RLHF
RLHF (Reinforcement Learning from Human Feedback) is a training method that improves AI behavior by using human judgments to guide and refine model outputs.
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.
RLHF (Reinforcement Learning from Human Feedback) is a training method that improves AI behavior by using human judgments to guide and refine model outputs.
RMSprop is an adaptive learning rate optimization algorithm primarily used in training artificial neural networks.
The RNN Transducer is a type of neural network architecture designed for sequence-to-sequence tasks, often used in speech recognition.
Road defect detection is the process of identifying and analyzing damages or irregularities on road surfaces to ensure maintenance and safety.
Road Network Generation is the process of creating and designing interconnected pathways and streets for transportation and urban planning purposes.
RoBERTa is a robustly optimized BERT approach, designed to improve the performance of NLP models by fine-tuning hyperparameters and training on more data.
Roboethics is the study of moral principles and ethical guidelines governing the design, development, and use of robots and autonomous systems.
A robot is a programmable machine designed to perform tasks automatically or with minimal human intervention.
Robot grasping is the ability of a robotic system to securely hold and manipulate objects using mechanical hands or grippers.
Robot Localization is the process by which a robot determines its position and orientation within a known environment.
Robot manipulation is the ability of robots to interact physically with objects and environments by using sensors and actuators to perform tasks.
Robot Navigation is the process by which a robot determines its position and plans paths to move safely and efficiently through its environment.
Robot Operating System (ROS) is an open-source framework that provides tools and libraries to help developers build robot applications efficiently.
Robotic Process Automation (RPA) is a technology that uses software robots to automate repetitive and rule-based tasks in business processes.
Robotics is the branch of technology that involves the design, construction, operation, and use of robots.
Robustness is the ability of a system, process, or model to maintain effective performance despite changes, errors, or unexpected conditions.
Robustness Certification is the process of formally verifying that a system, model, or product maintains reliable performance under various challenging or adversarial conditions.
ROC Curve is a graphical representation used to assess the performance of a binary classification model by plotting the true positive rate against the false positive rate.
Role is a defined function or position that an individual or entity assumes within a specific context or system.
Role-Based Access Control (RBAC) is a security approach that restricts system access to authorized users based on their assigned roles within an organization.
Roleplay is a method where individuals assume and act out specific characters or scenarios to explore behaviors, develop skills, or simulate real-life situations.
Roleplay AI is an artificial intelligence designed to simulate characters, scenarios, or conversations for interactive storytelling and immersive experiences.
Roleplaying AI is artificial intelligence designed to simulate characters or personas in interactive scenarios, enabling dynamic and immersive conversations or storytelling.
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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.