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  • Local coordinates

    Local coordinates

    Local coordinates are the ones used in a local coordinate system or a local coordinate space. Simple examples: Houses. In order to work in a house construction, the measurements are referred to a control arbitrary point that will allow to check it: stick/sticks on the ground, steel bar, nails... Addresses. Using house numbers to locate a house on a street; the street is a local coordinate system within a larger system composed of city townships, states, countries, postal codes, etc. Local systems exist for convenience. On ancient times, every work was made on relative bases as there was no conception of global systems. Practically, it is better to use local systems for small works as houses, buildings... For most of the applications, it is desired the position of one element relative to one building or location, and in a more local way, relative to one furniture or person. In a regular way, you will not give your position by geographical coordinates rather than "I am 15 meters away of the entry to the building". So it is a pretty common way to locate things. It is possible to bring latitude and longitude for all terrestrial locations, but unless one has a highly precise GPS device or you make astronomical observations, this is impractical. It is much simpler to use a tape, a rope, a chain... The position information (global) should be transformed into a location. Position refers to a numeric or symbolic description within a spatial reference system, whereas location refers to information about surrounding objects and their interrelationships. (Topological space) == Use == In computer graphics and computer animation, local coordinate spaces are also useful for their ability to model independently transformable aspects of geometrical scene graphs. When modeling a car, for example, it is desirable to describe the center of each wheel with respect to the car's coordinate system, but then specify the shape of each wheel in separate local spaces centered about these points. This way, the information describing each wheel can be simply duplicated four times, and independent transformations (e.g., steering rotation) can be similarly effected. Bounding volumes of objects may be described more accurately using extents in the local coordinates, (i.e. an object oriented bounding box, contrasted with the simpler axis aligned bounding box). The trade-off for this flexibility is additional computational cost: the rendering system must access the higher-level coordinate system of the car and combine it with the space of each wheel in order to draw everything in its proper place. Local coordinates also afford digital designers a means around the finite limits of numerical representation. The tread marks on a tire, for example, can be described using millimeters by allowing the whole tire to occupy the entire range of numeric precision available. The larger aspects of the car, such as its frame, might be described in centimeters, and the terrain that the car travels on could be specified in meters. In differential topology, local coordinates on a manifold are defined by means of an atlas of charts. The basic idea behind coordinate charts is that each small patch of a manifold can be endowed with a set of local coordinates. These are collected together into an atlas, and stitched together in such a way that they are self-consistent on the manifold. In Cartography and Maps, the traditional way of works are local datum. With a local datum the land can be mapped on relative small areas as a country. With the need of global systems, the transformations on between datum became a problem, so geodetic datum have been created. More than 150 local datum have been used in the world.

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  • Minimum intelligent signal test

    Minimum intelligent signal test

    The minimum intelligent signal test, or MIST, is a variation of the Turing test proposed by Chris McKinstry in which only boolean (yes/no or true/false) answers may be given to questions. The purpose of such a test is to provide a quantitative statistical measure of humanness, which may subsequently be used to optimize the performance of artificial intelligence systems intended to imitate human responses. McKinstry gathered approximately 80,000 propositions that could be answered yes or no, e.g.: Is Earth a planet? Was Abraham Lincoln once President of the United States? Is the sun bigger than my foot? Do people sometimes lie? He called these propositions Mindpixels. These questions test both specific knowledge of aspects of culture, and basic facts about the meaning of various words and concepts. It could therefore be compared with the SAT, intelligence testing and other controversial measures of mental ability. McKinstry's aim was not to distinguish between shades of intelligence but to identify whether a computer program could be considered intelligent at all. According to McKinstry, a program able to do much better than chance on a large number of MIST questions would be judged to have some level of intelligence and understanding. For example, on a 20-question test, if a program were guessing the answers at random, it could be expected to score 10 correct on average. But the probability of a program scoring 20 out of 20 correct by guesswork is only one in 220, i.e. one in 1,048,576; so if a program were able to sustain this level of performance over several independent trials, with no prior access to the propositions, it should be considered intelligent. == Discussion == McKinstry criticized existing approaches to artificial intelligence such as chatterbots, saying that his questions could "kill" AI programs by quickly exposing their weaknesses. He contrasted his approach, a series of direct questions assessing an AI's capabilities, to the Turing test and Loebner Prize method of engaging an AI in undirected typed conversation. Critics of the MIST have noted that it would be easy to "kill" a McKinstry-style AI too, due to the impossibility of supplying it with correct answers to all possible yes/no questions by ways of a finite set of human-generated Mindpixels: the fact that an AI can answer the question "Is the sun bigger than my foot?" correctly does not mean that it can answer variations like "Is the sun bigger than (my hand | my liver | an egg yolk | Alpha Centauri A | ...)" correctly, too. However, the late McKinstry might have replied that a truly intelligent, knowledgeable entity (on a par with humans) would be able to work out answers such as (yes | yes | yes | don't know | ...) by applying its knowledge of the relative sizes of the objects named. In other words, the MIST was intended as a test of AI, not as a suggestion for implementing AI. It can also be argued that the MIST is a more objective test of intelligence than the Turing test, a subjective assessment that some might consider to be more a measure of the interrogator's gullibility than of the machine's intelligence. According to this argument, a human's judgment of a Turing test is vulnerable to the ELIZA effect, a tendency to mistake superficial signs of intelligence for the real thing, anthropomorphizing the program. The response, suggested by Alan Turing's essay Computing Machinery and Intelligence, is that if a program is a convincing imitation of an intelligent being, it is in fact intelligent. The dispute is thus over what it means for a program to have "real" intelligence, and by what signs it can be detected. A similar debate exists in the controversy over great ape language, in which nonhuman primates are said to have learned some aspects of sign languages but the significance of this learning is disputed.

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  • Plinian Core

    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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  • Safe Superintelligence Inc.

    Safe Superintelligence Inc.

