人工智能专业英语教程（第2版） 习题答案 张强华

《AI英语教程》习题答案Unit1[Ex.1] 根据TextA回答以下问题ArtificialIntelligence(AI)isthesimulationofhumanintelligenceprocessesbymachines,especiallycomputersystems.Theseprocessesincludelearning(theacquisitionofinformationandrulesforusingtheinformation),reasoning(usingrulestoreachapproximateordefiniteconclusions)andself-correction.ParticularapplicationsofAIincludeexpertsystems,speechrecognitionandmachinevision.AIcanbecategorizedaseitherweakorstrong.WeakAI,alsoknownasnarrowAI,isanAIsystemthatisdesignedandtrainedforaparticulartask.StrongAI,alsoknownasartificialgeneralintelligence,isanAIsystemwithgeneralizedhumancognitiveabilities.AIasaServiceallowsindividualsandcompaniestoexperimentwithAIforvariousbusinesspurposesandsamplemultipleplatformsbeforemakingacommitment.ResearchersandmarketershopethelabelaugmentedintelligencewillhelppeopleunderstandthatAIwillsimplyimproveproductsandservices,notreplacethehumansthatusethem.ArendHintzecategorizesAIintofourtypes.TheyareType1:Reactivemachines,Type2:Limitedmemory,Type3:Theoryofmind.Type4:Self-awareness.AIisincorporatedintoavarietyofdifferenttypesoftechnology.Herearesomeexamples.automation,machinelearning,machinevision,NaturalLanguageProcessing(NLP),robotics,andself-drivingcars.Therearethreetypesofmachinelearningalgorithms.Theyaresupervisedlearning,unsupervisedlearningandreinforcementlearning.AIhasmadeitswayintoanumberofareas.Herearesixexamples.AIinhealthcare,AIinbusiness.AIineducation.AIinfinance.AIinlaw.AIinmanufacturing.Theywillstreamlineworkflows,enhancedecision-making,andunlocknewgrowthavenues,ultimatelyinjectingmorepowerfulintelligentimpetusintoglobalinnovationandsustainabledevelopment.〖Ex.2〗把下列单词或词组中英互译。1.n.预测，预报；预言1.prediction2.有监督学习2.supervisedlearning3.n.规章，规则adj.规定的3.regulation4.adj.认知的，认识的4.cognitive5.n.意识，观念；知觉5.consciousness6.signature6.n.签名；署名；识别标志7.artificialgeneralintelligence7.通用人工智能8.reinforcementlearning8.强化学习9.predictiveanalytic9.预测分析10.\o"/definition/voice-recognition"speechrecognition10.语音识别\o"学习"〖Ex.3〗短文翻译。强人工智能（强AI）强人工智能（强AI）是一种人工智能结构，能够模仿人类大脑的智力和功能。在强人工智能哲学中，该软件（即人工智能，它模仿人类大脑的行为）和人类的行为（包括理解力甚至意识）两者之间没有本质区别。强也被称为全AI。强人工智能更多的是一种哲学，而不是创建AI的实际方法。它是AI的不同观念，它将AI等同于人类。它规定可以对计算机编程使其实际上成为人类的头脑，能够理解各种情景中每个词的意义、具有感知、信任并且具有通常人类才会有的其他认知状态。然而，由于甚至正确定义人类智力，因此很难给出一个明确的标准，即在强人工智能发展中取得成功的标准。而弱AI尤其可以实现，因为它规定了智能是什么。弱AI专注于开发与特定任务或研究领域相关的智能，而不是试图完全模仿人类思维。这是一组活动，可以分解为更小的流程，因此可以按照为其设定的规模来实现。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。1simulation2rules3artificial4narrow5cognitive\6intelligence7powerful8conversations9human10learning〖Ex.5〗根据课文内容回答问题。A\t"/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/_self"NarrativeSciencesurveyfoundlastyearthat38%ofenterprisesarealreadyusingAI,growingto62%by2018.ArtificialIntelligencetodayincludesavarietyoftechnologiesandtools,sometime-tested,othersrelativelynew.Itiscurrentlyusedincustomerservice,reportgeneration,andsummarizingbusinessintelligenceinsights.Ittranscribesandtransformshumanspeechintoformatusefulforcomputerapplications.SamplevendorsareNICE,NuanceCommunications,OpenText,VerintSystems.Theyprovidealgorithms,APIs,developmentandtrainingtoolkits,data,aswellascomputingpowertodesign,train,anddeploymodelsintoapplications,processes,andothermachines.Itisusedinawidevarietyofenterpriseapplications,assistinginorperformingautomateddecision-making.SamplevendorsareAdvancedSystemsConcepts,Informatica,Maana,Pegasystems,UiPath.Theyareaspecialtypeofmachinelearningconsistingofartificialneuralnetworkswithmultipleabstractionlayers.Currentlyprimarilyusedinpatternrecognitionandclassificationapplicationssupportedbyverylargedatasets.Itiscurrentlyusedprimarilyinmarketresearch.Thesamplevendorsare3VR,Affectiva,Agnitio,FaceFirst,Sensory,Synqera,Tahzoo.Itiscurrentlyusedwhereit’stooexpensiveorinefficientforhumanstoexecuteataskoraprocess.ThesamplevendorsareAdvancedSystemsConcepts,AutomationAnywhere,BluePrism,UiPath,WorkFusion.Itusesandsupportstextanalyticsbyfacilitatingtheunderstandingofsentencestructureandmeaning,sentiment,andintentthroughstatisticalandmachinelearningmethods.

Unit2〖Ex.1〗根据课文内容回答问题。Knowledgeacquisitiontypicallyreferstotheprocessofacquiring,processing,understanding,andrecallinginformationthroughoneofanumberofmethods.Oneoftheprimarycomponentsofknowledgeacquisitionisthesuppositionthatpeoplearebornwithoutknowledge,andthatitisgainedduringaperson’slifetime.Knowledgeacquisitiontypicallybeginswiththeprocessofreceivingoracquiringnewinformation.Thisisusuallydonethroughvisual,aural,andtactilesignalsthatapersonreceivesthroughhisorhersenses.Onceinformationisreceived,knowledgeacquisitiontypicallycontinuesthroughencodingandunderstandingthatinformation.Thisencodingprocessallowsapersontobuildacognitivemodel,sometimescalledaschema,forapieceofinformation.Thefieldofknowledgerepresentationinvolvesconsideringartificialintelligenceandhowitpresentssomesortofknowledge,usuallyregardingaclosedsystem.ScientistsfromMIT’sAILabtalkaboutknowledgerepresentationas“asetofontologicalcommitments––––afragmentedtheoryofintelligentreasoning”and“asimulationofamediumofhumanexpression.”Aknowledge-basedsystem(KBS)isacomputersystemwhichgeneratesandutilizesknowledgefromdifferentsources,dataandinformation.Knowledge-basedsystemsareconsideredtobeamajorbranchofartificialintelligence.Theyarecapableofmakingdecisionsbasedontheknowledgeresidinginthem,andcanunderstandthecontextofthedatathatisbeingprocessed.Knowledge-basedsystemsbroadlyconsistofaninterfaceengineandknowledgebase.Theinterfaceengineactsasthesearchengine,andtheknowledgebaseactsastheknowledgerepository.Thelimitationsofknowledge-basedsystemsaretheabstractnatureoftheconcernedknowledge,acquiringandmanipulatinglargevolumesofinformationordata,andthelimitationsofcognitiveandotherscientifictechniques.〖Ex.2〗把下列单词或词组中英互译。1.intelligentreasoning1.智能推理2.hypertextmanipulationsystem2.超文本操作系统3.knowledgerepresentation3.知识表达，知识表现4.searchengine4.搜索引擎5.cognition5.n.认识，认知6.n.数据库6.database7.基于知识的系统7.knowledge-basedsystem8.vt.译成密码；编码8.encode9.n.参数9.parameter10.接口引擎10.interfaceengine\o"学习"〖Ex.3〗短文翻译。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。〖Ex.5〗根据课文内容回答问题。