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:Emailtonikunj.c.oza@nasa.gov 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