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.$

0$

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.

1$

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

2$

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.$

3$

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$hardware.$

4$

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.$

5$

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…$

6$

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!

7$

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.$

8$

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.!

9$

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.!

10$

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.!

11$

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$to.�$

12!

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

13$

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.$

14$

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.$

15$

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$don’t.$

16$

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

17$

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$interes8ng.$

18$

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$to.$

19$

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.$

20$

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

21$

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

22$

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$intelligent.$

23$

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

24$

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

25$

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

26$

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$work.$

27$

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.$

28$

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.$$Unfortunately,$sooner$than$we$think,$such$inaccurate$predic8ve$behavior$isn’t$going$to$be$an$isolated$incident.$Soon$we’re$going$to$have$100$connected$devices$simultaneously$ac8ng$on$predic8ons$about$us.$If$each$is$99%$accurate,$then$one$is$always$wrong.$So$the$problem$is:$How$can$you$design$a$user$experience$to$make$a$device$s8ll$func8onal,$s8ll$valuable,$s8ll$fun,$even$when$it’s$spewing$junk$behavior?$How$can$you$design$for$uncertainty?$$

29$

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$hose?$

30$

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$issues.$

31$

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.$

32$

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.$

33$

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$reached.$

34$

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$hasn’t.$

35$

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?$

36$

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

37$

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$8me.$

38$

Thank$you.$

39$