A Conversation with the Jensen Huang of Nvidia: Who Will Shape the Future of AI? (Full Interview)
Summary
TLDRJensen Huang, CEO of Nvidia, discusses how we are entering a new era of accelerated computing and AI. He explains how Nvidia GPUs have democratized high performance computing, enabling AI innovation. Huang advises governments to build AI infrastructure to activate researchers and companies to take ownership of developing national intelligence. He recommends focusing on digital biology and life sciences, stating we can now engineer solutions to previously unsolvable biological problems. Huang expresses optimism that AI will proliferate solutions for disease, resource limitations, and other global challenges.
Takeaways
- 💻 The transition from general-purpose computing to specialized, accelerated computing is essential for sustainable, energy-efficient, and high-performance computing, marking a new era in technology.
- 💡 AI's advancement is democratizing technology, making high-performance computing accessible to researchers worldwide, thus fostering innovation across various fields.
- 🚀 The importance of sovereign AI, where countries own their data and intelligence, is highlighted as essential for maintaining cultural, societal, and security integrity.
- 📚 The role of GPUs (Graphics Processing Units) in democratizing AI and high-performance computing is underscored, with NVIDIA's contributions being particularly emphasized for making advanced computing accessible to a broad audience.
- 📌 The concept of accelerated computing is not just about buying more computers but also improving their efficiency and performance over time, reducing the overall need for excessive hardware.
- 🔨 The imminent shift to AI requires infrastructure development akin to building farms for food production or generators for energy, emphasizing the foundational necessity of computing infrastructure for AI progress.
- 👨💻 Education in the AI era should focus less on programming skills and more on domain expertise, leveraging AI tools to amplify productivity and solve domain-specific problems.
- 📖 The potential for AI to democratize education and empower individuals with diverse skills to contribute to technology and innovation without traditional programming knowledge.
- 🛠 The discussion about regulating AI focuses on the use cases rather than the technology itself, advocating for safe development, application, and usage of AI in various industries.
- 🌱 The conversation about the future of AI and computing emphasizes the need for continued innovation, open-source development, and the democratization of technology to avoid centralizing power and capabilities.
Q & A
What major transitions is Jensen referring to that are happening simultaneously?
-The end of general purpose computing and the beginning of accelerated computing, along with the rise of AI enabled by accelerated computing.
How has Nvidia helped democratize high performance computing over the past decade?
-By developing gaming GPUs that could be used affordably by AI researchers worldwide to make breakthroughs in deep learning.
What does Jensen suggest is the first step a developing nation should take to mobilize AI?
-Build the infrastructure for accelerated computing to activate researchers and industry to create AI models.
What does Jensen see as the most significant AI development in 2022 that activated regional research?
-The release of open source large language models like LLAMa and Falcon which democratized access.
Why does Jensen say Nvidia GPUs are unique despite internal chips from large tech companies?
-Because Nvidia GPUs are the only accelerated computing platform available democratically to researchers on any cloud or platform.
Why does Jensen say now anyone can engage with AI unlike any other time?
-Because for the first time there is no technology divide - AI systems can understand natural language requests from any user.
What does Jensen see as the hottest area of study for students today?
-Digital biology and life sciences, to turn biology into a true engineering discipline improved yearly like computing.
How can developing nations build AI capability despite limited budgets?
-Leverage cloud-based accelerated computing which democratizes access to the needed infrastructure.
What is Jensen's vision for how AI will enhance applications across industries?
-AI will automate intelligence and be augmented into existing products and services across sectors.
Why does Jensen believe life sciences innovation has lagged compared to computing?
-Because innovations in computing are engineered yearly improvements but life sciences discoveries remain sporadic.
Outlines
🤝 Opening remarks and introducing Jensen Huang
The moderator welcomes Nvidia CEO Jensen Huang to the stage. He notes that Nvidia is at the center of artificial intelligence hype, possibilities and meaning. He asks Jensen to provide some insights, especially given Nvidia's upcoming GTC conference.
🚀 The AI revolution and infrastructure needed
Jensen explains we are entering a new era driven by specialized computing and AI. Countries realize they cannot rely on others for AI - they need "Sovereign AI" and to own their own data, models and intelligence. To activate AI, countries must build infrastructure to empower researchers.
📚 Codifying language and knowledge for AI
Jensen advises that countries should start by codifying their language and culture into large language models. But many AI revolutions are happening beyond language - in areas like biology, climate, materials, manufacturing etc. Infrastructure allows activation of researchers across domains.
