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Futurism – Wikipedia

Futurism (Italian: Futurismo) was an artistic and social movement that originated in Italy in the early 20th century. It emphasized speed, technology, youth, violence, and objects such as the car, the airplane, and the industrial city. Its key figures were the Italians Filippo Tommaso Marinetti, Umberto Boccioni, Carlo Carr, Gino Severini, Giacomo Balla, and Luigi Russolo. It glorified modernity and aimed to liberate Italy from the weight of its past.[1] Cubism contributed to the formation of Italian Futurism’s artistic style.[2] Important Futurist works included Marinetti’s Manifesto of Futurism, Boccioni’s sculpture Unique Forms of Continuity in Space, Balla’s painting Abstract Speed + Sound, and Russolo’s The Art of Noises. Although it was largely an Italian phenomenon, there were parallel movements in Russia, England, Belgium and elsewhere. The Futurists practiced in every medium of art, including painting, sculpture, ceramics, graphic design, industrial design, interior design, urban design, theatre, film, fashion, textiles, literature, music, architecture, and even Futurist meals. To some extent Futurism influenced the art movements Art Deco, Constructivism, Surrealism, Dada, and to a greater degree Precisionism, Rayonism, and Vorticism.

Futurism is an avant-garde movement founded in Milan in 1909 by the Italian poet Filippo Tommaso Marinetti.[1] Marinetti launched the movement in his Futurist Manifesto,[3] which he published for the first time on 5 February 1909 in La gazzetta dell’Emilia, an article then reproduced in the French daily newspaper Le Figaro on Saturday 20 February 1909.[4][5][6] He was soon joined by the painters Umberto Boccioni, Carlo Carr, Giacomo Balla, Gino Severini and the composer Luigi Russolo. Marinetti expressed a passionate loathing of everything old, especially political and artistic tradition. “We want no part of it, the past”, he wrote, “we the young and strong Futurists!” The Futurists admired speed, technology, youth and violence, the car, the airplane and the industrial city, all that represented the technological triumph of humanity over nature, and they were passionate nationalists. They repudiated the cult of the past and all imitation, praised originality, “however daring, however violent”, bore proudly “the smear of madness”, dismissed art critics as useless, rebelled against harmony and good taste, swept away all the themes and subjects of all previous art, and gloried in science.

Publishing manifestos was a feature of Futurism, and the Futurists (usually led or prompted by Marinetti) wrote them on many topics, including painting, architecture, religion, clothing and cooking.[7]

The founding manifesto did not contain a positive artistic programme, which the Futurists attempted to create in their subsequent Technical Manifesto of Futurist Painting (1914).[8] This committed them to a “universal dynamism”, which was to be directly represented in painting. Objects in reality were not separate from one another or from their surroundings: “The sixteen people around you in a rolling motor bus are in turn and at the same time one, ten four three; they are motionless and they change places. … The motor bus rushes into the houses which it passes, and in their turn the houses throw themselves upon the motor bus and are blended with it.”[9]

The Futurist painters were slow to develop a distinctive style and subject matter. In 1910 and 1911 they used the techniques of Divisionism, breaking light and color down into a field of stippled dots and stripes, which had been originally created by Giovanni Segantini and others. Later, Severini, who lived in Paris, attributed their backwardness in style and method at this time to their distance from Paris, the centre of avant-garde art.[10] Severini was the first to come into contact with Cubism and following a visit to Paris in 1911 the Futurist painters adopted the methods of the Cubists. Cubism offered them a means of analysing energy in paintings and expressing dynamism.

They often painted modern urban scenes. Carr’s Funeral of the Anarchist Galli (191011) is a large canvas representing events that the artist had himself been involved in, in 1904. The action of a police attack and riot is rendered energetically with diagonals and broken planes. His Leaving the Theatre (191011) uses a Divisionist technique to render isolated and faceless figures trudging home at night under street lights.

Boccioni’s The City Rises (1910) represents scenes of construction and manual labour with a huge, rearing red horse in the centre foreground, which workmen struggle to control. His States of Mind, in three large panels, The Farewell, Those who Go, and Those Who Stay, “made his first great statement of Futurist painting, bringing his interests in Bergson, Cubism and the individual’s complex experience of the modern world together in what has been described as one of the ‘minor masterpieces’ of early twentieth century painting.”[11] The work attempts to convey feelings and sensations experienced in time, using new means of expression, including “lines of force”, which were intended to convey the directional tendencies of objects through space, “simultaneity”, which combined memories, present impressions and anticipation of future events, and “emotional ambience” in which the artist seeks by intuition to link sympathies between the exterior scene and interior emotion.[11]

Boccioni’s intentions in art were strongly influenced by the ideas of Bergson, including the idea of intuition, which Bergson defined as a simple, indivisible experience of sympathy through which one is moved into the inner being of an object to grasp what is unique and ineffable within it. The Futurists aimed through their art thus to enable the viewer to apprehend the inner being of what they depicted. Boccioni developed these ideas at length in his book, Pittura scultura Futuriste: Dinamismo plastico (Futurist Painting Sculpture: Plastic Dynamism) (1914).[12]

Balla’s Dynamism of a Dog on a Leash (1912) exemplifies the Futurists’ insistence that the perceived world is in constant movement. The painting depicts a dog whose legs, tail and leashand the feet of the woman walking ithave been multiplied to a blur of movement. It illustrates the precepts of the Technical Manifesto of Futurist Painting that, “On account of the persistency of an image upon the retina, moving objects constantly multiply themselves; their form changes like rapid vibrations, in their mad career. Thus a running horse has not four legs, but twenty, and their movements are triangular.”[9] His Rhythm of the Bow (1912) similarly depicts the movements of a violinist’s hand and instrument, rendered in rapid strokes within a triangular frame.

The adoption of Cubism determined the style of much subsequent Futurist painting, which Boccioni and Severini in particular continued to render in the broken colors and short brush-strokes of divisionism. But Futurist painting differed in both subject matter and treatment from the quiet and static Cubism of Picasso, Braque and Gris. Although there were Futurist portraits (e.g. Carr’s Woman with Absinthe (1911), Severini’s Self-Portrait (1912), and Boccioni’s Matter (1912)), it was the urban scene and vehicles in motion that typified Futurist paintinge.g. Boccioni’s The Street Enters the House (1911), Severini’s Dynamic Hieroglyph of the Bal Tabarin (1912), and Russolo’s Automobile at Speed (1913)

In 1912 and 1913, Boccioni turned to sculpture to translate into three dimensions his Futurist ideas. In Unique Forms of Continuity in Space (1913) he attempted to realise the relationship between the object and its environment, which was central to his theory of “dynamism”. The sculpture represents a striding figure, cast in bronze posthumously and exhibited in the Tate Modern. (It now appears on the national side of Italian 20 eurocent coins). He explored the theme further in Synthesis of Human Dynamism (1912), Speeding Muscles (1913) and Spiral Expansion of Speeding Muscles (1913). His ideas on sculpture were published in the Technical Manifesto of Futurist Sculpture[13] In 1915 Balla also turned to sculpture making abstract “reconstructions”, which were created out of various materials, were apparently moveable and even made noises. He said that, after making twenty pictures in which he had studied the velocity of automobiles, he understood that “the single plane of the canvas did not permit the suggestion of the dynamic volume of speed in depth … I felt the need to construct the first dynamic plastic complex with iron wires, cardboard planes, cloth and tissue paper, etc.”[14]

In 1914, personal quarrels and artistic differences between the Milan group, around Marinetti, Boccioni, and Balla, and the Florence group, around Carr, Ardengo Soffici (18791964) and Giovanni Papini (18811956), created a rift in Italian Futurism. The Florence group resented the dominance of Marinetti and Boccioni, whom they accused of trying to establish “an immobile church with an infallible creed”, and each group dismissed the other as passiste.

Futurism had from the outset admired violence and was intensely patriotic. The Futurist Manifesto had declared, “We will glorify warthe world’s only hygienemilitarism, patriotism, the destructive gesture of freedom-bringers, beautiful ideas worth dying for, and scorn for woman.”[6][15] Although it owed much of its character and some of its ideas to radical political movements, it was not much involved in politics until the autumn of 1913.[14] Then, fearing the re-election of Giolitti, Marinetti published a political manifesto. In 1914 the Futurists began to campaign actively against the Austro-Hungarian empire, which still controlled some Italian territories, and Italian neutrality between the major powers. In September, Boccioni, seated in the balcony of the Teatro dal Verme in Milan, tore up an Austrian flag and threw it into the audience, while Marinetti waved an Italian flag. When Italy entered the First World War in 1915, many Futurists enlisted.[16] The experience of the war marked several Futurists, particularly Marinetti, who fought in the mountains of Trentino at the border of Italy and Austria-Hungary, actively engaging in propaganda.[17] The combat experience also influenced Futurist music.[18]

The outbreak of war disguised the fact that Italian Futurism had come to an end. The Florence group had formally acknowledged their withdrawal from the movement by the end of 1914. Boccioni produced only one war picture and was killed in 1916. Severini painted some significant war pictures in 1915 (e.g. War, Armored Train, and Red Cross Train), but in Paris turned towards Cubism and post-war was associated with the Return to Order.

After the war, Marinetti revived the movement. This revival was called il secondo Futurismo (Second Futurism) by writers in the 1960s. The art historian Giovanni Lista has classified Futurism by decades: “Plastic Dynamism” for the first decade, “Mechanical Art” for the 1920s, “Aeroaesthetics” for the 1930s.

Russian Futurism was a movement of literature and the visual arts. The poet Vladimir Mayakovsky was a prominent member of the movement. Visual artists such as David Burlyuk, Mikhail Larionov, Natalia Goncharova and Kazimir Malevich found inspiration in the imagery of Futurist writings and were poets themselves. It has also a larger impact on the all suprematism movement. Other poets adopting Futurism included Velimir Khlebnikov and Aleksey Kruchenykh. Poets and painters collaborated on theatre production such as the Futurist opera Victory Over the Sun, with texts by Kruchenykh and sets by Malevich.

The main style of painting was Cubo-Futurism, adopted in 1913 when Aristarkh Lentulov returned from Paris and exhibited his paintings in Moscow. Cubo-Futurism combines the forms of Cubism with the representation of movement. Like their Italian predecessors the Russian Futurists were fascinated with dynamism, speed and the restlessness of modern urban life.

The Russian Futurists sought controversy by repudiating the art of the past, saying that Pushkin and Dostoevsky should be “heaved overboard from the steamship of modernity”. They acknowledged no authority and professed not to owe anything even to Marinetti, whose principles they had earlier adopted, obstructing him when he came to Russia to proselytize in 1914.

The movement began to decline after the revolution of 1917. Some Futurists died, others emigrated. Mayakovsky and Malevich became part of the Soviet establishment and the Agitprop movement of the 1920s. Others were persecuted. Mayakovsky committed suicide on April 14, 1930.

The Futurist architect Antonio Sant’Elia expressed his ideas of modernity in his drawings for La Citt Nuova (The New City) (19121914). This project was never built and Sant’Elia was killed in the First World War, but his ideas influenced later generations of architects and artists. The city was a backdrop onto which the dynamism of Futurist life is projected. The city had replaced the landscape as the setting for the exciting modern life. Sant’Elia aimed to create a city as an efficient, fast-paced machine. He manipulates light and shape to emphasize the sculptural quality of his projects. Baroque curves and encrustations had been stripped away to reveal the essential lines of forms unprecedented from their simplicity. In the new city, every aspect of life was to be rationalized and centralized into one great powerhouse of energy. The city was not meant to last, and each subsequent generation was expected to build their own city rather than inheriting the architecture of the past.

Futurist architects were sometimes at odds with the Fascist state’s tendency towards Roman imperial-classical aesthetic patterns. Nevertheless, several Futurist buildings were built in the years 19201940, including public buildings such as railway stations, maritime resorts and post offices. Examples of Futurist buildings still in use today are Trento’s railway station, built by Angiolo Mazzoni, and the Santa Maria Novella station in Florence. The Florence station was designed in 1932 by the Gruppo Toscano (Tuscan Group) of architects, which included Giovanni Michelucci and Italo Gamberini, with contributions by Mazzoni.

Futurist music rejected tradition and introduced experimental sounds inspired by machinery, and would influence several 20th-century composers.

Francesco Balilla Pratella joined the Futurist movement in 1910 and wrote a Manifesto of Futurist Musicians in which he appealed to the young (as had Marinetti), because only they could understand what he had to say. According to Pratella, Italian music was inferior to music abroad. He praised the “sublime genius” of Wagner and saw some value in the work of other contemporary composers, for example Richard Strauss, Elgar, Mussorgsky, and Sibelius. By contrast, the Italian symphony was dominated by opera in an “absurd and anti-musical form”. The conservatories was said to encourage backwardness and mediocrity. The publishers perpetuated mediocrity and the domination of music by the “rickety and vulgar” operas of Puccini and Umberto Giordano. The only Italian Pratella could praise was his teacher Pietro Mascagni, because he had rebelled against the publishers and attempted innovation in opera, but even Mascagni was too traditional for Pratella’s tastes. In the face of this mediocrity and conservatism, Pratella unfurled “the red flag of Futurism, calling to its flaming symbol such young composers as have hearts to love and fight, minds to conceive, and brows free of cowardice.”

