AI Content Vs Human Content

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

Cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing paradigm developed from existing advanced manufacturing models (e.g., ASP, AM, NM, MGrid) and enterprise information technologies under the support of cloud computing, Internet of Things (IoT), virtualization and service-oriented technologies, and advanced computing technologies. It transforms manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and circulating of manufacturing resources and manufacturing capabilities. CMfg can provide safe and reliable, high quality, cheap and on-demand manufacturing services for the whole lifecycle of manufacturing. The concept of manufacturing here refers to big manufacturing that includes the whole lifecycle of a product (e.g. design, simulation, production, test, maintenance). The concept of Cloud manufacturing was initially proposed by the research group led by Prof. Bo Hu Li and Prof. Lin Zhang in China in 2010. Related discussions and research were conducted hereafter, and some similar definitions (e.g. Cloud-Based Design and Manufacturing (CBDM). ) to cloud manufacturing were introduced. Cloud manufacturing is a type of parallel, networked, and distributed system consisting of an integrated and inter-connected virtualized service pool (manufacturing cloud) of manufacturing resources and capabilities as well as capabilities of intelligent management and on-demand use of services to provide solutions for all kinds of users involved in the whole lifecycle of manufacturing. == Types == Cloud Manufacturing can be divided into two categories. The first category concerns deploying manufacturing software on the Cloud, i.e. a “manufacturing version” of Computing. CAx software can be supplied as a service on the Manufacturing Cloud (MCloud). The second category has a broader scope, cutting across production, management, design and engineering abilities in a manufacturing business. Unlike with computing and data storage, manufacturing involves physical equipment, monitors, materials and so on. In this kind of Cloud Manufacturing system, both material and non-material facilities are implemented on the Manufacturing Cloud to support the whole supply chain. Costly resources are shared on the network. This means that the utilisation rate of rarely used equipment rises and the cost of expensive equipment is reduced. According to the concept of Cloud technology, there will not be direct interaction between Cloud Users and Service Providers. The Cloud User should neither manage nor control the infrastructure and manufacturing applications. As a matter of fact, the former can be considered part of the latter. In CMfg system, various manufacturing resources and abilities can be intelligently sensed and connected into wider Internet, and automatically managed and controlled using IoT technologies (e.g., RFID, wired and wireless sensor network, embedded system). Then the manufacturing resources and abilities are virtualized and encapsulated into different manufacturing cloud services (MCSs), that can be accessed, invoked, and deployed based on knowledge by using virtualization technologies, service-oriented technologies, and cloud computing technologies. The MCSs are classified and aggregated according to specific rules and algorithms, and different kinds of manufacturing clouds are constructed. Different users can search and invoke the qualified MCSs from related manufacturing cloud according to their needs, and assemble them to be a virtual manufacturing environment or solution to complete their manufacturing task involved in the whole life cycle of manufacturing processes under the support of cloud computing, service-oriented technologies, and advanced computing technologies. Four types of cloud deployment modes (public, private, community and hybrid clouds) are ubiquitous as a single point of access. Private cloud refers to a centralized management effort in which manufacturing services are shared within one company or its subsidiaries. Enterprises' mission-critical and core-business applications are often kept in a private cloud. Community cloud is a collaborative effort in which manufacturing services are shared between several organizations from a specific community with common concerns. Public cloud realizes the key concept of sharing services with the general public in a multi-tenant environment. Hybrid cloud is a composition of two or more clouds (private, community or public) that remain distinct entities but are also bound together, offering the benefits of multiple deployment modes. == Resources == From the resource’s perspective, each kind of manufacturing capability requires support from the related manufacturing resource. For each type of manufacturing capability, its related manufacturing resource comes in two forms, soft resources and hard resources. === Soft resources === Software: software applications throughout the product lifecycle including design, analysis, simulation, process planning, and are only beginning to be embraced by the electronics manufacturing industry. Knowledge: experience and know-how needed to complete a production task, i.e. engineering knowledge, product models, standards, evaluation procedures and results, customer feedback, and manufacturing in the cloud provides just as many solutions as the number of questions it also raises for manufacturing executives wanting to make the best possible decision. Skill: expertise in performing a specific manufacturing task. Personnel: human resource engaged in the manufacturing process, i.e. designers, operators, managers, technicians, project teams, customer service, etc. Experience: performance, quality, client evaluation, etc. Business Network: business relationships and business opportunity networks that exist in an enterprise. === Hard resources === Manufacturing Equipment: facilities needed for completing a manufacturing task, e.g. machine tools, cutters, test and monitoring equipment and other fabrication tools. Monitoring/Control Resource: devices used to identify and control other manufacturing resource, for instance, RFID (Radio-Frequency IDentification), WSN (Wireless Sensor Network), virtual managers and remote controllers. Computational Resource: computing devices to support production process, e.g. servers, computers, storage media, control devices, etc. Materials: inputs and outputs in a production system, e.g. raw material, product-in-progress, finished product, power, water, lubricants, etc. Storage: automated storage and retrieval systems, logic controllers, location of warehouses, volume capacity and schedule/optimization methods. Transportation: movement of manufacturing inputs/outputs from one location to another. It includes the modes of transport, e.g. air, rail, road, water, cable, pipeline and space, and the related price, and time taken.
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Knowledge Engineering Environment

Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems. It was developed and sold by IntelliCorp, and was first released in 1983. It ran on Lisp machines, and was later ported to Lucid Common Lisp with the CLX library, an X Window System (X11) interface for Common Lisp. This version was available on several different UNIX workstations. On KEE, several extensions were offered: Simkit, a frame-based simulation library KEEconnection, database connection between the frame system and relational databases In KEE, frames are called units. Units are used for both individual instances and classes. Frames have slots and slots have facets. Facets can describe, for example, a slot's expected values, its working value, or its inheritance rule. Slots can have multiple values. Behavior can be implemented using a message passing model. KEE provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames. KEE also includes a frame-based rule system. In the KEE knowledge base, rules are frames. Both forward chaining and backward chaining inference are available. KEE supports non-monotonic reasoning through the concepts of worlds. Worlds allow providing alternative slot-values of frames. Through an assumption-based truth or reason maintenance system, inconsistencies can be detected and analyzed. ActiveImages allows graphical displays to be attached to slots of Units. Typical examples are buttons, dials, graphs, and histograms. The graphics are also implemented as Units via KEEPictures, a frame-based graphics library.
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CatBoost

CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey and as the top-7 most frequently used ML framework in the 2021 survey. As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository == Features == CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features Native handling for categorical features Fast GPU training Visualizations and tools for model and feature analysis Using oblivious trees or symmetric trees for faster execution Ordered boosting to overcome overfitting == History == In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results. Since 2009 MatrixNet has been used in different projects at Yandex, including recommendation systems and weather prediction. In 2014–2015 Andrey Gulin worked with a team of researchers to start a new project called Tensornet which was aimed at solving the problem of "how to work with categorical data". Their work resulted in several proprietary Gradient Boosting libraries with different approaches to handling categorical data. In 2016 the Machine Learning Infrastructure team led by Anna Dorogush started working on Gradient Boosting in Yandex, including Matrixnet and Tensornet. They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support for categorical and text data, GPU training, model analysis, and visualization tools. CatBoost was open-sourced in July 2017 and is under active development in Yandex and the open-source community. == Application == JetBrains uses CatBoost for code completion Cloudflare uses CatBoost for bot detection Careem uses CatBoost to predict future destinations of the rides
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IRCF360

Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. == Overview == The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with other robots and for simple IR inter-robot communications. To overcome certain limitation with Infra-red (IR) proximity sensing (e.g. detection of dark surfaces) the sensing module includes ambient light sensing and basic tactile sensing functionality during forward movement sensing/probing providing photovore and photophobe robot swarm behaviours and characteristics. A project named Sensorium Project was started aimed at broadening the Sensors audience beyond its typical robot sensor usage. To demonstrate the sensor's functionality, opensource Java based Integrated Development Environments (IDE) are used, such as Arduino and Processing (programming language).
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TSheets

TSheets was a web-based and mobile time tracking and employee scheduling app. The service was accessed via a web browser or a mobile app. TSheets was an alternative to a paper timesheet or punch cards. == History == Based in Eagle, Idaho, TSheets was co-founded in 2006 by CEO Matt Rissell and CTO Brandon Zehm. In 2008, TSheets released a native employee time tracking app for the iPhone. In 2012, TSheets released an integration with accounting and payroll software QuickBooks. In 2015, TSheets accepted $15 million in growth equity funding from Summit Partners, bought a building in Eagle, Idaho, and opened a second location in Sydney, Australia. On 5 December 2017, Intuit announced an agreement to acquire TSheets. The transaction was valued at approximately $340 million of cash and other consideration and closed on 11 January 2018. After the transaction closed, Time Capture became a new business unit within Intuit's Small Business and Self-Employed Group with Matt Rissell assuming the leader role reporting to Alex Chriss. TSheets's Eagle, Idaho site became an Intuit location.
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Plinian Core

Plinian Core is a set of vocabulary terms that can be used to describe different aspects of biological species information. Under "biological species Information" all kinds of properties or traits related to taxa—biological and non-biological—are included. Thus, for instance, terms pertaining descriptions, legal aspects, conservation, management, demographics, nomenclature, or related resources are incorporated. == Description == The Plinian Core is aimed to facilitate the exchange of information about the species and upper taxa. What is in scope? Species level catalogs of any kind of biological objects or data. Terminology associated with biological collection data. Striving for compatibility with other biodiversity-related standards. Facilitating the addition of components and attributes of biological data. What is not in scope? Data interchange protocols. Non-biodiversity-related data. Occurrence level data. This standard is named after Pliny the Elder, a very influential figure in the study of the biological species. Plinian Core design requirements includes: ease of use, to be self-contained, able to support data integration from multiple databases, and ability to handle different levels of granularity. Core terms can be grouped in its current version as follows: Metadata Base Elements Record Metadata Nomenclature and Classification Taxonomic description Natural history Invasive species Habitat and Distribution Demography and Threats Uses, Management and Conservation associatedParty, MeasurementOrFact, References, AncillaryData == Background == Plinian Core started as a collaborative project between Instituto Nacional de Biodiversidad and GBIF Spain in 2005. A series of iterations in which elements were defined and implanted in different projects resulted in a "Plinian Core Flat" [deprecated]. As a result, a new development was impulse to overcome them in 2012. New formal requirements, additional input and a will to better support the standard and its documentation, as well as to align it with the processes of TDWG, the world reference body for biodiversity information standards. A new version, Plinian Core v3.x.x was defined. This provides more flexibility to fully represent the information of a species in a variety of scenarios. New elements to deal with aspects such as IPR, related resources, referenced, etc. were introduced, and elements already included were better-defined and documented. Partner for the development of Plinian Core in this new phase incorporated the University of Granada (UG, Spain), the Alexander von Humboldt Institute (IAvH, Colombia), the National Commission for the Knowledge and Use of Biodiversity (Conabio, Mexico) and the University of São Paulo (USP, Brazil). A "Plinian Core Task Group" within TDWG "Interest Group on species Information" was constituted and currently working on its development. == Levels of the standard == Plinian Core is presented in to levels: the abstract model and the application profiles. The abstract model (AM), comprising the abstract model schema(xsd) and the terms' URIs, is the normative part. It is all comprehensive, and allows for different levels of granularity in describing species properties. The AM should be taken as a "menu" from which to choose terms and level of detail needed in any specific project. The subsets of the abstract model intended to be implemented in specific projects are the "application profiles" (APs). Besides containing part of the elements of the AM, APs can impose additional specifications on the included elements, such as controlled vocabularies. Some examples of APs in use follow: Application profile CONABIO Application profile INBIO Application profile GBIF.ES Application profile Banco de Datos de la Naturaleza.Spain Application profile SIB-COLOMBIA == Relation to other standards == Plinian incorporates a number of elements already defined by other standards. The following table summarizes these standards and the elements used in Plinian Core:
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TRAIGA

