GTI5GandCloudRobotics

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CloudRobotics:Trends,Technologies,

Communications

Abstract

Cloudrobotsarecontrolledfroma“brain”inthecloud.Thebrain,locatedinadatacenter,makesuseofArtificialIntelligenceandotheradvancedsoftwaretechnologiestodealwithtasksthatintraditionalrobotswereundertakenbyalocal,on-boardcontroller.Comparedtolocalrobots,cloudrobotswillgeneratenewvaluechains,newtechnologies,newarchitectures,newexperiencesandnewbusinessmodels,thiswhitepaperwillexploretheseaspects.

Introduction

Cloudroboticsisarelativelyrecentconcept.Earlyworkdatesbackto2010,whentheEuropeanCommission’sRoboEarthiprojectbegan.Thisaimedtoestablisha“WorldWideWebforrobots”.RoboEarthandlaterprojectssuchasRapyutaiiandRobohowiiiformalizedthebasicconceptandtechnologies,andarestillinfluencingcloudroboticresearchtoday.

Therearethreecoreadvantagesofcloudrobotscomparedtostand-alonerobots:

InformationsharingManycloudrobotscanbecontrolledfromonebrain,andthebraincanaccumulatevisual,verbal,andenvironmentaldatafromallconnectedrobots.Intelligencederivedfromthisdatacanbeusedbyalltherobotscontrolledbythebrain.Aswithothercloudservices,informationcollectedandprocessedoneachrobotwillalwaysbeup-to-dateandbacked-upsafely.Developersalsobenefit,astheycanbuildreusablesolutionsforallcloud-connectedrobots.

OffloadedcomputationSomerobottasksrequiremorecomputationalpowerthanalocalcontrollercaneconomicallydeliver.Offloading

totheclouddata-intensivetaskssuchasvoiceandimagerecognition,voicegeneration,environmentalmappingandmotionplanningwilllowerthehardwarerequirementsandpowerconsumptionofrobots,makingthemlighter,smaller,andcheaper.

CollaborationCloudrobotsdonotneedtoworkalone.Usingthecloudasacommonmedium,tworobotscanworktogethertocarryanobjecttooheavyforone,oragroupofsimpleworkerrobotscanworkwithalocalmap,providedbyaleaderrobotwithcostlysensors.

Figure1:Largescaledatacollectionwithanarrayofrobots(14robotsaresharingexperiencesofmachinelearningforgrasping)[Source:

/2016/03/deep-learning-for-robots-learning-from.html]

DistributedversionofAlphaGoexploited40searchthreads,1202CPUsand176GPUsx,noordinaryrobotcaninstallinside.Butcloudrobotcanmakeuseofit.

Applicationsforcloudrobots

Usingcloudresourcesempowersrobotsandgivesthemnewcapabilitiesinmanyareas:

Intelligentvisualprocessing:imageclassification,targetdetection,imagesegmentation,imagedescription,characterrecognition.

Naturallanguageprocessing:semanticunderstandingbasedondepthlearning,accurateidentificationofuserintent,multi-intentionanalysis,emotionalanalysis.Makesuseofapowerfulbackgroundknowledgebase.

Facialrecognition:facedetectionalgorithmbasedondepthlearning;Inthereal-timevideo

streamtoaccuratelydetecttheface;Anyfacemaskandreal-timedetectionundertheviewingangle;Toovercome:thesideface,halfobscured,blurredface;

Extensionfromcurrentrobotapplications:outdoormapnavigation,indoorpositioningandnavigation,typicalproductidentification,universalitemidentification,environmentalunderstanding,textreading,voiceprompts.

Theapplicationsthatwillemergeforcloudrobotsareofmanykinds;someareemergingnow–othersareatanearlystageofdevelopment.

