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Android (operating system)
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https://en.wikipedia.org/wiki?curid=12610483
| 8,197 |
The flash storage on Android devices is split into several partitions, such as codice_2 for the operating system itself, and codice_3 for user data and application installations. In 2021, journalists and researchers reported the discovery of spyware, called Pegasus, developed and distributed by a private company which can and has been used to infect both iOS and Android smartphones often – partly via use of 0-day exploits – without the need for any user-interaction or significant clues to the user and then be used to exfiltrate data, track user locations, capture film through its camera, and activate the microphone at any time. Analysis of data traffic by popular smartphones running variants of Android found substantial by-default data collection and sharing with no opt-out by this pre-installed software. Both of these issues are not addressed or cannot be addressed by security patches. In August 2015, Google announced that devices in the Google Nexus series would begin to receive monthly security patches. Google also wrote that "Nexus devices will continue to receive major updates for at least two years and security patches for the longer of three years from initial availability or 18 months from last sale of the device via the Google Store." The following October, researchers at the University of Cambridge concluded that 87.7% of Android phones in use had known but unpatched security vulnerabilities due to lack of updates and support. Ron Amadeo of "Ars Technica" wrote also in August 2015 that "Android was originally designed, above all else, to be widely adopted. Google was starting from scratch with zero percent market share, so it was happy to give up control and give everyone a seat at the table in exchange for adoption. [...] Now, though, Android has around 75–80 percent of the worldwide smartphone market—making it not just the world's most popular mobile operating system but arguably the most popular operating system, period. As such, security has become a big issue. Android still uses a software update chain-of-command designed back when the Android ecosystem had zero devices to update, and it just doesn't work". Following news of Google's monthly schedule, some manufacturers, including Samsung and LG, promised to issue monthly security updates, but, as noted by Jerry Hildenbrand in "Android Central" in February 2016, "instead we got a few updates on specific versions of a small handful of models. And a bunch of broken promises".
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Android (operating system)
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https://en.wikipedia.org/wiki?curid=12610483
| 8,221 |
Android smartphones have the ability to report the location of Wi-Fi access points, encountered as phone users move around, to build databases containing the physical locations of hundreds of millions of such access points. These databases form electronic maps to locate smartphones, allowing them to run apps like Foursquare, Google Latitude, Facebook Places, and to deliver location-based ads. Third party monitoring software such as TaintDroid, an academic research-funded project, can, in some cases, detect when personal information is being sent from applications to remote servers. In the second quarter of 2014, Android's share of the global smartphone shipment market was 84.7%, a new record. This had grown to 87.5% worldwide market share by the third quarter of 2016, leaving main competitor iOS with 12.1% market share. The recently released Android 12 is the most popular Android version on both smartphones and tablets.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,350 |
Albert Einstein ( ; ; 14 March 1879 – 18 April 1955) was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time. Einstein is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics. Relativity and quantum mechanics are the two pillars of modern physics. His mass–energy equivalence formula , which arises from relativity theory, has been dubbed "the world's most famous equation". His work is also known for its influence on the philosophy of science. He received the 1921 Nobel Prize in Physics "for his services to theoretical physics, and especially for his discovery of the law of the photoelectric effect", a pivotal step in the development of quantum theory. His intellectual achievements and originality resulted in "Einstein" becoming synonymous with "genius". Einsteinium, one of the synthetic elements in the Periodic Table was named in his honor. In 1905, a year sometimes described as his "annus mirabilis" ('miracle year'), Einstein published four groundbreaking papers. These outlined the theory of the photoelectric effect, explained Brownian motion, introduced special relativity, and demonstrated mass-energy equivalence. Einstein thought that the laws of classical mechanics could no longer be reconciled with those of the electromagnetic field, which led him to develop his special theory of relativity. He then extended the theory to gravitational fields; he published a paper on general relativity in 1916, introducing his theory of gravitation. In 1917, he applied the general theory of relativity to model the structure of the universe. He continued to deal with problems of statistical mechanics and quantum theory, which led to his explanations of particle theory and the motion of molecules. He also investigated the thermal properties of light and the quantum theory of radiation, which laid the foundation of the photon theory of light. However, for much of the later part of his career, he worked on two ultimately unsuccessful endeavors. First, despite his great contributions to quantum mechanics, he opposed what it evolved into, objecting that "God does not play dice". Second, he attempted to devise a unified field theory by generalizing his geometric theory of gravitation to include electromagnetism. As a result, he became increasingly isolated from the mainstream of modern physics.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,353 |
Einstein was born in the German Empire, but moved to Switzerland in 1895, forsaking his German citizenship (as a subject of the Kingdom of Württemberg) the following year. In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich, graduating in 1900. In 1901, he acquired Swiss citizenship, which he kept for the rest of his life, and in 1903 he secured a permanent position at the Swiss Patent Office in Bern. In 1905, he was awarded a PhD by the University of Zurich. In 1914, Einstein moved to Berlin in order to join the Prussian Academy of Sciences and the Humboldt University of Berlin. In 1917, Einstein became director of the Kaiser Wilhelm Institute for Physics; he also became a German citizen again, this time Prussian. In 1933, while Einstein was visiting the United States, Adolf Hitler came to power in Germany. Einstein, as a Jew, objected to the policies of the newly elected Nazi government; he settled in the United States and became an American citizen in 1940. On the eve of World War II, he endorsed a letter to President Franklin D. Roosevelt alerting him to the potential German nuclear weapons program and recommending that the US begin similar research. Einstein supported the Allies but generally denounced the idea of nuclear weapons. Albert Einstein was born in Ulm, in the Kingdom of Württemberg in the German Empire, on 14 March 1879 into a family of secular Ashkenazi Jews. His parents were Hermann Einstein, a salesman and engineer, and Pauline Koch. In 1880, the family moved to Munich, where Einstein's father and his uncle Jakob founded "Elektrotechnische Fabrik J. Einstein & Cie", a company that manufactured electrical equipment based on direct current. Albert attended a Catholic elementary school in Munich, from the age of five, for three years. At the age of eight, he was transferred to the Luitpold-Gymnasium (now known as the Albert-Einstein-Gymnasium), where he received advanced primary and secondary school education until he left the German Empire seven years later.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,357 |
In 1894, Hermann and Jakob's company lost a bid to supply the city of Munich with electrical lighting because they lacked the capital to convert their equipment from the direct current (DC) standard to the more efficient alternating current (AC) standard. The loss forced the sale of the Munich factory. In search of business, the Einstein family moved to Italy, first to Milan and a few months later to Pavia. When the family moved to Pavia, Einstein, then 15, stayed in Munich to finish his studies at the Luitpold Gymnasium. His father intended for him to pursue electrical engineering, but Einstein clashed with the authorities and resented the school's regimen and teaching method. He later wrote that the spirit of learning and creative thought was lost in strict rote learning. At the end of December 1894, he traveled to Italy to join his family in Pavia, convincing the school to let him go by using a doctor's note. During his time in Italy he wrote a short essay with the title "On the Investigation of the State of the Ether in a Magnetic Field". Einstein excelled at math and physics from a young age, reaching a mathematical level years ahead of his peers. The 12-year-old Einstein taught himself algebra and Euclidean geometry over a single summer. Einstein also independently discovered his own original proof of the Pythagorean theorem aged 12. A family tutor Max Talmud says that after he had given the 12-year-old Einstein a geometry textbook, after a short time "[Einstein] had worked through the whole book. He thereupon devoted himself to higher mathematics ... Soon the flight of his mathematical genius was so high I could not follow." His passion for geometry and algebra led the 12-year-old to become convinced that nature could be understood as a "mathematical structure". Einstein started teaching himself calculus at 12, and as a 14-year-old he says he had "mastered integral and differential calculus". At the age of 13, when he had become more seriously interested in philosophy (and music), Einstein was introduced to Kant's "Critique of Pure Reason". Kant became his favorite philosopher, his tutor stating: "At the time he was still a child, only thirteen years old, yet Kant's works, incomprehensible to ordinary mortals, seemed to be clear to him."
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,360 |
In 1895, at the age of 16, Einstein took the entrance examinations for the Swiss Federal polytechnic school in Zürich (later the Eidgenössische Technische Hochschule, ETH). He failed to reach the required standard in the general part of the examination, but obtained exceptional grades in physics and mathematics. On the advice of the principal of the polytechnic school, he attended the Argovian cantonal school ("gymnasium") in Aarau, Switzerland, in 1895 and 1896 to complete his secondary schooling. While lodging with the family of Jost Winteler, he fell in love with Winteler's daughter, Marie. Albert's sister Maja later married Winteler's son Paul. In January 1896, with his father's approval, Einstein renounced his citizenship in the German Kingdom of Württemberg to avoid military service. In September 1896 he passed the Swiss "Matura" with mostly good grades, including a top grade of 6 in physics and mathematical subjects, on a scale of 1–6. At 17, he enrolled in the four-year mathematics and physics teaching diploma program at the Federal polytechnic school. Marie Winteler, who was a year older, moved to Olsberg, Switzerland, for a teaching post. Einstein's future wife, a 20-year-old Serbian named Mileva Marić, also enrolled at the polytechnic school that year. She was the only woman among the six students in the mathematics and physics section of the teaching diploma course. Over the next few years, Einstein's and Marić's friendship developed into a romance, and they spent countless hours debating and reading books together on extra-curricular physics in which they were both interested. Einstein wrote in his letters to Marić that he preferred studying alongside her. In 1900, Einstein passed the exams in Maths and Physics and was awarded a Federal teaching diploma. There is eyewitness evidence and several letters over many years that indicate Marić might have collaborated with Einstein prior to his landmark 1905 papers, known as the "Annus Mirabilis" papers, and that they developed some of the concepts together during their studies, although some historians of physics who have studied the issue disagree that she made any substantive contributions.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,362 |
Early correspondence between Einstein and Marić was discovered and published in 1987 which revealed that the couple had a daughter named "Lieserl", born in early 1902 in Novi Sad where Marić was staying with her parents. Marić returned to Switzerland without the child, whose real name and fate are unknown. The contents of Einstein's letter in September 1903 suggest that the girl was either given up for adoption or died of scarlet fever in infancy. Einstein and Marić married in January 1903. In May 1904, their son Hans Albert Einstein was born in Bern, Switzerland. Their son Eduard was born in Zürich in July 1910. The couple moved to Berlin in April 1914, but Marić returned to Zürich with their sons after learning that, despite their close relationship before, Einstein's chief romantic attraction was now his cousin Elsa Löwenthal; she was his first cousin maternally and second cousin paternally. Einstein and Marić divorced on 14 February 1919, having lived apart for five years. As part of the divorce settlement, Einstein agreed to give Marić any future (in the event, 1921) Nobel Prize money. In letters revealed in 2015, Einstein wrote to his early love Marie Winteler about his marriage and his strong feelings for her. He wrote in 1910, while his wife was pregnant with their second child: "I think of you in heartfelt love every spare minute and am so unhappy as only a man can be." He spoke about a "misguided love" and a "missed life" regarding his love for Marie. Einstein married Löwenthal in 1919, after having had a relationship with her since 1912. They emigrated to the United States in 1933. Elsa was diagnosed with heart and kidney problems in 1935 and died in December 1936.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,366 |
In 1923, Einstein fell in love with a secretary named Betty Neumann, the niece of a close friend, Hans Mühsam. In a volume of letters released by Hebrew University of Jerusalem in 2006, Einstein described about six women, including Margarete Lebach (a blonde Austrian), Estella Katzenellenbogen (the rich owner of a florist business), Toni Mendel (a wealthy Jewish widow) and Ethel Michanowski (a Berlin socialite), with whom he spent time and from whom he received gifts while being married to Elsa. Later, after the death of his second wife Elsa, Einstein was briefly in a relationship with Margarita Konenkova. Konenkova was a Russian spy who was married to the Russian sculptor Sergei Konenkov (who created the bronze bust of Einstein at the Institute for Advanced Study at Princeton). Einstein's son Eduard had a breakdown at about age 20 and was diagnosed with schizophrenia. His mother cared for him and he was also committed to asylums for several periods, finally, after her death, being committed permanently to Burghölzli, the Psychiatric University Hospital in Zürich. After graduating in 1900, Einstein spent almost two years searching for a teaching post. He acquired Swiss citizenship in February 1901, but was not conscripted for medical reasons. With the help of Marcel Grossmann's father, he secured a job in Bern at the Swiss Patent Office, as an assistant examiner – level III. Einstein evaluated patent applications for a variety of devices including a gravel sorter and an electromechanical typewriter. In 1903, his position at the Swiss Patent Office became permanent, although he was passed over for promotion until he "fully mastered machine technology". Much of his work at the patent office related to questions about transmission of electric signals and electrical-mechanical synchronization of time, two technical problems that show up conspicuously in the thought experiments that eventually led Einstein to his radical conclusions about the nature of light and the fundamental connection between space and time. With a few friends he had met in Bern, Einstein started a small discussion group in 1902, self-mockingly named "The Olympia Academy", which met regularly to discuss science and philosophy. Sometimes they were joined by Mileva who attentively listened but did not participate. Their readings included the works of Henri Poincaré, Ernst Mach, and David Hume, which influenced his scientific and philosophical outlook.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,372 |
In 1900, Einstein's paper "Folgerungen aus den Capillaritätserscheinungen" ("Conclusions from the Capillarity Phenomena") was published in the journal "Annalen der Physik". On 30 April 1905 Einstein completed his dissertation, "A New Determination of Molecular Dimensions" with Alfred Kleiner, serving as "pro-forma" advisor. His thesis was accepted in July 1905, and Einstein was awarded a PhD on 15 January 1906. Also in 1905, which has been called Einstein's "annus mirabilis" (amazing year), he published four groundbreaking papers, on the photoelectric effect, Brownian motion, special relativity, and the equivalence of mass and energy, which were to bring him to the notice of the academic world, at the age of 26. By 1908, he was recognized as a leading scientist and was appointed lecturer at the University of Bern. The following year, after he gave a lecture on electrodynamics and the relativity principle at the University of Zurich, Alfred Kleiner recommended him to the faculty for a newly created professorship in theoretical physics. Einstein was appointed associate professor in 1909. Einstein became a full professor at the German Charles-Ferdinand University in Prague in April 1911, accepting Austrian citizenship in the Austro-Hungarian Empire to do so. During his Prague stay, he wrote 11 scientific works, five of them on radiation mathematics and on the quantum theory of solids. In July 1912, he returned to his alma mater in Zürich. From 1912 until 1914, he was a professor of theoretical physics at the ETH Zurich, where he taught analytical mechanics and thermodynamics. He also studied continuum mechanics, the molecular theory of heat, and the problem of gravitation, on which he worked with mathematician and friend Marcel Grossmann. When the "Manifesto of the Ninety-Three" was published in October 1914—a document signed by a host of prominent German intellectuals that justified Germany's militarism and position during the First World War—Einstein was one of the few German intellectuals to rebut its contents and sign the pacifistic "Manifesto to the Europeans".
