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.

252 terms

Other

Model Retraining

Model retraining is the process of updating a machine learning model by feeding it new data to improve its accuracy and adapt to changing conditions.

Other

Model Testing

Model Testing is the process of evaluating a predictive or analytical model to ensure its accuracy, reliability, and effectiveness before deployment.

Machine Learning

Model Training

Model training is the process of teaching an AI or machine learning model to recognize patterns by learning from data.

MLOps & Infrastructure

Model Versioning

Model versioning is the process of managing and tracking different iterations of machine learning models to ensure reproducibility, comparison, and deployment control.

Other

Model Visualization

Model Visualization is the process of creating graphical representations of machine learning or AI models to better understand their structure, behavior, and decision-making.

Other

Model-Agnostic Explanation

Model-agnostic explanation is a technique that interprets and explains the predictions of any machine learning model without relying on the model’s internal structure.

Generative AI

Model-as-a-Service

Model-as-a-Service is a cloud-based delivery model where machine learning or AI models are provided on-demand for integration into applications without the need for local development or infrastructure management.

Machine Learning

Model-Based Learning

Model-Based Learning is a cognitive process that involves using a mental model to predict outcomes and make decisions based on those predictions.

AI Agents & Systems

Model-Based Reflex Agent

Model-Based Reflex Agent is an intelligent agent that uses an internal model of the world to make decisions based on both current percepts and past knowledge.

Reinforcement Learning

Model-Based RL

Model-Based RL is a type of reinforcement learning where an agent builds and uses a predictive model of the environment to plan and make decisions.

Reinforcement Learning

Model-Free RL

Model-Free RL is a type of reinforcement learning where the agent learns optimal behaviors without building a model of the environment's dynamics.

AI Fundamentals

Modelling

Modelling is the process of creating representations or simulations of systems, phenomena, or processes to predict and analyze their behavior.

Generative AI

Moderation Endpoint

Moderation Endpoint is an API interface designed to automatically review and filter user-generated content for policy compliance and harmful material.

Natural Language Processing (NLP)

Modifier

A modifier is a word, phrase, or clause that provides description or detail to a sentence, enhancing its meaning by specifying attributes of other words.

Computer Vision

Moiré Pattern

Moiré Pattern is a visual interference effect that appears as wavy or rippled lines when two repetitive patterns overlap.

Generative AI

Molecular Generation

Molecular Generation is the process of designing and creating new molecular structures using computational methods, often to discover novel compounds with desired properties.

Machine Learning

Momentum

Momentum is the quantity of motion an object has, determined by its mass and velocity.

Data & Analytics

MongoDB

MongoDB is a document-oriented NoSQL database designed for scalability, flexibility, and high performance in handling large volumes of unstructured data.

AI Agents & Systems

Monitoring Agent

Monitoring Agent is a software tool that continuously collects and reports data on system performance, health, and security for IT infrastructure management.

MLOps & Infrastructure

Monitoring Dashboard

A monitoring dashboard is a visual tool that consolidates and displays real-time data to help track performance, identify issues, and make informed decisions.

AI Fundamentals

Monte Carlo Method

The Monte Carlo Method is a computational algorithm that uses random sampling to obtain numerical results, often used for estimating complex mathematical or physical systems.

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.