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Atlantic (disambiguation)
In the United States
In the United States Atlantic, Iowa Atlantic, Massachusetts Atlantic, North Carolina, an unincorporated community in eastern Carteret County Atlantic, Pennsylvania Atlantic, Seattle, a neighborhood in Washington state Atlantic, Virginia Atlantic City, New Jersey Atlantic County, New Jersey Atlantic Peak (Colorado), a mountain
Atlantic (disambiguation)
Art, entertainment, and media
Art, entertainment, and media
Atlantic (disambiguation)
Companies and labels
Companies and labels Atlantic Books, an independent British publishing house Atlantic Monthly Press, an American publishing house Atlantic Entertainment Group, a defunct movie studio company Atlantic FM, a former radio station serving Cornwall, United Kingdom Atlantic Records, a record company
Atlantic (disambiguation)
Music
Music The Atlantics, an Australian surf rock band formed in the early 1960s
Atlantic (disambiguation)
Albums
Albums Atlantic (Dufresne album)
Atlantic (disambiguation)
Songs
Songs "Atlantic" (song), by Keane "Atlantic", a song by Björk from Vessel (DVD) "Atlantic", a song by Thrice from Vheissu "Atlantic", a song by Sleep Token from This Place Will Become Your Tomb
Atlantic (disambiguation)
Other art, entertainment, and media
Other art, entertainment, and media Atlantic (film), a 1929 black and white British film The Atlantic, an American magazine founded as The Atlantic Monthly in 1857 Atlantic., a 2014 Dutch film Atlantic (2015 film), an Irish documentary film, awarded Best Irish Documentary at the 2016 Dublin International Film Festival
Atlantic (disambiguation)
Enterprises and organizations
Enterprises and organizations Atlantic (cinema), a movie theater in Warsaw, Poland Atlantic (toy company), a defunct Italian toy manufacturer Atlantic (supermarkets), a defunct supermarket chain in Greece Atlantic Broadband, a cable company in Massachusetts Atlantic City Electric, a division of Elexon supplying electricity in New Jersey Atlantic LNG, a liquefied natural gas producing company based in Trinidad and Tobago Atlantic Petroleum, a former oil company in the United States Atlantic Petroleum (Faroe Islands), an oil and gas production company Atlantic Philanthropies, a defunct private foundation Atlantic Technological University, north-western Ireland Atlantic University, Virginia Beach, Virginia Groupe Atlantic, a French climate control engineering company Real Atlantic Superstore, a Canadian supermarket chain
Atlantic (disambiguation)
Sports
Sports Atlantic Championship Series, developmental open-wheel racing series in North America Atlantic League of Professional Baseball, an American professional baseball league
Atlantic (disambiguation)
Structures
Structures Atlantic Building or Edificio Atlantic, a condominium building in Havana, Cuba The Atlantic (Atlanta), a skyscraper in Atlanta, Georgia, United States
Atlantic (disambiguation)
Transportation
Transportation
Atlantic (disambiguation)
Airlines
Airlines Air Atlantic, a Canadian airline Atlantic Airways, a Faroese airline company
Atlantic (disambiguation)
Aircraft
Aircraft Breguet Atlantic, a French long-range maritime patrol aircraft (1961)
Atlantic (disambiguation)
Motor vehicles
Motor vehicles Atlantic (1921 automobile), a defunct automobile company Austin Atlantic, a British car produced by the Austin Motor Company from 1949 to 1952 Fisker Atlantic, a 2012 plug-in electric concept car
Atlantic (disambiguation)
Railroads and trains
Railroads and trains Atlantic (locomotive), name of an early steam-powered locomotive of the Baltimore and Ohio Railroad Atlantic station (Los Angeles Metro) Atlantic station (Staten Island Railway) Atlantic (train), a named passenger train operated by Canadian Pacific Railway and later Via Rail Atlantic, a type of steam locomotive with a 4-4-2 wheel arrangement (UIC classification 2B1)
Atlantic (disambiguation)
Ships
Ships , any one of several vessels by that name Atlantic (yacht), a three-masted gaff-rigged schooner Atlantic 85-class lifeboats, lifeboats that serve the shores of the United Kingdom and Ireland as a part of the RNLI inshore fleet
Atlantic (disambiguation)
Other uses
Other uses Atlantic (period) of palaeoclimatology Atlantic languages (formerly West Atlantic), a language family in West Africa Atlantic (horse), British-bred Thoroughbred racehorse of the 1870s
Atlantic (disambiguation)
See also
See also Atlantik (disambiguation) Atlantique (disambiguation) Atlantic Beach (disambiguation) Atlantic Bridge (disambiguation) Atlantic City (disambiguation)
Atlantic (disambiguation)
Table of Content
Wiktionary, Places, In Canada, In the United States, Art, entertainment, and media, Companies and labels, Music, Albums, Songs, Other art, entertainment, and media, Enterprises and organizations, Sports, Structures, Transportation, Airlines, Aircraft, Motor vehicles, Railroads and trains, Ships, Other uses, See also
Algebraic number
Short description
thumb|200px|The square root of 2 is an algebraic number equal to the length of the hypotenuse of a right triangle with legs of length 1. An algebraic number is a number that is a root of a non-zero polynomial in one variable with integer (or, equivalently, rational) coefficients. For example, the golden ratio, , is an algebraic number, because it is a root of the polynomial . That is, it is a value for x for which the polynomial evaluates to zero. As another example, the complex number is algebraic because it is a root of . All integers and rational numbers are algebraic, as are all roots of integers. Real and complex numbers that are not algebraic, such as and , are called transcendental numbers. The set of algebraic (complex) numbers is countably infinite and has measure zero in the Lebesgue measure as a subset of the uncountable complex numbers. In that sense, almost all complex numbers are transcendental. Similarly, the set of algebraic (real) numbers is countably infinite and has Lebesgue measure zero as a subset of the real numbers, and in that sense almost all real numbers are transcendental.
Algebraic number
Examples
Examples All rational numbers are algebraic. Any rational number, expressed as the quotient of an integer and a (non-zero) natural number , satisfies the above definition, because is the root of a non-zero polynomial, namely .Some of the following examples come from Quadratic irrational numbers, irrational solutions of a quadratic polynomial with integer coefficients , , and , are algebraic numbers. If the quadratic polynomial is monic (), the roots are further qualified as quadratic integers. Gaussian integers, complex numbers for which both and are integers, are also quadratic integers. This is because and are the two roots of the quadratic . A constructible number can be constructed from a given unit length using a straightedge and compass. It includes all quadratic irrational roots, all rational numbers, and all numbers that can be formed from these using the basic arithmetic operations and the extraction of square roots. (By designating cardinal directions for +1, −1, +, and −, complex numbers such as are considered constructible.) Any expression formed from algebraic numbers using any finite combination of the basic arithmetic operations and extraction of th roots gives another algebraic number. Polynomial roots that cannot be expressed in terms of the basic arithmetic operations and extraction of th roots (such as the roots of ). That happens with many but not all polynomials of degree 5 or higher. Values of trigonometric functions of rational multiples of (except when undefined): for example, , , and satisfy . This polynomial is irreducible over the rationals and so the three cosines are conjugate algebraic numbers. Likewise, , , , and satisfy the irreducible polynomial , and so are conjugate algebraic integers. This is the equivalent of angles which, when measured in degrees, have rational numbers. Some but not all irrational numbers are algebraic: The numbers and are algebraic since they are roots of polynomials and , respectively. The golden ratio is algebraic since it is a root of the polynomial . The numbers and e are not algebraic numbers (see the Lindemann–Weierstrass theorem).Also, Liouville's theorem can be used to "produce as many examples of transcendental numbers as we please," cf.
Algebraic number
<span class="anchor" id="Degree of an algebraic number"></span> Properties
Properties thumb|Algebraic numbers on the complex plane colored by degree (bright orange/red = 1, green = 2, blue = 3, yellow = 4). The larger points come from polynomials with smaller integer coefficients. If a polynomial with rational coefficients is multiplied through by the least common denominator, the resulting polynomial with integer coefficients has the same roots. This shows that an algebraic number can be equivalently defined as a root of a polynomial with either integer or rational coefficients. Given an algebraic number, there is a unique monic polynomial with rational coefficients of least degree that has the number as a root. This polynomial is called its minimal polynomial. If its minimal polynomial has degree , then the algebraic number is said to be of degree . For example, all rational numbers have degree 1, and an algebraic number of degree 2 is a quadratic irrational. The algebraic numbers are dense in the reals. This follows from the fact they contain the rational numbers, which are dense in the reals themselves. The set of algebraic numbers is countable, and therefore its Lebesgue measure as a subset of the complex numbers is 0 (essentially, the algebraic numbers take up no space in the complex numbers). That is to say, "almost all" real and complex numbers are transcendental. All algebraic numbers are computable and therefore definable and arithmetical. For real numbers and , the complex number is algebraic if and only if both and are algebraic.
