Resource
The A-Z of Artificial Intelligence
A plain-language glossary of the AI terms that come up most in our work.
Whether you are leading a digital programme, exploring AI for the first time, or just want a quick refresher, this is a good place to start. We keep this updated as the landscape evolves. If something is missing or could be clearer, we would love to hear from you.
A
Artificial Intelligence (AI)
Technology that models human intelligence, enabling machines to perform tasks like reasoning, learning, and decision-making.
Artificial General Intelligence (AGI)
A form of AI capable of understanding, learning, and applying knowledge across a broad range of tasks at a level comparable to human cognition. Whether current frontier models represent early forms of AGI is now actively debated across the research community, with major AI labs holding divergent views on how to define it and how close we are.
Artificial Neural Network (ANN)
A machine learning model inspired by the human brain, forming the foundation of many deep learning systems.
Algorithm
A set of step-by-step rules or instructions used by AI systems to process information and solve problems.
Agents
AI systems that can act independently to complete tasks, make decisions, and interact with tools or services on behalf of users. Rather than responding to a single prompt, agents plan across multiple steps, use external tools such as web search or code execution, and adapt their approach based on results. Orchestrating multiple agents to work together on complex tasks is an increasingly common pattern in production AI systems.
Alignment
The challenge of ensuring AI systems pursue goals that are aligned with human values and intentions.
Automation
Using technology to perform tasks with minimal human input, often accelerated by AI.
B
Backpropagation
A training method for neural networks that adjusts weights by calculating gradients of error.
Benchmarking
The practice of testing AI systems against standardised tasks such as MMLU, BIG-bench, or ARC. Benchmarks help compare model capabilities, track improvements, and evaluate performance across different use cases.
Bias (AI Bias)
Systematic distortion in AI outputs caused by biased training data or flawed assumptions.
Big Data
Massive datasets used by AI to identify patterns, often beyond human processing capacity.
C
ChatGPT
A conversational AI developed by OpenAI, capable of generating human-like text based on prompts.
Claude
An AI assistant developed by Anthropic, available as a family of models ranging from fast and lightweight to its most capable versions. Claude is designed with a focus on safe and predictable behaviour, and supports extended reasoning, tool use, and long-context tasks. It is one of the leading frontier models alongside GPT-4o and Gemini.
Computer Vision
AI systems that analyse and interpret visual inputs such as images or videos.
Convolutional Neural Network (CNN)
A neural network particularly effective for processing visual data.
Context Engineering
The practice of structuring the information provided to an AI model, including system instructions, conversation history, retrieved documents, and tool outputs, to produce reliable and relevant results. As AI systems become more complex, how a model is given context has become as significant as the underlying model itself. Sometimes used as a more precise alternative to prompt engineering.
Context Window
The amount of input data an AI model can process in a single exchange. Long-context models can handle entire books, codebases, or multi-hour transcripts. This allows more coherent reasoning and helps reduce hallucination in complex tasks.
Conversational AI
AI designed to engage in dialogue, including chatbots and virtual assistants.
D
Data Mining
The process of discovering patterns and extracting useful information from large datasets using AI methods.
Deep Learning
A subset of machine learning using layered neural networks to model complex patterns in data.
DeepSeek
A family of AI models developed by a Chinese research organisation that attracted significant attention in early 2025. Its reasoning model demonstrated performance comparable to leading US frontier models at a fraction of the reported training cost, challenging prevailing assumptions about the relationship between compute investment and model capability.
Diffusion Models
Generative models that iteratively transform noise into coherent outputs, widely used in image generation.
Domain Adaptation
A technique allowing AI models trained in one domain to perform well in a different, but related, domain.
E
Embodied AI
AI systems that interact with the physical world through sensors and actuators, often used in robotics.
Ethics (AI Ethics)
Principles that guide the responsible design, deployment, and use of AI, including fairness, safety, and transparency.
EU AI Act
Legislation introduced by the European Union that regulates AI systems according to their potential risk. High-risk applications, such as those used in hiring, credit scoring, or critical infrastructure, face requirements around transparency, human oversight, and data governance. It came into force in 2024 with obligations phased in over subsequent years. It is the most significant piece of AI-specific regulation in force globally.
