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.

306 terms

Reinforcement Learning

Selection

Selection is the process of choosing the most suitable option from a set of alternatives based on specific criteria or goals.

AI Fundamentals

Self-Organization

Self-organization is a process where a structure or pattern emerges in a system without a central authority or external command.

Data & Analytics

Self-Service Analytics

Self-Service Analytics is a data analysis approach that empowers non-technical users to access, explore, and visualize data independently without relying on IT specialists.

Other

Semantic Analysis

Semantic Analysis is the process of understanding and interpreting the meaning and relationships within language data.

AI Agents & Systems

Semantic Interoperability

Semantic interoperability is the ability of different systems to exchange data with unambiguous, shared meaning that machines and humans can understand consistently.

Other

Semantic Relevance

Semantic relevance is the measure of how closely the meaning of content aligns with a user’s search intent or query.

Computer Vision

Semantic Segmentation

Semantic Segmentation is a computer vision technique that involves partitioning an image into segments to classify each pixel into a category.

Other

Semantic Understanding

Semantic understanding is the ability of computers and algorithms to interpret and derive meaningful context from language beyond mere keywords.

AI Fundamentals

Semantic Web

The Semantic Web is an extension of the World Wide Web that enables data to be shared and reused across application, enterprise, and community boundaries.

AI Agents & Systems

Semantic Web Services

Semantic Web Services are web services enhanced with semantic metadata to enable automated discovery, invocation, composition, and integration through machine-readable descriptions.

Data & Analytics

Semi-Structured Data

Semi-structured data is information that does not reside in a rigid database format but still contains organizational properties like tags or markers to separate data elements.

Machine Learning

Semi-Supervised Learning

Semi-supervised learning is a machine learning approach that trains models using a small amount of labeled data combined with a large amount of unlabeled data.

Machine Learning

Sensitivity

Sensitivity is the measure of how responsive a system, individual, or metric is to changes in external conditions or inputs.

Reinforcement Learning

Sensor

A sensor is a device that detects and measures physical or environmental changes and converts them into signals for monitoring or control.

Natural Language Processing (NLP)

Sentence Embeddings

Sentence embeddings are vector representations of entire sentences that capture their meaning, context, and semantic relationships for use in machine learning and natural language processing tasks.

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.