AI
also called Artificial Intelligence
Software that does things we used to think only people could do.
The umbrella term for systems that perform tasks associated with human intelligence — understanding language, recognising patterns, making decisions. In practice, almost everything called "AI" today is machine learning.
The term covers everything from a spam filter to a frontier model, which is why it is nearly useless in a technical conversation. When someone says "we should use AI for this", the useful follow-up is: which technique, on which data, evaluated how?
The useful move is to treat "AI" as a label for a product category rather than a technical claim. When someone says an "AI system", ask what it actually is underneath — almost always a model plus a harness, some retrieval, and a policy layer. The word hides all four.
The word is doing no work in the meeting
A vendor deck says "AI-powered". A stakeholder wants to know whether to buy it.
Ask three questions instead of arguing about the label: which model, what data reaches it, and what it is allowed to change without a human. That is answerable, and it is what actually determines whether the thing is safe to adopt.
Do not use "AI" as a synonym for "LLM". Most of this dictionary is about LLMs, which are one narrow and very recent branch of AI.
- guideIntroduction to large language models ↗
A plain-language official introduction to models, tokens and context. Teaching material rather than origin research — good for the definition, not for a claim about the frontier.
- standardAI Risk Management Framework: Generative AI Profile (NIST AI 600-1) ↗
The official record for the cross-sector GenAI companion to the AI RMF. Use it as the governance vocabulary; the PDF itself was not machine-readable in this research pass, so do not quote definitions from here.
Machine Learning · ML
Programs that improve from examples instead of explicit instructions.
Deep Learning
Machine learning with many-layered neural networks — the technique behind modern AI.
Foundation Model · Base model
A large model trained broadly, meant to be adapted to many downstream tasks.
LLM · Large Language Model
A very large model whose only job, at heart, is predicting the next token.