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ISO/IEC JTC 1/SC 24

ISO/IEC JTC 1/SC 24 Computer graphics, image processing and environmental data representation is a standardization subcommittee of the joint subcommittee ISO/IEC JTC 1 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of computer graphics, image processing, and environmental data representation. The international secretariat of ISO/IEC JTC 1/SC 24 is the British Standards Institute (BSI) located in the United Kingdom. == History == ISO/IEC JTC 1/SC 24 was formed in 1987 from ISO/TC 97 as a result of Resolution 21 at the ISO/IEC JTC 1 plenary. The group's origins began in computer graphics, the standardization of which was originally under ISO/IEC JTC 1/SC 21/WG 2. However, when ISO/IEC JTC 1/SC 24 was created, the standardization activity of ISO/IEC JTC 1/SC 21/WG 2 was carried over to the new subcommittee. The initial five working groups of ISO/IEC JTC 1/SC 24 were titled, “Architecture,” “Application programming interfaces,” “Metafiles and interfaces,” “Language bindings,” and “Validation, testing and registration.” The work of ISO/IEC JTC 1/SC 24 began with the Graphical Kernel System (GKS), which was adopted from ISO/IEC JTC 1/SC 21/WG 2. However, since GKS only addressed 2D functionality, attention turned to the standardization of 3D functionality. This resulted in two standards being published: GKS-3D in 1988 and PHIGS in 1989, both of which addressed 3D functionality. Since 1991, ISO/IEC JTC 1/SC 24 has held plenaries in a number of countries, including the Netherlands, Germany, United States, France, Canada, Japan, Sweden, Korea, United Kingdom, Australia, and Czech Republic. == Scope == The scope of ISO/IEC JTC 1/SC 24 is the “Standardization of interfaces for information technology based applications relating to”: Computer graphics Image processing Environmental data representation Support for the Mixed and Augmented Reality (MAR) Interaction with, and visual representation of, information Included are the following related areas: Modeling and simulation and related reference models Virtual reality with accompanying augmented reality/augmented virtuality aspects and related reference models Application program interfaces Functional specifications Representation models Interchange formats, encodings and their specifications, including metafiles Device interfaces Testing methods Registration procedures Presentation and support for creation of multimedia, hypermedia, and mixed reality documents Excluded are the following areas: Character and image coding Coding of multimedia, hypermedia, and mixed reality document interchange formats JTC 1 work in user system interfaces and document presentation ISO/TC 207 work on ISO 14000 environment management, ISO/TC 211 work on geographic information and geomatics Software environments as described by ISO/IEC JTC 1/SC 22 == Structure == ISO/IEC JTC 1/SC 24 is made up of four active working groups, each of which carries out specific tasks in standards development within the field of computer graphics, image processing and environmental data representation, together with ITU-T Study Group 16. As a response to changing standardization needs, working groups of ISO/IEC JTC 1/SC 24 can be disbanded if their area of work is no longer applicable, or established if new working areas arise. The focus of each working group is described in the group's terms of reference. Active working groups of ISO/IEC JTC 1/SC 24 are: == Collaborations == ISO/IEC JTC 1/SC 24 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC, in order to avoid conflicting or duplicative work. Organizations internal to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: ISO/IEC JTC 1/WG 7, Sensor Networks ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 32, Data management and interchange ISO/TAG 14, Imagery and technology ISO/TC 130, Graphic Technology ISO/TC 184/SC 4, Industrial data ISO/TC 211, Geographic information/Geomatics ISO/TC 215, Health informatics IEC TC 100, Audio, video and multimedia system and equipment Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: Defence Geospatial Information Working Group (DGIWG) Digital Imaging and Communications in Medicine (DICOM) International Hydrographic Organization (IHO) The Khronos Group NATO - Joint Intelligence Surveillance and Reconnaissance Capability Group (JISRCG) OMG Robotics DTF Open CGM Open Geospatial Consortium (OGC) SEDRIS Organization Simulation Interoperability Standards Organization (SISO) US National Imagery Transmission Format Standard (NITFS) Technical Board (US NTB) Web3D Consortium World Intellectual Property Organization (WIPO) World Wide Web Consortium (W3C) == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 11 "P" (participating) members of ISO/IEC JTC 1/SC 24 are: Australia, China, Egypt, France, India, Japan, Republic of Korea, Portugal, Russian Federation, United Kingdom, and United States. The 22 "O" (observer) members of ISO/IEC JTC 1/SC 24 are: Argentina, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Cuba, Czech Republic, Finland, Ghana, Hungary, Iceland, Indonesia, Islamic Republic of Iran, Italy, Kazakhstan, Malaysia, Poland, Romania, Serbia, Slovakia, Switzerland, and Thailand. == Published standards == ISO/IEC JTC 1/SC 24 currently has 80 published standards under their direct responsibility within the field of computer graphics, image processing, and environmental data representation, including:
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How to Choose an AI Copywriting Tool

