ArtificialInteligence. Integrity. Excelence. Results. CurentUsesAndLimitations.

Transkript

1 ArtificialInteligence CurentUsesAndLimitations Integrity. Excelence. Results.

2 Contents 3 Introduction 4 DefiningArtificialInteligence 4 MachineLearningandDeepLearning 6 How AreOrganisationsCurentlyUsingArtificialInteligence 7 Ilustration:ArtificialInteligenceUseAcrossAnInvestmentBank 8 CurentLimitationsOfArtificialInteligence 10 Conclusion 11 AboutCitihubConsulting Author Adam Evans AssociatePartner,CitihubConsulting adam.evans@citihub.com Withover12yearsexperienceinthefinancialservicesindustry,Adam hasbeeninvolvedinbusinessandit transformationprojectsincludingthedevelopmentoffronttobackbusinessoperatingmodels,shared servicessetupandstrategicc-levelcosteficiencyandlabourforcerestructuringinitiatives.inaddition,he hasexpertiseinplatform adoptionandvendormanagement.priortojoiningcitihubconsulting,adam workedforubs,bearstearnsandjpmorganinvariousrolesacrossequityderivatives. 10

3 Introduction ArtificialInteligencetechnologieshavereceivedmassiveinvestmentoverthepastcoupleof yearsascompaniessuchasgoogle,amazon,microsoft,ibm andfacebookdevelopsmarter, morepracticalapproachesinitsapplicationtorealworldbusinessproblems.amongstothers, thesecompaniesaredevelopingmachinesthatcanspeak,listen,understand,form ideas, make decisions and interact.a single instance ofartificialinteligence thatholisticaly encompassesalofthesedisciplinesinwaysthataresignificantlysuperiortowhatahumancan doisstilsomewayof,buttheredoexistmanyusecaseswherea.i.canprocessandexecute specifictasksfasterandbeterthanwecan(forexample,google salphagomachinerecently defeatedachinesegrandmasteratgo,acomplexstrategyboardgame).thesecapabilitiesare maturingrapidlyandmanyorganisationsthatareemployingthem areseeingrealbenefitsinthe supportand improvementofexisting processesand proceduresaswelasenabling the developmentofnew businesschannels. In thispaper,we discussthe scope ofartificialinteligence and which approachesand applicationsarebeingdeployedbyorganisations.usingtheexampleofinvestmentbanking,we takealookatspecificexamplesofartificialinteligenceuseinthatindustryandfinishthepaper byexaminingsomeofthelimitationsandissuesthatneedtoberesolvedinordertofacilitateits wideruse. 3

