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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.
2
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.
3
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.
4
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.
5
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.
6
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.
7
CrowdsourcingAIandMachineLearningsolutionsforSDGs
4Participation
ParticipationisopentoITUmembersandanyindividualfromanITUMemberState.“Participants”areindividualsorcompaniesthatparticipateintheITUAI/MLin5GChallenge,providingsolutionstoproblemsetsoftheChallenge.
Therearetwocategoriesofparticipants:studentandprofessional.
4.1MotivationtoParticipate
Aftereachiterationofthechallengeiscompleted,participantsareaskedtocompleteasurveypreparedbythechallengesecretariat.Oneofthekeyquestionsinthesurveyfocusesontheparticipants'motivationforjoiningthechallenge.ThefigurebelowillustratesthevariousreasonswhyindividualschoosetoparticipateintheITUAI/MLChallenges.Notably,theprimarymotivationformostparticipantsistheopportunitytoupskillorenhancetheirprofessionaloracademiccapabilities,ratherthanthepursuitofprizes.
Figure4:Motivationtoparticipateinthechallenge
8
CrowdsourcingAIandMachineLearningsolutionsforSDGs
4.2Statistics
ITU’smachinelearningchallengeshaveseenanexponentialincreaseinparticipationsince2020,welcomingover8,000participantsfrommorethan100countries,withdevelopingcountriesparticularlywellrepresented,asthechartbelowdemonstrates.
Figure5:Cumulativegrowthofparticipantsfromthetoptencountriessince2020
Thenumberofparticipantshasincreasedfourtimessince2020reachingaround8000intheyear2023.Seethegraphbelow:
Figure6:CombinedGrowthoftheChallengebyType
9
CrowdsourcingAIandMachineLearningsolutionsforSDGs
Participantsinthechallengehavemademorethan23’000submissionstothechallengebyJuneof2024.ThebelowtablesshowgranularparticipationdetailstosomeproblemstatementsoftheITUAI/MLChallengeproblemstatementsin2023.MostoftheseproblemstatementswerehostedthroughtheZindiplatform.
Table1:CompetitionDetails
10
CrowdsourcingAIandMachineLearningsolutionsforSDGs
Figure7:Participationandtotal#submissionsfor2023invariousdomainsoftheITUAI/MLChallenges
Thegenderdistributiongraphrevealsthatnearly80%oftheparticipantsaremale,highlightingtheimportanceofencouraginggreaterfemaleparticipation.
Figure8:ParticipantsGenderDistribution
4.3ChallengePhases/Timeline
TheITUAI/MLChallengeisrunthroughouttheyeardependingonproblemstatementsprovidedbypartners.Anexampleofachallengetimelineforthe2023ITUAI/MLin5GChallengeisillustratedbelowtoshowthevariousphasesofthechallenge.
11
CrowdsourcingAIandMachineLearningsolutionsforSDGs
Figure9:2023ITUAI/MLChallengeTimeline
12
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.
13
CrowdsourcingAIandMachineLearningsol
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