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

Topic Extraction

Topic Extraction is the process of identifying the main subjects or themes within a piece of text to summarize its content effectively.

Data & Analytics

Topology

Topology is the branch of mathematics that studies the properties of space that are preserved under continuous transformations such as stretching and bending, but not tearing or gluing.

MLOps & Infrastructure

TorchServe

TorchServe is an open-source model serving framework designed to deploy and manage PyTorch machine learning models efficiently in production environments.

MLOps & Infrastructure

Tracing

Tracing is the process of recording and analyzing the flow of execution within a software application or system to diagnose issues and optimize performance.

Computer Vision

Tracking

Tracking is the process of collecting and analyzing data about user interactions and behaviors on digital platforms.

Computer Vision

Traffic Monitoring

Traffic monitoring is the process of tracking and analyzing data about visitors to a website or network to understand behavior, performance, and security.

MLOps & Infrastructure

Traffic Splitting

Traffic splitting is the process of dividing website visitors or user traffic into multiple groups to test and compare different versions of web pages or marketing strategies.

AI Fundamentals

Training Data

Training data is the collection of examples used to teach an AI or machine learning model how to recognize patterns and make predictions.

MLOps & Infrastructure

Training-Serving Skew

Training-serving skew is the discrepancy between the data or environment used during machine learning model training and the conditions encountered when the model is deployed for serving predictions.

Other

Transcription Accuracy

Transcription accuracy is the measure of how precisely spoken language is converted into written text without errors or omissions.

Machine Learning

Transductive Learning

Transductive learning is a machine learning approach that makes predictions only for the given set of unlabeled data, leveraging both labeled and unlabeled data during training.

Machine Learning

Transfer Learning

Transfer learning is a machine learning technique where a model trained on one task is reused or adapted for a different but related task.

Other

Transformer Model

Transformer Model is a deep learning architecture designed for processing sequential data, primarily used in natural language processing tasks.

Other

Transformer Models

Transformer models are advanced neural network architectures designed to handle sequential data by using self-attention mechanisms for efficient and scalable natural language processing.

Natural Language Processing (NLP)

Transformer-XL

Transformer-XL is an advanced neural network architecture designed for natural language processing tasks, extending the traditional Transformer model with better handling of long-range dependencies.

Generative AI

Translating Code

Translating code is the process of converting source code written in one programming language into another to ensure compatibility and functionality across different platforms.

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.