The technology of building general humanoid robots, which is going to be the most useful, of course, because we fill the world around ourselves, that technology is incredibly hard to do.

當然，製造普通仿人機器人的技術將是最有用的，因為我們將自己周圍的世界填得滿滿的，但這項技術的難度令人難以置信。

But for the very first time with transformers and these large language models and the breakthroughs that we're seeing with foundation models, we finally have the technology necessary, we think, to be able to make a real contribution in this area.

但是，有了變壓器和這些大型語言模型，以及我們在基礎模型方面看到的突破，我們終於首次擁有了必要的技術，我們認為，我們能夠在這一領域做出真正的貢獻。

Jensen, you've led NVIDIA through gaming and so many AI breakthroughs.

詹森，你帶領英偉達在遊戲和人工智能領域取得了眾多突破。

Which emerging technology do you see or think will be the most impactful for us over the next decade?

未來十年，您認為哪項新興技術對我們的影響最大？

When you take a step back and ask yourself, what would happen if we could scale intelligence building robotics, the things that we're working on?

當你退一步捫心自問，如果我們能夠擴展智能建築機器人技術和我們正在研究的東西，會發生什麼？

Hi, Jensen.

嗨，詹森

Thanks for taking the time to chat with me today.

感謝您今天抽出時間和我哈拉。

I'm happy to be here.

我很高興來到這裡。

You made some groundbreaking announcements at CES and in particular, one area I'm very curious about is robotics.

你們在 CES 上發佈了一些開創性的消息，尤其是我對機器人技術非常好奇。

What excites you the most about the possibilities when it comes to robotics now with tools such as Cosmos or world foundational models?

當談到機器人技術的可能性時，最讓您興奮的是什麼？

We're in an incredible time with robotics.

在機器人技術方面，我們正處於一個令人難以置信的時代。

The critical technologies necessary to build general humanoid robotics is just around the corner.

製造通用仿人機器人所需的關鍵技術指日可待。

And one of the critical pieces of technology is an AI model that understands the world.

而其中一項關鍵技術就是能夠理解世界的人工智能模型。

Just as we have an AI model that understands language now with chat GPT and Lama and such, we need a world model, a language model of the world.

就像我們現在有了能理解語言的人工智能模型，有了哈拉 GPT 和喇嘛等等，我們還需要一個世界模型，一個世界的語言模型。

The world needs robots.

世界需要機器人

And one of the reasons for that is we just don't have enough workers.

其中一個原因就是我們沒有足夠的工人。

There's a aging population and a changing in preference of the type of work that people wanna do.

人口老齡化加劇，人們對工作類型的偏好也在發生變化。

And the birth rate is declining and the world needs more workers.

而出生率正在下降，世界需要更多的工人。

And so the timing is really relatively imperative that we have robotic systems.

是以，我們擁有機器人系統的時機確實相對緊迫。

The technology of building general humanoid robots, which is gonna be the most useful, of course, because we build a world around ourselves, that technology is incredibly hard to do.

當然，製造普通仿人機器人的技術將是最有用的，因為我們會圍繞自己構建一個世界，但這項技術卻非常難以實現。

But for the very first time with transformers and these large language models and the breakthroughs that we're seeing with foundation models, we finally have the technology necessary, we think, to be able to make a real contribution in this area.

但是，有了變壓器和這些大型語言模型，以及我們在基礎模型方面看到的突破，我們終於首次擁有了必要的技術，我們認為，我們能夠在這一領域做出真正的貢獻。

There are several things that we have to bring together.

我們必須把幾件事情結合起來。

First, the robot has to understand us.

首先，機器人必須理解我們。

And the breakthroughs in chat GPT, for example, has really made that possible.

例如，哈拉 GPT 的突破確實使這成為可能。

But what's missing is that we now need a AI that understands the physical world.

但我們現在缺少的是一種能夠理解物理世界的人工智能。

It has to understand the dynamics of the physical world, like gravity, inertia and friction.

它必須瞭解物理世界的動態，如重力、慣性和摩擦力。

And it has to understand spatial relationships and geometric relationships.

