the ux of predictive behavior in the consumer iot (ux week 2015)

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Good morning and thanks for having me here. In this talk I want to look at the design challenges of systems that an8cipate users’ needs and then act on them. That means that it sits at the intersec8on of the internet of things, user experience design and machine learning, which to me is new territory for designers who may have dealt with one of those disciplines before, but rarely all three at once. 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 selfA contained experiences, how predic8ve analy8cs enables key components of those services, and then I finish by trying to to iden8fy use experience issues around predic8ve behavior and sugges8ons for paCerns to ameliorate those issues. A couple of caveats: A I focus almost exclusively on the consumer internet of things. Although predic8ve analy8cs is an important part of the Industrial Internet of Things for things like predic8ve maintenance, I feel it’s REALLY key to the consumer IoT because of what experiences it creates for people. A I want to point out that few if any of the issues I raise are new. Though the term “internet of things” is hot right now, the ideas have been discussed in research circles for more than 20 years. Search for “ubiquitous compu8ng,” “ambient intelligence,” and “pervasive compu8ng” and it’ll help you keep from reinven8ng the wheel. A 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’m done. 0

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A big promise of the Internet of Things is that by analyzing millions of new sources of data from embedded, networked devices our experience of the world becomes better and more efficient. The environment automatically predicts our behavior and adjusts to it, anticipating problems and intercepting them before they occur. The notion is seductive and almost magical: an automatic espresso machine that starts a fresh latte as you’re thinking it’s a good time for coffee; office lights that dim when it’s sunny and electricity is expensive; a taco truck that arrives just as the crowd in the park is getting peckish. Exciting in theory, this promise is rather unspecific in the details. Exactly how will our experience of the world, our ability to use all the collected data, become more efficient and more pleasurable?We don’t have good examples for designing user experiences of predictive analytics, so this talk will lay out the challenges and some approaches to addressing them.

TRANSCRIPT

Page 1: The UX of Predictive Behavior in the Consumer IoT (UX Week 2015)

Good$morning$and$thanks$for$having$me$here.$In$this$talk$I$want$to$look$at$the$design$challenges$of$systems$that$an8cipate$users’$needs$and$then$act$on$them.$That$means$that$it$sits$at$the$intersec8on$of$the$internet$of$things,$user$experience$design$and$machine$learning,$which$to$me$is$new$territory$for$designers$who$may$have$dealt$with$one$of$those$disciplines$before,$but$rarely$all$three$at$once.$The$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$selfAcontained$experiences,$how$predic8ve$analy8cs$enables$key$components$of$those$services,$and$then$I$finish$by$trying$to$to$iden8fy$use$experience$issues$around$predic8ve$behavior$and$sugges8ons$for$paCerns$to$ameliorate$those$issues.$$A$couple$of$caveats:$A$I$focus$almost$exclusively$on$the$consumer$internet$of$things.$Although$predic8ve$analy8cs$is$an$important$part$of$the$Industrial$Internet$of$Things$for$things$like$predic8ve$maintenance,$I$feel$it’s$REALLY$key$to$the$consumer$IoT$because$of$what$experiences$it$creates$for$people.$$A$I$want$to$point$out$that$few$if$any$of$the$issues$I$raise$are$new.$Though$the$term$“internet$of$things”$is$hot$right$now,$the$ideas$have$been$discussed$in$research$circles$for$more$than$20$years.$Search$for$“ubiquitous$compu8ng,”$“ambient$intelligence,”$and$“pervasive$compu8ng”$and$it’ll$help$you$keep$from$reinven8ng$the$wheel.$$A$Finally,$most$of$my$slides$don’t$have$words$on$them,$so$I’ll$make$the$complete$deck$with$a$transcript$available$as$soon$I’m$done.$

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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’ve$also$worked$on$the$user$experience$design$of$a$lot$of$consumer$electronics$products$from$companies$you’ve$probably$heard$of.$

