducedthephrase‘ArtificialIntelligence’whichhelaterdefinedas[3]:

‘Thescienceandengineeringofmakingintelligentmachines,especiallyintelligentcomputerprograms.’

Bycontrast,thefieldofMachineLearningislessambitiousandcanberegardedasasub-domainofartificialintelligence[4]:

‘Machinelearningaddressesthequestionofhowtobuildcomputersthatimproveautomaticallythroughexperience.Itisoneoftoday'smostrapidlygrowingtechnicalfields,lyingattheintersectionofcomputerscienceandstatistics,andatthecoreofartificialintelligenceanddatascience.Recentprogressinmachinelearninghasbeendrivenbothbythedevelopmentofnewlearningalgorithmsandtheoryandbytheongoingexplosionintheavailabilityofonlinedataandlow-costcomputation.’

Finally,DeepLearningneuralnetworksareasubsetof

MachineLearning

methodsthatarebasedon

artificialneuralnetworks

(ANNs)[5]:

‘AnANNisbasedonacollectionofconnectedunitsornodescalled

artificial

neurons

,whichlooselymodelthe

neurons

inabiologicalbrain.Eachconnection,likethe

synapses

inabiologicalbrain,cantransmitasignaltootherneurons.Anartificialneuronthatreceivesasignalthenprocessesitandcansignalneuronsconnectedtoit.The"signal"ataconnectionisa

realnumber

,andtheoutputofeachneuroniscomputedbysomenon-linearfunctionofthesumofitsinputs.Theconnectionsarecallededges.Neuronsandedgestypicallyhavea

weight

thatadjustsaslearningproceeds.’

Theartificialneuronsinthesenetworksarearrangedinlayersgoingfromaninputlayertoanoutputlayerwithconnectionsbetweentheneuronsinthedifferentlayers.DeeplearningneuralnetworksaremerelyasubsetofsuchANNswithverylargenumbersofhiddenlayers.OntheImageNetImageRecognitionChallenge,the2015competitionwaswonbyateamfromMicrosoftResearchusingaverydeepneuralnetworkofover100layersandachievedanerrorrateforobjectrecognitioncomparabletohumanerrorrates[6].Figure1triestocapturetheessenceofthisAI,MachineLearning,andDeepLearninghierarchy[7]

Figure2attemptstodefinethreedifferentrolesforadatascientist[8].Thefirstroleisthatofadataengineerwhoisexpertatoperatingclosetothecomputers,instruments,andsensorsthatgeneratethedata.ThesecondroleisthatofadataanalystwhousesadvancedstatisticsandAI/MLmethodstoexploretheexperimentaldatasetsandassisttheresearchertoextractnewscience.Finally,inthisclassification,thereisathirdroleofdatacuratorwhoisexpertinmanaginglargedatasets,curatingthedatawithsuitablemetadataforre-use,andlaterarchiving.AllthreeoftheseaspectsofdatasciencearerelevantfortheproposedAIforScienceinitiative.

KeyFindings

FindingA

ThegrowingconvergenceofAI,Data,andHPCprovidesaonceinagenerationopportunitytoprofoundlyacceleratescientificdiscovery,createsynergiesacrossscientificareas,andimproveinternationalcompetitiveness.

Scienceandcomputingarenowinaneraofpost-Moore’sLawsilicontechnologiesandthereisanurgentneedforasea-changeintheprogrammabilityandproductiveuseofincreasinglycomplex/heterogeneoussystemsandtheseamlessintegrationofdata,algorithms,andcomputingresources.DoingsowillhelpmanagethechallengesofBigData,carryingoutscienceatscaleusingDOE’smostadvancedfacilities,leveragetheworkforceattheLabs,andsetthestagefortheemergenceanddevelopmentofrobustandreliableAIsystemswiththeabilitytolearnforthemselvesindomain-sciencespecificareas.

FindingB

SciencecangreatlybenefitfromAImethodsandtools.However,commercialsolutionsandexistingalgorithmsarenotsufficienttoaddresstheneedsofscienceautomationandscienceknowledgeextractionfromcurrentandfutureDOEfacilitiesanddata.

