physical ai

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OpenAI Introduces MRC (Multipath Reliable Connection): A New Open Networking Protocol for Large-Scale AI Supercomputer Training Clusters

Training frontier AI models is not just a compute problem — it is increasingly a networking problem. And OpenAI just introduced its solution. OpenAI announced the release of MRC (Multipath Reliable Connection), a novel networking protocol developed over the past two years in partnership with AMD, Broadcom, Intel, Microsoft, and NVIDIA. The specification was published […]

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Physical AI Training Data: The Missing Layer Between Vision and Action

A familiar pattern has emerged in robotics and autonomous systems: a flagship demo runs beautifully on stage, the same system stumbles in a live warehouse two weeks later, and the post-mortem blames “reality” for being messier than the test environment. Some voices in the field argue the missing layer is hardware — better grippers, force-torque

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What Is an Egocentric Dataset? A Guide for Robotics & Embodied AI

An egocentric dataset is a structured collection of first-person video and sensor recordings — captured from a head, chest, or wrist-mounted camera — used to train robotics and embodied AI systems on how people see, move, and act. It’s the closest match to what a robot’s onboard camera will see during operation, which is why

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Physical AI raises governance questions for autonomous systems

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

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

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

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

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

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

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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