AI fundamentals

A hypothetical AI able to match a capable person across most intellectual work rather than in narrow areas, with no agreement on whether or when it arrives.

Software that does work we used to assume needed a person, such as writing, summarising, reading images or judging what a request means.

An AI system that works towards a goal in steps rather than answering a single question: it can plan, use tools such as search or a calendar, check its own results and try again.

Sorting items into predefined categories, such as routing an incoming email as a complaint, a query or a renewal.

Machine learning built from many layers of connected calculations, which is what made today's language and image models possible.

A large general-purpose model trained once at great expense, then adapted by many companies for many jobs rather than built fresh for each one.

The largest and most capable models available at any moment, usually the most expensive and the ones vendors lead their marketing with.

A model built to handle many kinds of task rather than one, which is also the category European regulation now names directly.

AI that produces new text, images, audio or code rather than only sorting or scoring things that already exist.

A model trained on very large amounts of text to predict what comes next, which is what lets it write, summarise, translate and answer questions.

The broader field of software that learns patterns from examples instead of being given step-by-step rules by a programmer.

A model split into specialist sections where only a few run for any given request, giving large-model quality at smaller running cost.

The trained file at the centre of an AI product, holding what it learnt from its training data, which the chat window or app is wrapped around.

A set of models from one maker sharing a name and design, offered in sizes so you can trade cost against capability.

Pulling the people, companies, dates and amounts out of free text so they can be filed, matched or acted on.

The older name for getting software to work with human language, still used in job adverts and in established vendor products.

A stack of simple mathematical units that pass signals to each other, loosely inspired by brain cells, and the shape almost every model takes.

The learnt numbers inside a model that hold what it knows, usually counted in billions and often quoted as a rough measure of size.

AI that forecasts or scores something, such as which customers will churn, as distinct from generative AI that produces new content.

A model trained to work a problem through in steps before answering, which helps on maths, logic and planning at the cost of speed.

A deliberately compact language model that trades some breadth for speed, lower cost and the ability to run on modest hardware.

The model design behind almost every modern AI system, able to weigh every part of an input against every other part at once.