Autonomy
Autonomy is the ability of an individual or system to operate independently without external control or influence.
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.
Autonomy is the ability of an individual or system to operate independently without external control or influence.
Autonomy (Ethics) is the principle that individuals have the right and capacity to make informed, independent decisions about their own lives.
AV1 is an open, royalty-free video coding format designed for efficient compression and high-quality streaming.
Avatar Generation is the process of creating digital representations or icons that visually represent a user or character in online platforms, games, and virtual environments.
An Avatar Generator is a digital tool that creates personalized graphical representations or icons of users for online profiles, games, or virtual environments.
Average Pooling is a downsampling operation used in convolutional neural networks (CNNs) to reduce the spatial dimensions of an image by calculating the average value in each sub-region.
AVI is a digital video file format that stores audio and video data in a single file for synchronized playback.
AWS Glue is a fully managed cloud service that automates the discovery, preparation, and integration of data for analytics and machine learning.
AWS SageMaker is a fully managed machine learning service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates data movement and transformation across various sources and sinks.
Azure Machine Learning is a cloud-based platform by Microsoft that enables developers and data scientists to build, train, and deploy machine learning models efficiently.
B-Tree is a self-balancing tree data structure that maintains sorted data and allows efficient insertion, deletion, and search operations.
BabyAGI is a simplified, experimental artificial general intelligence framework designed to explore the principles of AGI in a controlled environment.
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.
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.
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.
Background subtraction is a technique used in image processing to separate moving objects from a static background.
Backpropagation is a training method used in neural networks to adjust model weights by propagating errors backward from the output to improve predictions.
A Backup Diagram is a visual representation that outlines the process and structure of data backup systems within an organization.
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.
Bag of Words is a text representation method used in natural language processing that models text as an unordered collection of words.
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.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.