Sakya Dasgupta: A Journey from Gaming To Enabling Embodied Intelligence
Summary
TLDRIn this episode of Silicon Grape Vine, host Nittin Dad interviews Sakya Duppa, CEO and founder of Edge Cortex, a company focused on creating energy-efficient AI systems for edge devices. Sakya shares his journey from writing a Tetris game at 9 to his PhD in brain-inspired computing and his work at IBM. He discusses the inspiration behind Edge Cortex, the importance of power efficiency in AI, and the future of AI hardware. Sakya also reflects on his personal interests, the significance of taking risks, and the concept of embodied intelligence in AI systems.
Takeaways
- 😀 Sakya Dupta is the founder and CEO of Edge Cortex, a company focused on creating energy-efficient systems for AI applications.
- 🌟 Edge Cortex aims to bring powerful AI models like GPT-7 and multimodal models to edge devices, inspired by the human brain's efficiency.
- 🌐 The company is headquartered in Tokyo, Japan, and operates globally with a presence in the US, Singapore, and India.
- 🎓 Sakya's journey in AI began with an early interest in computer science, inspired by his mother, a computer science professor, leading him to write his first game at a young age.
- 🏫 Sakya pursued higher education in computer engineering and machine learning, with a focus on applying AI to real-world devices and systems.
- 🏢 His professional experience includes working at IBM Research, where he co-invented the concept of Dynamic Bayesian Networks, used in time-series processing.
- 💡 Sakya emphasizes the importance of taking risks and learning from failures, which has been a core part of his entrepreneurial journey.
- 🌱 He highlights the cultural mix and government support in Japan as beneficial for startups, reflecting a growing and diverse tech ecosystem.
- 🔍 Sakya predicts that AI hardware will evolve towards more adaptable and domain-specific architectures to meet the changing demands of AI models.
- 🧠 He is intrigued by the concept of embodied intelligence, suggesting that the physical environment and body can influence intelligence, a concept he aims to apply in creating intelligent systems.
Q & A
What is the primary focus of Sakya's company, Edge Cortex?
-Edge Cortex focuses on building energy-efficient systems for running AI applications, especially recent models like multimodal, and bringing that power to edge devices.
How does Sakya's background in AI research influence Edge Cortex's approach to technology?
-Sakya's background in AI research, particularly in brain-inspired computing and neuromorphic computing, influences Edge Cortex's approach by emphasizing power efficiency and the development of systems that mimic the human brain's intelligence within power constraints.
What inspired Sakya to start Edge Cortex?
-Sakya's inspiration to start Edge Cortex came from his work at IBM Research, where he explored how to make algorithms and software efficient for standard hardware, and his experiences with power-constrained environments like robotics.
How does Sakya's early experience with programming influence his career?
-Sakya's early experience with programming, such as writing a Tetris game at a young age, sparked his interest in computer science and laid the foundation for his journey into AI and entrepreneurship.
What is the significance of the Sakura in relation to Edge Cortex's hardware?
-The Sakura, or cherry blossom, is significant to Edge Cortex as it represents the Japanese culture of renewed joy and inspiration, which is reflected in the company's hardware and work culture.
What is Sakya's perspective on the future of AI and high-performance computing?
-Sakya believes that the industry is still scratching the surface when it comes to AI. He anticipates an evolution in Transformer models towards more brain-like efficiency and adaptability, with a focus on domain-specific reconfigurable architectures.
What advice does Sakya have for young entrepreneurs?
-Sakya advises young entrepreneurs to learn to fail fast, take risks, and learn from those failures. He also suggests finding the right partners and investors to help in the growth of a company.
What is Sakya's view on the importance of embodied intelligence in AI systems?
-Sakya views embodied intelligence as a significant aspect of creating intelligent systems, where the hardware environment plays a crucial role in intelligence, just as much as the software substrate.
How does Sakya's personal interest in watches reflect his approach to technology?
-Sakya's interest in mechanical or electromechanical watches, which use silicon alongside mechanical systems, reflects his appreciation for the integration of technology with traditional craftsmanship, mirroring his approach to combining traditional hardware with advanced AI systems.
What hobbies does Sakya enjoy outside of his work in AI and technology?
-Outside of his work, Sakya enjoys outdoor activities like hiking and running, which allows him to explore and stay active, reflecting a balance between his professional and personal life.
What is Sakya's opinion on the current state of AI models and their efficiency?
