塑造人工智能对数十亿人生活的影响 Shaping AIs Impact on Billions of Lives

1

ShapingAI'sImpactonBillionsofLives

Mariano-FlorentinoCuéllar

,

JeffDean

,

FinaleDoshi-Velez

,

JohnHennessy

,

AndyKonwinski

,

SanmiKoyejo

,

PelonomiMoiloa

,

EmmaPierson

,and

DavidPatterson

Introduction1

I.PuttingPragmaticAIinContext3

HistoryofTechnologicalParadigmShifts3

ArtificialIntelligence(AI)5

ArtificialGeneralIntelligence(AGI)6

II.DemystifyingthePotentialImpactofAI7

Employment7

Education9

Healthcare12

Information/News/SocialNetworking14

Media/Entertainment16

Governance/NationalSecurity/OpenSource18

Science21

III.HarnessingAIforthePublicGood23

Milestones,Prizes,andResearchCenters23

Conclusion24

Acknowledgements25

AppendixI:EnergyUsageofAI26

AppendixII:TheRapidUpskillingPrize27

Bibliography27

Authors30

Introduction

ArtificialIntelligence(AI),likeanytransformative

technology,hasthepotentialtobeadouble-edged

sword,leadingeithertowardsignificant

advancementsordetrimentaloutcomesforsocietyasawhole.Asisoftenthecasewhenitcomesto

widely-usedtechnologiesinmarketeconomies(e.g.,carsandsemiconductorchips),commercialinteresttendstobethepredominantguidingfactor.TheAI

communityisatriskofbecomingpolarizedtoeithertakealaissez-faireattitudetowardAIdevelopment,ortocallforgovernmentoverregulation.BetweenthesetwopolesweargueforthecommunityofAI

practitionerstoconsciouslyandproactivelyworkfor

thecommongood.Thispaperoffersablueprintforanewtypeofinnovationinfrastructureincluding18

concretemilestonestoguideAIresearchinthat

direction.OurviewisthatwearestillintheearlydaysofpracticalAI,andfocusedeffortsbypractitioners,

policymakers,andotherstakeholderscanstillmaxi-mizetheupsidesofAIandminimizeitsdownsides.

Toofferasufficiently-broadandrealistic

perspectivethatcapturesthepossibilities,we’ve

assembledateamcomposedofseniorcomputer

scientists,policymakers,andrisingstarsinAIfrom

academia,startups,andbigtech—ateamthatcoversmanyAIdomains(seeAuthors).

Inadditiontoourownexpertise,ourperspectiveisinformedbyinterviewswithtwodozenexpertsin

2

variousfields.WetalkedtoluminariessuchasrecentNobelist

JohnJumper

onscience,President

Barack

Obama

ongovernance,formerUNAmbassadorandformerNationalSecurityAdvisor

SusanRice

on

security,philanthropist

EricSchmidt

onseveraltopics,andsciencefictionnovelist

NealStephenson

on

entertainment.Wealsometwithexpertsinlabor

economics,education,healthcare,andinformation.Thisongoingdialogueandcollaborativeefforthas

producedacomprehensive,realisticviewofwhattheactualimpactofAIcouldbe,fromadiverseassembly

ofthinkerswithdeepunderstandingofthistechnologyandthesedomains.

Ourviewisthatwearestillinthe

earlydaysofpracticalAI,andthat

focusedefortsbypractitioners,

policymakers,andotherstakeholders

canstillmaximizetheupsidesofAI

andminimizeitsdownsides.

ThesediscussionshavecrystallizedourconvictionthatrecentAImodelshaveshownaremarkable

promisetoinfluencetheworld,potentiallyaffectingbillionsoflivesforbetterorworse.WethinkthebestbetgoingforwardistoassumeAIprogresswill

continueorspeedup,andnotslowdown.AI'simpactonsocietywillbeprofound.

Fromtheseexchanges,fiverecurringguidelines

emerged,whichformthecornerstoneofaframeworkforbeginningtoharnessAIinserviceofthepublic

good.Theynotonlyguideoureffortsindiscoverybutalsoshapeourapproachtodeployingthis

transformativetechnologyresponsiblyandethically.