Logistics

Amazon,Jingdong,S.F.Expressandothercompanieshavedeployedlogisticsrobotsystems.ThewheeledAGV(AutomatedGuidedVehicle)isthemaintypeoflogisticsrobot(thoughlogisticscompaniesarealsotriallingtheuseofaerialdrones).Byconnectingtothecloud,AGVscanachieveunifiedscheduling(whereallAGVsareworkingasasinglesystemformaximumefficiency).Inaddition,AGVscanbeequippedwithmachinevisionsystems,andvideocanbetransmittedtocloud-basedsystemstohandleavarietyofsituationsontheroad.EventuallythiswillresultinAGVscomingoutofcontrolledareastotakeonmorework,includinginpublicplacesfordeliveryofparcelsorfood.

Securityandsurveillance

Inpublicplaces,cloudrobotscanperform24/7securityinspections,replacingsecuritypersonnel.Thecloudrobotwillcollectvideoandstillimagesandsendthemtothepublicsafetycloudforreal-timeidentificationofsuspiciouspeopleand

Personalassistanceandcare

Providingpersonalassistanceandcarefortheelderlyiswidelyconsideredthe“nextbigthing”inrobotics.Thepowerofthecloudmakescarerobotsbehavemorelikehumans.Theycancarryoutreal-timemonitoringofpersonalhealth,helppeoplemoveabout,andcompletehousework.AnexampleofthistypeofrobotisSoftbank’sRomeo.

activity.SuchrobotsarealreadybeingusedatShenzhenairportinChina.

Guidance

Inpublicplacessuchasenterprises,banksandhospitals,robotslikeSoftbank’sPepperarebeingusedtoguidevisitors.TheyarealsobeingusedtodeliverretailservicesbycompaniesincludingNestle,YamadaElectricandMizuhoBank.Cloudrobotscanmakeuseofavastknowledgedatabaseinthecloud,andcommunicateusingnaturallanguage;theycanevenrecogniseandrespondtopeople’sexpressionsusingcloud-AI-basedimageanalysis,toimprovetheuseexperience.

Education,entertainmentandcompanionship

Inrecentyears,theapplicationofmachinevisionandartificialintelligencehasresultedinthedevelopmentofmanyrobotsforeducationandentertainment.ExamplesincludeJibo,AsusZenbo,andSoftbankNao.Theserobotshaveahumanoidappearanceandtheabilitytousenaturallanguage.Theycandownloadcontentfromthecloudtoprovideeducationandentertainmentservices.

Figure2:Cloud-poweredsmartdevicesandcommunicationrobots[Source:Softbank]

Markettrends

Robotscanbecategorisedasindustrialrobotsorservicerobots,accordingtotheiruse.Service

226.2$bn

Accordingtomarketanalyst

robotscanbefurtherdividedintoprofessional

servicesrobotsandpersonalhomeservicerobots.Professionalservicerobotsareusedinthefieldsofmedicine,construction,underwaterengineering,logistics,defenceandsafety.Personalhomeservicerobotsareusedtoundertakehousework,providecompanionshipandpersonalassistance,andarealsousedinotherfields.

companyTractica,thevalueoftheglobalrobotmarketwillgrowfrom$34.1billionin2016to

$226.2billionin2021,withacompoundannualgrowthrate(CAGR)of46%invalueterms.Mostofthegrowthwillbeinthemarketfornon-industrialrobotsIX.

2bnOneofthemajordriversofthismarketgrowthistheagingpopulation.Therearefewerworking-agepeopletotakecareoftheincreasing

numbersoftheelderly.TheUNhasforecastthatby205021%oftheglobalpopulationwillbeovertheageof60–atotalofover2billionpeople.

Robotshavearoletoplayhere.Inaddition,industrialautomationcontinuestodevelopatarapidpace,withinitiativessuchasIndustry4.0inGermanyandMadeinChina2025.

AdvancesintechnologiesincludingArtificialIntelligence,theInternetofThingsandwirelesscommunicationsaremakingrobotsmorecapable.Theycannowidentifytheirsurroundings,calibratetheirposition,plantrajectories,andusenaturalinterfacestointeractwithhumans.Therehavebeenincreasesinthecapabilitiesofrobotsusedinindustry,agriculture,logisticsandeducation.Therapidriseintheuseofdronesisalsoevidenceoftheincreasingcapabilitiesofrobots.

