>> Hi and welcome back everybody to Tech Exceptions.
Today we have a very exceptional conversation with Aditya Sharma.
Aditya Sharma works for Microsoft,
is a Program Manager in the autonomous driving program.
We're going to talk about autonomous driving, the community,
and what is Microsoft doing to enable more people to be
successful in the autonomous driving ecosystem? Hi, Aditya.
>> Hey, Adi. How are you?
>> I'm good. How are you?
>> I'm pretty well.
>> What exactly do you do in Microsoft?
>> It's a good question. I'm a Program Manager
in the Azure core team,
basically responsible for working
with our startup community in the autonomous driving ecosystem.
So I started the
autonomous driving startups program about a year ago,
and so we've been working with
just our overall strategy for the autonomous driving space,
as well as our partnerships with startups in this space.
>> Cool. But how did you decide, "Hey,
I want to be in the autonomous driving area
and do things with autonomous driving ecosystem and community?"
>> Yeah. That was an interesting one.
When I joined Microsoft,
it was back in 2016, out of grad school, CMU.
I did a graduate degree in robotics
and I came here and I was like,
"That's something I need to continue working on."
We have this thing called the Microsoft Garage.
People may be familiar with it,
but it's basically a place where employees can go and work
on projects after hours, then over weekends.
Started the deep learning and robotics chapter there,
and that one thing led to another,
and we ended up starting
this project that we called project rotor,
which was basically a group of employees getting together
and just learning about autonomous driving,
doing autonomous driving research
in some specific areas like reinforcement learning,
as well as working with simulation for autonomists.
So things progressed from there,
and that turned into a full-time job,
and now we realize that there's
a huge potential here in this space for Azure to
play a major role specific to autonomous driving startups.
I think we'll talk about later how they're so important,
but that was an area where we saw Microsoft playing
a potentially pretty significant role in
advancing the whole space of autonomous driving further.
>> Yeah. Absolutely. We have so many tools, so many services,
so many amazing things happening on the Microsoft Cloud,
and we can support a lot of different industry.
I know you also work very closely with
the startup ecosystem around autonomous driving.
What is exactly that you do there?
>> Yeah. So if you think about everything
that has happened in this industry over the last five to six years,
startups have pretty much been
the gold innovation centers
for all the autonomous driving services happening.
There's your big players,
like the big automotive OEMs and the tier ones,
but then the real innovation is happening with startups.
What we've done is,
I mean being a startup is hard enough,
and then when you talk about being an autonomous driving startup,
the challenges just into different scale because
you have to build this technology that doesn't exist really.
It's going to be out there in the future.
But at the same time, you have to
face all the challenges that a startup faces.
But then on top of
that strictly speaking from an infrastructure standpoint,
we're looking at things and
workloads that were not possible before.
So as a startup, you also have to figure out those.
We as Microsoft figured that was
a great place for us to position ourselves in where we
could help these awesome startups
do what they do best and help them scale.
That was really the genesis of me getting involved in this
with the community and the program and all of what we do,
but then also enable
startups go-to-market and make sure that that's happening
at a very fast rate because of
the vast customer and
partner ecosystem that we
have in the automotive industry in general,
startups are able to leverage that as well.
So it's a combination of all of that, that has
basically led to this program and what we're doing here.
>> Yeah. Wow. That's exciting.
When thinking about it, there are
so many challenges when we talk about
autonomous driving ecosystem and
the technology and also the business and what is available,
and it's really pushing the industry,
the car industry to a new level.
I know you work closely with very targeted startups.
Do you want to share a little bit
about what you're doing with them?
>> Yeah. You rightly pointed it out,
so the automotive industry has mostly
been a mechanical engineering
and a very system engineering warehouse.
Autonomous driving signifies
a complete shift to software engineering,
which is something that the big giants weren't doing.
We being a software company have a huge role to play there,
and startups natively in this industry,
again, are coming from a very different background.
Most of them are at least not
coming from an automotive background.
They're coming from more of a robotics background and
AI background, machine learning.
The idea, again, is we start a scale conversations with
those startups where we're
talking about petabyte and even exabyte level scale,
and then we basically use leverage,
everything that Microsoft has to offer in
terms of our Cloud platform in Azure and
help one startups gain access
to, basically the technology itself,
but also have them gain
access to the people working on that technology,
so that our future roadmap is
directly influenced by what these startups need.
Because, again, like I said,
these startups are really pushing the boundaries
of just compute, and
storage, and networking, and everything else that goes with it,
just the requirements there and what Cloud can do.
So with this program,
our hope is to basically help them get there faster.
>> Yeah. That's impressive.
Like you mentioned, there's compute challenges.
There are storage challenges.
Of course, there's a lot of machine learning done in this area.
It depends on the different functionality that we want to solve.
We have the connected cars, and we have
the different technology for the autonomous driving itself.
There's a lot of component.