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I$wrote$a$couple$of$books$based$on$my$experience$as$a$designer.$One$is$a$cookbook$of$user$research$methods,$and$the$second$describes$what$I$think$are$some$of$the$core$concerns$when$designing$networked$computa8onal$devices.$

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I$also$cofounded$a$couple$of$companies.$The$first,$Adap8ve$Path,$you’re$familiar$with,$and$with$the$second$one,$ThingM,$I$got$deep$into$developing$hardware.$

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Today$I$work$for$PARC,$the$famous$hardware,$soWware$and$AI$research$lab,$as$a$principal$in$its$Innova8on$Services$group,$which$is$PARC’s$consul8ng$arm.$We$help$companies$reduce$the$risk$of$adop8ng$novel$technologies$using$a$mix$of$ethnographic$research,$user$experience$design$and$innova8on$strategy.$We$do$everything$from$coaching$teams$inside$companies$to$developing$novel$Internet$of$Things$services.$Consider$hiring$us$to$solve$your$unsolvable$problems.$

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I$want$start$by$focusing$on$what$I$feel$is$a$key$aspect$of$consumer$IoT$that’s$oWen$missed$when$people$focus$on$the$hardware$of$the$IoT,$which$is$that$consumer$IoT$products$have$a$very$different$business$model$than$tradi8onal$consumer$electronics.$Tradi8onally,$a$company$made$an$electronic$product,$say$a$turntable,$they$found$people$to$sell$it$for$them,$they$adver8sed$it$and$people$bought$it.$That$was$tradi8onally$the$end$of$the$company’s$rela8onship$with$the$consumer$un8l$that$person$bought$another$thing,$and$all$of$the$value$of$the$rela8onship$was$in$the$device.$With$the$IoT,$the$sale$of$the$device$is$just$the$beginning$of$the$rela8onship$and$holds$almost$no$value$for$either$the$customer$or$the$manufacturer.$Let$me$explain…$

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The core of this change is a shift from generic devices and software to specialized devices and software. When computing was expensive, you had general purpose devices and general purpose software that had deal with almost every situation. This necessitated design compromises that resulted in devices and software that could do almost everything, but did none of it well.!!Now that processing is cheap thanks to Moore’s Law, you can have a combination of 10, 20, or 30 computing devices and apps for the price of that one device, and you can acquire new functionality as needed. This means that every device and software package can have a narrower purpose.!!Adobe brush!Haiku deck!

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The$second$trend$is$that$much$of$the$value$of$compu8ng$is$now$remote.$Today,$most$people$understand$that$the$experience$you$see$on$one$device$is$oWen$a$part$of$something$that’s$distributed$throughout$the$world.$There’s$no$longer$a$need$to$pack$everything$into$a$single$piece$of$soWware,$and$there’s$no$expecta8on$that$everything$will$be$there.$$Foursquare$as$a$whole$doesn’t$live$on$your$phone$in$any$meaningful$way,$and$people$know$that.$

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If we chart these two tends, two broad classes of digital products emerge.!$If$we$follow$the$local$to$remote$axis,$we$find$generalApurpose$devices$that$do$roughly$the$same$set$of$things,$and$differ$primarily$in$size.$They exist to provide access to online services, in a form factor that’s appropriate to the context in which they’re used.!I$call$these$devices$terminals.!$If$we$follow$the$general$to$specific$axis,$we$see$a$shiW$is$to$more$narrowAfunc8on$devices$that$are$designed$to$do$a$small$set$of$things with specialized hardware. A parking meter can take quarters, which your phone can’t do, and a digital SLR has a giant lens on it, which you probably don’t want on your phone. These devices differ in their specialized hardware. I$call$these$devices$appliances.!

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The most interesting thing, however, to me is that these shifts are part of an even larger transition, one where devices are simultaneously specific AND deeply tied to online services. In this model, the service provides the majority of the value, and can be represented either as a dedicated appliance, an app running on a terminal, or anything in between. !!I call these devices service avatars. And this is where much of the Internet of Things currently lies, at least for consumer products.!

