Good evening and thanks forhaving me here. TodayI wantto lookat howour relationship tothe world changes when we’re surrounded by devices that anticipate our needs and act on them. That means it sits at the intersection of the internet of things, user experience design and machine learning, and although people have dealt with one of those disciplines before, I don’t think they’ve ever been combined in quite the ways they are now, or with the current enthusiasm. And, to be clear: I am neither a fan of, nor a critic of, these technologies. I think they’re too complex to be reduced that way and to maximize their positive impact we have to actively engage with them, and that’s what this talk is trying to do. The talk is divided into several parts: it starts with an overview of how I think Internet of Things devices are primarily components of services, rather than being self-contained experiences, how predictive behavior enables key components of those services, and then I finish by exploring some speculative ideas of what kind of impact they’re going to have on us, as individuals and as a society. At its core is an argument that everything is going to be connected to the Internet, that those things will each try to predict our immediate future, and that this is going to fundamentallychange our relationship to the world. A couple of caveats: - My current work in this field focuses almost exclusively on the consumer internet of things, so I see most things through that lens. - I want to point out that few if any of the issues I raise are new. Though the terms “internet of things” and “machine learning” are hot right now, the ideas have been discussed in research circles for decades. Search for “ubiquitous computing,” “ambient intelligence,” and “pervasive computing” and you’ll see a lot of great thought in the space. If you’re really ambitious, you can read the Artificial Intelligence and Cybernetics works of the 50s and60s andyou’ll be surprised by the prescience of the people working in this space when the entire world’s compute power was about as much as my key fob. - There are a lot of ideas here, and I will almost certainly under-explain something. For that I apologize in advance. My goal here is to give you a general sense of how these the pieces connect, rather than an in-depth explanationof any one of the pieces. - Finally, most of my slides don’t have words on them, so I’ll make the complete deck with a transcript available as soon I’mdone. 0
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Good eveningandthanksforhavingmehere.TodayIwanttolookathowourrelationshiptotheworldchangeswhenwe’resurroundedbydevicesthatanticipateourneedsandactonthem.Thatmeansitsitsattheintersectionoftheinternetofthings,userexperiencedesignandmachinelearning,andalthoughpeoplehavedealtwithoneofthosedisciplinesbefore,Idon’tthinkthey’veeverbeencombinedinquitethewaystheyarenow,orwiththecurrententhusiasm.And,tobeclear:Iamneitherafanof,noracriticof,thesetechnologies.Ithinkthey’retoocomplextobereducedthatwayandtomaximizetheirpositiveimpactwehavetoactivelyengagewiththem,andthat’swhatthistalkistryingtodo.
Thetalkisdividedintoseveralparts:itstartswithanoverviewofhowIthinkInternetofThingsdevicesareprimarilycomponentsofservices,ratherthanbeingself-containedexperiences,howpredictivebehaviorenableskeycomponentsofthoseservices,andthenIfinishbyexploringsomespeculativeideasofwhatkindofimpactthey’regoingtohaveonus,asindividualsandasasociety.AtitscoreisanargumentthateverythingisgoingtobeconnectedtotheInternet,thatthosethingswilleachtrytopredictourimmediatefuture,andthatthisisgoingtofundamentallychangeourrelationshiptotheworld.
Acoupleofcaveats:- Mycurrentworkinthisfieldfocusesalmostexclusivelyontheconsumerinternetofthings,soIseemostthingsthroughthatlens.- IwanttopointoutthatfewifanyoftheissuesIraisearenew.Thoughtheterms“internetofthings”and“machinelearning”arehotrightnow,theideashavebeendiscussedinresearchcirclesfordecades.Searchfor“ubiquitouscomputing,”“ambientintelligence,”and“pervasivecomputing”andyou’llseealotofgreatthoughtinthespace.Ifyou’rereallyambitious,youcanreadtheArtificialIntelligenceandCyberneticsworksofthe50sand60sandyou’llbesurprisedbytheprescienceofthepeopleworkinginthisspacewhentheentireworld’scomputepowerwasaboutasmuchasmykeyfob.- Therearealotofideashere,andIwillalmostcertainlyunder-explainsomething.ForthatIapologizeinadvance.Mygoalhereistogiveyouageneralsenseofhowthesethepiecesconnect,ratherthananin-depthexplanationofanyoneofthepieces.- Finally,mostofmyslidesdon’thavewordsonthem,soI’llmakethecompletedeckwithatranscriptavailableassoonI’mdone.
