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openPipeline is an open-source plug-in for Autodesk Maya that is designed to assist in a Production Pipeline structure and Computer animation. == Development == Created in Maya Embedded Language, openPipeline was initiated at Eyebeam Atelier and further developed at Pratt Institute in the Digital Arts Lab. The initial release date was December 28, 2006. == Contributors == Rob O'Neill (Creator) Paris Mavroidis Meng-Han Ho
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20Q is a computerized game of twenty questions that began as a test in artificial intelligence (AI). It was invented by Robin Burgener in 1988. The game was made handheld by Radica in 2003, but was discontinued in 2011 because Techno Source took the license for 20Q handheld devices. The game 20Q is based on the spoken parlor game known as twenty questions, and is both a website and a handheld device. 20Q asks the player to think of something and will then try to guess what they are thinking of with twenty yes-or-no questions. If it fails to guess in 20 questions, it will ask an additional 5 questions. If it fails to guess even with 25 (or 30) questions, the player is declared the winner. Sometimes the first guess of the object can be asked at question 14. == Principle and history == The principle is that the player thinks of something and the 20Q artificial intelligence asks a series of questions before guessing what the player is thinking. This artificial intelligence learns on its own with the information relayed back to the players who interact with it, and is not programmed. The player can answer these questions with: Yes, No, Unknown, and Sometimes. The experiment is based on the classic word game of Twenty Questions, and on the computer game "Animals," popular in the early 1970s, which used a somewhat simpler method to guess an animal. The 20Q AI uses an artificial neural network to pick the questions and to guess. After the player has answered the twenty questions posed (sometimes fewer), 20Q makes a guess. If it is incorrect, it asks more questions, then guesses again. It makes guesses based on what it has learned; it is not programmed with information or what the inventor thinks. Answers to any question are based on players’ interpretations of the questions asked. Newer editions were made for different categories, such as music 20Q which has the player think of a song, and Harry Potter 20Q, which has the player think of something from the world of the Harry Potter series. The 20Q AI can draw its own conclusions on how to interpret the information. It can be described as more of a folk taxonomy than a taxonomy. Its knowledge develops with every game played. In this regard, the online version of the 20Q AI can be inaccurate because it gathers its answers from what people think rather than from what people know. Limitations of taxonomy are often overcome by the AI itself because it can learn and adapt. For example, if the player was thinking of a "Horse" and answered "No" to the question "Is it an animal?," the AI will, nevertheless, guess correctly, despite being told that a horse is not an animal. Patent applications in the US and Europe were submitted in 2005. In August 2014, 20Q.net Inc., with Brashworks Studios, developed and released an iOS iPad version available at the Apple iTunes store. == Game show == On June 13, 2009, GSN began a TV version of the game, hosted by Cat Deeley, with Hal Sparks as the voice of Mr. Q.
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Knowledge Atlas Technology Joint Stock Co., Ltd., branded internationally as Z.ai, is a Chinese technology company specializing in artificial intelligence (AI). The company was formerly known as Zhipu AI outside China until its rebranding in 2025. Z.ai's flagship product is the GLM (General Language Model) family of large language models, which the company has released under the free and open-source MIT License since July 2025. As of 2024, it is one of China's "AI tiger" companies by investors and considered to be the third-largest LLM market player in China's AI industry according to the International Data Corporation. In January 2025, the United States Commerce Department blacklisted the company in its Entity List due to national security concerns. == History == Founded in 2019, the startup company began from Tsinghua University and was later spun out as an independent company. Researchers published an Association for Computational Linguistics conference paper in May 2022 introducing the GLM (General Language Model) training algorithm, which uses an "autoregressive blank infilling" strategy that creates cloze tests by randomly removing segments of input text and trains the model to autoregressively regenerate the removed text. In 2023, it raised 2.5 billion yuan (approx. 350 million in USD) from Alibaba Group and Tencent, along with Meituan, Ant Group, Xiaomi, and HongShan. In March 2024, Zhipu AI announced it was developing a Sora-like technology to achieve artificial general intelligence (AGI). In May 2024, the Saudi Arabian finance firm Prosperity7 Ventures, LLC participated in a USD $400 million financing round for Zhipu AI with a valuation of approximately 3 billion USD. In July 2024, they debuted the Ying text-to-video model. Zhipu released GLM-4-Plus in August 2024. In October 2024, Zhipu released GLM-4-Voice, an end-to-end speech large language model that can adjust its tone or dialect. Zhipu disclosed in April 2025 that it had started preparing for its initial public offering (IPO) and released two models under the free and open-source MIT License. In May 2025, the company sealed a 61.28 million yuan deal from the Chinese government for city projects in Hangzhou. In July 2025, Zhipu AI released GLM-4.5 and GLM-4.5 Air, their next generation language models, and the company rebranded itself as Z.ai internationally. In August 2025, Z.ai announced that their GLM models are compatible with Huawei's Ascend processors. On August 11, 2025, Z.ai released a new vision-language model (VLM) with a total of 106B parameters, GLM-4.5V. In late September 2025, the company released GLM-4.6 using China's domestic chips such as those from Cambricon Technologies. Z.ai released GLM-4.6V and GLM-4.7 in December 2025. That same year, the company changed its official name to Knowledge Atlas Technology JSC Ltd. On 8 January 2026, Z.ai held its IPO on the Hong Kong Stock Exchange to become a listed company. It is considered to be China's first major LLM company that went through an IPO. On February 11, 2026, Z.ai released GLM-5. In late February 2026, Z.ai's shares fell by 23%, and had a shortage of compute resources, leading to user complaints and Z.ai issuing a public call for support. Z.ai also restricted new user signups. In late March, 2026, Z.ai released the GLM-5.1 model to subscription users. On April 8th, 2026, Z.ai released GLM-5.1 as open-source. The same day, Z.ai increased its API prices by 10%, but maintained a lower price than its United States competitor Anthropic's Opus 4.6 model. On release, the company's share price increased 11.5%. == Description == Z.ai provides the following products and services: General Language Model (commonly abbreviated as GLM; formerly known as ChatGLM), a series of pre-trained dialogue models initially developed by Zhipu AI and Tsinghua KEG in 2023. GLM 4.5, released in July 2025 by Z.ai, can run on eight NVIDIA H20 chips. The release of GLM-4.6 in late September 2025 marked the first integration of FP8 and Int4 quantization on Cambricon chips. It also supports native FP8 on Moore Threads GPUs. Ying, a text-to-video model that generates image and text prompts into a six-second video clip for around 30 seconds. AutoGLM, an AI agent application that uses voice commands to complete tasks within a smartphone. The app can analyze complex tasks such as ordering an item from a nearby store and repeating an order based from the user's shopping history. AMiner, created by Jie Tang (co-founder of Z.ai) in March 2006, now owned by Z.ai. Z.ai has offices in the Middle East, United Kingdom, Singapore, and Malaysia, along with innovation center projects across Southeast Asia (2025). In January 2025, the United States Commerce Department added the company to its Entity List, citing national security concerns. == Models ==
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The DARPA AlphaDogfight was a 2019–2020 DARPA program that pitted computers using F-16 flight simulators against one another. The computers were managed by eight teams of humans, who competed in a single-round elimination for the right to battle a skilled human dogfighter. Heron Systems corporation wrote a deep reinforcement learning software tool that bested the human pilot by a score of 5–0. The tournament program was managed by the Applied Physics Laboratory. The trials took place in October 2019 and January 2020 while the finals were held in August 2020. In 2024 a successor version of the program was tested with in the physical world with the X-62A.