-Sakya believes that while today's Transformer models are impressive, there is room for evolution towards greater efficiency, similar to the human brain, which is the ultimate goal for AI systems.
Outlines
🌟 Introduction to Sakya Duppa and Edge Cortex
The video begins with host Nittin Dad introducing the guest, Sakya Duppa, a researcher in AI, entrepreneur, and CEO of Edge Cortex. Sakya's background in brain-inspired computing and his journey from his PhD at the Max Planck Institute to founding Edge Cortex are discussed. Edge Cortex focuses on creating energy-efficient systems for AI applications, particularly for edge devices, inspired by the power efficiency of the human brain. The company, headquartered in Tokyo, has a global presence with operations in the US, Singapore, and India.
🎮 Sakya's Early Inspirations and Career Path
Sakya shares his early inspirations, including his mother, a computer science professor, and his own interest in computer games, which led him to write a Tetris game at the age of 9. His academic journey from India to a Master's in Edinburgh and then to the Max Planck Institute is detailed. Sakya's professional experience includes time at Microsoft and IBM Research, where he explored machine learning and neural computation. His entrepreneurial spirit led him to found Edge Cortex in 2019, aiming to address the challenges of power efficiency in AI hardware.
🌐 Sakya's Move to Japan and the Founding of Edge Cortex
Sakya explains his move to Japan, initially for his PhD research and later for work at IBM Research, focusing on brain-inspired computing. He discusses the cultural and professional aspects of living in Japan, including the country's growing startup community and the revival of semiconductor interests. The decision to found Edge Cortex in Japan in 2019 was influenced by the global AI hardware trend and the opportunity to innovate in a market with a history of technological advancement.
🏆 Sakya's Achievements and Vision for Edge Cortex
Sakya reflects on his career achievements, including co-inventing Dynamic Bayesian Networks and their application in finance. He expresses pride in Edge Cortex's approach to hardware and software design, which prioritizes software efficiency before hardware architecture. Sakya's vision for the company involves creating adaptable, domain-specific architectures that can evolve with the changing AI landscape, aiming for brain-like efficiency in AI processing.
💡 Personal Life, Hobbies, and Advice to the Next Generation
In the final segment, Sakya talks about his personal life, including his love for watches and outdoor activities like hiking and running. He shares his views on Tokyo's vibrant culture and how it inspires his work. Sakya offers advice to the next generation, emphasizing the importance of taking risks, learning from failures, and finding the right partners for growth. He also discusses the concept of embodied intelligence and its relevance to creating intelligent systems, highlighting the importance of a holistic approach to AI development.
Mindmap
Keywords
💡AI Research
💡Neuromorphic Computing
💡Edge Cortex
💡Brain-Inspired Computing
💡Power Efficiency
💡Entrepreneurship
💡Machine Learning
💡Hardware Substrate
💡Deep Learning
💡Embodied Intelligence
💡Multimodal Systems
Highlights
Sakya Dupta, CEO of Edge Cortex, discusses the company's focus on creating energy-efficient AI systems for edge devices.
Edge Cortex aims to mimic the human brain's power efficiency, which operates on 15 to 20 watts.
The company was founded in 2019 and is headquartered in Tokyo, with a global presence.
Sakya's background in AI research at the Max Planck Institute in Germany influenced Edge Cortex's inception.
His early inspirations include his mother, a computer science professor, and his own experiences programming games.
Sakya's journey from India to Edinburgh for his Masters and then to the Max Planck Institute shaped his AI research.
He co-invented the concept of Dynamic Bayesian Networks during his time at IBM Research.
Edge Cortex's approach is to start with software design before creating hardware architecture.
Sakya is proud of Edge Cortex's achievements in a short period, particularly in software-first design and hardware systems.
He envisions AI models evolving towards greater efficiency, similar to the human brain.
Edge Cortex is working on adaptable domain-specific architectures for AI efficiency.
Sakya shares his personal interests, including a fondness for watches and programming.
He emphasizes the importance of taking risks and learning from failures in both entrepreneurship and life.
Sakya advises finding the right investors to support a company's growth and maintain control.
He discusses the concept of embodied intelligence and its relevance to AI systems and personal growth.
Edge Cortex is focused on creating intelligent systems that are power-efficient and sustainable.