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Let me begin by telling you a bit about my background. I�m a user experience designer. I was one of the first professional Web designers. This is the navigation for a hot sauce shopping site I designed in the spring of 1994.
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I’vealsoworkedontheuserexperiencedesignofalotofconsumerelectronicsproductsfromcompaniesyou’veprobablyheardof.
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Now, aquickaside.WhatisUserExperienceDesign?UXdesignisnotgraphicdesign,interfacedesign,ergonomics,industrialdesign,orproductdesign,butitincludesaspectsofallofthosethings.
UXdesignisahumanisticproblemsolvingapproachthatbringstogethertheneedsofpeopleandbusinessestocreatetechnologicalproductsthatarevaluableforbothgroups.It’smuchmoreaboutprocessthanmakingthingslookgood.
Thefieldisabout20yearsold.Thisishowitlookedabout15yearsago.
DiagrambyJessMcMullin.
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It’salittlemorecomplextoday,but’sroughlythesamething.
DiagrambyCoreyStern.
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Iwroteacoupleofbooksbasedonmyexperienceasadesigner.Oneisacookbookofuserresearchmethods,andtheseconddescribeswhatIthinkaresomeofthecoreconcernswhendesigningnetworkedcomputationaldevices.I’malsomarriedtooneoftheauthorsofthisbook,sothinkingabouttheimpactofthedesignofconnecteddevicesonpeopleiskindofafamilybusiness.
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Ialsostartedacoupleofcompanies.Thefirst,AdaptivePath,wasprimarilyfocusedontheweb, andwiththesecondone,ThingM,Igotdeepintodevelopinghardware.
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TodayIworkforPARC,thefamousresearchlabthatinventedthepersonalcomputer,objectorientedsoftware,thetabletcomputer,andlaserprinter,asaprincipalinitsInnovationServicesgroup.Wehelpcompaniesreducetheriskofadoptingnoveltechnologiesusingamixofsocialresearch,designandbusinessstrategy.
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PARCalsostartedthinkingaboutwhatwecalltheIoT longbeforemost othercompanies.
ItwasatPARCin1971thatDickShoup,andearlyPARCresearcher,wrotethateventuallyprocessorswouldbeascommon,andasinvisibleaselectricmotors.Thisclearlyoutlinesthedestinyofconnectedcomputer:thateventuallyitwillbecomeasboringandascommonaselectricmotorsaretoday.
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Inthelate80s,alsoatPARC,MarkWeisercoinedthetermubiquitouscomputingtodescribeafuturewhenthenumberofcomputerssurpassedthenumberofpeopleusingthem.Inthischartfrom20yearsago,hepredictedthatwouldhappenaround2005.Hedidn’tlivetoseethatcrossover,buthewasbasicallyright—theiPhonelaunchedin2007—andwenowliveintheworldheenvisioned.
Essentially,whatwenowseeasanovelphenomenonhasbeenforseen bypeopleintheindustryformanydecades.Thequestionshavealwaysbeennotaboutwherewe’regoing,butwhenwe’llgetthere,andhow.
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Buttheendvisiondoesn’tappearallatonce.We’veonlystartedthetransitiontotheubiquitouscomputingworld,andassuch,we’reseeingalotofbadideasaboutwhattheInternetofThingsisanditisn’t.Essentially,everythingthatcanbeconnectedtotheInternetwillbe,whichincludesalotofthingsthatshouldn’tbe.TherearesomanybadideasnowthatthereareentireTumblrs dedicatedtomockingstupidIoTideas.Oneisaboutdumbsmartthingsandtheotherisjustaboutdumbsmartrefrigerators.
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Mostofthesethingsarebadideasbecausesimplyconnectingexistingstufftotheinternetdoesnotproducecustomervalue…
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Simpleconnectivityhelpswhenyou’retryingtomaximizetheefficiencyofafixedprocess,butthat’snotaproblemthatmostpeoplehave.We’vebeenabletosimplyconnectvariousdevicestoacomputersinceaTandyColorComputerscouldlightsoffandonoverX10in1983.Theproblemisthatthatwasn’tveryusefulthen,andit’snotveryusefulnow.IfyoureplacetheTandywithaniPhoneandthelampwithawashingmachine…
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…oraneggcarton,youstillhavethesameproblem,andit’sauserexperienceproblem.
