Stochastic Gradient Descent
Stochastic Gradient Descent is an iterative optimization algorithm used to find the minimum of a function by updating parameters in small, random batches.
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.
Stochastic Gradient Descent is an iterative optimization algorithm used to find the minimum of a function by updating parameters in small, random batches.
A stochastic process is a mathematical object used to describe systems or phenomena that evolve over time with inherent randomness.
Stock prediction is the process of forecasting future stock prices or market trends using various analytical methods and data models.
Stop words are common words in a language that are often filtered out during text processing and search queries.
Story Generation is the automated process of creating coherent and engaging narratives using artificial intelligence or algorithmic methods.
A Story Generator is a digital tool or software that creates narrative content automatically based on user inputs or algorithms.
A story prompt is a brief idea or scenario designed to inspire creative writing or storytelling.
Storytelling AI is an artificial intelligence technology designed to create, enhance, or assist in crafting engaging narratives and stories.
Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, and randomly selecting samples from each stratum.
Streaming data is a continuous flow of real-time information generated by various sources that is processed and analyzed as it arrives.
Stress testing is a process that evaluates how a system, application, or business performs under extreme or unfavorable conditions beyond normal operational capacity.
STRIPS is a planning language and framework used in artificial intelligence to define and solve automated planning problems.
Structural Credit Assignment is the process of determining the contribution of individual components within a complex system to the overall outcome or performance.
Structural Similarity Index is a metric used to measure the similarity between two images by comparing their luminance, contrast, and structure.
Structure from Motion (SfM) is a photogrammetric technique used to estimate three-dimensional structures from two-dimensional image sequences.
Structured Data is a standardized format for organizing and labeling information on web pages to help search engines better understand their content.
Student Analytics is the collection and analysis of educational data to understand and improve student learning outcomes and experiences.
Style adjustment is the process of modifying design elements or content presentation to enhance clarity, appeal, and effectiveness based on context or audience preferences.
Style Loss is a metric used in neural networks to measure the difference in style between generated images and reference images.
Style recommendation is a system or guidance that suggests clothing, design, or presentation options tailored to individual preferences and trends.
Style transfer is a technique in image processing that applies the visual style of one image to the content of another.
StyleGAN is a type of generative adversarial network (GAN) that allows for the creation of high-quality, photorealistic images by manipulating the style of generated content.
A subgoal is a smaller, manageable objective set within a larger goal to help systematically achieve the main outcome.
A subject is the main theme or topic that is being discussed, described, or analyzed.
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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.