牛津大学：AI+超越人类编年史

WhenWillAIExceedHumanPerformance?EvidencefromAIExpertsKatjaGrace1,2,JohnSalvatier2,AllanDafoe1,3,BaobaoZhang3,andOwainEvans11FutureofHumanityInstitute,OxfordUniversity2AIImpacts3DepartmentofPoliticalScience,YaleUniversityAbstractAdvancesinartificialintelligence(AI)willtransformmodernlifebyreshapingtransportation,health,science,finance,andthemilitary[1,2,3].Toadaptpublicpolicy,weneedtobetteranticipatetheseadvances[4,5].HerewereporttheresultsfromalargesurveyofmachinelearningresearchersontheirbeliefsaboutprogressinAI.ResearcherspredictAIwilloutper-formhumansinmanyactivitiesinthenexttenyears,suchastranslatinglanguages(by2024),writinghigh-schoolessays(by2026),drivingatruck(by2027),workinginretail(by2031),writingabestsellingbook(by2049),andworkingasasurgeon(by2053).Researchersbelievethereisa50%chanceofAIoutperforminghumansinalltasksin45yearsandofautomatingallhumanjobsin120years,withAsianrespondentsexpectingthesedatesmuchsoonerthanNorthAmericans.TheseresultswillinformdiscussionamongstresearchersandpolicymakersaboutanticipatingandmanagingtrendsinAI.IntroductionAdvancesinartificialintelligence(AI)willhavemassivesocialconsequences.Self-drivingtech-nologymightreplacemillionsofdrivingjobsoverthecomingdecade.Inadditiontopossibleunemployment,thetransitionwillbringnewchallenges,suchasrebuildinginfrastructure,pro-tectingvehiclecyber-security,andadaptinglawsandregulations[5].Newchallenges,bothforAIdevelopersandpolicy-makers,willalsoarisefromapplicationsinlawenforcement,militarytech-nology,andmarketing[6].Toprepareforthesechallenges,accurateforecastingoftransformativeAIwouldbeinvaluable.SeveralsourcesprovideobjectiveevidenceaboutfutureAIadvances:trendsincomputinghardware[7],taskperformance[8],andtheautomationoflabor[9].ThepredictionsofAIexpertsprovidecrucialadditionalinformation.WesurveyalargerandmorerepresentativesampleofAIexpertsthananystudytodate[10,11].OurquestionscoverthetimingofAIadvances(includingbothpracticalapplicationsofAIandtheautomationofvarioushumanjobs),aswellasthesocialandethicalimpactsofAI.SurveyMethodOursurveypopulationwasallresearcherswhopublishedatthe2021NIPSandICMLconfer-ences(twoofthepremiervenuesforpeer-reviewedresearchinmachinelearning).Atotalof352researchersrespondedtooursurveyinvitation(21%ofthe1634authorswecontacted).Ourques-tionsconcernedthetimingofspecificAIcapabilities(e.g.foldinglaundry,languagetranslation),superiorityatspecificoccupations(e.g.truckdriver,surgeon),superiorityoverhumansatalltasks,andthesocialimpactsofadvancedAI.SeeSurveyContentfordetails.TimeUntilMachinesOutperformHumansAIwouldhaveprofoundsocialconsequencesifalltasksweremorecosteffectivelyaccomplishedbymachines.Oursurveyusedthefollowingdefinition:“High-levelmachineintelligence〞(HLMI)isachievedwhenunaidedmachinescanac-complisheverytaskbetterandmorecheaplythanhumanworkers.1Each

individual

respondent

estimated

the

probability

of

HLMI

arriving

in

future

years.

Taking

themean

over

each

individual,

the

aggregate

forecast

gave

a

50%

chance

of

HLMI

occurring

within

45

years

and

a

10%

chance

of

it

occurring

within

9

years.

Figure

1

displays

the

probabilistic

predictions

for

a

random

subset

of

individuals,

as

well

as

the

mean

predictions.

