Nvidia Unveils AI Breakthroughs for Real-World Applications in Biotech, Robotics, and Autonomous Vehicles

This is the future of AI, according to Nvidia

Recent breakthroughs in generative AI have centered largely on language and imagery -- from chatbots that compose sonnets and analyze text to voice models that mimic human speech and tools that transform prompts into vivid artwork. But global chip giant Nvidia is now making a bolder claim: the next chapter of AI is about systems that take action in high-stakes, real-world scenarios. At the recent International Conference on Learning Representations (ICLR 2025) in Singapore, Nvidia unveiled more than 70 research papers showcasing advances in AI systems designed to perform complex tasks beyond the digital realm. Driving this shift are agentic and foundational AI models. Nvidia's latest research highlights how combining these models can influence the physical world -- spanning adaptive robotics, protein design, and real-time reconstruction of dynamic environments for autonomous vehicles. As demand for AI grows across industries, Nvidia is positioning itself as a core infrastructure provider powering this new era of intelligent action. Bryan Catanzaro, vice president of applied deep learning research at Nvidia, described the company's new direction as a full-stack AI initiative.

Nvidia Wants to Use AI in the Real World, From Biotech to Transportation | PYMNTS.com

Nvidia is ramping up the artificial intelligence (AI) arms race, unveiling more than 70 research papers showing how the fast-evolving technology can perform in real-world settings that go far beyond text and images. The chip company is advancing what it calls "embodied intelligence," or AI that can perceive, reason and act in industries including manufacturing, biotechnology and transportation. As Fast Company reported on Monday (May 5), Nvidia sees these capabilities as essential to future breakthroughs in robotics, drug development and autonomous navigation. "For AI to be truly useful, it must engage meaningfully with real-world use cases," the magazine quoted Bryan Catanzaro, vice president of applied deep learning, as saying. The chip giant presented its company-authored papers, covering healthcare, robotics, autonomous vehicles and large language models, at a major technology conference held in Singapore at the end of last month. Nvidia is continuing its collaborative push into AI that has seen partnerships with Google, GE Healthcare and GM. One paper describes a key development called Skill Reuse via Skill Adaptation (SRSA), a system that enables robotic agents to perform unfamiliar tasks by adapting previously learned skills. Nvidia said the system improved task success by 19% and reduced training sample needs by more than half, helping speed deployment across logistics and industrial robotics. In the biotech sector, the company's Proteína model trains on 21 million synthetic protein structures to generate long-chain backbones of up to 800 amino acids. Nvidia says the model outperforms Google's DeepMind's Genie 2, a cutting-edge AI model, in accuracy and diversity. It says its structure-labeled outputs could accelerate vaccine development and enzyme design. STORM, short for Spatio-Temporal Occupancy Reconstruction Machine, builds 3D maps in under 200 milliseconds -- fast enough for use in drones, AR systems and autonomous vehicles navigating complex environments.