認知計算-helianghua1課件

認知計算概述何良華學習目標認知概述認知計算概述認知計算經典算法學習目標認知概述認知計算概述認知計算經典算法1．思維

人腦對客觀現(xiàn)實間接的、概括的反應，是認識事物本質特征及內部規(guī)律的理性認知過程。思維活動是人類認識活動的最高形式，常通過語言文字表達思維具有連續(xù)性，否則為思維障礙。

抽象思維、洞察力和判斷力是反映思維水平的主要指標。2．語言

是人們進行思維的工具，是思維的物質外殼學習語言的技巧與環(huán)境有關分接受性語言和表達性語言3．定向

人們對現(xiàn)實的感覺，對過去、現(xiàn)在、將來的察覺以及對自我存在的意識。包括時間定向、地點定向、空間定向和人物定向是大腦功能活動的綜合表現(xiàn)。即對環(huán)境的知覺狀態(tài)。4．意識

記憶-個人所經歷過的事物在人腦中的反映，是人腦積累經驗的功能表現(xiàn)。評估方法-短時記憶-長時記憶（一）思維能力的評估－抽象思維概念力人腦反映客觀事物本質特性的思維形式。通過抽象概括，把握事物的本質特性而形成。評估方法通過數(shù)次健康教育后，請被評估者概括相關內容（一）思維能力的評估－抽象思維推理力有已知判斷推出新判斷的思維過程歸納（從特殊到一般）演繹（從一般到特殊）評估方法根據被評估者的年齡特征提出一定的問題（一）思維能力的評估－抽象思維識別與理解客觀事物真實性的能力評估方法讓被評估者描述所處情形，再與實際情形作比較看有無差異你認為導致你來就診的主要問題是什么？你如何判斷你目前的這種情況？（一）思維能力的評估－洞察力

肯定或否定某事物具有某種屬性或某行動方案可行性的思維方式受個體的年齡、情緒、智力、受教育水平、社會經濟狀況、文化背景等的影響評估方法展示實物讓被評估者說出其屬性評價被評估者對將來打算的現(xiàn)實性與可行性進行評估（一）思維能力的評估－判斷力

（二）語言能力的評估

語言能力是人們認知水平的重要標志，對判斷個體認知水平很有價值。（二）語言能力的評估

評估方法

-提問-復述-自發(fā)性語言-命名-閱讀-書寫意識的臨床表現(xiàn)（四）意識的評估

影響認知的因素年齡受教育水平生活經歷文化背景疾病藥物作用酗酒吸毒

七個問題1.

認識的本質——兩條認識路線的對立2.

認識的能力3.

認識的來源4.

認識的過程5.

認識的途徑6.

認識的結果及其檢驗7.

認識的目的

認識論

Epistemology

TheTheoryofKnowledge

哲學認識論認知

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形學科六邊形artificialintelligencen.人工智能Anthropologyn.人類學Linguisticsn.語言學Psychologyn.心理學Philosophyn.哲學Neurosciencen.神經系統(tǒng)科學(指神經病學、神經化學等)學習目標認知概述認知計算概述認知計算經典算法ComparisonofSiliconComputersandCarbonComputersDigitalcomputersare

MadefromsiliconAccurate(essentiallynoerrors)Fast(nanoseconds)Executelongchainsofseriallogical

operations(billions)IrritatingtohumansComparisonofSiliconComputersandCarbonComputersBrainsareMadefromcarboncompounds

Inaccurate(lowprecision,noisy)Slow(milliseconds,106timesslower)Executeshortchainsofparallelalogical

associativeoperations(perhaps10operations)UnderstandabletohumansPerformanceofSiliconComputersandCarbonComputerHugedisadvantageforcarbon:morethan1012

intheproductofspeedandpower.Butwedobetterandfasterthantheminmanytasks:speechrecognition,objectrecognition,facerecognition,motorcontrolmostcomplexmemoryfunctions,informationintegration.Implication:Cognitive“software”usesonlyafewbutverypowerfulelementaryoperations.WhyBuildaBrain-LikeComputer?

1.Engineering.

Computersareallspecialpurposedevices.

Manyoftheimportantpracticalcomputerapplicationsofthenextfewdecadeswillbecognitive:

·

Languageunderstanding.

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Internetsearch.·

Cognitivedatamining.·

Decenthuman-computerinterfaces.

