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[SOURCE: https://en.wikipedia.org/wiki/History_of_the_Jews_in_Wales] | [TOKENS: 1088] |
Contents History of the Jews in Wales The history of the Jews in Wales begins in the 13th century. However, after the English conquest of Wales (1277–1283), Edward I issued the 1290 Edict of Expulsion expelling the Jews from England. From then until the formal return of the Jews to England in 1655, there is only one mention of Jews on Welsh soil. Jewish communities were recorded in the 18th century, while major Jewish settlement dates from the 19th century. The 2021 census recorded 2,044 Jews in Wales, representing 0.1% of the population, down 9.4% since 2001. Middle Ages Like the rest of Western Europe, medieval Wales was Christian. The clergyman and author Gerald of Wales (c. 1146 – c. 1223) wrote an account of his journey through Wales in 1188 in order to recruit soldiers for the Third Crusade, the Itinerarium Cambriae (1191). In it, he makes no reference to Jews in Wales but includes an allegorical narrative concerning a Jew and a Christian priest travelling in Shropshire, England. During the 13th century, there are records of Jews in Abergavenny, Caerleon and Chepstow, all of which were in the Marcher Lordships of South Wales. When Edward I established new borough towns in North Wales, both before and after 1290, he ensured that the charters banned the presence of Jews. The 1284 town charters of Bere, Caernarfon, Conwy, Criccieth, Flint, Harlech and Rhuddlan stated that "Jews shall not sojourn in the borough at any time". Despite the general expulsion in 1290, the same clauses were used in the charters of Beaumaris (1296) and Overton, (1292). It is likely that most, if not all, Jews left Wales after Edward I's act of 1290 although the writ of the English king would not have run in many of the Marcher Lordships. The Welsh chronicle Brut y Tywysogion refers to the act but only in the context of the Jews in neighbouring England. There is a record of an unnamed Jew in the commote of Manor Deilo in Carmarthenshire (outside the Marcher Lordships) in 1386/7. Early modern period In England, between 1290 and their formal return to that country in 1655, there are no other official traces of Jews as such except in connection with the Domus Conversorum, which kept a number of Jews who had converted to Christianity within its precincts up to 1551 and even later. There is no comparable evidence for Wales. The BBC notes, "The oldest non-Christian faith [in Wales] to be established was Judaism, with a presence in Swansea dating from around 1730. Jewish communities were formed in the next century in Cardiff, Merthyr Tydfil, Pontypridd and Tredegar." Modern period The rapid expansion of the coal mining industry in the 19th century led to major economic growth and a vast increase in immigration to Wales. The Jews immigrated to Wales in large numbers, leading to the founding of new Jewish communities, particularly in the heavily industrialized South Wales Valleys. While the Cardiff Jewish population was 13 families in 1852, after the influx of Jews fleeing from Russian pogroms in the 1880s the city's Jewish population rose to a peak of 5,500. A synagogue was founded in Merthyr Tydfil in 1875, and by the end of the century, most towns in the Valleys had small Jewish communities and trading stations. Generally, these communities appear to have been well tolerated, though there were some notable exceptions. In 1911, antisemitic sentiment came to a head in the Tredegar area, where working-class mobs attacked Jewish-owned businesses, causing thousands of pounds worth of damage. Early 20th-century Welsh Jewish society is featured in the 1999 film Solomon & Gaenor, which is set at the time of the Tredegar riots. Some of these topics were covered in the documentary The Kosher Comedian presented by Jewish-Welsh writer comedian Bennett Arron. Jewish communities continue to be substantial in Wales, being augmented by refugees from Nazi-dominated Europe in the late 1930s. See also Jews escaping from Nazi Europe to Britain. The modern community in South Wales is centred on the Cardiff Reform Synagogue and the Cardiff United Synagogue. There is also a synagogue in Swansea. The synagogue of Merthyr Tydfil, the major one north of Cardiff, ceased to hold regular services in the 1970s and was later sold. It is a listed building and, while there is planning permission to convert it into flats, there are calls for it to be moved to the National Museum of Wales at St Fagans, near Cardiff. The Welsh Jewish community held numerically steady between the 2011 and 2021 censuses. Notable people Notable people of Welsh-Jewish background include: Mythical history of the Jews in Wales See also References Further reading Books Articles and miscellanea External links |
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[SOURCE: https://en.wikipedia.org/wiki/Vaudeville] | [TOKENS: 7005] |
Contents Vaudeville Vaudeville (/ˈvɔːd(ə)vɪl, ˈvoʊ-/; French: [vodvil] ⓘ) is a theatrical genre of variety entertainment which became popular in the United States and Canada from the early 1870s until the early 1930s. In some ways analogous to music hall from Victorian Britain, a typical North American vaudeville performance was made up of a series of separate, unrelated acts grouped together on a common bill. Types of acts have included popular and classical musicians, singers, dancers, comedians, trained animals, magicians, ventriloquists, strongmen, female and male impersonators, acrobats, clowns, mimes, illustrated songs, jugglers, athletes, lecturing celebrities, or scenes from plays, one-act plays, minstrels, and films. A vaudeville performer is often referred to as a vaudevillian. Vaudeville developed from many sources, including the concert saloon, minstrelsy, freak shows, dime museums, and literary American burlesque. Called "the heart of American show business", vaudeville was one of the most popular types of entertainment in North America, South America, Australia, and Europe for several decades. Etymology The origin of the term has yet to be revealed but often explained as being derived from the French expression voix de ville 'voice of the city'. A second hypothesis is that it comes from the 15th-century songs on satire by poet Olivier Basselin, "Vau de Vire". In his Connections television series, science historian James Burke argues that the term is a corruption of the French Vau de Vire 'Vire River Valley', an area known for its bawdy drinking songs and where Basselin lived. In 1949, when vaudeville returned to New York's Palace Theatre after many years' absence, the New York Times wrote: "Dervied from Val De Vire (Valley of the Vire), a sector in Normandy where sprightly drinking songs originated." The Oxford English Dictionary also endorses the vau de vire origin, a truncated form of chanson du Vau de Vire 'song of the Valley of the Vire'. Around 1610, Jean le Houx collected these works as Le Livre des Chants nouveaux de Vaudevire, which is probably the direct origin of the word. Beginnings With its first subtle appearances within the early 1860s, vaudeville was not initially a common form of entertainment. The form gradually evolved from the concert saloon and variety hall into its mature form throughout the 1870s and 1880s. This more gentle form was known as "Polite Vaudeville". In the years before the American Civil War, entertainment existed on a different scale. Similar variety theatre existed before 1860 in Europe and elsewhere. In the US, as early as the first decades of the 19th century, theatergoers could enjoy a performance consisting of Shakespeare plays, acrobatics, singing, dancing, and comedy. As the years progressed, people seeking diversified amusement found an increasing number of ways to be entertained. Vaudeville was characterized by traveling companies touring through cities and towns. A handful of circuses regularly toured the country; dime museums appealed to the curious; amusement parks, riverboats, and town halls often featured "cleaner" presentations of variety entertainment; compared to saloons, music halls, and burlesque houses, which catered to those with a taste for the risqué. In the 1840s, the minstrel show, another type of variety performance, and "the first emanation of a pervasive and purely American mass culture", grew to enormous popularity and formed what Nick Tosches called "the heart of 19th-century show business". A significant influence also came from "Dutch" (i.e., German or faux-German) minstrels and comedians. Medicine shows traveled the countryside offering programs of comedy, music, jugglers, and other novelties along with displays of tonics, salves, and miracle elixirs, while "Wild West" shows provided romantic vistas of the disappearing frontier, complete with trick riding, music and drama. Vaudeville incorporated these various itinerant amusements into a stable, institutionalized form centered in America's growing urban hubs. From the mid-1860s, impresario Tony Pastor, a former singing circus clown who had become a prominent variety theater performer and manager, capitalized on middle class sensibilities and spending power when he began to feature "polite" variety programs in his New York City theatres. Pastor opened his first "Opera House" on the Bowery in 1865, later moving his variety theater operation to Broadway and, finally, to Fourteenth Street near Union Square. He only began to use the term "vaudeville" in place of "variety" in early 1876. Hoping to draw a potential audience from female and family-based shopping traffic uptown, Pastor barred the sale of liquor in his theatres, eliminated bawdy material from his shows, and offered gifts of coal and hams to attendees. Pastor's experiment proved successful, and other managers soon followed suit. Popularity The manager's comments, sent back to the circuit's central office weekly, follow each act's description. The bill illustrates the typical pattern of opening the show with a "dumb" act to allow patrons to find their seats, placing strong acts in second and penultimate positions, and leaving the weakest act for the end, to clear the house. In this bill, as in many vaudeville shows, acts often associated with "lowbrow" or popular entertainment (acrobats, a trained mule) shared a stage with acts more usually regarded as "highbrow" or classical entertainment (opera vocalists, classical musicians). B. F. Keith took the next step, starting in Boston, where he built an empire of theatres and brought vaudeville to the United States and Canada. Later, E. F. Albee, adoptive grandfather of the Pulitzer Prize-winning playwright Edward Albee, managed the chain to its greatest success. Circuits such as those managed by Keith-Albee provided vaudeville's greatest economic innovation and the principal source of its industrial strength. They enabled a chain of allied vaudeville houses that remedied the chaos of the single-theatre booking system by contracting acts for regional and national tours. These could easily be lengthened from a few weeks to two years. Albee also gave national prominence to vaudeville's trumpeting "polite" entertainment, a commitment to entertainment equally inoffensive to men, women and children. Acts that violated this ethos (e.g., those that used words such as "hell") were admonished and threatened with expulsion from the week's remaining performances or were canceled altogether. In spite of such threats, performers routinely flouted this censorship, often to the delight of the very audience members whose sensibilities were supposedly endangered. He eventually instituted a set of guidelines to be an audience member at his show, and these were reinforced by the ushers working in the theatre. This "polite entertainment" also extended to Keith's company members. He went to extreme measures to maintain this level of modesty. Keith even went as far as posting warnings backstage such as this: "Don't say 'slob' or 'son of a gun' or 'hully gee' on the stage unless you want to be canceled peremptorily... if you are guilty of uttering anything sacrilegious or even suggestive you will be immediately closed and will never again be allowed in a theatre where Mr. Keith is in authority." Along these same lines of discipline, Keith's theatre managers would occasionally send out blue envelopes with orders to omit certain suggestive lines of songs and possible substitutions for those words. If actors chose to ignore these orders or quit, they would get "a black mark" on their name and would never again be allowed to work on the Keith Circuit. Thus, actors learned to follow the instructions given to them by B. F. Keith for fear of losing their careers forever. By the late 1890s, vaudeville had large circuits, houses (small and large) in almost every sizable location, standardized booking, broad pools of skilled acts, and a loyal national following. One of the biggest circuits was Martin Beck's Orpheum Circuit. It incorporated in 1919 and brought together 45 vaudeville theatres in 36 cities throughout the United States and Canada and a large interest in two vaudeville circuits. Another major circuit was that of Alexander Pantages. In his heyday, Pantages owned more than 30 vaudeville theatres and controlled, through management contracts, perhaps 60 more in both the United States and Canada. At its height, vaudeville played across multiple strata of economic class and auditorium size. On the vaudeville circuit, it was said that if an act would succeed in Peoria, Illinois, it would work anywhere. The question "Will it play in Peoria?" has now become a metaphor for whether something appeals to the American mainstream public. The three most common levels were the "small time" (lower-paying contracts for more frequent performances in rougher, often converted theatres), the "medium time" (moderate wages for two performances each day in purpose-built theatres), and the "big time" (possible remuneration of several thousand dollars per week in large, urban theatres largely patronized by the middle and upper-middle classes). As performers rose in renown and established regional and national followings, they worked their way into the less arduous working conditions and better pay of the big time. The capital of the big time was New York City's Palace Theatre (or just "The Palace" in the slang of vaudevillians), built by Martin Beck in 1913 and operated by Keith. Featuring a bill stocked with inventive novelty acts, national celebrities, and acknowledged masters of vaudeville performance (such as comedian and trick roper Will Rogers), the Palace provided what many vaudevillians considered the apotheosis of remarkable careers. A standard show bill would begin with a sketch, follow with a single (an individual male or female performer); next would be an alley-oop (an acrobatic act); then another single, followed by yet another sketch such as a blackface comedy. The acts that followed these for the rest of the show would vary from musicals to jugglers to song-and-dance singles and end with a final extravaganza – either musical or drama – with the full company. These shows would feature such stars as ragtime and jazz pianist Eubie Blake, the famous and magical Harry Houdini, and child star Baby Rose Marie. In the New-York Tribune's article about vaudeville, it is said that at any given time, vaudeville was employing over twelve thousand people throughout its entire industry. Each entertainer would be on the road 42 weeks at a time while working a particular "Circuit" – or an individual theatre chain of a major company. While the neighborhood character of vaudeville attendance had always promoted a tendency to tailor fare to specific audiences, mature vaudeville grew to feature houses and circuits specifically aimed at certain demographic groups. Black patrons, often segregated into the rear of the second gallery in white-oriented theatres, had their own smaller circuits, as did speakers of Italian and Yiddish (see below). This foreign addition combined with comedy produced such acts as "minstrel shows of antebellum America" and Yiddish theatre. Many ethnic families joined in on this entertainment business, and for them, this traveling lifestyle was simply a continuation of the circumstances that brought them to America. Through these acts, they were able to assimilate themselves into their new home while also bringing bits of their own culture into this new world. White-oriented regional circuits, such as New England's "Peanut Circuit", also provided essential training grounds for new artists while allowing established acts to experiment with and polish new material. At its height, vaudeville was rivaled only by churches and public schools among the nation's premiere public gathering places. Another slightly different aspect of Vaudeville was an increasing interest in the female figure. The previously mentioned ominous idea of "the blue envelopes" led to the phrase "blue" material, which described the provocative subject matter present in many Vaudeville acts of the time. Many managers even saw this scandalous material as a marketing strategy to attract many different audiences. As stated in Andrew Erdman's book Blue Vaudeville, the Vaudeville stage was "a highly sexualized space ... where unclad bodies, provocative dancers, and singers of 'blue' lyrics all vied for attention." Such performances highlighted and objectified the female body as a "sexual delight", but more than that, historians think that Vaudeville marked a time in which the female body became its own "sexual spectacle". This sexual image began sprouting everywhere an American went: the shops, a restaurant, the grocery store, etc.[citation needed] The more this image brought in the highest revenue, the more Vaudeville focused on acts involving women. Even acts that were as innocent as a sister act were higher sellers than a good brother act. Consequently, Erdman adds that female Vaudeville performers such as Julie Mackey and Gibson's Bathing Girls began to focus less on talent and more on physical appeal through their figure, tight gowns, and other revealing attire. It eventually came as a surprise to audience members when such beautiful women actually possessed talent in addition to their appealing looks. This element of surprise colored much of the reaction to the female entertainment of this time. Women In the 1920s, announcements seeking all-girl bands for vaudeville performances appeared in industry publications like Billboard, Variety and in newspapers. Bands like The Ingenues and The Dixie Sweethearts were well-publicized, while other groups were simply described as "all-girl Revue". According to Feminist Theory, similar trends in theater and film objectified women, an example of male gaze, as women's role in public life was expanding. These expectations for women in the 19th century played a big role in the compelling aspects of vaudeville. Through vaudeville, many women were allowed to join their male counterparts on the stage and found success in their acts. Leila Marie Koerber, later Marie Dressler, was a Canadian actress who specialized in vaudeville comedy, and eventually won an Academy Award for Best Actress later in her career. Being the daughter of a musician, she moved to the United States of America in her childhood. At just fourteen years old, she left home to begin her career, lying about her age and sending her mother half of her paycheck. Dressler found great success and was known for her comedic timing and physical comedy, like carrying her male co-stars. She eventually worked on Broadway, where she had a great desire to become a serious actress but was advised to remain in comedy. She went on to star in a few films but again returned to vaudeville, her original career. Another famous vaudevillian actress was Trixie Friganza, born Delia O'Callaghan. She had a famous catchphrase: "You know Trixie with her bag of tricks." She began her career in opera, performing to help provide for her family. The oldest of three daughters, she wanted to help her family financially but had to do it secretly, as female performers were frowned on at the time. She worked largely in comedy and gained acclaim and success due to her willingness to step into other's roles who had fallen ill, and were otherwise unable to perform. In her acts she often emphasized her plus-size figure, calling herself the "perfect forty-six". Friganza was also a poet and writer. She used many of her performances as ways to raise money to support the poor or disenfranchised and went on record publicly numerous times to support these social causes. Friganza spent much of her life fighting for women's equality and pushing for self-acceptance for women, both publicly and within themselves, as well as their rights in comparison to men. Betty Felsen was an American ballerina, vaudeville star, and teacher. She was born on 9 June 1905, in Chicago, IL Betty began taking lessons at a local Chicago ballet school when she was eight years old, and often performed solo dances in shows presented by that school. Just before her tenth birthday in 1916, her parents enrolled her as a ballet student with the Pavley-Oukrainsky Ballet School within the Chicago Opera Association. Then, in 1919 Betty was accepted to be a member of the Chicago Opera’s Pavley-Oukrainsky Ballet corps de ballet. From December 1920 until the fall of 1922 Betty was a ballerina soloist and performed with them throughout North America. Under the name Buddye Felsen, Betty landed a starring dancing role in a new show at Fred Mann’s Million Dollar Rainbo Room in the Rainbo Gardens. The show, Rainbo Trail, directed by Frank Westphal, opened on 15 December 1922, and ran until 1 March 1923. In the winter of 1923 Betty began a partnership with Jack Broderick. From then until the end of 1927 Broderick & Felsen performed on the B.F. Keith and Pantages vaudeville circuits throughout the U.S. and Canada. Their act evolved from a simple dance act to one with over twenty dancers, an orchestra, and elaborate costumes and sets. From 1925 to 1926 they played for 20 straight weeks at the Colony Theater on Broadway in New York City. In 1926 and 1927, they starred in two spectacular musical productions, touring across the United States and Canada, first for about three months in Emil Boreo’s Mirage de Paris followed by nine months in their own Ballet Caprice. After Jack quit the act near the end of 1927, Betty continued to manage the troupe and, with a new dance partner, toured throughout the northeastern United States for the next six months as Betty Felsen and Company. The final performance of Ballet Caprice was on 4 June 1928, at Broadway’s Palace Theater in New York City. Another famous comedienne, one who brought in thousands of audience members with her signature improvisational skills, was May Irwin. She worked from about 1875 to 1914. Born Ada Campbell, she began her life on the stage at thirteen years old following the death of her father. She and her older sister created a singing act called the "Irwin Sisters". Many years later, their act had taken off and with performances in both vaudeville and burlesque at famous music halls, until Irwin decided to continue her career on her own. She then changed her approach to vaudeville, performing African-American-influenced songs, even later writing her songs. She introduced her signature in vaudeville, "The Bully Song", which was performed in a Broadway show. This is when she began experimenting with improvisational comedy and quickly found her unique success, even taking her performances global with acts in the U.K. Sophie Tucker, a Russian Jewish immigrant, was told by promoter Chris Brown that she was not attractive enough to succeed in show business without doing Blackface, so she performed that way for the first two years onstage, until one day she decided to go without it, and achieved much greater success being herself from that point on, especially with her song "Some of These Days". Moms Mabley was a comedienne who got her start in Vaudeville and the Chitlin circuits in the 1920s, and ended up with mainstream success in the 20th century. Other 20th century women performers who started in Vaudeville included blues singers Ma Rainey, Ida Cox and Bessie Smith. Women-led touring companies like Black Patti's Troubadours, the Whitman Sisters and the Hyers Sisters were popular acts. Other women worked the business side of Vaudeville, like Amanda Thorpe, a white woman who founded a black theater in Virginia, and the Griffin Sisters, who managed several theaters in their efforts to create a Black Vaudeville circuit. Black vaudeville Black performers and patrons participated in a racially segregated Vaudeville circuit. Though many popular acts like Lewis and Walker, Ernest Hogan, Irving Jones, and the Hyers Sisters played to both white and black audiences, early Vaudeville performances for white audiences were limited to one Black act per show, and performers faced discrimination in restaurants and lodging. Entertainers and entrepreneurs like The Whitman Sisters, Pat Chapelle and John Isham created and managed their own touring companies; others took on theater ownership and management and created Black Vaudeville circuits, as was the case for Sherman H. Dudley and the Griffin Sisters Later, in the 1920s, many bookings were managed by the Theatre Owners Booking Association. African-Americans challenged the prevailing Blackface stereotypes played by white performers by bringing their own authenticity and style to the stage, composing music, comedy and dance routines and laying the groundwork for distinctly American cultural phenomena like blues, jazz, ragtime and tap dance. Notable Black entertainers in Vaudeville included comedians Bert Williams, and George Walker, dancer/choreographer Ada Overton Walker, and many others. Black songwriters and composers like Bob Cole, Ernest Hogan, Irving Jones, Rosamond Johnson, George Johnson, Tom Lemonier, Gussie L. Davis, and Chris Smith, wrote many of the songs that were popularized onstage by white singers, and paved the way for African-American musical theater. Immigrant America In addition to vaudeville's prominence as a form of American entertainment, it reflected the newly evolving urban inner-city culture and interaction of its operators and audience. Making up a large portion of immigration to the United States in the mid-19th century, Irish Americans interacted with established Americans, with the Irish becoming subject to discrimination due to their perceived ethnic, physical, and cultural characteristics. The ethnic stereotypes of Irish through their greenhorn depiction alluded to their newly arrived status as immigrant Americans, with the stereotype portrayed in avenues of entertainment. Following the Irish immigration wave were several waves in which new immigrants from different backgrounds came in contact with the Irish in America's urban centers. Already settled and being native English speakers, Irish Americans took hold of these advantages and began to assert their positions in the immigrant racial hierarchy based on skin tone and assimilation status, cementing job positions that were previously unavailable to them as recently arrived immigrants. As a result, Irish Americans became prominent in vaudeville entertainment as curators and actors, creating a unique ethnic interplay between Irish American use of self-deprecation as humor and their diverse inner city surroundings. The interactions between newly arrived immigrants and settled immigrants within the backdrop of the unknown American urban landscape allowed vaudeville to be utilized as an avenue for expression and understanding. The often hostile immigrant experience in their new country was now used for comic relief on the vaudeville stage, where stereotypes of different ethnic groups were perpetuated. The crude stereotypes that emerged were easily identifiable not only by their distinct ethnic cultural attributes, but how those attributes differed from the mainstream established American culture and identity. Coupled with their historical presence on the English stage for comic relief, and as operators and actors of the vaudeville stage, Irish Americans became interpreters of immigrant cultural images in American popular culture. New arrivals found their ethnic group status defined within the immigrant population and in their new country as a whole by the Irish on stage. Unfortunately, the same interactions between ethnic groups within the close living conditions of cities also created racial tensions which were reflected in vaudeville. Conflict between Irish and African Americans saw the promotion of black-face minstrelsy on the stage, purposefully used to place African Americans beneath the Irish in the racial and social urban hierarchy. Although the Irish had a strong Celtic presence in vaudeville and in the promotion of ethnic stereotypes, the ethnic groups that they were characterizing also utilized the same humor. As the Irish donned their ethnic costumes, groups such as the Chinese, Italians, Germans and Jews utilized ethnic caricatures to understand themselves as well as the Irish. The urban diversity within the vaudeville stage and audience also reflected their societal status, with the working class constituting two-thirds of the typical vaudeville audience. The ethnic caricatures that now comprised American humor reflected the positive and negative interactions between ethnic groups in America's cities. The caricatures served as a method of understanding different groups and their societal positions within their cities. The use of the greenhorn immigrant for comedic effect showcased how immigrants were viewed as new arrivals, but also what they could aspire to be. In addition to interpreting visual ethnic caricatures, the Irish American ideal of transitioning from the shanty to the lace curtain became a model of economic upward mobility for immigrant groups. Selected vaudeville artists Decline The continued growth of the lower-priced cinema in the early 1910s dealt the heaviest blow to vaudeville (similar to the advent of free broadcast television's diminishing the cultural and economic strength of the cinema). Cinema was first regularly commercially presented in the US in vaudeville halls. The first public showing of movies projected on a screen took place at Koster and Bial's Music Hall in 1896. Lured by greater salaries and less arduous working conditions, many performers and personalities, such as Al Jolson, W. C. Fields, Mae West, Charlie Chaplin, Buster Keaton, the Marx Brothers, Jimmy Durante, Bill "Bojangles" Robinson, Edgar Bergen, Fanny Brice, Burns and Allen, and Eddie Cantor, used the prominence gained in live variety performance to vault into the new medium of cinema. In doing so, such performers often exhausted in a few moments of screen time the novelty of an act that might have kept them on tour for several years. Other performers who entered in vaudeville's later years, including Jack Benny, Abbott and Costello, Laurel and Hardy, Kate Smith, Cary Grant, Bob Hope, Milton Berle, Judy Garland, Rose Marie, Sammy Davis Jr., Red Skelton, Larry Storch and The Three Stooges, used vaudeville only as a launching pad for later careers. They left live performance before achieving the national celebrity of earlier vaudeville stars, and found fame in new venues. The line between live and filmed performances was blurred by the number of vaudeville entrepreneurs who made more or less successful forays into the movie business. For example, Alexander Pantages quickly realized the importance of motion pictures as a form of entertainment. He incorporated them in his shows as early as 1902. Later, he entered into a partnership with the Famous Players–Lasky, a major Hollywood production company and an affiliate of Paramount Pictures. By the late 1920s, most vaudeville shows included a healthy selection of cinema. Earlier in the century, many vaudevillians, cognizant of the threat represented by cinema, held out hope that the silent nature of the "flickering shadow sweethearts" would prevent movies from ever overtaking vaudeville in popularity. However, with the introduction of talking pictures in 1926, the burgeoning film studios removed what had remained the chief difference in favor of live theatrical performance: spoken dialogue. Historian John Kenrick wrote: Top vaudeville stars filmed their acts for one-time pay-offs, inadvertently helping to speed the death of vaudeville. After all, when "small time" theatres could offer "big time" performers on screen at a nickel a seat, who could ask audiences to pay higher amounts for less impressive live talent? The newly-formed RKO studios took over the famed Orpheum vaudeville circuit and swiftly turned it into a chain of full-time movie theatres. The half-century tradition of vaudeville was effectively wiped out within less than four years. Inevitably, managers further trimmed costs by eliminating the last of the live performances. Vaudeville also suffered due to the rise of broadcast radio following the greater availability of inexpensive receiver sets later in the decade. Even the hardiest in the vaudeville industry realized the form was in decline; the perceptive understood the condition to be terminal. The standardized film distribution and talking pictures of the 1930s confirmed the end of vaudeville. By 1930, the vast majority of formerly live theatres had been wired for sound, and none of the major studios were producing silent pictures. For a time, the most luxurious theatres continued to offer live entertainment, but most theatres were forced by the Great Depression to economize. Some in the industry blamed cinema's drain of talent from the vaudeville circuits for the medium's demise. Others argued that vaudeville had allowed its performances to become too familiar to its famously loyal, now seemingly fickle audiences. There was no abrupt end to vaudeville, though the form was clearly sagging by the late 1920s. Joseph Kennedy Sr. in a hostile buyout, acquired the Keith-Albee-Orpheum Theatres Corporation (KAO), which had more than 700 vaudeville theatres across the United States which had begun showing movies. The shift of New York City's Palace Theatre, vaudeville's center, to an exclusively cinema presentation on 16 November 1932, is often considered to have been the death knell of vaudeville. Though talk of its resurrection was heard during the 1930s and later, the demise of the supporting apparatus of the circuits and the higher cost of live performance made any large-scale renewal of vaudeville unrealistic. Architecture The most striking examples of Gilded Age theatre architecture were commissioned by the big time vaudeville magnates and stood as monuments of their wealth and ambition. Examples of such architecture are the theatres built by impresario Alexander Pantages. Pantages often used architect B. Marcus Priteca (1881–1971), who in turn regularly worked with muralist Anthony Heinsbergen. Priteca devised an exotic, neo-classical style that his employer called "Pantages Greek". Though classic vaudeville reached a zenith of capitalization and sophistication in urban areas dominated by national chains and commodious theatres, small-time vaudeville included countless more intimate and locally controlled houses. Small-time houses were often converted saloons, rough-hewn theatres, or multi-purpose halls, together catering to a wide range of clientele. Many small towns had purpose-built theatres. A small yet interesting example might include what is called Grange Halls in northern New England, still being used. These are old-fashioned, wooden buildings with creaky, dimly-lit, wooden stages, which were meant to offset the isolation of a farming lifestyle. These stages could offer anything from child performers to contra-dances to visits by Santa to local, musical talent, to homemade foods such as whoopee pies. Vaudeville's cultural influence and legacy Some of the most prominent vaudevillians successfully made the transition to cinema, though others were not as successful. Some performers such as Bert Lahr fashioned careers out of combining live performance with radio and film roles. Many others later appeared in the Catskill resorts that constituted the "Borscht Belt". Vaudeville was instrumental in the success of the newer media of film, radio, and television. Comedies of the new era adopted many of the dramatic and musical tropes of classic vaudeville acts. Film comedies of the 1920s through the 1940s used talent from the vaudeville stage and followed a vaudeville aesthetic of variety entertainment, both in Hollywood and in Asia, including China. The rich repertoire of the vaudeville tradition was mined for prominent prime-time radio variety shows such as The Rudy Vallée Show. The structure of a single host introducing a series of acts became a popular television style and can be seen consistently in the development of television, from The Milton Berle Show in 1948 to Late Night with David Letterman in the 1980s. The multi-act format had renewed success in shows such as Your Show of Shows with Sid Caesar and The Ed Sullivan Show. Today, performers such as Bill Irwin, a MacArthur Fellow and Tony Award-winning actor, are frequently lauded as "New Vaudevillians". References to vaudeville and the use of its distinctive argot continue throughout North American popular culture. Words such as "flop" and "gag" were terms created from the vaudeville era and have entered the American idiom. Vaudevillian techniques can commonly be witnessed on television and in movies, remarkably in the recent, worldwide phenomenon of TV shows such as America's Got Talent. In professional wrestling, there was a noted tag team, based in WWE, called The Vaudevillains. In 2018, noted film director Christopher Annino, maker of a new silent feature film, Silent Times, founded Vaudeville Con, a gathering to celebrate the history of vaudeville. The first meeting was held in Pawcatuck, Connecticut. Archives The records of the Tivoli Theatre are housed at the State Library of Victoria, Melbourne, Australia, with additional personal papers of vaudevillian performers from the Tivoli Theatre, including extensive costume and set design holdings, held by the Performing Arts Collection, Arts Centre Melbourne. The American Vaudeville Museum, one of the largest collections of vaudeville memorabilia, is located at the University of Arizona. The Elgin and Winter Garden Theatres in Toronto houses the world's largest collection of vaudeville props and scenery. The Benjamin Franklin Keith and Edward F. Albee Collection housed at the University of Iowa includes a large collection of managers' report books recording and commenting on the lineup and quality of the acts each night. See also References External links |
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[SOURCE: https://en.wikipedia.org/wiki/Javier_Oliv%C3%A1n] | [TOKENS: 653] |