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,378 |
In the spring of 1913, Einstein was enticed to move to Berlin with an offer that included membership in the Prussian Academy of Sciences, and a linked University of Berlin professorship, enabling him to concentrate exclusively on research. On 3 July 1913, he became a member of the Prussian Academy of Sciences in Berlin. Max Planck and Walther Nernst visited him the next week in Zurich to persuade him to join the academy, additionally offering him the post of director at the Kaiser Wilhelm Institute for Physics, which was soon to be established. Membership in the academy included paid salary and professorship without teaching duties at Humboldt University of Berlin. He was officially elected to the academy on 24 July, and he moved to Berlin the following year. His decision to move to Berlin was also influenced by the prospect of living near his cousin Elsa, with whom he had started a romantic affair. Einstein assumed his position with the academy, and Berlin University, after moving into his Dahlem apartment on 1 April 1914. As World War I broke out that year, the plan for Kaiser Wilhelm Institute for Physics was delayed. The institute was established on 1 October 1917, with Einstein as its director. In 1916, Einstein was elected president of the German Physical Society (1916–1918). In 1911, Einstein used his 1907 Equivalence principle to calculate the deflection of light from another star by the Sun's gravity. In 1913, Einstein improved upon those calculations by using Riemannian space-time to represent the gravity field. By the fall of 1915, Einstein had successfully completed his general theory of relativity, which he used to calculate that deflection, and the perihelion precession of Mercury. In 1919, that deflection prediction was confirmed by Sir Arthur Eddington during the solar eclipse of 29 May 1919. Those observations were published in the international media, making Einstein world-famous. On 7 November 1919, the leading British newspaper "The Times" printed a banner headline that read: "Revolution in Science – New Theory of the Universe – Newtonian Ideas Overthrown".
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,380 |
In 1920, he became a Foreign Member of the Royal Netherlands Academy of Arts and Sciences. In 1922, he was awarded the 1921 Nobel Prize in Physics "for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect". While the general theory of relativity was still considered somewhat controversial, the citation also does not treat even the cited photoelectric work as an "explanation" but merely as a "discovery of the law", as the idea of photons was considered outlandish and did not receive universal acceptance until the 1924 derivation of the Planck spectrum by S. N. Bose. Einstein was elected a Foreign Member of the Royal Society (ForMemRS) in 1921. He also received the Copley Medal from the Royal Society in 1925. Einstein resigned from the Prussian Academy in March 1933. Einstein's scientific accomplishments while in Berlin, included finishing the general theory of relativity, proving the gyromagnetic effect, contributing to the quantum theory of radiation, and Bose–Einstein statistics. Einstein visited New York City for the first time on 2 April 1921, where he received an official welcome by Mayor John Francis Hylan, followed by three weeks of lectures and receptions. He went on to deliver several lectures at Columbia University and Princeton University, and in Washington, he accompanied representatives of the National Academy of Sciences on a visit to the White House. On his return to Europe he was the guest of the British statesman and philosopher Viscount Haldane in London, where he met several renowned scientific, intellectual, and political figures, and delivered a lecture at King's College London. He also published an essay, "My First Impression of the U.S.A.", in July 1921, in which he tried briefly to describe some characteristics of Americans, much as had Alexis de Tocqueville, who published his own impressions in "Democracy in America" (1835). For some of his observations, Einstein was clearly surprised: "What strikes a visitor is the joyous, positive attitude to life ... The American is friendly, self-confident, optimistic, and without envy."
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,384 |
In 1922, his travels took him to Asia and later to Palestine, as part of a six-month excursion and speaking tour, as he visited Singapore, Ceylon and Japan, where he gave a series of lectures to thousands of Japanese. After his first public lecture, he met the emperor and empress at the Imperial Palace, where thousands came to watch. In a letter to his sons, he described his impression of the Japanese as being modest, intelligent, considerate, and having a true feel for art. In his own travel diaries from his 1922–23 visit to Asia, he expresses some views on the Chinese, Japanese and Indian people, which have been described as xenophobic and racist judgments when they were rediscovered in 2018. Because of Einstein's travels to the Far East, he was unable to personally accept the Nobel Prize for Physics at the Stockholm award ceremony in December 1922. In his place, the banquet speech was made by a German diplomat, who praised Einstein not only as a scientist but also as an international peacemaker and activist. On his return voyage, he visited Palestine for 12 days, his only visit to that region. He was greeted as if he were a head of state, rather than a physicist, which included a cannon salute upon arriving at the home of the British high commissioner, Sir Herbert Samuel. During one reception, the building was stormed by people who wanted to see and hear him. In Einstein's talk to the audience, he expressed happiness that the Jewish people were beginning to be recognized as a force in the world. Einstein visited Spain for two weeks in 1923, where he briefly met Santiago Ramón y Cajal and also received a diploma from King Alfonso XIII naming him a member of the Spanish Academy of Sciences. From 1922 to 1932, Einstein was a member of the International Committee on Intellectual Cooperation of the League of Nations in Geneva (with a few months of interruption in 1923–1924), a body created to promote international exchange between scientists, researchers, teachers, artists, and intellectuals. Originally slated to serve as the Swiss delegate, Secretary-General Eric Drummond was persuaded by Catholic activists Oskar Halecki and Giuseppe Motta to instead have him become the German delegate, thus allowing Gonzague de Reynold to take the Swiss spot, from which he promoted traditionalist Catholic values. Einstein's former physics professor Hendrik Lorentz and the Polish chemist Marie Curie were also members of the committee.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,389 |
In the months of March and April 1925, Einstein visited South America, where he spent about a month in Argentina, a week in Uruguay, and a week in Rio de Janeiro, Brazil. Einstein's visit was initiated by Jorge Duclout (1856–1927) and Mauricio Nirenstein (1877–1935) with the support of several Argentine scholars, including Julio Rey Pastor, Jakob Laub, and Leopoldo Lugones. The visit by Einstein and his wife was financed primarily by the Council of the University of Buenos Aires and the "Asociación Hebraica Argentina" (Argentine Hebraic Association) with a smaller contribution from the Argentine-Germanic Cultural Institution. In December 1930, Einstein visited America for the second time, originally intended as a two-month working visit as a research fellow at the California Institute of Technology. After the national attention he received during his first trip to the US, he and his arrangers aimed to protect his privacy. Although swamped with telegrams and invitations to receive awards or speak publicly, he declined them all. After arriving in New York City, Einstein was taken to various places and events, including Chinatown, a lunch with the editors of "The New York Times", and a performance of "Carmen" at the Metropolitan Opera, where he was cheered by the audience on his arrival. During the days following, he was given the keys to the city by Mayor Jimmy Walker and met the president of Columbia University, who described Einstein as "the ruling monarch of the mind". Harry Emerson Fosdick, pastor at New York's Riverside Church, gave Einstein a tour of the church and showed him a full-size statue that the church made of Einstein, standing at the entrance. Also during his stay in New York, he joined a crowd of 15,000 people at Madison Square Garden during a Hanukkah celebration. Einstein next traveled to California, where he met Caltech president and Nobel laureate Robert A. Millikan. His friendship with Millikan was "awkward", as Millikan "had a penchant for patriotic militarism", where Einstein was a pronounced pacifist. During an address to Caltech's students, Einstein noted that science was often inclined to do more harm than good.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,393 |
This aversion to war also led Einstein to befriend author Upton Sinclair and film star Charlie Chaplin, both noted for their pacifism. Carl Laemmle, head of Universal Studios, gave Einstein a tour of his studio and introduced him to Chaplin. They had an instant rapport, with Chaplin inviting Einstein and his wife, Elsa, to his home for dinner. Chaplin said Einstein's outward persona, calm and gentle, seemed to conceal a "highly emotional temperament", from which came his "extraordinary intellectual energy". Chaplin's film, "City Lights", was to premiere a few days later in Hollywood, and Chaplin invited Einstein and Elsa to join him as his special guests. Walter Isaacson, Einstein's biographer, described this as "one of the most memorable scenes in the new era of celebrity". Chaplin visited Einstein at his home on a later trip to Berlin and recalled his "modest little flat" and the piano at which he had begun writing his theory. Chaplin speculated that it was "possibly used as kindling wood by the Nazis". In February 1933, while on a visit to the United States, Einstein knew he could not return to Germany with the rise to power of the Nazis under Germany's new chancellor, Adolf Hitler. While at American universities in early 1933, he undertook his third two-month visiting professorship at the California Institute of Technology in Pasadena. In February and March 1933, the Gestapo repeatedly raided his family's apartment in Berlin. He and his wife Elsa returned to Europe in March, and during the trip, they learned that the German Reichstag had passed the Enabling Act on 23 March, transforming Hitler's government into a "de facto" legal dictatorship, and that they would not be able to proceed to Berlin. Later on, they heard that their cottage had been raided by the Nazis and Einstein's personal sailboat confiscated. Upon landing in Antwerp, Belgium on 28 March, Einstein immediately went to the German consulate and surrendered his passport, formally renouncing his German citizenship. The Nazis later sold his boat and converted his cottage into a Hitler Youth camp. In April 1933, Einstein discovered that the new German government had passed laws barring Jews from holding any official positions, including teaching at universities. Historian Gerald Holton describes how, with "virtually no audible protest being raised by their colleagues", thousands of Jewish scientists were suddenly forced to give up their university positions and their names were removed from the rolls of institutions where they were employed.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,398 |
A month later, Einstein's works were among those targeted by the German Student Union in the Nazi book burnings, with Nazi propaganda minister Joseph Goebbels proclaiming, "Jewish intellectualism is dead." One German magazine included him in a list of enemies of the German regime with the phrase, "not yet hanged", offering a $5,000 bounty on his head. In a subsequent letter to physicist and friend Max Born, who had already emigrated from Germany to England, Einstein wrote, "... I must confess that the degree of their brutality and cowardice came as something of a surprise." After moving to the US, he described the book burnings as a "spontaneous emotional outburst" by those who "shun popular enlightenment", and "more than anything else in the world, fear the influence of men of intellectual independence". Einstein was now without a permanent home, unsure where he would live and work, and equally worried about the fate of countless other scientists still in Germany. Aided by the Academic Assistance Council, founded in April 1933 by British liberal politician William Beveridge to help academics escape Nazi persecution, Einstein was able to leave Germany. He rented a house in De Haan, Belgium, where he lived for a few months. In late July 1933, he went to England for about six weeks at the personal invitation of British naval officer Commander Oliver Locker-Lampson, who had become friends with Einstein in the preceding years. Locker-Lampson invited him to stay near his home in a wooden cabin on Roughton Heath in the Parish of . To protect Einstein, Locker-Lampson had two bodyguards watch over him at his secluded cabin; a photo of them carrying shotguns and guarding Einstein was published in the "Daily Herald" on 24 July 1933. Locker-Lampson took Einstein to meet Winston Churchill at his home, and later, Austen Chamberlain and former Prime Minister Lloyd George. Einstein asked them to help bring Jewish scientists out of Germany. British historian Martin Gilbert notes that Churchill responded immediately, and sent his friend, physicist Frederick Lindemann, to Germany to seek out Jewish scientists and place them in British universities. Churchill later observed that as a result of Germany having driven the Jews out, they had lowered their "technical standards" and put the Allies' technology ahead of theirs.
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,401 |
Einstein later contacted leaders of other nations, including Turkey's Prime Minister, İsmet İnönü, to whom he wrote in September 1933 requesting placement of unemployed German-Jewish scientists. As a result of Einstein's letter, Jewish invitees to Turkey eventually totaled over "1,000 saved individuals". Locker-Lampson also submitted a bill to parliament to extend British citizenship to Einstein, during which period Einstein made a number of public appearances describing the crisis brewing in Europe. In one of his speeches he denounced Germany's treatment of Jews, while at the same time he introduced a bill promoting Jewish citizenship in Palestine, as they were being denied citizenship elsewhere. In his speech he described Einstein as a "citizen of the world" who should be offered a temporary shelter in the UK. Both bills failed, however, and Einstein then accepted an earlier offer from the Institute for Advanced Study, in Princeton, New Jersey, US, to become a resident scholar. On 3 October 1933, Einstein delivered a speech on the importance of academic freedom before a packed audience at the Royal Albert Hall in London, with "The Times" reporting he was wildly cheered throughout. Four days later he returned to the US and took up a position at the Institute for Advanced Study, noted for having become a refuge for scientists fleeing Nazi Germany. At the time, most American universities, including Harvard, Princeton and Yale, had minimal or no Jewish faculty or students, as a result of their Jewish quotas, which lasted until the late 1940s. Einstein was still undecided on his future. He had offers from several European universities, including Christ Church, Oxford, where he stayed for three short periods between May 1931 and June 1933 and was offered a five-year research fellowship (called a "studentship" at Christ Church), but in 1935, he arrived at the decision to remain permanently in the United States and apply for citizenship. Einstein's affiliation with the Institute for Advanced Study would last until his death in 1955. He was one of the four first selected (along with John von Neumann, Kurt Gödel, and Hermann Weyl) at the new Institute, where he soon developed a close friendship with Gödel. The two would take long walks together discussing their work. Bruria Kaufman, his assistant, later became a physicist. During this period, Einstein tried to develop a unified field theory and to refute the accepted interpretation of quantum physics, both unsuccessfully.
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Albert Einstein
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In 1939, a group of Hungarian scientists that included émigré physicist Leó Szilárd attempted to alert Washington to ongoing Nazi atomic bomb research. The group's warnings were discounted. Einstein and Szilárd, along with other refugees such as Edward Teller and Eugene Wigner, "regarded it as their responsibility to alert Americans to the possibility that German scientists might win the race to build an atomic bomb, and to warn that Hitler would be more than willing to resort to such a weapon." To make certain the US was aware of the danger, in July 1939, a few months before the beginning of World War II in Europe, Szilárd and Wigner visited Einstein to explain the possibility of atomic bombs, which Einstein, a pacifist, said he had never considered. He was asked to lend his support by writing a letter, with Szilárd, to President Roosevelt, recommending the US pay attention and engage in its own nuclear weapons research. The letter is believed to be "arguably the key stimulus for the U.S. adoption of serious investigations into nuclear weapons on the eve of the U.S. entry into World War II". In addition to the letter, Einstein used his connections with the Belgian Royal Family and the Belgian queen mother to get access with a personal envoy to the White House's Oval Office. Some say that as a result of Einstein's letter and his meetings with Roosevelt, the US entered the "race" to develop the bomb, drawing on its "immense material, financial, and scientific resources" to initiate the Manhattan Project. For Einstein, "war was a disease ... [and] he called for resistance to war." By signing the letter to Roosevelt, some argue he went against his pacifist principles. In 1954, a year before his death, Einstein said to his old friend, Linus Pauling, "I made one great mistake in my life—when I signed the letter to President Roosevelt recommending that atom bombs be made; but there was some justification—the danger that the Germans would make them ..." In 1955, Einstein and ten other intellectuals and scientists, including British philosopher Bertrand Russell, signed a manifesto highlighting the danger of nuclear weapons.
