Articulatory speech recognition

Articulatory speech recognition means the recovery of speech (in forms of phonemes, syllables or words) from acoustic signals with the help of articulatory modeling or an extra input of articulatory movement data. Speech recognition (or automatic speech recognition, acoustic speech recognition) means the recovery of speech from acoustics (sound wave) only. Articulatory information is extremely helpful when the acoustic input is in low quality, perhaps because of noise or missing data. Measurable information from the articulatory system (e.g. tongue, jaw movements) can supplement acoustic signals to improve phone recognition accuracy by 2%. However, attempts to estimate articulatory data from acoustic signals alone have not significantly enhanced recognition performance.

Gcore

Gcore is an edge AI, cloud, network, and security company headquartered in Luxembourg. Founded in 2014, the company provides low-latency services to industries including finance, healthcare, manufacturing, gaming, media and telecommunications internationally. As of March 2024, its global network includes over 180 Points of Presence (PoPs) across six continents. == History == Gcore was founded in 2014 in Luxembourg. The company built its own content delivery network, originally designed for the needs of the gaming industry. In 2016, Gcore's infrastructure expanded to multiple regions that were underserved by hyperscale cloud providers. In 2020, the company formed partnerships with Intel and Equinix. In 2022, Gcore launched the European AI Cloud, providing access to infrastructure for machine learning tasks. In March 2024, Gcore announced the acquisition of a web application and API protection (WAAP) solution from StackPath. In April 2024, Gcore received a commendation in the Industry Innovation category at the NVIDIA Partner Network Awards EMEA for developing the first speech-to-text technology for Luxembourgish, using the LuxemBERT AI model. In May 2024, Philipp Rösler, former vice-chancellor of Germany and federal minister of health joined the Gcore board. In July 2024, Gcore raised $60 million in a Series A funding round, marking the company's first external investment since its founding. In August 2024, Gcore was recognized as a Major Player in the IDC MarketScape report for European public cloud Infrastructure (IaaS) 2024 by IDC, the global market intelligence firm. In May 2025, Feiyu Xu became a member of the Gcore advisory board. == Network infrastructure == According to the company's website, Gcore has network locations in six continents: Europe, North America, Asia, South America, Africa, and Australia with over 14,000 peering partners and a network capacity exceeding 200 Tbps. According to a 2025 review by Geekflare, Gcore's CDN achieved an average global response time of around 30 milliseconds. Gcore offers AI cloud clusters, including a generative AI cluster with Nvidia GPUs in Luxembourg and additional sites in the Netherlands and Wales, as part of its European AI infrastructure. == Products and services == Gcore offers a range of services, including content delivery network (CDN), cloud computing,virtual machines, bare-metal servers, object storage AI infrastructure and inference, Kubernetes, video streaming, DDoS mitigation, web application and API protection (WAAP), Domain Name System (DNS). Gcore provides AI services and GPU cloud infrastructure to support model development, training, fine-tuning, and inference. In January 2025, the company introduced Everywhere Inference, a serverless inference solution that enables AI model deployment. == Controversies == Correctiv and Tageszeitung reported that Gcore supported the distribution of the TV network RT until April 2023, which has been under sanctions by the EU since March 2022. However, Gcore denies these allegations. == Collaborations == In 2024, Gcore and Qareeb Data Centres, a data center provider in the Middle East, launched a collaboration to integrate Gcore's AI, cloud and edge services across data centers in multiple Middle Eastern countries. In June 2025, Gcore joined the SmartSpires initiative, a €3.1 million smart city project co-funded by the Connecting Europe Facility. The three-year programme is coordinated by a public–private consortium including 5SKYE, the Luxembourg Institute of Science and Technology (LIST), Orange Luxembourg, and Gcore. The project aims to transform the Belval campus into a smart city by deploying 5G-enabled smart towers that integrate edge computing, artificial intelligence and IoT services. Within the consortium, Gcore acts as project coordinator and is responsible for the deployment of the edge infrastructure.