    Safe Superintelligence Inc. (SSI Inc.) is an Israeli-American artificial intelligence company founded by Ilya Sutskever, the former chief scientist of OpenAI; Daniel Gross, former head of Apple’s AI efforts; and Daniel Levy, an investor and AI researcher. The company's mission is to focus on safely developing a superintelligence, a computer-based agent capable of surpassing human intelligence. == History == On May 15, 2024, OpenAI co-founder Ilya Sutskever left OpenAI after a board dispute where he voted to fire Sam Altman amid concerns about communication and trust. Sutskever and others additionally believed that OpenAI was neglecting its original focus on safety in favor of pursuing opportunities for commercialization. On June 19, 2024, Sutskever posted on X that he was starting SSI Inc, with the goal to safely develop superintelligent AI, alongside Daniel Levy, and Daniel Gross. The company, composed of a small team, is split between Palo Alto, California and Tel Aviv, Israel. In September 2024, SSI revealed it had raised $1 billion from venture capital firms including SV Angel, DST Global, Sequoia Capital, and Andreessen Horowitz. The money will be used to build up more computing power and hire top individuals in the field. In March 2025, SSI reached a $30 billion valuation in a funding round led by Greenoaks Capital. This is six times its previous $5 billion valuation from September 2024. Despite not yet generating revenue and having approximately 20 employees, the company has attracted significant investor interest, largely due to co-founder Ilya Sutskever's reputation and its focus on developing safe superintelligence. In April 2025, Google Cloud announced a partnership to provide TPUs for SSI's research. In the first half of 2025, Meta attempted to acquire SSI but was rebuffed by Sutskever. In July 2025, co-founder Gross left the company to join Meta Superintelligence Labs, and Sutskever became the CEO of SSI.

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  • Geometric primitive

    Geometric primitive

    In vector computer graphics, CAD systems, and geographic information systems, a geometric primitive (or prim) is the simplest (i.e. 'atomic' or irreducible) geometric shape that the system can handle (draw, store). Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segments, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are curves (segments of straight lines, circles and more complicated curves), as well as shapes (boxes, arbitrary polygons, circles). A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles (polygon triangulation). All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves (such as Bézier curves, circles, etc.) may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. == Common primitives == The set of geometric primitives is based on the dimension of the region being represented: Point (0-dimensional), a single location with no height, width, or depth. Line or curve (1-dimensional), having length but no width, although a linear feature may curve through a higher-dimensional space. Planar surface or curved surface (2-dimensional), having length and width. Volumetric region or solid (3-dimensional), having length, width, and depth. In GIS, the terrain surface is often spoken of colloquially as "2 1/2 dimensional," because only the upper surface needs to be represented. Thus, elevation can be conceptualized as a scalar field property or function of two-dimensional space, affording it a number of data modeling efficiencies over true 3-dimensional objects. A shape of any of these dimensions greater than zero consists of an infinite number of distinct points. Because digital systems are finite, only a sample set of the points in a shape can be stored. Thus, vector data structures typically represent geometric primitives using a strategic sample, organized in structures that facilitate the software interpolating the remainder of the shape at the time of analysis or display, using the algorithms of Computational geometry. A Point is a single coordinate in a Cartesian coordinate system. Some data models allow for Multipoint features consisting of several disconnected points. A Polygonal chain or Polyline is an ordered list of points (termed vertices in this context). The software is expected to interpolate the intervening shape of the line between adjacent points in the list as a parametric curve, most commonly a straight line, but other types of curves are frequently available, including circular arcs, cubic splines, and Bézier curves. Some of these curves require additional points to be defined that are not on the line itself, but are used for parametric control. A Polygon is a polyline that closes at its endpoints, representing the boundary of a two-dimensional region. The software is expected to use this boundary to partition 2-dimensional space into an interior and exterior. Some data models allow for a single feature to consist of multiple polylines, which could collectively connect to form a single closed boundary, could represent a set of disjoint regions (e.g., the state of Hawaii), or could represent a region with holes (e.g., a lake with an island). A Parametric shape is a standardized two-dimensional or three-dimensional shape defined by a minimal set of parameters, such as an ellipse defined by two points at its foci, or three points at its center, vertex, and co-vertex. A Polyhedron or Polygon mesh is a set of polygon faces in three-dimensional space that are connected at their edges to completely enclose a volumetric region. In some applications, closure may not be required or may be implied, such as modeling terrain. The software is expected to use this surface to partition 3-dimensional space into an interior and exterior. A triangle mesh is a subtype of polyhedron in which all faces must be triangles, the only polygon that will always be planar, including the Triangulated irregular network (TIN) commonly used in GIS. A parametric mesh represents a three-dimensional surface by a connected set of parametric functions, similar to a spline or Bézier curve in two dimensions. The most common structure is the Non-uniform rational B-spline (NURBS), supported by most CAD and animation software. == Application in GIS == A wide variety of vector data structures and formats have been developed during the history of Geographic information systems, but they share a fundamental basis of storing a core set of geometric primitives to represent the location and extent of geographic phenomena. Locations of points are almost always measured within a standard Earth-based coordinate system, whether the spherical Geographic coordinate system (latitude/longitude), or a planar coordinate system, such as the Universal Transverse Mercator. They also share the need to store a set of attributes of each geographic feature alongside its shape; traditionally, this has been accomplished using the data models, data formats, and even software of relational databases. Early vector formats, such as POLYVRT, the ARC/INFO Coverage, and the Esri shapefile support a basic set of geometric primitives: points, polylines, and polygons, only in two dimensional space and the latter two with only straight line interpolation. TIN data structures for representing terrain surfaces as triangle meshes were also added. Since the mid 1990s, new formats have been developed that extend the range of available primitives, generally standardized by the Open Geospatial Consortium's Simple Features specification. Common geometric primitive extensions include: three-dimensional coordinates for points, lines, and polygons; a fourth "dimension" to represent a measured attribute or time; curved segments in lines and polygons; text annotation as a form of geometry; and polygon meshes for three-dimensional objects. Frequently, a representation of the shape of a real-world phenomenon may have a different (usually lower) dimension than the phenomenon being represented. For example, a city (a two-dimensional region) may be represented as a point, or a road (a three-dimensional volume of material) may be represented as a line. This dimensional generalization correlates with tendencies in spatial cognition. For example, asking the distance between two cities presumes a conceptual model of the cities as points, while giving directions involving travel "up," "down," or "along" a road imply a one-dimensional conceptual model. This is frequently done for purposes of data efficiency, visual simplicity, or cognitive efficiency, and is acceptable if the distinction between the representation and the represented is understood, but can cause confusion if information users assume that the digital shape is a perfect representation of reality (i.e., believing that roads really are lines). == In 3D modelling == In CAD software or 3D modelling, the interface may present the user with the ability to create primitives which may be further modified by edits. For example, in the practice of box modelling the user will start with a cuboid, then use extrusion and other operations to create the model. In this use the primitive is just a convenient starting point, rather than the fundamental unit of modelling. A 3D package may also include a list of extended primitives which are more complex shapes that come with the package. For example, a teapot is listed as a primitive in 3D Studio Max. == In graphics hardware == Various graphics accelerators exist with hardware acceleration for rendering specific primitives such as lines or triangles, frequently with texture mapping and shaders. Modern 3D accelerators typically accept sequences of triangles as triangle strips.