Transcripts
[Music]
hello and welcome to this edition of
silicon grape vine my name is nittin Dad
and today my guest is sakya dupta now he
is uh you will find if you look at
Google search he's a is known as a
researcher in Ai and stist but he's
actually also an entrepreneur and a
founder and CEO of a company called Edge
cortex hello
sakya hi naan thanks
for good Saka let's um maybe just let
let's start about um uh what you're
doing right now Edge CTIC uh so it sort
of stems from your whole background in
AI research and and sort of bra yeah I
call it brain inspired Computing but I
think it's more more that sort of
neuromorphic computing just tell us uh
the of edge cortex and you how that came
about and and what what you're doing and
then we'll go into a little bit of your
journey sure absolutely very happy so
you know at at let me just start with
what you're doing a little bit first and
then I'll walk back time so our primary
focus is to build energy efficient power
efficient systems uh for running uh AI
application especially recent models
like J VII multimodal and bring that to
powerin devices like Edge Etc uh which
is not very different from the brain
given that the brain is extremely
constrain in terms of the power it's
around 15 to 20 wats but it uh clearly
even now we canot match the intelligence
of a typical human brain so uh you know
Ed um with that goal of power efficient
AI processing at the edge we started in
2019 uh so roughly around five years um
we are headquartered in Japan uh in
Tokyo uh where I'm speaking to you from
but we are pretty much a global company
we operate out of the US Singapore and
recently we kick started a new center in
India in Hyderabad okay to tell you
about how we you know kind of the
background of otex I think um I have to
go back a little bit in time um when I
was doing my PhD at one of the max
planks at uh in Germany uh the key area
of focus was uh how do you take
inspiration from the as you call it
brain inspired Computing uh both From
algorithm's perspective as well as kind
of the efficient Hardware substrate that
it has and be able to apply that to
systems and we were doing that on
robotic systems like walking machines
and when you're on a robotic system is
very constrain in power size area it's
not unlike a typical Edge environment
yes and lle that's of a focus now the
critical question was how do we make the
algorithms and software efficient
and make it work across standard
Hardware that was the first thing that I
was trying to solve and then much later
years later when I was at IBM research
uh I came to kind of explore same
similar kind of questions that again if
you scale up the algorithm problem you
run latest deep learning algorithms lot
more compute heavy power hungry in the
DAT how do you bring that the and so
that's kind of the I would say starting
point for H cortex be able to solve that
problem and hopefully someday we will be
able to achieve the level of efficiency
that the brain does okay well um I mean
we'll go into a little bit about um uh
your journey and I think what we we talk
about inspiration I think you started uh
actually you inspired by your mom your
mother uh who was a computer science
professor and you you you create wrote
your own game in I think in GW basic
something like Tetris at 9 years old
tell us about that Journey yeah sort of
what uh what made you do
that that is correct wow yes so I think
my mother definitely I've had a number
of Inspirations over the years but I
would say my mother was my first
inspiration in terms of she's not only
computer science and of you know I would
call her a computer scientist in that
sense um so early
on I was I grew up in calata in India
and was exposed to at that time if I
recall correctly MS Doss uh very early
days of computers uh at least in India
uh and games was a major part of so I
started getting exposed to different
types of Dos games which I found
extremely interesting given the amount
of limited amount of Graphics but you
could add a lot of intelligence so once
I learned my first programming language
I believe it was gwpc at that time uh I
in fact the first major project I ever
did and this is I'm maybe n or 10 years
old was writing a tet game uh at that
time if I remember correctly there was
also a Nintendo Game Boy that was there
which also had a similar Tetris so I was
trying to kind of reproduce that myself
and you know that really got me started
with computer science um and much later
I found myself doing Computer
Engineering and kind of pursuing that uh
in a journey uh in fact my uh foray into
AI or machine learning was through games
there were a lot of games that exposed
to that was applying uh AI algorithms
and uh IED that uh for quite a long time
until my masters uh trying to understand
the use of machine learning in games and
then how do we bring that to a real
world devices that we interact with yeah
and and I think that that's sort of took
you from doing your your B bachelor in
India to going to the Masters in
Edinburgh which is renowned for you that
kind of um work there as well uh so and
and and then the max plank Institute so
I think you you you you basically built
your whole uh sort of foundation for for
doing all this AI research that then you
carried on doing with
IBM that's exactly right in fact in fact
after I finished my uh Bachelors I
actually joined Microsoft for some time
and this was pre aor days as well uh but
what I was really attracted towards was
to do machine learning and at that time
at least I was not getting enough
opportunity so I decided that you know