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As value shifts to services, 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 Flickr, while a TV becomes a nice Flickr display that you don’t have to log into every time, and a phone becomes a convenient way to take your Flickr pictures on the road.!!Hardware becomes simultaneously more specialized and devalued as users see “through” each device to the service it represents. The hardware exists to get better value out of the service.!

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Amazon$really$gets$this.$Here�s$a$telling$older$ad$from$Amazon$for$the$Kindle.$It’s$saying$�Look,$use$whatever$device$you$want.$We$don�t$care,$as$long$you$stay$loyal$to$our$service.$You$can$buy$our$specialized$devices,$but$you$don�t$have$to.�$

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When$Fire$was$released$3$years$ago,$Jeff$Bezos$even$called$it$a$service.$

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NetFlix$is$another$good$media$example.$It$feels$natural$to$pause$a$movie$on$one$device$and$con8nue$it$on$another$because$from$your$perspec8ve$there’s$only$ONE$Neglix.$Dropbox$creates$this$for$files,$Evernote$for$notes,$and$Angry$Birds$for$score$synchroniza8on.$The$service$is$where$your$aCen8on$is,$the$device$is$there$to$give$you$access$to$the$service,$but$other$than$a$convenient$form$factor,$the$hardware$is$largely$disposable.$

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Most$largeAscale$IoT$products$are$service$avatars.$They$use$specialized$sensors$and$actuators$to$support$a$service,$but$have$liCle$value—or$don’t$work$at$all—without$the$suppor8ng$service.$Smart$Things,$which$was$acquired$by$Samsung$last$year,$clearly$states$its$service$offering$right$up$front$on$their$site.$The$first$thing$they$say$about$their$product$line$is$not$what$the$func8onality$is,$but$what$effect$their$service$will$achieve$for$their$customers.$Their$hardware$products’$func8onality,$how$they$will$technically$sa8sfy$the$service$promise,$is$almost$an$aWerthought.$

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Compare$that$to$X10,$their$spiritual$predecessor$that’s$been$in$the$business$for$more$than$20$years.$All$that$X10$tells$is$you$is$what$the$devices$are,$not$what$the$service$will$accomplish$for$you.$I$don’t$even$know$if$there$IS$a$service.$Why$should$I$care$that$they$have$“modules”?$I$shouldn’t,$and$I$don’t.$

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So$what$do$these$services$offer?$

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Simple$connec8vity$helps$when$you’re$trying$to$maximize$the$efficiency$of$a$fixed$process,$but$that’s$not$a$problem$that$most$people$have.$We’ve$been$able$to$simply$connect$various$devices$to$a$computer$since$a$Tandy$Color$Computers$could$lights$off$and$on$over$X10$in$1983.$That$wasn’t$very$useful$then,$and$it’s$not$very$useful$now.$You$can$replace$the$Tandy$with$the$cloud$and$the$lamp$with$a$washing$machine$and$you$get$the$value$proposi8on$of$most$simple$connected$devices.$That’s$not$interes8ng.$

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I$think$the$real$consumer$value$connected$services$offer$is$their$ability$to$make$sense$of$the$world$on$people’s$behalf,$to$reduce$people’s$cogni8ve$load,$rather$than$increasing$it,$by$allowing$them$to$interact$with$devices$at$a$higher$level$than$simple$telemetry$and$control.$Fundamentally,$humans$are$good$paCern$matchers$at$certain$things,$but$we’re$not$built$to$collect$and$make$sense$of$huge$amounts$of$data$or$to$ar8culate$our$needs$as$complex$systems$of$mutually$interdependent$components.$Computers$are$great$at$it.$They$can$make$sta8s8cal$models$from$many$data$sources$across$space$and$8me$and$then$try$to$maximizes$the$probability$of$a$desired$outcome.$A$person$programming$a$device$can$express$what$they’re$familiar$with,$or$try$to$create$an$abstrac8on$based$on$their$past$experience,$some8mes$with$considerable$skill,$but$a$model$learned$from$the$outcome$of$thousands$of$situa8ons$across$many$people$and$long$periods$of$8me$can$compensate$for$much$wider$variety$of$situa8ons$in$a$more$nuanced$way$than$an$individual’s$perspec8ve$will$ever$be$able$to.$