TRAIGA, or the Texas Responsible Artificial Intelligence Governance Act, is a state law regulating the development and deployment of artificial intelligence (AI) systems in Texas. Sponsored by Representative Giovanni Capriglione, the Act establishes a framework governing certain uses of AI, outlines prohibited uses, and creates obligations on state government entities, among other provisions. TRAIGA was signed into law in 2025 and took effect on January 1, 2026. The law applies to AI developers and deployers that conduct business in Texas or whose systems are used by Texas residents. It prohibits the intentional development or deployment of AI systems to incite harm, violate constitutional rights, engage in unlawful discrimination, and produce child sexual abuse material or unlawful deepfakes. TRAIGA also establishes the Texas Artificial Intelligence Council and creates a regulatory sandbox program. The Texas Attorney General is charged with enforcement. It has received attention as one of the first comprehensive AI-related laws enacted by a U.S. state. Legal analysts have compared it to the European Union (EU) Artificial Intelligence Act and the Colorado AI Act, noting its intent-based discrimination standard and narrower scope relative to those frameworks. == Background == In June 2023, Texas Governor Greg Abbott signed House Bill 2060, which created an Artificial Intelligence Advisory Council within the Texas Department of Information Resources. The Council was tasked with monitoring the use of AI systems across state government. Its membership included representatives from law enforcement, academia, and the legal profession. After submitting a report to state policymakers, the Council was disbanded in December 2024. Separately, the Texas House Select Committee on Artificial Intelligence and Emerging Technologies was created in 2023 to examine the political and social implications of artificial intelligence. Among its recommendations was the creation of a regulatory sandbox to allow for controlled testing of AI systems. This recommendation informed the regulatory sandbox provision included in TRAIGA. == History == In December 2024, Representative Capriglione introduced House Bill 1709, the Texas Responsible Artificial Intelligence Governance Act. The bill sought to create a statewide framework for artificial intelligence, including transparency requirements for companies deploying AI systems, restrictions on certain uses of AI, and the creation of a regulatory sandbox. Modeled in part on the EU Artificial Intelligence Act and the Colorado AI Act, House Bill 1709 focused on "high-risk" AI systems and included provisions addressing private sector liability. House Bill 1709 did not advance during the legislative session. Industry stakeholders raised concerns that several provisions were overly burdensome. The bill informed the development of a revised proposal, House Bill 149, also titled the Texas Responsible Artificial Intelligence Governance Act. The revised version removed requirements for private companies to notify consumers when they interact with AI systems and to conduct impact assessments, among other provisions. In April 2025, an amended version of House Bill 149 passed the Texas House of Representatives and was referred to the Senate Committee on Business and Commerce. The bill later received approval from both chambers, where the House voted on amendments adopted by the Senate. On May 31, 2025, the state legislature passed House Bill 149, one of several AI-related bills considered during the legislative session. Governor Abbott signed TRAIGA into law on June 22, 2025. During the legislative process, a proposed federal moratorium on state-level AI regulation initially raised questions about the enforceability of state AI laws, including TRAIGA. At the time of signing, Governor Abbott stated that Texas would ensure compliance with applicable federal requirements. In July 2025, the United States Senate voted to remove the proposed moratorium from federal legislation. The Act took effect on January 1, 2026. == Provisions == === Definitions and scope === TRAIGA applies to AI developers and deployers that advertise or conduct business in Texas, develop products used by Texas residents, or develop or deploy AI systems within the state. The Act also applies to Texas state and local government entities. The Act defines a developer as a person who develops an AI system and a deployer as one who deploys an AI system in Texas. Consumers are defined as Texas residents. The Act defines an artificial intelligence system as a machine-based system that "infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments." === Government use === The Act requires government agencies to provide consumers with plain language notices before interacting with AI systems. It also prohibits government agencies from using artificial intelligence systems to assign social scores to consumers. It also restricts the use of AI systems to identify individuals using biometric data without the individual’s consent. === Prohibitions === The Act prohibits the development or deployment of artificial intelligence systems intended to cause harm, self-harm, or criminal activity. It also prohibits the development or deployment of AI systems designed to violate constitutional rights or unlawfully discriminate based on protected classes. In addition, the Act prohibits the development or deployment of AI systems that are intended to produce or distribute child sexual abuse material or unlawful deepfakes. === Enforcement === Enforcement authority under the Act rests with the Texas Attorney General. The Act does not create a private right of action. The Act requires the Texas Attorney General to create an online complaint system where consumers may submit allegations of potential violations. The Attorney General can investigate complaints received through this system and may request information relevant to the operation of an AI system, including information about training data. Before initiating an enforcement action, the Attorney General must provide a written notice to the alleged violator, who is then provided with a 60-day period to cure the alleged violation. === Penalties === If a violation is not cured, the Act authorizes civil penalties. Penalties range from $10,000 to $12,000 per curable violation and from $80,000 to $200,000 per non-curable violation. The Act also authorizes additional penalties of $2,000 to $40,000 for each day the violation continues. If the Attorney General determines that a person certified or licensed by a state agency has violated the Act and recommends enforcement, the relevant agency may impose additional administrative sanctions, including license suspension or further monetary penalties. === Safe harbor === The Act provides an affirmative defense for AI developers and deployers who identify potential violations through internal testing or auditing or who demonstrate compliance with National Institute of Standards and Technology (NIST)'s Artificial Intelligence Risk Management Framework or a comparable risk management framework. The Act also affords protection to developers and deployers when a third party uses their AI systems in a way that violates the Act. === Texas Artificial Intelligence Council === The Act creates the Texas Artificial Intelligence Council to assist the state legislatures in evaluating artificial intelligence policy and oversight. The Council is charged with developing recommendations for state agencies regarding the use of AI systems and with overseeing the regulatory sandbox. TRAIGA gives the Council the ability to organize AI-related training for state entities and issue reports concerning artificial intelligence. The Council does not have binding rulemaking authority. The Council consists of seven members appointed by the governor, the lieutenant governor, and the speaker of the Texas House of Representatives. === Regulatory sandbox === The Act directs the Texas Department of Information Resources to create a regulatory sandbox program that allows participants to test AI systems under state supervision in a modified regulatory setting. To join the program, companies must submit applications that describe their AI systems and intended use. Approved participants may operate within the sandbox for up to 36 months. During that period, the Attorney General is restricted from initiating enforcement actions for certain categories of violations. == Reception == === Support === During legislative testimony, the Texas Public Policy Foundation stated that TRAIGA would benefit Texas businesses by reducing legal ambiguity and creating clearer compliance standards. Representatives of business groups also expressed support, stating that the Act would not impose overly burdensome regulations. The consum
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Project Maven

