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

AI Agents & Systems

State Estimation

State estimation is the process of using mathematical models and sensor data to determine the internal state of a system that cannot be directly observed.

AI Fundamentals

State Space

State Space is a representation of all possible configurations or conditions in which a system can exist.

Reinforcement Learning

State-Value

State-value is a function that estimates the expected return or future rewards achievable from a given state in a decision-making process.

AI Agents & Systems

Statechart

Statechart is a graphical representation of a system’s states, transitions, and behaviors used to model dynamic processes.

AI Agents & Systems

Stationary Agent

A stationary agent is an entity that remains fixed in one location while performing tasks or interacting within a system.

AI Fundamentals

Statistics

Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data.

Generative AI

Steering

Steering is the mechanism or process of guiding and controlling the direction of a vehicle or system.

Other

Stem Separation

Stem separation is the process of isolating the root form of a word from its affixes to improve text analysis and information retrieval.

Computer Vision

Stereo Vision

Stereo vision is a technique used to perceive depth by comparing two images from slightly different viewpoints.

AI Agents & Systems

Stigmergy

Stigmergy is a mechanism of indirect coordination through environmental cues that enable complex collective behavior without centralized control.

Reinforcement Learning

Stochastic Game

Stochastic game is a strategic game model where outcomes depend on probabilistic transitions between states and the decisions of multiple players.

Machine Learning

Stochastic Gradient Descent

Stochastic Gradient Descent is an iterative optimization algorithm used to find the minimum of a function by updating parameters in small, random batches.

AI Fundamentals

Stochastic Process

A stochastic process is a mathematical object used to describe systems or phenomena that evolve over time with inherent randomness.

Other

Stock Prediction

Stock prediction is the process of forecasting future stock prices or market trends using various analytical methods and data models.

Generative AI

Story Generation

Story Generation is the automated process of creating coherent and engaging narratives using artificial intelligence or algorithmic methods.

Other

Story Generator

A Story Generator is a digital tool or software that creates narrative content automatically based on user inputs or algorithms.

Other

Story Prompt

A story prompt is a brief idea or scenario designed to inspire creative writing or storytelling.

Other

Storytelling AI

Storytelling AI is an artificial intelligence technology designed to create, enhance, or assist in crafting engaging narratives and stories.

Machine Learning

Stratified Sampling

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, and randomly selecting samples from each stratum.

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.