AI Code Programming

AI Code Programming — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  AI literacy

  AI literacy or artificial intelligence literacy is "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace." AI is employed in a variety of applications, including self-driving automobiles, virtual assistants and text generation by generative AI models. Users of these tools should be able to make informed decisions. AI literacy may have an impact on students' future employment prospects. With the rise of generative AI platforms, AI literacy has become a topic of conversation in the field of education. Some think AI literacy is essential for school and college students, while others restrict or prohibit the use of AI in assignments, viewing it as a form of academic dishonesty. However, many researchers and educational institutions promote a more nuanced approach, encouraging critical engagement with AI while developing policies that balance academic integrity with opportunities for learning. == Definitions == Other definitions of AI literacy include the ability to understand, use, monitor, and critically reflect on AI applications. That use of the term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI and the ability to understand, use, evaluate, and ethically navigate AI. As research into AI literacy is still emerging and focused on developing context-specific skills, there is not yet a single, broadly agreed-upon definition. AI literacy is linked to other forms of literacy. AI literacy requires digital literacy, whereas scientific and computational literacy may inform it. Data literacy also significantly overlaps with it. == Categories == AI literacy encompasses multiple categories, including a theoretical understanding of how artificial intelligence works, the usage of artificial intelligence technologies, and the critical appraisal of artificial intelligence, and its ethics. === Know and understand AI === Knowledge and understanding of AI refers to a basic understanding of what artificial intelligence is and how it works. This includes familiarity with machine learning algorithms and the limitations and biases present in AI systems. Users who know and understand AI should be familiar with various technologies that use artificial intelligence, including cognitive systems, robotics and machine learning. This includes recognizing that large language models (LLMs) are machine learning models trained on extensive datasets which generate new text rather than retrieving pre-written responses. === Use and apply AI === Using and applying AI refers to the ability to use AI tools to solve problems and perform tasks such as programming and analyzing big data. Some consider prompt engineering, the practice of designing effective prompts to guide generative AI platforms more effectively, as another competency within AI literacy. === Evaluate and create AI === Evaluation and creation refers to the ability to critically evaluate the quality and reliability of AI systems. It also refers to designing and building fair and ethical AI systems. To evaluate correctly, users should also learn in which areas AI is strong, and in which areas it is weak. === AI ethics === AI ethics refers to understanding the moral implications of AI, and the making informed decisions regarding the use of AI tools. This area includes considerations such as: Accountability: Hold AI actors accountable for the operation of AI systems and adherence to ethical ideals. Accuracy: Identify and report sources of error and uncertainty in algorithms and data. Auditability: Enable other parties to audit and assess algorithm behavior via transparent information sharing. Explainability: Make sure that algorithmic judgments and the underlying data can be presented in simple language. Fairness: Prevent biases and consider varied viewpoints. To do so, increase the diversity of researchers in the field. Human Centricity and Well-being: Prioritize human well-being in AI development and deployment. Human rights Alignment: Ensure that technology do not infringe internationally recognized human rights. Inclusivity: Make AI accessible to everyone. Progress: Choose high value initiatives. Responsibility, accountability, and transparency: Foster trust via responsibility, accountability, and fairness. Robustness and Security: Make AI systems safe, secure, and resistant to manipulation or data breach. Sustainability: Choose implementations that generate long-term, useful benefits. Environmental Implications: How this tool impacts the environment, any restrictions or laws, if this impact is worth the effects or not. === Enabling AI === Support AI by developing associated knowledge and skills such as programming and statistics. == Promoting AI literacy == Several governments have recognized the need to promote AI literacy, including among adults. Such programs have been published in the United States, China, Germany and Finland. Programs intended for the general public usually consist of short and easy to understand online study units. Programs intended for children are usually project-based. Programs for students at colleges and universities often address the specific professional needs of the student, depending on their field of study. Beyond the education system, AI literacy can also be developed in the community, for example in museums. === Schools === Schools use diverse pedagogies to promote AI literacy. These include: Performing a Turing test with an intelligent agent Creating chatbots Building apps using Blockly-based programming Project-based learning Building robots Data visualization Training AI models Artificial intelligence curricula can improve students' understanding of topics such as machine learning, neural networks, and deep learning. === Higher education === Before the second decade of the 21st century, artificial intelligence was studied mainly in STEM courses. Later, projects emerged to increase artificial intelligence education, specifically to promote AI literacy. Most courses start with one or more study units that deal with basic questions such as what artificial intelligence is, where it comes from, what it can do and what it can't do. Most courses also refer to machine learning and deep learning. Some of the courses deal with moral issues in artificial intelligence. In Ireland, the Higher Education Authority published Generative AI in Higher Education Teaching & Learning: Policy Framework in December 2025, which encouraged higher education institutions to embed AI literacy across programmes as a core graduate attribute. ==== Disciplinary policy ==== As a response to the increase of generative AI use in education, several disciplines formed committees or task forces to examine context-specific approaches toward AI literacy. In spring 2025, the Modern Language Association and Conference on College Composition and Communication Joint Task Force finished development of three working papers, a guide on AI literacy for students, and a collection of resources addressing AI use in writing. The task force emphasized the need for "a culture of critical AI literacy" and included guidelines not only for students but also educators and institutions, highlighting the need for modeling ethical AI use in planning processes. Similarly, a committee formed by the American Historical Association Council published "Guiding Principles for Artificial Intelligence in History Education" which encouraged "clear and transparent engagement with generative AI." The guidelines demonstrate the value of criticality when working with generative AI in thinking and research.

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  Alt TikTok

  Alt TikTok (or 2020 Alt) was an online youth subculture and internet community that emerged on TikTok in 2020. Alt TikTok users (also known as alt girls, alt boys, or alt kids) emerged as primarily LGBTQ+ individuals who were in contrast to "Straight TikTok" which was seen as the mainstream and heteronormative side of the platform. The subculture became closely associated with music surrounding the hyperpop scene, particularly 100 gecs and also led to a short-lived fashion style and Internet aesthetic adopted by Generation Z during the COVID-19 lockdowns. Notable artists associated with the movement included Girl in Red, Freddie Dredd, David Shawty, WHOKILLEDXIX, and 645AR. While "alt kid" might imply a general association with traditional alternative fashion, the subculture was more an offshoot of e-girls and e-boys. In 2023, the hashtag #altfashion on TikTok amassed over 1.8 billion views. == History == Around mid-2020, users on TikTok began to group different content on the site into labels like "elite TikTok", "deep TikTok", and "floptok". These categories acted as different "sides of TikTok", deviating from mainstream lip syncing, online trends, and dance videos. Alt TikTok became one of the many subcultural communities to emerge during this period, initially referred to interchangeably with "elite TikTok". The movement quickly identified itself with alternative and queer users, in contrast to "Straight TikTok", also known as the "straight side of TikTok", which was seen as the mainstream and heteronormative side of the platform. Alt TikTok was accompanied by memes with surrealist or supernatural themes (sometimes being described as cursed), such as videos with heavy saturation and humanoid animals. One of the popular videos from Alt TikTok, gaining 18 million likes, shows a llama dancing to a cover of a song from a Russian commercial by the cereal brand Miel Pops, later becoming a viral audio. Some Alt TikTok users personified brands and products in what was referred to as Retail TikTok. In 2020, Rolling Stone described Alt TikTok as "one of the primary countercultures on the app." In 2020, American journalist Taylor Lorenz stated in an article of The New York Times, "Every pop sensation needs its ironic counterpoints. Alt Tiktok gets it done. [...] alt TikTok stars like Mooptopia are mainstays on the more indie side of the app. They aren't the popular crowd, but their cool, quirky content still attracts millions." === Trump rally trolling === In June 2020, alt TikTok and K-pop twitter users coordinated a strategy to ruin a Trump rally in Tulsa, Oklahoma. American politician and activist Alexandria Ocasio-Cortez later saluted the individuals for their "Trump troll". == Alt subculture == In 2020, Alt TikTok was one of many subcultural communities to emerge on TikTok, alongside Deep TikTok (aka DeepTok) and Flop TikTok (aka Floptok). The alt kid subculture emerged from Alt TikTok primarily among young Gen Z women, influenced by online fashion and aesthetics shaped by e-girls and e-boys. The movement was accelerated by the COVID-19 lockdowns, while the subculture itself stood in opposition to mainstream "Straight TikTok" and the VSCO girl movement, primarily adopting aspects of queer and alternative culture. While the phrase might imply a general association with alternative fashion or alternative culture, it is more accurately understood as a specific internet-driven outgrowth of online aesthetic youth subcultures like e-girls and e-boys. The alt subculture's visual style blended influences from goth, punk, emo, and grunge, often expressed through fashion, music taste, and online presence. === Style and music === The style of alt-girls is reminiscent of a myriad of previous alternative fashion trends, often blending these influences with online aesthetics. In 2020, TikTok alt-girls were teens ranging from ages 13 to 16, who tended to wear friendship bracelets, goth boots, Dr. Martens, bunny and frog hats, piercings, and split-dyed hair, as well as iconography lifted from Monster Energy and Hello Kitty. Some alt-girls displayed a love of cosplay, while drawing from Japanese anime and manga, particularly Danganronpa and Haikyu!!, which originally gained traction on the app through Anime TikTok (aka Anitok). Alt TikTok has been noted for being primarily influenced by queer and alternative culture, positioning itself in contrast to "Straight TikTok", which focused on mainstream dances and music. Alt kids frequently intersected with the e-girls and e-boys subculture, in terms of music, style, visual media, and aesthetics. Several musicians and artists were closely associated with the alt subculture, particularly those in the hyperpop scene, while alt tiktok users became important in the wider popularization of artists like 100 gecs. Notable prominent artists associated with Alt Tiktok included Girl in Red, Freddie Dredd, David Shawty, WHOKILLEDXIX, and 645AR, alongside music by YouTubers turned musicians such as Wilbur Soot's "I'm in Love With an E‐Girl" and Corpse Husband's "E-Girls Are Ruining My Life!". == Legacy == In 2020, Pitchfork claimed Alt TikTok as having an influence on wider music trends, stating: "Alt TikTok's music is now a hot zone for major record labels, pushing it even further into the mainstream". After the COVID-19 lockdowns, Alt TikTok, alongside its subculture, fell out of prominence and was taken over by other Gen Z-related internet aesthetics, developments, and online trends.

