>> Hi, everyone. Welcome back to Tech Exception.
Today, we have an exceptional conversation
with a very special person,
and we're going to talk about how his company
is disrupting the autonomous driving world.
We have Alex Kendall,
CEO of Wayve with us today. Hi, Alex.
>> Hi, Adi. It's great to be here.
>> Fantastic to have you.
Alex, how did you come to the idea and said hey,
I want to do something new in the autonomous driving space?
>> Look, I think it's all about
excitement around building embodied intelligence.
Robotics has been an interesting space
where for a long time people
who have been building robots that we
hand-design how they behave and
we really program what they should do and what they shouldn't do.
But I think we've reached the limits of
this technology and what we're
excited to be building at Wayve is how we
can really expand this using machine learning.
How we can build robots that can learn to
do things more complex than we could previously imagine,
such as autonomous driving
with the power of data and machine learning.
>> Were you working in that space before you started Wayve?
>> I was doing a PhD at the University of Cambridge,
researching computer vision robotics.
I was really lucky enough to be doing this at the time when
deep learning started to take the computer vision world by storm.
We're just seeing the face models such as AlexNet
that we're able to do image recognition
over large datasets like ImageNet,
and there was starting to be
transmitted to more wider vision tasks.
I took deep learning from image recognition and showed that
I could work in seeing and understanding semantic segmentation,
really understanding where you are,
what's around you purely from a camera.
I think it's the technology
that gave us the confidence that we could take
a system that could understand
what it needs to be added and then make
decisions and actually drive an act within the world.
>> That's really cool. If I'm putting it back into context,
you said, ''Hey, I'm excited about this technology.
I'm excited about working in the software area,
and I'm going to continue to a PhD," and
then the PhD rowl your world and you said,
"Oh, this is a great challenge that no one targets yet,
and we can help improve the technology in this area."
>> Absolutely. I think the PhD was a special time of
reflection of thinking deeply about an area and also
looking to connect a bunch of
different dots from different fields with
its robotics deep learning
and some of the various sub-fields within these topics.
It was that introspection
and connecting things and just getting going and
building stuff without a repressive commercial agenda
that I think really lead to the formation of these ideas.
>> Yeah, that's interesting.
Billing onto that, can you take me back to
that moment that during your PhD,
you were writing articles,
working extensively in that field,
reading a lot of materials,
and looking also on
different companies and what people are doing in this industry.
Take me back to that moment when you realize,
hey, I have something,
I'm working on cutting edge technology and I can
actually turn it into a successful business.
>> There are a few key signals.
Let me walk through some of them.
The first one was,
I think when we were able to
put together a system that can
understand in one deep-learning model,
the semantics, motion and
geometry of the scene that really gives you-all of
the scene I'm sending you need is
a robot or a human to be able to drive and make decisions.
To run that in real time and demo it live on a vehicle I think
was the first key insight and mock sign in my mind.
The next one was, if you are going back to 2016/2017,
this was the time when we started to see
some early deployments of self-driving and particularly
Google Waymo went to Arizona,
and started servicing customers there.
I think this gave us a lot of confidence,
is this technology that now is the right time,
but over the next two years,
we just saw a challenge for
this technology to scale with the current paradigm,
one that was very capital-intensive to develop,
and one that inherently
didn't have the intelligence that would come to
see in some of the advances in machine learning.
Because then think about that 2017/2018 period where we also saw
massive advances in policy learning agents,
things like AlphaGo from Google,
DeepMind or some of the video game agents and StarCraft,
Odisha that we've seen come out of the deep learning community.
The ability for us to learn
these very complex game environments
and learn to be superhuman at them with machine learning.
A real thought on my behalf is,
why can't we take these to real life embodied robots?
Why can't we take them out of
simulation and put them in the hands of society?
I think it was scene understanding,
looking at what's happening in the self-driving industry and in
these massive breakthroughs in
machine learning through that period that lead to saying,
"Look, we can put this on a real life robot."
There are massive challenges to do so about
making it more data efficient and safe,
that is, quite frankly,
a paradigm shift from operating this technology in simulation.
But that's what we've really set out on us to go and resolve.
>> I really like how you
took specific technology and you said, hey,
that works well for robots,
that works well for gaming in that space.