CloudrobotswillsoonbecomethenormCloud-basedAIandconnectivitywillshapethedevelopmentoftherobotmarketsignificantlyin

thenextfewyears.Thesetechniqueshavealreadybeguntochangethewaythatpeopleinteract:technologygiantshavedevelopedAI-basedsystemsthatarebecomingwidelyused.ExamplesincludeGoogleCloudSpeechAPI,AmazonAlexa,BaiduDuer,IBMWatson,AppleSiriandMicrosoftCortana.

12%AccordingtoHuaweiGIV,by2025theuseofmobileconnectivityandartificialintelligencewillresultinrobotpenetrationinthefamilyof12%;intelligentrobotswillchangethefaceofallindustriesinthesamewaythattheautomotiveindustrywastransformativeinthe20thcentury.

GTIcloudroboticsworkinggroupresearchforecaststhatby2020connectedrobotswillaccountfor90%ofallrobots,andabout20millionnewconnectionswillbeneededeveryyeartosupporttheirday-to-dayoperations.

GTIcloudroboticsworkinggrouphasexaminedtheroboticsmarketindetail.Itsworksuggeststhatby2020theproportionofconnectedrobotsgloballywillbe90%,andabout20millionnewconnectionswillbeneededeveryyeartosupporttheirday-to-dayoperations.Figures3-7showprojectionsforsalesofconnectedrobots.

Figure3:Connectedrobotsales2016-2020(million)[Source:GTIcloudroboticsworkinggroup]

Figure4:Connectedlogisticssystemrobots(thousand)[Source:GTIcloudroboticsworkinggroup]

Figure5Connecteddomesticrobots(million)

[Source:GTIcloudroboticsworkinggroup] 8

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10

Figure6:Connectedentertainmentrobots(million)

[Source:GTIcloudroboticsworkinggroup]

Figure7Connecteddisabledcareassistantrobot(thousand)[Source:GTIcloudroboticsworkinggroup]

Inthenextfewyears,domesticrobotsandrecreationalrobotswilloccupymostoftheshipmentsofconnectedrobots.Withtheincrease

inthecapabilityofrobots,theneedsofindividualsandfamiliesforservicerobotswillcontinuetoincrease.

willing unwilling neither

TURKEY

QATARNETHERLANDS

NORWAYGERMANY

UK

60

45

40

35

30

27

Figure8:WillingnesstouseAIandrobotsforhealthcare[Source:PwC]

Figure9:Willingnesstohavesurgeryperformedbyrobot

[Source:PwC]

Thecurrentpublicacceptanceofroboticservices,especiallymedicalservices,isnothigh.Peopleareskepticalaboutwhetherrobotscanreachthelevelsofskillofhumandoctors.However,inthenextfewyears,withrobots’abilitiesgraduallyimproving,people’sacceptanceofroboticmedicalserviceswillincrease.

ResearchpublishedbytheOpenRoboethicsInitiativeshowsthatthemainexpectationofhomeservicerobotsistocompletehouseworktomakelifeeasier.Inaddition,education,inspectionandsecurityneedsarerelativelystrong.

9%

11%

17%

19%

26%

32%

32%

38%

75%

Other

Fancytoy

Petreplacement

Companionforfamlily Forcoolnessfactor Educationtoolforchild

Homesecurity ExtensionofelectronicdevicesHouseholdchores

Figure10:Reasonsforpurchaseahomerobot[Source:OpenRoboethicsInitiative]

Thecloudroboticsvaluechain

ThevaluechainofcloudroboticsisshowninFigure11.Therobotplatformproviderdeliverstherobotwhichrunsapplications;theseapplicationsuseintelligentservicesfromtheAIprovider,makinguseofthemobilenetworktoprovidea“smart”userexperienceforendusers.

Robot

Platform

Application

Provider

Mobile

Network

AIProvider

Endusers

Figure11:Cloudroboticsvaluechain[Source:GTIcloudroboticsworkinggroup]

Robotplatform–thetechnologiesbehindcloudrobots

Thedefinitionofrobotmayvarybycontext,butageneraldefinitionis“Amechanicalsystemwiththreeelements:controller,sensor,and

effector/actuator”.