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Predic8on$is$at$the$heart$of$the$value$proposi8on$many$of$the$most$compelling$IoT$products$offering,$star8ng$with$the$Nest.$The$Nest$says$that$it$knows$you.$How$does$it$know$you?$It$predicts$what$you’re$going$to$want$based$on$your$past$behavior.$

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Amazon’s$Echo$speaker$says$it’s$con8nually$learning.$How$is$that?$Predic8ve$analy8cs,$predic8ve$machine$learning.$

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The$Birdi$smart$smoke$alarm$says$it$will$learn$over$8me,$which$is$again$the$same$thing.$

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Jaguar$comes$right$out$with$it.$They$even$obliquely$reference$the$40$years$of$ar8ficial$intelligence$research$that$powers$predic8ve$analy8cs$by$calling$their$car$not$just$learning,$but$learning$AND$intelligent.$

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The$Edyn$plant$watering$system$adapts$to$every$change.$What$is$that$adapta8on?$Predic8ve$analy8cs.$

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Canary,$a$home$security$service.$

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Here’s$foobot,$an$air$quality$service.$

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Predic8ve$behavior$creates$a$preCy$seduc8ve$world$of$espresso$machines$that$start$brewing$as$you’re$thinking$it’s$a$good$8me$for$coffee,$and$reorder$your$favorite$blend;$office$lights$that$dim$when$it’s$sunny,$power$is$cheap$and$you’re$not$doing$anything$that$needs$them;$and$food$truck$caravans$that$show$up$just$as$the$crowd$in$the$park$is$genng$hungry.$The$problem$is$that$although$the$value$proposi8on$is$of$a$beCer$user$experience,$it’s$unspecific$in$the$details.$Exactly$how$will$our$experience$of$the$world,$our$ability$to$use$all$the$collected$data,$become$more$efficient$and$more$pleasurable?$$$Now$I’d$like$to$offer$some$ini8al$thoughts$on$user$experience$design$for$predic8ve$analy8cs$for$the$internet$of$things.$We’re$s8ll$early$in$our$understanding$of$design$for$predic8ve$devices,$so$right$now$the$problems$are$worse$than$solu8ons$and$I$want$to$start$by$ar8cula8ng$the$issues$I’ve$observed$in$our$work.$

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We’ve$never$had$mechanical$things$that$make$significant$decisions$on$their$own,$the$first$major$issue$is$around$expecta8ons.$As$devices$adapt$their$behavior,$how$will$they$communicate$that$they’re$doing$so?$Do$we$treat$them$like$animals?$Do$we$s8ck$a$sign$on$them$that$says$“adap8ng”,$like$the$light$on$a$video$camera$says$“recording”?$Should$my$chair$vibrate$when$adjus8ng$to$my$posture?$How$will$users,$or$just$passersAby,$know$which$things$adapt$and$which$merely$behave?$I$could$end$up$sinng$uncomfortable$for$a$long$8me,$wai8ng$for$my$chair$to$change,$before$realizing$it$doesn’t$adapt$on$its$own$or$sinng$in$the$dark$wai8ng$for$the$smart$lights$to$realize$it’s$now$produc8vity$8me.$How$should$smart$devices$set$the$expecta8on$that$they$may$behave$differently$in$what$appears$to$people$as$an$iden8cal$set$of$circumstances?$$$People$are$already$prone$to$project$more$intelligence$on$devices$than$those$devices$actually$have,$so$a$couple$of$accurate$predic8ons$may$imply$a$much$more$accurate$model$than$actually$exists.$Robo8cists$talk$about$an$“uncanny$valley”$where$robots$exhibit$just$enough$humanAlike$behavior$to$ini8ally$fool$us,$but$become$creepy$when$they’re$revealed$to$be$anything$but$human.$With$predic8ve$IoT$we$may$be$homesteading$that$valley$without$realizing$it.$$$Chair$by$Raffaello$D'Andrea,$MaC$Donovan$and$Max$Dean.$

