NVIDIA CEO Jensen Huang Reveals AI Future: "NIMS" Digital Humans, World Simulations & AI Factories.
Summary
TLDRIn a recent presentation, Nvidia CEO Jensen Huang unveiled the company's advancements in AI, emphasizing the transition from the AI era to the generative AI era. Huang discussed the concept of an 'AI Factory' capable of producing tokens representing anything of value, from language to physics. He introduced 'Nims' (Nvidia inference microservices), pre-packaged AI models that simplify AI deployment across industries. The presentation also touched on the potential of digital humans and quantum computing emulation, hinting at a future where AI becomes integral to every industry, driving a new industrial revolution.
Takeaways
- 🌟 Nvidia CEO Jensen Huang discussed the evolution and significance of AI, highlighting its transformation from perception-focused tasks to generative AI.
- 🤖 Generative AI, as demonstrated by Nvidia, can produce tokens of various types, including words, images, and even chemical structures, revolutionizing multiple industries.
- 💡 Nvidia's AI Factory concept emphasizes creating valuable commodities through AI, similar to how Nikola Tesla's AC generator produced electricity.
- 🏭 The AI Factory's scalability and repeatability are crucial for its impact, enabling rapid innovation and application across diverse sectors.
- 🧠 Nvidia introduced Nims (Nvidia Inference Microservices), which are pre-trained AI models designed to simplify and enhance AI deployment for various applications.
- 📈 The complexity of running AI models involves distributing workloads across multiple GPUs, ensuring high throughput and efficient data center utilization.
- 💻 Nvidia's comprehensive AI software stack integrates numerous dependencies, providing a seamless experience for users to implement AI solutions.
- 🌐 Nvidia's digital human technology aims to create more engaging and empathetic AI interactions, pushing the boundaries of realism in digital representations.
- 🔬 Nvidia's emulation system for quantum computers, Coo Quantum, aids researchers in designing quantum algorithms and simulating quantum computing tasks.
- 🌍 Nvidia's Earth 2 project aims to create a digital twin of the Earth for better climate prediction and disaster management, showcasing the potential of AI in global challenges.
Q & A
What is Jensen Huang's view on the role of AI in the future?
-Jensen Huang believes that AI should be seen as a tool to let robots do the work, emphasizing the mantra 'let the robots do the work'.
What is the fundamental difference that Jensen Huang highlighted in AI before and after GPT was revealed?
-Before GPT, AI was primarily about perception, including natural language understanding, computer vision, and speech recognition. After GPT, the world saw the emergence of generative AI, which produces tokens that could be words, images, or any other learnable entities.
What does Jensen Huang refer to as the 'AI Factory'?
-The 'AI Factory' is a concept where an AI system, which started as a supercomputer, has evolved into a data center that produces tokens. This factory generates tokens for almost anything of value, marking the shift to the generative AI era.
How does Jensen Huang compare Nvidia's AI generator to Nicola Tesla's AC generator?
-Jensen Huang draws a parallel between the AC generator, which generated electrons, and Nvidia's AI generator, which generates tokens. Both inventions have large market opportunities and are considered to be part of new industrial revolutions.
What is the significance of the IT industry's role in creating something that can serve a hundred trillion dollar industry, according to Jensen Huang?
-Jensen Huang points out that the IT industry, valued at $3 trillion, is on the cusp of creating something that can directly serve a hundred trillion dollar industry. This signifies a major shift from being just an instrument for information storage or data processing to a factory for generating intelligence for every industry.
What is the concept of 'Nims' introduced by Jensen Huang?
-Nims, or Nvidia Inference Microservices, is a concept where pre-trained AI models run inside an AI factory. These Nims are designed to be cloud-native and can auto-scale in a Kubernetes environment, making AI more accessible and manageable.
How does Jensen Huang describe the transformation of applications with the advent of AI?
