人工智能世代：从人工智能 1.0 到人工智能 4.0 AI Generations - From AI 1.0 to AI 4.0

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AIGenerations:FromAI1.0toAI4.0

JiahaoWu1,HengxuYou2,JingDu,Ph.D.3*

1Ph.D.Student,Informatics,CobotsandIntelligentConstruction(ICIC)Lab,EngineeringSchoolofSustainableInfrastructure&Environment,UniversityofFlorida,Gainesville,FL32611;e-mail:

jiahaowu@

2Ph.D.Candidate,Informatics,CobotsandIntelligentConstruction(ICIC)Lab,EngineeringSchoolofSustainableInfrastructure&Environment,UniversityofFlorida,Gainesville,FL32611;e-mail:

you.h@

3Professor,Informatics,Cobots,andIntelligentConstruction(ICIC)Lab,EngineeringSchoolofSustainableInfrastructure&Environment,DepartmentofMechanical&AerospaceEngineering,DepartmentofIndustrial&SystemEngineering,UniversityofFlorida,Gainesville,FL32611(correspondingauthor);Email:eric.du@

ABSTRACT

ThispaperproposesthatArtificialIntelligence(AI)progressesthroughseveraloverlappinggenerations:AI1.0(InformationAI),AI2.0(AgenticAI),AI3.0(PhysicalAI),andnowaspeculativeAI4.0(ConsciousAI).EachoftheseAIgenerationsisdrivenbyshiftingprioritiesamongalgorithms,computingpower,anddata.AI1.0usheredinbreakthroughsinpatternrecognitionandinformationprocessing,fuelingadvancesincomputervision,naturallanguageprocessing,andrecommendationsystems.AI2.0builtonthesefoundationsthroughreal-timedecision-makingindigitalenvironments,leveragingreinforcementlearningandadaptiveplanningforagenticAIapplications.AI3.0extendedintelligenceintophysicalcontexts,integratingrobotics,autonomousvehicles,andsensor-fusedcontrolsystemstoactinuncertainreal-worldsettings.Buildingonthesedevelopments,AI4.0putsforwardtheboldvisionofself-directedAIcapableofsettingitsowngoals,orchestratingcomplextrainingregimens,andpossiblyexhibitingelementsofmachineconsciousness.ThispapertracesthehistoricalfoundationsofAIacrossroughlyseventyyears,mappinghowchangesintechnologicalbottlenecksfromalgorithmicinnovationtohigh-performancecomputingtospecializeddata,havespurredeachgenerationalleap.ItfurtherhighlightstheongoingsynergiesamongAI1.0,2.0,3.0,and4.0,andexplorestheprofoundethical,regulatory,andphilosophicalchallengesthatarisewhenartificialsystemsapproach(oraspireto)human-likeautonomy.Ultimately,understandingtheseevolutionsandtheirinterdependenciesispivotalforguidingfutureresearch,craftingresponsiblegovernance,andensuringthatAI’stransformativepotentialbenefitssocietyasawhole.

KEYWORDS:ArtificialIntelligenceEvolution;MachineLearning;ReinforcementLearning;LargeLanguageModels;AIEthicsandGovernance

I.INTRODUCTION

ArtificialIntelligence(AI)hasexperiencedatransformativeevolutionoverthelastseventyyears,evolvingfromitsnascentstageoftheoreticalformulationstoitscurrentstatusasacornerstoneoftechnologicaladvancement[1].Initially,thefieldwasdominatedbyintellectualexplorationsintosymbolicreasoning,knowledgerepresentation,andtherudimentaryprinciplesofmachinelearning[2].Theseearlystagesweremarkedbyafocusonconceptualbreakthroughs,layingthegroundworkforwhatAIcouldpotentiallyachieve.Ascomputationalcapabilitiesexpandedanddatasourcesproliferated,AItransitionedfromtheoreticalmodelstopracticalapplicationscapableoflearningfrompatternsandmakingprecisepredictions[3].Thelasttwodecades,however,havewitnessedanunprecedentedaccelerationinAIdevelopment,propellingthefieldintorealmsthatsurpasseventhemostoptimisticprojectionsofitsearlypioneers.