1.HumansandAIsystemsworkingasateam

candomorethaneitherontheirown.ApplicationsofAIfocusedonhumanproductivityproducelarger

productivityincreasesthanthosefocusedon

replacinghumanlabor[Brynjolfsson][National

Academies].Inadditiontoincreasingpeople’s

employability,toolsaimedatmakingpeoplemore

productiveletthemactassafeguardsifAIsystems

veeroffcourse.AIattimescanleveltheplayingfieldbetweenthosewhohavemanyresourcesandthoseoflimitedresources.SincepeopleandAIsystems

tendtomakedifferentmistakes,collaboratingwithAImayimproveresults.Inshort,focusingonhuman

productivityhelpsbothpeopleandAItoolssucceed.Policiesshouldaimtowardinnovationsthatencour-agehuman-AIcollaborationwhilereducingrisks.

2.Toincreaseemployment,aimfor

productivityimprovementsinfieldsthatwould

createmorejobs.Despitetremendousproductivitygainsincomputingandairlinetravel,theUnited

Statesin2020had11timesmoreprogrammersand8timesmorecommercialairlinepilotsthanin1970.

Thisgrowthisbecauseprogrammingandairline

transportationwerefieldswithwhatlaboreconomistscallanelasticdemand.Goodswithelasticdemandarethosewhereadecreaseinpriceresultsinalarge

increaseinthequantityacquired.Agriculture,ontheotherhand,isinelasticintheU.S.,soproductivity

gainshavereducedthenumberofagriculturejobsfourfoldinonehumanlifetime(1940to2020).

Discussionswithexpertsinotherfieldswilllikely

uncovermoreopportunitiesforAItoincrease

productivity.IfpolicymakersandpractitionersaimAIsystemsatimprovingproductivityinelasticfields,AIcanincreaseemployment,despitepublicfearstothecontrary.AndasrecentNobelistJohnJumper

observed,onewaytoacceleratescientificprogressistoimprovetheproductivityofscientists,whichisthegoalofa“scientist’saide”(see

Science

).ProductivitygainsinsciencefromAIcouldprovetobeextremelyvaluabletosociety[NationalAcademies].

3.AIsystemsshouldinitiallyaimatremovingthedrudgeryofcurrenttasks.IfpolicymakersandpractitionersfirsttargetAIsystemsthatautomate

menialandunfulfillingaspectsofcurrentjobs,they

canmakeworkmoremeaningfulandenjoyable.

Doctorsandnurseschoosetheircareersbecause

theywanttohelppatients,nottodoendless

insurancedocumentation.Schoolteachersmayprefertospendtheirtimeonstudentinteractionratherthan

gradingandrecordkeeping.Ratherthanskipaheadto

3

newAIinnovations,firstprovideAItoolstoimprovethemeaningfulnessofpeople’scurrentworkin

hospitalsandclassrooms.Forexample,AI-powered“teacher’saide”tools(see

Education

)couldautomatetasksteachersfindunfulfilling,freeinguptimeto

spendwithstudentsandmakingteachingworkloadsmoremanageable.AsecondarybenefitisthattheymightbemorelikelytouseAItoolsinthefuture.

Sisyphus’sdrudgery.HecouldhaveusedAI’shelp.

4.TheimpactofAIvariesbygeography.

PhilanthropistEricSchmidtemphasizedthatwhile

nationswithadvancedeconomiesworryaboutAI

displacinghighlytrainedprofessionals,countrieswith

leaneconomiesfaceshortagesofthesesameskilledexperts(see

Employment

).AIcouldmakesuch

expertisemorewidelyavailableinplaceswith

extremescarcityoftrainedworkersandwithinsuf-ficientfunding,

potentiallyenhancingqualityoflife

andeconomicgrowth

.AIsystemscouldbecomeastransformativeforthelow-andmiddle-income

nationsas

mobilephoneshavebeen

[Rotondi].Forexample,a“healthcareaide”thatimprovedtheskillsetsandproductivityofnursesandphysician

assistantscouldalsogivemorepatientsaccessto

qualityhealthcareinregionsfacingphysician

shortages(see

Healthcare

).MultilingualAImodelsonsmartphonescangreatlyhelppeopleinlow-and

middle-incomecountriesgainaccesstoinformation,education,media/entertainment,andmore.Better

economiesandservicesmayevenoffer

alternatives

toemigrationforsomeinmiddle-incomecountries

.