Trying to pick the best AI copywriting tool? An AI copywriting tool is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI copywriting tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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AI Voice Assistants: Free vs Paid (2026)

Shopping for the best AI voice assistant? An AI voice assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI voice assistant slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Top 10 AI Photo Editors Compared (2026)

Looking for the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Pythia (machine learning)

Pythia is an ancient text restoration model that recovers missing characters from damaged text input using deep neural networks. It was created by Yannis Assael, Thea Sommerschield, and Jonathan Prag, researchers from Google DeepMind and the University of Oxford. To study the society and the history of ancient civilisations, ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts. Hundreds of thousands of these texts, known as inscriptions, have survived to our day, but are often damaged over the centuries. Illegible parts of the text must then be restored by specialists, called epigraphists, in order to extract meaningful information from the text and use it to expand our knowledge of the context in which the text was written. Pythia takes as input the damaged text, and is trained to return hypothesised restorations of ancient Greek inscriptions, working as an assistive aid for ancient historians. Its neural network architecture works at both the character- and word-level, thereby effectively handling long-term context information, and dealing efficiently with incomplete word representations. Pythia is applicable to any discipline dealing with ancient texts (philology, papyrology, codicology) and can work in any language (ancient or modern).
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Victor Yngve

Victor Huse Yngve (July 5, 1920 – January 15, 2012) was a professor of linguistics at the University of Chicago and the Massachusetts Institute of Technology (1953-1965). He was one of the earliest researchers in computational linguistics and natural language processing, the use of computers to analyze and process languages. He created the first program to produce random but well-formed output sentences, given a text, a children's book called Engineer Small and the Little Train. Most importantly, he showed in computer processing terms why the human brain can only process sentences of a certain kind of complexity, ones that do not exceed a "depth limit" (which has nothing to do with length) of the kind established independently by George Miller with his depth limit of "seven plus or minus two" sentence constituents in memory at any given time. Yngve was also the author of COMIT, the first string processing language (compare SNOBOL, TRAC, and Perl), which was developed on the IBM 700/7000 series computers by Yngve and collaborators at MIT from 1957-1965. Yngve created the language for supporting computerized research in the field of linguistics, and more specifically, the area of machine translation for natural language processing. In his 1970 paper "On Getting a Word in Edgewise", Yngve coined the term 'back channel behavior' to describe the conversational phenomenon that to this day is known in the linguistic literature as back-channeling. According to Duncan, Yngve's paper also suggested the term turn-taking, independently of Erving Goffman (Duncan, 1972: 283).
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Mark Heimann

Mark A. Heimann is an American chess grandmaster and machine learning researcher. == Chess career == Heimann began playing chess at the age of 5 after his father bought him and his twin brother Alexander a chess set. He then won several national grade-level championships as well as the Pennsylvania and Ohio state championships in middle school and high school. In October 2007, he was ranked as the national #2 under-14 player, only behind future grandmaster Marc Tyler Arnold. In the February 2008 national rankings, he moved up to being the top-ranked under-14 player. In December 2012, he played for Washington University St. Louis' "A" team in the Pan-American Intercollegiate Chess Championships, where he was the second-most successful player, recording 4 wins, 1 draw, and 1 loss. The university's team also won the Division II championship title. In three tournaments between September and December 2022, Heimann earned three international master title norms, earning the international master title at the age of 29. In November 2024, he scored a GM norm at the U.S. Masters Chess Championship. He finished the event in joint-6th place. The following week, at the Saint Louis Masters tournament, he earned his final grandmaster norm and crossed 2500 in live rating, achieving the Grandmaster title. It was formally awarded to him in April 2025. == Research career == He obtained a bachelor's degree from Washington University in St. Louis in the School of Arts and Sciences and got his PhD from the University of Michigan. He is a machine learning researcher at Lawrence Livermore National Laboratory. == Personal life == Outside of chess and research, he also plays several instruments and is a competitive powerlifter.
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Sasha Luccioni