⛔️ Avoid AI scaremongering
Jensen notes that scaremongering about AI being dangerous or overly complicated hurts progress. The most important recent event has been the release of open source models that allow more people to innovate. Safety, transparency and other innovations continue to advance.
🔬 Biology and engineering key future fields
Jensen suggests biology and "life engineering" will be vital fields, as biology is complex and impactful. Digital biology means engineering life systems - more sustinable, efficient etc. This will open new eras of engineering innovation versus sporadic scientific discovery.
Mindmap
Keywords
💡democratize
💡accelerated computing
💡sovereign AI
💡generative AI
💡industrial revolution
💡digital biology
💡language model
💡safety alignment
💡transparency
💡reinforcement learning
Highlights
We are experiencing two simultaneous transitions - the end of general purpose computing and the beginning of accelerated computing
Accelerated computing is the most sustainable and energy efficient way of computing going forward
AI has been democratized and put into the hands of every researcher due to the advancement of accelerated computing
Every country needs to own the production of their own intelligence through data and AI models - this concept is called Sovereign AI
The most important thing that happened in AI last year was the release of open source models like LLAMa-2, Falcon, Mistr which activated AI researchers globally
The first thing to do is build the infrastructure for AI - it is not that costly or difficult and can activate researchers to create models
AI revolutions are happening simultaneously across language, biology, physical sciences, IoT, robotics, manufacturing etc.
Regulations need to be extended and augmented for the safe and ethical application of AI across industries, like with any powerful new technology
The technology divide has been closed - AI makes every person a technologist and resets technology leadership across countries
Our job is to create computing technology such that nobody has to program - AI makes everyone a programmer by closing the gap between humans and machines
One of the most complex and impactful fields enabled by AI is biology and medicine - it will shift from sporadic discovery to systematic engineering
Digital biology will become a field of engineering, not just science - building solutions in a reliable, repeatable way
The technology to turn life sciences into life engineering is now possible with the rise of AI
A whole new generation of people can enjoy working with biology to engineer solutions that are efficient, sustainable and solve problems
We are entering an era of accelerating discovery and invention to overcome limitations and challenges
Transcripts
it's my pleasure and privilege to be
sitting in front of all of you here
today to moderate a Pioneer not just in
the technology space but in the
artificial space as well artificial
intelligence space Jensen who um is
leading probably the company that's at
the center of the eye of the storm when
it comes to artificial intelligence the
hype the possibilities and what this
technology with mean Jensen it's a
pleasure being with you on stage here
thank you it's great to be here when I'm
amazing
conference I um just want to say that we
really appreciate you taking the time
especially since you have GTC in 6 weeks
in six weeks I'm going to tell everybody
about a whole bunch of new things we've
been working on the next generation of
AI every single year they just push the
envelope when it comes to artificial
intelligence and GTC so um we're hoping
to get a few Snippets out of this okay
so I'd like to start with a um question
that was going on in my mind how many
gpus can we buy for 7
trillion well apparently all the
gpus I I I think this is one thing I'm
I'm waiting to ask Sam about because
it's it's a really big number talk about
ambition we have a lot of ambition here
in the UA we don't lack ambition but is
there a view that you can give the
government leaders today with regards to
compute capabilities and artificial
intelligence how can they plan well
where do you think the deployment is
going to make sense and what advice you
have uh well first of all these are
amazing times these are amazing times
because we're at the beginning of a new
Industrial
Revolution production of energy through
Steam production of
electricity it and information
revolution with PC and internet then now
artificial
intelligence uh we are experiencing two
simultaneous uh Transitions and this has
never happened before the first
transition is the end of general purpose
Computing and the beginning of
accelerated Computing it's like
specialized Computing using CPUs for
computation as the foundation of
everything we do is no longer possible
and the reason for that is because it's
been 60 years we invented central
processing units in 1964 the
announcement of the IBM uh system 360
we've been writing that wave for
literally UH 60 years now and this is
now the beginning of accelerated
Computing if you want sustainable
Computing energy efficient Computing
high performance Computing cost effect
cost Effective computing you can no
longer do it with general purpose
Computing you need specialized domain
specific acceleration and that's what
driving at the foundation our growth
accelerated Computing it's the most
sustainable way of doing uh Computing
going forward it's the most energy
efficient um it is so energy efficient
it's so cost effective it's so
performance so performant that it
enabled a new type of application called
AI the question is what's the cart and
and the horse you know first is
accelerated Computing and enabled a new
uh new application there's a whole bunch
of applications that are accelerated
today and so now we're in the beginning
of this new uh New Era uh and what's
going to happen is there's a about a
trillion dollar withth of installed base
of data centers around the world and
