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In a groundbreaking announcement at SIGGRAPH, NVIDIA unveiled a suite of generative physical AI advancements that promise to revolutionize how we interact with both digital and physical worlds. The highlight of the event was the introduction of NVIDIA NIM microservices and the NVIDIA Metropolis reference workflows, which are set to redefine the landscape of intelligent, immersive work environments.
Generative AI: From Text to Tangibility
While millions already rely on generative AI for tasks like writing and learning, NVIDIA’s latest innovations extend these capabilities into the physical realm. The new NVIDIA NIM (NVIDIA Intelligent Machines) microservices empower developers to train physical machines to better navigate and handle complex tasks. This is a significant leap forward in the field of physical AI, where advanced simulations and learning methods help robots and industrial automation systems perceive, reason, and navigate their surroundings more effectively.
NVIDIA Metropolis: Building Interactive Visual AI Agents
One of the cornerstone advancements is the NVIDIA Metropolis reference workflow, designed for building interactive visual AI agents. This workflow leverages NVIDIA’s deep learning framework for 3D worlds, including fVDB NIM microservices, and is complemented by USD Code, USD Search, and USD Validate NIM microservices for working with Universal Scene Description (OpenUSD).
These OpenUSD NIM microservices, combined with NVIDIA’s generative AI models for OpenUSD development, enable developers to integrate generative AI copilots and agents into USD workflows. This broadens the possibilities of creating and managing 3D worlds, making it easier to incorporate intelligent visual AI agents into various applications.
Transforming Industries with Physical AI
NVIDIA’s NIM microservices are tailored for specific models and industry domains, offering capabilities for speech and translation, vision, intelligence, and realistic animation and behavior. These microservices are already transforming industries such as manufacturing and healthcare by advancing smart spaces with robots, factory and warehouse technologies, surgical AI agents, and autonomous vehicles.
Vision Language Models: The Future of Visual AI Agents
A new class of generative AI models, known as vision language models (VLMs), powers highly perceptive and interactive visual AI agents. VLMs bridge digital perception and real-world interaction, enhancing decision-making, accuracy, interactivity, and performance in physical AI workloads. This enables the creation of vision AI agents capable of handling complex tasks in challenging environments, such as hospitals, factories, warehouses, retail stores, airports, and traffic intersections.
Real-World Applications: Palermo’s Traffic Management
A prime example of this technology in action is in Palermo, Italy, where city traffic managers have deployed visual AI agents using NVIDIA NIM microservices. Partnering with K2K, an NVIDIA Metropolis partner, the city has integrated VLMs into AI agents that analyze live traffic camera feeds in real-time. These agents provide fast, accurate insights and suggestions on how to improve city operations, such as adjusting traffic light timings to better manage roadways.
Bridging the Simulation-to-Reality Gap
Many AI-driven businesses are adopting a “simulation-first” approach for generative physical AI projects. This method is particularly valuable in complex environments like manufacturing and factory logistics, where intricate human-worker interactions and advanced facilities must be managed efficiently. NVIDIA’s physical AI software, tools, and platforms, including VLMs and fVDB NIM microservices, streamline the engineering required to create accurate digital representations or virtual environments.
Synthetic data generation, facilitated by tools like NVIDIA Omniverse Replicator, offers a powerful alternative to real-world datasets. This approach accelerates the creation of robust, diverse datasets for training physical AI models, enhancing their adaptability and performance across various industries and use cases.
Access and Availability
Developers can explore these cutting-edge AI models and NIM microservices at ai.nvidia.com. Additionally, the Metropolis NIM reference workflow is available on GitHub, and Metropolis VIA microservices are accessible in developer preview. OpenUSD NIM microservices can be previewed through the NVIDIA API catalog.
For an in-depth look at how accelerated computing and generative AI are transforming industries, watch NVIDIA founder and CEO Jensen Huang’s fireside chats from SIGGRAPH.
Conclusion
NVIDIA’s latest innovations in generative physical AI and NIM microservices are poised to transform how we interact with both digital and physical environments. By empowering developers with advanced tools and workflows, NVIDIA is paving the way for smarter, more efficient, and highly interactive AI agents that can revolutionize various industries.
Source: Nvidia