Aknowledgebaseisaself-serveonlinelibraryofinformationaboutaproduct,service,department,ortopic.TheknowledgebasecanincludeFAQs,troubleshootingguides,andanyothernittygrittydetailsyoumaywantorneedtoknow.Knowledgemanagementenablesyoutocreate,curate,share,utilizeandmanageknowledgeacrossyourwholecompanyandacrossindustries.Theotherwaysmentionedinthepassagethataknowledgebasecanmakeadifferenceareconsistentservice,higherresolutionandlowercosts.Aknowledgebaseissupportedbyastrongknowledgemanagementprogram.Itensuresnewhiresaretrainedwiththelatestinformationandgetconsistentguidance.Thattranslatestoabetterworkenvironmentandlowercosts.Theotherreasonstoorganizeagoodknowledgebaseare:•Itputseverythingpeopleneedtoknowinoneplaceand,well,it’sorganized.•Yourcompanylookssmart,up-to-date,andprofessional.•Youstandardizeanswersinsteadofofferingmultipleresponsesfromdifferentsources.•Yougetafeedbackloopandtheopportunitytoengagewithpeoplewhomatter.•It’sflexible.YoucanuseitforITtickets,foranydepartment.(Evenforconcerttickets.)TheyareIT,HRandLegal.Human-readableknowledgebasesaretheonespeoplecanaccessfordocuments,manuals,troubleshootinginformationandfrequentlyansweredquestions.Youstartbyaskingyourselfhowmuchtimeyou’dsaveifemployeesdidn’thavetoanswerthesamequestionsoverandoveragain.Thenlookatyourcustomersatisfactionandproductivitygoals;ifyourorganizationcoulddobetter,aknowledgebaseisagreatplacetostart.Thelasttiponmaintainingaknowledgebaseiskeepitrelevantanduptodate.

Unit3〖Ex.1〗根据课文内容回答问题。Inessence,theoperationofAIreliesontheclosecollaborationofdata,algorithms,andcomputingpower,servingasasimulationandinnovationofhumanintelligence.Dataisthecore"fuel"forAIoperation.Itislikeavasttreasuretroveofknowledge,providingmaterialsforAItolearnandanalyze.InAIsystems,datahasthreeforms.Theyareperceptuallayerdata,featurelayerdataandcognitivelayerdata.Relationshipannotation

constructsdataassociations(e.g.,"companyA-investsin-companyB"),servingasthecoreofknowledgegraphs(e.g.,Googleachievessearchcorrelationthroughbillionsofannotations).Supervisedlearningislikehavingastrict“teacher"guidingtheprocess.Commonsupervisedlearningalgorithmsincludedecisiontrees,supportvectormachines,logisticregression,etc.Unsupervisedlearningdealswithunlabeleddataandhasnoclear"answers"asareference,similartoanexplorerventuringintounchartedterritory.ReinforcementlearningenablesAItolearnthroughcontinuous"trialanderror"interactionswiththeenvironment.ThemostfamousexampleisAlphaGo.Atypicalneuralnetworkincludesaninputlayer,hiddenlayers,andanoutputlayer.Theinputlayerisresponsibleforreceivingdata.Theoutputlayerprovidesthefinalresult,suchasdeterminingwhetherthereisacatintheimage.Thehiddenlayers,locatedinthemiddle,undertaketheimportanttaskofextractingandprocessingcomplexfeaturesfromthedata.NaturalLanguageProcessing(NLP)aimstoenablecomputerstounderstand,process,andgeneratehumanlanguage.LanguagemodelsarethecoreofNLP,primarilyusedtopredicttheprobabilityofeachwordappearinginasentence.Syntacticanalysisanalyzesthegrammaticalstructureofsentencestodeterminedependencyrelationshipsbetweenwords.〖Ex.2〗把下列单词或词组中英互译。