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Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Typically, this involves processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. Recent advances in NLP techniques have allowed for significantly improved performance compared to previous years. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: MergerBetween ( c o m p a n y 1 , c o m p a n y 2 , d a t e ) {\displaystyle \operatorname {MergerBetween} (\mathrm {company} _{1},\mathrm {company} _{2},\mathrm {date} )} , from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow automated reasoning about the logical form of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to "understand" an attack article only enough to find data corresponding to the slots in this template. == History == Information extraction dates back to the late 1970s in the early days of NLP. An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc with the aim of providing real-time financial news to financial traders. Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference that focused on the following domains: MUC-1 (1987), MUC-3 (1989): Naval operations messages. MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries. MUC-5 (1993): Joint ventures and microelectronics domain. MUC-6 (1995): News articles on management changes. MUC-7 (1998): Satellite launch reports. Considerable support came from the U.S. Defense Advanced Research Projects Agency (DARPA), who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism. == Present significance == The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the World Wide Web, refers to the existing Internet as the web of documents and advocates that more of the content be made available as a web of data. Until this transpires, the web largely consists of unstructured documents lacking semantic metadata. Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. == Tasks and subtasks == Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include: Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack. Event extraction: Given an input document, output zero or more event templates. For instance, a newspaper article might describe multiple terrorist attacks. Knowledge Base Population: Fill a database of facts given a set of documents. Typically the database is in the form of triplets, (entity 1, relation, entity 2), e.g. (Barack Obama, Spouse, Michelle Obama) Named entity recognition: recognition of known entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions, by employing existing knowledge of the domain or information extracted from other sentences. Typically the recognition task involves assigning a unique identifier to the extracted entity. A simpler task is named entity detection, which aims at detecting entities without having any existing knowledge about the entity instances. For example, in processing the sentence "M. Smith likes fishing", named entity detection would denote detecting that the phrase "M. Smith" does refer to a person, but without necessarily having (or using) any knowledge about a certain M. Smith who is (or, "might be") the specific person whom that sentence is talking about. Coreference resolution: detection of coreference and anaphoric links between text entities. In IE tasks, this is typically restricted to finding links between previously extracted named entities. For example, "International Business Machines" and "IBM" refer to the same real-world entity. If we take the two sentences "M. Smith likes fishing. But he doesn't like biking", it would be beneficial to detect that "he" is referring to the previously detected person "M. Smith". Relationship extraction: identification of relations between entities, such as: PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.") PERSON located in LOCATION (extracted from the sentence "Bill is in France.") Semi-structured information extraction which may refer to any IE that tries to restore some kind of information structure that has been lost through publication, such as: Table extraction: finding and extracting tables from documents. Table information extraction : extracting information in structured manner from the tables. This task is more complex than table extraction, as table extraction is only the first step, while understanding the roles of the cells, rows, columns, linking the information inside the table and understanding the information presented in the table are additional tasks necessary for table information extraction. Comments extraction : extracting comments from the actual content of articles in order to restore the link between authors of each of the sentences Language and vocabulary analysis Terminology extraction: finding the relevant terms for a given corpus Audio extraction Template-based music extraction: finding relevant characteristic in an audio signal taken from a given repertoire; for instance time indexes of occurrences of percussive sounds can be extracted in order to represent the essential rhythmic component of a music piece. Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE. IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources. == World Wide Web applications == IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people
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RealSense is an American technology company that develops depth cameras and computer-vision systems used in robotics, access control, industrial automation and healthcare. The company’s stereoscopic 3D cameras and software are marketed as a perception platform for “physical AI”, particularly for humanoid robots and autonomous mobile robots (AMRs). RealSense was incubated for more than a decade inside Intel’s perceptual computing and depth-sensing group before being spun out as an independent company in July 2025 with a US$50 million Series A round backed by a semiconductor-focused private equity firm and strategic investors including Intel Capital and the MediaTek Innovation Fund. Following the spin-out, RealSense announced a strategic collaboration with Nvidia to integrate its AI depth cameras with the Nvidia Jetson Thor robotics platform, the Isaac Sim simulation environment and the Holoscan Sensor Bridge for low-latency sensor fusion. In November 2025, Swiss access-solutions provider dormakaba acquired a minority stake in RealSense and formed a partnership to develop AI-powered biometric access-control and security systems for data centres, airports and other critical infrastructure. == History == === Origins in Intel Perceptual Computing === Intel began developing depth-sensing and perceptual-computing technologies in the early 2010s under the Perceptual Computing brand, with research spanning gesture control, facial recognition and eye-tracking systems. The work led to a series of 3D cameras and developer challenge programmes intended to stimulate software ecosystems for natural-user interfaces. In 2014 Intel rebranded the effort as Intel RealSense, positioning the technology as a family of depth cameras and vision processors for PCs, mobile devices and embedded systems. Early devices such as the F200 and R200 were integrated into laptops and tablets from OEMs including Asus, HP, Dell, Lenovo and Acer, and were also sold as standalone webcams by partners such as Razer and Creative. === Refocus on robotics and near-closure === By the late 2010s Intel had steered RealSense away from mainstream PC peripherals toward robotics, industrial and embedded applications, adding stereo and lidar-based depth cameras to the portfolio. In August 2021, trade publication CRN reported that Intel planned to wind down the RealSense business as part of a broader restructuring, raising questions about the future of the product line. Despite that announcement, Intel continued to invest in new custom silicon for depth cameras, and RealSense remained widely used in mobile robots and automation projects. === Spin-out as RealSense Inc. (2025) === On 11 July 2025, Intel completed the spin-out of its RealSense 3D-camera business into a new privately held company, RealSense Inc., and the new entity announced a US$50 million Series A funding round. The round was led by a semiconductor-focused private equity investor with participation from Intel Capital, MediaTek Innovation Fund and other strategics. Independent coverage described RealSense as serving more than 3,000 active customers and supplying depth cameras to a large share of global AMR and humanoid robot platforms. The company stated that it would continue to support the existing Intel RealSense product roadmap while accelerating development of AI-enabled cameras and perception software. === Strategic partnerships and investments === In October 2025 RealSense and Nvidia announced a strategic collaboration centered on integrating RealSense AI depth cameras with Nvidia’s Jetson Thor robotics compute modules, the Isaac Sim simulation environment and the Holoscan Sensor Bridge for multi-sensor streaming. The collaboration is positioned as enabling “physical AI” workloads such as whole-body humanoid control, real-time mapping and safety-critical human–robot interaction. On 19 November 2025, dormakaba announced that it had acquired a minority stake in RealSense and entered into a partnership to co-develop intelligent access-control solutions, including biometric gates for airports and enterprise facilities. The partnership aims to combine RealSense’s depth and facial-authentication technology with dormakaba’s installed base of sensors, doors and turnstiles. == Products == === Depth-camera families === RealSense’s products are sold as modular components (depth modules, vision processors and complete cameras) and as integrated systems with on-device AI. The company continues to offer and support the Intel RealSense D400 family of active-stereo depth cameras (including the D415, D435 and D455), which are widely used in robotics and automation. These