Alexandra Sasha Luccioni (née Vorobyova; born 1990) is a computer scientist specializing in the intersection of artificial intelligence (AI) and climate change. Her work focuses on quantifying the environmental impact of AI technologies and promoting sustainable practices in machine learning development. == Early life and education == Alexandra Sasha Vorobyova was born in the Ukrainian Soviet Socialist Republic in 1990. When she was four years old, her family relocated to Ontario, Canada. Her interest in science is influenced by her family's history; her mother, grandmother, and great-grandmother all pursued careers in scientific fields. Luccioni earned a B.A. in language science from University of Paris III: Sorbonne Nouvelle in 2010. Subsequently, she completed a M.S. in cognitive science, with a minor in natural language processing, at École normale supérieure in Paris in 2012. Luccioni obtained her PhD in cognitive computing from Université du Québec à Montréal (UQAM) in 2018. == Career == Luccioni began her professional career at Nuance Communications in 2017, where she focused on natural language processing (NLP) and machine learning (ML) techniques to enhance conversational agents. She then joined Morgan Stanley’s AI/ML Center of Excellence in 2018, working on explainable artificial intelligence (AI) and decision-making systems. In 2019, she became a postdoctoral researcher at Université de Montréal and Mila, collaborating with computer scientist Yoshua Bengio on a project titled This Climate Does Not Exist. This initiative used generative adversarial networks to visualize the effects of climate change. During this time, she also contributed to integrating fairness and accountability into machine learning education at Mila. Luccioni briefly worked with the United Nations Global Pulse in 2021, developing tools to monitor COVID-19 misinformation. Later that year, she joined Hugging Face as a research scientist. Her role includes quantifying the carbon footprint of AI systems, co-chairing the carbon working group in the Big Science project, and advancing responsible machine learning practices. She helped create "CodeCarbon," an open-source software tool that estimates the carbon emissions produced during the training and operation of machine learning models. In addition to her research, she has developed tools to measure the environmental impact of AI models, communicated findings through media engagements, and presented at international conferences, including a TED Talk. In 2024, she was listed on BBC 100 Women and Time 100 AI.
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Wide-column store

A wide-column store (or extensible record store) is a type of NoSQL database. It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. Google's Bigtable is one of the prototypical examples of a wide-column store. == Wide-column stores versus columnar databases == Wide-column stores such as Bigtable and Apache Cassandra are not column stores in the original sense of the term, since their two-level structures do not use a columnar data layout. In genuine column stores, a columnar data layout is adopted such that each column is stored separately on disk. Wide-column stores do often support the notion of column families that are stored separately. However, each such column family typically contains multiple columns that are used together, similar to traditional relational database tables. Within a given column family, all data is stored in a row-by-row fashion, such that the columns for a given row are stored together, rather than each column being stored separately. Wide-column stores that support column families are also known as column family databases. == Notable examples == Notable wide-column stores include: Apache Accumulo Apache Cassandra Apache HBase Bigtable DataStax Enterprise (uses Apache Cassandra) DataStax Astra DB (uses Apache Cassandra) Hypertable Azure Tables ScyllaDB
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Eduard Hovy

Eduard Hovy is a Research Professor in the Language Technologies Institute at Carnegie Mellon University. He is one of the original 17 Fellows of the Association for Computational Linguistics. == Biography == Eduard Hovy received M.S. (December 1982) and Ph.D. (May 1987) degrees in Computer Science from Yale University. He was awarded honorary doctorates from the National University of Distance Education (UNED) in Madrid in 2013 and the University of Antwerp in 2015.
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Cognitive Technologies