1.聚类算法1.clusteringalgorithm2.激活函数2.activationfunction3.n.突触3.synapse4.n.权重4.weight5.n.原理5.putingpower6.计算能力7.featurelayerdata7.特征层数据8.knowledgegraph8.知识图谱9.reinforcementlearning9.强化学习10.logisticregression10.逻辑回归〖Ex.3〗短文翻译。机器学习模型全生命周期流程1.模型训练在模型训练阶段，将选定的算法应用于训练数据，以学习数据中的模式和关系。算法通过调整内部参数，最小化预先定义的损失函数，该函数用于衡量预测输出与实际输出之间的误差。这使模型能够从数据中归纳规律，并做出准确预测。2.模型评估与调优在验证阶段，通过准确率、精确率、召回率和F1分数等指标，在验证集上评估模型性能。若结果未达预期，则对超参数进行微调，或探索替代算法与配置以优化效果。此过程反复迭代，直至模型性能达到最优。3.模型部署当模型性能满足要求后，将其部署到生产环境中，用于对未知的新数据进行预测或决策。部署确保模型能够在现实场景中为终端用户或系统创造价值。4.模型监控与维护模型部署后，需持续监控其性能以确保有效性。随着时间推移，由于输入数据分布或环境条件的正常变化，模型准确率可能下降。因此，需定期进行重新训练或更新。通过主动监控和维护，模型能够抵御数据或环境的外部波动，保持鲁棒性、准确性和适应性。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。ynamicknowledgerepresentationreasoningapplicationsinformationdeeptrust9.processrequire

Unit4〖Ex.1〗根据课文内容回答问题。ProblemSpaceistheenvironmentinwhichthesearchtakesplace.Theyarestatedescription,asetofvalidoperators,initialstateandgoalstatedescription.Sinceeachlevelofnodesissavedforcreatingnextone,itconsumesalotofmemoryspace.Spacerequirementtostorenodesisexponential.ItisimplementedinrecursionwithLIFOstackdatastructure.ItcreatesthesamesetofnodesasBreadth-Firstmethod,onlyinthedifferentorder.Therecanbemultiplelongpaths.UniformCostsearchmustexplorethemall.Itiscomputedbycountingthenumberofmovesthateachtilemakesfromitsgoalstateandaddingthenumberofmovesforalltiles.Itisbest-knownformofBestFirstsearch.Itavoidsexpandingpathsthatarealreadyexpensive,butexpandsmostpromisingpathsfirst.Itisaniterativealgorithmthatstartswithanarbitrarysolutiontoaproblemandattemptstofindabettersolutionbychangingasingleelementofthesolutionincrementally.Annealingistheprocessofheatingandcoolingametaltochangeitsinternalstructureformodifyingitsphysicalproperties.Inthisalgorithm,theobjectiveistofindalow-costtourthatstartsfromacity,visitsallcitiesen-routeexactlyonceandendsatthesamestartingcity.〖Ex.2〗把下列单词或词组中英互译。1.admissibility1.n.可容许性，可接受性2.algorithm2.n.算法3.complexity3.n.复杂度，复杂性4.exponential4.adj.指数的，幂数的；越来越快的n.指数5.interrupt5.v.&n.中断；暂停6.n.最优性；最佳性6.optimality7.n.因素，因子7.factor8.n.递归，递推8.recursion9.贪婪最佳优先搜索9.greedybestfirstsearch10.局部集束搜索10.localbeamsearch〖Ex.3〗短文翻译。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。1traveling2considered3resources4referred5different6science7operations8development9problem10huge〖Ex.5〗根据课文内容回答问题。Best-firstsearchisagraph-basedsearchalgorithm,meaningthatthesearchspacecanberepresentedasaseriesofnodesconnectedbypaths.Thealgorithmmaintainstwolists,onecontainingalistofcandidatesyettoexplore(OPEN),andonecontainingalistofvisitednodes(CLOSED).Theadvantageofthisstrategyisthatifthealgorithmreachesadead-endnode,itwillcontinuetotryothernodes.ThefirststepistodefinetheOPENlistwithasinglenode,thestartingnode.Ifanysuccessoristhegoalnode,thealgorithmreturnssuccessandthesolution,whichconsistsofapathtracedbackwardsfromthegoaltothestartnode.Theseventhstepestablishesaloopingstructurebysendingthealgorithmbacktothesecondstep.Theparticularevaluationfunctionusedtodeterminethescoreofanodeisnotpreciselydefinedintheabovealgorithm,becausetheactualfunctionusedisuptothedeterminationoftheprogrammer,andmayvarydependingontheparticularitiesofthesearchspace.Inawebcrawler,eachwebpageistreatedasanode,andallthehyperlinksonthepagearetreatedasunvisitedsuccessornodes.Ingames,best-firstsearchmaybeusedasapath-findingalgorithmforgamecharacters.Somegamesdivideuptheterraininto“tiles”whichcaneitherbeblockedorunblocked.Insuchcases,thesearchalgorithmtreatseachtileasanode,withtheneighboringunblockedtilesbeingsuccessornodes,andthegoalnodebeingthedestinationtile.

Unit5〖Ex.1〗根据课文内容回答问题。Anartificialneuralnetworkisabiologicallyinspiredcomputationalmodelthatispatternedafterthenetworkofneuronspresentinthehumanbrain.Applicationsofartificialneuralnetworksincludepatternrecognitionandforecastinginfieldssuchasmedicine,business,puresciences,datamining,telecommunications,andoperationsmanagements.Themostbasictypeofneuralnetissomethingcalledafeedforwardneuralnetwork,inwhichinformationtravelsinonlyonedirectionfrominputtooutput.Amorewidelyusedtypeofnetworkistherecurrentneuralnetwork,inwhichdatacanflowinmultipledirections.Oncetheartificialneuralnetworkhasbeentrained,itcanaccuratelypredictoutputswhenpresentedwithinputs,aprocessreferredtoasneuralnetworkinference.Toperforminference,thetrainedneuralnetworkcanbedeployedinplatformsrangingfromthecloud,toenterprisedatacenters,toresource-constrainededgedevices.Whenresearchersorcomputerscientistssetouttotrainaneuralnetwork,theytypicallydividetheirdataintothreesets.Theyareatrainingset,avalidationdataset,andatestset.Therearemanykindsoftasksaneuralnetworkcando,frommakingcarsdriveautonomouslyontheroadstogeneratingshockinglyrealisticCGIfaces,tomachinetranslation,tofrauddetection,toreadingourminds,torecognizingwhenacatisinthegardenandturningonthesprinklers;neuralnetsarebehindmanyofthebiggestadvancesinA.I.GPUshavebecometheplatformofchoicefortraininglarge,complexNeuralNetwork-basedsystemsbecausetheyareabletoacceleratethesystems.Onatechnicallevel,oneofthebiggerchallengesistheamountoftimeittakestotrainnetworks,whichcanrequireaconsiderableamountofcomputepowerformorecomplextasks.〖Ex.2〗把下列单词或词组中英互译。1.latency1.n.延迟；潜伏2.artificialneuralnetwork2.人工神经网络3.computationalmodel3.计算模型4.convolutionalneuralnetwork4.卷积神经网络5.datamining5.数据挖掘6.语言识别6.languagerecognition7.学习算法7.learningalgorithm8.非线性函数8.nonlinearfunction9.模式识别9.patternrecognition10.n.前馈10.feedforward〖Ex.3〗短文翻译。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。〖Ex.5〗根据课文内容回答问题。Supervisedlearningisamachinelearningtaskoflearningafunctionthatmapsaninputtoanoutputbasedontheexampleinput-outputpairs.Unsupervisedlearningisthemachinelearningtaskofinferringafunctiontodescribehiddenstructurefromunlabelleddata.Someofthevariousalgorithmsrelatedtomachinelearningareregression,classificationandclustering.ThemostcommonprogramminglanguagesfordevelopingmachinelearningbasedapplicationsareRandPython.OtherlanguagessuchasJava,C++andMatlabcanalsobeused.Theregressionistheprocessofpredictingthetrendofthepreviousdatatopredicttheoutcomeofthenewdata.Thesimplestregressionmodelisalinearregression.Generally