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Einstein became an American citizen in 1940. Not long after settling into his career at the Institute for Advanced Study in Princeton, New Jersey, he expressed his appreciation of the meritocracy in American culture compared to Europe. He recognized the "right of individuals to say and think what they pleased" without social barriers. As a result, individuals were encouraged, he said, to be more creative, a trait he valued from his early education. Einstein joined the National Association for the Advancement of Colored People (NAACP) in Princeton, where he campaigned for the civil rights of African Americans. He considered racism America's "worst disease", seeing it as "handed down from one generation to the next". As part of his involvement, he corresponded with civil rights activist W. E. B. Du Bois and was prepared to testify on his behalf during his trial in 1951. When Einstein offered to be a character witness for Du Bois, the judge decided to drop the case. In 1946, Einstein visited Lincoln University in Pennsylvania, a historically black college, where he was awarded an honorary degree. Lincoln was the first university in the United States to grant college degrees to African Americans; alumni include Langston Hughes and Thurgood Marshall. Einstein gave a speech about racism in America, adding, "I do not intend to be quiet about it." A resident of Princeton recalls that Einstein had once paid the college tuition for a black student. Einstein has said, "Being a Jew myself, perhaps I can understand and empathize with how black people feel as victims of discrimination".
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In 1918, Einstein was one of the founding members of the German Democratic Party, a liberal party. Later in his life, Einstein's political view was in favor of socialism and critical of capitalism, which he detailed in his essays such as "Why Socialism?" His opinions on the Bolsheviks also changed with time. In 1925, he criticized them for not having a 'well-regulated system of government' and called their rule a 'regime of terror and a tragedy in human history'. He later adopted a more moderated view, criticizing their methods but praising them, which is shown by his 1929 remark on Vladimir Lenin: "In Lenin I honor a man, who in total sacrifice of his own person has committed his entire energy to realizing social justice. I do not find his methods advisable. One thing is certain, however: men like him are the guardians and renewers of mankind's conscience." Einstein offered and was called on to give judgments and opinions on matters often unrelated to theoretical physics or mathematics. He strongly advocated the idea of a democratic global government that would check the power of nation-states in the framework of a world federation. He wrote "I advocate world government because I am convinced that there is no other possible way of eliminating the most terrible danger in which man has ever found himself." The FBI created a secret dossier on Einstein in 1932, and by the time of his death his FBI file was 1,427 pages long. Einstein was deeply impressed by Mahatma Gandhi, with whom he exchanged written letters. He described Gandhi as "a role model for the generations to come". The initial connection was established on 27 September 1931, when Wilfrid Israel took his Indian guest V. A. Sundaram to meet his friend Einstein at his summer home in the town of Caputh. Sundaram was Gandhi's disciple and special envoy, whom Wilfrid Israel met while visiting India and visiting the Indian leader's home in 1925. During the visit, Einstein wrote a short letter to Gandhi that was delivered to him through his envoy, and Gandhi responded quickly with his own letter. Although in the end Einstein and Gandhi were unable to meet as they had hoped, the direct connection between them was established through Wilfrid Israel.
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Einstein was a figurehead leader in helping establish the Hebrew University of Jerusalem, which opened in 1925, and was among its first Board of Governors. Earlier, in 1921, he was asked by the biochemist and president of the World Zionist Organization, Chaim Weizmann, to help raise funds for the planned university. He made suggestions for the creation of an Institute of Agriculture, a Chemical Institute and an Institute of Microbiology in order to fight the various ongoing epidemics such as malaria, which he called an "evil" that was undermining a third of the country's development. He also promoted the establishment of an Oriental Studies Institute, to include language courses given in both Hebrew and Arabic. Einstein was not a nationalist and was against the creation of an independent Jewish state, which would be established without his help as Israel in 1948. He felt that the waves of arriving Jews of the Aliyah could live alongside existing Arabs in Palestine. Nevertheless, upon the death of Israeli president Weizmann in November 1952, Prime Minister David Ben-Gurion offered Einstein the largely ceremonial position of President of Israel at the urging of Ezriel Carlebach. The offer was presented by Israel's ambassador in Washington, Abba Eban, who explained that the offer "embodies the deepest respect which the Jewish people can repose in any of its sons". Einstein wrote that he was "deeply moved", but "at once saddened and ashamed" that he could not accept it. Einstein spoke of his spiritual outlook in a wide array of original writings and interviews. He said he had sympathy for the impersonal pantheistic God of Baruch Spinoza's philosophy. He did not believe in a personal god who concerns himself with fates and actions of human beings, a view which he described as naïve. He clarified, however, that "I am not an atheist", preferring to call himself an agnostic, or a "deeply religious nonbeliever". When asked if he believed in an afterlife, Einstein replied, "No. And one life is enough for me."
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Einstein was primarily affiliated with non-religious humanist and Ethical Culture groups in both the UK and US. He served on the advisory board of the First Humanist Society of New York, and was an honorary associate of the Rationalist Association, which publishes "New Humanist" in Britain. For the 75th anniversary of the New York Society for Ethical Culture, he stated that the idea of Ethical Culture embodied his personal conception of what is most valuable and enduring in religious idealism. He observed, "Without 'ethical culture' there is no salvation for humanity." In a German-language letter to philosopher Eric Gutkind, dated 3 January 1954, Einstein wrote:The word God is for me nothing more than the expression and product of human weaknesses, the Bible a collection of honorable, but still primitive legends which are nevertheless pretty childish. No interpretation no matter how subtle can (for me) change this. ... For me the Jewish religion like all other religions is an incarnation of the most childish superstitions. And the Jewish people to whom I gladly belong and with whose mentality I have a deep affinity have no different quality for me than all other people. ... I cannot see anything 'chosen' about them. Einstein had been sympathetic toward vegetarianism for a long time. In a letter in 1930 to Hermann Huth, vice-president of the German Vegetarian Federation (Deutsche Vegetarier-Bund), he wrote:Although I have been prevented by outward circumstances from observing a strictly vegetarian diet, I have long been an adherent to the cause in principle. Besides agreeing with the aims of vegetarianism for aesthetic and moral reasons, it is my view that a vegetarian manner of living by its purely physical effect on the human temperament would most beneficially influence the lot of mankind. He became a vegetarian himself only during the last part of his life. In March 1954 he wrote in a letter: "So I am living without fats, without meat, without fish, but am feeling quite well this way. It almost seems to me that man was not born to be a carnivore." His mother played the piano reasonably well and wanted her son to learn the violin, not only to instill in him a love of music but also to help him assimilate into German culture. According to conductor Leon Botstein, Einstein began playing when he was 5. However, he did not enjoy it at that age.
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When he turned 13, he discovered the violin sonatas of Mozart, whereupon he became enamored of Mozart's compositions and studied music more willingly. Einstein taught himself to play without "ever practicing systematically". He said that "love is a better teacher than a sense of duty." At the age of 17, he was heard by a school examiner in Aarau while playing Beethoven's violin sonatas. The examiner stated afterward that his playing was "remarkable and revealing of 'great insight. What struck the examiner, writes Botstein, was that Einstein "displayed a deep love of the music, a quality that was and remains in short supply. Music possessed an unusual meaning for this student." Music took on a pivotal and permanent role in Einstein's life from that period on. Although the idea of becoming a professional musician himself was not on his mind at any time, among those with whom Einstein played chamber music were a few professionals, including Kurt Appelbaum, and he performed for private audiences and friends. Chamber music had also become a regular part of his social life while living in Bern, Zürich, and Berlin, where he played with Max Planck and his son, among others. He is sometimes erroneously credited as the editor of the 1937 edition of the Köchel catalog of Mozart's work; that edition was prepared by Alfred Einstein, who may have been a distant relation. In 1931, while engaged in research at the California Institute of Technology, he visited the Zoellner family conservatory in Los Angeles, where he played some of Beethoven and Mozart's works with members of the Zoellner Quartet. Near the end of his life, when the young Juilliard Quartet visited him in Princeton, he played his violin with them, and the quartet was "impressed by Einstein's level of coordination and intonation". On 17 April 1955, Einstein experienced internal bleeding caused by the rupture of an abdominal aortic aneurysm, which had previously been reinforced surgically by Rudolph Nissen in 1948. He took the draft of a speech he was preparing for a television appearance commemorating the state of Israel's seventh anniversary with him to the hospital, but he did not live to complete it.
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Einstein refused surgery, saying, "I want to go when I want. It is tasteless to prolong life artificially. I have done my share; it is time to go. I will do it elegantly." He died in the University Medical Center of Princeton at Plainsboro early the next morning at the age of 76, having continued to work until near the end. During the autopsy, the pathologist Thomas Stoltz Harvey removed Einstein's brain for preservation without the permission of his family, in the hope that the neuroscience of the future would be able to discover what made Einstein so intelligent. Einstein's remains were cremated in Trenton, New Jersey, and his ashes were scattered at an undisclosed location. In a memorial lecture delivered on 13 December 1965 at UNESCO headquarters, nuclear physicist J. Robert Oppenheimer summarized his impression of Einstein as a person: "He was almost wholly without sophistication and wholly without worldliness ... There was always with him a wonderful purity at once childlike and profoundly stubborn." Einstein bequeathed his personal archives, library, and intellectual assets to the Hebrew University of Jerusalem in Israel. Throughout his life, Einstein published hundreds of books and articles. He published more than 300 scientific papers and 150 non-scientific ones. On 5 December 2014, universities and archives announced the release of Einstein's papers, comprising more than 30,000 unique documents. Einstein's intellectual achievements and originality have made the word "Einstein" synonymous with "genius". In addition to the work he did by himself he also collaborated with other scientists on additional projects including the Bose–Einstein statistics, the Einstein refrigerator and others. The "Annus Mirabilis" papers are four articles pertaining to the photoelectric effect (which gave rise to quantum theory), Brownian motion, the special theory of relativity, and E = mc that Einstein published in the "Annalen der Physik" scientific journal in 1905. These four works contributed substantially to the foundation of modern physics and changed views on space, time, and matter. The four papers are:
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Einstein's first paper submitted in 1900 to "Annalen der Physik" was on capillary attraction. It was published in 1901 with the title "Folgerungen aus den Capillaritätserscheinungen", which translates as "Conclusions from the capillarity phenomena". Two papers he published in 1902–1903 (thermodynamics) attempted to interpret atomic phenomena from a statistical point of view. These papers were the foundation for the 1905 paper on Brownian motion, which showed that Brownian movement can be construed as firm evidence that molecules exist. His research in 1903 and 1904 was mainly concerned with the effect of finite atomic size on diffusion phenomena. Einstein returned to the problem of thermodynamic fluctuations, giving a treatment of the density variations in a fluid at its critical point. Ordinarily the density fluctuations are controlled by the second derivative of the free energy with respect to the density. At the critical point, this derivative is zero, leading to large fluctuations. The effect of density fluctuations is that light of all wavelengths is scattered, making the fluid look milky white. Einstein relates this to Rayleigh scattering, which is what happens when the fluctuation size is much smaller than the wavelength, and which explains why the sky is blue. Einstein quantitatively derived critical opalescence from a treatment of density fluctuations, and demonstrated how both the effect and Rayleigh scattering originate from the atomistic constitution of matter. Einstein's ""Zur Elektrodynamik bewegter Körper"" ("On the Electrodynamics of Moving Bodies") was received on 30 June 1905 and published 26 September of that same year. It reconciled conflicts between Maxwell's equations (the laws of electricity and magnetism) and the laws of Newtonian mechanics by introducing changes to the laws of mechanics. Observationally, the effects of these changes are most apparent at high speeds (where objects are moving at speeds close to the speed of light). The theory developed in this paper later became known as Einstein's special theory of relativity. There is evidence from Einstein's writings that he collaborated with his first wife, Mileva Marić, on this work. The decision to publish only under his name seems to have been mutual, but the exact reason is unknown.
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This paper predicted that, when measured in the frame of a relatively moving observer, a clock carried by a moving body would appear to slow down, and the body itself would contract in its direction of motion. This paper also argued that the idea of a luminiferous aether—one of the leading theoretical entities in physics at the time—was superfluous. In his paper on mass–energy equivalence, Einstein produced "E" = "mc" as a consequence of his special relativity equations. Einstein's 1905 work on relativity remained controversial for many years, but was accepted by leading physicists, starting with Max Planck. Einstein originally framed special relativity in terms of kinematics (the study of moving bodies). In 1908, Hermann Minkowski reinterpreted special relativity in geometric terms as a theory of spacetime. Einstein adopted Minkowski's formalism in his 1915 general theory of relativity. General relativity (GR) is a theory of gravitation that was developed by Einstein between 1907 and 1915. According to general relativity, the observed gravitational attraction between masses results from the warping of space and time by those masses. General relativity has developed into an essential tool in modern astrophysics. It provides the foundation for the current understanding of black holes, regions of space where gravitational attraction is so strong that not even light can escape. As Einstein later said, the reason for the development of general relativity was that the preference of inertial motions within special relativity was unsatisfactory, while a theory which from the outset prefers no state of motion (even accelerated ones) should appear more satisfactory. Consequently, in 1907 he published an article on acceleration under special relativity. In that article titled "On the Relativity Principle and the Conclusions Drawn from It", he argued that free fall is really inertial motion, and that for a free-falling observer the rules of special relativity must apply. This argument is called the equivalence principle. In the same article, Einstein also predicted the phenomena of gravitational time dilation, gravitational redshift and deflection of light. In 1911, Einstein published another article "On the Influence of Gravitation on the Propagation of Light" expanding on the 1907 article, in which he estimated the amount of deflection of light by massive bodies. Thus, the theoretical prediction of general relativity could for the first time be tested experimentally.
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In 1916, Einstein predicted gravitational waves, ripples in the curvature of spacetime which propagate as waves, traveling outward from the source, transporting energy as gravitational radiation. The existence of gravitational waves is possible under general relativity due to its Lorentz invariance which brings the concept of a finite speed of propagation of the physical interactions of gravity with it. By contrast, gravitational waves cannot exist in the Newtonian theory of gravitation, which postulates that the physical interactions of gravity propagate at infinite speed. The first, indirect, detection of gravitational waves came in the 1970s through observation of a pair of closely orbiting neutron stars, PSR B1913+16. The explanation of the decay in their orbital period was that they were emitting gravitational waves. Einstein's prediction was confirmed on 11 February 2016, when researchers at LIGO published the first observation of gravitational waves, detected on Earth on 14 September 2015, nearly one hundred years after the prediction. While developing general relativity, Einstein became confused about the gauge invariance in the theory. He formulated an argument that led him to conclude that a general relativistic field theory is impossible. He gave up looking for fully generally covariant tensor equations and searched for equations that would be invariant under general linear transformations only. In June 1913, the Entwurf ('draft') theory was the result of these investigations. As its name suggests, it was a sketch of a theory, less elegant and more difficult than general relativity, with the equations of motion supplemented by additional gauge fixing conditions. After more than two years of intensive work, Einstein realized that the hole argument was mistaken and abandoned the theory in November 1915. In 1917, Einstein applied the general theory of relativity to the structure of the universe as a whole. He discovered that the general field equations predicted a universe that was dynamic, either contracting or expanding. As observational evidence for a dynamic universe was not known at the time, Einstein introduced a new term, the cosmological constant, to the field equations, in order to allow the theory to predict a static universe. The modified field equations predicted a static universe of closed curvature, in accordance with Einstein's understanding of Mach's principle in these years. This model became known as the Einstein World or Einstein's static universe.