-Jensen Huang describes a future where applications are no longer just written with instructions but are assemblies of teams of AI agents. These applications will involve breaking down problems and assembling teams of experts (Nims) to perform tasks, much like humans do.
What is the potential impact of generative AI on customer service agents across various industries?
-Generative AI has the potential to augment customer service agents in almost every industry, improving efficiency and service quality. This represents a significant shift in how customer service is managed and delivered.
What is the significance of digital humans in the context of AI interaction?
-Digital humans represent a more engaging and empathetic form of AI interaction. They have the potential to make interactions more natural and humanlike, which could be particularly useful in customer service and other interactive applications.
How does Jensen Huang envision the future of AI in relation to quantum computing?
-Jensen Huang discusses the potential for Nvidia chips to assimilate quantum computers, suggesting that AI and quantum computing could be closely intertwined in the future. This could lead to advancements in areas such as route planning and optimization.
What is the concept of a 'digital twin of Earth' and its relevance to AI?
-The concept of a 'digital twin of Earth' refers to creating a simulated version of the planet that can predict future scenarios and help understand the impact of climate change. This ambitious project leverages AI to process and analyze vast amounts of data, contributing to disaster aversion and climate change adaptation.
What does Jensen Huang mean by 'physical AI' and its importance?
-Physical AI refers to AI systems that understand the laws of physics and can interact within the physical world. This is crucial for AI to perform tasks that require a physical presence and understanding, such as robotics and automation.
Outlines
🤖 Generative AI and the AI Factory Revolution
Jensen Huang, CEO of Nvidia, discusses the latest developments at Nvidia, focusing on the concept of Generative AI and the AI Factory. He explains that AI has evolved from perception to a generative phase, where AI can produce tokens representing various forms of data, including text, images, and even physical laws. Huang likens the AI Factory to Nicola Tesla's invention of the AC generator, suggesting a new era of industrial revolution with AI generating tokens for every industry. The potential applications are vast, from steering wheel control to robotic arm articulation, emphasizing the scalability and repeatability of this approach.
🚀 Nvidia's AI Innovations and the Impact on Industries
The script delves into Nvidia's role at the intersection of computer graphics, simulations, and artificial intelligence. It introduces the concept of Nvidia's Omniverse, a virtual world powered by simulation and computer science, which is not animated but rather a product of advanced technology. The discussion then shifts to 'Nims' or Nvidia Inference Microservices, which are pre-trained AI models that run within an AI Factory environment. These Nims are designed to be cloud-native and easily scalable, with the aim of simplifying the complexity of AI deployment for companies. The potential for AI to transform industries by serving as a new type of software is highlighted, with a focus on customer service agents and the assembly of AI teams for various tasks.
🔍 The Future of AI: Nims and Digital Humans
This section explores the future applications of AI, particularly the concept of Nims and digital humans. Nims are presented as self-contained units of AI that can be downloaded and interacted with, much like chatting with an AI like chat GPT. The script discusses the integration of these Nims into various domains, such as healthcare, digital biology, and customer service. The idea of digital humans is introduced as a more engaging and empathetic form of AI interaction, with the potential to cross the uncanny valley of realism. The discussion also touches on Nvidia's capabilities in assimilating quantum computers for route planning and optimization, hinting at the broad implications of AI advancements.
🌏 Simulating Earth and the Emergence of Self-Improving AI
The script discusses the ambitious project of creating a digital twin of Earth, which aims to simulate our planet to predict and better understand phenomena such as climate change. This project represents a significant step forward in AI's ability to process and analyze vast amounts of data. The concept of synthetic data and self-play is introduced, suggesting that AI can improve itself by creating its own training data. The emergence of self-improving AI is highlighted, along with the need for AI to be physically based and understand the laws of physics for more accurate and realistic simulations and interactions.
