[MUSIC PLAYING]
TIMOTHY JORDAN: That was awesome.
Hi, and welcome to "I/O Life.
I'm Timothy Jordan, and I'm standing here with Magnus.
Magnus, how are you doing?
MAGNUS HYTTSTEN: Hi.
I'm fine, Timothy.
How are you?
TIMOTHY JORDAN: I am really good.
We're in the ML booth.
It's my favorite booth.
MAGNUS HYTTSTEN: Yep.
TIMOTHY JORDAN: Don't tell the other booths that.
MAGNUS HYTTSTEN: Of course not.
It's exciting in here.
TIMOTHY JORDAN: So in this sandbox,
we are looking at-- right now, we're in a track,
and there's cars going around.
MAGNUS HYTTSTEN: Exactly.
TIMOTHY JORDAN: What's happening here?
MAGNUS HYTTSTEN: This is a self-driving car track.
These are actually something called Donkey Car.
We'll post the link later on to these things.
You can build them yourself.
There are full instructions on the internet.
It's built on Raspberry Pi.
And we put a TensorFlow Lite model on there,
running [INAUDIBLE].
And then we trained them around this track.
And they're capable of going around like this.
TIMOTHY JORDAN: That's really cool.
MAGNUS HYTTSTEN: Yeah.
It's amazing.
So Raspberry Pi was just a supported platform
by TensorFlow Lite.
So it really opens up a lot of possibilities
to do these kinds of things.
So it's really, really exciting.
TIMOTHY JORDAN: Well, and this is just so delightful.
I saw them training it earlier where it's just a phone on it.
And he was operating it with another phone.
MAGNUS HYTTSTEN: Yeah.
TIMOTHY JORDAN: And then the autonomous vehicle
used a model built on those driving instructions.
MAGNUS HYTTSTEN: Exactly.
So it takes images when it's going around the track,
and then it's using those images to train the car,
so it knows where to go and also how fast
to go in each of the segments of the track.
TIMOTHY JORDAN: That's really cool.
MAGNUS HYTTSTEN: Yeah.
TIMOTHY JORDAN: All right.
What else do we got going on in this sandbox?
MAGNUS HYTTSTEN: So as we heard from the keynote,
we just launched a new version of the TPU, TPU v3.0.
Should we check that out?
TIMOTHY JORDAN: Yes.
MAGNUS HYTTSTEN: Cool!
TIMOTHY JORDAN: Over there?
MAGNUS HYTTSTEN: Yeah.
TIMOTHY JORDAN: Let's do it.
Wait a second.
I think I see Zak Stone over there.
Is that Zak?
MAGNUS HYTTSTEN: That's right.
TIMOTHY JORDAN: Do you think we could get him over here?
MAGNUS HYTTSTEN: Zak, come over.
TIMOTHY JORDAN: So this is TPU v3.0.
ZAK STONE: That's right.
This is our latest and greatest TPU.
Actually, this is just a part of the TPU pod
that contains many boards like this.
TIMOTHY JORDAN: How-- how many?
ZAK STONE: Enough to deliver more than 100 petaflops
of computers.
That's kind of hard to think about--
100 petaflops.
TIMOTHY JORDAN: How do you conceptualize 100 petaflops?
ZAK STONE: So it's not quite apples to apples,
but some of the top supercomputers in the world
are only doing a few more than 100 petaflops.
And so we use reduced precision that's
appropriate for machine learning.
So like I said, this is not a direct comparison,
but there are very few computers in the world that
can do more than that many mathematical operations
per second.
TIMOTHY JORDAN: That's really cool.
And we were talking to Jeff yesterday.
He mentioned that this is the first liquid cooled
component in our data centers.
ZAK STONE: That's right.
To the best of my knowledge, this
is the largest-scale liquid cooling system
that we've deployed here at Google.
TIMOTHY JORDAN: That's exciting.
And it's really pretty.
ZAK STONE: I'd love to have one of these in my living room,
on my wall, all over my house, actually.
That would be great.
TIMOTHY JORDAN: So a little bit more
background on what people are doing or will be doing
with something like this soon.
ZAK STONE: Sure.
Well, so today, if you look over here, that second generation
system, that's a cloud TPU.
Anybody with a GCP project, there's
no more requesting quota, waiting in line.