它還必須理解空間關係和幾何關係。

And common sense things like object permanence and things like that.

還有一些常識性的東西，比如物體永存之類的。

And so we went off to create essentially the chat GPT or the Lama of world models.

於是，我們創建了世界模型中的 "哈拉 GPT "或 "喇嘛"。

And it's called World Foundation Model, just like a language foundation model.

它被稱為 "世界基礎模型"，就像語言基礎模型一樣。

This is a foundation model that understands worlds.

這是一個瞭解世界的基礎模型。

And so if we could create such a thing, and that's what Cosmos is, and we made it available openly for everyone, hopefully this will really ignite and accelerate the development of robotics.

是以，如果我們能創造出這樣一個東西，這就是宇宙，並向所有人開放，希望這將真正點燃並加速機器人技術的發展。

I love that.

我喜歡這樣。

And when it comes to teaching robotics, I know there were some announcements made around Isaac Root as well, especially around virtual reality training.

說到機器人教學，我知道圍繞 Isaac Root 也發佈了一些消息，尤其是圍繞虛擬現實培訓。

Where do you see the future of that or the possibilities of that opening up for us?

您認為我們的未來或可能性在哪裡？

Well, the first part of training an AI is you have to give them foundation knowledge, common sense knowledge.

那麼，訓練人工智能的第一部分就是要讓它們掌握基礎知識和常識。

The second part is you have to fine tune them in skills.

第二部分是你必須在技能上對他們進行微調。

So you have to teach them things.

所以，你必須教他們一些東西。

And the way you teach a general robotics is kind of like the way you teach a person, you show it to them.

教普通機器人的方法就像教人一樣，你要展示給他們看。

And so you use human demonstration and you show them this is the way you pick up a glass.

是以，你要通過人體示範，告訴他們這就是你拿起杯子的方式。

But every time the glass is a little bit different, it's positioned a little different, the height's a little different and the shape's a little different.

但每次玻璃的位置、高度和形狀都有點不同。

And yet it's basically picking up a glass of water.

然而，這基本上就是拿起一杯水。

And so using Isaac Root, we could do a few human demonstrations and then using AI, using Cosmos and Omniverse to generate a whole bunch of future versions of it.

是以，利用艾薩克-羅特，我們可以做一些人類演示，然後利用人工智能、宇宙和全能宇宙來生成一大堆未來版本。

And so then we generate a whole bunch of versions of different sizes and different locations and placements.

然後，我們會生成一大堆不同尺寸、不同位置和位置的版本。

And we give all of that training data, like imitation data to the robot to learn from.

我們將所有的訓練數據，比如模仿數據，提供給機器人學習。

And so now it learned a whole bunch of generalized versions of it.

是以，現在它學會了一大堆通用版本。

Yes.

是的。

Because it feels like there's endless amounts of versions and that's what really what this is solving is by giving those versions for training the robot.

因為感覺就像有無窮無盡的版本，而這正是通過提供這些版本來訓練機器人所要解決的問題。

That's right.

這就對了。

So instead of giving it just one example, we're giving that robot millions of different examples. And you're mentioning Omniverse as well.

所以，我們不是隻給它一個例子，而是給機器人數百萬個不同的例子。 你還提到了 "全能宇宙"。

And that's something that I'm very, very fascinated with, especially when it comes to virtual training in industries such as manufacturing.

我對這一點非常非常著迷，尤其是涉及到製造業等行業的虛擬培訓時。

How do you see those industries evolving with using the Omniverse for training purposes?

您認為這些行業如何利用 Omniverse 進行培訓？

Well, the robotics industry has a hard time getting off the ground because it's hard to train a robot.

機器人產業很難起步，因為很難訓練機器人。

And you have to create a whole bunch of experiences for the robot.

你必須為機器人創造一大堆體驗。

And it's also hard, it's also dangerous to train a robot in the physical world.

在物理世界中訓練機器人也很困難，也很危險。

And so we created a virtual world where a robot could, you know, a playground for a robot essentially.