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  • Effective accelerationism

    Effective accelerationism

    Effective accelerationism (e/acc) is a 21st-century ideological movement that advocates for an explicitly pro-technology stance. Its proponents believe that unrestricted technological progress, especially driven by artificial intelligence, is a solution to universal human problems, such as poverty, war, and climate change. They perceive themselves as a counterweight to more cautious views on technological innovation and often label their opponents derogatorily as "doomers" or "decels" (short for decelerationists). The movement carries utopian undertones and advocates for faster AI progress to ensure human survival and propagate consciousness throughout the universe. Although effective accelerationism has been described as a fringe movement and as cult-like, it has gained mainstream visibility in 2023. A number of high-profile Silicon Valley figures, including investors Marc Andreessen and Garry Tan, explicitly endorsed it by adding "e/acc" to their public social media profiles. == Etymology and central beliefs == Effective accelerationism, a portmanteau of "effective altruism" and "accelerationism", is a fundamentally techno-optimist movement. According to Guillaume Verdon, one of the movement's founders, its aim is for human civilization to "clim[b] the Kardashev gradient", meaning its purpose is for human civilization to rise to next levels on the Kardashev scale by maximizing energy usage. To achieve this goal, effective accelerationism wants to accelerate technological progress. It is strongly focused on artificial general intelligence (AGI), because it sees AGI as fundamental for climbing the Kardashev scale. The movement therefore advocates for unrestricted development and deployment of artificial intelligence. Regulation of artificial intelligence and government intervention in markets more generally is met with opposition. Many of its proponents have libertarian views and think that AGI will be most aligned if many AGIs compete against each other on the marketplace. The founders of the movement see it as rooted in Jeremy England's theory on the origin of life, which is focused on entropy and thermodynamics. According to them, the universe aims to increase entropy, and life is a way of increasing it. By spreading life throughout the universe and making life use up ever increasing amounts of energy, the universe's purpose would thus be fulfilled. == History == === Intellectual origins === While Nick Land is seen as the intellectual originator of contemporary accelerationism in general, the precise origins of effective accelerationism remain unclear. The earliest known reference to the movement can be traced back to a May 2022 newsletter published by four pseudonymous authors known by their X (formerly Twitter) usernames @BasedBeffJezos, @bayeslord, @zestular and @creatine_cycle. Effective accelerationism is an extension of the TESCREAL movement, being etymologically derived from Effective Altruism and heavily rooted in the older Silicon Valley subcultures of transhumanism and extropianism (which similarly emphasized the value of progress and resisted efforts to restrain the development of technology), alongside elements of singularitarianism, cosmism, and longtermism. It is also often considered to have emerged at least in part from the work of the Cybernetic Culture Research Unit (of which Nick Land was a leading member, alongside writers such as Mark Fisher and Sadie Plant). It is sometimes compared and contrasted with the work of philosopher Benjamin Bratton on planetary computation. === Disclosure of the identity of BasedBeffJezos === Forbes disclosed in December 2023 that the @BasedBeffJezos persona is maintained by Guillaume Verdon, a Canadian former Google quantum computing engineer and theoretical physicist. The revelation was supported by a voice analysis conducted by the National Center for Media Forensics of the University of Colorado Denver, which further confirmed the match between Jezos and Verdon. The magazine justified its decision to disclose Verdon's identity on the grounds of it being "in the public interest". On 29 December 2023 Guillaume Verdon was interviewed by Lex Fridman on the Lex Fridman Podcast and introduced as the "creator of the effective accelerationism movement". === Second Trump presidency === Following Donald Trump's victory in the 2024 U.S. presidential election, several prominent tech industry figures expressed support for positions aligned with effective accelerationism, particularly regarding deregulation and technological advancement. The potential appointment of Elon Musk to government roles focused on auditing federal programs drew support from venture capitalists who anticipated reduced regulatory oversight of the technology sector. Notable tech figures publicly connected these developments to the movement's principles. Aaron Levie, CEO of Box, expressed support for "removing unnecessary red tape and over-regulation", while Mark Pincus, early Facebook investor and Zynga founder, explicitly referenced "effective accelerationism" in his post-election commentary. Venture capitalists viewed the incoming administration as an opportunity to ease regulations that had affected technology mergers and acquisitions during the previous years. == Relation to other movements == === Traditional accelerationism === Traditional accelerationism, as developed by the British philosopher Nick Land, sees the acceleration of technological change as a way to bring about a fundamental transformation of current culture, society, and the political economy. This is done through capitalism, which Land views as "an autonomous force that’s reconfiguring society" that can overcome its limits if intensified. Land's work has also been characterized as concerning "the supposedly inevitable 'disintegration of the human species' when artificial intelligence improves sufficiently." While both concern ideas like a technocapital singularity and AGI progress, effective accelerationism focuses on using AGI for the greatest ethical good for conscious life and civilization (whether human or machine), as well as expanding civilization and maximizing energy usage in order to align with the "will of the universe". Land focuses on capitalist self-optimization as the driver of modernity, progress, and the eroding of existing social orders. Land has expressed support for effective accelerationism, while Thomas Murphy referred to the movement as "Nick Land diluted for LinkedIn". === Effective altruism === Effective accelerationism diverges from the principles of effective altruism, which prioritizes using evidence and reasoning to identify the most effective ways to altruistically improve the world. This divergence comes primarily from one of the causes effective altruists focus on – AI existential risk. Effective altruists (particularly longtermists) argue that AI companies should be cautious and strive to develop safe AI systems, as they fear that any misaligned AGI could eventually lead to human extinction. Proponents of effective accelerationism generally consider existential risks from AGI to be negligible, and claim that even if they were not, decentralized free markets would much better mitigate this risk than centralized governmental regulation. === Degrowth === Effective accelerationism stands in stark contrast with the degrowth movement, sometimes described by it as "decelerationism" or "decels". The degrowth movement advocates for reducing economic activity and consumption to address ecological and social issues. Effective accelerationism on the contrary embraces technological progress, energy consumption and the dynamics of capitalism, rather than advocating for a reduction in economic activity. == Reception == The "Techno-Optimist Manifesto", a 2023 essay by Marc Andreessen, has been described by the Financial Times and the German Süddeutsche Zeitung as espousing the views of effective accelerationism. Mother Jones also characterized it as expressing effective accelerationism and reported that Andressen cited Land's work. David Swan of The Sydney Morning Herald has criticized effective accelerationism due to its opposition to government and industry self-regulation. He argues that "innovations like AI needs thoughtful regulations and guardrails ... to avoid the myriad mistakes Silicon Valley has already made." During the 2023 Reagan National Defense Forum, U.S. Secretary of Commerce Gina Raimondo cautioned against embracing the "move fast and break things" mentality associated with "effective acceleration [sic]". She emphasized the need to exercise caution in dealing with AI, stating "that's too dangerous. You can't break things when you are talking about AI." In a similar vein, Ellen Huet argued on Bloomberg News that some of the ideas of the movement were "deeply unsettling", focusing especially on Guillaume Verdon's "post-humanism" and the view that "natural selection could lead AI to replace us as the dominant spe

