人工智能英语教程参考试卷

参考试卷、写出以下单词的中文意思（每小题0.5分，共10分）1accuracy11customize2actuator12definition3adjust13defuzzification4agent14deployment5algorithm15effector6analogy16entity7attribute17extract8backtrack18feedback9blockchain19finite10cluster20framework二、根据给出的中文意思，写出英文单词（每小题0.5分，共10分）1V.收集，搜集11n.神经元；神经细胞2adj.嵌入的，内置的12n.节点3n.指示器；指标13V.运转；操作4n.基础设施，基础架构14n.模式5V.合并；集成15V.察觉，发觉6n.解释器，解释程序16n.前提7n.迭代；循环17adj.程序的；过程的8n.库18n.回归9n.元数据19adj.健壮的，强健的；结实的10V.监视；控制；监测20V.筛选三、根据给出的短语，写出中文意思（每小题I分，共10分）dataobjectcybersecuritysmartmanufacturingclusteredsystemdatavisualizationopensourceanalyzetextcloudcomputingcomputationpowerobjectrecognition四、根据给出的中文意思，写出英文短语（每小题1分，共10分）数据结构 决策树 演绎推理 4贪婪最佳优先搜索隐臧模式，隐含模式知识挖掘 逻辑推理预测性维护搜索引擎 文本挖掘技术五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）缩略语 完整形式 中文意思 ANNARBFSCVDFSESIAKNNNLPVR六、阅读短文，回答问题(每小题2分，共10分)ArtificialNeuralNetwork(ANN)Anartificialneuralnetwork(ANN)isthepieceofacomputingsystemdesignedtosimulatethewaythehumanbrainanalyzesandprocessesinformation.Itisthefoundationofartificialintelligence(AI)andsolvesproblemsthatwouldproveimpossibleordifficultbyhumanorstatisticalstandards.ANNshaveself-learningcapabilitiesthatenablethemtoproducebetterresultsasmoredatabecomesavailable.Artificialneuralnetworksarebuiltlikethehumanbrain,withneuronnodesinterconnectedlikeaweb.Thehumanbrainhashundredsofbillionsofcellscalledneurons.Eachneuronismadeupofacellbodythatisresponsibleforprocessinginformationbycarryinginformationtowards(inputs)andaway(outputs)fromthebrain.AnANNhashundredsorthousandsofartificialneuronscalledprocessingunits,whichareinterconnectedbynodes.Theseprocessingunitsaremadeupofinputandoutputunits.Theinputunitsreceivevariousformsandstructuresofinformationbasedonaninternalweightingsystem,andtheneuralnetworkattemptstolearnabouttheinformationpresentedtoproduceoneoutputreport.Justlikehumansneedrulesandguidelinestocomeupwitharesultoroutput,ANNsalsouseasetoflearningrulescalledbackpropagation,anabbreviationforbackwardpropagationoferror,toperfecttheiroutputresults.AnANNinitiallygoesthroughatrainingphasewhereitlearnstorecognizepatternsindata,whethervisually,aurally,ortextually.Duringthissupervisedphase,thenetworkcomparesitsactualoutputproducedwithwhatitwasmeanttoproduce—thedesiredoutput.Thedifferencebetweenbothoutcomesisadjustedusingbackpropagation.Thismeansthatthenetworkworksbackward,goingfromtheoutputunittotheinputunitstoadjusttheweightofitsconnectionsbetweentheunitsuntilthedifferencebetweentheactualanddesiredoutcomeproducesthelowestpossibleerror.Aneuralnetworkmaycontainthefollowing3layers:Inputlayer-Theactivityoftheinputunitsrepresentstherawinformationthatcanfeedintothenetwork.Hiddenlayer-Todeterminetheactivityofeachhiddenunit.Theactivitiesoftheinputunitsandtheweightsontheconnectionsbetweentheinputandthehiddenunits.Theremaybeoneormorehiddenlayers.Outputlayer-Thebehavioroftheoutputunitsdependsontheactivityofthehiddenunitsandtheweightsbetweenthehiddenandoutputunits.Whatisanartificialneuralnetwork(ANN)?Whatiseachneuronmadeupof?Whadotheinputunitsdo?WhatdoesanANNinitiallygothrough?Howmanylayersmayaneuralnetworkcontain?Whatarethey?七、将下列词填入适当的位置(每词只用一次)。