AI Content To Human

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

  • Software engineering demographics

    Software engineering demographics

    Software engineers make up a significant portion of the global workforce. As of 2022, there are an estimated 26.9 million professional software engineers worldwide, up from 21 million in 2016. == By country == === United States === In 2023, there were an estimated 1.6 million professional software developers in North America. There are 166 million people employed in the US workforce, making software developers 0.96% of the total workforce. ==== Summary ==== ==== Software engineers vs. traditional engineers ==== The following two tables compare the number of software engineers (611,900 in 2002) versus the number of traditional engineers (1,157,020 in 2002). There are another 1,500,000 people in system analysis, system administration, and computer support, many of whom might be called software engineers. Many systems analysts manage software development teams, and as analysis is an important software engineering role, many of them may be considered software engineers in the near future. This means that the number of software engineers may actually be much higher. It is important to note that the number of software engineers declined by 5 to 10 percent from 2000 to 2002. ==== Computer managers vs. construction and engineering managers ==== Computer and information system managers (264,790) manage software projects, as well as computer operations. Similarly, Construction and engineering managers (413,750) oversee engineering projects, manufacturing plants, and construction sites. Computer management is 64% the size of construction and engineering management. ==== Software engineering educators vs. engineering educators ==== Most people working in the field of computer science, whether making software systems (software engineering) or studying the theoretical and mathematical facts of software systems (computer science), acquire degrees in computer science. According to the U.S. Bureau of Labor Statistics (May 2023 data), there were approximately 44,800 postsecondary computer science teachers and 50,300 engineering teachers, indicating that the computer science educator workforce is nearly 89% as large as that of engineering educators. The combined number of postsecondary chemistry (25,400) and physics (17,100) teachers totaled 42,500, slightly less than the number of computer science educators. ==== Other software and engineering roles ==== ==== Relation to IT demographics ==== Software engineers are part of the much larger software, hardware, application, and operations community. In 2000 in the U.S., there were about 680,000 software engineers and about 10,000,000 IT workers. As of early 2025, there are an estimated 47.2 million software developers worldwide, representing a 50% increase from 31 million in Q1 2022. There are no numbers on testers in the BLS data. === India === There has been a healthy growth in the number of India's IT professionals over the past few years. From a base of 6,800 knowledge workers in 1985–86, the number increased to 522,000 software and services professionals by the end of 2001–02. It is estimated that out of these 528,000 knowledge workers, almost 170,000 are working in the IT software and services export industry; nearly 106,000 are working in the IT enabled services and over 230,000 in user organizations. === Australia === In May 2024, the Australian government reported that 169,300 Australians are employed as software and applications programmers, 17% of who are women. The role grew annually by 8,300 workers. === Russia === According to the Russian government, the number of IT specialists in the country increased by 13% in 2023, reaching approximately 857,000. During the initial phase of the 2022 invasion of Ukraine, an estimated 100,000 IT specialists left Russia.

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  • Hyperscale computing

    Hyperscale computing

    In computing, hyperscale is the ability of an architecture to scale appropriately as increased demand is added to the system. This typically involves the ability to seamlessly provide and add computing, memory, networking, and storage resources to a given node or set of nodes that make up a larger computing, distributed computing, or grid computing environment. Hyperscale computing is necessary in order to build a robust and scalable cloud, big data, map reduce, or distributed storage system and is often associated with the infrastructure required to run large distributed sites such as Google, Facebook, Twitter, Amazon, Microsoft, IBM Cloud, Oracle Cloud, or Cloudflare. Companies like Ericsson, AMD, and Intel provide hyperscale infrastructure kits for IT service providers. Companies like Scaleway, Switch, Alibaba, IBM, QTS, Neysa, Digital Realty Trust, Equinix, Oracle, Meta, Amazon Web Services, SAP, Microsoft, Google, and Cloudflare build data centers for hyperscale computing. Such companies are sometimes called "hyperscalers". They are recognized for their massive scale in cloud computing and data management, operating in environments that require extensive infrastructure to accommodate large-scale data processing and storage.

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  • List of operating systems

