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  • Skipper (computer software)

    Skipper (computer software)

    Skipper is a visualization tool and code/schema generator for PHP ORM frameworks like Doctrine2, Doctrine, Propel, and CakePHP, which are used to create database abstraction layer. Skipper is developed by Czech company Inventic, s.r.o. based in Brno, and was known as ORM Designer prior to rebranding in 2014. == Overview == Generates visual model from the schema definition files Repetitive import/export of schema definitions in supported formats (XML, YML, PHP annotations) Schema definition files are automatically generated from the visual model Visual representation uses ER diagram extended by concepts of inheritance and many-to-many Supports customization using .xml configuration files and JavaScript Does not support direct connections to the database Crude and simplistic visual representation and menus == Architecture == Skipper was built on the Qt framework. Import/export of the schema definitions uses XSL transformations powered by LibXslt library. Imported source files are first converted to XML format: no conversion for XML, simple conversion for YML, creating the Abstract Syntax Tree and its subsequent conversion to XML for PHP annotations. The import/export scripts are configured in JavaScript and can be freely customized. == Supported ORM frameworks == Frameworks supported for visual model and schema files generation: Doctrine2 Doctrine CakePHP == History == Skipper was created as an internal tool for the web applications developed by Inventic. It was first published as a commercial tool under the name ORM Designer in 2009. Application was reworked and optimized in January 2013, and released as ORM Designer 2. In May 2013 ORM Designer became part of the South Moravian Innovation Center Incubator program (support program for innovative technological startups). In June 2014, ORM Designer version 3 was released and rebranded under the name of Skipper

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  • Botler AI

    Botler AI

    Botler AI is a Montreal-based Canadian Artificial Intelligence company that helps users navigate the legal system. Launched in 2017 by Amir Morv and Ritika Dutt, Botler offers a free online tool which provides users who are unaware of their legal rights with information and guidance. Botler is known for its role in unveiling misconduct in the Government of Canada's procurement practices. Botler's findings have prompted numerous investigations, including by the Royal Canadian Mounted Police. == History == Botler's first AI was trained on over 300,000 U.S. and Canadian legal documents to help individuals identify and enforce their legal rights, without fear of judgment. Launched during the height of the #MeToo movement, the tool initially focused on sexual harassment with a goal of creating "a general artificial intelligence that would help the average person with any legal issue." === Department of Justice Canada === In 2020, Botler launched an expanded misconduct detection system in the form of an anonymous chatbot which provided users with an explanation of the law and relevant resources. In March 2021, the Minister of Justice and Attorney General of Canada announced the Government of Canada's support for Botler AI to assist complainants of sexual harassment in the workplace. The initiative, entitled Botler for Citizens and implemented with the support of the Department of Justice Canada, established an Artificial Intelligence-powered hybrid legal service delivery model. == Notable cases == On October 4, 2023, the RCMP confirmed to The Globe and Mail that they "are investigating a file referred from the CBSA (Canada Border Services Agency) that is based on allegations brought to their attention by Botler". In 2019, GCStrategies's managing partner, Kristian Firth, reached out to Botler on behalf of his client, the CBSA, to solicit their misconduct detection chatbot. After interactions with GCStrategies, Dalian Enterprises and Coradix Technology Consulting, the three main contractors involved in developing the controversial ArriveCAN app, Dutt and Morv alerted the CBSA to questionable contracting practices in federal government procurement in September, 2021, and again in November, 2022. In response to Botler's November 2022 report, the CBSA launched an internal review and referred the matter to the RCMP. During testimony before a parliamentary committee, the CBSA's President stated that the CBSA investigation to date has raised some concerns and shows "that there was a pattern of persistent collaboration between certain officials and GCStrategies... to circumvent or ignore certain established processes and roles and responsibilities". The Auditor General of Canada, which extended its study into ArriveCAN following the Botler revelations, found that GCStrategies was directly involved in setting narrow terms for a request for proposal for a $25-million government contract it ultimately won. The firm, which has just two employees, charges the government a commission of between 15 per cent and 30 per cent of each contract's value. The Office of the Procurement Ombudsman of Canada found "numerous examples" where GCStrategies "had simply copied and pasted" the required work experience to meet contracting requirements. To date, more than a dozen probes have been launched into the matter, including by the government, parliamentary committees, independent watchdogs and law-enforcement agencies. On April 17, 2024, GCStrategies' Firth was the first person summoned in over a century to answer questions before Members of Parliament in the House of Commons. During his appearance, Firth testified that the RCMP had raided "my property to obtain electronic goods surrounding the Botler allegations". === Government of Canada Reforms === One day after The Globe reported that the RCMP is investigating allegations of misconduct, the federal government responded by announcing new guidelines from the Treasury Board of Canada aimed at cutting back on the use of private consultants and that outsourcing contracts were under examination. Public Services and Procurement Canada (PSPC) invalidated and replaced all master level user agreements with government client departments in November 2023. The agreements set out the conditions for access to select Professional Services methods of supply which are used for outsourcing. In March 2024, PSPC announced its suspension of the respective security statuses of GCStrategies, Dalian and Coradix, barring them from participating in all federal procurements. Records show that the total value of contracts awarded to the three companies amounts to more than $1 Billion.

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  • Meta Content Framework

    Meta Content Framework

    Meta Content Framework (MCF) is a specification of a content format for structuring metadata about web sites and other data. == History == MCF was developed by Ramanathan V. Guha at Apple Computer's Advanced Technology Group between 1995 and 1997. Rooted in knowledge-representation systems such as CycL, KRL, and KIF, it sought to describe objects, their attributes, and the relationships between them. One application of MCF was HotSauce, also developed by Guha while at Apple. It generated a 3D visualization of a web site's table of contents, based on MCF descriptions. By late 1996, a few hundred sites were creating MCF files and Apple HotSauce allowed users to browse these MCF representations in 3D. When the research project was discontinued, Guha left Apple for Netscape, where, in collaboration with Tim Bray, he adapted MCF to use XML and created the first version of the Resource Description Framework (RDF). == MCF format == An MCF file consists of one or more blocks, each corresponding to an entity. A block looks like this:The identifier is a unique identifier for that entity (more on the scope of the identifier below) and is used to refer to that entity. The following lines each specify a property and one or more values, separated by commas. Each value can be a reference to another entity (via its identifier), a string (enclosed by double quotes) or a number. For example:NOTE: The identifier must not include a comma (,) and must not be enclosed within double quotes. A common parsing failure is due to odd number of unescaped double quotes in text. For instance, "foo bar" baz" needs to be "foo bar\" baz". Commas within double quotes are not considered as value separators. Every entity has at least one property: typeOf.