5.DeterminethebestmetricsandmethodstoevaluateAIinnovations.WemustmeasureAI

accuratelytoevaluateitsrealpotential.Inhigh-stakesdomains,becausewecan'triskharmingparticipants,

weneedtousegoldstandardtoolstoevaluate

innovationaccuratelyandidentifypossiblelimitationsbeforewidedeployment:

A/Btesting

,

randomized

controlledtrials

,and

naturalexperiments

.1Equally

urgentis

post-deploymentmonitoring

toevaluate

whetherAIinnovationsdowhattheysaytheyare

doing,whethertheyaresafe,andwhethertheyhaveexternalities.WealsoneedtocontinuouslymeasureAIsystemsinthefieldsoastobeabletoincre-

mentallyimprovethem.Inother,lowerrisksituations,themarketplaceandobservationalstudiescanassesseffectivenessofAItoolswithoutneedingthesame

rigor,suchasfor

AItoolsforprogrammers

.

Havingcoveredthefiveguidelines,thenextpartsetsthecontextforthecurrentexcitementaboutAI.

I.PuttingPragmaticAIinContext

HistoryofTechnologicalParadigmShifts

Similartothedawnoftelevision,computers,

nuclearpower,andtheinternet,uncompromising

antagonisticpositionsarebeingtakenintheseearlydaysofpracticalAI.Thepolarizeddiscourseonthis

newtechnologyhasdevolvedcurrentlyintoastandoffbetween“

accelerationists

”and“

doomers

.”LikemostpractitionersofAI,webelieverealityismorenuanced.

OnedebatedissueistheroleofthegovernmentinAI’sdevelopment.Recenteffortsbycompaniesto

developAIsystemshavebeenlikenedtothe

ManhattanProject

inthe1940sorthe

SpaceRace

ofthe1960s.Intermsofinvestmentsize,thenearly

$2B

1Anaturalexperimentisaresearchstudywhere

individualsareexposedtodifferentconditions,likea

controlgroup,notbytheresearcher'sdesignbutbya

naturallyoccurringeventorpolicychange.Researchers

treatsuchastudyasactingasifrandomassignment

occurred,allowingthemtoobserveandanalyzetheeffectswithoutactivelymanipulatingvariables.Thisoptionisoftenusedwhencontrolledexperimentsarenotfeasibleduetoethicalorpracticallimitations.

4

fortheManhattanProjectwouldbe$27Bintoday’s

dollars

,andthe

$26Btoputapersononthemoon

wouldbe$318Btoday

.WhilecurrentAIisroughlycomparableintermsofsizeofinvestment,thebigdifferenceisthattheU.S.governmentfundedthoseeffortswhileprivateindustrybacksthisone,and

mostofthetalentinvolvedareintheAIindustry.

Giventhisrelationship,weneedanewinnovationinfrastructure.PolicychangestoimprovetheimpactofAIarelikelybestaccomplishedviacollaboration

betweengovernment,industry,andacademia.2Asahistoricalprecedent,wecanlookattherolethe

governmentplayedinthedevelopmentofintegratedcircuitchipsandcars.

TheU.S.government’sApolloandMinutemanprogramsused>95%ofallchipsmadein1965.

Inthe1960s,thegovernmentwastheprimary

consumerofchips,asthesmallersizeandlower

powerofchipswasvitalintheSpaceRace.

Over95%

ofthechipsmadein1965wereusedbytheApollo

andMinutemanprograms

.Thismanufacturing

volumeallowedthenascentsemiconductorindustrytoimproveitsfabricationprowesssoitcouldenter

themuchlargercommercialmarketbytheendofthedecade.Twoyearslater,

Inteldeliveredthefirst

microprocessor

.Thegovernmentalsofunded

universityresearchthathelpedpushthefrontiersof

chipdesign

andmanufacturing,helping

Moore’sLaw

tocontinueformorethan50years.

2Inadditiontouniversitieshelpingadvancetheresearchfrontier,thepeopleinindustryandgovernmentpursuingAItechnologyandpolicyareeducatedatuniversities.EnablinguniversitiestoprepareindividualstoadvanceAI,aswellastoeducatethebroadpopulationtothriveinaworldof

ubiquitousAI,iscrucialtooursharedfuture.

