Nature 人工智能距离人类水平智能有多近

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HOWCLOSEISAI

N-?EL

LargelanguagemodelssuchasOpenAI’so1have

electrifiedthedebateoverachievingartificialgeneralintelligence.Buttheyareunlikelytoreachthis

milestoneontheirown.ByAnilAnanthaswamy

O

penAI’slatestartificialintelligence(AI)systemdroppedinSeptemberwithaboldpromise.Thecom-panybehindthechatbotChatGPTshowcasedo1—itslatestsuiteoflargelanguagemodels(LLMs)—ashavinga“newlevelofAIcapability”.OpenAI,whichisbasedinSanFran-

cisco,California,claimsthato1worksinawaythatisclosertohowapersonthinksthandopreviousLLMs.

Thereleasepouredfreshfuelonadebatethat’sbeensimmeringfordecades:justhowlongwillitbeuntilamachineiscapableofthewholerangeofcognitivetasksthathumanbrainscanhandle,includinggeneralizingfromonetasktoanother,abstractreasoning,plan-ningandchoosingwhichaspectsoftheworldtoinvestigateandlearnfrom?

Suchan‘artificialgeneralintelligence’,orAGI,couldtacklethornyproblems,includingclimatechange,pandemicsandcuresforcan-cer,Alzheimer’sandotherdiseases.Butsuchhugepowerwouldalsobringuncertainty—andposeriskstohumanity.“Badthingscould

happenbecauseofeitherthemisuseofAIorbecausewelosecontrolofit,”saysYoshuaBengio,adeep-learningresearcherattheUniversityofMontreal,Canada.

TherevolutioninLLMsoverthepastfewyearshaspromptedspeculationthatAGImightbetantalizinglyclose.ButgivenhowLLMsarebuiltandtrained,theywillnotbesufficienttogettoAGIontheirown,someresearcherssay.“Therearestillsomepiecesmissing,”saysBengio.

What’sclearisthatquestionsaboutAGIarenowmorerelevantthanever.“Mostofmylife,IthoughtpeopletalkingaboutAGIarecrack-pots,”saysSubbaraoKambhampati,acomputerscientistatArizonaStateUniversityinTempe.“Now,ofcourse,everybodyistalkingaboutit.Youcan’tsayeverybody’sacrackpot.”

WhytheAGIdebatechanged

Thephraseartificialgeneralintelligenceenteredthezeitgeistaround2007afteritsmentioninaneponymouslynamedbookeditedbyAIresearchersBenGoertzelandCassioPennachin.Itsprecisemeaningremains

elusive,butitbroadlyreferstoanAIsystemwithhuman-likereasoningandgeneralizationabilities.Fuzzydefinitionsaside,formostofthehistoryofAI,it’sbeenclearthatwehaven’tyetreachedAGI.TakeAlphaGo,theAIprogramcreatedbyGoogleDeepMindtoplaytheboardgameGo.Itbeatstheworld’sbesthumanplay-ersatthegame—butitssuperhumanqualitiesarenarrow,becausethat’sallitcando.

ThenewcapabilitiesofLLMshaveradicallychangedthelandscape.Likehumanbrains,LLMshaveabreadthofabilitiesthathavecausedsomeresearcherstoseriouslycon-sidertheideathatsomeformofAGImightbeimminent1,orevenalreadyhere.

Thisbreadthofcapabilitiesisparticularlystartlingwhenyouconsiderthatresearch-ersonlypartiallyunderstandhowLLMsachieveit.AnLLMisaneuralnetwork,amachine-learningmodellooselyinspiredbythebrain;thenetworkconsistsofartificialneurons,orcomputingunits,arrangedinlay-ers,withadjustableparametersthatdenotethestrengthofconnectionsbetweentheneurons.Duringtraining,themostpowerful