Contents Javier Oliván Javier Oliván López (born 1977) is a Spanish business executive who has been chief operating officer of Meta Platforms since 2022, having succeeded Sheryl Sandberg. Early life and education Oliván was born in 1977 in Sabiñánigo, Huesca, Spain. His father Florián is a retired businessman who ran a hardware store and an arcade, while his mother María Pilar is a retired professor from the Biello Aragón Institute. Between 1995 and 2002, Oliván studied electrical and industrial engineering at Tecnun, the School of Engineering of the University of Navarra in Pamplona. He then joined Siemens as a research and development engineer in Munich, Germany, where he patented an algorithmic system for digital image processing, before moving to Tokyo, Japan, to work for NTT Data on wireless video technologies. He later worked as product manager at Siemens Mobile where he led a team responsible for mobile device development. In 2005, he enrolled in the MBA program at Stanford University, where he took a course that analyzed case studies of startups, including Facebook. As a student, along with several friends, he worked on creating a Spanish-language version of Facebook, called Nosuni. Though the project was a failure, Mark Zuckerberg, the founder of Facebook, reached out and asked Oliván to work for him. He accepted the offer after graduating in 2007. Career Oliván joined Facebook (now Meta Platforms) in October 2007 as head of international growth under Chamath Palihapitiya. As a founding member of the growth team, he oversaw the company's international expansion—into new markets and languages. From 2011 to 2018, he was vice president of the growth team. He advocated the Internet.org and Facebook Lite initiatives during this period. He advised multiple acquisitions including that of WhatsApp and Onavo. In 2018, he took charge of the trust and safety team to tackle misinformation on its platforms. He was the vice president of Central Products between 2018 and 2022, after which he was promoted to chief growth officer. In August 2022, Oliván replaced Sheryl Sandberg as chief operating officer after she stepped down 14 years into the role. As part of a reorganization in January 2025, Oliván also oversees the business operations of the Reality Labs division. He was recognized as a Henry Crown Fellow by the Aspen Institute in 2014. He is on the board of the non-profit, Endeavor Global. Previously, he was a board member of the e-commerce company Mercado Libre for six years. He was also on the board of Vy Global Growth, a special-purpose acquisition company that invested in geospatial imaging firm Satellogic before the SPAC deal completed in 2022. Personal life Oliván lived in Palo Alto, California, before moving to Spain and working remotely as of 2023[update]. He is married and has two daughters. He met his wife while he was an Erasmus student in Munich. He is fluent in five languages, including French, German, and Japanese. He is fond of surfing and paragliding. References |
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[SOURCE: https://en.wikipedia.org/wiki/List_(computer_science)] | [TOKENS: 1138] |
Contents List (abstract data type) In computer science, a list or sequence is a collection of items that are finite in number and in a particular order. An instance of a list is a computer representation of the mathematical concept of a tuple or finite sequence. A list may contain the same value more than once, and each occurrence is considered a distinct item. The term list is also used for several concrete data structures that can be used to implement abstract lists, especially linked lists and arrays. In some contexts, such as in Lisp programming, the term list may refer specifically to a linked list rather than an array. In class-based programming, lists are usually provided as instances of subclasses of a generic "list" class, and traversed via separate iterators. Many programming languages provide support for list data types, and have special syntax and semantics for lists and list operations. A list can often be constructed by writing the items in sequence, separated by commas, semicolons, and/or spaces, within a pair of delimiters such as parentheses '()', brackets '[]', braces '{}', or angle brackets '<>'. Some languages may allow list types to be indexed or sliced like array types, in which case the data type is more accurately described as an array. In type theory and functional programming, abstract lists are usually defined inductively by two operations: nil that yields the empty list, and cons, which adds an item at the beginning of a list. A stream is the potentially infinite analog of a list.: §3.5 Operations Implementation of the list data structure may provide some or all of the following operations as low level primitives: Implementations Lists are typically implemented either as linked lists (either singly or doubly linked) or as arrays, usually variable length or dynamic arrays. The standard way of implementing lists, originating with the programming language Lisp, is to have each element of the list contain both its value and a pointer indicating the location of the next element in the list. This results in either a linked list or a tree, depending on whether the list has nested sublists. Some older Lisp implementations (such as the Lisp implementation of the Symbolics 3600) also supported "compressed lists" (using CDR coding) which had a special internal representation (invisible to the user). Lists can be manipulated using iteration or recursion. The former is often preferred in imperative programming languages, while the latter is the norm in functional languages. Lists can be implemented as self-balancing binary search trees holding index-value pairs, providing equal-time access to any element (e.g. all residing in the fringe, and internal nodes storing the right-most child's index, used to guide the search), taking the time logarithmic in the list's size, but as long as it doesn't change much will provide the illusion of random access and enable swap, prefix and append operations in logarithmic time as well. Programming language support Some languages do not offer a list data structure, but offer the use of associative arrays or some kind of table to emulate lists. For example, Lua provides tables. Although Lua stores lists that have numerical indices as arrays internally, they still appear as dictionaries. In Lisp, lists are the fundamental data type and can represent both program code and data. In most dialects, the list of the first three prime numbers could be written as (list 2 3 5). In several dialects of Lisp, including Scheme, a list is a collection of pairs, consisting of a value and a pointer to the next pair (or null value), making a singly linked list. Applications Unlike in an array, a list can expand and shrink. In computing, lists are easier to implement than sets. A finite set in the mathematical sense can be realized as a list with additional restrictions; that is, duplicate elements are disallowed and order is irrelevant. Sorting the list speeds up determining if a given item is already in the set, but in order to ensure the order, it requires more time to add a new entry to the list. In efficient implementations, however, sets are implemented using self-balancing binary search trees or hash tables, rather than a list. Lists also form the basis for other abstract data types including the queue, the stack, and their variations. Abstract definition The abstract list type L with elements of some type E (a monomorphic list) is defined by the following functions: with the axioms for any element e and any list l. It is implicit that Note that first (nil ()) and rest (nil ()) are not defined. These axioms are equivalent to those of the abstract stack data type. In type theory, the above definition is more simply regarded as an inductive type defined in terms of constructors: nil and cons. In algebraic terms, this can be represented as the transformation 1 + E × L → L. first and rest are then obtained by pattern matching on the cons constructor and separately handling the nil case. The list type forms a monad with the following functions (using E* rather than L to represent monomorphic lists with elements of type E): where append is defined as: Alternatively, the monad may be defined in terms of operations return, fmap and join, with: Note that fmap, join, append and bind are well-defined, since they're applied to progressively deeper arguments at each recursive call. The list type is an additive monad, with nil as the monadic zero and append as monadic sum. Lists form a monoid under the append operation. The identity element of the monoid is the empty list, nil. In fact, this is the free monoid over the set of list elements. See also References |
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[SOURCE: https://en.wikipedia.org/wiki/OpenAI#cite_note-intercept-africom-146] | [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 |
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Contents Birthday A birthday is the anniversary of the birth of a person or the figurative birth of an institution. Birthdays of people are celebrated in numerous cultures, often with birthday gifts, birthday cards, a birthday party, or a rite of passage. Many religions celebrate the birth of their founders or religious figures with special holidays (e.g. Christmas, Mawlid, Buddha's Birthday, Krishna Janmashtami, and Gurpurb). There is a distinction between birthday and birthdate (also known as date of birth): the former, except for February 29, occurs each year (e.g. January 15), while the latter is the complete date when a person was born (e.g. January 15, 2001). Coming of age In most legal systems, one becomes a legal adult on a particular birthday when they reach the age of majority (usually between 12 and 21), and reaching age-specific milestones confers particular rights and responsibilities. At certain ages, one may become eligible to leave full-time education, become subject to military conscription or to enlist in the military, to consent to sexual intercourse, to marry with parental consent, to marry without parental consent, to vote, to run for elected office, to legally purchase (or consume) alcohol and tobacco products, to purchase lottery tickets, or to obtain a driver's licence. The age of majority is when minors cease to legally be considered children and assume control over their persons, actions, and decisions, thereby terminating the legal control and responsibilities of their parents or guardians over and for them. Most countries set the age of majority at 18, though it varies by jurisdiction. Many cultures celebrate a coming of age birthday when a person reaches a particular year of life. Some cultures celebrate landmark birthdays in early life or old age. In many cultures and jurisdictions, if a person's real birthday is unknown (for example, if they are an orphan), their birthday may be adopted or assigned to a specific day of the year, such as January 1. Racehorses are reckoned to become one year old in the year following their birth on January 1 in the Northern Hemisphere and August 1 in the Southern Hemisphere.[relevant?] Birthday parties In certain parts of the world, an individual's birthday is celebrated by a party featuring a specially made cake. Presents are bestowed on the individual by the guests appropriate to their age. Other birthday activities may include entertainment (sometimes by a hired professional, i.e., a clown, magician, or musician) and a special toast or speech by the birthday celebrant. The last stanza of Patty Hill's and Mildred Hill's famous song, "Good Morning to You" (unofficially titled "Happy Birthday to You") is typically sung by the guests at some point in the proceedings. In some countries, a piñata takes the place of a cake. The birthday cake may be decorated with lettering and the person's age, or studded with the same number of lit candles as the age of the individual. The celebrated individual may make a silent wish and attempt to blow out the candles in one breath; if successful, superstition holds that the wish will be granted. In many cultures, the wish must be kept secret or it will not "come true". Birthdays as holidays Historically significant people's birthdays, such as national heroes or founders, are often commemorated by an official holiday marking the anniversary of their birth. Some notables, particularly monarchs, have an official birthday on a fixed day of the year, which may not necessarily match the day of their birth, but on which celebrations are held. In Mahayana Buddhism, many monasteries celebrate the anniversary of Buddha's birth, usually in a highly formal, ritualized manner. They treat Buddha's statue as if it was Buddha himself as if he were alive; bathing, and "feeding" him. Jesus Christ's traditional birthday is celebrated as Christmas Eve or Christmas Day around the world, on December 24 or 25, respectively. As some Eastern churches use the Julian calendar, December 25 will fall on January 7 in the Gregorian calendar. These dates are traditional and have no connection with Jesus's actual birthday, which is not recorded in the Gospels. Similarly, the birthdays of the Virgin Mary and John the Baptist are liturgically celebrated on September 8 and June 24, especially in the Roman Catholic and Eastern Orthodox traditions (although for those Eastern Orthodox churches using the Julian calendar the corresponding Gregorian dates are September 21 and July 7 respectively). As with Christmas, the dates of these celebrations are traditional and probably have no connection with the actual birthdays of these individuals. Catholic saints are remembered by a liturgical feast on the anniversary of their "birth" into heaven a.k.a. their day of death. In Hinduism, Ganesh Chaturthi is a festival celebrating the birth of the elephant-headed deity Ganesha in extensive community celebrations and at home. Figurines of Ganesha are made for the holiday and are widely sold. Sikhs celebrate the anniversary of the birth of Guru Nanak and other Sikh gurus, which is known as Gurpurb. Mawlid is the anniversary of the birth of Muhammad and is celebrated on the 12th or 17th day of Rabi' al-awwal by adherents of Sunni and Shia Islam respectively. These are the two most commonly accepted dates of birth of Muhammad. However, there is much controversy regarding the permissibility of celebrating Mawlid, as some Muslims judge the custom as an unacceptable practice according to Islamic tradition. In Iran, Mother's Day is celebrated on the birthday of Fatima al-Zahra, the daughter of Muhammad. Banners reading Ya Fatima ("O Fatima") are displayed on government buildings, private buildings, public streets and car windows. Religious views In Judaism, rabbis are divided about celebrating this custom, although the majority of the faithful accept it. In the Torah, the only mention of a birthday is the celebration of Pharaoh's birthday in Egypt (Genesis 40:20). Although the birthday of Jesus of Nazareth is celebrated as a Christian holiday on December 25, historically the celebrating of an individual person's birthday has been subject to theological debate. Early Christians, notes The World Book Encyclopedia, "considered the celebration of anyone's birth to be a pagan custom." Origen, in his commentary "On Levites," wrote that Christians should not only refrain from celebrating their birthdays but should look at them with disgust as a pagan custom. A saint's day was typically celebrated on the anniversary of their martyrdom or death, considered the occasion of or preparation for their entrance into Heaven or the New Jerusalem. Ordinary folk in the Middle Ages celebrated their saint's day (the saint they were named after), but nobility celebrated the anniversary of their birth.[citation needed] The "Squire's Tale", one of Chaucer's Canterbury Tales, opens as King Cambuskan proclaims a feast to celebrate his birthday. In the Modern era, the Catholic Church, the Eastern Orthodox Church and Protestantism, i.e. the three main branches of Christianity, as well as almost all Christian religious denominations, consider celebrating birthdays acceptable or at most a choice of the individual. An exception is Jehovah's Witnesses, who do not celebrate them for various reasons: in their interpretation this feast has pagan origins, was not celebrated by early Christians, is negatively expounded in the Holy Scriptures and has customs linked to superstition and magic. In some historically Roman Catholic and Eastern Orthodox countries,[a] it is common to have a 'name day', otherwise known as a 'Saint's day'. It is celebrated in much the same way as a birthday, but it is held on the official day of a saint with the same Christian name as the birthday person; the difference being that one may look up a person's name day in a calendar, or easily remember common name days (for example, John or Mary); however in pious traditions, the two were often made to concur by giving a newborn the name of a saint celebrated on its day of confirmation, more seldom one's birthday. Some are given the name of the religious feast of their christening's day or birthday, for example, Noel or Pascal (French for Christmas and "of Easter"); as another example, Togliatti was given Palmiro as his first name because he was born on Palm Sunday. The birthday does not reflect Islamic tradition, and because of this, the majority of Muslims refrain from celebrating it. Others do not object, as long as it is not accompanied by behavior contrary to Islamic tradition. A good portion of Muslims (and Arab Christians) who have emigrated to the United States and Europe celebrate birthdays as customary, especially for children, while others abstain. Hindus celebrate the birth anniversary day every year when the day that corresponds to the lunar month or solar month (Sun Signs Nirayana System – Sourava Mana Masa) of birth and has the same asterism (Star/Nakshatra) as that of the date of birth. That age is reckoned whenever Janma Nakshatra of the same month passes. Hindus regard death to be more auspicious than birth, since the person is liberated from the bondages of material society. Also, traditionally, rituals and prayers for the departed are observed on the 5th and 11th days, with many relatives gathering. Historical and cultural perspectives According to Herodotus (5th century BC), of all the days in the year, the one which the Persians celebrate most is their birthday. It was customary to have the board furnished on that day with an ampler supply than common: the richer people eat wholly baked cow, horse, camel, or donkey (Greek: ὄνον), while the poorer classes use instead the smaller kinds of cattle. On his birthday, the king anointed his head and presented gifts to the Persians. According to the law of the Royal Supper, on that day "no one should be refused a request". The rule for drinking was "No restrictions". In ancient Rome, a birthday (dies natalis) was originally an act of religious cultivation (cultus). A dies natalis was celebrated annually for a temple on the day of its founding, and the term is still used sometimes for the anniversary of an institution such as a university. The temple founding day might become the "birthday" of the deity housed there. March 1, for example, was celebrated as the birthday of the god Mars. Each human likewise had a natal divinity, the guardian spirit called the Genius, or sometimes the Juno for a woman, who was owed religious devotion on the day of birth, usually in the household shrine (lararium). The decoration of a lararium often shows the Genius in the role of the person carrying out the rites. A person marked their birthday with ritual acts that might include lighting an altar, saying prayers, making vows (vota), anointing and wreathing a statue of the Genius, or sacrificing to a patron deity. Incense, cakes, and wine were common offerings. Celebrating someone else's birthday was a way to show affection, friendship, or respect. In exile, the poet Ovid, though alone, celebrated not only his own birthday rite but that of his far distant wife. Birthday parties affirmed social as well as sacred ties. One of the Vindolanda tablets is an invitation to a birthday party from the wife of one Roman officer to the wife of another. Books were a popular birthday gift, sometimes handcrafted as a luxury edition or composed especially for the person honored. Birthday poems are a minor but distinctive genre of Latin literature. The banquets, libations, and offerings or gifts that were a regular part of most Roman religious observances thus became part of birthday celebrations for individuals. A highly esteemed person would continue to be celebrated on their birthday after death, in addition to the several holidays on the Roman calendar for commemorating the dead collectively. Birthday commemoration was considered so important that money was often bequeathed to a social organization to fund an annual banquet in the deceased's honor. The observance of a patron's birthday or the honoring of a political figure's Genius was one of the religious foundations for imperial cult or so-called "emperor worship." The Chinese word for "year(s) old" (t 歲, s 岁, suì) is entirely different from the usual word for "year(s)" (年, nián), reflecting the former importance of Chinese astrology and the belief that one's fate was bound to the stars imagined to be in opposition to the planet Jupiter at the time of one's birth. The importance of this duodecennial orbital cycle only survives in popular culture as the 12 animals of the Chinese zodiac, which change each Chinese New Year and may be used as a theme for some gifts or decorations. Because of the importance attached to the influence of these stars in ancient China and throughout the Sinosphere, East Asian age reckoning previously began with one at birth and then added years at each Chinese New Year, so that it formed a record of the suì one had lived through rather than of the exact amount of time from one's birth. This method—which can differ by as much as two years of age from other systems—is increasingly uncommon and is not used for official purposes in the PRC or on Taiwan, although the word suì is still used for describing age. Traditionally, Chinese birthdays—when celebrated—were reckoned using the lunisolar calendar, which varies from the Gregorian calendar by as much as a month forward or backward depending on the year. Celebrating the lunisolar birthday remains common on Taiwan while growing increasingly uncommon on the mainland. Birthday traditions reflected the culture's deep-seated focus on longevity and wordplay. From the homophony in some dialects between 酒 ("rice wine") and 久 (meaning "long" in the sense of time passing), osmanthus and other rice wines are traditional gifts for birthdays in China. Longevity noodles are another traditional food consumed on the day, although western-style birthday cakes are increasingly common among urban Chinese. Hongbaos—red envelopes stuffed with money, now especially the red 100 RMB notes—are the usual gift from relatives and close family friends for most children. Gifts for adults on their birthdays are much less common, although the birthday for each decade is a larger occasion that might prompt a large dinner and celebration. The Japanese reckoned their birthdays by the Chinese system until the Meiji Reforms. Celebrations remained uncommon or muted until after the American occupation that followed World War II.[citation needed] Children's birthday parties are the most important, typically celebrated with a cake, candles, and singing. Adults often just celebrate with their partner. In North Korea, the Day of the Sun, Kim Il Sung's birthday, is the most important public holiday of the country, and Kim Jong Il's birthday is celebrated as the Day of the Shining Star. North Koreans are not permitted to celebrate birthdays on July 8 and December 17 because these were the dates of the deaths of Kim Il Sung and Kim Jong Il, respectively. More than 100,000 North Koreans celebrate displaced birthdays on July 9 and December 18 instead to avoid these dates. A person born on July 8 before 1994 may change their birthday, with official recognition. South Korea was one of the last countries to use a form of East Asian age reckoning for many official purposes. Prior to June 2023, three systems were used together—"Korean ages" that start with 1 at birth and increase every January 1st with the Gregorian New Year, "year ages" that start with 0 at birth and otherwise increase the same way, and "actual ages" that start with 0 at birth and increase each birthday. First birthday celebrations was heavily celebrated, despite usually having little to do with the child's age. In June 2023, all Korean ages were set back at least one year, and official ages henceforth are reckoned only by birthdays. In Ghana, children wake up on their birthday to a special treat called oto, which is a patty made from mashed sweet potato and eggs fried in palm oil. Later they have a birthday party where they usually eat stew and rice and a dish known as kelewele, which is fried plantain chunks. Distribution through the year Birthdays are fairly evenly distributed throughout the year, with some seasonal effects. In the United States, there tend to be more births in September and October. This may be because there is a holiday season nine months before (the human gestation period is about nine months), or because the longest nights of the year also occur in the Northern Hemisphere nine months before. However, the holidays affect birth rates more than the winter: New Zealand, a Southern Hemisphere country, has the same September and October peak with no corresponding peak in March and April. The least common birthdays tend to fall around public holidays, such as Christmas, New Year's Day and fixed-date holidays such as Independence Day in the US, which falls on July 4. Between 1973 and 1999, September 16 was the most common birthday in the United States, and December 25 was the least common birthday (other than February 29 because of leap years). In 2011, October 5 and 6 were reported as the most frequently occurring birthdays. New Zealand's most common birthday is September 29, and the least common birthday is December 25. The ten most common birthdays all fall within a thirteen-day period, between September 22 and October 4. The ten least common birthdays (other than February 29) are December 24–27, January 1–2, February 6, March 22, April 1, and April 25. This is based on all live births registered in New Zealand between 1980 and 2017. Positive and negative associations with culturally significant dates may influence birth rates. The study shows a 5.3% decrease in spontaneous births and a 16.9% decrease in Caesarean births on Halloween, compared to dates occurring within one week before and one week after the October holiday. In contrast, on Valentine's Day, there is a 3.6% increase in spontaneous births and a 12.1% increase in Caesarean births. In Sweden, 9.3% of the population is born in March and 7.3% in November, when a uniform distribution would give 8.3%. In the Gregorian calendar (a common solar calendar), February in a leap year has 29 days instead of the usual 28, so the year lasts 366 days instead of the usual 365. A person born on February 29 may be called a "leapling" or a "leaper". In common years, they usually celebrate their birthdays on February 28. In some situations, March 1 is used as the birthday in a non-leap year since it is the day following February 28. Technically, a leapling will have fewer birthday anniversaries than their age in years. This phenomenon is exploited when a person claims to be only a quarter of their actual age, by counting their leap-year birthday anniversaries only. In Gilbert and Sullivan's 1879 comic opera The Pirates of Penzance, Frederic the pirate apprentice discovers that he is bound to serve the pirates until his 21st birthday rather than until his 21st year. For legal purposes, legal birthdays depend on how local laws count time intervals. An individual's Beddian birthday, named in tribute to firefighter Bobby Beddia, occurs during the year that their age matches the last two digits of the year they were born. Some studies show people are more likely to die on their birthdays, with explanations including excessive drinking, suicide, cardiovascular events due to high stress or happiness, efforts to postpone death for major social events, and death certificate paperwork errors. See also References Notes External links |
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Contents NetRexx NetRexx is an open source, originally IBM's, variant of the REXX programming language to run on the Java virtual machine. It supports a classic REXX syntax, with no reserved keywords, along with considerable additions to support object-oriented programming in a manner compatible with Java's object model, yet can be used as both a compiled and an interpreted language, with an option of using only data types native to the JVM or the NetRexx runtime package. The latter offers the standard Rexx data type that combines string processing with unlimited precision decimal arithmetic. Integration with the JVM platform is tight, and all existing Java class libraries can be used unchanged and without special setup; at the same time, a Java programmer can opt to just use the Rexx class from the runtime package for improved string handling in Java syntax source programs. NetRexx is free to download from the Rexx Language Association. IBM announced the transfer of NetRexx 3.00 source code to the Rexx Language Association (RexxLA) on June 8, 2011. History In 1995 Mike Cowlishaw ported Java to OS/2 and soon after started with an experiment to run REXX on the JVM. With REXX generally considered the first of the general purpose scripting languages, NetRexx is the first alternative language for the JVM. The 0.50 release, from April 1996, contained the NetRexx runtime classes and a translator written in REXX but tokenized and turned into an OS/2 executable. The 1.00 release came available in January 1997 and contained a translator bootstrapped to NetRexx. Release 2.00 became available in August 2000 and was a major upgrade, in which interpreted execution was added. Mike Cowlishaw left IBM in March 2010, and the future of IBM NetRexx as open source was unknown for a while. IBM finally announced the transfer of NetRexx source code to the Rexx Language Association (RexxLA) on June 8, 2011, 14 years after the v1.0 release. IBM released the NetRexx source code to RexxLA under the ICU license. RexxLA shortly after released this as NetRexx 3.00 and has followed up with regular releases, with 4.01 (2021-03-20) adding Java Platform Module System support to support Java versions 9 and higher. As of 2018[update] the ICU license has not been approved by OSI; it appears to be a variant of the Expat License. Syntax The syntax and object model of NetRexx differ from Object REXX, another IBM object-oriented variant of REXX which has been released as open source software. The successor ooREXX shares a few syntactical elements (LOOP, DO OVER) not found in classical REXX. NetRexx is written in NetRexx and uses the decimal arithmetic of REXX specified in ANSI X3.274. References External links |
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[SOURCE: https://en.wikipedia.org/wiki/Ashley_St._Clair] | [TOKENS: 757] |
Contents Ashley St. Clair Ashley St. Clair (born July 31, 1998) is an American right-wing influencer, author, and political commentator. She became known as an outspoken proponent of right-wing ideologies including anti-transgender activism, though she expressed remorse for the latter in 2026. She had a child with Elon Musk in 2024. Early life and political commentary Ashley St. Clair was born on July 31, 1998, in Florida but was subsequently raised in Colorado. She is Jewish. She became an outspoken proponent of right-wing ideologies and made appearances on Fox News to talk about declining birth rates. Among her works is being a longtime writer for the right-wing satirical news site The Babylon Bee, and serving as a brand ambassador for Turning Point USA. She has amassed over one million followers on Twitter, and has been an endorser of President Donald Trump. St. Clair also wrote the children's book Elephants Are Not Birds, which she once billed as an unapologetic rebuke of transgender acceptance and which was published by the Christian conservative publishing house BRAVE Books. The book follows Kevin the Elephant as he "learns that even though he can sing, he is not a bird, even if Culture insists that he is". Relationship with Elon Musk St. Clair first met Twitter owner Elon Musk on the site in 2023 and subsequently began exchanging direct messages with him. She gave birth to Musk's son in 2024. According to St. Clair, Musk was not on the child's birth certificate and had been unresponsive to her subsequent messages despite previously acknowledging paternity in writing. Reportedly, Musk met him on September 21, 2024, and spent two hours with him, followed by one hour the next day, then met their son for the last time on November 30, 2024, for only 30 minutes, and continued texting St. Clair thereafter. On Valentine's Day 2025, St. Clair publicly acknowledged the child's existence and Musk's paternity; on March 31, she was filmed selling her Tesla, which she said was because her child support payments had been cut by 60% as punishment by Musk for her "disobedience". Following this, St. Clair sought full custody over their son and had been locked in a battle with Musk since August 2025, which she described as "unplanned career suicide" and "a gap in my LinkedIn profile that can't be legally explained". St. Clair subsequently started a podcast to avoid an imminent eviction. Following this, St. Clair became a target for abuse from supporters of Musk. After the rollout of Twitter's Grok image-editing feature, St. Clair reported being deluged with images of herself that had been AI-edited to show her naked and in sexual positions, including some based on photos when she was underage, and which she said both Musk and Twitter refused to do anything to counteract. She subsequently filed suit in New York State against xAI for punitive and compensatory damages. Political shift In January 2026, St. Clair expressed strong remorse for her previous anti-trans activism, saying "I feel immense guilt for my role", in particular for pain caused to her child's half-sister, Vivian Wilson, and that she has been "trying incredibly hard privately to learn + advocate for those within the trans community that I’ve hurt", but that she didn't really know how to make amends. Following this apology, Musk filed for full custody of their son on the stated grounds that her apology implied she might try to "transition a one-year old boy", despite St. Clair at no point saying anything of the sort. See also References |
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[SOURCE: https://en.wikipedia.org/wiki/Computer#cite_note-84] | [TOKENS: 10628] |