Controller

Astherobotgainscomplexityanddemandsbecomemoreadvanced,thecontrollerparthasalsodevelopedandtoday’srobotsareoftencontrolledbyOSorrichmiddleware,suchasROS(

/

),OpenRTM-aist,middlewarecompliantwithObjectManagementGroup(OMG)RoboticTechnologyComponent(RTC)Specificationiv,andNAOqi(OSusedinSoftbank’sPepper).

Incloudrobots,thecontrollerpartisachievedbycoordinationofcloudandlocalsystems.

Sensors

Robotsusemanydifferenttypesofsensorsrelevanttotheirfunction.Themostimportanttypesare:

CamerasandmicrophonesSophisticatedcamerasandmicrophonesarerequiredtosensetheenvironment.Forinstance,Softbank’shuman-sizedcommunicationrobot

Peppervusesa3DcameraandtwoHDcameras(seeFigure12),andfourdirectionalmicrophonestodetectwheresoundsarecomingfromandlocateuser’sposition.

Figure12:MicrophonearrayandtopcamerainPepperrobot[Source:

http://techon.nikkeibp.co.jp/article/COLUMN/201506

23/424503/?P=2]

3Dcamerasareusedtoprovidepositiondetectionandmapping(oftenreferredtoasSLAM(simultaneouslocationandmapping)).Other3Dpositioningsensorsandtechnologiesarealsoused,frominexpensiveproximitysensing,sonarandphotoelectricsensingtomoreaccurateandcostlytechniquessuchasLiDARthatcanbeusedtobuilduphighresolution3Dpicturesacrossawidecoveragearea.

Wirelessnetworksshouldprovidesufficientbandwidthandlatencyperformancetosendsensordatatothecontroller.Astheaccuracyof

thesensorincreases,sodoesthebandwidthrequired.

Approach

Accuracy

Range

DataRate

3Dcamera

Stereotriangulation/structuredlight

Accurate

Middle

2.8Mbit/s(1280*960@16fps

binocular)

Sonar

Sonicwavemeasurement

Proximity

Short

<1kbit/s

Photoelectricsensor

Photoelectricsignal

measurement

Proximity

Short

<1kbit/s

LiDAR

Timeofflight

Accurate

Wide

0.1Mbit/s(4000

samples@10Hz)

Figure13:Imagesensordescriptionandrequirements[Source:GTIcloudroboticsworkinggroup]

Figure14:SLAMprocessvisualizedonRVIZ,visualizationtoolforROS,andasampleofobtainedmapdata[Source:SoftBank]

Gyroscopes,accelerometers,magnetometersandothersensorsThesesensorsenablearobottoknowitsownorientation,rotationandlocation

Sensor/technologyforlocation

Function

InertiaMeasurementUnit(IMU)

Orientationandrotation

Opticalandquantum-basedsensors

Orientationandrotation

Touchsensor

Contactdetection

GPS

Outdoorlocation

Cellularnetworkdata

Indoor/Outdoorlocation

Bluetoothbeacon

Indoorlocation

Ultrasoundsystem

Objectdetection

Effectors/actuators

Mostactuatorsusedforrobotsareelectric,thoughhydraulicandpneumaticactuatorsarealsoused.Eachtypehasadvantagesanddisadvantages(seeFigure15).