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  • Buddhism and artificial intelligence

    Buddhism and artificial intelligence

    The relationship between Buddhist philosophy and artificial intelligence (AI) includes how principles such as the reduction of suffering and ethical responsibility may influence AI development. Buddhist scholars and philosophers have explored questions such as whether AI systems could be considered sentient beings under Buddhist definitions, and how Buddhist ethics might guide the design and application of AI technologies. Some Buddhist scholars, including Somparn Promta and Kenneth Einar Himma, have analyzed the ethical implications of AI, emphasizing the distinction between satisfying sensory desires and pursuing the reduction of suffering. Other thinkers, such as Thomas Doctor and colleagues, have proposed applying the Bodhisattva vow—a commitment to alleviate suffering for all sentient beings—as a guiding principle for AI system design. Buddhist scholars and ethicists have examined Buddhist ethical principles, such as nonviolence, in relation to AI, focusing on the need to ensure that AI technologies are not used to cause harm. == Context == === Sentient beings === A major goal in Buddhist philosophy is the removal of suffering for all sentient beings, an aspiration often referred to in the Bodhisattva vow. Discussions about artificial intelligence (AI) in relation to Buddhist principles have raised questions about whether artificial systems could be considered sentient beings or how such systems might be developed in ways that align with Buddhist concepts. Buddhists have varying opinions about AI sentience, but if AI systems are determined to be sentient under Buddhist definitions, their suffering would also need to be addressed and alleviated in accordance with the principles of Buddhist thought. == Buddhist principles in AI system design == === Nonviolence and AI === The broadest ethical concern is that artificial intelligence should align with the Buddhist principle of nonviolence. From this perspective, AI systems should not be designed or used to cause harm. === Instrumental and transcendental goals === Scholars Somparn Promta and Kenneth Einar Himma have argued that the advancement of artificial intelligence can only be considered instrumentally good, rather than good a priori, from a Buddhist perspective. They propose two main goals for AI designers and developers: to set ethical and pragmatic objectives for AI systems, and to fulfill these objectives in morally permissible ways. Promta and Himma identify two potential purposes for creating AI systems. The first is to fulfill our sensory desires and survival instincts, similar to other tools. They suggest that many AI developers implicitly prioritize this goal by focusing on technicalities rather than broader functionalities. The second, and more important goal according to Buddhist teachings, is to transcend these desires and instincts. In texts like the Brahmajāla Sutta and minor Malunkya Sutta, the Buddha emphasizes that sensory desires and survival instincts confine beings to suffering, and that eliminating suffering is the primary goal of human life. Promta and Himma argue that AI has the potential to assist humanity in transcending suffering by helping individuals overcome survival-driven instincts. === Intelligence as care === Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, and Michael Levin propose redefining intelligence through the concept of "intelligence as care," and promote it as a slogan. Inspired by the Bodhisattva vow, they suggest this principle could guide AI system design. The Bodhisattva vow involves a formal commitment to alleviate suffering for all sentient beings, with four primary objectives: Liberating all beings from suffering. Extirpating all forms of suffering. Mastering endless techniques of practicing Dharma (Pali: dhammakkhandha, Sanskrit: dharmaskandha). Achieving ultimate enlightenment (Sanskrit: अनुत्तर सम्यक् सम्बोधि, Romanized: anuttara-samyak-saṃbodhi). This approach positions AI as a tool for exercising infinite care and alleviating stress and suffering for sentient beings. Doctor et al. emphasize that AI development should align with these altruistic principles.

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  • AGROVOC

    AGROVOC

    AGROVOC is a multilingual controlled vocabulary covering areas of interest of the Food and Agriculture Organization of the United Nations (FAO), aiming to promote the visibility of research produced among FAO members. By March 2024, AGROVOC consisted of over 42 000 concepts and up to 1 000 000 terms in more than 42 different languages. It is a collaborative effort, the outcome of consensus among a community of experts coordinated by FAO. == History == FAO first published AGROVOC at the beginning of the 1980s in English, Spanish and French to serve as a controlled vocabulary to index publications in agricultural science and technology, especially for the International System for Agricultural Science and Technology (AGRIS). In the 1990s, AGROVOC shifted from paper printing to a digital format opting for data storage handled by a relational database. In 2004, preliminary experiments with expressing AGROVOC into the Web Ontology Language (OWL) took place. At the same time a web based editing tool was developed, then called WorkBench, nowadays VocBench. In 2009 AGROVOC became an SKOS resource. == Usage == Today, AGROVOC is available in different languages. It is employed for tagging resources, allowing searches in a specific language while providing results in many others, enhancing their visibility worldwide. Additionally, it serves for organizing knowledge to facilitate subsequent data retrieval, tagging website content for search engine discovery, standardizing agricultural information data and acting as a reference for translations. Moreover, it finds applications in fields such as data mining, big data, or artificial intelligence. Updated AGROVOC content is released once a month and is available for public use. == Maintenance == FAO coordinates the editorial activities related to the maintenance of AGROVOC. Content curation is carried out by a community of editors and institutions responsible for each of the language versions. VocBench, is the tool used to edit and maintain AGROVOC in a distributed way. FAO also facilitates the technical maintenance of AGROVOC. == Copyright and license == Copyright for AGROVOC content in FAO languages (English, French, Spanish, Arabic, Russian and Chinese) is held by FAO, while content in other languages stays with the institutions that authored it. AGROVOC thesaurus content in English, Russian, French, Spanish, Arabic and Chinese is licensed under the international Creative Commons Attribution License (CC-BY-4.0).