devices combine a RealSense Vision Processor from the D4 family with dual infrared imagers and, on some models, an RGB camera. Earlier generations of Intel RealSense cameras, including the F200, R200, SR300 and the L515 lidar camera, remain in use in niche and legacy applications but are no longer the focus of the independent company’s roadmap. === D555 PoE depth camera === The first new hardware platform announced after the spin-out was the RealSense Depth Camera D555, a ruggedised stereo-depth device aimed at industrial and robotics deployments. The D555 uses the longer-range D450 optical module with a global shutter and integrates RealSense’s Vision SoC V5, a new generation of vision processor optimised for neural-network inference and depth computation. Key features highlighted in technical coverage include: Power over Ethernet (PoE), allowing power and data to be delivered over a single cable and supporting both RJ45 and ruggedised M12 connections; an IP-rated enclosure designed for harsh indoor and outdoor environments; a built-in inertial measurement unit (IMU) to support simultaneous localisation and mapping (SLAM) and motion tracking; native support for ROS 2 and integration with the open-source RealSense SDK. According to independent reporting, the D555 is used in AI-enabled embedded-vision applications in mobile robots and fixed industrial systems, and was among the first RealSense products to be tightly integrated with Nvidia’s Jetson Thor and Holoscan platforms for low-latency sensor fusion. === Software and SDK === RealSense cameras are supported by a cross-platform, open-source software stack historically branded as Intel RealSense SDK 2.0. The SDK provides device drivers, depth and point-cloud processing, tracking and calibration tools, and bindings for languages such as C++, Python and C#. The independent company has continued to maintain and extend the SDK for new hardware, including D555 and other Vision SoC V5-based devices, and publishes reference integrations for ROS 2 and industrial-automation frameworks. === Biometrics and access-control products === In addition to general-purpose depth cameras, RealSense offers facial-authentication hardware and software, commonly referred to as RealSense ID, for biometric access control and identity verification. These products combine an active depth sensor with a dedicated neural-network pipeline running on embedded processors, aimed at applications such as secure doors, turnstiles and kiosks. Use-case material published by partners describes deployments of RealSense-based biometric readers in school lunch programmes, agricultural biosecurity checkpoints and enterprise facilities. The dormakaba partnership announced in 2025 extends this portfolio to integrated biometric gates and sensor-equipped doors in airports and data centres. == Applications == === Robotics and automation === RealSense depth cameras are used in autonomous mobile robots, humanoid robots, drones and industrial automation systems for tasks such as obstacle avoidance, navigation and manipulation. Reuters reported in 2025 that RealSense cameras were embedded in around 60 percent of the world’s AMRs and humanoid robots, citing customers including Unitree Robotics and ANYbotics. Developers and integrators use RealSense systems with platforms such as Nvidia Jetson, ROS and proprietary motion-planning stacks. === Biometrics and security === RealSense technology is also applied in biometric access control and surveillance, where depth and infrared imaging are used to improve anti-spoofing performance for facial recognition. The dormakaba investment and collaboration is aimed at integrating these capabilities into boarding gates, staff entrances and secure facilities, with RealSense providing perception hardware and algorithms and dormakaba providing access-control infrastructure and global distribution. == Reception == Early coverage of Intel RealSense for consumer PCs noted that the technology’s impact would depend on the availability of compelling software and use cases for depth-sensing cameras. Later reporting on the spin-out has characterised the new company as part of a broader wave of investment in robotics and physical AI, with some analysts suggesting that RealSense’s installed base and patent portfolio give it an advantage as dep
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Group concept mapping is a structured methodology for organizing the ideas of a group on any topic of interest and representing those ideas visually in a series of interrelated maps. It is a type of integrative mixed method, combining qualitative and quantitative approaches to data collection and analysis. Group concept mapping allows for a collaborative group process with groups of any size, including a broad and diverse array of participants. Since its development in the late 1980s by William M.K. Trochim at Cornell University, it has been applied to various fields and contexts, including community and public health, social work, health care, human services,, instructional interventions, and biomedical research and evaluation. == Overview == Group concept mapping integrates qualitative group processes with multivariate analysis to help a group organize and visually represent its ideas on any topic of interest through a series of related maps. It combines the ideas of diverse participants to show what the group thinks and values in relation to the specific topic of interest. It is a type of structured conceptualization used by groups to develop a conceptual framework, often to help guide evaluation and planning efforts. Group concept mapping is participatory in nature, allowing participants to have an equal voice and to contribute through various methods. A group concept map visually represents all the ideas of a group and how they relate to each other, and depending on the scale, which ideas are more relevant, important, or feasible. == Process == Group concept mapping involves a structured multi-step process, including brainstorming, sorting and rating, multidimensional scaling and cluster analysis, and the generation and interpretation of multiple maps. The first step requires participants to brainstorm a large set of statements relevant to the topic of interest, usually in response to a focus prompt. Participants are then asked to individually sort those statements into categories based on their perceived similarity and rate each statement on one or more scales, such as importance or feasibility. The data is then analyzed using The Concept System software, which creates a series of interrelated maps using multidimensional scaling (MDS) of the sort data, hierarchical clustering of the MDS coordinates applying Ward's method, and the computation of average ratings for each statement and cluster of statements. The resulting maps display the individual statements in two-dimensional space with more similar statements located closer to each other, and grouped into clusters that partition the space on the map. The Concept System software also creates other maps that show the statements in each cluster rated on one or more scales, and absolute or relative cluster ratings between two cluster sets. As a last step in the process, participants are led through a structured interpretation session to better understand and label all the maps. == History == Group concept mapping was developed as a methodology in the late 1980s by William M.K. Trochim at Cornell University. Trochim is considered to be a leading evaluation expert, and he has taught evaluation and research methods at Cornell since 1980. Originally called "concept mapping", the methodology has evolved since its inception with the maturation of the field and the continued advancement of the software, which is now a Web application. == Uses == Group concept mapping can be used with any group for any topic of interest. It is often used by government agencies, academic institutions, national associations, not-for-profit and community-based organizations, and private businesses to help turn the ideas of the group into measurable actions. This includes in the areas of organizational development, strategic planning, needs assessment, curriculum development, research, and evaluation. Group concept mapping is well-documented, well-established methodology, and it has been used in hundreds of published papers. == Versus concept mapping and mind mapping == More generally, concept mapping is any process used for visually representing relationships between ideas in pictures or diagrams. A concept map is typically a diagram of multiple ideas, often represented as boxes or circles, linked in a graph (network) structure through arrows and words where each idea is connected to another. The technique was originally developed in the 1970s by Joseph D. Novak at Cornell University. Concept mapping may be done by an individual or a group. A mind map is a diagram used to visually represent information, centering on one word or idea with categories and sub-categories radiating off of it in a tree structure. Popularized by Tony Buzan in the 1970s, mind mapping is often a spontaneous exercise done by an individual or group to gather information about what they think around a single topic. Unlike Novak's concept maps and Buzan's mind maps, group concept mapping has a structured mathematical process (sorting and rating, multidimensional scaling and cluster analysis) for organizing and visually representing multiple ideas of a group through a series of specific steps. In other words, in group concept mapping, the resulting visual representations are mathematically generated from mixed (qualitative and quantitative) data collected from a group of research subjects, whereas in Novak's concept maps and Buzan's mind maps the visual representations are drawn directly by the subjects resulting in diagrams that are qualitative data and final product at the same time.