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  • InRule Technology

    InRule Technology

    InRule Technology is a software company that offers Business Rule Management System (BRMS) enterprise software products. == History == InRule Technology's Chief Executive Officer Rik Chomko and Chief Technology Officer Loren Goodman founded InRule Technology in Chicago in 2002. Paul Hessinger joined InRule Technology in 2004 as chief executive officer and chairman of the board and served until his retirement in 2015. They work with companies in several markets, including financial services, public sector, healthcare, and insurance. In 2007, InRule Technology became a charter member of the Microsoft Business Process Alliance. In August 2019, InRule was acquired by Open Gate Capital. == Products == On October 29, 2012, InRule Technology launched InRule for Microsoft Dynamics CRM. The program provides components to enable creation and update of rules within Microsoft Dynamics CRM, InRule for Microsoft Dynamics CRM provides a platform for shops that prefer to work with Microsoft's platforms. With the availability of InRule 4.6 in 2014, the company introduced deployment of InRule through REST services and allowed REST services to be called from InRule. This enables access to data exposed as a REST service and to package up a rule service for RESTful access. The product launch reflected the move of the company's core audience to use a broader array of technologies despite an earlier focus on .NET. In 2017, InRule introduced InRule for the Salesforce Platform, as well as a technology partnership with Work-Relay, a Business Process Management (BPM) application built on the Salesforce Platform. One year earlier the company introduced InRule for JavaScript, allowing enterprises to run rules on the client-side, server-side or both. The software architecture includes multiple components, including irAuthor, the primary authoring tool for creating and maintaining rules; irVerify, a real-time test environment to run and debug rule applications; and irSDK, a set of APIs that allows developers to integrate inRule into their applications. Additionally, irSOA allows users to access the InRule rule engine as a service. irSOA is now called the irServer Execution Service.

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  • Desktop Window Manager

    Desktop Window Manager

    Desktop Window Manager (DWM, previously Desktop Compositing Engine or DCE in builds of pre-reset Windows Longhorn) is the compositing window manager in Microsoft Windows since Windows Vista that enables the use of hardware acceleration to render the graphical user interface of Windows. It was originally created to enable portions of the new "Windows Aero" user experience, which allowed for effects such as transparency, 3D window switching and more. It is also included with Windows Server 2008, but requires the "Desktop Experience" feature and compatible graphics drivers to be installed. == Architecture == The Desktop Window Manager is a compositing window manager, meaning that each program has a buffer that it writes data to; DWM then composites each program's buffer into a final image. By comparison, the stacking window manager in Windows XP and earlier (and also Windows Vista and Windows 7 with Windows Aero disabled) comprises a single display buffer to which all programs write. DWM works in different ways depending on the operating system (Windows 7 or Windows Vista) and on the version of the graphics drivers it uses (WDDM 1.0 or 1.1). Under Windows 7 and with WDDM 1.1 drivers, DWM only writes the program's buffer to the video RAM, even if it is a graphics device interface (GDI) program. This is because Windows 7 supports (limited) hardware acceleration for GDI and in doing so does not need to keep a copy of the buffer in system RAM so that the CPU can write to it. Because the compositor has access to the graphics of all applications, it easily allows visual effects that string together visuals from multiple applications, such as transparency. DWM uses DirectX to perform the function of compositing and rendering in the GPU, freeing the CPU of the task of managing the rendering from the off-screen buffers to the display. However, it does not affect applications painting to the off-screen buffers – depending on the technologies used for that, this might still be CPU-bound. DWM-agnostic rendering techniques like GDI are redirected to the buffers by rendering the user interface (UI) as bitmaps. DWM-aware rendering technologies like WPF directly make the internal data structures available in a DWM-compatible format. The window contents in the buffers are then converted to DirectX textures. The desktop itself is a full-screen Direct3D surface, with windows being represented as a mesh consisting of two adjacent (and mutually inverted) triangles, which are transformed to represent a 2D rectangle. The texture, representing the UI chrome, is then mapped onto these rectangles. Window transitions are implemented as transformations of the meshes, using shader programs. With Windows Vista, the transitions are limited to the set of built-in shaders that implement the transformations. Greg Schechter, a developer at Microsoft has suggested that this might be opened up for developers and users to plug in their own effects in a future release. DWM only maps the primary desktop object as a 3D surface; other desktop objects, including virtual desktops as well as the secure desktop used by User Account Control are not. Because all applications render to an off-screen buffer, they can be read off the buffer embedded in other applications as well. Since the off-screen buffer is constantly updated by the application, the embedded rendering will be a dynamic representation of the application window and not a static rendering. This is how the live thumbnail previews and Windows Flip work in Windows Vista and Windows 7. DWM exposes a public API that allows applications to access these thumbnail representations. The size of the thumbnail is not fixed; applications can request the thumbnails at any size - smaller than the original window, at the same size or even larger - and DWM will scale them properly before returning. Aero Flip does not use the public thumbnail APIs as they do not allow for directly accessing the Direct3D textures. Instead, Aero Flip is implemented directly in the DWM engine. The Desktop Window Manager uses Media Integration Layer (MIL), the unmanaged compositor which it shares with Windows Presentation Foundation, to represent the windows as composition nodes in a composition tree. The composition tree represents the desktop and all the windows hosted in it, which are then rendered by MIL from the back of the scene to the front. Since all the windows contribute to the final image, the color of a resultant pixel can be decided by more than one window. This is used to implement effects such as per-pixel transparency. DWM allows custom shaders to be invoked to control how pixels from multiple applications are used to create the displayed pixel. The DWM includes built-in Pixel Shader 2.0 programs which compute the color of a pixel in a window by averaging the color of the pixel as determined by the window behind it and its neighboring pixels. These shaders are used by DWM to achieve the blur effect in the window borders of windows managed by DWM, and optionally for the areas where it is requested by the application. Since MIL provides a retained mode graphics system by caching the composition trees, the job of repainting and refreshing the screen when windows are moved is handled by DWM and MIL, freeing the application of the responsibility. The background data is already in the composition tree and the off-screen buffers and is directly used to render the background. In pre-Vista Windows OSs, background applications had to be requested to re-render themselves by sending them the WM_PAINT message. DWM uses double-buffered graphics to prevent flickering and tearing when moving windows. The compositing engine uses optimizations such as culling to improve performance, as well as not redrawing areas that have not changed. Because the compositor is multi-monitor aware, DWM natively supports this too. During full-screen applications, such as games, DWM does not perform window compositing and therefore performance will not appreciably decrease. On Windows 8 and Windows Server 2012, DWM is used at all times and cannot be disabled, due to the new "start screen experience" implemented. Since the DWM process is usually required to run at all times on Windows 8, users experiencing an issue with the process are seeing memory usage decrease after a system reboot. This is often the first step in a long list of troubleshooting tasks that can help. It is possible to prevent DWM from restarting temporarily in Windows 8, which causes the desktop to turn black, the taskbar grey, and break the start screen/modern apps, but desktop apps will continue to function and appear just like Windows 7 and Vista's Basic theme, based on the single-buffer renderer used by XP. They also use Windows 8's centered title bar, visible within Windows PreInstallation Environment. Starting up Windows without DWM will not work because the default lock screen requires DWM unlike the fallback lockscreen that appears as a command line interface program when Windows.UI.Logon.dll isn't present on Windows versions such as 1507 and later, so it can only be done on the fly, and does not have any practical purposes. Starting with Windows 10, disabling DWM in such a way will cause the entire compositing engine to break, even traditional desktop apps, due to Universal App implementations in the taskbar and new start menu. Windows can still be partially usable without the presence of DWM but requires Sihost.exe to not be present due to it relying on DWM. Most of the applications in Windows 11 require DWM to render UI elements and transparency, Windows 11's new task manager requires dwm to render menus unlike the fallback -d version. Unlike its predecessors, Windows 8 supports basic display adapters through Windows Advanced Rasterization Platform (WARP), which uses software rendering and the CPU to render the interface rather than the graphics card. This allows DWM to function without compatible drivers, but not at the same level of performance as with a normal graphics card. DWM on Windows 8 also adds support for stereoscopic 3D. == Redirection == For rendering techniques that are not DWM-aware, output must be redirected to the DWM buffers. With Windows, either GDI or DirectX can be used for rendering. To make these two work with DWM, redirection techniques for both are provided. With GDI, which is the most used UI rendering technique in Microsoft Windows, each application window is notified when it or a part of it comes in view and it is the job of the application to render itself. Without DWM, the rendering rasterizes the UI in a buffer in video memory, from where it is rendered to the screen. Under DWM, GDI calls are redirected to use the Canonical Display Driver (cdd.dll), a software renderer. A buffer equal to the size of the window is allocated in system memory and CDD.DLL outputs to this buffer rather than the video memory. Another buffer is allocated in the video memory to represent t

