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.

134 terms

Generative AI

Icon Generation

Icon Generation is the process of creating visual symbols or graphics that represent ideas, objects, or actions, often used in digital interfaces and branding.

Other

ID Document Recognition

ID Document Recognition is a technology that automatically identifies and extracts information from identity documents such as passports, driver’s licenses, or ID cards.

Other

IDE integration

IDE integration is the process of connecting an Integrated Development Environment with external tools or services to enhance software development workflows.

Data & Analytics

Identity and Access Management

Identity and Access Management (IAM) is a framework of policies and technologies that ensures the right individuals have appropriate access to an organization’s resources.

Other

Identity Verification

Identity verification is the process of confirming a person's identity using various methods to ensure they are who they claim to be.

AI Ethics

IEEE 7000

IEEE 7000 is a standard that provides a structured process for addressing ethical concerns in system design and development.

Other

iframe

An iframe is an HTML element that allows embedding another HTML document within the current web page.

Other

Image Annotation

Image annotation is the process of labeling or tagging images with metadata to enable accurate identification and analysis by machine learning models and digital systems.

Computer Vision

Image Captioning

Image captioning is the process of generating descriptive text that explains the content and context of an image.

Computer Vision

Image Classification

Image classification is a computer vision technique where an AI model identifies and assigns a label or category to an image based on its visual content.

Other

Image Colorization

Image colorization is the process of adding color to black-and-white or grayscale images to create a realistic or artistically enhanced colored version.

Computer Vision

Image Compression

Image compression is the process of reducing the file size of digital images to save storage space and improve loading times without significantly sacrificing quality.

Other

Image Deformation

Image deformation is the process of digitally altering the shape or structure of an image to achieve distortion or transformation effects.

Computer Vision

Image Dilation

Image dilation is a morphological operation that expands the boundaries of objects within a digital image to enhance or highlight features.

Computer Vision

Image Erosion

Image erosion is a morphological image processing technique that shrinks object boundaries by removing pixels from their edges.

Other

Image Filter

Image Filter is a digital tool or algorithm that modifies or enhances an image by applying specific effects or adjustments to its pixels.

Other

Image Filtering

Image filtering is a digital process that enhances or modifies an image by applying specific algorithms to adjust its visual characteristics.

Computer Vision

Image Generation

Image generation is the process of using artificial intelligence to create original images from text prompts, data inputs, or learned visual patterns.

Other

Image Generation AI

Image Generation AI is artificial intelligence technology designed to create visual content from text descriptions or other inputs.

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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.