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Despiteremarkablesuccessesinareaslikenaturallanguageprocessing,computervision,andlarge-scaledataanalytics,AIcontinuestofacechallengesininteractingseamlesslywithcomplex,dynamicreal-worldenvironments.ThisongoingstrugglesignalsanemergingphaseinAI’sevolution,markingashiftfromsystemsthatprimarilyprocessandpredictinformationtoonesthatcanplan,decide,andact,usheringinnewgenerationsofAI:InformationAI(AI1.0),AgenticAI(AI2.0),PhysicalAI(AI3.0)andConsciousAI(AI4.0).ThisclassificationnotonlyclarifiestheconceptualtransitionswithinthefieldbutalsohelpsdelineatetheevolutionofAIcapabilitiesfromdataextractiontomakingautonomousdecisionsindigitalrealms,andnowtoengagingdirectlywiththephysicalworld.

Understandingthesetransitionsisessential,notjustfromatechnologicalstandpointbutalsoforgraspingthesocietalandeconomicimplicationsofAI.EachphaseofAIhasbeenshapedbydistincttechnologicaldriversandbottlenecks:theearlyperiodwaslimitedbythelackofadvancedalgorithmsandcomputationalframeworks[4];theadventofpowerfulGPUsaround2012significantlyshiftedthelandscape,enablingmorecomplexneuralarchitectures[5];andtoday,thechallengehasmovedtowardsharnessingdomain-specific,high-qualitydatatofeedintothesesophisticatedsystems[6].Recognizingtheseshiftsiscrucialforstakeholders,includingpolicymakers,researchers,andindustryleaders,whomustnavigatetheethical,regulatory,andtechnicalcomplexitiesintroducedbyadvancedAIsystems.

TheobjectiveofthisreviewistoprovideacomprehensiveretrospectiveonthemilestonesthathavedefinedAI’sprogress.Bytracingthelineageofalgorithmicinnovations,increasesincomputingpower,andenhancementsindatautilization,weaimtoilluminatethesignificantmomentsthathaveshapedAIfromitsinceptiontoitscurrentstate.ThisexplorationisstructuredaroundtheAI1.0toAI4.0framework,illustratinghoweachgeneration’sdefiningfeaturesandlimitationscorrespondtobroaderhistoricalphasesfromapproximately1950tothepresent.Indoingso,wewillalsocontemplatethefuturetrajectoryofAI,consideringthepotentialtechnicalchallenges,societalimpacts,andstrategicdirectionsthatcoulddefinethenextphasesofAIresearchandapplication.

ThisarticleisstructuredtofirstrevisitthehistoricalfoundationsofAI,emphasizingtheshiftsinprimarydriversfromalgorithmstocomputingpowertodata.Wethendelveintothespecificcharacteristics,achievements,andlimitationsofAI1.0,AI2.0,AI3.0,andAI4.0.Followingthis,weexploretheconvergenceandfutureoutlookofAI,highlightingthesynergiesamongthefourgenerationsandoutliningthegrandchallengesthatlieahead.Finally,weconcludewithasynthesisofkeyinsightsandproposefuturedirectionsforsustainedprogressinthefield,aimingtobothinformandinspirecontinuedinnovationandthoughtfulintegrationofAIintoourdailylivesandsocietalstructures.

II.HISTORICALFOUNDATIONSOFAI

2.1Phase1(1950s-2010s):AgeofAlgorithmicInnovations

Sincethe1950s,AIhasadvancedthroughadynamicinterplayamongthreecoreingredients:algorithms,computingpower,anddata[7].Althoughthesethreefactorshavealwaysshapedthefield,theyhavenotalwayscontributedequallyateverystage.Intheearlydecades,thelimitingfactorwasinnovationinalgorithms.Frommid-centurydebatesaboutthefeasibilityofmachineintelligencetotheemergenceofexpertsystemsandneuralnetworks,itwasclearthatconceptualbreakthroughswoulddetermineAI’sboundaries[8].Meanwhile,althoughdataandcomputingpowerwereimportant,theyplayedmoresupportiveroles.Gradually,asnewhardwarearchitecturesappearedandaslarge-scaledatasetsbecamemoreaccessible,thefocusshiftedtowardharnessingimmensecomputationalcapabilityandvastamountsofinformation.