🤖 The Next Wave of AI: Physical AI and Robotics
The final paragraph outlines the next wave of AI, focusing on physical AI and robotics. It suggests that AI will become more integrated into our physical world, with the ability to understand and interact with the environment based on the laws of physics. The script predicts a future where robotics will be pervasive, not just in the form of humanoid robots but in all aspects of manufacturing and industry. The vision is one where factories are robotic, and AI systems drive intelligence and automation in every facet of production and service.
Mindmap
Keywords
💡AI Factory
💡Generative AI
💡Tokens
💡Nims (Nvidia Inference Microservices)
💡Digital Twins
💡Quantum Computing
💡Digital Humans
💡Physical AI
💡Robotics
💡Self-Improving AI
Highlights
Jensen Huang, CEO of Nvidia, discussed the staggering scale of AI and its potential impact, referring to it as an 'AI Factory'.
Generative AI is highlighted as a new era, capable of producing various types of tokens including words, images, and even physical phenomena.
The concept of the 'AI Generator' is compared to Tesla's AC generator, emphasizing its revolutionary market opportunities across industries.
Nvidia's AI models can now generate steering wheel controls for cars and articulation for robotic arms, showcasing the wide range of applications.
The transition from AI perception (natural language understanding, computer vision) to generative AI, which can create and produce new content.
Introduction of Nvidia Inference Microservices (NIMs) as pre-trained AI models that simplify the deployment and scaling of AI solutions.
Nvidia's 'AI in a box' concept includes integrated software such as Cuda, TensorRT, and Triton for streamlined AI deployment.
Nvidia's digital humans technology aims to create realistic, empathetic interactive agents for enhanced human-computer interactions.
The company's achievements in emulating quantum computers for research, holding 23 world records in route planning optimization and other complex problems.
The ambitious Earth 2 project aims to create a digital twin of Earth to better predict and mitigate the impacts of climate change.
Nvidia's approach to AI includes synthetic data and self-play for continuous AI improvement and training without human supervision.
The future of AI involves 'physical AI' that understands the laws of physics and can operate and interact within the physical world.
The role of robotics is emphasized, with the vision of factories orchestrating robots to build robotic products, highlighting the pervasiveness of AI.
Nvidia's collaboration with Hugging Face to make the Llama 3 model available, demonstrating their commitment to open and accessible AI technologies.
The potential of customer service agents powered by language models and AI to transform industries with efficient, intelligent interactions.
Transcripts
please welcome to the stage Nvidia
founder and CEO Jensen Wong Jensen hang
the CEO of Nvidia did an incredible
presentation recently in Taiwan talking
about the latest developments at Nvidia
here are the top most interesting
highlights that jumped out at me from
this conference but first take a listen
to this brief clip where he talks about
what AI is as AI takes over make this
your Mantra let the robots do the work
subscribe to St on top of AI news a lot
of people are failing to grasp what it
is that we've created when Jensen talks
about an AI Factory the scale of what
he's talking about is staggering take a
listen Chad gbt came along and um and
something is very important in this
slide here let me show you
something this
slide okay and this slide
the fundamental difference is
this until Chad
GPT revealed it to the
world AI was all about
perception natural language
understanding computer vision speech
recognition it's all about
perception and
detection this was the first time the
world World Sol a generative
AI It produced
tokens one token at a time and those
tokens were words some of the tokens of
course could now be images or charts or
tables songs words speech
videos those tokens could be anything
they anything that that you can learn
the meaning of it could be tokens of
chemicals
tokens of proteins
genes you saw earlier in Earth 2 we were
generating
tokens of the
weather we can we can learn physics if
you can learn physics you could teach an
AI model physics the AI model could
learn the meaning of physics and it can
generate physics we were scaling down to
1 kilometer not by using filtering it
was generating
and so we can use this method to
generate tokens for almost
anything almost anything of value we can
generate steering wheel control for a
car we can generate articulation for a
robotic
arm everything that we can learn we can
now
generate we have now arrived not at the
AI era but at generative AI era but
what's really important is
this this computer that started out as a
supercomputer has now evolved into a
Data Center and it
produces one thing it produces
tokens it's an AI
Factory this AI Factory is generating
creating producing something of Great
Value a new commodity
in the late
1890s Nicola Tesla invented an AC