Algebraic number
Degree of simple extensions of the rationals as a criterion to algebraicity
Degree of simple extensions of the rationals as a criterion to algebraicity For any , the simple extension of the rationals by , denoted by , is of finite degree if and only if is an algebraic number. The condition of finite degree means that there is a finite set in such that ; that is, every member in can be written as for some rational numbers (note that the set is fixed). Indeed, since the are themselves members of , each can be expressed as sums of products of rational numbers and powers of , and therefore this condition is equivalent to the requirement that for some finite , . The latter condition is equivalent to , itself a member of , being expressible as for some rationals , so or, equivalently, is a root of ; that is, an algebraic number with a minimal polynomial of degree not larger than . It can similarly be proven that for any finite set of algebraic numbers , ... , the field extension has a finite degree.
Algebraic number
Field
Field thumb|Algebraic numbers colored by degree (blue = 4, cyan = 3, red = 2, green = 1). The unit circle is black. The sum, difference, product, and quotient (if the denominator is nonzero) of two algebraic numbers is again algebraic: For any two algebraic numbers , , this follows directly from the fact that the simple extension , for being either , , or (for ) , is a linear subspace of the finite-degree field extension , and therefore has a finite degree itself, from which it follows (as shown above) that is algebraic. An alternative way of showing this is constructively, by using the resultant. Algebraic numbers thus form a field (sometimes denoted by , but that usually denotes the adele ring).
Algebraic number
Algebraic closure
Algebraic closure Every root of a polynomial equation whose coefficients are algebraic numbers is again algebraic. That can be rephrased by saying that the field of algebraic numbers is algebraically closed. In fact, it is the smallest algebraically closed field containing the rationals and so it is called the algebraic closure of the rationals. That the field of algebraic numbers is algebraically closed can be proven as follows: Let be a root of a polynomial with coefficients that are algebraic numbers , , ... . The field extension then has a finite degree with respect to . The simple extension then has a finite degree with respect to (since all powers of can be expressed by powers of up to ). Therefore, also has a finite degree with respect to . Since is a linear subspace of , it must also have a finite degree with respect to , so must be an algebraic number.
Algebraic number
Related fields
Related fields
Algebraic number
Numbers defined by radicals
Numbers defined by radicals Any number that can be obtained from the integers using a finite number of additions, subtractions, multiplications, divisions, and taking (possibly complex) th roots where is a positive integer are algebraic. The converse, however, is not true: there are algebraic numbers that cannot be obtained in this manner. These numbers are roots of polynomials of degree 5 or higher, a result of Galois theory (see Quintic equations and the Abel–Ruffini theorem). For example, the equation: has a unique real root, ≈ 1.1673, that cannot be expressed in terms of only radicals and arithmetic operations.
Algebraic number
Closed-form number
Closed-form number Algebraic numbers are all numbers that can be defined explicitly or implicitly in terms of polynomials, starting from the rational numbers. One may generalize this to "closed-form numbers", which may be defined in various ways. Most broadly, all numbers that can be defined explicitly or implicitly in terms of polynomials, exponentials, and logarithms are called "elementary numbers", and these include the algebraic numbers, plus some transcendental numbers. Most narrowly, one may consider numbers explicitly defined in terms of polynomials, exponentials, and logarithms – this does not include all algebraic numbers, but does include some simple transcendental numbers such as or ln 2.
Algebraic number
Algebraic integers
Algebraic integers thumb|Visualisation of the (countable) field of algebraic numbers in the complex plane. Colours indicate the leading integer coefficient of the polynomial the number is a root of (red = 1 i.e. the algebraic integers, green = 2, blue = 3, yellow = 4...). Points becomes smaller as the other coefficients and number of terms in the polynomial become larger. View shows integers 0,1 and 2 at bottom right, +i near top. An algebraic integer is an algebraic number that is a root of a polynomial with integer coefficients with leading coefficient 1 (a monic polynomial). Examples of algebraic integers are and Therefore, the algebraic integers constitute a proper superset of the integers, as the latter are the roots of monic polynomials for all . In this sense, algebraic integers are to algebraic numbers what integers are to rational numbers. The sum, difference and product of algebraic integers are again algebraic integers, which means that the algebraic integers form a ring. The name algebraic integer comes from the fact that the only rational numbers that are algebraic integers are the integers, and because the algebraic integers in any number field are in many ways analogous to the integers. If is a number field, its ring of integers is the subring of algebraic integers in , and is frequently denoted as . These are the prototypical examples of Dedekind domains.
Algebraic number
Special classes
Special classes Algebraic solution Gaussian integer Eisenstein integer Quadratic irrational number Fundamental unit Root of unity Gaussian period Pisot–Vijayaraghavan number Salem number
Algebraic number
Notes
Notes
Algebraic number
References
References
Algebraic number
Table of Content
Short description, Examples, <span class="anchor" id="Degree of an algebraic number"></span> Properties, Degree of simple extensions of the rationals as a criterion to algebraicity, Field, Algebraic closure, Related fields, Numbers defined by radicals, Closed-form number, Algebraic integers, Special classes, Notes, References
Automorphism
Short description
thumb|right|400px|An automorphism of the Klein four-group shown as a mapping between two Cayley graphs, a permutation in cycle notation, and a mapping between two Cayley tables. In mathematics, an automorphism is an isomorphism from a mathematical object to itself. It is, in some sense, a symmetry of the object, and a way of mapping the object to itself while preserving all of its structure. The set of all automorphisms of an object forms a group, called the automorphism group. It is, loosely speaking, the symmetry group of the object.
Automorphism
Definition
Definition In an algebraic structure such as a group, a ring, or vector space, an automorphism is simply a bijective homomorphism of an object into itself. (The definition of a homomorphism depends on the type of algebraic structure; see, for example, group homomorphism, ring homomorphism, and linear operator.) More generally, for an object in some category, an automorphism is a morphism of the object to itself that has an inverse morphism; that is, a morphism is an automorphism if there is a morphism such that where is the identity morphism of . For algebraic structures, the two definitions are equivalent; in this case, the identity morphism is simply the identity function, and is often called the trivial automorphism.
Automorphism
Automorphism group
Automorphism group The automorphisms of an object form a group under composition of morphisms, which is called the automorphism group of . This results straightforwardly from the definition of a category. The automorphism group of an object in a category is often denoted , or simply Aut(X) if the category is clear from context.
Automorphism
Examples
Examples In set theory, an arbitrary permutation of the elements of a set X is an automorphism. The automorphism group of X is also called the symmetric group on X. In elementary arithmetic, the set of integers, , considered as a group under addition, has a unique nontrivial automorphism: negation. Considered as a ring, however, it has only the trivial automorphism. Generally speaking, negation is an automorphism of any abelian group, but not of a ring or field. A group automorphism is a group isomorphism from a group to itself. Informally, it is a permutation of the group elements such that the structure remains unchanged. For every group G there is a natural group homomorphism G → Aut(G) whose image is the group Inn(G) of inner automorphisms and whose kernel is the center of G. Thus, if G has trivial center it can be embedded into its own automorphism group. In linear algebra, an endomorphism of a vector space V is a linear operator V → V. An automorphism is an invertible linear operator on V. When the vector space is finite-dimensional, the automorphism group of V is the same as the general linear group, GL(V). (The algebraic structure of all endomorphisms of V is itself an algebra over the same base field as V, whose invertible elements precisely consist of GL(V).) A field automorphism is a bijective ring homomorphism from a field to itself. The field of the rational numbers has no other automorphism than the identity, since an automorphism must fix the additive identity and the multiplicative identity ; the sum of a finite number of must be fixed, as well as the additive inverses of these sums (that is, the automorphism fixes all integers); finally, since every rational number is the quotient of two integers, all rational numbers must be fixed by any automorphism. The field of the real numbers has no automorphisms other than the identity. Indeed, the rational numbers must be fixed by every automorphism, per above; an automorphism must preserve inequalities since is equivalent to and the latter property is preserved by every automorphism; finally every real number must be fixed since it is the least upper bound of a sequence of rational numbers. The field of the complex numbers has a unique nontrivial automorphism that fixes the real numbers. It is the complex conjugation, which maps to The axiom of choice implies the existence of uncountably many automorphisms that do not fix the real numbers. The study of automorphisms of algebraic field extensions is the starting point and the main object of Galois theory. The automorphism group of the quaternions () as a ring are the inner automorphisms, by the Skolem–Noether theorem: maps of the form . This group is isomorphic to SO(3), the group of rotations in 3-dimensional space. The automorphism group of the octonions () is the exceptional Lie group G2. In graph theory an automorphism of a graph is a permutation of the nodes that preserves edges and non-edges. In particular, if two nodes are joined by an edge, so are their images under the permutation. In geometry, an automorphism may be called a motion of the space. Specialized terminology is also used: In metric geometry an automorphism is a self-isometry. The automorphism group is also called the isometry group. In the category of Riemann surfaces, an automorphism is a biholomorphic map (also called a conformal map), from a surface to itself. For example, the automorphisms of the Riemann sphere are Möbius transformations. An automorphism of a differentiable manifold M is a diffeomorphism from M to itself. The automorphism group is sometimes denoted Diff(M). In topology, morphisms between topological spaces are called continuous maps, and an automorphism of a topological space is a homeomorphism of the space to itself, or self-homeomorphism (see homeomorphism group). In this example it is not sufficient for a morphism to be bijective to be an isomorphism.