over the course of the next four or five
years we'll have $2 trillion worth of
data centers um that will be uh uh
powering software around the world and
all of it is going to be
accelerated and and this architecture
for Accelerated Computing is ideal for
this next generation of software called
generative Ai and so that's really at
the core of what is happening uh while
we're repl placing the install base of
general purpose Computing remember that
the performance of the architecture is
going to be improving at the same time
so you can't assume just that you will
buy more computers you have to also
assume that the computers are going to
become faster and therefore the total
amount that you need is not going to be
as much otherwise the mathematics if you
just assume you know that that computers
never get any faster you might come to
the con conclusion we need 14 different
planets and three different galaxies and
you know four four more Suns and um to
to fuel all this but but obviously uh
computer architecture continues to
advance in the last 10 years one of the
greatest contributions and I really
appreciate you mentioning that um the
rate of innovation one of the greatest
contributions we made was advancing
Computing and advancing AI by 1 million
times in the last 10 years and so
whatever demand that you think is going
to power the the world you have to
consider the fact that it is also going
to do it one million times larger faster
you know more
efficiently don't you think that creates
a risk of having a world of halves and
Have Nots since we need to constantly
invest to ensure that we have The
Cutting Edge and to ensure that we are
able to create the applications that are
going to reshape the world and
governments as we know them do you think
that there's going to be an issue of
countries that can afford uh these gpus
and countries that can't and if not
because you know it' be surprising if
you said the answer is no if not what
are going to be the drivers of equity
excellent question um first of all when
something improves by a million times
and the cost or the space or the energy
that it consumed did not grow up by a
million times in fact you've
democratized the technology um
researchers all over the world would
tell you that Nvidia single-handedly
democratized high performance Computing
we put it in the hands of every
researcher it is the reason why uh AI
researchers uh Jeff Hinton in University
of Toronto Yan Lun I think Yan's going
to be here uh University of uh New York
um Andrew a uh in uh Stanford
simultaneously discovered us they didn't
discover us because of supercomputers
they discovered us because of gaming
gpus that they used for deep learning we
put accelerated Computing or high
performance Computing in the hands of
every single researcher in the world and
so when we accelerate the rate of
innovation we're democratizing the
technology the cost of building
purchasing a supercomputer today is
really negligible and the reason for
that is because we're making it faster
and faster and faster whatever
performance you need costs a lot less
today than he used to it is absolutely
true we have to democratize this
technology and the reason the reason why
is very clear there's an Awakening of
every single country in probably the
last six
months that artificial
intelligence is a technology you can't
be mystified by you cannot be terrified
by it you have to find a way to activate
yourself to take advantage of it and the
reason for that is because this is the
beginning of a new Industrial Revolution
and this Industrial Revolution is about
the production not of energy not of food
but the production of intelligence and
every country needs to own the
production of their own intelligence
which is the reason why there's this
idea called Sovereign AI you own your
own data nobody owns it your country
owns the data your cult it it it
codifies your culture your society's
intelligence your common sense your
history you own your own data you
therefore must take that data refine
that data and own your own National
Intelligence you can't cannot allow that
to be done by other people and that is a
real realization now that we've
democratized the computation of AI the
infrastructure of AI the rest of it is
really up to you to take initiative
activate your uh your uh industry uh
build the infrastructure as fast as you
can so that the researchers the
companies your governments can take
advantage of this infrastructure to go
and create your own
AI I I think we completely subscribe to
that Vision um that's why the UAE is
moving aggressively on creating large
language models IM mobilizing compute
and maybe work with other partners of
this let's try to flip the Paradigm a
little bit let's today assume that
Jensen hang is the president of of a
developing nation that has a relatively
small GDP and you can focus on one AI
application what would it be let's call
it a hypothetical nation and say that
you know you have so many problems that
you need to deal with what is the first
thing that you're going to approach if
you're going to mobilize artificial
intelligence in that scenario the first
thing you have to do is you have to
build infrastructure if you want to if
you want to mobilize the production of
food you have to build farms if you want
to mobilize the production of energy you
have to build
AC generators if you want to if you want
to operationalize information digital if
you want to digitalize your economy you
have to build the internet um if you
want to automate the creation of
artificial intelligence you have to
build the infrastructure it is not that
cost it's not that it's not that costly
it is also not that hard um companies
all around the world of course wants to
mystify terrify glorify you know all of
those uh those those ideas but the fact
of the matter is they're computers you
can buy them off the shelf uh you can
install it uh every country needs
already has the expertise to do this uh
