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.

160 terms

Reinforcement Learning

Temporal Difference Learning

Temporal Difference Learning is a reinforcement learning method where an agent learns to predict future rewards by updating estimates based on the difference between successive predictions over time.

AI Fundamentals

Tensor Processing Unit

A Tensor Processing Unit (TPU) is a type of custom-developed application-specific integrated circuit (ASIC) designed by Google to accelerate machine learning workloads.

Other

Tensor Streaming Processor

A Tensor Streaming Processor is a specialized hardware unit designed to accelerate tensor operations for machine learning and AI workloads by efficiently streaming data through parallel computation pipelines.

MLOps & Infrastructure

TensorRT

TensorRT is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA to accelerate AI model deployment on GPUs.

Other

Term Extraction

Term extraction is the automated process of identifying and extracting relevant words or phrases from text that represent key concepts or domain-specific terminology.

MLOps & Infrastructure

Terraform

Terraform is an open-source infrastructure as code (IaC) tool that enables users to define, provision, and manage cloud and on-premises resources through declarative configuration files.

Generative AI

Terrain Generation

Terrain generation is the process of creating digital landscapes and natural environments using algorithms and computer graphics.

Machine Learning

Test Data

Test data is data specifically created or selected to be used in testing software applications or systems.

Generative AI

Text Adventure

Text Adventure is an interactive storytelling game where players navigate and influence the story through text-based commands.

Other

Text Analytics

Text Analytics is the process of extracting meaningful insights and patterns from unstructured text data using computational techniques.

Other

Text Annotation

Text annotation is the process of adding explanatory notes, labels, or metadata to text to enhance understanding, analysis, or machine learning applications.

Other

Text Chunking

Text chunking is the process of dividing a large piece of text into smaller, meaningful segments or units for easier processing and analysis.

Natural Language Processing (NLP)

Text Cleaning

Text cleaning is the process of removing errors, inconsistencies, and irrelevant elements from raw text to improve its quality, readability, and suitability for analysis, SEO, and machine learning tasks.

Other

Text Continuation

Text continuation is the process of extending a piece of written content by generating coherent and contextually relevant text that follows the original input.

Natural Language Processing (NLP)

Text Corpus

A text corpus is a large, structured collection of written or spoken texts used for linguistic analysis, natural language processing, and training AI or machine learning models.

Other

Text Digitization

Text Digitization is the process of converting physical or analog text into digital formats for easy storage, editing, and analysis.

Generative AI

Text Embedding Model

A text embedding model is an AI model that converts text into numerical vectors that represent the meaning and context of the text.

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.