AI Reference 099
Robotics, Finance & Governance
Wikipedia guided
Primary sources linked
AI Governance and Regulation
Definition
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.
PHYSICAL AI / CLOSED LOOP
What is AI governance?
AI governance is the internal system of policies, roles, controls, documentation and review used to decide how an organization develops or uses AI. Regulation is the external legal layer imposed by governments and regulators.
What governance covers
A governance program may define approved models, data restrictions, evaluation requirements, human-review rules, incident handling and monitoring. High-risk use cases typically receive more scrutiny than low-risk drafting tools.
Why governance cannot be only paperwork
Controls have to exist in software and operations: permissions, logs, model version tracking, testing and escalation paths. A written policy that is not reflected in the deployed system does little to prevent misuse or unnoticed failure.
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 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.
Robotics, Finance & Governance
Industrial Robots and AI
Industrial robots traditionally excel at repeatable structured tasks; AI expands their ability to perceive variable objects, adapt to changing conditions and plan more flexible work.
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.