AI Content Detector Zero

AI Content Detector Zero — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Nouvelle AI

Nouvelle artificial intelligence (Nouvelle AI) is an approach to artificial intelligence pioneered in the 1980s by Rodney Brooks, who was then part of MIT artificial intelligence laboratory. Nouvelle AI differs from classical AI by aiming to produce robots with intelligence levels similar to insects. Researchers believe that intelligence can emerge organically from simple behaviors as these intelligences interacted with the "real world", instead of using the constructed worlds which symbolic AIs typically needed to have programmed into them. == Motivation == The differences between nouvelle AI and symbolic AI are apparent in early robots Shakey and Freddy. These robots contained an internal model (or "representation") of their micro-worlds consisting of symbolic descriptions. As a result, this structure of symbols had to be renewed as the robot moved or the world changed. Shakey's planning programs assessed the program structure and broke it down into the necessary steps to complete the desired action. This level of computation required a large amount time to process, so Shakey typically performed its tasks very slowly. Symbolic AI researchers had long been plagued by the problem of updating, searching, and otherwise manipulating the symbolic worlds inside their AIs. A nouvelle system refers continuously to its sensors rather than to an internal model of the world. It processes the external world information it needs from the senses when it is required. As Brooks puts it, "the world is its own best model--always exactly up to date and complete in every detail." A central idea of nouvelle AI is that simple behaviors combine to form more complex behaviors over time. For example, simple behaviors can include elements like "move forward" and "avoid obstacles." A robot using nouvelle AI with simple behaviors like collision avoidance and moving toward a moving object could possibly come together to produce a more complex behavior like chasing a moving object. === The frame problem === The frame problem describes an issue with using first-order logic (FOL) to express facts about a robot in the world. Representing the state of a robot with traditional FOL requires the use of many axioms (symbolic language) to imply that things about an environment do not change arbitrarily. Nouvelle AI seeks to sidestep the frame problem by dispensing with filling the AI or robot with volumes of symbolic language and instead letting more complex behaviors emerge by combining simpler behavioral elements. === Embodiment === The goal of traditional AI was to build intelligences without bodies, which would only have been able to interact with the world via keyboard, screen, or printer. However, nouvelle AI attempts to build embodied intelligence situated in the real world. Brooks quotes approvingly from the brief sketches that Turing gave in 1948 and 1950 of the "situated" approach. Turing wrote of equipping a machine "with the best sense organs that money can buy" and teaching it "to understand and speak English" by a process that would "follow the normal teaching of a child." This approach was contrasted to the others where they focused on abstract activities such as playing chess. == Brooks' robots == === Insectoid robots === Brooks focused on building robots that acted like simple insects while simultaneously working to remove some traditional AI characteristics. He created insect-like robots, named Allen and Herbert after cognitive science and AI pioneers Allen Newell and Herbert A. Simon. Brooks's insectoid robots contained no internal models of the world. Herbert, for example, discarded a high volume of the information received from its sensors and never stored information for more than two seconds. ==== Allen ==== Allen had a ring of twelve ultrasonic sonars as its primary sensors and three independent behavior-producing modules. These modules were programmed to avoid both stationary and moving objects. With only this module activated, Allen stayed in the middle of a room until an object approached and then it ran away while avoiding obstacles in its way. ==== Herbert ==== Herbert used infrared sensors to avoid obstacles and a laser system to collect 3D data over a distance of about 12 feet. Herbert also carried a number of simple sensors in its "hand." The robot's testing ground was the real world environment of the busy offices and workspaces of the MIT AI lab where it searched for empty soda cans and carried them away, a seemingly goal-oriented activity that emerged as a result of 15 simple behavior units combining. As a parallel, Simon noted that an ant's complicated path is due to the structure of its environment rather than the depth of its thought processes. ==== Other insectoid robots ==== Other robots by Brooks' team were Genghis and Squirt. Genghis had six legs and was able to walk over rough terrain and follow a human. Squirt's behavior modules had it stay in dark corners until it heard a noise, then it would begin to follow the source of the noise. Brooks agreed that the level of nouvelle AI had come near the complexity of a real insect, which raised a question about whether or not insect level-behavior was and is a reasonable goal for nouvelle AI. === Humanoid robots === Brooks' own recent work has taken the opposite direction to that proposed by Von Neumann in the quotations "theorists who select the human nervous system as their model are unrealistically picking 'the most complicated object under the sun,' and that there is little advantage in selecting instead the ant, since any nervous system at all exhibits exceptional complexity." ==== Cog ==== In the 1990s, Brooks decided to pursue the goal of human-level intelligence and, with Lynn Andrea Stein, built a humanoid robot called Cog. Cog is a robot with an extensive collection of sensors, a face, and arms (among other features) that allow it to interact with the world and gather information and experience so as to assemble intelligence organically in the manner described above by Turing. The team believed that Cog would be able to learn and able to find a correlation between the sensory information it received and its actions, and to learn common sense knowledge on its own. As of 2003, all development of the project had ceased.
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Creator economy

The creator economy, also known as influencer economy, is a platform-driven economy in which creators produce content, products, or services and distribute them directly to their audience through social media platforms and emerging technologies. This economic model is based on the ability of creators to build and maintain communities of users, monetizing their creative activity through multiple channels including advertising, sponsorships, product sales, crowdfunding, and subscription-based services. Creators include various professional categories such as social media influencers, YouTubers, bloggers, artists, online educators, podcasters, and independent professionals, who use platforms as infrastructure to reach their audience without necessarily relying on traditional intermediaries in the cultural and media industry. According to Goldman Sachs Research, the ongoing growth of the creator economy will likely benefit companies that possess a combination of factors, including a large global user base, access to substantial capital, robust AI-powered recommendation engines, versatile monetization tools, comprehensive data analytics, and integrated e-commerce options. Examples of creator economy software platforms include YouTube, TikTok, Instagram, Facebook, Twitch, Spotify, Substack, OnlyFans and Patreon. == History == The term "creator" was coined by YouTube in 2011 to be used instead of "YouTube star", an expression that at the time could only apply to famous individuals on the platform. The term has since become omnipresent and is used to describe anyone creating any form of online content. A number of platforms such as TikTok, Snapchat, YouTube, and Facebook have set up funds with which to pay creators. == Criticism == The large majority of content creators derive no monetary gain for their creations, with most of the benefits accruing to the platforms who can make significant revenues from their uploads. As few as 0.1% of creators are able to earn a living through their channels.