Why not just bring it back to
autonomous driving in autonomous vehicles
and just connect all the dots,
and bring this awesomeness technologies
that we have and use it there?
I really like that approach because it connects well
to different things we do at Microsoft as well,
because we have tons of technology,
tons of services and we always try to enable people
to do more stuff on our platforms
by basically giving the generified version
of the things that we work with.
We can see it pretty widely on
the different services that we have in the Cloud as well though,
we try to give platforms for people to take
that technology and use it in
different areas and different domains.
I'm a big believer in that,
if you see something works in one area,
it might work as well as I know.
If we talk about autonomous driving,
there's also drones and a lot of
other autonomous systems that can be interested.
When I'm looking at Wayve,
what is the biggest challenge that you are solving today?
>> There is a plethora of
challenges and I think they come very unexpectedly,
but if I could talk
about the biggest technical challenge that we face,
I think it is really building the robustness in these models.
Because if you think about the problem
that was sitting out to solve,
we have a very high dimensional input space.
If you think about the driving intelligence,
the modal that we're building is one that can understand
the sensory input to a car and then make driving decisions.
Like a human would,
humans have their vision,
they had vestibular senses
and touch sensing on the actuation controls,
and then they control the speed and steering
and some other buttons within the vehicle.
In a similar way, a deep learning system is taking, in our case,
six or seven cameras around the vehicle of
about one megapixel of resolution,
so that's already six million input pixels.
Then you extend that to time,
not just space but time,
and you also have other signals like the initial measurement,
the GPS, the navigation.
All of these signals are set in
the vision input to the model of millions of dimensions,
need to just be distilled down to
a very few dimensions that control the speed and steering.
The limit is just those two numbers, speed and steering.
This model that we need to learn needs to be very robust,
because you can imagine
the weird and wonderful scenarios that you get on the road.
I'm sure you've all seen very bizarre things that
occur in the very long tail of the distribution,
and those are the ones we need to address.
How to do this in a way we can learn concepts
that generalize and we don't just say,
we've learned how to drive through
this specific traffic light but we've learned the understanding
that red light means that we should stop the vehicle,
green light means that we should go in
some circumstances and we can apply
this general like as humans do when we go into new scenarios.
How can we understand driving as a concept?
It's that model that forms a core challenge of what we're doing.
It's a very challenging statistical and reasoning adventure
for us to embark on.
In fact, one that is so hard to hand design as a human,
and I think that modal
from the six million input dimensions down to the twos in
output is really one that
machine learning is exceptional and something that we leave there.
>> Well, so many challenges that you're having in it.
It's like the first thing is just
collecting this huge, huge amount of data.
Because like you mentioned, you have
multiple sensors and you're collecting
around six million pixels, is that per second?
>> There are six million pixels per frame, and we run it about.
>> Per frame.
>> Thirty frames a second. So you
multiply that by 30 to get per second.
>> Here you go. We got a huge amount of
data that through you're collecting,
and then you need to filter it out,
sift it out, remove the noise,
understand what's relevant in that space and
also plug it back and create,
train different machine learning models,
and also deploy the different machine learning models
on multiple units.
It's like you took
all the challenges we have in Software Development and said,
"Hey, go do that and will make it awesome."
Let's go back to the data part where you're
collecting all the data. What do you do now?
Do you collect it on the cars
themselves or do you ship it somewhere to the Cloud?
>> Vision is really to build a driving school for robots
where we have a huge fleet of connected vehicles
that ship data back to as user Cloud
that we can use to actually train these models.
It's a paradigm shift away
from what you traditionally see in robotics.
We have a lot of compute on age,
a lot of processing power on the robot
itself to be able to run a lot of data processing and the like.
But we want to have a very lean sitter
compute on the robot at the age
that can run very
efficiently a deep learning model at inference time.
But we want to be able to take the state of X to
the Cloud and do very large-scale training our grid,
and this training will develop a model that can
understand that complexity is a driving like I described earlier.
We really do view it like a driving school,
and they're got that we want to get
data that is on the boundary of knowledge for our systems.
Not ones that's too easy like basic line following that,
we just see so many examples of that we want to ignore,
and not ones that are too difficult like
a very complex five-point turning maneuver
that might just confuse our systems.