Luigi Russolo (18851947) wrote The Art of Noises (1913),[19][20] an influential text in 20th-century musical aesthetics. Russolo used instruments he called intonarumori, which were acoustic noise generators that permitted the performer to create and control the dynamics and pitch of several different types of noises. Russolo and Marinetti gave the first concert of Futurist music, complete with intonarumori, in 1914. However they were prevented from performing in many major European cities by the outbreak of war.

Futurism was one of several 20th-century movements in art music that paid homage to, included or imitated machines. Ferruccio Busoni has been seen as anticipating some Futurist ideas, though he remained wedded to tradition.[21] Russolo’s intonarumori influenced Stravinsky, Arthur Honegger, George Antheil, Edgar Varse,[11] Stockhausen and John Cage. In Pacific 231, Honegger imitated the sound of a steam locomotive. There are also Futurist elements in Prokofiev’s The Steel Step and in his Second Symphony.

Most notable in this respect, however, is the American George Antheil. His fascination with machinery is evident in his Airplane Sonata, Death of the Machines, and the 30-minute Ballet Mcanique. The Ballet Mcanique was originally intended to accompany an experimental film by Fernand Lger, but the musical score is twice the length of the film and now stands alone. The score calls for a percussion ensemble consisting of three xylophones, four bass drums, a tam-tam, three airplane propellers, seven electric bells, a siren, two “live pianists”, and sixteen synchronized player pianos. Antheil’s piece was the first to synchronize machines with human players and to exploit the difference between what machines and humans can play.

Other composers offered more melodic variants of Futurist music, notably Franco Casavola, who was active with the movement at the invitation of Marinetti between 1924 and 1927, and Arthur-Vincent Louri, the first Russian Futurist musician, and a signatory of the St Petersburg Futurist Manifesto in 1914. His five Synthses offer a form of dodecaphony, while Formes en l’air was dedicated to Picasso and is a Cubo-Futurist concept. Born in Ukraine and raised in New York, Leo Ornstein gave his first recital of ‘Futurist Music’ at the Steinway Hall in London on 27 March 1914. According to the Daily Sketch newspaper “one listened with considerable distress. Nothing so horrible as Mr Ornstein’s music has been heard so far. Sufferers from complete deafness should attend the next recital.”

The Futuristic movement also influenced the concept of dance. Indeed, dancing was interpreted as an alternative way of expressing man’s ultimate fusion with the machine. The altitude of a flying plane, the power of a car’s motor and the roaring loud sounds of complex machinery were all signs of man’s intelligence and excellence which the art of dance had to emphasize and praise. This type of dance is considered futuristic since it disrupts the referential system of traditional, classical dance and introduces a different style, new to the sophisticated bourgeois audience. The dancer no longer performs a story, a clear content, that can be read according to the rules of ballet. One of the most famous futuristic dancers was the Italian Giannina Censi[it]. Trained as a classical ballerina, she is known for her “Aerodanze” and continued to earn her living by performing in classical and popular productions. She describes this innovative form of dance as the result of a deep collaboration with Marinetti and his poetry. Through these words, she explains: ” I launched this idea of the aerial-futurist poetry with Marinetti, he himself declaiming the poetry. A small stage of a few square meters;… I made myself a satin costume with a helmet; everything that the plane did had to be expressed by my body. It flew and, moreover, it gave the impression of these wings that trembled, of the apparatus that trembled,… And the face had to express what the pilot felt.”[22][23]

Futurism as a literary movement made its official debut with F.T. Marinetti’s Manifesto of Futurism (1909), as it delineated the various ideals Futurist poetry should strive for. Poetry, the predominate medium of Futurist literature, can be characterized by its unexpected combinations of images and hyper-conciseness (not to be confused with the actual length of the poem). The Futurists called their style of poetry parole in libert (word autonomy) in which all ideas of meter were rejected and the word became the main unit of concern. In this way, the Futurists managed to create a new language free of syntax punctuation, and metrics that allowed for free expression.

Theater also has an important place within the Futurist universe. Works in this genre have scenes that are few sentences long, have an emphasis on nonsensical humor, and attempt to discredit the deep rooted traditions via parody and other devaluation techniques.There are a number of examples of Futurist novels from both the initial period of Futurism and the neo-Futurist period, from Marinetti himself to a number of lesser known Futurists, such as Primo Conti, Ardengo Soffici and Giordano Bruno Sanzin (Zig Zag, Il Romanzo Futurista edited by Alessandro Masi, 1995). They are very diverse in style, with very little recourse to the characteristics of Futurist Poetry, such as ‘parole in libert’. Arnaldo Ginna’s ‘Le locomotive con le calze'(Trains with socks on)plunges into a world of absurd nonsense, childishly crude. His brother Bruno Corra wrote in Sam Dunn morto (Sam Dunn is Dead) a masterpiece of Futurist fiction, in a genre he himself called ‘Synthetic’ characterized by compression, and precision; it is a sophisticated piece that rises above the other novels through the strength and pervasiveness of its irony.

When interviewed about her favorite film of all times,[24] famed movie critic Pauline Kael stated that the director Dimitri Kirsanoff, in his silent experimental film Mnilmontant “developed a technique that suggests the movement known in painting as Futurism”.[25]

Many Italian Futurists supported Fascism in the hope of modernizing a country divided between the industrialising north and the rural, archaic South. Like the Fascists, the Futurists were Italian nationalists, radicals, admirers of violence, and were opposed to parliamentary democracy. Marinetti founded the Futurist Political Party (Partito Politico Futurista) in early 1918, which was absorbed into Benito Mussolini’s Fasci di combattimento in 1919, making Marinetti one of the first members of the National Fascist Party. He opposed Fascism’s later exaltation of existing institutions, calling them “reactionary”, and walked out of the 1920 Fascist party congress in disgust, withdrawing from politics for three years; but he supported Italian Fascism until his death in 1944. The Futurists’ association with Fascism after its triumph in 1922 brought them official acceptance in Italy and the ability to carry out important work, especially in architecture. After the Second World War, many Futurist artists had difficulty in their careers because of their association with a defeated and discredited regime.

Marinetti sought to make Futurism the official state art of Fascist Italy but failed to do so. Mussolini chose to give patronage to numerous styles and movements in order to keep artists loyal to the regime. Opening the exhibition of art by the Novecento Italiano group in 1923, he said, “I declare that it is far from my idea to encourage anything like a state art. Art belongs to the domain of the individual. The state has only one duty: not to undermine art, to provide humane conditions for artists, to encourage them from the artistic and national point of view.”[26] Mussolini’s mistress, Margherita Sarfatti, who was as able a cultural entrepreneur as Marinetti, successfully promoted the rival Novecento group, and even persuaded Marinetti to sit on its board. Although in the early years of Italian Fascism modern art was tolerated and even embraced, towards the end of the 1930s, right-wing Fascists introduced the concept of “degenerate art” from Germany to Italy and condemned Futurism.

Marinetti made numerous moves to ingratiate himself with the regime, becoming less radical and avant-garde with each. He moved from Milan to Rome to be nearer the centre of things. He became an academician despite his condemnation of academies, married despite his condemnation of marriage, promoted religious art after the Lateran Treaty of 1929 and even reconciled himself to the Catholic Church, declaring that Jesus was a Futurist.

Although Futurism mostly became identified with Fascism, it had leftist and anti-Fascist supporters. They tended to oppose Marinetti’s artistic and political direction of the movement, and in 1924 the socialists, communists and anarchists walked out of the Milan Futurist Congress. The anti-Fascist voices in Futurism were not completely silenced until the annexation of Abyssinia and the Italo-German Pact of Steel in 1939.[27] This association of Fascists, socialists and anarchists in the Futurist movement, which may seem odd today, can be understood in terms of the influence of Georges Sorel, whose ideas about the regenerative effect of political violence had adherents right across the political spectrum.

Futurism expanded to encompass many artistic domains and ultimately included painting, sculpture, ceramics, graphic design, industrial design, interior design, theatre design, textiles, drama, literature, music and architecture.

Aeropainting (aeropittura) was a major expression of the second generation of Futurism beginning in 1926. The technology and excitement of flight, directly experienced by most aeropainters,[28] offered aeroplanes and aerial landscape as new subject matter. Aeropainting was varied in subject matter and treatment, including realism (especially in works of propaganda), abstraction, dynamism, quiet Umbrian landscapes,[29] portraits of Mussolini (e.g. Dottori’s Portrait of il Duce), devotional religious paintings, decorative art, and pictures of planes.

Aeropainting was launched in a manifesto of 1929, Perspectives of Flight, signed by Benedetta, Depero, Dottori, Filla, Marinetti, Prampolini, Somenzi and Tato (Guglielmo Sansoni). The artists stated that “The changing perspectives of flight constitute an absolutely new reality that has nothing in common with the reality traditionally constituted by a terrestrial perspective” and that “Painting from this new reality requires a profound contempt for detail and a need to synthesise and transfigure everything.” Crispolti identifies three main “positions” in aeropainting: “a vision of cosmic projection, at its most typical in Prampolini’s ‘cosmic idealism’ …; a ‘reverie’ of aerial fantasies sometimes verging on fairy-tale (for example in Dottori …); and a kind of aeronautical documentarism that comes dizzyingly close to direct celebration of machinery (particularly in Crali, but also in Tato and Ambrosi).”[30]

Eventually there were over a hundred aeropainters. Major figures include Fortunato Depero, Enrico Prampolini, Gerardo Dottori and Crali. Crali continued to produce aeropittura up until the 1980s.

Futurism influenced many other twentieth-century art movements, including Art Deco, Vorticism, Constructivism, Surrealism, Dada, and much later Neo-Futurism.[31][32] Futurism as a coherent and organized artistic movement is now regarded as extinct, having died out in 1944 with the death of its leader Marinetti.

Nonetheless, the ideals of Futurism remain as significant components of modern Western culture; the emphasis on youth, speed, power and technology finding expression in much of modern commercial cinema and culture. Ridley Scott consciously evoked the designs of Sant’Elia in Blade Runner. Echoes of Marinetti’s thought, especially his “dreamt-of metallization of the human body”, are still strongly prevalent in Japanese culture, and surface in manga/anime and the works of artists such as Shinya Tsukamoto, director of the Tetsuo (lit. “Ironman”) films. Futurism has produced several reactions, including the literary genre of cyberpunkin which technology was often treated with a critical eyewhilst artists who came to prominence during the first flush of the Internet, such as Stelarc and Mariko Mori, produce work which comments on Futurist ideals. and the art and architecture movement Neo-Futurism in which technology is considered a driver to a better quality of life and sustainability values.[33][34]

A revival of sorts of the Futurist movement in theatre began in 1988 with the creation of the Neo-Futurist style in Chicago, which utilizes Futurism’s focus on speed and brevity to create a new form of immediate theatre. Currently, there are active Neo-Futurist troupes in Chicago, New York, San Francisco, and Montreal.[35]

Futurist ideas have been discerned in Western dance music since the 1980s.[36]

Japanese Composer Ryuichi Sakamoto’s 1986 album ‘Futurista’ was inspired by the movement. It features a speech from Tommaso Marinetti in the track ‘Variety Show’.[37]

In 2009, Italian director Marco Bellocchio included Futurist art in his feature film Vincere.[38]

In 2014, the Solomon R. Guggenheim Museum featured the exhibition “Italian Futurism, 19091944: Reconstructing the Universe”.[39] This was the first comprehensive overview of Italian Futurism to be presented in the United States.[40]

Estorick Collection of Modern Italian Art is a museum in London with a collection centered around Italian futurist artists and their paintings.

Umberto Boccioni, 1911, La rue entre dans la maison; Luigi Russolo, 1911, Souvenir dune nuit. Published in Les Annales politiques et littraires, 1 December 1912

Paintings by Gino Severini, 1911, La Danse du Pan-Pan, and Severini, 1913, Lautobus. Published in Les Annales politiques et littraires, Le Paradoxe Cubiste, 14 March 1920

Paintings by Gino Severini, 1911, Souvenirs de Voyage; Albert Gleizes, 1912, Man on a Balcony, LHomme au balcon; Severini, 191213, Portrait de Mlle Jeanne Paul-Fort; Luigi Russolo, 191112, La Rvolte. Published in Les Annales politiques et littraires, Le Paradoxe Cubiste (continued), n. 1916, 14 March 1920

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Futures studies – Wikipedia

Futures studies (also called futurology) is the study of postulating possible, probable, and preferable futures and the worldviews and myths that underlie them. In general, it can be considered as a branch of the social sciences and parallel to the field of history. Futures studies (colloquially called “futures” by many of the field’s practitioners) seeks to understand what is likely to continue and what could plausibly change. Part of the discipline thus seeks a systematic and pattern-based understanding of past and present, and to determine the likelihood of future events and trends.[1]

Unlike the physical sciences where a narrower, more specified system is studied, futures studies concerns a much bigger and more complex world system. The methodology and knowledge are much less proven as compared to natural science or even social science like sociology and economics. There is a debate as to whether this discipline is an art or science and sometimes described by scientists as pseudoscience.[2][3]

Futures studies is an interdisciplinary field, studying past and present changes, and aggregating and analyzing both lay and professional strategies and opinions with respect to future. It includes analyzing the sources, patterns, and causes of change and stability in an attempt to develop foresight and to map possible futures. Around the world the field is variously referred to as futures studies, strategic foresight, futuristics, futures thinking, futuring, and futurology. Futures studies and strategic foresight are the academic field’s most commonly used terms in the English-speaking world.

