🌱 AI Foundations

A set of step-by-step instructions for turning inputs into an output or action.

An umbrella term for computational systems using symbolic reasoning, search or learned models for tasks such as prediction, classification or content generation.

Automation uses a machine or software to carry out set steps with little or no human action after setup.

Recorded observations, measurements or examples that people or systems can store, examine and use for a purpose.

A dataset is an organised collection of examples that a system can use for analysis, training, testing or search.

A method that trains many-layered computer models to find useful patterns in large amounts of data.

A type of AI that produces new content such as text, images, audio or video from patterns learned from many examples.

A way of building computer systems that learn patterns from examples so they can make predictions, classifications or recommendations on new cases.

A parameterised mathematical function learned from data that maps inputs to outputs such as labels, scores, predictions or generated text.

An AI system built for a limited task or related tasks, rather than broad intellectual work across domains.

A system that uses past data to estimate what is likely to happen next, such as a future value, category or level of risk.

A rule-based system applies explicit if-then rules to given inputs to produce specified outputs.

A set of coded instructions that tells a computer how to take input, process it and produce output.

Attributing human-like traits, feelings, intentions or understanding to an AI system when its observable output does not establish those inner states.

A debated label for an AI system with broad capability across many intellectual tasks and domains, often compared with human general ability.

A machine-learning model pretrained at large scale on broad data can be adapted for many tasks without training a new model from scratch.

A label for AI models near the current leading edge of capability or scale, especially those seen as pushing technical limits and raising new governance questions.

The idea that a machine might have subjective experience or awareness, not just produce intelligent-seeming behaviour.

AI systems released under licence terms that let others inspect, use, modify and redistribute source code and other materials needed to run them.

A model whose trained numerical parameters are publicly released so others can run, inspect or adapt it, even if the training code, data or licence are not fully open.

An AI model whose owner uses licence terms or technical controls to restrict access to or reuse of key components, such as its weights or code.