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Autonomous AI systems test governance in physical environments

agentic ai, ai, AI (Artificial Intelligence), AI in Action, Artificial Intelligence, autonomous, governance, Governance, Regulation & Policy, physical ai

Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments. Most existing AI governance frameworks have focused on online harms and model outputs, including bias, misinformation, and harmful content. Embodied […]

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VLM vs VLA: Why Vision-Language Models Are Not Enough for Robotics

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Collection, physical ai, Shaip Blogs

Two model classes get conflated in robotics conversations: vision-language models and vision-language-action models. They sound similar, both ingest images and text, and both come from the same lineage of multimodal pretraining. But for anyone trying to deploy an AI system that moves — not just describes — the distinction is decisive. VLM vs VLA is

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NVIDIA AI Releases Gated DeltaNet-2: A Linear Attention Layer That Decouples Erase and Write in the Delta Rule

ai, AI (Artificial Intelligence), AI Infrastructure, AI Paper Summary, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, physical ai, Software engineering, Staff, Tech News, Technology

Linear attention replaces the unbounded KV cache of softmax attention with a fixed-size recurrent state. This cuts sequence mixing to linear time and decoding to constant memory. The hard part is not what to forget. It is how to edit a compressed memory without scrambling existing associations. NVIDIA has released Gated DeltaNet-2, a linear attention

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VLA Models: What Vision-Language-Action Models Need from Training Data

ai, AI (Artificial Intelligence), ai training data, Artificial Intelligence, physical ai, Shaip Blogs

The shift from chatbots to robots that follow natural-language commands runs through a single class of models. VLA models — vision-language-action models — combine visual perception, language understanding, and action generation in one neural network. Their power is real, but it depends almost entirely on the training data they ingest. This guide explains what VLA

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Enterprise AI roadblocks and roadmaps, security and physical AI: Day two at TechEx

ai, AI (Artificial Intelligence), Artificial Intelligence, copilot, industry insights, physical ai, pilot programmes, staggered deployments, TechEx Events

Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perform well in pilot, but don’t seem

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Tactile Sensing Data: The Training Signal Behind Robots That Can Actually Feel

ai, AI (Artificial Intelligence), ai training data, Artificial Intelligence, Data Collection, physical ai, Shaip Blogs

Robots can see. Internet-scale image datasets and a decade of refined models made that possible. But ask a robot to actually pick up a half-crushed carton, thread a cable, or hand a tool to a surgeon, and the wheels come off. Not because the cameras failed. Because nothing in the robot’s training ever taught it

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NVIDIA Introduces SANA-WM: A 2.6B-Parameter Open-Source World Model That Generates Minute-Scale 720p Video on a Single GPU

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, physical ai, Staff, Tech News, Technology, Vision Language Model

World models (systems that synthesize realistic video sequences from an initial image and a set of actions) are becoming central to embodied AI, simulation, and robotics research. The core challenge is scaling these systems to generate minute-long, high-resolution video without requiring prohibitively large clusters for both training and inference. Most competitive open-source baselines either require

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Physical AI moves closer to factory floors as companies test humanoid robots

ai, AI (Artificial Intelligence), AI in Action, Artificial Intelligence, data, manufacturing, Manufacturing & Engineering AI, physical ai, Robots, World of Work

British technology company Humanoid will deploy humanoid robots at factories operated by German industrial supplier Schaeffler, Reuters reported. The two companies’ agreement covers an estimated 1,000 to 2,000 robots in Schaeffler’s global manufacturing sites by 2032, according to a Humanoid spokesperson. The companies have not disclosed the contract value. The first deployment is scheduled between

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How to Annotate Robotics Data: Objects, Actions, Intent, Motion, and Failure Modes

ai, AI (Artificial Intelligence), ai training data, Artificial Intelligence, Data Collection, physical ai, Shaip Blogs

A robot that picks the wrong box, freezes in front of a person, or drops a fragile part rarely fails because of bad code. It fails because something it was taught to recognize wasn’t labeled correctly — or wasn’t labeled at all. Robotics data annotation is what stands between raw sensor streams and a robot

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Mira Murati’s Thinking Machines Lab Introduces Interaction Models: A Native Multimodal Architecture for Real-Time Human-AI Collaboration

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine Learning, New Releases, physical ai, Software engineering, Staff, Tech News, Technology

Most AI systems today work in turns. You type or speak, the model waits, processes your input, and then responds. That’s the entire interaction loop. Thinking Machines Lab, an AI research lab, is arguing that this model of interaction is a fundamental bottleneck. Thinking Machines Lab team introduced a research preview of a new class

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