Project Maven (officially Algorithmic Warfare Cross Functional Team) is a United States Department of Defense initiative launched in 2017 to accelerate the adoption of machine learning and data integration across U.S. military intelligence workflows, specifically in intelligence, surveillance, target acquisition, and reconnaissance as well as in geospatial intelligence. It initially focused on applying computer vision for processing images and videos for intelligence purposes. Currently, the program operates under the National Geospatial-Intelligence Agency (NGA) and encompasses multiple applications across the Department of Defense spanning military operation targeting support, data integration and visualization for analysts, and training machine learning models on labeled datasets of military assets and infrastructure. It integrates data from drones, satellites, and other sensors to flag potential targets, present findings to human analysts, and relay their decisions to operational systems. The program originated under Deputy Secretary Robert O. Work after he raised concerns about China's advances in defense applications of artificial intelligence. Project leaders, Colonel Drew Cukor, USMC, and Lt. Gen. Jack Shanahan, framed the program as human-in-the-loop decision support inside the Department of Defense rather than as an autonomous weapons platform. Contractors supporting Maven have included Google, which withdrew in 2018 after internal protests, and follow-on integrators such as Palantir, Anduril, Amazon Web Services, and Anthropic (withdrew in 2026). The Pentagon credits Maven with providing 2024 targeting support for U.S. airstrikes in Iraq, Syria, and Yemen, along with locating hostile maritime assets in the Red Sea. == Administrative history == Initially, the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017 proposing an "Algorithmic Warfare Cross-Functional Team". With the help of Defense Innovation Unit, the project obtained the support of top talents in AI outside of the traditional defense contracting base. It was initially funded for $70 million. Jack Shanahan was the director of the project during April 2017 to December 2018. At the second Defense One Tech Summit in July 2017, Cukor said that the investment in a "deliberate workflow process" was funded by the Department [of Defense] through its "rapid acquisition authorities" for about "the next 36 months". In the defense industry, the standard procedure for the military to acquire hardware is by way of research, development, test, and evaluation (RDT&E), followed by production and sustainment. In 2017, acquiring software was done in the same way as hardware. This created a problem, since software is constantly updated. Project Maven procured software using Broad Agency Announcements, a flexible contracting vehicle that categorized software as consistently RDT&E, allowing constant updating. Another issue was that the government usually acquired the intellectual property (IP) for procured software, and with the project, only parts of the IP of the software was acquired. Cukor used the principle of "platform IP belongs to the vendor, configurations on top are the customer's". For example, Palantir retained IP to their core platform, while the government obtained the IP to Maven-specific logic configured on top of it. According to US Air Force Lt. Gen. Jack Shanahan in November 2017, it is "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the ability to identify adversaries by harvesting data from sensors on UAVs and satellites. As of 2017 December, 150,000 images had been manually labelled to establish the first training data sets, and it was projected to reach one million by January 2018. Project Maven was funded for $221 million in fiscal 2020. In 2020, the House and Senate conferees on the National Defense Authorization Act for Fiscal Year 2021, agreed to the Senate's recommendation to fund the Pentagon's $250 million request for Project Maven. At the GEOINT Symposium of 2022, it was announced that Project Maven was transferred from the Office of the Under Secretary of Defense for Intelligence and Security to the NGA, under President Biden’s proposed budget for Fiscal Year 2023. It became a Program of Record on 2023 November 7. Frank "Trey" Whitworth, vice admiral, was the director of NGA from June 2022 to November 2025. Whitworth was initially skeptical of the program, suspecting it was incautious about the targeting principles, but later regarded it as "important work". As of 2024, the project is jointly administered by the NGA and the CDAO, and its director is Rachel Martin. Before 2025, Biden appointees within CDAO had held back AI development for safety and reliability concerns, though as of 2025, this has stopped. As of 2024, Maven provided the cloud infrastructure, software capabilities, and AI for CDAO's Combined Joint All-Domain Command and Control initiatives. As of summer 2025, there were eight Maven initiatives. Of these, five were in the NGA, including analyzing drone feeds and satellite imagery. On 18 September 2025, the UK government announced a new partnership with Palantir to develop AI-powered military capabilities for decision-making and targeting, identifying opportunities worth up to £750 million over five years. On 25 March 2025, the NATO Communications and Information Agency and Palantir finalized the acquisition of the Palantir Maven Smart System NATO (MSS NATO) for employment within NATO's Allied Command Operations. It was planned to be used within 30 days of acquisition. In a letter to Pentagon on 9 March 2026, Steve Feinberg stated that Project Maven will become an official program of record by September 2026, the close of the current fiscal year. The project would transfer from the NGA to the CDAO within 30 days. Future contracting with Palantir would be handled by the US Army. In 2026-03, it was announced that the US Army Combined Arms Command would integrate Maven into its training. == Technology == Project Maven uses machine learning algorithms to analyze and fuse vast amounts of surveillance data from multiple sources made possible through data integration using Palantir Technologies. The data sources include photographs, satellite imagery, geolocation data (IP address, geotag, metadata, etc) from communications intercepts, infrared sensors, synthetic-aperture radar, and more. The system is mainly used for assisting analysts in intelligence, surveillance, target acquisition, and reconnaissance. Machine learning systems, including object recognition systems, process the data and identify potential targets, such as enemy tanks or location of new military facility. The training dataset included at least 4 million images of military objects such as warships, labelled by humans. The user interface is called Maven Smart System. It could display information such as aircraft movements, logistics, locations of key personnel, locations on the no-strike list, ships, etc. Yellow-outlined boxes show potential targets. Blue-outlined boxes show friendly forces or no-strike zones. It could also transmit, directly to weapons, a human decision to fire weapons. Internal documentation referred to "Maven ATR: automatic target recognition". Initially the project focused on applications of computer vision. The project's leaders were particularly impressed by model performance on ImageNet. As of 2018, the purpose of the system was AI-enabled analysis of full-motion video. In 2022 it expanded to combatant commands under the AI and Data Acceleration Initiative. In 2022, it was reported that the project expanded to non-image data, including captured enemy material, maritime intelligence, and publicly available information. In 2024, it was stated that Maven's key technical contribution was data management: Maven standardizes heterogeneous data through an ontology layer so data can be fused, exchanged across cloud and edge systems, and used by multiple applications. The system was presented as a broader data-centric warfighting system that feeds apps for planning, preparing, and executing operations. In 2024, the Broad Area Surveillance-Targeting (BAS-T) is a part of Maven. The system detects objects in images and uses data fusion to produce a common operational picture containing "priority based, in-depth assessment of the enemy systems pre
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Security type system

