🎓AI Glossary For Beginners

With AI seemingly everywhere, I created this handy glossary to help understand in simple terms what is going on! Last updated May 2025.

Agentic AIAgentic AI refers to how independent a system is - demonstrating autonomy, initiative and problem solving, adapting as it goes, without needing input makes a system Agentic.

AI (Artificial Intelligence)When computers or machines do tasks that usually need human intelligence, like learning, understanding language, or solving problems. Examples: ChatGPT, Siri, Alexa.

AI AgentAn autonomous system that makes decisions and takes actions on its own to achieve a desired output, rather than needing constant instructions at each stage. Example: a restaurant booking app, that finds the best eatery, knows when you are available, books the table, invites the guests and emails you confirmation.

Artificial General Intelligence (AGI)A type of AI with the ability to understand, learn, and apply knowledge across a wide range of tasks at a human level or beyond. Unlike narrow AI, which is designed for specific tasks, AGI can reason, solve problems, and adapt in unfamiliar situations without being retrained. Examples: None! This is aspirational/hypothetical at the moment.

AlgorithmA set of step-by-step instructions a computer follows to solve a problem, like a recipe for a machine.

Algorithmic FairnessMaking sure AI systems make decisions that are fair and don’t discriminate against people.

API (Application Programming Interface)A tool that lets different computer programs talk to each other and share information.

AutomationAs it suggests, this is using computers and technology to perform tasks automatically, with little or no human input. Typically for routine or repetitive tasks. Example: Email sent in response to a form being completed.

Bias in AIWhen an AI system makes unfair or unbalanced decisions, often because of bias or flaws in the data it is trained on. Example: Its hard to generate an image of a watch or clock telling any time other than ten past ten because that is what the majority of images of watches look like.

Big DataVery large collections of information that are too big for normal computers to handle easily.Example: Social media posts, online shopping data.

ChatbotA computer program that can talk to people, usually by text or voice, to answer questions or help with tasks. Examples: ChatGPT, Gemini, customer service bots.

CopilotA smart virtual assistant that helps users with tasks, decision making and productivity by providing real-time suggestions and automation. Unlike basic chatbots, it understands context. Example: Microsoft Copilot, app-specific copilots for specific software

Computer VisionAI that helps computers “see” and understand images and videos. Examples: Face recognition on phones, self-driving cars.

Data MiningFinding useful patterns or information in large amounts of data.

Data Facts and information (like numbers, words, or pictures) that computers use to learn, make decisions, or solve problems.

Deep LearningA complex way computers learn from large neural networks with many layers to learn and recognise sophisticated patterns from data. Example: Voice assistants and image recognition.

Edge AI / Edge ComputingRunning AI on local devices (like smartphones or cameras) instead of sending data to the cloud, so things happen faster and more privately.

Ethics Moral rules about what is right or wrong when developing or using AI, such as protecting privacy or avoiding unfairness.

Explainable AIAI that can show or explain why it made a certain decision, instead of just giving a result with no reasoning.

Fine-tuning LLMMaking small adjustments to a large language model (LLM) so it works better for a specific task or company.

GANs (Generative Adversarial Networks)A type of AI where two computer models compete: one creates fake data, for example like a fake image, and the other tries to spot the fake. The competition between the two creates better and better fakes, until it produces convincing output. Example: Creating realistic photos of people who don’t exist, AI art, Deepfakes.

Generative AI (GenAI)AI that can create new content, such as text, images, music, or videos, which match what humans produce. Examples: ChatGPT (text), Midjourney (images), HeyGen (Video)

GPT (Generative Pre-trained Transformer)A type of large language model that can understand and generate human-like text. Example: ChatGPT is based on GPT.

Hallucinations When AI gives an answer that sounds correct but is actually made up or wrong. Check out our guide on this here:

IoT (Internet of Things)Everyday devices (like fridges, watches, or cars) connected to the internet, collecting and sharing data.

Language ModelAn AI program that learns patterns in human language so that it can understand and predict or generate text, but might be trained on limited or very specific data. Examples: Predictive text, autocomplete, early spam filters.

Large Language Model (LLM)A type of language model trained on huge amounts of text data and is able to answer questions, write text, and more, like a human. Examples: ChatGPT, Gemini (formally Bard), Claude.

Machine Learning (ML)A type of AI where computers learn from their own examples and experience, instead of being given exact instructions for every task.

Neural NetworkA computer system inspired by the human brain, made up of layers of connected “nodes” that process information and can find patterns. Example: Plant identification app

NLP (Natural Language Processing)AI that helps computers understand and work with human languages, such as reading text, translating or speech recognition.

PredictionWhen an AI system uses what it has learned to guess what might happen next or to answer a question.

PromptA question, instruction, or statement you give to an AI system to get a specific response.Example: Typing “Write a poem about spring” into ChatGPT.

Prompt EngineeringCrafting effective prompts to get better or more accurate results from AI systems, especially large language models.

Reinforcement LearningA way for AI to learn by trial and error, getting rewards for good choices and penalties for mistakes. Example: Teaching a robot to walk or an AI to play games.

Retrieval Augmented Generation (RAG)A technique where a database or knowledge source is searched first for relevant information - retrieval - which is then used to generate a more accurate or useful response - generation.

RoboticsDesigning and building robots, often using AI to help them act intelligently. Examples: Robot vacuum cleaners, industrial robots.

Semantic DatabaseA way of storing data so that computers can understand the meaning and relationships between different pieces of information.

Structured DataInformation organised in a clear, fixed way, like in tables or spreadsheets. Example: Names and addresses in a table.

Supervised LearningA way of training AI where the computer is given examples with the correct answers, so it can learn to make similar decisions on its own.

Transfer LearningUsing a model trained for one task to help learn a different, but related, task more quickly.

Unstructured DataInformation that doesn't have a fixed or organised format, making it harder to search or analyse. Example: emails, photos, videos, audio files, social media posts, documents, my notebook

Unsupervised LearningA way of training AI where the computer looks for patterns in data without being told what the correct answers are.

About the Author

Connect with me on LinkedIn here

Create your own Scribes here

Check out my Scribe Community here