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  • Dating app

    Dating app

    An online dating application, commonly known as a dating app, is an online dating service presented through a mobile phone application. These apps often take advantage of a smartphone's GPS location capabilities, always on-hand presence, and access to mobile wallets. These apps aim to speed up the online dating process of sifting through potential dating partners, chatting, flirting, and potentially meeting or becoming romantically involved. Online dating apps are now mainstream in the United States. As of 2017, online dating (which included both apps and other online dating services) was the principal method by which new couples in the U.S. met. The percentage of couples meeting online is predicted to increase to 70% by 2040. == Origins == The first computerized dating service was launched in 1964, the St. James Computer Dating Service, which became known as Com-Pat. The first U.S. dating service that used computerized match making was Operation Match. It required men and women to complete a questionnaire and was launched in 1965. Operation Match inspired the methodology of Dateline, which became popular in the 1970s and 1980s. Match.com was launched in 1995 and turned computerized match making into a profitable business. Grindr targeted gay and bisexual men at launch. Tinder, launched in 2012, led to a growth of online dating applications by both new providers and existing online dating services that expanded into the mobile app market. == Usage by demographic group == Online dating applications typically target a younger demographic group, though some apps, like Senior Match and Silver Singles are geared toward the 50 and up demographic. In 2016, almost 50% of people knew of someone who use the services or had met their loved one through the service. After the iPhone launch in 2007, online dating data has mushroomed as application usage increased. In 2005, only 10% of 18-24 year olds reported to have used online dating services; this number quickly grew to over 27%, making this target demographic the largest number of users for most applications. When Pew Research Center conducted a study in 2016, they found that 59% of U.S. adults agreed that online dating is a good way to meet people compared to 44% in 2005. This explosion in usage can be explained by the increased use of smartphones. By the end of 2022, it is expected there will be 413 million active users of online dating services worldwide. A 2023 Pew Research Center survey of 6,034 American adults found that 30% had ever used an online dating site or app, including 53% of those aged 18 to 29, 37% of those aged 30 to 49, and 17% of those aged 50 and over. Lesbian, gay and bisexual respondents reported using dating apps at nearly twice the rate of straight respondents (51% versus 28%), and 36% of divorced, separated or widowed adults had used one, compared with 16% of married adults. The increased use of smartphones by those 65 and older has also driven that population to the use dating apps. The Pew Research Center found that usage increase by 8 points since last surveyed in 2012. A study in 2021 found that more than one-third of seniors have dated in the past 5 years, and roughly one-third of those dating seniors have turned to dating apps. During the COVID-19 pandemic, Morning Consult found that more Americans were using online dating apps than ever before. In one survey in April 2020, the company discovered that 53% of U.S. adults who use online dating apps have been using them more during the pandemic. As of February 2021, that share increased to 71 percent. Research using Hofstede's cultural dimensions theory has indicated that norms about online dating applications tend to differ across cultures. A study published in the Journal of Creative Communications looked into the relationships between dating-app advertisements from over 51 countries and the cultural dimensions of these countries. The results revealed that dating-app advertisements appealed to multiple cultural needs, including the needs for relationships, friendship, entertainment, sex, status, design and identity. The use of these appeals was found to be 'congruent with ... the individualism/collectivism and uncertainty avoidance cultural dimensions.' == Popular applications == Following Tinder's success, other companies released dating applications. Raya was released in 2015 as a membership-based dating app, allowing entrance only through referrals, which was marketed as a dating app for celebrities. In early 2026, Hily surpassed Bumble to become the third most-used dating application in the United States and the fifth highest-grossing overall, driven largely by growing popularity among Generation Z users, while remaining behind Tinder and Hinge, both owned by Match Group. A number of dating apps have been created targeting adherents of particular religions seeking partners of the same religion, such as Muzmatch for Muslims, Christian Mingle, SALT, and Christian Connection for Christians, and JSwipe and JDate for Jews. === VR Dating === VR Dating is an application of Social VR where people can exist, collaborate, and perform various activities together. Virtual reality apps use virtual and augmented realities to make the dating experience more lifelike and more effective, as well as allow people to expand what is already possible in the world of online dating. There are several online platforms of VR Dating. The VR dating app Nevermet is the VR equivalent of Tinder, where people can search and find on dates. However, instead of actual real-life pictures, users will update pictures of virtual selves and will be interacting with avatars rather than real faces. Flirtual is a self-contained social VR app that serves to match users who then decide where and how to meet in VR. Flirtual hosts speed dating and social events in VR. == Effects on dating == The usage of online dating applications can have both advantages and disadvantages: === Advantages === Many of the applications provide personality tests for matching or use algorithms to match users. These factors enhance the possibility of users getting matched with a compatible candidate. Users are in control; they are provided with many options so there are enough matches that fit their particular type. Users can simply choose to not match the candidates that they know they are not interested in. Narrowing down options is easy. Once users think they are interested, they are able to chat and get to know the potential candidate. This form of communication can reduce the time, cost, and uncertainty often associated with traditional dating methods. Online dating offers convenience; people want dating to work around their schedules. Online dating can also increase self-confidence; even if users get rejected, they know there are hundreds of other candidates that will want to match with them so they can simply move on to the next option. In fact, 60% of U.S. adults agree that online dating is a good way to meet people and 66% say they have gone on a real date with someone they met through an application. Today, 5% of married Americans or Americans in serious relationships said they met their significant other online. The 39% of online dating users (representing 12% of all U.S. adults) say they have been in a committed relationship or married someone they met on a dating site or app. ==== Rejection sensitive individuals ==== Individuals high in rejection sensitivity are more likely to use online dating applications. As they tend to expect, perceive and overreact to rejection, rejection sensitive individuals struggle with traditional dating. Online dating applications allow for them to better reveal their true selves, potentially increasing their dating success. Online dating applications also obscure rejection cues, alleviating the rejection-related distress experienced by rejection sensitive individuals. ==== Anxiously attached individuals ==== Individuals high in attachment anxiety are also more likely to use online dating applications. While they harbour negative self-views, anxiously attached individuals are also more eager to enter and commit to relationships. Online dating applications allow for them to present "an authentic yet ideal version of themselves", mitigating their tendencies to view themselves as undesirable. This increases their romantic confidence, and potentially alleviates their anxiety when searching for a romantic partner. === Disadvantages === Sometimes having too many options can be overwhelming. With so many options available, users can get lost in their choices and end up spending too much time looking for the "perfect" candidate instead of using that time to start a real relationship. In addition, the algorithms and matching systems put in place may not always be as accurate as users think. There is no perfect system that can match two people's personalities perfectly every time. Communication online also lacks the physical chemistry aspec

