2025 中国学界对大语言建模的采购性思考人用工智能 AGI 的多渠道调查研究报告(英)-美国安全与新兴技术中心

CenterforSecurityandLargelanguagemodelshavegarneredinterestworldwideowingtotheirremarkableabilityto“generate”human-likeresponsestonaturallanguagequeries—athresholdthatatonetimewasconsidered“proof”ofsentience—andperformothertime-savingtasks.Indeed,LLMsintelligence(GAI)—thathypothesizedstatewherecomputersreach(orevenexceed)humanskillsatmostoralltasks.ThelureofachievingAI’sholygrailthroughLLMshasdrawninvestmentinthebillionsofdollarsbythosefocusedonthisgoal.IntheUnitedStatesandEuropeespecially,bigprivatesectorcompanieshaveledthewayandtheirfocusonLLMshasovershadowedresearchonotherapproachepowerconsumption,unreliableor“hallucinatory”output,anddeficitsinreasoningabilities.Ifthesecompanies’betsonLLMsfailtodeliveronexpectationsofprogresstowardGAI,westernAIdevelopersmaybepoorlypositionedtorapidlyfallbackonalternateapproaches.Incontrast,Chinafollowsastate-driven,diverseAIdevelopmentplan.LiketheUnitedStates,ChinaalsoinvestsinLLMsbutsimultaneouslypursuesalternatepathsincludingthosemoreexplicitlybrain-inspired.ThisreportdrawsonpublicstatementsannouncementstodocumentChina’smultifacetedapproach.TheChinesegovernmentalsosponsorsresearchtoinfuse“values”intoAIintendedtoguideautonomouslearning,provideAIsafety,andensurethatChina’sadvancedAIreflectstheneedsofthepeopleandthestate.ThisreportconcludesbyrecommendingU.S.governmentsupportforalternativegeneralAIprogramsandforcloserscrutinyofCenterforSecurityandEmergingTechIntroduction:GenerativeAIandGeneralAIAchievinggeneralartificialintelligenceorGAI—definedasAIthatreplicatesorunderstanding,continuallearning,planning,reasoning,skilltransfer,andcreativity1—isakeystrategicgoalofintenseresearcheffortsbothinChinaandtheUnitedStates.2ThereisvigorousdebateintheinternationalscientificcommunityregardingwhichpathwillleadtoGAImostquicklyandwhichpathsmaybefalsestarts.IntheUnitedStates,LLMshavedominatedthediscussion,yetquestionsremainabouttheirabilitytoachieveGAI.SincechoosingthewrongpathcanpositiontheUnitedStatesatastrategicdisadvantage,thisraisestheurgencyofexaminingalternativeapproachesthatothercountriesmaybepursuing.IntheUnitedStates,manyexpertsbelievethetransformativesteptoGAIwilloccurwiththerolloutofnewversionsofLLMssuchasOpenAI’so1,Google’sGemini,Anthropic’sClaude,andMeta’sLlama.3Othersargue,pointingtopersistentproblemssuchasLLMhallucinations,thatnoamountofcompute,feedback,ormultimodaldatasourceswillallowLLMstoachieveGAI.4StillotherAIscientistsseerolesforLLMsinGAIplatformsbutnotastheonly,orevenmain,component.5PonderingthequestionofhowGAIcanbeachievedisimportantbecauseittoucoptionsavailabletodeveloperspursuingAI’straditionalholygrail—human-levelintelligence.Isthepath—orapath—toGAIacontinuationofLLMdevelopment,possiblyaugmentedbyadditionalother,fundamentallydifferentapproachesthatarebasedonacloseremulationofhumancognitionandbrainfunction?GiventhesuccessofLLMs,thelevelsofinvestment,6endorsementsbyhighlyregardedAIscientists,optimismcreatedbyworkingexamples,andthedifficultyofreimaginingnewapproachesinthefaceofmodelsinwhichcompanieshavegreatcommitment,itiseasytooverlooktheriskofrelyingona“monoculture”basedonasingleresearchparadigm.7IftherearelimitationstowhatLLMscandeliver,withoutasufficientlydiversifiedresearchportfolio,itisunclearhowwellwesterncompaniesandgovernmentswillbeabletopursueothersolutionsthatcanovercomeLLMsproblemsaspathwaystoGAI.AdiversifiedresearchportfolioispreciselyChina’sapproachtoitsstate-sponsoredgoalofachieving“generalartificialintelligence”(通用人工智能).8Thisreportwillshowthat—inadditiontoChina’sknownandprodigiousefforttofieldChatGPT-likeLLMs,9—significantresourcesaredirectedinChinaatalternativepathwaystoGAIbyCenterforSecurityandEmergingTechnoscientistswhohavewell-foundedconcernsaboutthepotentialof“bigdata,smallAccordingly,thispaperaddressestwoquestions:WhatcriticismsdoChinesescientistshaveofLLMsaspathstogeneralshortcomings?Thepaperbegins(section1)withcritiquesbyprominentnon-ChinaAIscientistsoflargelanguagemodelsandtheirabilitytosupportGAI.ThesectionprovidescontextforviewsofChinesescientiststowardLLMs(section2)describedinonlinesources.Section3thencitesresearchthatsupportsChina’spublic-facingclaimsaboutthenon-recommendationsinsection5onwhyChina’salternativeprojectsmustbetakenseriously.CenterforSecurityandEmergingTechnolTheterm“largelanguagemodel”capturestwofacts:theyarelargenetworkstypicallywithbillionstotrillionsofparameters,andtheyaretrainedonnaturallanguage,terabytesoftextingestedfromtheinternetandothersources.LLMs,andneuralnetworks(NN)generally,aretypologicallydistinctfrom“goodold-fashioned”(GOFAI)symbolicAIthatdependsonrule-basedcoding.Inaddition,today’slargemodelscanmanage,todifferentdegrees,multimodalinputsandoutputs,includingimages,video,LLMsdebutedin2017,whenGoogleengineersproposedaNNarchitecture—calledatransformer—optimizedtofindpatternsinsequencesoftextbylearningto“payattention”totheco-occurrencerelationshipsbetween“tokens”(wordsorpartsofwords)inthetrainingcorpus.12Unlikehumanknowledge,knowledgecapturedinLLMsisnotobtainedthroughinteractionswiththenaturalenvironmentbutdependsonstatisticalprobabilitiesderivedfromthepositionalrelationshipsbetweenthetokensinsequences.MassiveexposuretocorporaduringtrainingallowstheLLMtoidentifyregularitiesthat,intheaggregate,canbeusedtogenerateresponsestohumanpromptsafterthetraining.Hence,theOpenAIproductname“GPT”(generativepre-trainedtransformer).TheabilityofLLMsto“blend”differentsourcesofinformation(whichplaystotraditionalstrengthsofneuralnetworksinpatternmatchinganduncoveringsimilaritiesincomplexspaces)hasgivensummarization,translation,codewriting,andtheoremproving.Yet,ithasbeenhotlydebatedwhetherthisabilitytofindandexploitregularitiesissufficienttoachieveGAI.Initialenthusiasticreportsregardingthe“sentience”ofLLMsareincreasinglysupplementedbyreportsshowingseriousdeficitsinLLMs’abilitytounderstandlanguageandtoreasoninahuman-likeway.13SomepersistentdeficitsinLLMs,asinbasicmath,14appearcorrectablebyplugins,15approach—ofanetworkofsystemsspecializedindifferentaspectsofcognition—wouldbemorelikethebrain,whichhasdedicatedmodules,e.g.,forepisodicmemory,SomescientistshopethatincreasesincomplexityalonemighthelpovercomeLLMs’defects.Forinstance,GeoffreyHinton,creditinganintuitionofIlyaSutskever(Opformerchiefscientist,whostudiedwithHinton),believesscalewillsolvesomeoftheseproblems.Inthisview,LLMsarealready“reasoning”byvirtueoftheirability“toCenterforSecurityandEmergingpredictthenextsymbol[and]predictionisaprettyplausibletheoryofhowthebrainislearning.”17Indeed,increasesincomplexity(fromGPT-2throughGPT-4)haveledtoincreasedperformanceonvariousbenchmarktasks,suchas“theoryofmind”18(reasoningaboutmentalstates),wheredeficitswerenotedforGPT-3.5.19Othersuchdeficitsarehardertoaddressandpersistcomplexity.Specifically,“hallucinations,”i.e.,LLMsmakingincorrectclaims(aprobleminherenttoneuralnetworksthataredesignedtointerpolateand,unlikethebrain,donotseparatethestorageoffactsfrominterpolations)anderrorsinreasoninghavebeendifficulttoovercome,20withrecentstudiesshowingthatthelikelihoodofincorrect/hallucinatoryanswersincreaseswithgreatermodelcomplexity.21Inaddition,thestrategyofincreasingmodelcomplexityinthehopeofachievingnovel,qualitativelydifferent“emergent”behaviorsthatappearonceacomputationalthresholdhasbeencrossedlikewisehasbeencalledintoquestioshowingthatpreviouslynoted“emergent”behaviorsinlargermodelswereartefactsofthemetricsusedandnotindicativeofanyqualitativechangesinmodelperformance.22Correspondingly,claimsof“emergence”inLLMshavedeclinedintherecentliterature,Indeed,thereisthejustifiedconcernthatthehighperformanceofLLMsonstandardizedtestscouldbeascribedmoretothewell-knownpatternmatchingprowessofneuralnetworksthanthediscoveryofnewstrategies.24StillothercriticismsofLLMscenteronfundamentalcognitiveandphilosophicalissuessuchastheabilitytogeneralize,formdeepabstractions,create,self-direct,modeltimeandspace,showcommonsense,reflectontheirownoutput,25manageambiguousexpressions,unlearnbasedonnewinformation,evaluateproandconargumentsWhilethesedeficitsarediscussedinthewesternresearchliterature,alongwithotherssuchasLLMs’inabilitytoeasilyaddknowledgebeyondthecontextwindowwithoutretrainingthebasemodel,orthehighcomputationalandenergydemandsofLLMtraining,mostcurrentinvestmentofcommercialplayersintheAIspace(e.g.,OpenAI,Anthropic)iscontinuingdownthissameroad.Theproblemisnotonlythat“weareinvestinginanidealfuturethatmaynotmaterialize”27butratherthatLLMs,inGoogleAIresearcherFranҫoisChollet’swords,“suckedtheoxygenoutoftheroom.EveryoneCenterforSecurityandEmergingTechnologAreviewofstatementsbyrankingscientistsatChina’stopAIresearchinstitutesrevealsahighdegreeofskepticismaboutLLMs’abilitytolead,bythemselves,toGAI.Thesecriticismsresemblethoseofinternationalexperts,becausebothgroupsfacethesameproblemsandbecauseChina’sAIexpertsinteractwiththeirglobalpeersasaHerefollowseveralChinesescientists’viewsonLLMsasapathtogeneralAI.TangJie(唐杰)isprofessorofcZhipu(智谱),30aleadingfigureintheBeijingAcademyofArtificialIntelligence(BAAI),31andthedesignerofseveralindigenousLLMs.32Despitehissuccesswithstatisticalmodels,Tangarguesthathuman-levelAIrequiresthemodelstobe“embodiedintheworld.”33Althoughhebelievesthescalinglaw(规模法则)34“stillhasalongwaytogo,”thatalonedoesnotguaranteeGAIwillbeachieved.35Amorefruitfulpathwouldtakecuesfrombiology.Inhiswords:“GAIormachineintelligencebasedonlargemodelsdoesnotnecessarilyhavetobethesameasthemechanismofhumanbraincognition,butanalyzingtheworkingmechanismofthehumanbrainmaybetterinspiretherealizationofGAI.”36formerpresidentofBaidu,foundingdeanofTsinghua’sInstituteforAIIndustrynamely,theirlowcomputationalefficiency,inabilityto“trulyunderstandthephysicalworld,”andso-called“boundaryissues”(边界问题),i.e.,tokenization.37Zhangbelieves(withGoertzel)that“weneedtoexplorehowtocombinelargegenerativeprobabilisticmodelswithexisting‘firstprinciples’[ofthephysicalworld]orrealmodelsandknowledgegraphs.”38HuangTiejun(黄铁军)isfounderandformUniversity’s(PKU)InstituteforArtificialIntelligence(人工智能研究院).HuangnamesthreepathstoGAI:“informationmodels”basedonbigdataandbigcompute,“embodiedmodels”trainedthroughreinforcementlearning,andbrainemulation—inwhichBAAIhasamajorstake.39HuangagreesthatLLMscalinglawswillcontinuetooperatebutadds“itisnotonlynecessarytocollectstaticdata,butalsotoobtainandprocessmultiplesensoryinformationinrealtime.”40Inhisview,GAIdependsonintegratingstatisticalmodelswithbrain-inspiredAIandembodiment,thatis:CenterforSecurityandEmergingTecLLMsrepresent“staticemergeBrain-inspiredintelligence,bycontrast,isbasedoncomplexdynamics.EmbodiedintelligencealsodiffersinthatitgeneratesnewabilitiesbyinteractingwiththeXuBo(徐波),deanoftheSchoolofArtificialIntelligenceatUniversityofChinese越创新中心)43believeembodimentandenvironmentalinteractionwillfacilitateLLMs’growthtowardGAI.AlthoughtheartificialneuralnetworksonwhichLLMsdependwereinspiredbybiology,theyscalebyadding“moreneurons,layersandconnections”anddonotbegintoemulatethebrain’scomplexityofneurontypes,selectiveconnectivity,andmodularstructure.Inparticular,“Computationallycostlybackpropagationalgorithms…couldbeimprovedorevenreplacedbybiologicallyplausiblelearningalgorithms.”Thesecandidatesincludespiketimesynapticplasticity,“neuromodulator-dependentmetaplasticity”and“short-termvs.long-termmemorystoragerulesthatsetthestabilityofsynapticweightIntelligenceanddirectoroftheBeijingInstituteforGeneralArtificialIntelligence(北京通用人工智能研究院)foundedBIGAIonthepremisethatbigdata-basedLLMsareadead-endintermsoftheirabilitytoemulatehuman-levelcognition.45Zhupullsno“Achievinggeneralartificialintelligenceistheoriginalintentionandultimategoalofartificialintelligenceresearch,butcontinuingtoexpandtheparameterscalebasedonexistinglargemodelscannotachievegeneralartificialintelligence.”ZhucomparesChina’sLLM’sachievementsto“climbingMt.Everest”whenthergoalistoreachthemoon.Inhisview,LLMsare“inherentlyuninteofdataleakage,donothaveacognitireasoningcapabilities,andotherlimitations,sotheycannotleadto‘generalartificial智能实验室)andfoundingdirectorofitsInternationalResearchCenterforAIEthicsandCenterforSecurityandEmergingTechnolGovernance,47isbuildingaGAIplatformbasedontime-dependentspikingneuralnetworks.Inhiswords:“Ourbrain-likecognitiveintelligenceteamfirmlybelievesthatonlybymirroringthestructureofthehumanbrainanditsintelligentmechanism,aswellasthelawsandmechanismsofnaturalevolution,canweachieveartificialintelligencethatistrulymeaningfulandbeneficialtohumans.”48CriticismsofLLMsbyotherChineseA•ShenXiangyang(沈向洋,HarryShumAKAHeung-YeungMicrosoftexecutiveVPanddirectoroftheAcademicCommitteeofPKU’sInstituteofArtificialIntelligence,lamentsthatAIresearchhasno“clearunderstandingofthenatureofintelligence.”ShensupportsaviewheattributestoNewYorkUniversityprofessoremeritusandLLMcriticGaryMarcusthat“nomatterhowChatGPTdevelops,thecurrenttechnicalroutewillnotbeabletobringusrealintelligence.”49•ZhengQinghua(郑庆华),presidentofTongjiUniversityandaChineseAcadeofEngineeringacademician,statedthatLLMshavemajorflaws:theyconsumetoomuchdataandcomputingresources,aresusceptibletocatastrophicforgetting,haveweaklogicalreasoningcapabilities,anddonotknowwhentheyarewrongorwhytheyarewrong.50•LiWu(李武),directoroftheStateKeyLaboratoryofCognitiveNeurLearningatBeijingNormalUniversity,statedhisbeliefthat“currentneuralnetworksarerelativelyspecializedanddonotconformtothewaythehumanbrainworks.Ifyoudesperatelyhypethelargemodelitselfandonlyfocusontheexpansionofparametersfrombillionsortensofbillionstohundredsofbillions,youwillnotbeabletoachievetrueintelligence.”51RecognitionoftheneedtosupplementLLMresearchwithalternativepathstoGAIisevidencedinstatementsbyChina’snationalandmunicipalgovernments.OnMay30,2023,Beijing’scitygovernment—withinwhosejurisdictionmuchofChina’sGAI-orientedLLMresearchistakingplace—issuedastatementcallingfordevelopmentof“largemodelsandothergeneralartificialintelligencetechnologysystems”(系统构建大模型等通用人工智能技术体系).52Sectionthreehasfiveitems(7-11),thefirstfourofwhichpertaintoLLMs(algorithms,trainingdata,evaluation,andabasicsoftwareandhardwaresystem).Item11reads“exploringnewpaths(新路径)forgeneralartificialintelligence”andcallsfor:CenterforSecurityandEmergingTechnologDevelopingabasictheoreticalsystem(基础理论体系)forGAI,autonomouscollaborationanddecision-making,embodiedintelligence,andbrain-inspired(类脑)intelligence,supportedbyaunifiedtheoreticalframework,ratingandtestingstandards,andprogramminglanguages.Embodiedsystems(robots)will[trainin]openenvironments,generalizedTheplanalsomandatesthefollowing:“Supporttheexplorationofbrain-likeintelligence,studytheconnectionpatterns,codingmechanisms,informationprocessingandothercoretechnologiesofbrainneurons,andinspirenewartificialneuralnetworkmodelingandtrainingmethods.”AlternativestoLLMswerecitedatthenationallevelinMarch2024,whenCASvicepresidentWuZhaohui(吴朝晖,formerlyviceministerofChina’sscienceministryandpresidentofZhejiangUniversity),53statedthatAIismovingtoward“synergybetweenlargeandsmallmodels”(大小模型协同),addingthatChinamust“explorethedevelopmentofGAIinmultipleways”(多路径地探索通用人工智能发展).Thelatterinclude“embodiedintelligence,distributedgroupintelligence,human-machinehybridintelligence,enhancedintelligence,andautonomousdecisionmaking.ThefollowingmonthBeijing’sHaidianDistrictgovernment,withjurisdictionover1,300AIcompanies,morethan90ofwhicharedevelopingbigmodels,55issuedathree-yearplantofacilitateresearchinembodied(具身)AI.Theplandefines“embodiment”as“theabilityofanintelligentsystemormachinetointeractwiththeenvironmentinrealtimethroughperceptionandinteraction”andismeanttoserveasaplatformfornationwidedevelopment.Itsdetailsincludeplansforhumanoidrobotsfacilitatedbyreplicatingbrainfunctionality.56OuranalysisofpublicstatementsbygovernmentinstitutionsandrankingChineseAIscientistsindicatesthataninfluentialpartofChina’sAIcommunitysharestheconcernsandmisgivingsheldbywesterncriticsofLLMsandseeksalternativepathwaystogeneralartificialintelligence.CenterforSecurityandEmergingTeWhatDoestheAcademicRecordShow?PublicstatementsbyscientistsareonemeasureofChintheirrecordofscholarship.PriorreviewsofChinesetechnicalliteraturedeterminedthatChinaispursuingGAIbymultiplemeans,includinggenerativelargelanguagemodels,57brain-inspiredmodels,58andbyenhancingcognitionthroughbrain-computerinterfaces.59OurpresenttaskistoexaminetheliteratureforevidencethatChinesescholars—beyondwhatpositivefeaturesbrain-basedmodelshave—arealsodriventoseekalternativepathsbyLLM’sshortcomings.Towardthatend,werankeywordsearchesinChineseandEnglishfor“AGI/GAI+LLM”andtheircommonvariantsinCSET’sMergedCorpus60forpaperspublishedin2021orlaterwithprimaryChineseauthorship.Some35documentswereobtained.Aseparatequeryusingweb-basedsearchesrecovered43morepapers.6115ofthe78paperswererejectedbythestudy’sleadanalystasofftopic.Theremaining63paperswerereviewedbythestudy’ssubjectmatterexpert,whohighlightedthefollowing24asexamplesofChineseresearchaddressingLLMproblemsthatstandinthewayoflargemodelsachievingthegeneralityassociatedwithGAI.62PretrainedLanguageModels?UnderstandingtheInvisibView,”arXivpreprintarXiv:2203.12258v1(2022).2.CHENGBing(程兵),“ArtificialIntelligenceGenerativeCont2023).“AdaptingLargeLanguageModelstoDomainsviaReadingComprehension,”arXivpreprintarXiv:2309.09530v4(2024).“Parameter-efficientFine-tuningofLarge-scalePre-trainedLanguageModels,”NatureMachineIntelligence,March2023.contextLearning,”arXivpreprintarXiv:2301.00234v4(2024).“AnEmbodiedGeneralistAgentin3DWorld,”Proceedingsofthe41st2024.CenterforSecurityandEmergingTetrainedLanguageModelsBetterFew-shotLearners,”IEEEInternationalADialogueDatasetforSituatedPragmaticReasoning,”37thConferenceonNeuralInformationProcessingSystemLong-ContextLanguageModelsUnderstandLongContext?”arXivpreprintarXiv:2311.04939v1(2023).LLMAgents:InsightsandSurveyabouttheCapability,EfficiencyandSecurity,”arXivpreprintarXiv:2401.05459v2(2024).towardsArtificialGeneralIntelligenceinLargeLanguageModels,”arXivpreprintarXiv:2307.03762v1(2023).(张宋扬),LINGHaibin(凌海滨),“forGeneralVideoRecognition,”arXivpreprintarXiv:2208.02816v1(2022).ArtificialGeneralIntelligencethroughDynamicEmbodiedPhysicalandSocialInteractions,”Engineering34,(2024).forSpikingNeuralNetworks,”PNAS39(2023).ZHANGMuhan(张牧涵),“LargeLanguageModelsAreIn-contextSReasonersRatherthanarXiv:2305.14825v2(2023).丽凤),“EvaluatingandModelingSocialIntelligence:aComparativeStudyofHumanandAICapabilities,”arXivpreprintarXiv:2405.11841v1(2024).ReallyGoodLogicalReasoners?”arXivpreprintarXiv:2306.09841v2(2023).PhotonicChipletTaichiEmpowers160-TOPS/WArtificialGeneralIntelligence,”Science,April2024.UnifiedLearningFormalism,”Engineering,March2023.CenterforSecurityandEmergingTeFew-shotConceptInductionthroughMinimaxEntropyLearning,”ScienceAdvances,April2024.21.ZHANGTielin(张铁林),XUBo(徐波),“ABrain-inspiredAlgorithmthatMitigatesCatastrophicForgettingofArtificialandSpikingNeuralNetworkswithLowComputationalCost,”ScienceAdvances,August2023.preprintarXiv:2309.01219v2(2023).SpikingNeuralNetworkImprovesMulti-agentCooperationandCompetition.”Patterns,August2023.fromPre-trainedLanguageModelsviaInversePrompting,”arXivpreprintarXiv:2103.10685v3(2021).ThestudiescollectivelyaddressthelitanyofLLMdeficitsdescribedinthispaper’ssections1and2,namely,thoseassociatedwiththeoryofmind(ToM)failures,inductive,deductive,andabductivereasoningdeficits,problemswithlearningnewtasksthroughanalogytoprevioustasks,lackofgrounding/embodiment,unpredictabilityoferrorsandhallucinations,lackofsocialintelligence,insufficientunderstandingofreal-worldinput,inparticularinvideoform,difficultyindealingwithlargercontexts,challengesassociatedwiththeneedtofinetuneoutputs,andcostofoperation.Proposedsolutionstotheseproblemsrangefromaddingmodules,emulatingbrainstructureandprocesses,rigorousstandardsandtesting,andreal-worldembedding,toreplacingthecomputingsubstrateoutrightwithimprovedchiptypes.SeveralprominentChinesescientistscitedinthisstudy’ssection2,whomadepublicstatementssupportingalternateGAImodels,includauthenticitytotheirdeclarations.Inaddition,virtuallyallofChina’stopinstitutionsandcompaniesengagedinGAIresearch,includingtheBeijingAcademyofArtificialIntelligence(北京智源人工智能研究院),theBeijingInstituteforGeneralArtificialIntelligence(北京通用人工智能研究院),theChineseAcademyofSciences’InstituteofAutomation(中国科学院自动化研究所),PekingUniversity(北京大学),TsinghuCenterforSecurityandEmergingTesupportthepresentstudy’scontentionthatmajorelementsinChina’sAIcommunityquestionLLMs’potentialtoachieveGAI—throughincreasesinscaleormodalities—andarecontemplatingorpursuingalternativepathways.CenterforSecurityandEmergingTeAssessment:DoAllPathsLeadtotheBuddha?WhenLLM-basedchatbotsfirstbecameavailable,earlyclaimsthatLLMsmightbesentient,i.e.,experiencefeelingsandsensations,orevenshowself-awareness,66wereprevalentandmuchdiscussed.Sincethen,coolerheadshaveprevailed,67andthefocushasshiftedfromphilosophicalspeculationsabouttheinteriorlivesofLLMstomoreconcretemeasurementsofLLMabilitiesonkeyindicatorsof“intelligent”behaviorandthestrategicallyimportantquestionofwhetherLLMsmightbecapableofgeneralartificialintelligence(GAI).WhileitisfarfromclearwhetherconsciousnessandthecapacityforemotionsarecriticaltoGAI,whatisclearisthataGAIsystemmustbeabletoreasonandtoseparatefactsfromhallucinations.Asthingsstand,LLMshavenoexplicitmechanismsthatwouldenablethemtoperformthesecorerequirementsofintelligentbehavior.Rather,thehopeofLLMenthusiastsisthat,somehow,reasoningabilitieswill“emerge”asLLMsaretrainedtobecomeeverbetteratpredictingthenextwordinaconversation.Yet,thereisnotheoreticalbasisforthisbelief.Tothecontrary,researhasshownthatLLMs’vasttextmemoryhasmaskeddeficienciesinreasonHeuristicattemptstoimprovereasoning(e.g.,chain-of-thought),69likelythebasisforimprovedperformanceinOpenAI’snewas“rephraseandrespond,”70“tree-of-thoughts”71or“graph-of-thoughts”72haveyieldedimprovements,butdonotsolvetheunderlyingproblemoftheabsenceofacore“reasoningengine.”Bythesametoken,multipleattemptstofixLLMs’hallucinationproblem73haverunintodeadendsbecausetheyfailtoaddressthecoreproblemthatisinherenttoLLMs’abilitytogeneralizefromtrainingdatatonewcontexts.Indeed,currenteffortstoimprovereasoningabilitiesandfixhallucinationsareabitlikeplaying“whack-a-mole”butwithmoleshidinginabillion-dimensionalweightspaceandwithamalletthatisuncertaintohitwhereintended.TheresultingsystemsmightbesufficientforsituationswherehumanscanassessthequalityofLLMoutput,e.g.,writingcoverletters,designingtravelitinerariesorcreatingessaysontopicsthatareperennialfavoritesofhighschoolteachers.Yet,thesecapabilitiesareafarcryfromGAThepublicdebatesinthewesternworldontheappropriatepathtoGAItendtobedrownedoutbycompanieswithfinancialinterestsinpromotingtheirlatestLLMswithclaimsof“human-likeintelligence”or“sparksofartificialgeneralintelligence,”74eveninthefaceofevermoreapparentshortcodominanceofcommercialintereststhatpromoteLLMsassurepathstoGAIhasCenterforSecurityandEmergingTealreadynegativelyaffectedtheabilityofacademicresearchintheU.S.topursueThesituationisdifferentinChina.WhiletherearealsocompaniesinChinadevelopingLLMsforcommerc