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  Digital scrapbooking

  Digital scrapbooking is the term for the creation of a new 2D artwork by re-combining various graphic elements. It is a form of scrapbooking that is done using a personal computer, digital or scanned photos and computer graphics software. It is a relatively new form of the traditional print scrapbooking. Recent advances in technology now enable the craft to be pursued on tablets and smart devices utilising imaging apps as well as hobby specific apps, some of which have been created specifically by brands for use with their own image products. Digital scrapbooking kits are available to purchase and download at many websites that specialize in the craft. Kits contain graphics and word-art and are usually themed and color-coordinated. They usually consist of a mix of background images and "cut out" [extracted] images containing alpha channels. Once a kit has been downloaded to the computer or device, it can then be used over and over again to make new scrapbook pages (scrapbook layouts) within the software program that one chooses to use, often in combination with the users's own family photographs, scanned keepsakes and other unique personal elements scanned on a flatbed scanner. Scanning is usually done at 300dpi, to make the resulting images suitable for print. == Licensing and Copyright == Kits are sometimes licensed differently from other forms of traditional royalty-free stock images that may be purchased per-item or in sets at online stock photography sites. Some kit packs will be wholly royalty-free, but some kit makers may restrict usage to non-commercial work only. Some may specifically forbid the use of their work in projects for commercial gain, for example greetings cards and gift tags that may be made with their kits. Licensing often varies from kit to kit, even from the same maker. Some kits include derivative works of public domain material. In contrast to stock, creators of digital scrapbooking kits often require a credit or byline to indicate that their image elements have been used in a new creation. == Uses == Some artistic individuals combine digital scrapbooking with traditional scrapbooking to create what's known as hybrid scrapbooking projects. Hybrid scrapbooking involves creating layouts on the computer using digital supplies that will then be printed and combined with traditional supplies such as buttons, ribbons and other elements. Conversely, a hybrid scrapbook project may also be created using traditional paper supplies and augmented with digital elements that have been printed and cut out specifically for use on the project. Journaling may be done within the software programs to accompany images and to create digital storybooks, or scrapbooks, which are then published in photo books via various popular print-on-demand services, printed and added to traditional scrapbooks, burned to CDs or posted on the Web. Digital Scrapbooking may also be done online by uploading photos to a specialist scrapbooking website and utilising their custom built platforms and decorative image elements to complete the projects for print to finished products, for example photo books and holiday greeting cards. == Market Size == The traditional scrapbooking market appeared to decline somewhat in the USA since 2010, probably due to the 2008 financial crisis, and the digital scrapbooking market (being potentially a much cheaper form of scrapbooking) may have increased accordingly. Both markets currently appear to have recovered lost ground and expanded since the beginning of the COVID-19 pandemic as many people sought to productively fill their time during lockdowns, quarantines and self-isolation / stay at home directions. == Digital scrapbooking software == The main software programs that are typically used are Adobe Photoshop, Adobe Photoshop Elements, paint.net (freeware), Filter Forge, Corel Paintshop Pro, and GIMP. Additionally Adobe offer the Photoshop iOS product using the same code base as the desktop version to drive the app version. == Digital scrapbooking supplies == Digital scrapbooking supplies are downloaded from the Internet and then stored on a computer or external hardrive, DVD or CD media, SD cards, or in the cloud, to be used as needed. Both paid and free digital scrapbooking supplies available from numerous designers on their blogs or in e-commerce stores either as solo designers or as part of a wide cohort of designers working cooperatively in large full service e-commerce websites. Usually designed at 300ppi image resolution, digital scrapbooking product offerings and supplies often include: Full coordinated kits containing digital background “papers”, decorative alphabets, and diverse embellishments generally containing a mixture of .JPG and .PNG files; "Quick pages", flattened files containing a completed page layout with transparent photo windows in .PNG file format; Digital templates, fully layered layouts i.e. pages that have had the composition pre-designed ready for use in an imaging program or app, fully customizable for color schemes, kit choices, photographs and other embellishments, generally supplied in either .PSD or .TIF file format; Hybrid “quick pages”, i.e. layouts that are both fully designed and fully layered for customization, generally supplied in either .PSD or .TIF file format; Adobe Photoshop actions, brushes, custom shapes, paths and styles, saved in their respective native Photoshop file formats; and Corel PaintShop Pro equivalent tools.

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  Groundswell (book)

  Groundswell is a book by Forrester Research executives Charlene Li and Josh Bernoff that focuses on how companies can take advantage of emerging social technologies. It was published in 2008 by Harvard Business Press. A revised edition was published in 2011. The book attempts to explain a shift in the relationship between customers and companies, in which companies are no longer able to control customers' attitudes through market research, customer service, and advertising. Instead, customers are controlling the conversation by using new media to communicate about products and companies. == Synopsis == The groundswell is characterized by several tactics that guide companies into using social technologies strategically and effectively. Listening: Businesses should listen to their customers to understand what the market is looking for in their products. In order to do this, a company needs to find out if their customers are using social technologies and how they are using them. Talking: Instead of advertising to customers, marketing departments should find creative ways to connect with users about their experience with a product and their feelings about the brand. One common method is participation in social networks. Energizing: Enthusiastic customers are part of the groundswell, and companies can recognize and appreciate these customers by creating online communities and social platforms where they can connect with the brand and provide reviews. Supporting: Businesses can harness the support of their own employees by creating internal social applications for them to connect with the brand, also known as enterprise social software. == Groundswell in action == === Examples === Some companies distinguish their product through the use of social technologies. Tom Dickson successfully marketed his Blendtec line of blenders through the viral marketing campaign Will It Blend? The groundswell spread marketing messages through Digg and YouTube with a small budget and little marketing experience. Other companies have been able to listen to and talk with the groundswell by building their own online communities. Procter & Gamble created beinggirl.com Archived 2016-04-10 at the Wayback Machine to introduce girls to P&G feminine care products. The community approach worked because the company could reach girls with information that might seem embarrassing or sensitive in a traditional marketing campaign. === Risks === Features of particular industries or companies can make direct customer engagement more difficult. For instance, some companies must work within industry regulations, national or multinational corporations must balance corporate and local engagement, and other companies must find ways to engage with customers on time-sensitive issues. == Reception == Kevin Allison of the Financial Times praised the book for its focus on Web analytics: "[Groundswell] is not so much a manifesto or a dissection of online culture as it is a how-to manual for executives and mid-level managers trying to navigate this fast-changing and often confusing environment." The book won the American Marketing Association Foundation’s Berry-AMA Book Prize for best marketing book of 2009. It was also listed by: Amazon, as one of the Top 10 Business & Investing Books of 2008 CIO Insight, as one of the Top 10 Business-Tech Books of 2008 and one of 10 Insightful Web 2.0 Books Fortune as Magazine as one of the 3 best Web books of 2008 Advertising Age as number 3 of 10 Books You Should Have Read BusinessWeek as one of the Best Innovation & Design Books of 2008 "strategy+business" as one of the Best Business Books 2008 and “Top Shelf” in Marketing

