Space & AI, Franck Marchis
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Lecture from The Hive's 2018 Annual Summit.
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Space & AI, Franck Marchis
- 1. AI & Space FRANCK MARCHIS Senior Planetary Astronomer at the SETI Institute Chair of the Exoplanet Group Chief Scientific Officer at Unistellar With contributions by the FDL Team
- 2. Overview of today’s discussion 1. SETI Institute & Space Science 2. NASA & FDL 3. A future with AI in Space Outline
- 7. SETI Institute and Astronomy 3 ways to answer to this question Technological Signal • Radio SETI • OSETI Intelligent Nearby & distant Life in Our Solar System • Ocean of Europa • Underneath Mars Surface Not-intelligent Nearby Life on Exoplanets • Remote sensing observations • Biological signatures • Technological artifacts Intelligent and Not-Intelligent Distant Are We Alone
- 8. SETI Search for ExtraTerrestrial Intelligence & Science Exploration Technology Innovation
- 9. It's not often that NASA asks you to come to Silicon Valley to save the world using AI… But that’s exactly what happened in summer 2016 to 12 researchers from around the world. and again in 2017… and soon 2018
- 10. ABOUT FDL GOAL: CLOSE KNOWLEDGE GAPS BY MATCHING DOCTORAL- LEVEL TALENT FROM THE PLANETARY SCIENCES WITH PEERS FROM THE MACHINE LEARNING COMMUNITY.
- 11. “To find all asteroid threats to Human Populations and know what to do about them”
- 12. To successfully deflect an asteroid, you need to know three things in advance…
- 13. 1) ’What is it made of?’
- 14. 2)‘What shape is it?’ 1) ’What is it made of?’
- 15. 3)’What is the best choice of technology to make a successful deflection of an Asteroid that poses a threat to Earth?’
- 16. ‘What are they made of?’ ‘What shape are they?’ ‘What is the best way to deflect them?’ (Better Data Gathering) (Better Insights) (Better Decisions)
- 17. “Only 31 meteorites have ever been found that can be linked to a source orbit” AI for Planetary Defense: ‘What is it made of?’ NASA needs more fragment recovery. Can AI help?
- 18. AI For Planetary Defense Fragment Recovery “Only 31 meteorites have ever been found that can be linked to a source orbit” 25,000 training images of meteorites six deep learning models (3 GoogleNet, 2 AlexNet + 1 combination) along with a 15 million image library. •Result: an automatic meteorite detection system, all driven by a user-friendly app for use in the field. Validation accuracy > 99.9%
- 19. 2)‘What shape is it?’
- 20. AI for Planetary Defense:‘What shape is it?
- 21. •Asteroid shapes are critical for asteroid deflection techniques - as any mitigation plan needs to know the center of mass. •Should an object be too close to shift, shape, density, internal structure are critical for understanding the potential for damage and planning effective disaster response. AI for Planetary Defense:‘What shape is it?
- 22. FOUR WEEKS This is currently a laborious manual process that takes a trained practitioner around four weeks. AI for Planetary Defense:‘What shape is it?
- 23. • Create 546,000 synthetic radar images from DAMIT and JPL shape database (1,620 models) • This approach may enable a rapid understanding of the shape of an asteroid - while it is still being tracked by radar, even as an incoming object. • Automatically determine the spin axis angles by automated calls of the existing SHAPE software using Bayesian Optimization (4-6h computer time) • Used a 3D-VAE (Variational Auto- encoder) to generate plausible 3D voxel shapes of asteroids AI for Planetary Defense: ‘What shape is it?
- 24. 3D SHAPE MODELING THE VARIATIONAL AUTO-ENCODER (VAE) GANs attract global attention With Deep Mind’s ‘Alpha Go” In 2017!
- 25. 3)’What is the best choice of technology to make a successful deflection of an Asteroid that poses a threat to Earth?’
- 26. AI for Planetary Defense: Deflecting Asteroids? ‘What is the best choice of technology to make a successful deflection of Asteroid that poses a threat to Earth?’ Nuclear Device ?
- 27. Why a Deflector Selector? A TRAINING SET BASED ON 1.5 MILLION ORBITAL SIMULATIONS OF THREE DIFFERENT KINDS OF DEFLECTION TECHNOLOGY. “There isn’t a tool of this sophistication available to the Planetary Defense community.” Astronomer, JL Galache from the IAU’s Minor Planet Center (& FDL Mentor) AI for Planetary Defense: Deflecting Asteroids?
- 28. 1.5 million orbital simulations were used train a decision tree to select a set of effective technologies (Nuclear Device, Gravity Tractor or Kinetic Impactor) for a given hazardous object. Once trained, it had an accuracy of 98% for determining which technology would produce a successful deflection. A Machine Learning Decision Tree… AI for Planetary Defense: Deflecting Asteroids?