    List of operating systems

    This is a list of operating systems. Computer operating systems can be categorized by technology, ownership, licensing, working state, usage, and by many other characteristics. In practice, many of these groupings may overlap. Criteria for inclusion is notability, as shown either through an existing Wikipedia article or citation to a reliable source. == Proprietary == === Acorn Computers === Arthur ARX MOS RISC iX RISC OS === Amazon === Fire OS === Amiga Inc. === AmigaOS AmigaOS 1.0-3.9 (Motorola 68000) AmigaOS 4 (PowerPC) Amiga Unix (a.k.a. Amix) === Amstrad === AMSDOS Contiki CP/M 2.2 CP/M Plus SymbOS === Apple === Apple II Apple DOS Apple Pascal ProDOS GS/OS GNO/ME Contiki Apple III Apple SOS Apple Lisa Mac Classic Mac OS A/UX (UNIX System V with BSD extensions) Copland MkLinux Pink Rhapsody macOS (formerly Mac OS X and OS X) macOS Server (formerly Mac OS X Server and OS X Server) Apple Network Server IBM AIX (Apple-customized) Apple MessagePad Newton OS iPhone and iPod Touch iOS (formerly iPhone OS) iPad iPadOS Apple Watch watchOS Apple TV tvOS Embedded operating systems bridgeOS Apple Vision Pro visionOS Embedded operating systems A/ROSE iPod software (unnamed embedded OS for iPod) Unnamed NetBSD variant for Airport Extreme and Time Capsule === Apollo Computer, Hewlett-Packard === Domain/OS – One of the first network-based systems. Run on Apollo/Domain hardware. Later bought by Hewlett-Packard. === Atari === Atari DOS (for 8-bit computers) Atari TOS Atari MultiTOS Contiki (for 8-bit, ST, Portfolio) === BAE Systems === XTS-400 === Be Inc. === BeOS BeIA BeOS r5.1d0 magnussoft ZETA (based on BeOS r5.1d0 source code, developed by yellowTAB) === Bell Labs === Unix ("Ken's new system," for its creator (Ken Thompson), officially Unics and then Unix, the prototypic operating system created in Bell Labs in 1969 that formed the basis for the Unix family of operating systems) UNIX Time-Sharing System v1 UNIX Time-Sharing System v2 UNIX Time-Sharing System v3 UNIX Time-Sharing System v4 UNIX Time-Sharing System v5 UNIX Time-Sharing System v6 MINI-UNIX PWB/UNIX USG CB Unix UNIX Time-Sharing System v7 (It is from Version 7 Unix (and, to an extent, its descendants listed below) that almost all Unix-based and Unix-like operating systems descend.) Unix System III Unix System IV Unix System V Unix System V Releases 2.0, 3.0, 3.2, 4.0, and 4.2 UNIX Time-Sharing System v8 UNIX Time-Sharing System v9 UNIX Time-Sharing System v10 Non-Unix Operating Systems: BESYS Plan 9 from Bell Labs Inferno === Burroughs Corporation, Unisys === Burroughs MCP === CII === Siris 8 === Commodore International === GEOS AmigaOS AROS Research Operating System === Control Data Corporation === ==== Lower 3000 series ==== SCOPE (Supervisory Control Of Program Execution) ==== Upper 3000 series ==== SCOPE (Supervisory Control Of Program Execution) Drum SCOPE ==== 6x00 and related Cyber ==== Chippewa Operating System (COS) MACE (Mansfield and Cahlander Executive) Kronos (Kronographic OS) NOS (Network Operating System) NOS/VE (NOS Virtual Environment) SCOPE (Supervisory Control Of Program Execution) NOS/BE NOS Batch Environment SIPROS (Simultaneous Processing Operating System) ==== Star-100 ==== Multiple Console Time Sharing System (MCTS), from General Motors Research === CloudMosa === Puffin OS === Convergent Technologies === Convergent Technologies Operating System (CTOS) – later acquired by Unisys === Cromemco === Cromemco DOS (CDOS) – a Disk Operating system compatible with CP/M Cromix – a multitasking, multi-user, Unix-like OS for Cromemco microcomputers with Z80A and/or 68000 CPU === Data General === AOS for 16-bit Data General Eclipse computers and AOS/VS for 32-bit (MV series) Eclipses, MP/AOS for microNOVA-based computers DG/UX RDOS Real-time Disk Operating System, with variants: RTOS and DOS (not related to PC DOS, MS-DOS etc.) === Datapoint === CTOS Cassette Tape Operating System for the Datapoint 2200 DOS Disk Operating System for the Datapoint 2200, 5500, and 1100 === DDC-I, Inc. === Deos – Time & Space Partitioned RTOS, Certified to DO-178B, Level A since 1998 HeartOS – POSIX-based Hard Real-Time Operating System === Digital Research, Inc. === CP/M CP/M CP/M for Intel 8080/8085 and Zilog Z80 Personal CP/M, a refinement of CP/M CP/M Plus with BDOS 3.0 CP/M-68K CP/M for Motorola 68000 CP/M-8000 CP/M for Zilog Z8000 CP/M-86 CP/M for Intel 8088/8086 CP/M-86 Plus Personal CP/M-86 MP/M Multi-user version of CP/M-80 MP/M II MP/M-86 Multi-user version of CP/M-86 MP/M 8-16, a dual-processor variant of MP/M for 8086 and 8080 CPUs. Concurrent CP/M, the successor of CP/M-80 and MP/M-80 Concurrent CP/M-86, the successor of CP/M-86 and MP/M-86 Concurrent CP/M 8-16, a dual-processor variant of Concurrent CP/M for 8086 and 8080 CPUs. Concurrent CP/M-68K, a variant for the 68000 DOS Concurrent DOS, the successor of Concurrent CP/M-86 with PC-MODE Concurrent PC DOS, a Concurrent DOS variant for IBM compatible PCs Concurrent DOS 8-16, a dual-processor variant of Concurrent DOS for 8086 and 8080 CPUs Concurrent DOS 286 Concurrent DOS XM, a real-mode variant of Concurrent DOS with EEMS support Concurrent DOS 386 Concurrent DOS 386/MGE, a Concurrent DOS 386 variant with advanced graphics terminal capabilities Concurrent DOS 68K, a port of Concurrent DOS to Motorola 68000 CPUs with DOS source code portability capabilities FlexOS 1.0 – 2.34, a derivative of Concurrent DOS 286 FlexOS 186, a variant of FlexOS for terminals FlexOS 286, a variant of FlexOS for hosts Siemens S5-DOS/MT, an industrial control system based on FlexOS IBM 4680 OS, a POS operating system based on FlexOS IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 386, a later variant of FlexOS for hosts IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 68K, a derivative of Concurrent DOS 68K Multiuser DOS, the successor of Concurrent DOS 386 CCI Multiuser DOS Datapac Multiuser DOS Datapac System Manager, a derivative of Datapac Multiuser DOS IMS Multiuser DOS IMS REAL/32, a derivative of Multiuser DOS IMS REAL/NG, the successor of REAL/32 DOS Plus 1.1 – 2.1, a single-user, multi-tasking system derived from Concurrent DOS 4.1 – 5.0 DR-DOS 3.31 – 6.0, a single-user, single-tasking native DOS derived from Concurrent DOS 6.0 Novell PalmDOS 1.0 Novell "Star Trek" Novell DOS 7, a single-user, multi-tasking system derived from DR DOS Caldera OpenDOS 7.01 Caldera DR-DOS 7.02 and higher === Digital Equipment Corporation, Compaq, Hewlett-Packard, Hewlett Packard Enterprise === Batch-11/DOS-11 OS/8 RSTS/E – multi-user time-sharing OS for PDP-11s RSX-11 – multiuser, multitasking OS for PDP-11s RT-11 – single user OS for PDP-11 TOPS-10 – for the PDP-10 TENEX – an ancestor of TOPS-20 from BBN, for the PDP-10 TOPS-20 – for the PDP-10 DEC MICA – for the DEC PRISM Digital UNIX – derived from OSF/1, became HP's Tru64 UNIX Ultrix VMS – originally by DEC (now by VMS Software Inc.) for the VAX mini-computer range; later renamed OpenVMS and ported to Alpha, and subsequently ported to Intel Itanium and then to x86-64 WAITS – for the PDP-6 and PDP-10 === ENEA AB === OSE – Flexible, small footprint, high-performance RTOS for control processors === Fujitsu === Towns OS XSP OS/IV MSP MSP-EX === GEC Computers === COS DOS OS4000 === General Electric, Honeywell, Bull === Real-Time Multiprogramming Operating System GCOS Multics === Google === ChromiumOS is an open source operating system development version of ChromeOS. Both operating systems are based on the Linux kernel. ChromeOS is designed to work exclusively with web applications, though has been updated to run Android apps with full support for Google Play Store. Announced on July 7, 2009, ChromeOS is currently publicly available and was released summer 2011. The ChromeOS source code was released on November 19, 2009, under the BSD license as ChromiumOS. Container-Optimized OS (COS) is an operating system that is optimized for running Docker containers, based on ChromiumOS. Android is an operating system for mobile devices. It consists of Android Runtime (userland) with Linux (kernel), with its Linux kernel modified to add drivers for mobile device hardware and to remove unused Vanilla Linux drivers. gLinux, a Linux distribution that Google uses internally Fuchsia is a capability-based real-time operating system (RTOS) scalable to universal devices, in early development, from the tiniest embedded hardware, wristwatches, tablets to the largest personal computers. Unlike ChromeOS and Android, it is not based on the Linux kernel, but instead began on a new microkernel called "Zircon", derived from "Little Kernel". Wear OS a version of Google's Android operating system designed for smartwatches and other wearables. === Green Hills Software === INTEGRITY – Reliable Operating system INTEGRITY-178B – A DO-178B certified version of INTEGRITY. μ-

