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.

122 terms

Computer Vision

Feature Detection

Feature detection is a computer vision technique used to identify and describe distinct elements within an image or video.

Machine Learning

Feature Engineering

Feature Engineering is the process of using domain knowledge to transform raw data into features that better represent the underlying problem to predictive models, resulting in improved model accuracy.

AI Fundamentals

Feature Extraction

Feature extraction is the process of transforming raw data into a set of features that can be effectively used in machine learning models.

Other

Feature Flagging

Feature flagging is a software development technique that allows teams to enable or disable features dynamically without deploying new code.

AI Ethics

Feature Importance

Feature Importance is a technique used in machine learning to identify which variables most influence the outcome of a predictive model.

MLOps & Infrastructure

Feature Management

Feature Management is the practice of controlling the release, configuration, and visibility of software features to optimize user experience and development workflow.

Computer Vision

Feature Map

A Feature Map is a visual or conceptual representation that outlines the key features, functionalities, and relationships within a product or system.

Machine Learning

Feature Selection

Feature Selection is the process of choosing the most relevant variables or features for use in model construction.

Machine Learning

Feature Space

Feature space is a multi-dimensional space where each dimension represents a feature used to characterize data.

MLOps & Infrastructure

Feature Store

Feature Store is a centralized platform that manages, stores, and serves machine learning features for consistent and efficient model training and deployment.

Machine Learning

Feature Vector

A feature vector is a list of numerical values that represent an object’s characteristics used in machine learning and statistical models.

Data & Analytics

Federated Analytics

Federated Analytics is a data analysis approach where insights are derived from decentralized data sources without pooling raw data centrally.

Machine Learning

Federated Learning

Federated Learning is a machine learning approach that enables model training across multiple decentralized devices or servers without sharing raw data.

Other

Feedback Analytics

Feedback Analytics is the process of collecting, analyzing, and interpreting customer feedback data to improve products, services, and customer experience.

AI Agents & Systems

Feedback Control

Feedback control is a process where a system automatically adjusts its behavior based on the difference between desired and actual outputs.

Generative AI

Feedback Generation

Feedback Generation is the process of creating, collecting, and analyzing responses or evaluations to improve products, services, or processes.

AI Fundamentals

Feedback Loop

A feedback loop is a process where outputs of a system are circled back and used as inputs, influencing the subsequent outputs.

Other

FFT

FFT is a fast algorithm that efficiently computes the discrete Fourier transform (DFT) of a sequence, converting data from the time domain to the frequency domain.

Deep Learning & Neural Networks

FGSM

FGSM, or Fast Gradient Sign Method, is an algorithm used in adversarial machine learning to generate adversarial examples by perturbing input data in the direction of the gradient.

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.