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1、MachineLearningthatMattersKiriL.Wagsta?kiri.l.wagstaff@jpl.nasa.govJetPropulsionLaboratory,CaliforniaInstituteofTechnology,4800OakGroveDrive,Pasadena,CA91109USAAbstracttivelysolvedspamemaildetection(Zdziarski,2005)andmachinetranslation(Koehnetal.,2003),twoMuchofcurre
2、ntmachinelearning(ML)re-problemsofglobalimport.Andsoon.searchhaslostitsconnectiontoproblemsofimporttothelargerworldofscienceandso-Andyetwestillobserveaproliferationofpublishedciety.Fromthisperspective,thereexistglar-MLpapersthatevaluatenewalgorithmsonahandfulinglimit
3、ationsinthedatasetsweinvesti-ofisolatedbenchmarkdatasets.Their“realworld”gate,themetricsweemployforevaluation,experimentsmayoperateondatathatoriginatedinandthedegreetowhichresultsarecommu-therealworld,buttheresultsarerarelycommunicatednicatedbacktotheiroriginatingdom
4、ains.backtotheorigin.Quantitativeimprovementsinper-Whatchangesareneededtohowwecon-formancearerarelyaccompaniedbyanassessmentofductresearchtoincreasetheimpactthatMLwhetherthosegainsmattertotheworldoutsideofhas?WepresentsixImpactChallengestoex-machinelearningresearch.p
5、licitlyfocusthe?eld’senergyandattention,Thisphenomenonoccursbecausethereisnoandwediscussexistingobstaclesthatmustwidespreademphasis,inthetrainingofgraduatestu-beaddressed.Weaimtoinspireongoingdis-dentresearchersorinthereviewprocessforsubmittedcussionandfocusonMLthatm
6、atters.papers,onconnectingMLadvancesbacktothelargerworld.Eventherichassortmentofapplications-drivenMLresearchoftenfailstotakethe?nalsteptotrans-1.Introductionlateresultsintoimpact.Atonetimeoranother,weallencounterafriend,Manymachinelearningproblemsarephrasedintermssp
7、ouse,parent,child,orconcernedcitizenwho,uponofanobjectivefunctiontobeoptimized.Itistimeforlearningthatweworkinmachinelearning,wondersustoaskaquestionoflargerscope:whatisthe?eld’s“What’sitgoodfor?”Thequestionmaybephrasedobjectivefunction?Doweseektomaximizeperfor-mores
8、ubtlyorelegantly,butnomatteritsform,itgetsmanceonisolateddatasets?Orcanwecharacterizeatthemotivationalunderpinningsoftheworkthatwep
