Plain language: An AI system that can take actions on your behalf, such as searching the web, sending emails and booking appointments, with varying degrees of independence.

Technical: An autonomous or semi-autonomous computational system capable of perceiving its environment, making decisions based on defined objectives, and executing multi-step actions. Agents may operate with a human in the loop or with minimal supervision, depending on design.

Misconception: AI agents are independent entities pursuing their own goals and will eventually act against human interests. They operate within parameters set by developers and users and do not have self-determined objectives.

Real-world example: An AI agent that checks your calendar, identifies a free slot, drafts a meeting invitation and sends it, completing a multi-step task without requiring you to do each step manually.

Related terms: AI · Generative AI · Assistive AI

Plain language: A trained system that has learned patterns from large amounts of data and uses those patterns to make predictions or generate outputs.

Technical: A mathematical structure derived from a training process in which parameters are adjusted to minimise error on a dataset. The model encodes learned statistical relationships and maps inputs to outputs at inference time.

Misconception: An AI model 'knows' things the way a human expert does. In practice, a model identifies and replicates statistical patterns in its training data, it does not understand or reason as humans do.

Real-world example: A medical imaging model trained on thousands of scans recognises patterns associated with specific conditions. When shown a new scan, it draws on those learned patterns to flag areas of concern for a clinician to review.

Related terms: AI · Generative AI · Algorithm · Hallucination

Plain language: A set of step-by-step rules or instructions that a computer follows to solve a particular problem or complete a task.

Technical: A finite, well-defined sequence of computational instructions used to process data, make decisions, or generate outputs. Algorithms are building blocks of all software, including AI systems.

Misconception: 'The algorithm' is used as a catch-all for all of AI, as though one all-knowing entity drives every AI outcome. In fact, an algorithm is a specific set of instructions for a specific task.

Real-world example: A social media platform's algorithm follows rules to decide which posts appear first, based on your past engagement. A chess-playing algorithm evaluates the best next move according to defined rules.

Related terms: AI · AI Model · Generative AI · Assistive AI · Machine Learning

Plain language: Technology enabling machines to perform tasks that typically require human thinking, such as recognising images, understanding language, making recommendations, or solving problems.

Technical: An umbrella field of computer science encompassing systems designed to simulate cognitive functions including learning, reasoning, pattern recognition and problem-solving. Covers both rule-based and machine learning approaches.

Misconception: AI is often understood as a single, unified system with its own intentions or desires. In reality, it is a broad category covering many distinct tools and approaches, none of which thinks, decides or wants anything.

Real-world example: When your email filters spam, when Google Maps reroutes you, or when a streaming service recommends a film, AI is at work, and none of these require human intervention in the moment.

Related terms: Algorithm · Generative AI · Assistive AI · AI Model · Intelligence (concept)