let me go and pursue a master's and
during those days there were not of not
many universities offering AI Masters in
Ai and Edinburgh were one of the few
places okay so so what year was that
what year was that this is um
2006 2007 yeah that time so pre- de
learning wom alexnet was not yet
published um so when I was at Edinburgh
I was actually focusing on Gan processes
and Asian systems uh but then uh one of
the professors um kind of got me into
neural computation and one of my all
time you know talking about Inspirations
alongside my mother I would say one
nyman has always been quite an inspiring
figure for me uh and in fact one nyman
also in early days has talked about kind
of brain inspired Computing um and you
know how we need to be able to get there
with Hardware as well yes so somehow I
found myself posting a PhD in that area
so I was never so much a researcher as
much you though Google says that I would
say I've always been more of an engineer
at creating systems um and uh I think
the entrepreneurship side also always
existed um the idea was to implement
something and make a functional business
out of it and much you know years later
in 2019 I I think the timing was correct
there was a huge taal vent happening uh
in especially in US I would see in China
as well and many of the other parts of
the world Japan was a little slow yeah
but it was clear indicated that a
hardware for AI was going to become a
big thing it it is absolutely important
and I could relate with it in terms of
the challenges I had faced with robotic
systems and others in constrain
environments so we started 2019 the
company and um but I mean how did you
end up in Japan of all places I mean
that's also a interesting story in
itself that that itself is as well I
would say so I think I keep getting that
question you why Japan I think one of
the unique non-japanese who running a
company in Japan uh it was a mixture so
I think uh my PhD kind of brought me to
Japan through an organization called Ren
if you know them uh who have the fastest
superc computer in the world for a for a
time at Le fugaku and that's how I
actually moved to Japan where I was
again focusing on brain inspired
Computing and how do you build systems
out of it um and then you know I've have
always been being attracted to Japanese
culture so that's how I ended up here
but interestingly being in Japan working
at IBM research as well gave me an
exposure towards the demand for AI as
well as
semiconductors unfortunately in the last
10 to 20 years the focus on
semiconductors in Japan had slowed down
yeah uh but I felt that really an
opportunity in 2019 so we jumped in and
started the company and L and beh we
started 2019 I think 2021 tsmc announced
the kumam plant and 2022 we had
foundaries talking about 2 nanometers so
clearly there's a Revival of sorts of
semiconductors in Japan right and I mean
culturally I think it must be very
different to sort of what you had in
India and then Europe uh and and sort of
like all your counterparts are all in
the
US absolutely I think uh you know it's
it has been uh challenging as well as
extremely rewarding Japan is the sixth
country if you believe that I'm living
in now so I've kind of been a little a
Glo Trotter in that sense but Japan
really has an Eclectic mix of um kind of
today at least is strong government
support there is a growing uh startup
Community there is lot of influx of um
capital from investors across the world
globally so there are us investors
European investors investing in Japan uh
and uh you can have a Silicon Valley
Style company so at a cortex we pretty
much run a company where English is the
common language and we have people from
10 11 different countries so extremely
an etic mix and that's kind of
reflective of today's Japan I would say
okay and uh I just change a little bit
to sort of um what are the things that
you think you've sort of greatest
achievement so far I know it's you're
still young in your career but uh uh
what are what what are you really proud
of in in your
career I would say the first thing that
I was immensely proud of was the first
game that I wrote as we talked about
because that I I recall the amount of
joy that I felt from that but you know
years later uh of course in terms of my
own Journey um you know I one of the co
inventors of uh a concept called Dynamic
busman machines which has been heavily
used I invented that when I was at IBM
research and has been used in uh time
series processing at that time lstms
were the norm in the industry but we
were focusing on how do you make a low
latency significantly low latency and
low power perform its sequences uh which
has implications much later now to
wordss temporal programming with uh uh
other types of models latest lstm as
well as current generation Transformers
so I'm very proud of that and uh you
know we had a very successful
application that in business in finance
domain prior to starting at cortex that
what I was doing and was a very
successful Endeavor in its own right uh
and then coming from that uh in at H
cortex that was really the starting
point where I had worked for a long
period of time on software first design
of hardware systems and I I would say we
very uh immensely proud of the way we
have been able to kind of achieve that
in a relatively short period of time
alongside some of our peers in the AI
industry uh I think they established
Norm has always been Hardware first you
start with the process architecture
design and then software becomes an
afterthought you kind of go and solve
the compiler problem yeah me coming from
