Mean Shift
Mean Shift is a clustering algorithm that identifies the dense areas of data points in a feature space to determine the center of clusters without specifying the number of clusters beforehand.
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.
Mean Shift is a clustering algorithm that identifies the dense areas of data points in a feature space to determine the center of clusters without specifying the number of clusters beforehand.
Mean Squared Error (MSE) is a measure of the average of the squares of the errors between observed and predicted values.
Mechanism design is a field in economics and game theory focused on creating rules or systems that lead to desired outcomes, even when participants act in their own self-interest.
Median filter is a nonlinear digital filtering technique used to remove noise from images or signals by replacing each value with the median of neighboring values.
Medical AI is the application of artificial intelligence technologies to enhance healthcare delivery, diagnosis, treatment, and patient management.
Medical Data Analysis is the process of examining healthcare-related data to extract meaningful insights that improve patient outcomes and optimize medical practices.
Medical imaging is a technique used to create visual representations of the interior of a body for clinical analysis and medical intervention.
Medical Imaging AI is artificial intelligence technology designed to analyze and interpret medical images to assist in diagnosis and treatment planning.
Meena is an open-domain chatbot developed by Google to improve conversational AI capabilities.
A meeting assistant is a digital tool or software designed to help organize, manage, and optimize meetings efficiently.
Meeting automation is the use of technology to streamline and manage the scheduling, organizing, and follow-up of meetings without manual intervention.
Meeting Intelligence is the use of technology to capture, analyze, and extract actionable insights from meetings to improve collaboration and decision-making.
Meeting Notes Automation is the use of software tools to automatically capture, transcribe, and organize key points from meetings without manual effort.
Meeting transcription is the process of converting spoken dialogue from a meeting into a written text format.
Megatron-Turing NLG is a large-scale natural language generation model developed to understand and generate human-like text.
Mel-Frequency Cepstral Coefficients (MFCCs) are features used in audio processing to represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel…
Melody generation is the process of creating a sequence of musical notes that form a coherent and appealing tune, often using algorithms or AI tools.
Melody synthesis is the process of creating musical melodies using computational algorithms and sound generation techniques.
Memcached is an open-source, high-performance distributed memory caching system used to speed up dynamic web applications by reducing database load.
A meme generator is a digital tool or software that allows users to create customized memes by adding text to images or videos.
Memory Game is a cognitive matching game that challenges players to recall and pair identical cards from a set.
Memory in AI is the capability of artificial intelligence systems to store, recall, and utilize information to improve learning and decision-making processes.
A Memory Network is a type of neural network architecture designed to improve the ability of models to store and retrieve information over long sequences.
Mental Health AI is the use of artificial intelligence technologies to support, assess, and improve mental health care and well-being.
Page 4 of 11
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.