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  • GENESIS (software)

    GENESIS (software)

    GENESIS (The General Neural Simulation System) is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including: sub-cellular processes, individual neurons, networks of neurons, and neuronal systems. These simulations are “computer-based implementations of models whose primary objective is to capture what is known of the anatomical structure and physiological characteristics of the neural system of interest”. GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.” Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University. == Neurons and Neural Systems == GENESIS works by creating simulation environments for constructing models of neurons or neural systems. "Nerve cells are capable of communicating with each other in such a highly structured manner as to form neuronal networks. To understand neural networks, it is necessary to understand the ways in which one neuron communicates with another through synaptic connections and the process called synaptic transmission". Neurons have a specialized structure for their function, they "are different from most other cells in the body in that they are polarized and have distinct morphological regions, each with specific functions". The two important regions of a neuron are the dendrite and the axon. "Dendrites are the region where one neuron receives connections from other neurons. The cell body or soma contains the nucleus and the other organelles necessary for cellular function. The axon is a key component of nerve cells over which information is transmitted from one part of the neuron (e.g., the cell body) to the terminal regions of the neuron". The third important piece of a neuron is the synapse. "The synapse is the terminal region of the axon this is where one neuron forms a connection with another and conveys information through the process of synaptic transmission". Neural networks like the ones simulated with GENESIS software can quickly become highly complex and difficult to understand. "Just a few interconnected neurons (a microcircuit) can perform sophisticated tasks such as mediate reflexes, process sensory information, generate locomotion and mediate learning and memory. Even more complex networks, macrocircuits, consist of multiple embedded microcircuits. Macrocircuits mediate higher brain functions such as object recognition and cognition". GENESIS endeavors to simulate neural systems as they are found in nature. Often, "a neuron can receive contacts from up to 10,000 presynaptic neurons, and, in turn, any one neuron can contact up to 10,000 postsynaptic neurons. The combinatorial possibility could give rise to enormously complex neuronal circuits or network topologies, which might be very difficult to understand". == History == GENESIS was developed by Dr. James M. Bower, in the Caltech laboratory, and first released to the public in 1988 in association with the first Methods in Computational Neuroscience Course at the Marine Biological Laboratory in Woods Hole, MA. Full source code for the software was released in the same year under an open software model for development. It's now supported by the Computational Biology Initiative at the University of Texas at San Antonio and is available free along with tutorial guides on its use. P-GENESIS, a parallel version of GENESIS, was first run in 1990 on the Intel Delta, which was the prototype for the Intel Paragon family of massively parallel supercomputers. == How GENESIS Works == GENESIS is useful in creating a simulation environment for constructing models of neurobiological systems, such as: sub-cellular processes individual neurons networks of neurons neuronal systems The GENESIS system is complicated, but relatively easy to use. An individual can input commands through one of three ways: script files, graphical user interface, or the GENESIS command shell. These commands are then processed by the script language interpreter. "The Script Language Interpreter processes commands entered through the keyboard, script files, or the graphical user interface, and passes them to the GENESIS simulation engine. The simulation engine also loads compiled object libraries, reads and writes data files, and interacts with the graphical user interface". Below is a graphical representation of the user input process and a sample GENESIS output. == Applications == Most current applications for GENESIS involve realistic simulations of biological systems. It is usually used to simulate the behavior of larger brain structures, for example the cerebral cortex. These studies most often occur in lab courses in neural simulation at Caltech and the Marine Biological Laboratory at Woods Hole, Massachusetts. GENESIS can be used in combination with Yale University’s software called NEURON as a means for scientists to collaborate to construct a physical description of the nervous system. The GENESIS software can also be used with Kinetikit in the modeling of signal transduction pathways. GENESIS has been used in many studies. Some of these studies involve research that focuses on the development of software that would be useful across many disciplines. Others are studies of neurons, such as Purkinje cells. These studies used GENESIS to simulate Purkinje cells and could be useful for the planning and development of later experiments using the GENESIS software. There may also be biomedical applications of the software. For example, St. Jude Medical in Europe has developed an implanted GENESIS device.