4 DefiningArtificialInteligence Theterm ArtificialInteligencewasfirstcoinedatDartmouthColegeinNew Hampshireinthesummerof1956whena groupoftheworld sleadingprofessorsandresearchersacrossthefieldsofmathematicsandsciencemettodiscuss awiderangeoftopicsincludinglanguagesimulationandthemanufacturingofinteligenceartificialy.sincethen, variousapproachestoartificialinteligencehaveemergedincludingtheoncepromisingdevelopmentof Expert Systems inthe80sand90swhichinvolvedinterviewingexpertsinafieldandcodifyingtheirknowledgeintoasetof rulesthatwouldenableacomputertomimictheirbehaviour. DuetothefactthattherearemultipleapproachesandapplicationsofArtificialInteligence,thereisnosingleagreed upondefinitionforitalthoughacommonthemeisthatitinvolvestheengineeringofcomputerstoperform tasksthat normalyrequirehumaninteligenceandbehaviour.thisrequirestheabilitytoreceiveinput(e.g.visualy,throughsound orthroughwritentext),toprocessit(bylearningfrom andinterpretingit),torespondmeaningfulytoit(bymaking decisionsoroferingadvice),andsometimestobeabletomovearoundandphysicalymanipulateobjects.integrating advancedformsofalofthesecapabilitiesatonceintoamultimodalapproachwouldconstituteagoodrepresentation ofhuman-likeinteligenceandbehaviourbutdespiteon-goingprogress,acommercialyavailablesolutionthatcando alofthisisunlikelyanytimesoon. MachineLearningandDeepLearning TheapproachtoArtificialInteligencethathasseenthemostrecentsuccessisMachineLearning. Assuch,the applicationsofartificialinteligencethatusesomeform ofmachinelearningtoperform theirfunctionhavealso prosperedoflate.theseincludenaturallanguageprocessing,cognitiveroboticprocessautomationandinteligent DecisionManagementSystems. MachineLearningisanapproachtoArtificialInteligencethatusesalgorithmstolearnfrom andmakepredictions aboutdatawithoutexplicitprogramming.machinelearningsystemshaveexistedformanyyears.however,since mid-2014,majoradvancesinmassiveparalelprocessing(e.g.usinggpus)andtheever-increasingavailabilityofbig datatechniqueshavefueledanexplosionofactivityaroundonetypeofmachinelearningknownasdeeplearning. DeepLearningusesmultiplelayersoflearningalgorithmsthatarecombinedtolearnincreasinglycomplextasks.For example,inavisualrecognitionsystem,thefirstlayermayrecogniseedgesandlines,thesecondlayermaycombine edgestorecognisesimpleshapes,andathirdlayermaycombineshapestorecogniseobjects.thesedeeplearning systemsoftenrequirevastamountsofexampledatatotrainthealgorithmsandthegreatertheamountdatathatisfed through,thegreatertheaccuracyofthesystem. TherearemanydiferenttypesofalgorithmsthatcanbeusedforMachineLearningandDeepLearningincluding NeuralNetworkswhichemulatebiologicalneuronsinterconnectedinlayerstoproduceanetworkofartificialneurons. DeepLearningsystemsoftenuseNeuralNetworkswithmanylayerswhichareknownasDeepNeuralNetworks.Deep NeuralNetworkshavebeenaroundfordecadesbutonlyrecentlyhastherebeenreadyaccesstothesuficientlylarge datasetsandcomputepowerrequiredtoprocessthem andtomakethem aviablesolutiontoenterprise-level problems.figure1describestherelationshipbetweentheseapproachesina.i. 4

5 ArtificialInteligence ApproachHierarchy Theengineeringof computerstoperform tasksthatnormalyrequire humaninteligenceand behaviour MachineLearning AsubsetofArtificial Inteligencewhichinvolves theconstructionof systemsthatuse algorithmstolearnfrom andmakepredictionson datawithoutbeing explicitlyprogrammed DeepLearning AsubsetofMachine Learningwhichcombines multiplelayersof algorithmstoself-learn increasinglycomplex problems DeepNeuralNetworks AtypeofDeepLearning algorithm looselymodeled onthemammalianbrainin whichdataisfedthrough threeormorelayersof interconnectednodes(or neurons)andclassifiedto everimprovinglevelsof accuracy Figure1 HierarchyofArtificialInteligenceapproaches Artificiallearningsystemscanautomaticalylearntoperform taskssuchasfindingandclassifyingfeaturesorpaterns ininputdata.therearethreemaintypesofmachinelearningthatcanbeimplementedinthisway: Supervisedlearningwherethenetworkmustbepre-trainedusingexampleinputandoutputdatabeforethe system canperform tasks e.g.recognisingobjectsinimagesorvideo Unsupervisedlearningwherethenetworkcanfindpaternsorstructureindatainputsanddetectanomalies withoutanytraining e.g.anomalydetectioninsystem monitoringdata Reinforcementlearningwherethenetworklearnstoperform ataskbyinteractingwiththeenvironmentand learningfrom feedback(rewards) e.g.self-drivingcars SuchhasbeentherecentprogressaroundDeepNeuralNetworksthatcurentnomenclatureoftenusesthetermsDeep NeuralNetworksandDeepLearninginterchangeablyaswelasregardingDeepLearningastheonlyapproachto ArtificialInteligence.Althoughthisisnotsurprisinggiventheconsiderableamountoffocusithasreceivedoflate,there areotherlesspublicisedapproachestoa.i.thatstilexistsuchaslogicbasedproblem solving. 5