- 29. •The most effective technology predicted by the decision tree is the nuclear explosive, due to the high ΔVs it can impart and its instantaneous effect. •This important work will help inform strategic decisions which deflection technologies should be prioritized, and what asteroid characteristics are the most important to be known in advance of taking action. Greenberg et al. Acta Astronautica 2018 Deflector Selector: Result
- 30. PLANETARY DEFENSE 2016 3 PROBLEM AREAS 12 RESEARCHERS 4 PLANETARY MENTORS 2 AI MENTORS RADAR SHAPE MODELING 2017 HELIOPHYSICS SPACE RESOURCES 5 PROBLEM AREAS 24 RESEARCHERS 6 PLANETARY MENTORS 6 AI MENTORS LONG PERIOD COMETS AI APPLIED Expanding FDL: The 2017 version
- 31. FDL 2017: RESULTS 5 CHALLENGES + WHITE PAPER SPACE RESOURCES HELIOPHYSICS PLANETARY DEFENSE WHITE PAPER
- 32. LOOKING TO 2018
- 33. PLANETARY DEFENSE 2016 3 PROBLEM AREAS 12 RESEARCHERS 4 PLANETARY MENTORS 2 AI MENTORS RADAR SHAPE MODELING 2017 HELIOPHYSICS SPACE RESOURCES 5 PROBLEM AREAS 24 RESEARCHERS 6 PLANETARY MENTORS 6 AI MENTORS 2018 LUNAR ROUTE PLANNINGS SOLAR STORMS / RADIATION WARNING LONG PERIOD COMETS 7 PROBLEM AREAS 28 RESEARCHERS 7 PLANETARY MENTORS 7 AI MENTORS AI APPLIED FDL 2018 ASTROBIOLOGY EXOPLANETS
- 34. FDL 2018 or FDL 3.0: Bigger, Bolder and Broader
- 35. Enhanced Vision for Exploration, Learning and Citizen Science. Unistellar : making astronomy fun and popular Enhanced Vision technology Beautiful images Light pollution compensation Portable and autonomous Fits in an urban backpack Connected Science campaign Data Sharing Automatic Field Detection Educative and Interactive
- 36. The Largest Network of Telescopes in the World Partnership
- 37. Future of Ground Based Astronomy Extremely Large telescopesAll the Southern Sky, every week: LSST 20 TB of data per night 20 million of asteroid 100 alerts per day AI to process AI to alert AI in optical and mechanical design AI in data analysis
- 38. Future of Space-Based Astronomy The picture of another Pale Blue Dot: Project Blue Searching for exoplanets: TESS 27 Gb of data per night 200,000 stars AI to process AI to Alert 2 years of continuous observations of Alpha Centauri stars AI to process the data
- 39. Future of Space Exploration 2018 2022+?1969
Editor's Notes
- We want to make sure there’s plenty of time for discussion, but of course if you have a question or reflection feel free to jump on in.
- 2 trillion of galaxies
- 100 billion stars
- Unambiguous earliest evidence for life on Earth dated to 3 billion years
- But that’s exactly what happened last Summer to 12 researchers from around the world.
- FDL is an Applied Research Accelerator: that is, the application of emerging technology to known ‘know-how’ gaps; by pairing the talent from academia and the private sector.
- When we talk about planetary defense, we mean Asteroids; or ‘Near Earth Objects’ and this project was a response to the White House Challenge to find all asteroid threats to Human Populations…AND KNOW WHAT TO DO ABOUT THEM.
- So the thing they never mention in the movies - you need to know three things in advance.
- What is it made of?
- What shape is it?
- What is the best choice of technology to make a successful defection?
- Problem choice is key too. We’ve endeavored to keep the problems related to a certain extent, so that the approaches and ideas created by one team, inspire the others. This slide shows the challenges from 2016, which also show the spread of solutions where AI can be applied: “Better Data Gathering” / “Better insights” / and “Better decisions”
- This is a very rare object - discovered this year; it’s a meteorite where the fireball - the shooting star - was tracked by fireball cameras. This has only happened 30 times. (Where a rock and the incoming trajectory are linked)
- The next thing we need to know is, what shape is it?
- When NEOs come near the Earth, it is possible to image them using radar. From these sparse 2D images, the 3D shape can be discerned…
- Why is this important? Any defection plan needs to know the object’s shape and center of mass; should an object be too close to move, knowing it’s shape can help with disaster response.
- but this is currently a laborious manual process.
- It may be possible to discern the shape of an object, even as an incoming object.
- Before we could do anything else, we needed data. Of course, in an ideal world, we would have lots of data of many different objects which humanity had tried deflecting. Actually maybe that’s a not-so-ideal world. Anyway, since we don’t have a sample of these kinds of objects, we had to resort to creating our own data with simulations.
- This year the team improved this approach by adding a GAN to the process, modifying the VAE to get further levels of precision.
- Lastly, now we know what it’s made of and its shape, what is the best technology to make a successful deflection?
- Although have been making strides in engineering solutions to NEO deflection, the best technology assessment was - up until FDL - based on only a handful of orbits.
- The team radically improved the resolution, by running 1.5 Million orbital simulations where NEOs where on a collision course with Earth and successfully deflected - to create training data for a decision making tool…
- A machine learning decision tree… once trained, it had an accuracy of 98% in determining which technology would produce a successful deflection…
- The most effective tool we have, then - as Bruce Willis demonstrated - is the nuclear device. However I know he didn’t do the math. Now we have the math.
- We’ve extended this concept of an ‘envelope of expertise’ to extend and branch the work of the preceding year. In 2017 we revisited PD with the same mentor team, adding Helio and Space Resources.
- We’re looking to build on the work of 2017 and branch out again - there’s appetite to translate the ‘foggy’ work done in Helio this year into quests. And of course we’d like to continue pushing the possibilities in Planetary Defense.
- Apollo Guidance Computer (AGC): It had approximately 64Kbyte of memory and operated at 0.043MHz. The scientist in charge of the software development program for the Apollo Guidance Computer was Margaret Hamilton, Director of the Software Engineering Division of the MIT Instrumentation Laboratory. Curiously, the world's first computer programmer was also female
- See you next year.