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  • Jensen Huang

    Jensen Huang

    Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i

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  • Lobsang Monlam

    Lobsang Monlam

    Geshe Lobsang Monlam (Tibetan: དགེ་བཤེས་བློ་བཟང་སྨོན་ལམ, Wylie: dge bshes blo bzang smon lam), born in 1976 in Ngawa eastern Tibet, is a Tibetan Buddhist scholar and programmer who uses digital technologies to preserve the Tibetan language and culture. He is best known for developing Tibetan typefaces and for the multi-volume Great Monlam Tibetan Dictionary. In 2025, he received the Snow Lion Award for Human Rights from the International Campaign for Tibet. He is also working on developing a "Dalai Lama AI," a specialized language model. == Biography == Lobsang Monlam was born in 1976 in Ngawa, eastern Tibet, anciently Tibetan Amdo, where he became a monk at the age of 12.. At the age of 17, in 1993, Lobsang Monlam fled Tibet by crossing the Himalayas to reach southern India and discovered computer science in a monastery. In 1993, he was ordained monk in the Sera Mey College in Bylakuppe, Karnataka, India, where he obtained a Geshe title in 2013.. By the early 2000s, Lobsang Monlam had already learned to paint thangkas and to compose plans and drawings. He used this knowledge to design a new assembly hall for Sera Mey, which the monks needed. Thanks to his work, Lobsang Monlam received donations from patrons of the monastery, which he was able to use to buy his first computer. He bought his first laptop in 2002 and largely taught himself how to use the hardware and software with the help of manuals. As a Buddhist scholar, he combines meditation practice with his digital work. In 2012, he founded and directs the Monlam Tibetan Information Technology Research Center in Dharamsala, which specializes in Tibetan language and software projects. Since then, he is its director, researching Tibetan language-related software. In 2019, advised by the 14th Dalai Lama, he founded Monlam IT and Research (OPC) Private Limited. Since the 2000s, Monlam has been developing Tibetan typefaces; the first Monlam Tibetan font was created in 2005. Under his direction, the Monlam Great Tibetan Dictionary was created, comprising 223 printed volumes and over 300,000 entries; approximately 150 people worked on this project for over nine years. On May 27, 2022, the Dalai Lama inaugurated the Monlam Tibetan Dictionary, produced by the Monlam Tibetan Information Technology Research Center, at Namgyal Monastery in McLeod Ganj. According to Penpa Tsering, this is the world's largest dictionary, created with guidance from the Dalai Lama, based on proposals from Lobsang Monlam and his team under the direction of Samdhong Rinpoche, and other lamas from all schools of Tibetan Buddhism and Yungdrung Bön. On December 5, 2024, Lobsang Monlam testified at a hearing of the US Congressional-Executive Commission on China in Washington, chaired by Christopher Smith, on the difficulties of preserving the Tibetan language and culture in Tibet and the Tibetan diaspora, and on the interest of the Monlam Tibetan Informatics Research Center in developing technologies for the preservation of the Tibetan language. On December 12, 2024, the work was presented to the Library of Congress in Washington, D.C., and launched at an event. The free Monlam Great Tibetan Dictionary app is available in several languages; the German version was created in collaboration with the Tibet Institute Rikon and has been downloaded millions of times. In total, Monlam has created over 37 apps related to the Tibetan language and translation; In 2023, its center launched the Monlam artificial intelligence platform, equipped with modules for machine translation, optical character recognition, speech transcription and speech synthesis.. For their efforts, he and Sophie Richardson received the Snow Lion Award in 2025, which was presented by Richard Gere and came with a prize of €3,000. In 2019, he started a PhD at Bangalore University on Library Science. He obtained his doctorate on November 30, 2023. Currently, he spearheads Monlam AI. Lobsang Monlam is developing "Dalai Lama AI" to digitally preserve the teachings of the 14th Dalai Lama, now 90 years old, for future generations. Lobsang Monlam states, "If we succeed in preserving the Dalai Lama, we also preserve the movement."