TheUXproblemisthatendusershavetoconnectallthedotstocoordinatebetweenawidevarietyofdevices,andtointerpretthemeaningofallofthesesensorstocreatepersonalvalue.Formanysimplyconnectedproductsthereissolittleefficiencytobehadrelativetothecognitiveloadthatit’s justnotworthit.What’sworse,theextracognitiveloadisexactlyoppositetowhattheproductpromises,andcustomersfeelintenselydisappointed,perhapsevenbetrayed,whentheyrealizehowlittletheygetoutofsuchaproductThatmakesmostsuchproductseffectivelyWORSEthanuseless.
Thatpromisegapiswhatdistinguishesagadgetfromatool,whythiseggcartonisfunny,andwhyQuirkywhomadeit,filedforbankruptcyafterburningthroughhundredsofmillionsofdollars.
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How doyoucreateatoolthatreducescognitiveloadinsteadofcreatingit,thatexchangespeople’sprecioustimeforsignificantvalue?Oneapproachistocouplecloud-basedserviceswithpredictivemachinelearningmodelstoanticipatewhatbehaviorswillmaximizethechancesofadesirableoutcomeinagivensituation.
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WhenI talkaboutservices,I’mtalkingaboutthinkingofhardwaredevicesasphysicalrepresentativesofcloudservices,whichmakesthemverydifferentthantraditionalconsumerelectronics.Historically,acompanymadeanelectronicproduct,sayaturntable,theyfoundpeopletosell itforthem,theyadvertiseditandpeopleboughtit.Thatwastraditionallytheendofthecompany’srelationshipwiththecustomeruntilthatpersonboughtanotherthing,andallofthevalueoftherelationshipwasinthedevice.WiththeIoT,thesaleofthedeviceisjustthebeginningoftherelationshipandphysicalthingholdsalmostnovalueforeitherthecustomerorthemanufacturer.
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Value now shifts to services and the devices, software applications and websites used to access it—its avatars—become secondary. A camera becomes a really good appliance for taking photos for Instagram, while a TV becomes a nice Instagram display that you don’t have to log into every time, and a phone becomes a convenient way to check your friends’ pictures on the road.
Hardware, physical things, become simultaneously more specialized and devalued as users see “through” each device to the service it represents. The avatars exist to get better value out of the service.
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Amazon reallygetsthis.Here�satellingolderadfromAmazonfortheKindle. It’ssaying�Look,usewhateverdevice youwant.Wedon�tcare,aslongyoustayloyaltoourservice.Youcanbuyourspecializeddevices,butyoudon�thaveto.�
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WhenFirewasreleased5yearsago,JeffBezosevencalled itaservice.
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AmazonDashisaservicethat’senabledbydedicateddevices.ADashbuttonisanetworkedcomputerwhoseonlypurposeistobeanavatarforamacaroniandcheeseservice.
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Mostlarge-scaleIoT productsareserviceavatars.Theyusespecialized sensorsandactuatorstosupportaservice,buthavelittlevalue—ordon’tworkatall—withoutthesupportingservice.SmartThings,whichwasacquiredbySamsung, clearlystatesitsserviceofferingrightupfrontontheirsite.Thefirstthingtheysayabouttheirproductlineisnotwhatthefunctionalityis,butwhateffecttheirservicewillachievefortheircustomers.Theirhardwareproducts’functionality,howtheywilltechnicallysatisfytheservicepromise,isalmostanafterthought.
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ComparethattoX10,theirspiritualpredecessorthat’sbeeninthebusinessfor30years.AllthatX10tellsisyouiswhatthedevicesare,notwhattheservicewillaccomplishforyou.Idon’tevenknowifthereISaservice.WhyshouldIcarethattheyhave“modules”?Ishouldn’t,andIdon’t.
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Ithink therealvalueconnectedservicesofferistheirabilitytomakesenseoftheworldonourbehalf,toreducecognitiveloadbyenablingpeopletointeractwithdevicesatahigherlevelthansimpletelemetry,atthelevelofintentionsandgoals,ratherthandataandcontrol.Humansarenotbuilttocollectandmakesenseofhugeamountsofdataacrossmanydevices,ortoarticulateourneedsassystemsofmutuallyinterdependentcomponents.Computersaregreatatit.