Inthefirsthalfofthe20thcentury,car

manufacturersbenefitedas

governmentsbuiltand

improvedroadsandfreewaysfundedbygasoline

taxes,createdtrafficlightsandtravelsigns,and

licenseddrivers

.Inthe1960s,theU.S.createdthe

NationalHighwayTrafficSafetyAdministration

andthe

EnvironmentalProtectionAgency

,whichset

societalbenefitingstandardsoncarsafetyand

emissionsforthewholeindustrythatmighthave

beendifficultforindividualcarmanufacturerstodoontheirown.Morerecently,thegovernmenthas

fundedacademicresearchtoimprovecars.ExamplesareDARPA’sself-drivingchallenge(

wonbyacademic

researchers

),

automotivesafety

,and

fuelefficiency

.

Weenvisionacoordinatedpublic-private

partnershipforAI.Itsgoalwouldbetoremove

bureaucraticroadblocks(e.g.,tosharingdata),ensuresafety,andprovidetransparencyandeducationto

policymakersandthepublic.Inadditiontolearning

fromhistoricalprecedentsforthedevelopmentofAIsystems,weshouldalsolearnfromthehistoryofhowtransformativetechnologieshavebeendeployed.

Onelessonlearnedfromtherolloutsofparadigm-shiftingtechnologieslikebroadbandinternet,cloud,mobiledevices,andsocialmediaisthattheir

deploymentwaslengthierthantechnologistspredicted,buttheirimpactwasevenmorewidespread.Quoting

BillGates

:

OnethingI’velearnedinmyworkwithMicrosoftisthatinnovationtakeslongerthanmany

peopleexpect,butitalsotendstobemorerevolutionarythantheyimagine.

Anotherlessonisthatpredictionsoftechnologicalimpactfrompeopleinotherfieldsaresimilarly

inaccurate[NationalAcademies]:

…commentatorsandexpertsofall

stripes—socialandnaturalscientists,historians,andjournalists—haveanalmostunblemishedrecordofincorrectlyforecastingthelong-run

consequencesoftechnologicalinnovations.

Athirdlessonisthatitisoftenhardtoaccuratelypredicttheunintendednegativesideeffectsuntil

afterthetechnologieswerewidelydeployed,withsocialnetworkingastheprimeexample.

5

TimewilltellifAIprovestobeanexceptiontothesethreelessons.

ArtificialIntelligence(AI)

BeforewediscussAI’simpactwithineachofourhalf-dozenfields,let’sreviewhowwegothere.ThetermArtificialIntelligence(AI)wascoinedtodefinethescienceandengineeringofmakingintelligentmachines

in1956,onlyfiveyearsafterthefirstcommercialcomputer.3

OnestrandofAIthatbecamepopularoverthenextdecadeswastocreateasetofrulesoftheform“if

thishappensdothat,ifthathappensdothis.”The

beliefwasthatwithsufficientlyaccurateandlarge

setsofrules,intelligencewouldemerge.Withinthe

bigtentofAI,acontrarianstranddidnotacceptthathumanswouldeverbeabletowritesuchasetof

rules.Theybelievedthattheonlyhopewastolearntherulesfromthedata.Thatis,itwasmuchhardertoprogramacomputertobecleverthanitwastoprogramacomputertolearntobeclever.JustthreeyearsafterAIwasdefined,theychristenedthisbottom-up

approachmachinelearning(ML).4

OnebranchoftheMLcommunitybelievedtheonlyhopeforcreatingaprogramthatcouldlearnfrom

datawouldbetoimitateouroneclearexampleofintelligence:thehumanbrain.Ourbrainsconsistof100billionneuronswith100trillionconnections

betweenthem.ThisversionofMLisbasedonavery

3In1961,Turinglaureate

DougEnglebart

tookthe

contrarianapproachofaugmentinghumanintellect

[Englebart],whichisthetermBrynjolfssonusedinhis

paper.Weinsteadusethephrase“improvinghuman

productivity”becausewethinkitiseasierforthepublicandpolicymakerstounderstandtheimplicationsofproductivitygainsthanofaugmentation.