Automorphism
History
History One of the earliest group automorphisms (automorphism of a group, not simply a group of automorphisms of points) was given by the Irish mathematician William Rowan Hamilton in 1856, in his icosian calculus, where he discovered an order two automorphism, writing: so that is a new fifth root of unity, connected with the former fifth root by relations of perfect reciprocity.
Automorphism
Inner and outer automorphisms
Inner and outer automorphisms In some categories—notably groups, rings, and Lie algebras—it is possible to separate automorphisms into two types, called "inner" and "outer" automorphisms. In the case of groups, the inner automorphisms are the conjugations by the elements of the group itself. For each element a of a group G, conjugation by a is the operation given by (or a−1ga; usage varies). One can easily check that conjugation by a is a group automorphism. The inner automorphisms form a normal subgroup of Aut(G), denoted by Inn(G); this is called Goursat's lemma. The other automorphisms are called outer automorphisms. The quotient group is usually denoted by Out(G); the non-trivial elements are the cosets that contain the outer automorphisms. The same definition holds in any unital ring or algebra where a is any invertible element. For Lie algebras the definition is slightly different.
Automorphism
See also
See also Antiautomorphism Automorphism (in Sudoku puzzles) Characteristic subgroup Endomorphism ring Frobenius automorphism Morphism Order automorphism (in order theory). Relation-preserving automorphism Fractional Fourier transform
Automorphism
References
References
Automorphism
External links
External links Automorphism at Encyclopaedia of Mathematics Category:Morphisms Category:Abstract algebra Category:Symmetry
Automorphism
Table of Content
Short description, Definition, Automorphism group, Examples, History, Inner and outer automorphisms, See also, References, External links
Accordion
short description
thumb|upright=1.2|An accordionist Accordions (from 19th-century German , from —"musical chord, concord of sounds")accordion, entry in Online Etymology Dictionary are a family of box-shaped musical instruments of the bellows-driven free reed aerophone type (producing sound as air flows past a reed in a frame). The essential characteristic of the accordion is to combine in one instrument a melody section, also called the diskant, usually on the right-hand keyboard, with an accompaniment or Basso continuo functionality on the left-hand. The musician normally plays the melody on buttons or keys on the right-hand side (referred to as the keyboard or sometimes the manual), and the accompaniment on bass or pre-set chord buttons on the left-hand side. A person who plays the accordion is called an accordionist. The accordion belongs to the free-reed aerophone family. Other instruments in this family include the concertina, harmonica, and bandoneon. The concertina and bandoneon do not have the melody–accompaniment duality. The harmoneon is also related and, while having the descant vs. melody dualism, tries to make it less pronounced. The harmonium and American reed organ are in the same family, but are typically larger than an accordion and sit on a surface or the floor. The accordion is played by compressing or expanding the bellows while pressing buttons or keys, causing pallets to open, which allow air to flow across strips of brass or steel, called reeds. These vibrate to produce sound inside the body. Valves on opposing reeds of each note are used to make the instrument's reeds sound louder without air leaking from each reed block.For the accordion's place among the families of musical instruments, see Henry Doktorski's Taxonomy of Musical Instruments (The Classical Free-Reed, Inc.) Also on this page is Diarmuid Pigott's The Free-Reed Family of Aerophones The accordion is widely spread across the world because of the waves of migration from Europe to the Americas and other regions. In some countries (for example: Argentina, Brazil, Colombia, the Dominican Republic, Mexico, and Panama) it is used in popular music (for example: Chamamé in Argentina; gaucho, forró, and sertanejo in Brazil; vallenato in Colombia; merengue in the Dominican Republic; and norteño in Mexico), whereas in other regions (such as Europe, North America, and other countries in South America) it tends to be more used for dance-pop and folk music. In Europe and North America, some popular music acts also make use of the instrument. Additionally, the accordion is used in cajun, zydeco, jazz, and klezmer music, and in both solo and orchestral performances of classical music. Many conservatories in Europe have classical accordion departments. The oldest name for this group of instruments is harmonika, from the Greek , meaning "harmonic, musical". Today, native versions of the name accordion are more common. These names refer to the type of accordion patented by Cyrill Demian, which concerned "automatically coupled chords on the bass side".Dyremose, Jeanette & Lars, Det levende bælgspil (2003), p. 133
Accordion
History
History thumb|left|Eight-key bisonoric diatonic accordion (c. 1830) The accordion's basic form is believed to have been invented in Berlin, in 1822, by Christian Friedrich Ludwig Buschmann,There is not a single document to back up this belief. Christian Friedrich Ludwig Buschmann was 16 years old at that time; handwritten evidence of C.F. Buschschmann and his father exists, but without any related notice within. The first mention of an aeoline was in a text dated 1829. although one instrument was discovered in 2006 that appears to have been built earlier.This is the accordion owned by Fredrik Dillner of Sweden, which has the name F. Löhner Nürnberg engraved (stamped) on it. The instrument was given to Johannes Dillner in 1830 or earlier"Interview with Fredrik DillnerThe Owner of What May Be the World's Oldest Accordion". The Free-Reed Journal, 22 June 2006Müller, Mette & Lisbet Torp (red.) Musikkens tjenere. Forsker, Instrument, Musiker – Musikhistorisk Museums 100 års Jubilæumsskrift 1998, 297 s., indb rigt illustreret Serie: Meddelelser fra Musikhistorisk Museum og Carl Claudius Samling The earliest history of the accordion in Russia is poorly documented. Nevertheless, according to Russian researchers, the earliest known simple accordions were made in Tula, Russia, by Ivan Sizov and Timofey Vorontsov around 1830, after they received an early accordion from Germany.Mirek, Alfred. Garmonika. Proshloe i nastoiashchee. Nauchno-istoricheskaia entsyklopedicheskaia kniga. Moscow, 1994. p. 50 By the late 1840s, the instrument was already very widespread;<ref>[http://publ.lib.ru/ARCHIVES/__Raritetnye_knigi/IRGO_Etnograficheskij_sbornik_02_1854.pdf Etnograficheskii sbornik Russkogo geograficheskogo obshchestva. Vol. 2] , Saint Petersburg, 1854. pp. 26, 162.</ref> together the factories of the two masters were producing 10,000 instruments a year. By 1866, over 50,000 instruments were being produced yearly by Tula and neighbouring villages, and by 1874 the yearly production was over 700,000.Mirek, Alfred. Iz istorii akkordeona i baiana. Moscow, 1967. pp. 43–45 By the 1860s, Novgorod, Vyatka and Saratov governorates also had significant accordion production. By the 1880s, the list included Oryol, Ryazan, Moscow, Tver, Vologda, Kostroma, Nizhny Novgorod and Simbirsk, and many of these places created their own varieties of the instrument. The accordion is one of several European inventions of the early 19th century that use free reeds driven by a bellows. An instrument called accordion was first patented in 1829 by Cyrill Demian in Vienna.A summary and pictures of this patent can be found at www.ksanti.net/free-reed/history/demian.html (Version of 20 Okt 4 – 19 Jun 9 Using Way Back Machine to Display: The Classical Free-Reed, Inc.) Demian's instrument bore little resemblance to modern instruments. It only had a left-hand buttonboard, with the right hand simply operating the bellows. One key feature for which Demian sought the patent was the sounding of an entire chord by depressing one key. His instrument could also sound two chords with the same key, one for each bellow direction (a bisonoric action). At that time in Vienna, mouth harmonicas with Kanzellen (chambers) had already been available for many years, along with bigger instruments driven by hand bellows. The diatonic key arrangement was also already used on mouth-blown instruments. Demian's patent thus covered an accompanying instrument: an accordion played with the left hand, opposite to how contemporary chromatic hand harmonicas were played, small and light enough for travelers to take with them and used to accompany singing. The patent also described instruments with both bass and treble sections, although Demian preferred the bass-only instrument owing to its cost and weight advantages.German Text: "Mit den Dekel des Balges, läßt sich das ganze Instrument verdoppeln, so daß man dadurch die Accorde vermehrt, oder auch mit einzelne Töne spielen kann, in diesem Fall, muß ein zweyter Einsatz mit Federn, und auch eine 2te Claviatur dazu kommen, der Blasebalg bleibt in der Mitte, jede Hand dirigirt abwechselnd, entweder die Claves, oder den Balg. Durch eine obengenannte Verdoplung des Instruments oder durch Vermehrung der Accorde, würde niemand etwas verbessern, oder was neues liefern, weil nur die Bestandtheile dadurch vermehrt, das Instrument theurer und schwerer wird." Translation of this snip: With the Cover of the bellows the instrument can be duplicated, so the number of Chords or single notes can be enlarged, or one can sound single notes, in this case, a second part with springs (free reeds) and also a second keyboard must be added, the bellows are in between these two parts, both hands push buttons and push and pull the bellows at the same time or alternatively. Through this doubling or increasing of chords within the instrument nothing new is invented or improved by someone else, because only the amount of similar parts is increased and the Instrument is heavier and more expensive.German