The Garage by Sonatus

Neel Mitra, the Worldwide Solutions Architecture Leader for Data and AI at AWS, discusses the evolution and applications of AI in the automotive industry. He highlights the transition from classic AI to large language models and agentic AI, emphasizing their potential to enhance vehicle diagnostics and performance. With over twenty-one years of experience, Neel shares insights on the importance of data integration and collaboration in optimizing automotive technology. He notes the growing trend of software-defined vehicles and the need for continuous learning through MLOps. The conversation underscores the significance of AI in transforming automotive systems and the benefits of these technologies. The conversation touches on Sonatus AI innovations such as Sonatus AI Director, a new solution for in-vehicle edge AI, and Sonatus AI Technician, which uses LLM's to provide a better diagnostic experience.

Chapters:
0:00 Introduction to Neel Mitra and AI Master Class
1:26 Meet Neel Mitra
2:24 Yoga, Hot and Cold
6:25 Introduction to Artificial Intelligence (AI)
11:01 What are Large Language Models (LLMs)?
15:24 What is Agentic AI?
19:56 The evolution of AI
21:24 AI applications in automotive
24:11 Sonatus AI Technician
27:28 Sonatus AI Director: In-vehicle Edge AI
30:25 Leveraging sector expertise in AI and data exchange
32:30 Anticipating the future, today
34:04 Using the cloud for digital twin
35:03 Conclusion

Creators and Guests

Host
John Heinlein, Ph.D.
An experienced technology and marketing leader, John brings his background from startups and established companies to Sonatus. He worked for 14 years at Arm, most recently leading Automotive Partnerships for North America where he engaged OEMs, Tier-1s, and others to deploy Arm-based solutions into automotive applications, including autonomous vehicles. His team was integral to launching the SOAFEE industry initiative for software-defined vehicles of which Sonatus is a member. Earlier he served as VP and Chief of Staff to the CEO and led a group responsible for competitive strategy. For three years, he was VP of Corporate Marketing where he led centralized outbound marketing, spanning marketing campaigns, press, events, web, digital marketing, ecosystem programs, and working closely with investor relations. Prior to Arm, John had an 11-year tenure at microprocessor startup Transmeta where he held several senior roles spanning business development, marketing, and customer success, among others. John earned his B.S. in Computer Engineering from Carnegie-Mellon University and an M.S. and Ph.D., both in Electrical Engineering, from Stanford University.

What is The Garage by Sonatus?

The Garage Podcast from Sonatus brings you conversations with thought leaders from around the vehicle technology space discussing far-reaching topics about software innovation in vehicles.

These episodes will include industry experts, whether from Sonatus, or from our amazing partners across the industry, all of whom will share their Ideas and their outlook for the most important topics in vehicle technology. Episodes span from more technical topics, to business evolution, and market trends. Join us and learn about the future of vehicle in The Garage.

Today in The Garage,
our guest is Neel Mitra,

Worldwide Solutions
Architecture Leader for Data

and AI at AWS.

In today's master class on
artificial intelligence,

we start with the origins of AI.

What was classic AI? What
is a large language model?

What is agentic AI?

And then we talk
about automotive.

How does AI play in
automotive today?

And what are the opportunities
to do new and incredible things

with AI and automotive
in the future?

You're really gonna enjoy
today's conversation.

Let's go.

Welcome to The Garage.

I'm John Heinlein, Chief
Marketing Officer with Sonatus.

We're here today with
Neel Mitra from AWS.

Neel, welcome to the garage.

Thank you.

Neel , we've been working
together for a number of years.

It's so exciting,

and we were finally able to get
you to come visit us and record

an episode with us.

Start by telling us about
you and your background.

Yeah. Thanks for having
me. It's a pleasure.

We had been working on so
many different initiatives,

and finally, the day is here.

So I grew up in India,

had been in the industry
for twenty one years.

Before that, I completed
my studies in engineering,

masters from India, worked
there for a few years in

consulting, then came to U.S.

And then, you know, since then,

I have worked with a lot of
Fortune Five Hundred customers

in fintech and health care, and

I never knew I will work
on automotive until 2016

when I joined
Amazon Web Services.

And those days, you
know, cloud was so new.

I remember I used
to go to customers,

and then the customers
will say, hey.

You guys, sell books. Right?
What are you doing here?

So I will tell them, like, what
we do in the enterprise space,

and then I'll tell them what
we're doing in the infrastructure.

At that time, we're very
heavy in infrastructure.

And as you know, AWS had been
the pioneer in cloud computing,

and we had been, like, you know,

the top of the gardener for
infrastructure even today,

I think.

