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.

986 terms

Generative AI

Ad Generation

Ad Generation is the automated process of creating digital advertisements using algorithms and data-driven techniques to optimize reach and engagement.

Machine Learning

AdaBoost

AdaBoost is a machine learning algorithm that combines multiple weak classifiers to create a strong classifier.

Machine Learning

Adagrad

Adagrad is an adaptive learning rate optimization algorithm used in training machine learning models.

Machine Learning

Adam Optimization

Adam Optimization is an adaptive learning rate optimization algorithm designed for training deep learning models.

Other

Adapter Model

Adapter Model is a technique in machine learning that efficiently fine-tunes pre-trained models by inserting small trainable modules, called adapters, without updating the entire model.

Other

Adaptive Logic

Adaptive Logic is a system that dynamically modifies its reasoning or decision-making processes based on changing inputs and environments.

Computer Vision

Adaptive Thresholding

Adaptive Thresholding is a dynamic image processing technique that determines threshold values locally for different regions of an image to effectively separate foreground from background.

Data & Analytics

Address Standardization

Address Standardization is the process of converting addresses into a consistent, uniform format to ensure accuracy and easy data management.

Machine Learning

Adjusted R-Squared

Adjusted R-Squared is a statistical measure that indicates how well a regression model fits the data, adjusted for the number of predictors used.

Other

Advanced Driver-Assistance System

Advanced Driver-Assistance System is a technology that enhances vehicle safety and driving ease by automatically assisting with tasks such as braking, steering, and lane keeping.

Reinforcement Learning

Advantage Function

Advantage Function is a concept in reinforcement learning that measures how much better an action is compared to the average action at a given state.

Generative AI

Adversarial Prompting

Adversarial prompting is a technique used to intentionally craft inputs that challenge or expose weaknesses in AI language models or systems.

Computer Vision

Aerial Imagery

Aerial imagery is photographic or digital images captured from an elevated position, typically from aircraft, drones, or satellites, used to visually represent the Earth's surface.

Generative AI

Aesthetic Gradient

Aesthetic Gradient is a smooth transition between colors used to create visually appealing designs that enhance user experience and engagement.

Other

Affective Computing

Affective computing is the study and development of systems that can recognize, interpret, and respond to human emotions.

Computer Vision

Affine Transformation

An affine transformation is a linear mapping method that preserves points, straight lines, and planes, commonly used in computer graphics and geometric modeling.

Machine Learning

Affinity Propagation

Affinity Propagation is a clustering algorithm that identifies exemplars among data points and forms clusters by exchanging messages between data points.

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.