full text The accordion was introduced from Germany into Britain in about the year 1828.The National Cyclopaedia of Useful Knowledge, Vol I, A–Arcesilaus, London, George Woodfall and Son, 1847, p. 107. The instrument was noted in The Times in 1831 as one new to British audiencesThe Times, Thursday 9 June 1831; p. 5; Issue 14560; col A: (Review of a performance by a flautist, Mr. Sedlatzek) "At the close of the concert Mr. Sedlatzek performed on a new instrument called the Accordion or Aeolian, which, however, has little beside its novelty to recommend it." and was not favourably reviewed, but nevertheless it soon became popular.The Times, Wednesday, 26 April 1837; p. 5; Issue 16400; col C : "Great Concert-room – King's Theatre ...There was also a novelty in the shape of an instrument called "a concertina", an improvement on the accordion, which has been such a favourite musical toy for the last two or three years". It had also become popular with New Yorkers by the mid-1840s.New York Times, 19 May 1907: 'The Lay of the Last of the Old Minstrels: Interesting Reminiscences of Isaac Odell, Who Was A Burnt Cork Artist Sixty Years Ago': "While we were drawing big crowds to the Palmer House on Chambers Street Charley White was making a great hit playing an accordion in Thalia Hall on Grand Street. In those days" (i.e. mid-1840s) "accordions were the real attraction to the public". After Demian's invention, other accordions appeared, some featuring only the right-handed keyboard for playing melodies. It took English inventor Charles Wheatstone to bring both chords and keyboard together in one squeezebox. His 1844 patent for what he called a concertina also featured the ability to easily tune the reeds from the outside with a simple tool. thumb|The first pages in Adolf Müller's accordion book The Austrian musician Adolf Müller described a great variety of instruments in his 1854 book . At the time, Vienna and London had a close musical relationship, with musicians often performing in both cities in the same year, so it is possible that Wheatstone was aware of this type of instrument and may have used them to put his key-arrangement ideas into practice. Jeune's flutina resembles Wheatstone's concertina in internal construction and tone colour, but it appears to complement Demian's accordion functionally. The flutina is a one-sided bisonoric melody-only instrument whose keys are operated with the right hand while the bellows are operated with the left. When the two instruments are combined, the result is quite similar to diatonic button accordions still manufactured today. Further innovations followed and continue to the present. Various buttonboard and keyboard systems have been developed, as well as voicings (the combination of multiple tones at different octaves), with mechanisms to switch between different voices during performance, and different methods of internal construction to improve tone, stability, and durability. Modern accordions may incorporate electronics such as condenser microphones and tone and volume controls so that the accordion can be plugged into a PA system or keyboard amplifier for live shows. Some 2010s-era accordions may incorporate MIDI sensors and circuitry, enabling the accordion to be plugged into a synth module and produce accordion sounds or other synthesized instrument sounds, such as piano or organ. Construction thumb|A diatonic button accordion being played Accordions have many configurations and types. What may be easy to do with one type of accordion could be technically challenging or impossible with another, and proficiency with one layout may not translate to another. The most obvious difference between accordions is their right-hand sides. Piano accordions use a piano-style musical keyboard; button accordions use a buttonboard. Button accordions are furthermore differentiated by their usage of a chromatic or diatonic buttonboard for the right-hand side. Accordions may be either bisonoric, producing different pitches depending on the direction of bellows movement, or unisonoric, producing the same pitch in both directions. Piano accordions are unisonoric. Chromatic button accordions also tend to be unisonoric, while diatonic button accordions tend to be bisonoric, though notable exceptions exist. Accordion size is not standardized, and may vary significantly from model to model. Accordions vary not only in their dimensions and weight, but also in the number of buttons or keys present in the right- and left-hand keyboards. For example, piano accordions may have as few as 8 bass buttons (two rows of four), or up to 140 (seven rows of twenty) or beyond. Accordions also vary by their available registers and by their specific tuning and voicing. Despite these differences, all accordions share several common components. Universal components Bellows thumb|265px|Bellows-driven instruments The bellows is the most recognizable part of the instrument, and the primary means of articulation. The production of sound in an accordion is in direct proportion to the motion of the bellows by the player. In a sense, the role of the bellows can be compared to the role of moving a violin's bow on bowed strings. For a more direct analogy, the bellows can be compared to the role of breathing for a singer. The bellows is located between the right- and left-hand keyboards, and is made from pleated layers of cloth and cardboard, with added leather and metal.How To Repair Bellows Ike's Accordion It is used to create pressure and vacuum, driving air across the internal reeds and producing sound by their vibrations, applied pressure increases the volume. The keyboard touch is not expressive and does not affect dynamics: all expression is effected through the bellows. Bellows effects include: Volume control, including swells and fades Repeated short, rapid changes of direction ("bellows shake"), which has been popularized by musicians such as Renato Borghetti (gaucho music) and Luiz Gonzaga, and extensively used in Forró, called resfulego in Brazil Constant bellows motion while applying pressure at intervals Constant bellows motion to produce clear tones with no resonance Subtly changing the intonation to mimic the expressiveness of a singer Using the bellows with the silent air button gives the sound of air moving ("whooshing"), which is sometimes used in contemporary compositions for this instrument Body thumb|left|Showroom of accordions (Petosa Accordions, Seattle, Washington) The accordion's body consists of two boxes, commonly made of wood, joined by the bellows. These boxes house reed chambers for the right- and left-hand keyboards. Each side has grilles in order to facilitate the transmission of air in and out of the instrument and to allow the sound to project. The grille at the right-hand side is usually larger and is often shaped for decorative purposes. The right-hand keyboard is normally used for playing the melody and the left-hand one for playing the accompaniment; however, skilled players can reverse these roles and play melodies with the left hand.Guido Deiro claimed he was the first accordionist to play a solo with the left hand: Sharpshooter's March (1908) Guido Deiro, Guido Deiro's Own Story of Sharpshooters March, The Pietro Musicordion, Volume 6, Number 2 (May–June 1948) The size and weight of an accordion varies depending on its type, layout and playing range, which can be as small as to have only one or two rows of basses and a single octave on the right-hand keyboard, to the most common 120-bass accordion and through to large and heavy 160-bass free-bass converter models. Pallet mechanism The accordion is an aerophone. The keyboard mechanisms of the instrument either enable the air flow, or disable it:Illustration made with reference from a similar illustration that can be found in both Det levende bælgspil (p. 9) by Jeanette & Lars Dyremose (2003), and Harmonikaens historie (p. 35a) by Bjarne Glenstrup (1972, The University of Copenhagen, Faculty of Music) upright=3.4|thumb|center|A side view of the pallet mechanism in a piano accordion. As the key is pressed down the pallet is lifted, allowing for air to enter the tone chamber in either direction and excite the reeds; air flow direction depends on the direction of bellows movement. A similar mechanical pallet movement is used in button accordions, as well as for bass mechanisms such as the Stradella bass machine that translates a single button press into multiple pallet openings for the notes of a chord.|alt=Accordion; cross-sectional view Variable components The term accordion covers a wide range of instruments, with varying components. All instruments have reed ranks of some format, apart from reedless digital accordions. Not all have switches to change registers or ranks, as some have only one treble register and one bass register. The most typical accordion is the piano accordion, which is used for many musical genres. Another type of accordion is the button accordion, which is used in musical traditions including Cajun, Conjunto and Tejano music, Swiss and Slovenian-Austro-German Alpine music, and Argentinian tango music. The Helikon-style accordion has multiple flared horns projecting out of the left side to strengthen the bass tone. The word "Helikon" refers to a deep-pitched tuba. Right-hand keyboard systems Different systems exist for the right-hand keyboard of an accordion, which is normally used for playing the melody (while it can also play chords). Some use a button layout arranged in one way or another, while others use a piano-style keyboard. Each system has different claimed benefits by those who prefer it. They are also used to define one accordion or another as a different "type": Chromatic button accordions and the bayan, a Russian variant, use a buttonboard where notes are arranged chromatically. Two major systems exist, referred to as the B-system and the C-system (there are also regional variants). Rarely, some chromatic button accordions have a decorative right-hand keyboard in addition to the rows of buttons, an approach used by the virtuoso accordionist Pietro Frosini. Diatonic button accordions use a buttonboard designed around the notes of diatonic scales in a small number of keys. The keys are often arranged in one row for each key available. Chromatic scales may be available by combining notes from different rows. The adjective "diatonic" is also commonly used to describe bisonic or bisonoric accordions—that is, instruments whose right-hand (and in some instances even bass) keys each sound two different notes depending on the direction of the bellows (for instance, producing major triad sequences while closing the bellows and dominant seventh or 7–9 while opening). Such is the case, for instance, with the Argentinian bandoneon, the Slovenian-Austro-German Steirische Harmonika, the Czech Heligonka Harmonika, the Italian organetto, the Swiss Schwyzerörgeli and the Anglo concertina. Piano accordions use a musical keyboard similar to a piano, at right angles to the cabinet, the tops of the keys inward toward the bellows. The rarely used bass accordion has only a right-hand keyboard, with ranks of 8', 16', and 32' reeds, with the lowest note being the deepest pitch on a pipe organ pedal keyboard (pedal C). It is intended for performing basslines in accordion orchestras. The rarely used piccolo accordion also has only a right-hand keyboard. 