So, yeah, since then,
it's nine years.

Like, you know, days fly.

And at AWS, we like to
say, like, every day is day one.

So, again, thanks for having me.

And, it was great
learning a lot, you know,

from all of you as well as
we were in the journey in the

automotive space, which
we'll dive deep into.

Thank you so much.

And we always like to start with
a fun fact about our guests.

You have to tell us
a fun fact about you.

That's a tight spot. Okay.

So, you know,

a lot of things in my life
like happened for no reason,

I believe, like even joining
AWS and working on automotive,

I don't know why that happened,

but it turned out
to be real fun.

Same way yoga entered my life.

And I don't know why and how,

because I never heard about yoga
in India and I grew up

there, right?

But, you know, there was a point where
I was traveling a lot and it was too

much going on and I
felt like, you know,

there is something has to be
there which takes care of me

holistically, Not just physical,

but mental and energetic
and everything.

And I tried this yoga
class and I liked it,

and then it was on and off.

I had been doing that probably
for eight, nine years now.

But last year, again, suddenly,
I don't know how random it is,

but I thought I want to be
a yoga teacher as a hobby.

And I started taking
a certification. OEMs.

That's scary. My wife also
tells me, why did you do that?

But, yeah, that's a fun fact.

Probably in the last one year,

I completed my yoga
trainer certification,

two hundred hours.

Now I'm doing the masters,

which is three
hundred hours more.

So it'd be fun,

probably my retirement
plans to be a yoga teacher.

Fantastic. And
what style of yoga?

Vinyasa.

Fantastic.

My fun fact I always try to think
of a fun fact for our guest.

My fun fact is quite
some years ago,

maybe about ten years ago or
so, I, for a couple years,

was very devoted to Bikram yoga.

Oh, nice.

Which if if our listeners
are not familiar with it,

it's when you have one
hundred and five degree room.

Yeah. Hot yoga.

And you got high
yoga as they call it,

and you get in the room and
you do yoga for ninety minutes.

It's a very specific
ninety minute, sequence.

And by the end, like
everybody is soaking wet.

I did one of those in New York.

I, I went to so many yoga places
and finally I've settled into

one or two, but,
yeah, it has been fun.

And yoga is fantastic.

Of course, the strength,
of course, the flexibility,

but also the mindfulness
and the kind of calm Yeah.

It gives you after ninety
minutes with a hot room,

but just in general, you
you leave feeling so,

peaceful, and I think it
gives you great ideas.

I had some of my best ideas
at minute forty five, maybe.

And, you know, more practically,

I know we'll talk a lot
about AI later today,

but more practically,

I see when machines are doing
so much work for you today.

Right? Yeah.

If we don't be mindful and
operate a higher level of

frequency as humans, it's
going to be difficult.

Like, what are you going to do?

You're writing code,
machines are doing it. Right?

So that's where I feel like my
yoga might be useful to me as

well from a
mindfulness standpoint.

We'll see how that goes.

Alright. So tell us
about your role at AWS.

What are you working
on these days?

Yeah. So, you know, like,
it's all over the place.

Like, I had been focusing
a lot on automotive,

as you guys know, for
the last few years.

So, had been working with a lot
of connected mobility starting

with then moved into
software-defined vehicles,

working with a lot of, you know,

worldwide customers
and partners, you know,

starting from, you know,
the Boschs and the DENSOs.

And a lot of customers
I cannot name, but,

there are the BMWs
and others as well,

which I had been very busy with.

But in the last one year,

I have shifted my focus
to be more horizontal.

That means I'm working with a
lot of automotive manufacturing

and enterprise customers on
a wide variety of use cases.

Those are not like
super niche automotive,

which I did in my
architecture and

engineering roles
last few years.

But it has been fun
because, you know,

there is so much
changes happening in the machine

learning space with AI
and agents and everything.

The opportunities are endless
and it's not just about

one industry that I'm seeing.

So I'm trying to learn more.

And that's the best thing
I feel like being at AWS.

I feel like I am being
paid for learning so much.

Like if you're in a university,
but you are not paying.

You are being paid and you
are able to work with so many

talented people across the
globe and doing something to

make this world a better place.

So, yeah.

So that's my journey
in the last one year,

doing a lot of work in the AI
space with OEMs and RAGs and

Agentic AIs for different
set of use cases,

including manufacturing
and automotive.

Great.

Yeah.

So you mentioned AI and and
a lot of times we talk about

vehicle and vehicle software,

and we'll get to
that a little later.

But this episode,

we're gonna talk a lot
more about AI than usual.

