ITUPublicationsInternationalTelecommunicationUnion

TelecommunicationStandardizationSector

CrowdsourcingAIand

MachineLearningsolutionsforSDGs

ITUAI/MLChallenges2024Report

ITU

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

ITUAI/MLChallenges2024Report

ITU

Foreword

i

TheITUArtificialIntelligenceandMachineLearning(AI/ML)ChallengesarecompetitionswhereanyonecanparticipatetosolveproblemstatementstoadvancetheachievementofSustainableDevelopmentGoals(SDGs)usingAI/ML.Thecompetitionsenableparticipantstoconnectwithnewpartners–andnewtoolsanddataresources–toachievegoalssetoutbyproblemstatementscontributedbyindustryandacademia.

Iampleasedtosaythatthesecompetitionshavewelcomedover8,000participantssincetheirlaunchin2020.

ThecompetitionsstimulateglobalaccesstoAI/MLexpertiseandcapabilitiesandempowerparticipantstocreate,train,and

deployMLmodelsbyofferingcuratedproblemstatements,data,technicalwebinars,mentoring,andhands-ontrainingsessions.Thisenhancesparticipants'skillsandglobalrecognitionandalsosupportsamoreinclusiveITUstandardizationprocessbypavingthewayforparticipantstomakevaluablecontributionstoITU'sspecifications.

Morethan70percentoftheparticipantsin2023werestudents,withalargemajorityfromtheAfricanregion.

Tosharetheoutcomeswiththelargercommunity,solutionssubmittedaresharedasopensourceinseveralrepositoriesontheChallengeGitHub:

/ITU-AI-ML-in-5G

-Challenge

.

Thisreporthighlightstheimportantworkofteamsacrosstheglobe.ItfeatureswinningsolutionsthataretheresultofinnovativeapproachestosolvingproblemswithapplicationsofAIacrossseveraldomains.

SeizoOnoe

DirectorITUTelecommunicationStandardizationBureau

i

Tableofcontents

Foreword

ii

Acronyms

vi

1ExecutiveSummary

1

2Introduction

3

3DomainsandAreasofCompetition

5

3.1AI/MLin5Gand6G(CommunicationNetworks)

5

3.2GeospatialArtificialIntelligence

6

3.3tinyML

6

3.4AIforClimateAction

7

3.5FusionEnergy

7

4Participation

8

4.1MotivationtoParticipate

8

4.2Statistics

9

4.3ChallengePhases/Timeline

11

5Problemstatements

13

6Winningsolutions

15

6.1AI/MLfor5G-EnergyConsumptionModelling

15

6.2Build-a-thon

16

6.3GraphNeuralNetworks(GNN)