CurrentAIsolutionscanbesuccessfullyappliedtoconductavarietyofdataanalyses.However,newalgorithms,foundations,andtoolsareessentialtoaddressinguniquescienceconcernsinabroadspectrumofscienceapplications.AIalgorithmsneedtobeabletodealwithsparse,heterogeneous,andun-labeleddatasetsthatareoftenexpensivetocollectandarchiveandbeabletogeneratemodelsthatincorporatedomainknowledgeandphysicalconstraints.AI-enabledexperimentaldesignandcontrolarenecessaryforoptimaluseofDOEfacilities.Inthesciencecontext,AImethodsneedtohaveprovablecorrectnessandperformance,beabletoexposebiases,andtoquantifyuncertainties,errors,andprecision.

FindingC

AdoptingAIforSciencetechnologiesthroughouttheOfficeofSciencewillenableUSscientiststotakeadvantageofthetremendousnewadvancesintheDOE’sscientificuserfacilities.

TheDOE’sOfficeofScienceprovidesUSresearcherswithaccesstothelargestandmostdiversesuiteofscientificexperimentalfacilitiesintheworld–fromX-raysynchrotronsandneutronsourcestointegrativegenomicsandatmosphericradiationfacilities–aswellastotheworld’smostcapablehighperformancecomputingfacilities.UpgradestotheseuserfacilitiesandnewnuclearphysicsfacilitiescomingonlinenowandoverthenextdecadewilldramaticallyincreasetheamountofnewdataproducedacrossallofthescientificdomainssupportedbytheOfficeofScience,posingnewchallengesandnewopportunities.Science-awareAItechnologieswillallowustoextractinformationandscientificunderstandingfromthesetremendousnewdatasources.

FindingD

RealizingthepotentialforagenerationalshiftinscientificexperimentationattheDOELaboratoriesduetoscience-drivenAI/MLtechnologiesrequiresfarmorethansimplycomputepowerandencompassesthefullspectrumofcomputinginfrastructures,rangingfromubiquitoussensorsandinterconnectivityacrossdevicestoreal-timemonitoringanddataanalytics,andwillrequireaconcertedandcoordinatedR&DeffortonAI/MLalgorithms,tools,andsoftwareinfrastructure.

AcrosstheSCprograms,scientificapplicationsofArtificialIntelligence(AI)andMachineLearning(ML)canbuildonthepowerofsensornetworks,edgecomputing,andhighperformancecomputerstotransformscienceandenergyresearchinthefuture.GiventhehighlyspecializednatureofmanyDOEfacilitiesandscientificresearchdomains,itisnotpossibletorelysolelyonthird-partyAI/MLresearchanddevelopment(R&D)forthistransformation.TheDOEwillneedtobuilditsownR&Dprogramsthatfocusonthemostchallengingscience-drivenapplications.SoftwareinfrastructurewillberequiredthatcombinesleadershipinAI/MLtoolsandalgorithmswiththeDOE’straditionalstrengthsinsimulationandmodelingtechnologiesandthatcanexecuteonnewcomputingplatformscapableofhighperformanceonbothtypesofapplications.TheanticipatedreturnswillhelpensurethattheUScontinuestomaintainandenhanceleadershipinbothdata-intensivescienceandhighperformancecomputing.

FindingE

TheDOELabsareuniquelypositionedtointegrateAI/MLtechnologiesacrossahostofscientificchallengesthankstotheenviablecultureofco-designteamsconsistingofscientificusers,instrumentproviders,theoreticalscientists,mathematiciansandcomputerscientiststhathasprovensosuccessfulintheExascaleComputingProject.

Thesubcommittee,therefore,seesacompellingneedforAI/MLtechnologiestobeincorporatedintoalloftheDOE’sscientificresearchcapabilitiesinordertoeffectivelysupporttheOfficeofScience’smissionsinenergy,nationalsecurity,fundamentalsciences,andtheenvironment.DOE’sNationalLaboratories,togetherwithUSuniversityandindustrypartners,havethenecessaryassetstoinitiatealarge-scaleprogramtoacceleratethedevelopmentofsuchcapabilitiesandthenecessaryworkforcetonotonlymeettheirSCmissionneedsbutalsobenefitallofDOE’sactivities.

