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Physical AI raises governance questions for autonomous systems

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Governance around Physical AI is becoming harder as autonomous AI systems move into robots, sensors, and industrial equipment. The issue is not only whether AI agents can complete tasks. It is how their actions are tested, monitored, and stopped when they interact with real-world systems. Industrial robotics already provides a large base for that discussion. […]

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What LG and NVIDIA’s talks reveal about the future of physical AI

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LG is currently engaged in exploratory discussions with NVIDIA concerning physical AI, data centres, and mobility. Following a meeting in Seoul between LG CEO Ryu Jae-cheol and Madison Huang, Senior Director of Product Marketing for Omniverse and Robotics at NVIDIA, the core operational dependencies required to run complex automated systems are becoming apparent. While the

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Kakao Mobility details Level 4 autonomous driving roadmap for physical AI

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Kakao Mobility has set out plans to develop Level 4 autonomous driving technologies in-house as part of its physical AI strategy. Kim Jin-kyu, vice president and head of Kakao Mobility’s Physical AI division, presented the roadmap at the 2026 World IT Show conference at COEX in Seoul. His session focused on autonomous driving services built

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Top 10 Physical AI Models Powering Real-World Robots in 2026

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Top 10 Physical AI ModelsNVIDIA Isaac GR00T N-Series (N1.5 / N1.6 / N1.7)Google DeepMind Gemini Robotics 1.5Physical Intelligence π0 / π0.5 / π0.7Figure AI HelixOpenVLAOctoAGIBOT BFM and GCFMGemini Robotics On-DeviceNVIDIA Cosmos World Foundation ModelsSmolVLA (HuggingFace LeRobot) The gap between language model capabilities and robotic deployment has been narrowing considerably over the past 18 months. A

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Google DeepMind Introduces Decoupled DiLoCo: An Asynchronous Training Architecture Achieving 88% Goodput Under High Hardware Failure Rates

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Training frontier AI models is, at its core, a coordination problem. Thousands of chips must communicate with each other continuously, synchronizing every gradient update across the network. When one chip fails or even slows down, the entire training run can stall. As models scale toward hundreds of billions of parameters, that fragility becomes increasingly untenable.

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NVIDIA and Google infrastructure cuts AI inference costs

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At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower

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Sony AI robot beats players as humanoid robot wins Beijing race

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An autonomous table tennis robot developed by Sony AI has competed against and defeated high-level human players in regulated matches, according to Reuters. The system is part of a broader category often referred to as “physical AI,” where artificial intelligence is applied to machines operating in real-world environments. The robot, named Ace, was designed to

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Siemens introduces AI system for automation engineering

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Siemens has introduced the Eigen Engineering Agent, an AI system designed to plan and validate automation engineering tasks in operational environments. The system uses multi-step reasoning and self-correction to carry out tasks autonomously and operates directly inside engineering platforms, letting it to complete workflows from initial design through to validation. Autonomous engineering workflows The agent

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Physical AI: How Vision AI Helps Machines Understand the Real World

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Physical AI is becoming one of the most important ideas in modern AI. Instead of working only with text prompts or digital workflows, physical AI operates in the real world. It has to interpret environments, understand movement, detect risk, and support action in spaces that are constantly changing. That is where vision AI becomes essential.

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NVIDIA Releases Ising: the First Open Quantum AI Model Family for Hybrid Quantum-Classical Systems

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Quantum computing has spent years living in the future tense. Hardware has improved, research has compounded, and venture dollars have followed — but the gap between a quantum processor running in a lab and one running a real-world application remains stubbornly wide. NVIDIA moved to close that gap with the launch of NVIDIA Ising, the

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