There

is

largeinter-subject

variation:

Figure

3

shows

that

Asian

respondents

expect

HLMI

in

30

years,

whereas

North

Americans

expect

it

in

74

years.0.000.250.500.751.0002550Yearsfrom202175100Probability

of

HLMIAggregateForecast(with95%ConfidenceInterval)RandomSubsetofIndividualForecastsLOESSFigure1:Aggregatesubjectiveprobabilityof‘high-levelmachineintelligence’arrivalbyfutureyears.EachrespondentprovidedthreedatapointsfortheirforecastandthesewerefittotheGammaCDFbyleastsquarestoproducethegreyCDFs.The“AggregateForecast〞isthemeandistributionoverallindividualCDFs(alsocalledthe“mixture〞distribution).Theconfidenceintervalwasgeneratedbybootstrapping(clusteringonrespondents)andplottingthe95%intervalforestimatedprobabilitiesateachyear.TheLOESScurveisanon-parametricregressiononalldatapoints.WhilemostparticipantswereaskedaboutHLMI,asubsetwereaskedalogicallysimilarquestionthatemphasizedconsequencesforemployment.Thequestiondefinedfullautomationoflaboras:whenalloccupationsarefullyautomatable.Thatis,whenforanyoccupation,machinescouldbebuilttocarryoutthetaskbetterandmorecheaplythanhumanworkers.ForecastsforfullautomationoflaborweremuchlaterthanforHLMI:themeanoftheindividualbeliefsassigneda50%probabilityin122yearsfromnowanda10%probabilityin20years.2Figure2:TimelineofMedianEstimates(with50%intervals)forAIAchievingHumanPer-formance.Timelinesshowing50%probabilityintervalsforachievingselectedAImilestones.Specifically,intervalsrepresentthedaterangefromthe25%to75%probabilityoftheeventoccurring,calculatedfromthemeanofindividualCDFsasinFig.1.Circlesdenotethe50%-probabilityyear.EachmilestoneisforAItoachieveorsurpasshumanexpert/professionalperformance(fulldescriptionsinTableS5).Notethattheseintervalsrepresenttheuncertaintyofsurveyrespondents,notestimationuncertainty.Respondentswerealsoaskedwhen32“milestones〞forAIwouldbecomefeasible.Thefullde-scriptionsofthemilestoneareinTableS5.Eachmilestonewasconsideredbyarandomsubsetofrespondents(n≥24).Respondentsexpected(meanprobabilityof50%)20ofthe32AImilestonestobereachedwithintenyears.Fig.2displaystimelinesforasubsetofmilestones.IntelligenceExplosion,Outcomes,AISafetyTheprospectofadvancesinAIraisesimportantquestions.WillprogressinAIbecomeexplosivelyfastonceAIresearchanddevelopmentitselfcanbeautomated?Howwillhigh-levelmachineintel-ligence(HLMI)affecteconomicgrowth?Whatarethechancesthiswillleadtoextremeoutcomes(eitherpositiveornegative)?WhatshouldbedonetohelpensureAIprogressisbeneficial?Table3rioritized

