Artificial intelligence has made extraordinary progress.

情態副詞

Now, we've been talking about AI for a little longer than that.

現在，我們談論人工智能的時間比這還要長一些。

But AI really came into the world's consciousness about a decade ago.

但人工智能真正進入人們的視野是在十年前。

It started with perception AI, computer vision, speech recognition, then generative AI.

首先是感知人工智能、計算機視覺、語音識別，然後是生成人工智能。

The last five years, we've largely focused on generative AI.

過去五年，我們主要關注生成式人工智能。

Teaching an AI how to translate from one modality to another, another modality, text to image, image to text, text to video, amino assets to proteins, properties to chemicals, all kinds of different ways that we can use AI to generate content.

教人工智能如何將一種模式轉換成另一種模式，將文本轉換成影像，將影像轉換成文本，將文本轉換成視頻，將氨基酸轉換成蛋白質，將屬性轉換成化學物質，我們可以利用人工智能生成內容的各種不同方式。

Generative AI fundamentally changed how computing is done.

生成式人工智能從根本上改變了計算方式。

From a retrieval computing model, we now have a generative computing model, whereas almost everything that we did in the past was about creating content in advance, storing multiple versions of it, and fetching whatever version we think is appropriate at the moment of use.

從檢索計算模式來看，我們現在採用的是生成計算模式，而過去我們所做的幾乎所有事情都是提前創建內容、存儲多個版本，然後在使用時獲取我們認為合適的版本。

Now, AI understands the context, understands what we're asking, understands the meaning of our request, and generates what it knows.

現在，人工智能可以理解上下文，理解我們的要求，理解我們請求的含義，並生成它所知道的東西。

If it needs, it'll retrieve information, augments its understanding, and generate answer for us.

如果有需要，它會檢索資訊，增強自己的理解力，併為我們生成答案。

Rather than retrieving data, it now generates answers.

現在，它不再是檢索數據，而是生成答案。

Fundamentally changed how computing is done.

Every single layer of computing has been transformed.

The last several years, the last couple, two, three years, major breakthrough happened.

在過去的幾年裡，在過去的一兩年、兩三年裡，我們取得了重大突破。

Fundamental advance in artificial intelligence.

Agentic AI basically means that you have an AI that has agency.

代理人工智能的基本含義是，人工智能具有代理權。

It can perceive and understand the context of the circumstance.

它能感知和理解環境的來龍去脈。

It can reason, very importantly, it can reason about how to answer or how to solve a problem, and it can plan an action.

它可以推理，非常重要的是，它可以推理如何回答或如何解決問題，還可以計劃行動。

It can use tools because it now understands multimodality information.

它能使用工具，因為它現在能理解多模態資訊。

It can go to a website and look at the format of the website, words and videos, maybe even play a video.

它可以訪問網站，查看網站的格式、文字和視頻，甚至播放視頻。

Learns from what it learns from that website, understands it, and come back and use that information, use that newfound knowledge to do its job.

它從網站上學習知識，理解知識，然後回來利用這些資訊，利用新發現的知識來完成自己的工作。

At the foundation of agentic AI, of course, something that's very new, reasoning.

當然，代理人工智能的基礎是一種全新的推理能力。

And then, of course, the next wave is already happening.

We're going to talk a lot about that today.

今天，我們就來談談這個問題。

Robotics, which has been enabled by physical AI.

機器人技術，通過物理人工智能得以實現。

It understands things like friction and inertia, cause and effect, object permanence.

它懂得摩擦和慣性、因果關係、物體永恆性等道理。

When something goes around the corner, it doesn't mean it has disappeared from this universe.

當某些東西消失在拐角處時，並不意味著它已經從這個宇宙中消失了。

And so that ability to understand the physical world, the three-dimensional world, is what's going to enable a new era of AI we call physical AI, and it's going to enable robotics.

是以，這種理解物理世界、三維世界的能力，將使我們稱之為物理人工智能的人工智能新時代成為可能，也將使機器人技術成為可能。

Each one of these phases, each one of these waves, opens up new market opportunities for all of us.

每一個階段，每一波浪潮，都會為我們所有人帶來新的市場機遇。

Robots have the benefit of being able to interact with the physical world and do things that otherwise digital information cannot.