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ILLUSTRATIONBYPETRAPÉTERFFY

LLMs—suchaso1,Claude(builtbyAnthropicinSanFrancisco)andGoogle’sGemini—relyonamethodcallednexttokenprediction,inwhichamodelisrepeatedlyfedsamplesoftextthathasbeenchoppedupintochunksknownastokens.Thesetokenscouldbeentirewordsorsimplyasetofcharacters.Thelasttokeninasequenceishiddenor‘masked’andthemodelisaskedtopredictit.Thetrainingalgorithmthencomparesthepredictionwiththemaskedtokenandadjuststhemodel’sparameterstoenableittomakeabetterpredictionnexttime.Theprocesscontinues—typicallyusing

YOUDON’TSEETHATKINDOFAUTHENTICAGENCYINLARGE

LANGUAGEMODELS.”

billionsoffragmentsoflanguage,scientifictextandprogrammingcode—untilthemodelcanreliablypredictthemaskedtokens.Bythisstage,themodelparametershavecapturedthestatisticalstructureofthetrainingdata,andtheknowledgecontainedtherein.Theparametersarethenfixedandthemodelusesthemtopre-dictnewtokenswhengivenfreshqueriesor‘prompts’thatwerenotnecessarilypresentinitstrainingdata,aprocessknownasinference. Theuseofatypeofneuralnetworkarchitec-tureknownasatransformerhastakenLLMssignificantlybeyondpreviousachievements.Thetransformerallowsamodeltolearnthatsometokenshaveaparticularlystronginfluenceonothers,eveniftheyarewidelyseparatedinasampleoftext.ThispermitsLLMstoparselanguageinwaysthatseemtomimichowhumansdoit—forexample,dif-ferentiatingbetweenthetwomeaningsoftheword‘bank’inthissentence:“Whentheriver’sbankflooded,thewaterdamagedthebank’sATM,makingitimpossibletowithdrawmoney.” Thisapproachhasturnedouttobehighlysuccessfulinawidearrayofcontexts,

includinggeneratingcomputerprogramstosolveproblemsthataredescribedinnaturallanguage,summarizingacademicarticlesandansweringmathematicsquestions.

Andothernewcapabilitieshaveemergedalongtheway,especiallyasLLMshaveincreasedinsize,raisingthepossibilitythatAGI,too,couldsimplyemergeifLLMsgetbigenough.Oneexampleischain-of-thought(CoT)prompting.ThisinvolvesshowinganLLManexampleofhowtobreakdownaproblemintosmallerstepstosolveit,orsimplyaskingtheLLMtosolveaproblemstep-by-step.CoTpromptingcanleadLLMstocorrectlyanswerquestionsthatpreviouslyflummoxedthem.Buttheprocessdoesn’tworkverywellwithsmallLLMs.

ThelimitsofLLMs

CoTpromptinghasbeenintegratedintotheworkingsofo1,accordingtoOpenAI,andunderliesthemodel’sprowess.FrancoisChollet,whowasanAIresearcheratGoogleinMountainView,California,andleftinNovembertostartanewcompany,thinks

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thatthemodelincorporatesaCoTgeneratorthatcreatesnumerousCoTpromptsforauserqueryandamechanismtoselectagoodpromptfromthechoices.Duringtraining,o1istaughtnotonlytopredictthenexttoken,butalsotoselectthebestCoTpromptforagivenquery.TheadditionofCoTreasoningexplainswhy,forexample,o1-preview—theadvancedversionofo1—correctlysolved83%ofprob-lemsinaqualifyingexamfortheInternationalMathematicalOlympiad,aprestigiousmathe-maticscompetitionforhigh-schoolstudents,accordingtoOpenAI.Thatcompareswithascoreofjust13%forthecompany’spreviousmostpowerfulLLM,GPT-4o.

But,despitesuchsophistication,o1hasitslimitationsanddoesnotconstituteAGI,sayKambhampatiandChollet.Ontasksthatrequireplanning,forexample,Kambhampati’steamhasshownthatalthougho1performsadmirablyontasksthatrequireupto16plan-ningsteps,itsperformancedegradesrapidlywhenthenumberofstepsincreasestobetween20and40(ref.2).Cholletsawsimilarlimita-tionswhenhechallengedo1-previewwithatestofabstractreasoningandgeneralizationthathedesignedtomeasureprogresstowardsAGI.Thetesttakestheformofvisualpuzzles.Solvingthemrequireslookingatexamplestodeduceanabstractruleandusingthattosolvenewinstancesofasimilarpuzzle,somethinghumansdowithrelativeease.