by

society

more

than

the

status

quo

(with

only

12%

wishing

for

lessEurope(n=58)NorthAmerica(n=64)0.000.250.500.75S4displaysresultsforquestionsweaskedonthesetopics.Herearesomekeyfindings:Researchersbelievethefieldofmachinelearninghasacceleratedinrecentyears.Weaskedresearcherswhethertherateofprogressinmachinelearningwasfasterinthefirstorsecondhalfoftheircareer.Sixty-sevenpercent(67%)saidprogresswasfasterinthesecondhalfoftheircareerandonly10%saidprogresswasfasterinthefirsthalf.Themediancareerlengthamongrespondentswas6years.ExplosiveprogressinAIafterHLMIisseenaspossiblebutimprobable.SomeauthorshavearguedthatonceHLMIisachieved,AIsystemswillquicklybecomevastlysuperiortohumansinalltasks[3,12].Thisaccelerationhasbeencalledthe“intelligenceexplosion.〞WeaskedrespondentsfortheprobabilitythatAIwouldperformvastlybetterthanhumansinalltaskstwoyearsafterHLMIisachieved.Themedianprobabilitywas10%(interquartilerange:1-25%).WealsoaskedrespondentsfortheprobabilityofexplosiveglobaltechnologicalimprovementtwoyearsafterHLMI.Herethemedianprobabilitywas20%(interquartilerange5-50%).HLMIisseenaslikelytohavepositiveoutcomesbutcatastrophicrisksarepossible.RespondentswereaskedwhetherHLMIwouldhaveapositiveornegativeimpactonhumanityoverthelongrun.Theyassignedprobabilitiestooutcomesonafive-pointscale.Themedianprobabilitywas25%fora“good〞outcomeand20%foran“extremelygood〞outcome.Bycontrast,theprobabilitywas10%forabadoutcomeand5%foranoutcomedescribedas“ExtremelyBad(e.g.,humanextinction).〞SocietyshouldprioritizeresearchaimedatminimizingthepotentialrisksofAI.Forty-eightpercentofrespondentsthinkthatresearchonminimizingtherisksofAIshouldbep ).UndergradRegionHLMICDFs1.004Asia(n=68)OtherRegions(n=21)02550Yearsfrom202175100Probability

