- Stay tuned.
Don't go anywhere. (upbeat rock music)
- We have come a long way since Amsterdam.
- The winner must be from Japan
because we are so competitive.
- She really, really wants to go to Las Vegas.
Number one, woo!
- Something's going on.
I don't know what it is.
It's a world record.
They're not posting the time.
(energetic rock music) (crowd cheering)
(laughs) I have no idea how he's done that.
Welcome to the world's first
autonomous racing league.
Developers of every skill level,
from all corners of the globe,
compete for the ultimate prize in machine learning.
Welcome to DeepRacer TV.
(upbeat rock music)
Tokyo is the city of the future.
Its fashion, food, and technology
are years ahead of any other place on earth.
Society 5.0, as the Japanese call it,
hopes to makes people's lives more comfortable
and sustainable through artificial intelligence.
And the future is now, as autonomous racing is underway
at the Makuhari Messe for AWS DeepRacer Tokyo.
- Ladies and gentlemen, AWS DeepRacer Japan, wow!
So much people here, get together.
- Tokyo is a three-day race.
The racing started yesterday.
We are seeing people that are standing in lines
that are going as long as four or five hours deep.
- Take-san. - Hi.
- My name is Blaine.
- My name is Ai Take.
- You were the number one competitor yesterday.
- Yes.
- And you work for DNP.
There's a lot of people from DNP that made the leaderboard.
Is this something you're doing as a group?
(speaking Japanese)
Take is part of DNP.
They are a publishing group here in Japan.
They have adopted DeepRacer as a tool
to train their employees on how to learn
machine learning, reinforcement learning,
and then be able to apply at the job.
They've got 15 employees here,
all of them trying to prove they're the best in the company.
So tell me what you're doing special with this model.
(speaking Japanese)
The model he's describing doesn't sound that powerful.
It's a centerline-based model.
As we've seen in other cities, a centerline-based model
tends to put a lot of jitter in the car,
(Blain imitating car wobbling)
just because it's trying to stay as close to center.
Now, Take says he saw that,
and has relaxed the model a little bit
so he can get more velocity.
But the truth is, it's still a centerline model.
I don't think it's going to be able to do that well.
Wonderful, hey, I think it's your turn.
Good luck. (speaking Japanese)
Good luck!
(dramatic electronic music)
(speaking Japanese)
- So the number one, Take, it's not Take, Take-san is 7.86.
- Take, 7.86 seconds.
(laughs) I have no idea how he's done that.
A centerline model should not get 7.86 seconds.
I got homework to do.
I got to figure out what's going on.
- [Announcer] Deep4Drive, 8.04.
- Maysaya, 8.04 seconds has you in second place right now.
- I'm so excited to try more.
(laid back rock music)
My name is Masaya.
I work in automotive industry,
focusing on self-driving systems
as a technical project manager in Bosch.
(group laughing)
When I was a student, I have a space balloon project.
By using the big balloon and helium inside,
we can release the camera to 30 kilometer.
Then we can picture the sky, and also the space.
We got a lot of interview from TV shows.
I got married last year,
and the next October we expecting a baby.
I make a team called Deep4Drive.
We have 11 people, from different companies
and different communities.
And then we find AWS DeepRacer, and we decide to join.
I bought a old car from America.
Then I made a course by myself in my room.
The winner in the DeepRacer League in Las Vegas
must be from Japan, because we are so competitive.
- What are you specifically doing with your model?
- We tried to make a model
which follow the path that we made.
- [Blaine] A waypoint space for more function.
- And also we try to log signals and messages,
and I try to analyze on the notebook.
- Masaya is diving so deep in the technology,
that we're looking at the logs of the training itself.
(laid back music)
Log analysis is how you find out what's specifically
happening to your vehicle as it's training.
Think about it like a phone bill.
You can look at your phone bill at the end of the month
and break it down by what's this tax
and what's this amount, and how much did I do?
Was there an international calling plan?
By looking at log analysis,
I'm not just looking at the end result.
Was I fast, was I slow?
Instead, I'm looking at all the little subpoints in between,
letting me tweak them, letting me adjust them.
So at the end of the day, it's not just a good sum.
It's a good series of parts that up to a good total.
(upbeat electronic music)
Ohno is also part of the DNP crew.
Now if I was just handicapping blind,
I would put him right at the top at the beginning,
because he is aggressively competing in the virtual league.
Last month, in the London Loop, Ohno placed second
out of all the international competitors.
Ohno is so much into the data,
the shirt he's wearing is a heat map of the London Loop.
I love it.
(speaking Japanese)
- [Announcer] Congratulations to Mr. Ohno,
number three, 8.23.
(speaking Japanese)
- Brand-new racer.
I hear she's a female racer.
Sola!
(laid back electronic music)
(speaking Japanese)
- Woo, fourth place, 7.82.
Sola, congratulations!
Big round of applause. (crowd cheering)
She wants to go, really, really wants to go Las Vegas.
Thank you very much. (speaks Japanese)
- Sola, Take, Ohno.
What I'm seeing is amazing work on the analytic side.
I'm seeing heat maps.
I'm seeing log files on their computer.
This is the kind of deep analytics work
that they can take their model and do fine-tuning with,
because I'm not just looking at an end result,
I'm looking at incremental changes.
That's how we start getting
all of these sub-eight-second performances.
Once you're sub-eight seconds,
I hate to say it, it's a bit of luck.
It's just who had that perfect lap,
where the car was behaving exactly right,
and it hits the perfect line.
It's sometimes not repeatable,
but you don't have to repeat it.
You just have to have one great lap.
- [Announcer] Congratulation of Mr. Ohno!