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  • Homeboyz Interactive

    Homeboyz Interactive

    Homeboyz Interactive (HBI) was a faith-based recruitment, training and job placement non-profit business in Milwaukee, Wisconsin, United States, founded by a Jesuit brother in 1996 to transform gang members into productive workers. == History == James Holub, a former Jesuit brother affiliated with Wheeling Jesuit University, asked gang members in the Southside of Milwaukee, WI how they could be helped, to break the cycle of poverty and violence. The youth suggested that they be trained for work they found exciting. To attract interest, the training must lead to jobs that paid at least a living wage, and computer skills seemed the most attractive. The non-profit Homeboyz Interactive was established to prepare professionals in web design, application development, and PC/network support. This non-profit outfit spawned the for-profit web design firm HBI Consulting, which provided trainees with work experience. It turned out more than 20 teachers yearly for computer and computer network programs for high schools and other clients, as well as for computer service providers. Some graduates of the program continued their education, some founded their own business, and others continued working at HBI. The Economist described this effort as "turning thugs into programmers" on Milwaukee's South Side, which has proportionally twice as many murders as New York. Holub had "buried his 28th gang member" before he implemented the Homeboyz plan, with the understanding that "nothing stops a bullet like a job." The programs would pass through about 80 prospects a year who successfully completed training and provide them with a job while studying for their high school equivalency test, before they were asked to decide in which direction to go. Most accepted a job or went on to community college but about 25 entered the Homeboyz training for computer programmers. Of first 150 graduates of this program none lost their job; their average pay after two years was US$63,000. Some preferred to return to full-time work at HBI. By 2002, a total of 142 people had graduated from HBI training and moved into full-time IT careers. The training curriculum as of 2000 included JavaScript and Photoshop, among other web-development tools. In 2000, HBI received a 14% ownership stake in reEmploy.com, a payrolling company, in exchange for the development of an electronic time sheet created by the organization. As of 2001, HBI Consulting, the for profit web design firm, had 72 clients. Among those clients were GE Medical, Toyota Forklift, Northwestern Mutual Life, Verizon Wireless, BP; and Marquette University. Companies that graduates of HBI's training programs secured positions have included Northwestern Mutual and Manpower Inc., United Community Center in Milwaukee and EKI Consulting. A pair of graduates also started their own company in 2002, Innovative Source, a web design firm, which itself has had clients such as the University of Wisconsin-Milwaukee and the Milwaukee Women's Center. This was a common path forward, graduates starting their own consulting firms. In 2004, HBI received a grant for General Support from the Vine and Branches Foundation in the amount of US$120,000. The product Project Foundry found its start in the difficulty of managing project-based learning across dozens of students with widely varying levels of skill, a problem encountered by Shane Krukowski, who developed the software while teaching at HBI. Krukowski subsequently an eponymous company to commercialize the software through a subscription-based business model. Some came to Homeboyz through the criminal courts or Department of Corrections. A Jesuit Volunteer (JV) was assigned to work with the program, and to add a spiritual dimension through regular reflection together. Gradually the market began prioritizing graphic design and flash images more than site construction. After 2006 Homeboyz HBI morphed into several spinoffs and ceased to exist as a separate entity.

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  • Grokking (machine learning)

    Grokking (machine learning)

    In machine learning, grokking, or delayed generalization, is a phenomenon observed in some settings where a model abruptly transitions from overfitting (performing well only on training data) to generalizing (performing well on both training and test data), after many training iterations with little or no improvement on the held-out data. This contrasts with what is typically observed in machine learning, where generalization occurs gradually alongside improved performance on training data. == Origin == Grokking was introduced by OpenAI researcher Alethea Power and colleagues in the January 2022 paper "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets". It is derived from the word grok coined by Robert Heinlein in his novel Stranger in a Strange Land. In ML research, "grokking" is not used as a synonym for "generalization"; rather, it names a sometimes-observed delayed‑generalization training phenomenon in which training and held‑out performance do not improve in tandem, and in which held‑out performance rises abruptly later. Authors also analyze the "grokking time", the epoch or step at which this transition occurs in those scenarios. == Interpretations == Grokking can be understood as a phase transition during the training process. In particular, recent work has shown that grokking may be due to a complexity phase transition in the model during training. While grokking has been thought of as largely a phenomenon of relatively shallow models, grokking has been observed in deep neural networks and non-neural models and is the subject of active research. One potential explanation is that the weight decay (a component of the loss function that penalizes higher values of the neural network parameters, also called regularization) slightly favors the general solution that involves lower weight values, but that is also harder to find. According to Neel Nanda, the process of learning the general solution may be gradual, even though the transition to the general solution occurs more suddenly later. Recent theories have hypothesized that grokking occurs when neural networks transition from a "lazy training" regime where the weights do not deviate far from initialization, to a "rich" regime where weights abruptly begin to move in task-relevant directions. Follow-up empirical and theoretical work has accumulated evidence in support of this perspective, and it offers a unifying view of earlier work as the transition from lazy to rich training dynamics is known to arise from properties of adaptive optimizers, weight decay, initial parameter weight norm, and more. This perspective is complementary to a unifying "pattern learning speeds" framework that links grokking and double descent; within this view, delayed generalization can arise across training time ("epoch‑wise") or across model size ("model‑wise"), and the authors report "model‑wise grokking".

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  • G7 Rapid Response Mechanism

    G7 Rapid Response Mechanism

    The G7 Rapid Response Mechanism (RRM) is an initiative introduced in the "Charlevoix Commitment on Defending Democracy from Foreign Threats", issued by the leaders of the Group of Seven (G7) countries—United States, Canada, Japan, United Kingdom, France, Germany and Italy—on June 9, 2018, during their summit in Charlevoix, Quebec. The RRM's mandate is to strengthen the coordination of G7 member countries, as well as "to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response" The G7 is an informal international intergovernmental economic organization that meets annually, whose members represent the seven wealthiest advanced economies in the world, as measured by the International Monetary Fund (IMF). == Constituents == The following countries and organisations are members and observers (associate members) of the G7 Rapid Response Mechanism: Australia Canada France Germany Italy Japan Netherlands New Zealand Poland Sweden United Kingdom United States European Union North Atlantic Treaty Organization == Mandate == The RRM was mandated to "strengthen coordination to prevent, thwart and respond to malign and evolving threats to G7 democracies." It "will share information and threat analysis related to various threats to democracy, and is an established mechanism to identify opportunities for coordinated response." According to the Institute for Research on Public Policy's Policy Options magazine, the "RRM initiative seeks to strengthen the leading democracies' coordination to identify and respond to diverse and evolving threats…including through sharing information and analysis, and identifying opportunities for a coordinated response." == Administration == The RRM initiative is led by Canada through Global Affairs Canada's Centre for International Digital Policy. Tara Denham, Director of the Centre for International Digital Policy at Global Affairs Canada, directed the team responsible for setting up the RRM Coordination Unit. Global Affairs Canada—the Department of Foreign Affairs, Trade and Development—is the federal Canadian ministry responsible for diplomatic and consular relations, international trade, and international development and humanitarian assistance. The Centre for International Digital Policy includes the Digital Inclusion Lab and the RRM. Denham is also the RRM's Canadian Focal Point. At a briefing on "the security and intelligence threats to elections" of the House of Commons Standing Committee on Access to Information, Privacy and Ethics, the chair Bob Zimmer (CPC), said that the 2019 general election "may be different" from past elections in Canada. as the "tools that were used to strengthen civic engagement are being used to undermine, disrupt and destabilize democracy." "Democracies around the world have entered a new era—an era of heightened threat and heightened vigilance—and 2019 will see a number of countries brace for volleys of attempted disruption: India, Australia, Ukraine, Switzerland, Belgium, the EU and, of course, Canada. Evidence has confirmed that the most recent Canadian general election, in 2015, was unencumbered by interference, although there were some relatively primitive attempts to disrupt, misinform and divide. These efforts were few in number and uncoordinated, and had no visible impact on the voter, either online or in line." Zimmer described the initiative's three pillars. "enhancing citizen preparedness" through the "digital citizen initiative" "improving organizational readiness" with national security and intelligence agencies supporting Elections Canada "ensure a comprehensive understanding of and response to any threats to Canada's democratic process." by establishing the Security and Intelligence Threats to Elections Task Force (SITE) which works as a team with the Communications Security Establishment (CSE), the Canadian Security Intelligence Service (CSIS), the Royal Canadian Mounted Police (RCMP), as well as Global Affairs Canada Zimmer said that as part of the third pillar, "We have activated the G7 rapid response mechanism, announced at the G7 leaders' summit in Charlevoix, to strengthen coordination among our G7 allies and to ensure that there is international collaboration and coordination in responding to foreign threats to democracy." == Background == === Charlevoix summit === The G7 met from June 8 to 9, 2018 during their summit at the Manoir Richelieu in Charlevoix, in La Malbaie, Quebec. The Charlevoix Summit was the 44th G7 summit. The group issued eight "Commitments" at the summit. They included: Commitment on Defending Democracy from Foreign Threats Commitment on Equality and Economic Growth Commitment to End Sexual and Gender-Based Violence, Abuse and Harassment in Digital Contexts Declaration on Quality Education for Girls, Adolescent Girls and Women in Developing Countries Commitment on Innovative Financing for Development. Prime Minister Justin Trudeau announced five themes for Canada's G7 presidency which began in January 2018. === Defending Democracy from Foreign Threats === "We commit to take concerted action in responding to foreign actors who seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security as outlined in the Charlevoix Commitment on Defending Democracy from Foreign Threats. We recognize that such threats, particularly those originating from state actors, are not just threats to G7 nations, but to international peace and security and the rules-based international order. We call on others to join us in addressing these growing threats by increasing the resilience and security of our institutions, economies and societies, and by taking concerted action to identify and hold to account those who would do us harm." They committed to "cooperate in defending democracies from foreign threats and establish a response mechanism for that purpose". "Democracy and the rules-based international order are increasingly being challenged by authoritarianism and the defiance of international norms. In particular, foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working with other governments that share our democratic values." The Charlevoix Commitment states that "foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working together with other governments that share our democratic values." The Charlevoix Summit resolved to "establish a G7 Rapid Response Mechanism to strengthen our coordination to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response." == Monitored elections == === 2019 European Parliament election === RRM Canada's comprehensive report on the 2019 European Parliament election analyzed open data "related to foreign interference during and leading up to the 2019 European Union Parliamentary Elections, May 23–26, 2019". RRM Canada did not find "significant evidence of state-based foreign interference, or any large-scale, organized and coordinated efforts by non-state actors". They did find that "national or international non-state actors" used tactics based on those used by the Russian sponsored Internet Research Agency (IRA) in previous elections, "such as the 2016 U.S. Elections". For example, blogs, webpages, and social media accounts on Twitter, Facebook and Reddit "were used to spread divisive and false information to damage and negatively impact social cohesion and trust in democratic processes and institutions" in coordinated networks of Facebook groups. === 2019 Alberta general election === RRM Canada's analyz report on the 2019 Alberta general election was intended to "identify any emerging tactics in foreign interference and draw lessons learned for the Canadian general elections scheduled to take place in October 2019." No foreign activity was detected, although the data revealed ""suspicious account creation pattern that is indicative of troll or bot activity". They found "automated inauthentic behaviour and trolling activities" but concluded that they were "very likely domestic". The data showed "suspicious account creation pattern that is indicative of troll or bot activity", and "spikes in account creation" which suggested the "presence of accounts developed for a specific purpose." The accounts were very likely domestic and were "mainly comprised of supporters of the United Conservative Party (UCP)." A seco