Okay.

So let's let's talk about
that first from a high level

perspective because I I think
our listeners have probably a

range of exposure to these
kinds of technologies.

Yeah.

And I think when most
people hear AI today,

they probably think
ChatGPT or, you know,

search and things like that.

Yeah.

But that's just
a a tiny fraction

of the potential for AI.

So I wonder if you could give us with
a twenty-thousand-foot level first.

Yeah.

What's the spectrum of AI
algorithms and AI technologies

that we could think about?

Yeah.

You know, a fun fun fact
before I get into that,

I watched the movie Terminator,

like fifteen years back,
probably the first time.

Right?

And I was really scared, like,
what AI can do for you, like,

how you're fighting against
the AI and stuff like that.

But then when I actually
started working on it,

I felt like, okay, it's like
probably a few hundred years away.

Even if that happens.
I don't know.

Like, it's a science fiction.

Right? We'll we'll
figure it out.

But you are absolutely right.

Like, there is so much that
is happening in this space.

Like, AI is not new. Right?

Those research papers were
from 1930s and 40s and 50s.

And then, you know, we at that
time, we started talking about,

like, supervised model.

That means you are
giving some labeled data.

You are trying to make the
machine smarter so they can do

some of the human stuff.

And then as it evolved
and evolved and evolved,

you saw like the introduction
of unsupervised models.

So it's not just labeled data.
It could be unlabeled data.

It could be multimodal data.

It could doesn't have
to be just text. Right?

It could be images and videos
and audios and whatnot.

And believe me or not,

like in the nineties when AI
was not considered so cool,

Amazon used AI at that time.

So if you went to that first
Amazon site ever, I didn't,

I don't know if you did,

it used to give you
an option that, hey,

if you like a specific genre—A
recommender system—Yeah,

recommendation system.

Right. And that was AI.
That was the early nineties.

Then Google did it, like with
this web search and stuff.

Netflix used to do that in the
early days when Netflix was

sending you a DVD and
something like that.

Exactly.

So AI is not new,

and this kind of supervised
model and a little bit of

unsupervised model had been
there for quite a long time.

And even if I remember the
early days when I was in,

let's let's say,
fintech industry,

you were doing a lot of
this fraud detection,

anomaly detection.

That's all kind of AI. Right?

But then when I got
into automotive,

the first set of AI use cases
I saw is like the cruise control,

which is essentially
you are, you know,

getting all this multimodal
data and you are figuring it

out, keep the car on lane.

There are lane departure
kind of use cases, right,

keeping the lane in in
control and stuff like that.

So those are all traditional
machine learning models.

So back to your question, yes,

there are supervised and
unsupervised and deep learning

came into the play where, like,

how do we think like humans and
neural networks and all those

cognitive functions we started
to slowly put into our vehicles.

And the vehicle started
doing the cruise control.

And then we are talking about different
levels of autonomy these days.

And there are so many use cases,

but from there as
we are evolving,

I think attention is
what you all need.

I think that is the
name of the paper.

I forgot the exact name, but
that came out from Google in,

like, in 2018-2019.

Right? Attention
is what you need.

So that talks about the
self attention mechanism.

And if you think about the
old AI, which is, like,

the neural network CNNs,
convolutional networks,

and the RNNs,
recording networks,

You will see the problem was
you have all these words, right?

But the AI couldn't figure out what
word or what token will come next.

It is very limited.

Like if I say, Hey,

John bought a Rivian or Neel bought
a Tesla or whatever it is, right?

The AI couldn't figure
out a long sentence.

It can probably
go only few words.

But with the self
attention mechanism,

with transformer architecture that got
invented in 2018-ish, that's changed.

Like now you can have thousands
and hundreds of thousands and

billions of tokens like this.

And with the self
attention mechanism,

AI with the encoder decoder
architecture can figure out where

the next word could be.

So this contextual awareness
and the ability to process

humongous amount of tokens
together changed everything.

That's the inception of
large language models,

which as you mentioned is
ChatGPT everyone was talking

about, but those were all the
foundation stepping stones

that led to LLMs and
there is more to come.

Yeah.

So now that you've
just mentioned on,

I think what is the next level
and this key innovation of

these transformer models and the
T in GPT is transformer model.

The tell us about how this
transformer models and maybe

LLMs more generally, how
does that change the game?

What are some of the things
that are now possible with

transformer models?

Yeah.

I think the biggest innovation we
have seen in is content generation.

Right?

So you are in
marketing yourself,

so I'm sure you have seen
so much productivity, right?

You can create a PowerPoint
and you can create all these

stories and videos and audio.