Expert System
An AI that mimics the decision-making ability of a human expert by applying predefined rules to a knowledge base.
Explainability
The ability to understand and interpret how an AI system arrived at its conclusions or decisions.
F
Fine-Tuning
A method of customising a pre-trained AI model on a smaller, task-specific dataset.
Foundation Model
A large AI model trained on broad data and designed to be adapted for a wide range of downstream tasks. Foundation models such as GPT-4o, Claude, and Gemini serve as the base for many commercial AI products and services, fine-tuned or prompted to meet specific use cases rather than trained from scratch for each application.
Frugal AI
The practice of designing efficient, cost-effective, and environmentally responsible AI systems.
Function Calling
A capability that allows AI models to trigger external tools, APIs, or predefined code functions in response to natural language commands. This is widely used in autonomous systems to extend the model's abilities beyond text generation.
Fuzzy Logic
A form of logic that handles imprecision, enabling AI to make decisions based on approximate information.
G
Generative AI (GenAI)
AI that produces new content, including text, images, audio, and video, based on learned data patterns.
Generative Adversarial Network (GAN)
A system of two neural networks competing to generate increasingly realistic data.
Generative Pre-trained Transformer (GPT)
A language model architecture trained on large text corpora to predict and generate coherent language.
Grounding
Connecting AI outputs to verifiable sources or real-world facts to reduce hallucinations.
Guardrails
Predefined constraints that keep AI systems within safe or acceptable limits. Guardrails help prevent harmful or inappropriate responses, enforce compliance, and align model behaviour with user expectations or legal requirements.
H
Hallucination
When an AI generates content that is plausible-sounding but factually incorrect or fabricated.
Human-in-the-Loop
An approach where humans provide oversight, validation, or feedback to AI systems during or after decision-making.
Hyperparameter
A setting used to control the training process of an AI model, such as learning rate or layer size.
I
Inference
Using a trained AI model to generate outputs or predictions from new input data.
Intelligent Agent
An autonomous AI that perceives its environment and takes actions to achieve specific goals.
Internet of Things (IoT)
A network of connected devices that collect and exchange data, often enhanced by AI for smarter operations.
J
Joint Embedding Predictive Architecture (JEPA)
A model that predicts relationships between different data embeddings, especially effective for multimodal or temporal tasks.
Job Displacement
A potential social impact of AI where automation replaces human roles in certain industries.
K
Knowledge Graph
A structured way to represent facts and their relationships, used by AI to reason and understand context.
Knowledge Distillation
A technique where a smaller model learns to replicate the behaviour of a larger, more complex one.
L
Large Language Model (LLM)
An AI model trained on large amounts of text data, capable of generating coherent and contextually appropriate language.
Low-Rank Adaptation (LoRA)
A fine-tuning technique that updates only a small number of parameters, improving efficiency.
Logic Programming
A programming method where logic-based rules determine actions or decisions.
M
Machine Learning (ML)
A subset of AI where systems learn patterns from data rather than being explicitly programmed.
Model Context Protocol (MCP)
An open standard developed by Anthropic that defines how AI models connect to external tools, data sources, and services. MCP allows developers to build integrations once and have them work across different AI systems. It has been widely adopted as a practical way to extend what AI models can do without building bespoke connectors for each application.
Model Training
The process of teaching an AI model to recognise patterns by exposing it to data and feedback.
Multimodal AI
AI systems that can process and relate different kinds of data, such as text, images, and sound.
N
Natural Language Processing (NLP)
The field of AI focused on enabling machines to understand, interpret, and generate human language.
Natural Language Understanding (NLU)
A subfield of NLP concerned with extracting meaning, intent, and context from text or speech.
Neural Network
A series of algorithms that recognise relationships in data by mimicking the way the human brain operates.
O
OpenAI
A research organisation focused on developing and deploying AI. Known for GPT, DALL-E, and ChatGPT.
Orchestration
The coordination of multiple AI models, agents, or tools working together to complete a task. An orchestration layer manages the flow of information between components, decides which model or tool to use at each step, and handles errors or unexpected outputs. As agentic AI systems become more common in production, orchestration has become a significant area of both engineering and product design.