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Following the discovery of the recession of the nebulae by Edwin Hubble in 1929, Einstein abandoned his static model of the universe, and proposed two dynamic models of the cosmos, The Friedmann-Einstein universe of 1931 and the Einstein–de Sitter universe of 1932. In each of these models, Einstein discarded the cosmological constant, claiming that it was "in any case theoretically unsatisfactory". In many Einstein biographies, it is claimed that Einstein referred to the cosmological constant in later years as his "biggest blunder", based on a letter George Gamow claimed to have received from him. The astrophysicist Mario Livio has recently cast doubt on this claim. In late 2013, a team led by the Irish physicist Cormac O'Raifeartaigh discovered evidence that, shortly after learning of Hubble's observations of the recession of the nebulae, Einstein considered a steady-state model of the universe. In a hitherto overlooked manuscript, apparently written in early 1931, Einstein explored a model of the expanding universe in which the density of matter remains constant due to a continuous creation of matter, a process he associated with the cosmological constant. As he stated in the paper, "In what follows, I would like to draw attention to a solution to equation (1) that can account for Hubbel's ["sic"] facts, and in which the density is constant over time" ... "If one considers a physically bounded volume, particles of matter will be continually leaving it. For the density to remain constant, new particles of matter must be continually formed in the volume from space." It thus appears that Einstein considered a steady-state model of the expanding universe many years before Hoyle, Bondi and Gold. However, Einstein's steady-state model contained a fundamental flaw and he quickly abandoned the idea. General relativity includes a dynamical spacetime, so it is difficult to see how to identify the conserved energy and momentum. Noether's theorem allows these quantities to be determined from a Lagrangian with translation invariance, but general covariance makes translation invariance into something of a gauge symmetry. The energy and momentum derived within general relativity by Noether's prescriptions do not make a real tensor for this reason.
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Einstein argued that this is true for a fundamental reason: the gravitational field could be made to vanish by a choice of coordinates. He maintained that the non-covariant energy momentum pseudotensor was, in fact, the best description of the energy momentum distribution in a gravitational field. This approach has been echoed by Lev Landau and Evgeny Lifshitz, and others, and has become standard. The use of non-covariant objects like pseudotensors was heavily criticized in 1917 by Erwin Schrödinger and others. In 1935, Einstein collaborated with Nathan Rosen to produce a model of a wormhole, often called Einstein–Rosen bridges. His motivation was to model elementary particles with charge as a solution of gravitational field equations, in line with the program outlined in the paper "Do Gravitational Fields play an Important Role in the Constitution of the Elementary Particles?". These solutions cut and pasted Schwarzschild black holes to make a bridge between two patches. If one end of a wormhole was positively charged, the other end would be negatively charged. These properties led Einstein to believe that pairs of particles and antiparticles could be described in this way. In order to incorporate spinning point particles into general relativity, the affine connection needed to be generalized to include an antisymmetric part, called the torsion. This modification was made by Einstein and Cartan in the 1920s. The theory of general relativity has a fundamental lawthe Einstein field equations, which describe how space curves. The geodesic equation, which describes how particles move, may be derived from the Einstein field equations. Since the equations of general relativity are non-linear, a lump of energy made out of pure gravitational fields, like a black hole, would move on a trajectory which is determined by the Einstein field equations themselves, not by a new law. So Einstein proposed that the path of a singular solution, like a black hole, would be determined to be a geodesic from general relativity itself. This was established by Einstein, Infeld, and Hoffmann for pointlike objects without angular momentum, and by Roy Kerr for spinning objects. In a 1905 paper, Einstein postulated that light itself consists of localized particles ("quanta"). Einstein's light quanta were nearly universally rejected by all physicists, including Max Planck and Niels Bohr. This idea only became universally accepted in 1919, with Robert Millikan's detailed experiments on the photoelectric effect, and with the measurement of Compton scattering.
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Einstein concluded that each wave of frequency "f" is associated with a collection of photons with energy "hf" each, where "h" is Planck's constant. He does not say much more, because he is not sure how the particles are related to the wave. But he does suggest that this idea would explain certain experimental results, notably the photoelectric effect. In 1907, Einstein proposed a model of matter where each atom in a lattice structure is an independent harmonic oscillator. In the Einstein model, each atom oscillates independently—a series of equally spaced quantized states for each oscillator. Einstein was aware that getting the frequency of the actual oscillations would be difficult, but he nevertheless proposed this theory because it was a particularly clear demonstration that quantum mechanics could solve the specific heat problem in classical mechanics. Peter Debye refined this model. Throughout the 1910s, quantum mechanics expanded in scope to cover many different systems. After Ernest Rutherford discovered the nucleus and proposed that electrons orbit like planets, Niels Bohr was able to show that the same quantum mechanical postulates introduced by Planck and developed by Einstein would explain the discrete motion of electrons in atoms, and the periodic table of the elements. Einstein contributed to these developments by linking them with the 1898 arguments Wilhelm Wien had made. Wien had shown that the hypothesis of adiabatic invariance of a thermal equilibrium state allows all the blackbody curves at different temperature to be derived from one another by a simple shifting process. Einstein noted in 1911 that the same adiabatic principle shows that the quantity which is quantized in any mechanical motion must be an adiabatic invariant. Arnold Sommerfeld identified this adiabatic invariant as the action variable of classical mechanics.
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In 1924, Einstein received a description of a statistical model from Indian physicist Satyendra Nath Bose, based on a counting method that assumed that light could be understood as a gas of indistinguishable particles. Einstein noted that Bose's statistics applied to some atoms as well as to the proposed light particles, and submitted his translation of Bose's paper to the "Zeitschrift für Physik". Einstein also published his own articles describing the model and its implications, among them the Bose–Einstein condensate phenomenon that some particulates should appear at very low temperatures. It was not until 1995 that the first such condensate was produced experimentally by Eric Allin Cornell and Carl Wieman using ultra-cooling equipment built at the NIST–JILA laboratory at the University of Colorado at Boulder. Bose–Einstein statistics are now used to describe the behaviors of any assembly of bosons. Einstein's sketches for this project may be seen in the Einstein Archive in the library of the Leiden University. Although the patent office promoted Einstein to Technical Examiner Second Class in 1906, he had not given up on academia. In 1908, he became a "Privatdozent" at the University of Bern. In ""Über die Entwicklung unserer Anschauungen über das Wesen und die Konstitution der Strahlung"" (""), on the quantization of light, and in an earlier 1909 paper, Einstein showed that Max Planck's energy quanta must have well-defined momenta and act in some respects as independent, point-like particles. This paper introduced the "photon" concept (although the name "photon" was introduced later by Gilbert N. Lewis in 1926) and inspired the notion of wave–particle duality in quantum mechanics. Einstein saw this wave–particle duality in radiation as concrete evidence for his conviction that physics needed a new, unified foundation.
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In a series of works completed from 1911 to 1913, Planck reformulated his 1900 quantum theory and introduced the idea of zero-point energy in his "second quantum theory". Soon, this idea attracted the attention of Einstein and his assistant Otto Stern. Assuming the energy of rotating diatomic molecules contains zero-point energy, they then compared the theoretical specific heat of hydrogen gas with the experimental data. The numbers matched nicely. However, after publishing the findings, they promptly withdrew their support, because they no longer had confidence in the correctness of the idea of zero-point energy. In 1917, at the height of his work on relativity, Einstein published an article in "Physikalische Zeitschrift" that proposed the possibility of stimulated emission, the physical process that makes possible the maser and the laser. This article showed that the statistics of absorption and emission of light would only be consistent with Planck's distribution law if the emission of light into a mode with n photons would be enhanced statistically compared to the emission of light into an empty mode. This paper was enormously influential in the later development of quantum mechanics, because it was the first paper to show that the statistics of atomic transitions had simple laws. Einstein discovered Louis de Broglie's work and supported his ideas, which were received skeptically at first. In another major paper from this era, Einstein gave a wave equation for de Broglie waves, which Einstein suggested was the Hamilton–Jacobi equation of mechanics. This paper would inspire Schrödinger's work of 1926. Einstein played a major role in developing quantum theory, beginning with his 1905 paper on the photoelectric effect. However, he became displeased with modern quantum mechanics as it had evolved after 1925, despite its acceptance by other physicists. He was skeptical that the randomness of quantum mechanics was fundamental rather than the result of determinism, stating that God "is not playing at dice". Until the end of his life, he continued to maintain that quantum mechanics was incomplete. The Bohr–Einstein debates were a series of public disputes about quantum mechanics between Einstein and Niels Bohr, who were two of its founders. Their debates are remembered because of their importance to the philosophy of science. Their debates would influence later interpretations of quantum mechanics.
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In 1935, Einstein returned to quantum mechanics, in particular to the question of its completeness, in a collaboration with Boris Podolsky and Nathan Rosen that laid out what would become known as the EPR paradox. In a thought experiment, they considered two particles, which had interacted such that their properties were strongly correlated. No matter how far the two particles were separated, a precise position measurement on one particle would result in equally precise knowledge of the position of the other particle; likewise, a precise momentum measurement of one particle would result in equally precise knowledge of the momentum of the other particle, without needing to disturb the other particle in any way. Given Einstein's concept of local realism, there were two possibilities: (1) either the other particle had these properties already determined, or (2) the process of measuring the first particle instantaneously affected the reality of the position and momentum of the second particle. Einstein rejected this second possibility (popularly called "spooky action at a distance"). Einstein's belief in local realism led him to assert that, while the correctness of quantum mechanics was not in question, it must be incomplete. But as a physical principle, local realism was shown to be incorrect when the Aspect experiment of 1982 confirmed Bell's theorem, which J. S. Bell had delineated in 1964. The results of these and subsequent experiments demonstrate that quantum physics cannot be represented by any version of the picture of physics in which "particles are regarded as unconnected independent classical-like entities, each one being unable to communicate with the other after they have separated." Although Einstein was wrong about local realism, his clear prediction of the unusual properties of its opposite, entangled quantum states, has resulted in the EPR paper becoming among the most influential papers published in "Physical Review". It is considered a centerpiece of the development of quantum information theory. Following his research on general relativity, Einstein attempted to generalize his theory of gravitation to include electromagnetism as aspects of a single entity. In 1950, he described his "unified field theory" in a "Scientific American" article titled "On the Generalized Theory of Gravitation". Although he was lauded for this work, his efforts were ultimately unsuccessful.
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Notably, Einstein's unification project did not accommodate the strong and weak nuclear forces, neither of which was well understood until many years after his death. Although mainstream physics long ignored Einstein's approaches to unification, Einstein's work has motivated modern quests for a theory of everything, in particular string theory, where geometrical fields emerge in a unified quantum-mechanical setting. Einstein conducted other investigations that were unsuccessful and abandoned. These pertain to force, superconductivity, and other research. In addition to longtime collaborators Leopold Infeld, Nathan Rosen, Peter Bergmann and others, Einstein also had some one-shot collaborations with various scientists. Einstein and De Haas demonstrated that magnetization is due to the motion of electrons, nowadays known to be the spin. In order to show this, they reversed the magnetization in an iron bar suspended on a torsion pendulum. They confirmed that this leads the bar to rotate, because the electron's angular momentum changes as the magnetization changes. This experiment needed to be sensitive because the angular momentum associated with electrons is small, but it definitively established that electron motion of some kind is responsible for magnetization. Einstein suggested to Erwin Schrödinger that he might be able to reproduce the statistics of a Bose–Einstein gas by considering a box. Then to each possible quantum motion of a particle in a box associate an independent harmonic oscillator. Quantizing these oscillators, each level will have an integer occupation number, which will be the number of particles in it. This formulation is a form of second quantization, but it predates modern quantum mechanics. Erwin Schrödinger applied this to derive the thermodynamic properties of a semiclassical ideal gas. Schrödinger urged Einstein to add his name as co-author, although Einstein declined the invitation. In 1926, Einstein and his former student Leó Szilárd co-invented (and in 1930, patented) the Einstein refrigerator. This absorption refrigerator was then revolutionary for having no moving parts and using only heat as an input. On 11 November 1930, was awarded to Einstein and Leó Szilárd for the refrigerator. Their invention was not immediately put into commercial production, and the most promising of their patents were acquired by the Swedish company Electrolux.
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While traveling, Einstein wrote daily to his wife Elsa and adopted stepdaughters Margot and Ilse. The letters were included in the papers bequeathed to the Hebrew University of Jerusalem. Margot Einstein permitted the personal letters to be made available to the public, but requested that it not be done until twenty years after her death (she died in 1986). Barbara Wolff, of the Hebrew University's Albert Einstein Archives, told the BBC that there are about 3,500 pages of private correspondence written between 1912 and 1955. Einstein's right of publicity was litigated in 2015 in a federal district court in California. Although the court initially held that the right had expired, that ruling was immediately appealed, and the decision was later vacated in its entirety. The underlying claims between the parties in that lawsuit were ultimately settled. The right is enforceable, and the Hebrew University of Jerusalem is the exclusive representative of that right. Corbis, successor to The Roger Richman Agency, licenses the use of his name and associated imagery, as agent for the university. Mount Einstein in New Zealand's Paparoa Range was named after him in 1970 by the Department of Scientific and Industrial Research. Einstein became one of the most famous scientific celebrities, beginning with the confirmation of his theory of general relativity in 1919. Despite the general public having little understanding of his work, he was widely recognized and received adulation and publicity. In the period before World War II, "The New Yorker" published a vignette in their "The Talk of the Town" feature saying that Einstein was so well known in America that he would be stopped on the street by people wanting him to explain "that theory". He finally figured out a way to handle the incessant inquiries. He told his inquirers, "Pardon me, sorry! Always I am mistaken for Professor Einstein." Einstein has been the subject of or inspiration for many novels, films, plays, and works of music. He is a favorite model for depictions of absent-minded professors; his expressive face and distinctive hairstyle have been widely copied and exaggerated. "Time" magazine's Frederic Golden wrote that Einstein was "a cartoonist's dream come true".
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Albert Einstein
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https://en.wikipedia.org/wiki?curid=736
| 11,489 |
Einstein received numerous awards and honors, and in 1922, he was awarded the 1921 Nobel Prize in Physics "for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect". None of the nominations in 1921 met the criteria set by Alfred Nobel, so the 1921 prize was carried forward and awarded to Einstein in 1922.
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Periodic table
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https://en.wikipedia.org/wiki?curid=23053
| 11,924 |
Another important property of elements is their electronegativity. Atoms can form covalent bonds to each other by sharing electrons in pairs, creating an overlap of valence orbitals. The degree to which each atom attracts the shared electron pair depends on the atom's electronegativity – the tendency of an atom towards gaining or losing electrons. The more electronegative atom will tend to attract the electron pair more, and the less electronegative (or more electropositive) one will attract it less. In extreme cases, the electron can be thought of as having been passed completely from the more electropositive atom to the more electronegative one, though this is a simplification. The bond then binds two ions, one positive (having given up the electron) and one negative (having accepted it), and is termed an ionic bond. The first one to systematically expand and correct the chemical potentials of Bohr's atomic theory was Walther Kossel in 1914 and in 1916. Kossel explained that in the periodic table new elements would be created as electrons were added to the outer shell. In Kossel's paper, he writes: "This leads to the conclusion that the electrons, which are added further, should be put into concentric rings or shells, on each of which ... only a certain number of electrons—namely, eight in our case—should be arranged. As soon as one ring or shell is completed, a new one has to be started for the next element; the number of electrons, which are most easily accessible, and lie at the outermost periphery, increases again from element to element and, therefore, in the formation of each new shell the chemical periodicity is repeated."