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Jess is a rule engine for the Java computing platform, written in the Java programming language. It was developed by Ernest Friedman-Hill of Sandia National Laboratories. It is a superset of the CLIPS language. It was first written in late 1995. The language provides rule-based programming for the automation of an expert system, and is often termed as an expert system shell. In recent years, intelligent agent systems have also developed, which depend on a similar ability. Rather than a procedural paradigm, where one program has a loop that is activated only one time, the declarative paradigm used by Jess applies a set of rules to a set of facts continuously by a process named pattern matching. Rules can modify the set of facts, or can execute any Java code. It uses the Rete algorithm to execute rules. == License == The licensing for Jess is freeware for education and government use, and is proprietary software, needing a license, for commercial use. In contrast, CLIPS, which is the basis and starting code for Jess, is free and open-source software. == Code examples == Code examples: Sample code:
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The Grammar of Graphics (GoG) is a grammar-based system for representing graphics to provide grammatical constraints on the composition of data and information visualizations. A graphical grammar differs from a graphics pipeline as it focuses on semantic components such as scales and guides, statistical functions, coordinate systems, marks and aesthetic attributes. For example, a bar chart can be converted into a pie chart by specifying a polar coordinate system without any other change in graphical specification. The grammar of graphics concept was launched by Leland Wilkinson in 2001 (Wilkinson et al., 2001; Wilkinson, 2005) and graphical grammars have since been written in a variety of languages with various parameterisations and extensions. The major implementations of graphical grammars are nViZn created by a team at SPSS/IBM, followed by Polaris focusing on multidimensional relational databases which is commercialised as Tableau, a revised Layered Grammar of Graphics by Hadley Wickham in Ggplot2, and Vega-Lite which is a visualisation grammar with added interactivity. The grammar of graphics continues to evolve with alternate parameterisations, extensions, or new specifications. == Wilkinson's Grammar of Graphics == === Theory === Wilkinson conceived the seven elements of a graphics to be Variables: mapping of objects to values represented in a graphic Algebra: operations to combine variables and specify dimensions of graphs Geometry: creation of geometric graphs from variables Aesthetics: sensory attributes Statistics: functions to change the appearance and representation of graphs Scales: represent variables on measured dimensions Coordinates: mapping to coordinate systems With these, Wilkinson hypothesised that These seven constructs are orthogonal and virtually all known statistical charts can be generated relatively parsimoniously This computational system is not a taxonomy of charts and rather it describes the meaning of what we do when we construct statistical graphics. === Implementations === Wilkinson wrote SYSTAT, a statistical software package, in the early 1980s. This program was noted for its comprehensive graphics, including the first software implementation of the heatmap display now widely used among biologists. After his company grew to 50 employees, he sold it to SPSS in 1995. At SPSS, he assembled a team of graphics programmers who developed the nViZn platform that produces the visualizations in SPSS, Clementine, and other analytics products. While at Stanford, Tableau founders Hanrahan and Stolte, as well as Diane Tang, created the predecessor to Tableau, named Polaris. Polaris was a data visualization software tool, built with the support of a United States Department of Energy defense program, the Accelerated Strategic Computing Initiative (ASCI). The main differences between Wilkinson's system and Polaris are the use of SQL relational algebra for database services and using shelves instead of cross and nest operators. == Wickham's Layered Grammar of Graphics == === Theory === Hadley Wickham conceived an alternate parameterisation of the syntax Wilkinson had derived, creating a layered grammar of graphics which he implemented as ggplot2 for R (programming language) users. This added a hierarchy of defaults based around the idea of building up a graphic from multiple layers. Wickham conceived these elements to be: Defaults: consists of data and mapping Data: dataset Mapping: aesthetic mappings Layer: consists of data, mapping, geom, stat, and position Data: dataset, or inherit from defaults Mapping: aesthetic mappings, or inherit from defaults Geom: geometric object Stat: statistical transformation Position: position adjustment Scale: mapping of data to aesthetic attributes Coord: mapping of data to the plane of the plot Facet: split up the data === Reception === Wilkinson is generally positive on Wickham's parameterisation and implementation of ggplot2, praising its elegance and expressivity whilst claiming that his original Grammar of Graphics is capable of representing a wider range of statistical graphics. === Implementations === ggplot2 is the first implementation of a layered grammar of graphics in R and implementations in other programming languages have ensued. These include direct ports plotnine for