<h2><strong>Nvidia CEO Jensen Huang Unveils Rubin AI Chips at GTC 2025</strong></h2> Nvidia founder and CEO Jensen Huang took the stage at GTC 2025—hailed as the “Super Bowl of AI”—to unveil the company’s latest advancements in artificial intelligence. Addressing a packed audience, Huang emphasized that AI is at a critical "inflection point" as it moves toward a future shaped by robotics and autonomous systems. <h3>The Future of AI: Blackwell Ultra and Rubin AI Chips</h3> One of the most highly anticipated moments of the keynote was Huang’s announcement of Nvidia’s next-generation graphics architectures. He introduced <strong>Blackwell Ultra</strong>, slated for release in the second half of 2025, followed by its successor, <strong>Rubin AI</strong>, launching in late 2026. A more advanced version, <strong>Rubin Ultra</strong>, is expected in 2027. The Rubin AI chips, named after renowned astronomer Vera Rubin, are designed to push the boundaries of AI-driven computing. Huang predicted that demand for GPUs from major cloud service providers will continue to surge, with <strong>Nvidia’s data center infrastructure revenue projected to reach $1 trillion by 2028</strong>. <h3>AI’s Evolution: From Generative Models to Agentic AI</h3> During his two-hour keynote, Huang outlined the rapid progress of AI, explaining how it has evolved from <strong>computer vision</strong> to <strong>generative AI</strong> and now to <strong>agentic AI</strong>—AI that can <strong>reason, understand context, and generate solutions dynamically</strong>. <blockquote>“AI understands the context, understands what we’re asking. It now generates answers. This has fundamentally changed how computing is done.” – Jensen Huang</blockquote> According to Huang, the next wave of AI is <strong>robotics</strong>—a field he calls “physical AI.” This next step in AI development will enable machines to understand real-world physics, including <strong>friction, inertia, cause and effect, and object permanence</strong>. <h3>Training AI with Synthetic Data</h3> A key theme in Huang’s address was the growing importance of <strong>synthetic data</strong>—computer-generated training data that allows AI to learn at speeds far beyond human capability. <blockquote>“There’s only so much data and so many human demonstrations we can perform. Reinforcement learning is the big breakthrough of the last few years.” – Jensen Huang</blockquote> To facilitate this, Nvidia introduced <strong>Isaac GR00T N1</strong>, an open-source AI model designed to train humanoid robots. The model will work in tandem with <strong>Cosmos AI</strong>, which generates high-quality synthetic data for AI training. <h3><strong>Revolutionizing Robotics and Autonomous Systems</strong></h3> Huang’s announcements underscored Nvidia’s focus on <strong>enhancing robotics training through simulated environments</strong>. Training robots in the real world is costly and time-consuming, making simulation-based reinforcement learning a crucial tool for researchers and engineers. <h3>Industry Reactions</h3> Benjamin Lee, a professor of electrical and systems engineering at the University of Pennsylvania, praised Nvidia’s open-source approach: <blockquote>“Providing an open-source platform will allow more researchers—beyond just the big players in the industry—to experiment with synthetic data and reinforcement learning.”</blockquote> <h3>Nvidia’s Expanding Role in Autonomous Vehicles</h3> Nvidia’s innovations are already making an impact in the automotive industry. Huang announced a partnership with <strong>General Motors</strong>, which plans to integrate Nvidia technology into its new fleet of self-driving cars. The companies will collaborate on building custom AI systems using <strong>Omniverse and Cosmos AI models</strong> to enhance AI-powered manufacturing and navigation. Additionally, Nvidia introduced <strong>Halos</strong>, an AI-driven safety system specifically designed for autonomous vehicles. Huang emphasized the rigorous safety standards met by Nvidia’s automotive AI: <blockquote>“We’re the first company in the world, I believe, to have every line of code safety assessed.”</blockquote> <h3>Introducing Newton: A New Era of Robotics Simulation</h3> In a surprise announcement, Huang introduced <strong>Newton</strong>, an open-source physics engine developed in partnership with <strong>Google DeepMind and Disney Research</strong>. This tool is designed to <strong>simulate real-world physics for robotics training</strong>, making AI-powered robots more adaptable to physical environments. To showcase Newton’s capabilities, a small robot named <strong>Blue</strong> made a dramatic entrance on stage. Rising from a hidden compartment in the floor, Blue beeped at Huang and executed a series of movements based on his commands. <blockquote>“The age of generalist robotics is here.” – Jensen Huang</blockquote> <h3>Conclusion</h3> Jensen Huang’s keynote at GTC 2025 made it clear that <strong>AI is rapidly advancing toward an era where machines will reason, learn, and operate autonomously</strong>. With the introduction of <strong>Rubin AI chips, synthetic data training, and robotics advancements</strong>, Nvidia is shaping the future of AI-driven industries—from <strong>cloud computing and autonomous vehicles to humanoid robotics and beyond</strong>. As AI continues to evolve, Nvidia remains at the forefront, driving the technological breakthroughs that will define the next decade. <em>Source: AP News - <a href="https://apnews.com/article/nvidia-gtc-jensen-huang-ai-457e9260aa2a34c1bbcc07c98b7a0555">Nvidia CEO Jensen Huang unveils new Rubin AI chips at GTC 2025</a></em>