3,135 articles
Security Identifier
A security identifier (SID), also called a Windows SID, is a variable-length value Windows uses to identify security principals such as accounts and groups in access control.
Segment (company)
Twilio Segment (formerly Segment.io), founded 2011 by Peter Reinhardt and three cofounders in San Francisco, is a customer data platform routing customer events via one API; Twilio acquired it in 2020 for about $3.2 billion in stock.
SegNet
SegNet is an encoder-decoder neural network for pixel-wise image segmentation proposed in 2015 by Vijay Badrinarayanan, Ankur Handa, and Roberto Cipolla of the University of Cambridge.
Select Star (company)
Select Star is a San Francisco data catalog company founded in 2020 by Shinji Kim, offering automated lineage, popularity metrics, and Ask AI; acquired by Snowflake in November 2025.
Selection (genetic algorithm)
Selection is the genetic algorithm operator that chooses which individuals become parents of the next generation, favoring fitter individuals through schemes such as roulette wheel, rank, and tournament selection.
Selection algorithm
A selection algorithm finds the k-th smallest, or i-th smallest, element of a collection of n unordered elements without sorting, studied through the minimum number of comparisons needed.
Selection sort
Selection sort is a simple in-place sorting algorithm that repeatedly selects the smallest remaining element and swaps it into position, always taking Θ(n²) comparisons and at most n swaps.
Selective search
Selective search is a region proposal algorithm for object recognition, introduced by Uijlings and colleagues in 2013, that generates class-independent candidate locations by hierarchically merging image regions, later serving as the proposal engine of R-CNN.
Self-attention model
Self-attention is a neural network mechanism that computes each sequence element's representation by comparing it with every other element, capturing long-range dependencies without recurrence as the core of the Transformer.
Self-attention network
A self-attention network is a neural network architecture that lets every position in a sequence attend directly to every other position, the core computation of Transformer models in machine translation.
Self-balancing binary search tree
A self-balancing binary search tree, also called a balanced or height-balanced tree, uses rotations and balance rules such as those of AVL and red-black trees to keep operations logarithmic.
Self-distillation
Self-distillation, also known as policy self-distillation, is a machine learning method in which a neural network imitates its own predictions, introduced as Born-Again Networks in 2018.
Self-driving laboratory
A self-driving laboratory, also called an autonomous laboratory, is a lab where robots run experiments and AI algorithms choose which experiment to test next, speeding up materials and molecule discovery.
Self-labeling (machine learning)
Self-labeling is a family of semi-supervised machine learning methods in which a model assigns pseudo-labels to unlabeled data and trains on them as ground truth, iterating; FixMatch reached 94.93% accuracy on CIFAR-10 with only 250 labels.
Self-organizing map
A self-organizing map, or Kohonen map, is an unsupervised neural network, introduced by Teuvo Kohonen in 1982, that maps high-dimensional data onto a grid so similar inputs neighbor each other.
Self-service analytics
Self-service analytics (SSA, self-service BI) is business intelligence that lets nontechnical users access, analyze and visualize data via tools like Tableau and Power BI, supported by governed data pipelines and central IT.
Self-supervised monocular depth estimation
Self-supervised monocular depth estimation is a computer vision method that trains a network to predict per-pixel depth from a single image without ground-truth labels, using photometric consistency.
Semantic clustering
Semantic clustering is a text-mining method that groups words, sentences, or documents by meaning, converting each into an embedding vector and clustering items whose vectors are close, unlike word-count-based methods.
Semantic layer
Semantic layer, also called metrics layer or business layer: a governed abstraction between raw data and BI tools, AI models, and agents defining metrics, dimensions, and relationships once; traces to the BusinessObjects Universe of 1990; the Open Semantic Interchange specification (now Apache Ossie, incubating) was published in 2026.
Semantic network
A semantic network, also known as a semantic net, is a knowledge representation in which concepts are graph nodes and semantic relations are labeled edges.
Semantic parsing
Semantic parsing is the natural language processing task of translating utterances into machine-readable meaning representations, so users can query databases without a formal query language.
Semantic role labeling
Semantic role labeling (SRL) is a natural language processing method that identifies verbs in a sentence and assigns roles such as agent or patient to their arguments.
Semantic Scholar
Semantic Scholar is a free, AI-powered search engine for scientific literature developed by the Allen Institute for Artificial Intelligence, launched in 2015 and now indexing over 200 million papers.
Semantic segmentation
Semantic segmentation is a computer vision task that assigns a semantic class to every pixel of an image, used in medical imaging, autonomous vehicles, and remote sensing.
Semantic similarity
Semantic similarity is a measure of how closely two linguistic items, such as words, sentences, or documents, match in meaning, and is central to natural language processing.
Semantic Textual Similarity
Semantic Textual Similarity (STS) is the natural language processing task of scoring how semantically equivalent two sentences are, on a scale from 0 to 5.
SemEval
SemEval (International Workshop on Semantic Evaluation) is a natural language processing workshop series, begun in 1998 as SENSEVAL for word sense disambiguation, where teams compete on shared evaluation tasks.
Semi-supervised clustering
Semi-supervised clustering is a machine learning method that groups data using must-link and cannot-link constraints or labeled seeds; canonical algorithms include COP-K-Means, seeded k-means, and MPCK-Means.
Semi-supervised learning
Semi-supervised learning is a machine learning approach that trains on a small labeled dataset together with a large unlabeled one, improving accuracy when labels are expensive to obtain.
Sensor fusion
Sensor fusion is a computational method that combines measurements from multiple sensors, such as GPS and inertial sensors, to produce more accurate and reliable estimates of position, orientation, and other states.