Cognitive Technologies is a Russian software corporation that develops corporate business applications, AI-based advanced driver assistance systems. Founded in 1993 in Moscow (Russia), the company has offices in Eastern Europe, with R&D Centers in Russia. == History == Cognitive Technologies was founded in 1993 by Olga Uskova and Vladimir Arlazarov. The first employees previously worked in the team that developed the first world computer chess champion "Kaissa". The first programs developed by Cognitive Technologies were optical image and character recognition software – Tiger and CuneiForm. In February 2015 Cognitive Technologies and Kamaz, Russian Dakar Rally-winning truck manufacturer, started working on the self-driving Kamaz truck project. The first field tests took place in June 2015. In 2015 Andrey Chernogorov was appointed CEO of the company. == Products == Cognitive Technologies develops business application software and self-driving vehicle artificial intelligence. The main products are: C-pilot, AI-based ADAS E1 Evfrat – electronic workflow system CognitiveLot – e-purchasing systems == Cooperation with global companies == Under the contract signed between Cognitive Technologies and Hewlett-Packard, all scanners sold in Russia had text recognition software developed by Cognitive Technologies. It was the first contract with HP for an Eastern European company. Afterwards, Cognitive Technologies signed OEM contracts and business agreements with several global IT-companies, including IBM, Canon, Corel, Samsung, Xerox, Brother, Epson, and Olivetti. In 1998 Cognitive Technologies became the first company in Eastern Europe to get the Oracle Complementary Software Provider status. In 2001 Cognitive Technologies sold its Russian language speech corpus to Intel. In 2010 Cognitive Technologies sold its text parsing module to Yandex. The company also signed an agreement with NVIDIA join efforts in the development of intelligent document recognition technologies. == Self-driving car project == The system developed by Cognitive Technologies does not require building smart cities and smart roads equipped with multiple sensors – it works the opposite way, trying to understand the situation on the road like humans do. The system uses a video camera like a driver who uses his eyes, analyzing the information and focusing on the relevant data. For this purpose the system uses a special type of computer vision – foveal computer vision. Only 5–7% of the data gathered by the video cameras and sensors is processed by the system as relevant. The prototype is being tested in Russia on rough roads, on roads without marking, with the goal to prepare the system for work in difficult situations and on bad roads all around the world. == C-Pilot ADAS project == In August 2016 Cognitive Technologies started its own ADAS development project C-Pilot for ground transport control automation. == Self-driving tractors and harvesters project == The experts from Cognitive Technologies claim that the system will track stones, poles, and other obstacles that might be dangerous for the vehicles. This data will enable the engineers to develop an interactive field map, with GPS coordinates for stones and other obstacles. Eventually, this will result in an alteration of the harvester's movement pattern preventing it from running into stones or other objects that may inflict damage. Harvesters will work autonomously on the field, on the territory that is narrowed by radio beacons. == Present international activities == In 2016 Cognitive Technologies has joined the international community OpenPower Foundation, a consortium of open source solutions to developers based on POWER technology from IBM, which includes the world's leading IT map of Google, NVidia, Mellanox, etc. Within the consortium Cognitive Technologies is the initiator of forming of an international working group to develop a single software standard for the self-driving vehicle control. == Awards == In 2016, the leading Russian business newspaper Kommersant, announced that Cognitive Technologies is the TOP-2 Russian software company. TOP-6 Russian software company in 2015 according to Russoft TOP-500 biggest Russian companies according to RBC TOP-2 company of the Russian EDMS market in 2014 according to IDC TOP-20 Russian biggest IT-companies in 2013 according to Cnews Analytics
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Regina Barzilay