16

6.4SmartWeatherStation

17

7Incentives

18

7.1Prizes

18

7.2Certificates

18

8Webinars

20

9Capacitybuilding

21

9.1TechnicalWebinars

21

9.2Hands-OnWorkshops

21

9.3MentoringSessions

21

9.4Round-TableDiscussions

21

iii

9.5OnlineLearningResources

22

9.6CertificationandRecognition

22

10Intellectualpropertyrights

23

11ChallengeSolutionContributions

24

11.1Standards

24

11.2OpenSource

24

11.3JournalandConferencePublications

24

11.4Ecosystemcreation

26

12Judgingthesubmissions

28

12.1Commonoutputformat

28

12.2Additionaloutputforopen-sourcecode

28

12.3Additionaloutputforproprietarycode

28

12.4EvaluationCriteria

28

13Resources

30

14Benefits

31

14.1Benefitsforpartnersandcollaborators

31

14.2BenefitsforParticipants

31

14.3SpecialBenefitsforCertainSponsorCategories

31

15Impact

32

15.1AdvancingTechnologicalInnovation

32

15.2PromotingGlobalCollaboration

32

15.3EnhancingPracticalSkills

32

15.4ContributingtoStandardsDevelopment

32

15.5AddressingSDGs

32

15.6RecognizingandRewardingExcellence

32

15.7BuildingaThrivingEcosystem

33

15.8ShowcasingandDisseminatingResearch

33

16Testimonials

34

17Conclusion

35

Annex1:Data

36

Annex2:ProblemStatementSample

38

Annex3:DataSharingGuidelines

39

iv

Annex4:HostOnboardingGuidelines

44

Listoffiguresandtables

Figures

Figure1:Geographicdistributionofparticipantsbycountry/regionfrom2020

-2023

3

Figure2:Distributionofparticipantsforthechallenge

3

Figure3:VariousdomainscoveredintheITUAI/MLChallenge

4

Figure4:Motivationtoparticipateinthechallenge

8

Figure5:Cumulativegrowthofparticipantsfromthetoptencountriessince2020

9

Figure6:CombinedGrowthoftheChallengebyType

9

Figure7:Participationandtotal#submissionsfor2023invariousdomainsof

theITUAI/MLChallenges

11

Figure8:ParticipantsGenderDistribution

11

Figure9:2023ITUAI/MLChallengeTimeline

12

Figure10:SampleChallengeproblemstatements

13

Figure11:WinnerannouncementofAI/MLfor5G-EnergyConsumption

ModellingchallengeatCOP28inDubai

15

Figure12:2ndGNNetWorkshop

17

Figure13:Aurorasmartweatherstation

17

Figure14:WinnerCertificates

19

Figure15:TheML5Gwebinarseriesin2020

20

Figure16:Thecallforpaperforthespecialissueofthepeer-reviewedITU

JournalforFutureandEvolvingTechnologies

25

Figure17:Ecosystem

26

Figure18:2024ChallengeannouncementinShanghaiduringtheAIfor

GoodInnovateforImpactatWorldAIconference

27

Figure19:TestimonialsfromChallengeorganizersandparticipants

34

Figure20:Guidelines

42

Tables

Table1:CompetitionDetails

10

Table2:ProblemStatementSample

38

Table3:DataClassificationCategories

39

v

Acronyms

ACM

AssociationforComputingMachinery

AI

ArtificialIntelligence

CSV

Comma-separatedValue

FGAN

FocusGroupAutonomousNetworks

GNN

GraphNeuralNetworks

IEEE

InstituteofElectricalandElectronicsEngineers

IPR

IntellectualPropertyRights

ITUJ-FET

InternationalTelecommunicationUnionJournalonFutureandEvolvingTechnologies

ML

MachineLearning

NDA

Non-disclosureAgreement

PoC

ProofofConcept

RRM

RadioResourceManagement

SDG

SustainableDevelopmentGoal

SG

StudyGroup

TSB

TelecommunicationStandardizationBureau

vi

CrowdsourcingAIandMachineLearningsolutionsforSDGs

1ExecutiveSummary

ArtificialIntelligence(AI)isadominanttechnologyandimpactseveryaspectofsociety.AsAIcontinuestoevolve,AI/ML-enabledapplicationsandservicesintegratedwiththefutureofcommunicationnetworkswoulddriveinnovationandrelatedstandards.ITUisattheforefrontofexploringhowbesttoapplyAI/MLthroughvariousinitiativesandprojectstoadvancetheachievementofsustainabledevelopmentgoals(SDGs).ITUAI/MLcompetitions,bringtogetherAI/MLstakeholderstobrainstorm,innovateandsolverelevantproblemsintelecommunicationnetworks,Geospatialchallenges,tinyMLusecases,etc.Buildingonitsstandardscommunity,ITUhasbeenconductingglobalITUAI/MLChallengesmappedtoseveralareasimpactingSDGs.

TheITUAI/MLin5GChallengeaimstosolvereal-worldcommunicationnetworkproblemsusingAIandML,focusingonthedevelopmentandoptimizationof5Gandemerging6Gtechnologies.Participantsengageintechnicalwebinars,mentoring,andhands-onsessions,creatinganddeployingMLmodels,andapplyingITUstandards,therebygainingglobalrecognitionfortheirinnovativesolutions.

TheGeoAIChallengeappliesAI/MLtoaddressreal-worldgeospatialproblemsrelatedtotheUNSDGs.Participantsgainpracticalexperiencebytacklingissuessuchasenvironmentalmonitoring,urbanplanning,anddisasterresponse,promotinginnovativesolutionsforsustainabledevelopment,andofferingprizes,recognition,andcertificatestotopperformers.