6-plus-6 accordions use a buttonboard with three rows of buttons in a "uniform" or "whole-tone" arrangement, generally known as a Jankó keyboard. The chromatic scale consists of two rows. The third row is a repetition of the first row, so there is the same fingering in all twelve scales. These accordions are produced only in special editions e.g. the logicordion produced by Harmona. Left-hand keyboard systems thumb|right|300px|Typical 120-button Stradella bass system. This is the left-hand keyboard system found on most unisonoric accordions today. Different systems are also in use for the left-hand keyboard, which is normally used for playing the accompaniment. These usually use distinct bass buttons and often have buttons with concavities or studs to help the player navigate the layout despite not being able to see the buttons while playing. There are three general categories: thumb|right|200px|The bass buttons trigger a complex mechanism of wires, rods, and levers, which is normally hidden inside the instrument. The Stradella bass system, also called standard bass, is arranged in a circle of fifths and uses single buttons for bass notes and additional rows of single buttons for preset major, minor, dominant seventh, and diminished chords. The dominant seventh and diminished chords are three-note chord voicings that omit the fifths of the chords. The Belgian bass system is a variation used in Belgian chromatic accordions. It is also arranged in a circle of fifths but in reverse order. This system has three rows of basses, three rows of chord buttons allowing easier fingering for playing melodies, combined chords, better use of fingers one and five, and more space between the buttons. This system was rarely used outside of its native Belgium. Various free-bass systems for greater access to playing melodies and complex basslines on the left-hand keyboard and to forming one's own chords note-by-note. These are often chosen for playing jazz and classical music. Some models can convert between free-bass and Stradella bass; this is called converter bass. The free-bass left hand notes are arranged chromatically in three rows with one additional duplicate row of buttons. Luttbeg double-keyboard piano accordions have a piano keyboard layout on both the treble and bass sides. This allows pianists, most notably Duke Ellington, to double up on the accordion without difficulty. The Bercandeon is an improved version of that instrument, also making it a "keyboard bandoneon". In 2021, a patent was published by Valerio Chiovarelli for a new bass system called the "Chiovarelli Jazz System". This system is a variation of the Stradella bass system where, instead of triads, the chordal buttons of this system produce bichords (chords with only 2 pitches instead of 3). The "Chiovarellia Jazz System" (or "CJS" for short) prioritizes the effectiveness of left hand accordion in jazz music, hence the name of the system, but according to the inventor, these chords can be useful when playing many varieties of music. Reed ranks and switches upright=0.75|thumb|right|Accordion reed ranks with closeup of reeds Inside the accordion are the reeds that generate the instrument tones. These are organized in different sounding banks, which can be further combined into registers producing differing timbres. All but the smaller accordions are equipped with switches that control which combination of reed banks operate, organized from high to low registers. Each register stop produces a separate sound timbre, many of which also differ in octaves or in how different octaves are combined. See the accordion reed ranks and switches article for further explanation and audio samples. All but the smaller accordions usually have treble switches. The larger and more expensive accordions often also have bass switches to give options for the reed bank on the bass side. Classification of chromatic and piano type accordions In describing or pricing an accordion, the first factor is size, expressed in number of keys on either side. For a piano type, this could for one example be 37/96, meaning 37 treble keys (three octaves plus one note) on the treble side and 96 bass keys. A second aspect of size is the width of the white keys, which means that even accordions with the same number of keys have keyboards of different lengths, ranging from for a child's accordion to for an adult-sized instrument. After size, the price and weight of an accordion is largely dependent on the number of reed ranks on either side, either on a cassotto or not, and to a lesser degree on the number of combinations available through register switches. The next, but important, factor is the quality of the reeds, the highest grade called "a mano" (meaning "hand-made"), the next "tipo a mano" ("like hand-made"), lower grades including "export" and several more. Price is also affected by the use of costly woods, luxury decorations, and features such as a palm switch, grille mute, and so on. Some accordion makers sell a range of different models, from a less-expensive base model to a more costly luxury model. Typically, the register switches are described as Reeds: 5 + 3, meaning five reeds on the treble side and three on the bass, and Registers: 13 + M, 7, meaning 13 register buttons on the treble side plus a special "master" that activates all ranks, like the "tutti" or "full organ" switch on an organ, and seven register switches on the bass side. Another factor affecting the price is the presence of electronics, such as condenser microphones, volume and tone controls, or MIDI sensors and connections. thumb|Accordion player on a street in the historic centre of Quito, Ecuador Straps The larger piano and chromatic button accordions are usually heavier than other smaller squeezeboxes, and are equipped with two shoulder straps to make it easier to balance the weight and increase bellows control while sitting, and avoid dropping the instrument while standing. Other accordions, such as the diatonic button accordion, have only a single shoulder strap and a right hand thumb strap. All accordions have a (mostly adjustable) leather strap on the left-hand side to keep the player's hand in position while drawing the bellows. There are also straps above and below the bellows to keep it securely closed when the instrument is not being played. Electronic and digital thumb|right|200px|Rainer von Vielen playing a Roland digital V-Accordion. The bank of electronic switches can change the accordion's sound, tone and volume. In the 2010s, a range of electronic and digital accordions were introduced. They have an electronic sound module which creates the accordion sound, and most use MIDI systems to encode the keypresses and transmit them to the sound module. A digital accordion can have hundreds of sounds, which can include different types of accordions and even non-accordion sounds, such as pipe organ, piano, or guitar. Sensors are used on the buttons and keys, such as magnetic reed switches. Sensors are also used on the bellows to transmit the pushing and pulling of the bellows to the sound module. Digital accordions may have features not found in acoustic instruments, such as a piano-style sustain pedal, a modulation control for changing keys, and a portamento effect. As an electronic instrument, these types of accordions are plugged into a PA system or keyboard amplifier to produce sound. Some digital accordions have a small internal speaker and amplifier, so they can be used without a PA system or keyboard amplifier, at least for practicing and small venues like coffeehouses. One benefit of electronic accordions is that they can be practiced with headphones, making them inaudible to other people nearby. On a digital accordion, the volume of the right-hand keyboard and the left-hand buttons can be independently adjusted. Acoustic-digital hybrid accordions also exist. They are acoustic accordions (with reeds, bellows, and so on), but they also contain sensors, electronics, and MIDI connections, which provides a wider range of sound options. An acoustic-digital hybrid may be manufactured in this form, or it may be an acoustic accordion which has had aftermarket electronics sensors and connections added. Several companies sell aftermarket electronics kits, but they are typically installed by professional accordion technicians, because of the complex and delicate nature of the internal parts of an accordion. Unusual accordions Various hybrid accordions have been created between instruments of different buttonboards and actions. Many remain curiosities – only a few have remained in use: The Schrammel accordion, used in Viennese chamber music and klezmer, which has the treble buttonboard of a chromatic button accordion and a bisonoric bass buttonboard, similar to an expanded diatonic