6 How AreOrganisationsCurentlyUsing ArtificialInteligence? MachineLearningcanbeapowerfultoolinaidingdecision-makingandaugmentingsystemsalreadyinplaceto manageabusinessprocess,oncetrainedandtailoredefectively.theabilitytofindpaternsandspotoutliersinhuge datasetslenditselftoanynumberofpracticalapplicationsincludingmanytypesofriskdetection.cybersecurity,fraud detectionsurveilanceand monitoringareotherexamplesofcurentapplicationsofmachinelearningthatare particularlywelsuitedtoit.findingcorelationsbetweennumerousvariablesalsomakesmachinelearningavery efectivetoolforuncoveringnonlinearinterdependenciesindataandhasbeenusedbygoogletomodelplant performanceandpredictpowerusageefectiveness(pue)towithinaverytightrange 1.Itcanalsoprovidefirmswith abeterunderstandingofconsumertasteswhichinturnenablesmoreindividualiseddigitalmarketingcampaignsand targetedproductoferings. NaturalLanguageProcessing(NLP)-ofwhichNaturalLanguageGenerationisacomponent-thatusesMachine Learningtointerpretorproducehumanlanguageineitherwritenorspokenform byunderstandingsentencestructure, meaningandsentimentcontinuestobeoneoftheapplicationsofartificialinteligencethatreceivesthemostatention, researchandfunding.smarthomedevicessuchamazonalexaandgooglehomeusenlp fortaskssuchas answeringquestions,orderinggoodsandcontrollightingandheating.enterprise-levelsolutionsincludeenhancing insightintocustomerinteractionsandsociallisteningwhencombinedwithcognitivesearchofunstructureddata acrossmultipledatasources,summarisingmarketreportsandperformingsentimentanalysis.nlpalsoprovidesthe abilityforchatbotstoconversewithhumansandtoprovideanswerstoquestionsfrom calcentresandothersupport functions.wordscanhavemultiplemeaningsdependingonthecontextandthatcanmakeliteraltranslationof sentencesimpreciseandambiguous.theabilityofnlptounderstandcontextandtoorganiseunstructuredlanguage intostructuredconsumabledataisaveryvaluableresourceforenablingdatadrivenanalysis. DecisionManagementSystems-whichincludeclientrelationshipmanagement,enterpriseresourceplanningand variousmonitoringsystems-areanotherareawheretheuseofartificialinteligencetechniquescanbuildonand improveexistingapplications.theabilitytocaptureandstoredataonpastactionsandtheiroutcomesenable rule-baseddecisionmanagementsystemstocognitivelylearn,andtoeitherexecuteactionsautomaticalyorprovide userswithinsightsastothebestcourseofaction.accordingtoforester,decisionmanagementsystemswereonthe highesttrajectoryforbusinessvalueadd-oninartificialinteligencetechnologiesasofq RoboticProcessAutomation(RPA)iscurentlyoneofthefastestgrowingareasofcomputerisedautomation.Itis definedastheapplicationoftechnologythatenablespeopletoconfigurecomputersoftwareora robot tocapture and interpretexisting applications forprocessing a transaction,manipulating data,triggering responses and communicatingwithotherdigitalsystems 3.DespitesomedebatearoundwhetherpureRPAshouldbedefinedas ArtificialInteligenceduetoitslimitationofonlyhandlingrule-basedwork,itcanbecombinedwithDeepLearningto interactwiththeuserinordertoidentifyrequirementsthatarethenfulfiledbyrpa(e.g.anautomatedhelpdesk).itis anemergingtechnologywiththeabilitytoenhancethespeedandaccuracyofrepetitivetaskswithpredictable outcomesacrossanorganisationandhasseenrapidadoptionacrossseveralindustriesoverthepastcoupleofyears. Attheendof2015,globalrevenuestotaled$2bnandby2024itisestimatedthatmarketrevenueswilgrow toover $22bn 4.Curentusesincludefinancialtransactionprocessingandtheautomationofcustomerservicerequestswith benefitssuchasareductioninerorratesandtherisksassociatedwiththem.automationofthesetasksproducean outputofconsistentandstructureddatathatcanbeusedforfurtheranalysisandcognitiveinsightandenablestafto focusonhigherlevelvalue-addingtasks. 1 MachineLearningApplicationsforDataCenterOptimization-Jim Gao,Google 2 TechRadar:ArtificialInteligenceTechnologies,Q InstituteforRoboticProcessAutomation&ArtificialInteligence 4 RoboticProcessAutomationandRiskMitigation:TheDefinitiveGuide MaryC.Lacity&LeslieP.Wilcocks,2017 6