Python, gramm for MATLAB, Lets-Plot for Kotlin and gadfly for Julia. Projects inspired by elements of Wickham's grammar include Vega-Lite which specifies plots in JSON and uses a JavaScript engine. Implementations for Python include Vega-Altair (built on top of Vega-Lite). == Vega-Lite: A Grammar of Interactive Graphics == === Theory === Vega-Lite combines ideas from Wilkinson's Grammar of Graphics and Wickham's Layered Grammar of Graphics with a composition algebra for layered and multi-view displays with a grammar of interaction. The Vega-Lite specification is instantiated in JSON and rendered by the lower-level Vega. The graphical grammar implemented by Vega-Lite is composed of the following: Unit: consists of data, transforms, mark-type and encoding Data: relational table consisting of records (rows) and named attributes (columns) Transforms: data transformations Mark-type: geometric object for visual encoding Encodings: mapping of data attributes to visual marks properties where each encoding consists of: Channel: e.g. colour, shape, size, or text Field: data attribute Data-type: e.g. nominal, ordinal, quantitative, or temporal Value: use a literal instead of a data-type Functions: e.g. binning, aggregation, and sorting Scale: maps from data domain to visual range Guide: axis or legend for visualising scale Composite Views: compose views from multiple unit specifications with operators: Layer: charts plotted on top of each other Hconcat/Vconcat: place views side-by-side Facet: subset data to produce a trellis plot Repeat: multiple plots similar to facet but with full data replication in each cell Interaction: selections identify the set of points a user is interested in manipulating, with components: Selection: get the minimal number of backing points Name: reference Type: how many backing values are stored Predicate: determine the set of selected points e.g. single, list, interval Domain|Range: store data domain or visual range Event: e.g. mouseover, mousedown, mouseup, Init: initialise with specific backing points Transforms: e.g. project, toggle, translate, zoom, and nearest Resolve: resolve selections to union or intersect ==== Implementations ==== Whilst Vega-Lite is the sole implementation of this graphics grammar specification with compilation to Vega, other implementations do create JSON files which can be interpreted by Vega-Lite. == Related projects == Ggplot2 is an R package for plotting Tableau Software (originally known as Polaris) is a commercial software built using the Grammar of Graphics nViZn built by Wilkinson. SYSTAT (statistics package) built by Wilkinson ggpy, ggplot for Python, but has not been updated since 20 November 2016 plotnine started as an effort to improve the scalability of ggplot for Python and is largely compatible with ggplot2 syntax. Plotly - Interactive, online ggplot2 graphs gramm, a plotting class for MATLAB inspired by ggplot2 gadfly, a system for plotting and visualization written in Julia, based largely on ggplot2 Chart::GGPlot - ggplot2 port in Perl, but has not been updated since 16 March 2023 The Lets-Plot for Python library includes a native backend and a Python API, which was mostly based on the ggplot2 package. Lets-Plot Kotlin API is an open-source plotting library for statistical data implemented using the Kotlin programming language, and is built on the principles of layered graphics first described in the Leland Wilkinson's work The Grammar of Graphics. ggplotnim, plotting library using the Nim programming language inspired by ggplot2. Vega and Vega-Lite are plotting libraries that use JSON to specify plots. Vega-Altair, a Python library built on top of Vega-Lite chart-parts - React-friendly Grammar of Graphics, but has not been updated since 10 Dec 2021 g2 - a JavaScript library
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John Schulman (born 1987 or 1988) is an American artificial intelligence researcher and co-founder of OpenAI. In August 2024, he announced he would be joining Anthropic. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Early life and education == Schulman had an interest in science and math from a young age. He enjoyed science fiction, especially the work of Isaac Asimov. When he was in seventh grade, he became deeply interested in the television program BattleBots, which featured combat between remote-controlled robots. In what he said was his first self-directed study, he read extensively in subject areas that would help him design a superior robot, but the robot he and his friends worked on was never built. He attended Great Neck South High School. He was a member of the US Physics olympiad Team in 2005. In 2010, he graduated from Caltech with a degree in physics. He has a PhD in electrical engineering and computer sciences from the University of California, Berkeley, where he was advised by Pieter Abbeel. == Career == In December 2015, shortly before finishing his PhD, Schulman co-founded OpenAI with Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. There, he led the reinforcement learning team that created ChatGPT. He has been referred to as the "architect" of ChatGPT. In August 2024, Schulman announced he would be joining Anthropic. He stated his move was to allow him to deepen his focus on AI alignment and return to more hands-on technical work. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Awards and honors == In 2025, Schulman received the Mark Bingham Award for Excellence in Achievement by Young Alumni from his alma mater, UC Berkeley.
