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  • [MUSIC PLAYING]

  • JASON MAYES: Hey, everyone.

  • My name is Jason Mayes.

  • I'm a developer advocate within the TensorFlow team

  • here at Google.

  • And today, I've got Meghna with me from the TensorFlow Lite

  • Micro team to talk more about what they

  • produced in the recent past.

  • So what have you made, and what is TensorFlow Lite?

  • MEGHNA NATRAJ: Oh, yeah.

  • Thanks for the introduction.

  • So I'm Meghna and I work on the TensorFlow

  • Lite for microcontroller steam at Google.

  • So my team basically has built a platform

  • that helps you to run machine learning models on the edge.

  • So that-- what that implies is you can try and build

  • machine learning models using [INAUDIBLE] TensorFlow,

  • and these are optimized to run on really tiny devices

  • like Spark Financial that we have

  • here or on [INAUDIBLE] and so on, and other platforms

  • that we support.

  • JASON MAYES: So what are the advantages of TF Micro?

  • MEGHNA NATRAJ: Yeah, so there are a lot of advantages,

  • one being that it's super tiny and lightweight so that it

  • can be a part of a lot of other appliances for doing machine

  • learning.

  • So it could be like a refrigerator

  • or a washing machine.

  • It's countless.

  • JASON MAYES: Very flexible.

  • MEGHNA NATRAJ: Yes.

  • And the second thing is that it needs no network connectivity.

  • So it runs without any Wi-Fi or any internet.

  • And that's because the model is completely running on device.

  • JASON MAYES: And that's great if in a remote location

  • like a field or something like this.

  • MEGHNA NATRAJ: Yeah.

  • So it has great applications there.

  • The third one is the fact that it's completely secure.

  • So there are a lot of security concerns with machine learning

  • models being used for various other applications,

  • and in this way, you can assure that the model is completely

  • running on device and the data is not being transmitted

  • anywhere else.

  • JASON MAYES: So can we see a demo in action?

  • MEGHNA NATRAJ: Yeah.

  • So as you can see here, the right most LED

  • is orange in color, which implies that it's detecting

  • that it's not a person.

  • And as I move it towards you, the LED

  • would turn yellow, so the one on the left

  • if it has just turned yellow, which

  • implies that it's detected that you're a person.

  • JASON MAYES: Awesome, great demo.

  • So how can people get started with this

  • if they want to at home?

  • MEGHNA NATRAJ: So we do have a GitHub page and a website

  • that you can check out.

  • So it's TensorFlow Lite for Microcontrollers.

  • Yeah.

  • That should be a great place to get started.

  • JASON MAYES: Awesome.

  • Well, thank you very much for the information today

  • and happy hacking to those at home.

  • Hey, everyone.

  • We're back with Pete Warden from the TensorFlow Lite Micro team,

  • and we've got a super magical demo for you today.

  • So tell us more, Pete.

  • What is this?

  • PETE WARDEN: So this is a magic wand believe it or not.

  • JASON MAYES: Excellent.

  • PETE WARDEN: And if you want to see what it does,

  • I'm going to try and demonstrate here by doing a W gesture,

  • and we should see that reflected on the screen.

  • And there we go.

  • JASON MAYES: Look at that, a beautiful W on the screen.

  • PETE WARDEN: And then we try a slope.

  • JASON MAYES: Uh huh.

  • And sure enough, we got the slope coming up as well.

  • PETE WARDEN: Perfect.

  • JASON MAYES: Awesome.

  • Cool.

  • So this is really awesome demo, but how did you

  • actually create it?

  • PETE WARDEN: Well, you might be able to tell

  • by the awesome craftsmanship here

  • that I actually used some masking tape

  • to attach this nano 33 BLE sense board from Arduino.

  • And it actually contains a tiny accelerometer,

  • which is how it's actually able to recognize the [INAUDIBLE]..

  • JASON MAYES: I see.

  • Yes.

  • Yeah, very cool.

  • And how much of these go for?

  • PETE WARDEN: This is about $30.

  • JASON MAYES: So fairly low cost a venture.

  • PETE WARDEN: Fairly low cost and you actually

  • don't need a magic wand.

  • You can use a stick.

  • JASON MAYES: Anything is usable.

  • It's perfect.

  • Awesome.

  • So how can we get started if you want to do this at home?

  • PETE WARDEN: So the nice thing is

  • we've been up to work with the Arduino community

  • and get TensorFlow Lite as an official library in the Arduino

  • ID.

  • So you can just go to the Manage library's entry and the menu

  • and get TensorFlow Lite for yourself.

  • JASON MAYES: So it's pretty much just plug and play then?

  • PETE WARDEN: Exactly.

  • JASON MAYES: Thank you very much, Pete.

[MUSIC PLAYING]

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