Regina Barzilay (Hebrew: רגינה ברזילי; born 1970) is an Israeli-American computer scientist. She is a professor at the Massachusetts Institute of Technology and a faculty lead for artificial intelligence at the MIT Jameel Clinic. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology. == Early life and education == Barzilay was born in Chișinău, Moldova and emigrated to Israel with her parents at the age of 20. She received bachelor's and master's degrees from Ben-Gurion University of the Negev in 1993 and 1998, respectively. She obtained a PhD in computer science from Columbia University in 2003 for research supervised by Kathleen McKeown. == Career and research == After her PhD, she spent a year as a postdoctoral researcher at Cornell University. She was appointed as Delta Electronics Professor of Electrical Engineering and Computer Science at MIT in 2016. She was diagnosed with breast cancer in 2014, which prompted her to conduct research in oncology. Barzilay won the MacArthur Fellowship in 2017. For her doctoral dissertation at Columbia University, she led the development of Newsblaster, which recognized stories from different news sources as being about the same basic subject, and then paraphrased elements from the stories to create a summary. In computational linguistics, Barzilay created algorithms that learned annotations from common languages (i.e. English) to analyze less understood languages. Prompted by her experience with breast cancer, Barzilay is applying machine learning to oncology. She is collaborating with physicians and students to devise deep learning models that utilize images, text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Frontline Documentary Following her battle with breast cancer in 2014, and her researching into applying artificial intelligence to improve early detection methods, she collaborated with Dr. Connie Lehman at Massachusetts General Hospital. While there Barzilay developed an AI-based system capable of predicting the likelihood of breast cancer up to five years in advance. The system leverages deep learning techniques to analyze mammograms and diagnostic notes, surpassing traditional pattern recognition by human radiologists. This breakthrough, while still in development, has the potential to significantly enhance early diagnosis and treatment outcomes. [1] Barzilay's work in this area was featured in the FRONTLINE documentary In the Age of AI, which explores the broader impact of artificial intelligence on society. === MIT Jameel Clinic === In 2018, Barzilay was appointed faculty lead for AI at the new MIT Jameel Clinic, a research center in the field of AI health sciences, including disease detection, drug discovery, and the development of medical devices. In 2020, she was part of the team—with fellow MIT Jameel Clinic faculty lead Professor James J. Collins—that announced the discovery through deep learning of halicin, the first new antibiotic compound for 30 years, which kills over 35 powerful bacteria, including antimicrobial-resistant tuberculosis, the superbug C. difficile, and two of the World Health Organization's top-three most deadly bacteria. In 2020, Collins, Barzilay and the MIT Jameel Clinic were also awarded funding through The Audacious Project to expand on the discovery of halicin in using AI to respond to the antibiotic resistance crisis through the development of new classes of antibiotics. == Awards and recognition == In 2017, Barzilay won the MacArthur Fellowship, known as the "Genius Grant", for "developing machine learning methods that enable computers to process and analyze vast amounts of human language data." She is also a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. Her teaching has also been recognized by MIT as she won the Jamieson Teaching Award in 2016. She was nominated an AAAI Fellow in 2018 by the Association for the Advancement of Artificial Intelligence. In 2020, she became the first recipient of the $1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. In 2023, she was elected to the National Academy of Medicine and the National Academy of Engineering.
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Empirical dynamic modeling