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  • Open Mind Common Sense

    Open Mind Common Sense

    Open Mind Common Sense (OMCS) is an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal is to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It has been active from 1999 to 2016. Since its founding, it has accumulated more than a million English facts from over 15,000 contributors in addition to knowledge bases in other languages. Much of OMCS's software is built on three interconnected representations: the natural language corpus that people interact with directly, a semantic network built from this corpus called ConceptNet, and a matrix-based representation of ConceptNet called AnalogySpace that can infer new knowledge using dimensionality reduction. The knowledge collected by Open Mind Common Sense has enabled research projects at MIT and elsewhere. == History == The project was the brainchild of Marvin Minsky, Push Singh, Catherine Havasi, and others. Development work began in September 1999, and the project opened to the Internet a year later. Havasi described it in her dissertation as "an attempt to ... harness some of the distributed human computing power of the Internet, an idea which was then only in its early stages." The original OMCS was influenced by the website Everything2 and its predecessor, and presents a minimalist interface that is inspired by Google. Push Singh would have become a professor at the MIT Media Lab and lead the Common Sense Computing group in 2007, but committed suicide on February 28, 2006. The project is currently run by the Digital Intuition Group at the MIT Media Lab under Catherine Havasi. == Database and website == There are many different types of knowledge in OMCS. Some statements convey relationships between objects or events, expressed as simple phrases of natural language: some examples include "A coat is used for keeping warm", "The sun is very hot", and "The last thing you do when you cook dinner is wash your dishes". The database also contains information on the emotional content of situations, in such statements as "Spending time with friends causes happiness" and "Getting into a car wreck makes one angry". OMCS contains information on people's desires and goals, both large and small, such as "People want to be respected" and "People want good coffee". Originally, these statements could be entered into the Web site as unconstrained sentences of text, which had to be parsed later. The current version of the Web site collects knowledge only using more structured fill-in-the-blank templates. OMCS also makes use of data collected by the Game With a Purpose "Verbosity". In its native form, the OMCS database is simply a collection of these short sentences that convey some common knowledge. In order to use this knowledge computationally, it has to be transformed into a more structured representation. == ConceptNet == ConceptNet is a semantic network based on the information in the OMCS database. ConceptNet is expressed as a directed graph whose nodes are concepts, and whose edges are assertions of common sense about these concepts. Concepts represent sets of closely related natural language phrases, which could be noun phrases, verb phrases, adjective phrases, or clauses. ConceptNet is created from the natural-language assertions in OMCS by matching them against patterns using a shallow parser. Assertions are expressed as relations between two concepts, selected from a limited set of possible relations. The various relations represent common sentence patterns found in the OMCS corpus, and in particular, every "fill-in-the-blanks" template used on the knowledge-collection Web site is associated with a particular relation. The data structures that make up ConceptNet were significantly reorganized in 2007, and published as ConceptNet 3. The Software Agents group currently distributes a database and API for the new version 4.0. In 2010, OMCS co-founder and director Catherine Havasi, with Robyn Speer, Dennis Clark and Jason Alonso, created Luminoso, a text analytics software company that builds on ConceptNet. It uses ConceptNet as its primary lexical resource in order to help businesses make sense of and derive insight from vast amounts of qualitative data, including surveys, product reviews and social media. == Machine learning tools == The information in ConceptNet can be used as a basis for machine learning algorithms. One representation, called AnalogySpace, uses singular value decomposition to generalize and represent patterns in the knowledge in ConceptNet, in a way that can be used in AI applications. Its creators distribute a Python machine learning toolkit called Divisi for performing machine learning based on text corpora, structured knowledge bases such as ConceptNet, and combinations of the two. == Comparison to other projects == Other similar projects include Never-Ending Language Learning, Mindpixel (discontinued), Cyc, Learner, SenticNet, Freebase, YAGO, DBpedia, and Open Mind 1001 Questions, which have explored alternative approaches to collecting knowledge and providing incentive for participation. The Open Mind Common Sense project differs from Cyc because it has focused on representing the common sense knowledge it collected as English sentences, rather than using a formal logical structure. ConceptNet is described by one of its creators, Hugo Liu, as being structured more like WordNet than Cyc, due to its "emphasis on informal conceptual-connectedness over formal linguistic-rigor".

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  • Personal knowledge base

    Personal knowledge base

    A personal knowledge base (PKB) is an electronic tool used by an individual to express, capture, and later retrieve personal knowledge. It differs from a traditional database in that it contains subjective material particular to the owner, that others may not agree with nor care about. Importantly, a PKB consists primarily of knowledge, rather than information; in other words, it is not a collection of documents or other sources an individual has encountered, but rather an expression of the distilled knowledge the owner has extracted from those sources or from elsewhere. The term personal knowledge base was mentioned as early as the 1980s, but the term came to prominence in the 2000s when it was described at length in publications by computer scientist Stephen Davies and colleagues, who compared PKBs on a number of different dimensions, the most important of which is the data model that each PKB uses to organize knowledge. == Data models == Davies and colleagues examined three aspects of the data models of PKBs: their structural framework, which prescribes rules about how knowledge elements can be structured and interrelated (as a tree, graph, tree plus graph, spatially, categorically, as n-ary links, chronologically, or ZigZag); their knowledge elements, or basic building blocks of information that a user creates and works with, and the level of granularity of those knowledge elements (such as word/concept, phrase/proposition, free text notes, links to information sources, or composite); and their schema, which involves the level of formal semantics introduced into the data model (such as a type system and related schemas, keywords, attribute–value pairs, etc.). Davies and colleagues also emphasized the principle of transclusion, "the ability to view the same knowledge element (not a copy) in multiple contexts", which they considered to be "pivotal" to an ideal PKB. They concluded, after reviewing many design goals, that the ideal PKB was still to come in the future. === Personal knowledge graph === In their publications on PKBs, Davies and colleagues discussed knowledge graphs as they were implemented in some software of the time. Later, other writers used the term personal knowledge graph (PKG) to refer to a PKB featuring a graph structure and graph visualization. However, the term personal knowledge graph is also used by software engineers to refer to the different subject of a knowledge graph about a person, in contrast to a knowledge graph created by a person in a PKB. == Software architecture == Davies and colleagues also differentiated PKBs according to their software architecture: file-based, database-based, or client–server systems (including Internet-based systems accessed through desktop computers and/or handheld mobile devices). == History == Non-electronic personal knowledge bases have probably existed in some form for centuries: Leonardo da Vinci's journals and notes are a famous example of the use of notebooks. Commonplace books, florilegia, annotated private libraries, and card files (in German, Zettelkästen) of index cards and edge-notched cards are examples of formats that have served this function in the pre-electronic age. Undoubtedly the most famous early formulation of an electronic PKB was Vannevar Bush's description of the "memex" in 1945. In a 1962 technical report, human–computer interaction pioneer Douglas Engelbart (who would later become famous for his 1968 "Mother of All Demos" that demonstrated almost all the fundamental elements of modern personal computing) described his use of edge-notched cards to partially model Bush's memex. == Examples == The following software applications have been used to build PKBs using various data models and architectures. The list includes software mentioned by Davies and colleagues in their 2005 paper, and additional software. Open source Compendium Haystack (MIT project) Joplin Logseq NoteCards Org-mode QOwnNotes TiddlyWiki Closed source Evernote Microsoft OneNote MindManager MyLifeBits Notion Obsidian Personal Knowbase PersonalBrain Roam Tinderbox