ThetinyMLChallengeexploresapplyingmachinelearningtotinydevicesandembeddedsystemstobuildcost-effective,low-power,reliable,andeasy-to-install,solutionsbyleveragingtinyMLtechnology.

TheITUAI/MLChallengeofferscarefullycuratedproblemstatements,amixofreal-worldandsimulateddata,technicalwebinars,mentoring,andhands-onsessions.TeamsparticipatingintheChallengeenable,create,train,anddeployMLmodelsfordifferentdomains.Thisenablesparticipantstonotonlyshowcasetheirtalent,testtheirconceptsonrealdataandreal-worldproblems,andcompeteforglobalrecognitionincludingprizemoneyandcertificates,butalsoentertheworldofITUstandardsbymappingtheirsolutionstoourspecifications.

TheITUAI/MLChallengehashadprofoundimpactsacrossmultipledimensions.

1

CrowdsourcingAIandMachineLearningsolutionsforSDGs

Standards:ThechallengehasfacilitatedtheintegrationofinnovativeAI/MLsolutionsintoITUspecifications,ensuringnewtechnologiesarestandardizedandwidelyadopted.

Research:Thechallengehasspurredcutting-edgeinvestigationsandpracticalapplications,leadingtonumerouspublicationsinjournalsandconferences.

Communitybuilding:ThechallengehasalsofosteredavibrantcommunityofAI/MLpractitioners,withmembersfromdiversebackgroundsandover100countries,creatingaglobalnetworkofcollaboratorsandinnovators.

Capacitybuilding:Thechallengehasprovidedparticipantswithinvaluableskillsthroughtechnicalwebinars,hands-onworkshops,andmentoringsessions,enhancingtheirabilitytotacklereal-worldproblems.

Overall,theITUAI/MLChallengehassignificantlycontributedtotechnologicaladvancement,globalcollaboration,andthedevelopmentofarobustecosystemthatdrivesprogressinAI/MLandcommunicationnetworks.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

2Introduction

TheITUAI/MLChallengewaslaunchedin2020.Thefirsteditionranonthetheme“HowtoapplyITU’sMLarchitecturein5Gnetworks”andappliedtothecommunicationnetworksdomain(ITUAI/MLin5GChallenge).ITUisattheforefrontofleveragingAI/MLtoachieveSDGs.Throughavarietyofactivitiesandprojects,ITUbringstogethermultiplestakeholderstobrainstorm,innovate,andsolverelevantproblemsacrossdifferentdomains.TheITUAI/MLChallengeisoneofthekeyinitiativesaimedatfosteringglobalcollaborationandinnovationintheapplicationofAI/MLtoSDGswithanemphasisoncommunicationnetworks.ThischallengehasbeeninstrumentalinexploringhowAIcanbeappliedto5G,geospatialtechnologies,tinyML,andotherareastodriveprogresstowardstheSDGs.

Figure1:Geographicdistributionofparticipantsbycountry/regionfrom2020-2023

Theboundariesandnamesshown,andthedesignationsusedonthismapdonotimplyofficialendorsementoracceptancebytheUnitedNations/ITU.

Note:participantsfrommorethan100countries/regionsparticipatedintheChallenge.Thetopfourcountriesareasfollows:India,UnitedStates,ChinaandNigeria.

Figure2:Distributionofparticipantsforthechallenge

Note:morethan57%ofparticipantsareprofessionalsandaround38%arestudents.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

Since2020,theITUAI/MLChallengehasevolvedtoincludemultipledomains,eachaddressingspecificareasofinterestandimpact.Thechallengeconnectsparticipantsfromover100countries,includingstudents,professionals,industryexperts,andacademia,tosolvereal-worldproblemsusingAI/ML.Thecompetitionsoffercarefullycuratedproblemstatements,amixofreal-worldandsimulateddata,technicalwebinars,mentoring,andhands-onsessions.Participantscreate,train,anddeployMLmodels,enablingthemtoshowcasetheirtalent,testtheirconceptsonrealdata,andcompeteforglobalrecognition,includingprizemoneyandcertificates.ThisinitiativealsoprovidesagatewaytotheworldofITUstandards,asparticipantsmaptheirsolutionstoITUspecifications.

