AI Reference 098
Robotics, Finance & Governance
Wikipedia guided
Primary sources linked
The NIST AI Risk Management Framework
Definition
The NIST AI Risk Management Framework is a voluntary framework organized around governing, mapping, measuring and managing AI risk. NIST's Generative AI Profile extends that framework with risks and suggested actions specific to generative systems.
PHYSICAL AI / CLOSED LOOP
What is the NIST AI RMF?
The NIST AI Risk Management Framework is a voluntary framework intended to help organizations manage risks from AI systems. Its core functions are Govern, Map, Measure and Manage.
What the four functions mean
Govern establishes policies, roles and accountability. Map describes the system and context. Measure evaluates risks and performance. Manage prioritizes and responds to the identified risks. The functions are intended to continue across the AI lifecycle.
Generative AI guidance
NIST later published a Generative AI Profile that applies the framework to issues such as confabulation, data privacy, harmful content, information integrity and cybersecurity. The framework is process guidance, not a certification that a model is safe.
Related terms, defined
These nearby terms are linked because they name distinct concepts that are easy to confuse with this page's subject.
Robotics, Finance & Governance
AI Governance and Regulation
AI governance is the set of policies, ownership, controls, documentation, evaluation and monitoring used to manage AI systems; regulation is one external component of that broader governance system.
Robotics, Finance & Governance
AI Roadmap 2026: Agents, Multimodality, Efficiency and Robotics
As of 2026, first-party releases and independent measurement show several major directions: more tool-using and agentic systems, stronger multimodality, smaller efficient models, longer context, model routing, open-weight competition and closer integration between foundation models and robotics.
Robotics, Finance & Governance
AI and Robotics
AI and robotics combine perception, planning, control and learning with physical machines. Unlike a text-only application, a robot must operate under sensor noise, timing constraints, changing environments and physical safety requirements.
Robotics, Finance & Governance
Physical AI
Physical AI refers to AI systems that perceive and act in the physical world through robots, autonomous machines or embodied devices. Current development increasingly combines foundation models, simulation, accelerated computing and on-device inference.
Robotics, Finance & Governance
Autonomous Vehicles and AI
Autonomous-vehicle systems combine sensors, perception, prediction, planning and control. Their safety is a property of the complete system, not a single neural network.
Reference guide and primary sources
Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.