Glossary of artificial intelligence

:Electronic environments that are sensitive and responsive to the presence of people. The concept was pioneered by Philips Research in the early 2000s as a vision for a post-PC era in which computing power would be embedded invisibly into everyday objects and spaces. An AmI environment continuously senses its surroundings, recognises the individuals within it, and adapts its behaviour to their needs, habits, and preferences — without requiring explicit interaction. :Key characteristics of ambient intelligence systems include being embedded (technology woven into the environment rather than foregrounded), context-aware (able to recognise situations and people), personalised (tailored to individual users), adaptive (capable of learning and changing behaviour over time), and anticipatory (proactively responding to needs without conscious instruction). :AmI draws on and overlaps with related fields including ubiquitous computing, pervasive computing, the Internet of Things (IoT), and affective computing. Practical applications include smart home systems, intelligent transport, assisted-living technologies for elderly or disabled users, and responsive public spaces. ==B==

;Retrieval-Augmented Generation (RAG) :A technique in natural language processing that combines a retrieval system with a generative language model. Rather than relying solely on knowledge encoded during training, a RAG system first retrieves relevant documents or passages from an external knowledge base in response to a query, then passes this retrieved context to the generative model to produce a more accurate and up-to-date response. RAG is commonly used to reduce hallucination in large language models and to enable models to answer questions about information not present in their training data. ==S==