Foresight was the original term and was first used in this sense by H.G. Wells in 1932.[4] “Futurology” is a term common in encyclopedias, though it is used almost exclusively by nonpractitioners today, at least in the English-speaking world. “Futurology” is defined as the “study of the future.”[5] The term was coined by German professor Ossip K. Flechtheim in the mid-1940s, who proposed it as a new branch of knowledge that would include a new science of probability. This term may have fallen from favor in recent decades because modern practitioners stress the importance of alternative and plural futures, rather than one monolithic future, and the limitations of prediction and probability, versus the creation of possible and preferable futures.[citation needed]

Three factors usually distinguish futures studies from the research conducted by other disciplines (although all of these disciplines overlap, to differing degrees). First, futures studies often examines not only possible but also probable, preferable, and “wild card” futures. Second, futures studies typically attempts to gain a holistic or systemic view based on insights from a range of different disciplines, generally focusing on the STEEP[6] categories of Social, Technological, Economic, Environmental and Political. Third, futures studies challenges and unpacks the assumptions behind dominant and contending views of the future. The future thus is not empty but fraught with hidden assumptions. For example, many people expect the collapse of the Earth’s ecosystem in the near future, while others believe the current ecosystem will survive indefinitely. A foresight approach would seek to analyze and highlight the assumptions underpinning such views.

As a field, futures studies expands on the research component, by emphasizing the communication of a strategy and the actionable steps needed to implement the plan or plans leading to the preferable future. It is in this regard, that futures studies evolves from an academic exercise to a more traditional business-like practice, looking to better prepare organizations for the future.

Futures studies does not generally focus on short term predictions such as interest rates over the next business cycle, or of managers or investors with short-term time horizons. Most strategic planning, which develops operational plans for preferred futures with time horizons of one to three years, is also not considered futures. Plans and strategies with longer time horizons that specifically attempt to anticipate possible future events are definitely part of the field. As a rule, futures studies is generally concerned with changes of transformative impact, rather than those of an incremental or narrow scope.

The futures field also excludes those who make future predictions through professed supernatural means.

Johan Galtung and Sohail Inayatullah[7] argue in Macrohistory and Macrohistorians that the search for grand patterns of social change goes all the way back to Ssu-Ma Chien (145-90BC) and his theory of the cycles of virtue, although the work of Ibn Khaldun (13321406) such as The Muqaddimah[8] would be an example that is perhaps more intelligible to modern sociology. Early western examples include Sir Thomas Mores Utopia, published in 1516, and based upon Platos Republic, in which a future society has overcome poverty and misery to create a perfect model for living. This work was so powerful that utopias have come to represent positive and fulfilling futures in which everyones needs are met.[9]

Some intellectual foundations of futures studies appeared in the mid-19th century. Isadore Comte, considered the father of scientific philosophy, was heavily influenced by the work of utopian socialist Henri Saint-Simon, and his discussion of the metapatterns of social change presages futures studies as a scholarly dialogue.[10]

The first works that attempt to make systematic predictions for the future were written in the 18th century. Memoirs of the Twentieth Century written by Samuel Madden in 1733, takes the form of a series of diplomatic letters written in 1997 and 1998 from British representatives in the foreign cities of Constantinople, Rome, Paris, and Moscow.[11] However, the technology of the 20th century is identical to that of Madden’s own era – the focus is instead on the political and religious state of the world in the future. Madden went on to write The Reign of George VI, 1900 to 1925, where (in the context of the boom in canal construction at the time) he envisioned a large network of waterways that would radically transform patterns of living – “Villages grew into towns and towns became cities”.[12]

In 1845, Scientific American, the oldest continuously published magazine in the U.S., began publishing articles about scientific and technological research, with a focus upon the future implications of such research. It would be followed in 1872 by the magazine Popular Science, which was aimed at a more general readership.[9]

The genre of science fiction became established towards the end of the 19th century, with notable writers, including Jules Verne and H. G. Wells, setting their stories in an imagined future world.

According to W. Warren Wagar, the founder of future studies was H. G. Wells. His Anticipations of the Reaction of Mechanical and Scientific Progress Upon Human Life and Thought: An Experiment in Prophecy, was first serially published in The Fortnightly Review in 1901.[13] Anticipating what the world would be like in the year 2000, the book is interesting both for its hits (trains and cars resulting in the dispersion of population from cities to suburbs; moral restrictions declining as men and women seek greater sexual freedom; the defeat of German militarism, the existence of a European Union, and a world order maintained by “English-speaking peoples” based on the urban core between Chicago and New York[14]) and its misses (he did not expect successful aircraft before 1950, and averred that “my imagination refuses to see any sort of submarine doing anything but suffocate its crew and founder at sea”).[15][16]

Moving from narrow technological predictions, Wells envisioned the eventual collapse of the capitalist world system after a series of destructive total wars. From this havoc would ultimately emerge a world of peace and plenty, controlled by competent technocrats.[13]

The work was a bestseller, and Wells was invited to deliver a lecture at the Royal Institution in 1902, entitled The Discovery of the Future. The lecture was well-received and was soon republished in book form. He advocated for the establishment of a new academic study of the future that would be grounded in scientific methodology rather than just speculation. He argued that a scientifically ordered vision of the future “will be just as certain, just as strictly science, and perhaps just as detailed as the picture that has been built up within the last hundred years to make the geological past.” Although conscious of the difficulty in arriving at entirely accurate predictions, he thought that it would still be possible to arrive at a “working knowledge of things in the future”.[13]

In his fictional works, Wells predicted the invention and use of the atomic bomb in The World Set Free (1914).[17] In The Shape of Things to Come (1933) the impending World War and cities destroyed by aerial bombardment was depicted.[18] However, he didn’t stop advocating for the establishment of a futures science. In a 1933 BBC broadcast he called for the establishment of “Departments and Professors of Foresight”, foreshadowing the development of modern academic futures studies by approximately 40 years.[4]

At the beginning of the 20th century future works were often shaped by political forces and turmoil. The WWI era led to adoption of futures thinking in institutions throughout Europe. The Russian Revolution led to the 1921 establishment of the Soviet Unions Gosplan, or State Planning Committee, which was active until the dissolution of the Soviet Union. Gosplan was responsible for economic planning and created plans in five year increments to govern the economy. One of the first Soviet dissidents, Yevgeny Zamyatin, published the first dystopian novel, We, in 1921. The science fiction and political satire featured a future police state and was the first work censored by the Soviet censorship board, leading to Zamyatins political exile.[9]

In the United States, President Hoover created the Research Committee on Social Trends, which produced a report in 1933. The head of the committee, William F. Ogburn, analyzed the past to chart trends and project those trends into the future, with a focus on technology. Similar technique was used during The Great Depression, with the addition of alternative futures and a set of likely outcomes that resulted in the creation of Social Security and the Tennessee Valley development project.[9]

The WWII era emphasized the growing need for foresight. The Nazis used strategic plans to unify and mobilize their society with a focus on creating a fascist utopia. This planning and the subsequent war forced global leaders to create their own strategic plans in response. The post-war era saw the creation of numerous nation states with complex political alliances and was further complicated by the introduction of nuclear power.

Project RAND was created in 1946 as joint project between the United States Army Air Forces and the Douglas Aircraft Company, and later incorporated as the non-profit RAND corporation. Their objective was the future of weapons, and long-range planning to meet future threats. Their work has formed the basis of US strategy and policy in regard to nuclear weapons, the Cold War, and the space race.[9]

Futures studies truly emerged as an academic discipline in the mid-1960s.[19] First-generation futurists included Herman Kahn, an American Cold War strategist for the RAND Corporation who wrote On Thermonuclear War (1960), Thinking about the unthinkable (1962) and The Year 2000: a framework for speculation on the next thirty-three years (1967); Bertrand de Jouvenel, a French economist who founded Futuribles International in 1960; and Dennis Gabor, a Hungarian-British scientist who wrote Inventing the Future (1963) and The Mature Society. A View of the Future (1972).[10]

Future studies had a parallel origin with the birth of systems science in academia, and with the idea of national economic and political planning, most notably in France and the Soviet Union.[10][20] In the 1950s, the people of France were continuing to reconstruct their war-torn country. In the process, French scholars, philosophers, writers, and artists searched for what could constitute a more positive future for humanity. The Soviet Union similarly participated in postwar rebuilding, but did so in the context of an established national economic planning process, which also required a long-term, systemic statement of social goals. Future studies was therefore primarily engaged in national planning, and the construction of national symbols.

By contrast, in the United States, futures studies as a discipline emerged from the successful application of the tools and perspectives of systems analysis, especially with regard to quartermastering the war-effort. The Society for General Systems Research, founded in 1955, sought to understand cybernetics and the practical application of systems sciences, greatly influencing the U.S. foresight community.[9] These differing origins account for an initial schism between futures studies in America and futures studies in Europe: U.S. practitioners focused on applied projects, quantitative tools and systems analysis, whereas Europeans preferred to investigate the long-range future of humanity and the Earth, what might constitute that future, what symbols and semantics might express it, and who might articulate these.[21][22]

By the 1960s, academics, philosophers, writers and artists across the globe had begun to explore enough future scenarios so as to fashion a common dialogue. Several of the most notable writers to emerge during this era include: sociologist Fred L. Polak, whose work Images of the Future (1961) discusses the importance of images to societys creation of the future; Marshall McLuhan, whose The Gutenberg Galaxy (1962) and Understanding Media: The Extensions of Man (1964) put forth his theories on how technologies change our cognitive understanding; and Rachel Carsons The Silent Spring (1962) which was hugely influential not only to future studies but also the creation of the environmental movement.[9]

Inventors such as Buckminster Fuller also began highlighting the effect technology might have on global trends as time progressed.

By the 1970s there was an obvious shift in the use and development of futures studies; its focus was no longer exclusive to governments and militaries. Instead, it embraced a wide array of technologies, social issues, and concerns. This discussion on the intersection of population growth, resource availability and use, economic growth, quality of life, and environmental sustainability referred to as the “global problematique” came to wide public attention with the publication of Limits to Growth, a study sponsored by the Club of Rome which detailed the results of a computer simulation of the future based on economic and population growth.[22] Public investment in the future was further enhanced by the publication of Alvin Tofflers bestseller Future Shock (1970), and its exploration of how great amounts of change can overwhelm people and create a social paralysis due to information overload.[9]

International dialogue became institutionalized in the form of the World Futures Studies Federation (WFSF), founded in 1967, with the noted sociologist, Johan Galtung, serving as its first president. In the United States, the publisher Edward Cornish, concerned with these issues, started the World Future Society, an organization focused more on interested laypeople.

The first doctoral program on the Study of the Future, was founded in 1969 at the University Of Massachusetts by Christoper Dede and Billy Rojas.The next graduate program (Master’s degree) was also founded by Christopher Dede in 1975 at the University of HoustonClear Lake,.[23] Oliver Markley of SRI (now SRI International) was hired in 1978 to move the program into a more applied and professional direction. The program moved to the University of Houston in 2007 and renamed the degree to Foresight.[24] The program has remained focused on preparing professional futurists and providing high-quality foresight training for individuals and organizations in business, government, education, and non-profits.[25] In 1976, the M.A. Program in Public Policy in Alternative Futures at the University of Hawaii at Manoa was established.[26] The Hawaii program locates futures studies within a pedagogical space defined by neo-Marxism, critical political economic theory, and literary criticism. In the years following the foundation of these two programs, single courses in Futures Studies at all levels of education have proliferated, but complete programs occur only rarely. In 2012, the Finland Futures Research Centre started a master’s degree Programme in Futures Studies at Turku School of Economics, a business school which is part of the University of Turku in Turku, Finland.[27]

As a transdisciplinary field, futures studies attracts generalists. This transdisciplinary nature can also cause problems, owing to it sometimes falling between the cracks of disciplinary boundaries; it also has caused some difficulty in achieving recognition within the traditional curricula of the sciences and the humanities. In contrast to “Futures Studies” at the undergraduate level, some graduate programs in strategic leadership or management offer masters or doctorate programs in “strategic foresight” for mid-career professionals, some even online. Nevertheless, comparatively few new PhDs graduate in Futures Studies each year.