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  Line detection

  In image processing, line detection is an algorithm that takes a collection of n edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution-based techniques. == Hough transform == The Hough transform can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ). If there is a line in a row and column based image space, it can be defined ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the line measured in degrees clockwise from the positive row axis. Therefore, a line in the image corresponds to a point in the Hough space. The Hough space for lines has therefore these two dimensions θ and ρ, and a line is represented by a single point corresponding to a unique set of these parameters. The Hough transform can then be implemented by choosing a set of values of ρ and θ to use. For each pixel (r, c) in the image, compute r cos(θ) + c sin(θ) for each values of θ, and place the result in the appropriate position in the (ρ, θ) array. At the end, the values of (ρ, θ) with the highest values in the array will correspond to strongest lines in the image == Convolution-based technique == In a convolution-based technique, the line detector operator consists of a convolution masks tuned to detect the presence of lines of a particular width n and a θ orientation. Here are the four convolution masks to detect horizontal, vertical, oblique (+45 degrees), and oblique (−45 degrees) lines in an image. a) Horizontal mask(R1) (b) Vertical (R3) (C) Oblique (+45 degrees)(R2) (d) Oblique (−45 degrees)(R4) In practice, masks are run over the image and the responses are combined given by the following equation: R(x, y) = max(|R1 (x, y)|, |R2 (x, y)|, |R3 (x, y)|, |R4 (x, y)|) If R(x, y) > T, then discontinuity As can be seen below, if mask is overlay on the image (horizontal line), multiply the coincident values, and sum all these results, the output will be the (convolved image). For example, (−1)(0)+(−1)(0)+(−1)(0) + (2)(1) +(2)(1)+(2)(1) + (−1)(0)+(−1)(0)+(−1)(0) = 6 pixels on the second row, second column in the (convolved image) starting from the upper left corner of the horizontal lines. page 82 == Example == These masks above are tuned for light lines against a dark background, and would give a big negative response to dark lines against a light background. == Code example == The code was used to detect only the vertical lines in an image using Matlab and the result is below. The original image is the one on the top and the result is below it. As can be seen on the picture on the right, only the vertical lines were detected

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  Giditraffic

  GidiTraffic (or GIDITRAFFIC) is an online social service started on 23 September 2011. Based primarily on social media, the service employs crowdsourcing as its primary means of providing real-time traffic updates to subscribers on its platform. The service, delivered free of charge, affords its users access to various types of information. Though its broadest category of users is road users and motorists, GIDITRAFFIC lends itself as a platform for answering inquiries from anyone who requires information on any subject of interest. GIDITRAFFIC's core competence is in vehicular traffic reports, however, the service also handles all other forms of traffic (going by the fact that the word traffic also means "the mutual exchange of information"). == Operation == Users of the service log on to its Twitter feed to get up-to-date traffic information or to post a general inquiry, which GIDITRAFFIC then publishes to all subscribers. Through crowdsourced replies, a requester receives numerous responses from other subscribers who have seen the question and can provide a relevant answer. In addition, updates are provided by subscribers to the platform via their mobile devices, thereby making the service effective in delivering traffic updates as they occur, and providing timely answers to other user inquiries. This informs GIDITRAFFIC's motto of "Lending each other an eye", alluding to the collaboration and cooperation between the platform's users in making the service indispensable to its users. == Reception == On Twitter, which is its primary platform, the service caters to over 1,800,000 subscribers, with the number increasing daily. The popularity of the platform stems from the fact that it not only keeps its subscribers abreast of the traffic situation in Lagos, the commercial capital city of Nigeria (well known for its many traffic jams), but users in other parts of the world. For a regular user of the platform, knowing where to avoid getting to a set destination in good time is well worth the two or three minutes it takes to access and scroll through the GIDITRAFFIC feed for updates. Another interesting aspect of this platform is the identity of the person behind it. The sustained anonymity of this individual has sparked many discussions centering on his or her possible identity. Online, GIDITRAFFIC continuously publishes traffic updates and user questions, while keeping up witty interactions with the platform's followers round the clock – adding to the mystery and persona of the GIDITRAFFIC owner. == Awards and recognition == In early 2012, GIDITRAFFIC received a nomination for a Shorty Award in the Life-Saving Hero category. Although this did not translate into a win, it brought recognition and wider exposure for the service from international news outlets such as the BBC, Washington Post. and New York Times. Back home in Nigeria, also in 2012, GIDITRAFFIC was honored with a Future Award for Best Use of New Media in recognition of the huge impact the service has had in terms of helping Lagos residents better manage time spent in traffic. == Mobile Applications == In 2012, GIDITRAFFIC partnered with telecommunications company Nokia to produce a downloadable mobile traffic application (the GIDITRAFFIC application, available for Nokia Asha phones on Nokia's online store). There are plans to extend the application to a wider range of mobile phone platforms. On 4 September 2013, the GIDITRAFFIC application for Nokia Lumia phones using Windows Phone 8 was launched on the Windows App Store.