Wefeelitwillbenecessarytohaveabrain-likearchitecturetoruntheseapplicationsefficiently.2.KinshipRecognition,HumanFactors:

Toberecognizedasintelligentbyhumans,amachinehastohaveasomewhathuman-likeintelligence.Theremaybemanykindsofintelligence,butwecanonlyunderstandandcommunicatewithoneofthem!Successfulhuman-computerinteractionswillrequireabrain-likecomputerdoingcognitivecomputation.“Ifoxenandhorseshadhandsandcouldcreateworksofart,horseswoulddrawpicturesofgodslikehorsesandoxen,godslikeoxen…”Xenophanes(C.530B.C.E.)3.Personal:

Itwouldbetheultimatecoolgadget.Atechnologicalvision:In2050thepersonalcomputeryoubuyinWal-MartwillhavetwoCPU’swithverydifferentarchitecture:

First,atraditionalvonNeumannmachinethatrunsspreadsheets,doeswordprocessing,keepsyourcalendarstraight,etc.Whattheydonow.

Second,abrain-likechip

·

TohandletheinterfacewiththevonNeumannmachine,·

GiveyouthedatathatyouneedfromtheWeboryourfiles(butdidn’tthinktoaskfor).·

Beyoursiliconfriend,guide,andconfidant.History:TechnicalIssuesManyhaveproposedtheconstructionofbrain-likecomputersforcognitivecomputation.

Theseattemptsusuallystartwith

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massivelyparallelarraysofneuralcomputingelements·

elementsbasedtosomedegreeonbiologicalneurons,·

thelayered2-Danatomyofmammaliancerebralcortex.

Suchattemptshavefailedcommercially.TheearlyconnectionmachinesfromThinkingMachines,Inc.,(W.D.Hillis,TheConnectionMachine,1987)wasthemostnearlysuccessfulcommercially..

Considertheextremesofcomputationalbrainmodels:FirstExtreme:BiologicalRealismThehumanbrainiscomposedofontheorderof1010neurons,connectedtogetherwithatleast1014neuralconnections.(Probablyunderestimates.)Biologicalneuronsandtheirconnectionsareextremelycomplexelectrochemicalstructures.Themorerealistictheneuronapproximationthesmallerthenetworkthatcanbemodeled.Thereisverygoodevidencethatforcerebralcortexabiggerbrainisabetterbrain.

Projectsthatmodelneuronsareofscientificinterest.

Theyarenotlargeenoughtomodelorsimulateinterestingcognition.

NeuralNetworks.

Themostsuccessfulbraininspiredmodelsareneuralnetworks.

Theyarebuiltfromsimpleapproximationsofbiologicalneurons:nonlinearintegrationofmanyweightedinputs.

Throwoutalltheotherbiologicaldetail.Cognitivecomputationisbasedonusefulapproximations.

SecondExtreme:AssociativelyLinkedNetworks.

Thesecondclassofbrain-likecomputingapproximationsisabasicpartofcomputerscience:

Associativelylinkedstructures.

Oneexampleofsuchastructureisasemanticnetwork.Suchstructuresunderliemostofthepracticallysuccessfulapplicationsofartificialintelligence.AssociativelyLinkedNetworks

(2)Theconnectionbetweenthebiologicalnervoussystemandsuchastructureisunclear.

Fewbelievethatnodesinasemanticnetworkcorrespondtosingleneuronsorgroupsofneurons.

Nodesarecomposedofmanypartsandcontainsignificantinternalstructure.

Physiology(fMRI)showsthatacomplexcognitivestructure–aword,forinstance–givesrisetowidelydistributedcorticalactivation.VirtueofLinkedNetworks:Theyhavesparselyconnectednodes.

Inpracticalsystems,thenumberoflinksconvergingonanoderangefromoneortwouptoadozenorso.

LookatSomeExamplesThebrain(andcognitivecomputation)dothingsdifferently:Ifyoubuildabrainexpecttogetweaknessesaswellasstrengths.Bothstrengthsandweaknessesareintrinsictothehardwareitself.Giveafewexamples.CognitiveStrengths

Strengths:Abilitytoapproximatecomplexeventsinusefulways(usingwords,concepts).Abilitytointegrateinformationfrommanysources.Effectivesearchofalargememory,thatis,integrationofpastexperiencewiththepresentsituation.Tightcouplingofhigher-levelcognitionwithperceptionNon-logicalprocessessuchas“intuition”forpredictionandunderstanding.CognitiveWeaknessesWeaknesses:

Higherrorrate.Slowresponsescomparedtosilicontimescales.Alogicalinformationprocessing,forexample,association.Oneresult:Greatdifficultywithlogicandformalreasoning.Lossofdetailinmemorystorage.Interferencefromothermemories.Prejudice(jumpingtoconclusions).Lackofexplanationforactions.ConclusionsBrainsareverydif