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Theydothisthroughprocessesthathavemanynames,butI’lllumpthemallunderMachineLearning,whichisabigpartofwhatusedtobecalledArtificialIntelligence.Manyofthecoreideasheregobacktothe1950sandit’sthebasisofeveryemailspamfilter,soifyou’vehadyourspamautomaticallyfiltered,you’veexperiencedthevalueofmachinelearning.
AbigpartofMachineLearningispatternrecognition.Wehumansevolvedverysophisticatedfacultiestorapidlyidentifyvisualimagesinallkindsofdifficultconditions.Youlookatapictureofanorangeonaredplateandyoucantellinstantlythatit’snotasunset,butuntilrecentlythatwasreally,reallyhardforacomputer.BecauseofacombinationofMoore’sLawandsomebreakthroughs,computershavegottenmuchbetteratpatternrecognitioninthelastcoupleofyears.
Foracomputer,recognizingsomethingstartswithaprocesswheresomebasicattributesofanimageareextracted,suchastheshapeofboundariesbetweenclustersofpixels,orthedominantcolorofapatchofanimage.Thesearecalledfeaturesinmachinelearning.Byexamininglotsandlotsofexamplesoffeaturesinanimage,amachinelearningsystembuildsastatisticalmodelofwhatthatclusterrepresents.
Basicformsofthiskindofimagerecognitionhasbeenusedindustriallyfordecades.Mostoftheorangesthatcomefromthecentralvalleyarescanned360timestoseparateoneswithblemishesfromoneswithout.LegohasacompletelyautomatedfactorythatinjectionmoldsamillionLegobricksanhour,examineseverysinglepiece,automaticallysorts,bagsandboxesthem,allusingcomputervision.That’srelativelyold.
Imagesfrom:Region-basedConvolutionalNetworksforAccurateObjectDetectionandSemanticSegmentation,R.Girshick,J.Donahue,T.Darrell,J.Malik,IEEETransactionsonPatternAnalysisandMachineIntelligence
Real-TimeImageandVideoProcessing:FromResearchtoRealitybyKehtarnavaz andGemadia
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What’snewisaclassofsystemsthatunderstandthecontentofimages.Theydon’tjustlookatfeatures,butclustersoffeatures,andclustersofclustersoffeatures,andtheycannowidentifyanorangefromthesettingsun,orapersonfromanairplane,orapolarbearfromadalmatian.
ThisiswhyFacebookasksyoutosaywhoisinanimage.It’snotjustforyou,it’sfortheirfacerecognizer.
Nowhere’stheinterestingpart:we’rebuilttoidentifypatternsinvisualphenomena,butwe’reprettybadatidentifyingtheminotherkindsofsituations.Forexample,ifyou’veevertriedtounderstandsomeone’sfoodsensitivities,it’sreallyhardtoextractwhatthatpersonisreactingto,evenifyoukeepverycarefultrackofwhatthey’veeaten.We’rejustnotbuiltforit.Itwasneverevolutionarilysufficientlyimportant,sowedidn’tevolveanorganforit.
Computers,ontheotherhand,don’tcare,andnowthatwe’vefoundreallygoodwaystofindpatternsinvisualimages,thesesametechniquescanfindpatternsinanything.
Insteadofamatrixofpixels,whatifyouhadamatrixofmedicalprescriptions,witheachrowasthehistoryofoneperson’sprescriptionsfromthefirsttimethatpersonwenttothedoctorforaproblem,throughwhentheywereprescribedcertainthings,towhentheygotbetter,ortheydidn’t.Thesamekindofsystemcouldlearnthetypicalpatternforprescribing,say,awheelchair.Itwouldessentiallyseethegeneralshapeofthesequencefortheprescriptionofachairovertimeandacrossmanypeople.
Thenifyousawawheelchairbeingprescribedthatwasoutsideofthetypicalpattern,youcouldidentifyit.That’scalledanomalydetection.That’sinfactexactlyhowwebuiltasystemtoidentifyMedicarefraudforthestateofCalifornia.Peopleareterribleatthatstuff,butcomputersaregreat.
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Whenoneofthedimensionsistimeandanotheristheoutcomeofaseriesofactionsyoucanmakeapatternrecognizerthatassociatesasequenceofactionswithasetofstatisticalprobabilitiesforpossibleoutcomesbasedondatacollectedacrossawidevarietyofsimilarsituations.Inotherwords,becausepeopleandmachinesbehaveinfairlyconsistentways,thesemachinelearningsystemscanincreasinglypredictthefutureandattempttoadaptthecurrentsituationtocreateamoredesirableoutcome.