generator we invented an AI
generator the AC generator generated
electrons nvidia's AI generator
generates
tokens both of these things have large
Market opportunities it's completely
fungible in almost every
industry and that's why it's a new
Industrial
Revolution we have now a new Factory
producing a new commodity for every
industry that is of extraordinary value
and the methodology for doing this is
quite scalable and the methodology of
doing this is quite
repeatable notice how quickly so many
different AI models generative AI models
are being invented literally daily every
single industry is now piling on
for the very first
time the IT industry which is $3
trillion $3 trillion IT industry is
about to create something that can
directly serve a hundred trillion dollar
of Industry no longer just an instrument
for information storage or data
processing but a factory for generating
intelligence for every industry a lot of
the clips you're going to see here they
look like animation they look like
something that in the past we would
think of as cartoon something that
somebody drew or animated on a computer
but that's not quite what it is what is
generative
AI what is its impact on our industry
and on every
industry a blueprint for how we will go
forward and engage this incredible
opportunity
and what's coming
next generative Ai and its impact our
blueprint and what comes
next these are really really exciting
times a
restart of our computer industry an
industry
that you have forged an industry that
you have
created and now now you're
prepared for the next major
Journey but before we
start Nvidia lives at the
intersection of computer
graphics
simulations and artificial
intelligence this is our
soul everything that I show you
today is
simulation it's math it's science it's
computer science it's amazing computer
architecture none of it's
animated and it's all
homemade this is NVIDIA soul and we put
it all into this virtual world we called
Omniverse next Jensen talks about Nims
and I think this is a bigger deal than
we realize the same way that Microsoft
changed the computer industry with
prepackaged software
Nims are going to change what we think
of AI and AI agents now the name might
change but understand the concept of
what he's talking about
remember the idea that Microsoft created
for packaging software revolutionize the
PC industry without packaged software
what would we use the PC to
do it drove this industry and now we
have a new Factory a new computer and
what we will run on top of this is a new
type of software and we call it Nims
Nvidia inference
microservices now what what happens is
the Nim runs inside this Factory and
this Nim is a pre-trained model it's an
AI well this AI is of course quite
complex in itself but the the Computing
stack that runs AIS are in insanely
complex when you go and use chat GPT
underneath their stack is a whole bunch
of software underneath that prompt is a
ton of software and it's incredibly
complex because the models are large
billions to trillions of parameters it
doesn't run on just one computer it runs
on multiple computers it has to
distribute the workload across multiple
gpus tensor parallelism pipeline
parallelism data parall all kinds of
parallelism expert parallelism all kinds
of parallelism Distributing the workload
across multiple gpus processing it as
fast as possible because if you in a
factory if you run a factory your
throughput directly correlates to your
revenues your throughput directly
correlates to quality of service and
your throughput directly correlates to
number of people who can use your
service we are now in a world where data
center throughput
utilization is vitally important it was
important in the past but not violently
important it was important in the past
but people don't measure it today every
parameter is measured start time uptime
utilization throughput idle time you
name it because it's a factory when
something is a factory its operations
directly correlate to the financial
performance of the company and so we
realize that this is incredibly complex
for most companies to do so what we did
was we created this AI in a box and it
containers an incredible AMS of
software inside this container is Cuda
cudnn tensor RT Triton for inference
Services it is cloud native so that you
could Auto scale in a kubernetes
environment it has Management Services
and hooks so that you can monitor your
AIS it has common apis standard API so
that you could literally chat with this
box you download this Nim and you can
talk to it so long as you have Cuda on
your
computer which is now of course
everywhere it's in every cloud available
from every computer maker it is
available in hundreds of millions of PCS
when you download this you have an AI
and you can chat with it like chat GPT
all of the software is now integrated
400 dependencies all integrated into one
we tested this Nim each one of these
pre-trained models against all kind our
entire installed base that's in the
cloud all the different versions of
Pascal and ampers and Hoppers
and all kinds of different versions I
even forget
some Nims incredible invention this is
