SentencePiece
SentencePiece is an unsupervised text tokenizer and detokenizer used to preprocess text data for machine learning models.
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.
SentencePiece is an unsupervised text tokenizer and detokenizer used to preprocess text data for machine learning models.
Sentiment analysis is the use of artificial intelligence to identify and measure emotions, opinions, or attitudes expressed in text.
Seq2Seq is a type of neural network architecture that is used to transform sequences from one domain to another, often in tasks like language translation or summarization.
Sequence length is the total number of elements or characters in a given set or string.
Serverless is a cloud computing model where developers build and run applications without managing the underlying servers.
Serverless Data Warehouse is a cloud-based data storage and analytics solution that automatically manages infrastructure, allowing users to query large datasets without managing servers.
A Service Agent is a professional who acts as the primary point of contact between a company and its customers, managing inquiries, support, and service delivery.
Service robot is a programmable machine designed to assist humans by performing specific tasks in various service-related environments.
Service-Oriented Architecture (SOA) is a design approach where software components are organized as interoperable services to support business processes.
Shadow Deployment is a software release strategy where new code runs alongside the current system without affecting live operations, allowing real-world testing before full rollout.
Shap-E is a machine learning model designed to generate 3D shapes from textual descriptions, enabling seamless text-to-3D content creation.
Shapefile is a popular geospatial vector data format used to represent geographic features like points, lines, and polygons in mapping and GIS applications.
Shapley values are a method from cooperative game theory used to fairly distribute the total gains or contributions among players based on their individual contributions.
Sharding is a database architecture technique that splits large datasets into smaller, more manageable parts called shards to improve performance and scalability.
A Shared Plan is a collaborative subscription model where multiple users or teams access a set of services or resources under a single account.
A Shopping Agent is a service or software that assists consumers in finding, comparing, and purchasing products from various retailers efficiently.
Shot noise is a type of electronic noise that arises from the discrete nature of electric charge causing random fluctuations in current or voltage.
ShuffleNet is a lightweight convolutional neural network architecture designed for efficient image classification on mobile devices.
Shutter speed is the length of time a camera's shutter remains open to expose light onto the camera sensor or film.
A Siamese Network is a type of neural network architecture used for comparing two inputs by learning a similarity function.
Side effects are unintended or secondary outcomes that occur in addition to the primary intended effect of an action, treatment, or process.
SIFT is an algorithm used in computer vision to detect and describe local features in images.
The sigmoid function is a mathematical function that maps any real-valued number into a value between 0 and 1, often used in machine learning for logistic regression and neural networks.
Signal Processing is the analysis, interpretation, and manipulation of signals.
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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.