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Theinterestingthingisthatthisnotjusttheory.
PredictionandresponseisattheheartofthevaluepropositionmanyofthemostcompellingIoT services,startingwiththeNest.TheNestsaysthatitknowsyou.Howdoesitknowyou?Itpredictswhatyou’regoingtowantbasedonyourpastbehavior.
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Amazon’sEchospeaker saysit’scontinuallylearning.Howisthat?Predictivemachinelearningbasedonyouractionsandyourwords.
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The Birdi smartsmokealarmsaysitwilllearnovertime,whichisagainthesamething.
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Jaguar, learning…ANDintelligent.
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TheEdyn plantwateringsystemadapts toeverychange.Whatisthatadaptation?Predictivemachinelearning.
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Canary,ahomesecurity service.
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Cocoon,anotherhomesecuritysystem knows.Howdoesitknow?Machinelearning.
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Here’sfoobot,anairqualityservice.
[Ialsolikehowoneof itsimplicitservicepromisesistoidentifywhenyourkidsaresmokingpot.]
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Silk’sSenseadapts
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Mistbox sprayswaterintoyourairconditionertoreduceyourenergybill.You’dthinkthat’saprettysimpleprocess,butno,it’salwayslearning.
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Anumberofcompaniesaremakingchipsthatmakemachinelearningmuchcheaperandmorepower-efficient,whichmeansthatit’sgoingtobeveryeasytoinstallitineverydevice,fromstreetlightstomedicalequipmenttotoys.It’snotjustlikely,it’sinevitable.Here’sonethatwasannouncedacoupleofweeksago.
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Here’saKickstarter for an“AIButler”thatpostedearlierthismonth.Whatdoesitdo?Idon’tknow,butitlearns.
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Theidealscenariothesethingspaintisprettyseductive.Imagineaworldofespressomachinesthatstartbrewingasyou’rethinkingit’sagoodtimeforcoffee;officelightsthatdimwhenit’ssunnytosaveenergy,andmacandcheesethatneverrunsout.Theproblemisthatalthoughthevaluepropositionisofabetteruserexperience,it’sunspecificinthedetails.Previousmachinelearningsystemswereusedinareassuchaspredictivemaintenance andfinance.Theyweremadebyandforspecialists.Nowthatthesesystemsareforgeneralconsumers,wehavesomesignificantquestions.Howexactlyhowwillourexperienceoftheworld,ourabilitytouseallthecollecteddata,becomemoreefficientandmorepleasurable?
We’restillearlyinourunderstandingofpredictivedevices,sorightnowtheproblemsareworsethansolutions.IwanttostartbyarticulatingtheissuesI’veobservedinourwork.
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We’veneverhadmechanicalthingsthatmakesignificantdecisionsontheirown.Asdevicesadapttheirbehavior,howwilltheycommunicatethatthey’redoingso?Dowestickasignonthemthatsays“adapting”,likethelightonavideocamerasays“recording”?Shouldmychairvibratewhenadjustingtomyposture?Howwillusers,orjustpassers-by,knowwhichthingsadapt?Icouldendupsittinguncomfortableforalongtime,waitingformychairtochange,beforerealizingitdoesn’tadaptonitsown.Howshouldsmartdevicessettheexpectationthattheymaybehavedifferentlyinwhatappearstobeidenticalcircumstances?
How doweknowHOWintelligentthesedevicesare?Peoplealreadyoftenprojectmoresmartsondevicesthanthosedevicesactuallyhave,soacoupleofaccuratepredictionsmayimplyamuchbettermodelthanactuallyexists.Howdoweknowwe’renotjust homesteadingtheuncannyvalleyhere?
ChairbyRaffaello D'Andrea,MattDonovanandMaxDean.
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Theironyinpredictivesystemsisthat they’reprettyunpredictable,atleastatfirst.Whenmachinelearningsystemsarenew,they’reofteninaccurate,whichisnotwhatweexpectfromourdigitaldevices.60%-70%accuracyistypicalforafirstpass,buteven90%accuracyisn’tenoughforapredictivesystemtofeelright,sinceifit’smakingdecisionsallthetime,it’sgoingtobemakingmistakesallthetime,too.It’sfineifyourhouseisacoupleofdegreescoolerthanyou’dlike,butwhatifyourwheelchairrefusestogotoadrinkingfountainnexttoadoorbecauseit’sbeentrainedondoorsanditcan’ttellthat’snotwhatyoumeaninthisoneinstance?Forallthetimesasystemgetsitright,it’sonthemistakesthatwejudgeitandacouplesuchinstancescanshatterpeople’sconfidence.Anxietyisakindofcognitiveload,andalittledoubtaboutwhetherasupposedlysmartsystemisgoingtodotherightthingisenoughtoturnaUXthat’srightmostofthetimeintoonethat’smoretroublethanit’sworth.Whenthathappens,it’s lostyou.
