machine learning workshop 2017 introduction, goals, conops · 2017. 9. 20. · 1.3 sec 10-50 sec 5...
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MachineLearningWorkshop2017Introduction,Goals,Conops
MichaelLowry,Nikunj OzaNASAAmes
FirstMarsDesignReferenceMission:70crewrequired (1950)
Lessons from Apollo: 5 of 7 Apollo Lunar Surface Exploration Missions Saved by
Real-Time Guidance from Mission Control11 12 13 14 15 16 17
Lightning Strike O2 Tank Explosion LM Abort Switch Short Circuit
CM Backup Gimbaling System
1202 AlarmsDuring LMDescent
ForDeepSpaceMissions:NeedtoforwardbaseReal-TimeMissionControlCapabilities on-board
1.3sec 10-50sec5- 18min!
Moon
Mars
Near-EarthObject2-9millionmiles
200millionmiles
60millionmiles
Space,Time,andDistance:CommunicationDelays
5-18Minone-way
4
MachineIntelligence
• ComputerHardware
• Software(ReasoningandSearchAlgorithms)
• Knowledge
ComputerHardware
197110,000 nm19851,000 nm1997250 nm2012 22 nm2017 10nm20205nm
Gate-Length(nm)
Capacitance:C~Gate-Length 2
SwitchEnergy:ESW=CV2SwitchTime:t >~h /ESWFaultRate:ESW>kT ln(MTBE /t)
DeviceLevel
ProcessorLevel:Entropy(energy~information)ofrunningacomputation
ARCH (SL +SS)=ST (kT / ESW)
SearchAlgorithms
10xSMTimprovementin4years
MachineLearning• 1997:IBMDeepBluewonworldchess– Supercomputer– Powerfulsearchalgorithms– Modestmachinelearning
• 2016AlphaGo wonworldgo– Yearsearlierthanexpectedbasedonprojectedhardwareandsearchalgorithmscapableoflargebranchingfactor
– InnovativeuseofmachinelearningbyGoogleDeepmind– Specialpurposehardware
• (tensorflow processingunits)
NASAisaLearningOrganization• Explore
– Human-machineteamingforMars– Deepspaceroboticexploration,EuropaLander
• Innovate– AutonomousUAVs,TrafficManagementforUAVs– UrbanAirMobility
• Learn– Istherelifeinotherpartsoftheuniverse?– Fundamentalquestionsofastro-physics– Earthecosystemandclimatescience
Plenary- BallroomTuesdayOpeningKeynoteSession(Plenary- Ballroom)8:30- 9:00WelcomeandIntroduction9:00- 10:00 IndustryKeynote.PeterNorvig10:00- 10:15 Break10:15- 10:30 AcademicKeynote.Vipin Kumar11:15- 12:15 NASAKeynote.Nikunj Oza
Wednesday8:30am- 9:30am.HPCKeynote Piyush Mehrotra
Thursday8:30am-9:30am.ProgramKeynote.MichaelLittle(AIST)1:30-3:00pm.BreakoutGroupSessionbrief-outs3:00pm KaiGoebel– PathForwardforMachineLearningatNASA
Presentations:[email protected] NLTnightbeforeBringonUSBsticktoputonBallroomorShowroomlaptopspriortosession
ParallelSessions
Tuesday1:30to4:00MachineLearningforAeronautics (Ballroom)MachineLearningforHumanSpaceExploration(Showroom)
Wednesday9:45– 12:30Hardware,SoftwareTools,andV&VforMachineLearning(Ballroom)LearningforHumanMachineInteraction (Showroom)
Wednesday1:30pm– 4:30pmMachineLearningforEarthScience (Ballroom)MachineLearningforAstrophysics/PlanetaryScience (Showroom)
BreakoutGroups
Tuesday 4:00– 5:30
Wednesday 4:30– 5:30
Thursday 10:00– 12:00Brief-outs 1:30- 3:00
BreakoutGroupsAeronautics(Northwing)Astrophysics(Ballroom1)EarthScience(Ballroom2)HumanSpace(Showroom)Hardware/SoftwareTechnologyandHumanInteraction(Mezzanine)
Firesideareaavailableasanalternatebreakoutroom
LunchandBreaks
Lunch– onyourown
SpacebarCafé11:00AMto7PM Lunch,Snacks,Beverages
Vendingmachines– nearfiresidearea
Mapstonearbyrestaurantareas(registrationdesk)
Lavatories– northsideofbuildingatcorners,onesetadjacenttoBallroom
BreakoutGroupBrief-Outs
40% Near-termopportunitiestoapplystate-of-artMachineLearningTechnologytoNASAmissions
20% GapsinmachinelearningtechnologyneededforfutureNASAmissions(MachineLearningtechnologyresearch)
20% HowbesttoleverageandintegrateMachineLearningR&DacrossNASA,Academia,Industry,andotheragencies
20% RecommendationsforNASAprograms