Nvidia Finally Reveals The Future Of AI In 2025...

So Nvidia's CEO Jensen Huang did a special address at an AI summit in India. Now, this talk was rather fascinating because it was one of those talks that gives you an insight with as to where we are headed overall in AI. I know most talks are about AI this or AI that, but this one covered three key topics that I think most people aren't focused on. One of them was the inference time with the new paradigm that AI is moving towards. As you know, the new 01, Model 3 thinks before it actually talks. And that means that the model gets smarter with its responses. There was also the talk about agents and how they're going to impact the workplace. And lastly, we got a really cool look at how physical AI is going to change the world in the future with humanoid robots. So this talk is going to be a shortened summary and I'm going to give you guys the key details you need to know. So coming in at the first point, one of the first talks that he actually gives was about the new inference time AI. The this is where he speaks about how this kind of AI is quite different. It's basically like how you have System one and System two thinking. System one being that short, snappy, where someone asks you something immediately, you immediately know the response. But System two is kind of deliberative and you know, planning and reasoning through certain steps to get to your response.

And this is a scaling law at a time of inference. The longer you think, the higher quality answer you can produce. This is not illogical. This is very, very intuitive to all of us. If you were to ask me what's my favorite Indian food, I would tell you chicken briyani. Okay? And I don't have to think about that very much and I don't have to reason about that. I just know it. And there are many things that you can ask it. Like for example, what's Nvidia good at? Nvidia is good at building AI supercomputers. Nvidia's great at building GPUs. And those are things that you know, that it's encoded into your knowledge. However, there are many things that requires reasoning. For example, if I had to travel from Mumbai to California, I want to do it in a way that allows me to enjoy four other cities along the way. Today I got here at 3am this morning I got here through Denmark. And right before Denmark I was in Orlando, Florida. And before Orlando, Florida, I was in California. That was two days ago. And I'm still trying to figure out what day we're in right now, but anyways, I'm happy to be here. If I were to tell it, I would like to go from California to Mumbai, I would like to do it within three days. And I give it all kinds of constraints about what time I'm willing to leave and able to leave, what hotels I like to stay at, so on and so forth, the people I have to meet, the number of permutations of that, of course quite high. And so the planning of that process, coming up with an optimal plan is very, very complicated. And so that's where thinking, reasoning, planning comes in. And the more you compute, the higher quality answer you could provide. And so we now have two fundamental scaling laws that is driving our technology development. First for training and now for inference. Next.

This is where we actually get to speak about agents. Now, agents are something that are right on the horizon. 2025 will largely be the year that autonomous AI takes over. And you'll see them in the workplace. It's likely you'll see them being able to do a various different amount of things for you as an individual. It's quite likely that towards the end of 2075, you're going to see a number of autonomous AI agent systems, paid and free, come online and be able to offer you variety of different goods and services.

Okay, so I'm going to introduce a couple of other ideas. And so earlier I told you that we have Blackwell, we have all of the libraries, acceleration libraries that we were talking about before. But on top, there are two very important platforms we're working on. One of them is called Nvidia AI Enterprise and the other one is called Nvidia Omniverse. And I'll explain each one of them very quick, quickly. First, Nvidia AI Enterprise. This is a time now where the large language models and the fundamental AI capabilities have reached a level of capabilities. We're able to now create what is called agents, large language models that understand understand the data that of course is being presented. It could be, it could be streaming data, video data, language model data. It could be data of all kinds. The first stage is perception. The second is reasoning about, given its observations, what is the mission and what is the task it has to perform in order to perform that task, the agent would break down that task into steps of other tasks and it would reason about what it would take and it would connect with other AI models. Some of them are good at produ, for example, understanding PDF. Maybe it's a model that understands how to generate images. Maybe it's a model that is able to Retrieve information, AI information, AI semantic data from a proprietary database. So each one of these large language models are connected to the central reasoning large language model we call agent. And so these agents are able to perform all kinds of tasks. Some of them are maybe marketing agents, some of them are customer service agents, some of them are chip design agents. Nvidia has chip design agents all over our company helping us design chips. Maybe they're software engineering agents, maybe, maybe they're able to do marketing campaigns, supply chain management. And so we're going to have agents that are helping our employees become super employees. These agents or agentic AI models augment all of our employees to supercharge them, make them more productive. Now when you think about these agents, it's really the way you would bring these agents into your company is not unlike the way you would onboard someone who's a new employee. You have to give them training, curriculum, you have to fine tune them, teach them how to use how to perform the skills and understand the vocabulary of your, of your company. You evaluate them and so their evaluation systems and you might guardrail them. If you're accounting agent, don't do marketing. If you're a marketing agent, don't report earnings at the end of the quarter, so on and so forth. And so each one of these agents are guardrailed. That entire process we put into essentially an agent life cycle suite of libraries and we call that nemo. Our partners are working with us to integrate these libraries into their platforms so that they could enable agents to be created, onboarded, deployed, improved into a life cycle of agents. And so this is what we call Nvidia Nemo. We have on the one hand the libraries on the other hand. What comes out of the output of it is a API inference microservice we call NIMS. Essentially this is a factory that builds AIs. And Nemo is a suite of libraries that onboard and help you operate the AIs. And ultimately your goal is to create a whole bunch of agents.