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The$irony$in$predic8ve$systems$is$that$they’re$preCy$unpredictable,$at$least$at$first.$When$machine$learning$systems$are$new,$they’re$oWen$inaccurate$and$unpredictable,$which$is$not$what$we$expect$from$our$digital$devices.$60%A70%$accuracy$is$typical$for$a$first$pass,$but$even$90%$accuracy$isn’t$enough$for$a$predic8ve$system$to$feel$right,$since$if$it’s$making$decisions$all$the$8me,$it’s$going$to$be$making$mistakes$all$the$8me,$too.$It’s$fine$if$your$house$is$a$couple$of$degrees$cooler$than$you’d$like,$but$what$if$your$wheelchair$refuses$to$go$to$a$drinking$fountain$next$to$a$door$because$it’s$been$trained$on$doors$and$it$can’t$tell$that’s$not$what$you$mean$in$this$one$instance?$For$all$the$8mes$a$system$gets$it$right,$it’s$on$the$mistakes$that$we$judge$it$and$a$couple$such$instances$can$shaCer$people’s$confidence.$Anxiety$is$a$kind$of$cogni8ve$load,$and$a$liCle$doubt$about$whether$a$system$is$going$to$do$the$right$thing$is$enough$to$turn$a$UX$that’s$right$most$of$the$8me$into$one$that’s$more$trouble$than$it’s$worth.$When$that$happens,$you’ve$more$than$likely$lost$your$customer.$$Unfortunately,$sooner$than$we$think,$such$inaccurate$predic8ve$behavior$isn’t$going$to$be$an$isolated$incident.$Soon$we’re$going$to$have$100$connected$devices$simultaneously$ac8ng$on$predic8ons$about$us.$If$each$is$99%$accurate,$then$one$is$always$wrong.$So$the$problem$is:$How$can$you$design$a$user$experience$to$make$a$device$s8ll$func8onal,$s8ll$valuable,$s8ll$fun,$even$when$it’s$spewing$junk$behavior?$How$can$you$design$for$uncertainty?$$

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The$last$issue$comes$as$a$result$of$the$previous$two:$control.$How$can$we$maintain$some$level$of$control$over$these$devices,$when$their$behavior$is$by$defini8on$sta8s8cal$and$unpredictable?$$On$the$one$hand$you$can$mangle$your$device’s$predic8ve$behavior$by$giving$it$too$much$data.$When$I$visited$Nest$once$they$told$me$that$none$of$the$Nests$in$their$office$worked$well$because$they’re$constantly$fiddling$with$them.$In$machine$learning$this$is$called$overtraining.$$The$other$hand,$if$I$have$no$direct$way$to$control$it$other$than$through$my$own$behavior,$how$do$I$adjust$it?$Amazon$and$Neglix’s$recommenda8on$systems,$which$is$a$kind$of$predic8ve$analy8cs$system,$give$you$some$context$about$why$they$recommended$something,$but$what$do$I$do$when$my$only$interface$is$a$garden$hose?$

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Here$are$4$paCerns$I’ve$observed$in$developing$predic8ve$systems$that$I$think$map$to$the$IoT.$For$most$of$these$I’m$going$to$be$using$examples$from$Nest$and$recommender$systems$like$Amazon’s,$Google’s$and$Neglix’s$which$have$been$using$similar$predic8ve$technologies$for$years$and$have$addressed$some$of$these$issues.$