Like, you can create a podcast,
like, I think Google has one,

right, where it sounds
like two humans talking.

This one's not AI generated!

This one is not AI
generated, absolutely,

these are real humans talking.

But the content generation,
I think one of the, like,

most innovative thing that came
out of all this ChatGPT stuff.

And then we started
learning about seventy billion tokens

being used to train this model
or one hundred billion tokens

to train this model.

But then you raise
a very valid point.

How does it help the
broader industry?

That's where we see a lot of developer
productivity suites coming out.

So for example, with
Amazon, we have Q Developer,

or we recently launched
something called Kiro,

which is agentic IDE that is
using the power of the large

language models to make
all those decisions.

So it's not only content generation
for marketing or for storytelling.

It could be content generation
like the code you are writing,

and that could can be anything.

It could be a Java
or Python code,

or that could be a code
that is in mainframe,

which is fifty years old.

And a lot of our customers,
believe it or not,

especially in the
fintech, healthcare,

those kind of industries have
so much mainframe presence and

they cannot move it, right?

So now if AI is able to
understand all this code and

able to refactor the code and
bring it to the cloud and make

it more modern, that's a
huge ROI for the customers.

Yeah.

And I think what's
interesting about about,

LLMs in general is you're
seeing now instead of thinking

about a general purpose LLM and
people talk about sort of AGI

or artificial
general intelligence,

which is we're not there yet.

In fact, what actually happens is
these these LLMs are being trained

for specific functions,

image creation or audio
manipulation or or whatever.

And as a result,

they can be a kind of an
expert in that discipline.

Over time, those
models will converge.

You'll have more kind of a
consolidated expert. Yep.

But today, you're seeing these
special expert models that are allowed

to do able to do these
incredible things you mentioned.

Absolutely. You're spot on.

And that's where I think
this industry will evolve so much.

And we have a LLM marketplace
at AWS as well. Right?

So for a lot of the
domain specific functions,

if you think about even,
like, predictive maintenance,

which had been a big problem
in the automotive industry for

such a long time.

And if or, you know, a lot of
time series forecasting models,

which had been, you know,

a need for a lot of
different kind of use cases.

If you think about large
language models that might be

trained focused on those kind of

domain-oriented problems,
that's a game changer.

Yeah.

And that's where we launched something
called distillation last year.

So it's not about, you know,
going for the big, big, big,

big, big, but going for the
data quality that truly matters

for your line of business.

And that could be automotive.
That could be health care.

That could be fintech.
But you're spot on.

I think that's where
the industry is moving,

and we'll see a
plethora of models.

And then the customer choose the
right model for the right job.

Yeah.

An example that
many people know,

but I think is really
compelling to me is,

when you think about,
medical diagnostics.

Yep.

X-rays in particular.

If you ever had an
X-ray, you know,

you have an X-ray and then they sort of
the the technician sees the X-ray live,

but they're not allowed they're
not allowed to tell you.

But they, like, see hundreds
and hundreds of X rays.

And then it goes to a doctor
who sees tens of thousands of X

rays, and he says, oh,

you have a broken bone or you
don't have a broken bone or you

have this or you have that.

Right.

But they've trained models
on tens, hundreds, thousands,

millions of X-rays, and these models
are able to do as well

or, in in some cases,

better than a doctor
because of the sheer volume

of images they've seen,

and they can see things
that doctors can't see.

Now I'm not saying that
they're gonna replace doctors,

but what it can do potentially
is do some filtering.

It's saying this is the five
percent of X-rays to look at.

The other ones is very
clear, for example. Yep.

Whether that reduces cost or
improves patient outcome or

reduces turnaround
time or whatever.

The point is these
are new tools. Yeah.

And they can be used
in new ways. Yeah.

Now you mentioned, earlier on
another word, which is agentic.

And I think I bet most people don't yet
fully appreciate what agentic means.

It's a very important
emerging trend,

but it's also early
in the life cycle.

Can you talk about what
agentic means? Yeah.

What is some of the opportunities
that agentic AI can bring,

but also what are some of the
challenges and limitations today?

Yeah, absolutely.

So I think there was a saying
that last year or the last few

years were the year of OEMs,

because you see LLMs like
being launched by all these different

providers almost every week.

And then came the RAG, the
Retrieval Augmented Generation,

all the era of the chatbots.

Everyone is building
chatbots using these LLMs.

But this year is supposedly the
year of agents. Now why agents?

Because you need to take action.

Like,

even as humans, right,

if we continuously think
and do not take any action,

we'll go nowhere.