Fromtheoutset,researcherswereenthralledbythequestionofwhethermachinescouldtrulythink.

AlanTuring’spioneeringpaper[9]setthestage,posingthefamous“imitationgame”asalitmustestforintelligence.In1956,theDartmouthConference[10]formallyintroducedtheterm“ArtificialIntelligence”andlaidouttheboldpropositionthattheessenceofhumanintelligencecouldbepreciselydescribedandreplicatedinmachines.Earlysystems,suchastheLogicTheoristandtheGeneralProblemSolver[2,11]underscoredthatsymbolicreasoningcouldbecomputationallyrealized.Theseproof-of-conceptattempts

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highlightedthecentralpremiseofthatera:ifwecoulddevisetherightalgorithms,computersmightreasonandsolveproblemswithnear-humanefficacy.

Bythe1960sand1970s,astrongemphasisonsymbolicAItookhold.InfluentialworksbyJohnMcCarthy[12]introducedLISPasalanguagesuitedtosymbolicprocessing,whileMinskyandPapert’s[13]criticalanalysisofsingle-layerperceptionscontributedtoapauseinneuralnetworkresearch,pushingmanyresearcherstowardknowledge-basedor“expert”systems.MilestonesliketheDENDRALproject[14]andMYCIN[15]showcasedhowcarefullycuratedrulesetscouldguideproblem-solvinginspecializeddomains.Thesesystemsillustratedthepowerofalgorithmicdesigninareassuchasmedicaldiagnosisorchemicalanalysis,evenwhenreal-worlddatawerescarceandcomputationalresourceslimited.

Neuralnetworksreboundedinthe1980swithworkonHopfieldnetworks[16](Fig.1)and,crucially,therediscoveryofbackpropagation[17].Thisgaveresearchersfreshinsightintohowmachinesmightlearnpatternsfromdata.Thoughthepotentialoftheseconnectionistapproacheswasclear,theyoftenstalledbecauselargedatasetswerenotwidelyavailableandspecializedhardwaredidnotyetexist.Evenso,foundationalcontributionslikeLeCun,etal.[18]applicationofconvolutionalneuralnetworkstohandwrittendigitrecognitionlaidthegroundworkforwhatwouldbecomemoderndeeplearning.

Fig.1TheHopfieldnetworks[16]introducedcontent-addressablememoryinneuralnetworks,markinga

majormilestoneinconnectionisminAI.

Bythe1990s,certainalgorithmicachievementshintedatdeeperarchitecturescapableoftacklingincreasinglycomplextasks.TheproposalofLongShort-TermMemory(LSTM)networkseffectivelyaddressedthevanishinggradientproblem,openingpossibilitiesformodelingsequentialdatamoreaccurately[19].However,therealtransformativemomentemergedaround2012,whenKrizhevsky,Sutskever,andHintondemonstratedthatImageNet-scaledatasetsandhigh-performanceGPUscoulddramaticallyimproveadeepneuralnetwork’sabilitytoclassifyimages,i.e.,theAlexNet[20](Fig.2).Althoughthiswatershedeventisoftenviewedasthedawnofthe“deeplearningera,”itcouldnothavehappenedwithoutthealgorithmicgroundworklaidovertheprecedingdecades.