是以，我們創建了一個虛擬世界，機器人可以在這個世界裡玩耍。

And so this Omniverse is a virtual playground to the robot.

是以，對於機器人來說，這個宇宙世界就是一個虛擬的遊樂場。

It feels like the real thing because it obeys the laws of physics and things look real.

它給人的感覺就像真的一樣，因為它遵守物理定律，而且看起來很真實。

And to the robot, it can't tell the difference.

對機器人來說，它根本分辨不出其中的差別。

And so we train the robot in this virtual world called Omniverse, and we create a whole bunch of scenarios for the robot to learn from.

是以，我們在這個名為 "Omniverse "的虛擬世界中對機器人進行訓練，為機器人創造了大量的學習場景。

Now, when the robot learned how to be in Omniverse and do a task in Omniverse, we take that robot brain and we put it into the real robot.

現在，當機器人學會了如何在 Omniverse 中生存，並在 Omniverse 中完成任務後，我們就可以將機器人大腦植入真正的機器人中。

And, you know, if the SIM, the real gap is as small as possible, the robot can't tell the difference.

要知道，如果 SIM 卡的實際間隙越小越好，機器人就無法分辨。

Yeah, that's the incredible part.

是啊，這就是不可思議的地方。

And so this virtual world, this digital twin of the world is what Omniverse was created for.

是以，這個虛擬世界，這個世界的數字孿生體，就是 Omniverse 的誕生目的。

It's amazing, and it saves so many, I'm sure, resources and time if the training was done otherwise.

這太神奇了，如果不這樣做，我相信會節省很多資源和時間。

Yeah, otherwise it'd just be impossible.

是啊，否則根本不可能。

If you were to train a robot, say, to learn how to walk in the physical world, it would be learning in human time, linear time.

如果你要訓練一個機器人，比如說，讓它學會如何在物理世界中行走，那麼它將在人類的線性時間中學習。

But in Omniverse, we could create so many different multiverses, if you will, that the robot is learning in parallel, you know, maybe 100,000 different ways.

但在 "全能宇宙 "中，我們可以創造出許多不同的 "多重宇宙"，機器人可以並行學習，你知道，可能有 10 萬種不同的學習方式。

And so we take what would have taken 10 years to train a robot to do, we basically reduced it down to a few hours.

是以，我們把原本需要 10 年時間訓練機器人才能完成的工作，縮短到了幾個小時。

And so, you know, this is the, imagine if we had a multiverse, how smart we would be, you know, so all the different versions of Tiffany would be learning math here, learning science there, learning English there, learning geography there, and we simultaneously learn all at the same time.

所以，你知道，這就是，想象一下，如果我們有一個多元宇宙，我們會有多聰明，你知道，所有不同版本的蒂芙尼都會在這裡學習數學，在那裡學習科學，在那裡學習英語，在那裡學習地理，我們同時學習所有的知識。

And that's essentially what Omniverse does.

而這正是 Omniverse 的主要工作。

Exactly, exactly.

沒錯，沒錯。

I wish that was possible for Danny Buffman.

我希望丹尼-布夫曼也能做到這一點。

You know, another area that was announced yesterday was around NVIDIA Drive AI, and really enhancing and helping the safety and security when it comes to autonomous vehicles.

昨天發佈的另一個領域是英偉達™（NVIDIA®）的人工智能駕駛技術（Drive AI），它能真正增強和幫助自動駕駛汽車的安全保障。

I know you also announced your partnership with Toyota as well, which is very exciting.

我知道你們也宣佈了與豐田的合作，這非常令人興奮。

Yeah, that was big news.

是啊，這可是個大新聞。

Really big news.

真正的大新聞

They're the largest car company in the world.

他們是世界上最大的汽車公司。

I know, it's very exciting.

我知道，這很令人興奮。

Where do you see that headed with NVIDIA Drive AI?

您認為英偉達™（NVIDIA®）Drive AI 的發展方向是什麼？

Well, we've been working on autonomous driving for some time, and it's already some $5 billion business for us.