FindingF

TheimpactofaDOE-drivenAI/MLstrategyforsciencewillhavenationalimplicationsfarbeyondtheOfficeofScienceandwilldrivenewindustrialinvestments,includingacceleratingengineeringdesigns,synthesizingmaterials,andoptimizingenergydevices,aswellasadvancinghardwareandsoftwarecomputingcapabilities.

Thebenefitstothenationindevelopingpowerfulandbroad-basedAIforSciencecapabilitiesintheDOELaboratorieswillextendwellbeyondtheDOE’sprograms.ThedevelopmentofcomprehensiveAI/MLcapabilitieswillbenefitothergovernmentagenciesandabroadrangeofindustriesinthiscountry,includingenergy,pharmaceutical,aircraft,automobile,entertainment,andothers.MorepowerfulAIcapabilitieswillallowthesediverseindustriestomorequicklyengineernewproductsthatcanimprovethenation’scompetitiveness.Inaddition,therewillbeconsiderableflow-downbenefitsthatresultfrommeetingboththehardwareandsoftwareAIchallenges.InitiatingamajorprogramfocusedonapplyingAI/MLtechnologiestotheDOEscientificchallengeswouldbelikelytoleadtosignificantgainsinUScompetitivenessinseveralcriticalareasandtechnologies.

FindingG

AworkforcetrainedinadvancedAI/MLtechnologieswouldplayapivotalroleinenhancingUScompetitiveness.

Thetraining,focusing,andretentionofacadreofyoungpeople,expertsinbothinventinganddeliveringthetechniquesandtechnologiesofAI/MLforscienceandengineeringapplications,iscriticaltothesuccessoftheAIforScienceagenda.TheOfficeofScienceDOELaboratoriescanplayakeyroleincooperationwiththeNationalScienceFoundation(NSF).Overthepast20years,theInformationTechnology(IT)industryhasexpandeddramatically,drivenbye-commerce,socialmedia,cloudservices,andsmartphones.Inrecentyears,theemergenceoftheInternetofThings(IoT),thewidespreaddeploymentofhealthcaresensors,increasingindustrialautomation,andthedevelopmentofautonomousvehicleshavefurtherexpandedthedomainofAI/MLdataanalyticsandservices.Inresponsetothesegrowingworkforcedemands,moststudentsarenowtrainedinsoftwaretoolsandtechniquesthattargetcommercialopportunities.Atpresent,commercialtoolsarerathergenericandnotwell-targetedtoscientificapplications.AnAIforScienceinitiativewoulddeliverscientificAI/MLtoolsandenvironmentsappropriatefortraininganewgenerationofscientistsandengineers.

FindingH

PartneringwithotherAgenciesandwithinternationaleffortswillbeimportanttodeliverontheambitiousgoalsofanAIforScienceinitiative.

TheNSFandNIH,thetwoothermajorscience-focusedfundingagenciesintheUS,alsohaveorareplanning,majorinvestmentsinAI/MLprogramsfortheirscientificdomains.InseveralareasthereareclearsynergiesofresearchinterestandtheDOEshouldexplorepossiblemechanisms

forcollaborativeprojectswithotheragenciessuchasNISTandDODinanyDOEAIforScience

initiative.

OthercountrieshavealsorecognizedthepotentialbenefitsofapplyingAL/MLtechnologiestoscience.ThesubcommitteebelievesthattherewouldbeabenefitintheDOEcollaboratingwith‘like-mindedinternationalpartners’onaspectsofanAIforScienceresearchagendathatarelikelytobeofmutualbenefit.

RecommendationsforDOE’sOfficeofScience

Creationofa10-yearAIforScienceInitiative

Inordertocreatetheworld-leadingAIsystemsandapplicationsneededtodrivescientificproductivityanddiscoveryinscienceandtechnologydramaticallybeyondthatachievablewithtraditionalscientificsupercomputing,werecommendthattheDOEOfficeofSciencestartaten-yearprogramtodevelopanambitiousAIforScienceinitiative,asrecommendedintherecentPCASTreport[9].Thisprogramshouldencompassfoundationalresearchintonew,science-awareAImethodologies,specificallydesignedforDOEmission-criticalchallenges,andAIsolutionsthatcanbedeployedinoperationalsettingsatleadingDOEresearchfacilities.Theinitiativeshouldprovideaclear,guidedroadmapfromresearchtodeployment.TheDOElaboratoriescanplayakeyrolehere,offeringleading-edgeexascalesupercomputersandlargeexperimentalfacilitiesgeneratingincreasinglylargescientificdatasets,aswellasprovidingcriticalexpertiseinmathematics,computerscience,andexperiencewithDOEmission-specificapplications.Nootheragencyhasthebreadth,criticalmass,orrecentlargeprojectmanagementexperiencetoundertakethiscross-disciplinaryAIforSciencechallenge.However,thereisaclearcaseforthebenefitsofcollaborationwithotheragenciesandothercountries,toleverageexistingexpertisetomaximumadvantage.Partnershipswithotherfundingagenciesandothercountriesarethereforestronglyencouraged.