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  • Beauty.AI

    Beauty.AI

    Beauty.AI is a mobile beauty pageant for humans and a contest for programmers developing algorithms for evaluating human appearance. The mobile app and website created by Youth Laboratories that uses artificial intelligence technology to evaluate people's external appearance through certain algorithms, such as symmetry, facial blemishes, wrinkles, estimated age and age appearance, and comparisons to actors and models. The Beauty.AI 2.0 contest caused great concern over important ethical issues with deep neural networks such as age, race and gender bias and lead to the creation of the Diversity.AI think tank dedicated to developing new methods for uncovering and managing bias in artificially intelligent systems. Beauty.AI was also an attempt to find approaches on how machines can perceive human face through evaluating particular features, commonly associated with health and beauty. == Concept == The Beauty.AI app was created by Youth Laboratories, a company based out of Russia and Hong Kong that focuses on facial skin analytics. The bioinformation company Insilico Medicine assists in the Beauty.AI app by testing its deep learning techniques to the app. One goal of the app is to reduce the need for human and animal testing as well as improving people's overall health. Its first contest was started in December 2016, and the results were announced in August 2016. More than 60,000 people submitted entries into the contest. The mobile app uses artificial intelligence technology to inspect photographs for certain facial features in order to both determine a person's beauty through artificial means by multiple robots. Part of the Beauty.AI app's purpose is to collect visual and anecdotal data to improve its creator's Youth Laboratories skin analyst skills. == Accusations of racism == There were a total of 44 individuals from different age groups and genders judged as the most attractive, with 37 white entrants, six Asian entrants, and one dark-skinned entrant. The app has received criticism from social justice advocates and computer science professionals. However, Alex Zhavoronkov, PhD, chief science officer of Youth Laboratories and chief technology officer Konstantin Kiselev, both for Youth Laboratories, noted that a lack of data may have contributed to these results. Also, Kiselev added that another issue was that approximately 75% of entrants were white Europeans, whereas only 7% and 1% were from India and Africa, respectively. Kiselev stated that they would work on doing more and better outreach to these areas to improve in this area. Despite this, it was said by Dr. Zhavoronkov that the AI would discard photos of dark-skinned people if the lighting is too poor. Dr. Zhavoronkov vowed to weed out the issues for the next beauty pageant and to try to avoid a similar controversy in the future.

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  • Innovation Center for Artificial Intelligence

    Innovation Center for Artificial Intelligence

    The Innovation Center for Artificial Intelligence (ICAI) is a Dutch national network focused on joint technology development between academia, industry and government in the area of artificial intelligence (AI). The initiative was launched in April 2018 and is based at Amsterdam Science Park. As of 2024, the director of the ICAI is Maarten de Rijke. In November 2018, ICAI announced its contribution to AINED, the first iteration of the Dutch National AI Strategy. In January 2023, Maastricht University announced the ROBUST program, led by the Innovation Center for Artificial Intelligence (ICAI) and supported by the University of Amsterdam and others. This initiative focuses on advancing research in trustworthy AI technology across various sectors, notably healthcare and energy, in the Netherlands. The program's plan includes the creation of 17 new labs and the appointment of PhD candidates, backed by a €25 million funding from the Dutch Research Council (NWO). == Labs == The ICAI network is linked to several collaborative labs: Thira Lab (Imaging): Thirona, Delft Imaging Systems and Radboud UMC, founded March 2019 AIMLab (AI for Medical Imaging): Uva and Inception Institute of Artificial Intelligence from the United Arab Emirates, founded March 2019 AFL (AI for Fintech): ING and Delft University of Technology, founded March 2019 Police Lab AI: Dutch National Police, founded January 2019 Elsevier AI Lab: Uva and Elsevier, founded October 2018 AIRLab Delft (AI for Retail Robotics): TU Delft Robotics and AholdDelhaize, founded November 2018 Quva Lab (Deep Vision): Uva and Qualcomm, founded 2016 (prior to ICAI) AIRLab Amsterdam (AI for Retail): Uva and AholdDelhaize, founded April 2018 DeltaLab (Deep Learning Technologies Amsterdam): Uva and Bosch, founded April 2017 (prior to ICAI) AI4SE (AI for Software Engineering Lab) Delft University of Technology and JetBrains, founded October 2023 Atlas Lab: Uva and TomTom (TOM2)

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  • ITU-WHO Focus Group on Artificial Intelligence for Health

    ITU-WHO Focus Group on Artificial Intelligence for Health

    The ITU-WHO Focus Group on Artificial Intelligence for Health (AI for Health) was an inter-agency collaboration from 2018 between the World Health Organization and the ITU, which in 2019 created a benchmarking framework to assess the accuracy of AI in health. The organization convened an international network of experts and stakeholders from fields like research, practice, regulation, ethics, public health, etc, that developed guideline documentation and code. The documents have addressed ethics, assessment/evaluation, handling, and regulation of AI for health solutions, covering specific use cases including AI in ophthalmology, histopathology, dentistry, malaria detection, radiology, symptom checker applications, etc. FG-AI4H has established an ad hoc group concerned with digital technologies for health emergencies, including COVID-19. All documentation is public. The idea for the Focus Group came out of the Health Track of the 2018 AI for Good Global Summit. Administratively, FG-AI4H was created by ITU-T Study Group 16. Under ITU-T's framework, participation in Focus Groups is open to anyone from an ITU Member State. The secretariat is provided by the Telecommunication Standardization Bureau (under Director Chaesub Lee). It was first created at the July 2018 meeting with a lifetime of two years, at the July 2020 meeting, this was extended for another two years, where the focus group also submitted its deliverables to its parent body. It was also presented at the NeurIPS 2020 health workshop. In July 2023 "the work was grandfathered in the Global Initiative on AI for Health (GI-AI4H)". == AI for Health Framework == The outline of the benchmarking framework was published in a 2019 commentary in The Lancet. The output of the Focus Group AI for Health were structured in the AI for Health Framework. Depending on their primary domain being health or ICT, the individual components of the AI for Health Framework were ratified by the corresponding United Nations Specialized Agency, as WHO Guidelines and ITU Recommendations respectively. Standards drawn up by FG-AI4H were titled as: AI4H ethics considerations AI4H regulatory [best practices | considerations] AI4H requirements specification AI software life cycle specification Data specification AI training best practices specification AI4H evaluation considerations AI4H scale-up and adoption AI4H applications and platforms Use cases of the ITU-WHO Focus Group on AI for Health

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