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Video game

A video game, computer game, or simply game is an electronic game that involves interaction with a user interface or input device (such as a joystick, controller, keyboard, or motion sensing device) to generate visual feedback from a display device, most commonly shown in a video format on a television set, computer monitor, flat-panel display or touchscreen on handheld devices, or a virtual reality headset. Most modern video games are audiovisual, with audio complement delivered through speakers or headphones, and sometimes also with other types of sensory feedback (e.g., haptic technology that provides tactile sensations). Some video games also allow microphone and webcam inputs for in-game chatting and livestreaming. Video games are typically categorized according to their hardware platform, which traditionally includes arcade video games, console games, and computer games (which includes LAN games, online games, and browser games). More recently, the video game industry has expanded onto mobile gaming through mobile devices (such as smartphones and tablet computers), virtual and augmented reality systems, and remote cloud gaming. Video games are also classified into a wide range of genres based on their style of gameplay and target audience. The first video game prototypes in the 1950s and 1960s were simple extensions of electronic games using video-like output from large, room-sized mainframe computers. The first consumer video game was the arcade video game Computer Space in 1971, which took inspiration from the earlier 1962 computer game Spacewar!. In 1972 came the now-iconic video game Pong and the first home console, the Magnavox Odyssey. The industry grew quickly during the "golden age" of arcade video games from the late 1970s to early 1980s but suffered from the crash of the North American video game market in 1983 due to loss of publishing control and saturation of the market. Following the crash, the industry matured, was dominated by Japanese companies such as Nintendo, Sega, and Sony, and established practices and methods around the development and distribution of video games to prevent a similar crash in the future, many of which continue to be followed. In the 2000s, the core industry centered on "AAA" games, leaving little room for riskier experimental games. Coupled with the availability of the Internet and digital distribution, this gave room for independent video game development (or "indie games") to gain prominence into the 2010s. Since then, the commercial importance of the video game industry has been increasing. The emerging Asian markets and proliferation of smartphone games in particular are altering player demographics towards casual and cozy gaming, and increasing monetization by incorporating games as a service. Today, video game development requires numerous skills, vision, teamwork, and liaisons between different parties, including developers, publishers, distributors, retailers, hardware manufacturers, and other marketers, to successfully bring a game to its consumers. As of 2020, the global video game market had estimated annual revenues of US$159 billion across hardware, software, and services, which is three times the size of the global music industry and four times that of the film industry in 2019, making it a formidable heavyweight across the modern entertainment industry. The video game market is also a major influence behind the electronics industry, where personal computer component, console, and peripheral sales, as well as consumer demands for better game performance, have been powerful driving factors for hardware design and innovation. == Origins == Early video games used interactive electronic devices with various display formats. The earliest example dates to 1947—a "cathode-ray tube amusement device" was filed for a patent on 25 January 1947, by Thomas T. Goldsmith Jr. and Estle Ray Mann, and issued on 14 December 1948, as U.S. Patent 2455992. Inspired by radar display technology, it consisted of an analog device allowing a user to control the parabolic arc of a dot on the screen to simulate a missile being fired at targets, which were paper drawings fixed to the screen. Other early examples include the Nimrod computer at the 1951 Festival of Britain; Christopher Strachey's Checkers, possibly the first game to display visuals on an electronic screen in 1952; OXO, a tic-tac-toe computer game by Alexander S. Douglas for the EDSAC in 1952; Tennis for Two, an electronic interactive game engineered by William Higinbotham in 1958; and Spacewar!, written by Massachusetts Institute of Technology students Martin Graetz, Steve Russell, and Wayne Wiitanen's on a DEC PDP-1 computer in 1962. Each game had different means of display: NIMROD had a panel of lights to play the game of Nim, OXO had a graphical display to play tic-tac-toe, Tennis for Two had an oscilloscope to display a side view of a tennis court, and Spacewar! had the DEC PDP-1's vector display to have two spaceships battle each other. These inventions laid the foundation for modern video games. In 1966, while working at Sanders Associates, Ralph H. Baer devised a system to play a basic table tennis game on a television screen. With the company's approval, Baer created the prototype known as the "Brown Box". Sanders patented Baer's innovations and licensed them to Magnavox, which commercialized the technology as the first home video game console, the Magnavox Odyssey, released in 1972. Separately, Nolan Bushnell and Ted Dabney, inspired by seeing Spacewar! running at Stanford University, devised a similar version running in a smaller coin-operated arcade cabinet using a less expensive computer. This was released as Computer Space, the first arcade video game, in 1971. Bushnell and Dabney went on to form Atari, Inc., and with Allan Alcorn, created their second arcade game in 1972, the hit ping pong-style Pong, which was directly inspired by the table tennis game on the Odyssey. Atari made a home version of Pong, which was released by Christmas 1975. The success of the Odyssey and Pong, both as an arcade game and home machine, launched the video game industry. Both Baer and Bushnell have been titled "Father of Video Games" for their contributions. == Terminology == The term "video game" was developed to describe electronic games played on a video display rather than on a teletype printer, audio speaker, or similar device. This also distinguished from handheld electronic games such as Merlin, which commonly used LED lights for indicators not in combination for imaging purposes. "Computer game" may also be used as a descriptor, as all these types of games essentially require the use of a computer processor; in some cases, it is used interchangeably with "video game". Particularly in the United Kingdom and Western Europe, this is common due to the historic relevance of domestically produced microcomputers. Other terms used include digital game, for example, by the Australian Bureau of Statistics. The term "computer game" can also refer to PC games, which are played primarily on personal computers or other flexible hardware systems, to distinguish them from console games, arcade games, or mobile games. Other terms, such as "television game", "telegame", or "TV game", had been used in the 1970s and early 1980s, particularly for home gaming consoles that rely on connection to a television set. However, these terms were also used interchangeably with "video game" in the 1970s, primarily due to "video" and "television" being synonymous. In Japan, where consoles like the Odyssey were first imported and then made within the country by the large television manufacturers such as Toshiba and Sharp Corporation, such games are known as "TV games", "TV geemu", or "terebi geemu". The term "TV game" is still commonly used into the 21st century. "Electronic game" may also be used to refer to video games, but this also incorporates devices like early handheld electronic games that lack any video output. The first appearance of the term "video game" emerged around 1973. The Oxford English Dictionary cited a 10 November 1973 BusinessWeek article as the first printed use of the term. Though Bushnell believed the term came from a vending magazine review of Computer Space in 1971, a review of the major vending magazines Vending Times and Cashbox showed that the term may have come even earlier, appearing first in a letter dated July 10, 1972. In the letter, Bushnell uses the term "video game" twice. Per video game historian Keith Smith, the sudden appearance suggested that the term had been proposed and readily adopted by those in the field. Around March 1973, Ed Adlum, who ran Cashbox's coin-operated section until 1972 and then later founded RePlay Magazine, covering the coin-op amusement field, in 1975, used the term in an article in March 1973. In a September 1982 issue of RePlay, Adlum is credited with first naming these games as "video games": "RePlay
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AMiner (database)

AMiner (formerly ArnetMiner) is a free online service used to index, search, and mine big scientific data. == Overview == AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, using social network analysis to identify connections between researchers, conferences, and publications. This allows it to provide services such as expert finding, geographic search, trend analysis, reviewer recommendation, association search, course search, academic performance evaluation, and topic modeling. AMiner was created as a research project in social influence analysis, social network ranking, and social network extraction. A number of peer-reviewed papers have been published arising from the development of the system. It has been in operation for more than three years, and has indexed 130,000,000 researchers and more than 265 million publications. The research was funded by the Chinese National High-tech R&D Program and the National Science Foundation of China. AMiner is commonly used in academia to identify relationships between and draw statistical correlations about research and researchers. It has attracted more than 10 million independent IP accesses from 220 countries and regions. The product has been used in Elsevier's SciVerse platform, and academic conferences such as SIGKDD, ICDM, PKDD, WSDM. == Operation == AMiner automatically extracts the researcher profile from the web. It collects and identifies the relevant pages, then uses a unified approach to extract data from the identified documents. It also extracts publications from online digital libraries using heuristic rules. It integrates the extracted researchers’ profiles and the extracted publications. It employs the researcher name as the identifier. A probabilistic framework has been proposed to deal with the name ambiguity problem in the integration. The integrated data is stored into a researcher network knowledge base (RNKB). The principal other product in the area are Google Scholar, Elsevier's Scirus, and the open source project CiteSeer. == History == It was initiated and created by professor Jie Tang from Tsinghua University, China. It was first launched in March 2006. The following provide a list of updates in the past years: March 2006, Version 0.1, Functions include researcher profiling, expert search, conference search, and publication search. The system was developed in Perl; August 2006, Version 1.0, The system was re-implemented in Java; July 2007, Version 2.0, New functions include researcher interest mining, association search, survey paper finding (unavailable now); April 2008, Version 3.0, New functions include query understanding, new GUI, and search log analysis; November 2008, Version 4.0, New functions include graph search, topic modeling, NSF/NSFC funding information extraction; April 2009, Version 5.0, New functions include Profile edition, open API service, Bole search, course search (unavailable now); December 2009, Version 6.0, New functions include academic performance evaluation, user feedback, conference analysis; May 2010, Version 7.0, New functions include name disambiguation, paper-reviewer recommendation, ArnetPage creation; March 2012, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. June 2014, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. December 2015, a completely new version got online. May 2017, professional version got online. April 2018, New functions include Trend Analysis, a deep learning based Name Disambiguation == Resources == AMiner published several datasets for academic research purpose, including Open Academic Graph, DBLP+citation (a data set augmenting citations into the DBLP data from Digital Bibliography & Library Project), Name Disambiguation, Social Tie Analysis. For more available datasets and source codes for research, please refer to.