Hardware backdoor

A hardware backdoor is a backdoor implemented within the physical components of a computer system, also known as its hardware. They can be created by introducing malicious code to a component's firmware, or even during the manufacturing process of an integrated circuit. Often, they are used to undermine security in smartcards and cryptoprocessors, unless investment is made in anti-backdoor design methods. They have also been considered for car hacking. Backdoors differ from hardware Trojans as backdoors are introduced intentionally by the original designer or during the design process, whereas hardware Trojans are inserted later by an external party. == Background == The existence of hardware backdoors poses significant security risks for several reasons. They are difficult to detect and are impossible to remove using conventional methods like antivirus software. They can also bypass other security measures, such as disk encryption. Hardware trojans can be introduced during manufacturing where the end-user lacks control over the production chain. == History == In 2008, the FBI reported the discovery of approximately 3,500 counterfeit Cisco network components in the United States, some of which were introduced in military and government infrastructure. In the same year, the possibility of a backdoor SPARC CPU was demonstrated with an FPGA running Linux that supported various hidden malicious services. A few years later, in 2011, Jonathan Brossard presented "Rakshasa", a proof-of-concept hardware backdoor. This backdoor could be installed by an individual with physical access to the hardware. It utilized coreboot to re-flash the BIOS with a SeaBIOS and iPXE-based bootkit composed of legitimate, open-source tools, allowing malware to be fetched from the internet during the boot process. The following year, in 2012, Sergei Skorobogatov and Christopher Woods from the University of Cambridge Computer Laboratory reported the discovery of a backdoor in a military-grade FPGA device, which could be exploited to access and modify sensitive information. It has been said that this was proven to be a software problem and not a deliberate attempt at sabotage. This still brought to attention that equipment manufacturers should ensure that microchips operate as intended. Later that year, two mobile phones developed by the Chinese company ZTE were found to carry a root access backdoor. According to security researcher Dmitri Alperovitch, the exploit used a hard-coded password in its software. Starting in 2012, the United States stated that Huawei might have backdoors present in their products. In 2013, researchers at the University of Massachusetts devised a method of breaking a CPU's internal cryptographic mechanisms by introducing specific impurities into the crystalline structure of transistors to change Intel's random-number generator. Documents revealed from 2013 onwards during the surveillance disclosures initiated by Edward Snowden showed that the Tailored Access Operations (TAO) unit and other NSA employees intercepted servers, routers, and other network gear being shipped to organizations targeted for surveillance to install covert implant firmware onto them before delivery. These tools include custom BIOS exploits that survive the reinstallation of operating systems and USB cables with spy hardware and radio transceiver packed inside. In June 2016 it was reported that University of Michigan Department of Electrical Engineering and Computer Science had built a hardware backdoor that leveraged "analog circuits to create a hardware attack" so that after the capacitors store up enough electricity to be fully charged, it would be switched on, to give an attacker complete access to whatever system or device − such as a PC − that contains the backdoored chip. In the study that won the "best paper" award at the IEEE Symposium on Privacy and Security they also note that microscopic hardware backdoor wouldn't be caught by practically any modern method of hardware security analysis, and could be planted by a single employee of a chip factory. In October 2018 Bloomberg reported that an attack by Chinese spies reached almost 30 U.S. companies, including Amazon and Apple, by compromising America's technology supply chain. == Countermeasures == Skorobogatov has developed a technique capable of detecting malicious insertions into chips. New York University Tandon School of Engineering researchers have developed a way to corroborate a chip's operation using verifiable computing whereby "manufactured for sale" chips contain an embedded verification module that proves the chip's calculations are correct and an associated external module validates the embedded verification module. Another technique developed by researchers at University College London (UCL) relies on distributing trust between multiple identical chips from disjoint supply chains. Assuming that at least one of those chips remains honest the security of the device is preserved. Researchers at the University of Southern California Ming Hsieh Department of Electrical and Computer Engineering and the Photonic Science Division at the Paul Scherrer Institute have developed a new technique called Ptychographic X-ray laminography. This technique is the only current method that allows for verification of the chips blueprint and design without destroying or cutting the chip. It also does so in significantly less time than other current methods. Anthony F. J. Levi Professor of electrical and computer engineering at University of Southern California explains “It’s the only approach to non-destructive reverse engineering of electronic chips—[and] not just reverse engineering but assurance that chips are manufactured according to design. You can identify the foundry, aspects of the design, who did the design. It’s like a fingerprint.” This method currently is able to scan chips in 3D and zoom in on sections and can accommodate chips up to 12 millimeters by 12 millimeters easily accommodating an Apple A12 chip but not yet able to scan a full Nvidia Volta GPU. "Future versions of the laminography technique could reach a resolution of just 2 nanometers or reduce the time for a low-resolution inspection of that 300-by-300-micrometer segment to less than an hour, the researchers say."