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  • Lighthill report

    Lighthill report

    Artificial Intelligence: A General Survey, commonly known as the Lighthill report, is a scholarly article by James Lighthill, published in Artificial Intelligence: a paper symposium in 1973. It was compiled by Lighthill for the British Science Research Council as an evaluation of academic research in the field of artificial intelligence (AI). The report gave a very pessimistic prognosis for many core aspects of research in this field, stating that "In no part of the field have the discoveries made so far produced the major impact that was then promised". It "formed the basis for the decision by the British government to end support for AI research in most British universities", contributing to an AI winter in the United Kingdom. == Publication history == It was commissioned by the SRC in 1972 for Lighthill to "make a personal review of the subject [of AI]". Lighthill completed the report in July. The SRC discussed the report in September, and decided to publish it, together with some alternative points of view by Stuart Sutherland, Roger Needham, Christopher Longuet-Higgins, and Donald Michie. The SRC's decision to invite the report was partly a reaction to high levels of discord within the University of Edinburgh's Department of Artificial Intelligence, one of the earliest and biggest centres for AI research in the UK. On May 9, 1973, Lighthill debated several leading AI researchers (Donald Michie, John McCarthy, Richard Gregory) at the Royal Institution in London concerning the report. == Content == While the report was supportive of research into the simulation of neurophysiological and psychological processes, it was "highly critical of basic research in foundational areas such as robotics and language processing". The report stated that AI researchers had failed to address the issue of combinatorial explosion when solving problems within real-world domains. That is, the report states that whilst AI techniques may have worked within the scope of small problem domains, the techniques would not scale up well to solve more realistic problems. The report represents a pessimistic view of AI that began after early excitement in the field. The report divides AI research into three categories: Advanced Automation ("A"): applications of AI, such as optical character recognition, mechanical component design and manufacture, missile perception and guidance, etc. Computer-based Central Nervous System research ("C"): building computational models of human brains (neurobiology) and behavior (psychology). Bridge, or Building Robots ("B"): research that combines categories A and C. This category is intentionally vague. Projects in category A had had some success, but only in restricted domains where a large quantity of detailed knowledge was used in designing the program. This was disappointing to researchers who hoped for generic methods. Due to the issue of the combinatorial explosion, the amount of detailed knowledge required by the program quickly grew too large to be entered by hand, thus restricting projects to restricted domains. Projects in category C had had some measure of success. Artificial neural networks were successfully used to model neurobiological data. SHRDLU demonstrated that human use of language, even in fine details, depends on the semantics or knowledge, and is not purely syntactical. This was influential in psycholinguistics. Attempts to extend SHRDLU to larger domains of discourse was considered impractical, again due to the issue of the combinatorial explosion. Projects in category B were held to be failures. One important project, that of "programming and building a robot that would mimic human ability in a combination of eye-hand co-ordination and common-sense problem solving", was considered entirely disappointing. Similarly, chess playing programs were no better than human amateurs. Due to the combinatorial explosion, the run-time of general algorithms quickly grew impractical, requiring detailed problem-specific heuristics. The report stated that it was expected that within the next 25 years, category A would simply become applied technologies engineering, C would integrate with psychology and neurobiology, while category B would be abandoned.

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  • Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing (CMAA) is an antialiasing technique originally developed by Filip Strugar at Intel. CMAA is an image-based, post processing technique similar to that of morphological antialiasing. CMAA uses 4 main steps which are image analysis for color discontinuities, locally dominant edge detection, simple shape handling, and lastly symmetrical long edge shape handling. A couple of years after CMAA was introduced, Intel unveiled an updated version which they named CMAA2.

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  • Stockfish (chess)

    Stockfish (chess)