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Lazy learning

(Not to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries. The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened to this movie/item/tune also ...") is that the data set is continuously updated with new entries (e.g., new items for sale at Amazon, new movies to view at Netflix, new clips at YouTube, new music at Spotify or Pandora). Because of the continuous update, the "training data" would be rendered obsolete in a relatively short time especially in areas like books and movies, where new best-sellers or hit movies/music are published/released continuously. Therefore, one cannot really talk of a "training phase". Lazy classifiers are most useful for large, continuously changing datasets with few attributes that are commonly queried. Specifically, even if a large set of attributes exist - for example, books have a year of publication, author/s, publisher, title, edition, ISBN, selling price, etc. - recommendation queries rely on far fewer attributes - e.g., purchase or viewing co-occurrence data, and user ratings of items purchased/viewed. == Advantages == The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes in the problem domain. At the same time they can reuse a lot of theoretical and applied results from linear regression modelling (notably PRESS statistic) and control. It is said that the advantage of this system is achieved if the predictions using a single training set are only developed for few objects. This can be demonstrated in the case of the k-NN technique, which is instance-based and function is only estimated locally. == Disadvantages == Theoretical disadvantages with lazy learning include: The large space requirement to store the entire training dataset. In practice, this is not an issue because of advances in hardware and the relatively small number of attributes (e.g., as co-occurrence frequency) that need to be stored. Particularly noisy training data increases the case base unnecessarily, because no abstraction is made during the training phase. In practice, as stated earlier, lazy learning is applied to situations where any learning performed in advance soon becomes obsolete because of changes in the data. Also, for the problems for which lazy learning is optimal, "noisy" data does not really occur - the purchaser of a book has either bought another book or hasn't. Lazy learning methods are usually slower to evaluate. In practice, for very large databases with high concurrency loads, the queries are not postponed until actual query time, but recomputed in advance on a periodic basis - e.g., nightly, in anticipation of future queries, and the answers stored. This way, the next time new queries are asked about existing entries in the database, the answers are merely looked up rapidly instead of having to be computed on the fly, which would almost certainly bring a high-concurrency multi-user system to its knees. Larger training data also entail increased cost. Particularly, there is the fixed amount of computational cost, where a processor can only process a limited amount of training data points. There are standard techniques to improve re-computation efficiency so that a particular answer is not recomputed unless the data that impact this answer has changed (e.g., new items, new purchases, new views). In other words, the stored answers are updated incrementally. This approach, used by large e-commerce or media sites, has long been used in the Entrez portal of the National Center for Biotechnology Information (NCBI) to precompute similarities between the different items in its large datasets: biological sequences, 3-D protein structures, published-article abstracts, etc. Because "find similar" queries are asked so frequently, the NCBI uses highly parallel hardware to perform nightly recomputation. The recomputation is performed only for new entries in the datasets against each other and against existing entries: the similarity between two existing entries need not be recomputed. == Examples of Lazy Learning Methods == K-nearest neighbors, which is a special case of instance-based learning. Local regression. Lazy naive Bayes rules, which are extensively used in commercial spam detection software. Here, the spammers keep getting smarter and revising their spamming strategies, and therefore the learning rules must also be continually updated.
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Bainu (website)

Bainu ("how are you?") is a Chinese social networking website written in the Mongolian language. As of 2020 it had about 400,000 users, concentrated in Inner Mongolia. == Core features and positioning == Language and Cultural Characteristics Bainu is based on Traditional Mongolian Script and supports social interactions in the Mongolian language, including various message formats such as text, voice, images, and video. This design aims to preserve and promote Mongolian language and culture, particularly appealing to users in Inner Mongolia and other Mongolian-populated areas. Social Features Instant Messaging: Supports one-on-one private chats and group chats. Users can create interest-based groups or join local communities. Life Sharing: Through the "Chomorlig" feature (similar to Moments or a dynamic feed), users can share daily highlights to enhance community interaction. Location-Based Socializing: Recommends nearby users based on location, making it easier to connect with Mongolian friends in the same city or neighboring regions. Multilingual Support The app interface is available in English, Mongolian, and Simplified Chinese. == Technical Features and User Experience == Cross-Platform Compatibility Supports iPhone, iPad, Mac (with M1 chip or above), and Apple Vision Pro devices, covering users across the Apple ecosystem. Pricing Model Free download and basic features are available. Premium services (e.g., ad-free experience, extended social functions) require a subscription, with pricing options including $0.99/month, $2.99/quarter, and $6.99/year. User Feedback Positive Reviews: Some users praise it as the "best Mongolian-language chat app," recognizing its cultural value and social convenience. Negative Feedback: Reports of app crashes and technical issues, with some users calling for improved stability (e.g., frequent crashes in the iOS version). == Privacy and Data Policy == Bainu collects user data such as location, contact information, and device identifiers, which are linked to user identities. Additionally, user behavior may be tracked through third-party services, raising some privacy concerns. == Current Development and Challenges == User Base As of 2020, Bainu had approximately 400,000 users, primarily concentrated in Inner Mongolia. Policy Impact It was reported by Voice of America (VOA) that the Chinese authorities blocked Bainu on 23 August 2020 in order to prohibit Mongolians from discussing the issue of the authorities’ implementation of "bilingual education" in elementary schools. But now, in 2025, this software is completely available for download and use. see:https://bainu.com/
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False answer supervision

False answer supervision (FAS) refers to VoIP fraud where the billed duration for the caller is more than the duration of the actual connection duration. The FAS is usually performed by VoIP wholesalers in their softswitches for randomly selected calls. Adding a small amount of extra billed seconds for many calls results in significant revenue for the VoIP wholesaler. == Implementation of FAS == The FAS fraud can be implemented in a softswitch in many different ways. These include: False billing of party A without calling a party B. Usually a fake ringback tone, loopback audio or voicemail message is played Start of billing before actual answer of party B Extra billing after disconnection of party B == Detection of FAS == The FAS can be detected and blocked in a softswitch. Common methods are: Manual verification of call detail records: listening to voice recordings Identification of FAS types and using algorithms to automatically detect the FAS RTP audio signal processing: detection of voice RTP audio signal processing: detection of silence RTP audio signal processing: detection of ringback tone
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Ajax (programming)

The Asynchronous JavaScript and XML, usually referred to as Ajax (or AJAX, ) is a set of web development techniques that uses various web technologies on the client-side to create asynchronous web applications. With Ajax, web applications can send and retrieve data from a server asynchronously (in the background) without interfering with the display and behaviour of the existing page. By decoupling the data interchange layer from the presentation layer, Ajax allows web pages and, by extension, web applications, to change content dynamically without the need to reload the entire page. In practice, modern implementations commonly utilize JSON instead of XML. Ajax is not a technology, but rather a programming pattern. HTML and CSS can be used in combination to mark up and style information. The webpage can be modified by JavaScript to dynamically display (and allow the user to interact with) the new information. The built-in XMLHttpRequest object is used to execute Ajax on webpages, allowing websites to load content onto the screen without refreshing the page. == History == In the early-to-mid 1990s, most Websites were based on complete HTML pages. Each user action required a complete new page to be loaded from the server. This process was inefficient, as reflected by the user experience: all page content disappeared, then the new page appeared. Each time the browser reloaded a page because of a partial change, all the content had to be re-sent, even though only some of the information had