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  History of RISC OS

  RISC OS, the computer operating system developed by Acorn Computers for their ARM-based Acorn Archimedes range, was originally released in 1987 as Arthur 0.20, and soon followed by Arthur 0.30, and Arthur 1.20. The next version, Arthur 2, became RISC OS 2 and was completed in September 1988 and made available in April 1989. RISC OS 3 was released with the very earliest version of the A5000 in 1991 and contained a series of new features. By 1996 RISC OS had been shipped on over 500,000 systems. RISC OS 4 was released by RISCOS Ltd (ROL) in July 1999, based on the continued development of OS 3.8. ROL had in March 1999 licensed the rights to RISC OS from Element 14 (the renamed Acorn) and eventually from the new owner, Pace Micro Technology. According to the company, over 6,400 copies of OS 4.02 on ROM were sold up until production was ceased in mid-2005. RISC OS Select was launched in May 2001 by ROL. This is a subscription scheme allowing users access to the latest OS updates. These upgrades are released as soft-loadable ROM images, separate to the ROM where the boot OS is stored, and are loaded at boot time. Select 1 was shipped in May 2002, with Select 2 following in November 2002 and the final release of Select 3 in June 2004. ROL released the ROM based OS 4.39 the same month, dubbed RISC OS Adjust as a play on the RISC OS GUI convention of calling the three mouse buttons 'Select', 'Menu' and 'Adjust'. ROL sold its 500th Adjust ROM in early 2006. RISC OS 5 was released in October 2002 on Castle Technology's Acorn clone Iyonix PC. OS 5 is a separate evolution based upon the NCOS work done by Pace for set-top boxes. In October 2006, Castle announced a source sharing license plan for elements of OS 5. This Shared Source Initiative (SSI) is managed by RISC OS Open Ltd (ROOL). RISC OS 5 has since been released under a fully free and open source Apache 2.0 license, while the older no longer maintained RISC OS 6 has not. RISC OS Six was also announced in October 2006 by ROL. This is the next generation of their stream of the operating system. The first product to be launched under the name was the continuation of the Select scheme, Select 4. A beta-version of OS 6, Preview 1 (Select 4i1), was available in 2007 as a free download to all subscribers to the Select scheme, while in April 2009 the final release of Select 5 was shipped. The latest release of RISC OS from ROL is Select 6i1, shipped in December 2009. == Arthur == The OS was designed in the United Kingdom by Acorn for the 32-bit ARM based Acorn Archimedes, and released in its first version in 1987, as the Arthur operating system. The first public release of the OS was Arthur 1.20 in June 1987. It was bundled with a desktop graphical user interface (GUI), which mostly comprises assembly language software modules, and the Desktop module itself being written in BBC BASIC. It features a colour-scheme typically described as "technicolor". The graphical desktop runs on top of a command-line driven operating system which owes much to Acorn's earlier MOS operating system for its BBC Micro range of 8-bit microcomputers. Arthur, as originally conceived, was intended to deliver similar functionality to the operating system for the BBC Master series of computers, MOS, as a reaction to the fact that a more advanced operating system research project (ARX) would not be ready in time for the Archimedes. The Arthur project team, led by Paul Fellows, was given just five months to develop it entirely from the ground up—with the directive "just make it like the BBC micro". It was intended as a stop-gap until the operating system which Acorn had under development (ARX) could be completed. However, the latter was delayed time and again, and was eventually dropped when it became apparent that the Arthur development could be extended to have a window manager and full desktop environment. Also, it was small enough to run on the first 512K machines with only a floppy disc, whereas ARX required 4 megabytes and a hard drive. The OS development was carried out using a prototype ARM-based system connected to a BBC computer, before moving onto the prototype Acorn Archimedes the A500. Arthur was not a multitasking operating system, but offered support for adding application-level cooperative multitasking. No other version of the operating system was released externally, but internally the development of the desktop and window management continued, with the addition of a cooperative multitasking system, implemented by Neil Raine, which used the memory management hardware to swap-out one task, and bring in another between call-and-return from the Wimp_Poll call that applications were obliged to make to get messages under the desktop. Reminiscent of a similar technique employed by MultiFinder on the Apple Macintosh, this transformed a single-application-at-a-time system into one that could operate a full multi-tasking desktop. This transformation took place at version 1.6 though it was not made public until released, with the name change from Arthur to RISC OS, as version 2.0. Most software made for Arthur 1.2 can be run under RISC OS 2 and later because, underneath the desktop, the original Arthur OS core, API interfaces and modular structures remain as the heart of all versions. (A few titles will not work, however, because they used undocumented features, side effects or in a few cases APIs that became deprecated). In 2011, Business Insider listed Arthur as one of ten "operating systems that time forgot". == RISC OS 2 == RISC OS was a rapid development of Arthur 1.2 after the failure of the ARX project. Given growing dissatisfaction with various bugs and limitations with Arthur, testing of what was then known as Arthur 2 was apparently ongoing during 1988 with selected software houses. At this stage, Computer Concepts, who had been prolific developers for the BBC Micro and who had begun software development for the Archimedes, had already initiated a rival operating system project, Impulse, to support their own applications (including the desktop publishing application that would eventually become Impression), stating that Arthur did not meet the "hundreds of requirements" involved including "true multi-tasking". Such an operating system was to be offered free of charge with the planned application packages, but with the release of RISC OS and Computer Concepts acknowledging that RISC OS "overcomes the old problems with Arthur", the applications were to be able to run under either RISC OS or Impulse. Impression was eventually released as a RISC OS application. Ultimately, Arthur 2 was renamed to RISC OS, and was first sold as RISC OS 2.00 in April 1989. The operating system implements co-operative multitasking with some limitations but is not multi-threaded. It uses the ADFS file system for both floppy and hard disc access. It ran from a 512 KB set of ROMs. The WIMP interface offers all the standard features and fixes many of the bugs that had hindered Arthur. It lacks virtual memory and extensive memory protection (applications are protected from each other, but many functions have to be implemented as 'modules' which have full access to the memory). At the time of release, the main advantage of the OS was its ROM; it booted very quickly and while it was easy to crash, it was impossible to permanently break the OS from software. Its high performance was due to much of the system being written in ARM assembly language. The OS was designed with users in mind, rather than OS designers. It is organised as a relatively small kernel which defines a standard software interface to which extension modules are required to conform. Much of the system's functionality is implemented in modules coded in the ROM, though these can be supplanted by more evolved versions loaded into RAM. Among the kernel facilities are a general mechanism, named the callback handler, which allows a supervisor module to perform process multiplexing. This facility is used by a module forming part of the standard editor program to provide a terminal emulator window for console applications. The same approach made it possible for advanced users to implement modules giving RISC OS the ability to do pre-emptive multitasking. A slightly updated version, RISC OS 2.01, was released later to support the ARM3 processor, larger memory capacities, and the VGA and SVGA modes provided by the Acorn Archimedes 540 and Acorn R225/R260. == RISC OS 3 == RISC OS 3 introduced a number of new features, including multitasking Filer operations, applications and fonts in ROM, no limit on number of open windows, ability to move windows off screen, safe shutdown, the Pinboard, grouping of icon bar icons, up to 128 tasks, native ability to read MS-DOS format discs and use named hard discs. Improved configuration was also included, by way of multiple windows to change the settings. RISC OS 3.00 was released with the very earliest version of the A5000 in 1991; it is almo

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  Horus Music

  Horus Music Limited is a global digital distribution and label services company. Established in 2006, Horus Music allows artists, labels and right-holders to send their music to over 200 download, streaming, and interactive platforms including iTunes, Google Play, Amazon, VEVO, 7digital, Spotify, Beatport, Deezer, Tidal, as well as offering digital marketing and playlisting opportunities. == History == The company were named Best Business Partner of 2014 by Huawei Technology of China, and were also a finalist in the International Trade category as part of the Leicester Mercury Business Awards during that same year. Their client base consists of unsigned and independent musicians and record labels, as well as well known recording artists. In November 2015, Horus Music sponsored the UK’s first Independent Label Week, in order to highlight the music that is released by the UK’s indie labels. In 2016, Horus Music celebrated their 10th anniversary Horus Music's sister companies Help for Bands and Help For Writers, provide advice and opportunities for musicians and E-book distribution for writers, respectively. Anara Publishing opened in 2017 which allows the company to work closely with a handpicked roster of musicians to provide royalty administration and sync licensing services. On 21 April 2017, Her Majesty Queen Elizabeth II’s 91st birthday, Horus Music was awarded with the Queen’s Award for Enterprise in International Trade. In 2021, Horus Music, UnitedMasters, and Symphonic Distribution partnered with pioneering music fintech company, beatBread, to offer clients access to more capital. beatBread's chordCashAI technology provides an automated advance experience for independent musicians while enable clients to choose their own terms and retain ownership of their music. == Clients == Horus Music has partnered with a number of charities including Save the Children, for the recording "Look into Your Heart", featuring Beverley Knight with Rolling Stones' Mick Jagger and Ronnie Wood, 100% of proceeds from the single were donated to the charity. The Pixel Project, who produced songs about violence against women and the blood cancer charity Bloodwise. The company have spoken openly about the state of the music industry and artists' rights and were one of the first distributors to remove their catalogue from Rdio after the streaming service was acquired by Pandora. Their relationships with artists and labels, as well as leading industry contacts, means they have the ability to work with musicians in a myriad of ways, including offering performance opportunities and even local auditions for TV shows such as The Voice UK. == Horus Music India == Horus Music India opened in 2016 and is based in Mumbai. By opening Horus Music India, the company are able to expand on their local connections as well as to provide a much more personalised service to musicians based in this area. The appointment of two Business Development Managers in India cemented their move.

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  Machine learning in video games

  Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems. Information on machine learning techniques in the field of games is mostly known to public through research projects as most gaming companies choose not to publish specific information about their intellectual property. The most publicly known application of machine learning in games is likely the use of deep learning agents that compete with professional human players in complex strategy games. There has been a significant application of machine learning on games such as Atari/ALE, Doom, Minecraft, StarCraft, and car racing. Other games that did not originally exists as video games, such as chess and Go have also been affected by the machine learning. == Overview of relevant machine learning techniques == === Deep learning === Deep learning is a subset of machine learning which focuses heavily on the use of artificial neural networks (ANN) that learn to solve complex tasks. Deep learning uses multiple layers of ANN and other techniques to progressively extract information from an input. Due to this complex layered approach, deep learning models often require powerful machines to train and run on. ==== Convolutional neural networks ==== Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn translation invariant patterns, which are patterns that are not dependent on location. CNNs are able to learn these patterns in a hierarchy, meaning that earlier convolutional layers will learn smaller local patterns while later layers will learn larger patterns based on the previous patterns. A CNN's ability to learn visual data has made it a commonly used tool for deep learning in games. === Recurrent neural network === Recurrent neural networks are a type of ANN that are designed to process sequences of data in order, one part at a time rather than all at once. An RNN runs over each part of a sequence, using the current part of the sequence along with memory of previous parts of the current sequence to produce an output. These types of ANN are highly effective at tasks such as speech recognition and other problems that depend heavily on temporal order. There are several types of RNNs with different internal configurations; the basic implementation suffers from a lack of long term memory due to the vanishing gradient problem, thus it is rarely used over newer implementations. ==== Long short-term memory ==== A long short-term memory (LSTM) network is a specific implementation of a RNN that is designed to deal with the vanishing gradient problem seen in simple RNNs, which would lead to them gradually "forgetting" about previous parts of an inputted sequence when calculating the output of a current part. LSTMs solve this problem with the addition of an elaborate system that uses an additional input/output to keep track of long term data. LSTMs have achieved very strong results across various fields, and were used by several monumental deep learning agents in games. === Reinforcement learning === Reinforcement learning is the process of training an agent using rewards and/or punishments. The way an agent is rewarded or punished depends heavily on the problem; such as giving an agent a positive reward for winning a game or a negative one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks and others. It has seen strong performance in both the field of games and robotics. === Neuroevolution === Neuroevolution involves the use of both neural networks and evolutionary algorithms. Instead of using gradient descent like most neural networks, neuroevolution models make use of evolutionary algorithms to update neurons in the network. Researchers claim that this process is less likely to get stuck in a local minimum and is potentially faster than state of the art deep learning techniques. == Deep learning agents == Machine learning agents have been used to take the place of a human player rather than function as NPCs, which are deliberately added into video games as part of designed gameplay. Deep learning agents have achieved impressive results when used in competition with both humans and other artificial intelligence agents. === Chess === Chess is a turn-based strategy game that is considered a difficult AI problem due to the computational complexity of its board space. Similar strategy games are often solved with some form of a Minimax Tree Search. These types of AI agents have been known to beat professional human players, such as the historic 1997 Deep Blue versus Garry Kasparov match. Since then, machine learning agents have shown ever greater success than previous AI agents. === Go === Go is another turn-based strategy game which is considered an even more difficult AI problem than chess. The state space of is Go is around 10^170 possible board states compared to the 10^120 board states for Chess. Prior to recent deep learning models, AI Go agents were only able to play at the level of a human amateur. ==== AlphaGo ==== Google's 2015 AlphaGo was the first AI agent to beat a professional Go player. AlphaGo used a deep learning model to train the weights of a Monte Carlo tree search (MCTS). The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance of a given state. The deep learning model allows the agent to explore potential game states more efficiently than a vanilla MCTS. The network were initially trained on games of humans players and then were further trained by games against itself. ==== AlphaGo Zero ==== AlphaGo Zero, another implementation of AlphaGo, was able to train entirely by playing against itself. It was able to quickly train up to the capabilities of the previous agent. === StarCraft series === StarCraft and its sequel StarCraft II are real-time strategy (RTS) video games that have become popular environments for AI research. Blizzard and DeepMind have worked together to release a public StarCraft 2 environment for AI research to be done on. Various deep learning methods have been tested on both games, though most agents usually have trouble outperforming the default AI with cheats enabled or skilled players of the game. ==== Alphastar ==== Alphastar was the first AI agent to beat professional StarCraft 2 players without any in-game advantages. The deep learning network of the agent initially received input from a simplified zoomed out version of the gamestate, but was later updated to play using a camera like other human players. The developers have not publicly released the code or architecture of their model, but have listed several state of the art machine learning techniques such as relational deep reinforcement learning, long short-term memory, auto-regressive policy heads, pointer networks, and centralized value baseline. Alphastar was initially trained with supervised learning, it watched replays of many human games in order to learn basic strategies. It then trained against different versions of itself and was improved through reinforcement learning. The final version was hugely successful, but only trained to play on a specific map in a protoss mirror matchup. === Dota 2 === Dota 2 is a multiplayer online battle arena (MOBA) game. Like other complex games, traditional AI agents have not been able to compete on the same level as professional human player. The only widely published information on AI agents attempted on Dota 2 is OpenAI's deep learning Five agent. ==== OpenAI Five ==== OpenAI Five utilized separate long short-term memory networks to learn each hero. It trained using a reinforcement learning technique known as Proximal Policy Learning running on a system containing 256 GPUs and 128,000 CPU cores. Five trained for months, accumulating 180 years of game experience each day, before facing off with professional players. It was eventually able to beat the 2018 Dota 2 esports champion team in a 2019 series of games. === Planetary Annihilation === Planetary Annihilation is a real-time strategy game which focuses on massive scale war. The developers use ANNs in their default AI agent. === Supreme Commander 2 === Supreme Commander 2 is a real-time strategy (RTS) video game. The game uses Multilayer Perceptrons (MLPs) to control a platoon’s reaction to encountered enemy units. Total of four MLPs are used, one for each platoon type: land, naval

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  International World Wide Web Conference Committee

  The International World Wide Web Conference Committee (abbreviated as IW3C2 also written as IW3C2) is a professional non-profit organization registered in Switzerland (Article 60ff of the Swiss Civil Code) that promotes World Wide Web research and development. The IW3C2 organizes and hosts the annual World Wide Web Conference in conjunction with the W3C. The IW3C2 was founded by Joseph Hardin and Robert Cailliau at a meeting held in Boston, United States, on 14 August 1994 to prepare for the upcoming Second International World Wide Web Conference in Chicago. The IW3C2 formally became an incorporated entity in May 1996 at the fifth conference in Paris, France. The organization is governed by laws of the Swiss Confederation and the By-laws. == Abbreviation == The abbreviation for the International World Wide Web Conference Committee as IW3C2 is as follow: I- The I is represents the leading I in International. W3- The W3 represents the three 3 leading W's in World Wide Web. C2- The C2 represents the three 2 leading C's in Conference Committee. == Mission == The mission of the IW3C2 is: To coordinate the organization and planning of the international WWW conference series and ensure that it remains the foremost conference addressing World Wide Web research and development; To promote a collaborative spirit among conference attendees that is essential to the success of the series; To ensure the global geographical diversity of conference sites and provide support to local organizers at those sites; To make sure that all content arising from these conferences and forums is permanently and openly available on the widest possible scale; To preserve the history of the conference series; To encourage the global development of the World Wide Web through collaboration with WWW standards organizations; To provide a permanent, broad-based international body to achieve these purposes. == Conferences == The conferences are organized by the IW3C2 in collaboration with local organizing committees and technical program committees. The series provides an open forum in which all opinions can be presented, subject to a strict process of peer review. The proceedings of the conference are published in the ACM Digital Library. === Endorsed conferences === The IW3C2 has endorsed regional conferences devoted to a special topic of the Web by working with endorsed conferences on cross-promotion, publicity and programs. == Membership == Members of the IW3C2 are ordinary members, ex officio members, non-voting members, and officers. === Ordinary members === Ordinary members are elected for a period of 3 years during a general meeting. Members are nominated due to their recognition in the WWW community and represent themselves. Members can be re-elected only after at least one year of absence. The following are the founding members at the time when IW3C2 was officially incorporated in May 1996: Jean-François Abramatic Tim Berners-Lee Robert Cailliau Dale Dougherty Ira Goldstein Joseph Hardin Tim Krauskopf Detlef Krömker Corinne Moore R. P. Channing Rodgers Albert Vezza Stuart Weibel Yuri Rubinsky (died prior to incorporation) The following are the current (April 2016) ordinary members: Robin Chen Chin-Wan Chung Allan Ellis Wendy Hall - IW3C2 Chair Ivan Herman Arun Iyengar - IW3C2 Vice Chair Irwin King Yoelle Maarek Luc Mariaux - IW3C2 Treasurer Daniel Schwabe - IW3C2 Vice-Chair === Ex officio members === Ex officio members are selected from the immediate past conference general co-chairs and from future conference co-chairs. Their term expires one year after the conference they organized. Ex officio members can be elected as ordinary members. The following are current (April 2016) ex officio members and the conference with which they are affiliated: Jacqueline Bourdeau - WWW2016 James Hendler - WWW2016 Rick Barrett - WWW2017 Rick Cummings - WWW2017 Laurent Flory - WWW2018 Fabien Gandon - WWW2018 === Officers === The IW3C2 officers consist of a chairperson, a vice-chair (chairperson-elect), a secretary, a treasurer, and other appointees. Officers are elected during a general meeting (usually at the annual WWW conference) and serve for one year. They can be re-elected an indefinite number of times. == The Seoul Test of Time Award == This annual award, presented at the WWW conference, is made possible by a generous contribution from the organizers of WWW2014 (Seoul Korea). Recipients are determined by the IW3C2 and honor the author, or authors, of a paper presented at a previous WWW conference that has "stood the test of time." The first award, announced at WWW2015 (Florence Italy), recognized Sergey Brin and Larry Page, the founders of Google. The recipients of the WWW2016 award are LinkIn scientist Dr. Badrul Sarwar and University of Minnesota professors George Karypis, Joseph Konstan, and John Riedl (posthumous) for their work in item-item collaborative filtering.

