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.

250 terms

Generative AI

Decentralized AI

Decentralized AI is an artificial intelligence system designed to operate across multiple distributed nodes without relying on a single central authority.

Other

Decision Automation

Decision Automation is the use of technology and algorithms to automatically make business or operational decisions without human intervention.

Other

Decision Intelligence

Decision Intelligence is a multidisciplinary approach that combines data, technology, and human judgment to improve decision-making processes.

Other

Decision Support System

Decision Support System is a computer-based tool that helps individuals and organizations make informed decisions by analyzing data and presenting actionable insights.

AI Fundamentals

Decision Theory

Decision Theory is a field of study that examines the principles and methods used to make choices among alternatives.

Machine Learning

Decision Tree

A Decision Tree is a flowchart-like structure used for making decisions and analyzing potential outcomes by mapping various possible choices and their consequences.

Natural Language Processing (NLP)

DeCLUTR

DeCLUTR is a neural network model designed for unsupervised learning of sentence embeddings by contrasting similar and dissimilar pairs of text.

Data & Analytics

Deduplication

Deduplication is the process of identifying and removing duplicate data to improve storage efficiency and data accuracy.

Deep Learning & Neural Networks

Deep Belief Network

A Deep Belief Network (DBN) is a type of artificial neural network composed of multiple layers of stochastic, latent variables, often used for unsupervised learning and feature extraction.

Other

Deep Neural Network

Deep Neural Network is a type of artificial neural network with multiple layers that enables complex pattern recognition and learning from large datasets.

Other

Deep Nostalgia

Deep Nostalgia is an AI-powered technology that animates still photos, bringing faces to life with realistic movements and expressions.

Deep Learning & Neural Networks

Deep Q-Network

A Deep Q-Network (DQN) is a type of reinforcement learning algorithm that uses deep neural networks to approximate the Q-value function, enabling agents to learn and optimize actions in complex environments.

Deep Learning & Neural Networks

DeepDream

DeepDream is a computer vision program created by Google that uses neural networks to enhance and modify images in surreal and dream-like ways.

Other

Deepfake

Deepfake is a synthetic media technique that uses artificial intelligence to create realistic but manipulated audio, video, or images that depict events or people in ways that never actually occurred.

Other

Deepfake Audio

Deepfake audio is synthetic sound generated by artificial intelligence to mimic a person's voice or create realistic but fabricated speech.

Generative AI

Deepfake Detection

Deepfake detection is the process of identifying and distinguishing synthetic media created using artificial intelligence from authentic content.

Other

Deepfake Technology

Deepfake technology is an advanced artificial intelligence method that creates hyper-realistic but fake audio, video, or images by manipulating or synthesizing content.

Other

DeepLIFT

DeepLIFT is an explainability method that attributes the output of a neural network to its input features by comparing activation differences from a reference state.

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.