AI Literacy / Glossary
AI Literacy glossary
Plain-language definitions of AI Literacy terms, each linked to a short visual lesson.
- AI agent
- An AI agent is an AI system that does not just answer but takes actions for you, such as searching the web, filling in forms, sending emails or booking things, often in several steps on its own.
- AI hallucination
- An AI hallucination is when an AI tool produces information that sounds confident and plausible but is false or made up, such as a fake fact, a quote nobody said or a source that does not exist.
- Algorithmic bias
- Algorithmic bias is when an AI system or other automated tool gives results that are unfairly better or worse for some groups of people, often because of skewed or missing examples in its data.
- Artificial intelligence (AI)
- Artificial intelligence (AI) is software that learns patterns from examples and uses them to make predictions, recommendations, decisions or new content, instead of only following rules a person wrote down.
- Deepfake
- A deepfake is an image, video or audio recording made or changed with AI so that it looks or sounds like a real person saying or doing something they never said or did.
- Diffusion model
- A diffusion model is a type of generative AI that makes images (and sometimes video or audio) by starting from random noise and removing it step by step, guided by a text prompt, until a picture appears.
- EU AI Act
- The EU AI Act is the European Union's law on artificial intelligence. It sorts AI uses by risk, bans a few harmful practices, sets strict rules for high-risk uses and requires transparency for chatbots and deepfakes.
- Generative AI
- Generative AI is AI that creates new content, such as text, images, music, video or code, based on patterns it learned from large amounts of existing examples.
- Large language model (LLM)
- A large language model (LLM) is an AI model trained on huge amounts of text to predict the next small piece of text. Repeating that prediction again and again lets it write answers, summaries, code and conversations.
- Machine learning
- Machine learning is the main way AI is built today: a computer is shown many examples, finds the patterns in them, and uses those patterns to handle new cases it has never seen.
- Neural network
- A neural network is a kind of AI model made of many layers of simple connected units. Each connection has a strength (a weight) that is tuned during training until the network gives good answers.
- Prompt
- A prompt is the instruction, question or example you give an AI tool to tell it what you want. Clear prompts with a goal, context and the format you need usually get much better results.
- Prompt injection
- Prompt injection is a trick where hidden or sneaky instructions in a web page, email, document or image make an AI tool ignore its real task and do something else, such as leaking data.
- Token
- A token is the small unit of text a language model reads and writes: a whole word, part of a word, a punctuation mark or a space. The model predicts one token at a time.
- Training data
- Training data is the collection of examples, such as texts, images, sounds or past records, that an AI model learns its patterns from. What is in it, and what is missing, shapes what the model can do.