The field currently faces the great challenge of creating a coherent conceptual framework, codified into a well-documented curriculum (or curricula) featuring widely accepted and consistent concepts and theoretical paradigms linked to quantitative and qualitative methods, exemplars of those research methods, and guidelines for their ethical and appropriate application within society. As an indication that previously disparate intellectual dialogues have in fact started converging into a recognizable discipline,[28] at least six solidly-researched and well-accepted first attempts to synthesize a coherent framework for the field have appeared: Eleonora Masini[sk]’s Why Futures Studies?,[29] James Dator’s Advancing Futures Studies,[30] Ziauddin Sardar’s Rescuing all of our Futures,[31] Sohail Inayatullah’s Questioning the future,[32] Richard A. Slaughter’s The Knowledge Base of Futures Studies,[33] a collection of essays by senior practitioners, and Wendell Bell’s two-volume work, The Foundations of Futures Studies.[34]

Some aspects of the future, such as celestial mechanics, are highly predictable, and may even be described by relatively simple mathematical models. At present however, science has yielded only a special minority of such “easy to predict” physical processes. Theories such as chaos theory, nonlinear science and standard evolutionary theory have allowed us to understand many complex systems as contingent (sensitively dependent on complex environmental conditions) and stochastic (random within constraints), making the vast majority of future events unpredictable, in any specific case.

Not surprisingly, the tension between predictability and unpredictability is a source of controversy and conflict among futures studies scholars and practitioners. Some argue that the future is essentially unpredictable, and that “the best way to predict the future is to create it.” Others believe, as Flechtheim, that advances in science, probability, modeling and statistics will allow us to continue to improve our understanding of probable futures, while this area presently remains less well developed than methods for exploring possible and preferable futures.

As an example, consider the process of electing the president of the United States. At one level we observe that any U.S. citizen over 35 may run for president, so this process may appear too unconstrained for useful prediction. Yet further investigation demonstrates that only certain public individuals (current and former presidents and vice presidents, senators, state governors, popular military commanders, mayors of very large cities, etc.) receive the appropriate “social credentials” that are historical prerequisites for election. Thus with a minimum of effort at formulating the problem for statistical prediction, a much reduced pool of candidates can be described, improving our probabilistic foresight. Applying further statistical intelligence to this problem, we can observe that in certain election prediction markets such as the Iowa Electronic Markets, reliable forecasts have been generated over long spans of time and conditions, with results superior to individual experts or polls. Such markets, which may be operated publicly or as an internal market, are just one of several promising frontiers in predictive futures research.

Such improvements in the predictability of individual events do not though, from a complexity theory viewpoint, address the unpredictability inherent in dealing with entire systems, which emerge from the interaction between multiple individual events.

Futurology is sometimes described by scientists as pseudoscience.[2][3]

In terms of methodology, futures practitioners employ a wide range of approaches, models and methods, in both theory and practice, many of which are derived from or informed by other academic or professional disciplines [1], including social sciences such as economics, psychology, sociology, religious studies, cultural studies, history, geography, and political science; physical and life sciences such as physics, chemistry, astronomy, biology; mathematics, including statistics, game theory and econometrics; applied disciplines such as engineering, computer sciences, and business management (particularly strategy).

The largest internationally peer-reviewed collection of futures research methods (1,300 pages) is Futures Research Methodology 3.0. Each of the 37 methods or groups of methods contains: an executive overview of each methods history, description of the method,primary and alternative usages, strengths and weaknesses, uses in combination with other methods, and speculation about future evolution of the method. Some also contain appendixes with applications, links to software, and sources for further information.

Given its unique objectives and material, the practice of futures studies only rarely features employment of the scientific method in the sense of controlled, repeatable and verifiable experiments with highly standardized methodologies. However, many futurists are informed by scientific techniques or work primarily within scientific domains. Borrowing from history, the futurist might project patterns observed in past civilizations upon present-day society to model what might happen in the future, or borrowing from technology, the futurist may model possible social and cultural responses to an emerging technology based on established principles of the diffusion of innovation. In short, the futures practitioner enjoys the synergies of an interdisciplinary laboratory.

As the plural term futures suggests, one of the fundamental assumptions in futures studies is that the future is plural not singular.[2] That is, the future consists not of one inevitable future that is to be predicted, but rather of multiple alternative futures of varying likelihood which may be derived and described, and about which it is impossible to say with certainty which one will occur. The primary effort in futures studies, then, is to identify and describe alternative futures in order to better understand the driving forces of the present or the structural dynamics of a particular subject or subjects. The exercise of identifying alternative futures includes collecting quantitative and qualitative data about the possibility, probability, and desirability of change. The plural term “futures” in futures studies denotes both the rich variety of alternative futures, including the subset of preferable futures (normative futures), that can be studied, as well as the tenet that the future is many.

At present, the general futures studies model has been summarized as being concerned with “three Ps and a W”, or possible, probable, and preferable futures, plus wildcards, which are low probability but high impact events (positive or negative). Many futurists, however, do not use the wild card approach. Rather, they use a methodology called Emerging Issues Analysis. It searches for the drivers of change, issues that are likely to move from unknown to the known, from low impact to high impact.

In terms of technique, futures practitioners originally concentrated on extrapolating present technological, economic or social trends, or on attempting to predict future trends. Over time, the discipline has come to put more and more focus on the examination of social systems and uncertainties, to the end of articulating scenarios. The practice of scenario development facilitates the examination of worldviews and assumptions through the causal layered analysis method (and others), the creation of preferred visions of the future, and the use of exercises such as backcasting to connect the present with alternative futures. Apart from extrapolation and scenarios, many dozens of methods and techniques are used in futures research (see below).

The general practice of futures studies also sometimes includes the articulation of normative or preferred futures, and a major thread of practice involves connecting both extrapolated (exploratory) and normative research to assist individuals and organizations to model preferred futures amid shifting social changes. Practitioners use varying proportions of collaboration, creativity and research to derive and define alternative futures, and to the degree that a preferred future might be sought, especially in an organizational context, techniques may also be deployed to develop plans or strategies for directed future shaping or implementation of a preferred future.

While some futurists are not concerned with assigning probability to future scenarios, other futurists find probabilities useful in certain situations, such as when probabilities stimulate thinking about scenarios within organizations [3]. When dealing with the three Ps and a W model, estimates of probability are involved with two of the four central concerns (discerning and classifying both probable and wildcard events), while considering the range of possible futures, recognizing the plurality of existing alternative futures, characterizing and attempting to resolve normative disagreements on the future, and envisioning and creating preferred futures are other major areas of scholarship. Most estimates of probability in futures studies are normative and qualitative, though significant progress on statistical and quantitative methods (technology and information growth curves, cliometrics, predictive psychology, prediction markets, crowdvoting forecasts,[31][better source needed] etc.) has been made in recent decades.

Futures techniques or methodologies may be viewed as frameworks for making sense of data generated by structured processes to think about the future.[35] There is no single set of methods that are appropriate for all futures research. Different futures researchers intentionally or unintentionally promote use of favored techniques over a more structured approach. Selection of methods for use on futures research projects has so far been dominated by the intuition and insight of practitioners; but can better identify a balanced selection of techniques via acknowledgement of foresight as a process together with familiarity with the fundamental attributes of most commonly used methods.[36]

Scenarios are a central technique in Futures Studies and are often confused with other techniques. The flowchart to the right provides a process for classifying a phenomena as a scenario in the intuitive logics tradition.[37]

Futurists use a diverse range of forecasting methods including:

Futurists use scenarios alternative possible futures as an important tool. To some extent, people can determine what they consider probable or desirable using qualitative and quantitative methods. By looking at a variety of possibilities one comes closer to shaping the future, rather than merely predicting it. Shaping alternative futures starts by establishing a number of scenarios. Setting up scenarios takes place as a process with many stages. One of those stages involves the study of trends. A trend persists long-term and long-range; it affects many societal groups, grows slowly and appears to have a profound basis. In contrast, a fad operates in the short term, shows the vagaries of fashion, affects particular societal groups, and spreads quickly but superficially.

Sample predicted futures range from predicted ecological catastrophes, through a utopian future where the poorest human being lives in what present-day observers would regard as wealth and comfort, through the transformation of humanity into a posthuman life-form, to the destruction of all life on Earth in, say, a nanotechnological disaster.

Futurists have a decidedly mixed reputation and a patchy track record at successful prediction. For reasons of convenience, they often extrapolate present technical and societal trends and assume they will develop at the same rate into the future; but technical progress and social upheavals, in reality, take place in fits and starts and in different areas at different rates.

Many 1950s futurists predicted commonplace space tourism by the year 2000, but ignored the possibilities of ubiquitous, cheap computers. On the other hand, many forecasts have portrayed the future with some degree of accuracy. Current futurists often present multiple scenarios that help their audience envision what “may” occur instead of merely “predicting the future”. They claim that understanding potential scenarios helps individuals and organizations prepare with flexibility.

Many corporations use futurists as part of their risk management strategy, for horizon scanning and emerging issues analysis, and to identify wild cards low probability, potentially high-impact risks.[39] Every successful and unsuccessful business engages in futuring to some degree for example in research and development, innovation and market research, anticipating competitor behavior and so on.[40][41]

In futures research “weak signals” may be understood as advanced, noisy and socially situated indicators of change in trends and systems that constitute raw informational material for enabling anticipatory action. There is some confusion about the definition of weak signal by various researchers and consultants. Sometimes it is referred as future oriented information, sometimes more like emerging issues. The confusion has been partly clarified with the concept ‘the future sign’, by separating signal, issue and interpretation of the future sign.[42]

A weak signal can be an early indicator of coming change, and an example might also help clarify the confusion. On May 27, 2012, hundreds of people gathered for a Take the Flour Back demonstration at Rothamsted Research in Harpenden, UK, to oppose a publicly funded trial of genetically modified wheat. This was a weak signal for a broader shift in consumer sentiment against genetically modified foods. When Whole Foods mandated the labeling of GMOs in 2013, this non-GMO idea had already become a trend and was about to be a topic of mainstream awareness.

“Wild cards” refer to low-probability and high-impact events, such as existential risks. This concept may be embedded in standard foresight projects and introduced into anticipatory decision-making activity in order to increase the ability of social groups adapt to surprises arising in turbulent business environments. Such sudden and unique incidents might constitute turning points in the evolution of a certain trend or system. Wild cards may or may not be announced by weak signals, which are incomplete and fragmented data from which relevant foresight information might be inferred.Sometimes, mistakenly, wild cards and weak signals are considered as synonyms, which they are not.[43] One of the most often cited examples of a wild card event in recent history is 9/11. Nothing had happened in the past that could point to such a possibility and yet it had a huge impact on everyday life in the United States, from simple tasks like how to travel via airplane to deeper cultural values. Wild card events might also be natural disasters, such as Hurricane Katrina, which can force the relocation of huge populations and wipe out entire crops to completely disrupt the supply chain of many businesses. Although wild card events cant be predicted, after they occur it is often easy to reflect back and convincingly explain why they happened.

A long-running tradition in various cultures, and especially in the media, involves various spokespersons making predictions for the upcoming year at the beginning of the year. These predictions sometimes base themselves on current trends in culture (music, movies, fashion, politics); sometimes they make hopeful guesses as to what major events might take place over the course of the next year.

Some of these predictions come true as the year unfolds, though many fail. When predicted events fail to take place, the authors of the predictions often state that misinterpretation of the “signs” and portents may explain the failure of the prediction.

Marketers have increasingly started to embrace futures studies, in an effort to benefit from an increasingly competitive marketplace with fast production cycles, using such techniques as trendspotting as popularized by Faith Popcorn.[dubious discuss]

Trends come in different sizes. A mega-trend extends over many generations, and in cases of climate, mega-trends can cover periods prior to human existence. They describe complex interactions between many factors. The increase in population from the palaeolithic period to the present provides an example.

Possible new trends grow from innovations, projects, beliefs or actions that have the potential to grow and eventually go mainstream in the future.

Very often, trends relate to one another the same way as a tree-trunk relates to branches and twigs. For example, a well-documented movement toward equality between men and women might represent a branch trend. The trend toward reducing differences in the salaries of men and women in the Western world could form a twig on that branch.

When a potential trend gets enough confirmation in the various media, surveys or questionnaires to show that it has an increasingly accepted value, behavior or technology, it becomes accepted as a bona fide trend. Trends can also gain confirmation by the existence of other trends perceived as springing from the same branch. Some commentators claim that when 15% to 25% of a given population integrates an innovation, project, belief or action into their daily life then a trend becomes mainstream.

Because new advances in technology have the potential to reshape our society, one of the jobs of a futurist is to follow these developments and consider their implications. However, the latest innovations take time to make an impact. Every new technology goes through its own life cycle of maturity, adoption, and social application that must be taken into consideration before a probable vision of the future can be created.

Gartner created their Hype Cycle to illustrate the phases a technology moves through as it grows from research and development to mainstream adoption. The unrealistic expectations and subsequent disillusionment that virtual reality experienced in the 1990s and early 2000s is an example of the middle phases encountered before a technology can begin to be integrated into society.[44]

Education in the field of futures studies has taken place for some time. Beginning in the United States of America in the 1960s, it has since developed in many different countries. Futures education encourages the use of concepts, tools and processes that allow students to think long-term, consequentially, and imaginatively. It generally helps students to:

Thorough documentation of the history of futures education exists, for example in the work of Richard A. Slaughter (2004),[45] David Hicks, Ivana Milojevi[46] to name a few.

