Automated Training
Automated training is the use of software and technology to deliver, manage, and assess learning programs without continuous human intervention.
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.
Automated training is the use of software and technology to deliver, manage, and assess learning programs without continuous human intervention.
Automated trimming is a process where software automatically cuts or edits digital content or data to improve quality, relevance, or format without manual intervention.
Automated Valuation Model is a technology-driven method that uses algorithms and data to estimate the value of real estate properties quickly and efficiently.
Automated video editing is the use of software tools and algorithms to edit video content with minimal or no manual intervention.
Automated video production is the process of creating videos using software and algorithms that minimize human intervention.
Automatic captioning is the process of generating text captions for audio or video content using speech recognition technology.
Automatic captions are machine-generated text overlays that transcribe spoken dialogue in videos in real time or after processing.
Automatic grading is a technology-driven process that evaluates and scores assessments or assignments using software without human intervention.
Automatic refactoring is the process where software code is transformed and improved by tools without changing its external behavior.
Automatic Speech Recognition (ASR) is the technology that enables the conversion of spoken language into text by computers.
Automatic transcription is the process of converting spoken language in audio or video files into written text using computer algorithms.
Automation is the use of technology to perform tasks with minimal human intervention.
AutoML is the process of automating the end-to-end process of applying machine learning to real-world problems.
An autonomous agent is an AI system that can perceive its environment, make decisions, and take actions independently to achieve specific goals.
Autonomous AI is artificial intelligence designed to operate independently without human intervention by making decisions and performing tasks on its own.
Autonomous delivery is the use of self-operating vehicles or robots to transport goods without human intervention.
Autonomous driving is the technology that enables vehicles to navigate and operate without human intervention using sensors, software, and artificial intelligence.
An autonomous drone is an unmanned aerial vehicle capable of performing tasks and navigating without human intervention.
Autonomous Mobile Robot (AMR) is a self-guided machine capable of navigating and performing tasks without human intervention using sensors and intelligent software.
Autonomous racing is a competitive motorsport where self-driving cars use artificial intelligence and sensors to race without human drivers.
Autonomous robots are machines capable of performing tasks and making decisions independently without human intervention.
An Autonomous System (AS) is a collection of IP networks and routers under the control of a single organization that presents a common routing policy to the Internet.
Autonomous vehicle is a self-driving car that uses advanced sensors and software to navigate and operate without human intervention.
Autonomous vehicles are self-driving cars equipped with technology that allows them to navigate and operate without human intervention.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.