機器人的優勢在於能夠與物理世界互動，做一些數字信息無法做到的事情。

We know very clearly that the world has severe shortage of human laborers, human workers.

我們清楚地知道，世界嚴重缺乏人類勞動力和人類工人。

By the end of this decade, the world is going to be at least 50 million workers short.

到本十年末，全世界將至少缺少 5000 萬名工人。

We'd be more than delighted to pay them each $50,000 to come to work.

我們非常樂意為他們每人支付 5 萬美元，讓他們來工作。

We're probably going to have to pay robots $50,000 a year to come to work.

我們可能要給機器人每年 5 萬美元的工資，讓他們來工作。

And so this is going to be a very, very large industry.

是以，這將是一個非常非常龐大的產業。

你們的基礎設施將是機器人式的。

Billions of cameras in warehouses and factories, 10, 20 million factories around the world.

倉庫和工廠裡有數十億個攝像頭，全球有 1,000 萬、2,000 萬個工廠。

Every car is already a robot, as I mentioned earlier, and then now we're building general robots.

正如我之前提到的，每輛汽車都已經是一個機器人，現在我們正在製造通用機器人。

讓我告訴你我們是如何做到這一點的。

Everything that moves will be autonomous.

Physical AI will embody robots of every kind in every industry.

物理人工智能將體現在各行各業的各類機器人中。

Three computers built by NVIDIA enable a continuous loop of robot AI simulation, training, testing, and real-world experience.

由英偉達™（NVIDIA®）公司製造的三臺計算機實現了機器人人工智能模擬、訓練、測試和實際體驗的連續循環。

Training robots requires huge volumes of data.

Internet-scale data provides common sense and reasoning, but robots need action and control data, which is expensive to capture.

互聯網規模的數據可以提供常識和推理，但機器人需要行動和控制數據，而獲取這些數據的成本很高。

With blueprints built on NVIDIA Omniverse and Cosmos, developers can generate massive amounts of diverse, synthetic data for training robot policies.

利用基於 NVIDIA Omniverse 和 Cosmos 構建的藍圖，開發人員可以生成大量多樣化的合成數據，用於訓練機器人策略。

First, in Omniverse, developers aggregate real-world sensor, or demonstration data, according to their different domains, robots, and tasks.

首先，在 Omniverse 中，開發人員根據不同的領域、機器人和任務，彙總真實世界的傳感器或演示數據。

Then use Omniverse to condition Cosmos, multiplying the original captures into large volumes of photoreal, diverse data.

然後使用 Omniverse 對 Cosmos 進行調節，將原始捕捉的影像轉化為大量逼真、多樣的數據。

Developers use Isaac Lab to post-train the robot policies with the augmented dataset, and let the robots learn new skills by cloning behaviors through imitation learning or through trial and error with reinforcement learning AI feedback.

開發人員利用 Isaac 實驗室使用增強數據集對機器人策略進行後期培訓，讓機器人通過模仿學習克隆行為，或通過強化學習人工智能反饋進行試錯來學習新技能。

Practicing in a lab is different than the real world.

在實驗室裡練習與現實世界不同。

Developers use Omniverse for software and hardware-in-the-loop testing, simulating the policies in a digital twin with real-world environmental dynamics, with domain randomization, physics feedback, and high-fidelity sensor simulation.

開發人員利用 Omniverse 進行軟件和硬件在環測試，在數字孿生系統中模擬具有真實環境動態、域隨機化、物理反饋和高保真傳感器模擬的政策。

Real-world operations require multiple robots to work together.

現實世界中的操作需要多個機器人協同工作。

Mega, an Omniverse blueprint, lets developers test fleets of post-train policies at scale.

Mega 是 Omniverse 的一個藍圖，可讓開發人員大規模測試車隊的列車後政策。

Here, Foxconn tests heterogeneous robots in a virtual NVIDIA Blackwell production facility.

在這裡，富士康在虛擬的英偉達 Blackwell 生產設施中測試異構機器人。

As the robot brains execute their missions, they perceive the results of their actions through sensor simulation, then plan their next action.

機器人大腦在執行任務時，會通過傳感器模擬感知行動結果，然後計劃下一步行動。

Mega lets developers test many robot policies, enabling the robots to work as a system, whether for spatial reasoning, navigation, mobility, or dexterity.