changed. This placed additional load on the server and made bandwidth a limiting factor in performance. The foundations of AJAX originate back in 1996 with the introduction of JavaScript 1. Developers quickly discovered that any HTML element which accepted a "src" attribute could be used to fetch remote data. By changing the src of a hidden frame, a developer could fetch remote data, process or display it without a page refresh. The remote data could be a string, JavaScript code, XML or a partial HTML page generated on the server. The same could be done with and tags, but many developers were alarmed at the concept of an executable GIF and preferred to use the hidden . This technique was used for both Netscape Navigator and Internet Explorer, the two main browsers at the time, until developers discovered the XMLHTTP server object (MSXML 1.0) was included in Internet Explorer 4. Browser detection was then used to either implement the hidden <iframe> in Netscape or use the XMLHTTP (MSXML 1.0) object in IE 4. In 1996, the iframe tag was introduced by Internet Explorer; like the object element, it can load a part of the web page asynchronously. In 1998, the Microsoft Outlook Web Access team developed the concept behind the XMLHttpRequest scripting object. It appeared as XMLHTTP in the second version of the MSXML library, which shipped with Internet Explorer 5.0 in March 1999. The functionality of the Windows XMLHTTP ActiveX control in IE 5 was later implemented by Mozilla Firefox, Safari, Opera, Google Chrome, and other browsers as the XMLHttpRequest JavaScript object. Microsoft adopted the native XMLHttpRequest model as of Internet Explorer 7. Support for the ActiveX version remained in Internet Explorer and on "Internet Explorer mode" in Microsoft Edge. The utility of these background HTTP requests and asynchronous Web technologies remained fairly obscure until it started appearing in large scale online applications such as Outlook Web Access (2000) and Oddpost (2002). Google made a wide deployment of standards-compliant, cross browser Ajax with Gmail (2004) and Google Maps (2005). In October 2004 Kayak.com's public beta release was among the first large-scale e-commerce uses of what their developers at that time called "the xml http thing". This increased interest in Ajax among web program developers. The term AJAX was publicly used on 18 February 2005 by Jesse James Garrett in an article titled Ajax: A New Approach to Web Applications, based on techniques used on Google pages. On 5 April 2006, the World Wide Web Consortium (W3C) released the first draft specification for the XMLHttpRequest object in an attempt to create an official Web standard. The latest draft of the XMLHttpRequest object was published on 6 October 2016, and the XMLHttpRequest specification is now a living standard. == Technologies == The term Ajax has come to represent a broad group of Web technologies that can be used to implement a Web application that communicates with a server in the background, without interfering with the current state of the page. In the article that coined the term Ajax, Jesse James Garrett explained that the following technologies are incorporated: HTML (or XHTML) and CSS for presentation The Document Object Model (DOM) for dynamic display of and interaction with data JSON or XML for the interchange of data, and XSLT for XML manipulation The XMLHttpRequest object for asynchronous communication JavaScript to bring these technologies together Since then, however, there have been a number of developments in the technologies used in an Ajax application, and in the definition of the term Ajax itself. XML is no longer required for data interchange and, therefore, XSLT is no longer required for the manipulation of data. JavaScript Object Notation (JSON) is often used as an alternative format for data interchange, although other formats such as preformatted HTML or plain text can also be used. A variety of popular JavaScript libraries, including jQuery, include abstractions to assist in executing Ajax requests. == Examples == === JavaScript example === An example of a simple Ajax request using the GET method, written in JavaScript. get-ajax-data.js: send-ajax-data.php: === Fetch example === Fetch is a native JavaScript API. According to Google Developers Documentation, "Fetch makes it easier to make web requests and handle responses than with the older XMLHttpRequest." ==== ES7 async/await example ==== Fetch relies on JavaScript promises. The fetch specification differs from Ajax in the following significant ways: The Promise returned from fetch() won't reject on HTTP error status even if the response is an HTTP 404 or 500. Instead, as soon as the server responds with headers, the Promise will resolve normally (with the ok property of the response set to false if the response isn't in the range 200–299), and it will only reject on network failure or if anything prevented the request from completing. fetch() won't send cross-origin cookies unless you set the credentials init option. (Since April 2018. The spec changed the default credentials policy to same-origin. Firefox changed since 61.0b13.) == Benefits == Ajax offers several benefits that can significantly enhance web application performance and user experience. By reducing server traffic and improving speed, Ajax plays a crucial role in modern web development. One key advantage of Ajax is its capacity to render web applications without requiring data retrieval, resulting in reduced server traffic. This optimization minimizes response times on both the server and client sides, eliminating the need for users to endure loading screens. Furthermore, Ajax facilitates asynchronous processing by simplifying the utilization of XmlHttpRequest, which enables efficient handling of requests for asynchronous data retrieval. Additionally, the dynamic loading of content enhances the application's performance significantly. Besides, Ajax enjoys broad support across all major web browsers, including Microsoft Internet Explorer versions 5 and above, Mozilla Firefox versions 1.0 and beyond, Opera versions 7.6 and above, and Apple Safari versions 1.2 and higher.</p> <a href="https://aizhi.co/html/323e999667.html" class="read-more" title="Ajax (programming)">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/354b499641.html" class="card-thumb-link" title="Active learning (machine learning)"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Active learning (machine learning)" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/354b499641.html" title="Active learning (machine learning)">Active learning (machine learning)</a></h2> <p class="article-excerpt">Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source) to label new data points with the desired outputs. The human user must possess expertise in the problem domain, including the ability to consult authoritative sources when necessary. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the teacher for labels. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. However, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning. Using active learning allows for faster development of a machine learning algorithm, when comparative updates would require a quantum or super computer. Large-scale active learning projects may benefit from crowdsourcing frameworks such as Amazon Mechanical Turk that include many humans in the active learning loop. == Definitions == Let T be the total set of all data under consideration. For example, in a protein engineering problem, T would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration, i, T is broken up into three subsets T K , i {\displaystyle \mathbf {T} _{K,i}} : Data points where the label is known. T U , i {\displaystyle \mathbf {T} _{U,i}} : Data points where the label is unknown. T C , i {\displaystyle \mathbf {T} _{C,i}} : A subset of TU,i that is chosen to be labeled. Most of the current research in active learning involves the best method to choose the data points for TC,i. == Scenarios == Pool-based sampling: In this approach, which is the most well known scenario, the learning algorithm attempts to evaluate the entire dataset before selecting data points (instances) for labeling. It is often initially trained on a fully labeled subset of the data using a machine-learning method such as logistic regression or SVM that yields class-membership probabilities for individual data instances. The candidate instances are those for which the prediction is most ambiguous. Instances are drawn from the entire data pool and assigned a confidence score, a measurement of how well the learner "understands" the data. The system then selects the instances for which it is the least confident and queries the teacher for the labels. The theoretical drawback of pool-based sampling is that it is memory-intensive and is therefore limited in its capacity to handle enormous datasets, but in practice, the rate-limiting factor is that the teacher is typically a (fatiguable) human expert who must be paid for their effort, rather than computer memory. Stream-based selective sampling: Here, each consecutive unlabeled instance is examined one at a time with the machine evaluating the informativeness of each item against its query parameters. The learner decides for itself whether to assign a label or query the teacher for each datapoint. As contrasted with Pool-based sampling, the obvious drawback of stream-based methods is that the learning algorithm does not have sufficient information, early in the process, to make a sound assign-label-vs ask-teacher decision, and it does not capitalize as efficiently on the presence of already labeled data. Therefore, the teacher is likely to spend more effort in supplying labels than with the pool-based approach. Membership query synthesis: This is where the learner generates synthetic data