Artificial intelligence glossary

A sequence of rules that a computer uses to complete a task. An algorithm takes an input (e.g. a dataset) and generates an output (e.g. a pattern that it has found in the data). Algorithms underpin the technology that makes our lives tick, from smartphones and social media to sat nav and online dating, and they are increasingly being used to make predictions and support decisions in areas as diverse as healthcare, employment, insurance and law. (The Alan Turing Institute, 2024)  

The UK Government’s 2023 policy paper on ‘A pro-innovation approach to AI regulation’ defined AI, AI systems or AI technologies as “products and services that are ‘adaptable’ and ‘autonomous’.” The adaptability of AI refers to AI systems, after being trained, often developing the ability to perform new ways of finding patterns and connections in data that are not directly envisioned by their human programmers. The autonomy of AI refers to some AI systems that can make decisions without the intent or ongoing control of a human. (UK Parliament, 2024) 

Unfairness that can arise from problems with an algorithm’s process or the way the algorithm is implemented, resulting in the algorithm inappropriately privileging or disadvantaging one group of users over another group. Algorithmic biases often result from biases in the data that has been used to train the algorithm, which can lead to the reinforcement of systemic prejudices around race, gender, sexuality, disability or ethnicity. (The Alan Turing Institute, 2024)

Any information that has been collected for analysis or reference. Data can take the form of numbers and statistics, text, symbols, or multimedia such as images, videos, sounds and maps. Data that has been collected but not yet processed, cleaned or analysed is known as ‘raw’ or ‘primary’ data. (The Alan Turing Institute, 2024) 

Pictures and video that are deliberately altered to generate misinformation and disinformation. Advances in generative AI have lowered the barrier for the production of deepfakes. (UK Parliament, 2024)

A subset of machine learning that uses artificial neural networks to recognise patterns in data and provide a suitable output, for example, a prediction. Deep learning is suitable for complex learning tasks, and has improved AI capabilities in tasks such as voice and image recognition, object detection and autonomous driving. (UK Parliament, 2024) 

An AI model that generates text, images, audio, video or other media in response to user prompts. It uses machine learning techniques to create new data that has similar characteristics to the data it was trained on. Generative AI applications include chatbots, photo and video filters, and virtual assistants. (UK Parliament, 2024) 

A system comprising a human and an artificial intelligence component, in which the human can intervene in some significant way, e.g. by training, tuning or testing the system’s algorithm so that it produces more useful results. It is a way of combining human and machine intelligence, helping to make up for the shortcomings of both. (The Alan Turing Institute, 2024) 

A type of foundation model that is trained on vast amounts of text to carry out natural language processing tasks. During training phases, large language models learn parameters from factors such as the model size and training datasets. Parameters are then used by large language models to infer new content. (UK Parliament, 2024)

A type of AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed. Machine learning systems learn by finding patterns in training datasets. They then create a model (with algorithms) encompassing their findings. This model is then typically applied to new data to make predictions or provide other useful outputs, such as translating text. (UK Parliament, 2024)