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My$first$paCern$isn’t$really$a$paCern,$but$a$general$approach.$To$design$these$systems$you$need$to$have$a$user$model$for$every$stage$of$the$machine$learning$and$predic8on$process.$There$needs$to$be$a$story$to$tell$about$each$step,$even$if$it’s$a$step$that$seems$like$it$would$invisible$to$customers.$Star8ng$with$acquisi8on:$how$will$you$incen8vize$people$o$add$data$to$the$system$at$all?$Why$should$I$upload$my$car’s$dashcam$video$to$your$traffic$predic8on$system$EVERY$DAY?$Next,$how$will$you$communicate$you’re$extrac8ng$features?$I$like$the$way$that$Google$speech$to$text$shows$you$par8al$phrases$as$you’re$speaking$into$it,$and$how$it$corrects$itself.$That$small$bit$of$feedback$tells$people$it’s$pulling$informa8on$out$and$it$trains$users$how$to$meet$the$algorithm$halfway.$How$do$machineAgenerated$classifica8ons$compare$to$people’s$organiza8on$of$the$same$phenomena?$How$is$a$context$model$presented$to$end$users$and$developers?$How$will$you$get$people$to$train$it$and$tell$you$when$the$model$is$wrong?$Does$the$final$behavior$actually$match$their$expecta8on?$$Machine$learning$algorithms$used$to$be$strictly$behindAtheAscenes,$but$in$the$IoT$they$are$actors$in$our$lives,$so$as$designers$it’s$our$responsibility$to$understand$the$situa8ons$where$the$algorithms$and$the$devices$they$control$interact$with$people’s$lives,$especially$since$there’s$a$deep$symbio8c$rela8onship$between$the$data$that$comprises$the$models,$the$behavior$those$models$induce$and$the$people$who$are$the$intended$beneficiaries.$

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Now$to$the$actual$UX$paCerns.$Our$current$expecta8on$for$digital$systems$is$that$they’ll$behave$consistently$and$the$reasons$for$their$behavior$will$be$clear.$Neither$of$these$is$true$for$the$user$experience$of$predic8ve$systems,$which$don’t$necessarily$behave$iden8cally$in$what$appear$to$be$similar$circumstances,$whose$behavior$changes$over$8me,$and$where$the$reasons$for$the$behavior$may$not$be$obvious.$If$we$undermine$people’s$confidence$in$a$system$by$viola8ng$their$expecta8ons,$they’re$likely$to$be$disappointed$and$stop$using$it.$$When$we’re$dealing$with$a$human$or$an$animal,$these$kinds$of$behaviors$are$expected$and$tolerated,$but$that’s$not$the$case$with$computers,$so$the$first$thing$a$UX$needs$to$do$is$to$set$people’s$expecta8ons$appropriately.$It$needs$to$explain$the$nature$of$the$device,$to$describe$it$is$trying$to$predict,$that$it’s$trying$to$adapt,$that$it’s$going$to$some8mes$be$wrong,$to$explain$how$it’s$learning,$and$how$long$it’ll$take$before$it$crosses$over$from$crea8ng$more$trouble$than$benefit.$$Recommender$systems,$such$as$Google$Now,$describe$why$a$certain$kind$of$content$was$selected,$and$that$sets$the$expecta8on$that$in$the$future$the$system$will$recommend$other$things$based$on$other$kinds$of$content$you’ve$requested.$Nest’s$FAQ$kind$of$buries$the$informa8on,$but$it$does$explain$that$you$shouldn’t$expect$your$thermostat$to$make$a$model$of$when$you’re$home$or$not$un8l$it’s$been$opera8ng$for$a$$week$or$so.$

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Predic8ve$behavior,$as$opposed$to$is$all$about$8me,$about$sequences$of$ac8vi8es.$Many$predic8ve$UX$issues$around$expecta8ons$and$uncertainty$have$8me$as$their$basis:$what$were$you$expec8ng$to$happen$and$why.$If$it$didn’t$happen,$why?$If$something$else$happened,$or$it$happened$at$an$unexpected$8me,$why$did$that$happen?$$Knowing$that$a$device$has$acted$on$your$behalf,$and$that$it’s$going$to$act—and$HOW$it’s$going$to$act—in$the$future$is$important$to$giving$people$a$model$of$how$it’s$working,$senng$their$expecta8ons,$reducing$the$uncertainty.$Nest,$for$example,$has$a$calendar$of$its$expected$behavior,$and$it$shows$that$it’s$ac8ng$on$your$behalf$to$change$the$temperature,$and$when$you$can$expect$that$temperature$will$be$reached.$