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Periodic table
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https://en.wikipedia.org/wiki?curid=23053
| 11,958 |
A significant controversy arose with elements 102 through 106 in the 1960s and 1970s, as competition arose between the LBNL team (now led by Albert Ghiorso) and a team of Soviet scientists at the Joint Institute for Nuclear Research (JINR) led by Georgy Flyorov. Each team claimed discovery, and in some cases each proposed their own name for the element, creating an element naming controversy that lasted decades. These elements were made by bombardment of actinides with light ions. IUPAC at first adopted a hands-off approach, preferring to wait and see if a consensus would be forthcoming. Unfortunately, it was also the height of the Cold War, and it became clear after some time that this would not happen. As such, IUPAC and the International Union of Pure and Applied Physics (IUPAP) created a Transfermium Working Group (TWG, fermium being element 100) in 1985 to set out criteria for discovery, which were published in 1991. After some further controversy, these elements received their final names in 1997, including seaborgium (106) in honour of Seaborg. Even if eighth-row elements can exist, producing them is likely to be difficult, and it should become even more difficult as atomic number rises. Although the 8s elements are expected to be reachable with present means, the first few elements are expected to require new technology, if they can be produced at all. Experimentally characterising these elements chemically would also pose a great challenge.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,320 |
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The "Oxford English Dictionary" of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,326 |
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals. and have been common in fiction, as in Mary Shelley's "Frankenstein" or Karel Čapek's "R.U.R." These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis. This, along with concurrent discoveries in neurobiology, information theory and cybernetics, led researchers to consider the possibility of building an electronic brain. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,331 |
By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. Closely associated with this approach was the "heuristic search" approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons. James Manyika and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of Descarte, Boole, Gottlob Frege, Bertrand Russell, and others. Connectionist approaches based on cybernetics or artificial neural networks were pushed to the background but have gained new prominence in recent decades. computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field. Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult. a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,338 |
However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began. Many researchers began to doubt that the symbolic approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Robotics researchers, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment. Interest in neural networks and "connectionism" was revived by Geoffrey Hinton, David Rumelhart and others in the middle of the 1980s. Soft computing tools were developed in the 1980s, such as neural networks, fuzzy systems, Grey system theory, evolutionary computation and many tools drawn from statistics or mathematical optimization. AI gradually restored its reputation in the late 1990s and early 21st century by finding specific solutions to specific problems. The narrow focus allowed researchers to produce verifiable results, exploit more mathematical methods, and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence". Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes". The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019. Numerous academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning. This concern has led to the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,347 |
The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). A truly intelligent program would also need access to commonsense knowledge; the set of facts that an average person knows. The semantics of an ontology is typically represented in description logic, such as the Web Ontology Language. AI research has developed tools to represent specific domains, such as objects, properties, categories and relations between objects; default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); as well as other domains. Among the most difficult problems in AI are: the breadth of commonsense knowledge (the number of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally).
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,358 |
Unsupervised learning finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization. allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include information retrieval, question answering and machine translation. Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic and the breadth of commonsense knowledge. Modern statistical techniques include co-occurrence frequencies (how often one word appears near another), "Keyword spotting" (searching for a particular word to retrieve information), transformer-based deep learning (which finds patterns in text), and others. They have achieved acceptable accuracy at the page or paragraph level, and, by 2019, could generate coherent text. is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,366 |
However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject. A machine with general intelligence can solve a wide variety of problems with breadth and versatility similar to human intelligence. There are several competing ideas about how to develop artificial general intelligence. Hans Moravec and Marvin Minsky argue that work in different individual domains can be incorporated into an advanced multi-agent system or cognitive architecture with general intelligence. Pedro Domingos hopes that there is a conceptually straightforward, but mathematically difficult, "master algorithm" that could lead to AGI. AI can solve many problems by intelligently searching through many possible solutions. Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies, heuristics can also serve to eliminate some choices unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,371 |
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other related optimization algorithms include random optimization, beam search and metaheuristics like simulated annealing. Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails). is used for knowledge representation and problem-solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic adds quantifiers and predicates and can express facts about objects, their properties, and their relations with each other. Fuzzy logic assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry), that are too linguistically imprecise to be completely true or false. Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as description logics; Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics. Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,379 |
are a very general tool that can be used for various problems, including reasoning (using the Bayesian inference algorithm), Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). A key concept from the science of economics is "utility", a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design. The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if diamond then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine the closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class is a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and machine learning approaches. The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability. Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,387 |
were inspired by the architecture of neurons in the human brain. A simple "neuron" "N" accepts input from other neurons, each of which, when activated (or "fired"), casts a weighted "vote" for or against whether neuron "N" should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks model complex relationships between inputs and outputs and find patterns in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of mathematical optimization – they perform gradient descent on a multi-dimensional topology that was created by training the network. The most common training technique is the backpropagation algorithm. Other learning techniques for neural networks are Hebbian learning ("fire together, wire together"), GMDH or competitive learning. The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. This can substantially reduce the number of weighted connections between neurons, and creates a hierarchy similar to the organization of the animal visual cortex.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,393 |
however long-term gradients which are back-propagated can "vanish" (that is, they can tend to zero) or "explode" (that is, they can tend to infinity), known as the vanishing gradient problem. Specialized languages for artificial intelligence have been developed, such as Lisp, Prolog, TensorFlow and many others. Hardware developed for AI includes AI accelerators and neuromorphic computing. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. In the 2010s, AI applications were at the heart of the most commercially successful areas of computing, and have become a ubiquitous feature of daily life. AI is used in search engines (such as Google Search), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones and self-driving cars), There are also thousands of successful AI applications used to solve problems for specific industries or institutions. A few examples are energy storage, deepfakes, medical diagnosis, military logistics, or supply chain management. Game playing has been a test of AI's strength since the 1950s. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a "Jeopardy!" quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest "Jeopardy!" champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Other programs handle imperfect-information games; such as for poker at a superhuman level, Pluribus and Cepheus. DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own. By 2020, Natural Language Processing systems such as the enormous GPT-3 (then by far the largest artificial neural network) were matching human performance on pre-existing benchmarks, albeit without the system attaining a commonsense understanding of the contents of the benchmarks. DeepMind's AlphaFold 2 (2020) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. Other applications predict the result of judicial decisions, create art (such as poetry or painting) and prove mathematical theorems.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,404 |
In 2019, WIPO reported that AI was the most prolific emerging technology in terms of number of patent applications and granted patents, the Internet of things was estimated to be the largest in terms of market size. It was followed, again in market size, by big data technologies, robotics, AI, 3D printing and the fifth generation of mobile services (5G). Since AI emerged in the 1950s, 340,000 AI-related patent applications were filed by innovators and 1.6 million scientific papers have been published by researchers, with the majority of all AI-related patent filings published since 2013. Companies represent 26 out of the top 30 AI patent applicants, with universities or public research organizations accounting for the remaining four. The ratio of scientific papers to inventions has significantly decreased from 8:1 in 2010 to 3:1 in 2016, which is attributed to be indicative of a shift from theoretical research to the use of AI technologies in commercial products and services. Machine learning is the dominant AI technique disclosed in patents and is included in more than one-third of all identified inventions (134,777 machine learning patents filed for a total of 167,038 AI patents filed in 2016), with computer vision being the most popular functional application. AI-related patents not only disclose AI techniques and applications, they often also refer to an application field or industry. Twenty application fields were identified in 2016 and included, in order of magnitude: telecommunications (15 percent), transportation (15 percent), life and medical sciences (12 percent), and personal devices, computing and human–computer interaction (11 percent). Other sectors included banking, entertainment, security, industry and manufacturing, agriculture, and networks (including social networks, smart cities and the Internet of things). IBM has the largest portfolio of AI patents with 8,290 patent applications, followed by Microsoft with 5,930 patent applications. He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks"
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,407 |
Russell and Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking". However, they are critical that the test compares machines to "people". "Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence". McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world." Another AI founder, Marvin Minsky similarly defines it as "the ability to solve hard problems". These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine -- and no other philosophical discussion is required, or may not even be possible. This definition stipulated the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence. No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, neat, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers. Symbolic AI (or "GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult. Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,414 |
Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree. The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neurosymbolic artificial intelligence attempts to bridge the two approaches. "Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems (especially in areas like common sense reasoning). This issue was actively discussed in the 70s and 80s, but in the 1990s mathematical methods and solid scientific standards became the norm, a transition that Russell and Norvig termed "the victory of the neats". Finding a provably correct or optimal solution is intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 80s and most successful AI programs in the 21st century are examples of soft computing with neural networks. AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence (general AI) directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The experimental sub-field of artificial general intelligence studies this area exclusively.
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Artificial intelligence
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| 18,421 |
The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field. Stuart Russell and Peter Norvig observe that most AI researchers "don't care about the [philosophy of AI] – as long as the program works, they don't care whether you call it a simulation of intelligence or real intelligence." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction. David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this "feels" or why it should feel like anything at all. Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to "know what red looks like". Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind. If a machine has a mind and subjective experience, then it may also have sentience (the ability to feel), and if so, then it could also "suffer", and thus it would be entitled to certain rights.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,427 |
and is now being considered by, for example, California's Institute for the Future; however, critics argue that the discussion is premature. A superintelligence, hyperintelligence, or superhuman intelligence, is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. "Superintelligence" may also refer to the form or degree of intelligence possessed by such an agent. If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. Its intelligence would increase exponentially in an intelligence explosion and could dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario the "singularity". Because it is difficult or impossible to know the limits of intelligence or the capabilities of superintelligent machines, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger. Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998. In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk".
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,437 |
Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; "The Economist" states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, face recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes aid in producing misinformation; advanced AI can make centralized decision making more competitive with liberal and decentralized systems such as markets. Terrorists, criminals and rogue states may use other forms of weaponized AI such as advanced digital warfare and lethal autonomous weapons. By 2015, over fifty countries were reported to be researching battlefield robots. Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours. AI programs can become biased after learning from real-world data. It is not typically introduced by the system designers but is learned by the program, and thus the programmers are often unaware that the bias exists. It can also emerge from correlations: AI is used to classify individuals into groups and then make predictions assuming that the individual will resemble other members of the group. In some cases, this assumption may be unfair. An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that the COMPAS-assigned recidivism risk level of black defendants is far more likely to be overestimated than that of white defendants, despite the fact that the program was not told the races of the defendants. Other examples where algorithmic bias can lead to unfair outcomes are when AI is used for credit rating or hiring.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,445 |
At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the Association for Computing Machinery, in Seoul, South Korea, presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed. Superintelligent AI may be able to improve itself to the point that humans could not control it. This could, as physicist Stephen Hawking puts it, "spell the end of the human race". Philosopher Nick Bostrom argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's, it might need to harm humanity to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. He concludes that AI poses a risk to mankind, however humble or "friendly" its stated goals might be. Political scientist Charles T. Rubin argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no "a priori" reason to believe that they would share our system of morality. The opinion of experts and industry insiders is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. Stephen Hawking, Microsoft founder Bill Gates, history professor Yuval Noah Harari, and SpaceX founder Elon Musk have all expressed serious misgivings about the future of AI. Prominent tech titans including Peter Thiel (Amazon Web Services) and Musk have committed more than $1 billion to nonprofit companies that champion responsible AI development, such as OpenAI and the Future of Life Institute. Mark Zuckerberg (CEO, Facebook) has said that artificial intelligence is helpful in its current form and will continue to assist humans. AI's decisions making abilities raises the questions of legal responsibility and copyright status of created works. This issues are being refined in various jurisdictions.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,453 |
Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk. Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. A common trope in these works began with Mary Shelley's "Frankenstein", where a human creation becomes a threat to its masters. This includes such works as and "" (both 1968), with HAL 9000, the murderous computer in charge of the "Discovery One" spaceship, as well as "The Terminator" (1984) and "The Matrix" (1999). In contrast, the rare loyal robots such as Gort from "The Day the Earth Stood Still" (1951) and Bishop from "Aliens" (1986) are less prominent in popular culture. Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity. Transhumanism (the merging of humans and machines) is explored in the manga "Ghost in the Shell" and the science-fiction series "Dune".
