AI Glossaries

AI prediction is the use of a trained model to estimate an unknown or future value based on patterns in past data, such as next month's sales, the chance a customer will leave or the likely delivery time of an order.

AI reasoning is the ability of an AI system to work through a problem in logical steps, combining information to reach a conclusion, solve a task or make a plan, rather than simply matching an input to a familiar pattern.

AI inference is the stage where a trained AI model is put to work: it receives new input and produces an output, such as a prediction, a label or generated text. Every time you use an AI app, inference is happening.

AI automation is the use of artificial intelligence to complete tasks with little or no human effort, especially tasks that involve reading unstructured information, making judgements or predicting outcomes, which traditional rule-based automation cannot handle.

An AI workflow is a series of connected steps in which AI performs one or more tasks, such as reading, classifying, summarising or deciding, to move work from a starting trigger to a finished outcome, often with people reviewing key steps.

An AI application is a piece of software that uses artificial intelligence to help people complete a task, such as writing, searching, translating, analysing data or answering questions. It is the part of AI most people actually see and use.

An AI algorithm is a set of step-by-step instructions that tells a computer how to learn from data or solve a problem. In machine learning, the algorithm is the method, and the trained model is what it produces.

An AI system is a complete machine-based setup that takes input, uses one or more AI models to produce outputs such as predictions, content, recommendations or decisions, and acts on them within a real environment.

An AI model is a program that has learned patterns from data and uses them to turn new input into an output, such as a prediction, a label or generated text. It is the trained 'brain' inside most AI products.

Machine intelligence is the ability of a machine to learn, reason and act on information in ways that look intelligent. The term is often used as a synonym for artificial intelligence, with a stronger focus on learning systems.

Superintelligence is a hypothetical form of AI that would far exceed the best human minds in almost every area, including science, strategy and social skills. It is the level beyond general AI and a central topic in AI safety.

AGI, or artificial general intelligence, is the term AI labs and researchers use for a system that matches or beats human ability across most intellectual tasks. It is the stated goal of several major AI companies, but definitions and timelines vary widely.

General AI is the idea of a machine that can learn and perform any intellectual task a human can, and transfer what it learns from one area to another. It sits between narrow AI and superintelligence and does not exist yet.

Narrow AI, also called weak AI, is artificial intelligence built to do one specific task or a small set of related tasks. Every AI system in use today, from spam filters to chatbots, is narrow AI.

Artificial intelligence (AI) is the field of building computer systems that perform tasks we normally link to human thinking, such as understanding language, recognising images, making predictions and solving problems.