NVIDIA CEO Jensen Huang Reveals AI Future: "NIMS" Digital Humans, World Simulations & AI Factories.

Summary

In 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

00:00

🤖 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.

05:02

🚀 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.

10:03

🔍 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.

15:03

🌏 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.

20:04

🤖 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.

Keywords

💡AI Factory

The term 'AI Factory' refers to the concept of an artificial intelligence system that operates at a large scale, producing valuable outputs akin to a manufacturing plant. In the video's context, it signifies Nvidia's vision for AI's role in generating tokens, which can represent anything from words to complex data structures. The AI Factory is central to the theme of generative AI and its potential to revolutionize various industries by creating new types of commodities and value.

💡Generative AI

Generative AI is a subset of artificial intelligence that focuses on creating new content rather than just recognizing or classifying existing data. It is a key theme in the video, with Jensen Huang explaining how Nvidia is moving beyond the traditional perception-based AI to a new era where AI can generate tokens that represent a wide range of data. This shift is portrayed as a significant advancement that will impact various industries.

💡Tokens

In the script, 'tokens' are used to describe the basic units generated by AI, which can represent words, images, or any other form of data. The concept is integral to understanding the video's message about the generative capabilities of AI. Tokens are the building blocks of the new commodities produced by the AI Factory, highlighting the versatility and potential applications of AI in creating value.

💡Nims (Nvidia Inference Microservices)

Nims, or Nvidia Inference Microservices, represent a new approach to software design where AI models are pre-packaged and made accessible as services. This concept is introduced as a significant development in the video, with the potential to change the way AI is deployed and utilized across industries. Nims encapsulate complex AI models and software, making it easier for companies to integrate AI into their operations.

💡Digital Twins

Digital Twins are virtual replicas of real-world entities or systems, used for simulation and analysis. In the video, the concept of a 'Digital Twin of Earth' is introduced as an ambitious project that aims to simulate the planet to predict and mitigate the impacts of climate change. This illustrates the video's theme of using advanced technologies like AI and simulation for global challenges.

💡Quantum Computing

Quantum Computing is a branch of computing that uses quantum-mechanical phenomena to perform operations on data. The video discusses how Nvidia's technology can simulate quantum computers, which is crucial for designing quantum algorithms and computers that don't yet exist. This showcases the video's theme of pushing the boundaries of computing and AI to solve complex problems.

💡Digital Humans

Digital Humans are virtual representations of humans that can interact with users in a natural and empathetic manner. The video highlights Nvidia's work in this area, suggesting that they have the potential to become engaging and realistic interactive agents. This concept ties into the video's overarching narrative of creating advanced AI systems that can mimic human capabilities.

💡Physical AI

Physical AI refers to artificial intelligence systems that understand and can interact with the physical world, following the laws of physics. The video positions Physical AI as the next wave in AI development, emphasizing the need for AI to be grounded in a physical world model to perform tasks effectively. This concept is central to the video's message about the future of AI in robotics and automation.

💡Robotics

Robotics is the branch of technology that deals with the design, construction, operation, and use of robots. In the video, robotics is discussed as a pervasive idea that extends beyond humanoid robots to include the automation of factories and the creation of robotic products. This illustrates the video's theme of AI and technology transforming industries and everyday life.

💡Self-Improving AI

Self-Improving AI is a concept where AI systems can learn and improve their performance over time without human intervention. The video mentions the emergence of such AI, which can generate synthetic data and use reinforcement learning to enhance its capabilities. This concept is part of the video's exploration of how AI is evolving to become more autonomous and intelligent.

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