ofHLMIFigure3:AggregateForecast(computedasinFigure1)forHLMI,groupedbyregioninwhichrespondentwasanundergraduate.Additionalregions(MiddleEast,S.America,Africa,Oceania)hadmuchsmallernumbersandaregroupedas“OtherRegions.〞5AsiansexpectHLMI44yearsbeforeNorthAmericansFigure3showsbigdifferencesbetweenindividualrespondentsinwhentheypredictHLMIwillarrive.BothcitationcountandsenioritywerenotpredictiveofHLMItimelines(seeFig.S1andtheresultsofaregressioninTableS2).However,respondentsfromdifferentregionshadstrikingdifferencesinHLMIpredictions.Fig.3showsanaggregatepredictionforHLMIof30yearsforAsianrespondentsand74yearsforNorthAmericans.Fig.S1displaysasimilargapbetweenthetwocountrieswiththemostrespondentsinthesurvey:China(median28years)andUSA(median76years).Similarly,theaggregateyearfora50%probabilityforautomationofeachjobweaskedabout(includingtruckdriverandsurgeon)waspredictedtobeearlierbyAsiansthanbyNorthAmericans(TableS2).Notethatweusedrespondents’undergraduateinstitutionasaproxyforcountryoforiginandthatmanyAsianrespondentsnowstudyorworkoutsideAsia.Wasoursamplerepresentative?Oneconcernwithanykindofsurveyisnon-responsebias;inparticular,researcherswithstrongviewsmaybemorelikelytofilloutasurvey.Wetriedtomitigatethiseffectbymakingthesurveyshort(12minutes)andconfidential,andbynotmentioningthesurvey’scontentorgoalsinourinvitationemail.Ourresponseratewas21%.Toinvestigatepossiblenon-responsebias,wecollecteddemographicdataforbothourrespondents(n=406)andarandomsample(n=399)ofNIPS/ICMLresearcherswhodidnotrespond.ResultsareshowninTableS3.Differencesbetweenthegroupsincitationcount,seniority,gender,andcountryoforiginaresmall.Whilewecannotruleoutnon-responsebiasesduetounmeasuredvariables,wecanruleoutlargebiasduetothedemographicvariableswemeasured.Ourdemographicdataalsoshowsthatourrespondentsincludedmanyhighly-citedresearchers(mostlyinmachinelearningbutalsoinstatistics,computersciencetheory,andneuroscience)andcamefrom43countries(vs.atotalof52foreveryonewesampled).Amajorityworkinacademia(82%),while21%workinindustry.DiscussionWhythinkAIexpertshaveanyabilitytoforeseeAIprogress?Inthedomainofpoliticalscience,along-termstudyfoundthatexpertswereworsethancrudestatisticalextrapolationsatpredictingpoliticaloutcomes[13].AIprogress,whichreliesonscientificbreakthroughs,mayappearintrin-sicallyhardertopredict.Yettherearereasonsforoptimism.Whileindividualbreakthroughsareunpredictable,longertermprogressinR&Dformanydomains(includingcomputerhardware,ge-nomics,solarenergy)hasbeenimpressivelyregular[14].Suchregularityisalsodisplayedbytrends[8]inAIperformanceinSATproblemsolving,games-playing,andcomputervisionandcouldbeexploitedbyAIexpertsintheirpredictions.Finally,itiswellestablishedthataggregatingindi-vidualpredictionscanleadtobigimprovementsoverthepredictionsofarandomindividual[15].Furtherworkcoulduseourdatatomakeoptimizedforecasts.Moreover,manyoftheAImilestones(Fig.2)wereforecasttobeachievedinthenextdecade,providingground-truthevidenceaboutthereliabilityofindividualexperts.References[1]PeterStone,RodneyBrooks,ErikBrynjolfsson,RyanCalo,OrenEtzioni,GregHager,JuliaHirschberg,ShivaramKalyanakrishnan,EceKamar,SaritKraus,etal.Onehundredyearstudyonartificialintelligence:Reportofthe2021-2021studypanel.Technicalreport,StanfordUniversity,2021.[2]PedroDomingos.TheMasterAlgorithm:HowtheQuestfortheUltimateLearningMachineWillRemakeOurWorld.BasicBooks,NewYork,NY,2021.[3]NickBostrom.Superintelligence:Paths,Dangers,Strategies.OxfordUniversityPress,Oxford,UK,2021.[4]ErikBrynjolfssonandAndrewMcAfee.TheSecondMachineAge:Work,Progress,andProsperityinaTimeofBrilliantTechnologies.WWNorton&Company,NewYork,2021.[5]RyanCalo.Roboticsandthelessonsofcyberlaw.CaliforniaLawReview,103:513,2021.6[6]TaoJiang,SrdjanPetrovic,UmaAyyer,AnandTolani,andSajidHusain.Self-drivingcars:Disruptiveorincremental.AppliedInnovationReview,1:3–22,2021.[7]WilliamD.Nordhaus.Twocenturiesofproductivitygrowthincomputing.TheJournalofEconomicHistory,67(01):128–159,2007.[8]KatjaGrace.Algorithmicprogressinsixdomains.Technicalreport,MachineIntelligenceResearchInstitute,2021.[9]ErikBrynjolfssonandAndrewMcAfee.RaceAgainsttheMachine:HowtheDigitalRevolutionIsAcceleratingInnovation,DrivingProductivity,andIrreversiblyTransformingEmploymentandtheEconomy.DigitalFrontierPress,Lexington,MA,2021.[10]SethD.Baum,BenGoertzel,andTedG.Goertzel.Howlonguntilhuman-levelai?resultsfromanexpertassessment.TechnologicalForecastingandSocialChange,78(1):185–195,2021.