While futures studies remains a relatively new academic tradition, numerous tertiary institutions around the world teach it. These vary from small programs, or universities with just one or two classes, to programs that offer certificates and incorporate futures studies into other degrees, (for example in planning, business, environmental studies, economics, development studies, science and technology studies). Various formal Masters-level programs exist on six continents. Finally, doctoral dissertations around the world have incorporated futures studies. A recent survey documented approximately 50 cases of futures studies at the tertiary level.[47]

The largest Futures Studies program in the world is at Tamkang University, Taiwan.[citation needed] Futures Studies is a required course at the undergraduate level, with between three and five thousand students taking classes on an annual basis. Housed in the Graduate Institute of Futures Studies is an MA Program. Only ten students are accepted annually in the program. Associated with the program is the Journal of Futures Studies.[48]

The longest running Future Studies program in North America was established in 1975 at the University of HoustonClear Lake.[49] It moved to the University of Houston in 2007 and renamed the degree to Foresight. The program was established on the belief that if history is studied and taught in an academic setting, then so should the future. Its mission is to prepare professional futurists. The curriculum incorporates a blend of the essential theory, a framework and methods for doing the work, and a focus on application for clients in business, government, nonprofits, and society in general.[50]

As of 2003, over 40 tertiary education establishments around the world were delivering one or more courses in futures studies. The World Futures Studies Federation[51] has a comprehensive survey of global futures programs and courses. The Acceleration Studies Foundation maintains an annotated list of primary and secondary graduate futures studies programs.[52]

Organizations such as Teach The Future also aim to promote future studies in the secondary school curriculum in order to develop structured approaches to thinking about the future in public school students. The rationale is that a sophisticated approach to thinking about, anticipating, and planning for the future is a core skill requirement that every student should have, similar to literacy and math skills.

Several corporations and government agencies utilize foresight products to both better understand potential risks and prepare for potential opportunities. Several government agencies publish material for internal stakeholders as well as make that material available to broader public. Examples of this include the US Congressional Budget Office long term budget projections,[53] the National Intelligence Center,[54] and the United Kingdom Government Office for Science.[55] Much of this material is used by policy makers to inform policy decisions and government agencies to develop long term plan. Several corporations, particularly those with long product development lifecycles, utilize foresight and future studies products and practitioners in the development of their business strategies. The Shell Corporation is one such entity.[56] Foresight professionals and their tools are increasingly being utilized in both the private and public areas to help leaders deal with an increasingly complex and interconnected world.

Design and futures studies have many synergies as interdisciplinary fields with a natural orientation towards the future. Both incorporate studies of human behavior, global trends, strategic insights, and anticipatory solutions.

Designers have adopted futures methodologies including scenarios, trend forecasting, and futures research. Design thinking and specific techniques including ethnography, rapid prototyping, and critical design have been incorporated into in futures as well. In addition to borrowing techniques from one another, futurists and designers have joined to form agencies marrying both competencies to positive effect. The continued interrelation of the two fields is an encouraging trend that has spawned much interesting work.

The Association for Professional Futurists has also held meetings discussing the ways in which Design Thinking and Futures Thinking intersect and benefit one another.

Imperial cycles represent an “expanding pulsation” of “mathematically describable” macro-historic trend.[57] The List of largest empires contains imperial record progression in terms of territory or percentage of world population under single imperial rule.

Chinese philosopher K’ang Yu-wei and French demographer Georges Vacher de Lapouge in the late 19th century were the first to stress that the trend cannot proceed indefinitely on the definite surface of the globe. The trend is bound to culminate in a world empire. K’ang Yu-wei estimated that the matter will be decided in the contest between Washington and Berlin; Vacher de Lapouge foresaw this contest between the United States and Russia and estimated the chance of the United States higher.[58] Both published their futures studies before H. G. Wells introduced the science of future in his Anticipations (1901).

Four later anthropologistsHornell Hart, Raoul Naroll, Louis Morano, and Robert Carneiroresearched the expanding imperial cycles. They reached the same conclusion that a world empire is not only pre-determined but close at hand and attempted to estimate the time of its appearance.[59]

As foresight has expanded to include a broader range of social concerns all levels and types of education have been addressed, including formal and informal education. Many countries are beginning to implement Foresight in their Education policy. A few programs are listed below:

By the early 2000s, educators began to independently institute futures studies (sometimes referred to as futures thinking) lessons in K-12 classroom environments.[62] To meet the need, non-profit futures organizations designed curriculum plans to supply educators with materials on the topic. Many of the curriculum plans were developed to meet common core standards. Futures studies education methods for youth typically include age-appropriate collaborative activities, games, systems thinking and scenario building exercises.[63]

Wendell Bell and Ed Cornish acknowledge science fiction as a catalyst to future studies, conjuring up visions of tomorrow.[64] Science fictions potential to provide an imaginative social vision is its contribution to futures studies and public perspective. Productive sci-fi presents plausible, normative scenarios.[64] Jim Dator attributes the foundational concepts of images of the future to Wendell Bell, for clarifying Fred Polaks concept in Images of the Future, as it applies to futures studies.[65][66] Similar to futures studies scenarios thinking, empirically supported visions of the future are a window into what the future could be. Pamela Sargent states, Science fiction reflects attitudes typical of this century. She gives a brief history of impactful sci-fi publications, like The Foundation Trilogy, by Isaac Asimov and Starship Troopers, by Robert A. Heinlein.[67] Alternate perspectives validate sci-fi as part of the fuzzy images of the future.[66] However, the challenge is the lack of consistent futures research based literature frameworks.[67] Ian Miles reviews The New Encyclopedia of Science Fiction, identifying ways Science Fiction and Futures Studies cross-fertilize, as well as the ways in which they differ distinctly. Science Fiction cannot be simply considered fictionalized Futures Studies. It may have aims other than prediction, and be no more concerned with shaping the future than any other genre of literature. [68] It is not to be understood as an explicit pillar of futures studies, due to its inconsistency of integrated futures research. Additionally, Dennis Livingston, a literature and Futures journal critic says, The depiction of truly alternative societies has not been one of science fictions strong points, especially preferred, normative envisages.[69]

Several governments have formalized strategic foresight agencies to encourage long range strategic societal planning, with most notable are the governments of Singapore, Finland, and the United Arab Emirates. Other governments with strategic foresight agencies include Canada’s Policy Horizons Canada and the Malaysia’s Malaysian Foresight Institute.

The Singapore government’s Centre for Strategic Futures (CSF) is part of the Strategy Group within the Prime Minister’s Office. Their mission is to position the Singapore government to navigate emerging strategic challenges and harness potential opportunities.[70] Singapores early formal efforts in strategic foresight began in 1991 with the establishment of the Risk Detection and Scenario Planning Office in the Ministry of Defence.[71] In addition to the CSF, the Singapore government has established the Strategic Futures Network, which brings together deputy secretary-level officers and foresight units across the government to discuss emerging trends that may have implications for Singapore.[71]

Since the 1990s, Finland has integrated strategic foresight within the parliament and Prime Ministers Office.[72] The government is required to present a Report of the Future each parliamentary term for review by the parliamentary Committee for the Future. Led by the Prime Ministers Office, the Government Foresight Group coordinates the governments foresight efforts.[73] Futures research is supported by the Finnish Society for Futures Studies (established in 1980), the Finland Futures Research Centre (established in 1992), and the Finland Futures Academy (established in 1998) in coordination with foresight units in various government agencies.[73]

In the United Arab Emirates, Sheikh Mohammed bin Rashid, Vice President and Ruler of Dubai, announced in September 2016 that all government ministries were to appoint Directors of Future Planning. Sheikh Mohammed described the UAE Strategy for the Future as an “integrated strategy to forecast our nations future, aiming to anticipate challenges and seize opportunities”.[74] The Ministry of Cabinet Affairs and Future(MOCAF) is mandated with crafting the UAE Strategy for the Future and is responsible for the portfolio of the future of UAE.[75]

Foresight is also applied when studying potential risks to society and how to effectively deal with them.[76][77] These risks may arise from the development and adoption of emerging technologies and/or social change. Special interest lies on hypothetical future events that have the potential to damage human well-being on a global scale – global catastrophic risks.[78] Such events may cripple or destroy modern civilization or, in the case of existential risks, even cause human extinction.[79] Potential global catastrophic risks include but are not limited to hostile artificial intelligence, nanotechnology weapons, climate change, nuclear warfare, total war, and pandemics.

Several authors have become recognized as futurists.[82] They research trends, particularly in technology, and write their observations, conclusions, and predictions. In earlier eras, many futurists were at academic institutions. John McHale, author of The Future of the Future, published a ‘Futures Directory’, and directed a think tank called The Centre For Integrative Studies at a university. Futurists have started consulting groups or earn money as speakers, with examples including Alvin Toffler, John Naisbitt and Patrick Dixon. Frank Feather is a business speaker that presents himself as a pragmatic futurist. Some futurists have commonalities with science fiction, and some science-fiction writers, such as Arthur C. Clarke, are known as futurists.[citation needed] In the introduction to The Left Hand of Darkness, Ursula K. Le Guin distinguished futurists from novelists, writing of the study as the business of prophets, clairvoyants, and futurists. In her words, “a novelist’s business is lying”.

A survey of 108 futurists found that they share a variety of assumptions, including in their description of the present as a critical moment in an historical transformation, in their recognition and belief in complexity, and in their being motivated by change and having a desire for an active role bringing change (versus simply being involved in forecasting).[83]

The Association for Professional Futurists recognizes the Most Significant Futures Works for the purpose of identifying and rewarding the work of foresight professionals and others whose work illuminates aspects of the future.[88]

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Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] More in detail, Kaplan and Haenlein define AI as a systems ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.[2] Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.”[4] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[5] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[7] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[8] Analytical AI has only characteristics consistent with cognitive intelligence generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive as well as emotional intelligence, understanding, in addition to cognitive elements, also human emotions considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[9][10] followed by disappointment and the loss of funding (known as an “AI winter”),[11][12] followed by new approaches, success and renewed funding.[10][13] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[14] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[15] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[16][17][18] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[14]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[15] General intelligence is among the field’s long-term goals.[19] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[20] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[21] Some people also consider AI to be a danger to humanity if it progresses unabated.[22] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[23]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[24][13]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[25] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[26] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[21]

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 ChurchTuring thesis.[27] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[28] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[30] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[31] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[33] (and by 1959 were reportedly playing better than the average human),[34] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[35] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[36] and laboratories had been established around the world.[37] AI’s founders were optimistic about the future: 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”.[9]

They 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”,[11] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[39] 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.[10] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[12]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[24] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[40] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, 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. 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.[43] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[44] as do intelligent personal assistants in smartphones.[45] 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.[7][46] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[47] who at the time continuously held the world No. 1 ranking for two years.[48][49] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

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. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[50] 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.[13] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[50] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[51][52] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[53][54]

A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] An AI’s intended goal function can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do actions mathematically similar to the actions that got you rewards in the past”). Goals can be explicitly defined, or can be induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior and punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems; this is similar to how animals evolved to innately desire certain goals such as finding food, or how dogs can be bred via artificial selection to possess desired traits. Some AI systems, such as nearest-neighbor, instead reason by analogy; these systems are not generally given goals, except to the degree that goals are somehow implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[57]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any function, including whatever combination of mathematical functions would best describe the entire world. These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful.[59] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[61]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[62] the best approach is often different depending on the problem.[64]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][67][68][69]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[72][73][74] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[75][76][77]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.[15]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[78] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[79]

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.[59] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[80]

Knowledge representation[81] and knowledge engineering[82] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[83] situations, events, states and time;[84] causes and effects;[85] knowledge about knowledge (what we know about what other people know);[86] and many other, less well researched domains. 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 semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[87] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[88] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[89] scene interpretation,[90] clinical decision support,[91] knowledge discovery (mining “interesting” and actionable inferences from large databases),[92] and other areas.[93]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[100] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[101]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[102] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[103]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[104]

Machine learning, a fundamental concept of AI research since the field’s inception,[105] is the study of computer algorithms that improve automatically through experience.[106][107]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[108] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. 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.[107] 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”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[109] In reinforcement learning[110] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[111] (NLP) gives machines the ability 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 natural language processing include information retrieval, text mining, question answering[112] and machine translation.[113] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[114]

Machine perception[115] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[116] facial recognition, and object recognition.[117] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[118]

AI is heavily used in robotics.[119] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[120] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[122][123] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[124][125] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[126]

Moravec’s paradox can be extended to many forms of social intelligence.[128][129] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[130] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[134]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[135] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[136]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[137] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[19][138] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[139][140][141] Besides transfer learning,[142] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[144][145]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[146] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[16]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[17]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[147] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[148] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[149]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.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[150][151]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless of whether people used the same algorithms.[16] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[152] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[153]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[154] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[17] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[155]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[156] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[39] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[157] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never 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.[18] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[158] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[159][160]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[163] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[164]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[40][165] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[174] 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.[175] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[176] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[120] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[177] 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 that are 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 entirely eliminate some choices that are 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.[178] Heuristics limit the search for solutions into a smaller sample size.