(每小题10分，共20分)填空题1供选择的答案:transactionsinformationtechniquesfraudnodesunstructuredsubsetsharedautomatedexplosionDeepLearningWhatIsDeepLearning?Deeplearningisanartificialintelligence(AI)functionthatimitatestheworkingsofthehumanbraininprocessingdataandcreatingpatternsforuseindecisionmaking.Deeplearningisa 1 ofmachinelearninginartificialintelligencethathasnetworkscapableoflearningunsupervisedfromdatathatis 2 orunlabeled.Alsoknownasdeepneurallearningordeepneuralnetwork.HowDoesDeepLearningWork?Deeplearninghasevolvedhand-in-handwiththedigitalera,whichhasbroughtaboutan 3 ofdatainallformsandfromeveryregionoftheworld.Thisdata,knownsimplyasbigdata,isdrawnfromsourceslikesocialmedia,internetsearchengines,e-commerceplatforms,andonlinecinemas,amongothers.Thisenormousamountofdataisreadilyaccessibleandcanbe 4 throughfintechapplicationslikecloudcomputing.However,thedata,whichnormallyisunstructured,issovastthatitcouldtakedecadesforhumanstocomprehenditandextractrelevant 5 .CompaniesrealizetheincrediblepotentialthatcanresultfromunravelingthiswealthofinformationandareincreasinglyadaptingtoAIsystemsfor 6 support.DeepLearningvs.MachineLearningOneofthemostcommonAI 7 usedforprocessingbigdataismachinelearning,aself-adaptivealgorithmthatgetsincreasinglybetteranalysisandpatternswithexperienceorwithnewlyaddeddata.Ifadigitalpaymentscompanywantedtodetecttheoccurrenceorpotential 8 initssystem,itcouldemploymachinelearningtoolsforthispurpose.Thecomputationalalgorithmbuiltintoacomputermodelwillprocessall 9 happeningonthedigitalplatform,findpatternsinthedataset,andpointoutanyanomalydetectedbythepattern.Deeplearningutilizesahierarchicallevelofartificialneuralnetworkstocarryouttheprocessofmachinelearning.Theartificialneuralnetworksarebuiltlikethehumanbrain,withneuron 10 connectedtogetherlikeaweb.Whiletraditionalprogramsbuildanalysiswithdatainalinearway,thehierarchicalfunctionofdeeplearningsystemsenablesmachinestoprocessdatawithanonlinearapproach.填空题2供选择的答案:storedresolutionmatchlookunlockdatabasephotographeyesreturn,identifyingFaceRecognitionFacerecognitionsystemsusecomputeralgorithmstopickoutspecific,distinctivedetailsaboutaperson'sface.Thesedetails,suchasdistancebetweenthe 1 orshapeofthechin,arethenconvertedintoamathematicalrepresentationandcomparedtodataonotherfacescollectedinafacerecognitiondatabase.Thedataaboutaparticularfaceisoftencalledafacetemplateandisdistinctfroma 2 becauseit'sdesignedtoonlyincludecertaindetailsthatcanbeusedtodistinguishonefacefromanother.Somefacerecognitionsystems,insteadofpositively 3 anunknownperson,aredesignedtocalculateaprobabilitymatchscorebetweentheunknownpersonandspecificfacetemplates 4 inthedatabase.Thesesystemswillofferupseveralpotentialmatches,rankedinorderoflikelihoodofcorrectidentification,insteadofjustreturningasingleresult.Facerecognitionsystemsvaryintheirabilitytoidentifypeopleunderchallengingconditionssuchaspoorlighting,lowqualityimage 5 ,andsuboptimalangleofview(suchasinaphotographtakenfromabovelookingdownonanunknownperson).Whenitcomestoerrors,therearetwokeyconceptstounderstand:A"falsenegative^^iswhenthefacerecognitionsystemfailsto 6 matchaperson'sfacetoanimagethatis,infact,containedinadatabase.Inotherwords,thesystemwillerroneously 7 zeroresultsinresponsetoaquery.A"falsepositive^^iswhenthefacerecognitionsystemdoesmatchaperson'sfacetoanimageina 8 ,butthatmatchisactuallyincorrect.Thisiswhenapoliceofficersubmitsanimageof"Joe,"butthesystemerroneouslytellstheofficerthatthephotoisof"Jack.”Whenresearchingafacerecognitionsystem,itisimportantto 9 closelyatthet€falsepositive“rateandthe“falsenegative''rate,sincethereisalmostalwaysatrade-off.Forexample,ifyouareusingfacerecognitionto 10 yourphone,itisbetterifthesystemfailstoidentifyyouafewtimes(falsenegative)thanitisforthesystemtomisidentifyotherpeopleasyouandletsthosepeopleunlockyourphone(falsepositive).Iftheresultofamisidentificationisthataninnocentpersongoestojail(likeamisidentificationinamugshotdatabase),thenthesystemshouldbedesignedtohaveasfewfalsepositivesaspossible.