tempo ai - designing ai interfaces
TRANSCRIPT
“Itisthescienceandengineeringofmakingintelligentmachines,especiallyintelligentcomputerprograms. Itisrelatedtothesimilartaskofusingcomputerstounderstandhumanintelligence,butAIdoesnothavetoconfineitselftomethodsthatarebiologicallyobservable.”
“Itpasses theTuringtest.”
“Thestudyanddevelopmentofintelligentagents.”
“Softwarethatlearnsandcompletestasksforyou.”
WhatIsAI?
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Raj:“WhatisAI?”Nephew:“It’srobotsandshit…”
AndthenthereisHollywood’sdefinition:
4RecommendationEngines- ImplicitandExplicitLearning
AIHasBeenOmnipresent
5ButthepuristssayWatsonisdumb!
ManyhavesaidWatsonwasthefirstmainstreamdemonstrationofAI
Anticipatory:“Predictyournextwantoraction”
Smart:“GivemeonlytheinformationIneed”
Assistant:“Completetasksforme”
Ihaveasmartphonebutit’snotsmart!
ButWhatIsMobileAI
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DesigninganAI-AssistedUXIsNotEasy
Designing95%UIYoudon’tknowifyourrecommendations arerightorwrong
Whatifthesuggestionwaswrong?Certainappsarealotmoreforgiving
Howdoestheusertrainthesystem?Mostsurveythattheywilltrainbutfewactuallydo.
Howdoyougetthedata?AIsuffersfromfalsestarts.
Allthataside,anticipatoryUIdesignisthenextfrontier!
SomeExamples(RecommendationEngines)
Pandora FourSquare LinkedIn
Doesthisbotheryou?
AnotherExample(RecommendationEngine)
Itdoesbecausetheserecommendationshaverightandwronganswers.
Flipboard vs Zite
ExtensiveML
LittletoNoML
• “Suggestions”– indicatesintelligence• “Recommendations”– slightlylowerbarthan“suggestions”• “Searched/Results”– lessintelligent
RecommendationLanguageMatters
TempoSearchedMeetingLocations
• Howdoyoudeterminethebalance?‒ Usertestingdoesn’talwayswork
• Constrainthedomain‒ Segment theusersviacohortanalysis‒ Whatistherightnumber ofsegments?
• The“More”buttoncanbeyourbestfriend‒ Infinitescroll
• Trainthesystem‒ Tempousertestingindicatedlessthan3%wouldtrain
‒ Thumbsup/down, ratingsvs implicitlearning?
BalanceofNoisevs Precision
PrismaticNewsTraining
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Siriisentertainingbutbeingunconstrainedkilledtheirengagement
• Itwouldannoyyouless• Youknowwhatyoucanaskanddo• You“settheexpectations”
ExamplesAutomatedsupportsystemsTellMe /Free411SalesforceVoiceAccess
ConstrainingCanSetExpectations
Lexee AppVoiceCommandsforSalesforce
• Animationsworkmosteffectively‒ Butifittakestoolong, ithinders theUX
• Speedofapplicationdirectlycorrelatedtoretentionrate‒ 15searchresultsvs 10searchresults(Google SearchResults)
• InTempo,wenumberedtheresults• Searchenginesusedtonumbertheirresultsaswell
IndicatingIntelligence
NumberedResultstoIndicateSmarts
• Falsestartsareverycommon‒ Introducerecommendations andanticipatoryactionsthrough use
• Keepon-boardingaslight-weightaspossible‒ Toomuchtimebetweenon-boardingandfirst-usewillcauseproblems
‒ Canyoucollectdataasyougoalong
• First3-Dayusagewillbeheavyexperimentationtoseewhatthesystemdoes‒ SiriusersexperimentbyaskingalotofQs
‒ Tempouserscreate10sofmtgs inthefirstfewdays
AINeedsData
Sosh Setup
BeingAnticipatory
• Anticipatewithoutthenoise‒ Pushnotificationsdriverepeatusagebutifnoisyresultinbounced users
• NotifyingyouwhentoleaveinTempo‒ Wewantedtobeveryanticipatorybutwe’renot95%yet
‒ Falsenotifications resultinangstandalostuser
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It’sJustBeginning!
FourSquare GoogleNow Prisimatic
Bigdatatosuggestplacestoexplore
Searchhistorytobecomemoreanticipatory
Machinelearningtosuggestrelevantnews
• Recommendationsworkbestwhentheusercan’ttellwhat’srightorwrong.
• Bespecificwithyourlanguagebecauseithelpssetexpectations• Bettertoundersellandover-deliver• Userswillwanttotrainthesystembutfewwilldoit.Beconsciousthattrainingmaycreateanaversereaction
• Cold-startsarecommon;needtohaveacompellingcaseandintegratetheuserdataover-time
• Useclustering/segmentationtoimprovethecold-start(egchooseyourinterests)
• Besensitiveaboutnotificationsandtrackengagementtomachinelearnonyournotifications
• UnderstandthatwhatyoumaythinkofasAI,theuserthinksisdumb(andvice-versa);incorporateanimationsorothertoindicateAI
Summarizing
