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.

3,885 terms

Computer Vision

RANSAC

RANSAC is an iterative algorithm used for robust model fitting in the presence of outliers.

AI Agents & Systems

Rapidly-exploring Random Tree

Rapidly-exploring Random Tree (RRT) is an algorithm designed to efficiently search high-dimensional spaces by incrementally building a space-filling tree through random sampling.

Data & Analytics

Raster Data

Raster data is a type of digital image represented by a grid of pixels, each holding specific values, commonly used to store and analyze spatial information in GIS and digital mapping.

Computer Vision

Rasterization

Rasterization is the process of converting vector graphics into a grid of pixels or dots for display on a screen or for printing.

AI Fundamentals

Rational Agent

A Rational Agent is an entity that acts to achieve the best possible outcome, based on its knowledge and the information available.

AI Agents & Systems

Rationality

Rationality is the ability to think, reason, and make decisions based on logic and evidence rather than emotions or biases.

Computer Vision

Raw Image Format

Raw Image Format is a type of digital photo file that captures unprocessed and uncompressed data directly from a camera's sensor.

Other

Ray Framework

Ray Framework is an open-source distributed computing framework designed to simplify the development of scalable, high-performance applications using parallel and distributed processing.

MLOps & Infrastructure

Ray Serve

Ray Serve is a scalable, distributed model serving library designed to deploy and manage machine learning models efficiently in production environments.

Computer Vision

Ray Tracing

Ray tracing is a rendering technique that simulates the way light interacts with objects to produce realistic images.

Machine Learning

RBF Kernel

The RBF Kernel is a popular function used in machine learning algorithms to transform data into a higher-dimensional space.

Data & Analytics

RDBMS

RDBMS is a Relational Database Management System that organizes data into structured tables with defined relationships for efficient storage and retrieval.

AI Agents & Systems

Reactivity

Reactivity is the ability of a system or component to automatically respond and update in real time when its underlying data or state changes.

Other

Read-Aloud

Read-Aloud is a technique where written text is spoken out loud to improve comprehension, engagement, or accessibility.

Other

Reading Assistant

Reading Assistant is a digital tool designed to support and enhance reading skills by providing personalized guidance, feedback, and interactive practice.

Other

Real-Time AI

Real-Time AI is artificial intelligence technology that processes data and delivers insights or actions instantly as events occur.

Other

Real-Time Captioning

Real-time captioning is the instant transcription of spoken words into text displayed simultaneously during live events or broadcasts.

Reinforcement Learning

Real-Time DP

Real-Time DP is a dynamic programming approach that processes data instantly as it becomes available to make immediate decisions or updates.

Other

Real-Time Image Processing

Real-Time Image Processing is the immediate analysis and manipulation of visual data as it is captured or received, enabling instant feedback or action.

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.