Electrical

Hydraulic

Pneumatic

Operatingprinciple

Electricity,electromagneticforce

Pressurechangeinliquids(oil,water)

Compressedgasisusedtopowerthesystem

Formfactor

Motors(DC,AC,geared,directdriveetc.)andcontrolcircuits

Cylinder,fluidmotor

Cylinder,pneumaticartificialmuscles(PAM)

Advantages

Easytostoreanddistributeelectricenergy,highcontrolflexibility,lowcost

Quickmovementsandgreatforce

Cleanerthanhydraulic,easyinstallation,lightweight

Disadvantages

Producedtorquesaresmallerthanhydraulicorpneumatic

Requirepump,liquidcancausecontamination,difficulttocontrolprecisely

Requirecompressor,lessforceandslowerspeedthanhydraulicduetocompressibility

Figure15:Comparisonofrobotactuatorprinciples[Source:Softbank]

Newdevelopmentsinmobility

Robotplatformneedstoevolve.Robotsneedtohavelongeruptime,highermobilityandrange,thecapabilitytounderstandtheirsurroundings,andtocarryoutsimultaneouslocalizationandmapping(SLAM).

Oneapproachtoachievehighermobility,especiallyinroughterrain,ortodealwithstairsanddoors,istheuseofbipedalorquadrupedsystem.Butcontinuousbalancingisrequiredinthesesystemsandthisrequiresgreaterpower,andtherearesomesafetyconcerns.Safetyrulesforrobotsmayvarybycountryandlocalarea:onepossiblearrangementmaybetotreatrobotsaspedestrians,ormobilityscooters.Speedlimitsand

Mobilenetworksupport

5GOverview

5Gisthenextgenerationofmobilecommunicationtechnology.Itisexpectedtobedefinedbytheendofthisdecadeandtobewidelydeployedintheearlyyearsofthenextdecade.

Thekeycapabilityof5Gisthepeakrateofmorethan10Gbit/s,1millionconnectionspersquarekilometer,andlessthan1msend-to-enddelay.Threeapplicationscenariosfor5Ghavebeendefined:eMBB(EnhancedMobileBroadband),mMTC(MassiveMachineTypeCommunications),andURLLC(Ultra-reliableandLow-latencyCommunications).

remotemonitoringmayberequired(perhapsnotasstrictaswithautonomousvehicles).SafetystandardsthatalreadyapplytorobotsincludeISO13482;otherrelevantstandardsarethosecoveringhomeelectricalappliancesandradiowavetransmitters.

Amorepracticalapproachthanarobotwithlegsisawheeledrobotequippedwith3Dcamerasandrangesystems,asdescribedabove.Anotherapproachisthe“wearable”robot–suchas

CloudMinds’Metaheadset,whichprovidessophisticatedvisualrecognition,SLAM,anddirectionindicationusingvibration.

Todeliverservicesforthesethreescenarios,theconceptofnetworkslicinghasbeendeveloped.Itisexpectedtoimprovetheoperationofcommunicationnetworks.Thisconceptessentiallyconsistsincreatingdifferentinstancesofnetworktechnologiessuitablefordifferentapplicationswithdifferentrequirements.Suchadynamicandflexiblecommunicationnetworkparadigmwillbeenabledbyanewcloud-basednetworkarchitecture,encompassingSoftwareDefinedNetworking(SDN)andNetworkFunctionVirtualization(NFV).

Figure16:5Gcloudarchitecturetosupportmultipleapplications[Source:HuaweiXLabs]

5Gwillmeetthenetworkrequirementforcloudrobotics

Incloudrobotics,fourtypesofbasicconnectionareneeded:

Monitoringandstatusreporting–therobotuploadingdataaboutitsstatustothecloudbrain

Real-timecontrol–mission-criticalcontrolsignalstotelltherobotwhattodo

Videoandvoiceprocessing–tousepowerfulcloudresourcestohelptherobotunderstanditsenvironment,andtointeractwithusers

Softwareandservicesdownload–for

updatingtherobot’ssoftware,ordownloadingusercontentsuchasmapsoreducationalmaterial.Figure17showstherequirementsofthoseconnectiontypes.

