Name: 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 (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: 2025-05-02T13:04:22.000Z
Duration: 21 min 29 s

You've positioned AI as a new industrial revolution with AI factories at the center of it.

您將人工智能定位為一場以人工智能工廠為中心的新工業革命。

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

您能向我們解釋一下什麼是人工智能工廠，以及在 21 世紀經濟中瞭解它的重要性嗎？

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

過去幾年，我們一直在談論人工智能。

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

它是多方面的，我認為從這個角度來看待它是有幫助的。

First, of course, AI is a new technology.

首先，人工智能當然是一項新技術。

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.

這是一種新技術，因為它的構造與過去的軟件不同，而這種新軟件可以做到過去的軟件做不到的事情。

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.

它能做的所有事情，我們為保證它的安全而必須做的所有事情，以及它將促成的所有令人驚歎的事情，太棒了。

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

第二層是比較新的，在上一個技術產業中，軟件生產是由人類打字完成的。

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

現在，我們有了新的產業，這種軟件是用機器生產出來的。

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

你需要一臺大型超級計算機，給它通電，從裡面飛出來的就是代幣。

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.

這些代幣可以重新組合成數字、文字、蛋白質、影像、視頻和三維結構，你可以把這些代幣重新組合成各種各樣的東西。

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

這種機器看起來與過去的機器不同，我之所以稱它為人工智能工廠，是因為它只做一件事。

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.

上面一層是基礎設施，這也是我們現在認為人工智能很可能是一場非同尋常的工業革命的原因所在，因為這項新技術將進入我剛才提到的一個新行業--人工智能工廠，即智能生產，但它將回過頭來徹底改變和變革其他所有行業。

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

所有這些代幣都將用於醫療和教育。

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

我每天都將它用於教育、金融服務和工程領域。

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

我們每天都在使用人工智能進行軟件編程和供應鏈管理。

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

它即將進入製造業，等等等等。

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

從這三個層面來思考，就會非常清楚。

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

這就像以前的電力一樣具有變革性和影響力，它將徹底改變每一個行業。

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?

你是否認為這是現代計算的一個範式轉變，現實世界中每一個製造實物的工廠都將伴隨著人工智能工廠的出現？

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

今天的每家公司都在製造東西，只要它們能動。

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

比方說，你製造割草機，他們製造建築機械。

Today, it's largely manually manipulated.

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

未來，它將是自動駕駛、高度自動駕駛、半自動駕駛或輔助駕駛。

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

但當它變得自主時，就會是軟件定義的了。

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

你必鬚生產那些代幣，那些為拖拉機提供動力的軟件。

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.

未來，每家制造產品的公司都會有一家工廠負責製造他們出售的產品，然後他們會有另一家工廠負責製造和生產在他們出售的產品上運行的人工智能。

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

是以，看看汽車公司就很清楚了。

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.

今天的汽車公司主要生產汽車，但很明顯，10 年後，每家汽車公司都將生產在這些汽車中運行的代幣。

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

去年，你談到了一些關於物理人工智能的話題，也就是物理人工智能的概念。

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?

對於正在思考美國未來政策的決策者來說，您能否向我們解釋一下什麼是物理人工智能，以及我們應該如何思考它？

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.

人工智能真正進入人們的意識，現代人工智能真正進入人們的意識大約是在 12、14 年前，當時 AlexNet 問世，計算機視覺取得了巨大的突破。

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

退一步說，計算機視覺是什麼？

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.

在最大的語境中，它是感知，感知世界，無論資訊的模式是什麼，當然，可以是影像，可以是聲音，可以是振動，也可以是溫度。

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

現在，我們已經開發出了人工智能模型，能夠理解所有這些資訊的含義，並且能夠相當智能地處理這些資訊。

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

第一層，第一波人工智能是感知人工智能。

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

其次，大家在五年前開始談論的是生成式人工智能。

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

而生成式人工智能則是指人工智能模型已經學會如何理解資訊的含義，並將其翻譯出來。

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

例如，您可以聽懂英語並將其翻譯成法語。

You could understand English and translate it to images.

你能聽懂英語並將其翻譯成影像。

And so you could prompt it to generate images.