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 optimization algorithms are simulated annealing, beam search and random optimization.[179]

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.[180] 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).[181][182]

Logic[183] 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[184] and inductive logic programming is a method for learning.[185]

Several different forms of logic are used in AI research. Propositional logic[186] involves truth functions such as “or” and “not”. First-order logic[187] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory 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. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][189][190]

Default logics, non-monotonic logics and circumscription[95] 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;[83] situation calculus, event calculus and fluent calculus (for representing events and time);[84] causal calculus;[85] belief calculus;[191] and modal logics.[86]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[193]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[194]

Bayesian networks[195] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[196] learning (using the expectation-maximization algorithm),[f][198] planning (using decision networks)[199] and perception (using dynamic Bayesian networks).[200] 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).[200] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

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,[201] and information value theory.[101] These tools include models such as Markov decision processes,[202] dynamic decision networks,[200] game theory and mechanism design.[203]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny 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 a 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 can be seen as 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.[204]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[205] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[207]k-nearest neighbor algorithm,[g][209]kernel methods such as the support vector machine (SVM),[h][211]Gaussian mixture model,[212] and the extremely popular naive Bayes classifier.[i][214] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the 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.[215]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast 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. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[218][219]

The study of non-learning artificial neural networks[207] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

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.[220] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[221]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[222][223] and was introduced to neural networks by Paul Werbos.[224][225][226]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[227]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[228]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[229] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[230][231][229]

According to one overview,[232] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[233] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[234] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[235][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[236] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[238]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[239] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[240]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[229]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[241]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[242] which are in theory Turing complete[243] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[229] RNNs can be trained by gradient descent[244][245][246] but suffer from the vanishing gradient problem.[230][247] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[248]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[249] LSTM is often trained by Connectionist Temporal Classification (CTC).[250] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[251][252][253] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[254] Google also used LSTM to improve machine translation,[255] Language Modeling[256] and Multilingual Language Processing.[257] LSTM combined with CNNs also improved automatic image captioning[258] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[259] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[260][261] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[262] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[126]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[263][264] E-sports such as StarCraft continue to provide additional public benchmarks.[265][266] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[267]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[268] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[270][271]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[274] prediction of judicial decisions[275] and targeting online advertisements.[276][277]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[278] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[279]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[280]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[281] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[282] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[283] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[284]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[285] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[286] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[287]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[288]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[289]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[290] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[291]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[292] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[293]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[294] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[295] In August 2001, robots beat humans in a simulated financial trading competition.[296] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[297]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[298] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[299][300]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[301][302] Military drones capable of autonomous action are widely considered a useful asset. In 2017, Vladimir Putin stated that “Whoever becomes the leader in (artificial intelligence) will become the ruler of the world”.[303][304] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[305]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[306]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[307] A documented case reports that online gambling companies were using AI to improve customer targeting.[308]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[309]

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Artificial intelligence – Wikipedia

Benefits & Risks of Artificial Intelligence – Future of Life …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence – Future of Life …

What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

Artificial Intelligence: The Robots Are Now Hiring – WSJ

Sept. 20, 2018 5:30 a.m. ET

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ’s Jason Bellini investigates.

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ’s Jason Bellini investigates.

Hiring is undergoing a profound revolution.

Nearly all Fortune 500 companies now use some form of automation — from robot avatars interviewing job candidates to computers weeding out potential employees by scanning keywords in resumes. And more and more companies are using artificial intelligence and machine learning tools to assess possible employees.

DeepSense, based in San Francisco and India, helps hiring managers scan peoples social media accounts to surface underlying personality traits. The company says it uses a scientifically based personality test, and it can be done with or without a potential candidates knowledge.

The practice is part of a general trend of some hiring companies to move away from assessing candidates based on their resumes and skills, towards making hiring decisions based on peoples personalities.

The Robot Revolution: An inside look at how humanoid robots are evolving.

WSJS Jason Bellini explores breakthrough technologies that are reshaping our world and beginning to impact human happiness, health and productivity. Catch the latest episode by signing up here.

Cornell sociology and law professor Ifeoma Ajunwa said shes concerned about these tools potential for bias. Given the large scale of these automatic assessments, she believes potentially faulty algorithms could do more damage than one biased human manager. And she wants scientists to test if the algorithms are fair, transparent and accurate.

In the episode of Moving Upstream above, correspondent Jason Bellini visits South Jordan, Utah-based HireVue, which is delivering AI-based assessments of digital interviews to over 50 companies. HireVue says its algorithm compares candidates tone of voice, word clusters and micro facial expressionsCC with people who have previously been identified as high performers on the job.

Write to Jason Bellini at jason.bellini@wsj.com and Hilke Schellmann at hilke.schellmann@wsj.com

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Artificial Intelligence: The Robots Are Now Hiring – WSJ

What is AI (artificial intelligence)? – Definition from …

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple’s Siri, are a form of weak AI. Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities. When presented with an unfamiliar task, a strong AI system is able to find a solution without human intervention.

Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings, as well as access to Artificial Intelligence as a Service (AIaaS) platforms. AI as a Service allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment. Popular AI cloud offerings include Amazon AI services, IBM Watson Assistant, Microsoft Cognitive Services and Google AI services.

While AI tools present a range of new functionality for businesses,the use of artificial intelligence raises ethical questions. This is because deep learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human selects what data should be used for training an AI program, the potential for human bias is inherent and must be monitored closely.

Some industry experts believe that the term artificial intelligence is too closely linked to popular culture, causing the general public to have unrealistic fears about artificial intelligence and improbable expectations about how it will change the workplace and life in general. Researchers and marketers hope the label augmented intelligence, which has a more neutral connotation, will help people understand that AI will simply improve products and services, not replace the humans that use them.

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

AI is incorporated into a variety of different types of technology. Here are seven examples.

Artificial intelligence has made its way into a number of areas. Here are six examples.

The application of AI in the realm of self-driving cars raises security as well as ethical concerns. Cars can be hacked, and when an autonomous vehicle is involved in an accident, liability is unclear. Autonomous vehicles may also be put in a position where an accident is unavoidable, forcing the programming to make an ethical decision about how to minimize damage.

Another major concern is the potential for abuse of AI tools. Hackers are starting to use sophisticated machine learning tools to gain access to sensitive systems, complicating the issue of security beyond its current state.

Deep learning-based video and audio generation tools also present bad actors with the tools necessary to create so-called deepfakes, convincingly fabricated videos of public figures saying or doing things that never took place.

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe’s GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

In 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Since that time the issue has received little attention from lawmakers.

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Artificial Intelligence – Journal – Elsevier

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Online Artificial Intelligence Courses | Microsoft …

The Microsoft Professional Program (MPP) is a collection of courses that teach skills in several core technology tracks that help you excel in the industry’s newest job roles.

These courses are created and taught by experts and feature quizzes, hands-on labs, and engaging communities. For each track you complete, you earn a certificate of completion from Microsoft proving that you mastered those skills.

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A.I. Artificial Intelligence – Wikipedia

A.I. Artificial Intelligence, also known as A.I., is a 2001 American science fiction drama film directed by Steven Spielberg. The screenplay by Spielberg and screen story by Ian Watson were based on the 1969 short story “Supertoys Last All Summer Long” by Brian Aldiss. The film was produced by Kathleen Kennedy, Spielberg and Bonnie Curtis. It stars Haley Joel Osment, Jude Law, Frances O’Connor, Brendan Gleeson and William Hurt. Set in a futuristic post-climate change society, A.I. tells the story of David (Osment), a childlike android uniquely programmed with the ability to love.

Development of A.I. originally began with producer-director Stanley Kubrick, after he acquired the rights to Aldiss’ story in the early 1970s. Kubrick hired a series of writers until the mid-1990s, including Brian Aldiss, Bob Shaw, Ian Watson, and Sara Maitland. The film languished in protracted development for years, partly because Kubrick felt computer-generated imagery was not advanced enough to create the David character, who he believed no child actor would convincingly portray. In 1995, Kubrick handed A.I. to Spielberg, but the film did not gain momentum until Kubrick’s death in 1999. Spielberg remained close to Watson’s film treatment for the screenplay.

The film divided critics, with the overall balance being positive, and grossed approximately $235 million. The film was nominated for two Academy Awards at the 74th Academy Awards, for Best Visual Effects and Best Original Score (by John Williams).

In a 2016 BBC poll of 177 critics around the world, Steven Spielberg’s A.I. Artificial Intelligence was voted the eighty-third greatest film since 2000.[3] A.I. is dedicated to Stanley Kubrick.

In the late 22nd century, rising sea levels from global warming have wiped out coastal cities such as Amsterdam, Venice, and New York and drastically reduced the world’s population. A new type of robots called Mecha, advanced humanoids capable of thought and emotion, have been created.

David, a Mecha that resembles a human child and is programmed to display love for his owners, is given to Henry Swinton and his wife Monica, whose son Martin, after contracting a rare disease, has been placed in suspended animation and not expected to recover. Monica feels uneasy with David, but eventually warms to him and activates his imprinting protocol, causing him to have an enduring childlike love for her. David is befriended by Teddy, a robotic teddy bear that belonged to Martin.

Martin is cured of his disease and brought home. As he recovers, he grows jealous of David. He tricks David into entering the parents’s bedroom at night and cutting off a lock of Monica’s hair. This upsets the parents, particularly Henry, who fears David intended to injure them. At a pool party, one of Martin’s friends pokes David with a knife, activating David’s self-protection programming. David grabs Martin and they fall into the pool. Martin is saved from drowning, but Henry persuades Monica to return David to his creators for destruction. Instead, she abandons David and Teddy in the forest. She warns David to avoid all humans, and tells him to find other unregistered Mecha who can protect him.

David is captured for an anti-Mecha “Flesh Fair”, where obsolete, unlicensed Mecha are destroyed before cheering crowds. David is placed on a platform with Gigolo Joe, a male prostitute Mecha who is on the run after being framed for murder. Before the pair can be destroyed with acid, the crowd, thinking David is a real boy, begins booing and throwing things at the show’s emcee. In the chaos, David and Joe escape. Since Joe survived thanks to David, he agrees to help him find Blue Fairy, whom David remembers from The Adventures of Pinocchio, and believes can turn him into a real boy, allowing Monica to love him and take him home.

Joe and David make their way to the decadent resort town of Rouge City, where “Dr. Know”, a holographic answer engine, directs them to the top of Rockefeller Center in the flooded ruins of Manhattan. There, David meets a copy of himself and destroys it. He then meets Professor Hobby, his creator, who tells David he was built in the image of the professor’s dead son David. The engineers are thrilled by his ability to have a will without being programmed. He reveals they have been monitoring him to see how he progresses and altered Dr. Know to guide him to Manhattan, back to the lab he was created in. David finds more copies of him, as well as female versions called Darlene, that have been made there.

Disheartened, David lets himself fall from a ledge of the building. He is rescued by Joe, flying an amphibicopter he has stolen from the police who were pursuing him. David tells Joe he saw the Blue Fairy underwater, and wants to go down to meet her. Joe is captured by the authorities, who snare him with an electromagnet. Before he is pulled up, he activates the amphibicopter’s dive function for David, telling him to remember him for he declares “I am, I was.” David and Teddy dive to see the Fairy, which turns out to be a statue at the now-sunken Coney Island. The two become trapped when the Wonder Wheel falls on their vehicle. David repeatedly asks the Fairy to turn him into a real boy. Eventually the ocean freezes and David’s power source is depleted.

Two thousand years later, humans are extinct, and Manhattan is buried under glacial ice. The Mecha have evolved into an advanced silicon-based form called Specialists. They find David and Teddy, and discover they are original Mecha who knew living humans, making them special. The Specialists revive David and Teddy. David walks to the frozen Fairy statue, which collapses when he touches it. The Mecha use David’s memories to reconstruct the Swinton home. David asks the Specialists if they can make him human, but they cannot. However, he insists they recreate Monica from DNA from the lock of her hair, which Teddy has kept. The Mecha warn David that the clone can live for only a day, and that the process cannot be repeated. David spends the next day with Monica and Teddy. Before she drifts off to sleep, Monica tells David she has always loved him. Teddy climbs onto the bed and watches the two lie peacefully together.