Remote scripting

Remote scripting is a technology which allows scripts and programs that are running inside a browser to exchange information with a server. The local scripts can invoke scripts on the remote side and process the returned information. The earliest form of asynchronous remote scripting was developed before XMLHttpRequest existed, and made use of very simple process: a static web page opens a dynamic web page (e.g. at other target frame) that is reloaded with new JavaScript content, generated remotely on the server side. The XMLHttpRequest and similar "client-side script remote procedure call" functions, open the possibility of use and triggering web services from the web page interface. The web development community subsequently developed a range of techniques for remote scripting in order to enable consistent results across different browsers. Early examples include JSRS library from 2000, the introduction of the Image/Cookie technique in 2000. == JavaScript Remote Scripting == JavaScript Remote Scripting (JSRS) is a web development technique for creating interactive web applications using a combination of: HTML (or XHTML) The Document Object Model manipulated through JavaScript to dynamically display and interact with the information presented A transport layer. Different technologies may be used, though using a script tag or an iframe is used the most because it has better browser support than XMLHttpRequest A data format. XML with WDDX can be used as well as JSON or any other text format. Schematic A similar approach is Ajax, though it depends on the XmlHttpRequest in newer web browsers. === Libraries === Brent Ashley's original JSRS library released in 2000 BlueShoes JSRS with added encoding and OO RPC abstractions Simple Tutorials: Javascript Remote Scripting with PHP at the Wayback Machine (archived 2006-04-14) MSDN article

Digital redlining

Digital redlining is the practice of creating and perpetuating inequities between already marginalized groups specifically through the use of digital technologies, digital content, and the internet. The concept of digital redlining is an extension of the practice of redlining in housing discrimination, a historical legal practice in the United States and Canada dating back to the 1930s where red lines were drawn on maps to indicate poor and primarily black neighborhoods that were deemed unsuitable for loans or further development, which created great economic disparities between neighborhoods. The term was popularized by Dr. Chris Gilliard, a privacy scholar, who defines digital redlining as "the creation and maintenance of tech practices, policies, pedagogies, and investment decisions that enforce class boundaries and discriminate against specific groups". Though digital redlining is related to the digital divide and techniques such as weblining and personalization, it is distinct from these concepts as part of larger complex systemic issues. It can refer to practices that create inequities of access to technology services in geographical areas, such as when internet service providers decide to not service specific geographic areas because they are perceived to be not as profitable and thus reduce access to crucial services and civic participation. It can also be used to refer to inequities caused by the policies and practices of digital technologies. For instance, with these methods inequities are accomplished through divisions that are created via algorithms which are hidden from the technology user; the use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. These algorithmic means are enabled through the use of unregulated data technologies that apply a score to individuals that statistically categorize personality traits or tendencies which are similar to a credit score but are proprietary to the technology companies and not under outside oversight. == Digital redlining and geography == While the roots of redlining lie in excluding populations based on geography, digital redlining occurs in both geographical and non-geographical contexts. An example of both contexts can be found in the charges brought against Facebook on March 28 of 2019, by the United States Department of Housing and Urban Development (HUD). HUD charged Facebook with violating the Fair Housing Act of 1968 by "encouraging, enabling, and causing housing discrimination through the company's advertising platform." HUD stated that Facebook allowed advertisers to “exclude people who live in a specified area from seeing an ad by drawing a red line around that area.” The discrimination called out by HUD included those that were racist, homophobic, ableist, and classist. Besides this example of geographically based digital redlining, HUD also charged that Facebook used profile information and designations to exclude classes of people. The charges stated: "Facebook enabled advertisers to exclude people