Photo CCBY2.0photo2011PopCultureGeektakenbyDougKline:https://www.flickr.com/photos/popculturegeek/6300931073/
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Thelastissuecomesasaresultoftheprevioustwo:control.Howcanwemaintainsomelevelofcontroloverthese devices,whentheirbehaviorisbydefinitionstatisticalandunpredictable?
Ontheonehandyoucanmangleyourdevice’spredictivebehaviorbygivingittoomuchdata.WhenIvisitedNestoncetheytoldmethatnoneoftheNestsintheirofficeworkedwellbecausethey’reconstantlyfiddlingwiththem.Inmachinelearningthisiscalledovertraining.Theotherhand,ifIhavenodirectwaytocontrolitotherthanthroughmyownbehavior,howdoIadjustit?AmazonandNetflix’srecommendationsystems,whicharemachinelearningsystemsforpredictingwhatyoumaylike,giveyousomecontextaboutwhytheyrecommendedsomething,butwhatdoIdowhenmyonlyinterfaceisagardenhose?
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Asinterestingastheseissuesare,Ithinkthat, moreimportantly,whattheyrepresentisthatwe’reentering intoanewrelationshipwithourdeviceecosystem,aseachangeinourrelationshiptothebuiltworld.
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Thinkofasewingmachine.It’sverycomplex,butitstillonlyactsinresponsetous.
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Computersacting autonomously erodethissimpletool/userrelationship.
Atthedawnofcomputinginthelate1940scyberneticistslikeNorbertWienerphilosophizedabouttheincreasinglycomplexrelationshipbetweenpeopleandcomputers,andhowitwasfundamentallydifferentthanthewayweinteractwithotherkindsofmachines.Developersworkinginsupervisorycontrolofmanufacturingmachinesandroboticshavehadtodealwiththesequestionspragmaticallyforabout30years,butthankstotheInternetofThings,thisisnowaproblemthateveryonewillhavetograpplewithgoingforward.
Here’sadiagrambythegreatsTomSheridanandBillVerplank from1978,inwhichtheyillustratefourwaysthatsemi-autonomouscomputersandhumanscanworktogethertosolveaproblem.
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By2000Sheridanexpandedtheseideastocreatethisframework,todefineaspectrumofresponsibilitybetweenpeopleandcomputers.Itrangesfromhumansdoingallthework(thisisyouwritinganessay)tocomputersdoingalltheworkcompletelyautonomously(thisisyourcar ’sfuelinjectioncontroller).Ofcoursethegoalistogetasystemtolevel9or10.That’sthemaximumreductionincognitiveload.However,forasystemtoqualifyforthat,ithastobeverystable,itseffectsneedtobehighlypredictableand,equallyimportantly,it’sroleneedstobeadequatelyembeddedinsociety.ItneedstobeOKforacomputertotakeonthatlevelofresponsibility.Attheairportwetrustthemonorailcomputerstoworkwithouthumanintervention,butwedon’ttrusttheplaneautopilottodothat,eventhough-–asIunderstandit—planescanbasicallyflythemselvesthesedays.
PredictiveIoT devicesgenerallyfallbetween5and7onthisscalerightnow.Theproblemisthatthisistheexactrangewhereyou’remaximizingsomeone’scognitiveload,butnotnecessarilydoingalltheworkforthem,sotheresultoftheautomationhadbetterbeworthit.Thisfundamentallyundermineswhatweexpectfromourtools,andwhenthattoolistryingtoanticipatewhatwe’retryingtodo,itfundamentallychangesourworkingrelationshipwithit.
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DannyHillis oftheLongNowtalksabouthowwehavegonepasttheEnlightenmentideawherewethoughtthatwecouldunderstandandcontroleverything,andbuilttoolsthatreflectedthatview.Inhisperspective,wearenolongerincontrolasmuchasweareentangledwiththem.