from an underlying natural distribution. For example, if the dataset are pictures of humans and animals, the learner could send a clipped image of a leg to the teacher and query if this appendage belongs to an animal or human. This is particularly useful if the dataset is small. The challenge here, as with all synthetic-data-generation efforts, is in ensuring that the synthetic data is consistent in terms of meeting the constraints on real data. As the number of variables/features in the input data increase, and strong dependencies between variables exist, it becomes increasingly difficult to generate synthetic data with sufficient fidelity. For example, to create a synthetic data set for human laboratory-test values, the sum of the various white blood cell (WBC) components in a white blood cell differential must equal 100, since the component numbers are really percentages. Similarly, the enzymes alanine transaminase (ALT) and aspartate transaminase (AST) measure liver function (though AST is also produced by other tissues, e.g., lung, pancreas) A synthetic data point with AST at the lower limit of normal range (8–33 units/L) with an ALT several times above normal range (4–35 units/L) in a simulated chronically ill patient would be physiologically impossible. == Query strategies == Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose: Balance exploration and exploitation: the choice of examples to label is seen as a dilemma between the exploration and the exploitation over the data space representation. This strategy manages this compromise by modelling the active learning problem as a contextual bandit problem. For example, Bouneffouf et al. propose a sequential algorithm named Active Thompson Sampling (ATS), which, in each round, assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for this sample point label. Expected model change: label those points that would most change the current model. Expected error reduction: label those points that would most reduce the model's generalization error. Exponentiated Gradient Exploration for Active Learning: In this paper, the author proposes a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration. Uncertainty sampling: label those points for which the current model is least certain as to what the correct output should be. Query by committee: a variety of models are trained on the current labeled data, and vote on the output for unlabeled data; label those points for which the "committee" disagrees the most Querying from diverse subspaces or partitions: When the underlying model is a forest of trees, the leaf nodes might represent (overlapping) partitions of the original feature space. This offers the possibility of selecting instances from non-overlapping or minimally overlapping partitions for labeling. Variance reduction: label those points that would minimize output variance, which is one of the components of error. Conformal prediction: predicts that a new data point will have a label similar to old data points in some specified way and degree of the similarity within the old examples is used to estimate the confidence in the prediction. Mismatch-first farthest-traversal: The primary selection criterion is the prediction mismatch between the current model and nearest-neighbour prediction. It targets on wrongly predicted data points. The second selection criterion is the distance to previously selected data, the farthest first. It aims at optimizing the diversity of selected data. User-centered labeling strategies: Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is asked to label the compiled data (categorical, numerical, relevance scores, relation between two instances). A wide variety of algorithms have been studied that fall into these categories. While the traditional AL strategies can achieve remarkable performance, it is often challenging to predict in advance which strategy is the most suitable in a particular situation. In recent years, meta-learning algorithms have been gaining in popularity. Some of them have been proposed to tackle the problem of learning AL strategies instead of relying on manually designed strategies. A benchmark which compares 'meta-learning approaches to active learning' to 'traditional heuristic-based Active Learning' may give intuitions if 'Learning active learning' is at the crossroads == Minimum marginal hyperplane == Some active learning algorithms are built upon support-vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin, W, of each u</p> <a href="https://aizhi.co/html/354b499641.html" class="read-more" title="Active learning (machine learning)">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/239d999751.html" class="card-thumb-link" title="Audience capture"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Audience capture" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/239d999751.html" title="Audience capture">Audience capture</a></h2> <p class="article-excerpt">Audience capture is the phenomenon where an influencer is affected by their audience, catering to it with what they believe it wants to hear or is willing to pay for. This creates a positive feedback loop, which can lead the influencer to express more extreme views and behaviors. A famous example of audience capture can be found in the story of the online influencer Nicholas Perry, known as Nikocado Avocado. Perry started off on YouTube with videos of himself playing the violin and supporting veganism. He then shifted to videos of himself eating known as mukbang. Audience capture led him to more and more extreme eating leading him in turn to obesity and poor health. The effect can cause ideological media creators to become more politically radical, based on the feedback of their audience.</p> <a href="https://aizhi.co/html/239d999751.html" class="read-more" title="Audience capture">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/476d999514.html" class="card-thumb-link" title="Military communications"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Military communications" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/476d999514.html" title="Military communications">Military communications</a></h2> <p class="article-excerpt">Military communications or military signals involve all aspects of communications, or conveyance of information, by armed forces. Examples from Jane's Military Communications include text, audio, facsimile, tactical ground-based communications, naval signalling, terrestrial microwave, tropospheric scatter, satellite communications systems and equipment, surveillance and signal analysis, security, direction finding and jamming. The most urgent purposes are to communicate information to commanders and orders from them. Military communications span from pre-history to the present. The earliest military communications were delivered by runners. Later, communications progressed to visual signals. For example, Naval ships would use flag signaling to communicate from ship to ship. These flags are a uniform set of easily identifiable nautical codes that would convey visual messages and codes between ships and from ship to shore. Then militaries discovered methods to use audible signaling to communicate with each other. This way of communicating was possible because of telegraphs. They are an electronic device that is used by a sender and when the sender presses on the telegraph key, they interrupt the current creating an audible pulse that is heard at the receiving station. The receiver then decodes the pulses to decode the messages. Since then, military communication has evolved and advanced much further. Today, there are many perspectives used to examine how troops around the world communicate. Anthony King states how Military sociologists have attempted to explain how military institutions develop and maintain high levels of social cohesion. == History == In past centuries communicating a message usually required someone to go to the destination, bringing the message. Thus, the term communication often implied the ability to transport people and supplies. A place under siege was one that lost communication in both senses. The association between transport and messaging declined in recent centuries. The first military communications involved the use of runners or the sending and receiving of simple signals (sometimes encoded to be unrecognizable). The first distinctive uses of military communications were called semaphore. Modern units specializing in these tactics are usually designated as signal corps. The Roman system of military communication (cursus publicus or cursus vehicularis) is an early example of this. Later, the terms signals and signaller became words referring to a highly-distinct military occupation dealing with general communications methods (similar to those in civil use) rather than with weapons. Present-day military forces of an informational society conduct intense and complicated communicating activities on a daily basis, using modern telecommunications and computing methods. Only a small portion of these activities are directly related to combat actions. Modern concepts of network-centric warfare (NCW) rely on network-oriented methods of communications and control to make existing forces more effective. == Military communications equipment == Drums, horns, flags, and riders on horseback were some of the early methods the military used to send messages over distances. The advent of distinctive signals led to the formation of the signal corps, a group specialized in the tactics of military communications. The signal corps evolved into a distinctive occupation where the signaller became a highly technical job dealing with all available communications methods including civil ones. In the middle 20th century radio equipment came to dominate the field. Many modern pieces of military communications equipment are built