六、将下面两篇短文翻译成中文(每小题10分，共20分)短文1DifferencesbetweenStrongAIandWeakAI1.MeaningStrongAIisatheoreticalformofartificialintelligencewhichsupportstheviewthatmachinescanreallydevelophumanintelligenceandconsciousnessinthesamewaythatahumaninconscious.StrongAIreferstoahypotheticalmachinethatexhibitshumancognitiveabilities.WeakAI(alsoknownasnarrowAI),ontheotherhand,isaformofartificialintelligencethatreferstotheuseofadvancedalgorithmstoaccomplishspecificproblemsolvingorreasoningtasksthatdonotencompassthefullrangeofhumancognitiveabilities.2.FunctionalityFunctionsarelimitedinweakAIascomparedtostrongAI.WeakAIdoesnotachieveself-awarenessordemonstrateawiderangeofhumancognitiveabilitiesthatahumanmayhave.WeakAIreferstosystemsthatareprogrammedtoaccomplishawiderangeproblemsbutoperatewithinapre-determinedorpre-definedrangeoffunctions.StrongAI,ontheotherhand,referstomachinesthatexhibithumanintelligence.Theideaistodevelopartificialintelligencetothepointwherehumaninteractwithmachinesthatareconscious,intelligentanddrivenbyemotionsandself-awareness.3.GoalThegoalofweakAlistocreateatechnologythatallowsallowsmachinesandcomputerstotoaccomplishspecificproblemsolvingorreasoningtasksatasignificantlyquickerpacethanahumancan.Butitdoesnotnecessarilyincorporateanyrealworldknowledgeabouttheworldoftheproblemthatisbeingsolved.ThegoalofstrongAIistodevelopartificialintelligencetothepointwhereitcanbeconsideredtruehumanintelligence.StrongAIisatypeofwhichdoesnotexistyetinitstrueform.短文2PatternRecognitionPatternRecognitionisdefinedastheprocessofidentifyingthetrends(globalorlocal)inthegivenpattern.Apatterncanbedefinedasanythingthatfollowsatrendandexhibitssomekindofregularity.Therecognitionofpatternscanbedonephysically,mathematicallyorbytheuseofalgorithms.Whenwetalkaboutpatternrecognitioninmachinelearning,itindicatestheuseofpowerfulalgorithmsforidentifyingtheregularitiesinthegivendata.Patternrecognitioniswidelyusedinthenewagetechnicaldomainslikecomputervision,speechrecognition,facerecognition,etc.Therearetwotypesofpatternrecognitionalgorithmsinmachinelearning.SupervisedAlgorithmsThepatternrecognitioninasupervisedapproachiscalledclassification.Thesealgorithmsuseatwo-stagemethodologyforidentifyingthepatterns.Thefirststageisthedevelopment/constructionofthemodelandthesecondstageinvolvesthepredictionfbrneworunseenobjects.Thekeyfeaturesinvolvingthisconceptarelistedbelow.Classifythegivendataintotwosets—trainingsetandtestingset.TrainthemodelusingasuitablemachinelearningalgorithmsuchasSVM(SupportVectorMachines),decisiontrees,randomforest,etc.Themodelistrainedonthetrainingsetandtestedonthetestingset.Theperformanceofthemodelisevaluatedbasedoncorrectpredictionsmade.UnsupervisedAlgorithmsIncontrasttothesupervisedalgorithmsforpatternmakeuseoftrainingandtestingsets,thesealgorithmsuseagroupbyapproach.Theyobservethepatternsinthedataandgroupthembasedonthesimilarityintheirfeaturessuchasdimensiontomakeaprediction.Let'ssaythatwehaveabasketofdifferentkindsoffruitssuchasapples,oranges,pears,andcherries.Weassumethatwedonotknowthenamesofthefruits.Wekeepthedataasunlabeled.Now,supposeweencounterasituationwheresomeonecomesandtellsustoidentifyanewfruitthatwasaddedtothebasket.Insuchacasewemakeuseofaconceptcalledclustering.Clusteringcombinesorgroupsitemshavingthesamefeatures.Nopreviousknowledgeisavailableforidentifyinganewitem.Theyusemachinelearningalgorithmslikehierarchicalandk-mansclustering.Basedonthefeaturesorpropertiesofthenewobject,itisassignedtoagrouptomakeaprediction.

