Nvdia CEO Just Revealed The NEXT STEP In AI....

So Nvidia CEO Jensen Huang at CES actually gave us a real insight with as to what is the next step for AI. It's not actually agentic AI, but rather physical AI. You can see on this chart right here we actually have generative AI which is the first step. Then of course we have agent AI which is going to be happening largely this year. And then of course right at the top we have physical AI and this is essentially embodied AI which is self driving cars and general robotics. So take a quick look because this video is going to break down into why this is absolutely incredible and why this is the next evolution in AI.

The next frontier of AI is physical AI. Model performance is directly related to data availability, but physical world data is costly to capture, curate and label. Nvidia Cosmos is a world foundation model development platform to advance physical physical AI. It includes auto regressive world foundation models, diffusion based world foundation models, advanced tokenizers and an Nvidia cuda, an AI accelerated data pipeline. Cosmos models ingest text, image or video prompts and generate virtual world states as videos. Cosmos generations prioritize the unique requirements of AV and robotics, use cases like real world environments, lighting and object permanence. Developers use Nvidia Omniverse to build physics based geospatially accurate scenarios. Then output Omniverse renders into Cosmos which generates photoreal physically based synthetic data. Whether diverse objects or environments, conditions like weather or time of day, or edge case scenarios. Developers use Cosmos to generate worlds for reinforcement learning, AI feedback to improve policy models, or to test and validate model.

Performance.

Even across multi sensor views. Cosmos can generate tokens in real time, bringing the power of foresight and multiverse simulation to AI models, generating every possible future to help the model select the right path. Working with the world's developer ecosystem, Nvidia is helping advance the next wave of physical AI.

Now next, this is where Jensen Huang actually talks about how we actually need a sort of World foundation model. Quite like how we have these, you know, foundational models for large language models and how they, you know, allow us to talk to these models in a natural language. We're going to need something very similar to that for robotics and this is essentially what they're trying to build with their Cosmos model.

Speaking of Linux, let's talk about physical AI. So physical AI, imagine, imagine. Whereas your large language model, you give it your context, your prompt on the left and it generates tokens one at a time to produce the output. That's basically how it works. The amazing thing is this model in the Middle is quite large, has billions of parameters. The context length is incredibly large because you might decide to load in a PDF. In my case, I might load in several PDFs before I ask it a question. Those PDFs are turned into tokens. The attention, the basic attention characteristic of a transformer has every single token find its relationship and relevance against every other token. So you could have hundreds of thousands of tokens and the computational load increases quadratically. And it does this. All of the parameters, all of the input sequence, process it through every single layer of the transformer and it produces one token. That's the reason why we needed Blackwell. And then the next token is produced. When the current token is done, it puts the current token into the input sequence and takes that whole thing and generates the next token and does it one at a time. This is the transformer model. It's the reason why it is so, so incredibly effective, computationally demanding. What if instead of PDFs, it's your surrounding? And what if instead of the prompt, a question, it's a request, go over there and pick up that, you know, that box and bring it back. And instead of what is produced in tokens as text, it produces action tokens. Well, that I just described is a very sensible thing for the future of robotics. And the technology is right around the corner. But what we need to do is we need to create the effective, effectively the world model of, you know, as opposed to GPT, which is a language model. And this world model has to understand the language of the world. It has to understand physical dynamics, things like gravity and friction and inertia. It has to understand geometric and spatial relationships. It has to understand cause and effect. If you drop something, it falls to the ground. If you poke at it, it tips over. It has to understand object permanence. If you roll a ball over the kitchen counter, when it goes off the other side, the ball didn't leave into another quantum universe. That's still there. And so all of these types of understanding is intuitive understanding that we know that most models today have a very hard time with.