Fig.2AlexNet[20]marksthebeginningoflarge-scale,GPU-acceleratedconvolutionalneuralnetworks

forhigh-performanceimageclassification

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2.2Phase2(2010s-Present):TheComputeRevolutionandDeepLearningRenaissance

AdramaticshiftinAIresearchtookholdaround2012,whenmountingcomputationalcapacitybegantoeclipsealgorithmicnoveltyastheprincipalengineofprogress.Whilethecoreconceptsunderlyingneuralnetworkshadbeenpresentsinceatleastthe1980s,itwasthewidespreadadoptionofGeneral-PurposeGraphicsProcessingUnits(GPUs)thatignitedwhatisoftentermedthe“deeplearningrenaissance”(Fig.3).WhenKrizhevsky,etal.[20]leveragedGPUstotrainalargeconvolutionalneuralnetworkfortheImageNetcompetition,theydecisivelydemonstratedhowparallelizedcomputingcouldunearthperformancegainspreviouslyunachievablewithsingle-threadedCentralProcessingUnits(CPUs).Thisturningpointcatalyzedawaveofresearchacrossmachinevision,speechrecognition,andnaturallanguageprocessing,withgroupsatGoogle,Microsoft,Baidu,andmanyacademicinstitutionsallracingtoscaleupnetworkarchitectures[21-23].Theessenceofthisperiodlayintheconvictionthat“biggerisbetter”,whetherintermsofmodelparameters,datasetsize,orsheercomputationalresources.Consequently,muchofthestate-of-the-artprogresshingedonharnessingspecializedhardware:firstGPUs,thentensorprocessingunits(TPUs)andothercustomaccelerators,tochurnthroughever-growingdatasetsinshortertrainingcycles.

Fig.3TheCUDAarchitecturepioneeredgeneral-purposeGPUcomputing,revolutionizingparallel

processingandacceleratingAIbreakthroughs.

Bythemid-2010s,theexplosiveriseofdeepreinforcementlearning[24]andbreakthroughsingame-playingAI,suchasAlphaGo[25],underscoredthatnotonlycouldAImodelslearnrepresentationsfrommassivedata,buttheycouldalsodiscoverwinningstrategiesthroughlarge-scalesimulations.Nevertheless,thepredominantrealmforthesesystemsremainedresolutelydigital.Whetherclassifyingimages,translatingtext[26,27],orplayingcomplexboardandvideogames,AIwasstilloperatinginanessentiallyinformationalcontext.Althoughdataavailabilitywascriticalandalgorithmslikeconvolutionalandrecurrentneuralnetworkscontinuedtoimprove,sheercomputationalpowerwasoftenthedecidingfactorinachievingsuperiorperformance.Researchersobservedemergentpatternsinscalinglaws[28],revealingthatlargermodelstrainedonlargerdatasetscouldunlockqualitativelynewcapabilities.SystemslikeGPT-2[29]andGPT-3[30]illustratedthisphenomenonvividlybydemonstratingastrikingabilitytogeneratehuman-liketextonceparametercountsandtrainingdatareachedcertainthresholds.Foralltheirsophistication,thesemodelscontinuedtoresideinthedigitalworld,makingthemrefinedandpowerfulversionsfocusedonbigdataanalyticsandpatternrecognitionatanunprecedentedscale.Evenso,theendofthisphasebegantohintatatransitiontowardgreaterautonomyanddecision-makingindigitalcontexts,anemerginghallmarkofagenticAI.Whilemanysystemsarestillcenteredonclassificationorprediction,theriseofadvancedreinforcementlearningagentsabletoadaptstrategieswithinsoftwareecosystemsforeshadowedanewkindofagency.Byapproximately2024,thescholarlyandcommercialdrivetodevelopgoal-directedvirtualassistants,automatedresourceallocationtools,andmulti-agentsimulationssuggested

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thatthechiefchallengewasnolongerpurelytolabeldataaccurately,buttoactindigitalenvironmentsinwaysthattranscendedtraditionalsupervisedlearning[31].ThisgrowingdesireforagenticAIremainedtiedtoabundantcomputingpower,yetitbegantorevealnewdependenciesonspecializeddatastreamsandreal-timefeedbackloops[32].ItsetthestageforthenextgenerationofAI,inwhichcomputationalneedswouldremainvital,butdataandcontext-specificknowledgewouldbecomeevenmorepivotalinenablingtrulyautonomous,adaptivesystems.