AnneGalloway,aNewZealandresearcherwholooksattheintersectionofanimalsanddigitaltechnology,callsittheendofhumanexceptionalism.Otherswouldsayit’sjustthePostmoderncondition,therecognitionthatthecomplexityoftheworldisbeyondourabilitytocontrol,andwehavetolearntocoaxandcoexist,ratherthancommandandcontrol.
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Becausesoonerthanweexpect, we’llbelivingwithhundredsofdevicesandservicestryingtomodelusandpredictwhatwillbegoodforus,andmostofthemwillrequireourattention.Theywillwantustoverifythings,touploadthings,toconfirmthings.Theywillwantustovalidatetheirexistence.Andtheywillbewrongalot.Ifyouhave100devicesandeachdevice is99%accurate—andmostpredictivealgorithmsrarelyachievethatlevelofaccuracy,atleastnotatfirst—thenoneisalwayswrong.
So howdoweengagewiththisworld?Howdoweapproachwranglingallthesethinkingtools?
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Youcanthinkaboutworkingsurroundedbyabunchofapprenticeassistants,asinamiddleageguild.
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…oryoucantakeananimistviewofassumingeverythingintheworldhasaconsciousness.PhilVanAllenofArtCenterhasrecentlystartedadvocating anapproachlikethis.Well,maybenotlikeTHIS.
ImagefromMiyazaki’sPrincessMononoke.
PhilVanAllen:https://medium.com/@philvanallen/rethink-ixd-e489b843bfb6#.6jszlfw9p
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I’dliketoexplorefarmingasametaphor,andnotbecauseofthesuperficialironyofusingpre-Enlightenmenttechnologytotalkaboutapost-Enlightenmentproblem.
Ireallywanttocreateausefulwayofthinkingaboutthechallengeofsmarttoolssowecandesignabetterrelationshipwiththemfromthebeginning.
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Farmingis oneofouroldesttechnologies,oneofthemostadvanced,andoneofthemostbrutalontheland,peopleandanimalsinvolved.Butitgotushere.
Also,anadmission:I’macitykid,myfamilyhasbeenlivingin citiesgoingbackmanygenerations.Ihavenotraisedsomuchasasingleedibleplantorownedapet,thoughIdohavechildren,butIdon’tthinkit’sthesame.Butthelongnowaskedmetodosomethingbrandnewandforageneralaudience,andthisiswhereIendedup,soifthistalkhasn’tgoneofftherailsforyouyet,it’llprobablygoofftherailsnow.
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For mefarmingisausefulmetaphorabouthowtosimultaneouslymanipulate thestateofmanyautonomous,independent,similarthings,foryourgain.Afarmerdoesn’traiseanearofcorn,sheraisesafieldofcorn,andsheisnotincontroloftheircropsasmuchassheisinsymbiosiswiththem.
Shereducesthecomplexityoffarmingbyplantingmanycopiesofthesameplant,anddividingherlandintoregionsforeachkindofplant.Rightnowslikeeachplantistotallydifferentandrequiresatotallydifferenttechniquetoworkwithit.
Sheselectscropsthatthriveinaspecificsetofconditionsandwhichcansynergisticallyusethesamerawmaterialtomaximizethevalueofthatmaterial.Whatifhadmultiplealgorithmsusingtheinformationfromthesamesensors—sayallthecamerasandtemperaturesensorsinyourenvironment—thenfusingtheirresults?
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Afarmerusesspecializedtoolstoworkonmanyplantsatthesametime,whetherit’saplow,aharvesterorascarecrow.That’swhyshechoosesmanyofthesamething.Inthealgorithmanalogy,howcanwegrouplargenumbersofalgorithmsandworkonthemallatonce?
Sheexpectspests.Rightnoweveryoneisshockedwhentheirsmartfridgestartspostingspambecauseit’sbeenhacked.That’skindoflikeafungusinfection,andfarmershavetoolsforthatandtrytomaintaingoodpracticestominimizeit,butwhenithappens,nooneissurprised.
Shedoesn’texpecttoextractthevaluefromitimmediately—thatmaytakemonthsoryears—yetsheknowsshewillhavetomaintainitthatwholetimeregardless.Rightnowweexpectourdigitalproductstoworkimmediatelyorwethinkthey’renotworthwhileordefectiveiftheydon’t.Whatifwedesignedthingssothattheywouldonlybeusefulafterwehadlivedwiththemforalongtime,butthenthey’dbeREALLYuseful?