Kubrick began development on an adaptation of “Super-Toys Last All Summer Long” in the late 1970s, hiring the story’s author, Brian Aldiss, to write a film treatment. In 1985, Kubrick asked Steven Spielberg to direct the film, with Kubrick producing.[6] Warner Bros. agreed to co-finance A.I. and cover distribution duties.[7] The film labored in development hell, and Aldiss was fired by Kubrick over creative differences in 1989.[8] Bob Shaw briefly served as writer, leaving after six weeks due to Kubrick’s demanding work schedule, and Ian Watson was hired as the new writer in March 1990. Aldiss later remarked, “Not only did the bastard fire me, he hired my enemy [Watson] instead.” Kubrick handed Watson The Adventures of Pinocchio for inspiration, calling A.I. “a picaresque robot version of Pinocchio”.[7][9]

Three weeks later, Watson gave Kubrick his first story treatment, and concluded his work on A.I. in May 1991 with another treatment of 90 pages. Gigolo Joe was originally conceived as a G.I. Mecha, but Watson suggested changing him to a male prostitute. Kubrick joked, “I guess we lost the kiddie market.”[7] Meanwhile, Kubrick dropped A.I. to work on a film adaptation of Wartime Lies, feeling computer animation was not advanced enough to create the David character. However, after the release of Spielberg’s Jurassic Park, with its innovative computer-generated imagery, it was announced in November 1993 that production of A.I. would begin in 1994.[10] Dennis Muren and Ned Gorman, who worked on Jurassic Park, became visual effects supervisors,[8] but Kubrick was displeased with their previsualization, and with the expense of hiring Industrial Light & Magic.[11]

“Stanley [Kubrick] showed Steven [Spielberg] 650 drawings which he had, and the script and the story, everything. Stanley said, ‘Look, why don’t you direct it and I’ll produce it.’ Steven was almost in shock.”

Producer Jan Harlan, on Spielberg’s first meeting with Kubrick about A.I.[12]

In early 1994, the film was in pre-production with Christopher “Fangorn” Baker as concept artist, and Sara Maitland assisting on the story, which gave it “a feminist fairy-tale focus”.[7] Maitland said that Kubrick never referred to the film as A.I., but as Pinocchio.[11] Chris Cunningham became the new visual effects supervisor. Some of his unproduced work for A.I. can be seen on the DVD, The Work of Director Chris Cunningham.[13] Aside from considering computer animation, Kubrick also had Joseph Mazzello do a screen test for the lead role.[11] Cunningham helped assemble a series of “little robot-type humans” for the David character. “We tried to construct a little boy with a movable rubber face to see whether we could make it look appealing,” producer Jan Harlan reflected. “But it was a total failure, it looked awful.” Hans Moravec was brought in as a technical consultant.[11]Meanwhile, Kubrick and Harlan thought A.I. would be closer to Steven Spielberg’s sensibilities as director.[14][15] Kubrick handed the position to Spielberg in 1995, but Spielberg chose to direct other projects, and convinced Kubrick to remain as director.[12][16] The film was put on hold due to Kubrick’s commitment to Eyes Wide Shut (1999).[17] After the filmmaker’s death in March 1999, Harlan and Christiane Kubrick approached Spielberg to take over the director’s position.[18][19] By November 1999, Spielberg was writing the screenplay based on Watson’s 90-page story treatment. It was his first solo screenplay credit since Close Encounters of the Third Kind (1977).[20] Spielberg remained close to Watson’s treatment, but removed various sex scenes with Gigolo Joe. Pre-production was briefly halted during February 2000, because Spielberg pondered directing other projects, which were Harry Potter and the Philosopher’s Stone, Minority Report and Memoirs of a Geisha.[17][21] The following month Spielberg announced that A.I. would be his next project, with Minority Report as a follow-up.[22] When he decided to fast track A.I., Spielberg brought Chris Baker back as concept artist.[16]

The original start date was July 10, 2000,[15] but filming was delayed until August.[23] Aside from a couple of weeks shooting on location in Oxbow Regional Park in Oregon, A.I. was shot entirely using sound stages at Warner Bros. Studios and the Spruce Goose Dome in Long Beach, California.[24]The Swinton house was constructed on Stage 16, while Stage 20 was used for Rouge City and other sets.[25][26] Spielberg copied Kubrick’s obsessively secretive approach to filmmaking by refusing to give the complete script to cast and crew, banning press from the set, and making actors sign confidentiality agreements. Social robotics expert Cynthia Breazeal served as technical consultant during production.[15][27] Haley Joel Osment and Jude Law applied prosthetic makeup daily in an attempt to look shinier and robotic.[4] Costume designer Bob Ringwood (Batman, Troy) studied pedestrians on the Las Vegas Strip for his influence on the Rouge City extras.[28] Spielberg found post-production on A.I. difficult because he was simultaneously preparing to shoot Minority Report.[29]

The film’s soundtrack was released by Warner Sunset Records in 2001. The original score was composed and conducted by John Williams and featured singers Lara Fabian on two songs and Josh Groban on one. The film’s score also had a limited release as an official “For your consideration Academy Promo”, as well as a complete score issue by La-La Land Records in 2015.[30] The band Ministry appears in the film playing the song “What About Us?” (but the song does not appear on the official soundtrack album).

Warner Bros. used an alternate reality game titled The Beast to promote the film. Over forty websites were created by Atomic Pictures in New York City (kept online at Cloudmakers.org) including the website for Cybertronics Corp. There were to be a series of video games for the Xbox video game console that followed the storyline of The Beast, but they went undeveloped. To avoid audiences mistaking A.I. for a family film, no action figures were created, although Hasbro released a talking Teddy following the film’s release in June 2001.[15]

A.I. had its premiere at the Venice Film Festival in 2001.[31]

A.I. Artificial Intelligence was released on VHS and DVD by Warner Home Video on March 5, 2002 in both a standard full-screen release with no bonus features, and as a 2-Disc Special Edition featuring the film in its original 1.85:1 anamorphic widescreen format as well as an eight-part documentary detailing the film’s development, production, music and visual effects. The bonus features also included interviews with Haley Joel Osment, Jude Law, Frances O’Connor, Steven Spielberg and John Williams, two teaser trailers for the film’s original theatrical release and an extensive photo gallery featuring production sills and Stanley Kubrick’s original storyboards.[32]

The film was released on Blu-ray Disc on April 5, 2011 by Paramount Home Media Distribution for the U.S. and by Warner Home Video for international markets. This release featured the film a newly restored high-definition print and incorporated all the bonus features previously included on the 2-Disc Special Edition DVD.[33]

The film opened in 3,242 theaters in the United States on June 29, 2001, earning $29,352,630 during its opening weekend. A.I went on to gross $78.62 million in US totals as well as $157.31 million in foreign countries, coming to a worldwide total of $235.93 million.[34]

Based on 192 reviews collected by Rotten Tomatoes, 73% of critics gave the film positive notices with a score of 6.6/10. The website’s critical consensus reads, “A curious, not always seamless, amalgamation of Kubrick’s chilly bleakness and Spielberg’s warm-hearted optimism. A.I. is, in a word, fascinating.”[35] By comparison, Metacritic collected an average score of 65, based on 32 reviews, which is considered favorable.[36]

Producer Jan Harlan stated that Kubrick “would have applauded” the final film, while Kubrick’s widow Christiane also enjoyed A.I.[37] Brian Aldiss admired the film as well: “I thought what an inventive, intriguing, ingenious, involving film this was. There are flaws in it and I suppose I might have a personal quibble but it’s so long since I wrote it.” Of the film’s ending, he wondered how it might have been had Kubrick directed the film: “That is one of the ‘ifs’ of film historyat least the ending indicates Spielberg adding some sugar to Kubrick’s wine. The actual ending is overly sympathetic and moreover rather overtly engineered by a plot device that does not really bear credence. But it’s a brilliant piece of film and of course it’s a phenomenon because it contains the energies and talents of two brilliant filmmakers.”[38] Richard Corliss heavily praised Spielberg’s direction, as well as the cast and visual effects.[39] Roger Ebert gave the film three stars, saying that it was “wonderful and maddening.”[40] Leonard Maltin, on the other hand, gives the film two stars out of four in his Movie Guide, writing: “[The] intriguing story draws us in, thanks in part to Osment’s exceptional performance, but takes several wrong turns; ultimately, it just doesn’t work. Spielberg rewrote the adaptation Stanley Kubrick commissioned of the Brian Aldiss short story ‘Super Toys Last All Summer Long’; [the] result is a curious and uncomfortable hybrid of Kubrick and Spielberg sensibilities.” However, he calls John Williams’ music score “striking”. Jonathan Rosenbaum compared A.I. to Solaris (1972), and praised both “Kubrick for proposing that Spielberg direct the project and Spielberg for doing his utmost to respect Kubrick’s intentions while making it a profoundly personal work.”[41] Film critic Armond White, of the New York Press, praised the film noting that “each part of David’s journey through carnal and sexual universes into the final eschatological devastation becomes as profoundly philosophical and contemplative as anything by cinema’s most thoughtful, speculative artists Borzage, Ozu, Demy, Tarkovsky.”[42] Filmmaker Billy Wilder hailed A.I. as “the most underrated film of the past few years.”[43] When British filmmaker Ken Russell saw the film, he wept during the ending.[44]

Mick LaSalle gave a largely negative review. “A.I. exhibits all its creators’ bad traits and none of the good. So we end up with the structureless, meandering, slow-motion endlessness of Kubrick combined with the fuzzy, cuddly mindlessness of Spielberg.” Dubbing it Spielberg’s “first boring movie”, LaSalle also believed the robots at the end of the film were aliens, and compared Gigolo Joe to the “useless” Jar Jar Binks, yet praised Robin Williams for his portrayal of a futuristic Albert Einstein.[45][not in citation given] Peter Travers gave a mixed review, concluding “Spielberg cannot live up to Kubrick’s darker side of the future.” But he still put the film on his top ten list that year for best movies.[46] David Denby in The New Yorker criticized A.I. for not adhering closely to his concept of the Pinocchio character. Spielberg responded to some of the criticisms of the film, stating that many of the “so called sentimental” elements of A.I., including the ending, were in fact Kubrick’s and the darker elements were his own.[47] However, Sara Maitland, who worked on the project with Kubrick in the 1990s, claimed that one of the reasons Kubrick never started production on A.I. was because he had a hard time making the ending work.[48] James Berardinelli found the film “consistently involving, with moments of near-brilliance, but far from a masterpiece. In fact, as the long-awaited ‘collaboration’ of Kubrick and Spielberg, it ranks as something of a disappointment.” Of the film’s highly debated finale, he claimed, “There is no doubt that the concluding 30 minutes are all Spielberg; the outstanding question is where Kubrick’s vision left off and Spielberg’s began.”[49]

Screenwriter Ian Watson has speculated, “Worldwide, A.I. was very successful (and the 4th highest earner of the year) but it didn’t do quite so well in America, because the film, so I’m told, was too poetical and intellectual in general for American tastes. Plus, quite a few critics in America misunderstood the film, thinking for instance that the Giacometti-style beings in the final 20 minutes were aliens (whereas they were robots of the future who had evolved themselves from the robots in the earlier part of the film) and also thinking that the final 20 minutes were a sentimental addition by Spielberg, whereas those scenes were exactly what I wrote for Stanley and exactly what he wanted, filmed faithfully by Spielberg.”[50]

In 2002, Spielberg told film critic Joe Leydon that “People pretend to think they know Stanley Kubrick, and think they know me, when most of them don’t know either of us”. “And what’s really funny about that is, all the parts of A.I. that people assume were Stanley’s were mine. And all the parts of A.I. that people accuse me of sweetening and softening and sentimentalizing were all Stanley’s. The teddy bear was Stanley’s. The whole last 20 minutes of the movie was completely Stanley’s. The whole first 35, 40 minutes of the film all the stuff in the house was word for word, from Stanley’s screenplay. This was Stanley’s vision.” “Eighty percent of the critics got it all mixed up. But I could see why. Because, obviously, I’ve done a lot of movies where people have cried and have been sentimental. And I’ve been accused of sentimentalizing hard-core material. But in fact it was Stanley who did the sweetest parts of A.I., not me. I’m the guy who did the dark center of the movie, with the Flesh Fair and everything else. That’s why he wanted me to make the movie in the first place. He said, ‘This is much closer to your sensibilities than my own.'”[51]

Upon rewatching the film many years after its release, BBC film critic Mark Kermode apologized to Spielberg in an interview in January 2013 for “getting it wrong” on the film when he first viewed it in 2001. He now believes the film to be Spielberg’s “enduring masterpiece”.[52]

Visual effects supervisors Dennis Muren, Stan Winston, Michael Lantieri and Scott Farrar were nominated for the Academy Award for Best Visual Effects, while John Williams was nominated for Best Original Music Score.[53] Steven Spielberg, Jude Law and Williams received nominations at the 59th Golden Globe Awards.[54] A.I. was successful at the Saturn Awards, winning five awards, including Best Science Fiction Film along with Best Writing for Spielberg and Best Performance by a Younger Actor for Osment.[55]

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A.I. Artificial Intelligence – Wikipedia

Artificial Intelligence: The Pros, Cons, and What to Really Fear

For the last several years, Russia has been steadily improving its ground combat robots. Just last year,Kalashnikov, the maker of the famous AK-47 rifle,announced it would builda range of products based on neural networks, including a fully automated combat module that promises to identify and shoot at targets.

According to Bendett,Russia delivered a white paperto the UN saying that from Moscow’s perspective,it would be inadmissible to leave UASwithout anyhuman oversight. In other words, Russia always wants a human in the loop and to be the one to push the final button to fire that weapon.