button accordion The Steirische Harmonika, a type of bisonoric diatonic button accordion particular to the Alpine folk music of Slovenia, Austria, the Czech Republic, the German state of Bavaria, and the Italian South Tyrol The schwyzerörgeli or Swiss organ, which usually has a three-row diatonic treble and 18 unisonoric bass buttons in a bass/chord arrangement – a subset of the Stradella system in reverse order like the Belgian bass – that travel parallel to the bellows motion The trikitixa of the Basque people, which has a two-row diatonic, bisonoric treble and a 12-button diatonic unisonoric bass The British chromatic accordion, the favoured diatonic accordion in Scotland. While the right hand is bisonoric, the left hand follows the Stradella system. The elite form of this instrument is generally considered the German manufactured Shand Morino, produced by Hohner with the input of Sir Jimmy Shand Pedal harmony, a type of accordion used sometimes in Polish folk music, which has a pair of pump organ-like bellows attached. The Finnish composer and accordionist Veli Kujala developed a quarter tone accordion together with the Italian accordion manufacturer Pigini in 2005, and has written works for it. It deploys the same system as the concert accordion, with a scale of five octaves, each divided into 24 quarter tones. Other notable composers who have written concertos for the quarter tone accordion include Jukka Tiensuu and Sampo Haapamäki. Manufacturing process The most expensive accordions are typically fully hand-made, particularly the reeds; completely hand-made reeds have a better tonal quality than even the best automatically manufactured ones. Some accordions have been modified by individuals striving to bring a more pure sound out of low-end instruments, such as the ones improved by Yutaka Usui,Yutaka Usuai, Japanese-born accordion craftsman. a Japanese craftsman. The manufacture of an accordion is only a partly automated process. In a sense, all accordions are handmade, since there is always some hand assembly of the small parts required. The general process involves making the individual parts, assembling the subsections, assembling the entire instrument, and final decorating and packaging. Notable centres of production are the Italian cities of Stradella and Castelfidardo, with many small and medium size manufacturers especially at the latter. Castelfidardo honours the memory of Paolo Soprani who was one of the first large-scale producers. has built accordions in the French town of Tulle since 1919, and the company is now the last complete-process manufacturer of accordions in France. German companies such as Hohner and Weltmeister made large numbers of accordions, but production diminished by the end of the 20th century. Hohner still manufactures its top-end models in Germany, and Weltmeister instruments are still handmade by HARMONA Akkordeon GmbH in Klingenthal. Use in various music genres thumb|A street performer playing the accordion The accordion has traditionally been used to perform folk or ethnic music, popular music, and transcriptions from the operatic and light-classical music repertoire.Henry Doktorski, CD booklet notes for "Guido Deiro: Complete Recorded Works, Vol. 1", Archeophone Records (2007) It was also used by the Kikuyu tribe in Kenya and is the main instrument in the traditional Mwomboko dance. Today the instrument is sometimes heard in contemporary pop styles, such as rock and pop-rock,Sometimes in modern pop music the accordion is not actually played, but its sound is heard by use of a MIDI instrument and sampled sound module. and occasionally even in serious classical music concerts, as well as advertisements. Use in traditional music right|thumb|A folk accordionist, 2009 The accordion's popularity spread rapidly: it has mostly been associated with the common people, and was propagated by Europeans who emigrated around the world. The accordion in both button and piano forms became a favorite of folk musiciansChristoph Wagner, "A Brief History of How the Accordion Changed the World", CD booklet notes for Planet Squeezebox, performed by various artists, (Roslyn, New York: Ellipsis Arts, 1995), 6 and has been integrated into traditional music styles all over the world: see the list of music styles that incorporate the accordion. Use in jazz Notable jazz accordionists Early jazz accordionists include Charles Melrose, who recorded Wailing Blues/Barrel House Stomp (1930, Voc. 1503) with the Cellar Boys; Buster Moten, who played second piano and accordion in the Bennie Moten orchestra; and Jack Cornell, who did recordings with Irving Mills. Later jazz accordionists from the United States include Steve Bach, Milton DeLugg, Orlando DiGirolamo, Angelo Di Pippo,The Biographical Encyclopedia of Jazz. Feather, Leonard. Gitler, Ira Eds. Oxford University Press. 2007 ebook ISBN 9780195320008 Angelo Di Pippo Biography on Google BooksAngelo Di Pippo Biography on allmusic.comAngelo Di Pippo Biography on imbdAngelo Di Pippo Biography on angelodipippo.com Dominic Frontiere, Guy Klucevsek, Yuri Lemeshev, Frank Marocco, Dr. William Schimmel, John Serry Sr.,Discography of American Historical Recordings: University of California Santa Barbara – Audio recordings online of John Serry and the Shep Fields Rippling Rhythm Jazz Orchestra 1937–1938 Lee Tomboulian, and Art Van Damme. French jazz accordionists include Richard Galliano, Bernard Lubat, and Vincent Peirani. Norwegian jazz accordionists include Asmund Bjørken, Stian Carstensen, Gabriel Fliflet, Frode Haltli, and Eivin One Pedersen. Left hand techniques The constraints of the Stradella bass system, limiting the left hand to preset chord buttons, is a barrier to some jazz chord conventions. Jazz accordionists expand the range of chord possibilities by using more than one chord button simultaneously, or by using combinations of a chord button and a bass note other than the typical root of the chord. An example of the former technique is used to play a minor seventh chord. To play an Am7(add9) chord, the Am and Em preset buttons are pressed simultaneously, along with an A bassnote. An example of the latter technique is used to play the half-diminished chord. To play an Eø7, a Gm preset button is pressed along with an E bassnote. For the left hand, the free-bass system is used in jazz as a means of creating complex chord voicings. Jazz harmony that would otherwise be difficult to replicate with the Stradella bass system, such as tritone substitutions, become more accessible using a free-bass accordion.Jacobson, M. [Squeeze This – A Cultural History of the Accordion in America]. University of Illinois Press. Use in popular music thumb|John Linnell of They Might Be Giants playing a Main Squeeze 911 The accordion appeared in popular music from the 1900s to the 1960s. This half-century is often called the "golden age of the accordion". Five players, Pietro Frosini, the two brothers Count Guido Deiro and Pietro Deiro and Slovenian brothers Vilko Ovsenik and Slavko Avsenik, Charles Magnante were major influences at this time. Most vaudeville theaters closed during the Great Depression, but accordionists during the 1930s–1950s taught and performed for radio. Included among this group was the concert virtuoso John Serry, Sr.The Los Angeles Examiner 9 October 1938, p. 1Jacobson, Marion (2012). Squeeze This: A Cultural History of the Accordion in America. University of Illinois Press, Chicago, p. 61. During the 1950s through the 1980s the accordion received significant exposure on television with performances by Myron Floren on The Lawrence Welk Show.Myron Floren and Randee Floren, Accordion Man, with a foreword by Lawrence Welk (The Stephen Greene Press, Brattleboro, Vermont: 1981) In the late 1950s and early 1960s, the accordion declined in popularity because of the rise of rock and roll. The first accordionist to appear and perform at the Newport Jazz Festival was Angelo DiPippo. He can be seen playing his accordion in the motion picture The Godfather. He also composed and performed with his accordion on part of the soundtrack of Woody Allen's movie To Rome With Love. He was featured twice on The Tonight Show with Johnny Carson. Richard Galliano is an internationally known accordionist whose repertoire covers jazz, tango nuevo, Latin, and classical. Some popular bands use the instrument to create distinctive sounds. A notable example is Grammy Award–winning parodist "Weird Al" Yankovic, who plays the accordion on many of his musical tracks, particularly his polkas. Yankovic was trained in the accordion as a child. The accordion has also been used in the rock genre, most notably by John Linnell of They Might Be Giants, featuring more prominently in the band's earlier works. The instrument is still frequently used during live performances, and continues to make appearances in their studio albums. Accordion is also used in the music of the Dropkick Murphys and Gogol Bordello. Tom Waits used the accordion extensively (often played by Dr. William Schimmel) on his album Rain Dogs and Franks Wild Years. The folk metal subgenre also employs accordionists, but they are otherwise generally rare in other genres. Full-time accordionists in folk metal seem even rarer, but they are still utilized for studio work, as flexible keyboardists are usually more accessible for live performances. The Finnish symphonic folk-metal band Turisas used to have a full-time accordionist, employing classical and polka sensibilities alongside a violinist. One of their accordionists, Netta Skog, is now a member of Ensiferum, another folk-metal band. Another Finnish metal band, Korpiklaani, invokes a type of Finnish polka called humppa, and also has a full-time accordionist. Sarah Kiener, the former hurdy-gurdy player for the Swiss melodic-death-folk metal band Eluveitie, played a Helvetic accordion known as a zugerörgeli. Use in classical music Although best known as a folk instrument, it has grown in popularity among classical composers. The earliest surviving concert piece is , written in 1836 by Louise Reisner of Paris. Other composers, including