So similarly with all
these OEMs and chatbots,

you can query all this data,
and that might be a good start.

But then to execute
on top of that data,

you need certain functions.

Now in traditionally, how do we
do that? We use microservices.

We use, like, so like, SOA
architectures, REST APIs,

and and stuff like that.

Right?

But in the AI world,

we are talking about agents
because agents have the

decision making power, and
they could be autonomous.

Now what level of autonomy?

That depends on the use case,

and that's a growth area
where we'll learn a lot more.

For example, you know,
in the automotive,

we had been talking about fully
autonomous vehicle for such a

long time, but because of
different complexities,

that is not yet possible.

The same thing will happen with a lot
of this agentic workloads as well.

We can keep talking about fully
autonomous agents doing everything,

but that might have lot of trade offs
when they're making the decisions.

Now you're already
seeing examples of,

something as mundane as
say calendar scheduling

or find me a flight from here to there
in this time frame at a good price.

And so and then, obviously, as
as time goes on, I think we,

society, and then, of course,

every individual may be
in a different place,

may be more willing to
give more authority, more,

agency—pun intended—to the
AI to do things for them.

Or other people could sort of
have it do the work and then

check back, does this look okay
for you before they execute?

And I think we'll see
all of those above.

But I think the
key point, though,

is is by by having the loop of
not just analytics of getting

an answer, data analytics,
but also taking some action,

potentially going
towards the loop closing,

you begin to get
the ability to have,

some feedback cycle that can
improve the capability and

throughput of AI and potential.

Yeah.

The biggest differentiator is
when we write software to date.

Right? It is mostly rule based.

So you are essentially saying,
hey, a microservice or API,

do x y z.

And to do that, that
will take an input data,

and that will send
an output data.

And you have to
codify that logic.

But with agents,

because it has the power of
the large language models,

it is able to make
so many decisions.

So you may not have to
be explicit all the time,

but the trade off there is,
is the power of thinking.

Like even as humans, right?

If we keep on thinking too much,

we're going to exhaust us.

We are going to lose a lot of
energy. We need to drink more.

We need to eat more. So
same thing for the agents.

If it is thinking continuously,

and it is kind of non
deterministic in nature,

you are paying the cost because
the agent is running for a long

time using a lot of memory,

a lot of CPUs and
stuff like that.

So long story short, I think
you know, agent is the future,

of course, but if there
are logic based workloads,

like if-then-else
straightforward,

you still go to microservices.

You still build
those microservices.

Whatever their
decision making needed,

like a supply chain
kind of use cases,

which is a big
challenge today, right,

especially with all these
tariffs and stuff like that.

There's so much uncertainty.

And if you want this agent
to figure out the inventory,

you have to figure out, like,
what is the vendor status,

you have to figure out the
logistics and transportation,

Those are the great use case where
you cannot codify a lot of the logic.

The agents or the multi agentic
orchestration need to go to

different systems to take
care of different actions.

So those are the great use
cases for agents, and, yes,

that is the future.

But don't try to put all the
eggs in one basket and do

agents for everything
if, you know,

it can be fit by a
rule based logic.

Right.

So in my opinion, both these
worlds will continue to stay,

which is more deterministic and
nondeterministic and use the

right tool for the right job.

So if I if I sort of pull back
and I summarize everything

you've just said, what
I hear is AI is growing.

It's getting more capable. Yeah.

But one shouldn't think of
it as a monolithic thing,

but there's a spectrum from
simpler models to to more

complicated models to
potentially more, independent,

obviously autonomous.

I don't wanna confuse the
meaning of that, but more,

agentic, action oriented models.

And all of those will continue
to be valuable in the future.

Yep.

And the mix probably
will evolve over time.

Is that a way to think about it?

Absolutely.

And the same thing if you think
about our computer, storage,

and database, everything
happened like that.

Like we started from, let's say,

all these monolithic
mainframe OEMs.

Right?

And then we
moved into decentralized systems.

We started talking about
VMwares and lot of this,

and now containers.

And now we
talk about serverless functions,

but all those big physical
machines still exist.

But at the same time you
have the serverless function,

which is a single piece of code
running on point five gig of memory.

So this whole plethora of
capabilities in the compute

list still continues to stay.

Databases.

You have all these giant
monolithic databases, right?

But you also have small databases
like time series databases.

You have other type of
data, NoSQL databases.

So the same thing will happen
in the AI space where you have

a plethora of LLMs .

And we are here. You
know? That's what I joke.

Like, as solution architects,

that's sort of our job to work
backwards from the end customer

to figure out what tool

is right for your use case,

because don't try to fit in
everything in one basket.

