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.

117 terms

Machine Learning

Lasso Regression

Lasso Regression is a type of linear regression that uses L1 regularization to reduce the complexity of the model by penalizing the absolute size of the coefficients.

Generative AI

Latent Diffusion

Latent Diffusion is a machine learning technique that generates high-quality images by learning patterns in compressed, abstract representations of data.

Natural Language Processing (NLP)

Latent Semantic Analysis

Latent Semantic Analysis is a natural language processing technique used to analyze relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and…

Generative AI

Latent Space

Latent space is a mathematical representation where complex data is transformed into simpler, abstract dimensions that capture essential features.

Other

Layer Fusion

Layer Fusion is a technique that combines multiple layers of data or features into a single unified representation to improve analysis or processing efficiency.

Generative AI

Layout Generation

Layout Generation is the process of automatically creating structured visual arrangements of content elements for websites, applications, or digital interfaces.

Deep Learning & Neural Networks

Leaky ReLU

Leaky ReLU is a type of activation function used in artificial neural networks that allows a small, non-zero gradient when the unit is not active.

AI Agents & Systems

Learning Agent

A learning agent is an artificial intelligence system designed to improve its performance over time by acquiring knowledge from experiences and adapting to new data.

Machine Learning

Learning Rate

Learning rate is a hyperparameter in machine learning models that controls how much to change the model in response to the estimated error each time the model weights are updated.

Machine Learning

Learning Rate Decay

Learning Rate Decay is a technique used in training neural networks to gradually reduce the learning rate over time to improve model convergence and performance.

Machine Learning

Leave-One-Out Cross-Validation

Leave-One-Out Cross-Validation (LOOCV) is a technique in machine learning where each observation in a dataset is used once as a test set while the rest serve as the training set.

Other

Legal AI

Legal AI is artificial intelligence technology designed to assist with legal tasks such as document review, contract analysis, and legal research.

Other

Legal Analytics

Legal Analytics is the use of data analysis and technology to extract insights from legal data for informed decision-making.

Other

Legal Automation

Legal automation is the use of technology to streamline, simplify, and automate routine legal tasks and processes.

Other

LegalTech

LegalTech is the use of technology and software to provide, enhance, or automate legal services and processes.

Generative AI

Length Penalty

Length penalty is a mechanism used in natural language processing models to adjust the preference for shorter or longer generated text sequences during decoding.

Other

Leptonica

Leptonica is an open-source software library designed for image processing and analysis, particularly useful in document and optical character recognition (OCR) applications.

Generative AI

Lesson Plan Generation

Lesson Plan Generation is the process of creating structured educational outlines that guide teaching activities and learning objectives.

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.