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The AgMES (Agricultural Metadata Element set) initiative was developed by the Food and Agriculture Organization (FAO) of the United Nations and aims to encompass issues of semantic standards in the domain of agriculture with respect to description, resource discovery, interoperability, and data exchange for different types of information resources. There are numerous other metadata schemas for different types of information resources. The following list contains a list of a few examples: Document-like Information Objects (DLIOs): Dublin Core, Agricultural Metadata Element Set (AgMES) Events: VCalendar Geographic and Regional Information: Geographic information—Metadata ISO/IEC 11179 Standards Persons: Friend-of-a-friend (FOAF), vCard Plant Production and Protection: Darwin Core (1.0 and 2.0) (DwC) AgMES as a namespace is designed to include agriculture specific extensions for terms and refinements from established standard metadata namespaces like Dublin Core, AGLS etc. Thus, to be used for Document-like Information Objects, for example like publications, articles, books, web sites, papers, etc., it will have to be used in conjunction with the standard namespaces mentioned before. The AgMES initiative strives to achieve improved interoperability between information resources in agricultural domain by enabling means for exchange of information. Describing a DLIO with AgMES means exposing its major characteristics and contents in a standard way that can be reused easily in any information system. The more institutions and organizations in the agricultural domain that use AgMES to describe their DLIOs, the easier it will be to interchange data in between information systems like digital libraries and other repositories of agricultural information. == Use of AgMES == Metadata on agricultural Document-like Information Objects (DLIOs) can be created and stored in various formats: embedded in a web site (in the manner as with the HTML meta tag) in a separate metadata database in an XML file in an RDF file AgMES defines elements that can be used to describe a DLIO that can be used together with other metadata standards such as the Dublin Core, the Australian Government Locator Service. A complete list of all elements, refinements and schemes endorsed by AgMES is available from the AgMES website. === Creating application profiles === Application profiles are defined as schemas which consist of data elements drawn from one or more namespaces, combined by implementers, and optimized for a particular local application. Application profiles share the following four characteristics: They draw upon existing pool of metadata definition standards to extract suitable application- or requirement oriented elements. An application profile cannot create new elements. Application profiles specify the application specific details such as the schemes or controlled vocabularies. An application profile also contains information such as the format for the element value, cardinality or data type. Lastly, an application profile can refine standardized definitions as long as it is "semantically narrower or more specific". This capability of application profiles caters to situations where a domain specific terminology is needed to replace a more general one. === Sample application profiles using AgMES === The AGRIS Application Profile is a standard created specifically to enhance the description, exchange and subsequent retrieval of agricultural Document-like Information Objects (DLIOs). It is a format that allows sharing of information across dispersed bibliographic systems and is based on well-known and accepted metadata standards. The Event Application Profile is a standard created to allow members of the Agricultural community to 'know' about an upcoming event and guide them to the event Web site where they can find further information. The information communicated is thus minimum yet interoperable across domains and organizations. == AgMES and the semantic web == One of the advantages of the AgMES metadata schema is the ability to link between the metadata element and controlled vocabularies. The use of controlled vocabulary provides a "known" set of options to the indexer (and the search programmer) as to how the field can be filled out. Often the values may come from a specific thesaurus (e.g. AGROVOC) or classification schemes (e.g. the AGRIS/CARIS classification scheme) etc. Thanks to the possibility to use controlled vocabularies for metadata elements, the user is provided with the most precise information. In this context, work is also being carried out on exploiting the power of controlled vocabularies expressed as using URIs and machine-understandable semantics. In this context, FAO is promoting the Agricultural Ontology Service (AOS) initiative with the objective of expressing more semantics within the traditional thesaurus AGROVOC and build a Concept Server as a repository from which it will be always possible to extract traditional KOS.