LLMs,saysChollet,irrespectiveoftheirsize,arelimitedintheirabilitytosolveproblemsthatrequirerecombiningwhattheyhavelearnttotacklenewtasks.“LLMscannottrulyadapttonoveltybecausetheyhavenoabilitytobasicallytaketheirknowledgeandthendoafairlysophisticatedrecombinationofthatknowledgeontheflytoadapttonewcontext.”

CanLLMsdeliverAGI?

So,willLLMseverdeliverAGI?Onepointintheirfavouristhattheunderlyingtransformerarchitecturecanprocessandfindstatisticalpatternsinothertypesofinformationinadditiontotext,suchasimagesandaudio,providedthatthereisawaytoappropriatelytokenizethosedata.AndrewWilson,whostudiesmachinelearningatNewYorkUni-versityinNewYorkCity,andhiscolleaguesshowedthatthismightbebecausethedif-ferenttypesofdataallshareafeature:suchdatasetshavelow‘Kolmogorovcomplexity’,definedasthelengthoftheshortestcomputerprogramthat’srequiredtocreatethem3.Theresearchersalsoshowedthattransformersarewell-suitedtolearningaboutpatternsindatawithlowKolmogorovcomplexityandthatthissuitabilitygrowswiththesizeofthemodel.Transformershavethecapacitytomodelawideswatheofpossibilities,increasingthechancethatthetrainingalgorithmwilldiscoveranappropriatesolutiontoaproblem,andthis‘expressivity’increaseswithsize.Theseare,

saysWilson,“someoftheingredientsthatwereallyneedforuniversallearning”.AlthoughWilsonthinksAGIiscurrentlyoutofreach,hesaysthatLLMsandotherAIsystemsthatusethetransformerarchitecturehavesomeofthekeypropertiesofAGI-likebehaviour.

Yettherearealsosignsthattransformer-basedLLMshavelimits.Forastart,thedatausedtotrainthemodelsarerunningout.ResearchersatEpochAI,aninstituteinSanFranciscothatstudiestrendsinAI,estimate4thattheexistingstockofpubliclyavailabletextualdatausedfortrainingmightrunoutsomewherebetween2026and2032.TherearealsosignsthatthegainsbeingmadebyLLMs

HUMANSAND

OTHERANIMALS

AREAPROOFOF

PRINCIPLETHAT

YOUCANGETTHERE.”

astheygetbiggerarenotasgreatastheyoncewere,althoughit’snotclearifthisisrelatedtotherebeinglessnoveltyinthedatabecausesomanyhavenowbeenused,orsomethingelse.ThelatterwouldbodebadlyforLLMs.

RaiaHadsell,vice-presidentofresearchatGoogleDeepMindinLondon,raisesanotherproblem.Thepowerfultransformer-basedLLMsaretrainedtopredictthenexttoken,butthissingularfocus,sheargues,istoolimitedtodeliverAGI.BuildingmodelsthatinsteadgeneratesolutionsallatonceorinlargechunkscouldbringusclosertoAGI,shesays.Thealgorithmsthatcouldhelptobuildsuchmodelsarealreadyatworkinsomeexisting,non-LLMsystems,suchasOpenAI’sDALL-E,whichgeneratesrealistic,sometimestrippy,imagesinresponsetodescriptionsinnaturallanguage.ButtheylackLLMs’broadsuiteofcapabilities.

Buildmeaworldmodel

TheintuitionforwhatbreakthroughsareneededtoprogresstoAGIcomesfromneuroscientists.Theyarguethatourintelli-genceistheresultofthebrainbeingabletobuilda‘worldmodel’,arepresentationofoursurroundings.Thiscanbeusedtoimaginedifferentcoursesofactionandpredicttheirconsequences,andthereforetoplanandrea-son.Itcanalsobeusedtogeneralizeskillsthathavebeenlearntinonedomaintonewtasksbysimulatingdifferentscenarios.