    Stockfish is a free and open-source chess engine, available for various desktop and mobile platforms. It can be used in chess software through the Universal Chess Interface. Stockfish has been one of the strongest chess engines in the world for several years. It has won all main events of the Top Chess Engine Championship (TCEC) and the Chess.com Computer Chess Championship (CCC) since 2020 and, as of May 2026, is the strongest CPU chess engine in the world with an estimated Elo rating of 3653 in a time control of 40/15 (15 minutes to make 40 moves), according to CCRL. The Stockfish engine was developed by Tord Romstad, Marco Costalba, and Joona Kiiski, and was derived from Glaurung, an open-source engine by Tord Romstad released in 2004. It is now being developed and maintained by the Stockfish community. Stockfish historically used only a classical hand-crafted function to evaluate board positions, but with the introduction of the efficiently updatable neural network (NNUE) in August 2020, Stockfish 12 adopted a hybrid evaluation system that primarily used the neural network and occasionally relied on the hand-crafted evaluation. In July 2023, Stockfish removed the hand-crafted evaluation and transitioned to a fully neural network-based approach. == Features == Stockfish uses a tree-search algorithm based on alpha–beta search with several hand-designed heuristics. Stockfish represents positions using bitboards. Stockfish supports Chess960, a feature it inherited from Glaurung. Support for Syzygy tablebases, previously available in a fork maintained by Ronald de Man, was integrated into Stockfish in 2014. In 2018, support for the 7-man Syzygy was added, shortly after the tablebase was made available. Stockfish supports an unlimited number of CPU threads in multiprocessor systems, with a maximum transposition table size of 32 TB. Stockfish has been a very popular engine on various platforms. On desktop, it is the default chess engine bundled with the Internet Chess Club interface programs BlitzIn and Dasher. On mobile, it has been bundled with the Stockfish app, SmallFish and Droidfish. Other Stockfish-compatible graphical user interfaces (GUIs) include Fritz, Arena, Stockfish for Mac, and PyChess. Stockfish can be compiled to WebAssembly or JavaScript, allowing it to run in the browser. Both Chess.com and Lichess provide Stockfish in this form in addition to a server-side program. Release versions and development versions are available as C++ source code and as precompiled versions for Microsoft Windows, macOS, Linux 32-bit/64-bit and Android. == History == The program originated from Glaurung, an open-source chess engine created by Tord Romstad and first released in 2004. Four years later, Marco Costalba forked the project, naming it Stockfish because it was "produced in Norway and cooked in Italy" (Romstad is Norwegian and Costalba is Italian). The first version, Stockfish 1.0, was released in November 2008. For a while, new ideas and code changes were transferred between the two programs in both directions, until Romstad decided to discontinue Glaurung in favor of Stockfish, which was the stronger engine at the time. The last Glaurung version (2.2) was released in December 2008. Around 2011, Romstad decided to abandon his involvement with Stockfish in order to spend more time on his new iOS chess app. On 18 June 2014 Marco Costalba announced that he had "decided to step down as Stockfish maintainer" and asked that the community create a fork of the current version and continue its development. An official repository, managed by a volunteer group of core Stockfish developers, was created soon after and currently manages the development of the project. === Fishtest === Since 2013, Stockfish has been developed using a distributed testing framework named Fishtest, where volunteers can donate CPU time for testing improvements to the program. Changes to game-playing code are accepted or rejected based on results of playing of tens of thousands of games on the framework against an older "reference" version of the program, using sequential probability ratio testing. Tests on the framework are verified using the chi-squared test, and only if the results are statistically significant are they deemed reliable and used to revise the software code. After the inception of Fishtest, Stockfish gained 120 Elo points in 12 months, propelling it to the top of all major rating lists. As of May 2026, the framework has used a total of more than 20,100 years of CPU time to play over 10 billion chess games. === NNUE === In June 2020, Stockfish introduced the efficiently updatable neural network (NNUE) approach, based on earlier work by computer shogi programmers. Instead of using manually designed heuristics to evaluate the board, this approach introduced a neural network trained on millions of positions which could be evaluated quickly on CPU. On 2 September 2020, the twelfth version of Stockfish was released, incorporating NNUE, and reportedly winning ten times more game pairs than it loses when matched against version eleven. In July 2023, the classical evaluation was completely removed in favor of the NNUE evaluation. == Competition results == === Top Chess Engine Championship === Stockfish is a TCEC multiple-time champion and the current leader in trophy count. Ever since TCEC restarted in 2013, Stockfish has finished first or second in every season except one. Stockfish finished second in TCEC Season 4 and 5, with scores of 23–25 first against Houdini 3 and later against Komodo 1142 in the Superfinal event. Season 5 was notable for the winning Komodo team as they accepted the award posthumously for the program's creator Don Dailey, who succumbed to an illness during the final stage of the event. In his honor, the version of Stockfish that was released shortly after that season was named "Stockfish DD". On 30 May 2014, Stockfish 170514 (a development version of Stockfish 5 with tablebase support) convincingly won TCEC Season 6, scoring 35.5–28.5 against Komodo 7x in the Superfinal. Stockfish 5 was released the following day. In TCEC Season 7, Stockfish again made the Superfinal, but lost to Komodo with a score of 30.5–33.5. In TCEC Season 8, despite losses on time caused by buggy code, Stockfish nevertheless qualified once more for the Superfinal, but lost 46.5–53.5 to Komodo. In Season 9, Stockfish defeated Houdini 5 with a score of 54.5–45.5. Stockfish finished third during season 10 of TCEC, the only season since 2013 in which Stockfish had failed to qualify for the superfinal. It did not lose a game but was still eliminated because it was unable to score enough wins against lower-rated engines. After this technical elimination, Stockfish went on a long winning streak, winning seasons 11 (59–41 against Houdini 6.03), 12 (60–40 against Komodo 12.1.1), and 13 (55–45 against Komodo 2155.00) convincingly. In Season 14, Stockfish faced a new challenger in Leela Chess Zero, eking out a win by one point (50.5–49.5). Its winning streak was finally ended in Season 15, when Leela qualified again and won 53.5–46.5, but Stockfish promptly won Season 16, defeating AllieStein 54.5–45.5, after Leela failed to qualify for the Superfinal. In Season 17, Stockfish faced Leela again in the superfinal, losing 52.5–47.5. However, Stockfish has won every Superfinal since: beating Leela 53.5–46.5 in Season 18, 54.5–45.5 in Season 19, 53–47 in Season 20, and 56–44 in Season 21. In Season 22, Komodo Dragon beat out Leela to qualify for the Superfinal, losing to Stockfish by a large margin 59.5–40.5. Stockfish did not lose an opening pair in this match. Leela made the Superfinal in Seasons 23 and 24, but was crushed by Stockfish both times (58.5–41.5 and 58–42). In Season 25, Stockfish once again defeated Leela, but this time by a narrower margin of 52–48. Stockfish also took part in the TCEC cup, winning the first edition, but was surprisingly upset by Houdini in the semifinals of the second edition. Stockfish recovered to beat Komodo in the third-place playoff. In the third edition, Stockfish made it to the finals, but was defeated by Leela Chess Zero after blundering in a 7-man endgame tablebase draw. It turned this result around in the fourth edition, defeating Leela in the final 4.5–3.5. In TCEC Cup 6, Stockfish finished third after losing to AllieStein in the semifinals, the first time it had failed to make the finals. Since then, Stockfish has consistently won the tournament, with the exception of the 11th edition which Leela won 8.5–7.5. === Chess.com Computer Chess Championship === Ever since Chess.com hosted its first Chess.com Computer Chess Championship in 2018, Stockfish has been the most successful engine. It dominated the earlier championships, winning six consecutive titles before finishing second in CCC7. Since then, its dominance has come under threat from the neural-network engines Leelenstein and Leela Chess Zero, but it has continued to perform w

