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

Reinforcement Learning

Monte Carlo Methods

Monte Carlo Methods are computational algorithms that use random sampling to solve complex mathematical problems and simulate systems with uncertain variables.

AI Ethics

Montreal Declaration

The Montreal Declaration is a set of ethical guidelines aimed at promoting responsible development and use of artificial intelligence.

Other

Mood Analysis

Mood Analysis is the process of identifying and interpreting the emotional tone behind written or spoken content to understand user sentiment.

Other

Mood Detection

Mood detection is the process of identifying and interpreting a person's emotional state using text, voice, facial expressions, or physiological signals.

AI Agents & Systems

Moore Machine

Moore Machine is a type of finite state machine where outputs depend solely on the current state, not on the inputs.

AI Fundamentals

Moore’s Law

Moore's Law is the observation that the number of transistors on a microchip doubles approximately every two years, while the cost of computing is halved.

AI Ethics

Moral Sycophancy

Moral sycophancy is the practice of insincerely aligning with popular ethical views to gain approval or social advantage.

Data & Analytics

Moran’s I

Moran's I is a statistical measure used to identify spatial autocorrelation, indicating how similar or dissimilar values are distributed across geographic areas.

Computer Vision

Morphological Closing

Morphological Closing is an image processing technique that smooths object contours by filling small holes and gaps using dilation followed by erosion.

Computer Vision

Morphological Opening

Morphological Opening is an image processing technique that smooths object contours by removing small objects and noise using erosion followed by dilation.

Other

Motion Capture

Motion capture is a technology that records human or object movements to create realistic digital animations.

AI Agents & Systems

Motion Planning

Motion planning is the computational process of determining a sequence of valid movements for a robot or autonomous system to achieve a specific goal while avoiding obstacles.

Computer Vision

MOV

MOV is a multimedia container file format developed by Apple, commonly used to store video, audio, and text data in a single file.

Computer Vision

MP4

MP4 is a digital multimedia container format used to store video, audio, subtitles, and other data in a single file.

Computer Vision

MPEG

MPEG is a set of digital video and audio compression standards designed to efficiently encode multimedia content for storage and transmission.

Natural Language Processing (NLP)

MPT

MPT (Multi-Party Transaction) is a financial transaction involving multiple parties, typically facilitated by a blockchain network.

Computer Vision

MRI

MRI is a non-invasive imaging technique that uses magnetic fields and radio waves to create detailed images of the inside of the body.

Other

MROps

MROps is the practice of managing, monitoring, and optimizing machine learning models in production environments to ensure reliable and scalable AI operations.

Natural Language Processing (NLP)

mT5

mT5 is a multilingual variant of the T5 (Text-to-Text Transfer Transformer) model designed to handle tasks in multiple languages.

Generative AI

MUD

MUD is a Multi-User Dungeon, an online text-based virtual world where multiple players interact, explore, and create stories together.

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.