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Remember$the$Wall$Street$Journal$story$about$a$guy$(Basil$Iwanyk)$who$thought$TiVo$thought$he$was$gay$because$of$the$shows$it$recommended?$That’s$funny,$but$the$second$part$is$frustra8ng:$he$didn’t$like$the$shows$it$recommended,$so$he$tried$to$correct$it$the$only$way$he$could$think$of,$by$watching$war$movies—which$I$guess$is$what$he$thought$was$the$opposite$of$gay—and$his$TiVo$then$decided$he$was$a$Nazi$(which$he$also$wasn’t).$You$have$to$give$people$a$clear$way$to$teach$the$system$and$tell$it$when$its$model$is$wrong.$Sta8s8cal$systems,$by$defini8on,$don’t$have$simple$rules$that$can$be$changed.$There$aren’t$obvious$handles$to$turn$or$dials$to$adjust,$because$everything$is$probabilis8c.$If$the$model$is$made$from$data$collected$by$several$devices,$which$device$should$I$interact$with$to$get$it$to$change$its$behavior?$Google$Now$asks$whether$I$want$more$informa8on$from$a$site$I$visited,$Amazon$shows$a$explana8on$of$why$it$gave$me$a$sugges8on.$Mapping$this$to$the$consumer$IoT$means$way$more$explana8on$than$we’re$currently$genng,$which$is$either$that$a$thing$has$happened,$or$it$hasn’t.$

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Finally,$don’t$automate.$These$system’s$shouldn’t$try$to$replace$people,$but$to$support$them,$to$augment$and$extent$their$capabili8es,$not$to$replace$them.$$One$of$my$favorite$current$examples$of$this$kind$of$system$is$from$Meshfire,$which$is$a$social$media$management$tool$that$has$a$machine$learning$assistant.$Its$machine$learning$assistant,$called$Ember,$doesn’t$try$to$replace$the$social$media$manager.$Instead$it$manages$the$media$manager’s$todo$list.$It$adds$things$that$it$thinks$are$going$to$be$interes8ng,$deletes$old$things,$and$repriori8zes$the$manager’s$list$based$on$what$it$thinks$is$important.$I$think$this$is$a$good$model$for$how$such$systems$can$add$value$to$a$person’s$experience$without$crea8ng$a$situa8on$where$random,$unexplained$behaviors$confuse$people,$frustrate$them$and$make$them$feel$powerless.$Ember$is$an$augmenta8on$to$the$social$media$manager,$it$helps$that$person$focus$on$what’s$important$so$that$they$can$be$smarter$about$their$decisions.$It$doesn’t$try$to$be$smarter$than$they$are.$How$can$our$devices$HELP$us,$rather$than$trying$to$replace$us?$

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Finally,$for$me$the$IoT$is$not$about$the$things,$but$the$experience$created$by$the$services$for$which$the$things$are$avatars.$

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Ul8mately$we$are$using$these$tools$to$extend$our$capabili8es,$to$use$the$digital$world$as$an$extension$of$our$minds.$To$do$that$well$we$have$to$respect$that$as$interes8ng$and$powerful$as$these$technologies$are,$they$are$s8ll$in$their$infancy,$and$our$job$as$entrepreneurs,$developers$and$designers$will$be$to$create$systems,$services,$that$help$people,$rather$than$adding$extra$work$in$the$name$of$simplis8c$automa8on.$What$we$want$to$create$is$a$symbio8c$rela8onship$where$we,$and$our$predic8ve$systems,$work$together$to$create$a$world$that$provides$the$most$value,$for$the$least$cost,$for$the$most$people,$for$the$longest$8me.$

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Thank$you.$

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