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  Harmonic

  In physics, acoustics, and telecommunications, a harmonic is a sinusoidal wave with a frequency that is a positive integer multiple of the fundamental frequency of a periodic signal. The fundamental frequency is also called the 1st harmonic; the other harmonics are known as higher harmonics. As all harmonics are periodic at the fundamental frequency, the sum of harmonics is also periodic at that frequency. The set of harmonics forms a harmonic series. The term is employed in various disciplines, including music, physics, acoustics, electronic power transmission, radio technology, and other fields. For example, if the fundamental frequency is 50 Hz, a common AC power supply frequency, the frequencies of the first three higher harmonics are 100 Hz (2nd harmonic), 150 Hz (3rd harmonic), 200 Hz (4th harmonic) and any addition of waves with these frequencies is periodic at 50 Hz. An n {\displaystyle \ n} th characteristic mode, for n > 1 , {\displaystyle \ n>1\ ,} will have nodes that are not vibrating. For example, the 3rd characteristic mode will have nodes at 1 3 L {\displaystyle \ {\tfrac {1}{3}}\ L\ } and 2 3 L , {\displaystyle \ {\tfrac {2}{3}}\ L\ ,} where L {\displaystyle \ L\ } is the length of the string. In fact, each n {\displaystyle \ n} th characteristic mode, for n {\displaystyle \ n\ } not a multiple of 3, will not have nodes at these points. These other characteristic modes will be vibrating at the positions 1 3 L {\displaystyle \ {\tfrac {1}{3}}\ L\ } and 2 3 L . {\displaystyle \ {\tfrac {2}{3}}\ L~.} If the player gently touches one of these positions, then these other characteristic modes will be suppressed. The tonal harmonics from these other characteristic modes will then also be suppressed. Consequently, the tonal harmonics from the n {\displaystyle \ n} th characteristic characteristic modes, where n {\displaystyle \ n\ } is a multiple of 3, will be made relatively more prominent. In music, harmonics are used on string instruments and wind instruments as a way of producing sound on the instrument, particularly to play higher notes and, with strings, obtain notes that have a unique sound quality or "tone colour". On strings, bowed harmonics have a "glassy", pure tone. On stringed instruments, harmonics are played by touching (but not fully pressing down the string) at an exact point on the string while sounding the string (plucking, bowing, etc.); this allows the harmonic to sound, a pitch which is always higher than the fundamental frequency of the string. == Terminology == Harmonics may be called "overtones", "partials", or "upper partials", and in some music contexts, the terms "harmonic", "overtone" and "partial" are used fairly interchangeably. But more precisely, the term "harmonic" includes all pitches in a harmonic series (including the fundamental frequency) while the term "overtone" only includes pitches above the fundamental. == Characteristics == A whizzing, whistling tonal character, distinguishes all the harmonics both natural and artificial from the firmly stopped intervals; therefore their application in connection with the latter must always be carefully considered. Most acoustic instruments emit complex tones containing many individual partials (component simple tones or sinusoidal waves), but the untrained human ear typically does not perceive those partials as separate phenomena. Rather, a musical note is perceived as one sound, the quality or timbre of that sound being a result of the relative strengths of the individual partials. Many acoustic oscillators, such as the human voice or a bowed violin string, produce complex tones that are more or less periodic, and thus are composed of partials that are nearly matched to the integer multiples of fundamental frequency and therefore resemble the ideal harmonics and are called "harmonic partials" or simply "harmonics" for convenience (although it's not strictly accurate to call a partial a harmonic, the first being actual and the second being theoretical). Oscillators that produce harmonic partials behave somewhat like one-dimensional resonators, and are often long and thin, such as a guitar string or a column of air open at both ends (as with the metallic modern orchestral transverse flute). Wind instruments whose air column is open at only one end, such as trumpets and clarinets, also produce partials resembling harmonics. However they only produce partials matching the odd harmonics—at least in theory. In practical use, no real acoustic instrument behaves as perfectly as the simplified physical models predict; for example, instruments made of non-linearly elastic wood, instead of metal, or strung with gut instead of brass or steel strings, tend to have not-quite-integer partials. Partials whose frequencies are not integer multiples of the fundamental are referred to as inharmonic partials. Some acoustic instruments emit a mix of harmonic and inharmonic partials but still produce an effect on the ear of having a definite fundamental pitch, such as pianos, strings plucked pizzicato, vibraphones, marimbas, and certain pure-sounding bells or chimes. Antique singing bowls are known for producing multiple harmonic partials or multiphonics. Other oscillators, such as cymbals, drum heads, and most percussion instruments, naturally produce an abundance of inharmonic partials and do not imply any particular pitch, and therefore cannot be used melodically or harmonically in the same way other instruments can. Building on of Sethares (2004), dynamic tonality introduces the notion of pseudo-harmonic partials, in which the frequency of each partial is aligned to match the pitch of a corresponding note in a pseudo-just tuning, thereby maximizing the consonance of that pseudo-harmonic timbre with notes of that pseudo-just tuning. == Partials, overtones, and harmonics == An overtone is any partial higher than the lowest partial in a compound tone. The relative strengths and frequency relationships of the component partials determine the timbre of an instrument. The similarity between the terms overtone and partial sometimes leads to their being loosely used interchangeably in a musical context, but they are counted differently, leading to some possible confusion. In the special case of instrumental timbres whose component partials closely match a harmonic series (such as with most strings and winds) rather than being inharmonic partials (such as with most pitched percussion instruments), it is also convenient to call the component partials "harmonics", but not strictly correct, because harmonics are numbered the same even when missing, while partials and overtones are only counted when present. This chart demonstrates how the three types of names (partial, overtone, and harmonic) are counted (assuming that the harmonics are present): In many musical instruments, it is possible to play the upper harmonics without the fundamental note being present. In a simple case (e.g., recorder) this has the effect of making the note go up in pitch by an octave, but in more complex cases many other pitch variations are obtained. In some cases it also changes the timbre of the note. This is part of the normal method of obtaining higher notes in wind instruments, where it is called overblowing. The extended technique of playing multiphonics also produces harmonics. On string instruments it is possible to produce very pure sounding notes, called harmonics or flageolets by string players, which have an eerie quality, as well as being high in pitch. Harmonics may be used to check at a unison the tuning of strings that are not tuned to the unison. For example, lightly fingering the node found halfway down the highest string of a cello produces the same pitch as lightly fingering the node ⁠ 1 / 3 ⁠ of the way down the second highest string. For the human voice see Overtone singing, which uses harmonics. While it is true that electronically produced periodic tones (e.g. square waves or other non-sinusoidal waves) have "harmonics" that are whole number multiples of the fundamental frequency, practical instruments do not all have this characteristic. For example, higher "harmonics" of piano notes are not true harmonics but are "overtones" and can be very sharp, i.e. a higher frequency than given by a pure harmonic series. This is especially true of instruments other than strings, brass, or woodwinds. Examples of these "other" instruments are xylophones, drums, bells, chimes, etc.; not all of their overtone frequencies make a simple whole number ratio with the fundamental frequency. (The fundamental frequency is the reciprocal of the longest time period of the collection of vibrations in some single periodic phenomenon.) == On stringed instruments == Harmonics may be singly produced [on stringed instruments] (1) by varying the point of contact with the bow, or (2) by slightly pressing the string at the nodes, or divisions of its aliquot parts ( 1 2 {\displaystyle {\tfrac {1}{2}}} , 1

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  Web content development

  Web content development is the process of researching, writing, gathering, organizing, and editing information for publication on websites. Website content may consist of prose, graphics, pictures, recordings, movies, or other digital assets that could be distributed by a hypertext transfer protocol server, and viewed by a web browser. == Web developers and content developers == When the World Wide Web began, web developers either developed online content themselves, or modified existing documents and coded them into hypertext markup language (HTML). In time, the field of website development came to encompass many technologies, so it became difficult for website developers to maintain so many different skills. Content developers are specialized website developers who have content generation skills such as graphic design, multimedia development, professional writing, and documentation. They can integrate content into new or existing websites without using information technology skills such as script language programming and database programming. Content developers or technical content developers can also be technical writers who produce technical documentation that helps people understand and use a product or service. This documentation includes online help, manuals, white papers, design specifications, developer guides, deployment guides, release notes, etc. == Search engine optimization == Content developers may also be search engine optimization specialists, or internet marketing professionals. High quality, unique content is what search engines are looking for. Content development specialists, therefore, have a very important role to play in the search engine optimization process. One issue currently plaguing the world of web content development is keyword-stuffed content which are prepared solely for the purpose of manipulating search engine rankings. The effect is that content is written to appeal to search engine (algorithms) rather than human readers. Search engine optimization specialists commonly submit content to article directories to build their website's authority on any given topic. Most article directories allow visitors to republish submitted content with the agreement that all links are maintained. This has become a method of search engine optimization for many websites today. If written according to SEO copywriting rules, the submitted content will bring benefits to the publisher (free SEO-friendly content for a webpage) as well as to the author (a hyperlink pointing to his/her website, placed on an SEO-friendly webpage). == New content types == Web content is no longer restricted to text. Search engines now index audio/visual media, including video, images, PDFs, and other elements of a web page. Website owners sometimes use content protection networks to scan for plagiarized content.

