Building smarter robots and autonomous vehicles (AVs) starts with physical AI models that understand real-world dynamics. These models serve two critical roles: accelerating synthetic data generation (SDG) to help autonomous machines learn about real-world physics and interactions—including rare edge cases—and serving as base models that can be post-trained for specialized tasks or adapted to…
]]>How can an AI system understand the difference between a plausible accident and a physically impossible event? Or plan a multi-step interaction across humans, objects, and environments in an edge-case scenario? These are questions at the core of physical intelligence—the kind that underpin how robots manipulate the world, how autonomous vehicles make split-second decisions, and how virtual agents…
]]>The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don’t get proper training and face testing risks due to poor generalization, limited exposure to real-world variations, and unpredictable behavior in edge cases. Collecting massive real-world datasets for…
]]>As robotics and autonomous vehicles advance, accelerating development of physical AI—which enables autonomous machines to perceive, understand, and perform complex actions in the physical world—has become essential. At the center of these systems are world foundation models (WFMs)—AI models that simulate physical states through physics-aware videos, enabling machines to make accurate decisions and…
]]>Generative AI has rapidly evolved from text-based models to multimodal capabilities. These models perform tasks like image captioning and visual question answering, reflecting a shift toward more human-like AI. The community is now expanding from text and images to video, opening new possibilities across industries. Video AI models are poised to revolutionize industries such as robotics…
]]>NVIDIA LLM Developer Day is a virtual event providing hands-on guidance for developers exploring and building LLM-based applications and services. You can gain an understanding of key technologies, their pros and cons, and explore example applications. The sessions also cover how to create, customize, and deploy applications using managed APIs, self-managed LLMs…
]]>The year 2022 has thus far been a momentous, thrilling, and an overwhelming year for AI aficionados. Get3D is pushing the boundaries of generative 3D modeling, an AI model can now diagnose breast cancer from MRIs as accurately as board-certified radiologists, and state-of-the-art speech AI models have widened their horizons to extended reality. Pretrained models from NVIDIA have redefined…
]]>Speech recognition technology is growing in popularity for voice assistants and robotics, for solving real-world problems through assisted healthcare or education, and more. This is helping democratize access to speech AI worldwide. As labeled datasets for unique, emerging languages become more widely available, developers can build AI applications readily, accurately, and affordably to enhance…
]]>