2.3Phase3(2024–Foreseeablefuture):Data-CentricParadigms

Inthewakeofaperioddefinedbydramaticincreasesincomputationalhorsepower,thefocalpointofAIadvancementhasshiftedonceagain.WherePhase2thrivedonscalingneuralnetworksthroughunprecedentedparallelprocessing,Phase3acknowledgesthatdata,especiallyspecialized,high-qualitydata,isfrequentlythegreatestobstacle.Researchershavediscoveredthatever-largermodelsalonedonotguaranteesuccessiftheylackcontext-richtrainingsets.Consequently,asurgeinlarge-scale,domain-specificdata-collectioneffortshasemerged,reshapingthefield’spriorities.Projectsthataggregatespecializedmedicaldatafordiagnosticsystems[33],simulatehigh-fidelityenvironmentsforroboticsandautonomousvehicles[34,35],orcompiledeepreinforcementlearningbenchmarkswithrealisticconstraints[36,37]attesttotheideathatharnessingrobustdatasetscanbeasdeterminativeasalgorithmicingenuityorrawcomputationalpower.

Despitethecontinuedimportanceofparallelcomputingandinnovativearchitectures,manycutting-edgesuccessesnowhingeondatastrategy.Researchershavechampioned“data-centricAI”[38],arguingthatrefiningtrainingsets,removingbiases,fillingincoveragegaps,orgeneratingsyntheticdatatohandleedgecases,oftenyieldsmoreimprovementthanaddinglayerstoaneuralnetwork.Thisphilosophyiscloselyrelatedtotheriseoffoundationmodels[39],whicharevastneuralarchitecturesthatcanbeadaptedtomyriadtasks,butrequiremassive,carefullycuratedcorporatorealizetheirfullpotential.Asdatabecomesthetruebottleneck,teamsmustgrapplewiththelogisticalandethicalchallengesofcollecting,storing,andlabelingit,aswellaswithprivacy,consent,andrepresentationissues.

Withinthisphase,AI’stransitionfrominformationalanalysistoagenticdecision-makingbecomesincreasinglytangible.Reinforcementlearningagentsnotonlyplanandlearnincomplexdigitalworldsbutalsobegintobridgeintoreal-worldapplications,wheretheymustreasonaboutnoisysensors,hardwareuncertainties,andhumancollaboration.PhysicalAI,exemplifiedbyadvancedrobotics,autonomousdrones,andintegratedcyber-physicalsystems,movesbeyondtheboundariesofsimulatedorpurelyinformationalspaces.Progressinroboticgraspingandmanipulation[35,40],self-drivingvehicles[41],androboticsurgery[42]signalshowthesesystemscanrobustlyinteractwiththeenvironment,handledynamicconditions,andlearnfromcontinuousfeedback.Thus,thehallmarkofthisnewphaseistherecognitionthatdataunlocksthefullerpotentialofagenticAIindigitalecosystems,aswellasphysicallyembodiedintelligenceintherealworld[43].

III.AIGENERATIONS

ThehistoricalreviewofAIunderscoresapivotalgenerationalshiftandevolutioninAIparadigms,callingforanovelframeworkforunderstandingandclassifyingAI.Inthiscontext,weavoidthetraditionaltechnicaldefinitionsthatcategorizeAIstrictlybytheiroperationaloralgorithmiccharacteristics.Instead,ouranalysisseekstounderstandAIthroughitsintrinsicqualities:Whatarethey?Whataretheydesignedtoachieve?Andwhataretheirconsistentbehavioralpatterns?Accordingly,weproposeataxonomythatidentifiesfourdistinctgenerationsofAI:AI1.0,characterizedasInformationAI,whichfocusesondataprocessingandknowledgemanagement;AI2.0,orAgenticAI,whichencompassessystemscapableofautonomousdecision-making;AI3.0,knownasPhysicalAI,whichintegratesAIintophysicaltasksthroughrobotics;andthespeculativeAI4.0,termedConsciousAI,whichpositsthepotentialemergenceofself-awareAIsystems.ThisclassificationaimstoprovideamoredetailedperspectivereflectingAItechnologies'complexevolution.Fig.4illustratesthegenerationalevolutionofartificialintelligence(AI)fromAI1.0(InformationAI)toAI4.0(ConsciousAI).