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KL-ONE (pronounced "kay ell won") is a knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network. == Overview == There is a whole family of KL-ONE-like systems. One of the innovations that KL-ONE initiated was the use of a deductive classifier, an automated reasoning engine that can validate a frame ontology and deduce new information about the ontology based on the initial information provided by a domain expert. Frames in KL-ONE are called concepts. These form hierarchies using subsume-relations; in the KL-ONE terminology a super class is said to subsume its subclasses. Multiple inheritance is allowed. Actually a concept is said to be well-formed only if it inherits from more than one other concept. All concepts, except the top concept (usually THING), must have at least one super class. In KL-ONE descriptions are separated into two basic classes of concepts: primitive and defined. Primitives are domain concepts that are not fully defined. This means that given all the properties of a concept, this is not sufficient to classify it. They may also be viewed as incomplete definitions. Using the same view, defined concepts are complete definitions. Given the properties of a concept, these are necessary and sufficient conditions to classify the concept. The slot-concept is called roles and the values of the roles are role-fillers. There are several different types of roles to be used in different situations. The most common and important role type is the generic RoleSet that captures the fact that the role may be filled with more than one filler.
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The T-Layout is an architectural and design concept for web applications, specifically tailored to improve the user experience on mobile devices. It features a horizontally scrollable container divided into three distinct sections, each spanning the full width of the screen, and was developed to optimise space usage and streamline navigation. == Background == The T-Layout introduces horizontal scrolling as a complementary method to the conventional pop-up-based navigation system in mobile web applications. In this layout, the central section which is visible by default upon accessing the application, facilitates the main content of a URL address and is flanked by two "helper" sections. This approach minimises the need for extensive user movements, in order to reach navigation controls typically located at the top of the screen. It is aimed at enhancing the user experience on mobile devices by providing an easier way to access essential content such as the main navigation, e-commerce related screens, or user account related information, ensuring that those elements are readily accessible while requiring minimal user effort. The T-Layout was first implemented by E (e-streetwear.com) in their mobile web app layout, and it was inspired by the interfaces of well-tested native mobile apps like Instagram and Revolut. A study titled "Mobile Navigation and User Preferences Survey" indicated a preference among mobile app users for one-handed usage, primarily navigating with their thumb. These insights led to the T-Layout Experiment, which compared the efficiency of using swipe gestures to access navigational elements against reaching traditional navigation controls. == Development history == It was first released as the mobile layout of E in early 2023. It was originally developed based on six principles: user-centric functionality, lightweight filesize, HTML and CSS implementation with minimal or no use of JavaScript required, suitable both for browser and server-rendering architectures, intuitive design, and improved SEO. The development of the T-Layout was driven by the necessity for more ergonomic and user-friendly interfaces in mobile web applications. Its design, reminiscent of the letter 'T', emerged as a solution to several usability challenges mobile device users face, emphasising ease of access and efficient screen space utilisation. In July 2023, E formalised the concept and its technical specifications, introducing it to the web design and development community. In October 2023 the "Mobile Navigation and User Preferences Survey" was conducted, establishing that the vast majority of individuals prefer to use mobile applications by holding the phone in a one-handed grip, utilising only the thumb for gestures when possible. The subsequent "T-Layout Experiment", designed to measure the time in seconds and the distance (user effort) in pixels, required to access navigational elements by traditionally tapping on fixed-positioned controls compared to swiping anywhere on the screen. The results proved that swipe gestures require less time and much less effort. == Styling and features == The main characteristic of the T-Layout is its horizontal scrolling feature, which can improve navigation efficiency while preserving the functionality of traditionally structured user interfaces. Its Implementation can be achieved with a combination of HTML and styling with CSS as well as precompiled Scss and Sass, CSS-in-JS, and styled JSX. It can be either a purely HTML/CSS solution but JavaScript can be utilised as well to add more specific functionalities, while It can be implemented to both existing and new applications. Its application in server-side rendering architectures will ensure that all its underlying principles apply. Although principally each section in the layout has a distinct role and facilitates specific types of content, the T-Layout as a concept is versatile, and it is adaptable allowing modifications in the layout or how it's implemented to cater to the specific needs of different applications.
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Sarah Guo is an American tech investor. She is the founder of the venture capital firm Conviction and formerly a general partner at Greylock Partners. == Early life and education == Guo grew up in Wisconsin. Her parents worked for Bell Labs. After attending Phillips Academy, she graduated from the University of Pennsylvania and its Wharton School. She received a Bachelor of Arts, a Bachelor of Science, a Master of Business Administration (M.B.A.), and a Master of Arts from the University of Pennsylvania. == Career == As a teenager, Guo worked at Casa Systems, a cloud networking company founded by her parents that launched in 2003 and went public in 2017. She then worked at Goldman Sachs. In 2013, Guo joined Greylock Partners. While still in her twenties, she became the firm's youngest General Partner. Guo left Greylock in July 2022, and in October of that year, launched a new early-stage venture capital firm focused on AI with $101 million. In 2025, Conviction raised a second fund in late 2024 with Mike Vernal. Conviction's investments include early investments in Baseten, Cognition AI, OpenEvidence, Harvey, HeyGen, Mistral AI, Sierra Platform, Sunday Robotics, and Thinking Machines Lab. Guo appears in media outlets, as an expert in AI, infrastructure, business software, cybersecurity, technology policy and software engineering. Guo is on the Midas List and the Midas Seed List of top investors. She co-hosts the podcast No Priors with tech founder and super angel Elad Gil. == Personal life == Guo is married to Pat Grady of Sequoia Capital.
