buildingSMART Data Dictionary (bSDD) is a service provided by buildingSMART which offers free data dictionaries for the international standardization of construction planning. The structure of bSDD was defined by the Nonprofit organization Buildingsmart and is used to describe objects and their attributes in a BIM process. == Aim == The aim of bSDD is to enable architects and planners to exchange and share building data across different specialists and language boundaries and thus avoid misunderstandings caused by different interpretations of terms. The bSDD standard extends the more general IFC. Software developers can access and use the dictionaries. In May 2025 over 300 dictionaries are available, including IFC, extensions to it such as Airport Domain IFC extension module or classification systems like Uniclass. == Structure == The main structural parts of bSDD are: Dictionary: A dictionary is a collection of classes: Class: A class describes the various object types, such as Bag drop or Baggage conveyor in airport planning. A class contains properties: Property: A property describes a part of a class, e.g. color or weight. Related properties are organized in a group: GroupOfProperties: A group organizes related properties, e.g. environmental properties or electrical properties. == Creating and managing a directory == Every dictionary in bSDD must be published in the name of a registered organization. As soon as the content is activated, it receives an unchangeable URI. This means that the content remains permanently in bSDD and cannot be deleted - this ensures stable use of the dictionary. It is only possible to change the status to inactive if it is no longer to be used - however, the dictionary remains permanently.
List of large language models
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. == List == For the training cost column, 1 petaFLOP-day equals 1 petaFLOP/sec × 1 day, or 8.64×1019 FLOP (floating point operations). Only the cost of the largest model is shown. The number of parameters is measured in billions, and the training cost is measured in petaFLOP-days. === 2018 === === 2019 === === 2020 === === 2021 === === 2022 === === 2023 === === 2024 === === 2025 === === 2026 ===
AI Content Generators Reviews: What Actually Works in 2026
In search of the best AI content generator? An AI content generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI content generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
How to Choose an AI Pair Programmer
In search of the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Peter Flach
Pieter "Peter" Adriaan Flach (born 8 April 1961, Sneek) is a Dutch computer scientist and a Professor of Artificial Intelligence in the Department of Computer Science at the University of Bristol. He is author of the acclaimed Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012). == Education == Flach received an MSc Electrical Engineering from Universiteit Twente in 1987 and a PhD in Computer Science from Tilburg University in 1995. == Research == Flach's research interests are in data mining and machine learning.
Landmark point
In morphometrics, landmark point or shortly landmark is a point in a shape object in which correspondences between and within the populations of the object are preserved. In other disciplines, landmarks may be known as vertices, anchor points, control points, sites, profile points, 'sampling' points, nodes, markers, fiducial markers, etc. Landmarks can be defined either manually by experts or automatically by a computer program. There are three basic types of landmarks: anatomical landmarks, mathematical landmarks or pseudo-landmarks. An anatomical landmark is a biologically-meaningful point in an organism. Usually experts define anatomical points to ensure their correspondences within the same species. Examples of anatomical landmark in shape of a skull are the eye corner, tip of the nose, jaw, etc. Anatomical landmarks determine homologous parts of an organism, which share a common ancestry. Mathematical landmarks are points in a shape that are located according to some mathematical or geometrical property, for instance, a high curvature point or an extreme point. A computer program usually determines mathematical landmarks used for an automatic pattern recognition. Pseudo-landmarks are constructed points located between anatomical or mathematical landmarks. A typical example is an equally spaced set of points between two anatomical landmarks to get more sample points from a shape. Pseudo-landmarks are useful during shape matching, when the matching process requires a large number of points.
Yun Sing Koh
Yun Sing Koh (born 1978) is a New Zealand computer science academic, and is a full professor at the University of Auckland, specialising in machine learning and artificial intelligence. She is a co-director of the Centre of Machine Learning for Social Good, and the Advanced Machine Learning and Data Analytics Research (MARS) Lab at Auckland. == Academic career == Koh earned a Bachelor of Science with Honours and a Master of Software Engineering at the University of Malaya. She then completed a PhD titled Generating sporadic association rules at the University of Otago in 2007. Koh joined the faculty of the University of Auckland in 2010, rising to full professor. As of 2024, she is director of the Centre of Machine Learning for Social Good at Auckland, alongside Gillian Dobbie and Daniel Wilson, and is director of the Master of AI course at the university. Koh also co-directs the Advanced Machine Learning and Data Analytics Research (MARS) Lab. Koh's research covers machine learning and artificial intelligence. She is especially interested in designing machine learning algorithms for data streams, and has led research using AI systems to identify individual stoats for pest population research. In 2018 she was awarded a Marsden grant for a research project "An Adaptive Predictive System for Life-long Learning on Data Streams", and has been part of three MBIE projects. In 2025 the stoat identification project Koh co-leads with Daniel Wilson was awarded $1 million per annum by the MBIE Smart Ideas fund. Koh was a finalist in the AI in Climate section of the Women in AI Australia and New Zealand Awards in 2022. She was a 2023 Fellow at the United States National Science Foundation-funded Convergence Research (CORE) Institute. Koh has chaired a number of sessions at international conferences on data mining. In March 2026 it was announced that Koh would be a member of the New Zealand Human Rights Commission's Expert Advisory Group on Artificial Intelligence, Emerging Digital Technologies and Human Rights. == Selected works == Philippe Fournier-Viger; Jerry Chun-Wei Lin; Rage Uday Kiran; Yun Sing Koh; Rincy Thomas (2017). "A Survey of Sequential Pattern Mining". Data Science and Pattern Recognition. 1 (1): 54–77. Wikidata Q138719481. Yun Sing Koh; Nathan Rountree; Richard O’Keefe (1 April 2006). "Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse". International Journal of Data Warehousing and Mining (in Ndonga). 2 (2): 38–54. doi:10.4018/JDWM.2006040102. ISSN 1548-3924. Wikidata Q125185222. Russel Pears; Sripirakas Sakthithasan; Yun Sing Koh (11 January 2014). "Detecting concept change in dynamic data streams". Machine Learning. 97 (3): 259–293. doi:10.1007/S10994-013-5433-9. ISSN 1573-0565. Zbl 1319.68186. Wikidata Q125185156. David Tse Jung Huang; Yun Sing Koh; Gillian Dobbie; Russel Pears (December 2014), Detecting Volatility Shift in Data Streams, Institute of Electrical and Electronics Engineers, doi:10.1109/ICDM.2014.50, Wikidata Q125185151 Sidney Tsang; Yun Sing Koh; Gillian Dobbie (2011). "RP-Tree: Rare Pattern Tree Mining". Lecture Notes in Computer Science: 277–288. doi:10.1007/978-3-642-23544-3_21. ISSN 0302-9743. Wikidata Q125185206. Yun Sing Koh; Sri Devi Ravana (24 May 2016). "Unsupervised Rare Pattern Mining". ACM Transactions on Knowledge Discovery from Data. 10 (4): 1–29. doi:10.1145/2898359. ISSN 1556-4681. Wikidata Q125185136. Jack Julian; Yun Sing Koh; Albert Bifet (1 October 2025), Building adaptive knowledge bases for evolving continual learning models (PDF), vol. 1, doi:10.1038/S44387-025-00028-4, Wikidata Q138719496