In computer science, a type system can be described as a syntactic framework which contains a set of rules that are used to assign a type property (int, boolean, char etc.) to various components of a computer program, such as variables or functions. A security type system works in a similar way, only with a main focus on the security of the computer program, through information flow control. Thus, the various components of the program are assigned security types, or labels. The aim of a such system is to ultimately be able to verify that a given program conforms to the type system rules and satisfies non-interference. Security type systems is one of many security techniques used in the field of language-based security, and is tightly connected to information flow and information flow policies. In simple terms, a security type system can be used to detect if there exists any kind of violation of confidentiality or integrity in a program, i.e. the programmer wants to detect if the program is in line with the information flow policy or not. == A simple information flow policy == Suppose there are two users, A and B. In a program, the following security classes (SC) are introduced: SC = {∅, {A}, {B}, {A,B}}, where ∅ is the empty set. The information flow policy should define the direction that information is allowed to flow, which is dependent on whether the policy allows read or write operations. This example considers read operations (confidentiality). The following flows are allowed: → = {({A}, {A}), ({B}, {B}), ({A,B}, {A,B}), ({A,B}, {A}), ({A,B}, {B}), ({A}, ∅), ({B}, ∅), ({A,B}, ∅)} This can also be described as a superset (⊇). In words: information is allowed to flow towards stricter levels of confidentiality. The combination operator (⊕) can express how security classes can perform read operations with respect to other security classes. For example: {A} ⊕ {A,B} = {A} — the only security class that can read from both {A} and {A,B} is {A}. {A} ⊕ {B} = ∅ — neither {A} nor {B} are allowed to read from both {A} and {B}. This can also be described as an intersection (∩) between security classes. An information flow policy can be illustrated as a Hasse diagram. The policy should also be a lattice, that is, it has a greatest lower-bound and least upper-bound (there always exists a combination between security classes). In the case of integrity, information will flow in the opposite direction, thus the policy will be inverted. == Information flow policy in security type systems == Once the policy is in place, the software developer can apply the security classes to the program components. Use of a security type system is usually combined with a compiler that can perform the verification of the information flow according to the type system rules. For the sake of simplicity, a very simple computer program, together with the information flow policy as described in the previous section, can be used as a demonstration. The simple program is given in the following pseudocode: if y{A} = 1 then x{A,B} := 0 else x{A,B} := 1 Here, an equality check is made on a variable y that is assigned the security class {A}. A variable x with a lower security class ({A,B}) is influenced by this check. This means that information is leaking from class {A} to class {A,B}, which is a violation of the confidentiality policy. This leak should be detected by the security type system. === Example === Designing a security type system requires a function (also known as a security environment) that creates a mapping from variables to security types, or classes. This function can be called Γ, such that Γ(x) = τ, where x is a variable and τ is the security class, or type. Security classes are assigned (also called "judgement") to program components, using the following notation: Types are assigned to read operations by: Γ ⊢ e : τ. Types are assigned to write operations by: Γ ⊢ S : τ cmd. Constants can be assigned any type. The following bottom-up notation can be used to decompose the program: ⁠assumption1 ... assumptionn/conclusion⁠. Once the program is decomposed into trivial judgements, by which the type can easily be determined, the types for the less trivial parts of the program can be derived. Each "numerator" is considered in isolation, looking at the type of each statement to see if an allowed type can be derived for the "denominator", based on the defined type system "rules". ==== Rules ==== The main part of the security type system is the rules. They say how the program should be decomposed and how type verification should be performed. This toy program consists of a conditional test and two possible variable assignments. Rules for these two events are defined as follows: Applying this to the simple program introduced above yields: The type system detects the policy violation in line 2, where a read operation of security class {A} is performed, followed by two write operations of a less strict security class {A,B}. In more formalized terms, {A} ⋢ {A,B}, {A,B} (from the rule of the conditional test). Thus, the program is classified as "not typeable". === Soundness === The soundness of a security type system can be informally defined as: If program P is well typed, P satisfies non-interference. Volpano, Smith and Irvine were the first to prove soundness of a security type system for a deterministic imperative programming language with a standard (non-instrumented) semantics using the notion of non-interference.
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Interim Measures for the Management of Anthropomorphic AI Interactive Services

The Interim Measures for the Management of Anthropomorphic AI Interactive Services (Chinese: 人工智能拟人化互动服务管理暂行办法) is a document proposed by the Cyberspace Administration of China to regulate anthropomorphic artificial intelligence systems. The draft was released on December 27, 2026 for public comment period until January 25, 2026. The proposed document would prohibit AI companies and users of AI services from generating certain types of content deemed harmful to national interests or the social order, and impose various regulatory and safety requirements on providers of AI systems. The proposed regulation is motivated by concerns about the psychological and social effects of AI systems that are perceived as personalities by their users, including addiction, encouragement of self-harm, or generation of illegal content. == Description == === Scope === The regulation would apply to AI systems that are offered to the general public within China. They would not apply to company-internal or research use, or to products that are only available outside of China. For the purpose of the regulation, anthropomorphic Ai systems are defined as those that "simulate human personality traits, modes of thinking, and communication styles, and that engage in emotional interaction with humans through text, images, audio, video, or other means". === Requirements === The regulation would require AI providers to monitor users for signs of harmful use and to take various interventions when indications of harmful use are detected. It would also prohibit AI systems from certain types of behaviors and generation of certain types of content. In some circumstances where a user appears to be at risk of self harm, the system would be required to hand over control to a human operator who would manually intervene. The regulation would also require more rigorous practices for managing the provenance of training data used to develop these systems, and would require explicit opt-in consent from users before their interactions with an AI system were used as training data. Data used to train the regulated systems would be required to reflect core socialist values and traditional Chinese culture.
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Fei-Fei Li

Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-born American computer scientist best known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is a professor of computer science at Stanford University, with research expertise in artificial intelligence, machine learning, deep learning, computer vision, and cognitive neuroscience. Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab, and served as Chief Scientist of AI/ML at Google Cloud and the director of the Stanford Artificial Intelligence Laboratory from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity in the field of artificial intelligence. In 2023, Li was named one of the Time 100 AI Most Influential People. Li received the Intel Lifetime Achievements Innovation Award in 2017 for her contributions to artificial intelligence, and was elected member of the National Academy of Engineering, the National Academy of Medicine in 2020 and the American Academy of Arts and Sciences in 2021. In 2025, she was named as one of the "Architects of AI" for Time's Person of the Year. On August 3, 2023, Li was appointed to the United Nations Scientific Advisory Board, established by Secretary-General Antonio Guterres. In 2024, Li was included on the Gold House's most influential Asian A100 list. In 2024, she raised $230 million for a startup called World Labs, which she and three colleagues founded to develop a "spatial intelligence" AI technology that can understand how the three-dimensional physical world works. In 2026, World Labs raised $1 Billion. == Early life and education == Li was born in Beijing, China, in 1976 and grew up in Chengdu, Sichuan. She studied at Sichuan Chengdu No.7 High School. When she was 12, her father immigrated to Parsippany, New Jersey. When she was 16, Li and her mother joined him in the United States. While attending Parsippany High School, Li worked weekends at her family's dry-cleaning shop. She graduated from Parsippany High School in 1995. She was inducted into the hall of fame at Parsippany High School in 2017. Li pursued undergraduate study at Princeton University, where she received a Bachelor of Arts with a major in physics in 1999. Li completed her senior thesis, "Auditory binaural correlogram difference: a new computational model for Huggins dichotic pitch", under the supervision of Bradley Dickinson, professor of electrical engineering. During her years at Princeton, Li returned home most weekends to help run her family's dry cleaning business and worked as a dishwasher to supplement the family income. Li pursued graduate study at the California Institute of Technology, where she received a Master of Science in electrical engineering in 2001 and a Doctor of Philosophy in electrical engineering in 2005. Li completed her dissertation, "Visual Recognition: Computational Models and Human Psychophysics", under the primary supervision of Pietro Perona and secondary supervision of Christof Koch. Her graduate studies were supported by the National Science Foundation Graduate Research Fellowship and The Paul & Daisy Soros Fellowships for New Americans. == Career and research == From 2005 to 2006, Li was an assistant professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign, and from 2007 to 2009, she was an assistant professor in the Computer Science Department at Princeton University. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012, and then full professor in 2018. At Stanford, Li served as the director of Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018. Her research has focused on computer vision, deep learning, and cognitive neuroscience, with over 300 peer-reviewed publications. She became the founding co-director of Stanford's University-level initiative - the Human-Centered AI Institute, along with co-director Dr. John Etchemendy, former provost of Stanford University. The institute aligns with Li's aims to advance AI research, education, policy, and practice to improve the human condition. While at Princeton in 2007, Li led the development of ImageNet, a massive visual database designed to advance object recognition in AI. The project involved labeling over 14 million images using Amazon Mechanical Turk and inspired the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which catalyzed progress in deep learning and led to dramatic improvements in image classification performance. The database addressed a key bottleneck in computer vision: the lack of large, annotated datasets for training machine learning models. Today, ImageNet is credited as a cornerstone innovation that underpins advancements in autonomous vehicles, facial recognition, and medical imaging. On her sabbatical from Stanford University from January 2017 to fall of 2018, Li joined Google Cloud as its Chief Scientist of AI/ML and Vice President. At Google, her team focused on democratizing AI technology and lowering the barrier for entrance to businesses and developers, including the developments of products like AutoML. In September 2017, Google secured a contract from the Department of Defense called Project Maven, which aimed to use AI techniques to interpret images captured by drone cameras. Google told employees who protested the company's work on Project Maven that their role was "specifically scoped to be for non-offensive purposes". In June 2018, Google told employees it would not seek renewal of the contract. In internal emails which were later leaked to reporters, Li expressed enthusiasm for the Google Cloud role in Project Maven, but warned against mentioning its AI component, saying that military AI is linked in the public mind with the danger of autonomous weapons. Asked about those leaked emails, Li told The New York Times, "I believe in human-centered AI to benefit people in positive and benevolent ways. It is deeply against my principles to work on any project that I think is to weaponize AI." In the fall of 2018, Li left Google and returned to Stanford University to continue her professorship. In 2023, Li co-led the launch of the RAISE-Health (Responsible AI for Safe and Equitable Health) initiative at Stanford University in collaboration with Stanford medicine. The initiative aims to develop frameworks for the responsible use of artificial intelligence in healthcare, including clinical care, biomedical research, and patient safety. According to her Stanford profile, she has been on partial academic leave from January 2024 through the end of 2025 to focus on entrepreneurial ventures. In 2024, Li said there was a disparity between private-sector investment in AI and support for academic and government research, and called for greater public funding for scientific uses of the technology and for studying its risks. Li is also known for her non-profit work as the co-founder and chairperson of nonprofit organization AI4ALL, whose mission is to educate the next generation of AI technologists, thinkers and leaders by promoting diversity and inclusion through human-centered AI principles. The program was created in collaboration with Melinda French Gates and Jensen Huang. Prior to establishing AI4ALL in 2017, Li and her former student Olga Russakovsky, currently an assistant professor in Princeton University, co-founded and co-directed the precursor program at Stanford called SAILORS (Stanford AI Lab OutReach Summers). SAILORS was an annual summer camp at Stanford dedicated to 9th grade high school girls in AI education and research, established in 2015 till it changed its name to AI4ALL @Stanford in 2017. In 2018, AI4ALL has successfully launched five more summer programs in addition to Stanford, including Princeton University, Carnegie Mellon University, Boston University, University of California Berkeley, and Canada's Simon Fraser University. We are at a turning point. AI's influence continues to grow, but representation and inclusion of a diversity of researchers in the field does not. It's critical that we seize this moment to create structures that will support long-term, positive changes. This won't happen via a single mechanism or quick fix. It starts with early education and extends to the existing structures of power within academia, work cultures among current AI researchers, and gatekeeping functions of research publishing, to name a few levers of change. Li has been described as a "researcher bringing humanity to AI". Li was elected as a member of the American Academy of Arts and Sciences in 2021, the National Academy of Engineering in 2020, and the National Academy of Medicine in 2020. In a November 2023 interview with The Guardian, Li said that while she would not refer to herself as the "godmother
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Seeing AI