ThedomainscoveredintheITUAI/MLChallengeincludeAI/MLin5Gand6G(orcommunicationnetworks),GeoAI,tinyML,AIforClimateAction,andFusion.Eachdomainoffersuniqueopportunitiesforparticipantstoapplytheirskillsandgainhands-onexperienceinaddressingcriticalissues.TheAI/MLin5GChallengefocusesontheapplicationofAI/MLincommunicationnetworks,optimizingthedevelopmentandperformanceof5Gand6Gtechnologies.TheGeoAIChallengeaddressesgeospatialproblemsrelatedtotheUNSDGs.ThetinyMLChallengeexplorestheapplicationofMLintinydevicesandembeddedsystems.TheAIforClimateActionInnovationFactoryaimstodevelopAIsolutionsforcombatingclimatechange,whiletheFusionChallengefocusesonusingMLforpredictivemodelinginfusionenergysystems.Throughthesediversedomains,theITUAI/MLChallengecontinuestodriveinnovationandcollaboration,contributingtotheadvancementofglobalstandardsandthedevelopmentofimpactfulsolutions.

Figure3:VariousdomainscoveredintheITUAI/MLChallenge

The2023ITUAI/MLChallengesawmorethan3300participantsfrom100+countriesinthechallenge.Theseparticipantscontributedover20'000submissionsandreceived56'267CHFinprizemoneyfromITUandsponsors.Detailedstatisticsofthechallengecanbefoundinsection4.2.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

3DomainsandAreasofCompetition

Since2020,theITUAI/MLChallengehasevolvedtoincludemultipledomains,eachaddressingspecificareasofinterestandimpact.Thesecompetitionsarerunannually,witheacheditionintroducingnewthemesandexpandingthescopeofthechallenge.ThecompetitionshaveincludedAI/MLin5Gand6G(i.e.communicationnetworks),GeoAI,tinyML,AIforClimateAction,andFusion.Eachdomainoffersuniqueopportunitiesforparticipantstoapplytheirskills,gainhands-onexperience,andcontributetosolvingpressingglobalissues.

3.1AI/MLin5Gand6G(CommunicationNetworks)

Applyingmachinelearningin

communicationnetworks

TheITU

AI/MLin5GChallenge

rallieslike-mindedstudentsandprofessionalsfromaroundtheglobetosolvereal-worldproblemsincommunicationnetworksbyapplyingAIandmachinelearning(ML).TheAI/MLin5GChallenge,launchedasthefirsteditionin2020,hasbecomeacornerstoneoftheITUAI/MLChallenge.ThiscompetitionfocusesonapplyingAI/MLincommunicationnetworks,particularlyinthedevelopmentandoptimizationof5Gandemerging6Gtechnologies.Astelecommunicationnetworksevolvetowards6G,AIisexpectedtobeintegraltothenetwork’sdesign,enablingadvancedfeatureslikeAI-nativeinfrastructure,pervasiveintelligence,andreal-timeresponsiveness.

ITUAI/MLin5GChallengeanalysespracticalproblemsinnetworksusingrealandsimulateddata.Asweaimforenhancedefficiency,reliability,andrichuserexperienceusingAI/MLincommunicationnetworks,ITUcallsfortheapplicationofitspre-standardandstandardconceptsinnetworkmanagement,security,optimization,andbeyondtosolvereal-worldproblems.IntheITUAI/MLin5GChallenge,participantsfromvariousbackgroundscollaboratetosolvereal-worldproblemsusingAI/ML,workingoncuratedproblemstatementswithaccesstoamixofreal-worldandsimulateddata.Thechallengeincludestechnicalwebinars,mentoring,andhands-onsessions,enablingparticipantstocreate,train,anddeployMLmodelsforcommunicationnetworks.ThecompetitionnotonlyshowcasestalentandinnovativesolutionsbutalsoprovidesapathwayforparticipantstoengagewithITUstandardsandgainglobalrecognition.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