Empirical dynamic modeling (EDM) is a framework for analysis and prediction of nonlinear dynamical systems. Applications include population dynamics, ecosystem service, medicine, neuroscience, dynamical systems, geophysics, and human-computer interaction. EDM was originally developed by Robert May and George Sugihara. It can be considered a methodology for data modeling, predictive analytics, dynamical system analysis, machine learning and time series analysis. == Description == Mathematical models have tremendous power to describe observations of real-world systems. They are routinely used to test hypothesis, explain mechanisms and predict future outcomes. However, real-world systems are often nonlinear and multidimensional, in some instances rendering explicit equation-based modeling problematic. Empirical models, which infer patterns and associations from the data instead of using hypothesized equations, represent a natural and flexible framework for modeling complex dynamics. Donald DeAngelis and Simeon Yurek illustrated that canonical statistical models are ill-posed when applied to nonlinear dynamical systems. A hallmark of nonlinear dynamics is state-dependence: system states are related to previous states governing transition from one state to another. EDM operates in this space, the multidimensional state-space of system dynamics rather than on one-dimensional observational time series. EDM does not presume relationships among states, for example, a functional dependence, but projects future states from localised, neighboring states. EDM is thus a state-space, nearest-neighbors paradigm where system dynamics are inferred from states derived from observational time series. This provides a model-free representation of the system naturally encompassing nonlinear dynamics. A cornerstone of EDM is recognition that time series observed from a dynamical system can be transformed into higher-dimensional state-spaces by time-delay embedding with Takens's theorem. The state-space models are evaluated based on in-sample fidelity to observations, conventionally with Pearson correlation between predictions and observations. == Methods == Primary EDM algorithms include Simplex projection, Sequential locally weighted global linear maps (S-Map) projection, Multivariate embedding in Simplex or S-Map, Convergent cross mapping (CCM), and Multiview Embeding, described below. Nearest neighbors are found according to: NN ( y , X , k ) = ‖ X N i E − y ‖ ≤ ‖ X N j E − y ‖ if 1 ≤ i ≤ j ≤ k {\displaystyle {\text{NN}}(y,X,k)=\|X_{N_{i}}^{E}-y\|\leq \|X_{N_{j}}^{E}-y\|{\text{ if }}1\leq i\leq j\leq k} === Simplex === Simplex projection is a nearest neighbor projection. It locates the k {\displaystyle k} nearest neighbors to the location in the state-space from which a prediction is desired. To minimize the number of free parameters k {\displaystyle k} is typically set to E + 1 {\displaystyle E+1} defining an E + 1 {\displaystyle E+1} dimensional simplex in the state-space. The prediction is computed as the average of the weighted phase-space simplex projected T p {\displaystyle Tp} points ahead. Each neighbor is weighted proportional to their distance to the projection origin vector in the state-space. Find k {\displaystyle k} nearest neighbor: N k ← NN ( y , X , k ) {\displaystyle N_{k}\gets {\text{NN}}(y,X,k)} Define the distance scale: d ← ‖ X N 1 E − y ‖ {\displaystyle d\gets \|X_{N_{1}}^{E}-y\|} Compute weights: For{ i = 1 , … , k {\displaystyle i=1,\dots ,k} } : w i ← exp ⁡ ( − ‖ X N i E − y ‖ / d ) {\displaystyle w_{i}\gets \exp(-\|X_{N_{i}}^{E}-y\|/d)} Average of state-space simplex: y ^ ← ∑ i = 1 k ( w i X N i + T p ) / ∑ i = 1 k w i {\displaystyle {\hat {y}}\gets \sum _{i=1}^{k}\left(w_{i}X_{N_{i}+T_{p}}\right)/\sum _{i=1}^{k}w_{i}} === S-Map === S-Map extends the state-space prediction in Simplex from an average of the E + 1 {\displaystyle E+1} nearest neighbors to a linear regression fit to all neighbors, but localised with an exponential decay kernel. The exponential localisation function is F ( θ ) = exp ( − θ d / D ) {\displaystyle F(\theta )={\text{exp}}(-\theta d/D)} , where d {\displaystyle d} is the neighbor distance and D {\displaystyle D} the mean distance. In this way, depending on the value of θ {\displaystyle \theta } , neighbors close to the prediction origin point have a higher weight than those further from it, such that a local linear approximation to the nonlinear system is reasonable. This localisation ability allows one to identify an optimal local scale, in-effect quantifying the degree of state dependence, and hence nonlinearity of the system. Another feature of S-Map is that for a properly fit model, the regression coefficients between variables have been shown to approximate the gradient (directional derivative) of variables along the manifold. These Jacobians represent the time-varying interaction strengths between system variables. Find k {\displaystyle k} nearest neighbor: N ← NN ( y , X , k ) {\displaystyle N\gets {\text{NN}}(y,X,k)} Sum of distances: D ← 1 k ∑ i = 1 k ‖ X N i E − y ‖ {\displaystyle D\gets {\frac {1}{k}}\sum _{i=1}^{k}\|X_{N_{i}}^{E}-y\|} Compute weights: For{ i = 1 , … , k {\displaystyle i=1,\dots ,k} } : w i ← exp ⁡ ( − θ ‖ X N i E − y ‖ / D ) {\displaystyle w_{i}\gets \exp(-\theta \|X_{N_{i}}^{E}-y\|/D)} Reweighting matrix: W ← diag ( w i ) {\displaystyle W\gets {\text{diag}}(w_{i})} Design matrix: A ← [ 1 X N 1 X N 1 − 1 … X N 1 − E + 1 1 X N 2 X N 2 − 1 … X N 2 − E + 1 ⋮ ⋮ ⋮ ⋱ ⋮ 1 X N k X N k − 1 … X N k − E + 1 ] {\displaystyle A\gets {\begin{bmatrix}1&X_{N_{1}}&X_{N_{1}-1}&\dots &X_{N_{1}-E+1}\\1&X_{N_{2}}&X_{N_{2}-1}&\dots &X_{N_{2}-E+1}\\\vdots &\vdots &\vdots &\ddots &\vdots \\1&X_{N_{k}}&X_{N_{k}-1}&\dots &X_{N_{k}-E+1}\end{bmatrix}}} Weighted design matrix: A ← W A {\displaystyle A\gets WA} Response vector at T p {\displaystyle Tp} : b ← [ X N 1 + T p X N 2 + T p ⋮ X N k + T p ] {\displaystyle b\gets {\begin{bmatrix}X_{N_{1}+T_{p}}\\X_{N_{2}+T_{p}}\\\vdots \\X_{N_{k}+T_{p}}\end{bmatrix}}} Weighted response vector: b ← W b {\displaystyle b\gets Wb} Least squares solution (SVD): c ^ ← argmin c ‖ A c − b ‖ 2 2 {\displaystyle {\hat {c}}\gets {\text{argmin}}_{c}\|Ac-b\|_{2}^{2}} Local linear model c ^ {\displaystyle {\hat {c}}} is prediction: y ^ ← c ^ 0 + ∑ i = 1 E c ^ i y i {\displaystyle {\hat {y}}\gets {\hat {c}}_{0}+\sum _{i=1}^{E}{\hat {c}}_{i}y_{i}} === Multivariate Embedding === Multivariate Embedding recognizes that time-delay embeddings are not the only valid state-space construction. In Simplex and S-Map one can generate a state-space from observational vectors, or time-delay embeddings of a single observational time series, or both. === Convergent Cross Mapping === Convergent cross mapping (CCM) leverages a corollary to the Generalized Takens Theorem that it should be possible to cross predict or cross map between variables observed from the same system. Suppose that in some dynamical system involving variables X {\displaystyle X} and Y {\displaystyle Y} , X {\displaystyle X} causes Y {\displaystyle Y} . Since X {\displaystyle X} and Y {\displaystyle Y} belong to the same dynamical system, their reconstructions (via embeddings) M x {\displaystyle M_{x}} , and M y {\displaystyle M_{y}} , also map to the same system. The causal variable X {\displaystyle X} leaves a signature on the affected variable Y {\displaystyle Y} , and consequently, the reconstructed states based on Y {\displaystyle Y} can be used to cross predict values of X {\displaystyle X} . CCM leverages this property to infer causality by predicting X {\displaystyle X} using the M y {\displaystyle M_{y}} library of points (or vice versa for the other direction of causality), while assessing improvements in cross map predictability as larger and larger random samplings of M y {\displaystyle M_{y}} are used. If the prediction skill of X {\displaystyle X} increases and saturates as the entire M y {\displaystyle M_{y}} is used, this provides evidence that X {\displaystyle X} is casually influencing Y {\displaystyle Y} . === Multiview Embedding === Multiview Embedding is a Dimensionality reduction technique where a large number of state-space time series vectors are combitorially assessed towards maximal model predictability. == Extensions == Extensions to EDM techniques include: Generalized Theorems for Nonlinear State Space Reconstruction Extended Convergent Cross Mapping Dynamic stability S-Map regularization Visual analytics with EDM Convergent Cross Sorting Expert system with EDM hybrid Sliding windows based on the extended convergent cross-mapping Empirical Mode Modeling Accounting for missing data and variable step sizes Accounting for observation noise Hierarchical Bayesian EDM via Gaussian processes Intelligent and Adaptive Control Optimal control via Empirical dynamic programming Multiview distance regularised S-map
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Top 10 AI Paraphrasing Tools Compared (2026)