Bandwidth

Latency

Reliability(%uptime)

Summary

Monitoringandstatusreporting

Uplink:1kbit/s

1s

99.9%

Highconnection

density

Real-timecontrol

Downlink:10kbit/s

20ms

99.999%

Lowlatency

Videoandvoiceprocessing

Uplink:3.3Mbit/s(1080p/H.264/30fps)

20ms

99.9%

Highuplinkbandwidthand

lowlatency

Softwareandservicesdownload

Downlink:10Mbit/s

100ms

99.9%

Highdownlink

bandwidth

Figure17:Robotnetworkrequirementanalysis[Source:HuaweiXLabs]

Figure18characterisesthenetworkrequirementsoffullycloudifiedversionsofcurrentrobottypes.Existingnetworkswillfinditdifficulttosupport

newrobotapplications,but5G’shighbandwidth,lowlatencyandhighreliabilitycanproviderobustsupportforfuturerobotapplications.

Figure18:Networkrequirementsforcloudrobotapplications[Source:HuaweiXLabs]

5Gnetworkslicingandmobileedgecomputingarewellsuitedforcloudroboticsapplications

Networkslicesthathavedifferentspecificperformancecharacteristicscanmatchtherequirementsofcloudrobotics,matchtheneedsforpowerconsumptionattherobotterminal,andprovideappropriateroaming.Usingtheseapproaches,5Gnetworkswillalsobeabletomeetthemostdemandingrequirementsintermsofbandwidth,latencyandsecurity.

Mobileedgecomputing(MEC)providesappropriatenetworkandothercomputingandstorageresourceslocatedatthemostappropriatepointtomeetthecloudroboticsapplicationrequirements.Byplacingresourcesclosertothe

user,networklatencycanbereduced.MECsolutionsmaybedeployedwiththeMECserverdeployedatagatewayorinthebasestation,providinglocalcontentcache,wirelessawareness-basedbusinessoptimization,localcontentforwarding,andnetworkcapability.Securityisalsoenhancedasmoredataisretainedclosertotheuseranddoesnottraversethecorenetwork.Forcloudrobotics,thecloserAIresourcescanbedeployedtotheenduserthelowerthelatency.

Softwarecontrolofvirtualizedresourcesthroughoutthenetworkwillensurethattheoptimumbalanceisachievedbetweenuseofcentralizedcloudresourcesanduseofmorelocaledge-basedresources,dependingonthelatencyrequirements.

Figure19:Cloudrobotfunctiondeploymentaccordingtolatency

AIprovider–deliveringcloudAIandMachineLearning

AI,MLandDL

AddingthepowerofcloudcomputingtoroboticswillenableArtificialIntelligence(AI),MachineLearning(ML)andDeepLearning(DL)tobeappliedtoabroadsetofnewapplicationswhererobotswillbeverymuchmorecapable,powerfulandintelligentthanbefore.Thiswillinturnaffectindustriesrangingfromsecuritytomanufacturing.DefinitionsofAI,MLandDLarenotuniversallyagreed,butinthispaper:

ArtificialIntelligenceisacomputersystemabletoperformtasksnormallyrequiringhumanintelligence(includingvisualperception,speechrecognition,decision-makingandtranslation)

MachineLearningistheuseofalgorithmsandmethodssuchasdecisiontrees,neural

networksandcase-basedreasoningtoimproveperformancethroughtraining

DeepLearningreferstotheuseofmulti-layeredartificialneuralnetworksthatenablethetrainingtobecarriedoutonahugescale,withtheresultthatdecisionsareverymuchbetter.

Theseconceptsenablerobotstobetaughttodoatask–andtolearnhowtoimprove–ratherthansimplyrespondingtoaprograminacontrolsystem.Machinelearningalgorithmsofvariouskindshelpcomputerstointerpretdataandmakedecisionsbasedonthedata.Theycanbetrainedtounderstandwhentheirdecisionsarerightorwrongsothattheirdecisionsgetbetterovertime.

Usingmachineordeeplearning,arobotcanbecomebetterabletocompleteatask,ortoundertakeanewone,throughanimprovedawarenessofitsenvironmentandthecontextofthetask.Theseapproacheswillalsoreducetheneed–andcost–toprogramrobotsforeachnewtask.Thisinturnopenstheprospectsformoreflexibleindustrialrobotsthatcancopewithchangesinfactoryconfigurationsandshorterproductionruns,andcapableofoptimizingtheprocessesthattheyarerequiredtoperform.Innon-industrialsettings,AIandmachinelearning

enableimagesandspokenwordstobeinterpretedandforrobotstorespondappropriately.Accesstothecomputingpowerrequiredformachineanddeeplearningisgreatlyenhancedthroughhigh-speednetworksandtheuseofcloudresources.

Thekeyareasinwhichthesetechnologieswillbeappliedincloudroboticsareinintelligentvisualprocessingforarealearningandautonavigation,facerecognition,andnatural-language(speech)processing.Theserequiredataprocessingpowerbeyondthatwhichissensiblybuiltintoarobotlocally.

Accesstothecomputingpowerrequiredformachineanddeeplearningisgreatlyenhancedthroughhigh-speednetworksandtheuseofcloudresources

28%

26%

16%

12%

7%

3.60%

3%

2010201120122013201420152016

Figure20:ImageNetLargeScaleVisualRecognitionChallenge(ILSVRC)errorrateofclassification(%)[Source:ImageNet]

Thankstotheimprovementincomputingpowerandthecontinuousimprovementofalgorithms,AItechnologyisprogressingrapidly.Asanexample,theerrorrateforobjectclassificationrecognitionintheannualImageNetcontesthasbeenreducedtolessthan3%.Itisworthnotingthatthecurrentvisualrecognitionhasnotyetreachedthecorrectrecognitionrateof100%,whichforhighsecurity

Figure21:objectclassification

applications,suchasautonomousvehicles,isstillachallenge

IntheImageNet2015competition,NVDIAandIBMprovidedtheparticipantswithacloudGPU(NVDIAK80s),demonstratingthefeasibilityofcloudAI.

FrombigdatatechnologystacktoAIstackDeeplearninghasemergedasthebestwaytoperformimageanalysis,inapplicationssuchasmedicalradiography,aswellasinlow-latency

applicationssuchasremovalofstreamingvideocontentthatisinbreachofpolicies.ThebiggestITcompaniessuchasBaidu,GoogleandFacebookhavecreatedspecializedAIinfrastructuretohandleAIusecases,butmanycompaniesdonothavethein-houseexpertiseorresourcestoexploit

thenewtechnologies.Anewbackendinfrastructureisrequired,andthiswillbeachievedwiththeuseofnewacceleratorchipssuchasGPUs(graphicsprocessorunits).Butasthesetechnologiesrequiremoreprocessingpower,puttinginfrastructureintopracticeishardandCIOswillneedtobecomemorefamiliarwiththesenewtrends.

Currently,companies’ITarchitecturesaredesignedtomakeuseoffaulttolerant,lowcoststoragethatallowsforeasyextensionofresourceclustersandcanmitigateequipmentfailure.ButAIrequiresthatbigdataanalyticssoftwareunderstandsbetterhowtoruncompute

workloadsbytakingfulladvantageofthesenewaccelerators.BigdatatechnologystackswillshifttoAIstacksthatwillallowenterprisestocapturemorevaluefromdatathatiscapturedbysensorsonrobotsandelsewhere.

AIPlatform

BigDataCloud

DataCollection&Connectivity

ImplementingcloudAIrequires:verylargestoragecapacityandcomputingpower,forward-lookinginvestmenttoattractsoftwaredevelopment,APIsandopensourcelibraries.HardwareneedstomovefromCPUstoGPUsorevenAIdedicatedprocessors

Robotsrequireslargedatacloudstostorethedata.Therearegreaterdemandsonsecurity,anonymityanddistributioncapacitythaninthepast.

HundredsofmillionsofconnectedsensorsarerequiredtocollecttrainingAIdatafromhumans,assetsandtheenvironment.

Figure22:AIstack[Source:GTIcloudroboticsworkinggroup]

Lowlatencyiscriticalforrobotexperience100msThedelayofthehumanneuralnetworkis100ms,andiftherobotcanrespondwithinthisdelay,itcanbeconsidered"seamless".Toachieve"seamless"robotresponsecapabilitiesneedsvideocapture,videocoding,networ