Overfitting
When a model performs well on training data but poorly on unseen data due to excessive specialisation.
Optimisation
Improving AI model performance by adjusting parameters, architecture, or training data.
P
Pattern Recognition
The ability of AI systems to detect regularities or structures in data.
Planning (AI Planning)
The ability of AI agents to break down high-level instructions into step-by-step tasks or sequences. Planning allows models to operate over time, manage dependencies, and pursue goals with limited human oversight.
Predictive Analytics
AI-powered analysis of data to forecast trends or future events.
Prompt Engineering
Crafting input prompts to guide or control the outputs of a generative AI system.
Pruning
Reducing model complexity by removing redundant components or connections.
Q
Quantisation
Reducing the precision of model parameters to improve computational efficiency with minimal performance loss.
Query
A structured or natural language request to an AI system to retrieve or generate information.
R
Reinforcement Learning
A training method where an AI learns by trial and error, receiving rewards or penalties based on its actions.
Reasoning
The process by which AI systems derive conclusions, explanations, or decisions from available information. See also: Reasoning Models.
Reasoning Models
A category of AI model that generates extended internal reasoning before producing a final response. Rather than answering immediately, these models work through a problem step by step, which improves performance on tasks that benefit from structured thinking such as mathematics, coding, and complex analysis. OpenAI's o-series and DeepSeek's R1 are examples. The underlying technique is sometimes called test-time compute scaling.
Retrieval-Augmented Generation (RAG)
Combining generative models with external information sources to improve factual accuracy.
Robotic Process Automation (RPA)
Automating routine business tasks using software bots, often enhanced by AI.
S
Supervised Learning
A method where AI is trained on labelled datasets to learn specific outputs for given inputs.
Small Language Model (SLM)
A compact AI model designed to run efficiently on devices with limited compute, such as laptops, phones, or edge hardware. While smaller than frontier models, recent SLMs have demonstrated strong performance on focused tasks. Their lower cost and ability to run locally make them practical for use cases where sending data to a cloud-based model is not feasible or appropriate.
Steerability
The ability to guide or constrain an AI model's behaviour according to user intent or parameters.
Stable Diffusion
A popular open-source model that generates images based on text prompts using diffusion-based techniques.
Superintelligence
A hypothetical future AI that surpasses human intelligence in all domains.
Synthetic Data
Artificially generated data used to train, fine-tune, or evaluate AI models. It can be used to simulate edge cases, balance biased datasets, or avoid reliance on private or sensitive real-world data.
T
Transfer Learning
Using a model trained on one task as a starting point for another related task.
Transformer
A neural network architecture that enables models to understand relationships in sequential data. Forms the basis for LLMs like GPT.
Test-Time Compute
The processing power used by an AI model at the point of generating a response, as distinct from the compute used during training. Reasoning models increase test-time compute deliberately, spending more processing time working through a problem before responding. This represents a shift in how AI capability is developed: rather than always training larger models, additional thinking at inference time can improve results on specific task types.
Turing Test
A measure of a machine's ability to exhibit intelligent behaviour indistinguishable from a human.
U
Unsupervised Learning
Training AI on data without labelled outputs, allowing it to find patterns or structures independently.
User Experience (UX) AI
The use of AI to personalise, adapt, or improve digital interfaces and user interactions.
V
Virtual Assistant
An AI-powered tool that performs tasks or provides information through voice or text, such as Alexa or Siri.
Vision Transformer (ViT)
A model architecture that applies transformer principles to computer vision tasks with strong performance.
W
Weak AI
Also called narrow AI. Systems designed for specific tasks, such as image classification or recommendation.
Weights
Numerical parameters in neural networks that are updated during training to influence learning outcomes.
X
eXplainable AI (XAI)
AI systems designed to make their decisions and logic understandable to humans.
Y
YOLO (You Only Look Once)
A real-time object detection algorithm that identifies multiple objects in images or video in a single pass.
Z
Zero-shot Learning
The ability of AI models to perform tasks they have not been explicitly trained for, by generalising from related knowledge.
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