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  • Usage share of operating systems

    Usage share of operating systems

    The usage share of an operating system is the percentage of computers running that operating system (OS). These statistics are estimates as wide scale OS usage data is difficult to obtain and measure. Reliable primary sources are limited and data collection methodology is not formally agreed. Currently devices connected to the internet allow for web data collection to approximately measure OS usage. As of December 2025, Android, which uses the Linux kernel, is the world's most popular operating system with 38.94% of the global market, followed by Windows with 29.99%, iOS with 15.66%, macOS with 2.14%, and other operating systems with 10.78%. This is for all device types excluding embedded devices. For smartphones and other mobile devices, Android has 72% market share, and Apple's iOS has 28%. For desktop computers and laptops, Microsoft Windows has 60.8%, followed by unknown operating systems at 19.7%, Mac OS at 14.4%, desktop Linux at 3.2%, then Google's ChromeOS at 1.6%, as of March 2026. For tablets, Apple's iPadOS (a variant of iOS) has 52% share and Android has 48% worldwide. For the top 500 most powerful supercomputers, Linux distributions have had 100% of the market share since 2017. The global server operating system market share has Linux leading with a 63.1% marketshare, followed by Windows, Unix and other operating systems. Linux is also most used for web servers, and the most common Linux distribution is Ubuntu, followed by Debian. Linux has almost caught up with the second-most popular (desktop) OS, macOS, in some regions, such as in South America, and in Asia it's at 6.4% (7% with ChromeOS) vs 9.7% for macOS. In the US, ChromeOS is third at 5.5%, followed by (desktop) Linux at 4.3%. The most numerous type of device with an operating system are embedded systems. Not all embedded systems have operating systems, instead running their application code on the "bare metal"; of those that do have operating systems, a high percentage are standalone or do not have a web browser, which makes their usage share difficult to measure. Some operating systems used in embedded systems are more widely used than some of those mentioned above; for example, modern Intel microprocessors contain an embedded management processor running a version of the Minix operating system. == Worldwide device shipments == Shipments (to stores) do not necessarily translate to sales to consumers, therefore suggesting the numbers indicate popularity and/or usage could be misleading. Not only do smartphones sell in higher numbers than PCs, but also a lot more by dollar value, with the gap only projected to widen, to well over double. According to Gartner, the following is the worldwide device shipments (referring to wholesale) by operating system from 2012 to 2016, which includes smartphones, tablets, laptops and PCs together. On 27 January 2016, Paul Thurrott summarized the operating system market, the day after Apple announced "one billion devices": Apple's "active installed base" is now one billion devices. [..] Granted, some of those Apple devices were probably sold into the marketplace years ago. But that 1 billion figure can and should be compared to the numbers Microsoft touts for Windows 10 (200 million, most recently) or Windows more generally (1.5 billion active users, a number that hasn’t moved, magically, in years), and that Google touts for Android (over 1.4 billion, as of September). My understanding of iOS is that the user base was previously thought to be around 800 million strong, and when you factor out Macs and other non-iOS Apple devices, that's probably about right. But as you can see, there are three big personal computing platforms. And only one of them is actually declining. We’ll see how Windows 10 fares over the long term, but even if Microsoft hits the 1 billion figure in 1-2 years as promised, it will by then still be the smallest of those three platforms. In 2018, Apple stopped revealing unit sales in its reports. Since 2018, the company have been publishing only revenues per device models which, nonetheless, allowed the analysers to extrapolate the unit sales from the model revenues by applying the wholesale device prices. Other hardware manufacturers usually do not report unit sales. === PC shipments === For 2015 (and earlier), Gartner reports for "the year, worldwide PC shipments declined for the fourth consecutive year, which started in 2012 with the launch of tablets" with an 8% decline in PC sales for 2015 (not including cumulative decline in sales over the previous years). Microsoft backed away from their goal of one billion Windows 10 devices in three years (or "by the middle of 2018") and reported on 26 September 2016 that Windows 10 was running on over 400 million devices, and in March 2019, on more than 800 million. In May 2020, Gartner predicted further decline in all market segments for 2020 due to COVID-19, predicting a decline of 13.6% for all devices. while the "Work from Home Trend Saved PC Market from Collapse", with only a decline of 10.5% predicted for PCs. However, in the end, according to Gartner, PC shipments grew 10.7% in the fourth quarter of 2020 and reached 275 million units in 2020, a 4.8% increase from 2019 and the highest growth in ten years." Apple in 4th place for PCs had the largest growth in shipments for a company in Q4 of 31.3%, while "the fourth quarter of 2020 was another remarkable period of growth for Chromebooks, with shipments increasing around 200% year over year to reach 11.7 million units. In 2020, Chromebook shipments increased over 80% to total nearly 30 million units, largely due to demand from the North American education market." Chromebooks sold more (30 million) than Apple's Macs worldwide (22.5 million) in pandemic year 2020. According to the Catalyst group, the year 2021 had record high PC shipments with total shipments of 341 million units (including Chromebooks), 15% higher than 2020 and 27% higher than 2019, while being the largest shipment total since 2012. According to Gartner, worldwide PC shipments declined by 16.2% in 2022, the largest annual decrease since the mid-1990s, due to geopolitical, economic, and supply chain challenges. In 2024 and 2025, due to lower adoption of Windows 11 and Microsoft ending its support to Windows 10, the number of PCs shipped with pre-installed Windows OS dropped. Pundits attribute the low Windows 11 acceptance to its steep hardware requirements and especially the TPM 2.0 ready chipset requirement and the 2024 CrowdStrike-related IT outages. Meanwhile, the macOS device market share in PC device shipments increased to new heights, with improved numbers seen for Linux devices too. In Q3 2025, the macOS pre-installed device shipments increased by 14.9% year-over-year (YoY), while the overall PC-shipments increased only by 8.1%, in Q2 2025, it grew 21.4% YoY while the global PC-shipments increased only by 6.5%, and in Q1 2025, it grew 7% YoY while the global PC-shipments increased by 4.8%. === Tablet computers shipments === In 2015, eMarketer estimated at the beginning of the year that the tablet installed base would hit one billion for the first time (with China's use at 328 million, which Google Play doesn't serve or track, and the United States's use second at 156 million). At the end of the year, because of cheap tablets – not counted by all analysts – that goal was met (even excluding cumulative sales of previous years) as: Sales quintupled to an expected 1 billion units worldwide this year, from 216 million units in 2014, according to projections from the Envisioneering Group. While that number is far higher than the 200-plus million units globally projected by research firms IDC, Gartner and Forrester, Envisioneering analyst Richard Doherty says the rival estimates miss all the cheap Asian knockoff tablets that have been churning off assembly lines.[..] Forrester says its definition of tablets "is relatively narrow" while IDC says it includes some tablets by Amazon — but not all.[..] The top tech purchase of the year continued to be the smartphone, with an expected 1.5 billion sold worldwide, according to projections from researcher IDC. Last year saw some 1.2 billion sold.[..] Computers didn’t fare as well, despite the introduction of Microsoft's latest software upgrade, Windows 10, and the expected but not realized bump it would provide for consumers looking to skip the upgrade and just get a new computer instead. Some 281 million PCs were expected to be sold, according to IDC, down from 308 million in 2014. Folks tend to be happy with the older computers and keep them for longer, as more of our daily computing activities have moved to the smartphone.[..] While Windows 10 got good reviews from tech critics, only 11% of the 1-billion-plus Windows user base opted to do the upgrade, according to Microsoft. This suggests Microsoft has a ways to go before the software gets "hit" status. Apple's new operating system El Capitan has been