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Fig.4TheEvolutionofAIGenerationsfromAI1.0toAI4.0

3.1AI1.0:InformationAI

TheconceptofAI1.0capturesastageinwhichcomputationalsystemsexcelatclassifyingandinterpretinginformationbutremainconfinedtoanalysesofstaticdata,ratherthanengaginginactivedecision-makingorreal-worldmanipulation.Fundamentally,AI1.0focusesonpatternrecognitionandinformationprocessing,techniquesthathavepoweredbreakthroughsincomputervision,naturallanguageprocessing(NLP),andrecommendationsystems.Althoughtheseachievementsmightseemcommonplacenow,theyrepresentthefruitsofdecadesofresearchdrivenbybothmathematicalinnovationandtheincreasingavailabilityofdigitaldata.

ManyofthecoreideasunderpinningAI1.0tracebacktoearlyneuralnetworkresearchandstatisticalmachinelearning.FromRosenblatt’sperceptroninthelate1950stothebackpropagationalgorithmspopularizedbyRumelhart,Hinton,andWilliams[17],thesedevelopmentslaidthegroundworkfordata-drivenlearningbydemonstratingthatmachinescoulduncoverpatternswithinexamplesratherthanrelyingsolelyonhand-codedrules.Classicapproachestosupervisedlearning,suchasSupportVectorMachines(SVMs)formalizedbyCortesandVapnik[44],laterprovedtobeformidablecontendersintasksrangingfromhandwritingrecognitiontotextclassification.Progressincomputationalhardware,alongwiththeaccumulationofsizeablelabeleddatasets,eventuallymadeitfeasibletotraindeeperandmorecomplexneuralnetworks,culminatinginmilestonesuccessesincomputervision.AwatershedmomentcamewhenKrizhevskyetal.[20]’sAlexNetleveragedparallelizedGPUtrainingtoconquertheImageNetchallenge,revealinghowconvolutionalarchitecturescouldoutperformallpriormethodsbylearningincreasinglyabstractfeaturesfromrawimagepixels.

Innaturallanguageprocessing,theinfluenceofAI1.0canbeseeninearlysequencemodelsandstatisticallanguagemodeling.AlthoughthesesystemsoftenreliedonsimplerMarkovorn-gramassumptions,theysetthestageformoreadvancedarchitecturesbyhighlightingthenecessityofabundanttextcorpora.Meanwhile,recommendationengines,suchasthosepopularizedbytheNetflixPrize[45],underscoredhowanalyzinglarge-scaleuserinteractionscoulddriveconsumerengagementonstreamingande-commerceplatforms.Today,manycompaniesstillrelyonthesecoreAI1.0technologies,sometimesenhancedwithshallowneuralarchitectures,tofilterspam,ranksearchresults,recommendproducts,ordetectfraudulenttransactions.Indeed,forstructuredorsemi-structureddata,thesepattern-recognitionapproachesremainbothcost-effectiveandhighlyaccurate.

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Despitetheirdeepsocietalimpact,AI1.0systemsgenerallylackautonomyorcontextualawarenessassociatedwithsubsequentgenerationsofAI.Theyexcelatpredictingoutcomeswhenprovidedwithsubstantialtrainingdata,buttheyrequirearelativelystableenvironmentandbenefitmostfromhumansupervisionindatacurationanddecision-making.Performanceoftendegradesiftheinputdistributionshiftssignificantly,avulnerabilityillustratedwhenfacerecognitionmodelsfalteronunderrepresentedgroupsorwhenlanguagemodelsencounterdomain-specificjargon.WhiletheconsiderablesuccessofAI1.0isundeniable-transformingindustriesfromfinancetohealthcarethroughimprovedanalyticsanddiagnostics

-itslimitationslieinitsreactivenature.Patternrecognitionaloneoffersnoguaranteeofproactivedecision-making,real-timeadaptation,orsafedeploymentindynamicsettings.Theseconstraints,whilehardlytrivial,becamethespringboardforfurtherdevelopmentsinAI2.0and3.0,inwhichsystemsaimtolearn,plan,andevenactwithinuncertaindigitalorphysicalworlds.