3.2GeospatialArtificialIntelligence

ApplyingMachineLearningtoGeospatialAnalysis

The

GeospatialArtificialIntelligenceChallenge

(GeoAI),nowenteringitsthirdeditionin2024,addressesreal-worldgeospatialproblemsbyapplyingAI/ML.ThiscompetitionaimstosolveissuesrelatedtotheUNSDGsusingreal-worlddata.ParticipantsgainpracticalexperienceinapplyingAI/MLtogeospatialdata,tacklingproblemssuchasenvironmentalmonitoring,urbanplanning,anddisasterresponse.Thechallengepromotesinnovativesolutionsthatcontributetosustainabledevelopment,offeringprizes,recognition,andcertificatestothetopperformers.

3.3tinyML

ApplyingMachineLearningtoEdgeDevices

The

tinyMLChallenge

,organizedincollaborationwithindustrypartners,explorestheapplicationofmachinelearninginthedomainoftinydevicesandembeddedsystems.Thesecondeditionofthischallengein2023focusedondevelopingaNext-GentinyMLSmartWeatherStationthatiscost-effective,low-power,reliable,andeasytoinstallandmaintain.Thisweatherstationwillmeasurevariousweatherconditions,particularlyrainandwind,usingtinyMLtechnology.Additionally,thetinyMLChallengeincludesprojectsonscalableandhigh-performancesolutionsforcropdiseasedetectionandwildlifemonitoring.Thiscompetitionencouragesinnovationinenvironmentalmonitoringandagriculture,leveragingthecapabilitiesoftinyML.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

3.4AIforClimateAction

AnacceleratorplatformforAI-poweredclimatechangesolutionsfromstart-ups

Climatechangeisasignificantglobalchallengewithfar-reachingimpacts.The

AIforClimateAction

InnovationFactory

,launchedattheAIforGoodSummitin2024,seekstoadvancetheuseofAIincombatingclimatechange.ThisinitiativebuildsonprevioussuccessesandfocusesondevelopingAIsolutionsthataddressclimate-relatedissues.The2024editionaimstoshowcasethesesolutionsatCOP29,theUnitedNationsClimateChangeConferenceinBaku,Azerbaijan.ThewinnersofthiscompetitionwillberecognizedfortheircontributionstotheGreenDigitalActiontrack,highlightingtheroleofAIinpromotingsustainablepracticesandmitigatingclimatechange.

3.5FusionEnergy

The

FusionChallenge

,partoftheIAEACoordinatedResearchProjectonAIforFusion,exploresthepotentialofMLinpredictivemodelingforfusionenergysystems.Fusionenergy,generatedbycombininglightelementstoformaheavierone,representsapromisingalternativeenergysource.Thischallengeengagesthescientificcommunityindevelopingcross-machinedisruptionpredictionmodelsusingML,utilizingdatafromfusiondevicessuchasAlcatorC-Mod,J-TEXT,andHL-2A.Participantsgainhands-onexperienceinAI/MLapplicationsrelevanttofusionenergyscience,competingforprizes,recognition,andcertificates.Thiscompetitionsupportstheglobalefforttomakefusionacommerciallyviableenergysource.

TheITUAI/MLChallenge,throughitsdiversedomainsandcompetitions,continuestodriveinnova-tionandcollaborationinAI/ML.Byaddressingcriticalissuesacrossvarioussectors,thechallengecontributestotheadvancementofglobalstandardsandthedevelopmentofsolutionsthathaveasignificantimpactonsociety.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

4Participation

ParticipationisopentoITUmembersandanyindividualfromanITUMemberState.“Participants”areindividualsorcompaniesthatparticipateintheITUAI/MLin5GChallenge,providingsolutionstoproblemsetsoftheChallenge.

Therearetwocategoriesofparticipants:studentandprofessional.