我們研究自動駕駛已經有一段時間了，這對我們來說已經是一項價值 50 億美元的業務。

Yeah, and so the way that we serve the autonomous vehicle industry is through the three computer systems, one for training the AI, one for simulating the AI called Omniverse, and one to put the AI in the car.

是的，我們為自動駕駛汽車行業提供服務的方式是通過三套計算機系統，一套用於訓練人工智能，一套用於模擬人工智能，稱為 "Omniverse"，還有一套用於將人工智能植入汽車。

And the car AI, safety is everything.

而汽車人工智能，安全就是一切。

And the way that you solve for safety, first, the algorithm has to be safe.

而解決安全問題的方法，首先是算法必須安全。

And so it has to be smart about what to avoid and how to drive safely and such.

是以，在避免什麼、如何安全駕駛之類的問題上，必須要有智慧。

But those are algorithm things beyond.

但這些都是算法之外的事情。

Even underneath that, the operating system has to be designed to be safe.

即便如此，作業系統的設計也必須是安全的。

The car computer has to be designed to be safe.

汽車電腦的設計必須安全。

In the sense that it can't fail.

從這個意義上說，它不會失敗。

And if it were to fail, it would fail safely.

即使失敗，也會安全地失敗。

There's a whole bunch of very complex technology that's associated with diversity of algorithms and redundancy of computing, and all of this complex technology makes it possible to be safe. It's so interesting you say that, because it is, you know, from a consumer standpoint, you think of safety more so from, you know, detecting objects or whatnot.

有一大堆非常複雜的技術與算法的多樣性和計算的冗餘性相關聯，所有這些複雜的技術使得安全成為可能。 你這麼說很有意思，因為你知道，從消費者的角度來看，你更多地是從探測物體或其他方面來考慮安全問題。

But to your point, there's so many layers to it.

但就你的觀點而言，這裡面有很多層面。

It goes all the way down to the algorithm, really, is where it begins.

這一切都要從算法說起。

That's right.

這就對了。

And the more diversity you have and the more redundancy that you have, the more safe that system will be. Jensen, you've led NVIDIA through gaming and so many AI breakthroughs.

多樣性越多，冗餘度越高，系統就越安全。 詹森，你帶領英偉達在遊戲和人工智能方面取得了很多突破。

Which emerging technology do you see or think will be the most impactful for us over the next decade?

未來十年，您認為哪項新興技術對我們的影響最大？

Well, artificial intelligence is unquestionably the single most important technology of our time.

人工智能無疑是我們這個時代最重要的一項技術。

And when you take a step back and ask yourself, what would happen if we could scale intelligence and apply it and channel that capability and direct it at healthcare for drug discovery or figuring out how to deal with climate change or just, you know, building robotics, for example, the things that we're working on so that we could deal with the aging population, declining population, and prevent and help alleviate the inflation that's going on everywhere by driving productivity into every single industry.

當你退一步捫心自問，如果我們能夠擴大智能的規模並加以應用，將這種能力引導到醫療保健領域，用於藥物研發，或者找出應對氣候變化的方法，或者只是，你知道的，例如，製造機器人，我們正在研究的東西會發生什麼，這樣我們就可以應對人口老齡化、人口減少的問題，並通過提高各行各業的生產力來預防和幫助緩解各地正在發生的通貨膨脹。

There's just so many things that artificial intelligence is gonna impact.

人工智能會影響很多事情。

And so that's why, as a company, we're all completely into it.

是以，作為一家公司，我們完全投入其中。

Now, artificial intelligence affects all of our other businesses, you know, from even though GeForce was really the vehicle that made artificial intelligence possible, AI has now gone back to GeForce and made computer graphics more amazing.

現在，人工智能影響著我們所有的其他業務，你知道，儘管 GeForce 是人工智能的真正載體，但人工智能現在又回到了 GeForce，使計算機圖形學變得更加令人驚歎。

Yeah.

是啊

Yeah.

是啊

And it's just incredible what we're able to do now, combining artificial intelligence and computer graphics.