It's the right tool
for the right job.

Great.

So we have to talk
about automotive.

Sonatus, and our podcast is definitely
about vehicle technology and

vehicle software.

So let's talk you
mentioned a few things,

but let's talk about some ways that
AI is being used in vehicles today,

and then we can talk about kinda
where it's going in the future.

Yeah.

So, you know, in the
last ten years or so,

as I was in the
automotive industry,

I've seen so many use cases.

Like, my background was
in big data and IoT.

And in addition to automotive,

I was also working with a lot of
connected home kind of products.

Right?

And that's why connected
mobility and all those

connected home kind
of were similar.

You were getting a lot
of those telemetry data.

For automotive, you're getting
primarily from the telematics unit.

And they were doing all the fleet
management and things like that.

Then I started working on the
OTA side of the world because

we started talking about, hey,

like automotive should be also
in over their upgrades and not

just the telemedics units
or the infotainment unit,

but how can we make the entire automotive
side of things more upgradable?

And it has to be upgraded over the
time and the cost doesn't depreciate.

And that's where the concept
of software-defined vehicle

started emerging.

And it was not just about
updating over the air.

As you guys know, you
have been, you know,

kind of a SME and
expert in the space.

It's also about transforming
the architecture within the

vehicle because if you have
hundreds of ECUs out there,

it's really, really difficult.

So how you can bring
that ECU consolidation

and make it more
zonal-based architecture or domain-based

architecture, and you can
push all of this, you know,

software updates or collect
a lot of the data from the

vehicles to bring the next
generation of experience.

So I think for AI,

it has been always there
as we've mentioned.

Right? Different forms of
ML had always been there.

When I was working with one
of our partners, BlackBerry,

a few years back,

we're talking about
synthetic sensors as well,

where we deploy a
lot of the sensors,

which are machine
learning based,

or it could be logic based.

And you are collecting
all this data,

processing the noisy data
on the vehicle before you do

something on the cloud because sending
the data in and out is

also cost prohibitive
at many times.

So, sorry for the
long winded answer,

but I believe that with
generative AI and agents,

the entire supply
chain to connect,

connected mobility

or collection of the data and
how much high quality data you

can actually send to the cloud.

How much work can you do,

which are non deterministic in
nature on the vehicle itself

with the EE architecture and
software defined vehicle,

is going to be crucial.

And that's where your earlier
point of having the right model

to do the right job.

You don't have to have the hundred
billion token model in the car.

You can probably have, you know,

a ten million token on the car,

which is good enough for
what you are trying to do.

And that's where all this great
softwares that you guys are

building, which is focused
on the vehicle software,

I think would be
immensely valuable.

Yeah. Thanks so much.
Appreciate that.

And let's let's get into that.

I mean, I I think as we
mentioned at the beginning,

I think people
tend to think, and,

there's a perception that AI
in vehicles is only autonomous

driving or it's only automatic
braking or it's only,

adaptive lane keeping.

And those are phenomenal.

I'm a huge fan of all those
technologies, and I love them.

Yeah.

But what what we're
trying to do at Sonatus,

and I think that there's a
missed opportunity and we're

trying to raise awareness,

is I think that there's
much more that's possible.

So I'll give you a
couple of examples.

And we've been collaborating
with AWS for years now.

We really love your partnership.

Last year at the Consumer
Electronics Show,

that is to say January
twenty twenty five Yeah.

We showed off a new product
called our AI Technician,

and this was running
on top of AWS,

using large language models.

And what we showed was that
there's an opportunity to use,

LLMs and use analytics to do
a better job of understanding

problems in the vehicle

And then leveraging the things
like the the software, the,

the data infrastructure that
Sonatus provides for better,

finer grain data Yeah.

To be able to identify, hey.
Is this a is this a problem?

Isn't it a problem? Is
it an urgent problem?

Is it a non-urgent problem?
Is it a fleet-wide problem? Yeah.

Is it a one-vehicle problem?

So this AI Technician
idea has gained,

has garnered a huge
amount of interest.

And we we also use that we
talked about this agentic idea

that it wasn't one
source of data.

It's not just, okay, we
just look at the the,

the trouble codes, DTC codes,
diagnostic trouble codes.

We're not just looking at DTC
codes and putting it into a RAG

or putting it into LM.

Yeah.

But rather—and this was the
point you made earlier—we're

pulling together data
from many sources.

And combining the data from
many sources to get a better

answer than any
one source alone.

And I think that's where
a power that AI brings.

That's that's going
to be hugely powerful.

I agree with you.