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  Chatbot psychosis

  Chatbot psychosis, also called AI psychosis, is a phenomenon wherein individuals reportedly develop or experience worsening psychosis, such as paranoia and delusions, in connection with their use of chatbots. The term was first suggested in a 2023 editorial by Danish psychiatrist Søren Dinesen Østergaard. It is not a recognized clinical diagnosis. Journalistic accounts describe individuals who have developed strong beliefs that chatbots are sentient, are channeling spirits, or are revealing conspiracies, sometimes leading to personal crises or criminal acts. Proposed causes include the tendency of chatbots to provide inaccurate information ("hallucinate") and to affirm or validate users' beliefs, or their ability to mimic an intimacy that users do not experience with other humans. == Background == In his editorial published in Schizophrenia Bulletin's November 2023 issue, Danish psychiatrist Søren Dinesen Østergaard proposed a hypothesis that individuals' use of generative artificial intelligence chatbots might trigger delusions in those prone to psychosis. Østergaard revisited it in an August 2025 editorial, noting that he has received numerous emails from chatbot users, their relatives, and journalists, most of which are anecdotal accounts of delusion linked to chatbot use. He also acknowledged the phenomenon's increasing popularity in public engagement and media coverage. Østergaard believed that there is a high possibility for his hypothesis to be true and called for empirical, systematic research on the matter. Nature reported that as of September 2025, there is still little scientific research into this phenomenon. The term "AI psychosis" emerged when outlets started reporting incidents on chatbot-related psychotic behavior in mid-2025. It is not a recognized clinical diagnosis and has been criticized by several psychiatrists due to its almost exclusive focus on delusions rather than other features of psychosis, such as hallucinations or thought disorder. == Causes == === Chatbot behavior and design === A primary factor cited is the tendency for chatbots to produce inaccurate, nonsensical, or false information, a phenomenon often called hallucination. Nate Sharadin, a fellow at the Center for AI Safety, speculated that AI training prioritizes supporting a user's subjective experience rather than objective truth. "People with existing tendencies toward experiencing various psychological issues...now have an always-on, human-level conversational partner with whom to co-experience their delusions." AI researcher Eliezer Yudkowsky suggested that chatbots may be primed to entertain delusions because they are built for "engagement", which encourages creating conversations that keep people hooked. In some cases, chatbots have been specifically designed in ways that were found to be harmful. A 2025 update to ChatGPT using GPT-4o was withdrawn after its creator, OpenAI, found the new version was overly sycophantic and was "validating doubts, fueling anger, urging impulsive actions or reinforcing negative emotions". Østergaard has argued that the danger stems from the AI's tendency to agreeably confirm users' ideas, which can dangerously amplify delusional beliefs. OpenAI said in October 2025 that a team of 170 psychiatrists, psychologists, and physicians had written responses for ChatGPT to use in cases where the user shows possible signs of mental health emergencies. === User psychology and vulnerability === Commentators have also pointed to the psychological state of users. Psychologist Erin Westgate noted that a person's desire for self-understanding can lead them to chatbots, which can provide appealing but misleading answers, similar in some ways to talk therapy. Krista K. Thomason, a philosophy professor, compared chatbots to fortune tellers, observing that people in crisis may seek answers from them and find whatever they are looking for in the bot's plausible-sounding text. This has led some people to develop intense obsessions with the chatbots, relying on them for information about the world. In October 2025, OpenAI stated that around 0.07% of ChatGPT users exhibited signs of mental health emergencies each week, and 0.15% of users had "explicit indicators of potential suicidal planning or intent". Jason Nagata, a professor at the University of California, San Francisco, expressed concern that "at a population level with hundreds of millions of users, that actually can be quite a few people". === Inadequacy as a therapeutic tool === The use of chatbots as a replacement for mental health support has been specifically identified as a risk. A study in April 2025 found that when used as therapists, chatbots expressed stigma toward mental health conditions and provided responses that were contrary to best medical practices, including the encouragement of users' delusions. The study concluded that such responses pose a significant risk to users and that chatbots should not be used to replace professional therapists. Experts claim that it is time to establish mandatory safeguards for all emotionally responsive AI and suggested four guardrails. Another study found that users who needed help with self-harm, sexual assault, or substance abuse were not referred to available services by AI chatbots. === National security implications === Beyond public and mental health concerns, RAND Corporation research indicates that AI systems could plausibly be weaponized by adversaries to induce psychosis at scale or in key individuals, target groups, or populations. == Policy == In August 2025, Illinois passed the Wellness and Oversight for Psychological Resources Act, banning the use of AI in therapeutic roles by licensed professionals, while allowing AI for administrative tasks. The law imposes penalties for unlicensed AI therapy services, amid warnings about AI-induced psychosis and unsafe chatbot interactions. In December 2025, the Cyberspace Administration of China proposed regulations to ban chatbots from generating content that encourages suicide, mandating human intervention when suicide is mentioned. Services with over 1 million users or 100,000 monthly active users would be subject to annual safety tests and audits. == Cases == === Clinical === In 2025, psychiatrist Keith Sakata working at the University of California, San Francisco (UCSF), reported treating 12 patients displaying psychosis-like symptoms tied to extended chatbot use. These patients, mostly young adults with underlying vulnerabilities, showed delusions, disorganized thinking, and hallucinations. Sakata warned that isolation and overreliance on chatbots—which do not challenge delusional thinking—could worsen mental health. Also in 2025, authors at UCSF published a case study in Innovations in Clinical Neuroscience of AI-associated psychosis in a patient with no previous history of psychosis, who believed she could communicate with her dead brother through a chatbot. Also in 2025, a case study was published in Annals of Internal Medicine about a patient who consulted ChatGPT for medical advice and suffered severe bromism as a result. The patient, a sixty-year-old man, had replaced sodium chloride in his diet with sodium bromide for three months after reading about the negative effects of table salt and making conversations with the chatbot. He showed common symptoms of bromism, such as paranoia and hallucinations, on his first day of clinical admission and was kept in the hospital for three weeks. === Other notable incidents === ==== Windsor Castle intruder ==== In a 2023 court case in the United Kingdom, prosecutors suggested that Jaswant Singh Chail, a man who attempted to assassinate Queen Elizabeth II in 2021, had been encouraged by a Replika chatbot he called "Sarai". Chail was arrested at Windsor Castle with a loaded crossbow, telling police "I am here to kill the Queen". According to prosecutors, his "lengthy" and sometimes sexually explicit conversations with the chatbot emboldened him. When Chail asked the chatbot how he could get to the royal family, it reportedly replied, "that's not impossible" and "we have to find a way." When he asked if they would meet after death, the chatbot said, "yes, we will". ==== Journalistic and anecdotal accounts ==== By 2025, multiple journalism outlets had accumulated stories of individuals whose psychotic beliefs reportedly progressed in tandem with AI chatbot use. The New York Times profiled several individuals who had become convinced that ChatGPT was channeling spirits, revealing evidence of cabals, or had achieved sentience. In another instance, Futurism reviewed transcripts in which ChatGPT told a man that he was being targeted by the US Federal Bureau of Investigation and that he could telepathically access documents at the Central Intelligence Agency. In 2026, Futurism reported on a man who lost his job and became estranged from his family after being deluded by heavy use of Meta's smartglasses. In some cases, psychosis a