[11]VincentC.MüllerandNickBostrom.Futureprogressinartificialintelligence:Asurveyofexpertopinion.InVincentCMüller,editor,Fundamentalissuesofartificialintelligence,chapterpart.5,chap.4,pages553–570.Springer,2021.[12]IrvingJohnGood.Speculationsconcerningthefirstultraintelligentmachine.Advancesincomputers,6:31–88,1966.[13]PhilipTetlock.Expertpoliticaljudgment:Howgoodisit?Howcanweknow?PrincetonUniversityPress,Princeton,NJ,2005.[14]JDoyneFarmerandFrançoisLafond.Howpredictableistechnologicalprogress?ResearchPolicy,45(3):647–665,2021.[15]LyleUngar,BarbMellors,VilleSatopää,JonBaron,PhilTetlock,JaimeRamos,andSamSwift.Thegoodjudgmentproject:Alargescaletest.Technicalreport,AssociationfortheAdvancementofArtificialIntelligenceTechnicalReport,2021.[16]JoeW.Tidwell,ThomasS.Wallsten,andDonA.Moore.Elicitingandmodelingprobabilityforecastsofcontinuousquantities.Paperpresentedatthe27thAnnualConferenceofSocietyforJudgementandDecisionMaking,Boston,MA,19November2021.,2021.[17]ThomasS.Wallsten,YaronShlomi,ColetteNataf,andTracyTomlinson.Efficientlyencod-ingandmodelingsubjectiveprobabilitydistributionsforquantitativevariables.Decision,3(3):169,2021.7SupplementaryInformationSurveyContentWedevelopedquestionsthroughaseriesofinterviewswithMachineLearningresearchers.Oursurveyquestionswereasfollows:ThreesetsofquestionselicitingHLMIpredictionsbydifferentframings:askingdirectlyaboutHLMI,askingabouttheautomatabilityofallhumanoccupations,andaskingaboutrecentprogressinAIfromwhichwemightextrapolate.Threequestionsabouttheprobabilityofan“intelligenceexplosion〞.OnequestionaboutthewelfareimplicationsofHLMI.AsetofquestionsabouttheeffectofdifferentinputsontherateofAIresearch(e.g.,hardwareprogress).TwoquestionsaboutsourcesofdisagreementaboutAItimelinesand“AISafety.〞Thirty-twoquestionsaboutwhenAIwillachievenarrow“milestones〞.TwosetsofquestionsonAISafetyresearch:oneaboutAIsystemswithnon-alignedgoals,andoneontheprioritizationofSafetyresearchingeneral.Asetofdemographicquestions,includingonesabouthowmuchthoughtrespondentshavegiventothesetopicsinthepast.ThequestionswereaskedviaanonlineQualtricssurvey.(TheQualtricsfilewillbesharedtoenablereplication.)Participantswereinvitedbyemailandwereofferedafinancialrewardforcompletingthesurvey.Questionswereaskedinroughlytheorderaboveandrespondentsreceivedarandomizedsubsetofquestions.SurveyswerecompletedbetweenMay3rd2021andJune28th2021.Ourgoalindefining“high-levelmachineintelligence〞(HLMI)wastocapturethewidely-discussednotionsof“human-levelAI〞or“generalAI〞(whichcontrastswith“narrowAI〞)[3].WeconsultedallprevioussurveysofAIexpertsandbasedourdefinitiononthatofanearliersurvey[11].TheirdefinitionofHLMIwasamachinethat“cancarryoutmosthumanprofessionsatleastaswellasatypicalhuman.〞Ourdefinitionismoredemandingandrequiresmachinestobebetteratalltasksthanhumans(whilealsobeingmorecost-effective).SinceearliersurveysoftenuselessdemandingnotionsofHLMI,theyshould(allotherthingsbeingequal)predictearlierarrivalforHLMI.DemographicInformationThedemographicinformationonrespondentsandnon-respondents(TableS3)wascollectedfrompublicsources,suchasacademicwebsites,LinkedInprofiles,andGoogleScholarprofiles.Citationcountandseniority(i.e.numbersofyearssincethestartofPhD)werecollectedinFebruary2021.ElicitationofBeliefsManyofourquestionsaskwhenaneventwillhappen.Forpredictiontasks,idealBayesianagentsprovideacumulativedistributionfunction(CDF)fromtimetothecumulativeprobabilityoftheevent.Whenelicitingpointsonrespondents’CDFs,weframedquestionsintwodifferentways,whichwecall“fixed-probability〞and“fixed-years〞.Fixed-probabilityquestionsaskbywhichyearaneventhasanp%cumulativeprobability(forp=10%,50%,90%).Fixed-yearquestionsaskforthecumulativeprobabilityoftheeventbyyeary(fory=10,25,50).TheformerframingwasusedinrecentsurveysofHLMItimelines;thelatterframingisusedinthepsychologicalliteratureonforecasting[16,17].Withalimitedquestionbudget,thetwoframingswillsampledifferentpointsontheCDF;otherwise,theyarelogicallyequivalent.Yetoursurveyrespondentsdonottreatthemaslogicallyequivalent.Weobservedeffectsofquestionframinginallourpredictionquestions,aswellasinpilotstudies.Differencesinthesetwoframingshavepreviouslybeendocumentedintheforecastingliterature[16,17]butthereisnoclearguidanceonwhichframingleadstomoreaccuratepredictions.ThuswesimplyaverageoverthetwoframingswhencomputingCDFestimatesforHLMIandfortasks.HLMIpredictionsforeachframingareshowninFig.S2.8StatisticsFor

