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

GPT-4o

GPT-4o is an advanced language model designed to understand and generate human-like text based on the GPT-4 architecture.

Other

GPT-4V

GPT-4V is an advanced multimodal AI model that processes both text and images to generate context-aware responses.

Other

GPU

GPU is a specialized processor designed to accelerate graphics rendering and parallel computing tasks.

Other

GPU Acceleration

GPU acceleration is the use of a Graphics Processing Unit (GPU) to perform computing tasks faster than a traditional Central Processing Unit (CPU) alone.

Other

GPU Cloud

GPU Cloud is a cloud computing service that provides access to powerful graphics processing units (GPUs) for high-performance computing tasks.

Other

GPU Compute

GPU Compute is the use of a graphics processing unit (GPU) to perform general-purpose computing tasks beyond traditional graphics rendering.

Other

GPU Computing

GPU Computing is the use of a Graphics Processing Unit (GPU) to perform complex calculations and data processing tasks beyond traditional graphics rendering.

MLOps & Infrastructure

GPU Sharing

GPU sharing is the technique of allocating a single graphics processing unit’s resources among multiple users or applications to maximize efficiency and reduce costs.

MLOps & Infrastructure

GPU Slicing

GPU slicing is the process of dividing a single physical GPU into multiple virtual GPUs to allocate resources efficiently across different workloads.

Other

GPU Worker

A GPU Worker is a computing process or unit that utilizes a Graphics Processing Unit (GPU) to perform parallel processing tasks, often accelerating complex computations.

Computer Vision

GrabCut

GrabCut is an interactive image segmentation technique that separates foreground objects from the background using iterative graph cuts and user input.

Deep Learning & Neural Networks

Grad-CAM

Grad-CAM is a visualization technique used in deep learning to highlight the important regions in an image that influence a model's prediction.

Machine Learning

Gradient Boosting Machine

Gradient Boosting Machine is a powerful machine learning technique used for regression and classification tasks by creating a series of decision trees that correct the errors of previous trees.

Data & Analytics

Grafana

Grafana is an open-source platform used for monitoring and visualizing data through customizable dashboards and graphs.

Other

Grammar Checker

Grammar checker is a software tool that automatically identifies and corrects grammatical errors in written text.

Data & Analytics

Graph Database

A graph database is a type of database designed to store, map, and query data structured as nodes, edges, and properties to represent relationships efficiently.

Other

Graph Embeddings

Graph embeddings are numerical representations of graph nodes, edges, or entire subgraphs designed to capture their structural and relational information in a low-dimensional vector space.

Generative AI

Graph Generation

Graph generation is the process of creating graphs—structured representations of data points and their relationships—using algorithms or models.

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.