Regret
Regret is the emotional experience of wishing one had made a different decision or acted differently in the past.
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.
Regret is the emotional experience of wishing one had made a different decision or acted differently in the past.
Regret minimization is a decision-making strategy that focuses on reducing the negative feeling of regret after making a choice.
A Regular Expression is a sequence of characters that defines a search pattern, typically used for pattern matching within strings.
Regularization is a technique in machine learning used to prevent overfitting by adding a penalty to more complex models.
Regulatory Technology (RegTech) is the use of technology to help businesses comply efficiently with regulations and manage regulatory processes.
Reinforce is the process of strengthening or supporting something to make it more effective or resilient.
Reinforcement learning is a machine learning approach where an AI system learns by taking actions and receiving rewards or penalties based on the results.
Reinforcement Learning from Human Feedback is a machine learning approach that uses human feedback to guide and improve the training of AI models.
Reinforcement Learning from Human Feedback (RLHF) is a training method where AI models learn to improve their behavior based on human preferences and evaluations.
Relation Networks are neural network architectures designed to model and reason about relationships between entities.
Relational database is a type of database that stores and organizes data in tables related by common fields to enable efficient data management and retrieval.
Relevance feedback is a process in information retrieval where user feedback is used to improve the accuracy of search results.
Reliability is the consistency and dependability of a system, product, or process to perform as expected under specified conditions.
ReLU is an activation function in neural networks that outputs the input directly if it is positive, otherwise, it outputs zero.
Remixing is the process of creatively adapting or combining existing content to produce a new, original work.
Remote Exam Monitoring is the use of technology to oversee and ensure the integrity of exams taken outside traditional testing centers.
Remote sensing is the process of collecting information about an object or area from a distance, typically using satellites or aircraft.
Rendering is the process of generating a visual representation from a model or script, often used to produce images, animations, or videos.
Repetition penalty is a mechanism used in language models and content generation to reduce the likelihood of repeating the same words or phrases excessively.
Report Generation is the automated or manual process of compiling data into organized, readable documents that summarize information for analysis, decision-making, or communication.
Reproducibility is the ability to consistently duplicate the results of an experiment or process using the same methods and conditions.
A reptile is a cold-blooded vertebrate animal that typically lays eggs and has skin covered in scales or scutes.
Research paper summarization is the process of condensing a research article into a concise and clear overview that highlights its key points and findings.
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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.