Can Narrow AI Incrementally Lead to General AI?狭义 AI 能否逐步导致通用 AI

AIandAGI:

Past,PresentandFuture

BenGoertzel,PhD

ArtificialGeneralIntelligence(AGI)

“Theabilitytoachievecomplexgoalsincomplexenvironmentsusinglimitedcomputationalresources”AutonomyPracticalunderstandingofselfandothersUnderstanding“whattheproblemis”asopposedtojustsolvingproblemsposedexplicitlybyprogrammers

NarrowAI

ThevastmajorityofAIresearchpracticedinacademiaandindustrytodayfitsintothe“NarrowAI”categoryEach“NarrowAI”programis(intheidealcase)highlycompetentatcarryingoutcertaincomplexgoalsincertainenvironments

Chess-playing,medicaldiagnosis,car-driving,etc.1950

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AlanTuringproposesatestformachineintelligence1956

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JohnMcCarthycoinstheterm“artificialintelligence”1959–ArthurSamuel’scheckersprogramwinsgamesagainstthebesthumanplayers1962–Firstindustrialrobotcompany,Unimation,founded1967–“HAL”starsin“2001:ASpaceOdyssey”1969–StanfordResearchInstitute:ShakeytheRobotdemonstratedcombiningmovement,perceptionandproblemsolvingAlanTuringShakeyJohnMcCarthyArthurSamuel1971

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TerryWinograd’sPhDthesis(M.I.T.)demonstratedtheabilityofcomputerstounderstandEnglishsentencesinarestrictedworldofchildren’sblocks1975-JohnHolland’sbookAdaptationinNaturalandArtificialSystemsformalizesandpopularizes

evolutionaryalgorithms1982-DougLenat’sself-modifyingheuristicAIprogramEURISKOwinstheTravelerTCScontestthesecondyearinarow1983-DannyHillisco-foundedThinkingMachinesCorporationduringhisdoctoralworkatMIT.ThecompanywastodevelopHillis'ConnectionMachinedesignintoacommercialparallelsupercomputerline.TerryWinogradDougLenatConnection

MachineJohnHolland1990-91

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AItechnologyplaysakeyroleintheGulfWar,fromautomatedco-pilotsandcruisemissiles,tooverallbattlecoordination,andmore1997–IBMsupercomputerDeepBluedefeatsworldchesschampionGarryKasparovina6-gamematch1998

-present--Googleleveragesanincreasingarsenalofnarrow-AItechnologiestoprovidecommerciallysuccessfulWebsearchandaddelivery2001-LionheadStudioreleasesBlackandWhite,apopularvideogameinwhichplayersteachAI-controlledcreatures

usingimitativeandreinforcementlearningGoogle’sFirstServerCreaturefrom

Black&WhiteGulfWarDeepBlue

2001Lotsofreal-world

achievementsLotsofdeep,fascinatingideasNothingclosetoaconsensusontherightpathtohuman-levelAGIInmanycases,AGIvisionshavegivenwaytonarrow-AIsuccessesInOntheneuroside,we’vecomealongwaysinceMcCulloughandPitts…Wecansimulatealotofspikingneurons--butwedon’tknowhowtoconnectthemtoyieldintelligentbehavior.

Today,NarrowAIDominatestheAIField

(inbothacademiaandapplications)DeepBlue-whoopsuspeskyhumansatchess-butcan’tlearntoplayanewgamebasedonadescriptionofthegamerules

DARPAGrandChallenge-agreatleapforward--butitcan’tlearntodrivedifferenttypesofvehiclesbesidescars(trucks,boats,motorcycles)

Google-fantasticservice:butcan’tanswercomplexquestions.WhateverhappenedtoAskJeeves?The“Common-senseBottleneck”

AI’scan’tyetpassthe“5-year-oldchildTuringtest”Narrow-AINLPcan’thandle

ParseselectionforcomplexsentencesSemanticdisambiguationbeyondnounsandverbsPrepositiondisambiguationReferenceresolution(esp.nominal)ContextualinterpretationofcomparativesEtc.WillNarrow-AINLPSoftwareEverCorrectlyInterpretThisParagraph?