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  • Fingerprint scanner

    Fingerprint scanner

    Fingerprint scanners are a type of biometric security device that identify an individual by identifying the structure of their fingerprints. They are used in police stations, security industries, smartphones, and other mobile devices. == Fingerprints == People have patterns of friction ridges on their fingers, these patterns are called the fingerprints. Fingerprints are uniquely detailed, durable over an individual's lifetime, and difficult to alter. Due to the unique combinations, fingerprints have become an ideal means of identification. == Types of fingerprint scanners == There are four types of fingerprint scanners: Optical scanners take a visual image of the fingerprint using a digital camera. Capacitive or CMOS scanners use capacitors and thus electric current to form an image of the fingerprint. This type of scanner tends to excel in terms of precision. Ultrasonic fingerprint scanners use high frequency sound waves to penetrate the epidermal (outer) layer of the skin. Thermal scanners sense the temperature differences on the contact surface, in between fingerprint ridges and valleys. All fingerprint scanners are susceptible to spoofing through fingerprints replicated using photographs and 3D printing. == Construction forms == Each type of fingerprint sensor can take two basic forms: the stagnant and the moving fingerprint scanner. Stagnant: The scanning module is mounted statically, and the user is required to swipe their fingers across it. This is cheaper but also less reliable than the moving form. Imaging can be less than ideal if the finger is not dragged over the scanning area at constant speed. Moving: The scanning module is mounted on a movable surface, while the user's finger can remain static. Because this layout allows the scanning module to pass the fingerprint at a constant speed, this method is generally more reliable. == Form factors == === Peripherals === Add-on fingerprint readers for PCs initially appeared in the late 1990's in the form of PCMCIA modules. Microsoft released a model in its IntelliMouse line with an integrated fingerprint reader in 2005. === Integrated readers === Laptops with built-in readers emerged around the same time as peripheral readers with devices such as NECs MC/R730F. IBM produced laptops with integrated readers starting in 2004. Apple introduced fingerprint scanners to their devices under the name Touch ID in 2013. These were initially released on the iPhone 5S, with the technology remaining exclusive to iPhones until the release of the 2016 MacBook Pro. On both laptops and smartphones, the fingerprint sensor usually uses a USB or I2C interface internally.

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  • Organoid intelligence

    Organoid intelligence

    Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. Such technologies may be referred to as OIs or the nervous filesystem. Organoid intelligent computer systems can be an example of biohybrid systems. == Differences with non-organic computing == As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware". While these structures are still far from being able to think like a regular human brain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments for neurological disorders such as dementia. Thomas Hartung, a professor from Johns Hopkins University, argued in 2023 that "while silicon-based computers are certainly better with numbers, brains are better at learning." He noted that transistor density in computer chip may be approaching its limits, whereas brains, being wired differently, are more energy-efficient and can store large amounts of information. Some researchers claim that even though human brains are slower than machines at processing simple information, they are far better at processing complex information as brains can deal with fewer and more uncertain data, perform both sequential and parallel processing, being highly heterogenous, use incomplete datasets, and is said to outperform non-organic machines in decision-making. Training OIs involve the process of biological learning (BL) as opposed to machine learning (ML) for AIs. == Bioinformatics in OI == OI generates complex biological data, necessitating sophisticated methods for processing and analysis. Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. Researchers have developed a platform named Neuroplatform for experimenting remotely with brain organoids via an API. == Intended functions == Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in AI technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as most examples are built on digital electronic principles. One study performed OI computation (which they termed Brainoware) by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics, and fading memory properties, as well as unsupervised learning from training data by reshaping the organoid functional connectivity, the study showed the potential of this technology by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. == Ethical concerns == While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Concerns include the possibility that an organoid could develop sentience or consciousness, and the question of the relationship between a stem cell donor (for growing the organoid) and the respective OI system.