So one of the things that most people might not know is that the reason that physical AI hasn't really caught up to the levels of, you know, these large language models is because, of course, we don't have enough data. ChatGPT and these large language models are trains on trillions and trillions of, you know, different pieces of text from around the world. And so if we want to actually get humanoid robots that work, we're going to need that Data from somewhere. So Nvidia's project Isaac Groot is essentially the framework that they are using to scale up their data collection efforts. And once this is something that becomes even more streamlined with future iterations, it's going to become easier for us to collect more data. And thus humanoid robots are going to start to rapidly increase their capabilities. And we all know just quickly how how Nvidia move, which is why I'm so bullish on robotics for the next coming years, it's quite likely to move a lot faster than you do predict. This is something that you should pay attention to as once again, quite like AI. This is something that's going to affect us maybe not as soon as AI agents this year and next year, but definitely in the very near future as they start to take roles at certain factories, automating a variety of tasks they couldn't before.

Developers around the world are building the next wave of physical AI embodied robots humanoids. Developing general purpose robot models requires massive amounts of real world data, which is costly to capture and curate. Nvidia Isaac Groot helps tackle these challenges, providing humanoid robot developers with four robot foundation models, data pipelines, simulation frameworks and a Thor robotics computer. The Nvidia Isaac Groot Blueprint for Synthetic Motion Generation is a simulation workflow for imitation learning, enabling developers to generate exponentially large datasets from a small number of human demonstrations. First, Groot Teleop enables skilled human workers to portal into a digital twin of their robot using the Apple Vision Pro. This means operators can capture data even without a physical robot. And they can operate the robot in a risk free environment, eliminating the chance of physical damage or wear and tear. To teach a robot a single task, operators capture motion trajectories through a handful of teleoperated demonstrations, then use Groot Mimic to multiply these trajectories into a much larger data set. Next, they use Grootgen built on Omniverse and Cosmos for domain randomization and 3D to real upscaling, generating an exponentially larger data set. The Omniverse and Cosmos multiverse simulation engine provides a massively scaled data set to train the robot policy. Once the policy is trained, developers can perform software in the loop testing and validation in Isaac Sim before deploying to the real robot. The age of general robotics is arriving. Powered by Nvidia Isaac Group we're going.

To have mountains of data to train robots with. Nvidia Isaac Group Nvidia Isaac Group this is our platform to provide technology platform technology elements to the robotics industry to accelerate the development of general robotics.

Now, if you're looking or wondering just exactly how this kind of AI is going to be revolutionizing the factories and the country, take no further to look at this example that they provide where they're already maximizing efficiency in this factory. By having these simulations, various processes running within the background, it really shows us how transformative IT AI technology is going to be once it's integrated into several parts of the economy.

Kion, the supply chain solution company Accenture, a global leader in professional services, and Nvidia are bringing physical AI to the $1 trillion warehouse and distribution center market. Managing high performance warehouse logistics involves navigating a complex web of decisions influenced by constantly shifting variables. These include daily and seasonal demand changes, space constraints, workforce availability, and the integration of diverse robotic and automated systems. And predicting operational KPIs of a physical warehouse is nearly impossible today. To tackle these challenges, Kion is adopting Mega, an Nvidia omniverse blueprint building industrial digital twins to test and optimize robotic fleets. First, Kihion's warehouse management solution assigns tasks to the industrial AI brains in the digital twin, such as moving a load from a buffer location to a shuttle storage solution. The robot's brains are in a simulation of a physical warehouse digitalized into omniverse. Using open USD connectors to aggregate CAD video and image to 3D LiDAR to point cloud and AI generated data, the fleet of robots execute tasks by perceiving and reasoning about their Omniverse digital twin environment, planning their next motion and acting. The robot brains can see the resulting state through sensor simulations and decide their next action. The loop continues while Mega precisely tracks the state of everything in the digital twin. Now Kion can simulate infinite scenarios at scale While measuring operational KPIs such as throughput, efficiency and utilization, all before deploying changes to the physical warehouse. Together with Nvidia, Kion and Accenture are reinventing industrial autonomy.

That's incredible. Everything is in simulation.