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Anotheraside:machinelearningalgorithmsarepatternrecognizers,sotheyneedtoknowwhichpatternsareimportant.Wheneveryoumarkemailasspamusingyouremailprogram,youaredoingwhat’scalledtrainingthealgorithmtounderstandwhatyouconsiderspam.
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Similarlywhenyoumakeachoiceusingvirtuallyanyadigitaldeviceorservice,you’retraininganalgorithm.Facebookasksyoutolabelpeopleinyourpicturestotrainitsalgorithmstoassociateasetoffacialfeatureswiththepersonyoulabeled.
Whathappenswhenyoutrainasingleanimal?Whatareyourmechanismsofcontrol?Whatareyourexpectations?
Well,youexpectthatitwillrequiretimeanditwillrequireacombinationofbothpositiveandnegativereinforcement.Then,youexpectthatitwillregularlymisbehaveandyouhavetoreinforcewhatyouteachit.Conversely,youcanexpectthatitwillprobablylearnabitfromotheranimalswithoutyouhavingtotelliteverythinganditsbehaviorwillsurpriseyouingoodwaysinadditiontobadways.
Imagesource:http://countingsheep.info/permalamb.html (AnneGalloway’sCountingSheepproject)
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Butwhathappenswhenafarmerhasalotofanimalstocontrol?Shecan’ttrainallofthemindividually,sooverthelast10000yearsshe’sdevelopedsometoolsformanagingthem.
First,sheselects animalsthatworkwellingroups.Ouralgorithmsarecurrentlybuiltoneatatimeandtheexpectationisthatourinteractionwiththemwillbeindividual.Thatdoesn’tscale.Weneedalgorithmsthatareexperiencedwelltogether,orelsewe’renotherdingsheep,we’reherdingcats.Next,shehasacrook.Whenyouneedtoassertcontrol,youneedaclearwaytodothatwhichworksonawidevarietyofanimalsandweneedconsistentwaystoassetimmediatecontroloverawidevarietyofsmartdevices.Shehas adog, whichisasmarterentitythatalso needstobetrained,butoncetrainedcanbeusedtoautonomouslycontrolmultipleotherindependententitiesitself.Shecanhandofftheworktoanassistant.Infarmingawholeclassofpeoplewhocantakeresponsibilityforallofthethingsandwhocanworktogether.Responsibilitycanbedelegated.AsTomCoatesofThington pointsout,mostIoT systemsarenotbuiltformanypeopletocontrolthemsimultaneously,eventhoughtheireffectsareoftenexperiencedinsharedenvironments.
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Todaywedon’thaveanInternetofThings,wehavemanyAOLsofthings.They’vebeenintentionallymademutuallyincompatibleandalthoughsomemaybecuteontheirown,whenyouhavealotofthem,andtheyhavetobedealtwithindividually,it’sabigproblem.
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Ithinkin 1000years,maybe100years fromnow,thisentirediscussionwillseemabsurd,likearguingaboutwhetherironisagoodthingorabadthing.We’llseeitasjustthewaytheworldis.Ourbodiesaregoingtobesemi-autonomouscomponentsthatwehavesomecontrolover,inanecosystemthatcombinesotherbiologicalanddigitalsemi-autonomouscomponents.Everythingisgoingtohavesomecontroloverandbecontrolledbyotherthings.
Someofthemaresmarterthanothers,somearemoreautonomousthanothers,someareevensmarterthanweareincertainways,somehavepositivesymbioticrelationships,someareparasites.Theboundariesbetweenmindsandbodies,betweennaturalandartificial,andbetweenhumanandnon-humanwillhavebeeneroded.Ourworldwillhavereconfigureditselfaroundassumptionsthateverythingismuchpermeableandmuchlessclearlydelineatedthanwehadfooledourselvesintobelieving.Wearenotasgods.Weare,andalwayshavebeen,animalsinanecosystem.
Anditwon’tallbegood.Therewillprobablybeterriblethingsthathappentopeople’sbodies,mindsandsocieties.Theremayalso,hopefully,begoodthings.
Image:CamillePissarro,“Shepherdesses,”1887
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Thisisthe lookingglassthatwe’vemade,andit’stimeforustostepthrough,andexplorethefield beyond,becausewehavenochoicebuttoengagewithit,tomakeitbewhatwewantittobe,whatweneedittobe,becauseitisnotandroidswhowilldreamingofelectricsheep,itwillbeus.
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Thankyou.
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