Severalreportshaveclaimedevidencefortheemergenceofrudimentaryworldmodels

insideLLMs.Inonestudy5,researchersWesGurneeandMaxTegmarkattheMassachusettsInstituteofTechnologyinCambridgeclaimedthatawidelyusedopen-sourcefamilyofLLMsdevelopedinternalrepresentationsoftheworld,theUnitedStatesandNewYorkCitywhentrainedondatasetscontaininginfor-mationabouttheseplaces,althoughotherresearchersnotedonX(formerlyTwitter)thattherewasnoevidencethattheLLMswereusingtheworldmodelforsimulationsortolearncausalrelationships.Inanotherstudy6,KennethLi,acomputerscientistatHarvardUniversityinCambridgeandhiscolleaguesreportedevi-dencethatasmallLLMtrainedontranscriptsofmovesmadebyplayersoftheboardgameOthellolearnttointernallyrepresentthestateoftheboardandusedthistocorrectlypredictthenextlegalmove.

Otherresults,however,showhowworldmodelslearntbytoday’sAIsystemscanbeunreliable.Inonesuchstudy7,computersci-entistKeyonVafaatHarvardUniversity,andhiscolleaguesusedagiganticdatasetoftheturnstakenduringtaxiridesinNewYorkCitytotrainatransformer-basedmodeltopredictthenextturninasequence,whichitdidwithalmost100%accuracy.

Byexaminingtheturnsthemodelgener-ated,theresearcherswereabletoshowthatithadconstructedaninternalmaptoarriveatitsanswers.Butthemapborelittleresem-blancetoManhattan(see‘TheimpossiblestreetsofAI’),“containingstreetswithimpos-siblephysicalorientationsandflyoversaboveotherstreets”,theauthorswrite.“Althoughthemodeldoesdowellinsomenavigationtasks,it’sdoingwellwithanincoherentmap,”saysVafa.Andwhentheresearcherstweakedthetestdatatoincludeunforeseendetoursthatwerenotpresentinthetrainingdata,itfailedtopredictthenextturn,suggestingthatitwasunabletoadapttonewsituations.

Theimportanceoffeedback

Oneimportantfeaturethattoday’sLLMslackisinternalfeedback,saysDileepGeorge,amemberoftheAGIresearchteamatGoogleDeepMindinMountainView,California.Thehumanbrainisfulloffeedbackconnectionsthatallowinformationtoflowbidirectionallybetweenlayersofneurons.Thisallowsinfor-mationtoflowfromthesensorysystemtohigherlayersofthebraintocreateworldmod-elsthatreflectourenvironment.Italsomeansthatinformationfromtheworldmodelscanripplebackdownandguidetheacquisitionoffurthersensoryinformation.Suchbidirec-tionalprocesseslead,forexample,topercep-tions,whereinthebrainusesworldmodelstodeducetheprobablecausesofsensoryinputs.Theyalsoenableplanning,withworldmodelsusedtosimulatedifferentcoursesofaction. ButcurrentLLMsareabletousefeedbackonlyinatacked-onway.Inthecaseofo1,the

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TruestreetsinManhattan,NewYork

Non-existent‘streets’reconstructed

Directionbyanartificial-intelligencesystem

oftravel

attheDalleMolleInstituteforArtificialIntelligenceStudiesinLugano-Viganelllo,Switzerland,reported9buildinganeuralnet-workthatcouldefficientlybuildaworldmodelofanartificialenvironment,andthenuseittotraintheAItoracevirtualcars.

