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

Disparity Map

A disparity map is a representation of the differences in pixel positions between two stereo images used to determine depth information.

Other

Distance Measurement

Distance measurement is the process of determining the physical space between two points or objects using various tools and techniques.

Other

Distance Metrics

Distance metrics are mathematical formulas used to quantify the similarity or dissimilarity between two points in a space.

AI Fundamentals

Distributed Computing

Distributed Computing is a model where multiple computer systems work together to complete complex tasks, often over a network.

Data & Analytics

Distributed Database

A distributed database is a collection of data spread across multiple physical locations, managed by a single database system to appear as one unified database.

Other

Distributed Training

Distributed training is a machine learning approach where multiple computers or devices work together to train a model faster and more efficiently.

MLOps & Infrastructure

Docker

Docker is an open-source platform that automates the deployment, scaling, and management of applications using containerization technology.

Other

Document AI

Document AI is a technology that uses artificial intelligence to automatically extract, analyze, and understand information from various types of documents.

Computer Vision

Document Analysis

Document Analysis is the process of examining and interpreting the content, structure, and context of documents to extract meaningful information and insights.

Other

Document Capture

Document Capture is the process of converting physical or digital documents into electronic formats for easier storage, management, and retrieval.

Other

Document Digitization

Document Digitization is the process of converting physical paper documents into digital formats for easier storage, access, and management.

Natural Language Processing (NLP)

Document Embeddings

Document embeddings are numerical vector representations of entire documents that capture their semantic meaning, allowing AI systems to compare, search, cluster, and analyze text based on conceptual similarity rather than keywords.

Other

Document Processing

Document processing is the automated method of capturing, analyzing, and managing information contained in digital or physical documents.

Data & Analytics

Document Store

Document Store is a type of NoSQL database designed to store, retrieve, and manage document-oriented information in formats like JSON or XML.

Other

Document Summarization

Document summarization is the process of creating a concise and coherent version of a longer text that highlights its key points and main ideas.

Other

Document Understanding

Document Understanding is the process of extracting meaningful information and insights from various types of documents using advanced technologies like AI and machine learning.

Generative AI

Documentation Generation

Documentation Generation is the automated process of creating structured, clear, and comprehensive documents that explain software, systems, or processes.

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.