7 Ilustration:ArtificialInteligenceUse AcrossAnInvestmentBank InvestmentBankingisanindustrywheremanyofthecorefunctionshavestartedtomakeuseofArtificialInteligence. From avendorlandscapestudywecariedoutacross68vendors,figure2showswhereexistingapplications, approachesanda.i.optimisedhardwarearecurentlybeingdeployed. Figure2 ArtificialInteligenceuseacrossanInvestmentBank TradingfunctionshavestartedtoadministerDeepLearningformultipleusesincludingsignalgenerationfrom the analysisofunstructureddata(suchastweetsandnewssources)aswelastrainingmachinesonhistoricstockmarket datatoenhancethepredictionofpricemovements.researchandsalesfunctionsaredeployingnaturallanguage ProcessingandNaturalLanguageGenerationtodramaticalyreducetimeneededtoanalysereportsandtoproduce summarisedviewsofindustriesandsectorsfortheirclients.riskmanagementandmiddleoficesupportfunctionsare alsomakinguseoftheabilitytoprocesshugedatasetstomakepredictionsandprovidereal-timeanalysis,whilstthe implementationofinteligentdecisionmanagementsystemsisenablingimproved consistencyand accuracyin multi-functionaltaskprocessing.roboticprocessautomationisseeingrapidgrowthinitsapplicationtorepeatable 7

8 andrepetitivebackofice,financeanditfunctions,andvirtualagentsorchatbotsarebeingroledouttodigitiseand streamlinehrandservicedesksupportfunctionsaswelasreducingemployeeworkloads. Acommonthemeacrosseachoftheseusecasesistheirabilitytoenhanceandsupportexistingworkflowsand interactionswithoutcausingdisruptionordamagetoservicelevelsandrevenuegeneratingactivities.aseachofthe applicationsand approachesofa.i.maturesand organisationsembed them moredeeplyintotheirday-to-day operations,theywilbenefitfrom significantimprovementsinriskmanagement,operationaleficiency,scalabilityand opportunitiesforrevenuegeneration. CurentLimitationsOf ArtificialInteligence DespiterealprogressmadeinArtificialInteligenceinrecentyears,thereremainsignificantlimitationsthathinderits wideruse.someofthekeylimitationsinclude: ImplementingenterpriseA.I.iscomplicated BuildingDeepNeuralNetworkarchitecturesisverycomplex andwhilsttherearedevelopmentframeworksthathelpprogram them,suchastensorflow from Google,more workisneededtomakea.i.capabilitiesmoreaccessible.thisincludesthecreationofhigherlevelprogramming languagesthathelporganisationsmoreeasilydevelopthesedeeplearningsystems,creatinga.i.thatcanfix badcodeinwaysthatdeveloperscaneasilyunderstandandforthelargerproviderstograntmoreaccessto samplecodeandsetsofopenapis. Significantefortisrequiredforsupervisedlearning SupervisedlearningcanbeappliedtoNeuralNetworks todiscoverunknownoutputsbyclassifyinginputs,groupingthem together,learningfrom mistakesandmaking beterestimationsovertimethroughbackwardpropagation.itisapowerfultoolfordiscoveringunknown paternsindatabutitsuseislimitedbythehumanefortrequiredtolabeldatasets-suchasphotosandvideos largeenoughtoenablethemachinetolearn. Applicationisnotpracticalforalbusinessproblems ForMachineLearningsystemstobeweltrainedand highlyefective,theyrequirelargeamountsofdatafrom whichtolearnandimprove.wheretrainingdatais abundant,suchashistoricalstockmarketdata,systemscanbefinelytunedandbecomehighlyeficientin providingpredictiveanalysis.likewise,bigtechfirmssuchasgooglehaveaccesstohugedatasetswithwhich theycantrainsystems(e.g.withobjectrecognition).whenmostfirmswanttoapplyittoeverydaybusiness problems,however,theydonotalwayspossesssuficientvolumesofreadilyavailabledataonwhichtotraina system.sometimesthatisbecausetheirdatamanagementstrategyisimmaturebutoftenitissimplybecause thevolumeofdatarequireddoesnotexist.thiscansignificantlylimitthepredictiveabilityofmachinelearning systemsandthusitsusefulapplicationtoeverydaybusinessproblems. 8