and you you have to you surely need to
have the imperative To Go activate that
um the first thing that I would do of
course is I would codify the uh language
the the data of your culture into your
own large language model and you're
doing that here uh core 42 um Saudi
ramco
uh uh uh uh
uh sad sad um really doing uh important
work to uh codify the Arabic language
and creating your own large language
model um but simultaneously remember
that AI is not just about language AI
we're seeing several AI revolutions
happening at the same time AI for
language AI for biology learning the
language of protein prot and and
chemicals uh AI for physical sciences
learning the AI of climate materials
energy Discovery AI of iot the language
of keeping places safe computer vision
and such um AI for iot AI for Robotics
and autonomous systems manufacturing and
such there's AI revolutions happening AI
great breakthroughs happening in all of
these different domains and if you build
the infrastructure you activate the
researchers in every one of these
domains without the internet how can you
be
digital without Farms how can you
produce food without an AI
infrastructure how can you activate all
of the researchers that are in your
region to go and create the AI
models you touched upon um the
issue of I would say authentic ignorance
the fear mongering a I taking over the
world and um I I think there's a
requirement for us to clarify where the
hype is real and where artificial
intelligence really has the power to
create a lot of disruption and to harm
us and where AI is going to be good what
do you think is the biggest issue when
it comes to artificial intelligence
right now because I think the the the
problem of regulating AI is like trying
to say we want to regulate a field of
computer science or regulate electricity
you don't regulate electricity as a
invention or as a discovery you regulate
a specific use case what is one use case
that you think we need to regulate
against and that government should
mobilize towards e excellent question um
first of all whatever new incredible
technology is being created uh you go
back to the earliest of times uh it is
absolutely true we have to develop the
technology safely we have to apply the
technology safely and we have to help
people use the technology safely and so
uh whether it's um uh the plane that I
came in uh cars uh Manufacturing Systems
medicine all of these different
Industries are heavily regulated today
those regulations have to be extended
augmented to consider artificial
intelligence artificial intelligence
will come to us through products and
services it is the automation of
intelligence and it will be augmented on
top of all of these various Industries
now it the case that that there are some
interests
to scare people about this uh new
technology to mystify this technology to
encourage other people to not do
anything about that technology and rely
on them to do it and I think that that's
a mistake we want to democratize this
technology let's face it the single most
important thing that has happened last
year if you were to ask me the one
single most important event last year
and how it has
activated AI researchers here in this
region it's actually llama 2 it's an
open source model or falcon or Falcon
another excellent model very true uh
mistr excellent model uh a I just I just
saw another one uh a smog um there's so
many open source models Innovations on
safety
alignment um uh uh
Guard railing reinforcement learning so
many different reasoning so many
different innovations that are happening
on top of transparencies explainability
all of this technology that has to be
built all were possible because of some
of these open source languages and so I
think that
democratizing activating every region
activating every country to join the AI
Advance is probably one of the most
important important thing rather than ex
convincing everybody it's too
complicated it's too dangerous it's too
my mystical and only two or three people
in the world should be able to do that
that I think is a huge
mistake uh the the uh Focus I think that
we have done in the UAE is to focus on
open source systems because we do
believe that anything that we develop
here should be given as um a opportunity
for others that can't develop
it most of this is developed using gpus
so graphic processing units that you
guys um are are supplying the world what
do you think the next era is going to
depend on is it going to continuously be
built on gpus is there something else as
a breakthrough that we're going to see
in the future you think actually uh you
know that that in just about all of the
large companies in the world uh there
are internal developments uh at Google
there's tpus at um AWS there's tranium
at Microsoft there's Maya uh uh meta has
um uh chips that they're building uh in
China just about every single CSP has
chips that they're building the reason
why you mention Invidia gpus is NVIDIA
GPU is the only platform that's
available to everybody on any platform
that's actually the observation it's not
that we're the only platform that's
being used we're simply the only
platform that's used that democratizes
AI for everybody's platform we're in
every single Cloud we're in every single
data center we're available in the cloud
uh in your private data centers all the
way out to the edge all the way out to
autonomous systems Robotics and
self-driving Cars one single
architecture spans all of that that's
what makes Nvidia unique that we can uh
in the beginning when cnns were popular
we were the right architecture because
we were programmable Aruda architecture
has the ability to adapt to any
architecture that comes along so when
CNN came along RNN came along Along
lstms came along and then eventually
Transformers came along and now Vision
Transformers bir eye view Transformers
um all kinds of different Transformers
are being uh created a Next Generation
State space uh models uh uh which is a
