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

Data & Analytics

Master Data Management

Master Data Management (MDM) is a comprehensive method for defining and managing an organization's critical data to provide a single, trusted source of information.

Other

Match Prediction

Match prediction is the process of forecasting the outcome of a sports or competitive event using data analysis and statistical models.

Deep Learning & Neural Networks

Matching Networks

Matching Networks are a type of neural network architecture designed for few-shot learning, where the model learns to classify new examples with only a few training instances.

AI Agents & Systems

Matchmaker Agent

A Matchmaker Agent is a software entity that connects two or more parties by identifying compatible matches based on predefined criteria.

Generative AI

Material Generation

Material Generation is the process of creating digital or physical resources designed to support content, products, or projects in marketing, education, or manufacturing.

Generative AI

Material Science

Material Science is the interdisciplinary study of the properties, structures, and applications of materials to improve technology and innovation.

Other

Math Problem Recognition

Math Problem Recognition is the process by which a system or individual identifies and understands the components and type of a mathematical problem to facilitate solving it effectively.

Other

Math Solver AI

Math Solver AI is an artificial intelligence tool designed to understand, analyze, and solve mathematical problems quickly and accurately.

Data & Analytics

Matplotlib

Matplotlib is a comprehensive Python library used for creating static, animated, and interactive visualizations in data analysis and scientific computing.

Machine Learning

Maximum A Posteriori

Maximum A Posteriori (MAP) is a statistical method used to estimate an unknown quantity by maximizing the posterior distribution.

Natural Language Processing (NLP)

mBART

mBART is a multilingual sequence-to-sequence model developed by Facebook AI for neural machine translation and other natural language processing tasks.

Deep Learning & Neural Networks

mBERT

mBERT is a multilingual version of the BERT model designed to process and understand multiple languages in natural language processing tasks.

Reinforcement Learning

MCTS Backpropagation

MCTS Backpropagation is the process in Monte Carlo Tree Search where simulation results are propagated up the search tree to update node values and guide future decisions.

Reinforcement Learning

MCTS Simulation

MCTS Simulation is a process within Monte Carlo Tree Search where random or guided plays are run to estimate the potential outcomes of moves in decision-making problems.

AI Agents & Systems

Mealy Machine

A Mealy Machine is a finite-state machine where the outputs depend on both the current state and the current inputs.

Machine Learning

Mean Absolute Error

Mean Absolute Error (MAE) is a statistical measure used to assess the accuracy of a predictive model by calculating the average of the absolute differences between predicted and actual values.

Reinforcement Learning

Mean Field Game

Mean Field Game is a mathematical framework used to analyze decision-making in large populations of interacting agents by approximating their collective behavior.

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.