Worth noting: “A lot of these are still kind of far-out applications,” Bendett said.

The same can be said for China’s more military-focused applications of AI, largely in surveillance and UAV operations for the PLA,said Elsa Kania, Technology Fellow at the Center for a New American Security. Speaking beside Bendett at the Genius Machines event in March, Kania said China’s military applications appear to beat a a fairly nascent stage in its development.

That is to say: There’snothing to fear about lethal AI applications yet unless you’re an alleged terrorist in the Middle East. For the rest of us, we have our Siris, Alexas, Cortanas and more, helping us shop, search, listen to music,and tag friends in images on social media.

Until the robot uprising comes, let us hope there will always be clips ofthe swearing Atlas Robot from Boston Dynamics available online whenever we need a laugh. It may be better to laugh before these robots start helping each other through doorwaysentirely independent of humans. (Too late.)

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A.I. Artificial Intelligence (2001) – IMDb

Nominated for 2 Oscars. Another 17 wins & 68 nominations. See more awards Learn more More Like This

Comedy | Drama | Sci-Fi

An android endeavors to become human as he gradually acquires emotions.

Director:Chris Columbus

Stars:Robin Williams,Embeth Davidtz,Sam Neill

Adventure | Sci-Fi | Thriller

As Earth is invaded by alien tripod fighting machines, one family fights for survival.

Director:Steven Spielberg

Stars:Tom Cruise,Dakota Fanning,Tim Robbins

Action | Crime | Mystery

In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.

Director:Steven Spielberg

Stars:Tom Cruise,Colin Farrell,Samantha Morton

Drama | History

In 1839, the revolt of Mende captives aboard a Spanish owned ship causes a major controversy in the United States when the ship is captured off the coast of Long Island. The courts must decide whether the Mende are slaves or legally free.

Director:Steven Spielberg

Stars:Djimon Hounsou,Matthew McConaughey,Anthony Hopkins

Drama | History | War

Young Albert enlists to serve in World War I after his beloved horse is sold to the cavalry. Albert’s hopeful journey takes him out of England and to the front lines as the war rages on.

Director:Steven Spielberg

Stars:Jeremy Irvine,Emily Watson,David Thewlis

Drama | Sci-Fi

Roy Neary, an electric lineman, watches how his quiet and ordinary daily life turns upside down after a close encounter with a UFO.

Director:Steven Spielberg

Stars:Richard Dreyfuss,Franois Truffaut,Teri Garr

Drama | History | War

A young English boy struggles to survive under Japanese occupation during World War II.

Director:Steven Spielberg

Stars:Christian Bale,John Malkovich,Miranda Richardson

Drama | History | Thriller

Based on the true story of the Black September aftermath, about the five men chosen to eliminate the ones responsible for that fateful day.

Director:Steven Spielberg

Stars:Eric Bana,Daniel Craig,Marie-Jose Croze

In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his “mother”, Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

Poll: Two Thirds of Americans Support Human Gene Editing to Cure Disease

The majority of U.S. adults would support gene editing embryos to protect babies against disease, according to a new poll.

Human Gene Editing

The majority of U.S. adults support human gene editing to protect babies against disease, according to a new poll.

But they wouldn’t support gene edits that make babies smarter or taller, according to the new research by the Associated Press-NORC Center for Public Affairs Research, which polled about a thousand U.S. adults this month to learn about public attitudes toward genetic engineering.

Deep Divides

The AP research found that 71 percent of respondents support gene editing to protect a baby from an inherited condition, and 67 percent support reducing the risk of diseases like cancer.

But just 12 percent would be okay with tampering with intelligence or athletic ability, and only 10 percent would consider altering physical characteristics like eye color or height.

CRISPR Drawer

Questions about using technologies like CRISPR to gene edit human embryos gained immediacy last month, when Chinese scientists claimed to have edited the genes of two babies in order to protect them against HIV — a move that prompted an international outcry, but also questions about when the technology will be ready for human testing.

“People appear to realize there’s a major question of how we should oversee and monitor use of this technology if and when it becomes available,” Columbia University bioethicist Robert Klitzman told the AP of the new research. “What is safe enough? And who will determine that? The government? Or clinicians who say, ‘Look, we did it in Country X a few times and it seems to be effective.

READ MORE: Poll: Edit baby genes for health, not smarts [Associated Press]

More on human gene editing: Chinese Scientists Claim to Have Gene-Edited Human Babies For the First Time

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Poll: Two Thirds of Americans Support Human Gene Editing to Cure Disease

Elon Musk Thinks the First Mars Settler Could Be an AI

On Friday, Elon Musk speculated that a sophisticated artificial intelligence might touch down on the Red Planet before the first human Mars settler.

The MartAIn

SpaceX CEO Elon Musk wants to establish a base on Mars — but he isn’t sure its first resident will be human.

On Friday, the mercurial billionaire responded to a question on Twitter about whether a sophisticated artificial intelligence might touch down on the Red Planet before human colonists. Musk’s answer: 30 percent.

30%

— Elon Musk (@elonmusk) December 27, 2018

AI Overlords

Musk has a fraught relationship with the topic of AI. He’s publicly warned about the danger’s of unregulated AI, even going so far as to found the organization Open AI to encourage the development of responsible machine learning systems.

It’s such a signature issue for Musk that other tech personalities have weighed in on his claims — including Facebook founder Mark Zuckerberg, who said the notion of killer AI was “pretty irresponsible,” and Reddit co-founder Alexis Ohanian, who quipped at an event earlier this month that Musk was “writing a great screenplay for a Black Mirror episode.”

Case For Optimism

But Musk also believes that AI could be made to help humankind — or that the two could even merge, ushering in a new era of evolution.

Or, as the Friday tweet shows, it seems that Musk could get on board with AI as long as it could help further his visions for the colonization of space.

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Elon Musk Thinks the First Mars Settler Could Be an AI

Leaked Documents Show How Facebook Controls Speech Across the Globe

Leaked documents showing how Facebook controls speech online raise deep questions about the future of the company's role in international discourse.

Unfriended

Documents obtained by the New York Times show how the social giant’s international content moderation strategy is dictated by thousands of pages of PowerPoint presentations and spreadsheets that “sometimes clumsily” tell thousands of moderators what to allow and what to delete. The revelation raises deep questions about the future of Facebook’s role in international discourse — especially in the wake of damaging revelations about how the platform allowed propaganda during the 2016 U.S. presidential elections.

“Facebook’s role has become so hegemonic, so monopolistic, that it has become a force unto itself,” political scientist Jasmin Mujanovic told the Times. “No one entity, especially not a for-profit venture like Facebook, should have that kind of power to influence public debate and policy.”

It’s Complicated

Facebook moderators who spoke to the Times under condition of anonymity said they felt hamstrung by the extraordinarily complex rule set, which forces them to make rapid decisions, sometimes using Google Translate, about fraught topics including terrorism and sectarian violence.

“You feel like you killed someone by not acting,” said a moderator who spoke to the paper on condition of anonymity.

The result, according to the Times, is that Facebook has become a “far more powerful arbiter of global speech than has been publicly recognized or acknowledged by the company itself.”

“A Lot of Mistakes”

Facebook executives pushed back against the implication that its content moderation efforts were murky or disorganized, arguing that the platform has a responsibility to moderate the content its users post and defending its efforts to do so.

“We have billions of posts every day, we’re identifying more and more potential violations using our technical systems,” Facebook’s head of global policy management Monika Bickert told the Times. “At that scale, even if you’re 99 percent accurate, you’re going to have a lot of mistakes.”

READ MORE: Inside Facebook’s Secret Rulebook for Global Political Speech [The New York Times]

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Leaked Documents Show How Facebook Controls Speech Across the Globe

Gov Shutdown Means 95 Percent of NASA Employees Aren’t At Work

The ongoing government shutdown means that 95 percent of NASA's workforce is home on furlough during New Horizons' historic flyby.

Get Furlough

When NASA’s New Horizons spacecraft soars by the space rock Ultima Thule on New Years Eve, it will be the most distant object humankind has ever explored.

Though you’ll be able to stream the historic flyby on the YouTube channel of Johns Hopkins Univerisity’s Applied Physics Laboratory, the event — which is arguably the most awe-inspiring item of space news all year — won’t be available on NASA TV, which typically offers extensive commentary and access to subject matter experts regarding the space agency’s projects. The reason: the ongoing government shutdown means that 95 percent of NASA’s workforce is home on furlough.

“Act of Ineptitude”

NASA employees are disgusted by the legislative dysfunction that’s keeping all but the most mission-critical workers home during the historic flyby, according to the Houston Chronicle — and their ire is reportedly focused on politicians who have allowed the science agency’s work to grind to a halt.

“We have not heard from a single member who supports the president’s inaction,” said the International Federation of Professional and Technical Engineers, a union that represents federal workers, in a statement quoted by the paper. “Most view this as an act of ineptitude.”

Heat Death

The Chronicle also pointed to a post by Casey Dreier, a senior space policy adviser to the nonprofit scientific advocacy organization The Planetary Society, that chastised leaders for failing the nation’s scientific workers — and worried that the political brinkmanship of a shutdown could lead talented workers away from government work entirely, altering the dynamics of space exploration.

“I fear that we will see more and more NASA employees ask themselves why they put up with such needless disruptions and leave for jobs the private sector,” Dreier wrote. “We know that NASA can get back to work, but how long will the best and the brightest want to work at an agency that continues to get callously tossed into political churn?”

READ MORE: NASA, other federal workers not as supportive of government shutdown as Trump claims, union rep says [Houston Chronicle]

More on government shutdowns and space travel: Government Shutdown Hampers SpaceX’s Falcon Heavy Testing

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Gov Shutdown Means 95 Percent of NASA Employees Aren’t At Work

Scientists to Test New Cancer Treatment on Human Patients in 2019

A new cancer treatment that uses the body's own immune system to fight cancer is scheduled to start human trials in 2019.

Cancer Treatment

A new cancer treatment that uses the body’s own immune system to fight cancer is scheduled to start human trials in 2019.

The U.K.’s Telegraph reports that the new treatment, devised by researchers at the Francis Crick Institute in London, uses implanted immune system cells from strangers to fight tumors, instead of old-school cancer treatments like chemotherapy — a new tack in oncology that the researchers say could boost cancer ten-year cancer survival rates from 50 percent to 75 percent.

Immune System

The scientists behind the project explained it as a “do-it-yourself” approach to cancer treatment in interviews with the Telegraph. Instead of relying on chemicals or radiation outside the body to fight tumors, the transplants aim to help the bodies of cancer patients fight the tumors on their own.

“It’s a very exciting time,” said Charlie Swanton, one of the Francis Crick researchers involved in the work, in an interview with the paper. “Using the body’s own immune cells to target the tumor is elegant because tumours evolve so quickly there is no way a pharmaceutical company can keep up with it, but the immune system has been evolving for over four billion years to do just that.”

“Rapidly Treated Diseases”

Swanton told the Telegraph that he believes the trials could lead to a whole new tool set that doctors will be able to use to fight cancer.

“I would go so far as to say that we might reach a point, maybe 20 years from now, where the vast majorities of cancers are rapidly treated diseases or long-term chronic issues that you can manage,” he said. “And I think the immune system will be essential in doing that.”

READ MORE: Cancer breakthrough: Scientists say immune system transplants mean ‘future is incredibly bright’ [The Telegraph]

More on cancer research: Researchers May Have Discovered a New Way to Kill off Cancer Cells

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Scientists to Test New Cancer Treatment on Human Patients in 2019

Holograms Are Resurrecting Dead Musicians, Raising Legal Questions

Dead Musicians

Michael Jackson. Amy Winehouse. Tupac. Roy Orbison.

Those are just a few of the dead musicians who have been resurrected on stage in recent years as holograms — and a new feature by the Australian Broadcasting Corporation explores not just the critical reception and technological frontiers of the new industry, but the legal minefield it raises to dust off the visage of a famous person and bring them out on the road.

Back to Life

According to University of Sydney digital human researcher Mike Seymour, today’s musical holograms have only started to tap the medium’s potential. In the future, he predicted to the ABC, machine learning will let these long-dead holograms interact with the crowd and improvise.

Additionally, according to the report, the law is still grappling with how to handle life-after-death performances. In the U.S., a legal concept called a “right to publicity” gives a person, or their estate, the right to profit from their likeness. But whether right to publicity applies after death, and for how long, differs between states.

Atrocity

Of course, no legal or technical measures will win over fans of an act who find it disrespectful to raise a performer from death and trot them out on tour.

“If you are appalled by [the idea], because you think it’s an atrocity to the original act, you are going to hate it,” Seymour told the broadcaster. “And if you are a fan that just loves seeing that song being performed again, you are going to think it’s the best thing ever.”

READ MORE: Dead musicians are touring again, as holograms. It’s tricky — technologically and legally [Australian Broadcasting Corporation]

More on hologram performances: Wildly Famous Japanese Pop Star Sells Thousands of Tickets in NYC. Also, She’s A Hologram

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