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

Data & Analytics

Synthetic Data

Synthetic data is artificially generated data that mimics real-world data without directly using actual personal or sensitive information.

Other

Synthetic Face

A synthetic face is a digitally created or artificially generated human face used in images, videos, or virtual environments.

Other

Synthetic Image

Synthetic image is a digitally created visual representation generated by computer algorithms rather than captured by a camera.

Other

Synthetic Media

Synthetic media is digitally created or manipulated content generated using artificial intelligence and machine learning techniques.

Other

Synthetic Speech

Synthetic Speech is computer-generated spoken language created using text-to-speech technology.

Other

Synthetic Voice

Synthetic voice is a computer-generated voice that mimics human speech using text-to-speech technology.

AI Ethics

System Cards

System Cards are structured digital or physical tools used to organize, display, and manage system components or functionalities in a clear and accessible format.

Machine Learning

t-SNE

t-SNE is a machine learning algorithm used for dimensionality reduction, particularly effective for visualizing high-dimensional data.

Generative AI

T2I-Adapter

T2I-Adapter is a technology that enhances text-to-image generation by efficiently integrating additional visual or semantic information into existing models.

Deep Learning & Neural Networks

T5

T5, or Text-to-Text Transfer Transformer, is a natural language processing model developed by Google that converts all text-based tasks into a text-to-text format.

Other

Table Recognition

Table Recognition is the process of automatically identifying and extracting tabular data from documents or images for further analysis or processing.

Data & Analytics

Tableau

Tableau is a powerful data visualization software that helps users transform raw data into interactive and easy-to-understand visual reports.

AI Agents & Systems

Tabu Search

Tabu Search is an advanced optimization algorithm that guides a local search procedure to explore solution spaces beyond local optima using memory structures.

Machine Learning

Target Encoding

Target Encoding is a technique used in data preprocessing where categorical variables are replaced with their corresponding mean target value.

Machine Learning

Target Variable

The target variable is the specific outcome or label that a model aims to predict in data analysis and machine learning.

AI Agents & Systems

Task

A task is a specific piece of work or activity that needs to be completed within a set timeframe or goal.

Other

Task Extraction

Task Extraction is the process of identifying and isolating specific actionable items or objectives from unstructured data or text.

AI Fundamentals

Taxonomy

Taxonomy is the practice and science of categorizing and organizing entities into hierarchical structures based on their relationships and characteristics.

Reinforcement Learning

TD(0)

TD(0) is a fundamental reinforcement learning algorithm that updates value estimates using immediate rewards and the estimated value of the next state.

Reinforcement Learning

TD(lambda)

TD(lambda) is a reinforcement learning algorithm that combines temporal difference learning with eligibility traces to efficiently predict future rewards.

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.