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.

294 terms

Machine Learning

Classification

Classification is the process of organizing data or information into categories based on shared characteristics or attributes.

Generative AI

Classifier-Free Guidance

Classifier-Free Guidance is a technique in generative AI that enhances output quality by steering models without relying on an explicit classifier.

AI Tools & Platforms

Claude AI

Claude AI is a large language model developed by Anthropic that is designed to generate, analyze, and summarize text with a strong focus on safety, reliability, and helpfulness.

Other

Clause Extraction

Clause Extraction is the process of identifying and isolating meaningful clauses within sentences for analysis or processing.

Generative AI

CLIP

CLIP is a neural network model that connects images and text by learning to understand and match visual content with natural language descriptions.

Generative AI

CLIP-Guidance

CLIP-Guidance is an AI technique that uses the CLIP model to steer image generation toward desired concepts by integrating text and visual understanding.

Generative AI

CLIPScore

CLIPScore is an evaluation metric that measures the similarity between images and text using a multimodal model to assess how well a caption describes an image.

Generative AI

Closed Source AI

Closed Source AI is artificial intelligence software whose source code is not publicly available, restricting access and modification to the original developers or authorized users.

Other

Cloud AI

Cloud AI is the delivery of artificial intelligence services and tools through cloud computing platforms, enabling scalable and accessible AI solutions.

Other

Cloud API

Cloud API is a set of protocols and tools that allow developers to interact with and manage cloud-based services and resources programmatically.

AI Fundamentals

Cloud Computing

Cloud computing is the delivery of computing services over the internet, allowing users to access and store data and applications remotely.

Data & Analytics

Cloud Data Warehouse

Cloud Data Warehouse is a scalable, cloud-based repository designed to store, manage, and analyze large volumes of structured and semi-structured data for business intelligence and analytics.

Other

Cloud GPU

Cloud GPU is a remote graphics processing unit service hosted on cloud infrastructure that enables high-performance computing and rendering without local hardware constraints.

Other

Cloud Spend Anomaly Detection

Cloud Spend Anomaly Detection is the process of identifying unexpected or unusual patterns in cloud service expenditures to prevent overspending and optimize budgets.

Data & Analytics

Cloud Storage

Cloud storage is a service that allows users to save and access data on remote servers via the internet instead of local devices.

Other

Cloud-Native Security

Cloud-Native Security is the practice of protecting applications, data, and infrastructure designed specifically for cloud environments using integrated, scalable, and automated security measures.

MLOps & Infrastructure

CloudFormation

CloudFormation is an AWS service that enables you to model, provision, and manage cloud infrastructure as code using templates.

Other

Cluster Analysis

Cluster analysis is a statistical method used to group similar objects or data points into clusters based on their characteristics or features.

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.