StructureofanSCAIforScienceInitiative

ItisrecommendedthatthisAIforScienceinitiativebestructuredaroundfourmajorAIR&Dthemes:

AI-enabledapplications

AIalgorithmsandfoundationalresearch

AIsoftwareinfrastructure

NewhardwaretechnologiesforAI

Thesubcommitteebelievesthatthisten-yearAIforScienceinitiativeshouldbefundedatthesamescaleasthesuccessfulExascaleComputingInitiative(ECI)andExascaleComputingProject(ECP).Essentialforthesuccessofsuchaninitiativeisthattheworkofthesefourthemesmustbeclosely-coupledinamannersimilartothatusedintheECP,astheadvancesandimprovementsinoneareacaninformadvancesandimprovementsinotherareas.

Figure3illustratesanoverviewofapossibleroadmapforsuchanAIforScienceinitiative.AsfortheECIandECP,theroadmapforthisproposedAIforScienceinitiativeenvisagesaninitial‘incubation’researchphaseofcoordinatedprojectswithco-designcentersconnectingthefourmajorthemes.PartnershipsacrossallOfficeofSciencedomains,withparticipationfromuniversitiesandprivateindustry,wouldbeinitiatedearlyintheprogram.ThegoalofthisresearchphaseistospecifytheapplicationgrandchallengesandAI/MLtoolsandservicesrequiredasdeliverablesinthemorefocusedprojectR&DandDeploymentphases,wherebroad

engagementoftheDOEresearchcommunitybecomescritical.SincetheseappliedR&DandDeploymentphaseswillinevitablygeneratenewquestionsandchallenges,havingtheresearchphasecontinuingandoverlappingwiththeR&DandDeploymentphaseswillsignificantlyincreasethechancesofsuccessfortheAIforScienceProject.

AnInstrument-to-EdgeInitiative

ThesubcommitteebelievesthatASCR,inclosecooperationwithBESandwiththeotherscienceprogramsintheOfficeofScience,shouldworkwithscientists,users,andthebroadacademiccommunitytodefinerequirements,conductresearch,competitiveprocurementanddesignahighlyintegratedend-to-endsystemandsoftwarestackthatconnectsinstrumentsattheedgetotheneededAIcomputingresources.Integratingnationalandglobaldatasources(largescaleexperimentalfacilities,observationalnetworksterrestrial&space-based,etc.)posesuniqueopportunitiesandchallengesthatrequireaddressingfoundationalresearchinthecontextofleading-edgescientificexperiments.Integratedsystemsforacquiring,analyzing,transforming,storing,andmaintainingscientificresults,capturingprovenance,andcontributingbroadlyaccessedanalyticalworkflowswithinDOEsupportedcomputationalinfrastructurecouldbetransformative.Thereare,however,severechallengesthatwillneedtobeconfrontedintermsofprivacy,security,commerciallicensingofdata,andintegrateddataservices.

BuildingonASCR’sco-designexperienceinECP,applicationusers,softwareinfrastructuredevelopers,AI/MLresearchers,andLabandindustryhardwarespecialistsshouldbeencouragedtodefine,develop,andcontributetoacommonsoftwarestackforAI/MLEdgecomputingresourcesacrossthedifferentfacilities.ThesoftwareinfrastructureshouldsupportsomegenericservicesatthefacilitiesbutalsoallowtheeasycreationofspecializedAI-basedsoftwarepipelinesspecifictothefacilityandcapableofsupportingcouplingtoparticularinstrumentsinsomecases.