Shopping for the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Yi Zeng (AI researcher)

Yi Zeng (Chinese: 曾毅) is a Chinese artificial intelligence researcher and professor at the Chinese Academy of Sciences, who also serves as the founding director of Center for Long-term AI, and as a member of the United Nations Advisory Body on AI. == Career == On May 25, 2019, Zeng led the team that published the Beijing Artificial Intelligence Principles, proposed as an initiative for the long-term research, governance and planning of AI, and the "realization of beneficial AI for mankind and nature". He was named on the Time 100 AI list, a list featuring the hundred most influential figures in artificial intelligence of the year, in 2023. In July 2023, Zeng addressed the United Nations Security Council in a meeting on the risks posed by recent strides in artificial intelligence. He said that AI models “cannot be trusted as responsible agents that can help humans to make decisions,” and warned of the risk of extinction posed by both near-term and long-term AI, arguing that “in the long term, we haven’t given superintelligence any practical reasons why they should protect humans”. Zeng stated that humans should always be responsible for final decision-making on the use of nuclear weapons, and that the United Nations must produce an international framework on AI development and governance, to ensure global peace and security. In October 2023, UN Secretary-General António Guterres announced the creation of an advisory body on issues surrounding the international governance of AI, of which Zeng would be a member. He leads teams of researchers at the Institute of Philosophy and the Institute of Automation of the Chinese Academy of Sciences, including doctoral candidates, postdoctoral fellows, research fellows, assistant professors, and associate professors. Among them is his first international PhD student, Ammar Younas, a lawyer and arbitrator whose research focuses on cross-cultural dimensions of AI ethics and governance.
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