9 Dataandmemorybandwidthrequirementsareconstrictive Computerarchitectureshavedevelopedwith processorchipsspecialisedforserialprocessinganddistributedram.althoughgraphicprocessingunits (GPUs)developedbythelikesofNvidiacanquicklyprocessmassivedatasets,theycanrequiretheuseofother programminglanguages(e.g.cuda).graphcore sipu aimstoincreaseperformanceby10to100times comparedtothefastestsystemsinusetodaybutinterfacesbetweendevicescanstilcreateamajorbotleneck thatintroduceslatencyandbandwidthlimitations.intel sxeonphiofersasolutionwithouttheneedforcputo GPUprogrammingwhilstearlyresearcharoundtheirupcomingLoihitestchiphasdemonstratedaonemilion timesimprovementinlearningratescomparedtootherspikingneuralnets 5,althoughitwon tbeforanother threetofiveyearsuntilitmakesitoutoftheresearchlab 6. A.I.systemsaredesignedforaspecificsingularpurpose Today,MachineLearningisonlypossiblewhena humanhasdesignedthenetworkarchitecture,algorithmsandinitialparameterstoperform aspecifictask. Machinescannotyetdesignoradjusttheirownarchitecture,learnalgorithmsorchangetheirinitialparameters, sotheycannotchangethescopeorboundariesoftheirdesignedpurpose. A.I.cannotexplainhow itreachedaconclusion Humanbeingscanprovidearationaleforhow adecisionis made.a.i.cannot.itcanprovidethebestoptionforanoptimaloutcomebasedonstatisticalanalysis(e.g. diagnosingcancerandrecommendingtreatment)butitcannotexplainhow itreachedthatconclusion.instead, itfindslinksindatathatarenotalwaystraceableintheirentiretyandthiscanhindertheabilitytoimproveitwhen somethinggoeswrong. 5 Intel.com:Intel snew Self-LearningChipPromisestoAccelerateArtificialInteligence, forbes.com:intelbeginsmakingchipsthatresemblethebrain,2017 9

10 Conclusions RecentadvancesinapproachestoA.I.haveenableditsapplicationtoavarietyofbusiness problems-from thecreationofnew productsandservicestooptimisingoperationsand enhancingthecustomerexperience Initscurent-state,A.I.isbestappliedtoaugmentingandimprovingexistingprocessesand systemswhereitcanaddvaluebyprovidingbeterinsightsintolargedatasets,provide cognitiveinsightsandautomatecertainlowerleveltasks InvestmentBanksareanexampleofanindustrymakinguseofapplicationsandapproaches toa.i.acrossmanyoftheircorefunctions TherearelimitationstoexistingusesofA.I.whichmeanthatafulmultimodalA.I.solutionwith fulhuman-likecapabilitiesisstilsomewayof Despitelimitations,enterpriseA.I.useisarapidlygrowingareaandcanbeaveryefectivetool thatprovidesrealbenefitstoanorganisation sabilitytodeliverproductsandservices 10

11 AboutCitihubConsulting CitihubConsultingisaglobal,independentITadvisoryfirm withdeepdomainexpertiseacrosseverylayer ofthetechnologystack from businessapplicationsanddataplatformsdowntocoreinfrastructure.from ITstrategy,architectureandsolutiondevelopment,throughtocostoptimisation,riskassessmentand implementation ourtrustedexpertsdelivertherightresultsforyourbusiness. Forus,consultancyispersonal.Wehavearelentlesscommitmenttogreatexecution,integrityandclient success.weaim toredefineperceptionsofourindustryandourcommitmenttodeliveringtherightresults forourclientshasneverchanged,evenasthebusinesshasgrownconsistentlyoverthelastdecades. Formoreinformation,pleasevisitwww.citihub.com ContactUs NorthAmerica EMEA AsiaPacific KeithMaitland RichardHamstead SteveRutherford 500FifthAve,Suite1610 New York,NY TheDineenBuilding 140YongeStreet,Suite200 Toronto,Ontario,M5C1X MoorPlace 1ForeStreet LondonEC2Y9DT PickeringStreet #01-64 Singapore