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,462 |
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's "R.U.R.", the films "A.I. Artificial Intelligence" and "Ex Machina", as well as the novel "Do Androids Dream of Electric Sheep?", by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence. As technology and research evolve and the world enters the third revolution of warfare following gunpowder and nuclear weapons, the artificial intelligence arms race ensues between the United States, China, and Russia, three countries with the world's top five highest military budgets. Intentions of being a world leader in AI research by 2030 have been declared by China's leader Xi Jinping, and President Putin of Russia has stated that "Whoever becomes the leader in this sphere will become the ruler of the world". If Russia were to become the leader in AI research, President Putin has stated Russia's intent to share some of their research with the world so as to not monopolize the field, similar to their current sharing of nuclear technologies, maintaining science diplomacy relations. The United States, China, and Russia, are some examples of countries that have taken their stances toward military artificial intelligence since as early as 2014, having established military programs to develop cyber weapons, control lethal autonomous weapons, and drones that can be used for surveillance.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,464 |
President Putin announced that artificial intelligence is the future for all mankind and recognizes the power and opportunities that the development and deployment of lethal autonomous weapons AI technology can hold in warfare and homeland security, as well as its threats. President Putin's prediction that future wars will be fought using AI has started to come to fruition to an extent after Russia invaded Ukraine on 24 February 2022. The Ukrainian military is making use of the Turkish Bayraktar TB2-drones that still require human operation to deploy laser-guided bombs but can take off, land, and cruise autonomously. Ukraine has also been using Switchblade drones supplied by the US and receiving information gathering by the United States's own surveillance operations regarding battlefield intelligence and national security about Russia. Similarly, Russia can use AI to help analyze battlefield data from surveillance footage taken by drones. Reports and images show that Russia's military has deployed KUB- BLA suicide drones into Ukraine, with speculations of intentions to assassinate Ukrainian President Volodymyr Zelenskyy. As research in the AI realm progresses, there is pushback about the use of AI from the Campaign to Stop Killer Robots and world technology leaders have sent a petition to the United Nations calling for new regulations on the development and use of AI technologies in 2017, including a ban on the use of lethal autonomous weapons due to ethical concerns for innocent civilian populations. With the ever evolving cyber-attacks and generation of devices, AI can be used for threat detection and more effective response by risk prioritization. With this tool, some challenges are also presented such as privacy, informed consent, and responsible use. According to CISA, the cyberspace is difficult to secure for the following factors: the ability of malicious actors to operate from anywhere in the world, the linkages between cyberspace and physical systems, and the difficulty of reducing vulnerabilities and consequences in complex cyber networks. With the increased technological advances of the world, the risk for wide scale consequential events rises. Paradoxically, the ability to protect information and create a line of communication between the scientific and diplomatic community thrives. The role of cybersecurity in diplomacy has become increasingly relevant, creating the term of cyber diplomacy – which is not uniformly defined and not synonymous with cyber defence. Many nations have developed unique approaches to scientific diplomacy in cyberspace.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,467 |
Dating back to 2011, when the Czech National Security Authority (NSA) was appointed as the national authority for the cyber agenda. The role of cyber diplomacy strengthened in 2017 when the Czech Ministry of Foreign Affairs (MFA) detected a serious cyber campaign directed against its own computer networks. In 2016, three cyber diplomats were deployed to Washington, D.C., Brussels and Tel Aviv, with the goal of establishing active international cooperation focused on engagement with the EU and NATO. The main agenda for these scientific diplomacy efforts is to bolster research on artificial intelligence and how it can be used in cybersecurity research, development, and overall consumer trust. CzechInvest is a key stakeholder in scientific diplomacy and cybersecurity. For example, in September 2018, they organized a mission to Canada in September 2018 with a special focus on artificial intelligence. The main goal of this particular mission was a promotional effort on behalf of Prague, attempting to establish it as a future knowledge hub for the industry for interested Canadian firms. Cybersecurity is recognized as a governmental task, dividing into three ministries of responsibility: the Federal Ministry of the Interior, the Federal Ministry of Defence, and the Federal Foreign Office. These distinctions promoted the creation of various institutions, such as The German National Office for Information Security, The National Cyberdefence Centre, The German National Cyber Security Council, and The Cyber and Information Domain Service. In 2018, a new strategy for artificial intelligence was established by the German government, with the creation of a German-French virtual research and innovation network, holding opportunity for research expansion into cybersecurity.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,469 |
The adoption of "The Cybersecurity Strategy of the European Union – An Open, Safe and Secure Cyberspace" document in 2013 by the European commission pushed forth cybersecurity efforts integrated with scientific diplomacy and artificial intelligence. Efforts are strong, as the EU funds various programs and institutions in the effort to bring science to diplomacy and bring diplomacy to science. Some examples are the cyber security programme Competence Research Innovation (CONCORDIA), which brings together 14 member states, and Cybersecurity for Europe (CSE), which brings together 43 partners involving 20 member states. In addition, The European Network of Cybersecurity Centres and Competence Hub for Innovation and Operations (ECHO) gathers 30 partners with 15 member states and SPARTA gathers 44 partners involving 14 member states. These efforts reflect the overall goals of the EU, to innovate cybersecurity for defense and protection, establish a highly integrated cyberspace among many nations, and further contribute to the security of artificial intelligence. With the 2022 invasion of Ukraine, there has been a rise in malicious cyber activity against the United States, Ukraine, and Russia. A prominent and rare documented use of artificial intelligence in conflict is on behalf of Ukraine, using facial recognition software to uncover Russian assailants and identify Ukrainians killed in the ongoing war. Though these governmental figures are not primarily focused on scientific and cyber diplomacy, other institutions are commenting on the use of artificial intelligence in cybersecurity with that focus. For example, Georgetown University's Center for Security and Emerging Technology (CSET) has the Cyber-AI Project, with one goal being to attract policymakers' attention to the growing body of academic research, which exposes the exploitive consequences of AI and machine-learning (ML) algorithms. This vulnerability can be a plausible explanation as to why Russia is not engaging in the use of AI in conflict per, Andrew Lohn, a senior fellow at CSET. In addition to use on the battlefield, AI is being used by the Pentagon to analyze data from the war, analyzing to strengthen cybersecurity and warfare intelligence for the United States.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,471 |
As artificial intelligence grows and the overwhelming amount of news portrayed through cyberspace expands, it is becoming extremely overwhelming for a voter to know what to believe. There are many intelligent codes, referred to as bots, written to portray people on social media with the goal of spreading misinformation. The 2016 US election is a victim of such actions. During the Hillary Clinton and Donald Trump campaign, artificial intelligent bots from Russia were spreading misinformation about the candidates in order to help the Trump campaign. Analysts concluded that approximately 19% of Twitter tweets centered around the 2016 election were detected to come from bots. YouTube in recent years has been used to spread political information as well. Although there is no proof that the platform attempts to manipulate its viewers opinions, Youtubes AI algorithm recommends videos of similar variety. If a person begins to research right wing political podcasts, then YouTube's algorithm will recommend more right wing videos. The uprising in a program called Deepfake, a software used to replicate someone's face and words, has also shown its potential threat. In 2018 a Deepfake video of Barack Obama was released saying words he claims to have never said. While in a national election a Deepfake will quickly be debunked, the software has the capability to heavily sway a smaller local election. This tool holds a lot of potential for spreading misinformation and is monitored with great attention. Although it may be seen as a tool used for harm, AI can help enhance election campaigns as well. AI bots can be programed to target articles with known misinformation. The bots can then indicate what is being misinformed to help shine light on the truth. AI can also be used to inform a person where each parts stands on a certain topic such as healthcare or climate change. The political leaders of a nation have heavy sway on international affairs. Thus, a political leader with a lack of interest for international collaborative scientific advancement can have a negative impact in the scientific diplomacy of that nation
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,472 |
The use of artificial intelligence (AI) has subtly grown to become part of everyday life. It is used every day in facial recognition software. It is the first measure of security for many companies in the form of a biometric authentication. This means of authentication allows even the most official organizations such as the United States Internal Revenue Service to verify a person's identity via a database generated from machine learning. As of the year 2022, the United States IRS requires those who do not undergo a live interview with an agent to complete a biometric verification of their identity via ID.me's facial recognition tool. In Japan and South Korea, artificial intelligence software is used in the instruction of English language via the company Riiid. Riiid is a Korean education company working alongside Japan to give students the means to learn and use their English communication skills via engaging with artificial intelligence in a live chat. Riid is not the only company to do this. American company Duolingo is well known for their automated teaching of 41 languages. Babbel, a German language learning program, also uses artificial intelligence in its teaching automation, allowing for European students to learn vital communication skills needed in social, economic, and diplomatic settings. Artificial intelligence will also automate the routine tasks that teachers need to do such as grading, taking attendance, and handling routine student inquiries. This enables the teacher to carry on with the complexities of teaching that an automated machine cannot handle. These include creating exams, explaining complex material in a way that will benefit students individually and handling unique questions from students. Unlike the human brain, which possess generalized intelligence, the specialized intelligence of AI can serve as a means of support to physicians internationally. The medical field has a diverse and profound amount of data in which AI can employ to generate a predictive diagnosis. Researchers at an Oxford hospital have developed artificial intelligence that can diagnose heart scans for heart disease and cancer. This artificial intelligence can pick up diminutive details in the scans that doctors may miss. As such, artificial intelligence in medicine will better the industry, giving doctors the means to precisely diagnose their patients using the tools available. The artificial intelligence algorithms will also be used to further improve diagnosis over time, via an application of machine learning called precision medicine. Furthermore, the narrow application of artificial intelligence can use "deep learning" in order to improve medical image analysis. In radiology imaging, AI uses deep learning algorithms to identify potentially cancerous lesions which is an important process assisting in early diagnosis.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,475 |
Data analysis is a fundamental property of artificial intelligence that enables it to be used in every facet of life from search results to the way people buy product. According to NewVantage Partners, over 90% of top businesses have ongoing investments in artificial intelligence. According to IBM, one of the world's leaders in technology, 45% of respondents from companies with over 1,000 employees have adopted AI. Recent data shows that the business market for artificial intelligence during the year 2020 was valued at $51.08 billion. The business market for artificial intelligence is projected to be over $640.3 billion by the year 2028. To prevent harm, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks. With the exponential surge of artificial technology and communication, the distribution of one's ideals and values has been evident in daily life. Digital information is spread via communication apps such as Whatsapp, Facebook/Meta, Snapchat, Instagram and Twitter. However, it is known that these sites relay specific information corresponding to data analysis. If a right-winged individual were to do a google search, Google's algorithms would target that individual and relay data pertinent to that target audience. US President Bill Clinton noted in 2000:"In the new century, liberty will spread by cell phone and cable modem. [...] We know how much the Internet has changed America, and we are already an open society. However, when the private sector uses artificial intelligence to gather data, a shift in power from the state to the private sector may be seen. This shift in power, specifically in large technological corporations, could profoundly change how diplomacy functions in society. The rise in digital technology and usage of artificial technology enabled the private sector to gather immense data on the public, which is then further categorized by race, location, age, gender, etc. "The New York Times" calculates that "the ten largest tech firms, which have become gatekeepers in commerce, finance, entertainment and communications, now have a combined market capitalization of more than $10 trillion. In gross domestic product terms, that would rank them as the world's third-largest economy." Beyond the general lobbying of congressmen/congresswomen, companies such as Facebook/Meta or Google use collected data in order to reach their intended audiences with targeted information.
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Artificial intelligence
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https://en.wikipedia.org/wiki?curid=1164
| 18,477 |
Multiple nations around the globe employ artificial intelligence to assist with their foreign policy decisions. The Chinese Department of External Security Affairs – under the Ministry of Foreign Affairs – uses AI to review almost all its foreign investment projects for risk mitigation. The government of China plans to use artificial intelligence in its $900 billion global infrastructure development plan, called the "Belt and Road Initiative" for political, economic, and environmental risk alleviation. Over 200 applications of artificial intelligence are being used by over 46 United Nations agencies, in sectors ranging from health care dealing with issues such as combating COVID-19 to smart agriculture, to assist the UN in political and diplomatic relations. One example is the use of AI by the UN Global Pulse program to model the effect of the spread of COVID-19 on internally displaced people (IDP) and refugee settlements to assist them in creating an appropriate global health policy. Novel AI tools such as remote sensing can also be employed by diplomats for collecting and analyzing data and near-real-time tracking of objects such as troop or refugee movements along borders in violent conflict zones. Artificial intelligence can be used to mitigate vital cross-national diplomatic talks to prevent translation errors caused by human translators. A major example is the 2021 Anchorage meetings held between US and China aimed at stabilizing foreign relations, only for it to have the opposite effect, increasing tension and aggressiveness between the two nations, due to translation errors caused by human translators. In the meeting, when United States National Security Advisor to President Joe Biden, Jacob Jeremiah Sullivan stated, "We do not seek conflict, but we welcome stiff competition and we will always stand up for our principles, for our people, and for our friends", it was mistranslated into Chinese as "we will face competition between us, and will present our stance in a very clear manner", adding an aggressive tone to the speech. AI's ability for fast and efficient natural language processing and real-time translation and transliteration makes it an important tool for foreign-policy communication between nations and prevents unintended mistranslation.
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Stephen Curry
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https://en.wikipedia.org/wiki?curid=5608488
| 19,830 |
NBA analysts say that Curry's scoring creates a "gravity" effect, forcing opposing defenders to double-team him even when he does not have the ball, which creates mismatches that his teammates exploit. With Curry, the Warriors average 10.8 isolations per game; without Curry, they average 15.3 isolations per game. His absence slows the Warriors offense and leads to less passing and ball movement. With Curry, the Warriors average 1.05 points every shot that comes after an off-ball screen; without Curry, it drops to 0.95 points per game. His absence makes it much easier for defenders to switch on screens. Of Curry's success with or without other elite teammates, Tom Haberstroh of NBC Sports said, "You can pluck All-Star after All-Star off the court like flower petals, and the Steph-led Warriors will still dominate like a champion. He's that transcendent of a player." During the 1992 NBA All-Star Game weekend, Curry's father entrusted him to Biserka Petrović, mother of future Hall of Fame player Dražen Petrović, while Dell competed in the Three-Point Contest. Following the 2015 NBA Finals, Curry gave Biserka one of his Finals-worn jerseys, which will reportedly be added to the collection of the Dražen Petrović Memorial Center, a museum to the late player in the Croatian capital of Zagreb. Curry suffers from keratoconus and wears contact lenses to correct his vision. Curry is also an avid golfer; he started playing golf at the age of 10, played golf in high school, and frequently plays golf with teammate Andre Iguodala. A 5-handicap golfer, Curry participates in celebrity golf tournaments and has played golf alongside Barack Obama. In August 2017, Curry competed in the Ellie Mae Classic on an unrestricted sponsor exemption. Although he missed the first cut, he scored 4-over-74 for both days he participated, surpassing most expectations for an amateur competing in the pro event. In August 2019, Curry and Howard University, a historically black institution in Washington, D.C., jointly announced that the school would add NCAA Division I teams in men's and women's golf starting in the 2020–21 school year, with Curry guaranteeing full funding of both teams for six years. Curry is also a fan of English soccer club Chelsea F.C.
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Stephen Curry
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https://en.wikipedia.org/wiki?curid=5608488
| 19,848 |
In 2012, Curry started donating three insecticide-treated mosquito nets for every three-pointer he made to the United Nations Foundation's Nothing But Nets campaign to combat malaria. He was first introduced to the malaria cause by Davidson teammate Bryant Barr when they were both in school. Curry visited the White House in 2015 and delivered a five-minute speech to dignitaries as part of President Barack Obama's launch of his President's Malaria Initiative strategy for 2015–2020.
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Lockheed Martin F-35 Lightning II
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https://en.wikipedia.org/wiki?curid=11812
| 22,145 |
The F-35 was the product of the Joint Strike Fighter (JSF) program, which was the merger of various combat aircraft programs from the 1980s and 1990s. One progenitor program was the Defense Advanced Research Projects Agency (DARPA) Advanced Short Take-Off/Vertical Landing (ASTOVL) which ran from 1983 to 1994; ASTOVL aimed to develop a Harrier jump jet replacement for the U.S. Marine Corps (USMC) and the U.K. Royal Navy. Under one of ASTOVL's classified programs, the Supersonic STOVL Fighter (SSF), Lockheed Skunk Works conducted research for a stealthy supersonic STOVL fighter intended for both U.S. Air Force (USAF) and USMC; a key technology explored was the shaft-driven lift fan (SDLF) system. Lockheed's concept was a single-engine canard delta aircraft weighing about empty. ASTOVL was rechristened as the Common Affordable Lightweight Fighter (CALF) in 1993 and involved Lockheed, McDonnell Douglas, and Boeing. The X-35A first flew on 24 October 2000 and conducted flight tests for subsonic and supersonic flying qualities, handling, range, and maneuver performance. After 28 flights, the aircraft was then converted into the X-35B for STOVL testing, with key changes including the addition of the SDLF, the three-bearing swivel module (3BSM), and roll-control ducts. The X-35B would successfully demonstrate the SDLF system by performing stable hover, vertical landing, and short takeoff in less than . The X-35C first flew on 16 December 2000 and conducted field landing carrier practice tests.
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Lockheed Martin F-35 Lightning II
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https://en.wikipedia.org/wiki?curid=11812
| 22,153 |
As the JSF program moved into the System Development and Demonstration phase, the X-35 demonstrator design was modified to create the F-35 combat aircraft. The forward fuselage was lengthened by to make room for mission avionics, while the horizontal stabilizers were moved aft to retain balance and control. The diverterless supersonic inlet changed from a four-sided to a three-sided cowl shape and was moved aft. The fuselage section was fuller, the top surface raised by along the centerline to accommodate weapons bays. Following the designation of the X-35 prototypes, the three variants were designated F-35A (CTOL), F-35B (STOVL), and F-35C (CV), all with a design service life of 8,000 hours. Prime contractor Lockheed Martin performs overall systems integration and final assembly and checkout (FACO), while Northrop Grumman and BAE Systems supply components for mission systems and airframe. The JSF program was expected to cost about $200 billion for acquisition in base-year 2002 dollars when SDD was awarded in 2001. As early as 2005, the Government Accountability Office (GAO) had identified major program risks in cost and schedule. The costly delays strained the relationship between the Pentagon and contractors. By 2017, delays and cost overruns had pushed the F-35 program's expected acquisition costs to $406.5 billion, with total lifetime cost (i.e., to 2070) to $1.5 trillion in then-year dollars which also includes operations and maintenance. The F-35A's unit cost for LRIP Lot 13 was $79.2 million. Delays in development and operational test and evaluation pushed full-rate production to 2023.