one of my favorites and of course
as you
know we now have the ability to create
large language models and pre-trained
models of all kinds and we we have all
of these various versions whether it's
language based or Vision based or
Imaging based or we have versions that
are available for Health Care digital
biology we have versions that are
digital humans that I'll talk to you
about and the way you use this just come
to ai. nvidia.com and today we uh just
posted up in hugging face the Llama 3
Nim fully optimized it's available there
for you to try and you can even take it
with you it's available to you for free
and so you could run it in the cloud run
it in any Cloud you could download this
container put it into your own Data
Center and you could host it make it
available for your customers we have as
I mentioned all kinds of different
domains physics some of it is for
semantic retrieval called Rags Vision
languages all kinds of different
languages and the way that you use
it is connecting these microservices
into large applications one of the most
important applications in the coming
future of course is customer service
agents customer service agents are
necessary in just about every single
industry it represents trillions of
dollars of of customer service around
the world
nurses or customer service agents um in
some ways some of them are
nonprescription or or non Diagnostics um
uh based nurses are essentially customer
service uh customer service for retail
for uh Quick Service Foods Financial
Services Insurance just tens and tens of
millions of customer service can now be
augmented by language models and
augmented by Ai and so these one these
boxes that you see are basically Nims
some of the NIMS are reasoning agents
given a task figure out what the mission
is break it down into a plan some of the
NIMS retrieve information some of the
NIMS might uh uh uh go and do search
some of the NIMS uh might use a tool
like kuop that I was talking about
earlier they could use a tool that uh
could be running on sap and so it has to
learn a particular uh language called
abap maybe some Nims have to uh uh do
SQL queries and so all of these Nims are
experts that are now assembled as a
team so what's
happening the application layer has been
changed what used to be applications
written with
instructions are now
applications that are assembling teams
assembling teams of
AIS very few people know how to write
programs almost everybody knows how to
break down a problem and assemble teams
very every company I believe in the
future will have a large collection of
Nims and you would bring down the
experts that you want you connect them
into a team and you you don't even have
to figure out
exactly how to connect
them you just give the mission to an
agent to a Nim to figure out who to
break the tasks down and who to give it
to and they that a that Central the
leader of the of the application if you
will the leader of the team would break
down the task and give it to the various
team members the team members would do
their perform their task bring it back
to the team leader the team leader would
reason about that and present an
information back to you just like humans
this is in our near future future this
is the way applications are going to
look now of
course we could interact with these
large these AI services with text
prompts and speech
prompts however there are many
applications where we would like to
interact with what what is otherwise a
humanlike form we call them digital
humans Nvidia has been working on
digital human technology for some time
let me show it to you digital humans has
the potential potential of being a great
interact interactive agent with you they
make much more engaging they could be
much more
empathetic and of course um we have to
uh uh cross this incredible Chasm this
uncanny Chasm of realism so that the
digital humans would appear much more
natural did you know that Nvidia chips
can assimilate quantum computers I was
not aware of this route planning
optimization the traveling salesman
problem incredibly complicated people
just people have well scientists have
largely concluded that you needed a
quantum computer to do that we created
an algorithm that runs on accelerated
Computing that runs Lightning Fast 23
World Records we hold every single major
world record
today cou Quantum is an emulation system
for a quantum computer if you want to
design a quantum computer you you need a
simulator to do so if you want to design
Quantum algorithms you need a Quantum
emulator to do so how would you do that
how would you design these quantum
computers create these Quantum
algorithms if the quantum computer
doesn't exist while you use the fastest
computer in the world that exists today
and we call it of course Nvidia Cuda and
on that we have an emulator that
simulates quantum computers it is used
by several hundred, researchers around
the world some people including some
pretty smart scientists believe that
it's possible that our world our reality
is assimilated this question becomes
even more fascinating now that we're
getting closer to potentially being able