whom Facebook classified as parents; non-American-born; non-Christian; interested in accessibility; interested in Hispanic culture; or a wide variety of other interests that closely align with the Fair Housing Act’s protected classes" Several media outlets pointed out HUDs own history of housing discrimination through redlining, the establishment of the Fair Housing Act to combat redlining, and how the digital platform was recreating this discriminatory practice. === Digital redlining within a geographical context === Although digital redlining refers to a complex and varied set of practices, it has been most commonly applied to practices with a geographical dimension. Common examples include when an internet service providers decide to not service specific geographic areas because those areas are seen to be not as profitable, resulting in discrimination against low-income communities, with resulting impacts on access to crucial services and civic participation. AT&T has faced specific scrutiny for this form of digital redlining, it has been reported that AT&T has been classist in its offerings of broadband internet service in areas that are more impoverished. Geographically based digital redlining can also apply to digital content or the distribution of goods sold online. Geographically based games such as Pokémon Go have been shown to offer more virtual stops and rewards in geographic areas that are less ethnically and racially diverse. In 2016, Amazon was rebuked for not offering their Prime same-day delivery service to many communities that were largely African American and had incomes that were beneath the national average. Even services such as email can be impacted, with many email administrators creating filters for flagging particular email messages as spam based on the geographical origin of the message. === Digital redlining based on personal identity === Although often aligned with discrimination that falls into a geographically based context digital redlining also refers to when vulnerable populations are targeted for or excluded from specific content or access to the internet in a way that harms them based on some aspect of their identity. Trade schools and community colleges, which typically have a more working class student body, have been found to block public internet content from their students where elite research institutions do not. The use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. For example, Facebook has been criticized for providing tools that allow advertisers to target ads by ethnic affinity and gender, effectively blocking minorities from seeing specific ads for housing and employment. In October 2019, a major class action lawsuit was filed against Facebook alleging gender and age discrimination in financial advertising. A broad array of consumers can be particularly vulnerable to digital redlining when it is used outside of a geographical context. Besides targeting vulnerable populations based on traditional and legally recognized classifications such as race, gender, age, etc., it has been shown that personal data mined and then resold by brokers can be used to target those who have been identified as suffering from Alzheimer's or dementia, or simply identified as impulse buyers or gullible. == Term distinctions == === Distinctions between weblining and digital redlining === Earlier distinctions have been made between weblining—the process of charging customers different prices based on profile information --- and internet or digital redlining, with digital redlining being focused not on pricing but access. As early as 2002 the Gale Encyclopedia of E-Commerce puts forth the distinction more in use today: weblining is the pervasive and generally accepted (or at least tolerated) practice of personalizing access to products and services in ways invisible to the user; digital redlining is when such personalized, data-driven schemes perpetuate traditional advantages of privileged demographics. As weblining has become more ubiquitous, the term has fallen out of use in favor of the more general term personalization. === Distinctions between the digital divide and digital redlining === Scholars have often drawn connections between the digital divide and digital redlining. In practice, the digital divide is seen as one of a number of impacts of digital redlining, and digital redlining is one of a number of ways in which the divide is maintained or extended. == Criticisms == A 2001 report looked to find if the reason for a gap in access to broadband internet by low-income and minority populations was due to a lack of availability or due to other factors. The report found that there was "little evidence of digital redlining based on income or black or Hispanic concentrations" but that there was mixed evidence of redlining based on areas in which Native American or Asian populations were larger.