software background as well as many of a
team we started with that software
mindset first compiler became the
prominent aspect and then we built art
architecture out of that and so I think
that we take quite a lot of pride in
that aspect where we can now see we can
achieve really significantly better
performance per what compared to many
other uh competing uh let's say gel
purpose architectures
okay um and uh you know um the I mean
the big thing right now is AI and high
performance compute and and um what's
your view on sort of where where we're
heading uh I mean I think part of your
story is obviously everybody's story is
AI theh and uh small language models but
uh what's what's your perspective on all
of this being somebody who's a real
scholar in this area as well I don't
know if I can still call myself a
scholar in that not an act s more but I
think the way I see the you know from
the perspective of edge cortex and you
know where we are seeing the industri I
think we just still scratching the
surface when it comes to latest I think
today's Transformer models are great uh
but I truly believe that there's going
to be an evolution uh continuously on
those types of models and I think the
Endeavor is towards getting lot more
brain likee at least in terms of
efficiency that's the ultimate goal and
I think uh St the industri is making
Headway towards there where today's
Hardware that we are creating uh it will
be not completely
neuromorphic uh but even the
neuromorphic hardware that we have today
even driven is not completely suited in
my opinion to PR like efficiency so it's
a middle ground and we as a company also
we are steadily moving towards that and
innovating how do we make a lot more uh
malleable so to speak plastic that it
can adapt to the changes of the changing
AI landscape and I think that's what I
expect move to happen uh today's very
fixed architectures where once you bring
a chip to the market and the AI model
has changed it's going to be very
difficult to you know adapt and G
purpose Hardware by definition you know
no free lunches in this world yeah so by
definition is going to be much less
efficient so I think we will move
towards a little bit more adaptable uh
domain specific architectures uh with
the software first I think adaptable
domain specific reconfigurable I think
those are the areas that we're heading
towards I guess to make make that
efficient to a certain extent you know
we have tried the industri has tried
things like fpgs or field prr great Aras
which are completely reconfigurable but
then again you sacrifice you leave a lot
on the table uh what we have seen is
that you know data flow architectures um
in the industry many have called it cgas
who G reconfigurable area so there's a
middle ground uh and you know we as a
company we are also in a similar space
and there is a lot of innovations that
can happen there where you can get
towards brain like efficiency and when
you combine that with latest things that
are happening in the uh system scaling
side for example not just monolithic DS
looking at uh multi- diet
disaggregation in combination of system
Skilling efficiency and this kind of
architectural efficiency I think that's
where the industry needs to move to be
able to achieve lot better power
efficiency and we as a company you will
hear a lot more from us we are also
moving in that direction okay let's move
to a little bit about you your personal
side um uh what's what's a piece of
technology that you can't do without uh
what what do you use in your daily life
you think okay you really wouldn't uh be
able to function if you would or just
what your favorite piece of technology
you know it's very interesting um so
there are two things that I absolutely
cannot live with up the first thing is
actually not electronic at all I guess
it's partially electronic so I'm a I
love watches so and I'm kind of I cannot
live without a watch okay uh and the
best watch that I like to use is not a
smart watch but rather a mechanical or
electromechanical watch which uses
essentially silicon inside yeah
alongside the mechanical systems I
definitely cannot do without that and I
find that a really a Marvel look at all
the time yeah and the second is perhaps
um unfortunately the smartphone yeah
given the lives that we so those would
be the two things I could imagine uh and
then you know I still like program you
would find it hard to believe that I
still try to squeeze in time and write a
piece of quote on our Hardware whenever
I can
so the third technology would be just
standard programming languages okay any
any new games you written which which
people might want to
play it's been a while I think uh you
know there's a collection of different
games that you I've worked on for a
number of years but um yeah it hasn't
been the case unfortunately haven't
found the time to work on a game
recently myself but I think the closest
that we came is applying reinforcement
learning with our own Hardware um and
essentially um using simulated
environments for for example um typical
self-driving cars or autonomous driving
there's a data problem so that's
something that I've been quite enamored
by looking at using simulation to
generate a lot of data but then use
Hardware like we create to speed up that
or some part of that simulation uh which
RS reinforcement learning for example
can run on Hardware like C so that's
something that still exploring to a
certain extent but I would say not as
actively given my full-time uh
day-to-day responsibilities see year
okay and and what kind of uh Hobbies do