是以，你可以促使它生成影像。

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

是以，如果你願意的話，生成式人工智能本質上是一種通用翻譯器，一種能夠理解人類語言的通用翻譯器。

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.

我們現在所處的浪潮是，人工智能可以理解，可以生成，但正如你所知道的，智能要求我們解決問題，識別我們從未見過的情況。

And the way we do that is we use reasoning.

我們這樣做的方式就是運用推理。

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.

我們運用過去學到的規則、法則和原則，一步一步地解決問題。

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

儘管我們以前從未解決過這個問題，但通過推理，我們可以解決它。

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

好吧，這就是情報的獨特能力之一。

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

是以，我們現在正處於一個叫做推理人工智能的時代，而推理人工智能可以讓你製造出一種數字機器人。

我們稱它們為雲端人工智能，即代理。

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.

是以，人工智能可以理解所賦予的任務，它可以去學習、閱讀、應用、使用計算器、網絡瀏覽器和電子表格等工具，然後回來為你做一些事情。

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

這可能與供應鏈或 SAP 訪問有關。

It could be something related to HR, access workday.

這可能與人力資源、訪問工作日有關。

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

是以，這些人工智能本質上是機器人，但它們是數字勞動力機器人。

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

是以，在未來，我們將成為管理生物勞動力和數字勞動力的一代首席執行官。

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

生物勞動力的人力資源部門和我們的信息技術部門將成為代理人工智能的人力資源部門，對嗎？

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.

下一波浪潮要求我們瞭解物理定律、摩擦力、慣性、因果關係等。

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

事實上，我把那東西翻過來，它就會掉下去。

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

現在，當我把瓶子放下時，它不會穿過桌子。

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

是以，所有這些常識、物理推理能力，孩子們有，我們的寵物有，大多數人工智能都沒有。

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

你知道，我在廚房的臺子上滾一個球，它就會越過臺子，消失不見。

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

人工智能認為它消失了，但你知道，你的狗知道它在另一邊。

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

它懂得 "物體永存 "這個概念，也沒有進入另一個元宇宙。

於是，它繞著桌子跑了一圈，拿到了它。

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.

機器人需要學會，如果你想從桌子的這一邊走到桌子的那一邊，就不能穿過桌子。

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.

所有這些物理推理就是所謂的物理人工智能。

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

當你把這種物理人工智能植入一個叫做機器人的物理物體時，你就得到了機器人技術。

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

這對我們來說非常重要，因為我們正在美國各地建廠。

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

我們希望以利用最新技術的方式來建設它。

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.

是以，希望在未來 10 年內，當我們建造出新一代的工廠和廠房時，它們都能高度自動化，幫助我們應對全球嚴重的勞動力短缺問題。

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

很多人都談到過這樣一個概念，即我們正在全球範圍內進行一場人工智能競賽。

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

你認為美國政府需要做些什麼才能贏得人工智能競賽，擁有最好的人工智能技術？

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

首先，要想在比賽中取得好成績，就必須瞭解比賽。

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.

你必須瞭解你正在使用的資源、你擁有的資產、你沒有的資產、你的優勢和劣勢。

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.

要認識到的一些事情是，從根本上說，人工智能處於核心層面，回到我們所說的三個層面，在每個層面上，我們都必須確保我們瞭解遊戲。

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

而這個遊戲不是，它沒有 60 分鐘的計時器，這是一個無限遊戲。

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

是以，大多數人都不擅長玩無限遊戲。

要知道，英偉達今年已經 33 歲了。

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

我們已經經歷了三次計算機革命，從 PC 革命到互聯網，再到移動，現在我們又迎來了人工智能。

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

是以，要想在所有這些不同的環境變化中茁壯成長，就必須瞭解如何玩遊戲。

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

是以，我剛才所說的事情，瞭解遊戲，瞭解你擁有的資產，真的很重要。

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

在第一層，即技術層，最重要的是要了解智力資本。

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

請記住，全球 50% 的人工智能研究人員都是中國人。

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

首先，退後一步，認識到這一重要因素對我們思考比賽的影響。

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 (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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