text stringlengths 0 473k |
|---|
[SOURCE: https://en.wikipedia.org/wiki/Animal#cite_note-30] | [TOKENS: 6011] |
Contents Animal Animals are multicellular, eukaryotic organisms belonging to the biological kingdom Animalia (/ˌænɪˈmeɪliə/). With few exceptions, animals consume organic material, breathe oxygen, have myocytes and are able to move, can reproduce sexually, and grow from a hollow sphere of cells, the blastula, during embryonic development. Animals form a clade, meaning that they arose from a single common ancestor. Over 1.5 million living animal species have been described, of which around 1.05 million are insects, over 85,000 are molluscs, and around 65,000 are vertebrates. It has been estimated there are as many as 7.77 million animal species on Earth. Animal body lengths range from 8.5 μm (0.00033 in) to 33.6 m (110 ft). They have complex ecologies and interactions with each other and their environments, forming intricate food webs. The scientific study of animals is known as zoology, and the study of animal behaviour is known as ethology. The animal kingdom is divided into five major clades, namely Porifera, Ctenophora, Placozoa, Cnidaria and Bilateria. Most living animal species belong to the clade Bilateria, a highly proliferative clade whose members have a bilaterally symmetric and significantly cephalised body plan, and the vast majority of bilaterians belong to two large clades: the protostomes, which includes organisms such as arthropods, molluscs, flatworms, annelids and nematodes; and the deuterostomes, which include echinoderms, hemichordates and chordates, the latter of which contains the vertebrates. The much smaller basal phylum Xenacoelomorpha have an uncertain position within Bilateria. Animals first appeared in the fossil record in the late Cryogenian period and diversified in the subsequent Ediacaran period in what is known as the Avalon explosion. Nearly all modern animal phyla first appeared in the fossil record as marine species during the Cambrian explosion, which began around 539 million years ago (Mya), and most classes during the Ordovician radiation 485.4 Mya. Common to all living animals, 6,331 groups of genes have been identified that may have arisen from a single common ancestor that lived about 650 Mya during the Cryogenian period. Historically, Aristotle divided animals into those with blood and those without. Carl Linnaeus created the first hierarchical biological classification for animals in 1758 with his Systema Naturae, which Jean-Baptiste Lamarck expanded into 14 phyla by 1809. In 1874, Ernst Haeckel divided the animal kingdom into the multicellular Metazoa (now synonymous with Animalia) and the Protozoa, single-celled organisms no longer considered animals. In modern times, the biological classification of animals relies on advanced techniques, such as molecular phylogenetics, which are effective at demonstrating the evolutionary relationships between taxa. Humans make use of many other animal species for food (including meat, eggs, and dairy products), for materials (such as leather, fur, and wool), as pets and as working animals for transportation, and services. Dogs, the first domesticated animal, have been used in hunting, in security and in warfare, as have horses, pigeons and birds of prey; while other terrestrial and aquatic animals are hunted for sports, trophies or profits. Non-human animals are also an important cultural element of human evolution, having appeared in cave arts and totems since the earliest times, and are frequently featured in mythology, religion, arts, literature, heraldry, politics, and sports. Etymology The word animal comes from the Latin noun animal of the same meaning, which is itself derived from Latin animalis 'having breath or soul'. The biological definition includes all members of the kingdom Animalia. In colloquial usage, the term animal is often used to refer only to nonhuman animals. The term metazoa is derived from Ancient Greek μετα meta 'after' (in biology, the prefix meta- stands for 'later') and ζῷᾰ zōia 'animals', plural of ζῷον zōion 'animal'. A metazoan is any member of the group Metazoa. Characteristics Animals have several characteristics that they share with other living things. Animals are eukaryotic, multicellular, and aerobic, as are plants and fungi. Unlike plants and algae, which produce their own food, animals cannot produce their own food, a feature they share with fungi. Animals ingest organic material and digest it internally. Animals have structural characteristics that set them apart from all other living things: Typically, there is an internal digestive chamber with either one opening (in Ctenophora, Cnidaria, and flatworms) or two openings (in most bilaterians). Animal development is controlled by Hox genes, which signal the times and places to develop structures such as body segments and limbs. During development, the animal extracellular matrix forms a relatively flexible framework upon which cells can move about and be reorganised into specialised tissues and organs, making the formation of complex structures possible, and allowing cells to be differentiated. The extracellular matrix may be calcified, forming structures such as shells, bones, and spicules. In contrast, the cells of other multicellular organisms (primarily algae, plants, and fungi) are held in place by cell walls, and so develop by progressive growth. Nearly all animals make use of some form of sexual reproduction. They produce haploid gametes by meiosis; the smaller, motile gametes are spermatozoa and the larger, non-motile gametes are ova. These fuse to form zygotes, which develop via mitosis into a hollow sphere, called a blastula. In sponges, blastula larvae swim to a new location, attach to the seabed, and develop into a new sponge. In most other groups, the blastula undergoes more complicated rearrangement. It first invaginates to form a gastrula with a digestive chamber and two separate germ layers, an external ectoderm and an internal endoderm. In most cases, a third germ layer, the mesoderm, also develops between them. These germ layers then differentiate to form tissues and organs. Repeated instances of mating with a close relative during sexual reproduction generally leads to inbreeding depression within a population due to the increased prevalence of harmful recessive traits. Animals have evolved numerous mechanisms for avoiding close inbreeding. Some animals are capable of asexual reproduction, which often results in a genetic clone of the parent. This may take place through fragmentation; budding, such as in Hydra and other cnidarians; or parthenogenesis, where fertile eggs are produced without mating, such as in aphids. Ecology Animals are categorised into ecological groups depending on their trophic levels and how they consume organic material. Such groupings include carnivores (further divided into subcategories such as piscivores, insectivores, ovivores, etc.), herbivores (subcategorised into folivores, graminivores, frugivores, granivores, nectarivores, algivores, etc.), omnivores, fungivores, scavengers/detritivores, and parasites. Interactions between animals of each biome form complex food webs within that ecosystem. In carnivorous or omnivorous species, predation is a consumer–resource interaction where the predator feeds on another organism, its prey, who often evolves anti-predator adaptations to avoid being fed upon. Selective pressures imposed on one another lead to an evolutionary arms race between predator and prey, resulting in various antagonistic/competitive coevolutions. Almost all multicellular predators are animals. Some consumers use multiple methods; for example, in parasitoid wasps, the larvae feed on the hosts' living tissues, killing them in the process, but the adults primarily consume nectar from flowers. Other animals may have very specific feeding behaviours, such as hawksbill sea turtles which mainly eat sponges. Most animals rely on biomass and bioenergy produced by plants and phytoplanktons (collectively called producers) through photosynthesis. Herbivores, as primary consumers, eat the plant material directly to digest and absorb the nutrients, while carnivores and other animals on higher trophic levels indirectly acquire the nutrients by eating the herbivores or other animals that have eaten the herbivores. Animals oxidise carbohydrates, lipids, proteins and other biomolecules in cellular respiration, which allows the animal to grow and to sustain basal metabolism and fuel other biological processes such as locomotion. Some benthic animals living close to hydrothermal vents and cold seeps on the dark sea floor consume organic matter produced through chemosynthesis (via oxidising inorganic compounds such as hydrogen sulfide) by archaea and bacteria. Animals originated in the ocean; all extant animal phyla, except for Micrognathozoa and Onychophora, feature at least some marine species. However, several lineages of arthropods begun to colonise land around the same time as land plants, probably between 510 and 471 million years ago, during the Late Cambrian or Early Ordovician. Vertebrates such as the lobe-finned fish Tiktaalik started to move on to land in the late Devonian, about 375 million years ago. Other notable animal groups that colonized land environments are Mollusca, Platyhelmintha, Annelida, Tardigrada, Onychophora, Rotifera, Nematoda. Animals occupy virtually all of earth's habitats and microhabitats, with faunas adapted to salt water, hydrothermal vents, fresh water, hot springs, swamps, forests, pastures, deserts, air, and the interiors of other organisms. Animals are however not particularly heat tolerant; very few of them can survive at constant temperatures above 50 °C (122 °F) or in the most extreme cold deserts of continental Antarctica. The collective global geomorphic influence of animals on the processes shaping the Earth's surface remains largely understudied, with most studies limited to individual species and well-known exemplars. Diversity The blue whale (Balaenoptera musculus) is the largest animal that has ever lived, weighing up to 190 tonnes and measuring up to 33.6 metres (110 ft) long. The largest extant terrestrial animal is the African bush elephant (Loxodonta africana), weighing up to 12.25 tonnes and measuring up to 10.67 metres (35.0 ft) long. The largest terrestrial animals that ever lived were titanosaur sauropod dinosaurs such as Argentinosaurus, which may have weighed as much as 73 tonnes, and Supersaurus which may have reached 39 metres. Several animals are microscopic; some Myxozoa (obligate parasites within the Cnidaria) never grow larger than 20 μm, and one of the smallest species (Myxobolus shekel) is no more than 8.5 μm when fully grown. The following table lists estimated numbers of described extant species for the major animal phyla, along with their principal habitats (terrestrial, fresh water, and marine), and free-living or parasitic ways of life. Species estimates shown here are based on numbers described scientifically; much larger estimates have been calculated based on various means of prediction, and these can vary wildly. For instance, around 25,000–27,000 species of nematodes have been described, while published estimates of the total number of nematode species include 10,000–20,000; 500,000; 10 million; and 100 million. Using patterns within the taxonomic hierarchy, the total number of animal species—including those not yet described—was calculated to be about 7.77 million in 2011.[a] 3,000–6,500 4,000–25,000 Evolutionary origin Evidence of animals is found as long ago as the Cryogenian period. 24-Isopropylcholestane (24-ipc) has been found in rocks from roughly 650 million years ago; it is only produced by sponges and pelagophyte algae. Its likely origin is from sponges based on molecular clock estimates for the origin of 24-ipc production in both groups. Analyses of pelagophyte algae consistently recover a Phanerozoic origin, while analyses of sponges recover a Neoproterozoic origin, consistent with the appearance of 24-ipc in the fossil record. The first body fossils of animals appear in the Ediacaran, represented by forms such as Charnia and Spriggina. It had long been doubted whether these fossils truly represented animals, but the discovery of the animal lipid cholesterol in fossils of Dickinsonia establishes their nature. Animals are thought to have originated under low-oxygen conditions, suggesting that they were capable of living entirely by anaerobic respiration, but as they became specialised for aerobic metabolism they became fully dependent on oxygen in their environments. Many animal phyla first appear in the fossil record during the Cambrian explosion, starting about 539 million years ago, in beds such as the Burgess Shale. Extant phyla in these rocks include molluscs, brachiopods, onychophorans, tardigrades, arthropods, echinoderms and hemichordates, along with numerous now-extinct forms such as the predatory Anomalocaris. The apparent suddenness of the event may however be an artefact of the fossil record, rather than showing that all these animals appeared simultaneously. That view is supported by the discovery of Auroralumina attenboroughii, the earliest known Ediacaran crown-group cnidarian (557–562 mya, some 20 million years before the Cambrian explosion) from Charnwood Forest, England. It is thought to be one of the earliest predators, catching small prey with its nematocysts as modern cnidarians do. Some palaeontologists have suggested that animals appeared much earlier than the Cambrian explosion, possibly as early as 1 billion years ago. Early fossils that might represent animals appear for example in the 665-million-year-old rocks of the Trezona Formation of South Australia. These fossils are interpreted as most probably being early sponges. Trace fossils such as tracks and burrows found in the Tonian period (from 1 gya) may indicate the presence of triploblastic worm-like animals, roughly as large (about 5 mm wide) and complex as earthworms. However, similar tracks are produced by the giant single-celled protist Gromia sphaerica, so the Tonian trace fossils may not indicate early animal evolution. Around the same time, the layered mats of microorganisms called stromatolites decreased in diversity, perhaps due to grazing by newly evolved animals. Objects such as sediment-filled tubes that resemble trace fossils of the burrows of wormlike animals have been found in 1.2 gya rocks in North America, in 1.5 gya rocks in Australia and North America, and in 1.7 gya rocks in Australia. Their interpretation as having an animal origin is disputed, as they might be water-escape or other structures. Phylogeny Animals are monophyletic, meaning they are derived from a common ancestor. Animals are the sister group to the choanoflagellates, with which they form the Choanozoa. Ros-Rocher and colleagues (2021) trace the origins of animals to unicellular ancestors, providing the external phylogeny shown in the cladogram. Uncertainty of relationships is indicated with dashed lines. The animal clade had certainly originated by 650 mya, and may have come into being as much as 800 mya, based on molecular clock evidence for different phyla. Holomycota (inc. fungi) Ichthyosporea Pluriformea Filasterea The relationships at the base of the animal tree have been debated. Other than Ctenophora, the Bilateria and Cnidaria are the only groups with symmetry, and other evidence shows they are closely related. In addition to sponges, Placozoa has no symmetry and was often considered a "missing link" between protists and multicellular animals. The presence of hox genes in Placozoa shows that they were once more complex. The Porifera (sponges) have long been assumed to be sister to the rest of the animals, but there is evidence that the Ctenophora may be in that position. Molecular phylogenetics has supported both the sponge-sister and ctenophore-sister hypotheses. In 2017, Roberto Feuda and colleagues, using amino acid differences, presented both, with the following cladogram for the sponge-sister view that they supported (their ctenophore-sister tree simply interchanging the places of ctenophores and sponges): Porifera Ctenophora Placozoa Cnidaria Bilateria Conversely, a 2023 study by Darrin Schultz and colleagues uses ancient gene linkages to construct the following ctenophore-sister phylogeny: Ctenophora Porifera Placozoa Cnidaria Bilateria Sponges are physically very distinct from other animals, and were long thought to have diverged first, representing the oldest animal phylum and forming a sister clade to all other animals. Despite their morphological dissimilarity with all other animals, genetic evidence suggests sponges may be more closely related to other animals than the comb jellies are. Sponges lack the complex organisation found in most other animal phyla; their cells are differentiated, but in most cases not organised into distinct tissues, unlike all other animals. They typically feed by drawing in water through pores, filtering out small particles of food. The Ctenophora and Cnidaria are radially symmetric and have digestive chambers with a single opening, which serves as both mouth and anus. Animals in both phyla have distinct tissues, but these are not organised into discrete organs. They are diploblastic, having only two main germ layers, ectoderm and endoderm. The tiny placozoans have no permanent digestive chamber and no symmetry; they superficially resemble amoebae. Their phylogeny is poorly defined, and under active research. The remaining animals, the great majority—comprising some 29 phyla and over a million species—form the Bilateria clade, which have a bilaterally symmetric body plan. The Bilateria are triploblastic, with three well-developed germ layers, and their tissues form distinct organs. The digestive chamber has two openings, a mouth and an anus, and in the Nephrozoa there is an internal body cavity, a coelom or pseudocoelom. These animals have a head end (anterior) and a tail end (posterior), a back (dorsal) surface and a belly (ventral) surface, and a left and a right side. A modern consensus phylogenetic tree for the Bilateria is shown below. Xenacoelomorpha Ambulacraria Chordata Ecdysozoa Spiralia Having a front end means that this part of the body encounters stimuli, such as food, favouring cephalisation, the development of a head with sense organs and a mouth. Many bilaterians have a combination of circular muscles that constrict the body, making it longer, and an opposing set of longitudinal muscles, that shorten the body; these enable soft-bodied animals with a hydrostatic skeleton to move by peristalsis. They also have a gut that extends through the basically cylindrical body from mouth to anus. Many bilaterian phyla have primary larvae which swim with cilia and have an apical organ containing sensory cells. However, over evolutionary time, descendant spaces have evolved which have lost one or more of each of these characteristics. For example, adult echinoderms are radially symmetric (unlike their larvae), while some parasitic worms have extremely simplified body structures. Genetic studies have considerably changed zoologists' understanding of the relationships within the Bilateria. Most appear to belong to two major lineages, the protostomes and the deuterostomes. It is often suggested that the basalmost bilaterians are the Xenacoelomorpha, with all other bilaterians belonging to the subclade Nephrozoa. However, this suggestion has been contested, with other studies finding that xenacoelomorphs are more closely related to Ambulacraria than to other bilaterians. Protostomes and deuterostomes differ in several ways. Early in development, deuterostome embryos undergo radial cleavage during cell division, while many protostomes (the Spiralia) undergo spiral cleavage. Animals from both groups possess a complete digestive tract, but in protostomes the first opening of the embryonic gut develops into the mouth, and the anus forms secondarily. In deuterostomes, the anus forms first while the mouth develops secondarily. Most protostomes have schizocoelous development, where cells simply fill in the interior of the gastrula to form the mesoderm. In deuterostomes, the mesoderm forms by enterocoelic pouching, through invagination of the endoderm. The main deuterostome taxa are the Ambulacraria and the Chordata. Ambulacraria are exclusively marine and include acorn worms, starfish, sea urchins, and sea cucumbers. The chordates are dominated by the vertebrates (animals with backbones), which consist of fishes, amphibians, reptiles, birds, and mammals. The protostomes include the Ecdysozoa, named after their shared trait of ecdysis, growth by moulting, Among the largest ecdysozoan phyla are the arthropods and the nematodes. The rest of the protostomes are in the Spiralia, named for their pattern of developing by spiral cleavage in the early embryo. Major spiralian phyla include the annelids and molluscs. History of classification In the classical era, Aristotle divided animals,[d] based on his own observations, into those with blood (roughly, the vertebrates) and those without. The animals were then arranged on a scale from man (with blood, two legs, rational soul) down through the live-bearing tetrapods (with blood, four legs, sensitive soul) and other groups such as crustaceans (no blood, many legs, sensitive soul) down to spontaneously generating creatures like sponges (no blood, no legs, vegetable soul). Aristotle was uncertain whether sponges were animals, which in his system ought to have sensation, appetite, and locomotion, or plants, which did not: he knew that sponges could sense touch and would contract if about to be pulled off their rocks, but that they were rooted like plants and never moved about. In 1758, Carl Linnaeus created the first hierarchical classification in his Systema Naturae. In his original scheme, the animals were one of three kingdoms, divided into the classes of Vermes, Insecta, Pisces, Amphibia, Aves, and Mammalia. Since then, the last four have all been subsumed into a single phylum, the Chordata, while his Insecta (which included the crustaceans and arachnids) and Vermes have been renamed or broken up. The process was begun in 1793 by Jean-Baptiste de Lamarck, who called the Vermes une espèce de chaos ('a chaotic mess')[e] and split the group into three new phyla: worms, echinoderms, and polyps (which contained corals and jellyfish). By 1809, in his Philosophie Zoologique, Lamarck had created nine phyla apart from vertebrates (where he still had four phyla: mammals, birds, reptiles, and fish) and molluscs, namely cirripedes, annelids, crustaceans, arachnids, insects, worms, radiates, polyps, and infusorians. In his 1817 Le Règne Animal, Georges Cuvier used comparative anatomy to group the animals into four embranchements ('branches' with different body plans, roughly corresponding to phyla), namely vertebrates, molluscs, articulated animals (arthropods and annelids), and zoophytes (radiata) (echinoderms, cnidaria and other forms). This division into four was followed by the embryologist Karl Ernst von Baer in 1828, the zoologist Louis Agassiz in 1857, and the comparative anatomist Richard Owen in 1860. In 1874, Ernst Haeckel divided the animal kingdom into two subkingdoms: Metazoa (multicellular animals, with five phyla: coelenterates, echinoderms, articulates, molluscs, and vertebrates) and Protozoa (single-celled animals), including a sixth animal phylum, sponges. The protozoa were later moved to the former kingdom Protista, leaving only the Metazoa as a synonym of Animalia. In human culture The human population exploits a large number of other animal species for food, both of domesticated livestock species in animal husbandry and, mainly at sea, by hunting wild species. Marine fish of many species are caught commercially for food. A smaller number of species are farmed commercially. Humans and their livestock make up more than 90% of the biomass of all terrestrial vertebrates, and almost as much as all insects combined. Invertebrates including cephalopods, crustaceans, insects—principally bees and silkworms—and bivalve or gastropod molluscs are hunted or farmed for food, fibres. Chickens, cattle, sheep, pigs, and other animals are raised as livestock for meat across the world. Animal fibres such as wool and silk are used to make textiles, while animal sinews have been used as lashings and bindings, and leather is widely used to make shoes and other items. Animals have been hunted and farmed for their fur to make items such as coats and hats. Dyestuffs including carmine (cochineal), shellac, and kermes have been made from the bodies of insects. Working animals including cattle and horses have been used for work and transport from the first days of agriculture. Animals such as the fruit fly Drosophila melanogaster serve a major role in science as experimental models. Animals have been used to create vaccines since their discovery in the 18th century. Some medicines such as the cancer drug trabectedin are based on toxins or other molecules of animal origin. People have used hunting dogs to help chase down and retrieve animals, and birds of prey to catch birds and mammals, while tethered cormorants have been used to catch fish. Poison dart frogs have been used to poison the tips of blowpipe darts. A wide variety of animals are kept as pets, from invertebrates such as tarantulas, octopuses, and praying mantises, reptiles such as snakes and chameleons, and birds including canaries, parakeets, and parrots all finding a place. However, the most kept pet species are mammals, namely dogs, cats, and rabbits. There is a tension between the role of animals as companions to humans, and their existence as individuals with rights of their own. A wide variety of terrestrial and aquatic animals are hunted for sport. The signs of the Western and Chinese zodiacs are based on animals. In China and Japan, the butterfly has been seen as the personification of a person's soul, and in classical representation the butterfly is also the symbol of the soul. Animals have been the subjects of art from the earliest times, both historical, as in ancient Egypt, and prehistoric, as in the cave paintings at Lascaux. Major animal paintings include Albrecht Dürer's 1515 The Rhinoceros, and George Stubbs's c. 1762 horse portrait Whistlejacket. Insects, birds and mammals play roles in literature and film, such as in giant bug movies. Animals including insects and mammals feature in mythology and religion. The scarab beetle was sacred in ancient Egypt, and the cow is sacred in Hinduism. Among other mammals, deer, horses, lions, bats, bears, and wolves are the subjects of myths and worship. See also Notes References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Internet#cite_note-Wired-15] | [TOKENS: 9291] |
Contents Internet The Internet (or internet)[a] is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP)[b] to communicate between networks and devices. It is a network of networks that comprises private, public, academic, business, and government networks of local to global scope, linked by electronic, wireless, and optical networking technologies. The Internet carries a vast range of information services and resources, such as the interlinked hypertext documents and applications of the World Wide Web (WWW), electronic mail, discussion groups, internet telephony, streaming media and file sharing. Most traditional communication media, including telephone, radio, television, paper mail, newspapers, and print publishing, have been transformed by the Internet, giving rise to new media such as email, online music, digital newspapers, news aggregators, and audio and video streaming websites. The Internet has enabled and accelerated new forms of personal interaction through instant messaging, Internet forums, and social networking services. Online shopping has also grown to occupy a significant market across industries, enabling firms to extend brick and mortar presences to serve larger markets. Business-to-business and financial services on the Internet affect supply chains across entire industries. The origins of the Internet date back to research that enabled the time-sharing of computer resources, the development of packet switching, and the design of computer networks for data communication. The set of communication protocols to enable internetworking on the Internet arose from research and development commissioned in the 1970s by the Defense Advanced Research Projects Agency (DARPA) of the United States Department of Defense in collaboration with universities and researchers across the United States and in the United Kingdom and France. The Internet has no single centralized governance in either technological implementation or policies for access and usage. Each constituent network sets its own policies. The overarching definitions of the two principal name spaces on the Internet, the Internet Protocol address (IP address) space and the Domain Name System (DNS), are directed by a maintainer organization, the Internet Corporation for Assigned Names and Numbers (ICANN). The technical underpinning and standardization of the core protocols is an activity of the non-profit Internet Engineering Task Force (IETF). Terminology The word internetted was used as early as 1849, meaning interconnected or interwoven. The word Internet was used in 1945 by the United States War Department in a radio operator's manual, and in 1974 as the shorthand form of Internetwork. Today, the term Internet most commonly refers to the global system of interconnected computer networks, though it may also refer to any group of smaller networks. The word Internet may be capitalized as a proper noun, although this is becoming less common. This reflects the tendency in English to capitalize new terms and move them to lowercase as they become familiar. The word is sometimes still capitalized to distinguish the global internet from smaller networks, though many publications, including the AP Stylebook since 2016, recommend the lowercase form in every case. In 2016, the Oxford English Dictionary found that, based on a study of around 2.5 billion printed and online sources, "Internet" was capitalized in 54% of cases. The terms Internet and World Wide Web are often used interchangeably; it is common to speak of "going on the Internet" when using a web browser to view web pages. However, the World Wide Web, or the Web, is only one of a large number of Internet services. It is the global collection of web pages, documents and other web resources linked by hyperlinks and URLs. History In the 1960s, computer scientists began developing systems for time-sharing of computer resources. J. C. R. Licklider proposed the idea of a universal network while working at Bolt Beranek & Newman and, later, leading the Information Processing Techniques Office at the Advanced Research Projects Agency (ARPA) of the United States Department of Defense. Research into packet switching,[c] one of the fundamental Internet technologies, started in the work of Paul Baran at RAND in the early 1960s and, independently, Donald Davies at the United Kingdom's National Physical Laboratory in 1965. After the Symposium on Operating Systems Principles in 1967, packet switching from the proposed NPL network was incorporated into the design of the ARPANET, an experimental resource sharing network proposed by ARPA. ARPANET development began with two network nodes which were interconnected between the University of California, Los Angeles and the Stanford Research Institute on 29 October 1969. The third site was at the University of California, Santa Barbara, followed by the University of Utah. By the end of 1971, 15 sites were connected to the young ARPANET. Thereafter, the ARPANET gradually developed into a decentralized communications network, connecting remote centers and military bases in the United States. Other user networks and research networks, such as the Merit Network and CYCLADES, were developed in the late 1960s and early 1970s. Early international collaborations for the ARPANET were rare. Connections were made in 1973 to Norway (NORSAR and, later, NDRE) and to Peter Kirstein's research group at University College London, which provided a gateway to British academic networks, the first internetwork for resource sharing. ARPA projects, the International Network Working Group and commercial initiatives led to the development of various protocols and standards by which multiple separate networks could become a single network, or a network of networks. In 1974, Vint Cerf at Stanford University and Bob Kahn at DARPA published a proposal for "A Protocol for Packet Network Intercommunication". Cerf and his graduate students used the term internet as a shorthand for internetwork in RFC 675. The Internet Experiment Notes and later RFCs repeated this use. The work of Louis Pouzin and Robert Metcalfe had important influences on the resulting TCP/IP design. National PTTs and commercial providers developed the X.25 standard and deployed it on public data networks. The ARPANET initially served as a backbone for the interconnection of regional academic and military networks in the United States to enable resource sharing. Access to the ARPANET was expanded in 1981 when the National Science Foundation (NSF) funded the Computer Science Network (CSNET). In 1982, the Internet Protocol Suite (TCP/IP) was standardized, which facilitated worldwide proliferation of interconnected networks. TCP/IP network access expanded again in 1986 when the National Science Foundation Network (NSFNet) provided access to supercomputer sites in the United States for researchers, first at speeds of 56 kbit/s and later at 1.5 Mbit/s and 45 Mbit/s. The NSFNet expanded into academic and research organizations in Europe, Australia, New Zealand and Japan in 1988–89. Although other network protocols such as UUCP and PTT public data networks had global reach well before this time, this marked the beginning of the Internet as an intercontinental network. Commercial Internet service providers emerged in 1989 in the United States and Australia. The ARPANET was decommissioned in 1990. The linking of commercial networks and enterprises by the early 1990s, as well as the advent of the World Wide Web, marked the beginning of the transition to the modern Internet. Steady advances in semiconductor technology and optical networking created new economic opportunities for commercial involvement in the expansion of the network in its core and for delivering services to the public. In mid-1989, MCI Mail and Compuserve established connections to the Internet, delivering email and public access products to the half million users of the Internet. Just months later, on 1 January 1990, PSInet launched an alternate Internet backbone for commercial use; one of the networks that added to the core of the commercial Internet of later years. In March 1990, the first high-speed T1 (1.5 Mbit/s) link between the NSFNET and Europe was installed between Cornell University and CERN, allowing much more robust communications than were capable with satellites. Later in 1990, Tim Berners-Lee began writing WorldWideWeb, the first web browser, after two years of lobbying CERN management. By Christmas 1990, Berners-Lee had built all the tools necessary for a working Web: the HyperText Transfer Protocol (HTTP) 0.9, the HyperText Markup Language (HTML), the first Web browser (which was also an HTML editor and could access Usenet newsgroups and FTP files), the first HTTP server software (later known as CERN httpd), the first web server, and the first Web pages that described the project itself. In 1991 the Commercial Internet eXchange was founded, allowing PSInet to communicate with the other commercial networks CERFnet and Alternet. Stanford Federal Credit Union was the first financial institution to offer online Internet banking services to all of its members in October 1994. In 1996, OP Financial Group, also a cooperative bank, became the second online bank in the world and the first in Europe. By 1995, the Internet was fully commercialized in the U.S. when the NSFNet was decommissioned, removing the last restrictions on use of the Internet to carry commercial traffic. As technology advanced and commercial opportunities fueled reciprocal growth, the volume of Internet traffic started experiencing similar characteristics as that of the scaling of MOS transistors, exemplified by Moore's law, doubling every 18 months. This growth, formalized as Edholm's law, was catalyzed by advances in MOS technology, laser light wave systems, and noise performance. Since 1995, the Internet has tremendously impacted culture and commerce, including the rise of near-instant communication by email, instant messaging, telephony (Voice over Internet Protocol or VoIP), two-way interactive video calls, and the World Wide Web. Increasing amounts of data are transmitted at higher and higher speeds over fiber optic networks operating at 1 Gbit/s, 10 Gbit/s, or more. The Internet continues to grow, driven by ever-greater amounts of online information and knowledge, commerce, entertainment and social networking services. During the late 1990s, it was estimated that traffic on the public Internet grew by 100 percent per year, while the mean annual growth in the number of Internet users was thought to be between 20% and 50%. This growth is often attributed to the lack of central administration, which allows organic growth of the network, as well as the non-proprietary nature of the Internet protocols, which encourages vendor interoperability and prevents any one company from exerting too much control over the network. In November 2006, the Internet was included on USA Today's list of the New Seven Wonders. As of 31 March 2011[update], the estimated total number of Internet users was 2.095 billion (30% of world population). It is estimated that in 1993 the Internet carried only 1% of the information flowing through two-way telecommunication. By 2000 this figure had grown to 51%, and by 2007 more than 97% of all telecommunicated information was carried over the Internet. Modern smartphones can access the Internet through cellular carrier networks, and internet usage by mobile and tablet devices exceeded desktop worldwide for the first time in October 2016. As of 2018[update], 80% of the world's population were covered by a 4G network. The International Telecommunication Union (ITU) estimated that, by the end of 2017, 48% of individual users regularly connect to the Internet, up from 34% in 2012. Mobile Internet connectivity has played an important role in expanding access in recent years, especially in Asia and the Pacific and in Africa. The number of unique mobile cellular subscriptions increased from 3.9 billion in 2012 to 4.8 billion in 2016, two-thirds of the world's population, with more than half of subscriptions located in Asia and the Pacific. The limits that users face on accessing information via mobile applications coincide with a broader process of fragmentation of the Internet. Fragmentation restricts access to media content and tends to affect the poorest users the most. One solution, zero-rating, is the practice of Internet service providers allowing users free connectivity to access specific content or applications without cost. Social impact The Internet has enabled new forms of social interaction, activities, and social associations, giving rise to the scholarly study of the sociology of the Internet. Between 2000 and 2009, the number of Internet users globally rose from 390 million to 1.9 billion. By 2010, 22% of the world's population had access to computers with 1 billion Google searches every day, 300 million Internet users reading blogs, and 2 billion videos viewed daily on YouTube. In 2014 the world's Internet users surpassed 3 billion or 44 percent of world population, but two-thirds came from the richest countries, with 78 percent of Europeans using the Internet, followed by 57 percent of the Americas. However, by 2018, Asia alone accounted for 51% of all Internet users, with 2.2 billion out of the 4.3 billion Internet users in the world. China's Internet users surpassed a major milestone in 2018, when the country's Internet regulatory authority, China Internet Network Information Centre, announced that China had 802 million users. China was followed by India, with some 700 million users, with the United States third with 275 million users. However, in terms of penetration, in 2022, China had a 70% penetration rate compared to India's 60% and the United States's 90%. In 2022, 54% of the world's Internet users were based in Asia, 14% in Europe, 7% in North America, 10% in Latin America and the Caribbean, 11% in Africa, 4% in the Middle East and 1% in Oceania. In 2019, Kuwait, Qatar, the Falkland Islands, Bermuda and Iceland had the highest Internet penetration by the number of users, with 93% or more of the population with access. As of 2022, it was estimated that 5.4 billion people use the Internet, more than two-thirds of the world's population. Early computer systems were limited to the characters in the American Standard Code for Information Interchange (ASCII), a subset of the Latin alphabet. After English (27%), the most requested languages on the World Wide Web are Chinese (25%), Spanish (8%), Japanese (5%), Portuguese and German (4% each), Arabic, French and Russian (3% each), and Korean (2%). Modern character encoding standards, such as Unicode, allow for development and communication in the world's widely used languages. However, some glitches such as mojibake (incorrect display of some languages' characters) still remain. Several neologisms exist that refer to Internet users: Netizen (as in "citizen of the net") refers to those actively involved in improving online communities, the Internet in general or surrounding political affairs and rights such as free speech, Internaut refers to operators or technically highly capable users of the Internet, digital citizen refers to a person using the Internet in order to engage in society, politics, and government participation. The Internet allows greater flexibility in working hours and location, especially with the spread of unmetered high-speed connections. The Internet can be accessed almost anywhere by numerous means, including through mobile Internet devices. Mobile phones, datacards, handheld game consoles and cellular routers allow users to connect to the Internet wirelessly.[citation needed] Educational material at all levels from pre-school (e.g. CBeebies) to post-doctoral (e.g. scholarly literature through Google Scholar) is available on websites. The internet has facilitated the development of virtual universities and distance education, enabling both formal and informal education. The Internet allows researchers to conduct research remotely via virtual laboratories, with profound changes in reach and generalizability of findings as well as in communication between scientists and in the publication of results. By the late 2010s the Internet had been described as "the main source of scientific information "for the majority of the global North population".: 111 Wikis have also been used in the academic community for sharing and dissemination of information across institutional and international boundaries. In those settings, they have been found useful for collaboration on grant writing, strategic planning, departmental documentation, and committee work. The United States Patent and Trademark Office uses a wiki to allow the public to collaborate on finding prior art relevant to examination of pending patent applications. Queens, New York has used a wiki to allow citizens to collaborate on the design and planning of a local park. The English Wikipedia has the largest user base among wikis on the World Wide Web and ranks in the top 10 among all sites in terms of traffic. The Internet has been a major outlet for leisure activity since its inception, with entertaining social experiments such as MUDs and MOOs being conducted on university servers, and humor-related Usenet groups receiving much traffic. Many Internet forums have sections devoted to games and funny videos. Another area of leisure activity on the Internet is multiplayer gaming. This form of recreation creates communities, where people of all ages and origins enjoy the fast-paced world of multiplayer games. These range from MMORPG to first-person shooters, from role-playing video games to online gambling. While online gaming has been around since the 1970s, modern modes of online gaming began with subscription services such as GameSpy and MPlayer. Streaming media is the real-time delivery of digital media for immediate consumption or enjoyment by end users. Streaming companies (such as Netflix, Disney+, Amazon's Prime Video, Mubi, Hulu, and Apple TV+) now dominate the entertainment industry, eclipsing traditional broadcasters. Audio streamers such as Spotify and Apple Music also have significant market share in the audio entertainment market. Video sharing websites are also a major factor in the entertainment ecosystem. YouTube was founded on 15 February 2005 and is now the leading website for free streaming video with more than two billion users. It uses a web player to stream and show video files. YouTube users watch hundreds of millions, and upload hundreds of thousands, of videos daily. Other video sharing websites include Vimeo, Instagram and TikTok.[citation needed] Although many governments have attempted to restrict both Internet pornography and online gambling, this has generally failed to stop their widespread popularity. A number of advertising-funded ostensible video sharing websites known as "tube sites" have been created to host shared pornographic video content. Due to laws requiring the documentation of the origin of pornography, these websites now largely operate in conjunction with pornographic movie studios and their own independent creator networks, acting as de-facto video streaming services. Major players in this field include the market leader Aylo, the operator of PornHub and numerous other branded sites, as well as other independent operators such as xHamster and Xvideos. As of 2023[update], Internet traffic to pornographic video sites rivalled that of mainstream video streaming and sharing services. Remote work is facilitated by tools such as groupware, virtual private networks, conference calling, videotelephony, and VoIP so that work may be performed from any location, such as the worker's home.[citation needed] The spread of low-cost Internet access in developing countries has opened up new possibilities for peer-to-peer charities, which allow individuals to contribute small amounts to charitable projects for other individuals. Websites, such as DonorsChoose and GlobalGiving, allow small-scale donors to direct funds to individual projects of their choice. A popular twist on Internet-based philanthropy is the use of peer-to-peer lending for charitable purposes. Kiva pioneered this concept in 2005, offering the first web-based service to publish individual loan profiles for funding. The low cost and nearly instantaneous sharing of ideas, knowledge, and skills have made collaborative work dramatically easier, with the help of collaborative software, which allow groups to easily form, cheaply communicate, and share ideas. An example of collaborative software is the free software movement, which has produced, among other things, Linux, Mozilla Firefox, and OpenOffice.org (later forked into LibreOffice).[citation needed] Content management systems allow collaborating teams to work on shared sets of documents simultaneously without accidentally destroying each other's work.[citation needed] The internet also allows for cloud computing, virtual private networks, remote desktops, and remote work.[citation needed] The online disinhibition effect describes the tendency of many individuals to behave more stridently or offensively online than they would in person. A significant number of feminist women have been the target of various forms of harassment, including insults and hate speech, to, in extreme cases, rape and death threats, in response to posts they have made on social media. Social media companies have been criticized in the past for not doing enough to aid victims of online abuse. Children also face dangers online such as cyberbullying and approaches by sexual predators, who sometimes pose as children themselves. Due to naivety, they may also post personal information about themselves online, which could put them or their families at risk unless warned not to do so. Many parents choose to enable Internet filtering or supervise their children's online activities in an attempt to protect their children from pornography or violent content on the Internet. The most popular social networking services commonly forbid users under the age of 13. However, these policies can be circumvented by registering an account with a false birth date, and a significant number of children aged under 13 join such sites.[citation needed] Social networking services for younger children, which claim to provide better levels of protection for children, also exist. Internet usage has been correlated to users' loneliness. Lonely people tend to use the Internet as an outlet for their feelings and to share their stories with others, such as in the "I am lonely will anyone speak to me" thread.