to simulate our own realities if we're
able to create simulated realities with
assimilated beings in them who's to say
that perhaps there's not another reality
above us the base reality we believe
that by reducing the cost of Compu in
incredibly the market developers
scientists inventors will continue to
discover new algorithms that consume
more and more and more Computing so that
one
day something
happens that a phase shift happens that
the marginal cost of computing is so low
that a new way of using computers
emerge in fact that's what we're seeing
now over the years we have driven down
the marginal cost of computing in the
last 10 years in one particular
algorithm by a million times well as a
result it is now very
logical and very common
sense to train large language models
with all of the data on the internet
nobody thinks
twice this idea that you could create a
computer that could process so much data
to write its own software the emergence
of artificial intelligence was made
possible because of this complete belief
that if we made Computing cheaper and
cheaper and cheaper somebody's going to
find a great use well today Cuda has
achieved the virtual cycle install base
is growing Computing cost is coming down
which causes more developers to come up
with more
ideas which drives more demand
and now we're on in the beginning of
something very very important but before
I show you that I want to show you what
is not possible if not for the fact that
we created Cuda that we created the
modern version of General the modern Big
Bang of AI generative AI what I'm about
to show you would not be possible this
is Earth to the idea that we would
create a digital twin of the Earth
that we would go and simulate the
Earth so that we could predict the
future of our planet to better
avert disasters or better understand the
impact of climate change so that we can
adapt better so that we could change our
habits now this digital twin of Earth is
probably one of the most ambitious
projects that the world's ever
undertaken and we're taking step large
steps every single year and I'll show
you results every single year but this
year we made some great breakthroughs
more and more people working on AI find
themselves talking about things like
synthetic data and self-play this idea
that AI can improve itself can create
data to train itself we're beginning to
see the emergence of self-improving AI
we enabled Transformers to be able to
train on enormously large data data sets
well what happened was in the beginning
the data was human
supervised it required human labeling to
train AIS unfortunately there's only so
much you can human label Transformers
made it possible for unsupervised
learning to happen now Transformers just
look at an enormous amount of data or
look at an enormous amount of video or
look at more enormous amount of uh
images and it can learn from studying an
enormous amount of data find the
patterns and relationships
itself while the next generation of AI
needs to be physically based most of the
AIS today uh don't understand the laws
of physics it's not grounded in the
physical world in order for us to
generate uh uh images and videos and 3D
graphics and many physics phenomenons we
need AI that are physically based and
understand the laws of physics
well the way that you could do that is
of course learning from video is One
Source another way is synthetic data
simulation data and another way is using
computers to learn with each other this
is really no different than using
alphago having alphao play itself
self-play and between the two
capabilities same capabilities playing
each other for a very long period of
time they emerge even smarter and so
you're going to start to see this type
of AI emerging well if the AI data is
synthetically generated and using
reinforcement learning it stands to
reason that the rate of data generation
will continue to advance and every
single time data generation grows the
amount of computation that we have to
offer needs to grow with it nvidia's AI
senior research scientist Dr Jim fan
once said that everything that moves
will be automated it will be intelligent
it will be driven by an AI system and as
you'll see here they're not kidding
around about that let me talk about
what's
next the next wave of AI is physical ai
ai that understands the laws of physics
AI that can work among us and so they
have to understand the world model so
that they understand how to interpret
the world how to perceive the world they
have to of of course have excellent
cognitive capabilities so they can
understand us understand what we asked
and perform the
tasks in the
future robotics is a much more per
pervasive idea of course when I say
robotics there's a humanoid robotics
that's usually the representation of
that but that's not at all true
everything is going to be robotic all of
the factories will be robotic the
factories will orchestrate robots and
those robots will be building products
that are
robotic robots interacting with robots
building products that are robotic my
name is Wes Roth and thank you for
watching
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