Seeing AI is an artificial intelligence application developed by Microsoft for iOS. Seeing AI uses the device camera to identify people and objects, and then the app audibly describes those objects for visually impaired people. == Capabilities == Seeing AI is primarily used to describe short text, documents, products, people, currency scenery, colors, handwriting and light. The app can scan a barcode to describe a product and uses sounds to assist the user in focusing on the barcode. When the app describes people, it attempts to estimate the person's age, gender, and emotional status. Additionally, in a test run by German journalists in December 2019, Seeing AI apparently used some sort of facial recognition system to identify people on photographs by name. Some functions are performed on the device, however more complex functions such as describing a scene or recognizing handwriting require an Internet connection. In December 2017, Seeing AI introduced the ability for currency recognition for US and Canadian dollar, British pounds and Euros. In December 2019, Seeing AI added support for five more languages, Dutch, French, German, Japanese, Spanish. Seeing AI is available in 70 countries such as Brazil, Argentina, Australia, Canada, Egypt, Albania, Bhutan, etc. Supported on iPhone 5C, 5S and later best performance with iPhone 6S, SE and later models
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Time-compressed speech

Time-compressed speech refers to an audio recording of verbal text in which the text is presented in a much shorter time interval than it would through normally-paced real time speech. The basic purpose is to make recorded speech contain more words in a given time, yet still be understandable. For example: a paragraph that might normally be expected to take 20 seconds to read, might instead be presented in 15 seconds, which would represent a time-compression of 25% (5 seconds out of 20). The term "time-compressed speech" should not be confused with "speech compression", which controls the volume range of a sound, but does not alter its time envelope. == Methods == While some voice talents are capable of speaking at rates significantly in excess of general norms, the term "time-compressed speech" most usually refers to examples in which the time-reduction has been accomplished through some form of electronic processing of the recorded speech. In general, recorded speech can be electronically time-compressed by: increasing its speed (linear compression); removing silences (selective editing); a combination of the two (non-linear compression). The speed of a recording can be increased, which will cause the material to be presented at a faster rate (and hence in a shorter amount of time), but this has the undesirable side-effect of increasing the frequency of the whole passage, raising the pitch of the voices, which can reduce intelligibility. There are normally silences between words and sentences, and even small silences within certain words, both of which can be reduced or removed ("edited-out") which will also reduce the amount of time occupied by the full speech recording. However, this can also have the effect of removing verbal "punctuation" from the speech, causing words and sentences to run together unnaturally, again reducing intelligibility. Vowels are typically held a minimum of 20 milliseconds, over many cycles of the fundamental pitch. DSP systems can detect the beginning and end of each cycle and then skip over some fraction of those cycles, causing the material to be presented at a faster rate, without changing the pitch, maintaining a "normal" tone of voice. The current preferred method of time-compression is called "non-linear compression", which employs a combination of selectively removing silences; speeding up the speech to make the reduced silences sound normally-proportioned to the text; and finally applying various data algorithms to bring the speech back down to the proper pitch. This produces a more acceptable result than either of the two earlier techniques; however, if unrestrained, removing the silences and increasing the speed can make a selection of speech sound more insistent, possibly to the point of unpleasantness. == Applications == === Advertising === Time-compressed speech is frequently used in television and radio advertising. The advantage of time-compressed speech is that the same number of words can be compressed into a smaller amount of time, reducing advertising costs, and/or allowing more information to be included in a given radio or TV advertisement. It is usually most noticeable in the information-dense caveats and disclaimers presented (usually by legal requirement) at the end of commercials—the aural equivalent of the "fine print" in a printed contract. This practice, however, is not new: before electronic methods were developed, spokespeople who could talk extremely quickly and still be understood were widely used as voice talents for radio and TV advertisements, and especially for recording such disclaimers. === Education === Time-compressed speech has educational applications such as increasing the information density of trainings, and as a study aid. A number of studies have demonstrated that the average person is capable of relatively easily comprehending speech delivered at higher-than-normal rates, with the peak occurring at around 25% compression (that is, 25% faster than normal); this facility has been demonstrated in several languages. Conversational speech (in English) takes place at a rate of around 150 wpm (words per minute), but the average person is able to comprehend speech presented at rates of up to 200-250 wpm without undue difficulty. Blind and severely visually impaired subjects scored similar comprehension levels at even higher rates, up to 300-350 wpm. Blind people have been found to use time-compressed speech extensively, for example, when reviewing recorded lectures from high school and college classes, or professional trainings. Comprehension rates in older blind subjects have been found to be as good, or in some cases better than those found in younger sighted subjects. Other studies have determined that the ability to comprehend highly time-compressed speech tends to fall off with increased age, and is also reduced when the language of the time-compressed speech is not the listener's native language. Non-native speakers can, however, improve their comprehension level of time-compressed speech with multiday training. === Voice Mail === Voice mail systems have employed time-compressed speech since as far back as the 1970s. In this application, the technology enables the rapid review of messages in high-traffic systems, by a relatively small number of people. === Streaming Multimedia === Time-compressed speech has been explored as one of a variety of interrelated factors which may be manipulated to increase the efficiency of streaming multimedia presentations, by significantly reducing the latency times involved in the transfer of large digitally encoded media files.
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Project Joshua Blue

Joshua Blue is a project under development by IBM that focuses on advancing the artificial intelligence field by designing and programming computers to emulate human mental functions. == Goals == According to researchers at IBM's Thomas J. Watson Research Center, the main goal of Joshua Blue is "to achieve cognitive flexibility that approaches human functioning". In short, IBM is aiming to design Joshua Blue to 'think like a human', mainly in terms of emotional thought. == How it will work == A model of Joshua Blue's learning pattern has been created. Similar to how young children learn human traits through interacting with their surroundings, Joshua Blue will acquire knowledge through external stimuli present in its environment. IBM believes that if computers evolve to learn in this way and then comprehend and analyze the knowledge gained using reason, computers could begin to possess a "mind", of sorts, capable of demonstrating complex social behaviors similar to those of humans. Thus far, IBM has revealed that Joshua Blue will be a computer with a network of wires and input nodes that function as a computer nervous system. This nervous system will be used by Joshua Blue to perceive affect or personal emotional feelings. Not only will this network of input nodes help Joshua Blue discover things physically, but it will also allow Joshua Blue to interpret the significance of events. The input nodes, or proprioceptors, will enable Joshua Blue to be aware of things that happen around itself, as well as recognize and attach meaning to the emotional effect produced by interacting with an object in a certain way. In addition, Joshua Blue's proprioceptors will function as pain and pleasure sensors, allowing Joshua Blue to employ a similar "reward and punishment" system that humans use to form behaviors.
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Tag (metadata)

In information systems, a tag is a keyword or term assigned to a piece of information (such as an Internet bookmark, multimedia, database record, or computer file). This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Tags are generally chosen informally and personally by the item's creator or by its viewer, depending on the system, although they may also be chosen from a controlled vocabulary. Tagging was popularized by websites associated with Web 2.0 and is an important feature of many Web 2.0 services. It is now also part of other database systems, desktop applications, and operating systems. == Overview == People use tags to aid classification, mark ownership, note boundaries, and indicate online identity. Tags may take the form of words, images, or other identifying marks. An analogous example of tags in the physical world is museum object tagging. People were using textual keywords to classify information and objects long before computers. Computer based search algorithms made the use of such keywords a rapid way of exploring records. Tagging gained popularity due to the growth of social bookmarking, image sharing, and social networking websites. These sites allow users to create and manage labels (or "tags") that categorize content using simple keywords. Websites that include tags often display collections of tags as tag clouds, as do some desktop applications. On websites that aggregate the tags of all users, an individual user's tags can be useful both to them and to the larger community of the website's users. Tagging systems have sometimes been classified into two kinds: top-down and bottom-up. Top-down taxonomies are created by an authorized group of designers (sometimes in the form of a controlled vocabulary), whereas bottom-up taxonomies (called folksonomies) are created by all users. This definition of "top down" and "bottom up" should not be confused with the distinction between a single hierarchical tree structure (in which there is one correct way to classify each item) versus multiple non-hierarchical sets (in which there are multiple ways to classify an item); the structure of both top-down and bottom-up taxonomies may be either hierarchical, non-hierarchical, or a combination of both. Some researchers and applications have experimented with combining hierarchical and non-hierarchical tagging to aid in information retrieval. Others are combining top-down and bottom-up tagging, including in some large library catalogs (OPACs) such as WorldCat. When tags or other taxonomies have further properties (or semantics) such as relationships and attributes, they constitute an ontology. In folder system a file cannot exist in two or more folders so tag system has been thought more convenient. But transitioning to tag system requires awareness of difference between properties of two systems. In folder system the information of classification is put outside of the file and we can change folder at once. In tag system the information of classification is put inside the file so changing its tag means changing the file and it needs to be saved again and takes time. Metadata tags as described in this article should not be confused with the use of the word "tag" in some software to refer to an automatically generated cross-reference; examples of the latter are tags tables in Emacs and smart tags in Microsoft Office. == History == The use of keywords as part of an identification and classification system long predates computers. Paper data storage devices, notably edge-notched cards, that permitted classification and sorting by multiple criteria were already in use prior to the twentieth century, and faceted classification has been used by libraries since the 1930s. In the late 1970s and early 1980s, Emacs, the text editor for Unix systems, offered a companion software program called Tags that could automatically build a table of cross-references called a tags table that Emacs could use to jump between a function call and that function's definition. This use of the word "tag" did not refer to metadata tags, but was an early use of the word "tag" in software to refer to a word index. Online databases and early websites deployed keyword tags as a way for publishers to help users find content. In the early days of the World Wide Web, the keywords meta element was used by web designers to tell web search engines what the web page was about, but these keywords were only visible in a web page's source code and were not modifiable by users. In 1997, the collaborative portal "A Description of the Equator and Some ØtherLands" produced by documenta X, Germany, used the folksonomic term Tag for its co-authors and guest authors on its Upload page. In "The Equator" the term Tag for user-input was described as an abstract literal or keyword to aid the user. However, users defined singular Tags, and did not share Tags at that point. In 2003, the social bookmarking website Delicious provided a way for its users to add "tags" to their bookmarks (as a way to help find them later); Delicious also provided browseable aggregated views of the bookmarks of all users featuring a particular tag. Within a couple of years, the photo sharing website Flickr allowed its users to add their own text tags to each of their pictures, constructing flexible and easy metadata that made the pictures highly searchable. The success of Flickr and the influence of Delicious popularized the concept, and other social software websites—such as YouTube, Technorati, and Last.fm—also implemented tagging. In 2005, the Atom web syndication standard provided a "category" element for inserting subject categories into web feeds, and in 2007 Tim Bray proposed a "tag" URN. == Examples == === Within a blog === Many systems (and other web content management systems) allow authors to add free-form tags to a post, along with (or instead of) placing the post into a predetermined category. For example, a post may display that it has been tagged with baseball and tickets. Each of those tags is usually a web link leading to an index page listing all of the posts associated with that tag. The blog may have a sidebar listing all the tags in use on that blog, with each tag leading to an index page. To reclassify a post, an author edits its list of tags. All connections between posts are automatically tracked and updated by the blog software; there is no need to relocate the page within a complex hierarchy of categories. === Within application software === Some desktop applications and web applications feature their own tagging systems, such as email tagging in Gmail and Mozilla Thunderbird, bookmark tagging in Firefox, audio tagging in iTunes or Winamp, and photo tagging in various applications. Some of these applications display collections of tags as tag clouds. === Assigned to computer files === There are various systems for applying tags to the files in a computer's file system. In Apple's Mac System 7, released in 1991, users could assign one of seven editable colored labels (with editable names such as "Essential", "Hot", and "In Progress") to each file and folder. In later iterations of the Mac operating system ever since OS X 10.9 was released in 2013, users could assign multiple arbitrary tags as extended file attributes to any file or folder, and before that time the open-source OpenMeta standard provided similar tagging functionality for Mac OS X. Several semantic file systems that implement tags are available for the Linux kernel, including Tagsistant. Microsoft Windows allows users to set tags only on Microsoft Office documents and some kinds of picture files. Cross-platform file tagging standards include Extensible Metadata Platform (XMP), an ISO standard for embedding metadata into popular image, video and document file formats, such as JPEG and PDF, without breaking their readability by applications that do not support XMP. XMP largely supersedes the earlier IPTC Information Interchange Model. Exif is a standard that specifies the image and audio file formats used by digital cameras, including some metadata tags. TagSpaces is an open-source cross-platform application for tagging files; it inserts tags into the filename. === For an event === An official tag is a keyword adopted by events and conferences for participants to use in their web publications, such as blog entries, photos of the event, and presentation slides. Search engines can then index them to make relevant materials related to the event searchable in a uniform way. In this case, the tag is part of a controlled vocabulary. === In research === A researcher may work with a large collection of items (e.g. press quotes, a bibliography, images) in digital form. If he/she wishes to associate each with a small number of themes (e.g. to chapters of a book, or to sub-themes of the overall subject), then a group of tags for these themes can be attached to each of the items in
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