Glossary
Algorithm
A set of step-by-step instructions a computer follows to solve a problem.
Artificial Intelligence (AI)
Computers doing tasks that usually need human thinking, like recognising speech or making decisions.
Bias
When an AI gives unfair results, often because its training data was unbalanced.
Chatbot
A program you can talk to using text or voice.
Classification
Sorting things into groups, like 'cat' or 'dog'.
Computer Vision
AI that lets computers see and understand pictures and videos.
Data
Information such as numbers, words, pictures or sounds that computers use.
Dataset
A big collection of data used to train or test an AI.
Deep Learning
Machine learning using neural networks with many layers.
Ethics
Thinking about what is right, fair and responsible.
Generative AI
AI that creates new content like text, images or music.
Hallucination
When an AI confidently says something that is false or made up.
Input
The information you give to a computer or AI.
Label
The correct answer attached to an example in training data.
Large Language Model (LLM)
A huge AI trained on lots of text so it can understand and write language.
Machine Learning
A type of AI where computers learn patterns from examples.
Model
The trained 'brain' of an AI that makes predictions or answers.
Natural Language Processing (NLP)
AI that understands and produces human language.
Neural Network
An AI system made of connected layers, loosely inspired by the brain.
Output
The result the computer or AI gives back.
Pattern
Something that repeats or follows a rule, which AI can learn to spot.
Prediction
A guess about what will happen, based on data.
Predictive AI
AI that uses past data to forecast future results.
Privacy
Keeping your personal information safe and under your control.
Prompt
The question or instruction you type into an AI tool.
Robot
A machine that can sense, think (sometimes with AI) and move.
Training
Teaching an AI by showing it lots of examples.
Training Data
The examples an AI learns from.