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  • Mosaik Solutions

    Mosaik Solutions

    Mosaik Solutions (formerly American Roamer) was a company that specializes in wireless coverage data and wireless coverage maps, based in Memphis, Tennessee before being acquired by Ookla. The company collects and crowdsources carrier signal quality from major telecommunications providers or users who have its consumer or enterprise mobile application installed. The data is used to provide insights into places around the world without access to cellular coverage and the development of new coverage patterns, as well as to provide maps showing what provider offers the best service in an area. In 2011, the Federal Communications Commission (FCC), recognized Mosaik Solutions as the "industry standard" for the presence of wireless service at the census-block level. == History == In 2016, Mosaik purchased Sensorly, a free app developed to crowdsource cellular network performance service and provide coverage mapping for wireless networks worldwide. == Products and services == === MapELEMENTS === MapELEMENTS software is a visualization tool that allows users to analyze data from the largest cellular coverage database in the world. === CellMaps === CellMaps is an interactive mapping solution that allows companies to show their network coverage directly on their website through an iframe or API. In 2013 Mosaik launched an android app for CellMaps that provides data directly from carriers so that users can determine what carrier meets their needs in a given area. On the map you can overlay multiple carriers, zoom to street-view level, and drop a pin onto any given spot to get a breakdown of carrier service in that area. === Signal Insights App === Signal Insights is an SaaS platform service available for android users that measures and analyzes the customer's experience in cellular or Wi-Fi networks. Indoor mode allows a user to upload a building floor plan and then map and test specific points in the building for cellular or Wi-Fi connectivity. === Sensorly App === Sensorly is a free app that crowdsources cellular network performance to provide coverage mapping worldwide and mobile speed data to help consumers make informed decisions when choosing a cellular carrier. In February 2017, Sensorly launched Map Trip, a feature that allows users to map their routes and share with others their signal data at a particular point in real time. === TowerSource === TowerSource is a resource for locating cell towers and identifying ownership, availability, fiber routes, type and height. It was acquired by Mosaik Solutions in September 2014. === Network Validator === Network Validator is a SaaS solution designed for users to quickly determine whether global cellular networks exist - by country, operator and wireless technology. === CoverageRight === CoverageRight is composed of licensed GIS file datasets that identify the marketed coverage of wireless operators in the United States and worldwide. It enables users to perform spatial analyses, monitor competitive build-outs, analyze coverage trends and assemble roaming footprints. This data has been utilized by the FCC to analyze wireless coverage nationwide. === Network QoE === Network QoE is an enterprise platform that uses crowdsourced data from cellular devices to detect wireless network issues including 3G, 4G and wifi accessibility, network coverage holes and data performance issues. === Wireless Spectrum Report === In March 2017, Mosaik Solutions launched the Wireless Spectrum Report, a tabular dataset detailing facts about spectrum ownership and availability in the United States.

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  • Randomized benchmarking

    Randomized benchmarking

    Randomized benchmarking is an experimental method for measuring the average error rates of quantum computing hardware platforms. The protocol estimates the average error rates by implementing long sequences of randomly sampled quantum gate operations. Randomized benchmarking is the industry-standard protocol used by quantum hardware developers such as IBM and Google to test the performance of the quantum operations. The original theory of randomized benchmarking, proposed by Joseph Emerson and collaborators, considered the implementation of sequences of Haar-random operations, but this had several practical limitations. The now-standard protocol for randomized benchmarking (RB) relies on uniformly random Clifford operations, as proposed in 2006 by Dankert et al. as an application of the theory of unitary t-designs. In current usage randomized benchmarking sometimes refers to the broader family of generalizations of the 2005 protocol involving different random gate sets that can identify various features of the strength and type of errors affecting the elementary quantum gate operations. Randomized benchmarking protocols are an important means of verifying and validating quantum operations and are also routinely used for the optimization of quantum control procedures. == Overview == Randomized benchmarking offers several key advantages over alternative approaches to error characterization. For example, the number of experimental procedures required for full characterization of errors (called tomography) grows exponentially with the number of quantum bits (called qubits). This makes tomographic methods impractical for even small systems of just 3 or 4 qubits. In contrast, randomized benchmarking protocols are the only known approaches to error characterization that scale efficiently as number of qubits in the system increases. Thus RB can be applied in practice to characterize errors in arbitrarily large quantum processors. Additionally, in experimental quantum computing, procedures for state preparation and measurement (SPAM) are also error-prone, and thus quantum process tomography is unable to distinguish errors associated with gate operations from errors associated with SPAM. In contrast, RB protocols are robust to state-preparation and measurement errors Randomized benchmarking protocols estimate key features of the errors that affect a set of quantum operations by examining how the observed fidelity of the final quantum state decreases as the length of the random sequence increases. If the set of operations satisfies certain mathematical properties, such as comprising a sequence of twirls with unitary two-designs, then the measured decay can be shown to be an invariant exponential with a rate fixed uniquely by features of the error model. == History == Randomized benchmarking was proposed in Scalable noise estimation with random unitary operators, where it was shown that long sequences of quantum gates sampled uniformly at random from the Haar measure on the group SU(d) would lead to an exponential decay at a rate that was uniquely fixed by the error model. Emerson, Alicki and Zyczkowski also showed, under the assumption of gate-independent errors, that the measured decay rate is directly related to an important figure of merit, the average gate fidelity and independent of the choice of initial state and any errors in the initial state, as well as the specific random sequences of quantum gates. This protocol applied for arbitrary dimension d and an arbitrary number n of qubits, where d=2n. The SU(d) RB protocol had two important limitations that were overcome in a modified protocol proposed by Dankert et al., who proposed sampling the gate operations uniformly at random from any unitary two-design, such as the Clifford group. They proved that this would produce the same exponential decay rate as the random SU(d) version of the protocol proposed in Emerson et al.. This follows from the observation that a random sequence of gates is equivalent to an independent sequence of twirls under that group, as conjectured in and later proven in. This Clifford-group approach to Randomized Benchmarking is the now standard method for assessing error rates in quantum computers. A variation of this protocol was proposed by NIST in 2008 for the first experimental implementation of an RB-type for single qubit gates. However, the sampling of random gates in the NIST protocol was later proven not to reproduce any unitary two-design. The NIST RB protocol was later shown to also produce an exponential fidelity decay, albeit with a rate that depends on non-invariant features of the error model In recent years a rigorous theoretical framework has been developed for Clifford-group RB protocols to show that they work reliably under very broad experimental conditions. In 2011 and 2012, Magesan et al. proved that the exponential decay rate is fully robust to arbitrary state preparation and measurement errors (SPAM). They also proved a connection between the average gate fidelity and diamond norm metric of error that is relevant to the fault-tolerant threshold. They also provided evidence that the observed decay was exponential and related to the average gate fidelity even if the error model varied across the gate operations, so-called gate-dependent errors, which is the experimentally realistic situation. In 2018, Wallman and Dugas et al., showed that, despite concerns raised in, even under very strong gate-dependence errors the standard RB protocols produces an exponential decay at a rate that precisely measures the average gate-fidelity of the experimentally relevant errors. The results of Wallman. in particular proved that the RB error rate is so robust to gate-dependent errors models that it provides an extremely sensitive tool for detecting non-Markovian errors. This follows because under a standard RB experiment only non-Markovian errors (including time-dependent Markovian errors) can produce a statistically significant deviation from an exponential decay The standard RB protocol was first implemented for single qubit gate operations in 2012 at Yale on a superconducting qubit. A variation of this standard protocol that is only defined for single qubit operations was implemented by NIST in 2008 on a trapped ion. The first implementation of the standard RB protocol for two-qubit gates was performed in 2012 at NIST for a system of two trapped ions

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  • Acquisition of DirecTV by AT&T