“Theworld'sprimarysourceofcaffeineisthecoffeebean(theseedofthecoffeeplant),fromwhichcoffeeisbrewed.Caffeinecontentincoffeevarieswidelydependingonthetypeofcoffee

beanandthemethodofpreparationused;evenbeanswithinagivenbushcanshowvariationsinconcentration.Ingeneral,oneservingofcoffeerangesfrom40milligrams,forasingleshot(30milliliters)ofarabica-varietyespresso,toabout100milligramsforacup(120milliliters)ofdripcoffee.”http://en.wikipedia.org/wiki/CaffeineWillNarrow-AINLPSoftwareEverCreateaChatBotthatKnowsWhatIt’sTalkingAbout?

Human:Whensomeonetalkstoyou,doyoureallyunderstandwhattheyaretalkingabout?

ALICE:Thatdoesnothappenveryoften.

Human:Whatdoesn'thappenveryoften?

ALICE:Whatdoyouthinkitdoes?

Human:Doyoumeanpeopledon'ttalktoyoumuch,orthatyoudon'tunderstandthemmuch?

ALICE:Yes,that'swhatImean,isn'titclear?

Human:Itwasaneither-orquestion.

ALICE:Howdidyoufeelaboutthat?

Human:Basicallyemotionallyneutral.

ALICE:Howinteresting.I'msometimesundecidedmyself.

Human:Whatdoyoudowhenyou'reundecidedaboutsomething?

ALICE:Igivethemostaccuraterepliespossible.

Human:Whatifyoudon'tknowwhichreplyismostaccurate?

ALICE:Tryitandsee.

Human:Anyway,isaccuracyalwaysthemostimportantthinginlife?

ALICE:Askagainlater.AskJohnLennonifitis.

Human:He'sdead.

ALICE:Maybeit'sanotherpersonusinghisname.

Human:WhydidyoubringupJohnLennon?

ALICE:Becausemyprogrammingdeterminedit.

CanNarrowAIIncrementallyLeadtoGeneralAI?IMHO,amajorlessonfromthehistoryofAIis:NarrowAIsuccessisgenerallyoffairlylimiteduseasastepping-stonetowardAGIsuccessTransferringinsightfromnarrow-AItoAGIinvolvesawholelotofcreativethinkingAndsomeofthekeyaspectsofAGImaynotariseinnarrow-AIworkatallTheTechnologicalandScientificContextLeavesusPoisedforDramaticAGIProgress

2008Credit:RayKurzweilGrowthinSupercomputerPowerLogarithmicPlotCredit:RayKurzweilExponentialGrowthofComputingExponentialImprovementofBrainScanningTechnologyMassivelyMultiplayerOnlineGame

