AI Glossary

Governance & Regulation

AI Literacy

Article 4 requires providers and deployers to take measures ensuring a sufficient level of AI literacy among staff dealing with AI systems, taking account of their role, context, and the people affected. This is one of the few obligations that lands on essentially every organisation using AI at work, not just high-risk ones. There is no prescribed curriculum, so it is proportionate rather than box-ticking. In practice: Documented training so the team knows what the tool can do, where it fails, and when to escalate. ...

Governance & Regulation

AI Regulatory Sandbox

Article 3(55) defines a controlled framework set up by a competent authority, allowing providers to develop, train, validate and test an innovative system for a limited time under supervision, following an agreed sandbox plan. Each member state must provide access to one. It is aimed particularly at SMEs and start-ups. In practice: Testing a novel high-risk system with a national authority watching, before market entry. Plain-English summary of Article 3(55). The binding text is Regulation (EU) 2024/1689. ...

Trust, Risk & Safety

AI Safety

AI safety spans near-term issues — a model giving dangerous instructions, a system failing silently in production — and longer-term concerns about highly capable systems. It is engineering practice as much as philosophy: evaluation, red teaming, monitoring, and rollback are all safety work. In practice: Testing what a model does with a request it should refuse, before customers find out.

Generative AI & LLMs

AI Slop

AI slop is the mass-produced output that floods search results, feeds, and inboxes because generating it costs almost nothing. It is a distribution problem more than a model problem: the models can do better, but nobody asked them to. For anyone publishing, it raises the bar — generic AI text now reads as a signal of low effort. In practice: Twelve near-identical listicles on the same topic, none of which answers the question. ...

Governance & Regulation

AI System (legal definition)

Article 3(1) defines an AI system as machine-based, designed to operate with varying levels of autonomy, possibly adaptive after deployment, which infers from its inputs how to generate outputs such as predictions, content, recommendations, or decisions that can influence environments. The definition is deliberately technology-neutral, and ‘infers’ is the operative word — it is what separates an AI system from ordinary software. The Commission issued guidelines in February 2025 to help draw that line. ...

Trust, Risk & Safety

Algorithmic Bias

Algorithmic bias emerges from skewed training data, proxy variables that stand in for protected attributes, and choices about what to optimise. It rarely requires anyone to intend it. Because it is systematic rather than random, it scales — which is exactly what makes it worse than an individual’s bias. In practice: A postcode feature quietly encoding ethnicity and driving loan decisions.

Building & Running AI

API

An API is how software talks to software. For AI it means you post a request containing your prompt and settings and receive generated output, paying per token. It is what makes a model a component you can build on rather than a website you visit. In practice: One HTTPS request in, one JSON response out, billed by tokens used.

Building & Running AI

API Key

An API key identifies your account to the provider. Anyone holding it can spend your budget and read your usage, so keys belong on a server or in a secrets manager, never in front-end code or a public repository. Rotate them and scope them wherever the provider allows. In practice: A key committed to a public repo gets found and used within minutes.

Foundations

Artificial General Intelligence

AGI describes AI that generalises across domains the way a person does, rather than excelling at a bounded set of tasks. It is not a technical specification: labs, researchers, and regulators use materially different definitions, which is why timeline claims are so hard to compare. Treat any confident AGI prediction as an opinion with a definition hidden inside it. In practice: Today’s systems can write and code well and still fail at tasks a child handles — breadth, not skill, is the gap. ...

Foundations

Artificial Intelligence

Artificial intelligence is an umbrella term for systems that take in data and produce useful outputs without being told, step by step, how to do it. It covers everything from a spam filter to a model that writes code. Most AI you use today is machine learning, and most of that is deep learning — so the three words often get used interchangeably even though they are nested, not identical. ...