Core Concepts
Autonomous
Autonomous AI systems operate independently in open-ended environments, perceiving inputs, making decisions, and taking actions without continuous human oversight. Examples range from self-driving vehicles to robotic process automation to autonomous AI agents that use tools and browse the web.
The degree of autonomy exists on a spectrum. Fully autonomous systems face safety and alignment challenges—ensuring they pursue intended goals without unintended side effects is a central concern of AI safety research.
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Related Terms
Applications
AI Agents
AI systems that combine a language model with tools, memory, and planning to autonomously execute multi-step tasks — observing outcomes, deciding next actions, and iterating until a goal is reached.
Techniques & Methods
AI Alignment
The research field and engineering practice of building AI systems that reliably pursue goals humans actually want, remain controllable, and avoid harmful side effects — operationalized through RLHF, Constitutional AI, evaluations, and interpretability.
Techniques & Methods
Reinforcement Learning
An agent learns by taking actions in an environment and receiving rewards or penalties.
Core Concepts
Strong AI
AI with the ability to understand, learn, and apply knowledge like human intelligence.