you have apart from programming anything
interesting you go I mean what do you do
in in
Tokyo to is fantastic if you're an
outdoors person uh and I do like to kind
of explore quite a bit so both my wife
and I uh we tend to kind of go out
hiking quite often uh you know so when I
was much more more younger than that I'm
now uh I was into bouldering uh and you
know climbing so uh I don't get that
much opportunity but you know we tend to
hike I would say or go for runs Tokyo is
fantastic for going for a run at night
and just soaking in wow okay well the
Tokyo seems to be like a city like
doesn't sleep here if you if you watch
the media over here in the in the west
so it seems to be very active all night
long it's it's it's a very eclectic um
city but I would say it has multiple
sheads to it it has a mixture of uh
complete modern as well as it preserves
uh you know the the history as well yeah
um and it's funny that you mentioned
that you know behind me you see this
Sakura yeah which is also the name or
inspiration of our Hardware okay and
that really stems from this Japanese
culture as well kind of this uh renewed
joy and inspiration uh which comes every
year and then kind of brings a new
outlook to life and Industry and that's
kind of the ethos for our own products
and work culture as well very good um
final question you so what would you
tell a young Su sua what you know now uh
in terms of Life advice or you sort of
how would you Mentor a young sua that's
such a tough question know I have a few
little kids and I constantly ponder that
question but I think the first thing
that I would see is learn to feel uh and
feel fast and quite often and I think
all the feel is little feel is that I've
had in my life has you know taught me
immense Les lessons so you know that's
in our company also we uh tend to take
you know another way of looking at
feeling fast is taking risks as
entrepreneurs we are always taking risk
and internally as a company culture as
well we tend to kind of think fast uh or
rather think big Implement fast and then
scale so you take risk and then you feel
you learn from that and you accelerate
and you grow so that's an EOS um I would
definitely share that with the Next
Generation if I had the opportunity
myself another thing that I would you
know caution people to be honest is you
know historically when we started the
company uh we were kind of bootstrapped
as well as um we never took funding from
any Venture capitals for a very long
period of time in fact up until first
few $5 million or so that we rais was
completely friends and family ourselves
Angel Investors uh that gave us a lot of
uh ability to control the way we wanted
to grow the company but also it brings
challenges so if in hindsight I would
say um you know if you find the right
partner if you're starting a company and
you know we are very lucky to have got
greed investors today it's good to find
those kind of investors and work
together with them because that might
help the journey um so that's another
lesson that we have learned uh which has
made us stronger in a
matter and and in in terms of like the
learning side obviously those are work
and career but uh in terms of other you
know on on the learning and the life
life life
Journey you know life journey I think
I'm still young comparatively so there's
a lot more wisdom to come from you know
kind of learn um I would see the as I
said you know feeling in general and
then learning from that is definitely a
life lesson that I've had several years
you know another thing that I think
about intelligence which is very
interesting is there's a book that I
read when I was in college it's from rol
Fifer if I remember correctly how the
how the mind shap or how the body shapes
the mind uh which is very interesting
which talks about embodied intelligence
you know the aspect of uh um your uh the
hardware environment or the kind of the
body that you're in uh plays as much
role in intelligence as the software
substrate and that's kind of a life
lesson so okay can
think perspective of um you know how you
kind of kind of the company that you
keep the way you work has an impact of
the way you think and at the same time
you can bring that to the systems that
we engineer uh sometimes we have to look
at it from the perspective of how that
system is going to get implemented from
the system perspective and uh take a lot
more higher up look rather than the M
micro side of what the semiconductor
does interesting I mean there's a
CEO who's talking about embodied AI at
the moment who's in this autonomous
driving area but I think it's quite
interesting that embodied intelligence
uh I think is is is something that is
going to be happening more and
more absolutely see I I would like to
think that we are in the business of
creating or enabling intelligent systems
that's the whole idea the reason why uh
we are creating software and Hardware so
we want to make a lot more power
efficient and sustainable and green but
ultimate goal is that these power
efficient systems should be a lot more
intelligent than what we have today and
I think we constantly think about those
kind of challenges embod intelligence as
well as not just making everything
Vision Centric make it a lot more
multimodal uh and you know the great
thing is today with uh recent transform
models we are now at least scratching
the surface of bringing such multimodal
systems alive in a lot more efficient
well s thank you very
much thank you Nathan lovely questions I
must say it was a great um speaking with
you thank you
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