    Acquisition of DirecTV by AT&T

    AT&T Inc. announced an agreement with the DirecTV Group on May 18, 2014, to acquire the company for $48.5 billion in a joint cash-stock transaction and assumed debts of $18.6 billion for a total offer of $67.1 billion. Due to stalling growth in the wireless sector, AT&T began diversifying into mass media to expand its consumer offerings. After regulatory agencies approved the purchase on July 24, 2015, AT&T briefly became the largest Pay-TV provider. DirecTV was brought under AT&T's communication segment and DirecTV Now was launched on November 30, 2016, as an alternative to cord-cutting. In the years following the purchase, DirecTV lost millions of subscribers across its satellite and streaming services and by 2019, calls grew for AT&T to divest itself off the business. Initially, AT&T rejected these calls and defended the acquisition, but by February 2021, it reached a deal with TPG Inc. to transfer ownership of DirecTV. Under the terms of the agreement, AT&T would retain a 70% majority stake in DirecTV but would no longer oversee its daily operations. The deal was finalized by August 2, 2021, with AT&T receiving $7.1 billion. By July 3, 2025, AT&T sold its majority stake to TPG, ending any ties of involvement. == Background and Development == === AT&T's history === The company to bear the name "AT&T" was founded on March 3, 1885, as American Telephone and Telegraph Company (or AT&T Corporation) by Theodore Newton Vail as a long-distance subsidiary of the Bell Telephone Company. By December 1899, the Bell Telephone's assets were transferred to AT&T, with the latter gaining control of the Bell System, a regional network of local telecom companies. Theodore Vail became AT&T's President in 1907 and under his leadership, AT&T gained a monopoly over the telephone sector in the United States. This near century dominance earned AT&T the nickname of "Ma Bell." In 1974, the U.S. Department of Justice sued AT&T on accounts of antitrust violations. AT&T challenged the lawsuit, but in 1982, it reached a settlement with the DOJ to break apart its Bell System monopoly into seven regional companies. On January 1, 1984, the Bell System came to an end and led to a reshaped telecom industry. One of these regional companies, Southwestern Bell, emerged as the smallest, but after the passage of the 1996 Telecom Act, deregulated telecom rules allowed SBC to become a major telecom company. AT&T briefly became the largest cable and broadband company by the end of the 20th Century, but later deconsolidated to exit those industries. In 2005, SBC acquired its former parent, AT&T, and took on its branding as AT&T Inc, while retaining its previous business history. The newly reincorporated AT&T acquired BellSouth in 2006 and reconstituted much of its former Bell System. === DirecTV's history === == Acquisition Timeline == == Managing DirecTV == == Divestment and Spinoff ==

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  • Standard test image

    Standard test image

    A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class of processing techniques would need to deal with. Other test images are chosen because they present a range of challenges to image reconstruction algorithms, such as the reproduction of fine detail and textures, sharp transitions and edges, and uniform regions. == Historical origins == Test images as transmission system calibration material probably date back to the original Paris to Lyon pantelegraph link. Analogue fax equipment (and photographic equipment for the printing trade) were the largest user groups of the standardized image for calibration technology until the coming of television and digital image transmission systems. == Common test image resolutions == The standard resolution of the images is usually 512×512 or 720×576. Most of these images are available as TIFF files from the University of Southern California's Signal and Image Processing Institute. Kodak has released 768×512 images, available as PNGs, that was originally on Photo CD with higher resolution, that are widely used for comparing image compression techniques.

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  • Comet (programming)

    Comet (programming)

    Comet is a web application model in which a long-held HTTPS request allows a web server to push data to a browser, without the browser explicitly requesting it. Comet is an umbrella term, encompassing multiple techniques for achieving this interaction. All these methods rely on features included by default in browsers, such as JavaScript, rather than on non-default plugins. The Comet approach differs from the original model of the web, in which a browser requests a complete web page at a time. The use of Comet techniques in web development predates the use of the word Comet as a neologism for the collective techniques. Comet is known by several other names, including Ajax Push, Reverse Ajax, Two-way-web, HTTP Streaming, and HTTP server push among others. The term Comet is not an acronym, but was coined by Alex Russell in his 2006 blog post. In recent years, the standardisation and widespread support of WebSocket and Server-sent events has rendered the Comet model obsolete. == History == === Early Java applets === The ability to embed Java applets into browsers (starting with Netscape Navigator 2.0 in March 1996) made two-way sustained communications possible, using a raw TCP socket to communicate between the browser and the server. This socket can remain open as long as the browser is at the document hosting the applet. Event notifications can be sent in any format – text or binary – and decoded by the applet. === The first browser-to-browser communication framework === The very first application using browser-to-browser communications was Tango Interactive, implemented in 1996–98 at the Northeast Parallel Architectures Center (NPAC) at Syracuse University using DARPA funding. TANGO architecture has been patented by Syracuse University. TANGO framework has been extensively used as a distance education tool. The framework has been commercialized by CollabWorx and used in a dozen or so Command&Control and Training applications in the United States Department of Defense. === First Comet applications === The first set of Comet implementations dates back to 2000, with the Pushlets, Lightstreamer, and KnowNow projects. Pushlets, a framework created by Just van den Broecke, was one of the first open source implementations. Pushlets were based on server-side Java servlets, and a client-side JavaScript library. Bang Networks – a Silicon Valley start-up backed by Netscape co-founder Marc Andreessen – had a lavishly financed attempt to create a real-time push standard for the entire web. In April 2001, Chip Morningstar began developing a Java-based (J2SE) web server which used two HTTP sockets to keep open two communications channels between the custom HTTP server he designed and a client designed by Douglas Crockford; a functioning demo system existed as of June 2001. The server and client used a messaging format that the founders of State Software, Inc. assented to coin as JSON following Crockford's suggestion. The entire system, the client libraries, the messaging format known as JSON and the server, became the State Application Framework, parts of which were sold and used by Sun Microsystems, Amazon.com, EDS and Volkswagen. In March 2006, software engineer Alex Russell coined the term Comet in a post on his personal blog. The new term was a play on Ajax (Ajax and Comet both being common household cleaners in the USA). In 2006, some applications exposed those techniques to a wider audience: Meebo’s multi-protocol web-based chat application enabled users to connect to AOL, Yahoo, and Microsoft chat platforms through the browser; Google added web-based chat to Gmail; JotSpot, a startup since acquired by Google, built Comet-based real-time collaborative document editing. New Comet variants were created, such as the Java-based ICEfaces JSF framework (although they prefer the term "Ajax Push"). Others that had previously used Java-applet based transports switched instead to pure-JavaScript implementations. == Implementations == Comet applications attempt to eliminate the limitations of the page-by-page web model and traditional polling by offering two-way sustained interaction, using a persistent or long-lasting HTTP connection between the server and the client. Since browsers and proxies are not designed with server events in mind, several techniques to achieve this have been developed, each with different benefits and drawbacks. The biggest hurdle is the HTTP 1.1 specification, which states "this specification... encourages clients to be conservative when opening multiple connections". Therefore, holding one connection open for real-time events has a negative impact on browser usability: the browser may be blocked from sending a new request while waiting for the results of a previous request, e.g., a series of images. This can be worked around by creating a distinct hostname for real-time information, which is an alias for the same physical server. This strategy is an application of domain sharding. Specific methods of implementing Comet fall into two major categories: streaming and long polling. === Streaming === An application using streaming Comet opens a single persistent connection from the client browser to the server for all Comet events. These events are incrementally handled and interpreted on the client side every time the server sends a new event, with neither side closing the connection. Specific techniques for accomplishing streaming Comet include the following: ==== Hidden iframe ==== A basic technique for dynamic web application is to use a hidden iframe HTML element (an inline frame, which allows a website to embed one HTML document inside another). This invisible iframe is sent as a chunked block, which implicitly declares it as infinitely long (sometimes called "forever frame"). As events occur, the iframe is gradually filled with script tags, containing JavaScript to be executed in the browser. Because browsers render HTML pages incrementally, each script tag is executed as it is received. Some browsers require a specific minimum document size before parsing and execution is started, which can be obtained by initially sending 1–2 kB of padding spaces. One benefit of the iframes method is that it works in every common browser. Two downsides of this technique are the lack of a reliable error handling method, and the impossibility of tracking the state of the request calling process. ==== XMLHttpRequest ==== The XMLHttpRequest (XHR) object, a tool used by Ajax applications for browser–server communication, can also be pressed into service for server–browser Comet messaging by generating a custom data format for an XHR response, and parsing out each event using browser-side JavaScript; relying only on the browser firing the onreadystatechange callback each time it receives new data. === Ajax with long polling === None of the above streaming transports work across all modern browsers without negative side-effects. This forces Comet developers to implement several complex streaming transports, switching between them depending on the browser. Consequently, many Comet applications use long polling, which is easier to implement on the browser side, and works, at minimum, in every browser that supports XHR. As the name suggests, long polling requires the client to poll the server for an event (or set of events). The browser makes an Ajax-style request to the server, which is kept open until the server has new data to send to the browser, which is sent to the browser in a complete response. The browser initiates a new long polling request in order to obtain subsequent events. IETF RFC 6202 "Known Issues and Best Practices for the Use of Long Polling and Streaming in Bidirectional HTTP" compares long polling and HTTP streaming. Specific technologies for accomplishing long-polling include the following: ==== XMLHttpRequest long polling ==== For the most part, XMLHttpRequest long polling works like any standard use of XHR. The browser makes an asynchronous request of the server, which may wait for data to be available before responding. The response can contain encoded data (typically XML or JSON) or Javascript to be executed by the client. At the end of the processing of the response, the browser creates and sends another XHR, to await the next event. Thus the browser always keeps a request outstanding with the server, to be answered as each event occurs. ==== Script tag long polling ==== While any Comet transport can be made to work across subdomains, none of the above transports can be used across different second-level domains (SLDs), due to browser security policies designed to prevent cross-site scripting attacks. That is, if the main web page is served from one SLD, and the Comet server is located at another SLD (which does not have cross-origin resource sharing enabled), Comet events cannot be used to modify the HTML and DOM of the main page, using those transports. This problem can be sidestepped by creating a proxy server in