to both encrypt and decode transmissions and survive rough treatment in hostile climates. They use different frequencies to send signals to other radio stations to communicate. Radios have played a major role in military communication. Since they are capable of sending radio waves to transmit voice signals over long distances. This can be helpful for communication on the battlefield since it is a good way to send messages undetected over long distances. Radios are also very reliable because even in harsh weather conditions they are still able to help communicate among the soldiers. Militaries still use radios and continue to improve the technology because of their durability and reliability for military communication. Spelling alphabets such as the NATO phonetic alphabet are used to aid radio communications by reducing ambiguity between letters. Military communications – or "comms" – are activities, equipment, techniques, and tactics used by the military in some of the most hostile areas of the earth and in challenging environments such as battlefields, on land (compare radio in a box), underwater and also in air. Military comms include command, control and communications and intelligence and were known as the C3I model before computers were fully integrated. The U.S. Army expanded the model to C4I when it recognized the vital role played by automated computer equipment to send and receive large, bulky amounts of data. In the modern world, most nations attempt to minimize the risk of war caused by miscommunication or inadequate communication. As a result, military communication is intense and complicated and often motivates the development of advanced technology for remote systems such as satellites. Satellites have been improving and are being used more and more for communication. They are being made to have higher transmission capacity to help with their communication abilities. The military is upgrading satellites to be immune to interference during combat operations. This advancement will establish stable, high-quality information highways for long distance communication. Aircraft are also beneficial for communication, both crewed and uncrewed, as well as computers. Computers and their varied applications have revolutionized military comms. Although military communication is designed for warfare, it also supports intelligence-gathering and communication between adversaries, and thus sometimes prevents war. The six categories of military comms are: alert measurement systems cryptography military radio systems command and control signal corps network-centric warfare The alert measurement systems are various states of alertness or readiness for the armed forces used around the world during a state of war, act of terrorism or a military attack against a state. They are known by different acronyms, such as DEFCON, or defense readiness condition, used by the U.S. Armed Forces. Cryptography is the study of methods of converting messages to a form unreadable except to one who knows how to decrypt them. This ancient military comms art gained new importance with the rise of radio systems whose signals traveled far and were easily intercepted. Cryptographic software is also widely used in civilian commerce. == Commercial refile == In United States military communications systems, commercial refile refers to sending a military message via a commercial communications network. The message may come from a military network, such as a tape relay network, a point-to-point telegraph network, a radio-telegraph network, or the Defense Switched Network. Commercial refiling of a message will usually require a reformatting of the message, particularly the heading.</p> <a href="https://aizhi.co/html/476d999514.html" class="read-more" title="Military communications">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/212e999778.html" class="card-thumb-link" title="Cringe culture"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Cringe culture" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/212e999778.html" title="Cringe culture">Cringe culture</a></h2> <p class="article-excerpt">Cringe culture () is an Internet phenomenon and neologism characterized by the mockery and ridicule of content, behaviors, or expressions deemed embarrassing or awkward. The term cringe evolved semantically from describing personal secondhand embarrassment to becoming a dismissive label applied to various forms of online expression and fan behavior. The phenomenon emerged in the early 2000s as a response to awkward online content but gradually transformed into a cultural force that impacted fan communities, creative expression, and social media behavior. Cringe culture gained particular prominence through online platforms like Reddit and 4chan, and has been observed to cause the decline of various fandoms when they become labeled as cringe. Cringe culture has extended beyond Internet communities into academic and professional settings. Educators have noticed increased self-consciousness among students about displaying effort in their work (known as tryharding). By the early 2020s, a cultural pushback against cringe culture began to emerge, with public figures and celebrities advocating for authentic self-expression and rejecting the fear of being perceived as "trying too hard". == Origin == The term cringe underwent semantic change from its original usage describing an involuntary physical response, then to embarrassment. The term gained popularity in online forums during the early 2000s, when public self-humiliation online was a relatively novel phenomenon. Early cringe culture drew much of its content from YouTube. According to Kaitlyn Tiffany of The Atlantic, the majority of cringe stemmed from people who did not seem to understand that anyone in the world could see their videos. The phenomenon initially focused on empathy and secondhand embarrassment, with viewers relating to the awkward situations they witnessed. Popular early examples of cringe include the 2002 viral video Star Wars Kid and "My Video for Briona for Our 7 Month", in which a man winks, licks his lips, and makes romantic declarations to his partner. Early cringe culture encompassed multiple styles, including self-deprecating, playful, and hostile forms. On /b/ (4chan's "random" board), early cringe discussions targeted groups like Tumblr users, social justice warriors, fangirls, and furries, while also being used to describe "normies" who lacked sufficient knowledge of Internet culture to understand its ironic humor. In July 2012, Reddit user Michael Dombkowski took over the dormant r/cringe subreddit after watching a KENS5 segment about teen werewolves. Dombkowski created RSS feeds to alert him whenever someone mentioned cringe anywhere on Reddit, then encouraged users to visit his subreddit. The subreddit collected 10,000 monthly pageviews in its first month, which grew to 941,000 by September 2012 and 5 million the following month. According to The Daily Dot, Dombkowski had intended the subreddit to elicit empathy from viewers rather than to mock its subjects. On November 9, 2012, Dombkowski banned all images from r/cringe and created r/cringepics as a spinoff subreddit for image-based content. The community initially opposed this decision, as users worried that it would fragment the community. In a few months, r/cringepics overtook r/cringe in traffic and subscribers. By 2014, the combined subreddits amassed over 500,000 subscribers and more than 30 million monthly pageviews. In a March 2013 company AMA ("Ask Me Anything"), Reddit's general manager Erik Martin stated that he hates "r/cringepics and anything cringe related and the whole idea." == Impact == Cringe culture has impacted various fandoms. Screen Rant dubbed the phenomenon in which a fandom abruptly dissipates when suddenly deemed cringe (due to the actions of individuals within the fandom or the fandom being re-evaluated as a whole) as the "My Hero Academia Effect". My Hero Academia initially enjoyed popularity in 2020 during the COVID-19 pandemic, but the resurfacing of embarrassing TikTok videos of convention-goers in 2020 caused the My Hero Academia fandom to be deemed cringy, and thus was abandoned by many anime fans. Similarly, the fandom of the Homestuck webcomic, which ran from 2009 to 2016, faced scrutiny when cosplayers filled bathtubs with Sharpies to achieve gray skin coloring (emulating the design of the Homestuck characters), which led to property damage at hotels and convention bans. Many fans subsequently abandoned the fandom, and as a result, according to Screen Rant, the Homestuck fandom was almost non-existent by 2024. It is worth noting that as of September 27, 2025 animation studio SpindleHorse, also responsible for the popular animated show Hazbin Hotel (another common recipient of Cringe Culture discussion) has released a Homestuck animated pilot episode on YouTube. Other fandoms that were deemed cringy include the Stranger Things and Hazbin Hotel fandoms. Isobel Heal of Varsity described being "far too insecure as a teen to even consider listening to songs inspired by My Little Pony or Five Nights at Freddy's regardless of how catchy they were," but found that attending a Living Tombstone concert allowed her to overcome these inhibitions. She wrote that everyone in the crowd was "completely unafraid to engage in the silliness of the entire night," which allowed her to "let my guard down and enjoy the evening without fear of feeling 'cringe.'" Heal described her experience of singing along to tracks like "Discord", a My Little Pony–themed song, provided what she described as healing "the wounds of the younger me" and represented a form of reclaiming interests that had been suppressed due to social pressure and bullying. == Reactions == New York University professor Ocean Vuong observed that students increasingly