uh probably in the next generation of
Transformers all of these different
architectures can live and breathe and
be created on invidious flexible
architecture and because it's available
literally everywhere any researcher can
get access to Nvidia gpus and invent the
Next Generation so so for those of you
who are non-technical and heard you know
a foreign language there with CNN and
and some of the other uh acronyms that
are being used the the thing about
artificial intelligence is it's going
through a lot of Evolutions over a very
short period of time so whatever the
infrastructure that was used probably 5
years ago is very different to the
infrastructure that's being used today
but what Jensen's point was I think it's
a very important point is NVIDIA has
always been relevant historically we see
companies that are relevant at one phase
of development and then as the
infrastructure changes they become
irrelevant but you guys were able to
innovate and and push through let's move
to a non- air related topic for a second
I want to talk about education so today
knowing what you know seeing what you
see and being at The Cutting Edge of the
technology what should people focus on
when it comes to education what should
they learn how should they educate their
kids and their societies wow excellent
question I'm going to say something and
it it's it's going to sound completely
opposite um of what people feel uh you
you you probably recall uh over the
course of the last 10 years 15 years um
almost everybody who sits on a stage
like this would tell you it is vital
that your children learn computer
science um everybody should learn how to
program and in fact it's it's almost
exactly the opposite it is our job to
create Computing technology such that
nobody has to
program and that the programming
language is
human everybody in the world is now a
programmer this is the miracle this is
the miracle of artificial intelligence
for the very first time we have closed
the Gap the technology divide has been
completely closed and this the reason
why so many people can engage artificial
intelligence it is the reason why every
single government every single
industrial conference every single
company is talking about artificial
intelligence today because for the very
first time you can imagine everybody in
your company being a
technologist and so this is a tremendous
time for uh all of you to realize that
the technology divide has been closed or
another way to say
it the te technology leadership of other
country has now been
reset the countries the people that
understand how to solve a domain problem
in digital biology or in education of
young people or in manufacturing or in
farming those people who understand
domain
expertise now can utilize technology
that is readily available to you you now
have a computer that will do what you
tell it to do to help automate your work
to amplify your productivity to make you
more efficient and so I think that this
is just a tremendous time um the impact
of course uh is is great and your
imperative to activate and take
advantage of the technology is
absolutely immediate um and also to
realize
that to engage AI is a lot easier now
than at any time in the history of
computing it is vital that we we upskill
everyone and the upskilling process I I
believe will be delightful surprising um
to realize that this computer can
perform all these things that you're
instructing it to do and doing it so
easily so if I was going to choose a uh
major and University as a degree that
I'm going to pursue what would you give
me as an advice for something to
pursue if I were starting all over again
um I would realize uh one thing that one
of the most
complex fields of science is the
understanding of biology human biology
not only is it complicated because it's
so diverse so complicated so hard to
understand living and breathing it is
also incredibly impactful complicated
technology complicated science
incredibly impactful for the very first
time and and remember we call this field
life
sciences and we call drug Discovery
Discovery as if you wander around the
univers ierse and all of a sudden hey
look what I discovered nobody in
computer science nobody in computers and
nobody in the traditional industries
that are very large today nobody says
car Discovery we don't say computer
Discovery we don't say software
Discovery we don't go home and say hey
honey look what I found
today this piece of software we call it
engineering and every single year our
science our computer science our
software becomes better and better than
the than the year before every single
year our chips get better every single
year our infrastructure gets
better however Life Sciences is
sporadic if I were to do it over again
right now I would realize that the
technology to turn life engineering life
science to life engineering is upon us
and that digital biology will be a field
of
engineering not a field of science it
will continue to have science of course
but not a field just of Science in the
future and so I I hope that that this is
going to start a whole generation of
people who enjoy working with proteins
and chemicals and and enzymes and um
materials and and they're engineering
these amazing things that are more
energy efficient that are lighter weight
that are stronger that are more
sustainable all of these inventions in
the future are going to be part of
engineering not scientific discovery so
I think we can end with a very positive
note hopefully we're going to enter an
era of Discovery an era of proliferating
a lot of the things that unfortunately
to they are challenges to us whether
it's disease whether it's limitations
and resources thank you so much Jess for
taking the time and being with us and I
know that we could have continued for
another hour but um thank you for taking
the stage and thank you for your Insight
thank you thank you
[Music]
everyone
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