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Ai2: Building physical AI with virtual simulation data

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Virtual simulation data is driving the development of physical AI across corporate environments, led by initiatives like Ai2’s MolmoBot. Instructing hardware to interact with the real world has historically relied on highly expensive and manually-collected demonstrations. Technology providers building generalist manipulation agents typically frame extensive real-world training as the basis for these systems. For some […]

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New partnership to offer smart robots for dangerous environments

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ADLINK Technology has signed a strategic alliance and joint development agreement with Under Control Robotics, the company behind the robotics startup Noble Machines. The two firms will combine ADLINK’s edge AI platforms with Noble Machines’ autonomy software to create a new generation of general-purpose robots for modern manufactories and engineering plants. The work focuses on

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How physical AI integration accelerates vehicle innovation

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The integration of physical AI into vehicles remains a primary objective for automakers looking to accelerate innovation. A technical collaboration between Qualcomm and Wayve offers a framework for how hardware and software providers can consolidate their efforts to supply production-ready advanced driver assistance systems to manufacturers worldwide. The partnership combines Wayve’s AI driving layer with

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ABB: Physical AI simulation boosts ROI for factory automation

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A new ABB and NVIDIA partnership shows physical AI simulation is driving real ROI in factory automation and solving production hurdles. Manufacturers have often found it difficult to make intelligent robotics work reliably outside testing environments. The core issue is the gap between digital training models and actual factory floors, where lighting, material physics, and

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Physical AI is having its moment–and everyone wants a piece of it

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There is a particular kind of momentum in the technology industry that announces itself not through a single breakthrough, but through the simultaneous convergence of many. Physical AI is having that moment right now–and paying attention to where it is coming from, and why, tells you more than any single product launch can. The term

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Google makes its industrial robotics AI play official–and this time, it means business

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When Google folds a moonshot into its core operations, it’s not cleaning house. It’s placing a bet. On February 25, Alphabet-owned Intrinsic–which builds AI models and software designed to make industrial robotics more accessible–officially joined Google.  The company will remain a distinct group within Google, working closely with Google DeepMind and tapping into Gemini AI models and

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Physical AI adoption boosts customer service ROI

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The adoption of physical AI drives ROI in frontline customer service by merging digital intelligence with human-like physical interaction. As businesses navigate shrinking labour pools, they are finding that simply automating routine workflows is no longer enough. A new partnership between KDDI and AVITA demonstrates how companies can address complex operational gaps through humanoid deployment.

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From Physical AI to AI-Augmented QA:  The Next Evolution of Testing

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Many of you may already be familiar with Physical AI — the evolution of artificial intelligence from purely digital intelligence to systems that understand and interact with the real world. Physical AI enables machines to: Understand physical environments Adapt to real-world conditions Make autonomous decisions Execute actions in dynamic systems This shift — from information

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Hitachi bets on industrial expertise to win the physical AI race

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Physical AI–the branch of artificial intelligence that controls robots and industrial machinery in the real world–has a hierarchy problem. At the top, OpenAI and Google are scaling multimodal foundation models. In the middle, Nvidia is building the platforms and tools for physical AI development.  And then there is a third camp: industrial manufacturers like Hitachi

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Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies

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Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different

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