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

Other

Knowledge Extraction

Knowledge extraction is the process of automatically retrieving structured information from unstructured or semi-structured data sources.

AI Fundamentals

Knowledge Graph

A knowledge graph is a structured data system that connects entities and concepts to represent real-world knowledge and relationships.

AI Fundamentals

Knowledge Representation

Knowledge representation is the method of structuring and encoding information so that AI systems can understand, reason, and make decisions with it.

Natural Language Processing (NLP)

Koala

A koala is a marsupial native to Australia, known for its tree-dwelling lifestyle and diet primarily consisting of eucalyptus leaves.

AI Agents & Systems

KQML

KQML is a communication language and protocol designed to facilitate information exchange between software agents.

Data & Analytics

Kriging

Kriging is a geostatistical interpolation method used to predict unknown values across a spatial field based on observed data points.

MLOps & Infrastructure

KServe

KServe is an open-source platform for deploying, serving, and managing machine learning models at scale on Kubernetes.

Deep Learning & Neural Networks

Kubeflow

Kubeflow is an open-source platform designed to simplify the deployment, management, and scaling of machine learning models on Kubernetes.

MLOps & Infrastructure

Kubernetes

Kubernetes is an open-source platform designed to automate the deployment, scaling, and management of containerized applications.

Machine Learning

L1 Regularization

L1 Regularization is a technique used in machine learning to prevent overfitting by adding a penalty equal to the absolute value of the magnitude of coefficients to the loss function.

Machine Learning

L2 Regularization

L2 Regularization is a machine learning technique that helps prevent overfitting by adding a penalty equal to the square of the magnitude of coefficients to the loss function.

Computer Vision

Lab Color Space

Lab Color Space is a color model designed to approximate human vision by representing colors in three dimensions: lightness and two color-opponent channels.

Machine Learning

Label

A label is a descriptive or identifying tag used to categorize or organize information.

Machine Learning

Label Encoding

Label encoding is a technique used to convert categorical data into numerical format for machine learning models.

Generative AI

Landing Page Generation

Landing Page Generation is the process of creating optimized web pages designed to capture visitor attention and drive specific user actions like conversions or lead collection.

Reinforcement Learning

Landmark

Landmark is a prominent or easily recognizable object or feature used as a point of reference for navigation or identification.

Generative AI

Langevine Dynamics

Langevine Dynamics is the study of particle motion and interactions within fluid environments, focusing on complex flow behaviors and dynamic responses.

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.