4ThisabbreviatedhistoryofAIissimplified.Inthe1950stherewasaferventenergyaroundtheconceptofintelligentmachinesinspiredbyhumanbrains/intelligence,andduringthe1960sthevarioustraditionsgrewapart.InthebigthreeCSAIdepartmentsofthetimethatwerefundedbyDARPA(MIT,Stanford,CMU),thetop-down“symbolicAI”traditiontookhold.Rule-basedsystemsmentionedabovearejust

onebranchofsymbolicAI.Neuralnetworksalsogotabig

boostinthemid-1980s,e.g.,theresearchbyTuringlaureateYannLeCunonhandwritingrecognitionusing

MNIST

.

simplemodelofaneuron,sothisformofML(thatisalsowithinthebigtentofAI)iscalledaneural

network.Atypicalneuralnetworkmightuse100

millionartificialneurons.Becausecurrentversionsofneuralnetworkshavemanymorelayersofartificialneuronsthaninthepast,recentincarnationsarealsocalleddeepneuralnetworksordeeplearning.

Neuralnetworkshavetwophases,trainingand

serving(alsocalledinference).Trainingisanalogoustobeingeducatedincollegeandservingislikeworkingaftergraduation.Traininganeuralnetworkinvolvesrepeatedlyshowingitlabeleddata(e.g.,images

identifiedascatsordogs)withthesystemadjustingitsartificialneuronsuntilitgivessufficientlyaccurateanswerstoquestionsaboutthatdata(e.g.,isitacatoradog).Oncetrained,thegoalisthatthemodel

shouldworkwellwithdataithasnotyetseen(e.g.,correctlydeterminingifanimageisofacatoradog).

TheRussiandollsofAI.

AfterdecadesofdebatesaboutwhichAI

philosophywasbest,in2012neuralnetworksstartedtosoundlybeatthecompetition.Thebreakthrough

12yearsagowasn’tsomuchtheinventionofnew

neuralnetworkalgorithmsasitwasthatMoore’sLawledtomachinesthatwere10,000timesfasterandwecouldnetworkmanytogethertoworkinconcert.Thatenabledtrainingusing

10,000timesmorelabeled

data

availablefromtheWorldWideWeb.VirtuallyallnewsstoriestodayconcerningAIbreakthroughsare,moreprecisely,aboutneuralnetworks.

TheexcitementaboutAIspikedbyChatGPTin2022isaboutmodelswithbillionsofneuronsthattake

6

monthstotrainontensofthousandsofchips

designedsolelyforneuralnetworktraining.Thesegiantneuralnetworkswereinitiallycalledlarge

languagemodels(LLMs)becausethefirstexamplesperformedamazingfeatsbasedontext.Eventuallythesemodelsbecamemoremultimodal,

incorporatingdatatypesbeyondtextsuchasimages,audio,andvideo.Theterminologyisevolvingwiththetechnology,andLLMsarenowoftencalledfoundationmodels[Bommasanietal.]orfrontiermodels.

Theadventoftheselargefrontiermodelshas

raisedunderstandableconcernsaboutenergyuseofAI.

AppendixI

coversthistopicindetail,butaquick

summaryisthatAIsystemstodayaccountfor

undera

quarter

of

1%ofglobalelectricityuse

,atenthofdigi-talhouseholdapplianceslikeTVs.The

International

EnergyAgencyconsidersevenastrongprojected

increasedenergyconsumptionbyAIfor2030tobe

modest

relativetootherlargertrendslikecontinuedeconomicgrowth,electriccars,andairconditioning.

WhileweusethebroadtermAI,thefieldis

fragmented,coveringmanytechnologies.Our

discussionwillprimarilyfocusongenerativeand

predictiveAIsystems,withabriefdiscussionofsomeotheraspectsofAIwhererelevant.ExamplesareAIassistants(e.g.,

NotebookLM

),chatbots(e.g.,

ChatGPT

),

retrieval-augmentedgeneration

(RAG)

systems5(e.g.,

Perplexity

),and

generativeAIsystems

(e.g.,

Midjourney

).

