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.

119 terms

Data & Analytics

B-Tree

B-Tree is a self-balancing tree data structure that maintains sorted data and allows efficient insertion, deletion, and search operations.

Natural Language Processing (NLP)

BabyAGI

BabyAGI is a simplified, experimental artificial general intelligence framework designed to explore the principles of AGI in a controlled environment.

Natural Language Processing (NLP)

Back-Translation

Back-Translation is the process of translating a document that has already been translated into another language back into the original language to ensure accuracy and consistency.

Other

Background Removal

Background removal is the process of isolating the main subject of an image by eliminating the surrounding background to create a transparent or new background.

Other

Background Replacement

Background replacement is the process of digitally substituting the original backdrop of an image or video with a new, often more suitable or creative, background.

Reinforcement Learning

Backup Diagram

A Backup Diagram is a visual representation that outlines the process and structure of data backup systems within an organization.

AI Agents & Systems

Backward Chaining

Backward chaining is a reasoning method that starts with a goal and works backward to find the necessary conditions or facts to achieve that goal.

Machine Learning

Bagging

Bagging is an ensemble machine learning technique that improves model accuracy by training multiple instances of the same model on random subsets of the data and aggregating their predictions.

Data & Analytics

Bar Chart

A bar chart is a graphical representation of data using rectangular bars to compare different categories or values.

AI Agents & Systems

Bargaining

Bargaining is the process of negotiating terms and conditions between parties to reach a mutually beneficial agreement.

Data & Analytics

BASE Properties

BASE properties describe a model of database consistency that prioritizes availability and partition tolerance over immediate consistency.

Reinforcement Learning

Baseline

Baseline is a reference point or standard used for comparison to measure progress or changes in a project, process, or study.

Data & Analytics

Batch Data

Batch data is a collection of information processed together as a group at a specific time rather than continuously.

Other

Batch Inference

Batch inference is the process of applying a trained machine learning model to a large set of data points at once to generate predictions or insights efficiently.

Machine Learning

Batch Learning

Batch learning is a type of machine learning where models are trained using a large collection of data all at once.

MLOps & Infrastructure

Batch Serving

Batch serving is the process of delivering large volumes of data or content in scheduled groups rather than in real-time.

Machine Learning

Batch Size

Batch size is the number of training examples utilized in one iteration during the training of a machine learning model.

Computer Vision

Bayer Filter

Bayer Filter is a color filter array used in digital cameras to capture color information by arranging red, green, and blue filters over image sensors.

Other

Bayesian Filtering

Bayesian filtering is a statistical technique that uses Bayes' theorem to classify data by updating probabilities based on new evidence.

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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.