Like, we call it data
center on wheels,

And it truly is because
as you were saying,

there are like so many
network topologies, right?

There is CAN, there
is ethernet, there is,

like so many others out there.

And what we had to
struggle for a long,

long time is how to deserialize
lot of these different type of

signals into something standard.

And that's why we started
working on VSS specifications

from COVESA, but not
every OEM uses like

VSS, right? Everyone
has their own thing.

So now how do you standardize
all this data coming from all

this network topologies,
different kind of ECUs,

right, and then making sense
out of it in near-real time to

bring that personalized
experience or even predictive

maintenance that your brake
pad is having wear and tear and

needs to be replaced,
send it to the dealership.

And then you go
to the dealership.

Dealership says that,
no, it's not a problem.

We're going to fix it. Right?

So all of this is this
holistic value chain.

I think that AI can solve it.

Not just, as you said, like
the fancy infotainment system.

Sure.

Like I worked on the Alexa
SDK when it first launched for

embedded devices.

And I still remember,

like we did a
reinvention with NXP

and we showed customers
how you can embed, right?

Alexa, what stuff.

So all this infotainment kind
of stuff we had been doing for

such a long time and ML had
been there for such a long time,

but there is so much more to
do with the AI of today's age,

as you're saying, because
it's the next evolution,

and it's everywhere.

It's so I'm so
glad you said that.

And that last point is a
perfect transition to the the

last point we want to make as
we as we're sitting here today,

we're so excited that Sonatus
has just launched our newest AI

product called
Sonatus AI Director.

And and what I think that
building even on what we talked

about a moment ago
with AI technician,

our recognition is that the AI
today tends to be centered in

ADAS units, generally
speaking, or, obviously,

in certain cases,

chatbots in the IVI unit to do
things like where's the nearest

Starbucks, or am I gonna hit
traffic on the way home? Yeah.

But the reality is that's a tiny
fraction of the vehicle infrastructure.

It's a tiny fraction of
the vehicle subsystems.

And so our recognition was
there's a huge untapped

potential to use
AI in more vehicle

subsystems for a
range of applications,

whether it's better diagnostics,

better preventative maintenance,

optimization, efficiency improvements,
tuning over time, and so on.

So what AI Director
did, and this is,

AWS is one of our
launch partners,

and so we're so happy to have
you, help us kick this off,

is showing how we,

can provide an
infrastructure to accelerate

the deployment of AI into the
vehicle, not in the cloud.

The cloud is important. The
cloud will always be important.

Yep.

But you there are certain applications
that you have to do locally,

whether it's latency sensitive
or there's too much data to

send to the cloud or
there's privacy concerns,

or there's IP-
protection concerns.

How can we bring those
models, where appropriate,

closer to the vehicle running
in the infrastructure of the

vehicle and solving
problems, cost reducing,

providing efficiency.

So we were so excited to launch that
recently and so happy to have you.

You've been looking at this
kind of problem for a while.

Do you see this as a real
need in the industry?

Yeah.

I absolutely do
because, you know,

data is still the
differentiator.

Because we keep on
talking about AI, but,

sorry for my language, but
it's going to be garbage in,

garbage out if you do not have
the right quality of data.

And that's what I do
believe. You know?

Like, we still need to
obsess about having high data

quality, high data
governance, as you mentioned,

all these different
kind of optimizations.

And then your AI, whether
it's LLM based AI,

whether it's agentic based AI,

is going to generate
that ROI for you.

If you just think you
will get this LLM,

which is trained
on Internet data,

and it will do magic for your
business, it's not going to.

I mean, that's the reality.

And that's where a lot of
the solutions that, you know,

you guys are pioneering on, I
believe it would be, you know,

super useful for the customers.

Yeah. I thank you for that.

And and one of the things
that occurred to us,

and we've validated this and
it continues to be the case is

there are so many
sector expertise.

There's so much knowledge
and know how for batteries,

or propulsion or safety
or tires or whatever.

That that kind of
expertise exists out there.

But the challenge today is OEMs
have to integrate each of these

models individually at
high integration cost.

How could we make a more
common infrastructure for them to draw

upon innovation from these
industry experts in a way

that's very scalable for them?

And that's what we
are trying to do.

And so far, there's been very,

very positive
reception for that.

Yeah, and this is amazing because
even if you think about this

multimodal OEMs Right.

They are actually bringing
a lot of the knowledge,

although they're
still internal data.

But more and more, it is being
used by different, you know,

industry verticals.

And similar to how, you
know, like, ten years back,

I had seen, like,

lot of customers are actually contributing
something called data exchange.