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Lockheed Martin F-35 Lightning II
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https://en.wikipedia.org/wiki?curid=11812
| 22,165 |
The F-35 has a wing-tail configuration with two vertical stabilizers canted for stealth. Flight control surfaces include leading-edge flaps, flaperons, rudders, and all-moving horizontal tails (stabilators); leading edge root extensions also run forwards to the inlets. The relatively short 35-foot wingspan of the F-35A and F-35B is set by the requirement to fit inside USN amphibious assault ship parking areas and elevators; the F-35C's larger wing is more fuel efficient. The fixed diverterless supersonic inlets (DSI) use a bumped compression surface and forward-swept cowl to shed the boundary layer of the forebody away from the inlets, which form a Y-duct for the engine. Structurally, the F-35 drew upon lessons from the F-22; composites comprise 35% of airframe weight, with the majority being bismaleimide and composite epoxy materials as well as some carbon nanotube-reinforced epoxy in later production lots. The F-35 is considerably heavier than the lightweight fighters it replaces, with the lightest variant having an empty weight of ; much of the weight can be attributed to the internal weapons bays and the extensive avionics carried. The F-35 was designed from the outset to incorporate improved processors, sensors, and software enhancements over its lifespan. Technology Refresh 3, which includes a new core processor and a new cockpit display, is planned for Lot 15 aircraft. Lockheed Martin has offered the Advanced EOTS for the Block 4 configuration; the improved sensor fits into the same area as the baseline EOTS with minimal changes. In June 2018, Lockheed Martin picked Raytheon for improved DAS. The USAF has studied the potential for the F-35 to orchestrate attacks by unmanned combat aerial vehicles (UCAVs) via its sensors and communications equipment.
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Lockheed Martin F-35 Lightning II
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https://en.wikipedia.org/wiki?curid=11812
| 22,171 |
Stealth is a key aspect of the F-35's design, and radar cross-section (RCS) is minimized through careful shaping of the airframe and the use of radar-absorbent materials (RAM); visible measures to reduce RCS include alignment of edges, serration of skin panels, and the masking of the engine face and turbine. Additionally, the F-35's diverterless supersonic inlet (DSI) uses a compression bump and forward-swept cowl rather than a splitter gap or bleed system to divert the boundary layer away from the inlet duct, eliminating the diverter cavity and further reducing radar signature. The RCS of the F-35 has been characterized as lower than a metal golf ball at certain frequencies and angles; in some conditions, the F-35 compares favorably to the F-22 in stealth. For maintainability, the F-35's stealth design took lessons learned from prior stealth aircraft such as the F-22; the F-35's radar-absorbent fibermat skin is more durable and requires less maintenance than older topcoats. The aircraft also has reduced infrared and visual signatures as well as strict controls of radio frequency emitters to prevent their detection. The F-35's stealth design is primarily focused on high-frequency X-band wavelengths; low-frequency radars can spot stealthy aircraft due to Rayleigh scattering, but such radars are also conspicuous, susceptible to clutter, and lack precision. To disguise its RCS, the aircraft can mount four Luneburg lens reflectors.
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Lockheed Martin F-35 Lightning II
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https://en.wikipedia.org/wiki?curid=11812
| 22,203 |
The United Kingdom's Royal Air Force and Royal Navy both operate the F-35B, known simply as the Lightning in British service; it has replaced the Harrier GR9, which was retired in 2010, and Tornado GR4, which was retired in 2019. The F-35 is to be Britain's primary strike aircraft for the next three decades. One of the Royal Navy's requirements for the F-35B was a Shipborne Rolling and Vertical Landing (SRVL) mode to increase maximum landing weight by using wing lift during landing. When operating on the aircraft carriers HMS Queen Elizabeth (R08) and HMS Prince of Wales (R09), British F-35Bs use ski-jumps. The Italian Navy use the same process. British F-35Bs are not intended to use the Brimstone 2 missile. In July 2013, Chief of the Air Staff, Air Chief Marshal Sir Stephen Dalton announced that No. 617 (The Dambusters) Squadron would be the RAF's first operational F-35 squadron. The second operational squadron will be the Fleet Air Arm's 809 Naval Air Squadron which will stand up in April 2023 or later. On 22 May 2018, IAF chief Amikam Norkin said that the service had employed their F-35Is in two attacks on two battle fronts, marking the first combat operation of an F-35 by any country. Norkin said it had been flown "all over the Middle East", and showed photos of an F-35I flying over Beirut in daylight. In July 2019, Israel expanded its strikes against Iranian missile shipments; IAF F-35Is allegedly struck Iranian targets in Iraq twice. On 11 May 2021, eight IAF F-35Is took part in an attack on 150 targets in Hamas' rocket array, including 50–70 launch pits in the northern Gaza Strip, as part of Operation Guardian of the Walls. Japan's F-35As were declared to have reached initial operational capability (IOC) on 29 March 2019. At the time Japan had taken delivery of 10 F-35As stationed in Misawa Air Base. Japan plans to eventually acquire a total of 147 F-35s, which will include 42 F-35Bs. It plans to use the latter variant to equip Japan’s Izumo-class multi-purpose destroyer.
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Stephen Hawking
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https://en.wikipedia.org/wiki?curid=19376148
| 26,132 |
Further work by Hawking in the area of arrows of time led to the 1985 publication of a paper theorising that if the no-boundary proposition were correct, then when the universe stopped expanding and eventually collapsed, time would run backwards. A paper by Don Page and independent calculations by Raymond Laflamme led Hawking to withdraw this concept. Honours continued to be awarded: in 1981 he was awarded the American Franklin Medal, and in the 1982 New Year Honours appointed a Commander of the Order of the British Empire (CBE). These awards did not significantly change Hawking's financial status, and motivated by the need to finance his children's education and home-expenses, he decided in 1982 to write a popular book about the universe that would be accessible to the general public. Instead of publishing with an academic press, he signed a contract with Bantam Books, a mass-market publisher, and received a large advance for his book. A first draft of the book, called "A Brief History of Time", was completed in 1984. Hawking continued his writings for a popular audience, publishing "The Universe in a Nutshell" in 2001, and "A Briefer History of Time", which he wrote in 2005 with Leonard Mlodinow to update his earlier works with the aim of making them accessible to a wider audience, and "God Created the Integers", which appeared in 2006. Along with Thomas Hertog at CERN and Jim Hartle, from 2006 on Hawking developed a theory of top-down cosmology, which says that the universe had not one unique initial state but many different ones, and therefore that it is inappropriate to formulate a theory that predicts the universe's current configuration from one particular initial state. Top-down cosmology posits that the present "selects" the past from a superposition of many possible histories. In doing so, the theory suggests a possible resolution of the fine-tuning question.
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Stephen Hawking
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https://en.wikipedia.org/wiki?curid=19376148
| 26,163 |
Hawking's speech deteriorated, and by the late 1970s he could be understood by only his family and closest friends. To communicate with others, someone who knew him well would interpret his speech into intelligible speech. Spurred by a dispute with the university over who would pay for the ramp needed for him to enter his workplace, Hawking and his wife campaigned for improved access and support for those with disabilities in Cambridge, including adapted student housing at the university. In general, Hawking had ambivalent feelings about his role as a disability rights champion: while wanting to help others, he also sought to detach himself from his illness and its challenges. His lack of engagement in this area led to some criticism. This relationship is conjectured to be valid not just for black holes, but also (since entropy is proportional to information) as an upper bound on the amount of information that can be contained in any volume of space, which has in turn spawned deeper reflections on the possible nature of reality.}} In June 2018, it was announced that Hawking's words, set to music by Greek composer Vangelis, would be beamed into space from a European space agency satellite dish in Spain with the aim of reaching the nearest black hole, 1A 0620-00. In June 2017, Hawking endorsed the Labour Party in the 2017 UK general election, citing the Conservatives' proposed cuts to the NHS. But he was also critical of Labour leader Jeremy Corbyn, expressing scepticism over whether the party could win a general election under him. Hawking was also a supporter of a universal basic income. He was critical of the Israeli government's position on the Israeli–Palestinian conflict, stating that their policy "is likely to lead to disaster." In 1988, Hawking, Arthur C. Clarke and Carl Sagan were interviewed in "God, the Universe and Everything Else". They discussed the Big Bang theory, God and the possibility of extraterrestrial life. Hawking used his fame to advertise products, including a wheelchair, National Savings, British Telecom, Specsavers, Egg Banking, and Go Compare. In 2015, he applied to trademark his name.
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Earth
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https://en.wikipedia.org/wiki?curid=9228
| 27,553 |
The angle of Earth's axial tilt is relatively stable over long periods of time. Its axial tilt does undergo nutation; a slight, irregular motion with a main period of 18.6 years. The orientation (rather than the angle) of Earth's axis also changes over time, precessing around in a complete circle over each 25,800-year cycle; this precession is the reason for the difference between a sidereal year and a tropical year. Both of these motions are caused by the varying attraction of the Sun and the Moon on Earth's equatorial bulge. The poles also migrate a few meters across Earth's surface. This polar motion has multiple, cyclical components, which collectively are termed quasiperiodic motion. In addition to an annual component to this motion, there is a 14-month cycle called the Chandler wobble. Earth's rotational velocity also varies in a phenomenon known as length-of-day variation. Viewed from Earth, the Moon is just far enough away to have almost the same apparent-sized disk as the Sun. The angular size (or solid angle) of these two bodies match because, although the Sun's diameter is about 400 times as large as the Moon's, it is also 400 times more distant. This allows total and annular solar eclipses to occur on Earth. The upper atmosphere, the atmosphere above the troposphere, is usually divided into the stratosphere, mesosphere, and thermosphere. Each layer has a different lapse rate, defining the rate of change in temperature with height. Beyond these, the exosphere thins out into the magnetosphere, where the geomagnetic fields interact with the solar wind. Within the stratosphere is the ozone layer, a component that partially shields the surface from ultraviolet light and thus is important for life on Earth. The Kármán line, defined as above Earth's surface, is a working definition for the boundary between the atmosphere and outer space. Earth's life has been shaping and inhabiting many particular ecosystems on Earth and has eventually expanded globally forming an overarching biosphere. Therefore, life has impacted Earth, significantly altering Earth's atmosphere and surface over long periods of time, causing changes like the Great oxidation event.
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Earth
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https://en.wikipedia.org/wiki?curid=9228
| 27,579 |
Extreme weather, such as tropical cyclones (including hurricanes and typhoons), occurs over most of Earth's surface and has a large impact on life in those areas. From 1980 to 2000, these events caused an average of 11,800 human deaths per year. Many places are subject to earthquakes, landslides, tsunamis, volcanic eruptions, tornadoes, blizzards, floods, droughts, wildfires, and other calamities and disasters. Human impact is felt in many areas due to pollution of the air and water, acid rain, loss of vegetation (overgrazing, deforestation, desertification), loss of wildlife, species extinction, soil degradation, soil depletion and erosion. Human activities release greenhouse gases into the atmosphere which cause global warming. This is driving changes such as the melting of glaciers and ice sheets, a global rise in average sea levels, increased risk of drought and wildfires, and migration of species to colder areas. Originating from earlier primates in eastern Africa 300,000 years ago humans have since been migrating and with the advent of agriculture in the 10th millennium BC increasingly settling Earth's land. In the 20th century Antarctica had been the last continent to see a first and until today limited human presence. Human population has since the 19th century grown exponentially to seven billion in the early 2010s, and is projected to peak at around ten billion in the second half of the 21st century. Most of the growth is expected to take place in sub-Saharan Africa. Distribution and density of human population varies greatly around the world with the majority living in south to eastern Asia and 90% inhabiting only the Northern Hemisphere of Earth, partly due to the hemispherical predominance of the world's land mass, with 68% of the world's land mass being in the Northern Hemisphere. Furthermore, since the 19th century humans have increasingly converged into urban areas with the majority living in urban areas by the 21st century.