Contents Computer A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can perform generic sets of operations known as programs, which enable computers to perform a wide range of tasks. The term computer system may refer to a nominally complete computer that includes the hardware, operating system, software, and peripheral equipment needed and used for full operation, or to a group of computers that are linked and function together, such as a computer network or computer cluster. A broad range of industrial and consumer products use computers as control systems, including simple special-purpose devices like microwave ovens and remote controls, and factory devices like industrial robots. Computers are at the core of general-purpose devices such as personal computers and mobile devices such as smartphones. Computers power the Internet, which links billions of computers and users. Early computers were meant to be used only for calculations. Simple manual instruments like the abacus have aided people in doing calculations since ancient times. Early in the Industrial Revolution, some mechanical devices were built to automate long, tedious tasks, such as guiding patterns for looms. More sophisticated electrical machines did specialized analog calculations in the early 20th century. The first digital electronic calculating machines were developed during World War II, both electromechanical and using thermionic valves. The first semiconductor transistors in the late 1940s were followed by the silicon-based MOSFET (MOS transistor) and monolithic integrated circuit chip technologies in the late 1950s, leading to the microprocessor and the microcomputer revolution in the 1970s. The speed, power, and versatility of computers have been increasing dramatically ever since then, with transistor counts increasing at a rapid pace (Moore's law noted that counts doubled every two years), leading to the Digital Revolution during the late 20th and early 21st centuries. Conventionally, a modern computer consists of at least one processing element, typically a central processing unit (CPU) in the form of a microprocessor, together with some type of computer memory, typically semiconductor memory chips. The processing element carries out arithmetic and logical operations, and a sequencing and control unit can change the order of operations in response to stored information. Peripheral devices include input devices (keyboards, mice, joysticks, etc.), output devices (monitors, printers, etc.), and input/output devices that perform both functions (e.g. touchscreens). Peripheral devices allow information to be retrieved from an external source, and they enable the results of operations to be saved and retrieved. Etymology It was not until the mid-20th century that the word acquired its modern definition; according to the Oxford English Dictionary, the first known use of the word computer was in a different sense, in a 1613 book called The Yong Mans Gleanings by the English writer Richard Brathwait: "I haue [sic] read the truest computer of Times, and the best Arithmetician that euer [sic] breathed, and he reduceth thy dayes into a short number." This usage of the term referred to a human computer, a person who carried out calculations or computations. The word continued to have the same meaning until the middle of the 20th century. During the latter part of this period, women were often hired as computers because they could be paid less than their male counterparts. By 1943, most human computers were women. The Online Etymology Dictionary gives the first attested use of computer in the 1640s, meaning 'one who calculates'; this is an "agent noun from compute (v.)". The Online Etymology Dictionary states that the use of the term to mean "'calculating machine' (of any type) is from 1897." The Online Etymology Dictionary indicates that the "modern use" of the term, to mean 'programmable digital electronic computer' dates from "1945 under this name; [in a] theoretical [sense] from 1937, as Turing machine". The name has remained, although modern computers are capable of many higher-level functions. History Devices have been used to aid computation for thousands of years, mostly using one-to-one correspondence with fingers. The earliest counting device was most likely a form of tally stick. Later record keeping aids throughout the Fertile Crescent included calculi (clay spheres, cones, etc.) which represented counts of items, likely livestock or grains, sealed in hollow unbaked clay containers.[a] The use of counting rods is one example. The abacus was initially used for arithmetic tasks. The Roman abacus was developed from devices used in Babylonia as early as 2400 BCE. Since then, many other forms of reckoning boards or tables have been invented. In a medieval European counting house, a checkered cloth would be placed on a table, and markers moved around on it according to certain rules, as an aid to calculating sums of money. The Antikythera mechanism is believed to be the earliest known mechanical analog computer, according to Derek J. de Solla Price. It was designed to calculate astronomical positions. It was discovered in 1901 in the Antikythera wreck off the Greek island of Antikythera, between Kythera and Crete, and has been dated to approximately c. 100 BCE. Devices of comparable complexity to the Antikythera mechanism would not reappear until the fourteenth century. Many mechanical aids to calculation and measurement were constructed for astronomical and navigation use. The planisphere was a star chart invented by Abū Rayhān al-Bīrūnī in the early 11th century. The astrolabe was invented in the Hellenistic world in either the 1st or 2nd centuries BCE and is often attributed to Hipparchus. A combination of the planisphere and dioptra, the astrolabe was effectively an analog computer capable of working out several different kinds of problems in spherical astronomy. An astrolabe incorporating a mechanical calendar computer and gear-wheels was invented by Abi Bakr of Isfahan, Persia in 1235. Abū Rayhān al-Bīrūnī invented the first mechanical geared lunisolar calendar astrolabe, an early fixed-wired knowledge processing machine with a gear train and gear-wheels, c. 1000 AD. The sector, a calculating instrument used for solving problems in proportion, trigonometry, multiplication and division, and for various functions, such as squares and cube roots, was developed in the late 16th century and found application in gunnery, surveying and navigation. The planimeter was a manual instrument to calculate the area of a closed figure by tracing over it with a mechanical linkage. The slide rule was invented around 1620–1630, by the English clergyman William Oughtred, shortly after the publication of the concept of the logarithm. It is a hand-operated analog computer for doing multiplication and division. As slide rule development progressed, added scales provided reciprocals, squares and square roots, cubes and cube roots, as well as transcendental functions such as logarithms and exponentials, circular and hyperbolic trigonometry and other functions. Slide rules with special scales are still used for quick performance of routine calculations, such as the E6B circular slide rule used for time and distance calculations on light aircraft. In the 1770s, Pierre Jaquet-Droz, a Swiss watchmaker, built a mechanical doll (automaton) that could write holding a quill pen. By switching the number and order of its internal wheels different letters, and hence different messages, could be produced. In effect, it could be mechanically "programmed" to read instructions. Along with two other complex machines, the doll is at the Musée d'Art et d'Histoire of Neuchâtel, Switzerland, and still operates. In 1831–1835, mathematician and engineer Giovanni Plana devised a Perpetual Calendar machine, which through a system of pulleys and cylinders could predict the perpetual calendar for every year from 0 CE (that is, 1 BCE) to 4000 CE, keeping track of leap years and varying day length. The tide-predicting machine invented by the Scottish scientist Sir William Thomson in 1872 was of great utility to navigation in shallow waters. It used a system of pulleys and wires to automatically calculate predicted tide levels for a set period at a particular location. The differential analyser, a mechanical analog computer designed to solve differential equations by integration, used wheel-and-disc mechanisms to perform the integration. In 1876, Sir William Thomson had already discussed the possible construction of such calculators, but he had been stymied by the limited output torque of the ball-and-disk integrators. In a differential analyzer, the output of one integrator drove the input of the next integrator, or a graphing output. The torque amplifier was the advance that allowed these machines to work. Starting in the 1920s, Vannevar Bush and others developed mechanical differential analyzers. In the 1890s, the Spanish engineer Leonardo Torres Quevedo began to develop a series of advanced analog machines that could solve real and complex roots of polynomials, which were published in 1901 by the Paris Academy of Sciences. Charles Babbage, an English mechanical engineer and polymath, originated the concept of a programmable computer. Considered the "father of the computer", he conceptualized and invented the first mechanical computer in the early 19th century. After working on his difference engine he announced his invention in 1822, in a paper to the Royal Astronomical Society, titled "Note on the application of machinery to the computation of astronomical and mathematical tables". He also designed to aid in navigational calculations, in 1833 he realized that a much more general design, an analytical engine, was possible. The input of programs and data was to be provided to the machine via punched cards, a method being used at the time to direct mechanical looms such as the Jacquard loom. For output, the machine would have a printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards to be read in later. The engine would incorporate an arithmetic logic unit, control flow in the form of conditional branching and loops, and integrated memory, making it the first design for a general-purpose computer that could be described in modern terms as Turing-complete. The machine was about a century ahead of its time. All the parts for his machine had to be made by hand – this was a major problem for a device with thousands of parts. Eventually, the project was dissolved with the decision of the British Government to cease funding. Babbage's failure to complete the analytical engine can be chiefly attributed to political and financial difficulties as well as his desire to develop an increasingly sophisticated computer and to move ahead faster than anyone else could follow. Nevertheless, his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906. In his work Essays on Automatics published in 1914, Leonardo Torres Quevedo wrote a brief history of Babbage's efforts at constructing a mechanical Difference Engine and Analytical Engine. The paper contains a design of a machine capable to calculate formulas like a x ( y − z ) 2 {\displaystyle a^{x}(y-z)^{2}} , for a sequence of sets of values. The whole machine was to be controlled by a read-only program, which was complete with provisions for conditional branching. He also introduced the idea of floating-point arithmetic. In 1920, to celebrate the 100th anniversary of the invention of the arithmometer, Torres presented in Paris the Electromechanical Arithmometer, which allowed a user to input arithmetic problems through a keyboard, and computed and printed the results, demonstrating the feasibility of an electromechanical analytical engine. During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticated analog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers. The first modern analog computer was a tide-predicting machine, invented by Sir William Thomson (later to become Lord Kelvin) in 1872. The differential analyser, a mechanical analog computer designed to solve differential equations by integration using wheel-and-disc mechanisms, was conceptualized in 1876 by James Thomson, the elder brother of the more famous Sir William Thomson. The art of mechanical analog computing reached its zenith with the differential analyzer, completed in 1931 by Vannevar Bush at MIT. By the 1950s, the success of digital electronic computers had spelled the end for most analog computing machines, but analog computers remained in use during the 1950s in some specialized applications such as education (slide rule) and aircraft (control systems).[citation needed] Claude Shannon's 1937 master's thesis laid the foundations of digital computing, with his insight of applying Boolean algebra to the analysis and synthesis of switching circuits being the basic concept which underlies all electronic digital computers. By 1938, the United States Navy had developed the Torpedo Data Computer, an electromechanical analog computer for submarines that used trigonometry to solve the problem of firing a torpedo at a moving target. During World War II, similar devices were developed in other countries. Early digital computers were electromechanical; electric switches drove mechanical relays to perform the calculation. These devices had a low operating speed and were eventually superseded by much faster all-electric computers, originally using vacuum tubes. The Z2, created by German engineer Konrad Zuse in 1939 in Berlin, was one of the earliest examples of an electromechanical relay computer. In 1941, Zuse followed his earlier machine up with the Z3, the world's first working electromechanical programmable, fully automatic digital computer. The Z3 was built with 2000 relays, implementing a 22-bit word length that operated at a clock frequency of about 5–10 Hz. Program code was supplied on punched film while data could be stored in 64 words of memory or supplied from the keyboard. It was quite similar to modern machines in some respects, pioneering numerous advances such as floating-point numbers. Rather than the harder-to-implement decimal system (used in Charles Babbage's earlier design), using a binary system meant that Zuse's machines were easier to build and potentially more reliable, given the technologies available at that time. The Z3 was not itself a universal computer but could be extended to be Turing complete. Zuse's next computer, the Z4, became the world's first commercial computer; after initial delay due to the Second World War, it was completed in 1950 and delivered to the ETH Zurich. The computer was manufactured by Zuse's own company, Zuse KG, which was founded in 1941 as the first company with the sole purpose of developing computers in Berlin. The Z4 served as the inspiration for the construction of the ERMETH, the first Swiss computer and one of the first in Europe. Purely electronic circuit elements soon replaced their mechanical and electromechanical equivalents, at the same time that digital calculation replaced analog. The engineer Tommy Flowers, working at the Post Office Research Station in London in the 1930s, began to explore the possible use of electronics for the telephone exchange. Experimental equipment that he built in 1934 went into operation five years later, converting a portion of the telephone exchange network into an electronic data processing system, using thousands of vacuum tubes. In the US, John Vincent Atanasoff and Clifford E. Berry of Iowa State University developed and tested the Atanasoff–Berry Computer (ABC) in 1942, the first "automatic electronic digital computer". This design was also all-electronic and used about 300 vacuum tubes, with capacitors fixed in a mechanically rotating drum for memory. During World War II, the British code-breakers at Bletchley Park achieved a number of successes at breaking encrypted German military communications. The German encryption machine, Enigma, was first attacked with the help of the electro-mechanical bombes which were often run by women. To crack the more sophisticated German Lorenz SZ 40/42 machine, used for high-level Army communications, Max Newman and his colleagues commissioned Flowers to build the Colossus. He spent eleven months from early February 1943 designing and building the first Colossus. After a functional test in December 1943, Colossus was shipped to Bletchley Park, where it was delivered on 18 January 1944 and attacked its first message on 5 February. Colossus was the world's first electronic digital programmable computer. It used a large number of valves (vacuum tubes). It had paper-tape input and was capable of being configured to perform a variety of boolean logical operations on its data, but it was not Turing-complete. Nine Mk II Colossi were built (The Mk I was converted to a Mk II making ten machines in total). Colossus Mark I contained 1,500 thermionic valves (tubes), but Mark II with 2,400 valves, was both five times faster and simpler to operate than Mark I, greatly speeding the decoding process. The ENIAC (Electronic Numerical Integrator and Computer) was the first electronic programmable computer built in the U.S. Although the ENIAC was similar to the Colossus, it was much faster, more flexible, and it was Turing-complete. Like the Colossus, a "program" on the ENIAC was defined by the states of its patch cables and switches, a far cry from the stored program electronic machines that came later. Once a program was written, it had to be mechanically set into the machine with manual resetting of plugs and switches. The programmers of the ENIAC were six women, often known collectively as the "ENIAC girls". It combined the high speed of electronics with the ability to be programmed for many complex problems. It could add or subtract 5000 times a second, a thousand times faster than any other machine. It also had modules to multiply, divide, and square root. High speed memory was limited to 20 words (about 80 bytes). Built under the direction of John Mauchly and J. Presper Eckert at the University of Pennsylvania, ENIAC's development and construction lasted from 1943 to full operation at the end of 1945. The machine was huge, weighing 30 tons, using 200 kilowatts of electric power and contained over 18,000 vacuum tubes, 1,500 relays, and hundreds of thousands of resistors, capacitors, and inductors. The principle of the modern computer was proposed by Alan Turing in his seminal 1936 paper, On Computable Numbers. Turing proposed a simple device that he called "Universal Computing machine" and that is now known as a universal Turing machine. He proved that such a machine is capable of computing anything that is computable by executing instructions (program) stored on tape, allowing the machine to be programmable. The fundamental concept of Turing's design is the stored program, where all the instructions for computing are stored in memory. Von Neumann acknowledged that the central concept of the modern computer was due to this paper. Turing machines are to this day a central object of study in theory of computation. Except for the limitations imposed by their finite memory stores, modern computers are said to be Turing-complete, which is to say, they have algorithm execution capability equivalent to a universal Turing machine. Early computing machines had fixed programs. Changing its function required the re-wiring and re-structuring of the machine. With the proposal of the stored-program computer this changed. A stored-program computer includes by design an instruction set and can store in memory a set of instructions (a program) that details the computation. The theoretical basis for the stored-program computer was laid out by Alan Turing in his 1936 paper. In 1945, Turing joined the National Physical Laboratory and began work on developing an electronic stored-program digital computer. His 1945 report "Proposed Electronic Calculator" was the first specification for such a device. John von Neumann at the University of Pennsylvania also circulated his First Draft of a Report on the EDVAC in 1945. The Manchester Baby was the world's first stored-program computer. It was built at the University of Manchester in England by Frederic C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948. It was designed as a testbed for the Williams tube, the first random-access digital storage device. Although the computer was described as "small and primitive" by a 1998 retrospective, it was the first working machine to contain all of the elements essential to a modern electronic computer. As soon as the Baby had demonstrated the feasibility of its design, a project began at the university to develop it into a practically useful computer, the Manchester Mark 1. The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first commercially available general-purpose computer. Built by Ferranti, it was delivered to the University of Manchester in February 1951. At least seven of these later machines were delivered between 1953 and 1957, one of them to Shell labs in Amsterdam. In October 1947 the directors of British catering company J. Lyons & Company decided to take an active role in promoting the commercial development of computers. Lyons's LEO I computer, modelled closely on the Cambridge EDSAC of 1949, became operational in April 1951 and ran the world's first routine office computer job. The concept of a field-effect transistor was proposed by Julius Edgar Lilienfeld in 1925. John Bardeen and Walter Brattain, while working under William Shockley at Bell Labs, built the first working transistor, the point-contact transistor, in 1947, which was followed by Shockley's bipolar junction transistor in 1948. From 1955 onwards, transistors replaced vacuum tubes in computer designs, giving rise to the "second generation" of computers. Compared to vacuum tubes, transistors have many advantages: they are smaller, and require less power than vacuum tubes, so give off less heat. Junction transistors were much more reliable than vacuum tubes and had longer, indefinite, service life. Transistorized computers could contain tens of thousands of binary logic circuits in a relatively compact space. However, early junction transistors were relatively bulky devices that were difficult to manufacture on a mass-production basis, which limited them to a number of specialized applications. At the University of Manchester, a team under the leadership of Tom Kilburn designed and built a machine using the newly developed transistors instead of valves. Their first transistorized computer and the first in the world, was operational by 1953, and a second version was completed there in April 1955. However, the machine did make use of valves to generate its 125 kHz clock waveforms and in the circuitry to read and write on its magnetic drum memory, so it was not the first completely transistorized computer. That distinction goes to the Harwell CADET of 1955, built by the electronics division of the Atomic Energy Research Establishment at Harwell. The metal–oxide–silicon field-effect transistor (MOSFET), also known as the MOS transistor, was invented at Bell Labs between 1955 and 1960 and was the first truly compact transistor that could be miniaturized and mass-produced for a wide range of uses. With its high scalability, and much lower power consumption and higher density than bipolar junction transistors, the MOSFET made it possible to build high-density integrated circuits. In addition to data processing, it also enabled the practical use of MOS transistors as memory cell storage elements, leading to the development of MOS semiconductor memory, which replaced earlier magnetic-core memory in computers. The MOSFET led to the microcomputer revolution, and became the driving force behind the computer revolution. The MOSFET is the most widely used transistor in computers, and is the fundamental building block of digital electronics. The next great advance in computing power came with the advent of the integrated circuit (IC). The idea of the integrated circuit was first conceived by a radar scientist working for the Royal Radar Establishment of the Ministry of Defence, Geoffrey W.A. Dummer. Dummer presented the first public description of an integrated circuit at the Symposium on Progress in Quality Electronic Components in Washington, D.C., on 7 May 1952. The first working ICs were invented by Jack Kilby at Texas Instruments and Robert Noyce at Fairchild Semiconductor. Kilby recorded his initial ideas concerning the integrated circuit in July 1958, successfully demonstrating the first working integrated example on 12 September 1958. In his patent application of 6 February 1959, Kilby described his new device as "a body of semiconductor material ... wherein all the components of the electronic circuit are completely integrated". However, Kilby's invention was a hybrid integrated circuit (hybrid IC), rather than a monolithic integrated circuit (IC) chip. Kilby's IC had external wire connections, which made it difficult to mass-produce. Noyce also came up with his own idea of an integrated circuit half a year later than Kilby. Noyce's invention was the first true monolithic IC chip. His chip solved many practical problems that Kilby's had not. Produced at Fairchild Semiconductor, it was made of silicon, whereas Kilby's chip was made of germanium. Noyce's monolithic IC was fabricated using the planar process, developed by his colleague Jean Hoerni in early 1959. In turn, the planar process was based on Carl Frosch and Lincoln Derick work on semiconductor surface passivation by silicon dioxide. Modern monolithic ICs are predominantly MOS (metal–oxide–semiconductor) integrated circuits, built from MOSFETs (MOS transistors). The earliest experimental MOS IC to be fabricated was a 16-transistor chip built by Fred Heiman and Steven Hofstein at RCA in 1962. General Microelectronics later introduced the first commercial MOS IC in 1964, developed by Robert Norman. Following the development of the self-aligned gate (silicon-gate) MOS transistor by Robert Kerwin, Donald Klein and John Sarace at Bell Labs in 1967, the first silicon-gate MOS IC with self-aligned gates was developed by Federico Faggin at Fairchild Semiconductor in 1968. The MOSFET has since become the most critical device component in modern ICs. The development of the MOS integrated circuit led to the invention of the microprocessor, and heralded an explosion in the commercial and personal use of computers. While the subject of exactly which device was the first microprocessor is contentious, partly due to lack of agreement on the exact definition of the term "microprocessor", it is largely undisputed that the first single-chip microprocessor was the Intel 4004, designed and realized by Federico Faggin with his silicon-gate MOS IC technology, along with Ted Hoff, Masatoshi Shima and Stanley Mazor at Intel.[b] In the early 1970s, MOS IC technology enabled the integration of more than 10,000 transistors on a single chip. System on a Chip (SoCs) are complete computers on a microchip (or chip) the size of a coin. They may or may not have integrated RAM and flash memory. If not integrated, the RAM is usually placed directly above (known as Package on package) or below (on the opposite side of the circuit board) the SoC, and the flash memory is usually placed right next to the SoC. This is done to improve data transfer speeds, as the data signals do not have to travel long distances. Since ENIAC in 1945, computers have advanced enormously, with modern SoCs (such as the Snapdragon 865) being the size of a coin while also being hundreds of thousands of times more powerful than ENIAC, integrating billions of transistors, and consuming only a few watts of power. The first mobile computers were heavy and ran from mains power. The 50 lb (23 kg) IBM 5100 was an early example. Later portables such as the Osborne 1 and Compaq Portable were considerably lighter but still needed to be plugged in. The first laptops, such as the Grid Compass, removed this requirement by incorporating batteries – and with the continued miniaturization of computing resources and advancements in portable battery life, portable computers grew in popularity in the 2000s. The same developments allowed manufacturers to integrate computing resources into cellular mobile phones by the early 2000s. These smartphones and tablets run on a variety of operating systems and recently became the dominant computing device on the market. These are powered by System on a Chip (SoCs), which are complete computers on a microchip the size of a coin. Types Computers can be classified in a number of different ways, including: A computer does not need to be electronic, nor even have a processor, nor RAM, nor even a hard disk. While popular usage of the word "computer" is synonymous with a personal electronic computer,[c] a typical modern definition of a computer is: "A device that computes, especially a programmable [usually] electronic machine that performs high-speed mathematical or logical operations or that assembles, stores, correlates, or otherwise processes information." According to this definition, any device that processes information qualifies as a computer. Hardware The term hardware covers all of those parts of a computer that are tangible physical objects. Circuits, computer chips, graphic cards, sound cards, memory (RAM), motherboard, displays, power supplies, cables, keyboards, printers and "mice" input devices are all hardware. A general-purpose computer has four main components: the arithmetic logic unit (ALU), the control unit, the memory, and the input and output devices (collectively termed I/O). These parts are interconnected by buses, often made of groups of wires. Inside each of these parts are thousands to trillions of small electrical circuits which can be turned off or on by means of an electronic switch. Each circuit represents a bit (binary digit) of information so that when the circuit is on it represents a "1", and when off it represents a "0" (in positive logic representation). The circuits are arranged in logic gates so that one or more of the circuits may control the state of one or more of the other circuits. Input devices are the means by which the operations of a computer are controlled and it is provided with data. Examples include: Output devices are the means by which a computer provides the results of its calculations in a human-accessible form. Examples include: The control unit (often called a control system or central controller) manages the computer's various components; it reads and interprets (decodes) the program instructions, transforming them into control signals that activate other parts of the computer.[e] Control systems in advanced computers may change the order of execution of some instructions to improve performance. A key component common to all CPUs is the program counter, a special memory cell (a register) that keeps track of which location in memory the next instruction is to be read from.[f] The control system's function is as follows— this is a simplified description, and some of these steps may be performed concurrently or in a different order depending on the type of CPU: Since the program counter is (conceptually) just another set of memory cells, it can be changed by calculations done in the ALU. Adding 100 to the program counter would cause the next instruction to be read from a place 100 locations further down the program. Instructions that modify the program counter are often known as "jumps" and allow for loops (instructions that are repeated by the computer) and often conditional instruction execution (both examples of control flow). The sequence of operations that the control unit goes through to process an instruction is in itself like a short computer program, and indeed, in some more complex CPU designs, there is another yet smaller computer called a microsequencer, which runs a microcode program that causes all of these events to happen. The control unit, ALU, and registers are collectively known as a central processing unit (CPU). Early CPUs were composed of many separate components. Since the 1970s, CPUs have typically been constructed on a single MOS integrated circuit chip called a microprocessor. The ALU is capable of performing two classes of operations: arithmetic and logic. The set of arithmetic operations that a particular ALU supports may be limited to addition and subtraction, or might include multiplication, division, trigonometry functions such as sine, cosine, etc., and square roots. Some can operate only on whole numbers (integers) while others use floating point to represent real numbers, albeit with limited precision. However, any computer that is capable of performing just the simplest operations can be programmed to break down the more complex operations into simple steps that it can perform. Therefore, any computer can be programmed to perform any arithmetic operation—although it will take more time to do so if its ALU does not directly support the operation. An ALU may also compare numbers and return Boolean truth values (true or false) depending on whether one is equal to, greater than or less than the other ("is 64 greater than 65?"). Logic operations involve Boolean logic: AND, OR, XOR, and NOT. These can be useful for creating complicated conditional statements and processing Boolean logic. Superscalar computers may contain multiple ALUs, allowing them to process several instructions simultaneously. Graphics processors and computers with SIMD and MIMD features often contain ALUs that can perform arithmetic on vectors and matrices. A computer's memory can be viewed as a list of cells into which numbers can be placed or read. Each cell has a numbered "address" and can store a single number. The computer can be instructed to "put the number 123 into the cell numbered 1357" or to "add the number that is in cell 1357 to the number that is in cell 2468 and put the answer into cell 1595." The information stored in memory may represent practically anything. Letters, numbers, even computer instructions can be placed into memory with equal ease. Since the CPU does not differentiate between different types of information, it is the software's responsibility to give significance to what the memory sees as nothing but a series of numbers. In almost all modern computers, each memory cell is set up to store binary numbers in groups of eight bits (called a byte). Each byte is able to represent 256 different numbers (28 = 256); either from 0 to 255 or −128 to +127. To store larger numbers, several consecutive bytes may be used (typically, two, four or eight). When negative numbers are required, they are usually stored in two's complement notation. Other arrangements are possible, but are usually not seen outside of specialized applications or historical contexts. A computer can store any kind of information in memory if it can be represented numerically. Modern computers have billions or even trillions of bytes of memory. The CPU contains a special set of memory cells called registers that can be read and written to much more rapidly than the main memory area. There are typically between two and one hundred registers depending on the type of CPU. Registers are used for the most frequently needed data items to avoid having to access main memory every time data is needed. As data is constantly being worked on, reducing the need to access main memory (which is often slow compared to the ALU and control units) greatly increases the computer's speed. Computer main memory comes in two principal varieties: RAM can be read and written to anytime the CPU commands it, but ROM is preloaded with data and software that never changes, therefore the CPU can only read from it. ROM is typically used to store the computer's initial start-up instructions. In general, the contents of RAM are erased when the power to the computer is turned off, but ROM retains its data indefinitely. In a PC, the ROM contains a specialized program called the BIOS that orchestrates loading the computer's operating system from the hard disk drive into RAM whenever the computer is turned on or reset. In embedded computers, which frequently do not have disk drives, all of the required software may be stored in ROM. Software stored in ROM is often called firmware, because it is notionally more like hardware than software. Flash memory blurs the distinction between ROM and RAM, as it retains its data when turned off but is also rewritable. It is typically much slower than conventional ROM and RAM however, so its use is restricted to applications where high speed is unnecessary.[g] In more sophisticated computers there may be one or more RAM cache memories, which are slower than registers but faster than main memory. Generally computers with this sort of cache are designed to move frequently needed data into the cache automatically, often without the need for any intervention on the programmer's part. I/O is the means by which a computer exchanges information with the outside world. Devices that provide input or output to the computer are called peripherals. On a typical personal computer, peripherals include input devices like the keyboard and mouse, and output devices such as the display and printer. Hard disk drives, floppy disk drives and optical disc drives serve as both input and output devices. Computer networking is another form of I/O. I/O devices are often complex computers in their own right, with their own CPU and memory. A graphics processing unit might contain fifty or more tiny computers that perform the calculations necessary to display 3D graphics.[citation needed] Modern desktop computers contain many smaller computers that assist the main CPU in performing I/O. A 2016-era flat screen display contains its own computer circuitry. While a computer may be viewed as running one gigantic program stored in its main memory, in some systems it is necessary to give the appearance of running several programs simultaneously. This is achieved by multitasking, i.e. having the computer switch rapidly between running each program in turn. One means by which this is done is with a special signal called an interrupt, which can periodically cause the computer to stop executing instructions where it was and do something else instead. By remembering where it was executing prior to the interrupt, the computer can return to that task later. If several programs are running "at the same time". Then the interrupt generator might be causing several hundred interrupts per second, causing a program switch each time. Since modern computers typically execute instructions several orders of magnitude faster than human perception, it may appear that many programs are running at the same time, even though only one is ever executing in any given instant. This method of multitasking is sometimes termed "time-sharing" since each program is allocated a "slice" of time in turn. Before the era of inexpensive computers, the principal use for multitasking was to allow many people to share the same computer. Seemingly, multitasking would cause a computer that is switching between several programs to run more slowly, in direct proportion to the number of programs it is running, but most programs spend much of their time waiting for slow input/output devices to complete their tasks. If a program is waiting for the user to click on the mouse or press a key on the keyboard, then it will not take a "time slice" until the event it is waiting for has occurred. This frees up time for other programs to execute so that many programs may be run simultaneously without unacceptable speed loss. Some computers are designed to distribute their work across several CPUs in a multiprocessing configuration, a technique once employed in only large and powerful machines such as supercomputers, mainframe computers and servers. Multiprocessor and multi-core (multiple CPUs on a single integrated circuit) personal and laptop computers are now widely available, and are being increasingly used in lower-end markets as a result. Supercomputers in particular often have highly unique architectures that differ significantly from the basic stored-program architecture and from general-purpose computers.[h] They often feature thousands of CPUs, customized high-speed interconnects, and specialized computing hardware. Such designs tend to be useful for only specialized tasks due to the large scale of program organization required to use most of the available resources at once. Supercomputers usually see usage in large-scale simulation, graphics rendering, and cryptography applications, as well as with other so-called "embarrassingly parallel" tasks. Software Software is the part of a computer system that consists of the encoded information that determines the computer's operation, such as data or instructions on how to process the data. In contrast to the physical hardware from which the system is built, software is immaterial. Software includes computer programs, libraries and related non-executable data, such as online documentation or digital media. It is often divided into system software and application software. Computer hardware and software require each other and neither is useful on its own. When software is stored in hardware that cannot easily be modified, such as with BIOS ROM in an IBM PC compatible computer, it is sometimes called "firmware". The defining feature of modern computers which distinguishes them from all other machines is that they can be programmed. That is to say that some type of instructions (the program) can be given to the computer, and it will process them. Modern computers based on the von Neumann architecture often have machine code in the form of an imperative programming language. In practical terms, a computer program may be just a few instructions or extend to many millions of instructions, as do the programs for word processors and web browsers for example. A typical modern computer can execute billions of instructions per second (gigaflops) and rarely makes a mistake over many years of operation. Large computer programs consisting of several million instructions may take teams of programmers years to write, and due to the complexity of the task almost certainly contain errors. This section applies to most common RAM machine–based computers. In most cases, computer instructions are simple: add one number to another, move some data from one location to another, send a message to some external device, etc. These instructions are read from the computer's memory and are generally carried out (executed) in the order they were given. However, there are usually specialized instructions to tell the computer to jump ahead or backwards to some other place in the program and to carry on executing from there. These are called "jump" instructions (or branches). Furthermore, jump instructions may be made to happen conditionally so that different sequences of instructions may be used depending on the result of some previous calculation or some external event. Many computers directly support subroutines by providing a type of jump that "remembers" the location it jumped from and another instruction to return to the instruction following that jump instruction. Program execution might be likened to reading a book. While a person will normally read each word and line in sequence, they may at times jump back to an earlier place in the text or skip sections that are not of interest. Similarly, a computer may sometimes go back and repeat the instructions in some section of the program over and over again until some internal condition is met. This is called the flow of control within the program and it is what allows the computer to perform tasks repeatedly without human intervention. Comparatively, a person using a pocket calculator can perform a basic arithmetic operation such as adding two numbers with just a few button presses. But to add together all of the numbers from 1 to 1,000 would take thousands of button presses and a lot of time, with a near certainty of making a mistake. On the other hand, a computer may be programmed to do this with just a few simple instructions. The following example is written in the MIPS assembly language: Once told to run this program, the computer will perform the repetitive addition task without further human intervention. It will almost never make a mistake and a modern PC can complete the task in a fraction of a second. In most computers, individual instructions are stored as machine code with each instruction being given a unique number (its operation code or opcode for short). The command to add two numbers together would have one opcode; the command to multiply them would have a different opcode, and so on. The simplest computers are able to perform any of a handful of different instructions; the more complex computers have several hundred to choose from, each with a unique numerical code. Since the computer's memory is able to store numbers, it can also store the instruction codes. This leads to the important fact that entire programs (which are just lists of these instructions) can be represented as lists of numbers and can themselves be manipulated inside the computer in the same way as numeric data. The fundamental concept of storing programs in the computer's memory alongside the data they operate on is the crux of the von Neumann, or stored program, architecture. In some cases, a computer might store some or all of its program in memory that is kept separate from the data it operates on. This is called the Harvard architecture after the Harvard Mark I computer. Modern von Neumann computers display some traits of the Harvard architecture in their designs, such as in CPU caches. While it is possible to write computer programs as long lists of numbers (machine language) and while this technique was used with many early computers,[i] it is extremely tedious and potentially error-prone to do so in practice, especially for complicated programs. Instead, each basic instruction can be given a short name that is indicative of its function and easy to remember – a mnemonic such as ADD, SUB, MULT or JUMP. These mnemonics are collectively known as a computer's assembly language. Converting programs written in assembly language into something the computer can actually understand (machine language) is usually done by a computer program called an assembler. A programming language is a notation system for writing the source code from which a computer program is produced. Programming languages provide various ways of specifying programs for computers to run. Unlike natural languages, programming languages are designed to permit no ambiguity and to be concise. They are purely written languages and are often difficult to read aloud. They are generally either translated into machine code by a compiler or an assembler before being run, or translated directly at run time by an interpreter. Sometimes programs are executed by a hybrid method of the two techniques. There are thousands of programming languages—some intended for general purpose programming, others useful for only highly specialized applications. Machine languages and the assembly languages that represent them (collectively termed low-level programming languages) are generally unique to the particular architecture of a computer's central processing unit (CPU). For instance, an ARM architecture CPU (such as may be found in a smartphone or a hand-held videogame) cannot understand the machine language of an x86 CPU that might be in a PC.[j] Historically a significant number of other CPU architectures were created and saw extensive use, notably including the MOS Technology 6502 and 6510 in addition to the Zilog Z80. Although considerably easier than in machine language, writing long programs in assembly language is often difficult and is also error prone. Therefore, most practical programs are written in more abstract high-level programming languages that are able to express the needs of the programmer more conveniently (and thereby help reduce programmer error). High level languages are usually "compiled" into machine language (or sometimes into assembly language and then into machine language) using another computer program called a compiler.[k] High level languages are less related to the workings of the target computer than assembly language, and more related to the language and structure of the problem(s) to be solved by the final program. It is therefore often possible to use different compilers to translate the same high level language program into the machine language of many different types of computer. This is part of the means by which software like video games may be made available for different computer architectures such as personal computers and various video game consoles. Program design of small programs is relatively simple and involves the analysis of the problem, collection of inputs, using the programming constructs within languages, devising or using established procedures and algorithms, providing data for output devices and solutions to the problem as applicable. As problems become larger and more complex, features such as subprograms, modules, formal documentation, and new paradigms such as object-oriented programming are encountered. Large programs involving thousands of line of code and more require formal software methodologies. The task of developing large software systems presents a significant intellectual challenge. Producing software with an acceptably high reliability within a predictable schedule and budget has historically been difficult; the academic and professional discipline of software engineering concentrates specifically on this challenge. Errors in computer programs are called "bugs". They may be benign and not affect the usefulness of the program, or have only subtle effects. However, in some cases they may cause the program or the entire system to "hang", becoming unresponsive to input such as mouse clicks or keystrokes, to completely fail, or to crash. Otherwise benign bugs may sometimes be harnessed for malicious intent by an unscrupulous user writing an exploit, code designed to take advantage of a bug and disrupt a computer's proper execution. Bugs are usually not the fault of the computer. Since computers merely execute the instructions they are given, bugs are nearly always the result of programmer error or an oversight made in the program's design.[l] Admiral Grace Hopper, an American computer scientist and developer of the first compiler, is credited for having first used the term "bugs" in computing after a dead moth was found shorting a relay in the Harvard Mark II computer in September 1947. Networking and the Internet Computers have been used to coordinate information between multiple physical locations since the 1950s. The U.S. military's SAGE system was the first large-scale example of such a system, which led to a number of special-purpose commercial systems such as Sabre. In the 1970s, computer engineers at research institutions throughout the United States began to link their computers together using telecommunications technology. The effort was funded by ARPA (now DARPA), and the computer network that resulted was called the ARPANET. Logic gates are a common abstraction which can apply to most of the above digital or analog paradigms. The ability to store and execute lists of instructions called programs makes computers extremely versatile, distinguishing them from calculators. The Church–Turing thesis is a mathematical statement of this versatility: any computer with a minimum capability (being Turing-complete) is, in principle, capable of performing the same tasks that any other computer can perform. Therefore, any type of computer (netbook, supercomputer, cellular automaton, etc.) is able to perform the same computational tasks, given enough time and storage capacity. In the 20th century, artificial intelligence systems were predominantly symbolic: they executed code that was explicitly programmed by software developers. Machine learning models, however, have a set parameters that are adjusted throughout training, so that the model learns to accomplish a task based on the provided data. The efficiency of machine learning (and in particular of neural networks) has rapidly improved with progress in hardware for parallel computing, mainly graphics processing units (GPUs). Some large language models are able to control computers or robots. AI progress may lead to the creation of artificial general intelligence (AGI), a type of AI that could accomplish virtually any intellectual task at least as well as humans. Professions and organizations As the use of computers has spread throughout society, there are an increasing number of careers involving computers. The need for computers to work well together and to be able to exchange information has spawned the need for many standards organizations, clubs and societies of both a formal and informal nature. See also Notes References Sources External links |
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[SOURCE: https://en.wikipedia.org/wiki/Entertainment_Tonight] | [TOKENS: 933] |
Contents Entertainment Tonight Entertainment Tonight (or simply ET) is an American first-run syndicated news broadcasting newsmagazine program that is distributed by CBS Media Ventures throughout the United States and owned by Paramount Streaming. Having premiered on September 14, 1981, it holds the Guinness World Record as the longest-running entertainment news program on television. International versions of the show are distributed by Paramount Global Content Distribution. Format The format of the program is composed of stories of interest from throughout the entertainment industry, exclusive set visits, first looks at upcoming film and television projects, and one-on-one interviews with actors, musicians and other entertainment personalities and newsmakers. A one-hour weekend edition, ET Weekend (known as Entertainment This Week until September 1991), originally offered a recap of the week's entertainment news, with most or all episodes later transitioning to center (either primarily or exclusively) around some sort of special theme; though the weekend edition now utilizes either format depending on the episode, most commonly, the format of those broadcasts consists of replays of stories that were shown during the previous week's editions. ET Radio Minute, a daily radio feature, is syndicated by Westwood One. As of 2021, the program's weekday broadcasts are anchored by Kevin Frazier and Nischelle Turner. Following the departure of Rachel Smith in September 2025, Frazier and Turner returned as anchors of the weekend editions. History In its early years from its September 1981 inception, Entertainment Tonight—following a local newscast-style format—consisted primarily of coverage of the latest movies, music and television releases and projects. They signed an exclusive agreement to cover the wedding of convicted child molester Mary Kay Letourneau, who married the student she had an affair with, Vili Fualaau; and attorney Howard K. Stern, who represented Daniel Birkhead in the Dannielynn Birkhead paternity case of the late Anna Nicole Smith's daughter Dannielynn. ET aired exclusive stories related to Smith, including coverage of her funeral, and her surviving daughter. In 1996, actor George Clooney decided to boycott Entertainment Tonight to protest the presence of intrusive paparazzi after Hard Copy did an exposé about his love life, violating an agreement that he had with Paramount, which produced and syndicated both shows. In a letter he sent to Paramount, Clooney stated that he would encourage his friends to do the same. On September 8, 2008, Entertainment Tonight began broadcasting in high-definition television; concurrently, the program moved its production and studio operations from its longtime home at Stage 28 on the Paramount Pictures studio lot to Stage 4 at CBS Studio Center, one of the final steps involving the incorporation of Paramount's former syndication arm, Paramount Domestic Television, into CBS' distribution arms and the adoption of the then-new CBS Television Distribution name, which all took place following the breakup of CBS and the original Viacom into separate companies in December 2005. After pressure via a social media campaign by actors Dax Shepard and Kristen Bell, ET announced in February 2014 that it would no longer accept footage or pictures of the children of celebrities from paparazzi photographers. In January 2020, Entertainment Tonight set the Guinness World Record for the longest-running entertainment news show on TV. In November 2018, CBS launched a free, 24-hour over-the-top streaming service known as ET Live; it features the correspondents from the linear show with expanded coverage of entertainment news. It is available via web browsers, apps, and most recently, the free streaming service Pluto TV (which added ET Live to its channel lineup in November 2019). In July 2022, it was announced that the service would be rebranded as Mixible, and continue to air a mixture of entertainment, lifestyle, and pop culture-related programming (including ET's The Download), but with expanded contributions from other Paramount Global properties such as MTV, VH1, Awesomeness, ComicBook.com (formerly), Inside Edition, and The Drew Barrymore Show. On-air staff Competition As of 2007[update], despite competition from The Insider and even the more general-focus newsmagazine Inside Edition, both also produced by CBS Television Distribution, Entertainment Tonight remained among the ten highest-rated syndicated programs, according to Nielsen ratings weekly ratings. During the 2007–08 season, the program's daytime ratings fluctuated between fourth and fifth place due to competition from fellow CBS-syndicated program Judge Judy. International versions The international rights are distributed by Paramount Global Content Distribution. References External links |
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[SOURCE: https://en.wikipedia.org/wiki/NewLISP] | [TOKENS: 1115] |
Contents newLISP newLISP is a scripting language, a dialect of the Lisp family of programming languages. It was designed and developed by Lutz Mueller. Because of its small resource requirements, newLISP is excellent for embedded systems applications. Most of the functions you will ever need are already built in. This includes networking functions, support for distributed and multicore processing, and Bayesian statistics. newLISP is free and open-source software released under the GNU General Public License, version 3 or later. History newLISP design is influenced by the two main Lisp dialects, Common Lisp and Scheme, and by other languages like Pascal and C.[citation needed] newLISP originated in 1991 and was originally developed on a Sun-4 workstation. It later moved to Windows 3.0, where version 1.3 was released on CompuServe around 1993, then became available as a Windows graphical user interface (GUI) graphics-capable application and an MS-DOS console application (both 16-bit). In 1995, with the release of Windows 95, newLISP moved to 32-bit. In April 1999, newLISP was ported to Linux; some of its core algorithms were rewritten, and all Windows-specific code removed. newLISP was released as an open-source software project licensed under the GPL, and development on Windows stopped after version 6.0.25. During the first half of 2001, newLISP was ported back to Windows on the Cygwin platform without graphics abilities. In the second half of 2001, a cross-platform Tcl/Tk frontend named newLISP-tk was released around version 6.3.0. In 2006, 64-bit precision was introduced for integer arithmetic and for some operations on files in version 9.0. Since the release of 6.5 in mid-2002, development has been very active, and many new features have been added. Philosophy newLISP attempts to provide a fast, powerful, cross-platform, full-featured scripting version of the language Lisp while using only a modest system resources such as data storage (e.g., disk space) and memory. It provides Lisp features such as lists, symbol processing, function mapping, anonymous functions (lambda expressions), s-expressions (excluding improper lists), and macros. It also provides the functions expected of a modern scripting language, including supporting regular expressions, XML, Unicode (UTF-8), networking via Transmission Control Protocol (TCP), Internet Protocol (IP), and User Datagram Protocol (UDP), matrix and array processing, advanced math, statistics and Bayesian statistical analysis, mathematical finance, and distributed computing. newLISP runs on the operating systems Berkeley Software Distribution (BSD), Linux, macOS, Solaris, and Windows. It supports MYSQL, SQLite and ODBC database access, Common Gateway Interface (CGI), Simple Mail Transfer Protocol (SMTP), Post Office Protocol (POP) 3, File Transfer Protocol (FTP) and XML remote procedure call (XML-RPC). It can run in server mode as a daemon. Language features newLISP supports namespaces termed contexts, which can be assigned to variables and passed to functions, but which are associated with globally unique symbols, limiting their use as first-class citizens (objects). A prototype-based object-oriented style of programming is possible in newLISP, using contexts as prototypes to construct objects. Variables inside contexts do not interfere with variables of the same name in other contexts, but inside a context, variables behave according to the rules of dynamic scoping. newLISP uses dynamic scoping. When a function is called, that function can see all variables of its caller, its caller's caller, and so on, within the same context or namespace. It supports both explicitly and implicitly defined local dynamic variables that shadow variables with the same name from the outer environment, thus preventing accidental use or change of the variables from caller environment. Parameter variables of the called function automatically shadow the caller's variable environment. Globally, variables can be grouped in separate namespaces. newLISP uses a method of automatic memory management different from traditional garbage collection schemes, termed one reference only (ORO) memory management. Each variable is referenced only by its context, and each context is referenced globally. Sharing of subobjects among objects, cyclic structures, or multiple variables pointing to the same object are unsupported in newLISP. Objects are copied when stored in data structures or passed to functions, except for certain built-in functions. The exceptions are symbols and contexts, which are shared instead of copied, and thus can be used for indirection. Symbols and contexts are globally named and are deleted explicitly; deleting a symbol or context scans all other objects to replace references to it with nil. newLISP graphical user interface (GUI) server (newLISP-GS) is a Java-based Internet protocol suite (TCP/IP) server providing a graphical programming interface. A newLISP-GS based development environment is included in newLISP binary distributions, and GTK-server, OpenGL, and Tcl/Tk-based programming interfaces are available. Any newLISP version allows building executable files, portable applications, for deployment which are self-contained and need no installing. newLISP has an import function, which allows importing functions from a dynamic-link library (DLL) on Windows API Win32, or from a shared library on Linux or Unix. Frameworks Web frameworks available for newLISP include Dragonfly and Rockets. References External links |
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[SOURCE: https://en.wikipedia.org/wiki/Very_large-scale_integration] | [TOKENS: 1125] |
Contents Very-large-scale integration Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining millions or billions of MOS transistors onto a single chip. VLSI began in the 1970s when MOS integrated circuit (metal oxide semiconductor) chips were developed and then widely adopted, enabling complex semiconductor and telecommunications technologies. Microprocessors and memory chips are VLSI devices. Before the introduction of VLSI technology, most ICs had a limited set of functions they could perform. An electronic circuit might consist of a CPU, ROM, RAM and other glue logic. VLSI enables IC designers to add all of these into one chip. History The history of the transistor dates to the 1920s when several inventors attempted devices that were intended to control current in solid-state diodes and convert them into triodes. Success came after World War II, when the use of silicon and germanium crystals as radar detectors led to improvements in fabrication and theory. Scientists who had worked on radar returned to solid-state device development. With the invention of the first transistor at Bell Labs in 1947, the field of electronics shifted from vacuum tubes to solid-state devices. With the small transistor at their hands, electrical engineers of the 1950s saw the possibilities of constructing far more advanced circuits. However, as the complexity of circuits grew, problems arose. One problem was the size of the circuit. A complex circuit like a computer was dependent on speed. If the components were large, the wires interconnecting them must be long. The electric signals took time to go through the circuit, thus slowing the computer. The invention of the integrated circuit by Jack Kilby and Robert Noyce solved this problem by making all the components and the chip out of the same block (monolith) of semiconductor material. The circuits could be made smaller, and the manufacturing process could be automated. This led to the idea of integrating all components on a single-crystal silicon wafer, which led to small-scale integration (SSI) in the early 1960s, and then medium-scale integration (MSI) in the late 1960s. General Micro-electronics introduced the first commercial MOS integrated circuit in 1964. In the early 1970s, MOS integrated circuit technology allowed the integration of more than 10,000 transistors in a single chip. This paved the way for VLSI in the 1970s and 1980s, with tens of thousands of MOS transistors on a single chip (later hundreds of thousands, then millions, and now billions). The first semiconductor chips held two transistors each. Subsequent advances added more transistors, and as a consequence, more individual functions or systems were integrated over time. The first integrated circuits held only a few devices, perhaps as many as ten diodes, transistors, resistors and capacitors, making it possible to fabricate one or more logic gates on a single device. Now known retrospectively as small-scale integration (SSI), improvements in technique led to devices with hundreds of logic gates, known as medium-scale integration (MSI). Further improvements led to large-scale integration (LSI), i.e. systems with at least a thousand logic gates. Current technology has moved far past this mark and today's microprocessors have many millions of gates and billions of individual transistors. At one time, there was an effort to name and calibrate various levels of large-scale integration above VLSI. Terms like ultra-large-scale integration (ULSI) were used. But the huge number of gates and transistors available on common devices has rendered such fine distinctions moot. Terms suggesting greater than VLSI levels of integration are no longer in widespread use. In 2008, billion-transistor processors became commercially available. This became more commonplace as semiconductor fabrication advanced from the then-current generation of 65 nanometer processors. Current designs, unlike the earliest devices, use extensive electronic design automation and automated logic synthesis to lay out the transistors, enabling higher levels of complexity in the resulting logic functionality. Certain high-performance logic blocks, like the SRAM (static random-access memory) cell, are still designed by hand to ensure the highest efficiency.[citation needed] Structured design Structured VLSI design is a modular methodology originated by Carver Mead and Lynn Conway for saving microchip area by minimizing the interconnect fabric area. This is obtained by repetitive arrangement of rectangular macro blocks which can be interconnected using wiring by abutment. An example is partitioning the layout of an adder into a row of equal bit slices cells. In complex designs this structuring may be achieved by hierarchical nesting. Structured VLSI design had been popular in the early 1980s, but lost its popularity later[citation needed] because of the advent of placement and routing tools wasting a lot of area by routing, which is tolerated because of the progress of Moore's law. When introducing the hardware description language KARL in the mid-1970s, Reiner Hartenstein coined the term "structured VLSI design" (originally as "structured LSI design"), echoing Edsger Dijkstra's structured programming approach by procedure nesting to avoid chaotic spaghetti-structured programs. Difficulties As microprocessors become more complex due to technology scaling (see Moore's law), microprocessor designers have encountered several challenges which force them to think beyond the design plane, and look ahead to post-silicon: See also References Further reading External links |
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[SOURCE: https://en.wikipedia.org/wiki/Social_network#cite_note-32] | [TOKENS: 5247] |
Contents Social network 1800s: Martineau · Tocqueville · Marx · Spencer · Le Bon · Ward · Pareto · Tönnies · Veblen · Simmel · Durkheim · Addams · Mead · Weber · Du Bois · Mannheim · Elias A social network is a social structure consisting of a set of social actors (such as individuals or organizations), networks of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities along with a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine dynamics of networks. For instance, social network analysis has been used in studying the spread of misinformation on social media platforms or analyzing the influence of key figures in social networks. Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations". Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s. Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science. Overview The social network is a theoretical construct useful in the social sciences to study relationships between individuals, groups, organizations, or even entire societies (social units, see differentiation). The term is used to describe a social structure determined by such interactions. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An axiom of the social network approach to understanding social interaction is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that individual agency is often ignored although this may not be the case in practice (see agent-based modeling). Precisely because many different types of relations, singular or in combination, form these network configurations, network analytics are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, sociology, and sociolinguistics. History In the late 1890s, both Émile Durkheim and Ferdinand Tönnies foreshadowed the idea of social networks in their theories and research of social groups. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (Gemeinschaft, German, commonly translated as "community") or impersonal, formal, and instrumental social links (Gesellschaft, German, commonly translated as "society"). Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. Georg Simmel, writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups. Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently. In psychology, in the 1930s, Jacob L. Moreno began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see sociometry). In anthropology, the foundation for social network theory is the theoretical and ethnographic work of Bronislaw Malinowski, Alfred Radcliffe-Brown, and Claude Lévi-Strauss. A group of social anthropologists associated with Max Gluckman and the Manchester School, including John A. Barnes, J. Clyde Mitchell and Elizabeth Bott Spillius, often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom. Concomitantly, British anthropologist S. F. Nadel codified a theory of social structure that was influential in later network analysis. In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure. Later, drawing upon Parsons' theory, the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory. By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist Harrison White and his students at the Harvard University Department of Social Relations. Also independently active in the Harvard Social Relations department at the time were Charles Tilly, who focused on networks in political and community sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis. Mark Granovetter and Barry Wellman are among the former students of White who elaborated and championed the analysis of social networks. Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as Duncan J. Watts, Albert-László Barabási, Peter Bearman, Nicholas A. Christakis, James H. Fowler, and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks. Levels of analysis In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system. These patterns become more apparent as network size increases. However, a global network analysis of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis. The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although levels of analysis are not necessarily mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level. At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context. Dyadic level: A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality. Triadic level: Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality. In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs. Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego." Egonetwork analysis focuses on network characteristics, such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals. Subset level: Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior. In general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks. Organizations: Formal organizations are social groups that distribute tasks for a collective goal. Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures. Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups. Randomly distributed networks: Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general degree-based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node-level, homophily and attribute-based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties. Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior. Scale-free networks: A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups. Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of vertices with a degree that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This distribution also follows a power law. The Barabási model of network evolution shown above is an example of a scale-free network. Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population. Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level." It is primarily used in social and behavioral sciences, and in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping). Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure (see stochastic block model), and hierarchical structure. In the case of agency-directed networks these features also include reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features. Theoretical links Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are Graph theory, Balance theory, Social comparison theory, and more recently, the Social identity approach. Few complete theories have been produced from social network analysis. Two that have are structural role theory and heterophily theory. The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties". Structural holes In the context of networks, social capital exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections. Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters. When two separate clusters possess non-redundant information, there is said to be a structural hole between them. Thus, a network that bridges structural holes will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes. Networks rich in structural holes are a form of social capital in that they offer information benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters. For example, in business networks, this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of weak ties, which rests on the basis that having a broad range of contacts is most effective for job attainment. Structural holes have been widely applied in social network analysis, resulting in applications in a wide range of practical scenarios as well as machine learning-based social prediction. Research clusters Research has used network analysis to examine networks created when artists are exhibited together in museum exhibition. Such networks have been shown to affect an artist's recognition in history and historical narratives, even when controlling for individual accomplishments of the artist. Other work examines how network grouping of artists can affect an individual artist's auction performance. An artist's status has been shown to increase when associated with higher status networks, though this association has diminishing returns over an artist's career. In J.A. Barnes' day, a "community" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through telecommunications devices and social network services. Such devices and services require extensive and ongoing maintenance and analysis, often using network science methods. Community development studies, today, also make extensive use of such methods. Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems, including techniques of dynamic network analysis. Mechanisms such as Dual-phase evolution explain how temporal changes in connectivity contribute to the formation of structure in social networks. The study of social networks is being used to examine the nature of interdependencies between actors and the ways in which these are related to outcomes of conflict and cooperation. Areas of study include cooperative behavior among participants in collective actions such as protests; promotion of peaceful behavior, social norms, and public goods within communities through networks of informal governance; the role of social networks in both intrastate conflict and interstate conflict; and social networking among politicians, constituents, and bureaucrats. In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, murders can be seen as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength. Diffusion of ideas and innovations studies focus on the spread and use of ideas from one actor to another or one culture and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation. A case in point is the social diffusion of linguistic innovation such as neologisms. Experiments and large-scale field trials (e.g., by Nicholas Christakis and collaborators) have shown that cascades of desirable behaviors can be induced in social groups, in settings as diverse as Honduras villages, Indian slums, or in the lab. Still other experiments have documented the experimental induction of social contagion of voting behavior, emotions, risk perception, and commercial products. In demography, the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents. The field of sociology focuses almost entirely on networks of outcomes of social interactions. More narrowly, economic sociology considers behavioral interactions of individuals and groups through social capital and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy. Analysis of social networks is increasingly incorporated into health care analytics, not only in epidemiological studies but also in models of patient communication and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and systems. Human ecology is an interdisciplinary and transdisciplinary study of the relationship between humans and their natural, social, and built environments. The scientific philosophy of human ecology has a diffuse history with connections to geography, sociology, psychology, anthropology, zoology, and natural ecology. In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo, De Nooy, Senekal, and Lotker, to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of Even-Zohar, can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using visualization from SNA. Research studies of formal or informal organization relationships, organizational communication, economics, economic sociology, and other resource transfers. Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. Many organizational social network studies focus on teams. Within team network studies, research assesses, for example, the predictors and outcomes of centrality and power, density and centralization of team instrumental and expressive ties, and the role of between-team networks. Intra-organizational networks have been found to affect organizational commitment, organizational identification, interpersonal citizenship behaviour. Social capital is a form of economic and cultural capital in which social networks are central, transactions are marked by reciprocity, trust, and cooperation, and market agents produce goods and services not mainly for themselves, but for a common good. Social capital is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also, the structural dimension of social capital indicates the level of ties among organizations. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and identifications of the bonds between partners. The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. Social capital is a sociological concept about the value of social relations and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use. In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity. This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales. In many organizations, members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities. Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist John Stuart Mill, writes, "it is hardly possible to overrate the value of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress." Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement. A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. Full communication with exploratory mindsets and information exchange generated by dynamically alternating positions in a social network promotes creative and deep thinking. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms. By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts. There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations. However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted. Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career. Computer networks combined with social networking software produce a new medium for social interaction. A relationship over a computerized social networking service can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a computer-mediated communication context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the "real" world. Social network analysis methods have become essential to examining these types of computer mediated communication. In addition, the sheer size and the volatile nature of social media has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data. Based on the pattern of homophily, ties between people are most likely to occur between nodes that are most similar to each other, or within neighbourhood segregation, individuals are most likely to inhabit the same regional areas as other individuals who are like them. Therefore, social networks can be used as a tool to measure the degree of segregation or homophily within a social network. Social Networks can both be used to simulate the process of homophily but it can also serve as a measure of level of exposure of different groups to each other within a current social network of individuals in a certain area. See also References Further reading External links |
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Contents Lod Lod (Hebrew: לוד, fully vocalized: לֹד), also known as Lydda (Ancient Greek: Λύδδα) and Lidd (Arabic: اللِّدّ, romanized: al-Lidd, or اللُّدّ, al-Ludd), is a city 15 km (9+1⁄2 mi) southeast of Tel Aviv and 40 km (25 mi) northwest of Jerusalem in the Central District of Israel. It is situated between the lower Shephelah on the east and the coastal plain on the west. The city had a population of 90,814 in 2023. Lod has been inhabited since at least the Neolithic period. It is mentioned a few times in the Hebrew Bible and in the New Testament. Between the 5th century BCE and up until the late Roman period, it was a prominent center for Jewish scholarship and trade. Around 200 CE, the city became a Roman colony and was renamed Diospolis (Ancient Greek: Διόσπολις, lit. 'city of Zeus'). Tradition identifies Lod as the 4th century martyrdom site of Saint George; the Church of Saint George and Mosque of Al-Khadr located in the city is believed to have housed his remains. Following the Arab conquest of the Levant, Lod served as the capital of Jund Filastin; however, a few decades later, the seat of power was transferred to Ramla, and Lod slipped in importance. Under Crusader rule, the city was a Catholic diocese of the Latin Church and it remains a titular see to this day.[citation needed] Lod underwent a major change in its population in the mid-20th century. Exclusively Palestinian Arab in 1947, Lod was part of the area designated for an Arab state in the United Nations Partition Plan for Palestine; however, in July 1948, the city was occupied by the Israel Defense Forces, and most of its Arab inhabitants were expelled in the Palestinian expulsion from Lydda and Ramle. The city was largely resettled by Jewish immigrants, most of them expelled from Arab countries. Today, Lod is one of Israel's mixed cities, with an Arab population of 30%. Lod is one of Israel's major transportation hubs. The main international airport, Ben Gurion Airport, is located 8 km (5 miles) north of the city. The city is also a major railway and road junction. Religious references The Hebrew name Lod appears in the Hebrew Bible as a town of Benjamin, founded along with Ono by Shamed or Shamer (1 Chronicles 8:12; Ezra 2:33; Nehemiah 7:37; 11:35). In Ezra 2:33, it is mentioned as one of the cities whose inhabitants returned after the Babylonian captivity. Lod is not mentioned among the towns allocated to the tribe of Benjamin in Joshua 18:11–28. The name Lod derives from a tri-consonantal root not extant in Northwest Semitic, but only in Arabic (“to quarrel; withhold, hinder”). An Arabic etymology of such an ancient name is unlikely (the earliest attestation is from the Achaemenid period). In the New Testament, the town appears in its Greek form, Lydda, as the site of Peter's healing of Aeneas in Acts 9:32–38. The city is also mentioned in an Islamic hadith as the location of the battlefield where the false messiah (al-Masih ad-Dajjal) will be slain before the Day of Judgment. History The first occupation dates to the Neolithic in the Near East and is associated with the Lodian culture. Occupation continued in the Levant Chalcolithic. Pottery finds have dated the initial settlement in the area now occupied by the town to 5600–5250 BCE. In the Early Bronze, it was an important settlement in the central coastal plain between the Judean Shephelah and the Mediterranean coast, along Nahal Ayalon. Other important nearby sites were Tel Dalit, Tel Bareqet, Khirbat Abu Hamid (Shoham North), Tel Afeq, Azor and Jaffa. Two architectural phases belong to the late EB I in Area B. The first phase had a mudbrick wall, while the late phase included a circulat stone structure. Later excavations have produced an occupation later, Stratum IV. It consists of two phases, Stratum IVb with mudbrick wall on stone foundations and rounded exterior corners. In Stratum IVa there was a mudbrick wall with no stone foundations, with imported Egyptian potter and local pottery imitations. Another excavations revealed nine occupation strata. Strata VI-III belonged to Early Bronze IB. The material culture showed Egyptian imports in strata V and IV. Occupation continued into Early Bronze II with four strata (V-II). There was continuity in the material culture and indications of centralized urban planning. North to the tell were scattered MB II burials. The earliest written record is in a list of Canaanite towns drawn up by the Egyptian pharaoh Thutmose III at Karnak in 1465 BCE. From the fifth century BCE until the Roman period, the city was a centre of Jewish scholarship and commerce. According to British historian Martin Gilbert, during the Hasmonean period, Jonathan Maccabee and his brother, Simon Maccabaeus, enlarged the area under Jewish control, which included conquering the city. The Jewish community in Lod during the Mishnah and Talmud era is described in a significant number of sources, including information on its institutions, demographics, and way of life. The city reached its height as a Jewish center between the First Jewish-Roman War and the Bar Kokhba revolt, and again in the days of Judah ha-Nasi and the start of the Amoraim period. The city was then the site of numerous public institutions, including schools, study houses, and synagogues. In 43 BC, Cassius, the Roman governor of Syria, sold the inhabitants of Lod into slavery, but they were set free two years later by Mark Antony. During the First Jewish–Roman War, the Roman proconsul of Syria, Cestius Gallus, razed the town on his way to Jerusalem in Tishrei 66 CE. According to Josephus, "[he] found the city deserted, for the entire population had gone up to Jerusalem for the Feast of Tabernacles. He killed fifty people whom he found, burned the town and marched on". Lydda was occupied by Emperor Vespasian in 68 CE. In the period following the destruction of Jerusalem in 70 CE, Rabbi Tarfon, who appears in many Tannaitic and Jewish legal discussions, served as a rabbinic authority in Lod. During the Kitos War, 115–117 CE, the Roman army laid siege to Lod, where the rebel Jews had gathered under the leadership of Julian and Pappos. Torah study was outlawed by the Romans and pursued mostly in the underground. The distress became so great, the patriarch Rabban Gamaliel II, who was shut up there and died soon afterwards, permitted fasting on Ḥanukkah. Other rabbis disagreed with this ruling. Lydda was next taken and many of the Jews were executed; the "slain of Lydda" are often mentioned in words of reverential praise in the Talmud. In 200 CE, emperor Septimius Severus elevated the town to the status of a city, calling it Colonia Lucia Septimia Severa Diospolis. The name Diospolis ("City of Zeus") may have been bestowed earlier, possibly by Hadrian. At that point, most of its inhabitants were Christian. The earliest known bishop is Aëtius, a friend of Arius. During the following century (200-300CE), it's said that Joshua ben Levi founded a yeshiva in Lod. In December 415, the Council of Diospolis was held here to try Pelagius; he was acquitted. In the sixth century, the city was renamed Georgiopolis after St. George, a soldier in the guard of the emperor Diocletian, who was born there between 256 and 285 CE. The Church of Saint George and Mosque of Al-Khadr is named for him. The 6th-century Madaba map shows Lydda as an unwalled city with a cluster of buildings under a black inscription reading "Lod, also Lydea, also Diospolis". An isolated large building with a semicircular colonnaded plaza in front of it might represent the St George shrine. After the Muslim conquest of Palestine by Amr ibn al-'As in 636 CE, Lod which was referred to as "al-Ludd" in Arabic served as the capital of Jund Filastin ("Military District of Palaestina") before the seat of power was moved to nearby Ramla during the reign of the Umayyad Caliph Suleiman ibn Abd al-Malik in 715–716. The population of al-Ludd was relocated to Ramla, as well. With the relocation of its inhabitants and the construction of the White Mosque in Ramla, al-Ludd lost its importance and fell into decay. The city was visited by the local Arab geographer al-Muqaddasi in 985, when it was under the Fatimid Caliphate, and was noted for its Great Mosque which served the residents of al-Ludd, Ramla, and the nearby villages. He also wrote of the city's "wonderful church (of St. George) at the gate of which Christ will slay the Antichrist." The Crusaders occupied the city in 1099 and named it St Jorge de Lidde. It was briefly conquered by Saladin, but retaken by the Crusaders in 1191. For the English Crusaders, it was a place of great significance as the birthplace of Saint George. The Crusaders made it the seat of a Latin Church diocese, and it remains a titular see. It owed the service of 10 knights and 20 sergeants, and it had its own burgess court during this era. In 1226, Ayyubid Syrian geographer Yaqut al-Hamawi visited al-Ludd and stated it was part of the Jerusalem District during Ayyubid rule. Sultan Baybars brought Lydda again under Muslim control by 1267–8. According to Qalqashandi, Lydda was an administrative centre of a wilaya during the fourteenth and fifteenth century in the Mamluk empire. Mujir al-Din described it as a pleasant village with an active Friday mosque. During this time, Lydda was a station on the postal route between Cairo and Damascus. In 1517, Lydda was incorporated into the Ottoman Empire as part of the Damascus Eyalet, and in the 1550s, the revenues of Lydda were designated for the new waqf of Hasseki Sultan Imaret in Jerusalem, established by Hasseki Hurrem Sultan (Roxelana), the wife of Suleiman the Magnificent. By 1596 Lydda was a part of the nahiya ("subdistrict") of Ramla, which was under the administration of the liwa ("district") of Gaza. It had a population of 241 households and 14 bachelors who were all Muslims, and 233 households who were Christians. They paid a fixed tax-rate of 33,3 % on agricultural products, including wheat, barley, summer crops, vineyards, fruit trees, sesame, special product ("dawalib" =spinning wheels), goats and beehives, in addition to occasional revenues and market toll, a total of 45,000 Akçe. All of the revenue went to the Waqf. In 1051 AH/1641/2, the Bedouin tribe of al-Sawālima from around Jaffa attacked the villages of Subṭāra, Bayt Dajan, al-Sāfiriya, Jindās, Lydda and Yāzūr belonging to Waqf Haseki Sultan. The village appeared as Lydda, though misplaced, on the map of Pierre Jacotin compiled in 1799. Missionary William M. Thomson visited Lydda in the mid-19th century, describing it as a "flourishing village of some 2,000 inhabitants, imbosomed in noble orchards of olive, fig, pomegranate, mulberry, sycamore, and other trees, surrounded every way by a very fertile neighbourhood. The inhabitants are evidently industrious and thriving, and the whole country between this and Ramleh is fast being filled up with their flourishing orchards. Rarely have I beheld a rural scene more delightful than this presented in early harvest ... It must be seen, heard, and enjoyed to be appreciated." In 1869, the population of Ludd was given as: 55 Catholics, 1,940 "Greeks", 5 Protestants and 4,850 Muslims. In 1870, the Church of Saint George was rebuilt. In 1892, the first railway station in the entire region was established in the city. In the second half of the 19th century, Jewish merchants migrated to the city, but left after the 1921 Jaffa riots. In 1882, the Palestine Exploration Fund's Survey of Western Palestine described Lod as "A small town, standing among enclosure of prickly pear, and having fine olive groves around it, especially to the south. The minaret of the mosque is a very conspicuous object over the whole of the plain. The inhabitants are principally Moslim, though the place is the seat of a Greek bishop resident of Jerusalem. The Crusading church has lately been restored, and is used by the Greeks. Wells are found in the gardens...." From 1918, Lydda was under the administration of the British Mandate in Palestine, as per a League of Nations decree that followed the Great War. During the Second World War, the British set up supply posts in and around Lydda and its railway station, also building an airport that was renamed Ben Gurion Airport after the death of Israel's first prime minister in 1973. At the time of the 1922 census of Palestine, Lydda had a population of 8,103 inhabitants (7,166 Muslims, 926 Christians, and 11 Jews), the Christians were 921 Orthodox, 4 Roman Catholics and 1 Melkite. This had increased by the 1931 census to 11,250 (10,002 Muslims, 1,210 Christians, 28 Jews, and 10 Bahai), in a total of 2475 residential houses. In 1938, Lydda had a population of 12,750. In 1945, Lydda had a population of 16,780 (14,910 Muslims, 1,840 Christians, 20 Jews and 10 "other"). Until 1948, Lydda was an Arab town with a population of around 20,000—18,500 Muslims and 1,500 Christians. In 1947, the United Nations proposed dividing Mandatory Palestine into two states, one Jewish state and one Arab; Lydda was to form part of the proposed Arab state. In the ensuing war, Israel captured Arab towns outside the area the UN had allotted it, including Lydda. In December 1947, thirteen Jewish passengers in a seven-car convoy to Ben Shemen Youth Village were ambushed and murdered.In a separate incident, three Jewish youths, two men and a woman were captured, then raped and murdered in a neighbouring village. Their bodies were paraded in Lydda’s principal street. The Israel Defense Forces entered Lydda on 11 July 1948. The following day, under the impression that it was under attack, the 3rd Battalion was ordered to shoot anyone "seen on the streets". According to Israel, 250 Arabs were killed. Other estimates are higher: Arab historian Aref al Aref estimated 400, and Nimr al Khatib 1,700. In 1948, the population rose to 50,000 during the Nakba, as Arab refugees fleeing other areas made their way there. A key event was the Palestinian expulsion from Lydda and Ramle, with the expulsion of 50,000-70,000 Palestinians from Lydda and Ramle by the Israel Defense Forces. All but 700 to 1,056 were expelled by order of the Israeli high command, and forced to walk 17 km (10+1⁄2 mi) to the Jordanian Arab Legion lines. Estimates of those who died from exhaustion and dehydration vary from a handful to 355. The town was subsequently sacked by the Israeli army. Some scholars, including Ilan Pappé, characterize this as ethnic cleansing. The few hundred Arabs who remained in the city were soon outnumbered by the influx of Jews who immigrated to Lod from August 1948 onward, most of them from Arab countries. As a result, Lod became a predominantly Jewish town. After the establishment of the state, the biblical name Lod was readopted. The Jewish immigrants who settled Lod came in waves, first from Morocco and Tunisia, later from Ethiopia, and then from the former Soviet Union. Since 2008, many urban development projects have been undertaken to improve the image of the city. Upscale neighbourhoods have been built, among them Ganei Ya'ar and Ahisemah, expanding the city to the east. According to a 2010 report in the Economist, a three-meter-high wall was built between Jewish and Arab neighbourhoods and construction in Jewish areas was given priority over construction in Arab neighborhoods. The newspaper says that violent crime in the Arab sector revolves mainly around family feuds over turf and honour crimes. In 2010, the Lod Community Foundation organised an event for representatives of bicultural youth movements, volunteer aid organisations, educational start-ups, businessmen, sports organizations, and conservationists working on programmes to better the city. In the 2021 Israel–Palestine crisis, a state of emergency was declared in Lod after Arab rioting led to the death of an Israeli Jew. The Mayor of Lod, Yair Revivio, urged Prime Minister of Israel Benjamin Netanyahu to deploy Israel Border Police to restore order in the city. This was the first time since 1966 that Israel had declared this kind of emergency lockdown. International media noted that both Jewish and Palestinian mobs were active in Lod, but the "crackdown came for one side" only. Demographics In the 19th century and until the Lydda Death March, Lod was an exclusively Muslim-Christian town, with an estimated 6,850 inhabitants, of whom approximately 2,000 (29%) were Christian. According to the Israel Central Bureau of Statistics (CBS), the population of Lod in 2010 was 69,500 people. According to the 2019 census, the population of Lod was 77,223, of which 53,581 people, comprising 69.4% of the city's population, were classified as "Jews and Others", and 23,642 people, comprising 30.6% as "Arab". Education According to CBS, 38 schools and 13,188 pupils are in the city. They are spread out as 26 elementary schools and 8,325 elementary school pupils, and 13 high schools and 4,863 high school pupils. About 52.5% of 12th-grade pupils were entitled to a matriculation certificate in 2001.[citation needed] Economy The airport and related industries are a major source of employment for the residents of Lod. Other important factories in the city are the communication equipment company "Talard", "Cafe-Co" - a subsidiary of the Strauss Group and "Kashev" - the computer center of Bank Leumi. A Jewish Agency Absorption Centre is also located in Lod. According to CBS figures for 2000, 23,032 people were salaried workers and 1,405 were self-employed. The mean monthly wage for a salaried worker was NIS 4,754, a real change of 2.9% over the course of 2000. Salaried men had a mean monthly wage of NIS 5,821 (a real change of 1.4%) versus NIS 3,547 for women (a real change of 4.6%). The mean income for the self-employed was NIS 4,991. About 1,275 people were receiving unemployment benefits and 7,145 were receiving an income supplement. Art and culture In 2009-2010, Dor Guez held an exhibit, Georgeopolis, at the Petach Tikva art museum that focuses on Lod. Archaeology A well-preserved mosaic floor dating to the Roman period was excavated in 1996 as part of a salvage dig conducted on behalf of the Israel Antiquities Authority and the Municipality of Lod, prior to widening HeHalutz Street. According to Jacob Fisch, executive director of the Friends of the Israel Antiquities Authority, a worker at the construction site noticed the tail of a tiger and halted work. The mosaic was initially covered over with soil at the conclusion of the excavation for lack of funds to conserve and develop the site. The mosaic is now part of the Lod Mosaic Archaeological Center. The floor, with its colorful display of birds, fish, exotic animals and merchant ships, is believed to have been commissioned by a wealthy resident of the city for his private home. The Lod Community Archaeology Program, which operates in ten Lod schools, five Jewish and five Israeli Arab, combines archaeological studies with participation in digs in Lod. Sports The city's major football club, Hapoel Bnei Lod, plays in Liga Leumit (the second division). Its home is at the Lod Municipal Stadium. The club was formed by a merger of Bnei Lod and Rakevet Lod in the 1980s. Two other clubs in the city play in the regional leagues: Hapoel MS Ortodoxim Lod in Liga Bet and Maccabi Lod in Liga Gimel. Hapoel Lod played in the top division during the 1960s and 1980s, and won the State Cup in 1984. The club folded in 2002. A new club, Hapoel Maxim Lod (named after former mayor Maxim Levy) was established soon after, but folded in 2007. Notable people Twin towns-sister cities Lod is twinned with: See also References Bibliography External links |
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File:PlayStationCross.svg Summary Licensing Original upload log File history Click on a date/time to view the file as it appeared at that time. File usage The following 27 pages use this file: Global file usage The following other wikis use this file: View more global usage of this file. Metadata This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file. |
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File:FFS Table bottom.jpg Summary Licensing العربية ∙ čeština ∙ Deutsch ∙ Zazaki ∙ English ∙ español ∙ eesti ∙ suomi ∙ français ∙ hrvatski ∙ magyar ∙ italiano ∙ 日本語 ∙ 한국어 ∙ македонски ∙ മലയാളം ∙ Plattdüütsch ∙ Nederlands ∙ polski ∙ português ∙ română ∙ русский ∙ sicilianu ∙ slovenščina ∙ Türkçe ∙ Tiếng Việt ∙ 简体中文 ∙ 繁體中文 ∙ +/− File history Click on a date/time to view the file as it appeared at that time. File usage More than 100 pages use this file. The following list shows the first 100 pages that use this file only. A full list is available. View more links to this file. Global file usage The following other wikis use this file: View more global usage of this file. Metadata This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file. |
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[SOURCE: https://en.wikipedia.org/wiki/Planum_Boreum] | [TOKENS: 1258] |
Contents Planum Boreum Planum Boreum (Latin: "the northern plain") is the northern polar plain on Mars. It extends northward from roughly 80°N and is centered at 88°00′N 15°00′E / 88.0°N 15.0°E / 88.0; 15.0. Surrounding the high polar plain is a flat and featureless lowland plain called Vastitas Borealis which extends for approximately 1500 kilometers southwards, dominating the northern hemisphere. Features The main feature of the Planum Boreum is a large fissure or canyon in the polar ice cap called Chasma Boreale. It is up to 100 kilometres (62 mi) wide and features scarps up to 2 kilometres (1.2 mi) high. By comparison, the Grand Canyon is approximately 1.6 kilometres (1 mi) deep in some places and 446 kilometres (277 mi) long but only up to 24 kilometres (15 mi) wide. Chasma Boreale cuts through polar deposits and ice, such as those present at Greenland. Planum Boreum interfaces with Vastitas Borealis west of Chasma Boreale at an irregular scarp named Rupes Tenuis. This scarp reaches heights of up to 1 km. At other places, the interface is a collection of mesas and troughs. Planum Boreum is surrounded by large fields of sand dunes spanning from 75°N to 85°N. These dune fields are named Olympia Undae, Abalos Undae, Siton Undae, and Hyperboreae Undae. Olympia Undae, by far the largest, covers from 100°E to 240°E. Abalos Undae covers from 261°E to 280°E and Hyperboreale Undae spans from 311°E to 341°E. See also List of extraterrestrial dune fields. The North Polar Layered Deposits (NPLD) are a kilometer-thick part of Mars's north polar ice cap. They are mostly water ice (about 95%) mixed with layers of dust. They form a geological record of Mars's climate over millions of years. Layers form because the climate has gone through many cycles of atmospheric ice and dust deposition. Planum Boreum is home to a permanent ice cap consisting mainly of water ice (with a 1 m thick veneer of carbon dioxide ice during the winter). It has a volume of 1.2 million cubic kilometres and covers an area equivalent to about 1.5 times the size of Texas. It has a radius of 600 km. The maximum depth of the cap is 3 km. The spiral troughs in the ice cap are formed by katabatic winds that entrain surface ice eroded from the equator-facing sides of the troughs, likely aided by solar ablation (sublimation), which is then redeposited on the colder pole-facing slopes. The troughs are roughly perpendicular to the wind direction, which is shifted by the Coriolis effect, leading to the spiral pattern. The troughs gradually migrate towards the pole over time; the central troughs have moved about 65 km in the last 2 million years. Chasma Boreale is a canyon-like feature older than the troughs, and in contrast is aligned parallel to the wind direction. The surface composition of the northern ice cap in middle spring (after a winter's accumulation of seasonal dry ice) has been studied from orbit. The outer edges of the ice cap are contaminated with dust (0.15% by weight) and are mostly water ice. As one moves toward the pole, the surface water ice content decreases and is replaced by dry ice. The purity of the ice also increases. At the pole, the surface seasonal ice consists of essentially pure dry ice with little dust content and 30 parts per million of water ice. The Phoenix lander, launched in 2007, arrived at Mars in May 2008 and successfully landed in the Vastitas Borealis region of the planet on May 25, 2008. The north polar cap of Mars has been proposed as a landing site for a human Mars expedition by Geoffrey A. Landis and by Charles Cockell. Recurring phenomena A February 2008 HiRISE observation captured four avalanches in progress off a 700 metres (2,300 ft) cliff. The cloud of fine material is 180 metres (590 ft) across and extends 190 metres (620 ft) from the base of the cliff. The reddish layers are known to be rock rich in water ice while the white layers are seasonal carbon dioxide frost. The landslide is thought to have originated from the uppermost red layer. Follow-up observations are planned to characterize the nature of the landslide debris. A large doughnut-shaped cloud appears in North polar region of Mars around the same time every Martian year and of about the same size. It forms in the morning, dissipates by the Martian afternoon. The outer diameter of the cloud is roughly 1,600 km (1,000 mi), and the inner hole or eye is 320 km (200 mi) across. The cloud is thought to be composed of water-ice, so it is white in color, unlike the more common dust storms. It looks like a cyclonic storm, similar to hurricane, but it does not rotate. The cloud appears during the northern summer and at high latitude. Speculation is that this is due to unique climate conditions near the northern pole. Cyclone-like storms were first detected during the Viking orbital mapping program, but the northern annular cloud is nearly three times larger. The cloud has also been detected by various probes and telescopes including the Hubble and Mars Global Surveyor. When Hubble Space Telescope viewed it in 1999, it was thought to be a cyclonic storm. The diameter was measured to be approximately 1750 km, and featured an "eye" 320 km in diameter. See also References External links |
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[SOURCE: https://en.wikipedia.org/wiki/NEWP] | [TOKENS: 11034] |
Contents Burroughs Large Systems The Burroughs Large Systems Group produced a family of large 48-bit mainframes using stack machine instruction sets with dense syllables.[NB 1] The first machine in the family was the B5000 in 1961, which was optimized for compiling ALGOL 60 programs extremely well, using single-pass compilers. The B5000 evolved into the B5500 (disk rather than drum) and the B5700 (up to four systems running as a cluster). Subsequent major redesigns include the B6500/B6700 line and its successors, as well as the separate B8500 line. In the 1970s, the Burroughs Corporation was organized into three divisions with very different product line architectures for high-end, mid-range, and entry-level business computer systems. Each division's product line grew from a different concept for how to optimize a computer's instruction set for particular programming languages. "Burroughs Large Systems" referred to all of these large-system product lines together, in contrast to the COBOL-optimized Medium Systems (B2000, B3000, and B4000) or the flexible-architecture Small Systems (B1000). Background Founded in the 1880s, Burroughs was the oldest continuously operating company in computing (Elliott Brothers was founded before Burroughs, but did not make computing devices in the 19th century). By the late 1950s its computing equipment was still limited to electromechanical accounting machines such as the Sensimatic. It had nothing to compete with its traditional rivals IBM and NCR, who had started to produce larger-scale computers, or with recently founded Univac. In 1956, they purchased ElectroData Corporation and rebranded its design as the B205. Burroughs' first internally developed machine, the B5000, was designed in 1961 and Burroughs sought to address its late entry in the market with the strategy of a completely different design based on the most advanced computing ideas available at the time. While the B5000 architecture is dead, it inspired the B6500 (and subsequent B6700 and B7700). Computers using that architecture were[citation needed] still in production as the Unisys ClearPath Libra servers which run an evolved but compatible version of the MCP operating system first introduced with the B6700. The third and largest line, the B8500, had no commercial success. In addition to a proprietary CMOS processor design, Unisys also uses Intel Xeon processors and runs MCP, Microsoft Windows and Linux operating systems on their Libra servers; the use of custom chips was gradually eliminated, and by 2018 the Libra servers had been strictly commodity Intel for some years. B5000, B5500, and B5700 The first member of the first series, the B5000, was designed beginning in 1961 by a team under the leadership of Robert (Bob) Barton. It had an unusual architecture. It has been listed by the computing scientist John Mashey as one of the architectures that he admires the most. "I always thought it was one of the most innovative examples of combined hardware/software design I've seen, and far ahead of its time." The B5000 was succeeded by the B5500, which used disks rather than drum storage, and the B5700, which allowed multiple CPUs to be clustered around shared disk. While there was no successor to the B5700, the B5000 line heavily influenced the design of the B6500, and Burroughs ported the Master Control Program (MCP) to that machine. The B5000 was unusual at the time in that the architecture and instruction set were designed with the needs of software taken into consideration. This was a large departure from the computer system design of the time, where a processor and its instruction set would be designed and then handed over to the software people. The B5000, B5500 and B5700 in Word Mode has two different addressing modes, depending on whether it is executing a main program (SALF off) or a subroutine (SALF on). For a main program, the T field of an Operand Call or Descriptor Call syllable is relative to the Program Reference Table (PRT). For subroutines, the type of addressing is dependent on the high three bits of T and on the Mark Stack FlipFlop (MSFF), as shown in B5x00 Relative Addressing. The B5000 was designed to exclusively support high-level languages. This was at a time when such languages were just coming to prominence with FORTRAN and then COBOL. FORTRAN and COBOL were considered weaker languages by some, when it comes to modern software techniques, so a newer, mostly untried language was adopted, ALGOL-60. The ALGOL dialect chosen for the B5000 was Elliott ALGOL, first designed and implemented by C. A. R. Hoare on an Elliott 503. This was a practical extension of ALGOL with I/O instructions (which ALGOL had ignored) and powerful string processing instructions. Hoare's famous Turing Award lecture was on this subject. Thus the B5000 was based on a very powerful language. Donald Knuth had previously implemented ALGOL 58 on an earlier Burroughs machine during the three months of his summer break, and he was peripherally involved in the B5000 design as a consultant. Many wrote ALGOL off, mistakenly believing that high-level languages could not have the same power as assembler, and thus not realizing ALGOL's potential as a systems programming language. The Burroughs ALGOL compiler was very fast — this impressed the Dutch scientist Edsger Dijkstra when he submitted a program to be compiled at the B5000 Pasadena plant. His deck of cards was compiled almost immediately and he immediately wanted several machines for his university, Eindhoven University of Technology in the Netherlands. The compiler was fast for several reasons, but the primary reason was that it was a one-pass compiler. Early computers did not have enough memory to store the source code, so compilers (and even assemblers) usually needed to read the source code more than once. The Burroughs ALGOL syntax, unlike the official language, requires that each variable (or other object) be declared before it is used, so it is feasible to write an ALGOL compiler that reads the data only once. This concept has profound theoretical implications, but it also permits very fast compiling. Burroughs large systems could compile as fast as they could read the source code from the punched cards, and they had the fastest card readers in the industry. The powerful Burroughs COBOL compiler was also a one-pass compiler and equally fast. A 4000-card COBOL program compiled as fast as the 1000-card/minute readers could read the code. The program was ready to use as soon as the cards went through the reader. B6500, B6700/B7700, and successors The B6500 (delivery in 1969) and B7500[citation needed] were the first computers in the only line of Burroughs systems to survive to the present day. While they were inspired by the B5000, they had a totally new architecture. Among the most important differences were Among other customers of the B6700 and B7700 were all five New Zealand universities in 1971. The ILLIAC IV supercomputer used a B6500 as its "I/O control computer", performing supervisory functions for the ILLIAC IV and setting up and initiating I/O operations to and from the ILLIAC IV memory. B8500 The B8500 line derives from the D825, a military computer that was inspired by the B5000. The B8500 was designed in the 1960s as an attempt to merge the B5500 and the D825 designs. The system used monolithic integrated circuits with magnetic thin-film memory. The architecture employed a 48-bit word, stack, and descriptors like the B5500, but was not advertised as being upward-compatible. The B8500 could never be gotten to work reliably, and the project was canceled after 1970, never having delivered a completed system. History The central concept of virtual memory appeared in the designs of the Ferranti Atlas and the Rice Institute Computer, and the central concepts of descriptors and tagged architecture appeared in the design of the Rice Institute Computer in the late 1950s. However, even if those designs had a direct influence on Burroughs, the architectures of the B5000, B6500 and B8500 were very different from those of the Atlas and the Rice machine; they are also very different from each other. The first of the Burroughs large systems was the B5000. Designed in 1961, it was a second-generation computer using discrete transistor logic and magnetic-core memory, followed by the B5500 and B5700. The first machines to replace the B5000 architecture were the B6500 and B7500. The successor machines to the B6500 and B7500 followed the hardware development trends to re-implement the architectures in new logic over the next 25 years, with the B6500, B7500, B6700, B7700, B6800, B7800, B5900,[NB 4] B7900 and finally the Burroughs A series. After a merger in which Burroughs acquired Sperry Corporation and changed its name to Unisys, the company continued to develop new machines based on the MCP CMOS ASIC. These machines were the Libra 100 through the Libra 500, with the Libra 590 being announced in 2005. Later Libras, including the 590, also incorporate Intel Xeon processors and can run the Burroughs large systems architecture in emulation as well as on the MCP CMOS processors. It is unclear if Unisys will continue development of new MCP CMOS ASICs. 1-2 HDUs (I/O), 1-2 APs, 1-4 CPUs, Soft implementation of NUMA memory allowed CPUs to float from memory space to memory space. Primary lines of hardware Hardware and software design, development, and manufacturing were split between two primary locations, in Orange County, California, and the outskirts of Philadelphia. The initial Large Systems Plant, which developed the B5000 and B5500, was located in Pasadena, California but moved to City of Industry, California, where it developed the B6500. The Orange County location, which was based in a plant in Mission Viejo, California but at times included facilities in nearby Irvine and Lake Forest, was responsible for the smaller B6x00 line, while the East Coast operations, based in Tredyffrin, Pennsylvania, handled the larger B7x00 line. All machines from both lines were fully object-compatible, meaning a program compiled on one could be executed on another. Newer and larger models had instructions which were not supported on older and slower models, but the hardware, when encountering an unrecognized instruction, invoked an operating system function which interpreted it. Other differences include how process switching and I/O were handled, and maintenance and cold-starting functionality. Larger systems included hardware process scheduling and more capable input/output modules, and more highly functional maintenance processors. When the Bxx00 models were replaced by the A Series models, the differences were retained but no longer readily identifiable by model number. ALGOL The Burroughs large systems implement ALGOL-derived stack architectures. The B5000 was the first stack-based system. While B5000 was specifically designed to support ALGOL, this was only a starting point. Other business-oriented languages such as COBOL were also well supported, most notably by the powerful string operators which were included for the development of fast compilers. The ALGOL used on the B5000 is an extended ALGOL subset. It includes powerful string manipulation instructions but excludes certain ALGOL constructs, notably unspecified formal parameters. A DEFINE mechanism serves a similar purpose to the #defines found in C, but is fully integrated into the language rather than being a preprocessor. The EVENT data type facilitates coordination between processes, and ON FAULT blocks enable handling program faults. The user level of ALGOL does not include many of the insecure constructs needed by the operating system and other system software. Two levels of language extensions provide the additional constructs: ESPOL and NEWP for writing the MCP and closely related software, and DCALGOL and DMALGOL to provide more specific extensions for specific kinds of system software. Originally, the B5000 MCP operating system was written in an extension of extended ALGOL called ESPOL (Executive Systems Programming Oriented Language). This superset of ALGOL 60, provided abilities of what would later be termed a system programming language or machine oriented high order language (mohol), such as interrupting a processor on a multiprocessing system (the Burroughs large systems were multiprocessor systems). ESPOL was used to write the Master Control Program (MCP) on Burroughs computer systems from the B5000 to the B6700. The single-pass compiler for ESPOL could compile over 250 lines per second. This was replaced in the mid-to-late 70s by a language called NEWP. Though NEWP probably just meant "New Programming language", legends surround the name. A common (perhaps apocryphal) story within Burroughs at the time suggested it came from "No Executive Washroom Privileges." Another story is that circa 1976, John McClintock of Burroughs (the software engineer developing NEWP) named the language "NEWP" after being asked, yet again, "does it have a name yet": answering "nyoooop", he adopted that as a name. NEWP, too, was a subset ALGOL extension, but it was more secure than ESPOL, and dropped some little-used complexities of ALGOL. In fact, all unsafe constructs are rejected by the NEWP compiler unless a block is specifically marked to allow those instructions. Such marking of blocks provide a multi-level protection mechanism. NEWP programs that contain unsafe constructs are initially non-executable. The security administrator of a system is able to "bless" such programs and make them executable, but normal users are not able to do this. (Even "privileged users", who normally have essentially root privilege, may be unable to do this depending on the configuration chosen by the site.) While NEWP can be used to write general programs and has a number of features designed for large software projects, it does not support everything ALGOL does. NEWP has a number of facilities to enable large-scale software projects, such as the operating system, including named interfaces (functions and data), groups of interfaces, modules, and super-modules. Modules group data and functions together, allowing easy access to the data as global within the module. Interfaces allow a module to import and export functions and data. Super-modules allow modules to be grouped. In the original implementation, the system used an attached specialized data communications processor (DCP) to handle the input and output of messages from/to remote devices. This was a 24-bit minicomputer with a conventional register architecture and hardware I/O capability to handle thousands of remote terminals. The DCP and the B6500 communicated by messages in memory, essentially packets in today's terms, and the MCS did the B6500-side processing of those messages. In the early years the DCP did have an assembler (Dacoma), an application program called DCPProgen written in B6500 ALGOL. Later the NDL (Network Definition Language) compiler generated the DCP code and NDF (network definition file). Ultimately, a further update resulted in the development of the NDLII language and compiler which were used in conjunction with the model 4 and 5 DCPs. There was one ALGOL function for each kind of DCP instruction, and if you called that function, then the corresponding DCP instruction bits would be emitted to the output. A DCP program was an ALGOL program comprising nothing but a long list of calls on these functions, one for each assembly language statement. Essentially ALGOL acted like the macro pass of a macro assembler. The first pass was the ALGOL compiler; the second pass was running the resulting program (on the B6500) which would then emit the binary for the DCP. Starting in the early 1980's, the DCP technology was replaced by the ICP (Integrated Communications Processor) which provided LAN based connectivity for the mainframe system. Remote devices, and remote servers/mainframes, were connected to the network via freestanding devices called CP2000s. The CP2000s were designed to provide network node support in a distributed network wherein the nodes were connected using the BNAV2 (Burroughs Network Architecture Version 2) networking technology. BNAV2 was a Burroughs functional equivalent of the IBM SNA product and did support interoperation with IBM environments in both PUT2 and PUT5 transport modes. The change in external datacommunications hardware did not require any change to existing MCS (Message Control System (discussed below)) software. On input, messages were passed from the DCP via an internal bus to the relevant MCP Datacom Control (DCC) DCP process stack. One DCC process was initiated for each DCP configured on the system. The DCP Process stack would then ensure that the inbound message was queued for delivery to the MCS identified to handle traffic from the particular source device and return any response to the DCP for delivery to the destination device. From a processing perspective no changes were required to the MCS software to handle different types of gateway hardware, be it any of the 5 styles of DCP or the ICP or ICP/CP2000 combinations. Apart from being a message delivery service, an MCS is an intermediate level of security between operating system code (in NEWP) and user programs (in ALGOL, or other application languages including COBOL, FORTRAN, and, in later days, JAVA). An MCS may be considered to be a middleware program and is written in DCALGOL (Data Communications ALGOL). As stated above, the MCS received messages from queues maintained by the Datacom Control Stack (DCC) and forwarded these messages to the appropriate application/function for processing. One of the original MCS's was CANDE (Command AND Edit) which was developed as the online program development environment. The University of Otago in New Zealand developed a skinny program development environment equivalent to CANDE which they called SCREAM/6700 in the same time that IBM was offering a remote time-sharing/program development service known as CALL/360 which ran on IBM 360 series systems. Another MCS named COMS was introduced around 1984 and developed as a high performance transaction processing control system. There were predecessor transaction processing environments which included GEMCOS (GEneralized Message COntrol System), and an Australian Burroughs subsidiary developed MCS called TPMCS (Transaction Processing MCS). The transaction processing MCS's supported the delivery of application data to online production environments and the return of responses to remote users/devices/systems. MCSs are items of software worth noting – they control user sessions and provide keeping track of user state without having to run per-user processes since a single MCS stack can be shared by many users. Load balancing can also be achieved at the MCS level. For example, saying that you want to handle 30 users per stack, in which case if you have 31 to 60 users, you have two stacks, 61 to 90 users, three stacks, etc. This gives B5000 machines a great performance advantage in a server since you don't need to start up another user process and thus create a new stack each time a user attaches to the system. Thus you can efficiently service users (whether they require state or not) with MCSs. MCSs also provide the backbone of large-scale transaction processing. Around 1988 an implementation of TCP/IP was developed principally for a U.S. government customer utilizing the CP2000 distributed communications processor as the protocol host. Two to three years later, the TCP/IP implementation was rewritten to be host/server based with significant performance and functionality improvements. In the same general time frame an implementation of the OSI protocol stacks was made, principally on the CP2000, but a large supporting infrastructure was implemented on the main system. All of the OSI standard defined applications were implemented including X.400 mail hosting and X.500 directory services. Another variant of ALGOL is DMALGOL (Data Management ALGOL). DMALGOL is ALGOL extended for compiling the DMSII database software from database description files created by the DASDL (Data Access and Structure Definition Language) compiler. Database designers and administrators compile database descriptions to generate DMALGOL code tailored for the tables and indexes specified. Administrators never need to write DMALGOL themselves. Normal user-level programs obtain database access by using code written in application languages, mainly ALGOL and COBOL, extended with database instructions and transaction processing directives. The most notable feature of DMALGOL is its preprocessing mechanisms to generate code for handling tables and indices. DMALGOL preprocessing includes variables and loops, and can generate names based on compile-time variables. This enables tailoring far beyond what can be done by preprocessing facilities which lack loops. DMALGOL is used to provide tailored access routines for DMSII databases. After a database is defined using the Data Access and Structure Definition Language (DASDL), the schema is translated by the preprocessor into tailored DMALGOL access routines and then compiled. This means that, unlike in other DBMS implementations, there is often no need for database-specific if/then/else code at run-time. In the 1970s, this "tailoring" was used very extensively to reduce the code footprint and execution time. It became much less used in later years, partly because low-level fine tuning for memory and speed became less critical, and partly because eliminating the preprocessing made coding simpler and thus enabled more important optimizations. An applications version of ALGOL to support the accessing of databases from application programs is called BDMSALGOL and included verbs like "FIND", "LOCK", "STORE", "GET", and "PUT" for database access and record manipulation. Additionally, the verbs "BEGINTRANSACTION" and "ENDTRANSACTION" were also implemented to solve the deadlock situation when multiple processes accessed and updated the same structures. Roy Guck of Burroughs was one of the main developers of DMSII. In later years, with compiler code size being less of a concern, most of the preprocessing constructs were made available in the user level of ALGOL. Only the unsafe constructs and the direct processing of the database description file remain restricted to DMALGOL. Stack architecture In many early systems and languages, programmers were often told not to make their routines too small. Procedure calls and returns were expensive, because a number of operations had to be performed to maintain the stack. The B5000 was designed as a stack machine – all program data except for arrays (which include strings and objects) was kept on the stack. This meant that stack operations were optimized for efficiency. As a stack-oriented machine, there are no programmer addressable registers. Multitasking is also very efficient on the B5000 and B6500 lines. There are specific instruction to perform process switches: Each stack and associated[NB 5] Program Reference Table (PRT) represents a process (task or thread) and tasks can become blocked waiting on resource requests (which includes waiting for a processor to run on if the task has been interrupted because of preemptive multitasking). User programs cannot issue an IP1,[NB 5] IP2[NB 5] or MVST,[NB 6] and there is only one place in the operating system where this is done. So a process switch proceeds something like this – a process requests a resource that is not immediately available, maybe a read of a record of a file from a block which is not currently in memory, or the system timer has triggered an interrupt. The operating system code is entered and run on top of the user stack. It turns off user process timers. The current process is placed in the appropriate queue for the resource being requested, or the ready queue waiting for the processor if this is a preemptive context switch. The operating system determines the first process in the ready queue and invokes the instruction move_stack, which makes the process at the head of the ready queue active. Stack performance was considered to be slow compared to register-based architectures, for example, such an architecture had been considered and rejected for the System/360. One way to increase system speed is to keep data as close to the processor as possible. In the B5000 stack, this was done by assigning the top two positions of the stack to two registers A and B. Most operations are performed on those two top of stack positions. On faster machines past the B5000, more of the stack may be kept in registers or cache near the processor. Thus the designers of the current successors to the B5000 systems can optimize in whatever is the latest technique, and programmers do not have to adjust their code for it to run faster – they do not even need to recompile, thus protecting software investment. Some programs have been known to run for years over many processor upgrades. Such speed up is limited on register-based machines.[citation needed] Another point for speed as promoted by the RISC designers was that processor speed is considerably faster if everything is on a single chip. It was a valid point in the 1970s when more complex architectures such as the B5000 required too many transistors to fit on a single chip. However, this is not the case today and every B5000 successor machine now fits on a single chip as well as the performance support techniques such as caches and instruction pipelines. In fact, the A Series line of B5000 successors included the first single chip mainframe, the Micro-A of the late 1980s. This "mainframe" chip (named SCAMP for Single-Chip A-series Mainframe Processor) sat on an Intel-based plug-in PC board. Here is an example of how programs map to the stack structure Each stack frame corresponds to a lexical level in the current execution environment. As you can see, lexical level is the static textual nesting of a program, not the dynamic call nesting. The visibility rules of ALGOL, a language designed for single pass compilers, mean that only variables declared before the current position are visible at that part of the code, thus the requirement for forward declarations. All variables declared in enclosing blocks are visible. Another case is that variables of the same name may be declared in inner blocks and these effectively hide the outer variables which become inaccessible. Lexical nesting is static, unrelated to execution nesting with recursion, etc. so it is very rare to find a procedure nested more than five levels deep, and it could be argued that such programs would be poorly structured. B5000 machines allow nesting of up to 32 levels. This could cause difficulty for some systems that generated Algol source as output (tailored to solve some special problem) if the generation method frequently nested procedure within procedure. Procedures can be invoked in four ways – normal, call, process, and run. The normal invocation invokes a procedure in the normal way any language invokes a routine, by suspending the calling routine until the invoked procedure returns. The call mechanism invokes a procedure as a coroutine. Coroutines are partner tasks established as synchronous entities operating in their own stack at the same lexical level as the initiating process. Control is explicitly passed between the initiating process and the coroutine by means of a CONTINUE instruction. The process mechanism invokes a procedure as an asynchronous task with a separate stack set up starting at the lexical level of the processed procedure. As an asynchronous task, there is no control over exactly when control will be passed between the tasks, unlike coroutines. The processed procedure still has access to the enclosing environment and this is a very efficient IPC (Inter Process Communication) mechanism. Since two or more tasks now have access to common variables, the tasks must be synchronized to prevent race conditions, which is handled by the EVENT data type, where processes can WAIT on one, or more, events until it is caused by another cooperating process. EVENTs also allow for mutual exclusion synchronization through the PROCURE and LIBERATE functions. If for any reason the child task dies, the calling task can continue – however, if the parent process dies, then all child processes are automatically terminated. On a machine with more than one processor, the processes may run simultaneously. This EVENT mechanism is a basic enabler for multiprocessing in addition to multitasking. The last invocation type is run. This runs a procedure as an independent task which can continue on after the originating process terminates. For this reason, the child process cannot access variables in the parent's environment, and all parameters passed to the invoked procedure must be call-by-value. Thus, Burroughs Extended ALGOL had some of the multi-processing and synchronization features of later languages like Ada. It made use of the support for asynchronous processes that was built into the hardware. One last possibility is that in NEWP a procedure may be declared INLINE, that is when the compiler sees a reference to it the code for the procedure is generated inline to save the overhead of a procedure call; this is best done for small pieces of code. Inline functions are similar to parameterized macros such as C #defines, except you don't get the problems with parameters that you can with macros. In the example program only normal calls are used, so all the information will be on a single stack. For asynchronous calls, a separate stack is initiated for each asynchronous process so that the processes share data but run asynchronously. A stack hardware optimization is the provision of D (or "display") registers. The D registers correspond to scope in source programs, that is nesting in the source. These are registers that point to the start of each called stack frame. These registers are updated automatically as procedures are entered and exited and are not accessible by any software other than the MCP. There are 32 D registers, which is what limits operations to 32 levels of lexical nesting. Consider how we would access a lexical level 2 (D) global variable from lexical level 5 (D). Suppose the variable is 6 words away from the base of lexical level 2. It is thus represented by the address couple (2, 6). If we don't have D registers, we have to look at the control word at the base of the D frame, which points to the frame containing the D environment. We then look at the control word at the base of this environment to find the D environment, and continue in this fashion until we have followed all the links back to the required lexical level. This is not the same path as the return path back through the procedures which have been called in order to get to this point. (The architecture keeps both the data stack and the call stack in the same structure, but uses control words to tell them apart.) As you can see, this is quite inefficient just to access a variable. With D registers, the D register points at the base of the lexical level 2 environment, and all we need to do to generate the address of the variable is to add its offset from the stack frame base to the frame base address in the D register. (There is an efficient linked list search operator LLLU, which could search the stack in the above fashion, but the D register approach is still going to be faster.) With D registers, access to entities in outer and global environments is just as efficient as local variable access. If we had invoked the procedure p as a coroutine, or a process instruction, the D environment would have become a separate D-based stack. This means that asynchronous processes still have access to the D environment as implied in ALGOL program code. Taking this one step further, a totally different program could call another program's code, creating a D stack frame pointing to another process' D environment on top of its own process stack. At an instant the whole address space from the code's execution environment changes, making the D environment on the own process stack not directly addressable and instead make the D environment in another process stack directly addressable. This is how library calls are implemented. At such a cross-stack call, the calling code and called code could even originate from programs written in different source languages and be compiled by different compilers. The D and D environments do not occur in the current process's stack. The D environment is the code segment dictionary, which is shared by all processes running the same code. The D environment represents entities exported by the operating system. Stack frames actually don't even have to exist in a process stack. This feature was used early on for file I/O optimization, the FIB (file information block) was linked into the display registers at D during I/O operations. In the early nineties, this ability was implemented as a language feature as STRUCTURE BLOCKs and – combined with library technology - as CONNECTION BLOCKs. The ability to link a data structure into the display register address scope implemented object orientation. Thus, the B6500 actually used a form of object orientation long before the term was ever used. On other systems, the compiler might build its symbol table in a similar manner, but eventually the storage requirements would be collated and the machine code would be written to use flat memory addresses of 16-bits or 32-bits or even 64-bits. These addresses might contain anything so that a write to the wrong address could damage anything. Instead, the two-part address scheme was implemented by the hardware. At each lexical level, variables were placed at displacements up from the base of the level's stack, typically occupying one word - double precision or complex variables would occupy two. Arrays were not stored in this area, only a one word descriptor for the array was. Thus, at each lexical level the total storage requirement was not great: dozens, hundreds or a few thousand in extreme cases, certainly not a count requiring 32-bits or more. And indeed, this was reflected in the form of the VALC instruction (value call) that loaded an operand onto the stack. This op-code was two bits long and the rest of the byte's bits were concatenated with the following byte to give a fourteen-bit addressing field. The code being executed would be at some lexical level, say six: this meant that only lexical levels zero to six were valid, and so just three bits were needed to specify the lexical level desired. The address part of the VALC operation thus reserved just three bits for that purpose, with the remainder being available for referring to entities at that and lower levels. A deeply nested procedure (thus at a high lexical level) would have fewer bits available to identify entities: for level sixteen upwards five bits would be needed to specify the choice of levels 0–31 thus leaving nine bits to identify no more than the first 512 entities of any lexical level. This is much more compact than addressing entities by their literal memory address in a 32-bit addressing space. Further, only the VALC opcode loaded data: opcodes for ADD, MULT and so forth did no addressing, working entirely on the top elements of the stack. Much more important is that this method meant that many errors possible on systems employing flat addressing could not occur because they were simply unspeakable even at the machine code level. A task had no way to corrupt memory in use by another task, because it had no way to develop its address. Offsets from a specified D-register would be checked by the hardware against the stack frame bound: rogue values would be trapped. Similarly, within a task, an array descriptor contained information on the array's bounds, and so any indexing operation was checked by the hardware: put another way, each array formed its own address space. In any case, the tagging of all memory words provided a second level of protection: a misdirected assignment of a value could only go to a data-holding location, not to one holding a pointer or an array descriptor, etc. and certainly not to a location holding machine code. Arrays were not stored contiguous in memory with other variables, they were each granted their own address space, which was located via the descriptor. The access mechanism was to calculate on the stack the index variable (which therefore had the full integer range potential, not just fourteen bits) and use it as the offset into the array's address space, with bound checking provided by the hardware. By default, should an array's length exceed 1,024 words, the array would be segmented, and the index be converted into a segment index and an offset into the indexed segment. There was, however, the option to prevent segmentation by specifying the array as LONG in the declaration. In ALGOL's case, a multidimensional array would employ multiple levels of such addressing. For a reference to A[i,j], the first index would be into an array of descriptors, one descriptor for each of the rows of A, which row would then be indexed with j as for a single-dimensional array, and so on for higher dimensions. Hardware checking against the known bounds of all the array's indices would prevent erroneous indexing. FORTRAN however regards all multidimensional arrays as being equivalent to a single-dimensional array of the same size, and for a multidimensional array simple integer arithmetic is used to calculate the offset where element A[i,j,k] would be found in that single sequence. The single-dimensional equivalent array, possibly segmented if large enough, would then be accessed in the same manner as a single-dimensional array in ALGOL. Although accessing outside this array would be prevented, a wrong value for one index combined with a suitably wrong value for another index might not result in a bounds violation of the single sequence array; in other words, the indices were not checked individually. Because an array's storage was not bounded on each side by storage for other items, it was easy for the system to "resize" an array - though changing the number of dimensions was precluded because compilers required all references to have the same number of dimensions. In ALGOL's case, this enabled the development of "ragged" arrays, rather than the usual fixed rectangular (or higher dimension) arrays. Thus in two dimensions, a ragged array would have rows that were of different sizes. For instance, given a large array A[100,100] of mostly-zero values, a sparse array representation that was declared as SA[100,0] could have each row resized to have exactly enough elements to hold only the non-zero values of A along that row. Because arrays larger than 1024 words were generally segmented but smaller arrays were not, on a system that was short of real memory, increasing the declared size of a collection of scratchpad arrays from 1,000 to say 1,050 could mean that the program would run with far less "thrashing" as only the smaller individual segments in use were needed in memory. Actual storage for an array segment would be allocated at run time only if an element in that segment were accessed, and all elements of a created segment would be initialised to zero. Not initialising an array to zero at the start therefore was encouraged by this, normally an unwise omission. Array equivalencing is also supported. The ARRAY declaration requested allocation of 48-bit data words which could be used to store any bit pattern but the general operational practice was that each allocated word was considered to be a REAL operand. The declaration of: requested the allocation of 100 words of type REAL data space in memory. The programmer could also specify that the memory might be referred to as character oriented data by the following equivalence declaration: or as hexadecimal data via the equivalence declaration: or as ASCII data via the equivalence declaration: The capability to request data type specific arrays without equivalencing is also supported, e.g. requested that the system allocated a 100 character array. Given that the architecture is word based, the actual space allocated is the requested number of characters rounded up to the next whole word boundary. The Data Descriptor generated at compilation time indicated the data type usage for which the array was intended. If an array equivalence declaration was made a copy descriptor indicating that particular usage type was generated but pointed back to the original, or MOM, descriptor. Thus, indexing the correct location in memory was always guaranteed. BOOLEAN arrays are also supported and may be used as a bit vector. INTEGER arrays may also be requested. The immediately preceding discussion uses the ALGOL syntax implementation to describe ARRAY declarations, but the same functionality is supported in COBOL and FORTRAN. One nice thing about the stack structure is that if a program does happen to fail, a stack dump is taken and it is very easy for a programmer to find out exactly what the state of a running program was. Compare that to core dumps and exchange packages of other systems. Another thing about the stack structure is that programs are implicitly recursive. FORTRAN was not expected to support recursion and perhaps one stumbling block to people's understanding of how ALGOL was to be implemented was how to implement recursion. On the B5000, this was not a problem – in fact, they had the reverse problem, how to stop programs from being recursive. In the end they didn't bother. The Burroughs FORTRAN compiler allowed recursive calls (just as every other FORTRAN compiler does), but unlike many other computers, on a stack-based system the returns from such calls succeeded as well. This could have odd effects, as with a system for the formal manipulation of mathematical expressions whose central subroutines repeatedly invoked each other without ever returning: large jobs were ended by stack overflow! Thus Burroughs FORTRAN had better error checking than other contemporary implementation of FORTRAN.[citation needed] For instance, for subroutines and functions it checked that they were invoked with the correct number of parameters, as is normal for ALGOL-style compilers. On other computers, such mismatches were common causes of crashes. Similarly with the array-bound checking: programs that had been used for years on other systems embarrassingly often would fail when run on a Burroughs system. In fact, Burroughs became known for its superior compilers and implementation of languages, including the object-oriented Simula (a superset of ALGOL), and Iverson, the designer of APL declared that the Burroughs implementation of APL was the best he'd seen.[citation needed] John McCarthy, the language designer of LISP disagreed, since LISP was based on modifiable code[citation needed], he did not like the unmodifiable code of the B5000[citation needed], but most LISP implementations would run in an interpretive environment anyway. The storage required for the multiple processes came from the system's memory pool as needed. There was no need to do SYSGENs on Burroughs systems as with competing systems in order to preconfigure memory partitions in which to run tasks. Tagged architecture The most defining aspect of the B5000 is that it is a stack machine as treated above. However, two other very important features of the architecture is that it is tag-based and descriptor-based. In the original B5000, a flag bit in each control or numeric word[NB 7] was set aside to identify the word as a control word or numeric word. This was partially a security mechanism to stop programs from being able to corrupt control words on the stack. Later, when the B6500 was designed, it was realized that the 1-bit control word/numeric distinction was a powerful idea and this was extended to three bits outside of the 48 bit word into a tag. The data bits are bits 0–47 and the tag is in bits 48–50. Bit 48 was the read-only bit, thus odd tags indicated control words that could not be written by a user-level program. Code words were given tag 3. Here is a list of the tags and their function: Internally, some of the machines had 60 bit words, with the extra bits being used for engineering purposes such as a Hamming code error-correction field, but these were never seen by programmers. The current incarnation of these machines, the Unisys ClearPath has extended tags further into a four bit tag. The microcode level that specified four bit tags was referred to as level Gamma. Even-tagged words are user data which can be modified by a user program as user state. Odd-tagged words are created and used directly by the hardware and represent a program's execution state. Since these words are created and consumed by specific instructions or the hardware, the exact format of these words can change between hardware implementation and user programs do not need to be recompiled, since the same code stream will produce the same results, even though system word format may have changed. Tag 1 words represent on-stack data addresses. The normal IRW simply stores an address couple to data on the current stack. The SIRW references data on any stack by including a stack number in the address. Amongst other things, SIRW's are used to provide addressing between discrete process stacks such as those generated in response to the CALL and PROCESS statements. Tag 5 words are descriptors, which are more fully described in the next section. Tag 5 words represent off-stack data addresses. Tag 7 is the program control word which describes a procedure entry point. When hardware operators hit a PCW, the procedure is entered. The ENTR operator explicitly enters a procedure (non-value-returning routine). Functions (value-returning routines) are implicitly entered by operators such as value call (VALC). Global routines are stored in the D environment as SIRWs that point to a PCW stored in the code segment dictionary in the D environment. The D environment is not stored on the current stack because it can be referenced by all processes sharing this code. Thus code is reentrant and shared. Tag 3 represents code words themselves, which won't occur on the stack. Tag 3 is also used for the stack control words MSCW, RCW, TOSCW. Descriptor-based architecture The figure to the left shows how the Burroughs Large System architecture was fundamentally a hardware architecture for object-oriented programming, something that still doesn't exist in conventional architectures. Instruction sets There are three distinct instruction sets for the Burroughs large systems. All three are based on short syllables that fit evenly into words. Programs on a B5000, B5500 and B5700 are made up of 12-bit syllables, four to a word. The architecture has two modes, Word Mode and Character Mode, and each has a separate repertoire of syllables. A processor may be either Control State or Normal State, and certain syllables are only permissible in Control State. The architecture does not provide for addressing registers or storage directly; all references are through the 1024 word Program Reference Table, current code segment, marked locations within the stack or to the A and B registers holding the top two locations on the stack. Burroughs numbers bits in a syllable from 0 (high bit) to 11 (low bit) Programs are made up of 8-bit syllables, which may be Name Call, Value Call, or form an operator, which may be from one to twelve syllables in length. There are less than 200 operators, all of which fit into 8-bit syllables. Many of these operators are polymorphic depending on the kind of data being acted on as given by the tag. If we ignore the powerful string scanning, transfer, and edit operators, the basic set is only about 120 operators. If we remove the operators reserved for the operating system such as MVST and HALT, the set of operators commonly used by user-level programs is less than 100. The Name Call and Value Call syllables contain address couples; the Operator syllables either use no addresses or use control words and descriptors on the stack. Multiple processors The B5000 line also were pioneers in having two processors connected together on a high-speed bus as master and slave. In the B6000, B7000 and B8000 lines the processors were symmetric. The B7000 line could have up to eight processors, as long as at least one was an I/O module. RDLK (ReaD with LocK) is a very low-level way of synchronizing between processors. RDLK operates in a single cycle. The higher level mechanism generally employed by user programs is the EVENT data type. The EVENT data type did have some system overhead. To avoid this overhead, a special locking technique called Dahm locks (named after a Burroughs software guru, Dave Dahm) can be used. Dahm locks used the READLOCK ALGOL language statement which generated a RDLK operator at the code level. Notable operators are: HEYU — send an interrupt to another processor RDLK — Low-level semaphore operator: Load the A register with the memory location given by the A register and place the value in the B register at that memory location in a single uninterruptible cycle. The Algol compiler produced code to invoke this operator via a special function that enabled a "swap" operation on single-word data without an explicit temporary value. x:=RDLK(x,y); WHOI — Processor identification IDLE — Idle until an interrupt is received Two processors could infrequently simultaneously send each other a 'HEYU' command resulting in a lockup known as 'a deadly embrace'. Influence of the B5000 The direct influence of the B5000 can be seen in the current Unisys ClearPath range of mainframes which are the direct descendants of the B6500, which was influenced by the B5000, and still have the MCP operating system after 40 years of consistent development. This architecture is now called emode (for emulation mode) since the B6500 architecture has been implemented on machines built from Intel Xeon processors running the x86 instruction set as the native instruction set, with code running on those processors emulating the B5000 instruction set. In those machines, there was also going to be an nmode (native mode), but this was dropped[citation needed], so you may often hear the B6500 successor machines being referred to as "emode machines". B5000 machines were programmed exclusively in high-level languages; there is no assembler. The B5000 stack architecture inspired Chuck Moore, the designer of the programming language Forth, who encountered the B5500 while at MIT. In Forth - The Early Years, Moore described the influence, noting that Forth's DUP, DROP and SWAP came from the corresponding B5500 instructions (DUPL, DLET, EXCH). B5000 machines with their stack-based architecture and tagged memory also heavily influenced the Soviet Elbrus series of mainframes and supercomputers. The first two generations of the series featured tagged memory and stack-based CPUs that were programmed only in high-level languages. There existed a kind of an assembly language for them, called El-76, but it was more or less a modification of ALGOL 68 and supported structured programming and first-class procedures. Later generations of the series, though, switched away from this architecture to the EPIC-like VLIW CPUs. The Hewlett-Packard designers of the HP 3000 business system had used a B5500 and were greatly impressed by its hardware and software; they aimed to build a 16-bit minicomputer with similar software. Several other HP divisions created similar minicomputer or microprocessor stack machines. Bob Barton's work on reverse Polish notation (RPN) also found its way into HP calculators beginning with the 9100A, and notably the HP-35 and subsequent calculators. The NonStop systems designed by Tandem Computers in the late 1970s and early 1980s were also 16-bit stack machines, influenced by the B5000 indirectly through the HP 3000 connection, as several of the early Tandem engineers were formerly with HP. Around 1990, these systems migrated to MIPS RISC architecture but continued to support execution of stack machine binaries by object code translation or direct emulation. Sometime after 2000, these systems migrated to Itanium architecture and continued to run the legacy stack machine binaries. Bob Barton was also very influential on Alan Kay. Kay was also impressed by the data-driven tagged architecture of the B5000 and this influenced his thinking in his developments in object-oriented programming and Smalltalk.[citation needed] Another facet of the B5000 architecture was that it was a secure architecture that runs directly on hardware. This technique has descendants in the virtual machines of today[citation needed] in their attempts to provide secure environments. One notable such product is the Java JVM which provides a secure sandbox in which applications run. The value of the hardware-architecture binding that existed before emode would be substantially preserved in the x86-based machines to the extent that MCP was the one and only control program, but the support provided by those machines is still inferior to that provided on the machines where the B6500 instruction set is the native instruction set. A little-known Intel processor architecture that actually preceded 32-bit implementations of the x86 instruction set, the Intel iAPX 432, would have provided an equivalent physical basis, as it too was essentially an object-oriented architecture. See also Notes References Further reading External links |
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[SOURCE: https://en.wikipedia.org/wiki/Life] | [TOKENS: 6329] |
Contents Life Life on Earth: Life is matter that has biological processes, such as signaling and the ability to sustain itself. It is defined descriptively by the capacity for homeostasis, organisation, metabolism, growth, adaptation, response to stimuli, and reproduction. All life over time eventually reaches a state of death, and none is immortal. Many philosophical definitions of living systems have been proposed, such as self-organizing systems. Defining life is further complicated by viruses, which replicate only in host cells, and the possibility of extraterrestrial life, which could be very different from life on Earth. Life exists all over the Earth in air, water, and soil, with many ecosystems forming the biosphere. Some of these are harsh environments occupied only by extremophiles. The life in a particular ecosystem is called its biota. Life has been studied since ancient times, with theories such as Empedocles's materialism asserting that it was composed of four eternal elements, and Aristotle's hylomorphism asserting that living things have souls and embody both form and matter. Life originated at least 3.5 billion years ago, resulting in a universal common ancestor. This evolved into all the species that exist now, by way of many extinct species, some of which have left traces as fossils. Attempts to classify living things, too, began with Aristotle. Modern classification began with Carl Linnaeus's system of binomial nomenclature in the 1740s. Living things are composed of biochemical molecules, formed mainly from a few core chemical elements. All living things contain two types of macromolecule, proteins and nucleic acids, the latter usually both DNA and RNA: these carry the information needed by each species, including the instructions to make each type of protein. The proteins, in turn, serve as the machinery which carries out the many chemical processes of life. The cell is the structural and functional unit of life. Smaller organisms, including prokaryotes (bacteria and archaea), consist of small single cells. Larger organisms, mainly eukaryotes, can consist of single cells or may be multicellular with more complex structure. Life is only known to exist on Earth but extraterrestrial life is thought probable. Artificial life is being simulated and explored by scientists and engineers. Definitions The definition of life has long been a challenge for scientists and philosophers. This is partially because life is a process, not a substance. This is complicated by a lack of knowledge of the characteristics of living entities, if any, that may have developed outside Earth. Philosophical definitions of life have also been put forward, with similar difficulties on how to distinguish living things from the non-living. Legal definitions of life have been debated, though these generally focus on the decision to declare a human dead, and the legal ramifications of this decision. At least 123 definitions of life have been compiled. A biota is the assemblage of living things, especially the animals and plants, that inhabit a specific place and time, such as an ecosystem or biome; thus, the goal of nature conservation is to preserve the biota of an ecosystem. Since there is no consensus for a definition of life, most current definitions in biology are descriptive. Life is considered a characteristic of something that preserves, furthers or reinforces its existence in the given environment. This implies all or most of the following traits: From a physics perspective, an organism is a thermodynamic system with an organised molecular structure that can reproduce itself and evolve as survival dictates. Thermodynamically, life has been described as an open system which makes use of gradients in its surroundings to create imperfect copies of itself. Another way of putting this is to define life as "a self-sustained chemical system capable of undergoing Darwinian evolution", a definition adopted by a NASA committee attempting to define life for the purposes of exobiology, based on a suggestion by Carl Sagan. This definition, however, has been widely criticised because according to it, a single sexually reproducing individual is not alive as it is incapable of evolving on its own. Others take a living systems theory viewpoint that does not necessarily depend on molecular chemistry. One systemic definition of life is that living things are self-organizing and autopoietic (self-producing). Variations of this include Stuart Kauffman's definition as an autonomous agent or a multi-agent system capable of reproducing itself, and of completing at least one thermodynamic work cycle. This definition is extended by the evolution of novel functions over time. Living systems are characterized by a multiscale, hierarchical organization, spanning from molecular machines to cells, organs, tissues, organisms, populations, ecosystems, up to the whole biosphere. Death is the termination of all vital functions or life processes in an organism or cell. One of the challenges in defining death is in distinguishing it from life. Death would seem to refer to either the moment life ends, or when the state that follows life begins. However, determining when death has occurred is difficult, as cessation of life functions is often not simultaneous across organ systems. Such determination, therefore, requires drawing conceptual lines between life and death. This is problematic because there is little consensus over how to define life. The nature of death has for millennia been a central concern of the world's religious traditions and of philosophical inquiry. Many religions maintain faith in either a kind of afterlife or reincarnation for the soul, or resurrection of the body at a later date. Whether or not viruses should be considered as alive is controversial. They are most often considered as just gene coding replicators rather than forms of life. They have been described as "organisms at the edge of life" because they possess genes, evolve by natural selection, and replicate by making multiple copies of themselves through self-assembly. However, viruses do not metabolise and they require a host cell to make new products. Virus self-assembly within host cells has implications for the study of the origin of life, as it may support the hypothesis that life could have started as self-assembling organic molecules. History of study Some of the earliest theories of life were materialist, holding that all that exists is matter, and that life is merely a complex form or arrangement of matter. Empedocles (430 BC) argued that everything in the universe is made up of a combination of four eternal "elements" or "roots of all": earth, water, air, and fire. All change is explained by the arrangement and rearrangement of these four elements. The various forms of life are caused by an appropriate mixture of elements. Democritus (460 BC) was an atomist; he thought that the essential characteristic of life was having a soul (psyche), and that the soul, like everything else, was composed of fiery atoms. He elaborated on fire because of the apparent connection between life and heat, and because fire moves. Plato, in contrast, held that the world was organised by permanent forms, reflected imperfectly in matter; forms provided direction or intelligence, explaining the regularities observed in the world. The mechanistic materialism that originated in ancient Greece was revived and revised by the French philosopher René Descartes (1596–1650), who held that animals and humans were assemblages of parts that together functioned as a machine. Gottfried Wilhelm Leibniz emphasised the hierarchical organization of living machines, noting in his book Monadology (1714) that "...the machines of nature, that is living bodies, are still machines in their smallest parts, to infinity." This idea was developed further by Julien Offray de La Mettrie (1709–1750) in his book L'Homme Machine. In the 19th century the advances in cell theory in biological science encouraged this view. The evolutionary theory of Charles Darwin (1859) is a mechanistic explanation for the origin of species by means of natural selection. At the beginning of the 20th century Stéphane Leduc (1853–1939) promoted the idea that biological processes could be understood in terms of physics and chemistry, and that their growth resembled that of inorganic crystals immersed in solutions of sodium silicate. His ideas, set out in his book La biologie synthétique, were widely dismissed during his lifetime, but has incurred a resurgence of interest in the work of Russell, Barge and colleagues. Hylomorphism is a theory first expressed by the Greek philosopher Aristotle (322 BC). The application of hylomorphism to biology was important to Aristotle, and biology is extensively covered in his extant writings. In this view, everything in the material universe has both matter and form, and the form of a living thing is its soul (Greek psyche, Latin anima). There are three kinds of souls: the vegetative soul of plants, which causes them to grow and decay and nourish themselves, but does not cause motion and sensation; the animal soul, which causes animals to move and feel; and the rational soul, which is the source of consciousness and reasoning, which (Aristotle believed) is found only in man. Each higher soul has all of the attributes of the lower ones. Aristotle believed that while matter can exist without form, form cannot exist without matter, and that therefore the soul cannot exist without the body. This account is consistent with teleological explanations of life, which account for phenomena in terms of purpose or goal-directedness. Thus, the whiteness of the polar bear's coat is explained by its purpose of camouflage. The direction of causality (from the future to the past) is in contradiction with the scientific evidence for natural selection, which explains the consequence in terms of a prior cause. Biological features are explained not by looking at future optimal results, but by looking at the past evolutionary history of a species, which led to the natural selection of the features in question. Spontaneous generation was the belief that living organisms can form without descent from similar organisms. Typically, the idea was that certain forms such as fleas could arise from inanimate matter such as dust or the supposed seasonal generation of mice and insects from mud or garbage. The theory of spontaneous generation was proposed by Aristotle, who compiled and expanded the work of prior natural philosophers and the various ancient explanations of the appearance of organisms; it was considered the best explanation for two millennia. It was decisively dispelled by the experiments of Louis Pasteur in 1859, who expanded upon the investigations of predecessors such as Francesco Redi. Disproof of the traditional ideas of spontaneous generation is no longer controversial among biologists. Vitalism is the belief that there is a non-material life-principle. This originated with Georg Ernst Stahl (17th century), and remained popular until the middle of the 19th century. It appealed to philosophers such as Henri Bergson, Friedrich Nietzsche, and Wilhelm Dilthey, anatomists like Xavier Bichat, and chemists like Justus von Liebig. Vitalism included the idea that there was a fundamental difference between organic and inorganic material, and the belief that organic material can only be derived from living things. This was disproved in 1828, when Friedrich Wöhler prepared urea from inorganic materials. This Wöhler synthesis is considered the starting point of modern organic chemistry. It is of historical significance because for the first time an organic compound was produced in inorganic reactions. During the 1850s Hermann von Helmholtz, anticipated by Julius Robert von Mayer, demonstrated that no energy is lost in muscle movement, suggesting that there were no "vital forces" necessary to move a muscle. These results led to the abandonment of scientific interest in vitalistic theories, especially after Eduard Buchner's demonstration that alcoholic fermentation could occur in cell-free extracts of yeast. Nonetheless, belief still exists in pseudoscientific theories such as homoeopathy, which interprets diseases and sickness as caused by disturbances in a hypothetical vital force or life force. Development The age of Earth is about 4.54 billion years. Life on Earth has existed for at least 3.5 billion years, with the oldest physical traces of life dating back 3.7 billion years. Estimates from molecular clocks, as summarised in the TimeTree public database, place the origin of life around 4.0 billion years ago. Hypotheses on the origin of life attempt to explain the formation of a universal common ancestor from simple organic molecules via pre-cellular life to protocells and metabolism. In 2016, a set of 355 genes from the last universal common ancestor was tentatively identified. The biosphere is postulated to have developed, from the origin of life onwards, at least some 3.5 billion years ago. The earliest evidence for life on Earth includes biogenic graphite found in 3.7 billion-year-old metasedimentary rocks from Western Greenland and microbial mat fossils found in 3.48 billion-year-old sandstone from Western Australia. More recently, in 2015, "remains of biotic life" were found in 4.1 billion-year-old rocks in Western Australia. In 2017, putative fossilised microorganisms (or microfossils) were announced to have been discovered in hydrothermal vent precipitates in the Nuvvuagittuq Belt of Quebec, Canada that were as old as 4.28 billion years, the oldest record of life on Earth, suggesting "an almost instantaneous emergence of life" after ocean formation 4.4 billion years ago, and not long after the formation of the Earth 4.54 billion years ago. Evolution is the change in heritable characteristics of biological populations over successive generations. It results in the appearance of new species and often the disappearance of old ones. Evolution occurs when evolutionary processes such as natural selection (including sexual selection) and genetic drift act on genetic variation, resulting in certain characteristics increasing or decreasing in frequency within a population over successive generations. The process of evolution has given rise to biodiversity at every level of biological organisation. Fossils are the preserved remains or traces of organisms from the remote past. The totality of fossils, both discovered and undiscovered, and their placement in layers (strata) of sedimentary rock is known as the fossil record. A preserved specimen is called a fossil if it is older than the arbitrary date of 10,000 years ago. Hence, fossils range in age from the youngest at the start of the Holocene Epoch to the oldest from the Archaean Eon, up to 3.4 billion years old. Extinction is the process by which a species dies out. The moment of extinction is the death of the last individual of that species. Because a species' potential range may be very large, determining this moment is difficult, and is usually done retrospectively after a period of apparent absence. Species become extinct when they are no longer able to survive in changing habitat or against superior competition. Over 99% of all the species that have ever lived are now extinct. Mass extinctions may have accelerated evolution by providing opportunities for new groups of organisms to diversify. Environmental conditions The diversity of life on Earth is a result of the dynamic interplay between genetic opportunity, metabolic capability, environmental challenges, and symbiosis. For most of its existence, Earth's habitable environment has been dominated by microorganisms and subjected to their metabolism and evolution. As a consequence of these microbial activities, the physical-chemical environment on Earth has been changing on a geologic time scale, thereby affecting the path of evolution of subsequent life. For example, the release of molecular oxygen by cyanobacteria as a by-product of photosynthesis induced global changes in the Earth's environment. Because oxygen was toxic to most life on Earth at the time, this posed novel evolutionary challenges, and ultimately resulted in the formation of Earth's major animal and plant species. This interplay between organisms and their environment is an inherent feature of living systems. The biosphere is the global sum of all ecosystems. It can also be termed as the zone of life on Earth, a closed system (apart from solar and cosmic radiation and heat from the interior of the Earth), and largely self-regulating. Organisms exist in every part of the biosphere, including soil, hot springs, inside rocks at least 19 km (12 mi) deep underground, the deepest parts of the ocean, and at least 64 km (40 mi) high in the atmosphere. For example, spores of Aspergillus niger have been detected in the mesosphere at an altitude of 48 to 77 km. Under test conditions, life forms have been observed to survive in the vacuum of space. Life forms thrive in the deep Mariana Trench, and inside rocks up to 580 m (1,900 ft; 0.36 mi) below the sea floor under 2,590 m (8,500 ft; 1.61 mi) of ocean off the coast of the northwestern United States, and 2,400 m (7,900 ft; 1.5 mi) beneath the seabed off Japan. In 2014, life forms were found living 800 m (2,600 ft; 0.50 mi) below the ice of Antarctica. Expeditions of the International Ocean Discovery Program found unicellular life in 120 °C sediment 1.2 km below seafloor in the Nankai Trough subduction zone. According to one researcher, "You can find microbes everywhere—they're extremely adaptable to conditions, and survive wherever they are." The inert components of an ecosystem are the physical and chemical factors necessary for life—energy (sunlight or chemical energy), water, heat, atmosphere, gravity, nutrients, and ultraviolet solar radiation protection. In most ecosystems, the conditions vary during the day and from one season to the next. To survive in these ecosystems, organisms must be able to tolerate a range of conditions defined as the "range of tolerance". Outside this range are the "zones of physiological stress", where the survival and reproduction are possible but not optimal. Beyond these zones are the "zones of intolerance", where survival and reproduction of that organism is unlikely or impossible. Organisms that have a wide range of tolerance are more widely distributed than organisms with a narrow range of tolerance. To survive, some microorganisms have evolved to withstand freezing, complete desiccation, starvation, high levels of radiation exposure, and other physical or chemical challenges. These extremophile microorganisms may survive exposure to such conditions for long periods. They excel at exploiting uncommon sources of energy. Characterization of the structure and metabolic diversity of microbial communities in such extreme environments is ongoing. Classification The first classification of organisms was made by the Greek philosopher Aristotle (384–322 BC), who grouped living things as either plants or animals, based mainly on their ability to move. He distinguished animals with blood from animals without blood, which can be compared with the concepts of vertebrates and invertebrates respectively, and divided the blooded animals into five groups: viviparous quadrupeds (mammals), oviparous quadrupeds (reptiles and amphibians), birds, fishes and whales. The bloodless animals were divided into five groups: cephalopods, crustaceans, insects (which included the spiders, scorpions, and centipedes), shelled animals (such as most molluscs and echinoderms), and zoophytes (animals that resembled plants). This theory remained dominant for more than a thousand years. In the late 1740s, Carl Linnaeus introduced his system of binomial nomenclature for the classification of species. Linnaeus attempted to improve the composition and reduce the length of the previously used many-worded names by abolishing unnecessary rhetoric, introducing new descriptive terms and precisely defining their meaning. The fungi were originally treated as plants. For a short period Linnaeus had classified them in the taxon Vermes in Animalia, but later placed them back in Plantae. Herbert Copeland classified the Fungi in his Protoctista, including them with single-celled organisms and thus partially avoiding the problem but acknowledging their special status. The problem was eventually solved by Whittaker, when he gave them their own kingdom in his five-kingdom system. Evolutionary history shows that the fungi are more closely related to animals than to plants. As advances in microscopy enabled detailed study of cells and microorganisms, new groups of life were revealed, and the fields of cell biology and microbiology were created. These new organisms were originally described separately in protozoa as animals and protophyta/thallophyta as plants, but were united by Ernst Haeckel in the kingdom Protista; later, the prokaryotes were split off in the kingdom Monera, which would eventually be divided into two separate groups, the Bacteria and the Archaea. This led to the six-kingdom system and eventually to the current three-domain system, which is based on evolutionary relationships. However, the classification of eukaryotes, especially of protists, is still controversial. As microbiology developed, viruses, which are non-cellular, were discovered. Whether these are considered alive has been a matter of debate; viruses lack characteristics of life such as cell membranes, metabolism and the ability to grow or respond to their environments. Viruses have been classed into "species" based on their genetics, but many aspects of such a classification remain controversial. The original Linnaean system has been modified many times, for example as follows: The attempt to organise the Eukaryotes into a small number of kingdoms has been challenged. The Protozoa do not form a clade or natural grouping, and nor do the Chromista (Chromalveolata). The ability to sequence large numbers of complete genomes has allowed biologists to take a metagenomic view of the phylogeny of the whole tree of life. This has led to the realisation that the majority of living things are bacteria, and that all have a common origin. Composition All life forms require certain core chemical elements for their biochemical functioning. These include carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulfur—the elemental macronutrients for all organisms. Together these make up nucleic acids, proteins and lipids, the bulk of living matter. Five of these six elements comprise the chemical components of DNA, the exception being sulfur. The latter is a component of the amino acids cysteine and methionine. The most abundant of these elements in organisms is carbon, which has the desirable attribute of forming multiple, stable covalent bonds. This allows carbon-based (organic) molecules to form the immense variety of chemical arrangements described in organic chemistry. Alternative hypothetical types of biochemistry have been proposed that eliminate one or more of these elements, swap out an element for one not on the list, or change required chiralities or other chemical properties. Deoxyribonucleic acid or DNA is a molecule that carries most of the genetic instructions used in the growth, development, functioning and reproduction of all known living organisms and many viruses. DNA and RNA are nucleic acids; alongside proteins and complex carbohydrates, they are one of the three major types of macromolecule that are essential for all known forms of life. Most DNA molecules consist of two biopolymer strands coiled around each other to form a double helix. The two DNA strands are known as polynucleotides since they are composed of simpler units called nucleotides. Each nucleotide is composed of a nitrogen-containing nucleobase—either cytosine (C), guanine (G), adenine (A), or thymine (T)—as well as a sugar called deoxyribose and a phosphate group. The nucleotides are joined to one another in a chain by covalent bonds between the sugar of one nucleotide and the phosphate of the next, resulting in an alternating sugar-phosphate backbone. According to base pairing rules (A with T, and C with G), hydrogen bonds bind the nitrogenous bases of the two separate polynucleotide strands to make double-stranded DNA. This has the key property that each strand contains all the information needed to recreate the other strand, enabling the information to be preserved during reproduction and cell division. Within cells, DNA is organised into long structures called chromosomes. During cell division these chromosomes are duplicated in the process of DNA replication, providing each cell its own complete set of chromosomes. Eukaryotes store most of their DNA inside the cell nucleus. Cells are the basic unit of structure in every living thing, and all cells arise from pre-existing cells by division. Cell theory was formulated by Henri Dutrochet, Theodor Schwann, Rudolf Virchow and others during the early nineteenth century, and subsequently became widely accepted. The activity of an organism depends on the total activity of its cells, with energy flow occurring within and between them. Cells contain hereditary information that is carried forward as a genetic code during cell division. There are two primary types of cells, reflecting their evolutionary origins. Prokaryote cells lack a nucleus and other membrane-bound organelles, although they have circular DNA and ribosomes. Bacteria and Archaea are two domains of prokaryotes. The other primary type is the eukaryote cell, which has a distinct nucleus bound by a nuclear membrane and membrane-bound organelles, including mitochondria, chloroplasts, lysosomes, rough and smooth endoplasmic reticulum, and vacuoles. In addition, their DNA is organised into chromosomes. All species of large complex organisms are eukaryotes, including animals, plants and fungi, though with a wide diversity of protist microorganisms. The conventional model is that eukaryotes evolved from prokaryotes, with the main organelles of the eukaryotes forming through endosymbiosis between bacteria and the progenitor eukaryotic cell. The molecular mechanisms of cell biology are based on proteins. Most of these are synthesised by the ribosomes through an enzyme-catalyzed process called protein biosynthesis. A sequence of amino acids is assembled and joined based upon gene expression of the cell's nucleic acid. In eukaryotic cells, these proteins may then be transported and processed through the Golgi apparatus in preparation for dispatch to their destination. Cells reproduce through a process of cell division in which the parent cell divides into two or more daughter cells. For prokaryotes, cell division occurs through a process of fission in which the DNA is replicated, then the two copies are attached to parts of the cell membrane. In eukaryotes, a more complex process of mitosis is followed. However, the result is the same; the resulting cell copies are identical to each other and to the original cell (except for mutations), and both are capable of further division following an interphase period. Most species of multicellular plants, animals and fungi as well as many protists are capable of sexual reproduction. Sexual reproduction, involving a meiotic process, is considered to have arisen very early in the evolution of eukaryotes. Multicellular organisms may have first evolved through the formation of colonies of identical cells. These cells can form group organisms through cell adhesion. The individual members of a colony are capable of surviving on their own, whereas the members of a true multi-cellular organism have developed specialisations, making them dependent on the remainder of the organism for survival. Such organisms are formed clonally or from a single germ cell that is capable of forming the various specialised cells that form the adult organism. This specialisation allows multicellular organisms to exploit resources more efficiently than single cells. About 800 million years ago, a minor genetic change in a single molecule, the enzyme GK-PID, may have allowed organisms to go from a single cell organism to one of many cells. Cells have evolved methods to perceive and respond to their microenvironment, thereby enhancing their adaptability. Cell signaling coordinates cellular activities, and hence governs the basic functions of multicellular organisms. Signaling between cells can occur through direct cell contact using juxtacrine signalling, or indirectly through the exchange of agents as in the endocrine system. In more complex organisms, coordination of activities can occur through a dedicated nervous system. In the universe Though life is confirmed only on Earth, many think that extraterrestrial life is not only plausible, but probable or inevitable, possibly resulting in a biophysical cosmology instead of a mere physical cosmology. Other planets and moons in the Solar System and other planetary systems are being examined for evidence of having once supported simple life, and projects such as SETI are trying to detect radio transmissions from possible alien civilisations. Other locations within the Solar System that may host microbial life include the subsurface of Mars, the upper atmosphere of Venus, and subsurface oceans on some of the moons of the giant planets. Investigation of the tenacity and versatility of life on Earth, as well as an understanding of the molecular systems that some organisms utilise to survive such extremes, is important for the search for extraterrestrial life. For example, lichen could survive for a month in a simulated Martian environment. Beyond the Solar System, the region around another main-sequence star that could support Earth-like life on an Earth-like planet is known as the habitable zone. The inner and outer radii of this zone vary with the luminosity of the star, as does the time interval during which the zone survives. Stars more massive than the Sun have a larger habitable zone, but remain on the Sun-like "main sequence" of stellar evolution for a shorter time interval. Small red dwarfs have the opposite problem, with a smaller habitable zone that is subject to higher levels of magnetic activity and the effects of tidal locking from close orbits. Hence, stars in the intermediate mass range such as the Sun may have a greater likelihood for Earth-like life to develop. The location of the star within a galaxy may also affect the likelihood of life forming. Stars in regions with a greater abundance of heavier elements that can form planets, in combination with a low rate of potentially habitat-damaging supernova events, are predicted to have a higher probability of hosting planets with complex life. The variables of the Drake equation are used to discuss the conditions in planetary systems where civilisation is most likely to exist, within wide bounds of uncertainty. A "Confidence of Life Detection" scale (CoLD) for reporting evidence of life beyond Earth has been proposed. Artificial Artificial life is the simulation of any aspect of life, as through computers, robotics, or biochemistry. Synthetic biology is a new area of biotechnology that combines science and biological engineering. The common goal is the design and construction of new biological functions and systems not found in nature. Synthetic biology includes the broad redefinition and expansion of biotechnology, with the ultimate goals of being able to design and build engineered biological systems that process information, manipulate chemicals, fabricate materials and structures, produce energy, provide food, and maintain and enhance human health and the environment. See also Notes References External links |
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[SOURCE: https://en.wikipedia.org/wiki/OpenAI#cite_note-147] | [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 |
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[SOURCE: https://en.wikipedia.org/wiki/Caf%C3%A9-chantant] | [TOKENS: 819] |
Contents Café-chantant Café-chantant (French pronunciation: [kafe ʃɑ̃tɑ̃]; French: lit. 'singing café'), café-concert, or caf'conc is a type of musical establishment associated with the Belle Époque in France. The music was generally lighthearted and sometimes risqué or even bawdy—but, as opposed to the cabaret tradition, not particularly political or confrontational. Origins Although there is much overlap of definition with cabaret, music hall, vaudeville, etc., the café-chantant was originally an outdoor café where small groups of performers performed popular music for the public. The tradition of such establishments as a venue for music has its origins in the Paris and London of the eighteenth century. Café Chantant establishments gained their widest popularity in the late nineteenth and early twentieth centuries with the growth of various other national "schools" of cafè-chantant (besides French). Thus, one spoke of an Italian café-chantant, German café-chantant, or Austrian café-chantant. For example, at least one Victorian-era premises in England was known as a café-chantant. One of the most famous performers in this medium was violinist Georges Boulanger, who performed in this style from 1910 until 1958, and singer Gorella Gori or Zaira Erba who died in 1963. National variations In Spain, such an establishment was known as a café-concert (such as the Café de las Salesas in Madrid) or café cantante, and became the centre for professional flamenco performances from the mid-nineteenth century to the 1920s. Cafés chantants were known as كافشانتان (kafeşantan) in Turkish, and many were opened in the Beyoğlu/Péra district of Istanbul in the early years of the twentieth century. They are described in great detail in the memoirs of such authors as Ahmed Rasim and Sermet Muhtar Alus. Earlier versions of the kafeşantan, known as kahvehane in Turkish, appeared in Istanbul during the Ottoman Era as early as 1554. Hundreds of them were opened continually, most of them with a social club status. In the Russian Empire, the term was taken wholesale into the Russian language as "kafe-shantan" (кафе-шантан). Odessa was the city best known for its numerous kafe-shantany. Twentieth century events In the twentieth century, Cafe Chantant events were held across the UK by the women's suffrage movement to bring together their supporters and to raise funds. The organization of the events of musical and other performances held the movement were intended to be of a high standard (and unlikely to be risqué although unconventional), so that fundraising this way was successful[citation needed] In 1900, a Thé and Café Chantant event was organised in Edinburgh by Alice Low and an actor to raise money for a patriotic fund for Scottish soldiers. In 1908, this type of fundraising was led by Jessie M.Soga, contralto, A programme for a London Cafe Chantant shows the variety of performances ranging from music or talks, to clairvoyance and jujitsu. starting in one branch, then rolling out across Scotland. In 1916, an event for prisoners of war comforts fund was organised by a 'tea committee' in Leamington Spa, during the First World War. Literary uses Le Café Concert was a book published by L'Estampe originale in 1893 about the French establishments of that day. The book contains text by Georges Montorgueil, and is illustrated with numerous lithographs by Toulouse-Lautrec and Henri-Gabriel Ibels that mostly feature famous performers or customers from the contemporary Paris scene. The name Cafe Chantant appears in See also References External links Commons:Category:Café-concerts |
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[SOURCE: https://en.wikipedia.org/wiki/Newspeak_(programming_language)] | [TOKENS: 617] |
Contents Newspeak (programming language) Newspeak is a programming language and platform in the tradition of Smalltalk and Self being developed by a team led by Gilad Bracha. The platform includes an integrated development environment (IDE), a graphical user interface (GUI) library, and standard libraries. Starting in 2006, Cadence Design Systems funded its development and employed the main contributors, but ended funding in January 2009. Overview Newspeak is a class-based and message-based language. Classes may be nested, as in BETA. This is one of the key differences between Newspeak and Smalltalk. Newspeak is distinguished by its unusual approach to modularity. The language has no global namespace. Top level classes act as module declarations. Modularity in Newspeak is based exclusively on class nesting. Module declarations are first class values (i.e., they may be stored in variables, passed as parameters, returned from methods, etc.) and are stateless. By design the newspeak lacks undeclared access to a global scope and therefore enforces dependency injection. As consequence it requires all class dependencies (instance variables referred as by "slots") to be explicitly referenced. This makes every class in Newspeak virtual. All names of dependencies in Newspeak are late-bound (dynamically bound), and are interpreted as message sends, as in Self. A notable feature of newspeak is its impossibility to directly access instance variables. It's done via automatically generated getters or setters.: 3 While developed at Cadence Newspeak was used to write its own IDE (compiler, debugger, class browsers, object inspectors, unit testing framework, a mirror based reflection API etc.), a portable GUI tool kit, an object serializer/deserializer, a parser combinator library, a regular expression package, core libraries for collections, streams, strings and files, parts of foreign function interface and CAD application code. The Newspeak platform as a whole took approximately 8 person years of work.: 17 Identity The name Newspeak is inspired by the Newspeak language appearing in George Orwell's dystopian novel Nineteen Eighty-Four. The heading on the programming language's website says "It's doubleplusgood". The motive for the name is that Orwell's Newspeak language grew smaller with each revision; Bracha views this as a desirable goal for a programming language. The language icon is supposed to be Big Brother's eye, as seen in page 3 of the documentation. It should not be confused with the safety critical programming language of the same name designed by Ian Currie of RSRE in 1984, for use with the VIPER microprocessor. Its principal characteristic was that its compiler would ensure all potential exceptional behaviour is explicitly handled by the program. Implementation Primordial Soup is a virtual machine (VM) that runs Newspeak binary snapshopts of serialized Newspeak files. Internal Object Representation was inspired by the Dart VM and provides basic VM primitives for the language. It can be compiled by SCons on various platforms. Code example "Hello World" example See also References External links |
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[SOURCE: https://en.wikipedia.org/wiki/Planum_Australe] | [TOKENS: 965] |
Contents Planum Australe Planum Australe (Latin: "the southern plain") is the southern polar plain on Mars. It extends southward of roughly 75°S and is centered at 83°54′S 160°00′E / 83.9°S 160.0°E / -83.9; 160.0. The geology of this region was to be explored by the failed NASA mission Mars Polar Lander, which lost contact on entry into the Martian atmosphere. In July 2018, scientists reported the discovery, based on MARSIS radar studies, of a subglacial lake on Mars, 1.5 km (0.93 mi) below the southern polar ice cap, and extending sideways about 20 km (12 mi), the first known stable body of water on the planet. Ice cap Planum Australe is partially covered by a permanent polar ice cap composed of frozen water and carbon dioxide about 3 km thick. A seasonal ice cap forms on top of the permanent one during the Martian winter, extending from 60°S southwards. It is, at the height of winter, approximately 1 meter thick. It is possible that the area of this ice cap may be shrinking due to localized climate change. Claims of more planetwide global warming based on imagery, however, ignore temperature data and global datasets. Spacecraft and microwave data indicate global average temperature is, at most, stable, and possibly cooling. In 1966, Leighton and Murray proposed that the Martian polar caps provided a store of CO2 much larger than the atmospheric reservoir. However it is now thought that both polar caps are made mostly of water ice. Both poles have a thin seasonal covering of CO2, while in addition the southern pole has a permanent residual CO2 cap, about 8 to 10 metres thick, that lies on top of the water ice. Perhaps the key argument that the bulk of the ice is water is that CO2 ice isn't mechanically strong enough to make a 3 km thick ice cap stable over long periods of time. Recent evidence from SHARAD ice penetrating radar has revealed a massive subsurface CO2 ice deposit approximately equal to 80% of the current atmosphere, or 4–5 mbar, stored in Planum Australe. Data from ESA's Mars Express indicates that there are three main parts to the ice cap. The most reflective part of the ice cap is approximately 85% dry ice and 15% water ice. The second part, where the ice cap forms steep slopes at the boundary with the surrounding plain, is almost exclusively water ice. Finally, the ice cap is surrounded by permafrost fields that extend for tens of kilometres north away from the scarps. The centre of the permanent ice cap is not located at 90°S but rather approximately 150 kilometres north of the geographical south pole. The presence of two massive impact basins in the western hemisphere – Hellas Planitia and Argyre Planitia – creates an immobile area of low pressure over the permanent ice cap. The resulting weather patterns produce fluffy white snow which has a high albedo. This is in contrast to the black ice that forms in the eastern part of the polar region, which receives little snow. Features There are two distinct subregions in Planum Australe – Australe Lingula and Promethei Lingula. It is dissected by canyons Promethei Chasma, Ultimum Chasma, Chasma Australe and Australe Sulci. It is theorised that these canyons were created by katabatic wind. The largest crater in Planum Australe is McMurdo Crater. The seasonal frosting and defrosting of the southern ice cap results in the formation of spider-like radial channels carved on 1 meter thick ice by sunlight. Then, sublimed CO2 (and probably water) increase pressure in their interior, producing geyser-like eruptions of cold fluids often mixed with dark basaltic sand or mud. This process is rapid, observed happening in the space of a few days, weeks or months, a growth rate rather unusual in geology – especially for Mars. The Mars Geyser Hopper lander is a concept mission that would investigate the geysers of Mars. In September 2020, scientists confirmed the existence of several large saltwater lakes under the ice in the south polar region of the planet Mars. According to one of the researchers, “We identified the same body of water [as suggested earlier in a preliminary initial detection], but we also found three other bodies of water around the main one ... It’s a complex system.” See also References External links |
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[SOURCE: https://en.wikipedia.org/wiki/Hypothetical_types_of_biochemistry] | [TOKENS: 5581] |
Contents Hypothetical types of biochemistry Several forms of biochemistry are agreed to be scientifically viable, but are not proven to exist at this time. The kinds of living organisms known on Earth, as of 2026[update], all use carbon compounds for basic structural and metabolic functions, water as a solvent, and deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) to define and control their form. If life exists on other celestial bodies (planets, moons), it may be chemically similar, though it is also possible that there are organisms with quite different chemistries – for instance, involving other classes of carbon compounds, compounds of another element, and/or another solvent in place of water. The possibility of life-forms being based on "alternative" biochemistries is the topic of an ongoing scientific discussion, informed by what is known about extraterrestrial environments and about the chemical behaviour of various elements and compounds. It is of interest in synthetic biology and is also a common subject in science fiction. The element silicon has been much discussed as a hypothetical alternative to carbon. Silicon is in the same group as carbon on the periodic table and, like carbon, it is tetravalent. Hypothetical alternatives to water include ammonia, which, like water, is a polar molecule, and cosmically abundant; and non-polar hydrocarbon solvents such as methane and ethane, which are known to exist in liquid form on the surface of Titan. Overview of hypothetical types of biochemistry In comparison, Hachimoji DNA changes the base pairs instead of the backbone. These new base pairs are P (2-Aminoimidazo[1,2a][1,3,5]triazin-4(1H)-one), Z (6-Amino-5-nitropyridin-2-one), B (Isoguanine), and S (rS=Isocytosine for RNA, dS=1-Methylcytosine for DNA). Shadow biosphere A shadow biosphere is a hypothetical microbial biosphere of Earth that uses radically different biochemical and molecular processes than currently known life. Although life on Earth is relatively well-studied, the shadow biosphere may still remain unnoticed because the exploration of the microbial world targets primarily the biochemistry of the macro-organisms. Alternative-chirality biomolecules Perhaps the least unusual alternative biochemistry would be one with differing chirality of its biomolecules. In known Earth-based life, amino acids are almost universally of the L form and sugars are of the D form. Molecules using D amino acids or L sugars may be possible; molecules of such a chirality, however, would be incompatible with organisms using the opposing chirality molecules. Amino acids which chirality is opposite to the norm are found on Earth, and these substances are generally thought to result from decay of organisms of normal chirality. However, physicist Paul Davies speculates that some of them might be products of "anti-chiral" life. It is questionable, however, whether such a biochemistry would be truly alien. Although it would certainly be an alternative stereochemistry, molecules that are overwhelmingly found in one enantiomer throughout the vast majority of organisms can nonetheless often be found in another enantiomer in different (often basal) organisms such as in comparisons between members of Archaea and other domains,[citation needed] making it an open topic whether an alternative stereochemistry is truly novel. Non-carbon-based biochemistries On Earth, all known living things have a carbon-based structure and system. Scientists have speculated about the advantages and disadvantages of using elements other than carbon to form the molecular structures necessary for life, but no one has proposed a theory employing such atoms to form all the necessary structures. However, as Carl Sagan argued, it is very difficult to be certain whether a statement that applies to all life on Earth will turn out to apply to all life throughout the universe. Sagan used the term "carbon chauvinism" for such an assumption. He regarded silicon and germanium as conceivable alternatives to carbon (other plausible elements include but are not limited to palladium and titanium); but, on the other hand, he noted that carbon does seem more chemically versatile and is more abundant in the cosmos. Norman Horowitz devised the experiments to determine whether life might exist on Mars that were carried out by the Viking Lander of 1976, the first U.S. mission to successfully land a probe on the surface of Mars. Horowitz argued that the great versatility of the carbon atom makes it the element most likely to provide solutions, even exotic solutions, to the problems of survival on other planets. He considered that there was only a remote possibility that non-carbon life forms could exist with genetic information systems capable of self-replication and the ability to evolve and adapt. The silicon atom has been much discussed as the basis for an alternative biochemical system, because silicon has many chemical similarities to carbon and is in the same group of the periodic table. Like carbon, silicon can create molecules that are sufficiently large to carry biological information. However, silicon has several drawbacks as a carbon alternative. Carbon is ten times more cosmically abundant than silicon, and its chemistry appears naturally more complex. By 1998, astronomers had identified 84 carbon-containing molecules in the interstellar medium, but only 8 containing silicon, of which half also include carbon. Even though Earth and other terrestrial planets are exceptionally silicon-rich and carbon-poor (silicon is roughly 925 times more abundant in Earth's crust than carbon), terrestrial life bases itself on carbon. This might be due to the comparatively lower diversity of functional groups observed in naturally-occurring Silicon-based polymers. Relative to carbon, silicon has a much larger atomic radius, and forms much weaker covalent bonds to atoms — except oxygen and fluorine, with which it forms very strong bonds. Almost no multiple bonds to silicon are stable, although silicon does exhibit varied coordination number. Silanes, silicon analogues to the alkanes, react rapidly with water, and long-chain silanes spontaneously decompose. Consequently, most terrestrial silicon is "locked up" in silica, and not a wide variety of biogenic precursors. Silicones, which alternate between silicon and oxygen atoms, are much more stable than silanes, and may even be more stable than the equivalent hydrocarbons in sulfuric acid-rich extraterrestrial environments. Alternatively, the weak bonds in silicon compounds may help maintain a rapid pace of life at cryogenic temperatures. Polysilanols, the silicon homologues to sugars, are among the few compounds soluble in liquid nitrogen.[unreliable source?] All known silicon macromolecules are artificial polymers, and so "monotonous compared with the combinatorial universe of organic macromolecules". Even so, some Earth life uses biogenic silica: diatoms' silicate skeletons. A. G. Cairns-Smith hypothesized that silicate minerals in water played a crucial role in abiogenesis, in that biogenic carbon compounds formed around their crystal structures. Although not observed in nature, carbon–silicon bonds have been added to biochemistry under directed evolution (artificial selection): a cytochrome c protein from Rhodothermus marinus has been engineered to catalyse new carbon–silicon bonds between hydrosilanes and diazo compounds. Arsenic as an alternative to phosphorus While arsenic, which is chemically similar to phosphorus, is poisonous for most life forms on Earth, it is incorporated into the biochemistry of some organisms. Some marine algae incorporate arsenic into complex organic molecules such as arsenosugars and arsenobetaines. Fungi and bacteria can produce volatile methylated arsenic compounds. Arsenate reduction and arsenite oxidation have been observed in microbes (Chrysiogenes arsenatis). Additionally, some prokaryotes can use arsenate as a terminal electron acceptor during anaerobic growth and some can utilize arsenite as an electron donor to generate energy. It has been speculated that the earliest life forms on Earth may have used arsenic biochemistry in place of phosphorus in the structure of their DNA. A common objection to this scenario is that arsenate esters are so much less stable to hydrolysis than corresponding phosphate esters that arsenic is poorly suited for this function. The authors of a 2010 geomicrobiology study, supported in part by NASA, have postulated that a bacterium named GFAJ-1, collected in the sediments of Mono Lake in eastern California, can employ such 'arsenic DNA' when cultured without phosphorus. They proposed that the bacterium may employ high levels of poly-β-hydroxybutyrate or other means to reduce the effective concentration of water and stabilize its arsenate esters. This claim was heavily criticized almost immediately after publication for the perceived lack of appropriate controls. Other authors were unable to reproduce their results and showed that the study had issues with phosphate contamination, suggesting that the low amounts present could sustain extremophile lifeforms. The 2010 paper was retracted in 2025. Non-water solvents In addition to carbon compounds, all currently known terrestrial life also requires water as a solvent. This has led to discussions about whether water is the only liquid capable of filling that role. The idea that an extraterrestrial life-form might be based on a solvent other than water has been taken seriously in recent scientific literature by the biochemist Steven Benner, and by the astrobiological committee chaired by John A. Baross. Solvents discussed by the Baross committee include ammonia, sulfuric acid, formamide, hydrocarbons, and (at temperatures much lower than Earth's) liquid nitrogen, or hydrogen in the form of a supercritical fluid. Water as a solvent limits the forms biochemistry can take. For example, Steven Benner, proposes the polyelectrolyte theory of the gene that claims that for a genetic biopolymer such as DNA to function in water, it requires repeated ionic charges. If water is not required for life, these limits on genetic biopolymers are removed. Carl Sagan once described himself as both a carbon chauvinist and a water chauvinist; however, on another occasion he said that he was a carbon chauvinist but "not that much of a water chauvinist". He speculated on hydrocarbons,: 11 hydrofluoric acid, and ammonia as possible alternatives to water. Some of the properties of water that are important for life processes include: Water as a compound is cosmically abundant, although much of it is in the form of vapor or ice. Subsurface liquid water is considered likely or possible on several of the outer moons: Enceladus and Europa (where geysers have been observed), Titan, and Ganymede. Earth and Titan are the only worlds currently known to have stable bodies of liquid on their surfaces. Not all properties of water are necessarily advantageous for life, however. For instance, water ice has a high albedo, meaning that it reflects a significant quantity of light and heat from the Sun. During ice ages, as reflective ice builds up over the surface of the water, the effects of global cooling are increased. There are some properties that make certain compounds and elements much more favourable than others as solvents in a successful biosphere. The solvent must be able to exist in liquid equilibrium over a range of temperatures the planetary object would normally encounter. Because boiling points vary with the pressure, the question tends not to be does the prospective solvent remain liquid, but at what pressure. For example, hydrogen cyanide has a narrow liquid-phase temperature range at 1 atmosphere, but in an atmosphere with the pressure of Venus, with 91 standard atmospheres (92 bar) of pressure, it can indeed exist in liquid form over a wide temperature range. The ammonia molecule (NH3), like the water molecule, is abundant in the universe, being a compound of hydrogen (the simplest and most common element) with another very common element, nitrogen. The possible role of liquid ammonia as an alternative solvent for life is an idea dating back to 1954 at least, when J. B. S. Haldane raised the topic at a symposium about life's origin. Many chemical reactions can occur in an ammonia solution, and liquid ammonia has chemical similarities with water. Ammonia dissolves most organic molecules at least as well as water does, and many elemental metals. Haldane indicated that various common water-related organic compounds have ammonia-related analogues; for instance, the ammonia-related amine group (−NH2) is analogous to the water-related hydroxyl group (−OH). Ammonia, like water, can either accept or donate an H+ ion. When ammonia accepts an H+, it forms the ammonium cation (NH4+), analogous to hydronium (H3O+). When it donates an H+ ion, it forms the amide anion (NH2−), analogous to the hydroxide anion (OH−). Compared to water, however, ammonia is more inclined to accept an H+ ion, and less inclined to donate one; it is a stronger nucleophile. Ammonia added to water functions as an Arrhenius base: it increases the concentration of the anion hydroxide. Conversely, using a solvent system definition of acidity and basicity, water added to liquid ammonia functions as an acid, because it increases the concentration of the cation ammonium. The carbonyl group (C=O), which is much used in terrestrial biochemistry, would not be stable in ammonia solution, but the analogous imine group (C=NH) could be used instead. However, ammonia has some problems as a basis for life. The hydrogen bonds between ammonia molecules are weaker than those in water, causing ammonia's heat of vaporization to be half that of water, its surface tension to be a third, and reducing its ability to concentrate non-polar molecules through a hydrophobic effect. Gerald Feinberg and Robert Shapiro have questioned whether ammonia could hold prebiotic molecules together well enough to allow the emergence of a self-reproducing system. Ammonia is also flammable in oxygen and could not exist sustainably in an environment suitable for aerobic metabolism. A biosphere based on ammonia would likely exist at temperatures or air pressures that are extremely unusual in relation to life on Earth. Life on Earth usually exists between the melting point and boiling point of water, at a pressure designated as normal pressure, between 0 and 100 °C (273 and 373 K). When also held to normal pressure, ammonia's melting and boiling points are −78 °C (195 K) and −33 °C (240 K) respectively. Because chemical reactions generally proceed more slowly at lower temperatures, ammonia-based life existing in this set of conditions might metabolize more slowly and evolve more slowly than life on Earth. On the other hand, lower temperatures could also enable living systems to use chemical species that would be too unstable at Earth temperatures to be useful. A set of conditions where ammonia is liquid at Earth-like temperatures would involve it being at a much higher pressure. For example, at 60 atm ammonia melts at −77 °C (196 K) and boils at 98 °C (371 K). Ammonia and ammonia–water mixtures remain liquid at temperatures far below the freezing point of pure water, so such biochemistries might be well suited to planets and moons orbiting outside the water-based habitability zone. Such conditions could exist, for example, under the surface of Saturn's largest moon Titan. Methane (CH4) is a simple hydrocarbon: that is, a compound of two of the most common elements in the cosmos: hydrogen and carbon. It has a cosmic abundance comparable with ammonia. Hydrocarbons could act as a solvent over a wide range of temperatures but would lack polarity. Isaac Asimov, the biochemist and science fiction writer, suggested in 1981 that poly-lipids could form a substitute for proteins in a non-polar solvent such as methane. Lakes composed of a mixture of hydrocarbons, including methane and ethane, have been detected on the surface of Titan by the Cassini spacecraft. There is debate about the effectiveness of methane and other hydrocarbons as a solvent for life compared to water or ammonia. Water is a stronger solvent than the hydrocarbons, enabling easier transport of substances in a cell. However, water is also more chemically reactive and can break down large organic molecules through hydrolysis. A life-form which solvent was a hydrocarbon would not face the threat of its biomolecules being destroyed in this way. Also, the water molecule's tendency to form strong hydrogen bonds can interfere with internal hydrogen bonding in complex organic molecules. Life with a hydrocarbon solvent could make more use of hydrogen bonds within its biomolecules. Moreover, the strength of hydrogen bonds within biomolecules would be appropriate to a low-temperature biochemistry. Astrobiologist Chris McKay has argued, on thermodynamic grounds, that if life does exist on Titan's surface, using hydrocarbons as a solvent, it is likely also to use the more complex hydrocarbons as an energy source by reacting them with hydrogen, reducing ethane and acetylene to methane. Possible evidence for this form of life on Titan was identified in 2010 by Darrell Strobel of Johns Hopkins University; a greater abundance of molecular hydrogen in the upper atmospheric layers of Titan compared to the lower layers, arguing for a downward diffusion at a rate of roughly 1025 molecules per second and disappearance of hydrogen near Titan's surface. As Strobel noted, his findings were in line with the effects Chris McKay had predicted if methanogenic life-forms were present. The same year, another study showed low levels of acetylene on Titan's surface, which were interpreted by Chris McKay as consistent with the hypothesis of organisms reducing acetylene to methane. While restating the biological hypothesis, McKay cautioned that other explanations for the hydrogen and acetylene findings are to be considered more likely: the possibilities of yet unidentified physical or chemical processes (e.g. a non-living surface catalyst enabling acetylene to react with hydrogen), or flaws in the current models of material flow. He noted that even a non-biological catalyst effective at 95 K would in itself be a startling discovery. A hypothetical cell membrane termed an azotosome, able to function in liquid methane in Titan conditions was computer-modelled in an article published in February 2015. Composed of acrylonitrile, a small molecule containing carbon, hydrogen, and nitrogen, it is predicted to have stability and flexibility in liquid methane comparable to that of a phospholipid bilayer (the type of cell membrane possessed by all life on Earth) in liquid water. An analysis of data obtained using the Atacama Large Millimeter / submillimeter Array (ALMA), completed in 2017, confirmed substantial amounts of acrylonitrile in Titan's atmosphere. Later studies questioned whether acrylonitrile would be able to self-assemble into azotosomes. However, in 2025 a new mechanism was proposed by scientists Christian Mayer and Conor Nixon to overcome the previous barriers to self-assembly of azotosomes in liquid methane, based on 'splashing' of a methane lake surface film by a hydrocarbon raindrop. Hydrogen fluoride (HF), like water, is a polar molecule, and due to its polarity it can dissolve many ionic compounds. At atmospheric pressure, its melting point is 189.15 K (−84.00 °C), and its boiling point is 292.69 K (19.54 °C); the difference between the two is a little more than 100 K. HF also makes hydrogen bonds with its neighbour molecules, as do water and ammonia. It has been considered as a possible solvent for life by scientists such as Peter Sneath and Carl Sagan. HF is dangerous to the systems of molecules that Earth-life is made of, but certain other organic compounds, such as paraffin waxes, are stable with it. Like water and ammonia, liquid hydrogen fluoride supports an acid–base chemistry. Using a solvent system definition of acidity and basicity, nitric acid functions as a base when it is added to liquid HF. However, hydrogen fluoride is cosmically rare, unlike water, ammonia, and methane. Hydrogen sulfide is the closest chemical analog to water, but is less polar and is a weaker inorganic solvent. Hydrogen sulfide is quite plentiful on Jupiter's moon Io and may be in liquid form a short distance below the surface; astrobiologist Dirk Schulze-Makuch has suggested it as a possible solvent for life there. On a planet with hydrogen sulfide oceans, the source of the hydrogen sulfide could come from volcanoes, in which case it could be mixed in with a bit of hydrogen fluoride, which could help dissolve minerals. Hydrogen sulfide life might use a mixture of carbon monoxide and carbon dioxide as their carbon source. They might produce and live on sulfur monoxide, which is analogous to oxygen (O2). Hydrogen sulfide, like hydrogen cyanide and ammonia, suffers from the small temperature range where it is liquid, though that, like that of hydrogen cyanide and ammonia, increases with increasing pressure. Silicon dioxide, also known as silica and quartz, is very abundant in the universe and has a large temperature range where it is liquid. However, its melting point is 1,600 to 1,725 °C (2,912 to 3,137 °F), so it would be impossible to make organic compounds in that temperature, because all of them would decompose. Silicates are similar to silicon dioxide and some have lower melting points than silica. Feinberg and Shapiro have suggested that molten silicate rock could serve as a liquid medium for organisms with a chemistry based on silicon, oxygen, and other elements such as aluminium. Other solvents sometimes proposed: Sulfuric acid in liquid form is strongly polar. It remains liquid at higher temperatures than water, its liquid range being 10 °C to 337 °C at a pressure of 1 atm, although above 300 °C it slowly decomposes. Sulfuric acid is known to be abundant in the clouds of Venus, in the form of aerosol droplets. In a biochemistry that used sulfuric acid as a solvent, the alkene group (C=C), with two carbon atoms joined by a double bond, could function analogously to the carbonyl group (C=O) in water-based biochemistry. A proposal has been made that life on Mars may exist and be using a mixture of water and hydrogen peroxide as its solvent. A 61.2% (by mass) mix of water and hydrogen peroxide has a freezing point of −56.5 °C and tends to super-cool rather than crystallize. It is also hygroscopic, an advantage in a water-scarce environment. Supercritical carbon dioxide has been proposed as a candidate for alternative biochemistry due to its ability to selectively dissolve organic compounds and assist the functioning of enzymes and because "super-Earth"- or "super-Venus"-type planets with dense high-pressure atmospheres may be common. Other speculations Physicists have noted that, although photosynthesis on Earth generally involves green plants, a variety of other-colored plants could also support photosynthesis, essential for most life on Earth, and that other colors might be preferred in places that receive a different mix of stellar radiation than Earth. These studies indicate that blue plants would be unlikely; however yellow or red plants may be relatively common. Many Earth plants and animals undergo major biochemical changes during their life cycles as a response to changing environmental conditions, for example, by having a spore or hibernation state that can be sustained for years or even millennia between more active life stages. Thus, it would be biochemically possible to sustain life in environments that are only periodically consistent with life as we know it. For example, frogs in cold climates can survive for extended periods of time with most of their body water in a frozen state, whereas desert frogs in Australia can become inactive and dehydrate in dry periods, losing up to 75% of their fluids, yet return to life by rapidly rehydrating in wet periods. Either type of frog would appear biochemically inactive (i.e. not living) during dormant periods to anyone lacking a sensitive means of detecting low levels of metabolism. The genetic code may have evolved during the transition from the RNA world to a protein world. The alanine world hypothesis postulates that the evolution of the genetic code (the so-called GC phase) started with only four basic amino acids: alanine, glycine, proline and ornithine (now arginine). The evolution of the genetic code ended with 20 proteinogenic amino acids. From a chemical point of view, most of them are Alanine-derivatives particularly suitable for the construction of α-helices and β-sheets – basic secondary structural elements of modern proteins. Direct evidence of this is an experimental procedure in molecular biology known as alanine scanning. A hypothetical proline world would create a possible alternative life with the genetic code based on the proline chemical scaffold as the protein backbone. Similarly, a glycine world and ornithine world are also conceivable, but nature has chosen none of them. Evolution of life with Proline, Glycine, or Ornithine as the basic structure for protein-like polymers (foldamers) would lead to parallel biological worlds. They would have morphologically radically different body plans and genetics from the living organisms of the known biosphere. Nonplanetary life In 2007, Vadim N. Tsytovich and colleagues proposed that lifelike behaviors could be exhibited by dust particles suspended in a plasma, under conditions that might exist in space. Computer models showed that, when the dust became charged, the particles could self-organize into microscopic helical structures, and the authors offer "a rough sketch of a possible model of...helical grain structure reproduction". In 2020, Luis A. Anchordoqu and Eugene M. Chudnovsky of the City University of New York hypothesized that cosmic necklace-based life composed of magnetic monopoles connected by cosmic strings could evolve inside stars. This would be achieved by a stretching of cosmic strings due to the star's intense gravity, thus allowing it to take on more complex forms and potentially form structures similar to the RNA and DNA structures found within carbon-based life. As such, it is theoretically possible that such beings could eventually become intelligent and construct a civilization using the power generated by the star's nuclear fusion. Because such use would use up part of the star's energy output, the luminosity would also fall. For this reason, it is thought that such life might exist inside stars observed to be cooling faster or dimmer than current cosmological models predict. Frank Drake suggested in 1973 that intelligent life could inhabit neutron stars. Physical models in 1973 implied that Drake's creatures would be microscopic. Scientists who have published on this topic Scientists who have considered possible alternatives to carbon-water biochemistry include: See also References Further reading |
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[SOURCE: https://en.wikipedia.org/wiki/NewtonScript] | [TOKENS: 729] |
Contents NewtonScript NewtonScript is a prototype-based programming language created to write programs for the Newton platform. It is heavily influenced by the Self programming language, but modified to be more suited to needs of mobile and embedded devices. History On August 3, 1993, Apple unveiled the Apple Newton MessagePad. The device had 640 KB RAM, 4 MB ROM, and a 20 MHz ARM 610 microprocessor. The main intention behind Newton project, was to develop a device capable of replacing a computer while being portable. With limited battery and memory, the developers were looking for programming language capable of meeting these challenges. The developers looked at the C++ programming language but realized that it lacked flexibility. They started focusing on prototype based languages and were impressed with Smalltalk and Self. Concurrently Apple was developing another dynamic programming language called Dylan, which was a strong candidate for the Newton platform. However, both Self and Dylan were dropped out of consideration, as they were both in nascent stage for proper integration. Instead, a team headed by Walter R. Smith developed a new language called NewtonScript. It was influenced by dynamic language like Smalltalk and prototype model based like Self. Features Although NewtonScript was heavily influenced by Self, there were some differences in both the languages. Differences arose due to three perceived problems with Self. The syntax was also modified to allow a more text-based programming style, as opposed to Self's widespread use of a GUI environment for programming. This allowed Newton programs to be developed on a computer running the Toolkit, where the programs would be compiled and then downloaded to a Newton device for running. One of the advantages of NewtonScript's prototype based inheritance was reduced memory usage, a key consideration in the 128 KB Newton. The prototype of a GUI object could actually be stored in ROM, so there was no need to copy default data or functions into working memory. Unlike class-based languages, where creation of an object involves memory being allocated to all of its attributes, NewtonScripts' use of prototype inheritance allowed it to allocated memory to few fields like _proto and _parent instead of creating whole new object. Here, _proto and _parent signifies whether the object is using prototype or parent inheritance. An example to illustrate above concept, a developer might create a new button instance. If the button uses the default font, accessing its font "slot" (i.e., property or member variable) will return a value that is actually stored in ROM; the button instance in RAM does not have a value in its own font slot, so the prototype inheritance chain is followed until a value is found. If the developer then changes the button's font, setting its font slot to a new value will override the prototype; this override value is stored in RAM. NewtonScript's "differential inheritance" therefore made efficient use of the Newton's expensive flash RAM by storing the bulk of the default data and code in the PDA's cheaper and much larger ROM. Important terms Advantages and disadvantages Influences With the cancellation of the Newton project by Apple in 1998, all further mainstream developments on NewtonScript were stopped. However, the features used in NewtonScript would continue to inspire other programming models and languages. The prototype-based object model of Self and NewtonScript was used in JavaScript, the most popular and visible language to use the concept so far. NewtonScript is also one of the conceptual ancestors (together with Smalltalk, Self, Act1, Lisp and Lua) of a general-purpose programming language called Io which implements the same differential inheritance model, which was used in NewtonScript to conserve memory. See also References External links |
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