人工智能和計算機圖形學的結合，讓我們現在所能做的一切令人難以置信。

And so we're using artificial intelligence to, we're combining it with physical sciences and revolutionizing the way we do scientific computing.

是以，我們正在利用人工智能，將其與物理科學相結合，徹底改變我們進行科學計算的方式。

We're combining it with, you know, the way that we design chips so that we design better chips and the way we develop better software.

我們正在將其與芯片設計方式結合起來，以便設計出更好的芯片和開發出更好的軟件。

And so artificial intelligence is affecting everything that we do.

是以，人工智能正在影響我們所做的一切。

Yes.

是的。

And it's gonna impact everything that, every industry out there.

這將對所有行業產生影響。

So it's the single most important thing, undoubtedly, Manji.

毫無疑問，這是最重要的一件事，曼吉。

And that brings me to a question.

這讓我想到了一個問題。

I have a lot of followers or viewers on my channel who are either, you know, in computer science or, you know, working in technology.

我的頻道上有很多粉絲或觀眾，他們要麼是計算機專業的，要麼是從事技術工作的。

And a common question asked is, there's so many areas within tech that you can get into or kind of grow your career into.

一個經常被問到的問題是，在科技領域有很多你可以涉足或發展的領域。

You know, it seems like artificial intelligence from both the business and technical standpoint is definitely a great area for them to continue to pursue.

你知道，從商業和技術角度看，人工智能無疑是他們繼續追求的一個偉大領域。

Yeah, I think the, of course, there's the contributing to the basic science of artificial intelligence.

是的，我認為，當然，還有對人工智能基礎科學的貢獻。

And I think that that's terrific.

我認為這非常好。

However, the next decade, the application of artificial intelligence, the applied sciences is going to be really important. You know, how does, how, I work with ChatGPT as a companion every day, you know?

然而，未來十年，人工智能的應用、應用科學將變得非常重要。 你知道嗎，我每天都與 ChatGPT 作為伴侶一起工作。

Yeah, and so I have ChatGPT on all the time and I'm asking you questions and working with it to solve problems.

是的，所以我一直開著 ChatGPT，向你提問，用它來解決問題。

You have to learn how to interact with AI.

你必須學會如何與人工智能互動。

And prompting, as you know, has a real art to it.

如你所知，提示是一門真正的藝術。

And there's art and science associated with prompting.

提示是一門藝術，也是一門科學。

And so the way you interact with people, the way you interact with AIs, you're going to have to learn how to do that.

是以，你必須學會如何與人互動，如何與人工智能互動。

And how do you apply AI to content creation?

如何將人工智能應用於內容創作？

How do you apply AI to engineering?

如何將人工智能應用於工程學？

Or how do you apply AI to software development?

或者，如何將人工智能應用於軟件開發？

Or how do you apply AI to marketing or finance or the legal profession?

或者，如何將人工智能應用於市場營銷、金融或法律行業？

Whatever field that you're interested in, how do you apply AI to that?

無論你對哪個領域感興趣，你如何將人工智能應用於其中？

That's an area that I think is worthy of a lot of research and a lot of development.

我認為這是一個值得進行大量研究和開發的領域。

And so I think the, whereas my generation was really about how do we apply computers to solve chip design and software engineering, this generation is how do we apply AI to solve those, answer all of those same basic questions.

是以，我認為，我們這一代人真正關心的是如何應用計算機來解決芯片設計和軟件工程問題，而這一代人關心的是如何應用人工智能來解決這些問題，回答所有這些相同的基本問題。

How do I apply AI to forestry?

如何將人工智能應用於林業？

How do I apply AI to oceanography?

如何將人工智能應用於海洋學？

How do I, you know, so on and so forth.

我該怎麼做，你知道的，諸如此類。

It's, yeah, every industry, every field of science.

是的，每個行業、每個科學領域都是如此。

Jansen, thank you so much for taking the time to chat with me today.

詹森，非常感謝你今天抽出時間和我哈拉。

It's, I'm leaving this conversation feeling so excited about the future and what's to come.

離開這次談話時，我對未來和即將發生的事情感到非常興奮。