hesitate to reveal effort behind their creative work. Vuong stated that students often say "I want to be a good writer, but it's a bit cringe" and perform cynicism because it can be misread as intelligence. In May 2022, Taylor Swift addressed cringe culture in her commencement speech at New York University: she advised graduates to "learn to live alongside cringe" and that "cringe is unavoidable over a lifetime." Other celebrities have made public speeches fighting against the perceived notion that "tryharding" is cringe. In his 31st Screen Actors Guild Awards acceptance speech, Timothée Chalamet emphasized his pursuit of greatness and the effort he invested in his roles, which diverged from typical humble acceptance speeches. In her 67th Annual Grammy Awards acceptance speech, rapper Doechii also stressed her dedication and hard work. According to The Daily Dot, X users called Chalamet and Doechii's speeches "refreshing" and decried those who embrace cringe culture as "miserable losers". In 2023, Critical Role dungeon master Matthew Mercer spoke against cringe culture at New York Comic Con: "We live in an odd time of 'cringe culture' where anything that's honest can be called cringe. And I don't agree with that." Mercer argued that much of what is dismissed as cringe consists of "people being their authentic self." In October 2025, actress and singer Ariana Grande discussed her experience with cringe culture in an interview on the podcast Shut Up Evan. She described the phenomenon as "unfair", stating that people should be allowed to express passion and happiness without judgement. She further explained that in the wake of her leading role in the 2024 film Wicked there were those who perceived the behavior of her and costar Cynthia Erivo during the film's press tour as "inauthentic" and therefore cringe. == Analysis == In 2021, Steven Dashiell wrote in the journal Studies in Popular Culture that cringe culture functions as a mechanism for social boundaries within the My Little Pony: Friendship Is Magic fandom, and that cringe culture operates not only between different communities but also within fandoms themselves. In his analysis, Dashiell examined a Reddit thread where a brony (an adult fan of My Little Pony: Friendship Is Magic) expressed embarrassment about other bronies. The thread received over 400 comments in which participants engaged in what Dashiell termed other-izing: distancing themselves from behaviors they deemed cringeworthy. Rather than defending the criticized bronies, commenters consistently used the term cringe to describe their reactions to certain fan behaviors while distinguishing themselves from the so-called "deviant brony" to normalize their own participation in the fandom. A February 2024 Hinge report revealed that more than half of Generation Z worries about cringe while dating and are 50 percent more likely than millennials to delay responding to avoid seeming overeager.</p> <a href="https://aizhi.co/html/212e999778.html" class="read-more" title="Cringe culture">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/476b299521.html" class="card-thumb-link" title="Yahoo Groups"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Yahoo Groups" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/476b299521.html" title="Yahoo Groups">Yahoo Groups</a></h2> <p class="article-excerpt">Yahoo! Groups was a free-to-use system of electronic mailing lists offered by Yahoo!. Prior to February 2020, Yahoo! Groups was one of the world's largest collections of online discussion boards. It allowed members to subscribe to various groups, read subscribed discussions online, view and share photos, files and bookmarks within a group, access a group calendar, create polls for group members, and receive email notifications of new discussion topics. Some groups were simply announcement boards, to which only the group moderators could post, while others were discussion forums. Depending on each group's settings, membership could be open to everyone or only to invited or approved people. On February 1, 2020, Yahoo! removed online access to discussions and all other features except simple membership management, essentially turning all groups into mailing lists, and on October 13, 2020, it announced that Yahoo Groups would shut down completely on December 15, 2020. == History == In 1998 Yahoo! Clubs was launched as an extension of services developed by Yahoo! Messenger. In August 2000 Yahoo acquired eGroups.com. Yahoo! Groups was launched in early 2001 as an integration of technology from eGroups.com and community groups from both eGroups.com and Yahoo! Clubs. In 2001 Yahoo! deleted adult groups from its search directory, making it very difficult to locate Yahoo! groups with adult content. The Groups Updates Email feature was introduced in 2010. It summarized, in a single email, all the updates that occurred every twenty-four hours in all groups. In September 2010, a major facelift was rolled out, making Yahoo! Groups look very similar to Facebook. In December, Yahoo! Groups Japan emailed its users and posted a notice on its homepage, to announce that its service, which commenced in February 2004, would be closing on May 28, 2014. In October 2019, Yahoo! announced that all content that had been posted to Yahoo! Groups will be deleted on December 14, 2019; that date was later amended to January 31, 2020. Yahoo! announced that adding new content would be blocked on October 28, 2019. Once the content was deleted, users of Yahoo! Groups were only able to browse the group directory, request invitations and, if members of a group, send messages to that group. On October 13, 2020, Yahoo! announced they would be shutting down Yahoo! Groups on December 15, 2020. The site was closed down a few days after the advertised date, displaying a message that the service was officially shut down. This message stopped appearing at the end of January 2021 and the Yahoo! Groups web address began redirecting to the main Yahoo! page. === Criticism and controversy === On August 31, 2010, Yahoo! Groups started rolling out a major software change, which was denounced by a large number of users. The re-model was completely abandoned on January 12, 2011. == Site statistics == In August 2008, Yahoo! Group staff reported that there were 113 million users, and nine million Groups using 22 languages. In July 2010, the web analytics website Quantcast reported around 915 thousand unique visitors daily to the Yahoo! Groups website (US). In January 2011, that number had increased to 933 thousand unique visitors daily. The number did not include Yahoo! Group members who accessed the Groups site via email. In September 2010, at its "Product Runway" event, Yahoo! told reporters that Yahoo! Groups had 115 million group members and that there were 10 million Yahoo! groups. == Archives ==</p> <a href="https://aizhi.co/html/476b299521.html" class="read-more" title="Yahoo Groups">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/417b999573.html" class="card-thumb-link" title="Access-independent services"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Access-independent services" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/417b999573.html" title="Access-independent services">Access-independent services</a></h2> <p class="article-excerpt">Access-independent service (AIS) is a service concept in which a service does not depend on guaranteed access network cooperation for service delivery. Telecommunications industry analyst Dean Bubley first used the term in a report on Telco-OTT in February 2012. Traditionally, most telecom company or internet service provider services are access-dependent, because they rely heavily on guaranteed access cooperation on the network the service is delivered over. For instance, traditional IP-based TV service (IPTV) delivered by a telecom company is generally a managed service. This means that IPTV service assumes the IPTV service provider has control over the access network that the IPTV service is delivered over, and network quality of service (QoS) guarantees are available for IPTV service delivery. As a result, the reach of a telecom company's IPTV service is generally restricted by the reach of the telecom company's access network. In contrast, services offered by non-traditional video content delivery service providers such as Netflix, Hulu, and Amazon Video are considered access-independent services. Netflix's video content streaming service, for example, dynamically adapts to network conditions in real-time to strive for the best overall quality of experience (QoE) and does not assume guaranteed cooperation from the underlying IP network, such as QoS. As a result, without considering content rights and different countries' government restrictions, the reach of Netflix's video content streaming service is, in theory, the reach of the Internet. Skype is another example of AIS, because Skype offers an IP-based telephony service over the Internet without depending on IP network cooperation guarantees other than basic IP network connectivity. In the context of telecom service delivery, the concept of access independent services is also commonly described by the term "over-the-top" (OTT) services. OTT service providers such as but not limited to Facebook, WeChat, and Netflix generally do not own or directly manage any wide-area access network to begin with, so they design their services for overall quality of experience, with no assumptions on guaranteed access network cooperation.