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

Data Entry Automation

Data Entry Automation is the use of technology to automatically input, process, and manage data without manual intervention.

AI Ethics

Data Exploitation

Data Exploitation is the process of extracting valuable insights and actionable information from raw data to support decision-making and business growth.

Data & Analytics

Data Fabric

Data Fabric is an integrated architecture and set of data services that provide consistent, secure, and intelligent data management across diverse platforms and environments.

Data & Analytics

Data Governance

Data Governance is the framework of policies, processes, and standards that ensures effective management, quality, and security of an organization's data assets.

Data & Analytics

Data Hashing

Data hashing is the process of converting input data into a fixed-size string of characters, typically for security or data integrity purposes.

Data & Analytics

Data Integration

Data Integration is the process of combining data from different sources into a unified and consistent view for analysis and decision-making.

AI Fundamentals

Data Labeling

Data labeling is the process of adding meaningful tags or annotations to raw data so it can be used to train machine learning models.

Data & Analytics

Data Lake

Data Lake is a centralized repository that stores vast amounts of raw, unstructured, and structured data in its native format for flexible analysis and processing.

Data & Analytics

Data Lakehouse

Data Lakehouse is a modern data architecture that combines the scalability of data lakes with the management and performance features of data warehouses.

Machine Learning

Data Leakage

Data leakage is the unintended or unauthorized transmission of data from within an organization to an external recipient.

Data & Analytics

Data Lineage

Data lineage is the process of tracking the origin, movement, and transformation of data throughout its lifecycle within an organization’s systems.

Data & Analytics

Data Localization

Data localization is the practice of storing and processing data within the geographic boundaries of a specific country or region.

Data & Analytics

Data Loss Prevention

Data Loss Prevention is a strategy and set of tools designed to prevent sensitive information from being lost, leaked, or accessed by unauthorized users.

Data & Analytics

Data Mart

Data Mart is a focused subset of a data warehouse designed to serve specific business lines or departments with relevant, streamlined data for analysis.

Data & Analytics

Data Masking

Data Masking is the process of obscuring sensitive information by replacing it with fictitious but realistic data to protect privacy and maintain data usability.

Data & Analytics

Data Mesh

Data Mesh is a decentralized approach to managing and scaling data architecture by treating data as a product owned by domain-specific teams.

Other

Data Mining

Data mining is the process of discovering patterns, trends, and useful information from large datasets using statistical and computational techniques.

Data & Analytics

Data Modeling

Data Modeling is the process of creating a structured representation of data to organize, define, and manage information efficiently.

Data & Analytics

Data Munging

Data munging is the process of cleaning, transforming, and organizing raw data into a usable format for analysis or business applications.

Data & Analytics

Data Observability

Data Observability is the practice of monitoring, tracking, and understanding the health and quality of data systems to ensure reliable and accurate data insights.

Data & Analytics

Data Pipeline

Data Pipeline is a series of processes that move data from one system to another, transforming and preparing it for analysis or storage.

Data & Analytics

Data Preparation

Data Preparation is the process of cleaning, transforming, and organizing raw data into a usable format for analysis or modeling.

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.

Read more Read less

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.