(crowd applauding)
Give it up for him, big round of applause.
First place, 7.7.
- It's getting hard to keep up with the DNP competitors.
It is their world.
The rest of us are just playing in it.
(upbeat music)
DeepRacer is powered by the Intel Atom Processor.
Many people think of Intel just for the chips.
But the truth is, Intel is a hardware
and a software provider.
They also provide the OpenVINO platform
used by machine learning scientists,
which is integrated with DeepRacer
and the AWS platform all together.
AWS DeepRacer also benefits from RL Coach,
which is an Intel product.
It's a reinforcement learning research library
that's is already integrated with the Sagemaker,
which means machine learning scientists can start
using a wide range of libraries native to Sagemaker,
then incorporate that in the DeepRacer.
(upbeat electronic music)
- AWS DeepRacer.
Give it up for her, Sola. (crowd applauding)
She's a female racer.
(speaking Japanese)
Good speed.
Look at this, very smooth.
- I am watching one, two, three, four laps in a row
that she has gotten under eight seconds.
This is unheard of.
- [Announcer] Two minutes left to the end.
- I've been talking all day about the idea
of a lucky lap, one beautiful lap.
Sola has proven me wrong.
She is doing this consistently, every time.
- Time is up, time is up.
Stay tuned.
Don't go anywhere.
I'm so excited.
Everybody ready for this?
Just a moment, please.
We are waiting for the time.
I'm so excited, ah!
7.44, first place. (crowd applauding)
Sola, congratulations.
Number one, please, big round of applause.
- She has broken the world record, 7.44 seconds.
She has shattered the previous record.
- You went so fast!
(speaking Japanese)
Congratulations.
(upbeat rock music)
- So, as we wrap up today,
we think we know who the major players are.
But this is Tokyo.
Someone could come out of nowhere
with a race that could surprise us.
(upbeat rock music)
- [Announcer] AWS DeepRacer, day three.
- And we're back for the final day of racing
at the Makuhari Messe,
where Sola sits atop the leaderboard
with a world record time of 7.44 seconds.
Team DNP is already here,
ready to continue their assault on the record books.
Meanwhile, team Deep4Drive hopes to clinch
a spot on the podium, as well.
With the racing queues barely set so far,
and a wait time of over 250 minutes,
our competitors will get one, maybe two times in.
(upbeat electronic music)
Take is about to take the track.
- [Announcer] Take.
- It was good, but it wasn't stable.
He had a couple good times, an 8.16, an 8.20.
He got one sub-eight-second at 7.95.
He's still going to hold at 3rd place at his current time.
- He'd like to go one more time, but he can't.
(speaking Japanese)
The beautiful heart, Sola!
- Sola, overnight, decided that she needed more stability,
which is impressive considering how stable
her run was yesterday.
She did it but adjusting the hyper-parameters,
specifically on batch size.
By increasing batch size, that allows the engine
to have more scenes to look at to evaluate
what decisions it wants to make based on the input.
The question is, was overnight enough time
that the increase in batch size can make a difference?
(upbeat electronic music)
Her vehicle was all over the place.
Couldn't stay on the track, not consistent, not stable.
This is not the same ride we saw yesterday.
Times of 11.96.
She got one good run in at 8.96 seconds.
That's a second and a half slower
than her current world record.
This was not her race.
Is she nervous?
Yeah, but I'd be nervous too,
sitting at the number one spot
with this many people gunning for me.
(laid back electronic music)
Masaya spent all last night trying to build a new model,
with severe penalties for going off the track.
This is the last chance to disrupt DNP's chance
to own the entire podium.
(crowd cheering)
And he starts out right into the wall again.
He hits the wall, he hits the wall again.
This is not helping him out.
He is not hitting his stride.
Most of his times are faults.
He's only getting one time in, 9.18 seconds.
Here's another time, 8.77 seconds.
One minute left to go, he is unable to get momentum.
He is not able to get this car on the track.
And that's it, time's up, 8.54 seconds.
Masaya, a great run, but it does not improve
his time of 8.04 seconds.
(dramatic music)
We're coming near the end of the day,
but it looks like Ohno is going to get
one more shot at the crown.
Can it happen?
Absolutely.
- [Announcer] Go really fast now.
Three, two, one, woo!
- I can see here on the scores table,
Ohno has a world record, he's got it.
But they're not posting the time.
Something's going on.
I don't know what it is.
It's a world record.
We're not seeing it on the screen.
- [Announcer] Just a moment please.
I repeat, just a moment please.
- A lot of judges came around to decide
if this one lap was good or bad.
(tense orchestral music)
Was he on the track?
Did he still have a wheel on the white line?
- Everybody ready for this?
I got new information.
7.56, second place, Ohno.
(speaking Japanese)
- The judges decided that the car was off the track.
It was a fault
They invalidated that lap.
He still has a great time, 7.56,
but not enough to overtake Sola.
- [Announcer] Ohno.
(speaking Japanese) (crowd applauding)
(jaunty music)
- That wraps it up for DeepRacer Tokyo.
Team DNP finishes first, second, and third.
Take finishes the day with 7.86 seconds.
A great time, third place finish.
(speaking Japanese)
Ohno's coming in at 7.56 seconds.
He finishes in 2nd place.
(speaking Japanese)
Sola, with a world record, comes in at 7.44 seconds.
(speaking Japanese)
If you'd like to participate with AWS DeepRacer,
come to any one of the AWS summits,
or go to deepracerleague.com
to compete in the virtual league.
And remember to like, comment, and subscribe
on this and every episode of DeepRacer TV.
See you next time, at AWS DeepRacer New York City.