</p> <a href="https://aizhi.co/html/417b999573.html" class="read-more" title="Access-independent services">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/html/2b999988.html" class="card-thumb-link" title="Timeline of operating systems"><img class="card-thumb" src="https://aizhi.co/uploads/images/x.jpg" alt="Timeline of operating systems" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/html/2b999988.html" title="Timeline of operating systems">Timeline of operating systems</a></h2> <p class="article-excerpt">This article presents a timeline of events in the history of computer operating systems from 1951 to the current day. For a narrative explaining the overall developments, see the History of operating systems. == 20th Century == == 1940s == 1949 EDSAC was considered the first operating system developed by Maurice Wilkes and manufactured by the University of Cambridge == 1950s == 1951 LEO I 'Lyons Electronic Office' was the commercial development of EDSAC computing platform, supported by British firm J. Lyons and Co. 1953 DYSEAC - an early machine capable of distributing computing 1955 General Motors Operating System made for IBM 701 MIT's Tape Director operating system made for UNIVAC 1103 1956 GM-NAA I/O for IBM 704, based on General Motors Operating System 1957 Atlas Supervisor (Manchester University) (Atlas computer project start) BESYS (Bell Labs), for IBM 704, later IBM 7090 and IBM 7094 1958 University of Michigan Executive System (UMES), for IBM 704, 709, and 7090 1959 SHARE Operating System (SOS), based on GM-NAA I/O == 1960s == 1960 IBSYS (IBM for its 7090 and 7094) 1961 CTSS demonstration (MIT's Compatible Time-Sharing System for the IBM 7094) MCP (Burroughs Master Control Program) for B5000 1962 Atlas Supervisor (Manchester University) (Atlas computer commissioned) BBN Time-Sharing System GCOS (GE's General Comprehensive Operating System, originally GECOS, General Electric Comprehensive Operating Supervisor) 1963 ADMIRAL AN/FSQ-32, another early time-sharing system begun CTSS becomes operational (MIT's Compatible Time-Sharing System for the IBM 7094) JOSS, an interactive time-shared system that did not distinguish between operating system and language Titan Supervisor, early time-sharing system begun 1964 Berkeley Timesharing System (for Scientific Data Systems' SDS 940) Chippewa Operating System (for CDC 6600 supercomputer) Dartmouth Time-Sharing System (Dartmouth College's DTSS for GE computers) EXEC 8 (UNIVAC) KDF9 Timesharing Director (English Electric) – an early, fully hardware secured, fully pre-emptive process switching, multi-programming operating system for KDF9 (originally announced in 1960) OS/360 (IBM's primary OS for its S/360 series) (announced) PDP-6 Monitor (DEC) descendant renamed TOPS-10 in 1970 SCOPE (CDC 3000 series) 1965 BOS/360 (IBM's Basic Operating System) DECsys TOS/360 (IBM's Tape Operating System) Livermore Time Sharing System (LTSS) Multics (MIT, GE, Bell Labs for the GE-645) (announced) Pick operating system SIPROS 66 (Simultaneous Processing Operating System) THE multiprogramming system (Technische Hogeschool Eindhoven) development TSOS (later VMOS) (RCA) 1966 DOS/360 (IBM's Disk Operating System) GEORGE 1 & 2 for ICT 1900 series Mod 1 Mod 2 Mod 8 MS/8 (Richard F. Lary's DEC PDP-8 system) MSOS (Mass Storage Operating System) OS/360 (IBM's primary OS for its S/360 series) PCP and MFT (shipped) RAX Remote Users of Shared Hardware (RUSH), a time-sharing system developed by Allen-Babcock for the IBM 360/50 SODA for Elwro's Odra 1204 Universal Time-Sharing System (XDS Sigma series) 1967 CP-40, predecessor to CP-67 on modified IBM System/360 Model 40 CP-67 (IBM, also known as CP/CMS) Conversational Programming System (CPS), an IBM time-sharing system under OS/360 Michigan Terminal System (MTS) (time-sharing system for the IBM S/360-67 and successors) ITS (MIT's Incompatible Timesharing System for the DEC PDP-6 and PDP-10) OS/360 MVT ORVYL (Stanford University's time-sharing system for the IBM S/360-67) TSS/360 (IBM's Time-sharing System for the S/360-67, never officially released, canceled in 1969 and again in 1971) WAITS (SAIL, Stanford Artificial Intelligence Laboratory, time-sharing system for DEC PDP-6 and PDP-10, later TOPS-10) 1968 Airline Control Program (ACP) (IBM) B1 (NCR Century series) CALL/360, an IBM time-sharing system for System/360 HP Real-Time Executive (HP RTE) – Hewlett-Packard HP Time-Shared BASIC (HP TSB) – Hewlett-Packard (time-sharing system for the HP 2000) THE multiprogramming system (Eindhoven University of Technology) publication TSS/8 (DEC for the PDP-8) VP/CSS 1969 B2 (NCR Century series) B3 (NCR Century series) GEORGE 3 For ICL 1900 series MINIMOP Multics (MIT, GE, Bell Labs for the GE-645 and later the Honeywell 6180) (opened for paying customers in October) RC 4000 Multiprogramming System (RC) TENEX (Bolt, Beranek and Newman for DEC systems, later TOPS-20) Unics (later Unix) (AT&T, initially on DEC computers) Xerox Operating System == 1970s == 1970 DOS-11 (PDP-11) 1971 EMAS Kronos RSTS-11 2A-19 (First released version; PDP-11) RSX-15 OS/8 1972 B4 (NCR Century series) COS-300 Data General RDOS Edos MUSIC/SP OS/4 OS 1100 OS/2000 (Honeywell 2000-series) Operating System/Virtual Storage 1 (OS/VS1) Operating System/Virtual Storage 2 R1 (OS/VS2 SVS) PRIMOS (written in FORTRAN IV, that didn't have pointers, while later versions, around version 18, written in a version of PL/I, called PL/P) Virtual Machine/Basic System Extensions Program Product (BSEPP or VM/SE) Virtual Machine/System Extensions Program Product (SEPP or VM/BSE) Virtual Machine Facility/370 (VM/370), sometimes known as VM/CMS 1973 Эльбрус-1 (Elbrus-1) – Soviet computer – created using high-level language uЭль-76 (AL-76/ALGOL 68) Alto OS CP-V (Control Program V) RSX-11D RT-11 VME – implementation language S3 (ALGOL 68) 1974 ACOS-2 (NEC) ACOS-4 ACOS-6 CP/M DOS-11 V09-20C (Last stable release, June 1974) Hydra – capability-based, multiprocessing OS kernel MONECS Multi-Programming Executive (MPE) – Hewlett-Packard Operating System/Virtual Storage 2 R2 (MVS) OS/7 OS/16 OS/32 Sintran III 1975 BS2000 V2.0 (First released version) COS-350 ISIS NOS (Control Data Corporation) OS/3 (Univac) VS/9 (formerly RCA's TSOS, later named VMOS) Version 6 Unix XVM/DOS XVM/RSX 1976 Cambridge CAP computer – all operating system procedures written in ALGOL 68C, with some closely associated protected procedures in BCPL Cray Operating System DX10 FLEX TOPS-20 TX990/TXDS Tandem Nonstop OS v1 Thoth 1977 1BSD AMOS KERNAL OASIS operating system OS68 OS4000 RMX-80 System 88 (Exec) System Support Program (IBM System/34 and System/36) TRSDOS Virtual Memory System (VMS) V1.0 (Initial commercial release, October 25) VRX (Virtual Resource eXecutive) VS Virtual Memory Operating System 1978 2BSD Apple DOS Control Program Facility (IBM System/38) Cray Time Sharing System (CTSS) DPCX (IBM) DPPX (IBM) HDOS KSOS – secure OS design from Ford Aerospace KVM/370 – security retro-fit of IBM VM/370 Lisp machine (CADR) MVS/System Extensions (MVS/SE) OS4 (Naked Mini 4) PTDOS TRIPOS UCSD p-System (First released version) Z80-RIO 1979 Atari DOS 3BSD CP-6 Idris MP/M MVS/System Extensions R2 (MVS/SE2) NLTSS POS Sinclair BASIC Transaction Processing Facility (TPF) (IBM) UCLA Secure UNIX – an early secure UNIX OS based on security kernel UNIX/32V DOS/VSE Version 7 Unix == 1980s == 1980 86-DOS AOS/VS (Data General) Business Operating System CTOS DOSPLUS (TRS-80) MVS/System Product (MVS/SP) V1 NewDos/80 OS-9 RMX-86 RS-DOS SOS Virtual Machine/System Product (VM/SP) Xenix 1981 Acorn MOS Aegis SR1 (First Apollo/DOMAIN systems shipped on March 27) CP/M-86 DRX (Distributed Resource Executive) iMAX – OS for Intel's iAPX 432 capability machine MCS (Multi-user Control System) MS-DOS PC DOS Pilot (Xerox Star operating system) UNOS UTS V VERSAdos VRTX VSOS (Virtual Storage Operating System) Xinu first release 1982 Commodore DOS LDOS (By Logical Systems, Inc. – for the Radio Shack TRS-80 Models I, II & III) PCOS (Olivetti M20) pSOS QNX Stratus VOS Sun UNIX (later SunOS) 0.7 Ultrix Unix System III VAXELN 1983 Coherent DNIX EOS GNU (project start) Lisa Office System 7/7 LOCUS – UNIX compatible, high reliability, distributed OS MVS/System Product V2 (MVS/Extended Architecture, MVS/XA) Novell NetWare (S-Net) PERPOS ProDOS RTU (Real-Time Unix) STOP – TCSEC A1-class, secure OS for SCOMP hardware SunOS 1.0 VSE/System Package (VSE/SP) Version 1 1984 AMSDOS CTIX (Unix variant) DYNIX Mac OS (System 1.0) MSX-DOS NOS/VE PANOS PC/IX ROS Sinclair QDOS SINIX UNICOS Venix 2.0 Virtual Machine/Extended Architecture Migration Assistance (VM/XA MA) 1985 AmigaOS Atari TOS DG/UX DOS Plus Graphics Environment Manager Harmony MacOS 2 MIPS RISC/os Oberon – written in Oberon SunOS 2.0 Version 8 Unix Virtual Machine/Extended Architecture System Facility (VM/XA SF) Windows 1.0 Windows 1.01 Xenix 2.0 1986 AIX 1.0 Cronus distributed OS FlexOS GEMSOS – TCSEC A1-class, secure kernel for BLACKER VPN & GTNP GEOS Genera 7.0 HP-UX MacOS 3 SunOS 3.0 TR-DOS TRIX Version 9 Unix 1987 Arthur (much improved version came in 1989 under the name RISC OS) BS2000 V9.0 IRIX (3.0 is first SGI version) MacOS 4 MacOS 5 MDOS MINIX 1.0 OS/2 (1.0) PC-MOS/386 Topaz – semi-distributed OS for DEC Firefly workstation written in Modula-2+ and garbage collected VxWorks Windows 2.0 1988 A/UX (Apple Computer) AOS/VS II (Data General) CP/M rebranded as DR-DOS Flex machine – tagged, capability machine with OS and other software written </p> <a href="https://aizhi.co/html/2b999988.html" class="read-more" title="Timeline of operating systems">Read more →</a> </div> </article> </li> </ul> <nav class="pagination" aria-label="Pagination"> <a href="https://aizhi.co/aicontentdetectorzero/17/" class="page-num">1</a><a href="https://aizhi.co/aicontentdetectorzero/18/" class="page-num">2</a><a href="https://aizhi.co/aicontentdetectorzero/19/" class="page-num">3</a><a href="https://aizhi.co/aicontentdetectorzero/20/" class="page-num">4</a><a href="https://aizhi.co/aicontentdetectorzero/21/" class="page-num">5</a><a href="https://aizhi.co/aicontentdetectorzero/22/" class="page-num">6</a><a href="https://aizhi.co/aicontentdetectorzero/23/" 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