,theresultsgeneratedfromsupervisedlearningmethodsaremoreaccurateandreliablebecausetheinputdataiswellknownandlabeled.Whyaretheunsupervisedlearningalgorithmsharderthansupervisedlearningalgorithmsgenerally?Generally,theunsupervisedlearningalgorithmsareharderthansupervisedlearningalgorithmsbecausethereislittleinformation.Generally,theresultsgeneratedfromunsupervisedlearningalgorithmsarenotmuchaccurateandreliablebecausethemachinehastodefineandlabeltheinputdatabeforedeterminingthehiddenpatternsandfunctions.Inunsupervisedlearning,themodelpredictstheoutcomewithoutlabeleddatabyidentifyingthepatternsonitsown.

Unit6〖Ex.1〗根据TextA课文内容回答问题。themachineislearning,thetechnique,artificialintelligenceourhistoricaldata,observations,experiments,taketheresponses3)datamining,statistics,dataoriented4)thedifferentcases(inputs),thelabels(output)ofthesecases5)validatethemodel,makeitevenbetter.6)iftheoutputisrightorwrong7)rewardbasedlearning,feedbackorientedlearning8)collectdata,preparetheinputdata,analyzingdata,trainmodel,testthemodel,deployitintheapplication9).csvfile(commaseparatedvalue),clustering10)developingthealgorithm,processtheinput,givebacktheoutput〖Ex.2〗把下列单词或词组中英互译。1.knowledgeset1.知识集2.wirelesscommunication2.无线通信3.dataoriented3.数据导向4.bulk4.n.大块，大量5.backend5.n.后端6.adj.任意的；随机的6.random7.vt.更新7.update8.vt.确认；证实8.validate9.n.设想；可能发生的情况9.scenario10.n.函数10.function〖Ex.3〗短文翻译。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。1finance2345678910〖Ex.5〗根据课文内容回答问题。Deeplearningisamachinelearningtechniquethatteachescomputerstodowhatcomesnaturallytohumans:learnbyexample.Indeeplearning,acomputermodellearnstoperformclassificationtasksdirectlyfromimages,text,orsound.Oneisthatdeeplearningrequireslargeamountsoflabeleddata,theotherisdeeplearningrequiressubstantialcomputingpower.Deeplearningapplicationsareusedinautomateddriving,aerospaceanddefense,medicalresearch,industrialautomation,andelectronics.Mostdeeplearningmethodsuseneuralnetworkarchitectures.Theterm“deep”usuallyreferstothenumberofhiddenlayersintheneuralnetwork.Oneofthemostpopulartypesofdeepneuralnetworksisknownasconvolutionalneuralnetworks(CNNorConvNet).ACNNconvolveslearnedfeatureswithinputdata,anduses2Dconvolutionallayers.Deeplearningisaspecializedformofmachinelearning.Amachinelearningworkflowstartswithrelevantfeaturesbeingmanuallyextractedfromimages.Anotherkeydifferenceisdeeplearningalgorithmsscalewithdata,whereasshallowlearningconverges.Akeyadvantageofdeeplearningnetworksisthattheyoftencontinuetoimproveasthesizeofyourdataincreases.Asuccessfuldeeplearningapplicationrequiresaverylargeamountofdata(thousandsofimages)totrainthemodel,aswellasGPUs,orgraphicsprocessingunits,torapidlyprocessyourdata.Mostdeeplearningapplicationsusethetransferlearningapproach.Itisaprocessthatinvolvesfine-tuningapretrainedmodel.Transferlearningrequiresaninterfacetotheinternalsofthepre-existingnetwork,UsingGPUaccelerationcanspeeduptheprocesssignificantly.UsingMATLABwithaGPUreducesthetimerequiredtotrainanetworkandcancutthetrainingtimeforanimageclassificationproblemfromdaysdowntohours.