3.2AI2.0:AgenticAI

AdefiningcharacteristicofAI2.0istheemergenceofsystemscapableofautonomousdecision-makingwithindigitalcontexts.Ratherthanmerelyclassifyingstaticdata,theseagentsadapttheirbehaviortoachievegoals,oftenincomplexorcontinuouslyevolvingenvironments.Reinforcementlearning(RL)hasplayedapivotalroleinthisshift,enablingmachinestolearnstrategiesbyinteractingwithsimulatedorreal-worldsettingsandreceivingfeedbackintheformofrewardsorpenalties.PioneeringworkondeepRL[24]andsubsequentachievementssuchasAlphaGo[25]underscoredhowsufficientlypowerfulalgorithmsandamplecomputingresourcescouldsurpasshumanperformanceintasksthatdemandlong-termplanningandstrategicadaptation.Acommonthreadamongthesesystemsistheconceptofgoal-directedplanning:softwareagentsallocateresources,scheduletasks,orcoordinatewithotheragents,leveragingsophisticatedRLorhybridRL-languagemodelalgorithms[30]thatintegratescontextualunderstanding(Fig.5).

Fig.5AgenticAIusesadaptivepolicies,enablingautonomousactionandcontinuousself-improvement.

AlthoughtheconceptualleapfromAI1.0’spatternrecognitiontoAI2.0’sagenticbehaviormightappearseamless,itdemandsauniqueconfluenceoftechnicalelements.Computingpoweriscrucialbecauseagenticsystemsfrequentlyrequirereal-timeinferenceandtheabilitytoruncomplexsimulations,whethertheyinvolveamarketplace,amultiplayerenvironment,ortherobustschedulingofcloudresources.ThepursuitofthesecomputationallyintensivetaskshasspurredthedevelopmentofGPUclusters,tensorprocessingunits(TPUs),andotherspecializedacceleratorsdesignedforiterativetrainingandlow-latencydecision-making.

Alongsiderawcomputing,datanowshiftstowardcontextual,time-varyinginputs.Insteadofstaticimagesets,thesesystemsofteningeststreamsoflogs,marketquotes,eventtriggers,oruserinteractions.

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Traininganagenttotradestocksautomaticallyortooperatearecommendationengineinreal-timerequiresongoingingestionofbehavioraldataandacapacitytoadaptasmarketconditionsoruserpreferencesevolve.Inparallel,algorithmsforplanningandmulti-agentcoordinationcontinuetomature.RLframeworkshavegrownmorerefined,incorporatinghierarchicalstrategies[46],policyoptimizationmethods[47],andcombinationswithlargelanguagemodelstogeneratemoreadaptiveandcontext-awaredecisions.

PracticalapplicationsofAI2.0alreadyabound,evenifmanyarenotlabeled“reinforcementlearning”byname.Automatedtradingsystemsinfinanceexemplifyhowagentsmakehigh-frequencydecisionsunderuncertainty,guidedbystreamingdatafeeds.Recommendationsystems,evolvingfromstaticcollaborativefiltering,increasinglyincorporatefeedbackloopstoadaptsuggestionsinrealtime,improvinguserengagementacrosse-commerceandmediaplatforms.Digitalassistantsandsoftwareschedulers,whilenotyetubiquitouslyagentic,offerglimpsesofafuturewhereAIhandlestaskslikeresourceallocation,taskdelegation,andmulti-agentcoordinationwithincorporateorconsumersoftwareeco