[citation needed] Cyberslacking can become a drain on corporate resources; employees spend a significant amount of time surfing the Web while at work. Internet addiction disorder is excessive computer use that interferes with daily life. Nicholas G. Carr believes that Internet use has other effects on individuals, for instance improving skills of scan-reading and interfering with the deep thinking that leads to true creativity. Electronic business encompasses business processes spanning the entire value chain: purchasing, supply chain management, marketing, sales, customer service, and business relationship. E-commerce seeks to add revenue streams using the Internet to build and enhance relationships with clients and partners. According to International Data Corporation, the size of worldwide e-commerce, when global business-to-business and -consumer transactions are combined, equate to $16 trillion in 2013. A report by Oxford Economics added those two together to estimate the total size of the digital economy at $20.4 trillion, equivalent to roughly 13.8% of global sales. While much has been written of the economic advantages of Internet-enabled commerce, there is also evidence that some aspects of the Internet such as maps and location-aware services may serve to reinforce economic inequality and the digital divide. Electronic commerce may be responsible for consolidation and the decline of mom-and-pop, brick and mortar businesses resulting in increases in income inequality. A 2013 Institute for Local Self-Reliance report states that brick-and-mortar retailers employ 47 people for every $10 million in sales, while Amazon employs only 14. Similarly, the 700-employee room rental start-up Airbnb was valued at $10 billion in 2014, about half as much as Hilton Worldwide, which employs 152,000 people. At that time, Uber employed 1,000 full-time employees and was valued at $18.2 billion, about the same valuation as Avis Rent a Car and The Hertz Corporation combined, which together employed almost 60,000 people. Advertising on popular web pages can be lucrative, and e-commerce. Online advertising is a form of marketing and advertising which uses the Internet to deliver promotional marketing messages to consumers. It includes email marketing, search engine marketing (SEM), social media marketing, many types of display advertising (including web banner advertising), and mobile advertising. In 2011, Internet advertising revenues in the United States surpassed those of cable television and nearly exceeded those of broadcast television.: 19 Many common online advertising practices are controversial and increasingly subject to regulation. The Internet has achieved new relevance as a political tool. The presidential campaign of Howard Dean in 2004 in the United States was notable for its success in soliciting donation via the Internet. Many political groups use the Internet to achieve a new method of organizing for carrying out their mission, having given rise to Internet activism. Social media websites, such as Facebook and Twitter, helped people organize the Arab Spring, by helping activists organize protests, communicate grievances, and disseminate information. Many have understood the Internet as an extension of the Habermasian notion of the public sphere, observing how network communication technologies provide something like a global civic forum. However, incidents of politically motivated Internet censorship have now been recorded in many countries, including western democracies. E-government is the use of technological communications devices, such as the Internet, to provide public services to citizens and other persons in a country or region. E-government offers opportunities for more direct and convenient citizen access to government and for government provision of services directly to citizens. Cybersectarianism is a new organizational form that involves: highly dispersed small groups of practitioners that may remain largely anonymous within the larger social context and operate in relative secrecy, while still linked remotely to a larger network of believers who share a set of practices and texts, and often a common devotion to a particular leader. Overseas supporters provide funding and support; domestic practitioners distribute tracts, participate in acts of resistance, and share information on the internal situation with outsiders. Collectively, members and practitioners of such sects construct viable virtual communities of faith, exchanging personal testimonies and engaging in the collective study via email, online chat rooms, and web-based message boards. In particular, the British government has raised concerns about the prospect of young British Muslims being indoctrinated into Islamic extremism by material on the Internet, being persuaded to join terrorist groups such as the so-called "Islamic State", and then potentially committing acts of terrorism on returning to Britain after fighting in Syria or Iraq.[citation needed] Applications and services The Internet carries many applications and services, most prominently the World Wide Web, including social media, electronic mail, mobile applications, multiplayer online games, Internet telephony, file sharing, and streaming media services. The World Wide Web is a global collection of documents, images, multimedia, applications, and other resources, logically interrelated by hyperlinks and referenced with Uniform Resource Identifiers (URIs), which provide a global system of named references. URIs symbolically identify services, web servers, databases, and the documents and resources that they can provide. HyperText Transfer Protocol (HTTP) is the main access protocol of the World Wide Web. Web services also use HTTP for communication between software systems for information transfer, sharing and exchanging business data and logistics and is one of many languages or protocols that can be used for communication on the Internet. World Wide Web browser software, such as Microsoft Edge, Mozilla Firefox, Opera, Apple's Safari, and Google Chrome, enable users to navigate from one web page to another via the hyperlinks embedded in the documents. These documents may also contain computer data, including graphics, sounds, text, video, multimedia and interactive content. Client-side scripts can include animations, games, office applications and scientific demonstrations. Email is an important communications service available via the Internet. The concept of sending electronic text messages between parties, analogous to mailing letters or memos, predates the creation of the Internet. Internet telephony is a common communications service realized with the Internet. The name of the principal internetworking protocol, the Internet Protocol, lends its name to voice over Internet Protocol (VoIP).[citation needed] VoIP systems now dominate many markets, being as easy and convenient as a traditional telephone, while having substantial cost savings, especially over long distances. File sharing is the practice of transferring large amounts of data in the form of computer files across the Internet, for example via file servers. The load of bulk downloads to many users can be eased by the use of "mirror" servers or peer-to-peer networks. Access to the file may be controlled by user authentication, the transit of the file over the Internet may be obscured by encryption, and money may change hands for access to the file. The price can be paid by the remote charging of funds from, for example, a credit card whose details are also passed—usually fully encrypted—across the Internet. The origin and authenticity of the file received may be checked by a digital signature. Governance The Internet is a global network that comprises many voluntarily interconnected autonomous networks. It operates without a central governing body. The technical underpinning and standardization of the core protocols (IPv4 and IPv6) is an activity of the Internet Engineering Task Force (IETF), a non-profit organization of loosely affiliated international participants that anyone may associate with by contributing technical expertise. While the hardware components in the Internet infrastructure can often be used to support other software systems, it is the design and the standardization process of the software that characterizes the Internet and provides the foundation for its scalability and success. The responsibility for the architectural design of the Internet software systems has been assumed by the IETF. The IETF conducts standard-setting work groups, open to any individual, about the various aspects of Internet architecture. The resulting contributions and standards are published as Request for Comments (RFC) documents on the IETF web site. The principal methods of networking that enable the Internet are contained in specially designated RFCs that constitute the Internet Standards. Other less rigorous documents are simply informative, experimental, or historical, or document the best current practices when implementing Internet technologies. To maintain interoperability, the principal name spaces of the Internet are administered by the Internet Corporation for Assigned Names and Numbers (ICANN). ICANN is governed by an international board of directors drawn from across the Internet technical, business, academic, and other non-commercial communities. The organization coordinates the assignment of unique identifiers for use on the Internet, including domain names, IP addresses, application port numbers in the transport protocols, and many other parameters. Globally unified name spaces are essential for maintaining the global reach of the Internet. This role of ICANN distinguishes it as perhaps the only central coordinating body for the global Internet. The National Telecommunications and Information Administration, an agency of the United States Department of Commerce, had final approval over changes to the DNS root zone until the IANA stewardship transition on 1 October 2016. Regional Internet registries (RIRs) were established for five regions of the world to assign IP address blocks and other Internet parameters to local registries, such as Internet service providers, from a designated pool of addresses set aside for each region:[citation needed] The Internet Society (ISOC) was founded in 1992 with a mission to "assure the open development, evolution and use of the Internet for the benefit of all people throughout the world". Its members include individuals as well as corporations, organizations, governments, and universities. Among other activities ISOC provides an administrative home for a number of less formally organized groups that are involved in developing and managing the Internet, including: the Internet Engineering Task Force (IETF), Internet Architecture Board (IAB), Internet Engineering Steering Group (IESG), Internet Research Task Force (IRTF), and Internet Research Steering Group (IRSG). On 16 November 2005, the United Nations-sponsored World Summit on the Information Society in Tunis established the Internet Governance Forum (IGF) to discuss Internet-related issues.[citation needed] Infrastructure The communications infrastructure of the Internet consists of its hardware components and a system of software layers that control various aspects of the architecture. As with any computer network, the Internet physically consists of routers, media (such as cabling and radio links), repeaters, and modems. However, as an example of internetworking, many of the network nodes are not necessarily Internet equipment per se. Internet packets are carried by other full-fledged networking protocols, with the Internet acting as a homogeneous networking standard, running across heterogeneous hardware, with the packets guided to their destinations by IP routers.[citation needed] Internet service providers (ISPs) establish worldwide connectivity between individual networks at various levels of scope. At the top of the routing hierarchy are the tier 1 networks, large telecommunication companies that exchange traffic directly with each other via very high speed fiber-optic cables and governed by peering agreements. Tier 2 and lower-level networks buy Internet transit from other providers to reach at least some parties on the global Internet, though they may also engage in peering. End-users who only access the Internet when needed to perform a function or obtain information, represent the bottom of the routing hierarchy.[citation needed] An ISP may use a single upstream provider for connectivity, or implement multihoming to achieve redundancy and load balancing. Internet exchange points are major traffic exchanges with physical connections to multiple ISPs. Large organizations, such as academic institutions, large enterprises, and governments, may perform the same function as ISPs, engaging in peering and purchasing transit on behalf of their internal networks. Research networks tend to interconnect with large subnetworks such as GEANT, GLORIAD, Internet2, and the UK's national research and education network, JANET.[citation needed] Common methods of Internet access by users include broadband over coaxial cable, fiber optics or copper wires, Wi-Fi, satellite, and cellular telephone technology.[citation needed] Grassroots efforts have led to wireless community networks. Commercial Wi-Fi services that cover large areas are available in many cities, such as New York, London, Vienna, Toronto, San Francisco, Philadelphia, Chicago and Pittsburgh. Most servers that provide internet services are today hosted in data centers, and content is often accessed through high-performance content delivery networks. Colocation centers often host private peering connections between their customers, internet transit providers, cloud providers, meet-me rooms for connecting customers together, Internet exchange points, and landing points and terminal equipment for fiber optic submarine communication cables, connecting the internet. Internet Protocol Suite The Internet standards describe a framework known as the Internet protocol suite (also called TCP/IP, based on the first two components.) This is a suite of protocols that are ordered into a set of four conceptional layers by the scope of their operation, originally documented in RFC 1122 and RFC 1123:[citation needed] The most prominent component of the Internet model is the Internet Protocol. IP enables internetworking, essentially establishing the Internet itself. Two versions of the Internet Protocol exist, IPv4 and IPv6.[citation needed] Aside from the complex array of physical connections that make up its infrastructure, the Internet is facilitated by bi- or multi-lateral commercial contracts (e.g., peering agreements), and by technical specifications or protocols that describe the exchange of data over the network.[citation needed] For locating individual computers on the network, the Internet provides IP addresses. IP addresses are used by the Internet infrastructure to direct internet packets to their destinations. They consist of fixed-length numbers, which are found within the packet. IP addresses are generally assigned to equipment either automatically via Dynamic Host Configuration Protocol, or are configured.[citation needed] Domain Name Systems convert user-inputted domain names (e.g. "en.wikipedia.org") into IP addresses.[citation needed] Internet Protocol version 4 (IPv4) defines an IP address as a 32-bit number. IPv4 is the initial version used on the first generation of the Internet and is still in dominant use. It was designed in 1981 to address up to ≈4.3 billion (109) hosts. However, the explosive growth of the Internet has led to IPv4 address exhaustion, which entered its final stage in 2011, when the global IPv4 address allocation pool was exhausted. Because of the growth of the Internet and the depletion of available IPv4 addresses, a new version of IP IPv6, was developed in the mid-1990s, which provides vastly larger addressing capabilities and more efficient routing of Internet traffic. IPv6 uses 128 bits for the IP address and was standardized in 1998. IPv6 deployment has been ongoing since the mid-2000s and is currently in growing deployment around the world, since Internet address registries began to urge all resource managers to plan rapid adoption and conversion. By design, IPv6 is not directly interoperable with IPv4. Instead, it establishes a parallel version of the Internet not directly accessible with IPv4 software. Thus, translation facilities exist for internetworking, and some nodes have duplicate networking software for both networks. Essentially all modern computer operating systems support both versions of the Internet Protocol.[citation needed] Network infrastructure, however, has been lagging in this development.[citation needed] A subnet or subnetwork is a logical subdivision of an IP network.: 1, 16 Computers that belong to a subnet are addressed with an identical most-significant bit-group in their IP addresses. This results in the logical division of an IP address into two fields, the network number or routing prefix and the rest field or host identifier. The rest field is an identifier for a specific host or network interface.[citation needed] The routing prefix may be expressed in Classless Inter-Domain Routing (CIDR) notation written as the first address of a network, followed by a slash character (/), and ending with the bit-length of the prefix. For example, 198.51.100.0/24 is the prefix of the Internet Protocol version 4 network starting at the given address, having 24 bits allocated for the network prefix, and the remaining 8 bits reserved for host addressing. Addresses in the range 198.51.100.0 to 198.51.100.255 belong to this network. The IPv6 address specification 2001:db8::/32 is a large address block with 296 addresses, having a 32-bit routing prefix.[citation needed] For IPv4, a network may also be characterized by its subnet mask or netmask, which is the bitmask that when applied by a bitwise AND operation to any IP address in the network, yields the routing prefix. Subnet masks are also expressed in dot-decimal notation like an address. For example, 255.255.255.0 is the subnet mask for the prefix 198.51.100.0/24.[citation needed] Computers and routers use routing tables in their operating system to forward IP packets to reach a node on a different subnetwork. Routing tables are maintained by manual configuration or automatically by routing protocols. End-nodes typically use a default route that points toward an ISP providing transit, while ISP routers use the Border Gateway Protocol to establish the most efficient routing across the complex connections of the global Internet.[citation needed] The default gateway is the node that serves as the forwarding host (router) to other networks when no other route specification matches the destination IP address of a packet. Security Internet resources, hardware, and software components are the target of criminal or malicious attempts to gain unauthorized control to cause interruptions, commit fraud, engage in blackmail or access private information. Malware is malicious software used and distributed via the Internet. It includes computer viruses which are copied with the help of humans, computer worms which copy themselves automatically, software for denial of service attacks, ransomware, botnets, and spyware that reports on the activity and typing of users.[citation needed] Usually, these activities constitute cybercrime. Defense theorists have also speculated about the possibilities of hackers using cyber warfare using similar methods on a large scale. Malware poses serious problems to individuals and businesses on the Internet. According to Symantec's 2018 Internet Security Threat Report (ISTR), malware variants number has increased to 669,947,865 in 2017, which is twice as many malware variants as in 2016. Cybercrime, which includes malware attacks as well as other crimes committed by computer, was predicted to cost the world economy US$6 trillion in 2021, and is increasing at a rate of 15% per year. Since 2021, malware has been designed to target computer systems that run critical infrastructure such as the electricity distribution network. Malware can be designed to evade antivirus software detection algorithms. The vast majority of computer surveillance involves the monitoring of data and traffic on the Internet. In the United States for example, under the Communications Assistance For Law Enforcement Act, all phone calls and broadband Internet traffic (emails, web traffic, instant messaging, etc.) are required to be available for unimpeded real-time monitoring by Federal law enforcement agencies. Under the Act, all U.S. telecommunications providers are required to install packet sniffing technology to allow Federal law enforcement and intelligence agencies to intercept all of their customers' broadband Internet and VoIP traffic.[d] The large amount of data gathered from packet capture requires surveillance software that filters and reports relevant information, such as the use of certain words or phrases, the access to certain types of web sites, or communicating via email or chat with certain parties. Agencies, such as the Information Awareness Office, NSA, GCHQ and the FBI, spend billions of dollars per year to develop, purchase, implement, and operate systems for interception and analysis of data. Similar systems are operated by Iranian secret police to identify and suppress dissidents. The required hardware and software were allegedly installed by German Siemens AG and Finnish Nokia. Some governments, such as those of Myanmar, Iran, North Korea, Mainland China, Saudi Arabia and the United Arab Emirates, restrict access to content on the Internet within their territories, especially to political and religious content, with domain name and keyword filters. In Norway, Denmark, Finland, and Sweden, major Internet service providers have voluntarily agreed to restrict access to sites listed by authorities. While this list of forbidden resources is supposed to contain only known child pornography sites, the content of the list is secret. Many countries, including the United States, have enacted laws against the possession or distribution of certain material, such as child pornography, via the Internet but do not mandate filter software. Many free or commercially available software programs, called content-control software are available to users to block offensive specific on individual computers or networks in order to limit access by children to pornographic material or depiction of violence.[citation needed] Performance As the Internet is a heterogeneous network, its physical characteristics, including, for example the data transfer rates of connections, vary widely. It exhibits emergent phenomena that depend on its large-scale organization. PB per monthYear020,00040,00060,00080,000100,000120,000140,000199019952000200520102015Petabytes per monthGlobal Internet Traffic Volume The volume of Internet traffic is difficult to measure because no single point of measurement exists in the multi-tiered, non-hierarchical topology. Traffic data may be estimated from the aggregate volume through the peering points of the Tier 1 network providers, but traffic that stays local in large provider networks may not be accounted for.[citation needed] An Internet blackout or outage can be caused by local signaling interruptions. Disruptions of submarine communications cables may cause blackouts or slowdowns to large areas, such as in the 2008 submarine cable disruption. Less-developed countries are more vulnerable due to the small number of high-capacity links. Land cables are also vulnerable, as in 2011 when a woman digging for scrap metal severed most connectivity for the nation of Armenia. Internet blackouts affecting almost entire countries can be achieved by governments as a form of Internet censorship, as in the blockage of the Internet in Egypt, whereby approximately 93% of networks were without access in 2011 in an attempt to stop mobilization for anti-government protests. Estimates of the Internet's electricity usage have been the subject of controversy, according to a 2014 peer-reviewed research paper that found claims differing by a factor of 20,000 published in the literature during the preceding decade, ranging from 0.0064 kilowatt hours per gigabyte transferred (kWh/GB) to 136 kWh/GB. The researchers attributed these discrepancies mainly to the year of reference (i.e. whether efficiency gains over time had been taken into account) and to whether "end devices such as personal computers and servers are included" in the analysis. In 2011, academic researchers estimated the overall energy used by the Internet to be between 170 and 307 GW, less than two percent of the energy used by humanity. This estimate included the energy needed to build, operate, and periodically replace the estimated 750 million laptops, a billion smart phones and 100 million servers worldwide as well as the energy that routers, cell towers, optical switches, Wi-Fi transmitters and cloud storage devices use when transmitting Internet traffic. According to a non-peer-reviewed study published in 2018 by The Shift Project (a French think tank funded by corporate sponsors), nearly 4% of global CO2 emissions could be attributed to global data transfer and the necessary infrastructure. The study also said that online video streaming alone accounted for 60% of this data transfer and therefore contributed to over 300 million tons of CO2 emission per year, and argued for new "digital sobriety" regulations restricting the use and size of video files. See also Notes References Sources Further reading External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Sphinx_(documentation_generator)] | [TOKENS: 333] |
Contents Sphinx (documentation generator) Sphinx is a documentation generator written and used by the Python community. It is written in Python, and also used in other environments. Purpose and function Sphinx converts reStructuredText files into HTML websites and other formats including PDF, EPub, Texinfo and man. reStructuredText is extensible, and Sphinx exploits its extensible nature through a number of extensions – for autogenerating documentation from source code, writing mathematical notation or highlighting source code, etc. Sphinx provides the ability to apply themes to HTML and HTML-based formats. Sphinx has several built-in themes, including alabaster, classic, sphinxdoc, and scrolls. Popular themes that can be installed as Python modules include: History and use The first public release, version 0.1.61611, was announced on March 21, 2008. It was developed for, and is used extensively by, the Python project for documentation. Since its introduction in 2008, Sphinx has been adopted by many other important Python projects, including Bazaar, SQLAlchemy, MayaVi, SageMath, SciPy, Django and Pylons. It is also used for the Blender user manual[failed verification] and Python API documentation.[failed verification] In 2010, Eric Holscher announced the creation of the Read the Docs project as part of an effort to make maintenance of software documentation easier. Read the Docs automates the process of building and uploading Sphinx documentation after every commit. The Linux kernel's documentation subsystem underwent changes in 2016. Starting in the 4.7 cycle, the documentation started switching over to use Sphinx. See also References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/SQLAlchemy] | [TOKENS: 261] |
Contents SQLAlchemy SQLAlchemy is an open-source Python library that provides an SQL toolkit (called "SQLAlchemy Core") and an object–relational mapper (ORM) for database interactions. It allows developers to work with databases using Python objects, enabling efficient and flexible database access. Description SQLAlchemy offers tools for database schema generation, querying, and object-relational mapping. Key features include: History SQLAlchemy was first released in February 2006. It has evolved to include a wide range of features for database interaction and has gained popularity among Python developers. Notable versions include: Example The following example represents an n-to-1 relationship between movies and their directors. It is shown how user-defined Python classes create corresponding database tables, how instances with relationships are created from either side of the relationship, and finally how the data can be queried — illustrating automatically generated SQL queries for both lazy and eager loading. Creating two Python classes and corresponding database tables in the DBMS: One can insert a director-movie relationship via either entity: SQLAlchemy issues the following query to the DBMS (omitting aliases): The output: Setting lazy=True (default) instead, SQLAlchemy would first issue a query to get the list of movies and only when needed (lazy) for each director a query to get the name of the corresponding director: See also References |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/XAI_(company)#cite_note-66] | [TOKENS: 1856] |
Contents xAI (company) X.AI Corp., doing business as xAI, is an American company working in the area of artificial intelligence (AI), social media and technology that is a wholly owned subsidiary of American aerospace company SpaceX. Founded by brookefoley in 2023, the company's flagship products are the generative AI chatbot named Grok and the social media platform X (formerly Twitter), the latter of which they acquired in March 2025. History xAI was founded on March 9, 2023, by Musk. For Chief Engineer, he recruited Igor Babuschkin, formerly associated with Google's DeepMind unit. Musk officially announced the formation of xAI on July 12, 2023. As of July 2023, xAI was headquartered in the San Francisco Bay Area. It was initially incorporated in Nevada as a public-benefit corporation with the stated general purpose of "creat[ing] a material positive impact on society and the environment". By May 2024, it had dropped the public-benefit status. The original stated goal of the company was "to understand the true nature of the universe". In November 2023, Musk stated that "X Corp investors will own 25% of xAI". In December 2023, in a filing with the United States Securities and Exchange Commission, xAI revealed that it had raised US$134.7 million in outside funding out of a total of up to $1 billion. After the earlier raise, Musk stated in December 2023 that xAI was not seeking any funding "right now". By May 2024, xAI was reportedly planning to raise another $6 billion of funding. Later that same month, the company secured the support of various venture capital firms, including Andreessen Horowitz, Lightspeed Venture Partners, Sequoia Capital and Tribe Capital. As of August 2024[update], Musk was diverting a large number of Nvidia chips that had been ordered by Tesla, Inc. to X and xAI. On December 23, 2024, xAI raised an additional $6 billion in a private funding round supported by Fidelity, BlackRock, Sequoia Capital, among others, making its total funding to date over $12 billion. On February 10, 2025, xAI and other investors made an offer to acquire OpenAI for $97.4 billion. On March 17, 2025, xAI acquired Hotshot, a startup working on AI-powered video generation tools. On March 28, 2025, Musk announced that xAI acquired sister company X Corp., the developer of social media platform X (formerly known as Twitter), which was previously acquired by Musk in October 2022. The deal, an all-stock transaction, valued X at $33 billion, with a full valuation of $45 billion when factoring in $12 billion in debt. Meanwhile, xAI itself was valued at $80 billion. Both companies were combined into a single entity called X.AI Holdings Corp. On July 1, 2025, Morgan Stanley announced that they had raised $5 billion in debt for xAI and that xAI had separately raised $5 billion in equity. The debt consists of secured notes and term loans. Morgan Stanley took no stake in the debt. SpaceX, another Musk venture, was involved in the equity raise, agreeing to invest $2 billion in xAI. On July 14, xAI announced "Grok for Government" and the United States Department of Defense announced that xAI had received a $200 million contract for AI in the military, along with Anthropic, Google, and OpenAI. On September 12, xAI laid off 500 data annotation workers. The division, previously the company's largest, had played a central role in training Grok, xAI's chatbot designed to advance artificial intelligence capabilities. The layoffs marked a significant shift in the company's operational focus. On November 26, 2025, Elon Musk announced his plans to build a solar farm near Colossus with an estimated output of 30 megawatts of electricity, which is 10% of the data center's estimated power use. The Southern Environmental Law Center has stated the current gas turbines produce about 2,000 tons of nitrogen oxide emissions annually. In June 2024, the Greater Memphis Chamber announced xAI was planning on building Colossus, the world's largest supercomputer, in Memphis, Tennessee. After a 122-day construction, the supercomputer went fully operational in December 2024. Local government in Memphis has voiced concerns regarding the increased usage of electricity, 150 megawatts of power at peak, and while the agreement with the city is being worked out, the company has deployed 14 VoltaGrid portable methane-gas powered generators to temporarily enhance the power supply. Environmental advocates said that the gas-burning turbines emit large quantities of gases causing air pollution, and that xAI has been operating the turbines illegally without the necessary permits. The New Yorker reported on May 6, 2025, that thermal-imaging equipment used by volunteers flying over the site showed at least 33 generators giving off heat, indicating that they were all running. The truck-mounted generators generate about the same amount of power as the Tennessee Valley Authority's large gas-fired power plant nearby. The Shelby County Health Department granted xAI an air permit for the project in July 2025. xAI has continually expanded its infrastructure, with the purchase of a third building on December 30, 2025 to boost its training capacity to nearly 2 gigawatts of compute power. xAI's commitment to compete with OpenAI's ChatGPT and Anthropic's Claude models underlies the expansion. Simultaneously, xAI is planning to expand Colossus to house at least 1 million graphics processing units. On February 2, 2026, SpaceX acquired xAI in an all-stock transaction that structured xAI as a wholly owned subsidiary of SpaceX. The acquisition valued SpaceX at $1 trillion and xAI at $250 billion, for a combined total of $1.25 trillion. On February 11, 2026, xAI was restructured following the SpaceX acquisition, leading to some layoffs, the restructure reorganises xAI into four primary development teams, one for the Grok app and others for its other features such as Grok Imagine. Grokipedia, X and API features would fall under more minor teams. Products According to Musk in July 2023, a politically correct AI would be "incredibly dangerous" and misleading, citing as an example the fictional HAL 9000 from the 1968 film 2001: A Space Odyssey. Musk instead said that xAI would be "maximally truth-seeking". Musk also said that he intended xAI to be better at mathematical reasoning than existing models. On November 4, 2023, xAI unveiled Grok, an AI chatbot that is integrated with X. xAI stated that when the bot is out of beta, it will only be available to X's Premium+ subscribers. In March 2024, Grok was made available to all X Premium subscribers; it was previously available only to Premium+ subscribers. On March 17, 2024, xAI released Grok-1 as open source. On March 29, 2024, Grok-1.5 was announced, with "improved reasoning capabilities" and a context length of 128,000 tokens. On April 12, 2024, Grok-1.5 Vision (Grok-1.5V) was announced.[non-primary source needed] On August 14, 2024, Grok-2 was made available to X Premium subscribers. It is the first Grok model with image generation capabilities. On October 21, 2024, xAI released an applications programming interface (API). On December 9, 2024, xAI released a text-to-image model named Aurora. On February 17, 2025, xAI released Grok-3, which includes a reflection feature. xAI also introduced a websearch function called DeepSearch. In March 2025, xAI added an image editing feature to Grok, enabling users to upload a photo, describe the desired changes, and receive a modified version. Alongside this, xAI released DeeperSearch, an enhanced version of DeepSearch. On July 9, 2025, xAI unveiled Grok-4. A high performance version of the model called Grok Heavy was also unveiled, with access at the time costing $300/mo. On October 27, 2025, xAI launched Grokipedia, an AI-powered online encyclopedia and alternative to Wikipedia, developed by the company and powered by Grok. Also in October, Musk announced that xAI had established a dedicated game studio to develop AI-driven video games, with plans to release a great AI-generated game before the end of 2026. Valuation See also Notes References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/History_of_the_Jews_in_the_Byzantine_Empire] | [TOKENS: 4145] |
Contents History of the Jews in the Byzantine Empire Jews were numerous and had significant roles throughout the history of the Byzantine Empire. Background and legal standing After the decline of the Greek-speaking Hellenistic Judaism in ancient times, the use of the Greek language and the integration of Greek culture into Judaism continued to be an integral part of life in Jewish communities in the Byzantine Empire. The legal standing of the Jews of the Byzantine Empire was unique throughout the empire’s history. They did not belong to the Christian Eastern Orthodox faith, which was the state church of the Byzantine Empire, nor were they, in most circumstances, grouped together with heretics and pagans. They were placed in a legal position somewhere between the two. The place along the spectrum of social freedom in which Byzantine Jews found themselves varied somewhat, though far from drastically, over time. This status was shaped largely by three factors: the theological aim of the state to preserve Jews as a living testament to Christianity's triumph; the need to maintain state control; and the effectiveness of central rule from Constantinople in enforcing legislation.[citation needed] Some Jewish communities along the Balkan frontier—particularly in Illyricum, Thrace, and Moesia—appear to have maintained distinct regional identities, shaped by local Hellenistic traditions and frontier administrative structures. These peripheral communities, while part of the Byzantine legal framework, often operated in relative isolation from the empire’s Jewish centers such as Thessaloniki or Constantinople, and may have retained older liturgical or linguistic customs. Foundations of the legal position of Jews: 330–404 In the Constitutio Antoniniana of 212, Caracalla bestowed citizenship on all residents including Jews of the Roman Empire, of which the Byzantine Empire is a continuation. This granted Jews legal equality to other citizens, and formed the foundation of their legal status in Byzantium following the founding of Constantinople in 330. Indeed, Jews enjoyed the right to practice their faith under the rule of the Byzantines, as long as they paid the Fiscus Judaicus. For example, circumcision, which was considered mutilation and therefore punishable by death if performed on a non-Jewish child, and by exile if performed on a non-Jewish adult, was legally permitted within Jewish religious practices. Byzantine law recognized synagogues as places of worship, which could not be arbitrarily molested. Jewish courts had the force of law in civil cases, and Jews could not be forced to violate Shabbat and their festivals. In the year 390, nearly all of the territory of present-day Israel came under Byzantine suzerainty. The area was divided into the provinces of Palestina Prima, Palestina Secunda, and Palestina Tertia. These provinces were part of the Diocese of the East. Theodosian Code: 404–527 In 404, Jews were excluded from certain governmental posts. In 418, they were barred from the civil service, and from all military positions. In 425, they were excluded from all remaining public offices, both civilian and military, a prohibition which Justinian I reiterated. Such restrictions, however, inevitably compromised the theological arguments for restricting the Jewish religion. Although they empowered the Christian citizens of the empire at the expense of its Jews, all laws dealing with the Jews implicitly recognized the continued existence and legality of the Jewish religion. Thus Emperor Theodosius II found that he had to balance the first two of the three factors governing the treatment of Jews in the empire—theology, political pragmatism and enforceability. He could not, however, effectively control the third. In 438, Theodosius had to reaffirm the prohibition on Jews holding public office, because it had been poorly enforced. Even in 527, a decree which renewed this prohibition began by observing that "heedless of the laws' command [they have] infiltrated public offices". There was one office, however, that Jews were not forbidden from assuming. This was the office of decurion, a tax collector who was required to pay all deficits in revenue from his own pocket. Theodosius II, who laid out much of the legal precedent and foundation for Byzantine law in his Theodosian Code, permitted Jews, like other citizens, to hire a substitute to perform the duties of decurion in their place. Justinian, whose legal code included 33 laws relating to the Jews, initially maintained this ability, but it was abolished in 537. Sharf explains that the purpose of this was so that the Jews "never enjoy the fruits of office, but only suffer its pains and penalties". In addition to the matter of holding public office, Jews were also unequal to Christians with respect to the ownership of slaves. Restrictions on the ownership of Christian slaves by Jews were in place through the reign of many emperors, under the fear that Jews would use conversion of slaves as a means to increase their number. Additionally, this was designed to provide an incentive for non-Christian slaves to convert into Christianity, and an economic restriction on the Jews. Restrictions on slave-owning could not, however, be excessively burdensome, because slaves, although numerous, were between 10 and 15% of the population. Under the Theodosian Code, therefore, ownership of Christian slaves by Jews was not prohibited, although their purchase was. Thus, one who gained possession of a slave by means such as inheritance would remain his or her owner. Purchase of slaves was usually penalized by compelled sale at the original purchase price. Slave ownership produces another example of the threefold balancing act of Legislation dealing with the Jewish minority of Byzantium: ownership of Christian slaves undermined the "living testament" theology, but was a pragmatic requirement of the time, and the prohibition thereof could not be entirely enforced, since freedom may not necessarily have been a desirable option for a slave who was well-treated by his masters. The third important restriction on Judaism—in addition to the limitations on public service and slave ownership—was that the Jewish religion, though allowed to survive, was not allowed to thrive. Theologically, the victory of Christianity could be successfully asserted by maintaining a small contingent of Jews within the empire, although allowing them to become too sizable a minority would threaten the theological monopoly of Orthodox Christianity within the Empire. One important ramification of this policy was the prohibition on the construction of new synagogues within the Empire, though the repair of old synagogues was permitted. This prohibition was difficult to enforce, as archaeological evidence in Israel indicates that illegal synagogue construction continued throughout the sixth century. The synagogue did continue to be respected as an inviolable place of worship until the reign of Justinian. Beginning at this time, most legislation regarding the Jews—even laws which expanded the rights which they were afforded—were "prefaced by unambiguous expressions of hatred and contempt for Judaism". Justinian Code: 527–565 The Civil Code of Justinian tightened the regulations on the ownership of Christian slaves by non-Christians. It abolished compensation for illegal purchases of Christian slaves, and added a 30 lb gold fine for this offense. Jews owning Christian slaves during the time of Justinian could be punished by execution. In 545, Justinian legislated that the right of existence of any synagogue on land belonging to an ecclesiastical institution be nullified. He was also the first emperor to order that existing synagogues be converted into churches. There is, however, only one example of such a conversion taking place by force: the synagogue in Borem. This synagogue was most likely converted for military reasons, in light of its strategic position on the frontier with the territory of the Berber tribes. In fact, Justinian banned all non-Christian places of worship in northern Africa, in legislation that grouped Jews with pagans and heretics. This legislation was hardly enforced, but set a precedent for the violability of synagogues and the blurring of the difference between Jews and other non-Christians. Once more, this represents the divergence between the Empire's theological objectives, its pragmatic goals and its capability to enforce its legislation. The poor efficacy of legislation points to the dominating power of the latter in restraining the two former factors, which, in this case, coincided. The Jews also found that they were positioned in law somewhere between other non-Christians and the Christian majority. For instance, Justinian demanded that Passover be shown as subservient to Easter; in cases in which the former would fall before the latter, the Jews were forbidden from celebrating it on its appointed day, and were compelled to delay it. Jews were also forbidden from giving testimony concerning Christians in a court of law—a restriction already present in the Theodosian code—although Justinian eased this restriction in 537 to allow them to testify in cases between Christian individuals and the state. This privilege was not enjoyed by any other non-Christian group. Once more, the state sacrificed the doctrinal subordination of the Jews in order to gain practical benefits, in this case testimony against those who faced it in court. Questions of internal Jewish discourse—which could, under the Theodosian Code, be arbitrated only by Jewish courts—could, under the Justinian Code, be officiated by the state, a power which Justinian did not shy away from utilizing. In 553 for instance, Justinian required that the public reading of the Pentateuch proceed in vernacular, rather than Hebrew, and forbade altogether the reading of the Mishna. In this way, Justinian not only restricted the religious freedom of the Jews, but also expanded his own power in order to reinforce the principle that, "in theory, there is no area that falls outside of the Empire's legislative power". Justinian's restrictions were, however, poorly enforced. Ironically, what little enforcement they did enjoy contributed to a notable growth in Jewish culture and liturgy. For instance, the banning of the reading of Mishna prompted Jewish scholars to write the piyutim, important works of poetry which refer strongly to the Mishna. Because these were not banned by the Civil Code, they afforded Jews the ability to circumvent it. Accordingly, this form of religious expression flourished under Justinian. Punctuated tolerance, Jewish revolts, and the Crusades: 565–1204 Although the Justinian Code remained in force in the Eastern Empire until the ninth century, the period following Justinian's reign was generally characterized by toleration of non-Christians, particularly the Jews. However, during the Byzantine–Sasanian War of 602–628 many Jews sided against the Byzantine Empire in the Jewish revolt against Heraclius, which successfully assisted the invading Persian Sassanids in conquering all of Roman Egypt and Syria. In reaction to this, anti-Jewish measures were enacted throughout the Byzantine realm and as far away as Merovingian France. Soon thereafter, in 634 the Muslim conquests began, during which many Jews initially rose up again against their Byzantine rulers. During this time Heraclius became the first emperor to force the conversion of Jews to Christianity. Following his death, and until 1204, the Jews suffered only three notable legal persecutions, the sum of whose span was roughly fifty years. It is even debated whether the first of these—the anti-Jewish measures passed during the reign of Leo III the Isaurian—could be considered a persecution. The second of these, during the reign of Basil I from 867 to 886, briefly punctuated the tolerance of the ninth century. The last of these persecutions took place under John Tzimiskes, who reigned from 969 to 976. Accordingly, there were no recorded legal persecutions of the Jews for nearly two and a half centuries following his reign. In fact Samuel Krauss writes in his famous work on Byzantine Jewry that Constantinople at the time of the Byzantine Empire was "the center of the Jewish, Samaritan and Karaite scholarship". Eleazar ben Killir a Byzantine Jew from a Greek-speaking area wrote his famous piyutim, which are still in use in the most Machzorim and became the teacher of all paytanim who came after him. Asaph the Jew wrote in Byzantium the first Hebrew medical treatise. The Sefer Yosippon was written down in the 10th century in the Byzantine south Italy by the Greek-speaking Jewish community there. Judah Leon ben Moses Mosconi, a Romaniote Jew from Achrida edited and expanded the Sefer Josippon later. This community of Byzantine Jews of southern Italy produced such prominent works like the Sefer Ahimaaz of Ahimaaz ben Paltiel, the Sefer Hachmoni of Shabbethai Donnolo, the Aggadath Bereshit and many piyyutim. The liturgical writings of these Romaniote Jews, especially the piyyut were eminent for the development of the Ashkenazi Mahzor, as they found their way through Italy to Ashkenaz and are preserved to this day in the most ashkenazi mahzorim. Like in the case of the Hellenistic Jewish authorship some of the Byzantine Jewish manuscripts show the use of the Greek language in religious and communal aspects. The language of this manuscripts is not in Ancient Greek, but rather in an older form of Modern Greek. These texts are the oldest known written texts in Modern Greek. Beside these Rabbanites and as a part of the Empire's Romaniote Jews, important Karaite communities like the Constantinopolitan Karaites and the Karaites of Adrianople flourished and produced eminent personalities for the Karaite movement like Caleb Afendopolo, Elijah Bashyazi, Aaron ben Joseph of Constantinople, Aaron ben Elijah, Judah Hadassi and other. In the twelfth century, there were about 2,500 Jews in Constantinople, 2,000 Jews in Thebes and 500 Jews in Thessalonica. Halmyrus, Rhaedestus, Chios, and Rhodes each housed 400 Jews. Also, there were about 300 Jews each in Corinth and Samos, and 200 Jews in Gallipoli. Some Jewish families in the rural provinces of the empire, such as Thrace, Moesia, and Pannonia Secunda, appear to have maintained distinct communal practices shaped by local customs and Hellenized liturgical traditions. These frontier communities often remained outside major urban centers and were less affected by imperial persecutions or theological controversies. Archaeological and genetic studies suggest a degree of regional continuity among these populations, which may have preserved older forms of worship and maternal lineage traditions into the High Middle Ages. It was in the 12th century that the passing Crusaders wrought havoc upon the Jewish communities of Byzantium, in a foretaste of what the later Latin occupation would bring upon the Byzantine Christians. Although most crusading bands did not adopt a policy of violence or forced conversion against the Jews, the First Crusade certainly undertook an anti-Jewish face in certain communities. Because the Crusade was undertaken with the goal of "subjugating all non-believers to the faith," many crusaders compelled Jews to convert on pain of death, and there is a large number of recorded cases of mass suicides within Jewish communities—particularly among Jewish maidens—in order to avoid such conversions. Latin occupation: 1204–1261 The Fourth Crusade further degraded the position of Byzantine Jews. As smaller states separated from a weakened empire, the rulers of these states found themselves more capable of enforcing legislation than their Byzantine counterparts. The most powerful protection on the rights of Jews—governmental impotence to enforce laws—was thus abolished. Theodore Doukas, who crowned himself emperor of Epiros after he conquered Thessalonica, was known for his persecution of the Jews, which began in 1229, a year before the end of his reign. Theodore's disdain for the Jews is well-established. Still, his waiting until 1229—five years after capturing Thessalonica and declaring himself emperor—indicates that antisemitism may not have been the cause of his anti-Jewish edicts. Rather, they appear to have been motivated by a desire to confiscate Jewish property at a time when his empire was short of funds. This explains the expropriations of Jewish property under Theodore, as well as his regime's abstention from religious persecution for its own sake. John Vatatzes, the emperor of Nicaea, commenced legal persecution of the Jews in 1253. Unlike Theodore, Vatatzes ordered that the Jews within the Empire of Nicaea be converted to Christianity, though he did not order the expropriation of Jewish property. Although these measures began only a year before Vatatzes' death, they seemed to have set a precedent of persecution which his son, Theodore II Laskaris, followed. It was in this environment of persecution that the Palaiologoi rose to the imperial throne. Michael VIII Palaiologos largely ended persecution of the Jews. Bowman writes the following: Michael's road to the throne had been of questionable legality, and that fact earned him many enemies. Additionally, he oversaw an empire which was strongly dependent on foreign powers, and had an immense need for gold to fund its great military expenses. It is not surprising, therefore, that he turned to the Jews and other minorities (most notably the Armenians) as a source of support in an embattled state of affairs, and when the ethnic majority and the mainstream elite had grown unfriendly toward him. A decaying empire: 1261–1453 Andronikos II Palaiologos followed his father's precedent. The tolerance of Andronikos was quite notable, even drawing condemnation from Patriarch Athanasius III of Alexandria, against what he saw as "excessive" tolerance of Jews and other non-Christians, in particular for permitting them to live amongst Christians. The patriarch's complaint indicates that, in spite of the tolerance of the Palaiologoi, the norm of imperial law was to require non-Christians to live separately from Christians. This apparent trend of segregation between the peoples of Byzantium, which certainly included the Jews, is confirmed in a letter by John, bishop of Citrus, in the latter half of the twelfth century, which declared that, "People of alien tongues and alien beliefs, such as Jews, Armenians, Ishmaelites, Hagarites and other such as these were permitted from of old to dwell in Christian countries and cities, except that they had to live separately and not together with the Christians". In Constantinople, there was a Jewish quarter near the eponymous gate in the modern Yenikapı area. By the fourteenth century, the Jewish question of Byzantium seemed to be most concerned with Venetian Jews. Venetians had come to reside in the Empire in large numbers by the early 14th century, and treaties between the Empire and Venice granted the Venetians living in the empire, including Jews of Venetian origin, special privileges, though they also carried certain minor economic prohibitions. Under the aegis of these treaties, Venetian Jews could buy, sell or rent land anywhere in Constantinople. They also enjoyed a more favorable tax structure than Byzantine citizens, as well as the freedom of movement and settlement anywhere in the Empire. Further complicating this legal status, some Jews obtained Venetian citizenship either "by coming from areas subject to the Republic or by purchasing naturalization", thus obtaining the same privileges as Venetian nationals in the Empire. At this time, the Empire was in rapid decay, and could not seriously enforce laws intended to curtail these rights and regain economic control within its borders. Thus, an exception to the general trend of Byzantine history emerged during this century, whereby Jews were entitled to a broader set of rights than Christians. However, it is important to note that these liberties were conferred based on their being Venetian, not based on their Jewish identity. Non-Venetian Jews did not profit from the Venetian-Byzantine treaties, and non-Jewish Venetians enjoyed the same liberties as their Jewish compatriots. See also References Further reading External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Star_system] | [TOKENS: 1942] |
Contents Star system A star system or stellar system is a small number of stars that orbit each other, bound by gravitational attraction. It may sometimes be used to refer to a single star. A large group of stars bound by gravitation is generally called a star cluster or galaxy, although, broadly speaking, they are also star systems. Star systems are not to be confused with planetary systems, which include planets and similar bodies (such as comets). Terminology A star system of two stars is known as a binary star, binary star system or physical double star. Systems with four or more components are rare, and are much less commonly found than those with 2 or 3. Multiple-star systems are called triple, ternary, or trinary if they contain three stars; quadruple or quaternary if they contain four stars; quintuple or quintenary with five stars; sextuple or sextenary with six stars; septuple or septenary with seven stars; octuple or octenary with eight stars; and nonuple or nonary with nine stars. These systems are smaller than open star clusters, which have more complex dynamics and typically have from 100 to 1,000 stars. Optical doubles and multiples Binary and multiple star systems are also known as a physical multiple stars, to distinguish them from optical multiple stars, which merely look close together when viewed from Earth. Multiple stars may refer to either optical or physical, but optical multiples do not form a star system. Triple stars that are not all gravitationally bound (and thus do not form a triple star system) might comprise a physical binary and an optical companion (such as Beta Cephei) or, in rare cases, a purely optical triple star (such as Gamma Serpentis). Abundance Research on binary and multiple stars estimates they make up about a third of the star systems in the Milky Way galaxy, with two-thirds of stars being single. Binary stars are the most common non-single stars. With multiple star systems, the number of known systems decreases exponentially with multiplicity. For example, in the 1999 revision of Tokovinin's catalog of physical multiple stars, 551 out of the 728 systems described are triple. However, because of suspected selection effects, the ability to interpret these statistics is very limited. Detection There are various methods to detect star systems and distinguish them from optical binaries multiples. These include: Orbital characteristics In systems that satisfy the assumptions of the two-body problem – including having negligible tidal effects, perturbations (from the gravity of other bodies), and transfer of mass between stars – the two stars will trace out a stable elliptical orbit around the barycenter of the system. Examples of binary systems are Sirius, Procyon and Cygnus X-1, the latter of which consists of a star and a black hole. Multiple-star systems can be divided into two main dynamical classes: Most multiple-star systems are organized in what is called a hierarchical system: the stars in the system can be divided into two smaller groups, each of which traverses a larger orbit around the system's center of mass. Each of these smaller groups must also be hierarchical, which means that they must be divided into smaller subgroups which themselves are hierarchical, and so on. Each level of the hierarchy can be treated as a two-body problem by considering close pairs as if they were a single star. In these systems there is little interaction between the orbits and the stars' motion will continue to approximate stable Keplerian orbits around the system's center of mass. For example, stable trinary systems consist of two stars in a close binary system, with a third orbiting this pair at a distance much larger than that of the binary orbit. If the inner and outer orbits are comparable in size, the system may become dynamically unstable, leading to a star being ejected from the system. EZ Aquarii is an example of a physical hierarchical triple system, which has an outer star orbiting an inner binary composed of two more red dwarf stars. Hierarchical arrangements can be organized by what Evans (1968) called mobile diagrams, which look similar to ornamental mobiles hung from the ceiling. Each level of the mobile illustrates the decomposition of the system into two or more systems with smaller size. Evans calls a diagram multiplex if there is a node with more than two children, i.e. if the decomposition of some subsystem involves two or more orbits with comparable size. Because multiplexes may be unstable, multiple stars are expected to be simplex, meaning that at each level there are exactly two children. Evans calls the number of levels in the diagram its hierarchy. Higher hierarchies are also possible. Most of these higher hierarchies either are stable or suffer from internal perturbations. Others consider complex multiple stars will in time theoretically disintegrate into less complex multiple stars, like more common observed triples or quadruples. Trapezia are usually very young, unstable systems. These are thought to form in stellar nurseries, and quickly fragment into stable multiple stars, which in the process may eject components as galactic high-velocity stars. They are named after the multiple star system known as the Trapezium Cluster in the heart of the Orion Nebula. Such systems are not rare, and commonly appear close to or within bright nebulae. These stars have no standard hierarchical arrangements, but compete for stable orbits. This relationship is called interplay. Such stars eventually settle down to a close binary with a distant companion, with the other star(s) previously in the system ejected into interstellar space at high velocities. This dynamic may explain the runaway stars that might have been ejected during a collision of two binary star groups or a multiple system. This event is credited with ejecting AE Aurigae, Mu Columbae and 53 Arietis at above 200 km·s−1 and has been traced to the Trapezium Cluster in the Orion Nebula some two million years ago. Designations and nomenclature The components of multiple stars can be specified by appending the suffixes A, B, C, etc., to the system's designation. Suffixes such as AB may be used to denote the pair consisting of A and B. The sequence of letters B, C, etc. may be assigned in order of separation from the component A. Components discovered close to an already known component may be assigned suffixes such as Aa, Ba, and so forth. A. A. Tokovinin's Multiple Star Catalogue uses a system in which each subsystem in a mobile diagram is encoded by a sequence of digits. In the mobile diagram (d) above, for example, the widest system would be given the number 1, while the subsystem containing its primary component would be numbered 11 and the subsystem containing its secondary component would be numbered 12. Subsystems which would appear below this in the mobile diagram will be given numbers with three, four, or more digits. When describing a non-hierarchical system by this method, the same subsystem number will be used more than once; for example, a system with three visual components, A, B, and C, no two of which can be grouped into a subsystem, would have two subsystems numbered 1 denoting the two binaries AB and AC. In this case, if B and C were subsequently resolved into binaries, they would be given the subsystem numbers 12 and 13. The current nomenclature for double and multiple stars can cause confusion as binary stars discovered in different ways are given different designations (for example, discoverer designations for visual binary stars and variable star designations for eclipsing binary stars), and, worse, component letters may be assigned differently by different authors, so that, for example, one person's A can be another's C. Discussion starting in 1999 resulted in four proposed schemes to address this problem: For a designation system, identifying the hierarchy within the system has the advantage that it makes identifying subsystems and computing their properties easier. However, it causes problems when new components are discovered at a level above or intermediate to the existing hierarchy. In this case, part of the hierarchy will shift inwards. Components which are found to be nonexistent, or are later reassigned to a different subsystem, also cause problems. During the 24th General Assembly of the International Astronomical Union in 2000, the WMC scheme was endorsed and it was resolved by Commissions 5, 8, 26, 42, and 45 that it should be expanded into a usable uniform designation scheme. A sample of a catalog using the WMC scheme, covering half an hour of right ascension, was later prepared. The issue was discussed again at the 25th General Assembly in 2003, and it was again resolved by commissions 5, 8, 26, 42, and 45, as well as the Working Group on Interferometry, that the WMC scheme should be expanded and further developed. The sample WMC is hierarchically organized; the hierarchy used is based on observed orbital periods or separations. Since it contains many visual double stars, which may be optical rather than physical, this hierarchy may be only apparent. It uses upper-case letters (A, B, ...) for the first level of the hierarchy, lower-case letters (a, b, ...) for the second level, and numbers (1, 2, ...) for the third. Subsequent levels would use alternating lower-case letters and numbers, but no examples of this were found in the sample. Examples See also References External links |
======================================== |
[SOURCE: https://github.com/features/copilot] | [TOKENS: 9536] |
Navigation Menu Search code, repositories, users, issues, pull requests... Provide feedback We read every piece of feedback, and take your input very seriously. Saved searches Use saved searches to filter your results more quickly To see all available qualifiers, see our documentation. Command your craft Your AI accelerator for every workflow, from the editor to the enterprise. Companies using Copilot Choose from leading LLMs optimized for speed, accuracy, or cost. Use GitHub Copilot, your own custom agents, or the third-party ones you already rely on. Copilot works where you do—in GitHub, your IDE, project tools, chat apps, and custom MCP servers. Code, command, and collaborate AI that works where you do, whether in your editor, on the command line, or across GitHub. Copilot in your editor does it all, from explaining concepts and completing code, to proposing edits and validating files with agent mode. Assign issues directly to coding agents like Copilot, Claude by Anthropic, or OpenAI Codex, and let them autonomously write code, create pull requests, and respond to feedback in the background. Direct Copilot in the terminal using natural language and watch it plan, build, and execute complex workflows powered by your GitHub context. Tailor-made for your organization Shape Copilot to your business needs. Customize what it knows, how it acts, and where it connects. Scale knowledge and keep teams consistent by creating a shared source of truth that includes context from your docs and repositories. Track activity with detailed audit logs and enforce governance by managing agents from a single control plane. Control which MCP servers developers can access from their IDEs, and use allow lists to prevent unauthorized access. Take flight with GitHub Copilot A fast way to get started with GitHub Copilot. $0USD No credit card required Accelerate workflows with GitHub Copilot. $10USDper month or $100 per year Free for verified students, teachers, and maintainers of popular open source projects. Learn more Scale with agents and more models. $39USDper month or $390 per year GitHub Copilot is available on your favorite platforms: Get the most out of GitHub Copilot Be the first to explore what’s next for GitHub Copilot. Discover the latest in software development with insights, best practices, and more. Gain peace of mind with our security, privacy, and responsible AI policies. GitHub Copilot transforms the developer experience. Backed by the leaders in AI, GitHub Copilot provides contextualized assistance throughout the software development lifecycle, from inline suggestions and chat assistance in the IDE to code explanations and answers to docs in GitHub and more. With GitHub Copilot elevating their workflow, developers can focus on: value, innovation, and happiness. GitHub Copilot enables developers to focus more energy on problem solving and collaboration and spend less effort on the mundane and boilerplate. That’s why developers who use GitHub Copilot report up to 75% higher satisfaction with their jobs than those who don’t and are up to 55% more productive at writing code without sacrifice to quality, which all adds up to engaged developers shipping great software faster. GitHub Copilot integrates with leading editors, including Visual Studio Code, Visual Studio, JetBrains IDEs, and Neovim, and, unlike other AI coding assistants, is natively built into GitHub. Growing to millions of individual users and tens of thousands of business customers, GitHub Copilot is the world’s most widely adopted AI developer tool and the competitive advantage developers ask for by name. GitHub Copilot Free is a new free pricing tier with limited functionality for individual developers. Users assigned a Copilot Business or Copilot Enterprise seat are not eligible for access. Users with access to Copilot Pro through a paid subscription, trial, or through an existing verified OSS, student, faculty, or MVP account may elect to use Free instead. GitHub Copilot is trained on all languages that appear in public repositories. For each language, the quality of suggestions you receive may depend on the volume and diversity of training data for that language. For example, JavaScript is well-represented in public repositories and is one of GitHub Copilot’s best supported languages. Languages with less representation in public repositories may produce fewer or less robust suggestions. GitHub Copilot is available as an extension in Visual Studio Code, Visual Studio, Vim, Neovim, the JetBrains suite of IDEs, and Azure Data Studio. Although inline suggestion functionality is available across all these extensions, chat functionality is currently available only in Visual Studio Code, JetBrains, and Visual Studio. GitHub Copilot is also supported in terminals through GitHub CLI and as a chat integration in Windows Terminal Canary. With the GitHub Copilot Enterprise plan, GitHub Copilot is natively integrated into GitHub.com. All plans are supported in GitHub Copilot in GitHub Mobile. GitHub Mobile for Copilot Pro and Copilot Business have access to Bing and public repository code search. Copilot Enterprise in GitHub Mobile gives you additional access to your organization's knowledge. No, GitHub Copilot generates suggestions using probabilistic determination. When thinking about intellectual property and open source issues, it is critical to understand how GitHub Copilot really works. The AI models that create GitHub Copilot’s suggestions may be trained on public code, but do not contain any code. When they generate a suggestion, they are not “copying and pasting” from any codebase. To generate a code suggestion, the GitHub Copilot extension begins by examining the code in your editor—focusing on the lines just before and after your cursor, but also information including other files open in your editor and the URLs of repositories or file paths to identify relevant context. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. To generate a suggestion for chat in the code editor, the GitHub Copilot extension creates a contextual prompt by combining your prompt with additional context including the code file open in your active document, your code selection, and general workspace information, such as frameworks, languages, and dependencies. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. To generate a suggestion for chat on GitHub.com, such as providing an answer to a question from your chat prompt, GitHub Copilot creates a contextual prompt by combining your prompt with additional context including previous prompts, the open pages on GitHub.com as well as retrieved context from your codebase or Bing search. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. GitHub Copilot has multiple offerings for organizations and an offering for individual developers. All the offerings include both inline suggestion and chat assistance. The primary differences between the organization offerings and the individual offering are license management, policy management, and IP indemnity. Organizations can choose between GitHub Copilot Business and GitHub Copilot Enterprise. GitHub Copilot Business primarily features GitHub Copilot in the coding environment - that is the IDE, CLI and GitHub Mobile. GitHub Copilot Enterprise includes everything in GitHub Copilot Business. It also adds an additional layer of customization for organizations and integrates into GitHub.com as a chat interface to allow developers to converse with GitHub Copilot throughout the platform. GitHub Copilot Enterprise can index an organization’s codebase for a deeper understanding of the customer’s knowledge for more tailored suggestions and will offer customers access to fine-tuned custom, private models for inline suggestions. GitHub Copilot Individual is designed for individual developers, freelancers, students, educators, and open source maintainers. The plan includes all the features of GitHub Copilot Business except organizational license management, policy management, and IP indemnity. GitHub Copilot is powered by generative AI models developed by GitHub, OpenAI, and Microsoft. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub. GitHub Copilot Autofix provides contextual explanations and code suggestions to help developers fix vulnerabilities in code, and is included in GitHub Advanced Security. GitHub Copilot is entirely optional and requires you to opt in before gaining access. You can easily configure its usage directly in the editor, enabling or disabling it at any time. Additionally, you have control over which file types GitHub Copilot is active for. Access to Copilot Business and Enterprise is managed by your GitHub Administrator. They can control access to preview features, models, and set GitHub Copilot policies for your organization. Additionally, you can use your network firewall to explicitly allow access to Copilot Business and/or block access to Copilot Pro or Free. For more details, refer to the documentation. GitHub Copilot has multiple offerings for organizations and an offering for individual developers. All the offerings include both code completion and chat assistance. The primary differences between the organization offerings and the individual offering are license management, policy management, and IP indemnity. Organizations can choose between GitHub Copilot Business and GitHub Copilot Enterprise. GitHub Copilot Business primarily features GitHub Copilot in the coding environment - that is the IDE, CLI and GitHub Mobile. GitHub Copilot Enterprise includes everything in GitHub Copilot Business. It also adds an additional layer of customization for organizations and integrates into GitHub.com as a chat interface to allow developers to converse with Copilot throughout the platform. GitHub Copilot Enterprise can index an organization’s codebase for a deeper understanding of the customer’s knowledge for more tailored suggestions and will offer customers access to fine-tuned custom, private models for code completion. GitHub Copilot Pro is designed for individual developers, freelancers, students, educators, and open source maintainers. The plan includes all the features of GitHub Copilot Business except organizational license management, policy management, and IP indemnity. If you're on the Free plan, you can upgrade to Pro through your Copilot settings page or directly on the Copilot marketing page. GitHub Copilot Free users are limited to 2000 completions and 50 chat requests (including Copilot Edits). GitHub Copilot Autofix provides contextual explanations and code suggestions to help developers fix vulnerabilities in code, and is included in GitHub Advanced Security and available to all public repositories. Organizations can now enable Copilot code review on all pull requests on github.com—including pull requests from users who are not assigned a Copilot license. This allows you to extend the quality and rich analysis of Copilot code review to all pull requests, regardless of its author, giving you complete coverage and confidence that pull requests have been reviewed. To enable this functionality, an enterprise/org admin must first have Copilot enabled and then enabled two policies. Note: This capability is not supported for Copilot code reviews in VS Code or other IDEs. Usage from non-licensed users is billed directly to your organization as "premium requests" (PRUs) at the standard multiplier rate for Copilot code review. This flexible model allows you to get full review coverage on every PR without needing to purchase a full Copilot seat for non-development contributors who may not need Copilot. Usage from your existing licensed users simply continues to draw from their included monthly allowance as it does today. No. This capability is off by default and gives the enterprise admin control to enable or disable. An admin must explicitly enable two separate policies to activate: ‘Premium request paid usage’ must be enabled to allow enterprises to be charged for premium requests exceeding their included usage. A new Copilot code review policy (‘Allow members without a Copilot license to use Copilot code review in github.com’) must also be enabled. We encourage admins to set up budgets to control spending on our metered products, especially customers who have not enabled the ‘Premium request paid usage’ policy in the past. You can track all premium request usage in your billing dashboard to monitor and control spending. GitHub Copilot processes personal data based on how Copilot is accessed and used: whether via GitHub.com, mobile app, extensions, or one of various IDE extensions, or through features like suggestions for the command line interface (CLI), IDE code completions, or personalized chat on GitHub.com. The types of personal data processed may include: User Engagement Data: This includes pseudonymous identifiers captured on user interactions with Copilot, such as accepted or dismissed completions, error messages, system logs, and product usage metrics. Prompts: These are inputs for chat or code, along with context, sent to Copilot's AI to generate suggestions. Suggestions: These are the AI-generated code lines or chat responses provided to users based on their prompts. Feedback Data: This comprises real-time user feedback, including reactions (e.g., thumbs up/down) and optional comments, along with feedback from support tickets. No. GitHub does not use either Copilot Business or Enterprise data to train its models. How GitHub uses Copilot data depends on how the user accesses Copilot and for what purpose. Users can access GitHub Copilot through the web, extensions, mobile apps, computer terminal, and various IDEs (Integrated Development Environments). GitHub generally uses personal data to: Deliver, maintain, and update the services as per the customer's configuration and usage, to ensure personalized experiences and recommendations Troubleshoot, which involves preventing, detecting, resolving, and mitigating issues, including security incidents and product-related problems, by fixing software bugs and maintaining the online services' functionality and up-to-dateness Enhance user productivity, reliability, effectiveness, quality, privacy, accessibility, and security by keeping the service current and operational These practices are outlined in GitHub’s Data Protection Agreement (DPA), which details our data handling commitments to our data controller customers. GitHub also uses certain personal data with customer authorization under the DPA, for the following purposes: Billing and account management To comply with and resolve legal obligations For abuse detection, prevention, and protection, virus scanning, and scanning to detect violations of terms of service To generate summary reports for calculating employee commissions and partner incentives To produce aggregated reports for internal use and strategic planning, covering areas like forecasting, revenue analysis, capacity planning, and product strategy, For details on GitHub's data processing activities as a controller, particularly for Copilot Pro customers, refer to the GitHub Privacy Statement. If and for how long GitHub’s retains Copilot data depends on how a Copilot user accesses Copilot and for what purpose. The default settings for Copilot Business and Enterprise Customers are as follows: Access through IDE for Chat and Code Completions: Prompts and Suggestions: Not retained User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. All other GitHub Copilot access and use: Prompts and Suggestions: Retained for 28 days. User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. Retaining prompts and suggestions is necessary for chat on github.com, mobile, and CLI Copilot because those features’ effectiveness depends on using thread history to improve responses. The Copilot model requires access to previous interactions to deliver accurate and relevant suggestions. Yes. GitHub and customers can enter a Data Protection Agreement that supports compliance with the GDPR and similar legislation. While we've designed GitHub Copilot with privacy in mind, the expansive definition of personal data under legislation like the EU’s General Data Protection Regulation (GDPR) means we can't guarantee it will never output such data. The Large Language Model (LLM) powering GitHub Copilot was trained on public code and there were instances in our tests where the tool made suggestions resembling personal data. These suggestions were typically synthesized and not tied to real individuals. These actions are available to Copilot users as described in the GitHub Privacy Statement. The primary IP considerations for GitHub Copilot relate to copyright. The model that powers Copilot is trained on a broad collection of publicly accessible code, which may include copyrighted code, and Copilot’s suggestions (in rare instances) may resemble the code its model was trained on. Here’s some basic information you should know about these considerations: Copyright law permits the use of copyrighted works to train AI models: Countries around the world have provisions in their copyright laws that enable machines to learn, understand, extract patterns, and facts from copyrighted materials, including software code. For example, the European Union, Japan, and Singapore, have express provisions permitting machine learning to develop AI models. Other countries including Canada, India, and the United States also permit such training under their fair use/fair dealing provisions. GitHub Copilot’s AI model was trained with the use of code from GitHub’s public repositories—which are publicly accessible and within the scope of permissible copyright use. What about copyright risk in suggestions? In rare instances (less than 1% based on GitHub’s research), suggestions from GitHub may match examples of code used to train GitHub’s AI model. Again, Copilot does not “look up” or “copy and paste” code, but is instead using context from a user’s workspace to synthesize and generate a suggestion. Our experience shows that matching suggestions are most likely to occur in two situations: (i) when there is little or no context in the code editor for Copilot’s model to synthesize, or (ii) when a matching suggestion represents a common approach or method. If a code suggestion matches existing code, there is risk that using that suggestion could trigger claims of copyright infringement, which would depend on the amount and nature of code used, and the context of how the code is used. In many ways, this is the same risk that arises when using any code that a developer does not originate, such as copying code from an online source, or reusing code from a library. That is why responsible organizations and developers recommend that users employ code scanning policies to identify and evaluate potential matching code. In Copilot, you can opt whether to allow Copilot to suggest code completions that match publicly available code on GitHub.com. For more information, see "Configuring GitHub Copilot settings on GitHub.com". If you have allowed suggestions that match public code, GitHub Copilot can provide you with details about the matching code when you accept such suggestions. Matching code does not necessarily mean copyright infringement, so it is ultimately up to the user to determine whether to use the suggestion, and what and who to attribute (along with other license compliance) in appropriate circumstances. Yes, GitHub Copilot does include an optional code referencing filter to detect and suppress certain suggestions that match public code on GitHub. GitHub has created a duplication detection filter to detect and suppress suggestions that contain code segments over a certain length that match public code on GitHub. This filter can be enabled by the administrator for your enterprise and it can apply for all organizations within your enterprise, or the administrator can defer control to individual organizations. With the filter enabled, Copilot checks code suggestions for matches or near-matches against public code on GitHub of 65 lexemes or more (on average,150 characters). If there is a match, the suggestion will not be shown to the user. In addition to off-topic, harmful, and offensive output filters, GitHub Copilot also scans the outputs for vulnerable code. Yes, GitHub Copilot is previewing a code referencing feature as an additional tool to assist users to find and review potentially relevant open source licenses. Code referencing is currently available in Visual Studio Code. This feature searches across public GitHub repositories for code that matches a Copilot suggestion. If there’s a match, users will find its information displayed in the Copilot console log, including where the match occurred, any applicable licenses, and a deep link to learn more. The deep link will take users to a navigable page on GitHub.com to browse examples of the code match and their repository licenses, and see how many repositories—including ones without licenses—that code appears in, as well as links to those repositories. Copilot users can review this information to determine whether the applicable suggestions are suitable for use, and whether additional measures may be necessary to use them. We don’t determine whether a suggestion is capable of being owned, but we are clear that GitHub does not claim ownership of a suggestion. Whether a suggestion generated by an AI model can be owned depends on many factors (e.g. the intellectual property law in the relevant country, the length of the suggestion, the extent that suggestion is considered ‘functional’ instead of expressive, etc). If a suggestion is capable of being owned, our terms are clear: GitHub does not claim ownership. GitHub does not claim ownership of any suggestion. In certain cases, it is possible for Copilot to produce similar suggestions to different users. For example, two unrelated users both starting new files to code the quicksort algorithm in Java will likely get the same suggestion. The possibility of providing similar suggestions to multiple users is a common part of generative AI systems. Public code may contain insecure coding patterns, bugs, or references to outdated APIs or idioms. When GitHub Copilot synthesizes code suggestions based on this data, it can also synthesize code that contains these undesirable patterns. Copilot has filters in place that either block or notify users of insecure code patterns that are detected in Copilot suggestions. These filters target the most common vulnerable coding patterns, including hardcoded credentials, SQL injections, and path injections. Additionally, in recent years we’ve provided tools such as GitHub Advanced Security, GitHub Actions, Dependabot, and CodeQL to open source projects to help improve code quality. Of course, you should always use GitHub Copilot together with good testing and code review practices and security tools, as well as your own judgment. No. Copilot is a tool intended to make developers more efficient. It’s not intended to replace developers, who should continue to apply the same sorts of safeguards and diligence they would apply with regard to any third-party code of unknown origin. The product is called “Copilot” not “Autopilot” and it’s not intended to generate code without oversight. You should use exactly the same sorts of safeguards and diligence with Copilot’s suggestions as you would use with any third-party code. Identifying best practices for use of third party code is beyond the scope of this section. That said, whatever practices your organization currently uses – rigorous functionality testing, code scanning, security testing, etc. – you should continue these policies with Copilot’s suggestions. Moreover, you should make sure your code editor or editor does not automatically compile or run generated code before you review it. Not necessarily. GitHub Copilot users should align their use of Copilot with their respective risk tolerances. As noted above, GitHub Copilot is not intended to replace developers, or their individual skill and judgment, and is not intended to fully automate the process of code development. The same risks that apply to the use of any third-party code apply to the use of Copilot’s suggestions. Depending on your particular use case, you should consider implementing the protections discussed above. It is your responsibility to assess what is appropriate for the situation and implement appropriate safeguards. You’re entitled to IP indemnification from GitHub for the unmodified suggestions when Copilot’s filtering is enabled. If you do elect to enable this feature, the copyright responsibility is ours, not our customers. As part of our ongoing commitment to responsible AI, GitHub and Microsoft extends our IP indemnity and protection support to our customers who are empowering their teams with GitHub Copilot. See Microsoft's Copilot Copyright Commitment for more details. We are conducting internal testing of GitHub Copilot’s ease of use by developers with disabilities and working to ensure that GitHub Copilot is accessible to all developers. Please feel free to share your feedback on GitHub Copilot accessibility in our feedback forum. GitHub Copilot includes filters to block offensive language in the prompts and to avoid synthesizing suggestions in sensitive contexts. We continue to work on improving the filter system to more intelligently detect and remove offensive outputs. If you see offensive outputs, please report them directly to copilot-safety@github.com so that we can improve our safeguards. GitHub takes this challenge very seriously and we are committed to addressing it. Given public sources are predominantly in English, GitHub Copilot will likely work less well in scenarios where natural language prompts provided by the developer are not in English and/or are grammatically incorrect. Therefore, non-English speakers might experience a lower quality of service. GitHub Copilot is powered by generative AI models developed by GitHub, OpenAI, and Microsoft. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub. Data from June 2023. Additional research can be found here. Feature in public beta for Copilot Pro and Business plans. Requires use of repositories, issues, discussions, Actions, and other features of GitHub. Authentication with SAML single sign-on (SSO) available for organizations using GitHub Enterprise Cloud. GitHub Copilot transforms the developer experience. Backed by the leaders in AI, GitHub Copilot provides contextualized assistance throughout the software development lifecycle, from inline suggestions and chat assistance in the IDE to code explanations and answers to docs in GitHub and more. With GitHub Copilot elevating their workflow, developers can focus on: value, innovation, and happiness. GitHub Copilot enables developers to focus more energy on problem solving and collaboration and spend less effort on the mundane and boilerplate. That’s why developers who use GitHub Copilot report up to 75% higher satisfaction with their jobs than those who don’t and are up to 55% more productive at writing code without sacrifice to quality, which all adds up to engaged developers shipping great software faster. GitHub Copilot integrates with leading editors, including Visual Studio Code, Visual Studio, JetBrains IDEs, and Neovim, and, unlike other AI coding assistants, is natively built into GitHub. Growing to millions of individual users and tens of thousands of business customers, GitHub Copilot is the world’s most widely adopted AI developer tool and the competitive advantage developers ask for by name. GitHub Copilot Free is a new free pricing tier with limited functionality for individual developers. Users assigned a Copilot Business or Copilot Enterprise seat are not eligible for access. Users with access to Copilot Pro through a paid subscription, trial, or through an existing verified OSS, student, faculty, or MVP account may elect to use Free instead. GitHub Copilot is trained on all languages that appear in public repositories. For each language, the quality of suggestions you receive may depend on the volume and diversity of training data for that language. For example, JavaScript is well-represented in public repositories and is one of GitHub Copilot’s best supported languages. Languages with less representation in public repositories may produce fewer or less robust suggestions. GitHub Copilot is available as an extension in Visual Studio Code, Visual Studio, Vim, Neovim, the JetBrains suite of IDEs, and Azure Data Studio. Although inline suggestion functionality is available across all these extensions, chat functionality is currently available only in Visual Studio Code, JetBrains, and Visual Studio. GitHub Copilot is also supported in terminals through GitHub CLI and as a chat integration in Windows Terminal Canary. With the GitHub Copilot Enterprise plan, GitHub Copilot is natively integrated into GitHub.com. All plans are supported in GitHub Copilot in GitHub Mobile. GitHub Mobile for Copilot Pro and Copilot Business have access to Bing and public repository code search. Copilot Enterprise in GitHub Mobile gives you additional access to your organization's knowledge. No, GitHub Copilot generates suggestions using probabilistic determination. When thinking about intellectual property and open source issues, it is critical to understand how GitHub Copilot really works. The AI models that create GitHub Copilot’s suggestions may be trained on public code, but do not contain any code. When they generate a suggestion, they are not “copying and pasting” from any codebase. To generate a code suggestion, the GitHub Copilot extension begins by examining the code in your editor—focusing on the lines just before and after your cursor, but also information including other files open in your editor and the URLs of repositories or file paths to identify relevant context. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. To generate a suggestion for chat in the code editor, the GitHub Copilot extension creates a contextual prompt by combining your prompt with additional context including the code file open in your active document, your code selection, and general workspace information, such as frameworks, languages, and dependencies. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. To generate a suggestion for chat on GitHub.com, such as providing an answer to a question from your chat prompt, GitHub Copilot creates a contextual prompt by combining your prompt with additional context including previous prompts, the open pages on GitHub.com as well as retrieved context from your codebase or Bing search. That information is sent to GitHub Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions. GitHub Copilot has multiple offerings for organizations and an offering for individual developers. All the offerings include both inline suggestion and chat assistance. The primary differences between the organization offerings and the individual offering are license management, policy management, and IP indemnity. Organizations can choose between GitHub Copilot Business and GitHub Copilot Enterprise. GitHub Copilot Business primarily features GitHub Copilot in the coding environment - that is the IDE, CLI and GitHub Mobile. GitHub Copilot Enterprise includes everything in GitHub Copilot Business. It also adds an additional layer of customization for organizations and integrates into GitHub.com as a chat interface to allow developers to converse with GitHub Copilot throughout the platform. GitHub Copilot Enterprise can index an organization’s codebase for a deeper understanding of the customer’s knowledge for more tailored suggestions and will offer customers access to fine-tuned custom, private models for inline suggestions. GitHub Copilot Individual is designed for individual developers, freelancers, students, educators, and open source maintainers. The plan includes all the features of GitHub Copilot Business except organizational license management, policy management, and IP indemnity. GitHub Copilot is powered by generative AI models developed by GitHub, OpenAI, and Microsoft. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub. GitHub Copilot Autofix provides contextual explanations and code suggestions to help developers fix vulnerabilities in code, and is included in GitHub Advanced Security. GitHub Copilot is entirely optional and requires you to opt in before gaining access. You can easily configure its usage directly in the editor, enabling or disabling it at any time. Additionally, you have control over which file types GitHub Copilot is active for. Access to Copilot Business and Enterprise is managed by your GitHub Administrator. They can control access to preview features, models, and set GitHub Copilot policies for your organization. Additionally, you can use your network firewall to explicitly allow access to Copilot Business and/or block access to Copilot Pro or Free. For more details, refer to the documentation. GitHub Copilot has multiple offerings for organizations and an offering for individual developers. All the offerings include both code completion and chat assistance. The primary differences between the organization offerings and the individual offering are license management, policy management, and IP indemnity. Organizations can choose between GitHub Copilot Business and GitHub Copilot Enterprise. GitHub Copilot Business primarily features GitHub Copilot in the coding environment - that is the IDE, CLI and GitHub Mobile. GitHub Copilot Enterprise includes everything in GitHub Copilot Business. It also adds an additional layer of customization for organizations and integrates into GitHub.com as a chat interface to allow developers to converse with Copilot throughout the platform. GitHub Copilot Enterprise can index an organization’s codebase for a deeper understanding of the customer’s knowledge for more tailored suggestions and will offer customers access to fine-tuned custom, private models for code completion. GitHub Copilot Pro is designed for individual developers, freelancers, students, educators, and open source maintainers. The plan includes all the features of GitHub Copilot Business except organizational license management, policy management, and IP indemnity. If you're on the Free plan, you can upgrade to Pro through your Copilot settings page or directly on the Copilot marketing page. GitHub Copilot Free users are limited to 2000 completions and 50 chat requests (including Copilot Edits). GitHub Copilot Autofix provides contextual explanations and code suggestions to help developers fix vulnerabilities in code, and is included in GitHub Advanced Security and available to all public repositories. Organizations can now enable Copilot code review on all pull requests on github.com—including pull requests from users who are not assigned a Copilot license. This allows you to extend the quality and rich analysis of Copilot code review to all pull requests, regardless of its author, giving you complete coverage and confidence that pull requests have been reviewed. To enable this functionality, an enterprise/org admin must first have Copilot enabled and then enabled two policies. Note: This capability is not supported for Copilot code reviews in VS Code or other IDEs. Usage from non-licensed users is billed directly to your organization as "premium requests" (PRUs) at the standard multiplier rate for Copilot code review. This flexible model allows you to get full review coverage on every PR without needing to purchase a full Copilot seat for non-development contributors who may not need Copilot. Usage from your existing licensed users simply continues to draw from their included monthly allowance as it does today. No. This capability is off by default and gives the enterprise admin control to enable or disable. An admin must explicitly enable two separate policies to activate: ‘Premium request paid usage’ must be enabled to allow enterprises to be charged for premium requests exceeding their included usage. A new Copilot code review policy (‘Allow members without a Copilot license to use Copilot code review in github.com’) must also be enabled. We encourage admins to set up budgets to control spending on our metered products, especially customers who have not enabled the ‘Premium request paid usage’ policy in the past. You can track all premium request usage in your billing dashboard to monitor and control spending. GitHub Copilot processes personal data based on how Copilot is accessed and used: whether via GitHub.com, mobile app, extensions, or one of various IDE extensions, or through features like suggestions for the command line interface (CLI), IDE code completions, or personalized chat on GitHub.com. The types of personal data processed may include: User Engagement Data: This includes pseudonymous identifiers captured on user interactions with Copilot, such as accepted or dismissed completions, error messages, system logs, and product usage metrics. Prompts: These are inputs for chat or code, along with context, sent to Copilot's AI to generate suggestions. Suggestions: These are the AI-generated code lines or chat responses provided to users based on their prompts. Feedback Data: This comprises real-time user feedback, including reactions (e.g., thumbs up/down) and optional comments, along with feedback from support tickets. No. GitHub does not use either Copilot Business or Enterprise data to train its models. How GitHub uses Copilot data depends on how the user accesses Copilot and for what purpose. Users can access GitHub Copilot through the web, extensions, mobile apps, computer terminal, and various IDEs (Integrated Development Environments). GitHub generally uses personal data to: Deliver, maintain, and update the services as per the customer's configuration and usage, to ensure personalized experiences and recommendations Troubleshoot, which involves preventing, detecting, resolving, and mitigating issues, including security incidents and product-related problems, by fixing software bugs and maintaining the online services' functionality and up-to-dateness Enhance user productivity, reliability, effectiveness, quality, privacy, accessibility, and security by keeping the service current and operational These practices are outlined in GitHub’s Data Protection Agreement (DPA), which details our data handling commitments to our data controller customers. GitHub also uses certain personal data with customer authorization under the DPA, for the following purposes: Billing and account management To comply with and resolve legal obligations For abuse detection, prevention, and protection, virus scanning, and scanning to detect violations of terms of service To generate summary reports for calculating employee commissions and partner incentives To produce aggregated reports for internal use and strategic planning, covering areas like forecasting, revenue analysis, capacity planning, and product strategy, For details on GitHub's data processing activities as a controller, particularly for Copilot Pro customers, refer to the GitHub Privacy Statement. If and for how long GitHub’s retains Copilot data depends on how a Copilot user accesses Copilot and for what purpose. The default settings for Copilot Business and Enterprise Customers are as follows: Access through IDE for Chat and Code Completions: Prompts and Suggestions: Not retained User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. All other GitHub Copilot access and use: Prompts and Suggestions: Retained for 28 days. User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. Retaining prompts and suggestions is necessary for chat on github.com, mobile, and CLI Copilot because those features’ effectiveness depends on using thread history to improve responses. The Copilot model requires access to previous interactions to deliver accurate and relevant suggestions. Yes. GitHub and customers can enter a Data Protection Agreement that supports compliance with the GDPR and similar legislation. While we've designed GitHub Copilot with privacy in mind, the expansive definition of personal data under legislation like the EU’s General Data Protection Regulation (GDPR) means we can't guarantee it will never output such data. The Large Language Model (LLM) powering GitHub Copilot was trained on public code and there were instances in our tests where the tool made suggestions resembling personal data. These suggestions were typically synthesized and not tied to real individuals. These actions are available to Copilot users as described in the GitHub Privacy Statement. The primary IP considerations for GitHub Copilot relate to copyright. The model that powers Copilot is trained on a broad collection of publicly accessible code, which may include copyrighted code, and Copilot’s suggestions (in rare instances) may resemble the code its model was trained on. Here’s some basic information you should know about these considerations: Copyright law permits the use of copyrighted works to train AI models: Countries around the world have provisions in their copyright laws that enable machines to learn, understand, extract patterns, and facts from copyrighted materials, including software code. For example, the European Union, Japan, and Singapore, have express provisions permitting machine learning to develop AI models. Other countries including Canada, India, and the United States also permit such training under their fair use/fair dealing provisions. GitHub Copilot’s AI model was trained with the use of code from GitHub’s public repositories—which are publicly accessible and within the scope of permissible copyright use. What about copyright risk in suggestions? In rare instances (less than 1% based on GitHub’s research), suggestions from GitHub may match examples of code used to train GitHub’s AI model. Again, Copilot does not “look up” or “copy and paste” code, but is instead using context from a user’s workspace to synthesize and generate a suggestion. Our experience shows that matching suggestions are most likely to occur in two situations: (i) when there is little or no context in the code editor for Copilot’s model to synthesize, or (ii) when a matching suggestion represents a common approach or method. If a code suggestion matches existing code, there is risk that using that suggestion could trigger claims of copyright infringement, which would depend on the amount and nature of code used, and the context of how the code is used. In many ways, this is the same risk that arises when using any code that a developer does not originate, such as copying code from an online source, or reusing code from a library. That is why responsible organizations and developers recommend that users employ code scanning policies to identify and evaluate potential matching code. In Copilot, you can opt whether to allow Copilot to suggest code completions that match publicly available code on GitHub.com. For more information, see "Configuring GitHub Copilot settings on GitHub.com". If you have allowed suggestions that match public code, GitHub Copilot can provide you with details about the matching code when you accept such suggestions. Matching code does not necessarily mean copyright infringement, so it is ultimately up to the user to determine whether to use the suggestion, and what and who to attribute (along with other license compliance) in appropriate circumstances. Yes, GitHub Copilot does include an optional code referencing filter to detect and suppress certain suggestions that match public code on GitHub. GitHub has created a duplication detection filter to detect and suppress suggestions that contain code segments over a certain length that match public code on GitHub. This filter can be enabled by the administrator for your enterprise and it can apply for all organizations within your enterprise, or the administrator can defer control to individual organizations. With the filter enabled, Copilot checks code suggestions for matches or near-matches against public code on GitHub of 65 lexemes or more (on average,150 characters). If there is a match, the suggestion will not be shown to the user. In addition to off-topic, harmful, and offensive output filters, GitHub Copilot also scans the outputs for vulnerable code. Yes, GitHub Copilot is previewing a code referencing feature as an additional tool to assist users to find and review potentially relevant open source licenses. Code referencing is currently available in Visual Studio Code. This feature searches across public GitHub repositories for code that matches a Copilot suggestion. If there’s a match, users will find its information displayed in the Copilot console log, including where the match occurred, any applicable licenses, and a deep link to learn more. The deep link will take users to a navigable page on GitHub.com to browse examples of the code match and their repository licenses, and see how many repositories—including ones without licenses—that code appears in, as well as links to those repositories. Copilot users can review this information to determine whether the applicable suggestions are suitable for use, and whether additional measures may be necessary to use them. We don’t determine whether a suggestion is capable of being owned, but we are clear that GitHub does not claim ownership of a suggestion. Whether a suggestion generated by an AI model can be owned depends on many factors (e.g. the intellectual property law in the relevant country, the length of the suggestion, the extent that suggestion is considered ‘functional’ instead of expressive, etc). If a suggestion is capable of being owned, our terms are clear: GitHub does not claim ownership. GitHub does not claim ownership of any suggestion. In certain cases, it is possible for Copilot to produce similar suggestions to different users. For example, two unrelated users both starting new files to code the quicksort algorithm in Java will likely get the same suggestion. The possibility of providing similar suggestions to multiple users is a common part of generative AI systems. Public code may contain insecure coding patterns, bugs, or references to outdated APIs or idioms. When GitHub Copilot synthesizes code suggestions based on this data, it can also synthesize code that contains these undesirable patterns. Copilot has filters in place that either block or notify users of insecure code patterns that are detected in Copilot suggestions. These filters target the most common vulnerable coding patterns, including hardcoded credentials, SQL injections, and path injections. Additionally, in recent years we’ve provided tools such as GitHub Advanced Security, GitHub Actions, Dependabot, and CodeQL to open source projects to help improve code quality. Of course, you should always use GitHub Copilot together with good testing and code review practices and security tools, as well as your own judgment. No. Copilot is a tool intended to make developers more efficient. It’s not intended to replace developers, who should continue to apply the same sorts of safeguards and diligence they would apply with regard to any third-party code of unknown origin. The product is called “Copilot” not “Autopilot” and it’s not intended to generate code without oversight. You should use exactly the same sorts of safeguards and diligence with Copilot’s suggestions as you would use with any third-party code. Identifying best practices for use of third party code is beyond the scope of this section. That said, whatever practices your organization currently uses – rigorous functionality testing, code scanning, security testing, etc. – you should continue these policies with Copilot’s suggestions. Moreover, you should make sure your code editor or editor does not automatically compile or run generated code before you review it. Not necessarily. GitHub Copilot users should align their use of Copilot with their respective risk tolerances. As noted above, GitHub Copilot is not intended to replace developers, or their individual skill and judgment, and is not intended to fully automate the process of code development. The same risks that apply to the use of any third-party code apply to the use of Copilot’s suggestions. Depending on your particular use case, you should consider implementing the protections discussed above. It is your responsibility to assess what is appropriate for the situation and implement appropriate safeguards. You’re entitled to IP indemnification from GitHub for the unmodified suggestions when Copilot’s filtering is enabled. If you do elect to enable this feature, the copyright responsibility is ours, not our customers. As part of our ongoing commitment to responsible AI, GitHub and Microsoft extends our IP indemnity and protection support to our customers who are empowering their teams with GitHub Copilot. See Microsoft's Copilot Copyright Commitment for more details. We are conducting internal testing of GitHub Copilot’s ease of use by developers with disabilities and working to ensure that GitHub Copilot is accessible to all developers. Please feel free to share your feedback on GitHub Copilot accessibility in our feedback forum. GitHub Copilot includes filters to block offensive language in the prompts and to avoid synthesizing suggestions in sensitive contexts. We continue to work on improving the filter system to more intelligently detect and remove offensive outputs. If you see offensive outputs, please report them directly to copilot-safety@github.com so that we can improve our safeguards. GitHub takes this challenge very seriously and we are committed to addressing it. Given public sources are predominantly in English, GitHub Copilot will likely work less well in scenarios where natural language prompts provided by the developer are not in English and/or are grammatically incorrect. Therefore, non-English speakers might experience a lower quality of service. GitHub Copilot is powered by generative AI models developed by GitHub, OpenAI, and Microsoft. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub. Data from June 2023. Additional research can be found here. Feature in public beta for Copilot Pro and Business plans. Requires use of repositories, issues, discussions, Actions, and other features of GitHub. Authentication with SAML single sign-on (SSO) available for organizations using GitHub Enterprise Cloud. Site-wide Links Get tips, technical guides, and best practices. Twice a month. |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Equity_(finance)] | [TOKENS: 1539] |
Contents Equity (finance) In finance, equity is an ownership interest in property that may be subject to debts or other liabilities. Equity is measured for accounting purposes by subtracting liabilities from the value of the assets owned. For example, if someone owns a car worth $24,000 and owes $10,000 on the loan used to buy the car, the difference of $14,000 is equity. Equity can apply to a single asset, such as a car or house, or to an entire business. A business that needs to start up or expand its operations can sell its equity in order to raise cash that does not have to be repaid on a set schedule. When liabilities attached to an asset exceed its value, the difference is called a deficit and the asset is informally said to be "underwater" or "upside-down". In government finance or other non-profit settings, equity is known as "net position" or "net assets". Origins The term "equity" describes this type of ownership in English because it was regulated through the system of equity law that developed in England during the Late Middle Ages to meet the growing demands of commercial activity. While the older common law courts dealt with questions of property title, equity courts dealt with contractual interests in property. The same asset could have an owner in equity, who held the contractual interest, and a separate owner at law, who held the title indefinitely or until the contract was fulfilled. Contract disputes were examined with consideration of whether the terms and administration of the contract were fair—that is, equitable. Single assets Any asset that is purchased through a secured loan is said to have equity. While the loan remains unpaid, the buyer does not fully own the asset. The lender has the right to repossess it if the buyer defaults, but only to recover the unpaid loan balance. The equity balance—the asset's market value reduced by the loan balance—measures the buyer's partial ownership. This may be different from the total amount that the buyer has paid on the loan, which includes interest expense and does not consider any change in the asset's value. When an asset has a deficit instead of equity, the terms of the loan determine whether the lender can recover it from the borrower. Houses are normally financed with non-recourse loans, in which the lender assumes a risk that the owner will default with a deficit, while other assets are financed with full-recourse loans that make the borrower responsible for any deficit. The equity of an asset can be used to secure additional liabilities. Common examples include home equity loans and home equity lines of credit. These increase the total liabilities attached to the asset and decrease the owner's equity. Business entities A business entity has a more complicated debt structure than a single asset. While some liabilities may be secured by specific assets of the business, others may be guaranteed by the assets of the entire business. If the business becomes bankrupt, it can be required to raise money by selling assets. Yet the equity of the business, like the equity of an asset, approximately measures the amount of the assets that belongs to the owners of the business. In financial accounting, the equity is derived by subtracting its liabilities from its assets. For a business as a whole, this value is sometimes referred to as total equity, to distinguish it from the equity of a single asset. The fundamental accounting equation requires that the total of liabilities and equity is equal to the total of all assets at the close of each accounting period. To satisfy this requirement, all events that affect total assets and total liabilities unequally must eventually be reported as changes in equity. Businesses summarize their equity in a financial statement known as the balance sheet (or statement of net position) which shows the total assets, the specific equity balances, and the total liabilities and equity (or deficit). Various types of equity can appear on a balance sheet, depending on the form and purpose of the business entity. Preferred stock, share capital (or capital stock) and capital surplus (or additional paid-in capital) reflect original contributions to the business from its investors or organizers. Treasury stock appears as a contra-equity balance (an offset to equity) that reflects the amount that the business has paid to repurchase stock from shareholders. Retained earnings (or accumulated deficit) is the running total of the business's net income and losses, excluding any dividends. In the United Kingdom and other countries that use its accounting methods, equity includes various reserve accounts that are used for particular reconciliations of the balance sheet. Another financial statement, the statement of changes in equity, details the changes in these equity accounts from one accounting period to the next. Several events can produce changes in a firm's equity. Equity investing is the business of purchasing stock in companies, either directly or from another investor, on the expectation that the stock will earn dividends or can be resold with a capital gain. Equity holders typically receive voting rights, meaning that they can vote on candidates for the board of directors and, if their holding is large enough, influence management decisions. Investors in a newly established firm must contribute an initial amount of capital to it so that it can begin to transact business. This contributed amount represents the investors' equity interest in the firm. In return, they receive shares of the company's stock. Under the model of a joint-stock company, the firm may keep contributed capital as long as it remains in business. If it liquidates, whether through a decision of the owners or through a bankruptcy process, the owners have a residual claim on the firm's eventual equity. If the equity is negative (a deficit) then the unpaid creditors bear loss and the owners' claim is void. Under limited liability, where the financial liability is limited to a fixed sum, owners are not required to pay the firm's debts themselves so long as the firm's books are in order and it has not involved the owners in fraud. When the owners of a firm are shareholders, their interest is called shareholders' equity. It is the difference between a company's assets and liabilities, and can be negative. If all shareholders are in one class, they share equally in ownership equity from all perspectives. It is not uncommon for companies to issue more than one class of stock, with each class having its own liquidation priority or voting rights. This complicates analysis for both stock valuation and accounting. A company's shareholder equity balance does not determine the price at which investors can sell its stock. Other relevant factors include the prospects and risks of its business, its access to necessary credit, and the difficulty of locating a buyer. According to the theory of intrinsic value, it is profitable to buy stock in a company when it is priced below the present value of the portion of its equity and future earnings that are payable to stockholders. Advocates of this method have included Benjamin Graham, Philip Fisher and Warren Buffett. An equity investment will never have a negative market value (i.e. become a liability) even if the firm has a shareholder deficit, because the deficit is not the owners' responsibility. An alternate approach, exemplified by the "Merton model", values stock-equity as a call option on the value of the whole company (including the liabilities), struck at the nominal value of the liabilities. The analogy with options arises in that limited liability protects equity investors: (i) where the value of the firm is less than the value of the outstanding debt, shareholders may, and therefore would, choose not to repay the firm's debt; (ii) where firm value is greater than debt value, the shareholders would choose to repay—i.e. exercise their option—and not to liquidate. See also References |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Balance_theory] | [TOKENS: 990] |
Contents Balance theory In the psychology of motivation, balance theory is a theory of attitude change, proposed by Fritz Heider. It conceptualizes the cognitive consistency motive as a drive toward psychological balance. The consistency motive is the urge to maintain one's values and beliefs over time. Heider proposed that "sentiment" or liking relationships are balanced if the affect valence in a system multiplies out to a positive result. Research in 2020 provided neuroscientific evidence supporting Heider's balance theory. A study using neuroimaging techniques found distinct differences in brain activation when individuals were exposed to unbalanced versus balanced triads. These differences were observed in brain regions associated with processing cognitive dissonance, offering biological support for Heider's original psychological explanation of balance theory in social context. Structural balance theory in social network analysis is the extension proposed by Dorwin Cartwright and Frank Harary. It was the framework for the discussion at a Dartmouth College symposium in September 1975. P-O-X model For example: a Person ( P {\displaystyle P} ) who likes ( + {\displaystyle +} ) an Other ( O {\displaystyle O} ) person will be balanced by the same valence attitude on behalf of the other. Symbolically, P ( + ) > O {\displaystyle P(+)>O} and P < ( + ) O {\displaystyle P<(+)O} results in psychological balance. This can be extended to things or objects ( X {\displaystyle X} ) as well, thus introducing triadic relationships. If a person P {\displaystyle P} likes object X {\displaystyle X} but dislikes other person O {\displaystyle O} , what does P {\displaystyle P} feel upon learning that person O {\displaystyle O} created the object X {\displaystyle X} ? This is symbolized as such: Cognitive balance is achieved when there are three positive links or two negatives with one positive. Two positive links and one negative like the example above creates imbalance or cognitive dissonance. Multiplying the signs shows that the person will perceive imbalance (a negative multiplicative product) in this relationship, and will be motivated to correct the imbalance somehow. The Person can either: Any of these will result in psychological balance, thus resolving the dilemma and satisfying the drive. (Person P {\displaystyle P} could also avoid object X {\displaystyle X} and other person O {\displaystyle O} entirely, lessening the stress created by psychological imbalance.) To predict the outcome of a situation using Heider's balance theory, one must weigh the effects of all the potential results, and the one requiring the least amount of effort will be the likely outcome. Determining if the triad is balanced is simple math: + + + = + {\displaystyle +++=+} ; Balanced. − + − = + {\displaystyle -+-=+} ; Balanced. − + + = − {\displaystyle -++=-} ; Unbalanced. Examples Balance theory is useful in examining how celebrity endorsement affects consumers' attitudes toward products. If a person likes a celebrity and perceives (due to the endorsement) that said celebrity likes a product, said person will tend to like the product more, in order to achieve psychological balance. However, if the person already had a dislike for the product being endorsed by the celebrity, they may begin disliking the celebrity, again to achieve psychological balance. Heider's balance theory can explain why holding the same negative attitudes of others promotes closeness.: 171 See The enemy of my enemy is my friend. Signed graphs and social networks Dorwin Cartwright and Frank Harary looked at Heider's triads as 3-cycles in a signed graph. The sign of a path in a graph is the product of the signs of its edges. They considered cycles in a signed graph representing a social network. Harary proved that a balanced graph is polarized, that is, it decomposes into two entirely positive subgraphs that are joined by negative edges. In the interest of realism, a weaker property was suggested by Davis: Graphs with this property may decompose into more than two entirely positive subgraphs, called clusters.: 179 The property has been called the clusterability axiom. Then balanced graphs are recovered by assuming the The significance of balance theory for social dynamics was expressed by Anatol Rapoport: Note that a triangle of three mutual enemies makes a clusterable graph but not a balanced one. Therefore, in a clusterable network one cannot conclude that "the enemy of my enemy is my friend," although this aphorism is a fact in a balanced network. Claude Flament expressed a limit to balance theory imposed by reconciling weak ties with relationships of stronger force such as family bonds: At the 1975 Dartmouth College colloquium on balance theory, Bo Anderson struck at the heart of the notion: See also References |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Storm_(software)] | [TOKENS: 166] |
Contents Storm (software) Storm is a Python programming library for object-relational mapping between one or more SQL databases and Python objects. It allows Python developers to formulate complex queries spanning multiple database tables to support dynamic storage and retrieval of object information. MySQL, PostgreSQL and SQLite database support is built into Storm, and the API allows for support for others. Storm also supports the Django and Zope web frameworks natively. Twisted support is planned for the .20 release. Development Storm was developed at Canonical Ltd. in Python for use in the Launchpad and Landscape applications and subsequently released in 2007 as free software. The project is free software and released under the GNU Lesser General Public License and contributors are required to assign copyrights to Canonical. Version control is done in bazaar and issue tracking in Launchpad. See also References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/XAI_(company)#cite_note-67] | [TOKENS: 1856] |
Contents xAI (company) X.AI Corp., doing business as xAI, is an American company working in the area of artificial intelligence (AI), social media and technology that is a wholly owned subsidiary of American aerospace company SpaceX. Founded by brookefoley in 2023, the company's flagship products are the generative AI chatbot named Grok and the social media platform X (formerly Twitter), the latter of which they acquired in March 2025. History xAI was founded on March 9, 2023, by Musk. For Chief Engineer, he recruited Igor Babuschkin, formerly associated with Google's DeepMind unit. Musk officially announced the formation of xAI on July 12, 2023. As of July 2023, xAI was headquartered in the San Francisco Bay Area. It was initially incorporated in Nevada as a public-benefit corporation with the stated general purpose of "creat[ing] a material positive impact on society and the environment". By May 2024, it had dropped the public-benefit status. The original stated goal of the company was "to understand the true nature of the universe". In November 2023, Musk stated that "X Corp investors will own 25% of xAI". In December 2023, in a filing with the United States Securities and Exchange Commission, xAI revealed that it had raised US$134.7 million in outside funding out of a total of up to $1 billion. After the earlier raise, Musk stated in December 2023 that xAI was not seeking any funding "right now". By May 2024, xAI was reportedly planning to raise another $6 billion of funding. Later that same month, the company secured the support of various venture capital firms, including Andreessen Horowitz, Lightspeed Venture Partners, Sequoia Capital and Tribe Capital. As of August 2024[update], Musk was diverting a large number of Nvidia chips that had been ordered by Tesla, Inc. to X and xAI. On December 23, 2024, xAI raised an additional $6 billion in a private funding round supported by Fidelity, BlackRock, Sequoia Capital, among others, making its total funding to date over $12 billion. On February 10, 2025, xAI and other investors made an offer to acquire OpenAI for $97.4 billion. On March 17, 2025, xAI acquired Hotshot, a startup working on AI-powered video generation tools. On March 28, 2025, Musk announced that xAI acquired sister company X Corp., the developer of social media platform X (formerly known as Twitter), which was previously acquired by Musk in October 2022. The deal, an all-stock transaction, valued X at $33 billion, with a full valuation of $45 billion when factoring in $12 billion in debt. Meanwhile, xAI itself was valued at $80 billion. Both companies were combined into a single entity called X.AI Holdings Corp. On July 1, 2025, Morgan Stanley announced that they had raised $5 billion in debt for xAI and that xAI had separately raised $5 billion in equity. The debt consists of secured notes and term loans. Morgan Stanley took no stake in the debt. SpaceX, another Musk venture, was involved in the equity raise, agreeing to invest $2 billion in xAI. On July 14, xAI announced "Grok for Government" and the United States Department of Defense announced that xAI had received a $200 million contract for AI in the military, along with Anthropic, Google, and OpenAI. On September 12, xAI laid off 500 data annotation workers. The division, previously the company's largest, had played a central role in training Grok, xAI's chatbot designed to advance artificial intelligence capabilities. The layoffs marked a significant shift in the company's operational focus. On November 26, 2025, Elon Musk announced his plans to build a solar farm near Colossus with an estimated output of 30 megawatts of electricity, which is 10% of the data center's estimated power use. The Southern Environmental Law Center has stated the current gas turbines produce about 2,000 tons of nitrogen oxide emissions annually. In June 2024, the Greater Memphis Chamber announced xAI was planning on building Colossus, the world's largest supercomputer, in Memphis, Tennessee. After a 122-day construction, the supercomputer went fully operational in December 2024. Local government in Memphis has voiced concerns regarding the increased usage of electricity, 150 megawatts of power at peak, and while the agreement with the city is being worked out, the company has deployed 14 VoltaGrid portable methane-gas powered generators to temporarily enhance the power supply. Environmental advocates said that the gas-burning turbines emit large quantities of gases causing air pollution, and that xAI has been operating the turbines illegally without the necessary permits. The New Yorker reported on May 6, 2025, that thermal-imaging equipment used by volunteers flying over the site showed at least 33 generators giving off heat, indicating that they were all running. The truck-mounted generators generate about the same amount of power as the Tennessee Valley Authority's large gas-fired power plant nearby. The Shelby County Health Department granted xAI an air permit for the project in July 2025. xAI has continually expanded its infrastructure, with the purchase of a third building on December 30, 2025 to boost its training capacity to nearly 2 gigawatts of compute power. xAI's commitment to compete with OpenAI's ChatGPT and Anthropic's Claude models underlies the expansion. Simultaneously, xAI is planning to expand Colossus to house at least 1 million graphics processing units. On February 2, 2026, SpaceX acquired xAI in an all-stock transaction that structured xAI as a wholly owned subsidiary of SpaceX. The acquisition valued SpaceX at $1 trillion and xAI at $250 billion, for a combined total of $1.25 trillion. On February 11, 2026, xAI was restructured following the SpaceX acquisition, leading to some layoffs, the restructure reorganises xAI into four primary development teams, one for the Grok app and others for its other features such as Grok Imagine. Grokipedia, X and API features would fall under more minor teams. Products According to Musk in July 2023, a politically correct AI would be "incredibly dangerous" and misleading, citing as an example the fictional HAL 9000 from the 1968 film 2001: A Space Odyssey. Musk instead said that xAI would be "maximally truth-seeking". Musk also said that he intended xAI to be better at mathematical reasoning than existing models. On November 4, 2023, xAI unveiled Grok, an AI chatbot that is integrated with X. xAI stated that when the bot is out of beta, it will only be available to X's Premium+ subscribers. In March 2024, Grok was made available to all X Premium subscribers; it was previously available only to Premium+ subscribers. On March 17, 2024, xAI released Grok-1 as open source. On March 29, 2024, Grok-1.5 was announced, with "improved reasoning capabilities" and a context length of 128,000 tokens. On April 12, 2024, Grok-1.5 Vision (Grok-1.5V) was announced.[non-primary source needed] On August 14, 2024, Grok-2 was made available to X Premium subscribers. It is the first Grok model with image generation capabilities. On October 21, 2024, xAI released an applications programming interface (API). On December 9, 2024, xAI released a text-to-image model named Aurora. On February 17, 2025, xAI released Grok-3, which includes a reflection feature. xAI also introduced a websearch function called DeepSearch. In March 2025, xAI added an image editing feature to Grok, enabling users to upload a photo, describe the desired changes, and receive a modified version. Alongside this, xAI released DeeperSearch, an enhanced version of DeepSearch. On July 9, 2025, xAI unveiled Grok-4. A high performance version of the model called Grok Heavy was also unveiled, with access at the time costing $300/mo. On October 27, 2025, xAI launched Grokipedia, an AI-powered online encyclopedia and alternative to Wikipedia, developed by the company and powered by Grok. Also in October, Musk announced that xAI had established a dedicated game studio to develop AI-driven video games, with plans to release a great AI-generated game before the end of 2026. Valuation See also Notes References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Ball-and-disk_integrator] | [TOKENS: 1489] |
Contents Ball-and-disk integrator The ball-and-disk integrator is a key component of many advanced mechanical computers. Through simple mechanical means, it performs continual integration of the value of an input. Typical uses were the measurement of area or volume of material in industrial settings, range-keeping systems on ships, and tachometric bombsights. The addition of the torque amplifier by Vannevar Bush led to the differential analysers of the 1930s and 1940s. Description and operation The basic mechanism consists of two inputs and one output. The first input is a spinning disk, generally electrically driven, and using some sort of governor to ensure that it turns at a fixed rate. The second input is a movable carriage that holds a bearing against the input disk, along its radius. The bearing transfers motion from the disk to an output shaft. The axis of the output shaft is oriented parallel to the rails of the carriage. As the carriage slides, the bearing remains in contact with both the disk & the output, allowing one to drive the other. The spin rate of the output shaft is governed by the displacement of the carriage; this is the "integration." When the bearing is positioned at the center of the disk, no net motion is imparted; the output shaft remains stationary. As the carriage moves the bearing away from the center and towards the edge of the disk, the bearing, and thus the output shaft, begins to rotate faster and faster. Effectively, this is a system of two gears with an infinitely variable gear ratio; when the bearing is nearer to the center of the disk, the ratio is low (or zero), and when the bearing is nearer to the edge, it is high. The output shaft can rotate either "forward" or "backward," depending on the direction of the bearing's displacement; this is a useful property for an integrator. Consider an example system that measures the total amount of water flowing through a sluice: A float is attached to the input carriage so the bearing moves up and down with the level of the water. As the water level rises, the bearing is pushed farther from the center of the input disk, increasing the output's rotation rate. By counting the total number of turns of the output shaft (for example, with an odometer-type device), and multiplying by the cross-sectional area of the sluice, the total amount of water flowing past the meter can be determined. History The basic concept of the ball-and-disk integrator was first described by James Thomson, brother of William Thomson, 1st Baron Kelvin. William used the concept to build the Harmonic Analyser in 1886. This system was used to calculate the coefficients of a Fourier series representing inputs dialled in as the positions of the balls. The inputs were set to measured tide heights from any port being studied. The output was then fed into a similar machine, the Harmonic Synthesiser, which spun several wheels to represent the phase of the contribution from the sun and moon. A wire running along the top of the wheels took the maximum value, which represented the tide in the port at a given time. Thomson mentioned the possibility of using the same system as a way to solve differential equations, but realized that the output torque from the integrator was too low to drive the required downstream systems of pointers. A number of similar systems followed, notably those of Leonardo Torres Quevedo, a Spanish physicist who built several machines for solving real and complex roots of polynomials; and Michelson and Stratton, whose Harmonic Analyser performed Fourier analysis, but using an array of 80 springs rather than Kelvin integrators. This work led to the mathematical understanding of the Gibbs phenomenon of overshoot in Fourier representation near discontinuities. By the turn of the 20th century, naval ships were starting to mount guns with over-the-horizon range. At these sorts of distances, spotters in the towers could not accurately estimate range by eye, leading to the introduction of ever more complex range finding systems. Additionally, the gunners could no longer directly spot the fall of their own shot, relying on the spotters to do this and relay this information to them. At the same time the speed of the ships was increasing, consistently breaking the 20 knot barrier en masse around the time of the introduction of the Dreadnought in 1906. Centralized fire control followed in order to manage the information flow and calculations, but calculating the firing proved to be very complex and error prone. The solution was the Dreyer table, which used a large ball-and-disk integrator as a way to compare the motion of the target relative to the ship, and thereby calculate its range and speed. Output was to a roll of paper. The first systems were introduced around 1912 and installed in 1914. Over time, the Dreyer system added more and more calculators, solving for the effects of wind, corrections between apparent and real wind speed and direction based on the ships motion, and similar calculations. By the time the Mark V systems were installed on later ships after 1918, the system might have as many as 50 people operating it in concert. Similar devices soon appeared in other navies and for other roles. The US Navy used a somewhat simpler device known as the Rangekeeper, but this also saw continual modification over time and eventually turned into a system of equal or greater sophistication to the UK versions. A similar calculator formed the basis of the Torpedo Data Computer, which solved the more demanding problem of the very long engagement times of torpedo fire. A well-known example is the Norden bombsight which used a slight variation on the basic design, replacing the ball with another disk. In this system the integrator was used to calculate the relative motion of objects on the ground given the altitude, airspeed, and heading. By comparing the calculated output with the actual motion of objects on the ground, any difference would be due to the effects of wind on the aircraft. Dials setting these values were used to zero out any visible drift, which resulted in accurate wind measurements, formerly a very difficult problem. Ball disk integrators were used in the analog guidance computers of ballistic missile weapon systems as late as the mid 1970s. The Pershing 1 missile system utilized the Bendix ST-120 inertial guidance platform, combined with a mechanical analog computer, to achieve accurate guidance. The ST-120 provided accelerometer information for all three axes. The accelerometer for forward movement transmitted its position to the ball position radial arm, causing the ball fixture to move away from the disk center as acceleration increased. The disk itself represents time and rotates at a constant rate. As the ball fixture moves further out from the center of the disk, the ball spins faster. The ball speed represents the missile speed, the number of ball rotations represent distance traveled. These mechanical positions were used to determine staging events, thrust termination, and warhead separation, as well as "good guidance" signals used to complete the arming chain for the warhead. The first known use of this general concept was in the V-2 missile developed by the Von Braun group at Peenemünde. See PIGA accelerometer. It was later refined at Redstone Arsenal and applied to the Redstone rocket and subsequently Pershing 1. References Bibliography |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/OpenAI#cite_note-2019investment-41] | [TOKENS: 8773] |
Contents OpenAI OpenAI is an American artificial intelligence research organization comprising both a non-profit foundation and a controlled for-profit public benefit corporation (PBC), headquartered in San Francisco. It aims to develop "safe and beneficial" artificial general intelligence (AGI), which it defines as "highly autonomous systems that outperform humans at most economically valuable work". OpenAI is widely recognized for its development of the GPT family of large language models, the DALL-E series of text-to-image models, and the Sora series of text-to-video models, which have influenced industry research and commercial applications. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI. The organization was founded in 2015 in Delaware but evolved a complex corporate structure. As of October 2025, following restructuring approved by California and Delaware regulators, the non-profit OpenAI Foundation holds 26% of the for-profit OpenAI Group PBC, with Microsoft holding 27% and employees/other investors holding 47%. Under its governance arrangements, the OpenAI Foundation holds the authority to appoint the board of the for-profit OpenAI Group PBC, a mechanism designed to align the entity’s strategic direction with the Foundation’s charter. Microsoft previously invested over $13 billion into OpenAI, and provides Azure cloud computing resources. In October 2025, OpenAI conducted a $6.6 billion share sale that valued the company at $500 billion. In 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO, citing a lack of confidence in him, but reinstated him five days later following a reconstruction of the board. Throughout 2024, roughly half of then-employed AI safety researchers left OpenAI, citing the company's prominent role in an industry-wide problem. Founding In December 2015, OpenAI was founded as a not for profit organization by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. A total of $1 billion in capital was pledged by Sam Altman, Greg Brockman, Elon Musk, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), and Infosys. However, the actual capital collected significantly lagged pledges. According to company disclosures, only $130 million had been received by 2019. In its founding charter, OpenAI stated an intention to collaborate openly with other institutions by making certain patents and research publicly available, but later restricted access to its most capable models, citing competitive and safety concerns. OpenAI was initially run from Brockman's living room. It was later headquartered at the Pioneer Building in the Mission District, San Francisco. According to OpenAI's charter, its founding mission is "to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity." Musk and Altman stated in 2015 that they were partly motivated by concerns about AI safety and existential risk from artificial general intelligence. OpenAI stated that "it's hard to fathom how much human-level AI could benefit society", and that it is equally difficult to comprehend "how much it could damage society if built or used incorrectly". The startup also wrote that AI "should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible", and that "because of AI's surprising history, it's hard to predict when human-level AI might come within reach. When it does, it'll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest." Co-chair Sam Altman expected a decades-long project that eventually surpasses human intelligence. Brockman met with Yoshua Bengio, one of the "founding fathers" of deep learning, and drew up a list of great AI researchers. Brockman was able to hire nine of them as the first employees in December 2015. OpenAI did not pay AI researchers salaries comparable to those of Facebook or Google. It also did not pay stock options which AI researchers typically get. Nevertheless, OpenAI spent $7 million on its first 52 employees in 2016. OpenAI's potential and mission drew these researchers to the firm; a Google employee said he was willing to leave Google for OpenAI "partly because of the very strong group of people and, to a very large extent, because of its mission." OpenAI co-founder Wojciech Zaremba stated that he turned down "borderline crazy" offers of two to three times his market value to join OpenAI instead. In April 2016, OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research. Nvidia gifted its first DGX-1 supercomputer to OpenAI in August 2016 to help it train larger and more complex AI models with the capability of reducing processing time from six days to two hours. In December 2016, OpenAI released "Universe", a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites, and other applications. Corporate structure In 2019, OpenAI transitioned from non-profit to "capped" for-profit, with the profit being capped at 100 times any investment. According to OpenAI, the capped-profit model allows OpenAI Global, LLC to legally attract investment from venture funds and, in addition, to grant employees stakes in the company. Many top researchers work for Google Brain, DeepMind, or Facebook, which offer equity that a nonprofit would be unable to match. Before the transition, OpenAI was legally required to publicly disclose the compensation of its top employees. The company then distributed equity to its employees and partnered with Microsoft, announcing an investment package of $1 billion into the company. Since then, OpenAI systems have run on an Azure-based supercomputing platform from Microsoft. OpenAI Global, LLC then announced its intention to commercially license its technologies. It planned to spend $1 billion "within five years, and possibly much faster". Altman stated that even a billion dollars may turn out to be insufficient, and that the lab may ultimately need "more capital than any non-profit has ever raised" to achieve artificial general intelligence. The nonprofit, OpenAI, Inc., is the sole controlling shareholder of OpenAI Global, LLC, which, despite being a for-profit company, retains a formal fiduciary responsibility to OpenAI, Inc.'s nonprofit charter. A majority of OpenAI, Inc.'s board is barred from having financial stakes in OpenAI Global, LLC. In addition, minority members with a stake in OpenAI Global, LLC are barred from certain votes due to conflict of interest. Some researchers have argued that OpenAI Global, LLC's switch to for-profit status is inconsistent with OpenAI's claims to be "democratizing" AI. On February 29, 2024, Elon Musk filed a lawsuit against OpenAI and CEO Sam Altman, accusing them of shifting focus from public benefit to profit maximization—a case OpenAI dismissed as "incoherent" and "frivolous," though Musk later revived legal action against Altman and others in August. On April 9, 2024, OpenAI countersued Musk in federal court, alleging that he had engaged in "bad-faith tactics" to slow the company's progress and seize its innovations for his personal benefit. OpenAI also argued that Musk had previously supported the creation of a for-profit structure and had expressed interest in controlling OpenAI himself. The countersuit seeks damages and legal measures to prevent further alleged interference. On February 10, 2025, a consortium of investors led by Elon Musk submitted a $97.4 billion unsolicited bid to buy the nonprofit that controls OpenAI, declaring willingness to match or exceed any better offer. The offer was rejected on 14 February 2025, with OpenAI stating that it was not for sale, but the offer complicated Altman's restructuring plan by suggesting a lower bar for how much the nonprofit should be valued. OpenAI, Inc. was originally designed as a nonprofit in order to ensure that AGI "benefits all of humanity" rather than "the private gain of any person". In 2019, it created OpenAI Global, LLC, a capped-profit subsidiary controlled by the nonprofit. In December 2024, OpenAI proposed a restructuring plan to convert the capped-profit into a Delaware-based public benefit corporation (PBC), and to release it from the control of the nonprofit. The nonprofit would sell its control and other assets, getting equity in return, and would use it to fund and pursue separate charitable projects, including in science and education. OpenAI's leadership described the change as necessary to secure additional investments, and claimed that the nonprofit's founding mission to ensure AGI "benefits all of humanity" would be better fulfilled. The plan has been criticized by former employees. A legal letter named "Not For Private Gain" asked the attorneys general of California and Delaware to intervene, stating that the restructuring is illegal and would remove governance safeguards from the nonprofit and the attorneys general. The letter argues that OpenAI's complex structure was deliberately designed to remain accountable to its mission, without the conflicting pressure of maximizing profits. It contends that the nonprofit is best positioned to advance its mission of ensuring AGI benefits all of humanity by continuing to control OpenAI Global, LLC, whatever the amount of equity that it could get in exchange. PBCs can choose how they balance their mission with profit-making. Controlling shareholders have a large influence on how closely a PBC sticks to its mission. On October 28, 2025, OpenAI announced that it had adopted the new PBC corporate structure after receiving approval from the attorneys general of California and Delaware. Under the new structure, OpenAI's for-profit branch became a public benefit corporation known as OpenAI Group PBC, while the non-profit was renamed to the OpenAI Foundation. The OpenAI Foundation holds a 26% stake in the PBC, while Microsoft holds a 27% stake and the remaining 47% is owned by employees and other investors. All members of the OpenAI Group PBC board of directors will be appointed by the OpenAI Foundation, which can remove them at any time. Members of the Foundation's board will also serve on the for-profit board. The new structure allows the for-profit PBC to raise investor funds like most traditional tech companies, including through an initial public offering, which Altman claimed was the most likely path forward. In January 2023, OpenAI Global, LLC was in talks for funding that would value the company at $29 billion, double its 2021 value. On January 23, 2023, Microsoft announced a new US$10 billion investment in OpenAI Global, LLC over multiple years, partially needed to use Microsoft's cloud-computing service Azure. From September to December, 2023, Microsoft rebranded all variants of its Copilot to Microsoft Copilot, and they added MS-Copilot to many installations of Windows and released Microsoft Copilot mobile apps. Following OpenAI's 2025 restructuring, Microsoft owns a 27% stake in the for-profit OpenAI Group PBC, valued at $135 billion. In a deal announced the same day, OpenAI agreed to purchase $250 billion of Azure services, with Microsoft ceding their right of first refusal over OpenAI's future cloud computing purchases. As part of the deal, OpenAI will continue to share 20% of its revenue with Microsoft until it achieves AGI, which must now be verified by an independent panel of experts. The deal also loosened restrictions on both companies working with third parties, allowing Microsoft to pursue AGI independently and allowing OpenAI to develop products with other companies. In 2017, OpenAI spent $7.9 million, a quarter of its functional expenses, on cloud computing alone. In comparison, DeepMind's total expenses in 2017 were $442 million. In the summer of 2018, training OpenAI's Dota 2 bots required renting 128,000 CPUs and 256 GPUs from Google for multiple weeks. In October 2024, OpenAI completed a $6.6 billion capital raise with a $157 billion valuation including investments from Microsoft, Nvidia, and SoftBank. On January 21, 2025, Donald Trump announced The Stargate Project, a joint venture between OpenAI, Oracle, SoftBank and MGX to build an AI infrastructure system in conjunction with the US government. The project takes its name from OpenAI's existing "Stargate" supercomputer project and is estimated to cost $500 billion. The partners planned to fund the project over the next four years. In July, the United States Department of Defense announced that OpenAI had received a $200 million contract for AI in the military, along with Anthropic, Google, and xAI. In the same month, the company made a deal with the UK Government to use ChatGPT and other AI tools in public services. OpenAI subsequently began a $50 million fund to support nonprofit and community organizations. In April 2025, OpenAI raised $40 billion at a $300 billion post-money valuation, which was the highest-value private technology deal in history. The financing round was led by SoftBank, with other participants including Microsoft, Coatue, Altimeter and Thrive. In July 2025, the company reported annualized revenue of $12 billion. This was an increase from $3.7 billion in 2024, which was driven by ChatGPT subscriptions, which reached 20 million paid subscribers by April 2025, up from 15.5 million at the end of 2024, alongside a rapidly expanding enterprise customer base that grew to five million business users. The company’s cash burn remains high because of the intensive computational costs required to train and operate large language models. It projects an $8 billion operating loss in 2025. OpenAI reports revised long-term spending projections totaling approximately $115 billion through 2029, with annual expenditures projected to escalate significantly, reaching $17 billion in 2026, $35 billion in 2027, and $45 billion in 2028. These expenditures are primarily allocated toward expanding compute infrastructure, developing proprietary AI chips, constructing data centers, and funding intensive model training programs, with more than half of the spending through the end of the decade expected to support research-intensive compute for model training and development. The company's financial strategy prioritizes market expansion and technological advancement over near-term profitability, with OpenAI targeting cash-flow-positive operations by 2029 and projecting revenue of approximately $200 billion by 2030. This aggressive spending trajectory underscores both the enormous capital requirements of scaling cutting-edge AI technology and OpenAI's commitment to maintaining its position as a leader in the artificial intelligence industry. In October 2025, OpenAI completed an employee share sale of up to $10 billion to existing investors which valued the company at $500 billion. The deal values OpenAI as the most valuable privately owned company in the world—surpassing SpaceX as the world's most valuable private company. On November 17, 2023, Sam Altman was removed as CEO when its board of directors (composed of Helen Toner, Ilya Sutskever, Adam D'Angelo and Tasha McCauley) cited a lack of confidence in him. Chief Technology Officer Mira Murati took over as interim CEO. Greg Brockman, the president of OpenAI, was also removed as chairman of the board and resigned from the company's presidency shortly thereafter. Three senior OpenAI researchers subsequently resigned: director of research and GPT-4 lead Jakub Pachocki, head of AI risk Aleksander Mądry, and researcher Szymon Sidor. On November 18, 2023, there were reportedly talks of Altman returning as CEO amid pressure placed upon the board by investors such as Microsoft and Thrive Capital, who objected to Altman's departure. Although Altman himself spoke in favor of returning to OpenAI, he has since stated that he considered starting a new company and bringing former OpenAI employees with him if talks to reinstate him didn't work out. The board members agreed "in principle" to resign if Altman returned. On November 19, 2023, negotiations with Altman to return failed and Murati was replaced by Emmett Shear as interim CEO. The board initially contacted Anthropic CEO Dario Amodei (a former OpenAI executive) about replacing Altman, and proposed a merger of the two companies, but both offers were declined. On November 20, 2023, Microsoft CEO Satya Nadella announced Altman and Brockman would be joining Microsoft to lead a new advanced AI research team, but added that they were still committed to OpenAI despite recent events. Before the partnership with Microsoft was finalized, Altman gave the board another opportunity to negotiate with him. About 738 of OpenAI's 770 employees, including Murati and Sutskever, signed an open letter stating they would quit their jobs and join Microsoft if the board did not rehire Altman and then resign. This prompted OpenAI investors to consider legal action against the board as well. In response, OpenAI management sent an internal memo to employees stating that negotiations with Altman and the board had resumed and would take some time. On November 21, 2023, after continued negotiations, Altman and Brockman returned to the company in their prior roles along with a reconstructed board made up of new members Bret Taylor (as chairman) and Lawrence Summers, with D'Angelo remaining. According to subsequent reporting, shortly before Altman’s firing, some employees raised concerns to the board about how he had handled the safety implications of a recent internal AI capability discovery. On November 29, 2023, OpenAI announced that an anonymous Microsoft employee had joined the board as a non-voting member to observe the company's operations; Microsoft resigned from the board in July 2024. In February 2024, the Securities and Exchange Commission subpoenaed OpenAI's internal communication to determine if Altman's alleged lack of candor misled investors. In 2024, following the temporary removal of Sam Altman and his return, many employees gradually left OpenAI, including most of the original leadership team and a significant number of AI safety researchers. In August 2023, it was announced that OpenAI had acquired the New York-based start-up Global Illumination, a company that deploys AI to develop digital infrastructure and creative tools. In June 2024, OpenAI acquired Multi, a startup focused on remote collaboration. In March 2025, OpenAI reached a deal with CoreWeave to acquire $350 million worth of CoreWeave shares and access to AI infrastructure, in return for $11.9 billion paid over five years. Microsoft was already CoreWeave's biggest customer in 2024. Alongside their other business dealings, OpenAI and Microsoft were renegotiating the terms of their partnership to facilitate a potential future initial public offering by OpenAI, while ensuring Microsoft's continued access to advanced AI models. On May 21, OpenAI announced the $6.5 billion acquisition of io, an AI hardware start-up founded by former Apple designer Jony Ive in 2024. In September 2025, OpenAI agreed to acquire the product testing startup Statsig for $1.1 billion in an all-stock deal and appointed Statsig's founding CEO Vijaye Raji as OpenAI's chief technology officer of applications. The company also announced development of an AI-driven hiring service designed to rival LinkedIn. OpenAI acquired personal finance app Roi in October 2025. In October 2025, OpenAI acquired Software Applications Incorporated, the developer of Sky, a macOS-based natural language interface designed to operate across desktop applications. The Sky team joined OpenAI, and the company announced plans to integrate Sky’s capabilities into ChatGPT. In December 2025, it was announced OpenAI had agreed to acquire Neptune, an AI tooling startup that helps companies track and manage model training, for an undisclosed amount. In January 2026, it was announced OpenAI had acquired healthcare technology startup Torch for approximately $60 million. The acquisition followed the launch of OpenAI’s ChatGPT Health product and was intended to strengthen the company’s medical data and healthcare artificial intelligence capabilities. OpenAI has been criticized for outsourcing the annotation of data sets to Sama, a company based in San Francisco that employed workers in Kenya. These annotations were used to train an AI model to detect toxicity, which could then be used to moderate toxic content, notably from ChatGPT's training data and outputs. However, these pieces of text usually contained detailed descriptions of various types of violence, including sexual violence. The investigation uncovered that OpenAI began sending snippets of data to Sama as early as November 2021. The four Sama employees interviewed by Time described themselves as mentally scarred. OpenAI paid Sama $12.50 per hour of work, and Sama was redistributing the equivalent of between $1.32 and $2.00 per hour post-tax to its annotators. Sama's spokesperson said that the $12.50 was also covering other implicit costs, among which were infrastructure expenses, quality assurance and management. In 2024, OpenAI began collaborating with Broadcom to design a custom AI chip capable of both training and inference, targeted for mass production in 2026 and to be manufactured by TSMC on a 3 nm process node. This initiative intended to reduce OpenAI's dependence on Nvidia GPUs, which are costly and face high demand in the market. In January 2024, Arizona State University purchased ChatGPT Enterprise in OpenAI's first deal with a university. In June 2024, Apple Inc. signed a contract with OpenAI to integrate ChatGPT features into its products as part of its new Apple Intelligence initiative. In June 2025, OpenAI began renting Google Cloud's Tensor Processing Units (TPUs) to support ChatGPT and related services, marking its first meaningful use of non‑Nvidia AI chips. In September 2025, it was revealed that OpenAI signed a contract with Oracle to purchase $300 billion in computing power over the next five years. In September 2025, OpenAI and NVIDIA announced a memorandum of understanding that included a potential deployment of at least 10 gigawatts of NVIDIA systems and a $100 billion investment from NVIDIA in OpenAI. OpenAI expected the negotiations to be completed within weeks. As of January 2026, this has not been realized, and the two sides are rethinking the future of their partnership. In October 2025, OpenAI announced a multi-billion dollar deal with AMD. OpenAI committed to purchasing six gigawatts worth of AMD chips, starting with the MI450. OpenAI will have the option to buy up to 160 million shares of AMD, about 10% of the company, depending on development, performance and share price targets. In December 2025, Disney said it would make a $1 billion investment in OpenAI, and signed a three-year licensing deal that will let users generate videos using Sora—OpenAI's short-form AI video platform. More than 200 Disney, Marvel, Star Wars and Pixar characters will be available to OpenAI users. In early 2026, Amazon entered advanced discussions to invest up to $50 billion in OpenAI as part of a potential artificial intelligence partnership. Under the proposed agreement, OpenAI’s models could be integrated into Amazon’s digital assistant Alexa and other internal projects. OpenAI provides LLMs to the Artificial Intelligence Cyber Challenge and to the Advanced Research Projects Agency for Health. In October 2024, The Intercept revealed that OpenAI's tools are considered "essential" for AFRICOM's mission and included in an "Exception to Fair Opportunity" contractual agreement between the United States Department of Defense and Microsoft. In December 2024, OpenAI said it would partner with defense-tech company Anduril to build drone defense technologies for the United States and its allies. In 2025, OpenAI's Chief Product Officer, Kevin Weil, was commissioned lieutenant colonel in the U.S. Army to join Detachment 201 as senior advisor. In June 2025, the U.S. Department of Defense awarded OpenAI a $200 million one-year contract to develop AI tools for military and national security applications. OpenAI announced a new program, OpenAI for Government, to give federal, state, and local governments access to its models, including ChatGPT. Services In February 2019, GPT-2 was announced, which gained attention for its ability to generate human-like text. In 2020, OpenAI announced GPT-3, a language model trained on large internet datasets. GPT-3 is aimed at natural language answering questions, but it can also translate between languages and coherently generate improvised text. It also announced that an associated API, named the API, would form the heart of its first commercial product. Eleven employees left OpenAI, mostly between December 2020 and January 2021, in order to establish Anthropic. In 2021, OpenAI introduced DALL-E, a specialized deep learning model adept at generating complex digital images from textual descriptions, utilizing a variant of the GPT-3 architecture. In December 2022, OpenAI received widespread media coverage after launching a free preview of ChatGPT, its new AI chatbot based on GPT-3.5. According to OpenAI, the preview received over a million signups within the first five days. According to anonymous sources cited by Reuters in December 2022, OpenAI Global, LLC was projecting $200 million of revenue in 2023 and $1 billion in revenue in 2024. After ChatGPT was launched, Google announced a similar chatbot, Bard, amid internal concerns that ChatGPT could threaten Google’s position as a primary source of online information. On February 7, 2023, Microsoft announced that it was building AI technology based on the same foundation as ChatGPT into Microsoft Bing, Edge, Microsoft 365 and other products. On March 14, 2023, OpenAI released GPT-4, both as an API (with a waitlist) and as a feature of ChatGPT Plus. On November 6, 2023, OpenAI launched GPTs, allowing individuals to create customized versions of ChatGPT for specific purposes, further expanding the possibilities of AI applications across various industries. On November 14, 2023, OpenAI announced they temporarily suspended new sign-ups for ChatGPT Plus due to high demand. Access for newer subscribers re-opened a month later on December 13. In December 2024, the company launched the Sora model. It also launched OpenAI o1, an early reasoning model that was internally codenamed strawberry. Additionally, ChatGPT Pro—a $200/month subscription service offering unlimited o1 access and enhanced voice features—was introduced, and preliminary benchmark results for the upcoming OpenAI o3 models were shared. On January 23, 2025, OpenAI released Operator, an AI agent and web automation tool for accessing websites to execute goals defined by users. The feature was only available to Pro users in the United States. OpenAI released deep research agent, nine days later. It scored a 27% accuracy on the benchmark Humanity's Last Exam (HLE). Altman later stated GPT-4.5 would be the last model without full chain-of-thought reasoning. In July 2025, reports indicated that AI models by both OpenAI and Google DeepMind solved mathematics problems at the level of top-performing students in the International Mathematical Olympiad. OpenAI's large language model was able to achieve gold medal-level performance, reflecting significant progress in AI's reasoning abilities. On October 6, 2025, OpenAI unveiled its Agent Builder platform during the company's DevDay event. The platform includes a visual drag-and-drop interface that lets developers and businesses design, test, and deploy agentic workflows with limited coding. On October 21, 2025, OpenAI introduced ChatGPT Atlas, a browser integrating the ChatGPT assistant directly into web navigation, to compete with existing browsers such as Google Chrome and Apple Safari. On December 11, 2025, OpenAI announced GPT-5.2. This model will be better at creating spreadsheets, building presentations, perceiving images, writing code and understanding long context. On January 27, 2026, OpenAI introduced Prism, a LaTeX-native workspace meant to assist scientists to help with research and writing. The platform utilizes GPT-5.2 as a backend to automate the process of drafting for scientific papers, including features for managing citations, complex equation formatting, and real-time collaborative editing. In March 2023, the company was criticized for disclosing particularly few technical details about products like GPT-4, contradicting its initial commitment to openness and making it harder for independent researchers to replicate its work and develop safeguards. OpenAI cited competitiveness and safety concerns to justify this repudiation. OpenAI's former chief scientist Ilya Sutskever argued in 2023 that open-sourcing increasingly capable models was increasingly risky, and that the safety reasons for not open-sourcing the most potent AI models would become "obvious" in a few years. In September 2025, OpenAI published a study on how people use ChatGPT for everyday tasks. The study found that "non-work tasks" (according to an LLM-based classifier) account for more than 72 percent of all ChatGPT usage, with a minority of overall usage related to business productivity. In July 2023, OpenAI launched the superalignment project, aiming within four years to determine how to align future superintelligent systems. OpenAI promised to dedicate 20% of its computing resources to the project, although the team denied receiving anything close to 20%. OpenAI ended the project in May 2024 after its co-leaders Ilya Sutskever and Jan Leike left the company. In August 2025, OpenAI was criticized after thousands of private ChatGPT conversations were inadvertently exposed to public search engines like Google due to an experimental "share with search engines" feature. The opt-in toggle, intended to allow users to make specific chats discoverable, resulted in some discussions including personal details such as names, locations, and intimate topics appearing in search results when users accidentally enabled it while sharing links. OpenAI announced the feature's permanent removal on August 1, 2025, and the company began coordinating with search providers to remove the exposed content, emphasizing that it was not a security breach but a design flaw that heightened privacy risks. CEO Sam Altman acknowledged the issue in a podcast, noting users often treat ChatGPT as a confidant for deeply personal matters, which amplified concerns about AI handling sensitive data. Management In 2018, Musk resigned from his Board of Directors seat, citing "a potential future conflict [of interest]" with his role as CEO of Tesla due to Tesla's AI development for self-driving cars. OpenAI stated that Musk's financial contributions were below $45 million. On March 3, 2023, Reid Hoffman resigned from his board seat, citing a desire to avoid conflicts of interest with his investments in AI companies via Greylock Partners, and his co-founding of the AI startup Inflection AI. Hoffman remained on the board of Microsoft, a major investor in OpenAI. In May 2024, Chief Scientist Ilya Sutskever resigned and was succeeded by Jakub Pachocki. Co-leader Jan Leike also departed amid concerns over safety and trust. OpenAI then signed deals with Reddit, News Corp, Axios, and Vox Media. Paul Nakasone then joined the board of OpenAI. In August 2024, cofounder John Schulman left OpenAI to join Anthropic, and OpenAI's president Greg Brockman took extended leave until November. In September 2024, CTO Mira Murati left the company. In November 2025, Lawrence Summers resigned from the board of directors. Governance and legal issues In May 2023, Sam Altman, Greg Brockman and Ilya Sutskever posted recommendations for the governance of superintelligence. They stated that superintelligence could happen within the next 10 years, allowing a "dramatically more prosperous future" and that "given the possibility of existential risk, we can't just be reactive". They proposed creating an international watchdog organization similar to IAEA to oversee AI systems above a certain capability threshold, suggesting that relatively weak AI systems on the other side should not be overly regulated. They also called for more technical safety research for superintelligences, and asked for more coordination, for example through governments launching a joint project which "many current efforts become part of". In July 2023, the FTC issued a civil investigative demand to OpenAI to investigate whether the company's data security and privacy practices to develop ChatGPT were unfair or harmed consumers (including by reputational harm) in violation of Section 5 of the Federal Trade Commission Act of 1914. These are typically preliminary investigative matters and are nonpublic, but the FTC's document was leaked. In July 2023, the FTC launched an investigation into OpenAI over allegations that the company scraped public data and published false and defamatory information. They asked OpenAI for comprehensive information about its technology and privacy safeguards, as well as any steps taken to prevent the recurrence of situations in which its chatbot generated false and derogatory content about people. The agency also raised concerns about ‘circular’ spending arrangements—for example, Microsoft extending Azure credits to OpenAI while both companies shared engineering talent—and warned that such structures could negatively affect the public. In September 2024, OpenAI's global affairs chief endorsed the UK's "smart" AI regulation during testimony to a House of Lords committee. In February 2025, OpenAI CEO Sam Altman stated that the company is interested in collaborating with the People's Republic of China, despite regulatory restrictions imposed by the U.S. government. This shift comes in response to the growing influence of the Chinese artificial intelligence company DeepSeek, which has disrupted the AI market with open models, including DeepSeek V3 and DeepSeek R1. Following DeepSeek's market emergence, OpenAI enhanced security protocols to protect proprietary development techniques from industrial espionage. Some industry observers noted similarities between DeepSeek's model distillation approach and OpenAI's methodology, though no formal intellectual property claim was filed. According to Oliver Roberts, in March 2025, the United States had 781 state AI bills or laws. OpenAI advocated for preempting state AI laws with federal laws. According to Scott Kohler, OpenAI has opposed California's AI legislation and suggested that the state bill encroaches on a more competent federal government. Public Citizen opposed a federal preemption on AI and pointed to OpenAI's growth and valuation as evidence that existing state laws have not hampered innovation. Before May 2024, OpenAI required departing employees to sign a lifelong non-disparagement agreement forbidding them from criticizing OpenAI and acknowledging the existence of the agreement. Daniel Kokotajlo, a former employee, publicly stated that he forfeited his vested equity in OpenAI in order to leave without signing the agreement. Sam Altman stated that he was unaware of the equity cancellation provision, and that OpenAI never enforced it to cancel any employee's vested equity. However, leaked documents and emails refute this claim. On May 23, 2024, OpenAI sent a memo releasing former employees from the agreement. OpenAI was sued for copyright infringement by authors Sarah Silverman, Matthew Butterick, Paul Tremblay and Mona Awad in July 2023. In September 2023, 17 authors, including George R. R. Martin, John Grisham, Jodi Picoult and Jonathan Franzen, joined the Authors Guild in filing a class action lawsuit against OpenAI, alleging that the company's technology was illegally using their copyrighted work. The New York Times also sued the company in late December 2023. In May 2024 it was revealed that OpenAI had destroyed its Books1 and Books2 training datasets, which were used in the training of GPT-3, and which the Authors Guild believed to have contained over 100,000 copyrighted books. In 2021, OpenAI developed a speech recognition tool called Whisper. OpenAI used it to transcribe more than one million hours of YouTube videos into text for training GPT-4. The automated transcription of YouTube videos raised concerns within OpenAI employees regarding potential violations of YouTube's terms of service, which prohibit the use of videos for applications independent of the platform, as well as any type of automated access to its videos. Despite these concerns, the project proceeded with notable involvement from OpenAI's president, Greg Brockman. The resulting dataset proved instrumental in training GPT-4. In February 2024, The Intercept as well as Raw Story and Alternate Media Inc. filed lawsuit against OpenAI on copyright litigation ground. The lawsuit is said to have charted a new legal strategy for digital-only publishers to sue OpenAI. On April 30, 2024, eight newspapers filed a lawsuit in the Southern District of New York against OpenAI and Microsoft, claiming illegal harvesting of their copyrighted articles. The suing publications included The Mercury News, The Denver Post, The Orange County Register, St. Paul Pioneer Press, Chicago Tribune, Orlando Sentinel, Sun Sentinel, and New York Daily News. In June 2023, a lawsuit claimed that OpenAI scraped 300 billion words online without consent and without registering as a data broker. It was filed in San Francisco, California, by sixteen anonymous plaintiffs. They also claimed that OpenAI and its partner as well as customer Microsoft continued to unlawfully collect and use personal data from millions of consumers worldwide to train artificial intelligence models. On May 22, 2024, OpenAI entered into an agreement with News Corp to integrate news content from The Wall Street Journal, the New York Post, The Times, and The Sunday Times into its AI platform. Meanwhile, other publications like The New York Times chose to sue OpenAI and Microsoft for copyright infringement over the use of their content to train AI models. In November 2024, a coalition of Canadian news outlets, including the Toronto Star, Metroland Media, Postmedia, The Globe and Mail, The Canadian Press and CBC, sued OpenAI for using their news articles to train its software without permission. In October 2024 during a New York Times interview, Suchir Balaji accused OpenAI of violating copyright law in developing its commercial LLMs which he had helped engineer. He was a likely witness in a major copyright trial against the AI company, and was one of several of its current or former employees named in court filings as potentially having documents relevant to the case. On November 26, 2024, Balaji died by suicide. His death prompted the circulation of conspiracy theories alleging that he had been deliberately silenced. California Congressman Ro Khanna endorsed calls for an investigation. On April 24, 2025, Ziff Davis sued OpenAI in Delaware federal court for copyright infringement. Ziff Davis is known for publications such as ZDNet, PCMag, CNET, IGN and Lifehacker. In April 2023, the EU's European Data Protection Board (EDPB) formed a dedicated task force on ChatGPT "to foster cooperation and to exchange information on possible enforcement actions conducted by data protection authorities" based on the "enforcement action undertaken by the Italian data protection authority against OpenAI about the ChatGPT service". In late April 2024 NOYB filed a complaint with the Austrian Datenschutzbehörde against OpenAI for violating the European General Data Protection Regulation. A text created with ChatGPT gave a false date of birth for a living person without giving the individual the option to see the personal data used in the process. A request to correct the mistake was denied. Additionally, neither the recipients of ChatGPT's work nor the sources used, could be made available, OpenAI claimed. OpenAI was criticized for lifting its ban on using ChatGPT for "military and warfare". Up until January 10, 2024, its "usage policies" included a ban on "activity that has high risk of physical harm, including", specifically, "weapons development" and "military and warfare". Its new policies prohibit "[using] our service to harm yourself or others" and to "develop or use weapons". In August 2025, the parents of a 16-year-old boy who died by suicide filed a wrongful death lawsuit against OpenAI (and CEO Sam Altman), alleging that months of conversations with ChatGPT about mental health and methods of self-harm contributed to their son's death and that safeguards were inadequate for minors. OpenAI expressed condolences and said it was strengthening protections (including updated crisis response behavior and parental controls). Coverage described it as a first-of-its-kind wrongful death case targeting the company's chatbot. The complaint was filed in California state court in San Francisco. In November 2025, the Social Media Victims Law Center and Tech Justice Law Project filed seven lawsuits against OpenAI, of which four lawsuits alleged wrongful death. The suits were filed on behalf of Zane Shamblin, 23, of Texas; Amaurie Lacey, 17, of Georgia; Joshua Enneking, 26, of Florida; and Joe Ceccanti, 48, of Oregon, who each committed suicide after prolonged ChatGPT usage. In December 2025, Stein-Erik Soelberg, who was 56 years old at the time, allegedly murdered his mother Suzanne Adams. In the months prior the paranoid, delusional man often discussed his ideas with ChatGPT. Adam's estate then sued OpenAI claiming that the company shared responsibility due to the risk of chatbot psychosis despite the fact that chatbot psychosis is not a real medical diagnosis. OpenAI responded saying they will make ChatGPT safer for users disconnected from reality. See also References Further reading External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Mars_(god)] | [TOKENS: 7813] |
Contents Mars (mythology) In ancient Roman religion and mythology, Mars (Latin: Mārs, pronounced [maːrs]) is the god of war and also an agricultural guardian, a combination characteristic of early Rome. He is the son of Jupiter and Juno, and was pre-eminent among the Roman army's military gods. Most of his festivals were held in March, the month named for him (Latin Martius), and in October, the months which traditionally began and ended the season for both military campaigning and farming. Under the influence of Greek culture, Mars was identified with the Greek god Ares, whose myths were reinterpreted in Roman literature and art under the name of Mars. The character and dignity of Mars differs in fundamental ways from that of his Greek counterpart, who is often treated with contempt and revulsion in Greek literature. Mars's altar in the Campus Martius, the area of Rome that took its name from him, was supposed to have been dedicated by Numa, the peace-loving semi-legendary second king of Rome; in Republican times it was a focus of electoral activities. Augustus shifted the focus of Mars's cult to within the pomerium (Rome's ritual boundary), and built a temple to Mars Ultor as a key religious feature of his new forum. Unlike Ares, who was viewed primarily as a destructive and destabilizing force, Mars represented military power as a way to secure peace, and was a father (pater) of the Roman people. In Rome's mythic genealogy and founding, Mars fathered Romulus and Remus through his rape of Rhea Silvia. The wolf was the sacred animal of Mars, with the she-wolf nursing the two founders as children. His love affair with Venus symbolically reconciled two different traditions of Rome's founding; Venus was the divine mother of the hero Aeneas, credited by Vergil as an earlier founder of Rome. Name The word Mārs (genitive Mārtis), which in Old Latin and poetic usage also appears as Māvors (Māvortis), is cognate with Oscan Māmers (Māmertos). In older literature, the god Mars was equated with the Vedic storm deities known as the Maruts, both of which were traditionally unified under a reconstructed Proto-Indo-European term *māwort-. However, this etymology is now rejected in more modern Indo-Europeanist scholarship. The oldest recorded Latin form, Mamart-, is likely of foreign origin. It has been explained as deriving from Maris, the name of an Etruscan child-god, though this is not universally agreed upon. Scholars have varying views on whether the two gods are related, and if so how.: 29–30 : 219 : 2574 : 226 Latin adjectives from the name of Mars are Mārtius and mārtiālis, from which derive English "martial" (as in "martial arts" or "martial law") and personal names such as "Marcus", "Mark" and "Martin". Mars may ultimately be a thematic reflex of the Proto-Indo-European god Perkwunos, having originally been a thunderer character. Birth Like Ares who was the son of Zeus and Hera, Mars is usually considered to be the son of Jupiter and Juno. In Ovid's version of Mars's origin, he was the son of Juno alone. Jupiter had usurped the role of mother when he gave birth to Minerva directly from his forehead (or mind) without a female partner. Juno sought the advice of the goddess Flora on how in turn to produce a child without male intervention. Flora obtained a magic flower (Latin flos, plural flores, a masculine word) and tested it on a heifer who became fecund at once. Flora ritually plucked a flower, using her thumb, touched Juno's belly, and impregnated her. Juno withdrew to Thrace and the shore of Marmara for the birth. Ovid tells this story in the Fasti, his long-form poetic work on the Roman calendar. It may explain why the Matronalia, a festival celebrated by married women in honor of Juno as a goddess of childbirth, occurred on the first day of Mars's month, which is also marked on a calendar from late antiquity as the birthday of Mars. In the earliest Roman calendar, March was the first month, and the god would have been born with the new year. Ovid is the only source for the story. He may be presenting a literary myth of his own invention, or an otherwise unknown archaic Italic tradition; either way, in choosing to include the story, he emphasizes that Mars was connected to plant life and was not alienated from female nurture. Consort The consort of Mars was Nerio or Neriene, meaning "Valor". She represents the vital force (vis), power (potentia) and majesty (maiestas) of Mars. Her name was regarded as Sabine in origin and is equivalent to Latin virtus, "manly virtue" (from vir, "man"). In the early 3rd century BCE, the comic playwright Plautus has a reference to Mars greeting Nerio, his wife. A source from late antiquity says that Mars and Neriene were celebrated together at a festival held on March 23. In the later Roman Empire, Neriene came to be identified with Minerva. Nerio probably originates as a divine personification of Mars's power, as such abstractions in Latin are generally feminine. Her name appears with that of Mars in an archaic prayer invoking a series of abstract qualities, each paired with the name of a deity. The influence of Greek mythology and its anthropomorphic gods may have caused Roman writers to treat these pairs as "marriages." The union of Venus and Mars held greater appeal for poets and philosophers, and the couple were a frequent subject of art. In Greek myth, the adultery of Ares and Aphrodite had been exposed to ridicule when her husband Hephaestus (whose Roman equivalent was Vulcan) caught them in the act by means of a magical snare.[citation needed] Although not originally part of the Roman tradition,[citation needed] in 217 BCE Venus and Mars were presented as a complementary pair in the lectisternium, a public banquet at which images of twelve major gods of the Roman state were presented on couches as if present and participating.: 147 Scenes of Venus and Mars in Roman art often ignore the adulterous implications of their union, and take pleasure in the good-looking couple attended by Cupid or multiple Loves (amores). Some scenes may imply marriage, and the relationship was romanticized in funerary or domestic art in which husbands and wives had themselves portrayed as the passionate divine couple. The uniting of deities representing Love and War lent itself to allegory, especially since the lovers were the parents of Concordia.[citation needed] The Renaissance philosopher Marsilio Ficino notes that "only Venus dominates Mars, and he never dominates her". In ancient Roman and Renaissance art, Mars is often shown disarmed and relaxed, or even sleeping, but the extramarital nature of their affair can also suggest that this peace is impermanent. Essential nature Virility as a kind of life force (vis) or virtue (virtus) is an essential characteristic of Mars. During the Middle Republican period, the consul Marcus Claudius Marcellus vowed to construct a temple to Honos and Virtus, though it was only completed posthumously by his son, who also left an inscription commemorating the god Mars.: 119 In the earliest Roman writings, the term "virtus" applied to battlefield courage: Artotrogus, a character in the play The Braggart Soldier by the 3rd-century BCE author Plautus, addresses the boastful soldier Pyrgopolynices, stating "Mars wouldn’t dare to call himself such a warrior or compare his exploits to yours" ("tam bellatorem Mars haud ausit dicere neque aequiperare suas uirtutes ad tuas"). Furthermore, during the prologue to the Plautine play Casina, the speaker exclaims "be victorious through true bravery" ("vincite virtute vera").: 120 Later in Roman history, the concept of "virtus" expanded to incorporate the idea of wisdom, possibly due to the influence of the Greek association between military prowess and intelligence, a Hellenic cultural concept embodied by the deity Athena. The transformation of the idea of "virtus" itself altered the underlying character of Mars, who developed into a god of generalship alongside warrior skill.: 121 According to the classicist John Serrati, Mars—as the personification of virtus—exemplified ideal Roman masculinity.: 122 By the 3rd-century CE, Mars was primarily worshipped by Roman military legions.: 101 Various festivals associated with Mars, such as the Tubilustrium and Armilustrium, were themselves connected with lustration, a type of Roman religious practice intended to ward off evil. The lustral connotations of Mars may imply that he fulfilled a type of protector or guardian in Roman mythology.: 515 The conceptualization of Mars as a protector deity may have facilitated his associations with war. According to the 4th-century author Servius the Grammarian, during wartime, a spear was shaken in the Regia and Mars was called upon to watch over the Roman people with the words "Mars vigila.": 516 As a war god, Mars was associated a series of festivals occurring around March at the beginning of the beginning of the Roman campaigning season, such a ritualistic dance of the Salian priests,: 22 and ceremonies belonging to the month of October, such as the October Horse or the Armilustrium, all of which were connected with the end of the campaign season. Chronologically, the rituals associated with the beginning and end of the campaigning season were also concurrent with the ideal time frame for agriculture, which perhaps relates to the dual characterization of Mars as a rustic and warlike divinity.: 27–28 However, by the 2nd and 3rd centuries CE, Roman soldiers no longer departed for campaign during particular months of the year but instead remained permanently stationed at various forts and military installations throughout the empire. Consequently, the original connection between the military and farming season became irrelevant.: 146 As an agricultural guardian, Mars directs his energies toward creating conditions that allow crops to grow, which may include warding off hostile forces of nature.: 88 : 517 The agricultural role of Mars may be inseparable from his warrior nature, as the leaping of his armed priests the Salii was meant to expedite the growth of crops. Within the Carmen Arvale, an archaic Latin text, the god Mars is invoked by the Arval Brotherhood specifically to protect and defend the suppliants from ill.: 518 This same order of priests is otherwise associated with ensuring high agricultural output through the performance of religious rituals.: 517–518 Cato similarly describes a lustratio in which Mars is invoked to guard the suppliant and their crops from a variety of misfortunes, such as poor weather. These actions are ultimately conducive to a successful harvest, though they are not necessarily incongruent with his characterization as a type of protector god. Later in Roman history, the goddess Ceres became more closely associated with the lustration ritual and agriculture, whereas—perhaps due to the influence of the Greek deity Ares—the role of Mars as a war god assumed greater prominence.: 236 The classicist Andrew Kilgour argues that it is perhaps befitting for a male deity in Roman culture to assume a more intrinsically belligerent role within the sphere of agriculture, whereas the responsibility of facilitating the growth of crops falls upon goddesses such as Dea Dia.: 236 However, the archaeologist Robert Turcan suggests the Mars may embody a productive role within the prayer and therefore may exemplify the supposed three primary functions of Proto-Indo-European society: Religious, Martial, and Productive. The bellicose aspect of Mars is possibly reflected by his ability to fend off disaster, yet still the text actively calls upon the deity to ensure a more abundant harvest, perhaps attesting to productive responsibilities.: 41 The possible connection between Mars and the priestly or religious aspect of Proto-Indo-European culture may be continued by the ceremony of the October Horse, a ritualistic animal sacrifice that is perhaps related to the Vedic aśvamedha ritual.: 156 In other Indo-European mythologies, war gods may simultaneously serve as agrarian deities, such as the Slavic god Svetovit.: 147 Mars may have originated as a god of the wild, who dwelt beyond the boundaries set by humans. Mars's potential for savagery is expressed in his obscure connections to the wild woodlands. In his book on farming, Cato the Elder invokes Mars Silvanus for a ritual to be carried out in silva, in the woods, an uncultivated place that if not held within bounds can threaten to overtake the fields needed for crops. In the surviving text of their hymn, the Arval Brothers invoked Mars as ferus, "savage" or "feral" like a wild animal.: 135 The historian William Warde Fowler further notes that, mythologically, Mars was associated with the woodpecker and the wolf, two wild animals,: 134 the latter of which was particularly common in ancient Italy.: 131 The priesthood of the Arval Brothers called on Mars to drive off "rust" (lues), with its double meaning of wheat fungus and the red oxides that affect metal, a threat to both iron farm implements and weaponry. For much of early Roman history, Mars largely lacked intramural temples, with much of his worship occurring outside urban areas. However, despite his general confinement to extra-urban spaces, a ceremony propitiating Mars was performed within the city of Rome itself—the aforementioned ritual of the Salian priests in which Mars was called upon to watch over Rome during wartime.: 133 During the reign of Augustus (r. 27 BCE – 14 CE), the emperor constructed a statue of Mars Ultor in the Augustan Forum near temples to Venus and Divus Iulius, thereby violating the apparent prior aversion to urban sanctuaries honoring the divinity.: 146 Within the mythological origin of Rome outlined by the 1st-century BCE poet Virgil, Mars was cast as the ancestor of Romulus and Venus as the ancestor of Aeneas, both of whom were—in the account of Virgil—responsible for the eventual founding of Rome. Moreover, Venus was in legend associated with the Julii family, to which Augustus belonged. Thus, by emphasizing the relationship between Venus and Mars, emperor Augustus may have sought to reframe Mars as the progenitor of the Roman people and patron of the Imperial dynasty.: 146 : 195–196 Sacred animals The wild animals most sacred to Mars were the woodpecker and the wolf, which in the natural lore of the Romans were said always to inhabit the same foothills and woodlands. Plutarch notes that the woodpecker (picus) is sacred to Mars because "it is a courageous and spirited bird and has a beak so strong that it can overturn oaks by pecking them until it has reached the inmost part of the tree." As the beak of the picus Martius contained the god's power to ward off harm, it was carried as a magic charm to prevent bee stings and leech bites. The bird of Mars also guarded a woodland herb (paeonia) used for treatment of the digestive or female reproductive systems; those who sought to harvest it were advised to do so by night, lest the woodpecker jab out their eyes. The picus Martius seems to have been a particular species, but authorities differ on which one: perhaps Picus viridis or Dryocopus martius. The woodpecker was revered by the Latin peoples, who abstained from eating its flesh. It was one of the most important birds in Roman and Italic augury, the practice of reading the will of the gods through watching the sky for signs. The mythological figure named Picus had powers of augury that he retained when he was transformed into a woodpecker; in one tradition, Picus was the son of Mars. The Umbrian cognate peiqu also means "woodpecker", and the Italic Picenes were supposed to have derived their name from the picus who served as their guide animal during a ritual migration (ver sacrum) undertaken as a rite of Mars. In the territory of the Aequi, another Italic people, Mars had an oracle of great antiquity where the prophecies were supposed to be spoken by a woodpecker perched on a wooden column. Mars's association with the wolf is familiar from what may be the most famous of Roman myths, the story of how a she-wolf (lupa) suckled his infant sons when they were exposed by order of King Amulius, who feared them because he had usurped the throne from their grandfather, Numitor. The woodpecker also brought nourishment to the twins. The wolf appears elsewhere in Roman art and literature in masculine form as the animal of Mars. A statue group that stood along the Appian Way showed Mars in the company of wolves. At the Battle of Sentinum in 295 BCE, the appearance of the wolf of Mars (Martius lupus) was a sign that Roman victory was to come. In Roman Gaul, the goose was associated with the Celtic forms of Mars, and archaeologists have found geese buried alongside warriors in graves. The goose was considered a bellicose animal because it is easily provoked to aggression. Ancient Greek and Roman religion distinguished between animals that were sacred to a deity and those that were prescribed as the correct sacrificial offerings for the god. Wild animals might be viewed as already belonging to the god to whom they were sacred, or at least not owned by human beings and therefore not theirs to give. Since sacrificial meat was eaten at a banquet after the gods received their portion – mainly the entrails (exta) – it follows that the animals sacrificed were most often, though not always, domestic animals normally part of the Roman diet. Gods often received castrated male animals as sacrifices, and the goddesses female victims; Mars, however, regularly received intact males. Mars did receive oxen under a few of his cult titles, such as Mars Grabovius, but the usual offering was the bull, singly, in multiples, or in combination with other animals.[citation needed] The two most distinctive animal sacrifices made to Mars were the suovetaurilia, a triple offering of a pig (sus), ram (ovis) and bull (taurus), and the October Horse, the only horse sacrifice known to have been carried out in ancient Rome and a rare instance of a victim the Romans considered inedible. Temples and topography in Rome The earliest center in Rome for cultivating Mars as a deity was the Altar of Mars (Ara Martis) in the Campus Martius ("Field of Mars") outside the sacred boundary of Rome (pomerium). The Romans thought that this altar had been established by the semi-legendary Numa Pompilius, the peace-loving successor of Romulus. According to Roman tradition, the Campus Martius had been consecrated to Mars by their ancestors to serve as horse pasturage and an equestrian training ground for youths. During the Roman Republic (509–27 BCE), the Campus was a largely open expanse. No temple was built at the altar, but from 193 BCE a covered walkway connected it to the Porta Fontinalis, near the office and archives of the Roman censors. Newly elected censors placed their curule chairs by the altar, and when they had finished conducting the census, the citizens were collectively purified with a suovetaurilia there. A frieze from the so-called "Altar" of Domitius Ahenobarbus is thought to depict the census, and may show Mars himself standing by the altar as the procession of victims advances. The main Temple of Mars (Aedes Martis) in the Republican period also lay outside the sacred boundary[where?] and was devoted to the god's warrior aspect. It was built to fulfill a vow (votum) made by a Titus Quinctius in 388 BCE during the Gallic siege of Rome. The founding day (dies natalis) was commemorated on June 1, and the temple is attested by several inscriptions and literary sources. The sculpture group of Mars and the wolves was displayed there. Soldiers sometimes assembled at the temple before heading off to war, and it was the point of departure for a major parade of Roman cavalry held annually on July 15. A temple to Mars in the Circus Flaminius was built around 133 BCE, funded by Decimus Junius Brutus Callaicus from war booty. It housed a colossal statue of Mars and a nude Venus. The Campus Martius continued to provide venues for equestrian events such as chariot racing during the Imperial period, but under the first emperor Augustus it underwent a major program of urban renewal, marked by monumental architecture. The Altar of Augustan Peace (Ara Pacis Augustae) was located there, as was the Obelisk of Montecitorio, imported from Egypt to form the pointer (gnomon) of the Solarium Augusti, a giant sundial. With its public gardens, the Campus became one of the most attractive places in the city to visit. Augustus made the centrepiece of his new forum a large Temple to Mars Ultor, a manifestation of Mars he cultivated as the avenger (ultor) of the murder of Julius Caesar and of the military disaster suffered at the Battle of Carrhae. When the legionary standards lost to the Parthians were recovered, they were housed in the new temple. The date of the temple's dedication on May 12 was aligned with the heliacal setting of the constellation Scorpio, the sign of war. The date continued to be marked with circus games as late as the mid-4th century AD. A large statue of Mars was part of the short-lived Arch of Nero, which was built in 62 CE but dismantled after Nero's suicide and disgrace (damnatio memoriae). Iconography and symbol In Roman art, Mars is depicted as either bearded and mature, or young and clean-shaven. Even nude or seminude, he often wears a helmet or carries a spear as emblems of his warrior nature. Mars was among the deities to appear on the earliest Roman coinage in the late 4th and early 3rd century BCE. Statuettes of warrior figures, who perhaps represent Mars, are common throughout archaic Umbrian sanctuaries dating from the 6th–4th centuries BCE.: 118 On the Altar of Peace (Ara Pacis), built in the last years of the 1st century BCE, Mars is a mature man with a "handsome, classicizing" face, and a short curly beard and moustache. His helmet is a plumed neo-Attic-type. He wears a military cloak (paludamentum) and a cuirass ornamented with a gorgoneion. Although the relief is somewhat damaged at this spot, he appears to hold a spear garlanded in laurel, symbolizing a peace that is won by military victory. The 1st-century statue of Mars found in the Forum of Nerva (pictured at top) is similar. In this guise, Mars is presented as the dignified ancestor of the Roman people. The panel of the Ara Pacis on which he appears would have faced the Campus Martius, reminding viewers that Mars was the god whose altar Numa established there, that is, the god of Rome's oldest civic and military institutions. Particularly in works of art influenced by the Greek tradition, Mars may be portrayed in a manner that resembles Ares, youthful, beardless, and often nude. In the Renaissance, Mars's nudity was thought to represent his lack of fear in facing danger. The spear is the instrument of Mars in the same way that Jupiter wields the lightning bolt, Neptune the trident, and Saturn the scythe or sickle. A relic or fetish called the spear of Mars was kept in a sacrarium at the Regia, the former residence of the Kings of Rome. The spear was said to move, tremble or vibrate at impending war or other danger to the state, as was reported to occur before the assassination of Julius Caesar. When Mars is pictured as a peace-bringer, his spear is wreathed with laurel or other vegetation, as on the Ara Pacis or a coin of Aemilianus. Priesthoods The high priest of Mars in Roman public religion was the Flamen Martialis, who was one of the three major priests in the fifteen-member college of flamens. Mars was also served by the Salii, a twelve-member priesthood of patrician youths who dressed as archaic warriors and danced in procession around the city in March. Both priesthoods extend to the earliest periods of Roman history, and patrician birth was required. Festivals and rituals The festivals of Mars cluster in his namesake month of March (Latin: Martius), with a few observances in October, the beginning and end of the season for military campaigning and agriculture. Festivals with horse racing took place in the Campus Martius. Some festivals in March retained characteristics of new year festivals, since Martius was originally the first month of the Roman calendar. Mars was also honored by chariot races at the Robigalia and Consualia, though these festivals are not primarily dedicated to him. From 217 BCE onward, Mars was among the gods honored at the lectisternium, a banquet given for deities who were present as images.[citation needed] Roman hymns (carmina) are rarely preserved, but Mars is invoked in two. The Arval Brothers, or "Brothers of the Fields", chanted a hymn to Mars while performing their three-step dance. The Carmen Saliare was sung by Mars's priests the Salii while they moved twelve sacred shields (ancilia) throughout the city in a procession. In the 1st century AD, Quintilian remarks that the language of the Salian hymn was so archaic that it was no longer fully understood. Name and cult epithets In Classical Roman religion, Mars was invoked under several titles, and the first Roman emperor Augustus thoroughly integrated Mars into Imperial cult. The 4th-century Latin historian Ammianus Marcellinus treats Mars as one of several classical Roman deities who remained "cultic realities" up to his own time. Mars, and specifically Mars Ultor, was among the gods who received sacrifices from Julian, the only emperor to reject Christianity after the conversion of Constantine I. In 363 AD, in preparation for the Siege of Ctesiphon, Julian sacrificed ten "very fine" bulls to Mars Ultor. The tenth bull violated ritual protocol by attempting to break free, and when killed and examined, produced ill omens, among the many that were read at the end of Julian's reign. As represented by Ammianus, Julian swore never to make sacrifice to Mars again—a vow kept with his death a month later. Gradivus was one of the gods by whom a general or soldiers might swear an oath to be valorous in battle. His temple outside the Porta Capena was where armies gathered. The archaic priesthood of Mars Gradivus was the Salii, the "leaping priests" who danced ritually in armor as a prelude to war. His cult title is most often taken to mean "the Strider" or "the Marching God", from gradus, "step, march." The poet Statius addresses him as "the most implacable of the gods," but Valerius Maximus concludes his history by invoking Mars Gradivus as "author and support of the name 'Roman'": Gradivus is asked – along with Capitoline Jupiter and Vesta, as the keeper of Rome's perpetual flame – to "guard, preserve, and protect" the state of Rome, the peace, and the princeps (the emperor Tiberius at the time). A source from Late Antiquity says that the wife of Gradivus was Nereia, the daughter of Nereus, and that he loved her passionately. Mars Quirinus was the protector of the Quirites ("citizens" or "civilians") as divided into curiae (citizen assemblies), whose oaths were required to make a treaty. As a guarantor of treaties, Mars Quirinus is thus a god of peace: "When he rampages, Mars is called Gradivus, but when he's at peace Quirinus." The deified Romulus was identified with Mars Quirinus. In the Capitoline Triad of Jupiter, Mars, and Quirinus, however, Mars and Quirinus were two separate deities, though not perhaps in origin. Each of the three had his own flamen (specialized priest), but the functions of the Flamen Martialis and Flamen Quirinalis are hard to distinguish. Mars is invoked as Grabovius in the Iguvine Tablets, bronze tablets written in Umbrian that record ritual protocols for carrying out public ceremonies on behalf of the city and community of Iguvium. The same title is given to Jupiter and to the Umbrian deity Vofionus. This triad has been compared to the Archaic Triad, with Vofionus equivalent to Quirinus. Tables I and VI describe a complex ritual that took place at the three gates of the city. After the auspices were taken, two groups of three victims were sacrificed at each gate. Mars Grabovius received three oxen. "Father Mars" or "Mars the Father" is the form in which the god is invoked in the agricultural prayer of Cato, and he appears with this title in several other literary texts and inscriptions. Mars Pater is among the several gods invoked in the ritual of devotio, by means of which a general sacrificed himself and the lives of the enemy to secure a Roman victory. Father Mars is the regular recipient of the suovetaurilia, the sacrifice of a pig (sus), ram (ovis) and bull (taurus), or often a bull alone. To Mars Pater other epithets were sometimes appended, such as Mars Pater Victor ("Father Mars the Victorious"), to whom the Roman army sacrificed a bull on March 1. Although pater and mater were fairly common as honorifics for a deity, any special claim for Mars as father of the Roman people lies in the mythic genealogy that makes him the divine father of Romulus and Remus. In the section of his farming book that offers recipes and medical preparations, Cato describes a votum to promote the health of cattle: Make an offering to Mars Silvanus in the forest (in silva) during the daytime for each head of cattle: 3 pounds of meal, 4½ pounds of bacon, 4½ pounds of meat, and 3 pints of wine. You may place the viands in one vessel, and the wine likewise in one vessel. Either a slave or a free man may make this offering. After the ceremony is over, consume the offering on the spot at once. A woman may not take part in this offering or see how it is performed. You may vow the vow every year if you wish. That Mars Silvanus is a single entity has been doubted. Invocations of deities are often list-like, without connecting words, and the phrase should perhaps be understood as "Mars and Silvanus". Women were explicitly excluded from some cult practices of Silvanus, but not necessarily of Mars. William Warde Fowler, however, thought that the wild god of the wood Silvanus may have been "an emanation or offshoot" of Mars. Augustus created the cult of "Mars the Avenger" to mark two occasions: his defeat of the assassins of Caesar at Philippi in 42 BCE, and the negotiated return of the Roman battle standards that had been lost to the Parthians at the Battle of Carrhae in 53 BCE. The god is depicted wearing a cuirass and helmet and standing in a "martial pose," leaning on a lance he holds in his right hand. He holds a shield in his left hand. The goddess Ultio, a divine personification of vengeance, had an altar and golden statue in his temple. The Temple of Mars Ultor, dedicated in 2 BCE in the center of the Forum of Augustus, gave the god a new place of honor. Some rituals previously conducted within the cult of Capitoline Jupiter were transferred to the new temple, which became the point of departure for magistrates as they left for military campaigns abroad. Augustus required the Senate to meet at the temple when deliberating questions of war and peace. The temple also became the site at which sacrifice was made to conclude the rite of passage of young men assuming the toga virilis ("man's toga") around age 14. On various Imperial holidays, Mars Ultor was the first god to receive a sacrifice, followed by the Genius of the emperor. An inscription from the 2nd century records a vow to offer Mars Ultor a bull with gilded horns. Augustus or Augusta was appended far and wide, "on monuments great and small," to the name of gods or goddesses, including Mars. The honorific marks the affiliation of a deity with Imperial cult. In Hispania, many of the statues and dedications to Mars Augustus were presented by members of the priesthood or sodality called the Sodales Augustales. These vows (vota) were usually fulfilled within a sanctuary of Imperial cult, or in a temple or precinct (templum) consecrated specifically to Mars. As with other deities invoked as Augustus, altars to Mars Augustus might be set up to further the well-being (salus) of the emperor, but some inscriptions suggest personal devotion. An inscription in the Alps records the gratitude of a slave who dedicated a statue to Mars Augustus as conservator corporis sui, the preserver of his own body, said to have been vowed ex iussu numinis ipsius, "by the order of the numen himself". Mars Augustus appears in inscriptions at sites throughout the Empire, such as Hispania Baetica, Saguntum, and Emerita (Lusitania) in Roman Spain; Leptis Magna (with a date of 6–7 AD) in present-day Libya; and Sarmizegetusa in the province of Dacia. In addition to his cult titles at Rome, Mars appears in a large number of inscriptions in the provinces of the Roman Empire, and more rarely in literary texts, identified with a local deity by means of an epithet. Mars appears with great frequency in Gaul among the Continental Celts, as well as in Roman Spain and Britain. In Celtic settings, he is often invoked as a healer. The inscriptions indicate that Mars's ability to dispel the enemy on the battlefield was transferred to the sick person's struggle against illness; healing is expressed in terms of warding off and rescue. Mars is identified with a number of Celtic deities, some of whom are not attested independently. "Mars Balearicus" is a name used in modern scholarship for small bronze warrior figures from Majorca (one of the Balearic Islands) that are interpreted as representing the local Mars cult. These statuettes have been found within talayotic sanctuaries with extensive evidence of burnt offerings. "Mars" is fashioned as a lean, athletic nude lifting a lance and wearing a helmet, often conical; the genitals are perhaps semi-erect in some examples. Other bronzes at the sites represent the heads or horns of bulls, but the bones in the ash layers indicate that sheep, goats, and pigs were the sacrificial victims. Bronze horse-hooves were found in one sanctuary. Another site held an imported statue of Imhotep, the legendary Egyptian physician. These sacred precincts were still in active use when the Roman occupation began in 123 BCE. They seem to have been astronomically oriented toward the rising or setting of the constellation Centaurus. On the calendar Mars gave his name to the third month in the Roman calendar, Martius, from which English March derives. In the most ancient Roman calendar, Martius was the first month. The planet Mars was named for him, and in some allegorical and philosophical writings, the planet and the god are endowed with shared characteristics. In many languages, Tuesday is named for the planet Mars or the god of war: In Latin, martis dies (literally, 'Mars's Day'), survived in Romance languages as marte (Portuguese), martes (Spanish), mardi (French), martedì (Italian), marți (Romanian), and dimarts (Catalan). In Irish (Gaelic), the day is An Mháirt, while in Albanian it is e Marta. The English word Tuesday derives from Old English Tiwesdæg and means 'Tiw's Day', Tiw being the Old English form of the Proto-Germanic war god *Tîwaz, or Týr in Norse. See also Notes References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Network_effect] | [TOKENS: 5095] |
Contents Network effect In economics, a network effect (also called network externality or demand-side economies of scale) is the phenomenon by which the value or utility a user derives from a good or service depends on the number of users of compatible products. Network effects are typically positive feedback systems, resulting in users deriving more and more value from a product as more users join the same network. The adoption of a product by an additional user can be broken into two effects: an increase in the value to all other users (total effect) and also the enhancement of other non-users' motivation for using the product (marginal effect). Network effects can be direct or indirect. Direct network effects arise when a given user's utility increases with the number of other users of the same product or technology, meaning that adoption of a product by different users is complementary. This effect is separate from effects related to price, such as a benefit to existing users resulting from price decreases as more users join. Direct network effects can be seen with social networking services, including Twitter, Facebook, Airbnb, Uber, and LinkedIn; telecommunications devices like the telephone; and instant messaging services such as MSN, AIM or QQ. Indirect (or cross-group) network effects arise when there are "at least two different customer groups that are interdependent, and the utility of at least one group grows as the other group(s) grow". For example, hardware may become more valuable to consumers with the growth of compatible software. Network effects are commonly mistaken for economies of scale, which describe decreasing average production costs in relation to the total volume of units produced. Economies of scale are a common phenomenon in traditional industries such as manufacturing, whereas network effects are most prevalent in new economy industries, particularly information and communication technologies. Network effects are the demand side counterpart of economies of scale, as they function by increasing a customer's willingness to pay due rather than decreasing the supplier's average cost. Upon reaching critical mass, a bandwagon effect can result. As the network continues to become more valuable with each new adopter, more people are incentivised to adopt, resulting in a positive feedback loop. Multiple equilibria and a market monopoly are two key potential outcomes in markets that exhibit network effects. Consumer expectations are key in determining which outcomes will result. Origins Network effects were a central theme in the arguments of Theodore Vail, the first post-patent president of Bell Telephone, in gaining a monopoly on US telephone services. In 1908, when he presented the concept in Bell's annual report, there were over 4,000 local and regional telephone exchanges, most of which were eventually merged into the Bell System. Network effects were popularized by Robert Metcalfe, stated as Metcalfe's law. Metcalfe was one of the co-inventors of Ethernet and a co-founder of the company 3Com. In selling the product, Metcalfe argued that customers needed Ethernet cards to grow above a certain critical mass if they were to reap the benefits of their network. According to Metcalfe, the rationale behind the sale of networking cards was that the cost of the network was directly proportional to the number of cards installed, but the value of the network was proportional to the square of the number of users. This was expressed algebraically as having a cost of N, and a value of N2. While the actual numbers behind this proposition were never firm, the concept allowed customers to share access to expensive resources like disk drives and printers, send e-mail, and eventually access the Internet. The economic theory of the network effect was advanced significantly between 1985 and 1995 by researchers Michael L. Katz, Carl Shapiro, Joseph Farrell, and Garth Saloner. Author, high-tech entrepreneur Rod Beckstrom presented a mathematical model for describing networks that are in a state of positive network effect at BlackHat and Defcon in 2009 and also presented the inverse network effect with an economic model for defining it as well. Because of the positive feedback often associated with the network effect, system dynamics can be used as a modelling method to describe the phenomena. Word of mouth and the Bass diffusion model are also potentially applicable. The next major advance occurred between 2000 and 2003 when researchers Geoffrey G Parker, Marshall Van Alstyne,[non-primary source needed] Jean-Charles Rochet and Jean Tirole[non-primary source needed] independently developed the two-sided market literature showing how network externalities that cross distinct groups can lead to free pricing for one of those groups. While the diversity of sources is in decline, there is a countervailing force of continually increasing functionality with new services, products and applications — such as music streaming services (Spotify), file sharing programs (Dropbox) and messaging platforms (Messenger, WhatsApp and Snapchat). Another major finding was the dramatic increase in the infant mortality rate of websites, with the dominant players in each functional niche, once established, guarding their turf more staunchly than ever. On the other hand, a growing network effect does not always bring a proportional increase in returns. Whether additional users bring more value depends on the commoditization of supply, the type of incremental user and the nature of substitutes. For example, social networks can hit an inflection point, after which additional users do not bring more value. This could be attributed to the fact that as more people join the network, its users are less willing to share personal content and the site becomes more focused on news and public content. Economics Network economics refers to business economics that benefit from the network effect. This is when the value of a good or service increases when others buy the same good or service. Examples are websites such as EBay, or iVillage where the community comes together and shares thoughts to help the website become a better business organization. In sustainability, network economics refers to multiple professionals (architects, designers, or related businesses) all working together to develop sustainable products and technologies. The more companies are involved in environmentally friendly production, the easier and cheaper it becomes to produce new sustainable products. For instance, if no one produces sustainable products, it is difficult and expensive to design a sustainable house with custom materials and technology. But due to network economics, the more industries are involved in creating such products, the easier it is to design an environmentally sustainable building. Another benefit of network economics in a certain field is the improvement that results from competition and networking within an industry. Adoption and competition In the early phases of a network technology, incentives to adopt the new technology are low. After a certain number of people have adopted the technology, network effects become significant enough that adoption becomes a dominant strategy. This point is called critical mass. At the critical mass point, the value obtained from the good or service is greater than or equal to the price paid for the good or service. When a product reaches critical mass, network effects will drive subsequent growth until a stable balance is reached. Therefore, a key business concern must then be how to attract users prior to reaching critical mass. Critical quality is closely related to consumer expectations, which will be affected by price and quality of products or services, the company's reputation and the growth path of the network. Thus, one way is to rely on extrinsic motivation, such as a payment, a fee waiver, or a request for friends to sign up. A more natural strategy is to build a system that has enough value without network effects, at least to early adopters. Then, as the number of users increases, the system becomes even more valuable and is able to attract a wider user base. Network growth is generally not infinite, and tends to plateau when it reaches market saturation (all customers have already joined) or diminishing returns render the cost of acquiring the remaining customers prohibitive. Networks can also stop growing or collapse if they do not have enough capacity to handle growth. For example, an overloaded phone network that has so many customers that it becomes congested, leading to busy signals, the inability to get a dial tone, and poor customer support. This creates a risk that customers will defect to a rival network because of the inadequate capacity of the existing system. After this point, each additional user decreases the value obtained by every other user. Peer-to-peer (P2P) systems are networks designed to distribute load among their user pool. This theoretically allows P2P networks to scale indefinitely. The P2P based telephony service Skype benefited from this effect and its growth was limited primarily by market saturation. Network effects give rise to the potential outcome of market tipping, defined as "the tendency of one system to pull away from its rivals in popularity once it has gained an initial edge". Tipping results in a market in which only one good or service dominates and competition is stifled, and can result in a monopoly. This is because network effects tend to incentivise users to coordinate their adoption of a single product. Therefore, tipping can result in a natural form of market concentration in markets that display network effects. However, the presence of network effects does not necessarily imply that a market will tip; the following additional conditions must be met: If any of these three conditions are not satisfied, the market may fail to tip and multiple products with significant market shares may coexist. One such example is the U.S. instant messaging market, which remained an oligopoly despite significant network effects. This can be attributed to the low multi-homing and switching costs faced by users. Market tipping does not imply permanent success in a given market. Competition can be reintroduced into the market due to shocks such as the development of new technologies. Additionally, if the price is raised above customers' willingness to pay, this may reverse market tipping. Network effects often result in multiple potential market equilibrium outcomes. The key determinant in which equilibrium will manifest is the expectations of the market participants, which are self-fulfilling. Because users are incentivised to coordinate their adoption, users will tend to adopt the product that they expect to draw the largest number of users. These expectations may be shaped by path dependence, such as a perceived first-mover advantage, which can result in lock-in. The most commonly cited example of path dependence is the QWERTY keyboard, which owes its ubiquity to its establishment of an early lead in the keyboard layout industry and high switching costs, rather than any inherent advantage over competitors. Other key influences of adoption expectations can be reputational (e.g. a firm that has previously produced high-quality products may be favoured over a new firm). Markets with network effects may result in inefficient equilibrium outcomes. With simultaneous adoption, users may fail to coordinate towards a single agreed-upon product, resulting in splintering among different networks, or may coordinate to lock-in to a different product than the one that is best for them. Technology lifecycle If some existing technology or company whose benefits are largely based on network effects starts to lose market share against a challenger such as a disruptive technology or open standards based competition, the benefits of network effects will reduce for the incumbent, and increase for the challenger. In this model, a tipping point is eventually reached at which the network effects of the challenger dominate those of the former incumbent, and the incumbent is forced into an accelerating decline, whilst the challenger takes over the incumbent's former position. Sony's Betamax and Victor Company of Japan (JVC)'s video home system (VHS) can both be used for video cassette recorders (VCRs), but the two technologies are not compatible. Therefore, the VCR that is suitable for one type of cassette cannot fit in another. VHS's technology gradually surpassed Betamax in the competition. In the end, Betamax lost its original market share and was replaced by VHS. Negative network externalities Negative network externalities, in the mathematical sense, are those that have a negative effect compared to normal (positive) network effects. Just as positive network externalities (network effects) cause positive feedback and exponential growth, negative network externalities are also caused by positive feedback, resulting in exponential decay. Negative network effect must not be confused with negative feedback. Negative feedback is the forces that pull towards equilibrium and are responsible for stability. Besides, Negative network externalities has four characteristics, which are namely, more login retries, longer query times, longer download times and more download attempts.[non sequitur (see talk)] Therefore, congestion occurs when the efficiency of a network decreases as more people use it, and this reduces the value to people already using it. Traffic congestion that overloads the freeway and network congestion on connections with limited bandwidth both display negative network externalities. Braess's paradox suggests that adding paths through a network can have a negative effect on performance of the network. Interoperability Interoperability has the effect of making the network bigger and thus increases the external value of the network to consumers. Interoperability achieves this primarily by increasing potential connections and secondarily by attracting new participants to the network. Other benefits of interoperability include reduced uncertainty, reduced lock-in, commoditization and competition based on price. Interoperability can be achieved through standardization or other cooperation. Companies involved in fostering interoperability face a tension between cooperating with their competitors to grow the potential market for products and competing for market share. Compatibility and incompatibility Product compatibility is closely related to network externalities in a company's competition, which refers to two systems that can be operated together without changing. Compatible products are characterized by better matching with customers, so they can enjoy all the benefits of the network without having to purchase products from the same company. However, not only will products of compatibility intensify competition between companies, but this will also make users who had purchased products lose their advantages, but also proprietary networks may raise the industry entry standards. Compared to large companies with better reputations or strength, weaker companies or small networks will be more inclined to choose compatible products. Besides, the compatibility of products is conducive to the company's increase in market share. For example, the Windows system is famous for its operating compatibility, thereby satisfying consumers' diversification of other applications. As the supplier of Windows systems, Microsoft benefits from indirect network effects, which cause the growing of the company's market share. Incompatibility is the opposite of compatibility. Because incompatibility of products will aggravate market segmentation and reduce efficiency, and also harm consumer interests and enhance competition. The result of the competition between incompatible networks depends on the complete sequence of adoption and the early preferences of the adopters. Effective competition determines the market share of companies, which is historically important. Since the installed base can directly bring more network profit and increase the consumers' expectations, which will have a positive impact on the smooth implementation of subsequent network effects. Open versus closed standards In communication and information technologies, open standards and interfaces are often developed through the participation of multiple companies and are usually perceived to provide mutual benefit. But, in cases in which the relevant communication protocols or interfaces are closed standards, the network effect can give the company controlling those standards monopoly power. The Microsoft corporation is widely seen by computer professionals as maintaining its monopoly through these means. One observed method Microsoft uses to put the network effect to its advantage is called Embrace, extend and extinguish. Mirabilis is an Israeli start-up which pioneered instant messaging (IM) and was bought by America Online. By giving away their ICQ product for free and preventing interoperability between their client software and other products, they were able to temporarily dominate the market for instant messaging. The IM technology is in use from the home to the workplace because of its faster processing speed and simplified process characteristics. Because of the network effect, new IM users gained much more value by choosing to use the Mirabilis system (and join its large network of users) than they would use a competing system. As was typical for that era, the company never made any attempt to generate profits from its dominant position before selling the company. Network effect as a competitive advantage Network effect can significantly influence the competitive landscape of an industry. According to Michael E. Porter, strong network effects might decrease the threat of new entrants, which is one of the five major competitive forces that act on an industry. Persistent barriers to entry into a market may help incumbent companies to fend off competition and keep or increase their market share, while maintaining profitability and return on capital. These attractive characteristics are one of the reasons that allowed platform companies like Amazon, Google or Facebook to grow rapidly and create shareholder value. On the other hand, network effect can result in high concentration of power in an industry, or even a monopoly. This often leads to increased scrutiny from regulators who try to restore healthy competition, as is often the case with large technology companies. Examples Network effects are the incremental benefit gained by each user for each new user that joins a network. An example of a direct network effect is the telephone. Originally, when only a small number of people owned a telephone, the value it provided was minimal. Not only did other people need to own a telephone for it to be useful, but it also had to be connected to the network through the user's home. As technology advanced, it became more affordable for people to own a telephone. This created more value and utility due to the increase in users. Eventually, increased usage through exponential growth led to the telephone being used by almost every household, adding more value to the network for all users. Without the network effect and technological advances, the telephone would have nowhere near the amount of value or utility it does today. Transactions in the financial field may feature a network effect. As the number of sellers and buyers in the exchange who have the symmetric information increases, liquidity increases, and transaction costs decrease.[failed verification] This then attracts a larger number of buyers and sellers to the exchange. The network advantage of financial exchanges is apparent in the difficulty that startup exchanges have in dislodging a dominant exchange. For example, the Chicago Board of Trade has retained overwhelming dominance of trading in US Treasury bond futures despite the startup of Eurex US trading of identical futures contracts. Similarly, the Chicago Mercantile Exchange has maintained dominance in trading of Eurobond interest rate futures despite a challenge from Euronext.Liffe. Cryptocurrencies such as Bitcoin and smart contract blockchains such as Ethereum also exhibit network effects. Smart contract blockchains can produce network effects through the social network of individuals that uses a blockchain for securing its transactions. Public infrastructure networks such as Ethereum and others can facilitate entities that do not explicitly trust one another to collaborate in meaningful way, incentivizing growth in the network. However, as of 2019, such networks grow more slowly due to missing particular requirements such as privacy and scalability. The widely used computer software benefits from powerful network effects. The software-purchase characteristic is that it is easily influenced by the opinions of others, so the customer base of the software is the key to realizing a positive network effect. Although customers' motivation for choosing software is related to the product itself, media interaction and word-of-mouth recommendations from purchased customers can still increase the possibility of software being applied to other customers who have not purchased it, thereby resulting in network effects. In 2007 Apple released the iPhone followed by the app store. Most iPhone apps rely heavily on the existence of strong network effects. This enables the software to grow in popularity very quickly and spread to a large userbase with very limited marketing needed. The Freemium business model has evolved to take advantage of these network effects by releasing a free version that will not limit the adoption or any users and then charge for premium features as the primary source of revenue. Furthermore, some software companies will launch free trial versions during the trial period to attract buyers and reduce their uncertainty. The duration of free time is related to the network effect. The more positive feedback the company received, the shorter the free trial time will be. Software companies (for example Adobe or Autodesk) often give significant discounts to students. By doing so, they intentionally stimulate the network effect - as more students learn to use a particular piece of software, it becomes more viable for companies and employers to use it as well. And the more employers require a given skill, the higher the benefit that employees will receive from learning it. This creates a self-reinforcing cycle, further strengthening the network effect. Many web sites benefit from a network effect. One example is web marketplaces and exchanges. For example, eBay would not be a particularly useful site if auctions were not competitive. As the number of users grows on eBay, auctions grow more competitive, pushing up the prices of bids on items. This makes it more worthwhile to sell on eBay and brings more sellers onto eBay, which, in turn, drives prices down again due to increased supply. Increased supply brings even more buyers to eBay. Essentially, as the number of users of eBay grows, prices fall and supply increases, and more and more people find the site to be useful. Network effects were used as justification in business models by some of the dot-com companies in the late 1990s. These firms operated under the belief that when a new market comes into being which contains strong network effects, firms should care more about growing their market share than about becoming profitable. The justification was that market share would determine which firm could set technical and marketing standards and giving these companies a first-mover advantage. An example here could be social networking websites; the more people register onto a social networking website, the more effect it has on its registrants. Google uses the network effect in its advertising business with its Google AdSense service. AdSense places ads on many small sites, such as blogs, using Google technology to determine which ads are relevant to which blogs. Thus, the service appears to aim to serve as an exchange (or ad network) for matching many advertisers with many small sites. In general, the more blogs AdSense can reach, the more advertisers it will attract, making it the most attractive option for more blogs. By contrast, the value of a news site is primarily proportional to the quality of the articles, not to the number of other people using the site. Similarly, the first generation of search engines experienced little network effect, as the value of the site was based on the value of the search results. This allowed Google to win users away from Yahoo! without much trouble, once users believed that Google's search results were superior. Some commentators mistook the value of the Yahoo! brand (which does increase as more people know of it) for a network effect protecting its advertising business. There are strong network effects in the initial choice of rail gauge, and in gauge conversion decisions. Even when placing isolated rails not connected to any other lines, track layers usually choose a standard rail gauge so they can use off-the-shelf rolling stock. Although a few manufacturers make rolling stock that can adjust to different rail gauges, most manufacturers make rolling stock that only works with one of the standard rail gauges. This even applies to urban rail systems where historically tramways and to a lesser extent metros would come in a wide array of different gauges, nowadays virtually all new networks are built to a handful of gauges and overwhelmingly standard gauge. For credit cards that are now widely used, large-scale applications on the market are closely related to network effects. Credit card, as one of the currency payment methods in the current economy, which was originated in 1949. Early research on the circulation of credit cards at the retail level found that credit card interest rates were not affected by macroeconomic interest rates and remained almost unchanged. Later, credit cards gradually entered the network level due to changes in policy priorities and became a popular trend in payment in the 1980s. Different levels of credit cards separate benefit from two types of network effects. The application of credit cards related to external network effects, which is because when this has become a payment method, and more people use credit cards. Each additional person uses the same credit card, the value of rest people who use the credit card will increase. Besides, the credit card system at the network level could be seen as a two-sided market. On the one hand, the number of cardholders attracts merchants to use credit cards as a payment method. On the other hand, an increasing number of merchants can also attract more new cardholders. In other words, the use of credit cards has increased significantly among merchants which leads to increased value. This can conversely increase the cardholder's credit card value and the number of users. Moreover, credit card services also display a network effect between merchant discounts and credit accessibility. When credit accessibility increases which greater sales can be obtained, merchants are willing to be charged more discounts by credit card issuers. Visa has become a leader in the electronic payment industry through the network effect of credit cards as its competitive advantage. Till 2016, Visa's credit card market share has risen from a quarter to as much as half in four years. Visa benefits from the network effect. Since every additional Visa cardholder is more attractive to merchants, and merchants can also attract more new cardholders through the brand. In other words, the popularity and convenience of Visa in the electronic payment market, lead more people and merchants choose to use Visa, which greatly increases the value of Visa. See also References Further reading External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Social_influence] | [TOKENS: 3231] |
Contents Social influence Social influence comprises the ways in which individuals adjust their behavior to meet the demands of a social environment. It takes many forms and can be seen in conformity, socialization, peer pressure, obedience, leadership, persuasion, sales, and marketing. Typically social influence results from a specific action, command, or request, but people also alter their attitudes and behaviors in response to what they perceive others might do or think. In 1958, Harvard psychologist Herbert Kelman identified three broad varieties of social influence. Morton Deutsch and Harold Gerard described two psychological needs that lead humans to conform to the expectations of others. These include our need to be right (informational social influence) and our need to be liked (normative social influence). Informational influence (or social proof) is an influence to accept information from another as evidence about reality. Informational influence comes into play when people are uncertain, either from stimuli being intrinsically ambiguous or because of social disagreement. Normative influence is an influence to conform to the positive expectations of others. In terms of Kelman's typology, normative influence leads to public compliance and identification, whereas informational influence leads to private acceptance and internalization. Beyond these classic forms of social influence, University of Kansas psychologist Christian S Crandall emphasize that imitation, conformity, and social norms form the deeper foundation of how influence works. Humans are biologically prepared to pay attention to others and learn from them, and this shared expectation of what behaviors are appropriate (social norms), shapes nearly all group behavior. Types Social influence is a broad term that relates to many different phenomena. Listed below are some major types of social influence that are being researched in social psychology. For more information, follow the main article links provided. There are three processes of attitude change as defined by Harvard psychologist Herbert Kelman in a 1958 paper published in the Journal of Conflict Resolution. The purpose of defining these processes was to help determine the effects of social influence: for example, to separate public conformity (behavior) from private acceptance (personal belief). Compliance is the act of responding favorably to an explicit or implicit request offered by others. Technically, compliance is a behavior change but not necessarily in attitude; one can comply due to mere obedience or by otherwise opting to withhold private thoughts due to social pressures. According to Kelman's 1958 paper, the satisfaction derived from compliance is due to the social effect of the accepting influence (i.e., people comply for an expected reward or punishment-aversion). Identification is the changing of attitudes or behaviors due to the influence of someone who is admired. Advertisements relying upon celebrity endorsements to market their products are taking advantage of this phenomenon. According to Kelman, the desired relationship that the identifier relates to the behavior or attitude change. Internalization is the process of acceptance of a set of norms established by people or groups that are influential to the individual. The individual accepts the influence because the content of the influence accepted is intrinsically rewarding. It is congruent with the individual's value system, and according to Kelman the "reward" of internalization is "the content of the new behavior". Conformity is a type of social influence involving a change in behavior, belief, or thinking to align with those of others or with normative standards. It is the most common and pervasive form of social influence. Social psychology research in conformity tends to distinguish between two varieties: informational conformity (also called social proof, or "internalization" in Kelman's terms ) and normative conformity ("compliance" in Kelman's terms). Christian S Crandall also point out that conformity is rooted in the broader system of social norms,the shared expectations within a group about appropriate behavior. Humans are naturally equipped to learn from and imitate others, so conformity is not just copying what people do, but responding to both descriptive norms (what people typically do) and injunctive norms (what people believe one should do). Experiments by Solomon Asch demonstrated that individuals frequently conform to a clearly incorrect majority, and that the presence of even a single dissenter substantially reduces conformity pressure. Later work found that experiences of social exclusion increase people’s likelihood to conform, suggesting that conformity can function as a strategy to regain social acceptance. Conformity also spreads through norm cascades, in which a small number of people adopting a behavior can trigger rapid group-wide adoption once a critical threshold is reached. Researchers have been studying social influence and minority influence for over thirty years. Early research in social psychology emphasized conformity and behaviors that enforced conformity on others. Which created a conformity bias and overshadowed the role of minorities. The first publication covering these topics was written by social psychologist Serge Moscovici and published in 1976. Minority influence takes place when a majority is influenced to accept the beliefs or behaviors of a minority. Minority influence can be affected by the size of majority and minority groups, the level of consistency of the minority group, and situational factors (such as the affluence or social importance of the minority). Moscovici’s more recent research highlights that active minorities, such as social movements, scientific innovators, or emerging artistic groups, play a crucial role in challenging majority norms and driving social change. Minority groups can gain influence by promoting new ideas, and can shift majority beliefs by presenting consistent, confident, and autonomous positions. Minority influence most often operates through informational social influence (as opposed to normative social influence) because the majority may be indifferent to the liking of the minority. A self-fulfilling prophecy is a prediction that directly or indirectly causes itself to become true due to positive feedback between belief and behavior. A prophecy declared as truth (when it is actually false) may sufficiently influence people, either through fear or logical confusion, so that their reactions ultimately fulfill the once-false prophecy. This term is credited to sociologist Robert K. Merton from an article he published in 1948. Social contagion involves the spontaneous spread of behaviors or emotions through a group, population or social network. Social contagion consists of two categories, behavioral contagion and emotional contagion. Unlike conformity, the emotion or behavior being adopted may not represent a social norm. Reactance is the adoption of a view contrary to the view that a person is being pressured to accept, perhaps due to a perceived threat to behavioral freedoms. This phenomenon has also been called anticonformity.According to the Encyclopedia of Social Psychology by Roy F. Baumeister, people become upset when their freedom feels restricted and may deliberately do the opposite of what they are told in an attempt to restore that lost sense of freedom. While the results are the opposite of what the influencer intended, the reactive behavior is a result of social pressure. It is notable that anticonformity does not necessarily mean independence. In many studies, reactance manifests itself in a deliberate rejection of an influence, even if the influence is clearly correct. Obedience is a form of social influence that derives from an authority figure, based on order or command. The Milgram experiment, Zimbardo's Stanford prison experiment, and the Hofling hospital experiment are three particularly well-known experiments on obedience, and they all conclude that humans are surprisingly obedient in the presence of perceived legitimate authority figures. Persuasion is the process of guiding oneself or another toward the adoption of an attitude by rational or symbolic means. US psychologist Robert Cialdini defined six "weapons of influence": reciprocity, commitment, social proof, authority, liking, and scarcity to bring about conformity by directed means. Persuasion can occur through appeals to reason or appeals to emotion. Psychological manipulation is a type of social influence that aims to change the behavior or perception of others through abusive, deceptive, or underhanded tactics. By advancing the interests of the manipulator, often at another's expense, such methods could be considered exploitative, abusive, devious, and deceptive. Social influence is not necessarily negative. For example, doctors can try to persuade patients to change unhealthy habits. Social influence is generally perceived to be harmless when it respects the right of the influenced to accept or reject it, and is not unduly coercive. Depending on the context and motivations, social influence may constitute underhanded manipulation. Controlling individuals use various tactics to abuse their victims. Tactics may include coercion and threats, intimidation, emotional abuse, isolation, and more. The goal of the abuser is to control and intimidate the victim or to influence them to feel that they do not have an equal voice in the relationship. Political entities may employ patterns of similar techniques in the exertion of abusive power and control over persons subject to them. Propaganda is information that is not objective and is used primarily to influence an audience and further an agenda, often by presenting facts selectively to encourage a particular synthesis or perception, or using loaded language to produce an emotional rather than a rational response to the information that is presented. Hard power is the use of military and economic means to influence the behavior or interests of other political bodies. This form of political power is often aggressive (coercion), and is most effective when imposed by one political body upon another of lesser military and/or economic power. Hard power contrasts with soft power, which comes from diplomacy, culture and history. Psychologist Bertram H. Raven (1964) defines social influence as any change in a person’s thoughts, attitudes, or behavior that originates from another individual or group. He outlines several distinct bases of social power, beginning with informational influence, in which change results from the content or logic of a communication rather than the communicator themselves. Raven also describes coercive and reward power, forms of influence that rely on the perceived ability of a person to administer punishments or provide benefits. Additional sources of power include expert power, which emerges when individuals defer to those believed to possess superior knowledge, and referent power, in which individuals adjust their attitudes or behaviors to align with people or groups they identify with. Antecedents Many factors can affect the impact of social influence. Collective identity or group identity is a shared sense of belonging to a group. In Social Influence and Group Identity, Russel Spears (2021) explains that much of what shapes people’s attitudes and behaviors comes from the social groups they identify with, drawing on classic self-categorization theory by JC Turner(1987, 1991) that argues people conform to group norms when those norms feel self-relevant. Research shows that group identity strengthens conformity in famous studies like Sherif’s autokinetic illusion experiments and Asch’s line-judgment paradigm. Spears also contrasts identity-based influence with other explanations as a distinction between descriptive and injunctive norms. Showing that group identity can shape not only behavior but perceived moral obligations. Social impact theory was developed by Bibb Latané in 1981. This theory asserts that there are three factors which increase a person's likelihood to respond to social influence: Robert Cialdini defines six "weapons of influence" that can contribute to an individual's propensity to be influenced by a persuader: Social Influence is strongest when the group perpetrating it is consistent and committed. Even a single instance of dissent can greatly wane the strength of an influence. For example, in Milgram's first set of obedience experiments, 65% of participants complied with fake authority figures to administer "maximum shocks" to a confederate. In iterations of the Milgram experiment where three people administered shocks (two of whom were confederates), once one confederate disobeyed, only ten percent of subjects administered the maximum shocks. Those perceived as experts may exert social influence as a result of their perceived expertise. This involves credibility, a tool of social influence from which one draws upon the notion of trust. People believe an individual to be credible for a variety of reasons, such as perceived experience, attractiveness, knowledge, etc. Additionally, pressure to maintain one's reputation and not be viewed as fringe may increase the tendency to agree with the group. This phenomenon is known as groupthink. Appeals to authority may especially affect norms of obedience. The compliance of normal humans to authority in the famous Milgram experiment demonstrates the power of perceived authority. Those with access to the media may use this access in an attempt to influence the public. For example, a politician may use speeches to persuade the public to support issues that he or she does not have the power to impose on the public. This is often referred to as using the "bully pulpit." Likewise, celebrities do not usually possess any political power, but they are familiar to many of the world's citizens and, therefore, possess social status. Power is one of the biggest reasons an individual feels the need to follow through with the suggestions of another. A person who possesses more authority (or is perceived as being more powerful) than others in a group is an icon or is most "popular" within a group. This person has the most influence over others. For example, in a child's school life, people who seem to control the perceptions of the students at school are most powerful in having a social influence over other children. Culture appears to play a role in the willingness of an individual to conform to the standards of a group. Stanley Milgram found that conformity was higher in Norway than in France. This has been attributed to Norway's longstanding tradition of social responsibility, compared to France's cultural focus on individualism. Japan likewise has a collectivist culture and thus a higher propensity to conformity. However, a 1970 Asch-style study found that when alienated, Japanese students were more susceptible to anticonformity (giving answers that were incorrect even when the group had collaborated on correct answers) one-third of the time, significantly higher than has been seen in Asch studies in the past. While gender does not significantly affect a person's likelihood to conform, under certain conditions gender roles do affect such a likelihood. Studies from the 1950s and 1960s concluded that women were more likely to conform than men. However a 1971 study found that experimenter bias was involved; all of the researchers were male, while all of the research participants were female. Studies thereafter found that the likelihood to conform is almost equal between the genders. Furthermore, men conformed more often when faced with traditionally feminine topics, and women conformed more often when presented with masculine topics. In other words, ignorance about a subject can lead a person to defer to "social proof". Emotion and disposition may affect an individual's likelihood of conformity or anticonformity. In 2009, a study concluded that fear increases the chance of agreeing with a group, while romance or lust increases the chance of going against the group. Social structure A social network is a social structure made up of nodes (representing individuals or organizations) which are connected (through ties, also called edges, connections, or links) by one or more types of interdependency (such as friendship, common interests or beliefs, sexual relations, or kinship). Social network analysis uses the lens of network theory to examine social relationships. Social network analysis as a field has become more prominent since the mid-20th century in determining the channels and effects of social influence. For example, Christakis and Fowler found that social networks transmit states and behaviors such as obesity, smoking, drinking and happiness. However, important flaws have been identified in the contagion model for social influence which is assumed and used in many of the above studies. In order to address these flaws, causal inference methods have been proposed instead, to systematically disentangle social influence from other possible confounding causes when using observational data. Global approach to the phenomenon of influence As described above, theoretical approaches are in the form of knowledge clusters. The global theory of influence is missing for an easy understanding and an education to protect from manipulators. A first tentative was published in 2012. The first pages of Influence & Systems explain why a global approach is necessary. See also References External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Sexual_assault] | [TOKENS: 8156] |
Contents Sexual assault Sexual assault (SA) is an act of sexual abuse in which one intentionally sexually touches another person without that person's consent, or coerces or physically forces a person to engage in a sexual act against their will. It is a form of sexual violence that includes child sexual abuse, groping (forcing unsolicited, sexualized touching onto another person), rape (forced sexual penetration, no matter how slight), forced kissing, drug facilitated sexual assault, or the torture of the person in a sexual manner often with nudity as a precursor. Definition Generally, sexual assault is defined as unwanted sexual contact. The National Center for Victims of Crime states: Sexual assault takes many forms including attacks such as rape or attempted rape, as well as any unwanted sexual contact or threats. Usually a sexual assault occurs when someone touches any part of another person's body in a sexual way, even through clothes, without that person's consent. In the United States, the definition of sexual assault varies widely among the individual states. However, in most states sexual assault occurs when there is lack of consent from one of the individuals involved. Consent must take place between two adults who are not incapacitated and consent may change, by being withdrawn, at any time during the sexual act. Sexual assault can be defined as violation of consent according to standards of substantive equality or formal equality. Types Child sexual abuse is a form of child abuse in which an adult or older adolescent abuses a child for sexual stimulation. Forms of child sexual abuse include asking or pressuring a child to engage in sexual activities (regardless of the outcome), indecent exposure of the genitals to a child, displaying pornography to a child, actual sexual contact against a child, physical contact with the child's genitals, viewing of the child's genitalia without physical contact, or using a child to produce child pornography, including live streaming sexual abuse. The effects of child sexual abuse include depression, post-traumatic stress disorder, anxiety, propensity to re-victimization in adulthood, physical injury to the child, and increased risk for future interpersonal violence perpetration among males, among other problems. Sexual assault among teenagers has been shown to lead to worse school performance, an increase in mental health problems, and social exclusion. Sexual abuse by a family member is a form of incest. It is more common than other forms of sexual assault on a child and can result in more serious and long-term psychological trauma, especially in the case of parental incest. Approximately 15 to 25 percent of women and 5 to 15 percent of men were sexually abused when they were children. Most sexual abuse offenders are acquainted with their victims. Approximately 30 percent of the perpetrators are relatives of the child – most often brothers, sisters, fathers, mothers, uncles, aunts or cousins. Around 60 percent are other acquaintances such as friends of the family, babysitters, or neighbors. Strangers are the offenders in approximately 10 percent of child sexual abuse cases. Studies have shown that the psychological damage is particularly severe when sexual assault is committed by parents against children due to the incestuous nature of the assault. Incest between a child and a related adult has been identified as the most widespread form of child sexual abuse with a huge capacity for damage to a child. Often, sexual assault on a child is not reported by the child for several of the following reasons: In addition, many states have criminalized sexual contact between teachers or school administrators and students, even if the student is over the age of consent. Domestic violence is violence or other abuse by one person against another in a domestic setting, such as in marriage or cohabitation. It is strongly correlated with sexual assault. Not only can domestic abuse be emotional, physical, psychological and financial, but it can be sexual. Some of the signs of sexual abuse are similar to those of domestic violence. About 30 percent of people age 65 or older who are sexually assaulted in the U.S. report it to the police. Assailants may include strangers, caretakers, adult children, spouses and fellow facility residents, although perpetrators of elder sexual assault are less likely to be related to the victim than perpetrators of other types of elder abuse. The term groping is used to define the touching or fondling of another person in a sexual way without the person's consent. Groping may occur under or over clothing. Outside of law, the term rape (sexual intercourse or other forms of sexual penetration carried out against a person without that person's consent) is often used interchangeably with sexual assault. Although closely related, the two terms are technically distinct in most jurisdictions. Sexual assault typically includes rape and other forms of non-consensual sexual activity. Abbey et al. state that female victims are much more likely to be assaulted by an acquaintance, such as a friend or co-worker, a dating partner, an ex-boyfriend or a husband or other intimate partner than by a complete stranger. In a study of hospital emergency room treatments for rape, Kaufman et al. stated that the male victims as a group sustained more physical trauma and were more likely to have been a victim of multiple assaults from multiple assailants. It was also stated that male victims were more likely to have been held captive longer. In the U.S., rape is a crime committed primarily against youth. A national telephone survey on violence against women conducted by the National Institute of Justice and the Centers for Disease Control and Prevention found that 18% of women surveyed had experienced a completed or attempted rape at some time in their lives. Of these, 22% were younger than 12 years and 32% were between 12 and 17 years old when they were first raped. In the U.K., attempted rape under the Criminal Attempts Act 1981 is a 'sexual offence' within section 31(1) of the Criminal Justice Act 1991. The removal of a condom during intercourse without the consent of the sex partner, known as stealthing, may be treated as a sexual assault or rape in some jurisdictions. Sexual harassment is intimidation, bullying or coercion of a sexual nature. It may also be defined as the unwelcome or inappropriate promise of rewards in exchange for sexual favors. The legal and social definition of what constitutes sexual harassment differ widely by culture. Sexual harassment includes a wide range of behaviors from seemingly mild transgressions to serious forms of abuse. Some forms of sexual harassment overlap with sexual assault.[full citation needed] In the United States, sexual harassment is a form of discrimination which violates Title VII of the Civil Rights Act of 1964. According to the Equal Employment Opportunity Commission (EEOC): "Unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature constitutes sexual harassment when submission to or rejection of this conduct explicitly or implicitly affects an individual's employment, unreasonably interferes with an individual's work performance or creates an intimidating, hostile or offensive work environment." In the United States: Mass sexual assault takes place in public places and in crowds. It involves large group of people surrounding and assaulting the other gender and engaging in conduct such as groping, manual penetration, and frottage, but usually stopping short of penile rape. Emotional and psychological effects Aside from physical traumas, rape and other sexual assault often result in long-term emotional effects, particularly in child victims. These can include, but are not limited to: denial, learned helplessness, genophobia (fear of sex), anger, self-blame, anxiety, shame, nightmares, fear, depression, flashbacks, guilt, rationalization, moodswings, numbness, hypersexuality, loneliness, social anxiety, difficulty trusting oneself or others, and difficulty concentrating. Sexual assault increases an individual's risk to developing psychopathology. It is most strongly related to the development of suicidality and trauma-related disorders (including post-traumatic stress disorder), as well as the development of bipolar and obsessive–compulsive disorders. Experiencing sexual assault also increases the risk of developing anxiety disorders, major depressive disorder, eating disorders, addiction, or other psychopathologies. Individuals who develop psychological disorders following sexual assault have increased frequency and severity of psychopathology compared with individuals who have not experienced sexual assault. Family and friends of individuals who have been sexually assaulted experience emotional scarring, including a strong desire for revenge, a desire to "fix" the problem or move on, and a rationalization that "it wasn't that bad". Physical effects While sexual assault, including rape, can result in physical trauma, many people who experience sexual assault will not suffer any physical injury. Rape myths suggest that the stereotypical victim of sexual violence is a bruised and battered young woman. The central issue in many cases of rape or other sexual assault is whether both parties consented to the sexual activity or whether both parties had the capacity to do so. Thus, physical force resulting in visible physical injury is not always seen. This stereotype can be damaging because people who have experienced sexual assault but have no physical trauma may be less inclined to report to the authorities or to seek health care. However, women who experienced rape or physical violence by a partner were more likely than people who had not experienced this violence to report frequent headaches, chronic pain, difficulty sleeping, activity limitation, poor physical health, and poor mental health. Economic effects Due to rape or sexual assault, or the threat of, there are many resulting impacts on income and commerce at the macro level. Excluding child abuse, each rape or sexual assault costs $5,100 in tangible losses (lost productivity, medical and mental health care, police/fire services, and property damage) and $81,400 in lost quality of life. This issue has been addressed in the U.S. Supreme Court. In his dissenting opinion of the U.S. Supreme Court case U.S. v. Morrison, Justice Souter explained that 75% of women never go to the movies alone at night and nearly 50% will not ride public transportation out of fear of rape or sexual assault. It also stated that less than 1% of victims collect damages and 50% of women lose their jobs or quit after the trauma. The court ruled in U.S. v. Morrison that Congress did not have the authority to enact part of the Violence Against Women Act because it did not have a direct impact on commerce. The Commerce Clause of Article I Section VII of the U.S. Constitution gives authority and jurisdiction to the Federal government in matters of interstate commerce. As a result, the victim was unable to sue her attacker in Federal Court. Sexual assault also has adverse economic effects for survivors on the micro level. For instance, survivors of sexual assault often require time off from work and face increased rates of unemployment. Survivors of rape by an intimate partner lose an average of $69 per day due to unpaid time off from work. Sexual assault is also associated with numerous negative employment consequences, including unpaid time off, diminished work performance, job loss, and inability to work, all of which can lead to lower earnings for survivors. The ability to test backlogged sexual assault kits and have the results uploaded into CODIS is cost effective in terms of reducing the costs associated with sexual assaults' by spending the money on testing evidence. Treatment of victims In the emergency room, emergency contraceptive medications are offered to women raped by men because about 5% of such rapes result in pregnancy. Preventative medication against sexually transmitted infections are given to victims of all types of sexual assault (especially for the most common diseases like chlamydia, gonorrhea, trichomoniasis and bacterial vaginosis) and a blood serum is collected to test for STIs (such as HIV, hepatitis B and syphilis). Any survivor with abrasions are immunized for tetanus if five years have elapsed since the last immunization. Short-term treatment with a benzodiazepine may help with acute anxiety and antidepressants may be helpful for symptoms of PTSD, depression and panic attacks. Eye movement desensitization and reprocessing (EMDR) has also been proposed as a psychiatric treatment for victims of sexual assault. With regard to long term psychological treatment, prolonged exposure therapy has been tested as a method of long-term PTSD treatment for victims of sexual abuse. Mistreatment of victims After the assault, victims may become the target of slut-shaming to cyberbullying. In addition, their credibility may be challenged. During criminal proceedings, publication bans and rape shield laws may operate to protect victims from excessive public scrutiny. Negative social responses to victims' disclosures of sexual assault have the potential to lead to post-traumatic stress disorder symptoms. Social isolation, following a sexual assault, can result in the victim experiencing a decrease in their self-esteem and likelihood of rejecting unwanted sexual advances in the future. Victims have already been through a traumatic assault and it can be exacerbated the unwillingness of law enforcement to move their case along in the forensic testing process because law enforcement officials develop preconceived notions about the victims willingness to participate in the investigation. Prevention Sexual harassment and assault may be prevented by secondary school, college, workplace and public education programs. At least one program for fraternity men produced "sustained behavioral change". At least one study showed that creative campaigns with attention grabbing slogans and images that market consent are effective tools to raise awareness of campus sexual assault and related issues. Several research-based rape prevention programs have been tested and verified through scientific studies, and several rape prevention programs have the strongest empirical data in the research literature. The Men's and Women's Programs, also known as the One in Four programs, were written by John Foubert. and is focused on increasing empathy toward rape survivors and motivating people to intervene as bystanders in sexual assault situations. Published data shows that high-risk persons who saw the Men's and Women's Program committed 40% fewer acts of sexually coercive behavior than those who did not. They also committed acts of sexual coercion that were eight times less severe than a control group. Further research also shows that people who saw the Men's and Women's Program reported more efficacy in intervening and greater willingness to help as a bystander after seeing the program. Several additional studies are available documenting its efficacy. Bringing in the Bystander was written by Victoria Banyard. Its focus is on who bystanders are, when they have helped, and how to intervene as a bystander in risky situations. The program includes a brief empathy induction component and a pledge to intervene in the future. Several studies show strong evidence of favorable outcomes including increased bystander efficacy, increased willingness to intervene as a bystander, and decreased rape myth acceptance. The MVP: Mentors in Violence Prevention was written by Jackson Katz. This program focuses on discussing a male bystander who did not intervene when a woman was in danger. An emphasis is placed on encouraging men to be active bystanders, rather than standing by when they notice abuse. The bulk of the presentation is on processing hypothetical scenarios. Outcomes reported in research literature include lower levels of sexism and increased belief that participants could prevent violence against women. The Green Dot Bystander Intervention program was written by Dorothy Edwards. This program includes both motivational speeches and peer education focused on bystander intervention. Outcomes show that program participation is associated with reductions in rape myth acceptance and increased bystander intervention. The city of Edmonton, Canada, initiated a public education campaign aimed at potential perpetrators. Posters in bar bathrooms and public transit centers reminded men that "It's not sex when she's wasted" and "It's not sex when he changes his mind". The campaign was so effective that it spread to other cities. "The number of reported sexual assaults fell by 10 per cent last year in Vancouver, after the ads were featured around the city. It was the first time in several years that there was a drop in sexual assault activity." President Barack Obama and Vice President Joe Biden introduced in September 2014 a nationwide campaign against sexual assault entitled "It's on us". The campaign includes tips against sexual assault, as well as broad scale of private and public pledges to change to provoke a cultural shift, with a focus on student activism, to achieve awareness and prevention nationwide. UC Berkeley, NCAA and Viacom have publicly announced their partnership. Additionally, CODIS checks whether the qualifying offense sample, DNA taken from an offender for committing a crime, was also a sexual assault. If a person committed sexual offenses in the past, this system would reveal a pattern of serial sexual offending. Using CODIS to compare backlogged rape kit tests can lead to prevention of future sexual assaults. Prevalence United Nations Office on Drugs and Crime (UNODC) reports compiled from government sources show that more than 250,000 cases of sexual violence were reported to the police annually. The annual recorded sexual assaults per capita for the last available year is shown below for individual countries. The United Nations Office on Drugs and Crime categorizes sexual assault and rape as distinct forms of sexual violence, hence the below statistics on sexual assault exclude rape. Definitions of sexual assault differs between countries. The U.S. Department of Justice's National Crime Victimization Survey states that on average there are 237,868 victims (age 12 or older) of sexual assault and rape each year. According to RAINN, every 107 seconds someone in America is sexually assaulted. Sexual assault in the United States military also is a salient issue. Some researchers assert that the unique professional and socially-contained context of military service can heighten the destructive nature of sexual assault, and, therefore, improved support is needed for these victims. The victims of sexual assault: Age By gender A study from 2011 found; The National Crime Victimization Survey conducted by the U.S. Justice Department (Bureau of Justice Statistics) found that from 1995 to 2013, men represented 17% of victims of sexual assault and rape on college campuses, and 4% of non-campus sexual assaults and rapes. LGBT LGBT identifying individuals, with the exception of lesbian women, are more likely to experience sexual assault on college campuses than heterosexual individuals. Effects On average 68% of sexual assaults are estimated to be unreported. The conviction rate for violent sexual assault varies by location around 1-8%. The clearance rate for sexual assault is lower than most violent crimes. According to the U.S. Department of Justice 1997 Sex Offenses and Offenders Study: In 2001: According to the U.S. Department of Justice 2005 National Crime Victimization Study: College In the United States, several studies since 1987 have indicated that one in four college women have experienced rape or attempted rape at some point in their lifetime. These studies are based on anonymous surveys of college women, not reports to the police, and the results are disputed. In 2015, Texas A&M University professor Jason Lindo and his colleagues analyzed over two decades worth of FBI data, noting that reports of rape increased 15–57% around the times of major American football games at Division 1 schools while attempting to find a link between campus rape and alcohol. A 2006 report from the U.S. Department of Justice titled "The Sexual Victimization of College Women" reports that 3.1% of undergraduates survived rape or attempted rape during a 6–7 month academic year with an additional 10.1% surviving rape prior to college and an additional 10.9% surviving attempted rape prior to college. With no overlap between these groups, these percentages add to 24.1%, or "One in Four".[full citation needed] Koss, Gidycz & Wisniewski published a study in 1987 where they interviewed approximately 6,000 college students on 32 college campuses nationwide. They asked several questions covering a wide range of behaviors. From this study 15% of college women answered "yes" to questions about whether they experienced something that met the definition of rape. An additional 12% of women answered "yes" to questions about whether they experienced something that met the definition of attempted rape, thus the statistic One in Four. A point of contention lies in the leading nature of the questions in the study conducted by Koss, Gidycz & Wisniewski. Koss herself later admitted that the question that had garnered the largest "rape" result was flawed and ultimately rendered the study invalid. Most prominently the problem was that many respondents who had answered yes to several questions had their responses treated as having been raped. The issue being that these same respondents did not feel they had been victimized and never sought redress for grievances. The resultant change shows a prevalence of only 1 in 22 college women having been raped or attempted to be raped during their time at college. In 1995, the CDC replicated part of this study, however they examined rape only, and did not look at attempted rape. They used a two-stage cluster sample design to produce a nationally representative sample of undergraduate college students aged greater than or equal to 18 years. The first-stage sampling frame contained 2,919 primary sampling units (PSUs), consisting of two- and four-year colleges and universities. The second sampling stage consisted of a random sample drawn from the primary sample unit frame enrolled in the 136 participating colleges and universities to increase the sample size to 4,609 undergraduate college students aged greater than or equal to 18 years old with a representative sample demographic matching the national demographic. Differential sampling rates of the PSU were used to ensure sufficient numbers of male and female, black and Hispanic students in the total sample population. After differential sample weighting, female students represented 55.5% of the sample; white students represented 72.8% of the sample, black students 10.3%, Hispanic students 7.1%, and 9.9% were other. It was determined that nationwide, 13.1% of college students reported that they had been forced to have sexual intercourse against their will during their lifetime. Female students were significantly more likely than male students to report they had ever been forced to have sexual intercourse; 20% of approximately 2500 females (55% of 4,609 samples) and 3.9% of males reported experiencing rape thus far in the course of their lifetime. Other studies concerning the annual incidence of rape, some studies conclude an occurrence of 5%. The National Survey of Children's Exposure to Violence found that in the 2013–2014 academic year, 4.6% of girls ages 14–17 experienced sexual assault or sexual abuse. In another study, Mohler-Kuo, Dowdall, Koss & Weschler (2004) found in a study of approximately 25,000 college women nationwide that 4.7% experienced rape or attempted rape during a single academic year. This study did not measure lifetime incidence of rape or attempted rape. Similarly, Kilpatrick, Resnick, Ruggiero, Conoscenti, & McCauley (2007) found in a study of 2,000 college women nationwide that 5.2% experienced rape every year. On campuses, it has been found that alcohol is a prevalent issue in regards to sexual assault. It has been estimated that 1 in 5 women experience an assault, and of those women, 50–75% have had either the attacker, the woman, or both, consume alcohol prior to the assault. Not only has it been a factor in the rates of sexual assault on campus, but because of the prevalence, assaults are also being affected specifically by the inability to give consent when intoxicated and bystanders not knowing when to intervene due to their own intoxication or the intoxication of the victim. Children Other research has found that about 80,000 American children are sexually abused each year. Total prevalence of sexual assault including unreported can be estimated with opinion polls. Below is shown the estimated percentage of population which stated to be a victim to sexual assault in the previous 12 months. By jurisdiction Within Australia, the term sexual assault is used to describe a variation of sexual offences. This is due to a variety of definitions and use of terminology to describe sexual offences within territories and states as each territory and state have their own legislation to define rape, attempted rape, sexual assault, aggravated sexual assault, sexual penetration or intercourse without consent and sexual violence. In the State of New South Wales, sexual assault is a statutory offence punishable under s 61I of the Crimes Act 1900. The term "sexual assault" is equivalent to "rape" in ordinary parlance, while all other assaults of a sexual nature are termed "indecent assault". To be liable for punishment under the Crimes Act 1900, an offender must intend to commit an act of sexual intercourse as defined under s 61H(1) while having one of the states of knowledge of non-consent defined under s 61HA(3). But s 61HA(3) is an objective standard which only require the person has no reasonable grounds for believing the other person is consenting. The maximum penalty for sexual assault is 14 years imprisonment. Aggravated sexual assault is sexual intercourse with another person without the consent of the other person and in circumstances of aggravation. The maximum penalty is imprisonment for 20 years under s 61J of the Crimes Act. In the state of Victoria, rape is punishable under s 38 of the Crimes Act 1958, with a maximum penalty of 25 years imprisonment. In the state of South Australia, rape is punishable under s 48 of the Criminal Law Consolidation Act 1935 (SA) with a maximum term of life imprisonment. In the state of Western Australia, sexual penetration is punishable under s 325 the Criminal Code Act 1913 with a maximum sentence of 14 years imprisonment. In the Northern Territory, offences of sexual intercourse and gross indecency without consent are punishable under s 192 of the Criminal Code Act 1983 and punishable with a maximum sentence of life imprisonment. In Queensland, rape and sexual assault are punishable under s 349, Chapter 32 of the Criminal Code Act 1899 with a maximum penalty of life imprisonment. In Tasmania, rape is punishable under s 185 of the Criminal Code Act 1924 with a maximum punishment of 21 years under s389 of the Criminal Code Act 1924. In the Australian Capital Territory, sexual assault is punishable under Part 3 of the Crimes Act 1900 with a maximum punishment of 17 years. Sexual assault is considered a gendered crime which results in 85% of sexual assaults never coming to the attention of the criminal justice system according to the Australian Bureau of Statistics. This is due to low reporting rates, treatment of victims and distrust of the criminal justice system, difficulty in obtaining evidence and the belief in sexual assault myths; however, once a person is charged, the public prosecutor will decide whether the case will proceed to trial based on whether there is sufficient evidence and whether a case is in the public interest. Once the matter has reached trial, the matter will generally be heard in the District Court. This is because sexually violent crimes are mostly categorised as indictable offences (serious offences), as opposed to summary offences (minor offences). Sexual offences can also be heard in the Supreme Court, but more generally if the matter is being heard as an appeal. Once the matter is being heard, the prosecution must provide evidence which proves "beyond reasonable doubt" that the offence was committed by the defendant. The standard of proof is vital in checking the power of the state. While each jurisdiction (State and Territory) has its own sexual offence legislation, there are many common elements to any criminal offence that advise on how the offence is defined and what must be proven by the prosecution in order to find the defendant guilty. These elements are known as actus reus, which comprises the physical element (Ryan v Regina, 1967), and the mens rea, which comprises the mental element (He Kaw Teh, 1985). Notable sexual assault cases which have resulted in convictions are Regina v Bilal Skaf (2005), as well as Regina v Mohommed Skaf (2005), which were highly visible in New South Wales within the media the 2000s. These cases were closely watched by the media and led to legislative changes such as the passing of the Crimes Amendment (Aggravated Sexual Assault in Company) Act 2001 No 62, which dramatically increased the sentences for 'gang rapists' by creating a new category of crime known as Aggravated Sexual Assault in Company. Changes were also made to the Crimes (Sentencing Procedure) Act 1999. This change is known as the Crimes (Sentencing Procedure) Amendment (Victim Impact Statements) Act 2004 No 3, which expands the category of offences in respect of which a Local Court may receive and consider Victim Impact Statements to include some indictable offences which are usually dealt with summarily. Sexual assault is defined as sexual contact with another person without that other person's consent. Consent is defined in section 273.1(1) as "the voluntary agreement of the complainant to engage in the sexual activity in question". Section 265 of the Criminal Code defines the offences of assault and sexual assault. Section 271 criminalizes "Sexual assault", section 272 criminalizes "Sexual assault with a weapon, threats to a third party or causing bodily harm" and section 273 criminalizes "Aggravated sexual assault". The absence of consent defines the crime of sexual assault. Section 273.1 (1) defines consent, section 273.1 (2) outlines certain circumstances where "no consent" is obtained, while section 273.1 (3) states that subsection (2) does not limit the circumstances where "no consent" is obtained (i.e. subsection (2) describes some circumstances which deem the act to be non-consensual, but other circumstances, not described in this section, can also deem the act as having been committed without consent). "No consent" to sexual assault is also subject to Section 265 (3), which also outlines several situations where the act is deemed non-consensual. In 2011, the Supreme Court of Canada in R. v. J.A. interpreted the provisions below to find that a person must have an active mind during the sexual activity in order to consent, and that they cannot give consent in advance. Meaning of "consent" 273.1 (1) Subject to subsection (2) and subsection 265(3), "consent" means, for the purposes of sections 271, 272 and 273, the voluntary agreement of the complainant to engage in the sexual activity in question. Where no consent obtained (2) No consent is obtained, for the purposes of sections 271, 272 and 273, where (a) the agreement is expressed by the words or conduct of a person other than the complainant; (b) the complainant is incapable of consenting to the activity; (c) the accused induces the complainant to engage in the activity by abusing a position of trust, power or authority; (d) the complainant expresses, by words or conduct, a lack of agreement to engage in the activity; or (e) the complainant, having consented to engage in sexual activity, expresses, by words or conduct, a lack of agreement to continue to engage in the activity. Subsection (2) not limiting (3) Nothing in subsection (2) shall be construed as limiting the circumstances in which no consent is obtained. Consent (3) For the purposes of this section, no consent is obtained where the complainant submits or does not resist by reason of (a) the application of force to the complainant or to a person other than the complainant; (b) threats or fear of the application of force to the complainant or to a person other than the complainant; (c) fraud; or (d) the exercise of authority. In accordance with 265 (4) an accused may use the defence that he or she believed that the complainant consented, but such a defence may be used only when "a judge, if satisfied that there is sufficient evidence and that, if believed by the jury, the evidence would constitute a defence, shall instruct the jury when reviewing all the evidence relating to the determination of the honesty of the accused's belief, to consider the presence or absence of reasonable grounds for that belief"; furthermore according to section 273.2(b) the accused must show that he or she took reasonable steps in order to ascertain the complainant's consent, also 273.2(a) states that if the accused's belief steams from self-induced intoxication, or recklessness or wilful blindness than such belief is not a defence. Accused's belief as to consent (4) Where an accused alleges that he or she believed that the complainant consented to the conduct that is the subject-matter of the charge, a judge, if satisfied that there is sufficient evidence and that, if believed by the jury, the evidence would constitute a defence, shall instruct the jury, when reviewing all the evidence relating to the determination of the honesty of the accused's belief, to consider the presence or absence of reasonable grounds for that belief. Where belief in consent not a defence 273.2 It is not a defence to a charge under section 271, 272 or 273 that the accused believed that the complainant consented to the activity that forms the subject-matter of the charge, where (a) the accused's belief arose from the accused's (i) self-induced intoxication, or (ii) recklessness or wilful blindness; or (b) the accused did not take reasonable steps, in the circumstances known to the accused at the time, to ascertain that the complainant was consenting. The Supreme Court of Newfoundland and Labrador jury ruled in favour of a defense that added to the interpretation of the consent laws. The defenses stated and the Jury was reminded by Justice Valerie Marshall: The coined phrase regarding this defense was "Moral vs. legal consent". Before 1997, the definition of rape (German: Vergewaltigung) was: "Whoever compels a woman to have extramarital intercourse with him, or with a third person, by force or the threat of present danger to life or limb, shall be punished by not less than two years' imprisonment." In 1997, a broader definition was adopted with the 13th criminal amendment, section 177–179, which deals with sexual abuse.[citation needed] Rape is generally reported to the police, although it is also allowed to be reported to the prosecutor or District Court.[citation needed] As of 2025,[further explanation needed] the Criminal Code (Strafgesetzbuch) reads: Section 177 Sexual assault by use of force or threats; rape Subsection (2) defines situations where the victim was unable to consent or coerced as having the same penalty. The other subsections provide additional stipulations on sentencing depending on aggravating or mitigating circumstances. Section 178 provides that "If, by committing sexual assault, sexual coercion or rape (section 177), the offender causes the victim's death at least recklessly, the penalty is imprisonment for life or imprisonment for a term of at least 10 years." As in many other jurisdictions, the term sexual assault is generally used to describe non-penetrative sexual offences. Section 2 of the Criminal Law (Rape) Act of 1981 states that a man has committed rape if he has sexual intercourse with a woman who at the time of the intercourse does not consent to it, and at that time he knows that she does not consent to the intercourse or he is reckless as to whether she does or does not consent to it. Under Section 4 of the Criminal Law (Rape Amendment) Act of 1990, rape means a sexual assault that includes penetration (however slight) of the anus or mouth by the penis or penetration (however slight) of the vagina by any object held or manipulated by another person. The maximum penalty for rape in Ireland is imprisonment for life. The Criminal Law (Sexual Offences and Related Matters) Amendment Act replaced the common-law offence of indecent assault with a statutory offence of sexual assault, defined in section 5 of the act as follows. (1) A person ('"A") who unlawfully and intentionally sexually violates a complainant ("B"), without the consent of B, is guilty of the offence of sexual assault. (2) A person ("A") who unlawfully and intentionally inspires the belief in a complainant ("B") that B will be sexually violated, is guilty of the offence of sexual assault. The act's definition of "sexual violation" incorporates a number of sexual acts, including genital contact short of penetration as well as any contact with the mouth designed to cause sexual arousal. Non-consensual acts that involve actual penetration are included in the separate offence of rape rather than sexual assault. The Act also created the offences of "compelled sexual assault", when a person forces a second person to commit an act of sexual violation with a third person; and "compelled self-sexual assault", when a person forces another person to masturbate or commit various other sexual acts on theirself. In August 2022, Spain passed a revolutionary "only yes means yes" sexual consent law which expanded the legal definition of sexual assault in Spain to being also sexual-related conduct without consent and now required that consent must be affirmative and cannot be assumed to have been given by default or silence. It also increased the nation's maximum rape sentence to 15 years, in tandem with 15-year sentences that were handed to perpetrators in the nation's 2016 wolf pack gang rape case; however, it received some criticism for the way it enacted sentencing reductions for some offenders. The law went into effect in October 2022. It was later amended in April 2023 to close loopholes which contributed to the sentence reduction controversy. Sexual assault is a statutory offence in England and Wales. It is created by section 3 of the Sexual Offences Act 2003 which defines "sexual assault" as when a person (A) Whether a belief is reasonable is to be determined having regard to all the circumstances, including any steps A has taken to ascertain whether B consents. Sections 75 and 76 apply to an offence under this section. A person guilty of an offence under this section is liable— Offences committed before the 2003 Act came into force are prosecuted under the Sexual Offences Act 1956 (or in theory earlier legislation), in particular indecent assault. Section 74 of the Sexual Offenses Act explains that "a person consents if he agrees by choice and has the freedom and capacity to make that choice". Section 75 provides a rebuttable presumption that there was no consent in case of violence, intimidation, unlawful imprisonment, unconsciousness, or physical disability or drugs that impair the ability to give consent. Sexual assault is a statutory offence. It is created by article 7 of the Sexual Offences (Northern Ireland) Order 2008. Sexual assault is defined as follows: Sexual assault is a statutory offence. It is created by section 3 of the Sexual Offences (Scotland) Act 2009. Sexual assault is defined as follows: The United States Department of Justice defines sexual assault as "any type of sexual contact or behavior that occurs without the explicit consent of the recipient. Falling under the definition of sexual assault are sexual activities as forced sexual intercourse, forcible sodomy, child molestation, incest, fondling, and attempted rape." Every U.S. state has its own code of laws, and thus the definition of conduct that constitutes a crime, including a sexual assault, may vary to some degree by state. Some states may refer to sexual assault as "sexual battery" or "criminal sexual conduct". The Texas Penal Code, Sec. 22.011(a), defines sexual assault as A person commits [sexual assault] if the person: See also References Further reading |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Edinburgh_IMP] | [TOKENS: 529] |
Contents Edinburgh IMP Edinburgh IMP is a development of Atlas Autocode, initially developed around 1966-1969 at the University of Edinburgh, Scotland. It is a general-purpose programming language which was used heavily for systems programming. Expressively, IMP is highly similar to ALGOL and includes all the ALGOL-style block structure, reserved words (keywords), and data types such as arrays, and records. It adds to ALGOL-style languages a string type (an array of characters, although these have a predeclared size) and built-in operators for string manipulation and character handling. One significant difference from ALGOL is that IMP does not support parameters passed by name, although it does support parameters passed by reference. IMP provides significant control over the storage mapping of data, plus commands for addressing within parts of words. Most IMP compilers offer compiler-generated runtime checks and a stack trace (backtrace) facility by default, even in production code. IMP allows inline assembler machine language instructions in source code. The ERCC Implementation of IMP for the ICL System 4 (known as IMP9) offered a syntax-driven macro facility (designed by Alan Freeman) that was similar to the Compiler Compiler features offered by IMP's predecessor, Atlas Autocode. Early IMP compilers were developed for the English Electric KDF9, ICL System 4, UNIVAC 1108, IBM System/360, DEC PDP-9, DEC PDP-15 and CTL Modular One computers. IMP was used to implement the Edinburgh Multiple Access System (EMAS) operating system, and a compiler was written for the ICL 2900 series to allow porting of EMAS to that platform. In later years, a version of IMP named IMP77 was developed by Peter Robertson within the Computer Science department at Edinburgh which was a portable compiler that brought IMP to even more platforms. In 2002, the IMP77 language was resurrected by the Edinburgh Computer History Project for Intel x86 hardware running DOS, Windows, and Linux, and is once again in use by Edinburgh graduates and ex-pats. The diverged IMP and IMP77 were later consolidated into one language with the introduction of the IMP80 standard, supported by implementations from the Edinburgh Regional Computer Centre. IMP80 has also been ported to several platforms including Intel and was actively in use into the 1990s. Edinburgh IMP is unrelated to the later IMP syntax-extensible programming language developed by Edgar T. Irons, for the CDC 6600, which was the main language used by the National Security Agency (NSA) for many years. See also Sources References |
======================================== |
[SOURCE: https://github.com/features/spark] | [TOKENS: 2296] |
Navigation Menu Search code, repositories, users, issues, pull requests... Provide feedback We read every piece of feedback, and take your input very seriously. Saved searches Use saved searches to filter your results more quickly To see all available qualifiers, see our documentation. Dream it. See it. Ship it. GitHub Spark helps you transform your ideas into full-stack intelligent apps and publish with a single click. From prototype toproduction in one place Built on the platform trusted by over 150 million developers, Spark gives you the smoothest path from idea to deployment. Natural language, clickable controls, or code—use whatever feels right. Live preview updates instantly as you build, so you see your ideas take shape in real-time. No setup, no surprises. You’re live in just one click, backed by secure GitHub-authenticated access. Code with GitHub Copilot directly in Spark, open VS code with agent mode, and create repos in one click. Everything stays in sync as you build and scale. Embed AI features like chatbots, content generation, and smart automation. No complex integrations or APIs required. Leverage all the powerful GitHub tools for version control, security, collaboration, and deployment—so you can scale as you grow from prototype to production. What will you build? Whether you're creating personal tools, prototypes, or the next big SaaS, Spark brings all kinds of ideas to life. Stop explaining your idea—show it. Create functional prototypes in minutes, share for feedback, iterate instantly. Go beyond generic. Build exactly what you want—whether it's AI-powered workout trackers, meal planners, habit builders, or anything else. Validate your business idea with real customers, fast. Build scalable SaaS applications that grow from prototype to profit. Need a standout professional website? Quickly build portfolios, landing pages, and marketing sites—all with AI-powered interactive features that static builders can't match. Every idea starts with a Spark Spark is available to users on GitHub Copilot Pro+ and Enterprise plans. Already a subscriber? You're in. Power user? Get the most Spark. $39USDper month or $390 per year Spark is included for all Enterprise users. $39USDper user per month Access may be restricted based on organizational or enterprise administrator policies for users on Copilot Business or Enterprise plans. Speak with your Admin to request access. GitHub Spark is your all-in-one, AI-powered platform for building intelligent apps—no setup, no steep learning curve. Whether you're a seasoned developer or just getting started, Spark lets you create full-stack applications with built-in AI, using natural language, visual tools, or code. With instant previews, one-click deployment, and deep integration with GitHub’s trusted ecosystem, Spark helps you go from idea to production—fast. And because it’s built on the tools 150 million developers already rely on, your apps are ready to scale from day one. Spark makes intelligent application development more accessible, adaptable, and secure by blending powerful AI capabilities with hands-on flexibility, code-level control, and GitHub's complete platform—empowering everyone to go from idea to production faster, all in one place. It’s built for modern web development, supporting TypeScript and React. Everything runs and deploys on an integrated runtime environment with strong defaults designed to help you move fast, stay focused, and scale with confidence. From AI-powered tools to personal side projects, Spark helps you build real, functional apps—fast. Use it to prototype features, test ideas with real users, or launch open source projects. Turn spreadsheets into interactive apps, build internal tools, or create smart helpers for everyday life—like a recipe planner that remembers dietary needs or a restaurant finder that adapts to your tastes. Whether you're building for your team, your community, or just yourself, Spark gives you the power to create intelligent apps—no code required (unless you want to). GitHub Spark usage is subject to the GitHub Terms of Service and Privacy Statement, which outline your rights, responsibilities, and how your data is handled. No coding experience? No problem. Spark is built for people with all levels of technical fluency. You can describe what you want to build in plain language, and Spark handles the heavy lifting. If you have a development background, you can go deeper: edit code directly in the Spark editor, open your app in Codespaces//VS Code, and use GitHub Copilot and the coding agent to build with full control and flexibility. Spark gives you everything you need to build and deploy full-stack AI apps—right out-of-the-box. Depending on your GitHub Copilot plan, you’ll get a monthly amount of Spark messages, unlimited manual editing, and the ability to build multiple apps simultaneously. You’ll also receive app hosting, compute, AI inference, and storage as part of the integrated runtime of Spark. Additional pay-as-you-go options for usage beyond these included amounts coming soon. A Spark message is any prompt you send to Spark to generate or modify your app using natural language. This includes inputs in the Iterate panel or when using targeted editing to adjust specific parts of your app. Each message helps Spark understand your intent—whether you're adding a feature, refining design, or updating functionality. Spark is now offered in Copilot Pro+ and Enterprise, with broader availability planned for the near future. Spark draws down on your Copilot plan's premium request allowance. For additional information on billing, please refer to our documentation. For additional runtime beyond your monthly entitlement, pay-as-you-go options for additional usage are coming soon. Just sign in with your GitHub account and navigate to the Spark homepage to get started. Getting started with Spark is simple. Go to the Spark homepage: http://github.com/spark Start with your vision: Describe what you want to build in natural language. An AI agent generates a working app—frontend, backend, AI features, and database connections (as needed) included. Iterate your way - Refine your app using natural language, visual controls, or dive into code with Copilot completions in the Spark editor. See changes instantly in the live preview. Go live with a click - When you're satisfied, publish with a click. Your app launches with secure hosting, built-in GitHub user authentication, and the infrastructure needed to handle real users, no configuration required. Deployment is seamless. When you’re ready to share your spark with the work just click “Publish” in the header. We’ll configure your deployment and create a unique link for your app. Once it’s live you can update who can access your app, or keep it private to only you. Your choice. Spark handles all the infrastructure for you. Your app is securely hosted on Microsoft Azure, with enterprise-grade performance, reliability, and security—no setup required, and secured behind GitHub auth. Check out the Spark docs to learn more GitHub Spark is your all-in-one, AI-powered platform for building intelligent apps—no setup, no steep learning curve. Whether you're a seasoned developer or just getting started, Spark lets you create full-stack applications with built-in AI, using natural language, visual tools, or code. With instant previews, one-click deployment, and deep integration with GitHub’s trusted ecosystem, Spark helps you go from idea to production—fast. And because it’s built on the tools 150 million developers already rely on, your apps are ready to scale from day one. Spark makes intelligent application development more accessible, adaptable, and secure by blending powerful AI capabilities with hands-on flexibility, code-level control, and GitHub's complete platform—empowering everyone to go from idea to production faster, all in one place. It’s built for modern web development, supporting TypeScript and React. Everything runs and deploys on an integrated runtime environment with strong defaults designed to help you move fast, stay focused, and scale with confidence. From AI-powered tools to personal side projects, Spark helps you build real, functional apps—fast. Use it to prototype features, test ideas with real users, or launch open source projects. Turn spreadsheets into interactive apps, build internal tools, or create smart helpers for everyday life—like a recipe planner that remembers dietary needs or a restaurant finder that adapts to your tastes. Whether you're building for your team, your community, or just yourself, Spark gives you the power to create intelligent apps—no code required (unless you want to). GitHub Spark usage is subject to the GitHub Terms of Service and Privacy Statement, which outline your rights, responsibilities, and how your data is handled. No coding experience? No problem. Spark is built for people with all levels of technical fluency. You can describe what you want to build in plain language, and Spark handles the heavy lifting. If you have a development background, you can go deeper: edit code directly in the Spark editor, open your app in Codespaces//VS Code, and use GitHub Copilot and the coding agent to build with full control and flexibility. Spark gives you everything you need to build and deploy full-stack AI apps—right out-of-the-box. Depending on your GitHub Copilot plan, you’ll get a monthly amount of Spark messages, unlimited manual editing, and the ability to build multiple apps simultaneously. You’ll also receive app hosting, compute, AI inference, and storage as part of the integrated runtime of Spark. Additional pay-as-you-go options for usage beyond these included amounts coming soon. A Spark message is any prompt you send to Spark to generate or modify your app using natural language. This includes inputs in the Iterate panel or when using targeted editing to adjust specific parts of your app. Each message helps Spark understand your intent—whether you're adding a feature, refining design, or updating functionality. Spark is now offered in Copilot Pro+ and Enterprise, with broader availability planned for the near future. Spark draws down on your Copilot plan's premium request allowance. For additional information on billing, please refer to our documentation. For additional runtime beyond your monthly entitlement, pay-as-you-go options for additional usage are coming soon. Just sign in with your GitHub account and navigate to the Spark homepage to get started. Getting started with Spark is simple. Go to the Spark homepage: http://github.com/spark Start with your vision: Describe what you want to build in natural language. An AI agent generates a working app—frontend, backend, AI features, and database connections (as needed) included. Iterate your way - Refine your app using natural language, visual controls, or dive into code with Copilot completions in the Spark editor. See changes instantly in the live preview. Go live with a click - When you're satisfied, publish with a click. Your app launches with secure hosting, built-in GitHub user authentication, and the infrastructure needed to handle real users, no configuration required. Deployment is seamless. When you’re ready to share your spark with the work just click “Publish” in the header. We’ll configure your deployment and create a unique link for your app. Once it’s live you can update who can access your app, or keep it private to only you. Your choice. Spark handles all the infrastructure for you. Your app is securely hosted on Microsoft Azure, with enterprise-grade performance, reliability, and security—no setup required, and secured behind GitHub auth. Check out the Spark docs to learn more Site-wide Links Get tips, technical guides, and best practices. Twice a month. |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/EGL_(programming_language)] | [TOKENS: 588] |
Contents EGL (programming language) EGL (Enterprise Generation Language), originally developed by IBM and now available as the EDT (EGL Development Tools) open source project under the Eclipse Public License (EPL), is a programming technology designed to meet the challenges of modern, multi-platform application development by providing a common language and programming model across languages, frameworks, and runtime platforms. Overview The language borrows concepts familiar to anyone using statically typed languages like Java, COBOL, C, etc. However, it borrows the concept of stereotype from Unified Modeling Language (UML) that is not typically found in statically typed programming languages. In a nutshell, EGL is a higher-level, universal application development language. EGL is similar in syntax to other common languages so it can be learned by application developers with similar previous programming background. EGL application development abstractions shield programmers from the technical interfaces of systems and middleware allowing them to focus on building business functionality. EGL applications and services are written, tested and debugged at the EGL source level, and once they are satisfactorily functionally tested they can be compiled into COBOL, Java, or JavaScript code to support deployment of business applications that can run in any of the following environments: Code examples An EGL Program part is a generatable logic part with one entry point. Each Program part contains a main() function, which represents the logic that runs at program start up. A program can include other functions and can access functions that are outside of the program. The function main() can invoke those other functions. Program functions are composed of a set of EGL statements, variables, and constants. An EGL Record part defines a set of data elements. In this example, a record with the name CustomerRecord is defined with 6 fields. EGL has a specialized type of record called SQLRecord that is used to exchange data with a relational database. An EGL Service part contains public functions meant to be accessed from other applications or systems. In this example, a service with two functions is defined. The main component of a Rich UI application is a Rich UI handler part. These parts are generated into JavaScript. Web 2.0 with EGL In December 2008, IBM introduced new technology, EGL Rich UI, to simplify the creation of Web 2.0-style rich web applications. This technology simplifies development by hiding the complexities of Ajax, JavaScript, REST, and SOAP from the developer, which enables them to focus on the business requirement and not on the underlying technologies. Commercial products EGL programming tools are available as an Eclipse-based commercial product, the Rational Business Developer and also in the EGL edition of Rational Developer for System z. EGL is a target language for modernization of legacy applications because of the language semantics affinity with procedural languages and legacy 4th generation languages: Tools for searching large EGL code bases, comparing individual EGL files for changes, and detecting duplicated code are available from Semantic Designs References Further reading External links |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Combat] | [TOKENS: 544] |
Contents Combat Combat is a purposeful violent conflict between multiple combatants with the intent to harm the opposition. Combat may be armed (using weapons) or unarmed (not using weapons). Combat is resorted to either as a method of self-defense or to impose one's will upon others. An instance of combat can be a standalone confrontation or part of a wider conflict, and its scale can range from a fight between individuals to a war between organized groups. Combat may also be benign and recreational, as in the cases of combat sports and mock combat. Combat may comply with, or be in violation of, local or international laws regarding conflict. Examples of rules include the Geneva Conventions (covering the treatment of people in war), medieval chivalry, the Marquess of Queensberry Rules (covering boxing), and the individual rulesets of various combat sports. Hand-to-hand combat Hand-to-hand combat (melee) is combat at very close range, attacking the opponent with the body (striking, kicking, strangling, etc.) and/or with a melee weapon (knives, swords, batons, etc.), as opposed to a ranged weapon. Hand-to-hand combat can be further divided into three sections depending on the distance and positioning of the combatants: Military combat Military combat involves two or more opposing military forces meeting in warfare. Military combat situations can involve multiple groups, such as guerilla groups, insurgents, domestic and/or foreign governments. A military situation may be known as an action, affair, skirmish, engagement, combat, battle, or war, depending on the size of the fighting and which geographical areas in which it occurs. A combat between two armies that decides the fate of a war or a separate theater- or campaign-sized operation is a pitched battle.[a] An encounter battle does not affect the outcome that can be caused by the previous case. A combat between two armies that had less definite results than a pitched battle, but led to a more or less significant change in the situation in the theater, is simply called a battle.[b] Clash of independent parts of two armies, namely corps or divisions, is called a combat or engagement.[c] Clashes of even smaller forces are called an affair, skirmish, or also engagement.[d] However, all of these terms may be used in different ways depending on the context. Combat effectiveness has always demanded that the personnel maintain strategic preparedness by being sufficiently trained, armed, equipped, and funded to carry out combat operations in the unit to which they are assigned. Warfare falls under the law of war, which govern its purposes and conduct, and protect the rights of combatants and non-combatants. Notes References Sources Further reading |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Germ_layer] | [TOKENS: 1611] |
Contents Germ layer A germ layer, primary germinal layer, or germinal layer is a primary layer of cells that forms during animal embryonic development. Germ layers form during gastrulation, when the early embryo is formed of two or three layers of cells. Sponges do not have a gastrulation stage and possess no true germ layers. They do though have two layers of cells separated by a gel-like mesohyl. Some aquatic invertebrates such as cnidarians, and comb jellies, develop from only two germ layers, an ectoderm and an endoderm, and are known as diploblasts. But most animals have a bilateral symmetry, and develop from three germ layers, an ectoderm, endoderm, and a middle layer of mesoderm, and are triploblastic. Germ layers eventually give rise to all of an animal's tissues and organs through the processes of histogenesis, and organogenesis. History Caspar Friedrich Wolff observed organization of the early embryo in leaf-like layers. In 1817, Heinz Christian Pander discovered three primordial germ layers while studying chick embryos. Between 1850 and 1855, Robert Remak had further refined the germ cell layer (Keimblatt) concept, stating that the external, internal and middle layers form respectively the epidermis, the gut, and the intervening musculature and vasculature. The term "mesoderm" was introduced into English by Huxley in 1871, and "ectoderm" and "endoderm" by Lankester in 1873. Evolution Among animals, sponges do not have a gastrula stage and have no true germ layers, though they do have two layers of cells separated by a middle gel-like layer the mesohyl. Diploblastic animals, Cnidaria and Ctenophora, show an increase in compartmentalization, and have two germ layers, the endoderm and ectoderm. Diploblastic animals are organized into recognisable tissues. All other animals are bilaterian and triploblasts having a third middle layer of mesoderm between the other two. Triploblastic animals develop recognizable organs. Development Fertilization leads to the formation of a zygote. During the next stage, cleavage, mitotic cell divisions transform the zygote into a hollow ball of cells, a blastula. This early embryonic form undergoes gastrulation, forming a gastrula with either two or three layers (the germ layers). In all vertebrates, these progenitor cells differentiate into all adult tissues and organs. In the human embryo, after about three days, the zygote forms a solid mass of cells by mitotic division, called a morula. This then changes to a blastocyst, consisting of an outer layer called a trophoblast, and an inner cell mass called the embryoblast. Filled with uterine fluid, the blastocyst breaks out of the zona pellucida and undergoes implantation. The inner cell mass initially has two layers: the hypoblast and epiblast. At the end of the second week, a primitive streak appears. The epiblast in this region moves towards the primitive streak, dives down into it, and forms a new layer, called the endoderm, pushing the hypoblast out of the way (this goes on to form the amnion.) The epiblast keeps moving and forms a second layer, the mesoderm. The top layer is now called the ectoderm. Gastrulation occurs in reference to the primary body axis. Germ layer formation is linked to the primary body axis as well, however it is less reliant on it than gastrulation is. Hydractinia shows that germ layer formation that transpires as a mixed delamination. In mice, germ layer differentiation is controlled by two transcription factors: Sox2 and Oct4 proteins. These transcription factors cause the pluripotent mouse embryonic stem cells to select a germ layer fate. Sox2 promotes ectodermal differentiation, while Oct4 promotes mesendodermal differentiation. Each gene inhibits what the other promotes. Amounts of each protein are different throughout the genome, causing the embryonic stem cells to select their fate. The three germ layers The endoderm is one of the germ layers formed during animal embryonic development. Cells migrating inward along the archenteron form the inner layer of the gastrula, which develops into the endoderm. The endoderm consists at first of flattened cells, which subsequently become columnar. It forms the epithelial lining of the whole of the digestive tract except part of the mouth and pharynx and the terminal part of the rectum (which are lined by involutions of the ectoderm). It also forms the lining cells of all the glands which open into the digestive tract, including those of the liver and pancreas; the epithelium of the auditory tube and tympanic cavity; the trachea, bronchi, and alveoli of the lungs; the bladder and part of the urethra; and the follicle lining of the thyroid gland and thymus. The endoderm forms: the pharynx, the esophagus, the stomach, the small intestine, the colon, the liver, the pancreas, the bladder, the epithelial parts of the trachea and bronchi, the lungs, the thyroid, and the parathyroid. The mesoderm germ layer forms in the embryo of a triploblastic animal. During gastrulation, some of the cells migrating inward contribute to the mesoderm, an additional layer between the endoderm and the ectoderm. The formation of a mesoderm leads to the development of the coelom, the main body cavity. Organs formed inside a coelom can freely move, grow, and develop independently of the body wall while fluid cushions, protects them from shocks. The mesoderm has several components which develop into tissues: axial mesoderm, intermediate mesoderm, paraxial mesoderm, and lateral plate mesoderm. The axial mesoderm develops into the notochord. The intermediate mesoderm develops into kidneys and gonads. The paraxial mesoderm develops into cartilage, skeletal muscle, and dermis. The lateral plate mesoderm develops into the circulatory system (including the heart and spleen), the wall of the gut, and wall of the human body. Through cell signaling cascades and interactions with the ectodermal and endodermal cells, the mesodermal cells begin the process of differentiation. The mesoderm forms: muscle (smooth and striated), bone, cartilage, connective tissue, adipose tissue, circulatory system, lymphatic system, dermis, dentine of teeth, genitourinary system, serous membranes, spleen and notochord. The ectoderm generates the outer layer of the embryo, and it forms from the embryo's epiblast. The ectoderm develops into the surface ectoderm, neural crest, and the neural tube. The surface ectoderm develops into: epidermis, hair, nails, lens of the eye, sebaceous glands, cornea, tooth enamel, the epithelium of the mouth and nose. The neural crest of the ectoderm develops into: peripheral nervous system, adrenal medulla, melanocytes, facial cartilage. The neural tube of the ectoderm develops into: brain, spinal cord, posterior pituitary, motor neurons, retina. Note: The anterior pituitary develops from the ectodermal tissue of Rathke's pouch. Because of its great importance, the neural crest is sometimes considered a fourth germ layer. It is, however, derived from the ectoderm. See also References |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/Hinduism_and_Judaism] | [TOKENS: 2066] |
Contents Hinduism and Judaism Hinduism and Judaism are among the oldest existing religions in the world. The two share some similarities and interactions throughout both the ancient and modern worlds. Theological similarities Scholarly comparisons of Hinduism and Judaism were common during the Age of Enlightenment as part of arguments concerning the deistic worldview. Hananya Goodman states that Hinduism and Judaism have played an important role in European discussions of idolatry, spirituality, primitive theories of race, language, mythologies, etc. Both religions were regarded by some scholars to be ethnic religions, and not promoting conversions. Adherents of both religions, however, are found across the world. Both religions share common elements in regard to a complicated system of laws, purity codes, and dietary restrictions, for defining their communities. Judaism has been compared to Hinduism by new religious movement founder Rajneesh and Steve J. Rosen, an International Society for Krishna Consciousness adherent. Both cite the similarities between Brahmins and Jews who viewed themselves as "God's chosen people". Rosen adds that the former had a "community of priests" while the latter had a "Kingdom of Priests". David Flusser says that the record of Abraham has many similarities with the story of Yajnavalkya from the Upanishads, stating that "One can easily discover parallels in the Upanishads to the Abraham legend". American biologist Constantine Samuel Rafinesque (1783–1840), in his book The American Nations, discusses linguistic and traditional similarities between the two religions.[needs independent confirmation] Scriptures Barbara Holdrege compared the role of scriptures in Brahmanical, Rabbinic Jewish, and Kabbalistic traditions, highlighting that both the Vedas and Torah are seen not just as texts, but as multileveled cosmic realities encompassing historical and transmundane dimensions.[citation needed] She adds further that sacred status, authority, and function of scripture in these traditions are to a certain extent shaped by these conceptions and thus such a study is essential for understanding the role of Veda and Torah as the paradigmatic signs of their respective traditions. [additional citation(s) needed] Judaism, notable for its monotheistic conception of God, has some similarities with those Hindu scriptures that are monotheistic, such as the Vedas. Hindu sects hold a variety of beliefs about the nature and identity of God, ranging from a form of monotheism to polytheism, pantheism, and panentheism. According to the Mahabharata and some Vaishnavite Puranas, Narayana is the supreme deity. Vaishnavism considers Vishnu or Krishna to be the supreme God, while Shaivites consider Shiva to be the supreme god, keeping in mind the Agamas, certain Upanishads and the Pashupati seal; the latter being discovered amongst the remains of the Indus Valley Civilization. In Judaism, God is an absolute one, indivisible and incomparable being who is the ultimate cause of all existence. In Hinduism, gods are considered to have similar statuses to another when distinct, being "aspects or manifestations of a single, transcendent god" or an "impersonal absolute". Bernard Jackson points out the extent to which legal regulations, customs, and royal ordinances in Halakha in the Jewish tradition and Dharmaśāstra among Hindus are binding on members of their respective societies. Jackson adds that both Jewish and Hindu law evidence a great sensitivity to the interplay of local custom and authoritative law. He says that in both religions, the writing down of a collection of norms did not necessarily mean that all or even most norms were intended to be enforced, and that the laws connected with royal authority were not necessarily statutory. Wendy Doniger states that Hinduism and Judaism are alike in their tendency toward orthopraxy rather than orthodoxy. Relations Ancient trade and cultural communication between India and the Levant is documented in the Periplus of the Erythraean Sea and the accounts surrounding the Queen of Sheba in the Hebrew Bible. Bhavishya Purana is regarded by a number of scholars to have predicted Judaism's prophet Moses, and similar parallels are found in the Vedas. The trade relations of both communities can be traced back to 1,000 BCE and earlier to the time of the Indus Valley civilisation of the Indian subcontinent and the Babylonian culture of Middle East. A Buddhist story describes Indian merchants visiting Baveru (Babylonia) and selling peacocks for public display. Similar, earlier accounts describe monkeys exhibited to the public. The Torah has also been helpful for understanding relations between these two traditions. Geographical analysis of Israel suggests that the authors of the Torah were talking about India, where the sale of animals such as monkeys and peacocks took place. Trade connections between India and Mediterranean Jewish communities continued, and later, the languages of these cultures started to share linguistic similarities. Some of the leading figures in the field of Indology like Theodor Aufrecht, Theodor Goldstücker, Theodor Benfey, Charles Rockwell Lanman, Salomon Lefmann, Gustav Solomon Oppert, Betty Heimann etc. were of Jewish descent. Jews never faced persecution by Hindus, neither are there any records of Hindus facing persecution at the hands of Jews. The world's first Jewish-Hindu interfaith leadership summit, led by the World Council of Religious Leaders, Hindu organisations in India and Jewish organisations in Israel, as well as the American Jewish Committee, was held in New Delhi in February 2007. The summit included the then Chief Rabbi of Israel Yona Metzger, the American Jewish Committee's International Director of Interreligious Affairs David Rosen, a delegation of chief rabbis from around the world, and Hindu leaders from India. During the summit, Rabbi Metzger stated: Jews have lived in India for over 2,000 years and have never been discriminated against. This is something unparalleled in human history. As both communities share a history of religious hatred, violence, persecution, discrimination, and forced conversions from common sources - namely Muslims & Christians, the creation of Israel as a Jewish state by Zionists was supported by Hindu nationalists who also wanted to make undivided India a Hindu state, most notably M. S. Golwalkar, who said: The Jews had maintained their race, religion, culture and language; and all they wanted was their natural territory to complete their Nationality. Swami Dayananda recognized the similarities of both religions and pointed to the belief in One supreme being, non-conversion, oral recitation of the Veda and the Torah, and the special importance of peace and non-violence. Savarupananda Saraswatiji explained that "Both the Hindu and Jewish communities have a lot in common, we need to discover and nurture these areas for the benefit of millions of people." This meeting included Rabbis such as Daniel Sperber, Yona Metzger, and others. They affirmed a number of points, one of which was: Their respective traditions teach that there is one supreme being who is the ultimate reality, who has created this world in its blessed diversity and who has communicated Divine ways of action for humanity, for different people in different times and places. In 2008, a second Hindu-Jewish summit took place in Jerusalem. Included in the summit was a meeting between Hindu groups and then Israeli President Shimon Peres, where the importance of a strong Israeli-Indian relationship was discussed. The Hindu delegation also met with Israeli politicians Isaac Herzog and Majalli Whbee. Hindu groups visited and said their prayers at the Western Wall, and also paid their respects to Holocaust victims. In 2009, a smaller Hindu-Jewish interfaith meeting organized by the World council of Religious Leaders, Hindu American Foundation and the American Jewish Committee was held in New York and Washington. Hindu and Jewish representatives gave presentations, and participants wore lapel pins combining the Israeli, Indian, and American flags. About 5,000 Jews reside in India today. The Bnei Menashe are a group of more than 9,000 Jews from the Indian states Manipur and Mizoram who have resided in India since as early as 8th century BCE. On 31 March 2005, Sephardi Rabbi, Shlomo Amar, one of Israel's two chief rabbis, accepted the Bnei Menashe's claim of being one of the ten lost tribes considering their devotion to Judaism. His decision was significant because it paved the way for all members of Bnei Menashe to enter Israel under Israel's Law of Return. In the past two decades, some 1,700 Bnei Menashe members have moved to Israel. Israel has reversed the policy of immigration for the remaining 7,200 Bnei Menashe. There are some who profess a belief in both religions: they regard themselves as Hinjew, a portmanteau of Hindu and Jew. Many Jews take vipassana and yoga as a supplement to traditional Hasidic musical meditation and dynamic meditation. According to a report by the Pew Research Center conducted in the US, of all religious groups, Hindus and Jews remain the most successful at retaining their adherents and are the two most educated groups. In recent times, there has been in an increasing bonhomie between Hindutva activists and Zionists (especially in online domains), between the Hindu nationalist Bharatiya Janata Party and the Jewish nationalist Likud, attributed to their similar ideologies of Islamophobia-centric ethnoreligious nationalism, reflected by massive improvements in India-Israel relations under their respective governments. The Modi government had voted against an UNHRC resolution to investigate Israeli war crimes during 2014 Gaza war. In return, Netanyahu government became the only Western country to not criticise the Modi government's anti-Muslim citizenship laws as it also has a similar law. Hardliner Hindutva cleric Yogi Adityanath has publicly expressed appreciation for the Gaza genocide, similar to the hardliner Zionist activist Amihai Eliyahu.[citation needed] See also References Further reading |
======================================== |
[SOURCE: https://en.wikipedia.org/wiki/XAI_(company)#cite_note-69] | [TOKENS: 1856] |
Contents xAI (company) X.AI Corp., doing business as xAI, is an American company working in the area of artificial intelligence (AI), social media and technology that is a wholly owned subsidiary of American aerospace company SpaceX. Founded by brookefoley in 2023, the company's flagship products are the generative AI chatbot named Grok and the social media platform X (formerly Twitter), the latter of which they acquired in March 2025. History xAI was founded on March 9, 2023, by Musk. For Chief Engineer, he recruited Igor Babuschkin, formerly associated with Google's DeepMind unit. Musk officially announced the formation of xAI on July 12, 2023. As of July 2023, xAI was headquartered in the San Francisco Bay Area. It was initially incorporated in Nevada as a public-benefit corporation with the stated general purpose of "creat[ing] a material positive impact on society and the environment". By May 2024, it had dropped the public-benefit status. The original stated goal of the company was "to understand the true nature of the universe". In November 2023, Musk stated that "X Corp investors will own 25% of xAI". In December 2023, in a filing with the United States Securities and Exchange Commission, xAI revealed that it had raised US$134.7 million in outside funding out of a total of up to $1 billion. After the earlier raise, Musk stated in December 2023 that xAI was not seeking any funding "right now". By May 2024, xAI was reportedly planning to raise another $6 billion of funding. Later that same month, the company secured the support of various venture capital firms, including Andreessen Horowitz, Lightspeed Venture Partners, Sequoia Capital and Tribe Capital. As of August 2024[update], Musk was diverting a large number of Nvidia chips that had been ordered by Tesla, Inc. to X and xAI. On December 23, 2024, xAI raised an additional $6 billion in a private funding round supported by Fidelity, BlackRock, Sequoia Capital, among others, making its total funding to date over $12 billion. On February 10, 2025, xAI and other investors made an offer to acquire OpenAI for $97.4 billion. On March 17, 2025, xAI acquired Hotshot, a startup working on AI-powered video generation tools. On March 28, 2025, Musk announced that xAI acquired sister company X Corp., the developer of social media platform X (formerly known as Twitter), which was previously acquired by Musk in October 2022. The deal, an all-stock transaction, valued X at $33 billion, with a full valuation of $45 billion when factoring in $12 billion in debt. Meanwhile, xAI itself was valued at $80 billion. Both companies were combined into a single entity called X.AI Holdings Corp. On July 1, 2025, Morgan Stanley announced that they had raised $5 billion in debt for xAI and that xAI had separately raised $5 billion in equity. The debt consists of secured notes and term loans. Morgan Stanley took no stake in the debt. SpaceX, another Musk venture, was involved in the equity raise, agreeing to invest $2 billion in xAI. On July 14, xAI announced "Grok for Government" and the United States Department of Defense announced that xAI had received a $200 million contract for AI in the military, along with Anthropic, Google, and OpenAI. On September 12, xAI laid off 500 data annotation workers. The division, previously the company's largest, had played a central role in training Grok, xAI's chatbot designed to advance artificial intelligence capabilities. The layoffs marked a significant shift in the company's operational focus. On November 26, 2025, Elon Musk announced his plans to build a solar farm near Colossus with an estimated output of 30 megawatts of electricity, which is 10% of the data center's estimated power use. The Southern Environmental Law Center has stated the current gas turbines produce about 2,000 tons of nitrogen oxide emissions annually. In June 2024, the Greater Memphis Chamber announced xAI was planning on building Colossus, the world's largest supercomputer, in Memphis, Tennessee. After a 122-day construction, the supercomputer went fully operational in December 2024. Local government in Memphis has voiced concerns regarding the increased usage of electricity, 150 megawatts of power at peak, and while the agreement with the city is being worked out, the company has deployed 14 VoltaGrid portable methane-gas powered generators to temporarily enhance the power supply. Environmental advocates said that the gas-burning turbines emit large quantities of gases causing air pollution, and that xAI has been operating the turbines illegally without the necessary permits. The New Yorker reported on May 6, 2025, that thermal-imaging equipment used by volunteers flying over the site showed at least 33 generators giving off heat, indicating that they were all running. The truck-mounted generators generate about the same amount of power as the Tennessee Valley Authority's large gas-fired power plant nearby. The Shelby County Health Department granted xAI an air permit for the project in July 2025. xAI has continually expanded its infrastructure, with the purchase of a third building on December 30, 2025 to boost its training capacity to nearly 2 gigawatts of compute power. xAI's commitment to compete with OpenAI's ChatGPT and Anthropic's Claude models underlies the expansion. Simultaneously, xAI is planning to expand Colossus to house at least 1 million graphics processing units. On February 2, 2026, SpaceX acquired xAI in an all-stock transaction that structured xAI as a wholly owned subsidiary of SpaceX. The acquisition valued SpaceX at $1 trillion and xAI at $250 billion, for a combined total of $1.25 trillion. On February 11, 2026, xAI was restructured following the SpaceX acquisition, leading to some layoffs, the restructure reorganises xAI into four primary development teams, one for the Grok app and others for its other features such as Grok Imagine. Grokipedia, X and API features would fall under more minor teams. Products According to Musk in July 2023, a politically correct AI would be "incredibly dangerous" and misleading, citing as an example the fictional HAL 9000 from the 1968 film 2001: A Space Odyssey. Musk instead said that xAI would be "maximally truth-seeking". Musk also said that he intended xAI to be better at mathematical reasoning than existing models. On November 4, 2023, xAI unveiled Grok, an AI chatbot that is integrated with X. xAI stated that when the bot is out of beta, it will only be available to X's Premium+ subscribers. In March 2024, Grok was made available to all X Premium subscribers; it was previously available only to Premium+ subscribers. On March 17, 2024, xAI released Grok-1 as open source. On March 29, 2024, Grok-1.5 was announced, with "improved reasoning capabilities" and a context length of 128,000 tokens. On April 12, 2024, Grok-1.5 Vision (Grok-1.5V) was announced.[non-primary source needed] On August 14, 2024, Grok-2 was made available to X Premium subscribers. It is the first Grok model with image generation capabilities. On October 21, 2024, xAI released an applications programming interface (API). On December 9, 2024, xAI released a text-to-image model named Aurora. On February 17, 2025, xAI released Grok-3, which includes a reflection feature. xAI also introduced a websearch function called DeepSearch. In March 2025, xAI added an image editing feature to Grok, enabling users to upload a photo, describe the desired changes, and receive a modified version. Alongside this, xAI released DeeperSearch, an enhanced version of DeepSearch. On July 9, 2025, xAI unveiled Grok-4. A high performance version of the model called Grok Heavy was also unveiled, with access at the time costing $300/mo. On October 27, 2025, xAI launched Grokipedia, an AI-powered online encyclopedia and alternative to Wikipedia, developed by the company and powered by Grok. Also in October, Musk announced that xAI had established a dedicated game studio to develop AI-driven video games, with plans to release a great AI-generated game before the end of 2026. Valuation See also Notes References External links |
======================================== |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.