AI for Automotive 150218 · • 4,000 patents, 500 new inventions per year ... Robotics Machine...
Transcript of AI for Automotive 150218 · • 4,000 patents, 500 new inventions per year ... Robotics Machine...
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AIforAutomotive
Dr.BillMarkPresident,InformationandComputingSciencesSRIInternationalMenloPark,CA
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SanFrancisco
StanfordUniversitySandHillRoad
SanJose
Independent,non-profitresearchinstitutefoundedbyStanfordUniversityin1946• 2,000+staffmembers;2/3advanceddegrees
• $500M+inannualrevenues
>$4Binresearchoverlast10years• 1,000R&Dprojectsperyear
• 4,000patents,500newinventionsperyear
§ 60+spin-offcompanies&100+licenses
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acquiredbyApple
acquiredbyNokia
acquiredbyYahoo
acquiredbyRedbrickHealth
acquiredbyGoogle
acquiredbyDow
acquiredbyLightingScience
acquiredbyParkerHannifin
acquiredbyConVerge One
NUAN(NASDAQ)ISRG(NASDAQ)
ORCH(NASDAQ)
acquiredbySalesForce
IndustrialAutomation
OnlineGaming
Education
Biometrics VideoContentDistribution
AgriculturalRoboticsAIforFinancialTech
VoiceAnalytics
Exits
Active
SpaceServices
WearableRobotics
acquiredbyChanZuckerbergInitiative
LiDARSystems
acquiredbyMorpho/Sarfan
Metal“printandplate”manufacturingprocess
SRISpinoffCompanies
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IntellectualpropertyKeyIPinvision,speech,informationsecurity,virtualassistant,computationalbiology
LicensesVPADynaSpeakATSESenSay AnalyticsTMBotHunterVisionIP
Ventures
FormalDesign
VirtualPersonalAssistance
CyberSecurity
SpeechRecognition
VisionSystems&Robotics
MachineLearning
AugmentedReality
SpeakerIdentification
EmotionDetection
ArtificialIntelligenceCenter
ComputerScienceLaboratory
CenterforVisionTechnologies
SpeechTechnologyandResearchLab
InformationandComputingSciencesDivision
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Big Data: the power of correlation
Trusting and collaborating with AI
Small Data: the power of interaction
3 Themes
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BigData
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BILLIONSOFIMAGESAND
VIDEOS
BigDataMachineLearningVisualUnderstanding
DEEPLEARNING
Changingatire
CocaCola
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BigDataMachineLearningPedestriandetection
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SmallData
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PeopleGENERATE smalldatathroughINTERACTION
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ConversationenablespersonalizationUserstyleandcontentshapessystemresponse
IneedtogobacktotheofficebeforeIgohome.CanImakeitonthischarge?
CanIekethebatterytogetbacktotheofficeandthenhome?
Howlongareyougoingtospendatthe
office?
Youraverageonthatrouteis54%,butyou‘vedoneitwith44%,whichwouldwork.
Justneedtopicksomethingup.
Whatwastrafficlikeonthe44%day?
Itwouldbebesttochargefor30minutes.Wouldthatwork?
Normal.Youraccelerationwasespeciallygentle...
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VirtualPersonalAssistantArchitecture
GeneralVirtualPersonalAssistantPlatformAutomaticSpeech
Recognition
Controller
NaturalLanguage
Understanding Reasoning OutputGenerationInterpretation
TextToSpeech(TTS)
UserInterface&ClientApplication
BackendSystems
Acoustic&LanguageModel
StatisticalLanguageModel Domain
SpecificOntology
WebServices
DomainSpecific
Workflows
DomainSpecificApplicationResources
OutputTemplates
ImageUnderstanding
ObjectandGestureModels
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Conversationismorethanwords
13
CombinesvisualandothersensorinformationUnderstands(andrespondsto)emotion
NextGenerationVirtualPersonalAssistant
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• Improveefficiencythroughinteractivestep-by-stepprocedures
WithAR,VPAshiftsfromassistanttomentor…
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TrustingandCollaboratingwithAI
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AsdevicesbecomemoreCOMPLEX andINTELLIGENT
wecan/mustdelegatetothem
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Whatisyourmosttrustedcomputercollaboration?
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Brakepedal
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HUMANSandMACHINEShavedifferentapproaches
toproblemsolving
TheENVIRONMENT isDYNAMIC,making
autonomyunpredictable
AUTONOMOUSSYSTEMSarecomplexandevolveovertime
BarrierstoappropriateTrust
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BridgingtheGap
FILLEXPLAINABILITY
GAPSMeaningfuldifferencesHuman-levelinterpretation
Directedexploration
INFERCAUSALSTRUCTURE
AssignmeaningtonumericfeaturesProvidedecisionrationale
Projectlong-termeffects
CHARACTERIZESTATE
Featureaggregation
Coverageofagentmodel
AssesspredictabilityIncreaseconfidence
StochasticoutcomesConfidencelevels
CHARACTERIZEUNCERTAINTY
Simulation
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Smalldatabecomesmoreimportantfordifferentiation
Trustbecomesthegating
conditionforcollaboration
What lies ahead
Widespreadexperimentationwithpersonalizationandemotionalconnection