the Russian Pyotr Ilyich Tchaikovsky, the Italian Umberto Giordano, and the American Charles Ives, wrote works for the diatonic button accordion. thumb|Finnish accordionist Esa Pakarinen (Feeliks Esaias Pakarinen, 1911–1989) The first composer to write specifically for the chromatic accordion was Paul Hindemith.Accordion Composers in German Accordion Online In 1922, the Austrian Alban Berg included an accordion in Wozzeck, Op. 7. In 1937, the first accordion concerto was composed in Russia. Other notable composers have written for the accordion during the first half of the 20th century.Henry Doktorski, "The Classical Squeezebox: A Short History of the Accordion and Other Free-Reed Instruments in Classical Music", The Classical Free-Reed, Inc. (1997) Included among this group was the Italian-American John Serry Sr., whose Concerto for Free Bass Accordion was completed in 1964.Library of Congress Copyright Office, "Concerto in C Major for Bassetti Accordion", Composer: John Serry, 4 June 1968, Copyright # EP247602.Eastman School of Music - University of Rochester - Sibley Music Library: John J. Serry Sr. Collection score "Concerto in C Major (1967) for Free Bass Accordion", Folder 15 & 16 p. 10 archived at the University of Rochester Eastman School of Music Sibley Music Library Special collections on esm.rochester.eduAccordion World, Bedford Hills, NY, 1968. In addition, the American accordionist Robert Davine composed his Divertimento for Flute, Clarinet, Bassoon and Accordion as a work for chamber orchestra. American composer William P. Perry featured the accordion in his orchestral suite Six Title Themes in Search of a Movie (2008). The experimental composer Howard Skempton began his musical career as an accordionist, and has written numerous solo works for it. In his work Drang (1999), British composer John Palmer pushed the expressive possibilities of the accordion/bayan. Luciano Berio wrote Sequenza XIII (1995) for accordionist Teodoro Anzellotti. Accordionists like Mogens Ellegaard, Joseph Macerollo, Nick Ariondo, Friedrich Lips, Hugo Noth, Dr. William Schimmel (also a composer), Stefan Hussong, Teodoro Anzellotti, and Geir Draugsvoll, encouraged composers to write new music for the accordion (solo and chamber music) and also started playing baroque music on the free bass accordion. French composer Henri Dutilleux used an accordion in both his late song cycles Correspondences (2003) and Le Temps L'Horloge (2009). Russian-born composer Sofia Gubaidulina has composed solos, concertos, and chamber works for accordion. Astor Piazzolla's concert tangos are performed widely. Piazzolla performed on the bandoneon, but his works are also performed on or accordion. Dr. William schimmel and "The Tango Project" recorded a number of hit recordings and appeared in the movie Scent of a Woman with Al Pacino which earned Pacino an Oscar. Their recordings were used in many films. Australia The earliest mention of the novel accordion instrument in Australian music occurs in the 1830s. The accordion initially competed against cheaper and more convenient reed instruments such as mouth organ, concertina and melodeon. Frank Fracchia was an Australian accordion composer and copies of his works "My dear, can you come out tonight" and "Dancing with you" are preserved in Australian libraries. Other Australian composers who arranged music for accordion include Reginald Stoneham. The popularity of the accordion peaked in the late 1930s and continued until the 1950s. The accordion was particularly favoured by buskers. Bosnia and Herzegovina The accordion is a traditional instrument in Bosnia and Herzegovina. It is the dominant instrument used in sevdalinka, a traditional genre of folk music from Bosnia and Herzegovina. Brazil thumb|right|150px|Brazilian accordionist Dominguinhos (José Domingos de Morais, 1941–2013) The accordion was brought to Brazil by settlers and immigrants from Europe, especially from Italy and Germany, who mainly settled in the south (Rio Grande do Sul, Santa Catarina and Paraná). The first instrument brought was a "Concertina" (a 120 button chromatic accordion). The instrument was popular in the 1950s, and it was common to find several accordions in the same house. There are many different configurations and tunes which were adapted from the cultures that came from Europe. Accordion is the official symbol instrument of the Rio Grande do Sul state, where was voted by unanimity in the deputy chamber. During the boom of accordions there were around 65 factories in Brazil, where most of them (52) in the south, in Rio Grande do Sul state, with only 7 outside the south. One of the most famous and genuinely Brazilian brands was Accordeões Todeschini from Bento Gonçalves-RS, closed in 1973. The Todeschini accordion is very appreciated today and survives with very few maintainers. The most notable musicians of button accordions are Renato Borghetti, Adelar Bertussi, Albino Manique and Edson Dutra. Compared to many other countries, the instrument is very popular in mainstream pop music. In some parts of the country, such as the northeast it is the most popular melodic instrument. As opposed to most European folk accordions, a very dry tuning is usually used in Brazil. Outside the south, the accordion (predominantly the piano accordion) is used in almost all styles of Forró (in particular in the subgenres of Xote and Baião) as the principal instrument, Luiz Gonzaga (the "King of the Baião") and Dominguinhos being among the notable musicians in this style from the northeast. In this musical style the typical combination is a trio of accordion, triangle and zabumba (a type of drum). This style has gained popularity recently, in particular among the student population of the southeast of the country (in the Forró Universitário genre, with important exponents today being Falamansa, and trios such as Trio Dona Zefa, Trio Virgulino and Trio Alvorada). Moreover, the accordion is the principal instrument in Junina music (music of the São João Festival), with Mario Zan having been a very important exponent of this music. It is an important instrument in Sertanejo (and Caipira) music, which originated in the midwest and southeast of Brazil, and subsequently has gained popularity throughout the country. China The number of accordionists in China exceeds every other country in the world, and possibly every country combined. Introduced in 1926, the accordion has risen to popularity in China throughout the years, thanks to Russian teachers and its being a popular instrument in the People's Liberation Army, and remains popular. In the late 20th century, the development of high performance standards for the accordion within China's halls of academe was also influenced by several American virtuosos including Robert Davine, who was invited by the Ministry of Culture of the People's Republic to present Master Classes and to broaden its national program of music for the accordion in 1984. Colombia The accordion is also a traditional instrument in Colombia, commonly associated with the vallenato and cumbia genres. The accordion has been used by tropipop musicians such as Carlos Vives, Andrés Cabas, Fonseca (singer) and Bacilos, as well as rock musicians such as Juanes and pop musicians as Shakira. Vallenato, who emerged in the early twentieth century in Valledupar, and have come to symbolize the folk music of Colombia. Every year in April, Colombia holds one of the most important musical festivals in the country: the Vallenato Legend Festival. The festival holds contests for best accordion player. Once every decade, the "King of Kings" accordion competition takes place, where winners of the previous festivals compete for the highest possible award for a vallenato accordion player: the Pilonera Mayor prize.Smithsonian Channel, "The Accordion Kings", 15 August 2010. This is the world's largest competitive accordion festival. Czech Republic thumb|At U Flekú, Prague Accordion is often played at traditional Czech pubs, such as U Flekú, Prague. Mexico thumb|right|200px|A Norteño band, including an accordion Norteño heavily relies on the accordion; it is a genre related to polka. Ramón Ayala, known in Mexico as the "King of the Accordion", is a norteño musician. Cumbia, which features the accordion, is also popular with musicians such as Celso Piña, creating a more contemporary style. U.S.-born Mexican musician Julieta Venegas incorporates the sound of the instrument into rock, pop and folk. She was influenced by her fellow Chicanos Los Lobos who also use the music of the accordion. North Korea According to Barbara Demick in Nothing to Envy, the accordion is known as "the people's instrument" and all North Korean teachers were expected to learn the accordion. United States Accordions are played in Tejano music, Cajun and Creole music, zydeco, klezmer, and polka. During the post-World War II era from the 1940s to the 1960s, accordions were widely used in the United States for performances of traditional Western classical music within the configuration of large free-reed symphonic orchestras both in live performances on the concert hall stage and in phonograph recordings.Squeese This! A Cultural History of the Accordion in America. Jacobson, Marion. University of Illinois Press. 2012. p. 78-80 ebook ISBN 9780252093852Accordion Orchestra Joe Biviano on Google Books on Google BooksMusic Trades: "Accordion Orchestra Featured on New Coral 12" Record" Vol. 109, 1961 p. 84 John Serry on Google Books"Pietro Deiro Presents The Accordion Orchestra - Under Direction of Joe Biviano" Coral Records (CRL-57323, 1960) See album cover for performers credits including John Serry, Eugene Ettore, Carmen Carrozza and Angelo Di Pippo. Pietro Deiro on Discogs.comPietro The Billboard- Reviews and Ratings of New albums: "Pietro Deiro Presents The Accordion Orchestra" (Coral, CRL-57323), 27 June 1960 p. 33 Pietro Deiro Presents the Accordion Orchestra on Google BooksThe Coral Album Discography. Edwards, David. Callahan, Mike. Eyrles, Patrice. Watts, Randy. Neely, Timothy. April 27, 2014. CRL-57323 "Pietro Deiro Presents the Accordion Orchestra" (1960) on bsnpubs.com Included among the leading accordion orchestras were: The New York Accordion Symphony in New York City, The Springfield Accordion Orchestra in Massachusetts, The Houston Accordion Symphony in Houston, Texas and The Philadelphia Accordion Orchestra in Philadelphia, Pennsylvania.Squeeze This! A Cultural History of the Accordion in America. Jacobson, Marion. University of Illinois Press. 