Name: 實戰練英文 | NVIDIA CEO 黃仁勳談 AI 工業革命：各行各業遲了就來不及了，AI 工廠與未來製造 | Hill & Valley Forum 2025 | 長文、註解、大字版、中英對照
Uploaded: 2025-05-02T13:03:58.000Z
Duration: 21 min 29 s
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You've positioned AI as a new industrial revolution with AI factories at the center of it.

Can you explain to us what is an AI factory and why is it important to understand in the 21st century economy?

We've been talking a lot about AI over the last several years.

It's multifaceted and I think it's helpful to look at it in this lens.

First, of course, AI is a new technology.

It's a new technology in the sense that it's built differently than software of the past, and this new software can do things that software of the past could not do.

All the things it can do, all the things that we have to do to keep it safe, all the amazing things that it's going to enable, fantastic.

The second layer, which is rather new, in the last technology industry, software production, it was done by humans typing.

Now we have a new industry and this software is produced with machinery.

You need a large supercomputer, you apply electricity to it, and what comes flying out of it are tokens.

These tokens could be reformulated into numbers, and words, and proteins, and images, and videos, and three-dimensional structures, and you could reformulate these tokens into all kinds of things.

This machinery looks different than the machinery of the past, and I call it an AI factory because it does one thing.

The layer above that is infrastructural, and this is the reason why we now internalize that AI is likely going to be quite an extraordinary industrial revolution in the sense that this new technology is going to a new industry that I just mentioned, AI factories, the production of intelligence, but it's going to go back and revolutionize and transform every other industry.

All of these tokens are going to go into healthcare and education.

I use it every day for education, financial services, engineering.

We use AI every day for software programming and supply chain management.

It's about to go into manufacturing and so on and so forth.

When you think about it from these three layers, it's very clear.

This is as transformative, as impactful as electricity was before, and it's going to revolutionize every industry.

Do you think it's a paradigm shift in modern computing and that every factory that's building physical things in the real world will also be accompanied by an AI factory?

Every company that makes things today, so long as they move.

Let's say you make lawnmowers or they build construction machinery.

Today, it's largely manually manipulated.

In the future, it'll be autonomous or highly autonomous or semi-autonomous or assisted.

But when it becomes autonomous, then it'll be software-defined.

You're going to have to produce those tokens, those software that feeds that tractor.

In the future, every company that builds things will have a factory that builds the things that they sell, and then they'll have another factory that builds and produces the AI that runs on that thing that they sell.

And so it's very clear when you look at car companies.

Today's car companies largely make cars, but it's very clear that in 10 years' time, every car company will also produce the tokens that runs in those cars.

You've talked a little bit about physical AI, the concept of physical AI over the last year.

For policymakers who are thinking about the future of American policy, can you explain to us a little bit about what physical AI is and how we should be thinking about it?

AI really came to consciousness, modern AI really came into consciousness about 12, 14 years ago when AlexNet came out and computer vision was this big, giant breakthrough.

Around that time, when you take a step back, what is computer vision?

In its largest context, it's perception, perceiving the world, whether whatever modality of information it could be, of course, images, it could be sounds, it could be vibration, temperature.

And we've now developed AI models that understand the meaning of all of that information and can be quite smart about it.

The first layer, the first wave of AI was perception AI.

The second, what everybody started talking about maybe five years ago was generative AI.

And generative AI is where the AI model has learned how to understand the meaning of the information but translate it.

So for example, you could understand English and translate it to French.

You could understand English and translate it to images.

And so you could prompt it to generate images.

So generative AI is essentially a universal translator, if you will, a universal translator that understand the language of humans.

The wave that we're in now is where you now have AIs that can understand, it can generate, but as you know, intelligence requires us to solve problems and recognize conditions that we've never seen before.

And the way we do that is we use reasoning.

We apply rules and laws and principles that we've learned in the past and we break the problem down step-by-step-by-step.

And even though we've never solved this problem before, through reasoning, we can solve it.

Okay, and so one of the unique capabilities of intelligence.

And so we're now in this age called reasoning AI and reasoning AIs allow you to produce a form of digital robots.

And so AI that can understand the task that's been given, it can go off and learn and read, apply, use tools like calculators and web browsers and spreadsheets and then come back and does something for you.

And it could be something related to supply chains or access as SAP.

It could be something related to HR, access workday.

So these gentic AIs are essentially robots, but they're digital workforce robots.

So in the future, we're gonna be the generation of CEOs that are gonna manage biological workforce as well as digital workforce.

And HR department for the biological workforce and our IT department are gonna become the HR of agentic AI, right?

This is kind of the phase we're in today.

The next wave requires us to understand things like the laws of physics, friction, inertia, cause and effect.

The fact that I tipped that thing over, it's gonna fall.

Now, when I set the bottle down, it's not gonna go through the table.

And so all of these common sense, physical reasoning abilities that children have, that our pets have, most AIs don't have.

You know, I roll a ball over the kitchen counter and it goes up over the top and it disappears.

Well, the AI thinks it's disappeared, but you know, your dog knows it's on the other side.

It understands this concept called object permanence and it didn't go into another metaverse.

And a robot needs to learn that if you wanna go from this side of the table to that side of the table, you can't go through the table.

You're gonna have to reason about how to go around the table.

And so all of these type of physical reasoning things is what's called physical AI.

And when you take that physical AI and then you put it into a physical object called a robot, you get robotics.

And this is really important for us now because we're building plants and factories all over the United States.

And we'd like to build it in a way that takes advantage of the latest technology.

And so hopefully, you know, in the next 10 years, as we build out this new generation of plants and factories, they're highly robotic and they're helping us deal with the severe labor shortage that we have all over the world.

So many people have talked about this concept about the fact that we're in an AI race globally.

What do you think the US government needs to do to win that AI race, to have the very best AI technology?

First, in order to have a race to do well in a race, you have to understand the race.

And you have to understand the resources that you're working with, the assets that you have, the assets you don't have, your advantages and your disadvantages.

And some of the things to realize is that that AI is fundamentally at this core level, going back to the three levels that we're talking about, at each level, we have to make sure that we understand the game.

And this game isn't, there's no 60 minute clock on this thing, this is an infinite game.

And so most people aren't very good at playing infinite games.

We've been through three computer revolutions, from the PC revolution to the internet, to mobile, and now we're in AI.

And so in order to thrive across all of these different changes in the environment, you have to understand how to play games.

And so the things that I just described, understanding the game, understanding the assets you have, really important.

At the first layer, at the technology layer, the most important thing to understand is the intellectual capital.

And remember, 50% of the world's AI researchers are Chinese.

First, just take a step back and recognize that important factor has to play into how we think about the game.

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實戰練英文 | NVIDIA CEO 黃仁勳談 AI 工業革命：各行各業遲了就來不及了，AI 工廠與未來製造 | Hill & Valley Forum 2025 | 長文、註解、大字版、中英對照

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