Training,focusing,andretentionofAI/MLworkforce

Industry,nationallaboratories,government,andbroadareasofacademicresearcharemakingmoreusethaneverbeforeofAI,ML,andsimulation-baseddecision-making.Thistrendisapparentacrossmanydomainssuchasenergy,manufacturing,finance,andtransportation.TheseareallareasinwhichAIisplayinganincreasinglysignificantrole,withmanymoreexamplesacrossscience,engineering,business,andgovernment.Researchandinnovation,bothinacademiaandintheprivatesector,areincreasinglydrivenbylarge-scalecomputationalapproachesusingAIandMLtechnologies.WiththissignificantandincreasedusecomesademandforaworkforceversedintechnologiesnecessaryforeffectiveandefficientAI/ML-basedcomputationalmodelingandsimulationandbigdataanalytics,aswellasthefundamentalsofAI/MLalgorithms.Graduateswiththeinterdisciplinaryexpertiseneededtodevelopand/orutilizeAItechniquesandmethodsinordertoadvancetheunderstandingofphysicalphenomenainaparticularscientific,engineering,orbusinessfieldandalsotosupportbetterdecision-makingareinhighdemand.

Astrongresearchprogramwillcruciallyrelyonacomplementaryeducationandskillscomponent,whichisasimportantasprovidingadequateinfrastructuresupport.AsemphasizedintheASCRECPTransitionreport[10],thisisalsoatimelyandimportantopportunitytofocusSCeffortstocreateamorediverseandinclusiveworkforce.Acontinuingsupplyofhigh-qualitycomputationalanddatascientistsavailableforworkatDOElaboratoriesisofvitalimportance.Inhighperformancemodelingandsimulation,forexample,theDOEComputationalScienceGraduateFellowship(CSGF)programhassuccessfullyprovidedsupportandguidancetosomeofthenation'sbestscientificgraduatestudents,andmanyofthesestudentsarenowemployedinDOElaboratories,privateindustry,andeducationalinstitutions.Weneedasimilarfellowshipprogramtomeettheincreasingrequirementforcomputationalanddatascientiststrainedtotackleexascaleanddata-intensivecomputingchallenges.Inaddition,theDOESCshouldexplorethepossibilitiesforcollaborationwiththeNSFabouttheprovisionofrelevanttrainingprogramsinAI/MLtechnologiesandtheirapplicationtoscience.

Inter-Agencycollaboration

AlthoughtheNSFhaslongbeenregardedastheleadagencyforfundamentalAIresearch,DOEisclearlytheleadagencyforresearchinvolvingtheintersectionof‘BigScience,BigData,andBigComputing.’DOEhasnotonlyestablishednationalandinternationalleadershipinHPCandsupercomputingbutisalsoaleaderintheapplicationofAI/MLtechnologiestotheverylargescientificdatasetsgeneratedbytheirlarge-scaleexperimentalfacilities.

WiththeNIH,theDOESChasasuccessfulcollaborationwiththeNationalCancerInstitute(NCI)intheCANDLEproject[11].DOEisnowdevelopinganMOUwithboththeNSFandNIHonaprogramofcollaborativeresearchinComputationalNeuroscience.Thesubcommittee,therefore,recommendsthattheSCexplorenewopportunitiestoworkwithbothNSFandNIHinareaswheretherewouldbeaclearbenefitforscientificprogressunderaDOE-ledAIforScienceinitiative.TheremayalsobeopportunitiestoworkwithotherUSfundingagencies,suchasNISTandDOD,inareasofmutualinterest.

Internationalcollaboration

Thereisaneedforbroad-based,coordinatedactionbylike-mindedinternationalpartnerstoharnesstheglobalscientificsoftwarecommunitytoaddressthetremendousopportunitiesindata-intensivesciencestemmingfromthehugeincreaseinscientificdatacollectionrates.ComputationalanddataanalyticalmethodsdrivenbyAI/MLarenowuniversallyacceptedasindispensableforfutureprogressinscienceandengineering.

InternationalleadershipinAIforScienceoverthecomingdecadewillhingeontherealizationofanintegratedsetofprogramsspanningthefourinterdependentareasnotedabove–AI-enabledapplications,AIalgorithmsandfoundationalresearch,AIsoftwareinfrastructure,andnewhardwa