Unit7〖Ex.1〗根据课文内容回答问题。Expertsystemsarecomputerapplicationsthatcombinecomputerequipment,software,andspecializedinformationtoimitateexperthumanreasoningandadvice.Typically,expertsystemsfunctionbestwithspecificactivitiesorproblemsandadiscretedatabaseofdigitizedfacts,rules,cases,andmodels.Thebasicroleofanexpertsystemistoreplicateahumanexpertandreplacehimorherinaproblem-solvingactivity.Factsencompassthedefinitivelyknowndataandthedefinedvariablesthatcompriseanygivenactivity.Initsdiagnosticrole,anexpertsystemofferstosolveaproblembyanalyzingyesornowiththelikelihoodofcorrectlyidentifyingacauseofaproblemordisturbance.Inapredictiverole,theexpertsystemforecastsfutureeventsandactivitiesbasedonpastinformation.Theinferenceenginechumsthroughcountlesspotentialpathsandpossibilitiesbasedonsomecombinationofrules,cases,models,ortheories.Theknowledgedatabaseiscomposedoffacts,records,rules,books,andcountlessotherresourcesandmaterials.Expertsystemsarecapableofhandlingenormouslycomplextasksandactivitiesaswellasanextremelyrichknowledge-databasestructureandcontent.Anexpertshellisakindofoff-the-shelfcomputerprogramforbuildinganexpertapplication.Theexpertshellsimplifiestheexpertsystembyprovidingpreprogrammedmodulesandaready-to-useinferenceenginestructure.〖Ex.2〗把下列单词或词组中英互译。1.anatomy1.n.分解，分析2.capture2.vt.&n.捕获，捕捉3.conceptualization3.n.化为概念，概念化4.discrete4.adj.分离的，不相关联的5.diagnostic5.adj.诊断的，判断的；特征的6.n.解释；说明6.explanation7.adj.模糊的7.fuzzy8.诊断系统8.diagnosticsystem9.知识数据库9.knowledgedatabase10.最佳性能10.optimumperformance〖Ex.3〗短文翻译。专家系统在人工智能中，专家系统是一个模拟人类专家决策能力的计算机系统。专家系统旨在通过推理知识来解决复杂问题，它像一个专家，而不像传统编程那样遵循开发人员编制的程序来解决问题。第一个专家系统创建于20世纪70年代，然后在20世纪80年代专家系统激增。专家系统是最早真正成功的AI软件之一。不同于传统的程序，专家系统具有的独特结构，它分为两部分：一个是固定的独立于专家系统即推理引擎、另一个是变化部分即知识库。为了运行专家系统，引擎会像人类一样对知识库进行推理。在80年代出现了第三部分，即与用户通信的对话界面。这种与用户进行对话的能力后来被称为“会话”。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。1reasoning2stored3determine4expert5engine6inference7unreliable8conclusion9inconsistent10fuzzy〖Ex.5〗根据课文内容填空。understand,interpret,manipulatehumanlanguagemachinecode,machinelanguage,incomprehensiblecommunicatewithhumans,scalesotherlanguage-relatedtasks,morelanguage-baseddata,consistent,unbiasedunsupervised,deeplearning,syntactic,semantic,domainexpertiseinterpreting,,statistical,machinelearning,rules-based,algorithmictokenization,parsing,part-of-speechtagging,languagedetection,semanticrelationshipsshorter,elemental,explore,createmeaningtextualcontent,newvariables,filtered,statisticalmethodsinvestigativediscovery,subject-matterexpertise,socialmediaanalyticsnaturallanguageunderstanding(NLU),cognitiveandAIapplications,interpretintent,wordambiguity,well-formedhumanlanguage

