Placeholder Image

字幕表 動画を再生する

  • [MUSIC PLAYING]

  • CHRIS MATTMANN: Hey, everybody, this is Chris Mattmann.

  • And I'm not able to attend physically the TensorFlow Dev

  • Summit.

  • So I'm giving my talk remotely.

  • My talk is about TensorFlow and Machine Learning

  • from the Trenches.

  • I'm the deputy CTO at the NASA Jet Propulsion Laboratory.

  • And I'm going to talk about our experience using TensorFlow

  • in the Innovation Experience Center at JPL.

  • What's JPL?

  • JPL is a federally-funded research and development

  • center.

  • It's NASA's only FFRDC.

  • They call these the National Labs.

  • Its goal is to do first-of-a-kind missions

  • in autonomy, technology development for space, in situ,

  • on-the-ground remote sensing of the earth,

  • and various other really nationally critically

  • functions.

  • It's nestled there in the beautiful mountains of La

  • Cañada, Flintridge.

  • We have about 6,000 employees, about a $2.6 billion-business

  • base.

  • We have a pretty large facility, about 167 acres.

  • At JPL, I am the lead for the Innovation Experience Center.

  • I'm the deputy chief technology and innovation officer.

  • And what does our Innovation Experience Center look like?

  • Our recipe for it is to find the most difficult space,

  • the space that looks the ugliest, and make it our own.

  • Take it, gut it, have the actual engineers and data scientists

  • put it together and put it back together in the way

  • that they want-- sit-stand desks, basically,

  • follow the sun sunshades, IoT internet devices

  • that both frost and unfrost the conference

  • room for smart-glass and privacy,

  • and so on and so forth.

  • So that's our team.

  • We're working in all of these areas.

  • And we're really excited to be doing machine

  • learning with TensorFlow.

  • In particular, we're excited in a few different areas.

  • Our organization is responsible for using TensorFlow

  • in the following ways-- the first is the M2020 Rover

  • that you just see right there.

  • It's now named Perseverance.

  • In that rover, in that clean room,

  • we measure particulates in the air

  • to determine if we're adding any sort of biocontamination.

  • Because we don't want to do that when

  • we send this to another planet.

  • If we discover life, we actually want to do that.

  • So we have small-commodity IoT, internet

  • of things, sensors that are measuring particulates,

  • increasing our ability to do that,

  • increasing the density of the measurements that we have.

  • And we're doing predictions using TensorFlow

  • in machine learning to determine the next measurements,

  • the next contaminations, if we had them,

  • and intervening if necessary.

  • In the bottom right, you can see our people counter.

  • That's another IoT device.

  • It uses TensorFlow, and object detection,

  • and facial recognition, and so forth

  • to, basically, count people's heads

  • as they go in and out of our tents at events,

  • like our IT Expo and so forth, so that we can tell people

  • when the right time is to actually attend these events,

  • so that they're not overcrowded.

  • Besides that, we're not just doing institutional things

  • with TensorFlow.

  • We're looking beyond that.

  • Today, our MAARS rovers are currently

  • running on what's called the RAD750 processor.

  • That's a radiation-hardened PowerPC 750 Lite processor.

  • That's, basically, the amount of power

  • that we had on an iPhone 1.

  • Tomorrow, we'll have the ability to have

  • high-performance spaceflight computing and the ability

  • to use things like Snapdragons from Qualcomm.

  • So real GPU, like a deep-learning chip,

  • so that we can do actual computing onboard.

  • And if we could do high-performance spaceflight

  • computing onboard, we could do really cool things,

  • like make the rovers intelligent,

  • make our rovers smart, do things like drive-by science, which

  • you see there highlighted on the right,

  • as one of our three ongoing tasks and initiatives to use

  • and leverage high-performance spaceflight computing.

  • Can we make rovers smarter?

  • Absolutely, we can.

  • In particular, we can take models

  • like terrain classifiers, which we've built with TensorFlow.

  • We call it SPOC.

  • There is a theme here, "Star Trek."

  • Our terrain classifier, SPOC, is a CNN.

  • It's a convolutional neural network using TensorFlow to do

  • terrain classification-- ripples, smooth,

  • smooth with rocks-- to figure out where the rover should

  • drive and where it shouldn't.

  • We test this in our Arroyo Seco, which is right by JPL,

  • using our test Athena Rover.

  • Another TensorFlow-based model that we've been using

  • and leveraging is the Google Show-and-Tell model for that,

  • which is a combination of a convolutional neural network

  • and LSTM--

  • or recurrent neural network, long short-term memory--

  • to, basically, do labeling, figure out

  • the labels for a particular image for the rover,

  • and then take the labels and actually learn a sentence

  • description for it, so that scientists can review them

  • and so that the rover, when it's on Mars, can,

  • instead of sending back 200 images a day

  • to plan what to do the next day, it can send back millions

  • of image captions that are scientifically validated

  • and to increase our density of observation.

  • In terms of our terrain classifier, just

  • some examples of that--

  • SPOC looks at the geometric features and so forth.

  • And it's actually capable of recognizing terrain types

  • from images.

  • So this is really important, both for Mars surface

  • operations, but, also, to potentially plan

  • where we should do future Mars missions and landings.

  • Beyond that, one of the things we've been really challenged

  • with-- and it's been a big area of research for us--

  • is putting TensorFlow models, and taking them,

  • and porting them to TensorFlow Lite,

  • and moving them on to exotic hardware--

  • some of which isn't even physically here and we only

  • have emulators for, like the high-performance spaceflight

  • computing emulator-- that we've been trying to look at various

  • TensorFlow models-- like DeepLab, Mobilenetv2--

  • and then figuring out how do we port them

  • into a TensorFlow Lite quantized model or a TensorFlow Lite

  • floating-point model and measuring the computation time

  • from that.

  • One of our key observations here is that Mobilenetv2 tests were

  • conducted on smaller imagery.

  • And actually, Mobilenetv2 tests performed the fastest

  • of any of the models that we were actually

  • testing when we used TensorFlow Lite in a quantized fashion

  • for that.

  • So we've got ongoing research.

  • And we're working on porting these models

  • into these TF-Lite environments.

  • In particular, if we have drive-by science,

  • if our rovers are smarter and you look on the right,

  • we don't want to miss that unnoticed-green-monster

  • problem, where the rover simply doesn't have enough power to be

  • able to--

  • in light time and bandwidth, it misses recognizing something

  • that, actually, we really wanted to see, like our little buddy

  • right there in green.

  • And one of the challenges with that

  • is that the rover has an eight-minute light time round

  • trip, from Earth to Mars, to, basically,

  • send out a communication and to hear back from it.

  • So it's got to do a lot of science and things onboard.

  • It's got to recognize things, even

  • without human intervention.

  • Additionally, our geologists, they've

  • got a headache with too many images and so forth.

  • So having the ability to have the rover be smart, do

  • drive-by science onboard, and just send back,

  • again, those textual captions and descriptions of images

  • is really key.

  • Because then it can get beyond only

  • being able to send 200 images per day

  • and could actually send millions of captions.

  • In particular, all of the work that we're

  • doing on TensorFlow in the book, I've been collecting it.

  • I've been capturing it.

  • And I'm writing a second version of the "Machine Learning

  • with TensorFlow" book.

  • It's called "Machine Learning with TensorFlow Second

  • Edition."

  • It's currently in the Manning Early Access Program, or MEAP.

  • Please check out the link right there.

  • And I would love for you to, basically,

  • ask me any questions.

  • There's an online developer forum for it.

  • Please let me know.

  • And I'd be happy to get back to you.

  • And you know what?

  • I'm not there physically.

  • But you can find me online at Twitter, Chris Mattmann

  • @ChrisMattmann.

  • And thank you for giving me the opportunity to present today.

  • [MUSIC PLAYING]

[MUSIC PLAYING]

字幕と単語

ワンタップで英和辞典検索 単語をクリックすると、意味が表示されます

B1 中級

トレンチから見たTensorFlowとML。JPLのイノベーション体験センター (TF Dev Summit '20) (TensorFlow and ML from the trenches: The Innovation Experience Center at JPL (TF Dev Summit '20))

  • 1 0
    林宜悉 に公開 2021 年 01 月 14 日
動画の中の単語