4.1MotivationtoParticipate

Aftereachiterationofthechallengeiscompleted,participantsareaskedtocompleteasurveypreparedbythechallengesecretariat.Oneofthekeyquestionsinthesurveyfocusesontheparticipants'motivationforjoiningthechallenge.ThefigurebelowillustratesthevariousreasonswhyindividualschoosetoparticipateintheITUAI/MLChallenges.Notably,theprimarymotivationformostparticipantsistheopportunitytoupskillorenhancetheirprofessionaloracademiccapabilities,ratherthanthepursuitofprizes.

Figure4:Motivationtoparticipateinthechallenge

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

4.2Statistics

ITU’smachinelearningchallengeshaveseenanexponentialincreaseinparticipationsince2020,welcomingover8,000participantsfrommorethan100countries,withdevelopingcountriesparticularlywellrepresented,asthechartbelowdemonstrates.

Figure5:Cumulativegrowthofparticipantsfromthetoptencountriessince2020

Thenumberofparticipantshasincreasedfourtimessince2020reachingaround8000intheyear2023.Seethegraphbelow:

Figure6:CombinedGrowthoftheChallengebyType

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

Participantsinthechallengehavemademorethan23’000submissionstothechallengebyJuneof2024.ThebelowtablesshowgranularparticipationdetailstosomeproblemstatementsoftheITUAI/MLChallengeproblemstatementsin2023.MostoftheseproblemstatementswerehostedthroughtheZindiplatform.

Table1:CompetitionDetails

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

Figure7:Participationandtotal#submissionsfor2023invariousdomainsoftheITUAI/MLChallenges

Thegenderdistributiongraphrevealsthatnearly80%oftheparticipantsaremale,highlightingtheimportanceofencouraginggreaterfemaleparticipation.

Figure8:ParticipantsGenderDistribution

4.3ChallengePhases/Timeline

TheITUAI/MLChallengeisrunthroughouttheyeardependingonproblemstatementsprovidedbypartners.Anexampleofachallengetimelineforthe2023ITUAI/MLin5GChallengeisillustratedbelowtoshowthevariousphasesofthechallenge.

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

Figure9:2023ITUAI/MLChallengeTimeline

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CrowdsourcingAIandMachineLearningsolutionsforSDGs

5Problemstatements

ParticipantsoftheITUAI/MLChallengecansolvereal-worldproblems(includingthosewithsocialrelevance).ProblemstatementsarecontributedeitherfromITU’sstandardsandspecifications,orfromhostsofproblemstatementswhoareinstitutionsinterestedinadvancingSDGsorcanbedecidedbytheparticipant(s)themselves.Problemstatementswillfallintoaspecificchallengedomainbasedontheproblemowner(host)interestandresources.

The

AIforGoodGlobalSummit

identifiespracticalapplicationsofAI/MLwiththepotentialtoaccelerateprogresstowardsthe

UnitedNationsSustainableDevelopmentGoals

.Solutionsareinvitedinfieldssuchaseducation,healthcareandwellbeing,socialandeconomicequality,climateaction,naturaldisastermanagement,space,andsmartandsafemobility.SelectedteamswillbeinvitedtoparticipateintheAIforGoodSummit.

Figure10:SampleChallengeproblemstatements

TheITUAI/MLChallengecontinuestohostproblemstatementsfromhostsaroundtheworld.Someofthescheduledproblemstatementsareasfollows:

?GreenTelecom:SmartEnergySupplyScheduling[Smartenergysupplyschedulingforbothcarbonfootprintreductionandnetworkreliabilityguarantee]

?Beam-levelTrafficPrediction

?SpecializingLargeLanguageModelsforTelecomNetworks

?Ground-levelNO2EstimationChallenge

?RadioResourceManagement(RRM)for6Gin-XSubnetworks

TheITUAI/MLChallengeservesasacrucialbridgebetweencurrentinnovationsandfutureresearchandstandards.Byengagingparticipantsinsolvingreal-worldproblemsusingAIandML,thechallengefostersthedevelopmentofpracticalsolutionsthatcaninformfutureresearchdirections.Thesesolutionsoftenleadtonewinsightsanddiscoveries,fuellingfurtherinvestigationsandacademicstudies.

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CrowdsourcingAIandMachineLearningsol