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  1. 1. 2 #02, 2017 ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW #02, 2017 3 STANDARDIZING NARROWBAND TECHNOLOGY T R E N D S TECHNOLOGY T R E N D S technology trends driving innovation Our industry has an increasingly important role to play in creating the foundation for new business in a broad range of industry sectors in countries all around the world. As Ericssons new Chief Technology Officer, its my job to keep track of technological advancements on the horizon and leverage them to create new value streams for society, consumers and industries. The challenge is timing, and to see new things in the context of the present without losing sight of history. I have selected the five trends presented here based on my understanding of the ongoing transformation of the industry, including rapid digitalization, mobilization and continuous technology evolution, and how they affect the future development of network platforms one of the essential components of the emergent digital economy. At Ericsson, our role is to keep these top trends in sight to guide our innovation, test our limits and ultimately create a thriving market for the next generation of technology. #1 AN ADAPTABLE TECHNOLOGY BASE Blending technologies in new ways to unleash next generation computational networks #2 THE DAWN OF TRUE MACHINE INTELLIGENCE (MI) Moving from cognitive MI toward augmented human intelligence #3 END-TO-END SECURITY AND IDENTITY FOR THE INTERNET OF THINGS (IOT) A holistic approach to trust in all dimensions #4 AN EXTENDED DISTRIBUTED IOT PLATFORM Acceleration toward a distributed and connected IoT platform #5 OVERLAYING REALITY WITH KNOWLEDGE Immersive communication that ties user experience to the physical world 21 by erik ekudden, cto five to watch
  2. 2. 3 TECHNOLOGY T R E N D S TECHNOLOGY T R E N D S 4#02, 2017 ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW #02, 2017 T O R E L E A S E the full potential of the digital economy, the underlying technology components will rely on a symbiotic evolution in the software and hardware dimension. It is in the lowest layers of the technology stack at the intersection between software and hardware where more powerful and flexible solutions will become available, enabled by virtualization technologies and horizontal architectures. ARCHITECTURALADJUSTMENTS AHEAD Geometricalscalinghaslongbeenthe mainpathforthetransistortechnology evolutionbutextensiveresearchis currentlyunderwaytofindalternative methodstoincreasetransistor performance.Significanteffortsare beingputintobuildingmoreadvanced architecturalstructures,suchasvarious typesofnon-monolithicintegration technologies,toincreaseintegration andtherebymaintaintheperformance evolutiontrack. Inthecomputingdomain,the dominatingCPU architecture trendismassivemulticoretomeet parallelprocessingdemands.With moreprocessorsonachip,memory architecturesanddatatransferwill becomekeytechnologiesinhardware. Non-volatilememoryandintegrated siliconphotonicswillreachmaturity andchangetheentirememory/storage hierarchy.Thenewtechnologiesare expectedtohavesignificantlyhigher performanceaswellaslowerlatencyand energyconsumption. Tofurtherenhancecomputational powerandperformanceindatacenters, specializedresourcessuchassmart networkinterfacecontrollers,general purposegraphicsprocessorsandfield programmablegatearrayswillbemade availableforvirtualapplicationsthrough abstractions.Asimilarco-processing architecturalapproachcanbeexpected forquantumcomputing. Therapidadvancesofbasecomponents willmeanthatthefirstexascalesystemswith computingpoweratdoublethecapacityof todaystop500computerscombinedcanbe expectedwithinfiveyears. Incombinationwithspecifictypes ofalgorithmsandapplications,these technologyshiftscouldprovetobe disruptive. SPURRINGINNOVATIONTHROUGH ALGORITHMEVOLUTION Massivedatacollectionfrom,forexample, IoTsensorswilldrivetheneedfornew predictivesoftwarealgorithmsthat alsotakeadvantageoftheincreasingly parallelcomputationalpower.Algorithm developmentwillplayanevenmore significantroleinsoftwaredesign.One exampleisdeeplearning,abranchof machinelearningthatusesalayered algorithmstructuretolearnhierarchical concepts. Theamountofcodeneededtoreach ahigherlevelofcomplexityisverysmall, areductionbyafactorof10ormore, comparedwithtraditionalsoftware systemapproaches.Thistypeofsystem learnsfromexamples:itutilizesageneric algorithmthatusestheexamplestoset parametersinthealgorithmtofitthe particulartaskathand. Reinforcementlearningisatechnology todevelopself-learningsoftwareagents, whichcanlearnandoptimizeonan observedstateoftheenvironmentanda rewardsystem.Thisenablesdevelopment ofself-learningsystemsthatrequire neitherhumaninterventionnorhand- engineered,threshold-basedpolicies. ENABLINGTHEFUTURE COMMUNICATIONSYSTEM Fromaconnectivityperspective,one interestingareaisbeamforminginfuture 5Gnetworks,wheresymbiosisofsoftware andhardwareplaysanimportantrole. Atmm-wavefrequencies,hundredsof antennasandtransceiverchainswork togetherwithadvancedcontrolalgorithms togenerate,formandsteerradiowaves inrealtimetoaccomplishmultiuser MIMO(multiple-input,multiple-output). Eachdeviceisaccessedbyasingleuser dedicatedbeamtooptimizenetworkand usercapacity,efficiencyandquality. AtEricsson,wearecurrentlyinvesting inavarietyofcollaborativeeffortswith academiaandthetechnologyindustry tofosteranopenenvironmentinwhich toshareideasandvisionsthatwillenable themostsuccessfulfuturenetworkfrom bothasocietalandindividualhuman perspective. an adaptable technology base #1
  3. 3. 65 #02, 2017 ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW #02, 2017 TECHNOLOGY T R E N D S TECHNOLOGY T R E N D S the dawn of true machine intelligence ARTIFICIAL INTELLIGENCE (AI) first emerged in the early 1950s when symbolic logic and rule-based systems were used to generate logical conclusions. Another important step included artificial neural networks, capable of performing pattern recognition by learning from data through iterative optimization. The advancements in these computational-intense algorithms have progressed in performance and cost through the evolution of computing power, data availability and connectivity. Machineintelligence(MI)combines machinelearningandAImethodsto createdata-drivenintelligent,non-fragile systemsforautomation,augmentation andamplifications.MIisabouttheabilityto augmenthumanintelligence.Humanswill beempoweredbyanecosystemofsharing dataandinsights,withsupportfromdigital assistantsguidingandaugmentinghuman awareness.MIwillcreateanewtype ofautonomouscoachingenvironment wherehumansandmachinescantrain andmentoreachother.Thissituationis comparabletoateacherinaclassroom guiding,mentoring,discussingwithand learningfromthestudents. MIwillhelpusunderstandand accomplishthingsweneverwouldhave discoveredbyourselves.Itestablishesa wholenewfoundationandpotentialfor innovation,withtheabilitytobemuch moreinfluentialthanindustrialization. Human-machinecommunicationwill furtherevolvetowardamultifaceted communicationplatformthatincludes capabilitiessuchassituationandsocial awareness.Adeeperdialoguebetween humansandmachineswillemerge,moving beyondcognitiveintelligencetoward augmentedhumanintelligence. SIMPLICITYTHROUGHANALYTICS ANDAUTOMATION AmongthemanytoolsintheMItoolbox isanalyticscoveringeverythingfrom straightforwardanalyticsovermultiple nodesandpetabytesofdata,tocomplex multidimensionalanalyticsonparallel processorsystems.Today,analyticsis oftenahuman-involvedprocessthat requirestheconsiderationofseveral aspects,includinghowtohandledata volumes,dataspeedandthemultitudeof datatypes. Anexampleofcomplexeventprocess handlingwithamassiveamountofreal- timedataiselectronictrading.Thistype oftooliscapableofhandling,analyzing anddrawingconclusionsbasedupon millionsofmessagespersecond.Froma networkperspective,wewillseethese typesofusecasesfurthersubstantiated bytheevolutionoftheIoT.Connectedcars andothertypesofconnecteddeviceswill comeonline,providingmoreusecases thatrequirereal-timemessagingfroma varietyofdecentralizeddatasources. Jettisonandmetadataareessentialto handlethehugevarietyindatastructuring. Automatingthesetasksisofgreat importancetoanyenterprisewithdigital aspirationsbecausehuman-intense interventionsarenotscalable. Automationisbestdescribedasa closed-loopsystem.Theautomation closedlooprefinessystemintervention dependingonrecordedimpact,with minimallatency.Theinterventionis updatedbasedonfeedbackonthesystem performance.Theclosedloopintroduces thenecessarychangeswithouthuman intervention,basedonperformancegoals definedbyhumans. Weareenteringtheeraofearly enablementofsentientMIthatcanbeused tocreatedigitalattention,agilememory andgoalmanagement. NETWORKEVOLUTIONBRINGS MUCHMORETHANRAWDATA Overthenextdecade,industryandsociety willestablishafoundationofinsight-driven systemsleveragingMItechnologies.New serviceengagementswillemerge,driven bypredictivemodellingandautomated operations.Furthermore,avarietyof deploymentmodelsservingdifferent usecaseswillemerge,suchaspure cloud-nativeapplications,on-premises datacenteroperations,anddistributed deploymentsacrossmultiplesites.All ofthiswillbebeneficialtomanydigital businessesandindustries. Analytics,MIandautomation capabilitieswillbeanintegralpartof futurenetworks,substantiatinginnovation fromnetworkoperationtonewbusiness opportunitieswithintheIoT,forinstance. #2
  4. 4. 87 #02, 2017 ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW #02, 2017 TECHNOLOGY T R E N D S TECHNOLOGY T R E N D S Thiscomprehensiveapproachincludes end-to-endmanagementofsecurityand privacythatprovidespredictivesecurity insightsand,inmanycases,automated adjustmentsbasedonpolicies.Security andprivacyarenotthingsthatcanbe addedon;theymustbefullyintegrated intodomainsandcomponents,processes, storageandcommunication. ASYSTEMBUILTANDENABLEDBY TRUSTCOMPONENTS Thecomprehensiveapproachtosecurity andprivacywillbebasedonanindustry- wideagreementonhowtosecuretrustin development,deploymentandoperations. Itwillincludesecurityandprivac