ArtificialGeneralIntelligence

(AGI)

BeforewecangettotheimpactofneartermAI,wefirstneedtoconsidertheprospectofartificialgeneralintelligence(AGI).AnAIsystemcaneasilywriteanewbedtimestorydailyfeaturingyourchildrenasmain

characters.AdifferentAIsystemcouldbeatany

humanbeingattheclassicstrategygameofGo.Asofnow,nosingleAIsystemcandobothofthesethings.

5Retrieval-augmentedgeneration(RAG)isanAIframeworkthatcombinesLLMswithtraditionalinformationretrievalsystemstoproducemoreaccurateandrelevanttext.

Eachcandeliveramazingcapabilities,buttheyarepracticallyuselessiftheystrayoutsidetheirlanes.IncontrasttoexistingAI,proponentsarguethatanAGIthatwouldbemultitalented—capableenoughtowinstrategygames,diagnosediseases,analyzepoetry,andcontributetoappliedcomputerscience

innovationsthatcanfurtherenhancethecapacityofAGIsystems.

Ageneralknife.

AGIhasmanydefinitions,

butoneframework

gainingpopularity

emphasizestherangeoftasksthatanAIsystemreachesatargetthresholdcomparedtopeopleandhowwellitcomparestohuman-level

performanceforagiventask[Morrisetal.].Thresholdsarelabeledbasedontheportionofpeoplethatthe

systemoutperforms:competent(>50%),expert

(>90%),virtuoso(>99%),andsuperhuman(>100%).

AlphaGo

isratedsuperhuman,butonlyforplayingGo,andisnotcompetentatanythingelse.This

breadthversusdepthmetrichelpsclarifyAGIdiscussions.

TremendousattentionisbeingpaidtoAGI,

deservedlysogivenitslargepotentialpositiveand

negativeimpactontheworld.WeapplaudtheseriousinvestigationsofAGI,includingscientificworkthat

aimstoclarifyrelevantdefinitionsandlikelyimpacts.

Aswefocusonimpactsofcurrentandnear-termAIsystems,wewillnotdiscussAGIfurther,beyond

mentioningthatprogressonthetopicmayaccelerateboththebenefitsandrisksweoutlinehere.

ThenextpartofthepaperdelvesintotheimpactofAIsystemsinthehalfdozenfieldsweinvestigated.

7

II.DemystifyingthePotentialImpactofAI

Employment

Ourfirsttopicfornearer-termAIisamajor

concern:theimpactonjobs[NationalAcademies].Indeed,a

GlobalPublicOpinionPollonAI

foundthatthemajorityexpecttobereplacedatworkbyanAIsysteminthecomingdecade[Loewenetal.].

Technologicaladvancementshavelongledtothedeclineofsomejobsandthecreationofnewones.

FortheU.S.workforce,

63%hadjobsin2018thatdid

notexistin1940

[Autor2022].Figure1shows

examplesoffourjobswherenumberschangedstrikinglyfrom1970to2020.

Despitethedownsideofjobdisruption,ahealthyeconomyreliesonimprovingworkerproductivity.

Two-thirdsoftheworld’spopulationlivesincountries

withbelow-replacementbirthlevels

[Eberstadt]andmanynationsarefacing

laborshortages

[Duarte].

TheU.S.alreadylackscriticalpositionsasvariedas

K-12teachers

,

passengerairlinepilots

,

physicians

,

registerednurses

,

softwareengineers

,and

school

busdrivers

.Tosupplyneededservices,high-income

countriesmusteithergreatlyexpandtheirworkingpopulationorsignificantlyimproveworker

productivity[ManyikaandSpence].

Theimpactofproductivitygainsonjobsdependsonwhetherthedemandforgoodsproducedbythatworkiselasticorinelastic.Ifdemandisinelastic,

productivitygainsmeansjobswillbelost

[Bessen].Forexample,agricultureisinelasticintheU.S.,so

gainsmeantdramaticdeclinesinabsolutenumbers(fourfold)anditsportionoftheworkforce(

from40%

in1900to20%in1940,4%in1970,and2%today

)

[Daly].

Ifproductdemandissufficientlyelastic,

productivity-enhancingtechnologywillincrease

industryemployment

[Bessen].

Forexample,programmerstodayare

tremendouslymoreproductivethantheywerein1970—theyhavemorepowerfulprogramming

languagesandtools,plusMoore’sLawhelped

improvehardwareamillionfold—yettherewere11timesmoreprog