参考试卷答案\写出以下单词的中文意思（每小题0.5分，共10分）accuracyn.精确（性），准确（性）11customizeVt.定制，定做；用户化2actuatorn.执行器12definitionn.定义3adjustV.调整，调节；适应；校准13defuzzificationn.逆模糊化，去模糊化4agentn.实体：代理14deploymentn.部署5algorithmn.算法15effectorn.效应器6analogyn.类推16entityn.实体7attributen.属性；性质；特征17extractV.提取，提炼8backtrackvi.回溯18feedbackn.反馈9blockchainn.区块链19finiteadj.有限的：限定的1020n.构架；框架；（体系的）clusterv.聚集n.团，群，簇framework结构二、根据给出的中文意思，写出英文单词（每小题0.5分，共10分）1V.收集，搜集gather11n.神经元；神经细胞neuron2adj.嵌入的，内置的inbuilt12n.节点node3n.指示器；指标indicator13V.运转；操作operate4n.基础设施，基础架构infrastructure14n.模式pattern5v.合并：集成integrate15V.察觉，发觉perceive6n.解释器，解释程序interpreter16n.前提premise7n.迭代；循环iteration17adj.程序的：过程的procedural8n.库library18n.回归regression919adi.健壮的，强健的；n.元数据metadatarobust结实的10V.监视；控制；监测monitor20V.筛选screen三、根据给出的短语，写出中文意思（每小题1分，共10分）1dataobject数据对象2cybersecurity网络安全3smartmanufacturing智能制造4clusteredsystem集群系统5datavisualization数据可视化6opensource开源7analyzetext分析文本8cloudcomputing云计算9computationpower计算能力10objectrecognition物体识别四、根据给出的中文意思，写出英文短语（每小题1分，共10分）

1数据结构datastructure2决策树decisiontree3演绎推理deductivereasoning4贪婪最佳优先搜索greedybest-firstsearch5隐藏模式，隐含模式hiddenpattern6知识挖掘knowledgemining7逻辑推理logicalreasoning8预测性维护predictivemaintenance9搜索引擎searchengine10文本挖掘技术textminingtechnique五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）缩略语完整形式中文意思1ANNArtificialNeuralNetwork人工神经网络2ARAugmentedReality增强现实3BFSBreadth-FirstSearch宽度优先搜索4CVComputerVision计算机视觉5DFSDepth-FirstSearch深度优先搜索6ESExpertSystem专家系统7IAIntelligentAgent智能体8KNNK-NearestNeighborK最近邻算法9NLPNaturalLanguageProcessing自然语言处理10VRVirtualReality虚拟现实六、阅读短文，回答问题（每小题2分，共10分）Anartificialneuralnetwork(ANN)isthepieceofacomputingsystemdesignedtosimulatethewaythehumanbrainanalyzesandprocessesinformation.Itisthefoundationofartificialintelligence(AI)andsolvesproblemsthatwouldproveimpossibleordifficultbyhumanorstatisticalstandards.Eachneuronismadeupofacellbodythatisresponsibleforprocessinginformationbycarryinginformationtowards(inputs)andaway(outputs)fromthebrain.Theinputunitsreceivevariousformsandstructuresofinformationbasedonaninternalweightingsystem.A