Anybody today can start up a cloud TPU
and use one of our open source reference models
for image recognition, for speech recognition,
for machine translation, language modeling,
even generating images from scratch, pixel by pixel.
And it all runs on this amazing, self-contained supercomputer
here.
That's 180 teraflops over there.
And so that's pretty exciting.
We're trying to make machine learning
acceleration affordable and accessible to everybody.
And then we are just relentless about pushing
the envelope of performance with these new generations
of hardware, like you're seeing here.
TIMOTHY JORDAN: Awesome, Zak.
Thank you, so much.
ZAK STONE: Any time.
TIMOTHY JORDAN: All right, Magnus, what's next?
MAGNUS HYTTSTEN: OK, cool.
Let's go over and check out an object detection
robot at the TF Lite stand.
TIMOTHY JORDAN: I love robots.
Let's do it.
SPEAKER 2: So basically here we have a TensorFlow Lite model
running on a Raspberry Pi.
This is a very cheap and easy to find computer.
And what we've done with it is we've
added a servo motor, or two servo motors,
that can actually point the camera in different directions.
So the TensorFlow Lite model detects objects,
and then it tries to center them on the screen.
So it's like a virtual camera person
that's able to keep things in frame as they move.
So if you take the apple or the banana
and you put it in the frame, it will start
to actually move the camera.
MAGNUS HYTTSTEN: So what this thing is doing
is that it's presenting us with words.
And then it knows related words by having
been trained on the huge data set that puts those words close
together.
And then we use our brains to do the association.
And if the answers are matching each other,
then I'm successful.
Otherwise, I'm not.
TIMOTHY JORDAN: And this seems like just fun.
But what's represented there is a dramatic reduction
in the work to build something like this,
because the associations are created by a model
rather than a whole bunch of people working all those rules
together.
MAGNUS HYTTSTEN: Yeah.
And, yeah, exactly.
And these models, they actually exist pre-built.
So you can actually reuse one in your own app.
You should definitely check out a technology
called TensorFlow Hub on tensorflow.org
where we have these types of models available.
The classical example for this relationship
is if you take Paris, and then you add France,
and then you subtract Italy, what do you get?
TIMOTHY JORDAN: Something else?
MAGNUS HYTTSTEN: You get Rome, right?
TIMOTHY JORDAN: Sure.
MAGNUS HYTTSTEN: Exactly.
And that's how we calculate the distances
between these vectors.
So Paris is related to France in the same way
that Italy is related to Rome.
TIMOTHY JORDAN: Oh, OK.
MAGNUS HYTTSTEN: Yeah.
So I'm playing a game here.
And I need to associate things with the word
here and bring it down into the green area here.
So you can see, I wasn't--
so school, what is the association to school for you?
Pupil?
TIMOTHY JORDAN: Homework?
MAGNUS HYTTSTEN: Yeah.
Pupil is fine.
OK.
Because that's nice.
I took that out.
So what the machine learning algorithm knows,
it knows about all of these associations.
So garden is related to hose.
Shoe is related to--
TIMOTHY JORDAN: Foot.
MAGNUS HYTTSTEN: Foot?
Yeah.
Good job, Timothy.
TIMOTHY JORDAN: Thank you, Magnus.
MAGNUS HYTTSTEN: The newspaper is--
so what should we do?
"New York Times?"
Yeah.
It's a newspaper, right?
So I bring these words down.
And eventually, it's going to build up.
So I got to get faster and faster at these things.
Horse, riding?
Riding.
OK.
So let's see here.
We now have hamster.
Hamster?
Fluffy?
Fluffy!
Fluffy worked!
Yeah!
Motorcycle, fast?
No.
Motorcycle?
TIMOTHY JORDAN: Riding.
MAGNUS HYTTSTEN: Ride?
Well, yeah, that's right.
Summer, winter.
Hot?
Well, summer, hot.
TIMOTHY JORDAN: Thanks, for the tour, Magnus.
MAGNUS HYTTSTEN: You're so welcome, Timothy.
TIMOTHY JORDAN: And if you'd like
to learn more about TensorFlow, head on over to tensorflow.org.
If you'd like to learn more about any of our developer
products, sign up at g.co/dev/form.
See you next time.
[MUSIC PLAYING]