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  • Sasha Stiles

    Sasha Stiles

    Sasha Stiles (born 1980) is an American artist and poet. After discovering natural language processing, she created the 2021 poetry collection Technelegy through an eponymous AI model, before presenting the 2025–2026 installation A Living Poem at the Museum of Modern Art. In addition to artificial intelligence, binary code and non-fungible tokens have been important aspects of her work. == Biography == Stiles was born in 1980 in Pasadena, California, to documentary filmmaker parents whose work includes Cosmos: A Personal Voyage. She was interested in science fiction during her youth, particularly how they addressed human-machine collaboration and posthumanism. She graduated magna cum laude from Harvard University with a Bachelor of Arts in 2002 and she graduated with high honors from the University of Oxford with a Master of Studies in 2004. Originally, Stiles's poetry focused on technology. In 2017, she discovered natural language processing, piquing her interest in its ability to process thoughts and words comparably to its human counterparts. Despite lacking a technological background, she managed to channel people like Gwern Branwen, Ross Goodwin, and Allison Parrish as inspirations for her AI work, and in 2019, she started training an AI model named Technelegy. In 2021, Black Spring Press published her poetry collection Technelegy, where she combines AI-generated content produced by the titular AI model with her own traditionally-created work; the AI-generated content was produced by processing Stiles's own poetry onto GPT-2 and GPT-3. She and Technelegy later co-created A Living Poem, which ran at the Museum of Modern Art's Hyundai Card Digital Wall from September 2025 to March 2026. Stiles also has used non-fungible tokens as a platform for her poetry, having been inspired to go into blockchain by her experiences working with a metaverse exhibition curated by Jess Conatser. She has used Christie's and SuperRare to sell several of her poems as tokenized real-world assets, including Daughter of E.V.E. (Ex-Vivo Uterine Environment), a 2021 single-channel video using freeze-frame shots to hide poetry. In 2021, she co-founded TheVerseVerse (stylized as theVERSEverse), a non-fungible token gallery specializing in poetry. She later created Four Core Texts: Humanifesto and Other Poems, involving four NFT videos of poetry written in looping handwriting and powered by Technelegy. Stiles uses binary code as an inspiration for her work, citing in part its "quite antagonistic system of a binary 'EITHER / OR'", which she connected to several dichotomies pitting humanity and the present against technology and the future. In 2018, she started Analog Binary Code, where she creates sculptures by arranging objects in binary code ciphers. She also created Cursive Binary, where she combines binary with cursive handwriting, after writing zeros and ones on a steamed wall while showering. Stiles and the robot BINA48 co-created the 2020 ArtYard exhibition A Valentine for the Future. She was part of the 2021 group exhibition Computational Poetics at the Beall Center for Art and Technology. From February 24 to March 18, 2023, she held her solo show Binary Odes (stylized as B1NARY 0DES) at Annka Kultys Gallery. By 2024, her work had appeared in places such as Gucci storefronts and Times Square billboards. She designed Words Beyond Words, the official poster for Art Basel in Basel 2025. Stiles is based in Milford, New Jersey, where she lives with her husband, musician Kris Bones. She has also lived in Jersey City and Bucks County, Pennsylvania. She is Kalmyk-American on her mother's side, and she has also announced plans to create a version of Technelegy in her ancestral language Kalmyk.

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  • Maia and Marco

    Maia and Marco

    Maia and Marco are artificial intelligence used by GMA Network. Unveiled in 2023, they are used to fulfill the role of sports newscasters. == Background == Maia and Marco are artificial intelligence (AI) which take the form of three-dimensional human avatars. Maia makes use of a female avatar while Marco uses a male likeness. They have aesthetic features that are typical to Filipino showbusiness personalities. Among the technologies used in making and operating the AI include image generation, text-to-speech AI voice synthesis/generation, and deep learning face animation. They are also demonstrated to be bilingual, being able to speak in English and Tagalog (Filipino). == Use == The AI pair was unveiled by GMA Network on September 24, 2023, for their coverage of Season 99 of the National Collegiate Athletic Association (NCAA). Fulfilling the role of sports newscasters, Maia and Marco would join GMA's courtside human reporters. The AI pair are scheduled to appear four times a month on GMA's digital media platforms. They will not appear in traditional television broadcast. == Reception == The launch of the Maia and Marco was met with strong reactions. Various journalists and other personalities across the Philippine media industry expressed concern that their employment be at risk with the introduction of AI. The quality of the AI ability to emulate human behavior was characterized by critics as "soulless". GMA responding to concerns has stated that the AI would complement rather than replace its live human journalists including sportscasters. The National Union of Journalists of the Philippines urged dialogue among its peers in the newsroom on policy on how to use AI, which the group acknowledge as "inevitable".

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  • Hekaton (database)

    Hekaton (database)

    Hekaton (also known as SQL Server In-Memory OLTP) is an in-memory database for OLTP workloads built into Microsoft SQL Server. Hekaton was designed in collaboration with Microsoft Research and was released in SQL Server 2014. Traditional RDBMS systems were designed when memory resources were expensive, and were optimized for disk storage. Hekaton is instead optimized for a working set stored entirely in main memory, but is still accessible via T-SQL like normal tables. It is fundamentally different from the "DBCC PINTABLE" feature in earlier SQL Server versions. Hekaton was announced at the Professional Association for SQL Server (PASS) conference 2012.

