Understanding AI: Essential Terms for Creative and IT Decision-Makers

Our AI glossary brings together clear, concise definitions of the key terms you’re likely to encounter so you can confidently evaluate AI-powered tools, brief your teams and make more informed decisions.

Short on time? Download the PDF version today.

Whether you’re exploring machine learning for personalised campaigns or understanding large language models for automated content creation, this guide equips you with the precise vocabulary to bridge the gap between technical experts and strategic stakeholders:

1. Algorithm: A set of rules or step-by-step instructions that a computer follows to solve a problem or perform a task.
2. Artificial Intelligence (AI): The branch of computer science focused on creating machines that can mimic human abilities such as learning, reasoning and decision-making.
3. Bias: Unintended or unfair behaviour in an AI system, often caused by skewed or unrepresentative training data.
4. Chatbot: A programme designed to simulate conversation with people, often used in customer support or as virtual assistants like ChatGPT.
5. Deep Learning: A form of machine learning that uses large neural networks with many layers to identify patterns in data—commonly applied in speech and image recognition.
6. Generative AI: AI that creates new content—such as text, images, music or video—instead of only analysing existing data.
7. GPT (Generative Pre-trained Transformer): A type of large language model developed by OpenAI. “Generative” means it creates content; “pre-trained” means it’s initially trained on vast text datasets before fine-tuning; “transformer” refers to its underlying architecture optimised for language tasks.
8. Inference: The act of using a trained model to make predictions or generate outputs when given new data.
9. Large Language Model (LLM): An AI system trained on massive amounts of text to understand and generate human-like language. For example, GPT-4 is an LLM.
10. Machine Learning (ML): A subset of AI in which machines learn from data and improve their performance over time without explicit programming.
11. Model: A trained algorithm that processes inputs to produce outputs based on patterns it has learned from data.
12. Natural Language Processing (NLP): The field of AI concerned with enabling computers to understand, interpret and generate human language.
13. Neural Network: A computing model inspired by the human brain, composed of interconnected units (“neurons”) that work together in layers to detect patterns.
14. OpenAI: An AI research organisation and company known for developing models like GPT-4 and ChatGPT.
15. Overfitting: When a model learns the training data—including its noise and outliers—so well that it performs poorly on new, unseen data.
16. Prompt: The input (such as a question or instruction) you give to an AI model to generate a response—for example, what you type into ChatGPT.
17. Reinforcement Learning: A learning method where an AI agent learns by trial and error, receiving rewards for good actions and penalties for bad ones.
18. Supervised Learning: A machine-learning approach where models are trained on labelled data, meaning each example comes with the correct answer.
19. Token: A piece of text—either a whole word or part of a word—that AI models use to process and generate language. For instance, “chatting” might split into “chat” + “ting”.
20. Training: The process of feeding data into a model so it can learn patterns and relationships to make future predictions or decisions.
21. Turing Test: A test proposed by Alan Turing to assess whether a machine’s behaviour is indistinguishable from that of a human.
22. Unsupervised Learning: A learning method where models work with unlabeled data and automatically seek out patterns or groupings.

By familiarising yourself with these terms, you’ll be better equipped to follow discussions about and evaluate AI.

Does your organisation have an AI policy in place? Let’s chat!

Download a PDF version today.