Unit8〖Ex.1〗根据课文内容回答问题。ComputerVision(CV)isafieldofAIthatdealswithcomputationalmethodstohelpcomputersunderstandandinterpretthecontentofdigitalimagesandvideos.Computervisionseektoenablecomputersystemstoautomaticallysee,identify,andunderstandthevisualworld,simulatinghumanvisionusingcomputationalmethods.Duetotheirspeed,objectivity,continuity,accuracy,andscalability,computervisionsystemscanquicklysurpasshumancapabilities.Generally,computervisionworksinthreebasicsteps:Step#1:Acquiringtheimage/videofromacamera,Step#2:Processingtheimage,andStep#3:Understandingtheimage.Manyhigh-performingmethodsinmoderncomputervisionsoftwarearebasedonaconvolutionalneuralnetwork(CNN).TheAImodelusesthelabelstoperformconvolutionsandmakepredictionsaboutwhatitis“seeing”andcheckstheaccuracyofthepredictionsiterativelyuntilthepredictionsmeettheexpectation(starttocometrue).Theimageprocessingalgorithm,mostpopularlyadeeplearningmodel(DLmodel),performsimagerecognition,objectdetection,imagesegmentation,andimageclassificationoneveryimageorvideoframe.Traditionalmachinevisionsystemscommonlydependonspecialcamerasandhighlystandardizedsettings.Incontrast,moderndeeplearningalgorithmsaremuchmorerobust,easytore-useandre-train,andallowthedevelopmentofapplicationsacrossindustries.CurrentpopulardeeplearningAIhardwareincludesedgecomputingdevicessuchasembeddedcomputersandSoCdevices.EdgeAIusesedgecomputingandtheInternetofThings(IoT)tomovemachinelearningfromthecloudtoedgedevicesnearthedatasourcesuchascameras.〖Ex.2〗把下列单词或词组中英互译。1.v.识别；确认1.identify2.n.场景；场面2.scene3.n.分类；类别3.classification4.n.卷积4.convolution5.n.预处理5.pre-processing6.machineperception6.机器感知7.objectdetection7.目标检测8.layeredneuralnetwork8.分层神经网络9.deepneuralnetwork9.深度神经网络10.edgecomputing10.边缘计算〖Ex.3〗短文翻译。〖Ex.4〗将下列词填入适当的位置（每词只用一次）。〖Ex.5〗根据课文内容回答问题。Patternrecognitionistheabilityofmachinestoidentifypatternsindata,andthenusethosepatternstomakedecisionsorpredictionsusingcomputeralgorithms.Patternrecognitionisdefinedasthestudyofhowmachinescanobservetheenvironment,learntodistinguishvariouspatternsofinterestfromtheirbackground,andmakelogicaldecisionsaboutthecategoriesofthepatterns.Incomputerscience,patternrecognitionreferstotheprocessofmatchinginformationalreadystoredinadatabasewithincomingdatabasedontheirattributes.Inmachinelearning,patternrecognitionistheprocessofdiscoveringsimilaritieswithinsmallproblemstosolvelarger,morecomplicatedproblems.Unsupervisedlearningreferstotheprocesswherecomputersfindcorrelationsinunlabeleddatawithouthumanassistance.Popularpatternrecognitiontechniques,categorizedbytechnicalprinciples,includestatisticalpatternrecognition,neuralpatternrecognition,templatematching,andsyntacticpatternrecognition.Thereareseveralcomponentstoapatternrecognitionsystem,includingdata