This is where we got the very fascinating talk about how we're going to get physical AI. Of course, once you do have agents and AI, it's very good because they are digital and they're able to move at hyperspeed. But how do you impact the physical world? How do you manipulate physical objects and achieve things in the real physical world whilst maintaining that scale? Of course, it's humanoid robots and physical AI. This is where he gives the interesting insight into where physical AI is truly headed.

What happens after agents? Now remember, every single company has Employees. But most companies, the goal is to build something, to produce something, to make something. And that those things that people make could be factories, it could be warehouses, it could be cars and planes and trains and ships and so on, so forth, all kinds of things. Computers and servers. The servers that Nvidia builds, it could be phones. Most companies in the largest of industries ultimately produces something, sometimes produce production of service, which is the IT industry. But many of your customers are about producing something. That next generation of AI needs to understand the physical world. We call it physical AI. In order to create physical AI, we need three computers. And we created three computers to do so. The DGX computer, which Blackwell, for example, is a reference design and architecture to create things like DGX computers for training the model. That model needs a place to be refined. It needs a place to learn. It needs a place to apply its physical capability, its robotics capability. We call that Omniverse, a virtual world that obeys the laws of physics, where robots can learn to be robots. And then when you're done with the training of it, that AI model could then run in the actual robotic system. That robotic system could be a car, it could be a robot, it could be av, it could be a autonomous moving robot, it could be a picking arm, it could be an entire factory or an entire warehouse that's robotic. And that computer we call agx, Jetson, agx, DGX for training, and then Omniverse for doing the digital twin. Now here in India, we've got a really great ecosystem who is working with us to take this infrastructure, take this ecosystem of capabilities to help the world build physical AI systems.

Then we got a very short summary about the entire talk. This is from software 1 to software 2.0 about agents and mainly about about how the humanoid robot is going to go completely crazy. Nvidia are actually doing so much in that area that I can't wait to show you guys a new video I've been working on that covers how the Nvidia company is about to change the entire AI ecosystem. So take a look at this because this one gives you pretty much everything you need to know.

For 60 years, software 1.0 code written by programmers ran on general purpose CPUs. Then software 2.0 arrived. Machine learning neural networks running on GPUs. This led to the big bang of generative AI models that learn and generate anything. Today, generative AI is revolutionizing $100 trillion in industries. Knowledge enterprises use Agentic AI to automate digital work. Hello, I'm James A digital Human industrial enterprises use physical AI to automate physical work. Physical AI embodies robots like self driving cars that safely navigate the real world, manipulators that perform complex industrial tasks and humanoid robots who work collaboratively alongside us. Plants and factories will be embodied by physical AI capable of monitoring and adjusting its operations or speaking to us. Nvidia builds three computers to enable developers to create physical AI. The models are first trained on dgx. Then the AI is fine tuned and tested using reinforcement learning, physics feedback in Omniverse and the trained AI runs on Nvidia Jetson AGX Robotics computers. Nvidia Omniverse is a physics based operating system for physical AI simulation. Robots learn and fine tune their skills in Isaac Lab, a robot gym built on Omniverse. This is just one robot. Future factories will orchestrate teams of robots and monitor entire operations through thousands of sensors. For factory digital twins, they use an Omniverse blueprint called Mega. With Mega, the factory digital twin is populated with virtual robots and their AI models the robots brains. The robots execute a task by perceiving their environment, reasoning, planning their next motion and finally converting it to actions. These actions are simulated in the environment by the world simulator in Omniverse and the results are perceived by the robot brains through Omniverse sensor simulation. Based on the sensor simulations, the robot brains decide the next action and the loop continues while Mega precisely tracks the state and position of everything in the factory digital twin. This software in the loop testing brings software defined processes to physical spaces and embodiments, letting industrial enterprises simulate and validate changes in an Omniverse digital twin before deploying to the physical world, saving massive risk and cost. The era of physical AI is here transforming the world's heavy industries and robotics.