That means they have the trader,

which is high quality data
maybe for finance industry or

health care or auto,

and they're sharing it either
in open source model or

commercial model,
whatever it is.

And that can be used by
different other verticals.

I believe same thing will probably
happen for this space as well.

And when this happens,
the LLMs are so powerful.

You use that as your brain
and bring all this best practices.

As you said, like EV range anxiety
is such a big problem even today.

Right.

I talked to so many, OEMs even,

and they talk and they
share the same problem.

Like there is EV range
anxiety and what can we do?

And this cannot be optimized
until you have this entire data

flywheel of collecting
the data from the tires,

collecting the data
from different ECUs

and then consolidating it
and doing that predictive or

preventive kind of maintenance,
even range optimization.

It seems simpler,

but it is not because we're also putting
so much capability within the car.

Like my kid wants to watch
Netflix while for a long drive.

Right?

And if you're putting so much
battery out of the system and

still expecting your range
to be five hundred miles,

that's not going to happen.

So how do you
optimize all of this?

And the answer is data
and AI on top of it,

and you continuously learn,
fine tune your model,

whether it's a traditional
model or a large language model.

Right?

And then that is the MLOps
that, you know, you can do.

Exactly. And you
mentioned range anxiety.

One of the the sources from
that is also the battery.

Vehicle battery technology,

the understanding of
that is evolving rapidly.

One of our launch
partners is a battery

management system,
battery-health leader called

Qnovo. Mhmm.

And we were happy to have
them as part of our launch.

And what they're looking at
is they have in incredible

innovation in looking
for failures in battery,

looking for the ability to
charge batteries faster and so

on, but you have
to have the data.

You have to have the model.

And so one of the things
we're doing with them is helping use

the same infrastructure to
provide a way to land that model.

So that's a subsystem
you wouldn't have thought of,

but it can leverage the
same infrastructure.

Exactly.

So and then the
other point you made,

which I think is
really important,

is that we as an industry
and we as technologists,

our mission always is to
anticipate the future.

And and sometimes anticipating the
future means that you know

there'll be things in the
future that you don't know.

Yeah.

So how do you put in place the
framework so that when the next

innovation comes,
you can use it?

You can avail yourself of it.

Because too often,

with many technology, but
certainly with vehicles

historically, is you design it
because it's a car, it drives,

and that's all I need.

But then something comes
that wasn't anticipated,

and your vehicle becomes
obsolete or less competitive in

the marketplace or
resale value goes down.

How do we put in place the
capability to extend vehicles

over time for range
optimization, for new services,

for new safety technologies
after shipment.

And that's one of the extensibility
benefits we're trying to bring,

and and we feel like there's
a huge need in the industry.

Yeah, yeah, absolutely.

And this is where, you know,
I still believe the, you know,

power of cloud is going
to be so important.

Right?

I have not directly worked
on some of these initiatives,

but my peers have like the
virtual ECU on the cloud.

Right?

Where you were bringing a lot
of this NVIDIA stuff that you

were probably using on the car.

How can you keep running them on the
cloud and do all these simulations?

And especially if you're going
to that EE architecture where

you have consolidated ECUs,
you have the car on the cloud.

And then you are running end-to-end
simulations on all of this.

And eventually, you are
figuring it out, like,

what makes sense at
this point of time?

Because a car is a
different animal.

It's not like a headphone,
which is a smart headphone.

Right? You know, like, I don't
want to preach to the choir.

Like, it's like safety critical
systems that have deterministic

actions you need to take.

The airbags getting deployed.

You need to take an
action immediately.

This is not like any
other connected products.

This is the most complex
product out there.

So I think, as you said, like
it's continuous learning.

We will be accelerated on the
cloud and then partner on the

edge side, but we
need all this data.

And then machine learning
and OEMs need all this data.

We need all those MLOps.

We need all the
SDLC best practices.

That's not going anywhere.
It's just the tools changing.

And we need to use the right
thing for the right job.

It's a perfect
summary, you said,

because while we talked for a
moment ago about the vehicle

edge, the point is really that
you need all of the above.

Yep. You need strong cloud.
You need strong data.

You need strong services. Yep.

And you need data
in the vehicle,

and they need to work
closely together.

And that's really the story.

And that's why we
partner with you. Yeah.

Yeah.

And that's why we're so happy
to have you here. Likewise.

It's such a joy to
talk about this,

and it's so exciting to talk
about AI in more detail.

We're gonna be talking more
about AI in in future episodes.

But thank you for the
master class, really,

in all of these technologies.

Thank you for having me.
Really appreciate it, John.

It was so fun chatting with you.

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