There's robotics, autonomous systems, storage, IoT.
There's a lot of challenges that exist in that space.
Which one do you think is the most
prominent that the community is now facing?
What do you think?
>> So from a technology perspective, I think it's all of that,
but the key, all I think is one scaling all of that.
So as I talked about before,
we're talking about ingesting
multiple petabytes of data every single day in some cases,
and bringing that to the Cloud,
then running large-scale AI jobs.
We're talking about large-scale simulation,
large-scale training and validation on that data.
Again, this is petabytes of data coming in every day.
Then basically running those processes
to then continually update
your models and then deploying them back onto your cars,
and not just doing that at a very large scale,
but also doing this in a very small amount of time.
We're talking code to road,
road to code in a few hours,
maybe a day, day and a half.
That is, I think,
the biggest technological challenge.
Apart from obviously the obvious,
will AI ever happen?
Do we need artificial general intelligence
to solve the problem of autonomous?
I think aside from that,
the infrastructure scale is really the biggest challenge,
especially because it comes with a huge financial investment,
if you speak, on the startups part
and they have to be ahead of the curve.
They have to be thinking, let's say,
three years down the line when they're
thinking about how they plan on scaling.
So I would say that's the biggest challenge I
see outside of solving the problem itself.
>> I remember around
eight years ago when I read about autonomous cars,
one of the biggest questions that existed there was the ethics.
If now, the car is going to accidentally hit someone,
who is to blame?
Is that the AI?
Is that the person that was sitting in the car?
But they weren't driving.
They were just sitting, or should we even give
AI this responsibility to say?
But again, that was eight years ago.
Where are we standing now with
AI when we talk about the autonomous driving?
>> I think we're still asking the same questions to be honest.
It's always a debate that comes up.
I can give the standard answers.
Statistically speaking,
autonomous vehicles have proven to be more safe,
but again, any life lost is bad.
So I'm not sure I have an answer to that.
We're making progress as an industry,
and hopefully we will reach
a place where we don't have to ask that question anymore.
But again, I think we're basically still in the same place.
I will say though,
as we talk to these companies and not just startups,
but also the bigger companies working in this space,
there is a heavy,
heavy focus put on
safety certification and making sure
that the autonomous vehicles that are on the road,
even the test vehicles,
everything is as safe as possible.
There has been a lot of work recently over
the last couple of years done
in terms of building standards for safety.
We have standards like SOTIF, Safety Of
The Intended Functionality that basically talk about this.
But again, not sure there is an answer yet,
but it is very much front and center,
and the focus of what this industry is working on currently.
>> So it's safe to say
that security first and safety first for everyone.
>> Absolutely.
>> Interesting. I'm happy to hear that.
I remember articles I read many,
many years ago and this was the main focus.
Let's keep the past in the past and focus on the future.
What do you think the next exciting challenges
to solve in the industry?
>> Yeah. This one is interesting. So I personally
feel there's going to be a shift.
If you look at autonomous driving that's been happening
over the last four to five, six years,
everything has been very supervised learning-based,
especially when you look at how our perception systems are built.
It's all pattern recognition with a huge amount of data.
I think in order, and again,
this is just my opinion.
I think in order to make autonomous driving happen,
we will need to start looking into unique approaches,
like unsupervised learning and reinforcement learning, especially,
where basically the idea is to start thinking
how humans think and start learning to drive like humans do.
I think that is the thing that gets me most excited,
and I think that is our best shot at making this thing a reality.
Again, I'm not saying it's going to be easy to solve that problem,
but that at least sitting here today in 2020,
when I think about this,
is the closest we are going to get to having a solution
to that one percent long-tail AI problem
that's famously we all struggle with it in this industry.
>> Yeah. Absolutely. I'm definitely
following closely and looking forward to see what's come up.
Last question.
What tips do you have for people who want
to get into the autonomous driving industry?
>> I always say
autonomous driving is more complicated than rocket science.
Because we have rockets, we don't have autonomous cars.
But getting into the autonomous driving space is
actually much more easier.
There's a variety of skills that
you can have that are needed in this industry.
Right now, there are definitely more jobs than there are people,
or at least there is a high demand
for really smart people in this industry.
I would say any background from
mechanical engineering to embedded systems.
You have a place, even like business backgrounds,
like finance and we talked
about safety certification, that has a place.
But then obviously in the code
sits your traditional software engineering roles.
I would say the easiest path to a job in
the industry would really be
through machine learning and robotics,
because autonomous driving is
essentially a robotics and AI problem.
So if you have those skills,
you're setting yourselves up for success in this space.
>> Yes. Absolutely. So many opportunities.
So many ways to skill up and get into the industry.
Aditya, thank you so much for joining our meeting today.
It was lovely to have you and looking
forward to hearing more about the programs,
the different things you're doing at Microsoft.
>> Yeah. Thank you for having me. It's a pleasure.