(MMOG)Subscriptions

66%yearlygrowthrate14million

WhatWeHaveNow

Fastcomputersinternetworked

DecentvirtualworldsforAIembodimentHalfway-decentrobotbodies

Lotsof

AIalgorithmsandrepresentations

oftenusefulinspecializedareas

oftennotveryscalableontheirown

Abasicunderstandingof

humancognitive

architectureA

cruderbutusefulunderstandingof

brain

structureanddynamics

Atheoreticalunderstandingof

generalintelligence

underconditionsofmassivecomputationalresources

…wemaybeonthevergeofanswering…

BigQuestionsWhat’saWorkableCognitiveCycle?CanAbstractKnowledgeRepresentationsServeAsanAdequateFoundationfortheAdaptiveCreationofContext-SpecificKnowledgeRepresentations?(andifso,whatkind?)MustanAGIWhollyLearnLanguage,orCanLinguisticResources,StatisticalNLPandCommonsenseKB’sHelp?Questioning:Message(truth-query,useful)Questioning:Message(truth-query_1,useful)Existence:Circumstances(truth-query_1,useful)Existence:Circumstances(truth-query,useful)Usefulness:Purpose(useful,intelligence)Usefulness:Entity(useful,this)Communicate_categorization:Category(general,intelligence)Communicate_categorization:Item(general,intelligence)Communicate_categorization:Category(artificial,intelligence)Communicate_categorization:Item(artificial,intelligence)WhatMustaWorldBeThatanAGICanDevelopInIt?CanLogicServeasaScalableFoundationforSensorimotorLearning?HowDoesNeuralLearningRelatetoAbstractFormalModelsofLearning?CanIntegrativeDesignAllowMultipleAIAlgorithmstoQuellEachOthers’CombinatorialExplosions?ProbabilisticEvolutionaryProgramLearningProbabilisticLogicalInferenceEconomicAttentionAllocationforexampleAseriousattemptatpowerful,virtuallyembodiedAGI

TheNovamente

CognitionEngineTechnology:

CognitionEngineNovamenteCognitionEngine(NCE)isanintegrativeAIframeworkaimedatpowerfulartificialgeneralintelligence,andinvolvesauniquesystem-theoreticframeworkincorporating:Knowledgerepresentationusingweighted, labeledhyper-graphsProbabilisticinferenceusingProbabilistic LogicNetworks

ProcedurelearningusingMOSES

probabilisticevolutionarylearning

algorithmEconomicmethodsforattentionallocation andcreditassignment

AIalgorithmsintegratedinsuchawayasto

palliateeachothers’internal

combinatorialexplosionsNCEiscapableofintegratingmultipleformsofcognitiveprocessingandknowledgerepresentation,includinglanguage,quantitativeandrelationaldata,andvirtualagentcontrol/perceptionTechnology:

AtomTableHypergraphKnowledgeRepresentationTechnology:

MOSES&PLNMOSESProbabilisticEvolutionaryLearningCombinesthepoweroftwoleadingAIparadigms:evolutionarylearningprobabilisticlearningBroadapplicabilitywithsuccessfultrackrecordinbioinformatics,textanddataminingandvirtualagentcontrol.ProbabilisticLogicNetworks(PLN)PLNisthefirstgeneral,practicalintegrationofprobabilitytheoryandsymboliclogic.Ithasbroadapplicabilitywithasuccessfultrackrecordinbiotextminingandvirtualagentcontrol.BasedonmathematicsdescribedinProbabilisticLogicNetworks,publishedbySpringerin2008IntelligentVirtualPets

PetBrainincorporatesMOSESlearningtoallowpetstolearntricks,andProbabilisticLogicNetworks(PLN)inferenceregulatesemotion-behaviorinteractions,andallowsgeneralizationbasedonexperience.Novamente’sPetBrainutilizesaspecializedversionofNCEtoprovideunprecedentedintelligentvirtualpetswithindividualpersonalities,andtheabilitytolearnspontaneouslyandthroughtraining.PetsunderstandsimpleEnglish,andfutureversionswillincludelanguagegenerationIntelligentPets:

TrainingExampleNovamente-poweredintelligentpetscanbetaughttodosimpleorcomplextricks-fromsittingtoplayingsoccerorlearningadance-bylearningfromacombinationofencouragement,reinforcementanddemonstration.give“sit”command…claptoreinforce.showexample…successfulsit,great…reinforcedemoteachencourageWhatthismeansforVirtualWorldsandGames

uniquecontenttodifferentiateandattractmoreuserslongerandmoremeaningfulengagementbyusersopportunitiestobuildcommunityplaydatesparadesagilitycompetitionstrainingclasses

socialnaturedrivesviralmarketingeffect?HowDoWeGuideaSuccessfulFuturefortheAGIField??OpenCog.org Anopen-sourceAGIframework,tobelaunchedin2008

SponsoredinitiallybySingularityInstituteforAI Seededwithk