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In information systems, a tag is a keyword or term assigned to a piece of information (such as an Internet bookmark, multimedia, database record, or computer file). This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Tags are generally chosen informally and personally by the item's creator or by its viewer, depending on the system, although they may also be chosen from a controlled vocabulary. Tagging was popularized by websites associated with Web 2.0 and is an important feature of many Web 2.0 services. It is now also part of other database systems, desktop applications, and operating systems. == Overview == People use tags to aid classification, mark ownership, note boundaries, and indicate online identity. Tags may take the form of words, images, or other identifying marks. An analogous example of tags in the physical world is museum object tagging. People were using textual keywords to classify information and objects long before computers. Computer based search algorithms made the use of such keywords a rapid way of exploring records. Tagging gained popularity due to the growth of social bookmarking, image sharing, and social networking websites. These sites allow users to create and manage labels (or "tags") that categorize content using simple keywords. Websites that include tags often display collections of tags as tag clouds, as do some desktop applications. On websites that aggregate the tags of all users, an individual user's tags can be useful both to them and to the larger community of the website's users. Tagging systems have sometimes been classified into two kinds: top-down and bottom-up. Top-down taxonomies are created by an authorized group of designers (sometimes in the form of a controlled vocabulary), whereas bottom-up taxonomies (called folksonomies) are created by all users. This definition of "top down" and "bottom up" should not be confused with the distinction between a single hierarchical tree structure (in which there is one correct way to classify each item) versus multiple non-hierarchical sets (in which there are multiple ways to classify an item); the structure of both top-down and bottom-up taxonomies may be either hierarchical, non-hierarchical, or a combination of both. Some researchers and applications have experimented with combining hierarchical and non-hierarchical tagging to aid in information retrieval. Others are combining top-down and bottom-up tagging, including in some large library catalogs (OPACs) such as WorldCat. When tags or other taxonomies have further properties (or semantics) such as relationships and attributes, they constitute an ontology. In folder system a file cannot exist in two or more folders so tag system has been thought more convenient. But transitioning to tag system requires awareness of difference between properties of two systems. In folder system the information of classification is put outside of the file and we can change folder at once. In tag system the information of classification is put inside the file so changing its tag means changing the file and it needs to be saved again and takes time. Metadata tags as described in this article should not be confused with the use of the word "tag" in some software to refer to an automatically generated cross-reference; examples of the latter are tags tables in Emacs and smart tags in Microsoft Office. == History == The use of keywords as part of an identification and classification system long predates computers. Paper data storage devices, notably edge-notched cards, that permitted classification and sorting by multiple criteria were already in use prior to the twentieth century, and faceted classification has been used by libraries since the 1930s. In the late 1970s and early 1980s, Emacs, the text editor for Unix systems, offered a companion software program called Tags that could automatically build a table of cross-references called a tags table that Emacs could use to jump between a function call and that function's definition. This use of the word "tag" did not refer to metadata tags, but was an early use of the word "tag" in software to refer to a word index. Online databases and early websites deployed keyword tags as a way for publishers to help users find content. In the early days of the World Wide Web, the keywords meta element was used by web designers to tell web search engines what the web page was about, but these keywords were only visible in a web page's source code and were not modifiable by users. In 1997, the collaborative portal "A Description of the Equator and Some ØtherLands" produced by documenta X, Germany, used the folksonomic term Tag for its co-authors and guest authors on its Upload page. In "The Equator" the term Tag for user-input was described as an abstract literal or keyword to aid the user. However, users defined singular Tags, and did not share Tags at that point. In 2003, the social bookmarking website Delicious provided a way for its users to add "tags" to their bookmarks (as a way to help find them later); Delicious also provided browseable aggregated views of the bookmarks of all users featuring a particular tag. Within a couple of years, the photo sharing website Flickr allowed its users to add their own text tags to each of their pictures, constructing flexible and easy metadata that made the pictures highly searchable. The success of Flickr and the influence of Delicious popularized the concept, and other social software websites—such as YouTube, Technorati, and Last.fm—also implemented tagging. In 2005, the Atom web syndication standard provided a "category" element for inserting subject categories into web feeds, and in 2007 Tim Bray proposed a "tag" URN. == Examples == === Within a blog === Many systems (and other web content management systems) allow authors to add free-form tags to a post, along with (or instead of) placing the post into a predetermined category. For example, a post may display that it has been tagged with baseball and tickets. Each of those tags is usually a web link leading to an index page listing all of the posts associated with that tag. The blog may have a sidebar listing all the tags in use on that blog, with each tag leading to an index page. To reclassify a post, an author edits its list of tags. All connections between posts are automatically tracked and updated by the blog software; there is no need to relocate the page within a complex hierarchy of categories. === Within application software === Some desktop applications and web applications feature their own tagging systems, such as email tagging in Gmail and Mozilla Thunderbird, bookmark tagging in Firefox, audio tagging in iTunes or Winamp, and photo tagging in various applications. Some of these applications display collections of tags as tag clouds. === Assigned to computer files === There are various systems for applying tags to the files in a computer's file system. In Apple's Mac System 7, released in 1991, users could assign one of seven editable colored labels (with editable names such as "Essential", "Hot", and "In Progress") to each file and folder. In later iterations of the Mac operating system ever since OS X 10.9 was released in 2013, users could assign multiple arbitrary tags as extended file attributes to any file or folder, and before that time the open-source OpenMeta standard provided similar tagging functionality for Mac OS X. Several semantic file systems that implement tags are available for the Linux kernel, including Tagsistant. Microsoft Windows allows users to set tags only on Microsoft Office documents and some kinds of picture files. Cross-platform file tagging standards include Extensible Metadata Platform (XMP), an ISO standard for embedding metadata into popular image, video and document file formats, such as JPEG and PDF, without breaking their readability by applications that do not support XMP. XMP largely supersedes the earlier IPTC Information Interchange Model. Exif is a standard that specifies the image and audio file formats used by digital cameras, including some metadata tags. TagSpaces is an open-source cross-platform application for tagging files; it inserts tags into the filename. === For an event === An official tag is a keyword adopted by events and conferences for participants to use in their web publications, such as blog entries, photos of the event, and presentation slides. Search engines can then index them to make relevant materials related to the event searchable in a uniform way. In this case, the tag is part of a controlled vocabulary. === In research === A researcher may work with a large collection of items (e.g. press quotes, a bibliography, images) in digital form. If he/she wishes to associate each with a small number of themes (e.g. to chapters of a book, or to sub-themes of the overall subject), then a group of tags for these themes can be attached to each of the items in
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