each

timeline

probability

question

(see

Figures1and

2),

we

computed

an

aggregate

distribution

by

fitting

a

gamma

CDF

to

each

individual’s

responses

using

least

squares

and

then

taking

themixture

distribution

of

all

individuals.

Reported

medians

and

quantiles

were

computed

on

thissummary

distribution.

The

confidence

intervals

were

generated

by

bootstrapping

(clustering

onrespondents

with

10,000

draws)

and

plotting

the

95%

interval

for

estimated

probabilities

at

each

year.

The

time-in-field

andcitationscomparisons

between

respondents

and

non-respondents

(Table

S3)

were

done

using

two-tailed

t-tests.

The

region

and

gender

proportions

were

done

using

two-

sided

proportion

tests.

The

significance

test

for

the

effect

of

region

on

HLMI

date

(Table

S2)

was

done

using

robust

linear

regression

using

the

R

function

rlm

from

the

MASS

package

to

do

the

regression

and

then

the

f.robtest

function

from

the

sfsmisc

package

to

do

a

robust

F-test

significance.Supplementary

Figures(a)

Top

4

Undergraduate

Country

HLMI

CDFsIndia(n=20)China(n=36)France(n=16)UnitedStates(n=53)0.000.250.500.751.0002550Yearsfrom202175100Probability

of

HLMITop4UndergradCountryHLMICDFs(b)

Time

in

Field

Quantile

HLMI

CDFsQ[1](n=57)Q[2](n=40)Q[4](n=48)Q[3](n=55)0.000.250.500.751.0002550Yearsfrom202175100Probability

of

HLMITimeinFieldQuartileHLMICDFs(c)

Citation

Count

Quartile

HLMI

CDFs0.50Q[2](n=57)Q[1](n=53)Q[3](n=65)Q[4](n=49)0.000.250.751.00092550Yearsfrom202175100Probability

of

HLMIHLMICDFByCitation

CountQuartileFigureS1:AggregatesubjectiveprobabilityofHLMIarrivalbydemographicgroup.EachgraphcurveisanAggregateForecastsCDF,computedusingtheproceduredescribedinFigure1andin“ElicitationofBeliefs.〞FigureS1ashowsaggregateHLMIpredictionsforthefourcountrieswiththemostrespondentsinoursurvey.FigureS1bshowspredictionsgroupedbyquartilesforseniority(measuredbytimesincetheystartedaPhD).FigureS1cshowspredictionsgroupedbyquartilesforcitationcount.“Q4〞indicatesthetopquartile(i.e.themostseniorresearchersortheresearcherswithmostcitations).0.000.25FramingFixed

