AI Glossary: 3,885 terms, defined

Plain-language definitions of the vocabulary used across machine learning, model architectures, tooling and AI safety. Every entry opens with a one-sentence definition, then explains where the term is used.

252 terms

AI Agents & Systems

Machine Ethics

Machine Ethics is the field of study focused on designing artificial intelligence systems that make morally responsible decisions.

Machine Learning

Machine Learning

Machine learning is a type of artificial intelligence that enables systems to learn from data and improve performance without being explicitly programmed.

Other

Machine Learning API

Machine Learning API is an application programming interface that allows developers to integrate machine learning models and functionalities into their applications without building models from scratch.

Other

Machine Learning Dataset

A machine learning dataset is a structured collection of data used to train and evaluate machine learning models.

Other

Machine Learning Deployment

Machine Learning Deployment is the process of integrating a trained machine learning model into a production environment to make real-time or batch predictions.

Other

Machine Learning Ethics

Machine Learning Ethics is the study and application of moral principles to ensure the responsible design, deployment, and use of machine learning systems.

Other

Machine Learning Framework

A Machine Learning Framework is a software library or environment that provides tools and components to build, train, and deploy machine learning models efficiently.

Other

Machine Learning Inference

Machine Learning Inference is the process of using a trained machine learning model to make predictions or decisions based on new data.

Other

Machine Learning Lifecycle

Machine Learning Lifecycle is the structured process that guides the development, deployment, and maintenance of machine learning models from data collection to continuous improvement.

Other

Machine Learning Metrics

Machine Learning Metrics are quantitative measures used to evaluate the performance and effectiveness of machine learning models.

Other

Machine Learning Model

A machine learning model is a mathematical framework or algorithm that enables computers to recognize patterns and make predictions or decisions based on data.

Other

Machine Learning Model Deployment

Machine learning model deployment is the process of integrating a trained machine learning model into a production environment where it can make real-time or batch predictions.

Other

Machine Learning Models

Machine learning models are algorithms designed to recognize patterns in data and make predictions or decisions without being explicitly programmed.

Other

Machine Learning Observability

Machine Learning Observability is the practice of monitoring, tracking, and understanding machine learning models in production to ensure their performance, reliability, and accuracy over time.

Other

Machine Learning Pipeline

A Machine Learning Pipeline is a structured workflow that automates the process of developing, training, and deploying machine learning models.

Other

Machine Learning Training Data

Machine Learning Training Data is the curated dataset used to teach algorithms how to recognize patterns and make accurate predictions or decisions.

Other

Machine Learning Transparency

Machine Learning Transparency is the practice of making the processes, decisions, and data behind machine learning models understandable and accessible.

Other

Machine Learning Workflow

Machine Learning Workflow is a structured sequence of steps involved in developing, training, and deploying machine learning models to solve real-world problems.

Other

Machine Teaching

Machine teaching is the process of designing and delivering training data to efficiently guide a machine learning model toward desired performance.

Computer Vision

Machine Vision

Machine Vision is a technology that enables computers to interpret and process visual information from the world.

Other

Machine-Generated Text

Machine-generated text is written content produced automatically by computer algorithms without direct human input.

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About the TiorAI AI Glossary

The TiorAI AI Glossary is a free reference of 3,885 artificial intelligence terms, each defined in plain English. Every entry opens with a one-sentence definition that stands on its own, then explains where the term is actually used and how it sits next to the ideas around it. There is nothing to sign up for and nothing to download: each definition has its own page and its own URL, so a term you needed once can be linked to and found again.

Three ways to find a term

They combine, and none of them cancels the others. The A to Z strip narrows the index to a single initial, with the # key collecting the entries that begin with a digit or a symbol and that no letter would reach. The category filter cuts the glossary to one of sixteen subject areas, among them Machine Learning, Deep Learning and Neural Networks, Natural Language Processing, Computer Vision, Generative AI, Reinforcement Learning, AI Agents and Systems, MLOps and Infrastructure, Data and Analytics, and AI Ethics. The search field matches names and definitions, so a concept you can only half-name still surfaces. Every combination has its own URL, which means a narrowed view can be bookmarked or sent to a colleague.

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What the definitions cover

The vocabulary of modern AI, at the three depths people arrive needing. The fundamentals a first week runs into — model, parameter, training, inference, overfitting. The architecture behind current systems — transformers, attention, embeddings, diffusion, retrieval-augmented generation. And the operational and governance language that turns up in a procurement review or a policy document — fine-tuning, hallucination, alignment, model drift, bias. Terms are defined as they are used in practice rather than as they appear in a paper abstract.

Definitions that answer first

Each entry leads with the answer. The opening sentence says what the term means without assuming the rest of the page, which is what makes an entry useful to someone who arrived from a search with exactly one question. The explanation underneath is for the reader who wants more than that: what the idea is commonly confused with, what it looks like in practice, and which neighbouring terms are worth reading next. The card in the index above shows the opening of that definition, so you can tell from the results whether an entry is the one you want.

Where the glossary fits

A glossary answers what something means; it is not the place that tells you what to do about it. Where a concept has products behind it, the AI tools directory is where they are listed, and where it has a working instruction behind it, the prompt library is where that lives. Entries link out to those when the link earns its place, and the glossary stays what a glossary is — the definitions, ordered so they can be browsed, and written so a single one can be read on its own.

Using this glossary

How is the glossary ordered?

Alphabetically by term name, which is the order a reference work is browsed in and the order every page of this archive has always used. The sort menu can switch it to reverse alphabetical, or to recently added and recently updated if you want to see what has changed.

What does the letter strip do?

It narrows the index to terms whose name begins with that character, and it keeps whatever else you have set — a category filter, a search, a sort — so you can move between letters without starting again. The # entry collects the terms that begin with a digit or a symbol, which no letter would reach.

Why do some terms have no specific category?

Entries that span several areas, or that predate the current category set, sit under “Other”. It holds a large share of the glossary, so the filter is most useful for narrowing to a named topic rather than for excluding one.

What is in a glossary entry?

A short definition in plain language, then a longer explanation of where the term is used and how it relates to neighbouring concepts. The card above shows the opening of that definition; the entry itself also links to related terms and, where relevant, to the AI tools that implement the idea.

How do I search the glossary?

The field at the top matches term names and definitions, so a concept you can only half-name will usually surface. Search combines with the category filter and the letter strip, and every combination has its own URL, so a narrowed view can be bookmarked or shared.