IfyouthinkthatAIsystemswiththislevelofautonomysoundscary,youarenotalone.AswellasresearchinghowtobuildAGI,BengioisanadvocateofincorporatingsafetyintothedesignandregulationofAIsystems.Hearguesthatresearchmustfocusontrainingmodelsthatcanguaranteethesafetyoftheirownbehaviour—forinstance,byhavingmech-anismsthatcalculatetheprobabilitythatthemodelisviolatingsomespecifiedsafetycon-straintandrejectactionsiftheprobabilityistoohigh.Also,governmentsneedtoensuresafeuse.“Weneedademocraticprocessthatmakessureindividuals,corporations,eventhemilitary,useAIanddevelopAIinwaysthataregoingtobesafeforthepublic,”hesays.

SOURCE:REF.7

THEIMPOSSIBLESTREETSOFAI

Theabilitytobuildrepresentationsofour

environment,calledworldmodels,helpshumansto

reasonandplan.ItisthoughtthatAIsystemswillneedthiscapacity,too,iftheyaretodevelophuman-level

intelligence.InthecaseofanAIsystemthatwas

trainedtopredictroutestakenbytaxisinManhattan,NewYork,itsinternalmapdidnotresemblethereal

world.Inlatertesting,thisledtoaninabilitytohandledetoursthatwerenotpresentinthetrainingdata.

TheAIsystem’smap

containsstreetswith

impossibleorientations

andbridgesthatdon’texist.

SowilliteverbepossibletoachieveAGI?Computerscientistssaythereisnoreasontothinkotherwise.“Therearenotheoreticalimpediments,”saysGeorge.MelanieMitchell,acomputerscientistattheSantaFeInstituteinNewMexico,agrees.“Humansandsomeotheranimalsareaproofofprinciplethatyoucangetthere,”shesays.“Idon’tthinkthere’sanythingparticularlyspecialaboutbiologicalsystemsversussystemsmadeofothermaterialsthatwould,inprinciple,preventnon-biologicalsystemsfrombecomingintelligent.”

internalCoTpromptingthatseemstobeatwork—inwhichpromptsaregeneratedtohelpansweraqueryandfedbacktotheLLMbeforeitproducesitsfinalanswer—isaformoffeed-backconnectivity.But,asseenwithChollet’stestsofo1,thisdoesn’tensurebullet-proofabstractreasoning.

Researchers,includingKambhampati,havealsoexperimentedwithaddingexternalmod-ules,calledverifiers,ontoLLMs.ThesecheckanswersthataregeneratedbyanLLMinaspe-cificcontext,suchasforcreatingviabletravelplans,andasktheLLMtorerunthequeryiftheanswerisnotuptoscratch8.Kambhampati’steamshowedthatLLMsaidedbyexternalverifi-erswereabletocreatetravelplanssignificantlybetterthanwerevanillaLLMs.Theproblemisthatresearchershavetodesignbespokeverifi-ersforeachtask.“Thereisnouniversalverifier,”saysKambhampati.Bycontrast,anAGIsystemthatusedthisapproachwouldprobablyneedtobuilditsownverifierstosuitsituationsastheyarise,inmuchthesamewaythathumanscanuseabstractrulestoensuretheyarereasoningcorrectly,evenfornewtasks.

EffortstousesuchideastohelpproducenewAIsystemsareintheirinfancy.Bengio,forexample,isexploringhowtocreateAIsys-temswithdifferentarchitecturestotoday’stransformer-basedLLMs.Oneofthese,which

useswhathecallsgenerativeflownetworks,wouldallowasingleAIsystemtolearnhowtosimultaneouslybuildworldmodelsandthemodulesneededtousethemforreasoningandplanning.

AnotherbighurdleencounteredbyLLMsisthattheyaredataguzzlers.KarlFriston,athe-oreticalneuroscientistatUniversityCollegeLondon,suggeststhatfuturesystemscouldbemademoreefficientbygivingthemtheabilitytodecidejusthowmuchdatatheyneedtosam-plefromtheenvironmenttoconstructworldmodelsandmakereasonedpredictions,ratherthansimplyingestingallthedatatheyarefed.This,saysFriston,wouldrepresentaformofagencyorautonomy,whichmightbeneededforAGI.“Youdon’tseethatkindofauthen-ticagency,insay,largelanguagemodels,orgenerativeAI,”hesays.“Ifyou’vegotanykindofinte