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  • Algorithmic curation

    Algorithmic curation

    Algorithm curation is the selection of online media by technologies such as recommender systems and personalized search. Curation entails the selective sharing of online content and recommendations based on inferred interests. Curation algorithms implement different filter approaches, such as collaborative filtering and content-based filtering. Examples include search engine and social media products such as the Twitter feed, Facebook's News Feed, and Google Personalized Search. == History == === Early algorithmic curation === Online platforms use newsfeed algorithms to determine what content to present to each user. The volume of content published on social media platforms created a need for automated filtering, as manual review of all available content by users is not feasible. These systems function as a form of gatekeeper, shaping which new material users are exposed to and influencing knowledge, attention, and political exposure. ==== Information overload ==== Early ranking algorithms addressed information overload by surfacing the most recent or most popular posts. Later systems shifted toward ranking content based on predicted engagement, aiming to increase the time users spend on a platform. Research has found that these engagement-oriented systems can increase the spread of misinformation and contribute to political polarization as a side effect of optimising for user interaction. ==== How algorithm changes users' feeds over time ==== Algorithmic curation has been found to increase source diversity in some respects while simultaneously reducing the number of external links presented to users, which limits exposure to off-platform content. Research using agent-based modelling has examined how user behaviour, information quality, and algorithmic design interact with one another over time. === Emergence of AI === Platforms increasingly shifted from rule-based ranking systems toward machine-learning and AI-driven approaches, which allow feeds to be personalised at a larger scale and with greater responsiveness to user behaviour. For example, X (formerly Twitter) moved away from a chronological feed toward an AI-powered ranking system that personalises content for each user. These systems are capable of making ranking decisions across volumes of content and user interactions that would not be practical to handle manually. == Approach == === Filter types === ==== Collaborative filtering ==== Collaborative filtering (CF) methods create recommendations based on a person's usage patterns. CF predicts a person's preference for an item by matching their interests with those of users who have similar interests. This process allows for the sharing of ratings between users with similar profiles. CF is based on patterns of human behaviour rather than machine analysis of content itself. Users of CF systems rate items they have interacted with, and these ratings form a profile of interests. The CF system then matches that user with others who have similar profiles, and uses their ratings to generate recommendations. Collaborative filtering can be applied across various content types including text, images, music, and financial products, and can account for complex attributes such as taste and quality that are difficult to represent explicitly. ==== Content-based filtering ==== Content-based filtering (CBF) builds a user profile to represent the types of items a user has engaged with, based on keywords and attributes used to describe those items. Recommendations are generated by presenting items similar to those the user has previously engaged with or is currently viewing. The CBF method creates a profile for each item based on discrete attributes and features, and then constructs a content-based user profile using a weighted vector of those features derived from items the user has rated, purchased, or interacted with. The weights represent the relative importance of each feature, and can be computed using techniques such as Bayesian classifiers, cluster analysis, decision trees, and artificial neural networks, with the goal of estimating the probability that a user will engage with a suggested item. One application of content-based filtering is Pandora Radio, where users provide an artist, genre, or composer to generate a station, and the system surfaces music with similar attributes. == Technology == === Recommender system === Recommender systems rank and suggest content to users based on a combination of implicit and explicit user input. Implicit signals include time spent viewing or engaging with a specific item. Explicit signals include actions such as liking posts, saving store pages, reading news articles, or sharing content. === Personalized search === Personalized search aims to retrieve results most relevant to the user by incorporating contextual factors beyond the explicit query, such as past queries, browsing history, and inferred interests. Social media platforms such as X (formerly Twitter) and Bluesky generate recommendations based on similar users and the content those users interact with. Personalized search may also allow users to explicitly filter results by blocking content containing certain phrases or hashtags. For first-time users without prior history, personalized search may draw on content-based filtering to establish an initial context. Similar processes are used by search engines and retail platforms to tailor results and product recommendations to individual users. == AI contribution == Artificial intelligence contributes to algorithmic curation through machine-learning models capable of processing large volumes of data. Techniques such as deep learning and reinforcement learning allow curation algorithms to model user preferences with greater granularity alongside established filtering approaches. This enables platforms to adjust content rankings rapidly in response to user behaviour. In social media and streaming contexts, AI-driven systems arrange feeds according to predicted relevance, with the outputs shaped by patterns present in the training data. == Social media and potential impact == === Echo chambers === Social media algorithms, such as those used by X (formerly Twitter), recommend content that the system predicts a user will engage with positively. Content from accounts with differing perspectives is less likely to be surfaced, which may reduce source and topic diversity and contribute to the formation of echo chambers. For example, Facebook's news feed is designed to surface content aligned with users' prior engagement, which may reinforce existing views. This dynamic may contribute to filter bubbles, in which users are seldom exposed to content outside their existing interests. Users may further narrow their feeds by actively blocking certain content or accounts. === Over-representation === A pattern observed across social media platforms is the concentration of algorithmic visibility among a small subset of users. Content from the most active users, those with the largest followings, or those generating the most engagement tends to be surfaced more frequently, meaning a small number of accounts can account for a disproportionate share of what appears in other users' feeds.

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