Mega 可讓開發人員測試多種機器人策略，使機器人作為一個系統工作，無論是空間推理、導航、移動還是靈巧性。

Today, we're introducing NVIDIA iZake Groot N1.

今天，我們要介紹的是 NVIDIA iZake Groot N1。

Groot N1 is a generalist foundation model for humanoid robots.

Groot N1 是仿人機器人的通用基礎模型。

It's built on the foundations of synthetic data generation and learning in simulation.

它建立在合成數據生成和模擬學習的基礎之上。

Groot N1 features a dual-system architecture for thinking fast and slow, inspired by principles of human cognitive processing.

Groot N1 採用雙系統架構，既能快速思考，也能慢速思考，其靈感來源於人類的認知處理原則。

The slow thinking system lets the robot perceive and reason about its environment and instructions, and plan the right actions to take.

慢速思維繫統可讓機器人感知和推理其所處的環境和指令，並計劃採取正確的行動。

The fast thinking system translates the plan into precise and continuous robot actions.

快速思維繫統可將計劃轉化為機器人精確而連續的動作。

Groot N1's generalization lets robots manipulate common objects with ease and execute multi-step sequences collaboratively.

Groot N1 的通用性使機器人能夠輕鬆操縱普通物體，並協同執行多步驟序列。

And with this entire pipeline of synthetic data generation and robot learning, humanoid robot developers can post-train Groot N1 across multiple embodiments and tasks across many environments.

有了這一整套合成數據生成和機器人學習管道，仿人機器人開發人員就可以在多種環境下對 Groot N1 的多種實施方案和任務進行後期訓練。

Around the world, in every industry, developers are using NVIDIA's three computers to build the next generation of embodied AI.

在全球各行各業，開發人員都在使用英偉達™（NVIDIA®）的三臺計算機構建下一代人工智能。

Physical AI and robotics are moving so fast.

物理人工智能和機器人技術發展如此之快。

This could very well likely be the largest industry of all.

At its core, we have the same challenges.

其核心是，我們面臨著同樣的挑戰。

As I mentioned before, there are three that we focus on.

正如我之前提到的，我們的重點有三個。

How, where do you create the data necessary to train the AI?

如何、在哪裡創建訓練人工智能所需的數據？

How can we scale either the data, the compute, or both so that we can make AIs smarter and smarter and smarter?

我們如何才能擴大數據、計算或兩者的規模，從而讓人工智能變得越來越聰明？

And those two, those fundamental problems exist in robotics as well.

機器人技術中也存在這兩個基本問題。

In robotics, we created a system called Omniverse.

在機器人領域，我們創建了一個名為 "Omniverse "的系統。

It's our operating system for physical AIs.

這是我們的物理人工智能作業系統。

You've heard me talk about Omniverse for a long time.

今天，我要向你們展示兩件事。

One of them is so that we could scale AI with generative capabilities and generative model that understand the physical world.

其中之一是，我們可以利用生成能力和能夠理解物理世界的生成模型來擴展人工智能。

Using Omniverse to condition Cosmos and using Cosmos to generate an infinite number of environments allows us to create data that is grounded, grounded, controlled by us, and yet be systematically infinite at the same time.

用 "宇宙 "來調節 "宇宙"，用 "宇宙 "來生成無限多的環境，這樣我們就能創造出腳踏實地的數據，這些數據由我們控制，但同時又是系統性的無限數據。

We use candy colors to give you an example of us controlling the robot in the scenario perfectly, and yet Cosmos can create all these virtual environments.

我們用糖果色來舉例說明，我們可以完美地控制場景中的機器人，而宇宙號卻能創造出所有這些虛擬環境。

The second thing, just as we were talking about earlier, one of the incredible scaling capabilities of language models today is reinforcement learning, verifiable rewards.

第二件事，就像我們之前談到的，如今語言模型令人難以置信的擴展能力之一是強化學習，即可驗證的獎勵。

The question is, what's the verifiable rewards in robotics?

問題是，機器人技術的可驗證獎勵是什麼？

中英大字幕 | 15分鐘掌握最新科技動態 | 黃仁勳揭露未來最大產業是它 | 2025 GTC 最重要的發表 (中英大字幕 | 15分鐘掌握最新科技動態 | 黃仁勳揭露未來最大產業是它 | 2025 GTC 最重要的發表)

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