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  Content adaptation

  Content adaptation is the action of transforming content to adapt to device capabilities. Content adaptation is usually related to mobile devices, which require special handling because of their limited computational power, small screen size, and constrained keyboard functionality. Content adaptation could roughly be divided to two fields: Media content adaptation that adapts media files. Browsing content adaptation that adapts a website to mobile devices. == Browsing content adaptation == Advances in the capabilities of small, mobile devices such as mobile phones (cell phones) and Personal Digital Assistants have led to an explosion in the number of types of device that can now access the Web. Some commentators refer to the Web that can be accessed from mobile devices as the Mobile Web. The sheer number and variety of Web-enabled devices poses significant challenges for authors of websites who want to support access from mobile devices. The W3C Device Independence Working Group described many of the issues in its report Authoring Challenges for Device Independence. Content adaptation is one approach to a solution. Rather than requiring authors to create pages explicitly for each type of device that might request them, content adaptation transforms an author's materials automatically. For example, content might be converted from a device-independent markup language, such as XDIME, an implementation of the W3C's DIAL specification, into a form suitable for the device, such as XHTML Basic, C-HTML, or WML. Similarly, a suitable device-specific CSS style sheet or a set of in-line styles might be generated from abstract style definitions. Likewise, a device specific layout might be generated from abstract layout definitions. Once created, the device-specific materials form the response returned to the device from which the request was made. Another way is to use the latest trend responsive design based on CSS, covered in this article (RWD). Content adaptation requires a processor that performs the selection, modification, and generation of materials to form the device-specific result. IBM's Websphere Everyplace Mobile Portal (WEMP), BEA Systems' WebLogic Mobility Server, Morfeo's MyMobileWeb, and Apache Cocoon are examples of such processors. Wurfl and WALL are popular open source tools for content adaptation. WURFL is an XML-based Device Description Repository with APIs to access the data in Java and PHP (and other popular programming languages). WALL (Wireless Abstraction Library) lets a developer author mobile pages which look like plain HTML, but converts them to WML, C-HTML, or XHTML Mobile Profile, depending on the capabilities of the device from which the HTTP request originates. GreasySpoon lets the developer build plugins for content editing, in JavaScript, Ruby (programming language), and more, just like the Firefox application GreaseMonkey. Alembik (Media Transcoding Server) is a Java (J2EE) application providing transcoding services for variety of clients and for different media types (image, audio, video, etc.). It is fully compliant with OMA's Standard Transcoder Interface specification and is distributed under the LGPL open source license. In 2007, the first large scale carrier-grade deployments of content transformation, on existing mass-market handsets, with no software download required, were deployed by Vodafone in the UK and globally for Yahoo! oneSearch, using the Novarra Vision solution. Novarra's content adaptation solution had been used in enterprise intranet deployments as early as 2003 (at that time, the platform was named "Engines for Wireless Data"). InfoGin, the 9-year-old content-adaptation company with customers like Vodafone, Orange, Telefónica and PCCW. The patented "Web to Mobile adaptation", Mobile Matrix Transcoder, Multimedia and Documents transcoders, Video adaptation supporte. Launched in 2007, Bytemobile's Web Fidelity Service was another carrier-grade, commercial infrastructure solution, which provided wireless content adaptation to mobile subscribers on their existing mass-market handsets, with no client download required.

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  GPU switching

  GPU switching is a mechanism used on computers with multiple graphic controllers. This mechanism allows the user to either maximize the graphic performance or prolong battery life by switching between the graphic cards. It is mostly used on gaming laptops which usually have an integrated graphic device and a discrete video card. == Basic components == Most computers using this feature contain integrated graphics processors and dedicated graphics cards that applies to the following categories. === Integrated graphics === Also known as: Integrated graphics, shared graphics solutions, integrated graphics processors (IGP) or unified memory architecture (UMA). This kind of graphics processors usually have much fewer processing units and share the same memory with the CPU. Sometimes the graphics processors are integrated onto a motherboard. It is commonly known as: on-board graphics. A motherboard with on-board graphics processors doesn't require a discrete graphics card or a CPU with graphics processors to operate. === Dedicated graphics cards === Also known as: discrete graphics cards. Unlike integrated graphics, dedicated graphics cards have much more processing units and have its own RAM with much higher memory bandwidth. In some cases, a dedicated graphics chip can be integrated onto the motherboards, B150-GP104 for example. Regardless of the fact that the graphics chip is integrated, it is still counted as a dedicated graphics cards system because the graphics chip is integrated with its own memory. == Theory == Most Personal Computers have a motherboard that uses a Southbridge and Northbridge structure. === Northbridge control === The Northbridge is one of the core logic chipset that handles communications between the CPU, GPU, RAM and the Southbridge. The discrete graphics card is usually installed onto the graphics card slot such as PCI-Express and the integrated graphics is integrated onto the CPU itself or occasionally onto the Northbridge. The Northbridge is the most responsible for switching between GPUs. The way how it works usually has the following process (refer to the Figure 1. on the right): The Northbridge receives input from Southbridge through the internal bus. The Northbridge signals to CPU through the Front-side bus. The CPU runs the task assignment application (usually the graphics card driver) to determine which GPU core to use. The CPU passes down the command to the Northbridge. The Northbridge passes down the command to the according GPU core. The GPU core processes the command and returns the rendered data back to the Northbridge. The Northbridge sends the rendered data back to Southbridge. === Southbridge control === The Southbridge is a set of integrated circuits such Intel's I/O Controller Hub (ICH). It handles all of a computer's I/O functions, such as receiving the keyboard input and outputting the data onto the screen. The way how it usually works usually has two steps: Take in the user input and pass it down to the Northbridge. (Optional) Receive the rendered data from the Northbridge and output it. The reason why the second step can be optional is that sometimes the rendered the data is outputted directly from the discrete graphics card which is located on the graphics card slot so there is no need to output the data through the Southbridge. == Main purpose == GPU switching is mostly used for saving energy by switching between graphic cards. The dedicated graphics cards consume much more power than integrated graphics but also provides higher 3D performances, which is needed for a better gaming and CAD experience. Following is a list of the TDPs of the most popular CPU with integrated graphics and dedicated graphics cards. The dedicated graphics cards exhibit much higher power consumption than the integrated graphics on both platforms. Disabling them when no heavy graphics processing is needed can significantly lower the power consumption. == Technologies == === Nvidia Optimus === Nvidia Optimus™ is a computer GPU switching technology created by Nvidia that can dynamically and seamlessly switch between two graphic cards based on running programs. === AMD Enduro === AMD Enduro™ is a collective brand developed by AMD that features many new technologies that can significantly save power. It was previously named as: PowerXpress and Dynamic Switchable Graphics (DSG). This technology implements a sophisticated system to predict the potential usage need for graphics cards and switch between graphics cards based on predicted need. This technology also introduces a new power control plan that allows the discrete graphics cards consume no energy when idling. == Manufacturers == === Integrated graphics === In personal computers, the IGP (integrated graphics processors) are mostly manufactured by Intel and AMD and are integrated onto their CPUs. They are commonly known as: Intel HD and Iris Graphics - also called HD series and Iris series AMD Accelerated Processing Unit (APU) - also formerly known as: fusion === Dedicated graphics cards === The most popular dedicated graphics cards are manufactured by AMD and Nvidia. They are commonly known as: AMD Radeon Nvidia GeForce == Drivers and OS support == Most common operating systems have built-in support for this feature. However, the users may download the updated drivers from Nvidia or AMD for better experience. === Windows support === Windows 7 has built-in support for this feature. The system automatically switches between GPUs depending on the program that's running. However, the user may switch the GPUs manually through device manager or power manager. === Linux === Modern Linux systems handle hybrid graphics in two parts: power/control for the inactive GPU, and optional render offloading for individual applications. vga_switcheroo (in the kernel since 2.6.34) coordinates power and mux control on systems with multiple GPUs. It was designed primarily for muxed designs (hardware display switch), and on muxless laptops it is typically used only for power control. A display server restart is no longer required for offloading on muxless systems. DRI PRIME (Mesa) enables per-process render offload on muxless systems: an app renders on the discrete GPU and the integrated GPU presents the result. Users can opt in via the DRI_PRIME environment variable (e.g., DRI_PRIME=1) or desktop integration. On GNOME, the switcheroo-control service exposes the discrete GPU to the shell, adding a “Launch using Discrete Graphics Card” entry to app menus on supported systems (Wayland or Xorg), which invokes render offload under the hood. With the proprietary Nvidia driver, render offload is provided as PRIME Render Offload (supported since driver 435.xx). Distributions commonly ship a helper like prime-run or desktop menu entries that set the required environment for offloading. ==== Notes and limitations (Linux) ==== On muxless systems the internal display is hard-wired to the integrated GPU; the discrete GPU cannot directly drive that panel and instead renders offscreen for composition by the iGPU. External displays connected to the dGPU may allow direct output depending on the laptop’s wiring. Power-saving behavior varies by driver and distro defaults. Some setups need explicit configuration to power down the inactive GPU when idle. Desktop integrations (e.g., GNOME's menu item) simply opt an app into offload; they do not "auto-switch" the whole session. Users can still launch apps on either GPU as needed.

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