The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
arXiv:2606. 00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions.
arXiv:2607. 18548v1 Announce Type: new Abstract: Agentic artificial intelligence systems, capable of autonomous perception, planning, tool use, and multi-step action, are increasingly proposed for critical engineering domains where decisions carry physical, operational, or economic consequences.
Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings revea...
arXiv:2608. 16237v1 Announce Type: cross Abstract: Building operations are energy-inefficient.
arXiv:2606. 18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture.
The article discusses the necessity of building trust in AI for railway applications, highlighting that AI is currently limited to non‑safety critical uses due to stringent industry standards. It proposes focusing on three key areas—robustness, Operational Design Domain (ODD), and explainability—to meet compliance and safety requirements. By integrating these domains within a safe MLOps environment, the authors argue that regulatory acceptance and public confidence can be achieved, enabling broader AI adoption in mission‑critical railway systems.
arXiv:2607. 26121v1 Announce Type: cross Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .
Ensuring that AI systems are built, deployed, and used safely is critical to our mission.
arXiv:2505. 23397v3 Announce Type: replace Abstract: This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making.
arXiv:2606. 23768v1 Announce Type: cross Abstract: We propose cryptographic certificates of validity for agentic AI systems.