Electronic business

Electronic business (also known as online business or e-business) is any kind of business or commercial activity that includes sharing information across the internet. Commerce constitutes the exchange of products and services between businesses, groups, and individuals; and can be seen as one of the essential activities of any business. E-commerce focuses on the use of ICT to enable the external activities and relationships of the business with individuals, groups, and other organizations, while e-business does not only deal with online commercial operations of enterprises, but also deals with their other organizational matters such as human resource management and production. The term "e-business" was coined by IBM's marketing and Internet team in 1996. == Market participants == Electronic business can take place between a very large number of market participants; it can be between business and consumer, private individuals, public administrations, or any other organizations such as non-governmental organizations (NGOs). These various market participants can be divided into three main groups: Business (B) Consumer (C) Administration (A) All of them can be either buyers or service providers within the market. There are nine possible combinations for electronic business relationships. B2C and B2B belong to E-commerce, while A2B and A2A belong to the E-government sector which is also a part of the electronic business. == History == One of the founding pillars of electronic business was the development of the Electronic Data Interchange (EDI) electronic data interchange. This system replaced traditional mailing and faxing of documents with a digital transfer of data from one computer to another, without any human intervention. Michael Aldrich is considered the developer of the predecessor to online shopping. In 1979, the entrepreneur connected a television set to a transaction processing computer with a telephone line and called it "teleshopping", meaning shopping at distance. From the mid-nineties, major advancements were made in the commercial use of the Internet. Amazon, which launched in 1995, started as an online bookstore and grew to become nowadays the largest online retailer worldwide, selling food, toys, electronics, apparel and more. Other successful stories of online marketplaces include eBay or Etsy. In 1994, IBM, with its agency Ogilvy & Mather, began to use its foundation in IT solutions and expertise to market itself as a leader of conducting business on the Internet through the term "e-business." Then CEO Louis V. Gerstner, Jr. was prepared to invest $1 billion to market this new brand. After conducting worldwide market research in October 1997, IBM began with an eight-page piece in The Wall Street Journal that would introduce the concept of "e-business" and advertise IBM's expertise in the new field. IBM decided not to trademark the term "e-business" in the hopes that other companies would use the term and create an entirely new industry. However, this proved to be too successful and by 2000, to differentiate itself, IBM launched a $300 million campaign about its "e-business infrastructure" capabilities. Since that time, the terms, "e-business" and "e-commerce" have been loosely interchangeable and have become a part of the common vernacular. According to the U.S. Department Of Commerce, the estimated retail e-commerce sales in Q1 2020 were representing almost 12% of total U.S. retail sales, against 4% for Q1 2010. == Business model == The transformation toward e-business is complex and in order for it to succeed, there is a need to balance between strategy, an adapted business model (e-intermediary, marketplaces), right processes (sales, marketing) and technology (Supply Chain Management, Customer Relationship Management). When organizations go online, they have to decide which e-business models best suit their goals. A business model is defined as the organization of product, service and information flows, and the source of revenues and benefits for suppliers and customers. The concept of the e-business model is the same but used in online presence. === Revenue model === A key component of the business model is the revenue model or profit model, which is a framework for generating revenues. It identifies which revenue source to pursue, what value to offer, how to price the value, and who pays for the value. It is a key component of a company's business model. It primarily identifies what product or service will be created in order to generate revenues and the ways in which the product or service will be sold. Without a well-defined revenue model, that is, a clear plan of how to generate revenues, new businesses will more likely struggle due to costs that they will not be able to sustain. By having a revenue model, a business can focus on a target audience, fund development plans for a product or service, establish marketing plans, begin a line of credit and raise capital. ==== E-commerce ==== E-commerce (short for "electronic commerce") is trading in products or services using computer networks, such as the Internet. Electronic commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection. Modern electronic commerce typically uses the World Wide Web for at least one part of the transaction's life cycle, although it may also use other technologies such as e-mail. == Concerns == While much has been written of the economic advantages of Internet-enabled commerce, there is also evidence that some aspects of the internet such as maps and location-aware services may serve to reinforce economic inequality and the digital divide. Electronic commerce may be responsible for consolidation and the decline of mom-and-pop, brick and mortar businesses resulting in increases in income inequality. === Security === E-business systems naturally have greater security risks than traditional business systems, therefore it is important for e-business systems to be fully protected against these risks. A far greater number of people have access to e-businesses through the internet than would have access to a traditional business. Customers, suppliers, employees, and numerous other people use any particular e-business system daily and expect their confidential information to stay secure. Hackers are one of the great threats to the security of e-businesses. Some common security concerns for e-Businesses include keeping business and customer information private and confidential, the authenticity of data, and data integrity. Some of the methods of protecting e-business security and keeping information secure include physical security measures as well as data storage, data transmission, anti-virus software, firewalls, and encryption to list a few. ==== Privacy and confidentiality ==== Confidentiality is the extent to which businesses makes personal information available to other businesses and individuals. With any business, confidential information must remain secure and only be accessible to the intended recipient. However, this becomes even more difficult when dealing with e-businesses specifically. To keep such information secure means protecting any electronic records and files from unauthorized access, as well as ensuring safe transmission and data storage of such information. Tools such as encryption and firewalls manage this specific concern within e-business. ==== Authenticity ==== E-business transactions pose greater challenges for establishing authenticity due to the ease with which electronic information may be altered and copied. Both parties in an e-business transaction want to have the assurance that the other party is who they claim to be, especially when a customer places an order and then submits a payment electronically. One common way to ensure this is to limit access to a network or trusted parties by using a virtual private network (VPN) technology. The establishment of authenticity is even greater when a combination of techniques are used, and such techniques involve checking "something you know" (i.e. password or PIN), "something you need" (i.e. credit card), or "something you are" (i.e. digital signatures or voice recognition methods). Many times in e-business, however, "something you are" is pretty strongly verified by checking the purchaser's "something you have" (i.e. credit card) and "something you know" (i.e. card number). ==== Data integrity ==== Data integrity answers the question "Can the information be changed or corrupted in any way?" This leads to the assurance that the message received is identical to the message sent. A business needs to be confident that data is not changed in transit, whether deliberately or by accident. To help with data integrity, firewalls protect stored data against unauthorized access, while

Algorithmic amplification

Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users