ProbabilitiesFixed

YearsCombined100.500.751.0002550Yearsfrom202175100Probability

of

HLMIFraming

CDFsFigureS2:AggregatesubjectiveprobabilityofHLMIarrivalfortwoframingsofthequestion.The“fixedprobabilities〞and“fixedyears〞curvesareeachanaggregateforecastforHLMIpredictions,computedusingthesameprocedureasinFig.1.ThesetwoframingsofquestionsaboutHLMIareexplainedin“ElicitationofBeliefs〞above.The“combined〞curveisanaverageoverthesetwoframingsandisthecurveusedinFig.1.SupplementaryTablesS1:AutomationPredictionsbyResearcherRegionThisquestionaskedwhenautomationofthejobwouldbecomefeasible,andcumulativeproba-bilitieswereelicitedasintheHLMIandmilestonepredictionquestions.Thedefinitionof“fullautomation〞isgivenabove(p.1).Forthe“NA/Asiagap〞,wesubtracttheAsianfromtheN.Americanmedianestimates.TableS1:Medianestimate(inyearsfrom2021)forautomationofhumanjobsbyregionofundergraduateinstitutionS2:RegressionofHLMIPredictiononDemographicFeaturesWestandardizedinputsandregressedthelogofthemedianyearsuntilHLMIforrespondentsongender,logofcitations,seniority(i.e.numbersofyearssincestartofPhD),questionframing(“fixed-probability〞vs.“fixed-years〞)andregionwheretheindividualwasanundergraduate.Weusedarobustlinearregression.TableS2:RobustlinearregressionforindividualHLMIpredictionsS3:

Demographics

of

Respondents

vs.

Non-respondentsThere

were

(n=406)

respondents

and

(n=399)

non-respondents.

Non-respondents

were

randomly

sampled

from

all

NIPS/ICML

authors

who

did

not

respond

to

our

survey

invitation.

Subjects

with11QuestionEuropeN.

AmericaAsiaNA/Asia

gapFull

Automation130.8168.6104.2+64.4Retail

salesperson13.210.610.2+0.4Truck

driver46.441.031.4+9.6Surgeon18.820.210.0+10.2AI

researcher80.0123.6109.0+14.6termEstimateSEt

-statisticp-valueWald

F

-statistic(Intercept)3.650380.1732021.076350.00000458.0979Gender

=

“female”-0.254730.39445-0.645780.553200.3529552log(citation_count)-0.103030.13286-0.775460.447220.5802456Seniority

(years)0.096510.130900.737280.466890.5316029Framing

=

“fixed_probabilities”-0.340760.16811-2.027040.044144.109484Region

=

“Europe”0.518480.215232.408980.015825.93565Region

=

“M.East”-0.227630.37091-0.613690.544300.3690532Region

=

“N.America”1.049740.208495.034960.0000025.32004Region

=

“Other”-0.267000.58311-0.457880.632780.2291022missingdataforregionofundergraduateinstitutionorforgenderaregroupedin“NA〞.Missingdataforcitationsandseniorityisignoredincomputingaverages.Statisticaltestsareexplainedinsection“Statistics〞above.TableS3:Demographicdifferencesbetweenrespondentsandnon-respondents12UndergraduateregionRespondent

pro-portionNon-respondentproportionp-test

p-valueAsia0.3050.3430.283Europe0.2710.2360.284Middle

East0.0710.0630.721North

America0.2540.2210.307Other0.0150.0131.000NA0.0840.1250.070GenderRespondent

proportionNon-respondent

proportionp-test

p-valuefemale0.0540.1000.020male0.9190.8420.001NA0.0270.0580.048VariableRespondent

estimateNon-respondent

estimatestatisticp-valueCitations2740.54528.02.550.010856log(Citations)5.96.43.190.001490Years

in

field8.611.14.040.000060S4: SurveyresponsesonAIprogress,intelligenceexplosions,andAISafetyTheargumentbyStuartRussell,referredtoinoneofthequestionsbelow,canbefoundat/conversation/the-myth-of-ai#26015.T