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  • No Fakes Act

    No Fakes Act

    The NO FAKES Act or the Nurture Originals, Foster Art, and Keep Entertainment Safe Act, is proposed United States federal legislation concerning digital replicas. The bill was first introduced in 2023 as a discussion draft, formally introduced in 2024, and reintroduced in 2025. If enacted, the bill would establish a federal right of publicity, giving public figures and private individuals greater control over the creation and use of digital replicas of their likenesses, including artificial intelligence (AI)-generated content. If passed, the NO FAKES Act would create a legal framework for licensing digital replicas, including provisions for liability, safe harbors, and statutory exceptions. The proposal has received broad support from the entertainment and technology industries. However, digital rights organizations have raised concerns that the Act risks chilling protected speech. == Background == === Entertainment industry concerns === Actors’ concerns over studios' use of their digital likeness were one of the primary drivers of the Screen Actors Guild–American Federation of Television and Radio Artists (SAG-AFTRA) strike in 2023. Negotiators for SAG-AFTRA alleged that the Alliance of Motion Picture and Television Producers (AMPTP) sought to use the digital likenesses of actors in perpetuity and would try to replace union members, especially background actors. The AMPTP denied SAG-AFTRA's interpretation of its proposal. In November 2023, AMPTP and SAG-AFTRA reached an agreement on the use of actors’ digital replicas, which included requirements for consent and compensation. Recording labels have also expressed concerns over unauthorized digital replicas of their performers' likeness. In 2023, TikTok user Ghostwriter977 released "Heart on My Sleeve," an AI-produced song in the styles of Drake and the Weeknd. After the song received millions of streams, the Universal Music Group (UMG) initiated takedown requests to TikTok and YouTube, which removed the song from their platforms. The legal arguments attorneys made were not disclosed; however, commentators noted that they likely used the Digital Millennium Copyright Act (DMCA). This presented a novel scenario, since UMG did not have licensing rights to "Heart on My Sleeve." According to The Verge, UMG based its DMCA takedown request on an unauthorized sample used at the start of the song for the producer tag. While legal commentators noted that UMG could have asserted a violation of the artists’ rights of publicity, existing state right of publicity laws do not provide notice-and-takedown mechanisms comparable to those under the DMCA. === Legal landscape === Legal scholars have observed that AI-generated digital replicas raise questions under existing copyright and intellectual property law. U.S. copyright law generally requires that original authorship be attributable to a human; however, the extent of human intervention needed to satisfy this requirement is not clear. Copyright holders have filed lawsuits against AI companies alleging unauthorized usage of copyrighted material to train their models, though many of these cases remain pending. In terms of outputs, record labels often hold rights to artists’ musical works but do not necessarily control the artists’ voice, appearance, or likeness in the same way. As a result, AI-generated recordings such as "Heart on My Sleeve" may fall outside the scope of certain traditional copyright protections. Individuals' likenesses have historically been governed under the Lanham Act, the Federal Trade Commission Act, and right of publicity laws. The right of publicity, recognized in many state-level statutes and common law, allows individuals to bring legal claims against unauthorized commercial use of their identities. It has often, but not exclusively, been applied to celebrities or other recognizable individuals. There is no federal-level right to publicity, and state-level protections vary, especially on issues relating to digital replicas and posthumous rights, which makes it difficult for creators or other individuals to prevent unauthorized use of their likenesses. In July 2024, the U.S. Copyright Office released a report on digital replicas and recommended that Congress create a federal law to protect individuals from unauthorized uses of their digital replicas, noting the inadequacy, narrowness, and inconsistency of existing laws. == Provisions == Under the NO FAKES Act of 2025, a digital replica is defined as "a newly created, computer-generated, highly realistic electronic representation that is readily identifiable as the voice or visual likeness of an individual," living or dead. A digital replica can be embodied in sound recordings, images, or audiovisual works in which the individual did not perform or in which the individual did perform but the "fundamental character of the performance or appearance has been materially altered." The Act specifies that digital replicas do not include reproduced samples of works authorized by the copyright holder. The Act defines a "right holder" as either the individual who is the subject of a digital replica or an entity that has acquired the rights to that individual’s likeness. The Act grants right holders the exclusive right to authorize the use of an individual’s likeness in a digital replica. This right is not assignable during the individual’s lifetime; however, it can be licensed to a living individual for up to 10 years under certain conditions. Postmortem rights The Act provides that the right does not automatically expire upon an individual’s death. It may be transferred to executors, heirs, or other parties designated by the individual. The right is held by the right holder for 10 years following the individual’s death. If the right holder demonstrates active use of the digital replica within the 2 years preceding the end of the 10-year term, the right may be extended for an additional 5-year period. These five-year extensions may be renewed for up to 70 years after the individual’s death. Liability The Act establishes liability for individuals who knowingly distribute a digital replica without authorization from the right holder, as well as for entities that make available a service primarily designed to produce unlawful digital replicas. Safe harbor provisions Similar to the Communications Decency Act and the DMCA, the Act establishes safe harbor provisions for online service providers. Providers are shielded from liability if they adopt and inform users of a policy for terminating accounts that repeatedly violate the Act. The NO FAKES Act does not require online services to proactively monitor content. Instead, it creates a notice-and-takedown mechanism under which providers must promptly respond to notifications seeking the removal of unauthorized digital replicas. These safe harbor protections apply only if the online service provider designates an agent with the U.S. Copyright Office to receive notifications of alleged violations. Remedies The NO FAKES Act provides remedies that are similar to those available under U.S. copyright law. Under the Act, individuals may be held liable for either statutory damages of $5,000 or actual damages for creating or distributing an unauthorized digital replica. The legislation also establishes a tiered liability framework for online service providers. Those that make good faith efforts to comply with the Act may face statutory damages of up to $25,000 per work for violations or actual damages. Providers that do not undertake such compliance efforts may be liable for $5,000 per unauthorized display or transmission of a digital replica, with damages capped at $750,000 per work. Exclusions The Act includes several exceptions to liability that are modeled in part on fair use principles. Digital replicas are excluded from liability when "used in a bona fide news, public affairs, or sports broadcast or account;" in a documentary or historical context; or in a way that is "consistent with the public interest." These exclusions do not apply to de minimis uses or to digital replicas that are sexually explicit in nature. The Act further states that licensing requirements do not apply to licenses established through collective bargaining agreements that contain provisions governing the use of digital replicas. The Act does not impose secondary liability on providers of generative artificial intelligence tools or services whose primary purpose is not the creation of unauthorized digital replicas. Preemption The NO FAKES Act preempts laws that protect "an individual's voice and visual likeness rights in connection with a digital replica, as defined in this Act, in an expressive work." However, the Act preserves state laws governing digital replicas enacted before January 2, 2025, as well as state laws addressing digital replicas that portray sexually explicit conduct. == History == In 2023, Senators Marsha Blackburn, Chris Coons, Amy Klobuchar, and Th

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  • Simple Knowledge Organization System