We want to find it on the boundary of knowledge
that we can extract those driving examples, and in particular,
it's like if you go out for a driving lesson,
your instructor will put you in a scenario where
you will learn from it and it'll be challenging for you.
We want to create the same environment for
our vehicles through
this distributed connected vehicle driving school.
>> Driving school for robotics, is that it?
>> Absolutely.
>> I like it.
I really like that concept, it's super cool.
You mentioned basically, well,
you're building a school for robots to
train everyone to know how to drive on
the roads and train robots to drive
on the roads, I really like it.
You also touched on the area that you
work closely with Azure and Microsoft technologies.
Are there specific technologies that you currently work with?
>> Yeah, it's across the stack.
You can imagine deploying this connected fleet of vehicles,
and being able to ingest data at scale to
the Cloud is very much one that aligns well with the IoT,
the Internet of Things, services that Azure offers.
There's that, and then there's also
many other services around authentication and device management.
But then within the Cloud, it's all
about large-scale data saving and training.
I mean, we need to [inaudible] of
data at 400 gigabytes a second to compute
nodes that can run very massively
scaled parallel practicing for deep learning.
Some of these technologies with it at Nvidia,
POV 100 GPUs, or whether it's technologies like Graphcore on IPUs.
It's getting as much data as we can to
that parallel processing to run distributed training at scale.
That's very much the world that we're in,
that Azure can offer on the training side.
>> Yeah, it's a massive scale.
We see different levels of scale and
I think what you're doing is actually
challenging everything that I personally
saw in the area of how to do machine learning at scale,
and it's exciting to see how
you're building your product and how are you going to
make all of our lives basically easier
and push forward what we do with
the technology for autonomous driving.
Last question? What the future holds for Wayve?
>> What does the future hold? Well, what I think we're going
to see is that this technology is really
going to allow self-driving to
scale at a right that we haven't been mentioned before.
What I mean by that is that you can think
about self-driving scaling on a number of axes.
It's not going to be a technology that is initially everywhere.
It's going to have a very initial limited deployment
around a specific domain.
In particular, you can imagine, for example,
we're ride-hailing marketplace where
ride-hailing my addresses city,
self-driving won't be auto, just all of those rides from day 1.
They might be ones that go through areas that are too complex or
perhaps in weather conditions that are too challenging.
So there needs to be some interaction between
the human marketplace and the ride-hailing marketplace.
That's right in the autonomous services within that marketplace.
But the key thing is that we need to provide
a positive feedback that grows the amount
of autonomous services within that marketplace very quickly.
Not only within that marketplace,
but other marketplaces, and so
there's very much a demanded action problem.
But how do we go from,
let's say, ride-hailing A to ride-hailing B,
or some delivery service A to
public transport within different types of mobility within a city.
Then there's also different demanded
action to different scenarios and weathers,
and then ultimately to different geographies.
Everything is about adapting the domain.
Then we not only consider that domain
adaption to these different services,
but also demanded action to the shifting demands of
society of a time as [inaudible] will
exchange as we get pandemics,
as we get things that disrupt urban mobility.
Everything is about shifting data.
So we needed a technology that can very rapidly
understand and adapt to
these changing demands and driving domains.
Now, this is what machine learning can do.
It's demonstrated this in many other fields,
whether it's natural language processing
or whether it's image recognition,
I think the domain adoption of
using pre-trained models and being able to
very quickly adapt to
new tasks with what's called few-shot learning.
I think this has been very aptly shown from deep learning.
Now, this is something that we can do in driving as well,
and what this will mean is that we'll have
self-driving solution that can scale societies needs.
It's not just going to be in a small, affluent area,
but it's really going to grow in a way
that leads us to deploy it over hundreds of cities.
I think this is the future that we
really envisioned from ability and what we'll be
able to bring transport for people down to low latency,
sustainable and very efficient practices.
>> That's incredible. I'm looking forward
to interact more actively with
your products that everything that you build in Wayve.
Alex, thank you so much for being with us here
today and sharing your vast knowledge and expertise.
It was indeed an exceptional conversation. Thank you.
>> [inaudible] , keep an eye out and I think you'll be riding in
autonomous mobility in the years to come very shortly.
>> Looking forward, I'll stay tuned.