Name: 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
Uploaded: 2025-04-28T07:35:06.000Z
Duration: 15 min 27 s
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Artificial intelligence has made extraordinary progress.

Adverbs of manner

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

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

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

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

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.

Generative AI fundamentally changed how computing is done.

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.

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

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

Rather than retrieving data, it now generates answers.

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.

Fundamental advance in artificial intelligence.

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

It can perceive and understand the context of the circumstance.

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

It can use tools because it now understands multimodality information.

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

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.

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

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

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

Robotics, which has been enabled by physical AI.

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

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

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.

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

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

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

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

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

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

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

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

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

Everything that moves will be autonomous.

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

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

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.

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

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

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

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.

Practicing in a lab is different than the real world.

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.

Real-world operations require multiple robots to work together.

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

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

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

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

Today, we're introducing NVIDIA iZake Groot N1.

Groot N1 is a generalist foundation model for humanoid robots.

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

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

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

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

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

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.

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

Physical AI and robotics are moving so fast.

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

At its core, we have the same challenges.

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

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

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

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

In robotics, we created a system called Omniverse.

It's our operating system for physical AIs.

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

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

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.

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.

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.

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