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Earth
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https://en.wikipedia.org/wiki?curid=9228
| 27,585 |
Earth has resources that have been exploited by humans. Those termed non-renewable resources, such as fossil fuels, are only replenished over geological timescales. Large deposits of fossil fuels are obtained from Earth's crust, consisting of coal, petroleum, and natural gas. These deposits are used by humans both for energy production and as feedstock for chemical production. Mineral ore bodies have also been formed within the crust through a process of ore genesis, resulting from actions of magmatism, erosion, and plate tectonics. These metals and other elements are extracted by mining, a process which often brings environmental and health damage. It was only during the 19th century that geologists realized Earth's age was at least many millions of years. Lord Kelvin used thermodynamics to estimate the age of Earth to be between 20 million and 400 million years in 1864, sparking a vigorous debate on the subject; it was only when radioactivity and radioactive dating were discovered in the late 19th and early 20th centuries that a reliable mechanism for determining Earth's age was established, proving the planet to be billions of years old. </math>, where "m" is the mass of Earth, "a" is an astronomical unit, and "M" is the mass of the Sun. So the radius in AU is about formula_1.</ref>
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,048 |
JavaScript (), often abbreviated as JS, is a programming language that is one of the core technologies of the World Wide Web, alongside HTML and CSS. As of 2022, 98% of websites use JavaScript on the client side for webpage behavior, often incorporating third-party libraries. All major web browsers have a dedicated JavaScript engine to execute the code on users' devices. JavaScript is a high-level, often just-in-time compiled language that conforms to the ECMAScript standard. It has dynamic typing, prototype-based object-orientation, and first-class functions. It is multi-paradigm, supporting event-driven, functional, and imperative programming styles. It has application programming interfaces (APIs) for working with text, dates, regular expressions, standard data structures, and the Document Object Model (DOM). The ECMAScript standard does not include any input/output (I/O), such as networking, storage, or graphics facilities. In practice, the web browser or other runtime system provides JavaScript APIs for I/O. JavaScript engines were originally used only in web browsers, but are now core components of some servers and a variety of applications. The most popular runtime system for this usage is Node.js. Although Java and JavaScript are similar in name, syntax, and respective standard libraries, the two languages are distinct and differ greatly in design. The first popular web browser with a graphical user interface, Mosaic, was released in 1993. Accessible to non-technical people, it played a prominent role in the rapid growth of the nascent World Wide Web. The lead developers of Mosaic then founded the Netscape corporation, which released a more polished browser, Netscape Navigator, in 1994. This quickly became the most-used. During these formative years of the Web, web pages could only be static, lacking the capability for dynamic behavior after the page was loaded in the browser. There was a desire in the flourishing web development scene to remove this limitation, so in 1995, Netscape decided to add a scripting language to Navigator. They pursued two routes to achieve this: collaborating with Sun Microsystems to embed the Java programming language, while also hiring Brendan Eich to embed the Scheme language.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,055 |
Netscape management soon decided that the best option was for Eich to devise a new language, with syntax similar to Java and less like Scheme or other extant scripting languages. Although the new language and its interpreter implementation were called LiveScript when first shipped as part of a Navigator beta in September 1995, the name was changed to JavaScript for the official release in December. The choice of the JavaScript name has caused confusion, implying that it is directly related to Java. At the time, the dot-com boom had begun and Java was the hot new language, so Eich considered the JavaScript name a marketing ploy by Netscape. Microsoft debuted Internet Explorer in 1995, leading to a browser war with Netscape. On the JavaScript front, Microsoft reverse-engineered the Navigator interpreter to create its own, called JScript. JScript was first released in 1996, alongside initial support for CSS and extensions to HTML. Each of these implementations was noticeably different from their counterparts in Navigator. These differences made it difficult for developers to make their websites work well in both browsers, leading to widespread use of "best viewed in Netscape" and "best viewed in Internet Explorer" logos for several years. In November 1996, Netscape submitted JavaScript to Ecma International, as the starting point for a standard specification that all browser vendors could conform to. This led to the official release of the first ECMAScript language specification in June 1997. The standards process continued for a few years, with the release of ECMAScript 2 in June 1998 and ECMAScript 3 in December 1999. Work on ECMAScript 4 began in 2000. Meanwhile, Microsoft gained an increasingly dominant position in the browser market. By the early 2000s, Internet Explorer's market share reached 95%. This meant that JScript became the de facto standard for client-side scripting on the Web. Microsoft initially participated in the standards process and implemented some proposals in its JScript language, but eventually it stopped collaborating on Ecma work. Thus ECMAScript 4 was mothballed. During the period of Internet Explorer dominance in the early 2000s, client-side scripting was stagnant. This started to change in 2004, when the successor of Netscape, Mozilla, released the Firefox browser. Firefox was well received by many, taking significant market share from Internet Explorer.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,064 |
In 2005, Mozilla joined ECMA International, and work started on the ECMAScript for XML (E4X) standard. This led to Mozilla working jointly with Macromedia (later acquired by Adobe Systems), who were implementing E4X in their ActionScript 3 language, which was based on an ECMAScript 4 draft. The goal became standardizing ActionScript 3 as the new ECMAScript 4. To this end, Adobe Systems released the Tamarin implementation as an open source project. However, Tamarin and ActionScript 3 were too different from established client-side scripting, and without cooperation from Microsoft, ECMAScript 4 never reached fruition. Meanwhile, very important developments were occurring in open-source communities not affiliated with ECMA work. In 2005, Jesse James Garrett released a white paper in which he coined the term Ajax and described a set of technologies, of which JavaScript was the backbone, to create web applications where data can be loaded in the background, avoiding the need for full page reloads. This sparked a renaissance period of JavaScript, spearheaded by open-source libraries and the communities that formed around them. Many new libraries were created, including jQuery, Prototype, Dojo Toolkit, and MooTools. Google debuted its Chrome browser in 2008, with the V8 JavaScript engine that was faster than its competition. The key innovation was just-in-time compilation (JIT), so other browser vendors needed to overhaul their engines for JIT. In July 2008, these disparate parties came together for a conference in Oslo. This led to the eventual agreement in early 2009 to combine all relevant work and drive the language forward. The result was the ECMAScript 5 standard, released in December 2009. Ambitious work on the language continued for several years, culminating in an extensive collection of additions and refinements being formalized with the publication of ECMAScript 6 in 2015. The creation of Node.js in 2009 by Ryan Dahl sparked a significant increase in the usage of JavaScript outside of web browsers. Node combines the V8 engine, an event loop, and I/O APIs, thereby providing a stand-alone JavaScript runtime system. As of 2018, Node had been used by millions of developers, and npm had the most modules of any package manager in the world.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,070 |
The ECMAScript draft specification is currently maintained openly on GitHub, and editions are produced via regular annual snapshots. Potential revisions to the language are vetted through a comprehensive proposal process. Now, instead of edition numbers, developers check the status of upcoming features individually. The current JavaScript ecosystem has many libraries and frameworks, established programming practices, and substantial usage of JavaScript outside of web browsers. Plus, with the rise of single-page applications and other JavaScript-heavy websites, several transpilers have been created to aid the development process. "JavaScript" is a trademark of Oracle Corporation in the United States. The trademark was originally issued to Sun Microsystems on 6 May 1997, and was transferred to Oracle when they acquired Sun in 2010. JavaScript is the dominant client-side scripting language of the Web, with 98% of all websites using it for this purpose. Scripts are embedded in or included from HTML documents and interact with the DOM. All major web browsers have a built-in JavaScript engine that executes the code on the user's device. Over 80% of websites use a third-party JavaScript library or web framework for their client-side scripting. jQuery is by far the most popular library, used by over 75% of websites. Facebook created the React library for its website and later released it as open source; other sites, including Twitter, now use it. Likewise, the Angular framework created by Google for its websites, including YouTube and Gmail, is now an open source project used by others. In contrast, the term "Vanilla JS" has been coined for websites not using any libraries or frameworks, instead relying entirely on standard JavaScript functionality. The use of JavaScript has expanded beyond its web browser roots. JavaScript engines are now embedded in a variety of other software systems, both for server-side website deployments and non-browser applications. Initial attempts at promoting server-side JavaScript usage were Netscape Enterprise Server and Microsoft's Internet Information Services, but they were small niches. Server-side usage eventually started to grow in the late 2000s, with the creation of Node.js and other approaches. Electron, Cordova, React Native, and other application frameworks have been used to create many applications with behavior implemented in JavaScript. Other non-browser applications include Adobe Acrobat support for scripting PDF documents and GNOME Shell extensions written in JavaScript.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,080 |
The following features are common to all conforming ECMAScript implementations unless explicitly specified otherwise. JavaScript supports much of the structured programming syntax from C (e.g., codice_1 statements, codice_2 loops, codice_3 statements, codice_4 loops, etc.). One partial exception is scoping: originally JavaScript only had function scoping with codice_5; block scoping was added in ECMAScript 2015 with the keywords codice_6 and codice_7. Like C, JavaScript makes a distinction between expressions and statements. One syntactic difference from C is automatic semicolon insertion, which allow semicolons (which terminate statements) to be omitted. JavaScript is weakly typed, which means certain types are implicitly cast depending on the operation used. Values are cast to numbers by casting to strings and then casting the strings to numbers. These processes can be modified by defining codice_15 and codice_16 functions on the prototype for string and number casting respectively. JavaScript has received criticism for the way it implements these conversions as the complexity of the rules can be mistaken for inconsistency. For example, when adding a number to a string, the number will be cast to a string before performing concatenation, but when subtracting a number from a string, the string is cast to a number before performing subtraction. Often also mentioned is codice_17 resulting in codice_18 (number). This is misleading: the codice_19 is interpreted as an empty code block instead of an empty object, and the empty array is cast to a number by the remaining unary codice_8 operator. If you wrap the expression in parentheses codice_21 the curly brackets are interpreted as an empty object and the result of the expression is codice_22 as expected. In JavaScript, an object is an associative array, augmented with a prototype (see below); each key provides the name for an object property, and there are two syntactical ways to specify such a name: dot notation (codice_24) and bracket notation (codice_25). A property may be added, rebound, or deleted at run-time. Most properties of an object (and any property that belongs to an object's prototype inheritance chain) can be enumerated using a codice_26 loop.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,087 |
JavaScript functions are first-class; a function is considered to be an object. As such, a function may have properties and methods, such as codice_36 and codice_37. A "nested" function is a function defined within another function. It is created each time the outer function is invoked. In addition, each nested function forms a lexical closure: the lexical scope of the outer function (including any constant, local variable, or argument value) becomes part of the internal state of each inner function object, even after execution of the outer function concludes. JavaScript also supports anonymous functions. Variables in JavaScript can be defined using either the codice_5, codice_6 or codice_7 keywords. Variables defined without keywords will be defined at the global scope. There is no built-in Input/output functionality in JavaScript, instead it is provided by the run-time environment. The ECMAScript specification in edition 5.1 mentions that "there are no provisions in this specification for input of external data or output of computed results". However, most runtime environments have a codice_50 object that can be used to print output. Here is a minimalist Hello World program in JavaScript in a runtime environment with a console object: // After setting this, the tag will look like this: `<span class="foo" id="bar" data-attr="baz"></span>` myElem.setAttribute('data-attr', 'baz'); // Which could also be written as `myElem.dataset.attr = 'baz'` // Elements can be imperatively grabbed with querySelector for one element, or querySelectorAll for multiple elements that can be looped with forEach document.querySelectorAll('.multiple'); // Returns an Array of all elements with the "multiple" class This example shows that, in JavaScript, function closures capture their non-local variables by reference. Arrow functions were first introduced in 6th Edition - ECMAScript 2015. They shorten the syntax for writing functions in JavaScript. Arrow functions are anonymous, so a variable is needed to refer to them in order to invoke them after their creation, unless surrounded by parenthesis and executed immediately.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,097 |
// An arrow function, like other function definitions, can be executed in the same statement as they are created. const five_multiples = generate_multiplier_function(5); // The supplied argument "seeds" the expression and is retained by a. Immediately-invoked function expressions are often used to create closures. Closures allow gathering properties and methods in a namespace and making some of them private: Generator objects (in the form of generator functions) provide a function which can be called, exited, and re-entered while maintaining internal context (statefulness). }).sort((a, b) => a.lcm() - b.lcm()) // sort with this comparative function; => is a shorthand form of a function, called "arrow function" JavaScript and the DOM provide the potential for malicious authors to deliver scripts to run on a client computer via the Web. Browser authors minimize this risk using two restrictions. First, scripts run in a sandbox in which they can only perform Web-related actions, not general-purpose programming tasks like creating files. Second, scripts are constrained by the same-origin policy: scripts from one Web site do not have access to information such as usernames, passwords, or cookies sent to another site. Most JavaScript-related security bugs are breaches of either the same origin policy or the sandbox. There are subsets of general JavaScript—ADsafe, Secure ECMAScript (SES)—that provide greater levels of security, especially on code created by third parties (such as advertisements). Closure Toolkit is another project for safe embedding and isolation of third-party JavaScript and HTML. Content Security Policy is the main intended method of ensuring that only trusted code is executed on a Web page. A common JavaScript-related security problem is cross-site scripting (XSS), a violation of the same-origin policy. XSS vulnerabilities occur when an attacker can cause a target Web site, such as an online banking website, to include a malicious script in the webpage presented to a victim. The script in this example can then access the banking application with the privileges of the victim, potentially disclosing secret information or transferring money without the victim's authorization. A solution to XSS vulnerabilities is to use "HTML escaping" whenever displaying untrusted data.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,106 |
Some browsers include partial protection against "reflected" XSS attacks, in which the attacker provides a URL including malicious script. However, even users of those browsers are vulnerable to other XSS attacks, such as those where the malicious code is stored in a database. Only correct design of Web applications on the server-side can fully prevent XSS. Another cross-site vulnerability is cross-site request forgery (CSRF). In CSRF, code on an attacker's site tricks the victim's browser into taking actions the user did not intend at a target site (like transferring money at a bank). When target sites rely solely on cookies for request authentication, requests originating from code on the attacker's site can carry the same valid login credentials of the initiating user. In general, the solution to CSRF is to require an authentication value in a hidden form field, and not only in the cookies, to authenticate any request that might have lasting effects. Checking the HTTP Referrer header can also help. "JavaScript hijacking" is a type of CSRF attack in which a codice_42 tag on an attacker's site exploits a page on the victim's site that returns private information such as JSON or JavaScript. Possible solutions include: Developers of client-server applications must recognize that untrusted clients may be under the control of attackers. The application author cannot assume that their JavaScript code will run as intended (or at all) because any secret embedded in the code could be extracted by a determined adversary. Some implications are:
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
| 28,110 |
Package management systems such as npm and Bower are popular with JavaScript developers. Such systems allow a developer to easily manage their program's dependencies upon other developers' program libraries. Developers trust that the maintainers of the libraries will keep them secure and up to date, but that is not always the case. A vulnerability has emerged because of this blind trust. Relied-upon libraries can have new releases that cause bugs or vulnerabilities to appear in all programs that rely upon the libraries. Inversely, a library can go unpatched with known vulnerabilities out in the wild. In a study done looking over a sample of 133,000 websites, researchers found 37% of the websites included a library with at least one known vulnerability. "The median lag between the oldest library version used on each website and the newest available version of that library is 1,177 days in ALEXA, and development of some libraries still in active use ceased years ago." Another possibility is that the maintainer of a library may remove the library entirely. This occurred in March 2016 when Azer Koçulu removed his repository from npm. This caused tens of thousands of programs and websites depending upon his libraries to break. JavaScript provides an interface to a wide range of browser capabilities, some of which may have flaws such as buffer overflows. These flaws can allow attackers to write scripts that would run any code they wish on the user's system. This code is not by any means limited to another JavaScript application. For example, a buffer overrun exploit can allow an attacker to gain access to the operating system's API with superuser privileges. Plugins, such as video players, Adobe Flash, and the wide range of ActiveX controls enabled by default in Microsoft Internet Explorer, may also have flaws exploitable via JavaScript (such flaws have been exploited in the past). In Windows Vista, Microsoft has attempted to contain the risks of bugs such as buffer overflows by running the Internet Explorer process with limited privileges. Google Chrome similarly confines its page renderers to their own "sandbox". Web browsers are capable of running JavaScript outside the sandbox, with the privileges necessary to, for example, create or delete files. Such privileges are not intended to be granted to code from the Web.
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JavaScript
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https://en.wikipedia.org/wiki?curid=9845
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Incorrectly granting privileges to JavaScript from the Web has played a role in vulnerabilities in both Internet Explorer and Firefox. In Windows XP Service Pack 2, Microsoft demoted JScript's privileges in Internet Explorer. Microsoft Windows allows JavaScript source files on a computer's hard drive to be launched as general-purpose, non-sandboxed programs (see: Windows Script Host). This makes JavaScript (like VBScript) a theoretically viable vector for a Trojan horse, although JavaScript Trojan horses are uncommon in practice. In 2015, a JavaScript-based proof-of-concept implementation of a rowhammer attack was described in a paper by security researchers. In 2017, a JavaScript-based attack via browser was demonstrated that could bypass ASLR. It's called "ASLR⊕Cache" or AnC. In 2018, the paper that announced the Spectre attacks against Speculative Execution in Intel and other processors included a JavaScript implementation. A common misconception is that JavaScript is the same as Java. Both indeed have a C-like syntax (the C language being their most immediate common ancestor language). They are also typically sandboxed (when used inside a browser), and JavaScript was designed with Java's syntax and standard library in mind. In particular, all Java keywords were reserved in original JavaScript, JavaScript's standard library follows Java's naming conventions, and JavaScript's and objects are based on classes from Java 1.0. Java and JavaScript both first appeared in 1995, but Java was developed by James Gosling of Sun Microsystems and JavaScript by Brendan Eich of Netscape Communications. The differences between the two languages are more prominent than their similarities. Java has static typing, while JavaScript's typing is dynamic. Java is loaded from compiled bytecode, while JavaScript is loaded as human-readable source code. Java's objects are class-based, while JavaScript's are prototype-based. Finally, Java did not support functional programming until Java 8, while JavaScript has done so from the beginning, being influenced by Scheme. JSON, or JavaScript Object Notation, is a general-purpose data interchange format that is defined as a subset of JavaScript's object literal syntax.
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