    Simple Knowledge Organization System

    Simple Knowledge Organization System (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data. == History == === DESIRE II project (1997–2000) === The most direct ancestor to SKOS was the RDF Thesaurus work undertaken in the second phase of the EU DESIRE project . Motivated by the need to improve the user interface and usability of multi-service browsing and searching, a basic RDF vocabulary for Thesauri was produced. As noted later in the SWAD-Europe workplan, the DESIRE work was adopted and further developed in the SOSIG and LIMBER projects. A version of the DESIRE/SOSIG implementation was described in W3C's QL'98 workshop, motivating early work on RDF rule and query languages: A Query and Inference Service for RDF. === LIMBER (1999–2001) === SKOS built upon the output of the Language Independent Metadata Browsing of European Resources (LIMBER) project funded by the European Community, and part of the Information Society Technologies programme. In the LIMBER project CCLRC further developed an RDF thesaurus interchange format which was demonstrated on the European Language Social Science Thesaurus (ELSST) at the UK Data Archive as a multilingual version of the English language Humanities and Social Science Electronic Thesaurus (HASSET) which was planned to be used by the Council of European Social Science Data Archives CESSDA. === SWAD-Europe (2002–2004) === SKOS as a distinct initiative began in the SWAD-Europe project, bringing together partners from both DESIRE, SOSIG (ILRT) and LIMBER (CCLRC) who had worked with earlier versions of the schema. It was developed in the Thesaurus Activity Work Package, in the Semantic Web Advanced Development for Europe (SWAD-Europe) project. SWAD-Europe was funded by the European Community, and part of the Information Society Technologies programme. The project was designed to support W3C's Semantic Web Activity through research, demonstrators and outreach efforts conducted by the five project partners, ERCIM, the ILRT at Bristol University, HP Labs, CCLRC and Stilo. The first release of SKOS Core and SKOS Mapping were published at the end of 2003, along with other deliverables on RDF encoding of multilingual thesauri and thesaurus mapping. === Semantic web activity (2004–2005) === Following the termination of SWAD-Europe, SKOS effort was supported by the W3C Semantic Web Activity in the framework of the Best Practice and Deployment Working Group. During this period, focus was put both on consolidation of SKOS Core, and development of practical guidelines for porting and publishing thesauri for the Semantic Web. === Development as W3C Recommendation (2006–2009) === The SKOS main published documents — the SKOS Core Guide, the SKOS Core Vocabulary Specification, and the Quick Guide to Publishing a Thesaurus on the Semantic Web — were developed through the W3C Working Draft process. Principal editors of SKOS were Alistair Miles, initially Dan Brickley, and Sean Bechhofer. The Semantic Web Deployment Working Group, chartered for two years (May 2006 – April 2008), put in its charter to push SKOS forward on the W3C Recommendation track. The roadmap projected SKOS as a Candidate Recommendation by the end of 2007, and as a Proposed Recommendation in the first quarter of 2008. The main issues to solve were determining its precise scope of use, and its articulation with other RDF languages and standards used in libraries (such as Dublin Core). === Formal release (2009) === On August 18, 2009, W3C released the new standard that builds a bridge between the world of knowledge organization systems – including thesauri, classifications, subject headings, taxonomies, and folksonomies – and the linked data community, bringing benefits to both. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use SKOS to leverage the power of linked data. === Historical view of components === SKOS was originally designed as a modular and extensible family of languages, organized as SKOS Core, SKOS Mapping, and SKOS Extensions, and a Metamodel. The entire specification is now complete within the namespace http://www.w3.org/2004/02/skos/core#. == Overview == In addition to the reference itself, the SKOS Primer (a W3C Working Group Note) summarizes the Simple Knowledge Organization System. The SKOS defines the classes and properties sufficient to represent the common features found in a standard thesaurus. It is based on a concept-centric view of the vocabulary, where primitive objects are not terms, but abstract notions represented by terms. Each SKOS concept is defined as an RDF resource. Each concept can have RDF properties attached, including: one or more preferred index terms (at most one in each natural language) alternative terms or synonyms definitions and notes, with specification of their language Concepts can be organized in hierarchies using broader-narrower relationships, or linked by non-hierarchical (associative) relationships. Concepts can be gathered in concept schemes, to provide consistent and structured sets of concepts, representing whole or part of a controlled vocabulary. === Element categories === The principal element categories of SKOS are concepts, labels, notations, documentation, semantic relations, mapping properties, and collections. The associated elements are listed in the table below. === Concepts === The SKOS vocabulary is based on concepts. Concepts are the units of thought—ideas, meanings, or objects and events (instances or categories)—which underlie many knowledge organization systems. As such, concepts exist in the mind as abstract entities which are independent of the terms used to label them. In SKOS, a Concept (based on the OWL Class) is used to represent items in a knowledge organization system (terms, ideas, meanings, etc.) or such a system's conceptual or organizational structure. A ConceptScheme is analogous to a vocabulary, thesaurus, or other way of organizing concepts. SKOS does not constrain a concept to be within a particular scheme, nor does it provide any way to declare a complete scheme—there is no way to say the scheme consists only of certain members. A topConcept is (one of) the upper concept(s) in a hierarchical scheme. === Labels and notations === Each SKOS label is a string of Unicode characters, optionally with language tags, that are associated with a concept. The prefLabel is the preferred human-readable string (maximum one per language tag), while altLabel can be used for alternative strings, and hiddenLabel can be used for strings that are useful to associate, but not meant for humans to read. A SKOS notation is similar to a label, but this literal string has a datatype, like integer, float, or date; the datatype can even be made up (see 6.5.1 Notations, Typed Literals and Datatypes in the SKOS Reference). The notation is useful for classification codes and other strings not recognizable as words. === Documentation === The Documentation or Note properties provide basic information about SKOS concepts. All the properties are considered a type of skos:note; they just provide more specific kinds of information. The property definition, for example, should contain a full description of the subject resource. More specific note types can be defined in a SKOS extension, if desired. A query for skos:note ? will obtain all the notes about , including definitions, examples, and scope, history and change, and editorial documentation. Any of these SKOS Documentation properties can refer to several object types: a literal (e.g., a string); a resource node that has its own properties; or a reference to another document, for example using a URI. This enables the documentation to have its own metadata, like creator and creation date. Specific guidance on SKOS documentation properties can be found in the SKOS Primer Documentary Notes. === Semantic relations === SKOS semantic relations are intended to provide ways to declare relationships between concepts within a concept scheme. While there are no restrictions precluding their use with two concepts from separate schemes, this is discouraged because it is likely to overstate what can be known about the two schemes, and perhaps link them inappropriately. The property related simply makes an association relationship between two concepts; no hierarchy or generality relation is implied. The properties broader and narrower are used to assert a direct hierarchical link between two concepts. The meaning may be unexpected; the relat

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