2012, pp. 78-79 ebook ISBN 978-0-252-09385-2 Accordion Orchestra on Google Books Prominent orchestra members included: Joe Biviano (President of the American Accordionists Association)"American Accordionists Association: Joe Biviano and Charles Magnante - A Lifetime Commitment to the American Accordionists Association". Joe Biviano BBiography on ameraccord.com"Squeeze This! A Cultural History of the Accordion in the United States". Jacobson, Marion. University of Illinois Press 2012 p. 93 ISBN 9780252093852 Joe Biviano on google books Carmen Carrozza,"Squeeze This! A Cultural History of the Accordion in the United States". Jacobson, Marion. University of Illinois Press 2012 p. 85 ISBN 9780252093852 Carmen Carrozza on google books"Accordion World" Gerstner Publications 1957 Vol. 22 p. 55 Pietro Deiro and Carmen Carrozza on Google Books Orlando Di Girolamo (President of the American Symphony Society),"Music Trades", Music Trades Corporation, "Accordion Orchestra Featured On New Coral 12" Record" 1961 Vol. 109, p. 84 Orlando Girolamo on Google books( Tony Mecca (who collaborated with Leonard Bernstein),"Accordion Journal" Vol 12-13 p. 21 Tony Mecca on Google Books"The Accordion in all its Guises". Miller, Malcom. Harwood Academic Publishers 2010 p. 88 Tony Mecca on google books Angelo Di Pippo (jazz accordionist and arranger for Robert Merrill),"The Biographical Encyclopedia of Jazz" Feather, Leonard. Gitler, Ira. Ed. Oxford University Prress 2007 p. 181 ISBN 9780195074185 Angelo Di Pippo Biography on Google Books John Serry Sr."Music Trades", Music Trades Corporation, "Accordion Orchestra Featured On New Coral 12" Record" 1961 Vol. 109, p. 84 John Serry on Google booksInternational Musician – "Accordion Instrument Played with A Smile", Hope Stoddard, May 1951, pp. 10-11 Contributing authors: Charles Nunzio and Sergei Matsusewitch – Photographs of John Serry, Joe Biviano and Anthony Mecca in the article published by the American Federation of Musicians' magazine "International Musician" on worldradiohistory.com and Alfonso Veltri (Director of the National Conservatory of Music)."Accordion Journal - Pietro Deiro Presents the Accordion Orchestra - under the Direction of Joe Biviano" Vol 12-13 p. 21 Alfonso Veltri on Google Books By the 1960s recordings by such orchestras were even praised for their high level of musicality in The Billboard'' magazine.Pietro The Billboard- Reviews and Ratings of New albums: "Pietro Deiro Presents The Accordion Orchestra" (Coral, CRL-57323), 27 June 1960 p. 33 "Pietro Deiro Presents the Accordion Orchestra" album review on Google BooksAmerican Music - "Searching for the Rockordion: The Changing Image of the Accordion in America". Jacobson, Marion S. University of Illinois Press Vol. 25, No. 2 (Sommer 2007) pp.216-247, See pp. 220-225 on https://www.jstor.org/stable/40071656
Accordion
Other audio samples
Other audio samples
Accordion
See also
See also List of accordionists Steirische Harmonika Confédération internationale des accordéonistes
Accordion
Notes
Notes
Accordion
References
References
Accordion
External links
External links Category:Folk music instruments Category:Articles containing video clips Category:German inventions Category:19th-century inventions Category:Symbols of Rio Grande do Sul
Accordion
Table of Content
short description, History, Other audio samples, See also, Notes, References, External links
Artificial intelligence
Short description
Artificial intelligence (AI) refers to the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."AI set to exceed human brain power CNN.com (July 26, 2006) Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, perception, and support for robotics. To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.. Some AI companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI)—AI that can complete virtually any cognitive task at least as well as humans. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest vastly increased after 2012 when graphics processing units started being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, the period of rapid progress marked by advanced generative AI became known as the AI boom. Generative AI and its ability to create and modify content exposed several unintended consequences and harms in the present and raised ethical concerns about AI's long-term effects and potential existential risks, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.
Artificial intelligence
Goals
Goals The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.
Artificial intelligence
Reasoning and problem-solving
Reasoning and problem-solving Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.Problem-solving, puzzle solving, game playing, and deduction: , (constraint satisfaction), , , By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.Uncertain reasoning: , , , Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.Intractability and efficiency and the combinatorial explosion: Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: , , , Accurate and efficient reasoning is an unsolved problem.
Artificial intelligence
Knowledge representation
Knowledge representation thumb|upright=1.2|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts. Knowledge representation and knowledge engineeringKnowledge representation and knowledge engineering: , , , allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas. A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;Representing categories and relations: Semantic networks, description logics, inheritance (including frames, and scripts): , , , situations, events, states, and time;Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): , , causes and effects;Causal calculus: knowledge about knowledge (what we know about what other people know);Representing knowledge about knowledge: Belief calculus, modal logics: , default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction: , , , (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"). and many other aspects and domains of knowledge. Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);Breadth of commonsense knowledge: , , , (qualification problem) and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.
Artificial intelligence
Planning and decision-making
Planning and decision-making An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal.Automated planning: . In automated decision-making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.Automated decision making, Decision theory: . In classical planning, the agent knows exactly what the effect of any action will be.Classical planning: . In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning): . In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences.Uncertain preferences: Inverse reinforcement learning: Information value theory can be used to weigh the value of exploratory or experimental actions.Information value theory: . The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.Markov decision process: . Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.Game theory and multi-agent decision theory: .
Artificial intelligence
Learning
Learning Machine learning is the study of programs that can improve their performance on a given task automatically.Learning: , , , It has been a part of AI from the beginning. right|upright=1.4|frameless There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.Unsupervised learning: (definition), (cluster analysis), (word embedding) Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).Supervised learning: (Definition), (Techniques) In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".Reinforcement learning: , Transfer learning is when the knowledge gained from one problem is applied to a new problem.Transfer learning: , Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.Computational learning theory: ,
Artificial intelligence
Natural language processing
Natural language processing Natural language processing (NLP)Natural language processing (NLP): , , allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.Subproblems of NLP: Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure. Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others.Modern statistical and deep learning approaches to NLP: , In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.
Artificial intelligence
Perception
Perception Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.Computer vision: , The field includes speech recognition, image classification, facial recognition, object recognition,object tracking, and robotic perception.
Artificial intelligence
Social intelligence
Social intelligence thumb|Kismet, a robot head which was made in the 1990s; it is a machine that can recognize and simulate emotions. Affective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood.Affective computing: , , , For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.
Artificial intelligence
General intelligence
General intelligence A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.Artificial general intelligence: Proposal for the modern version: Warnings of overspecialization in AI from leading researchers: , ,
Artificial intelligence
Techniques
Techniques AI research uses a wide variety of techniques to accomplish the goals above.
Artificial intelligence
Search and optimization
Search and optimization AI can solve many problems by intelligently searching through many possible solutions.Search algorithms: , , , There are two very different kinds of search used in AI: state space search and local search.
Artificial intelligence
State space search
State space search State space search searches through a tree of possible states to try to find a goal state.State space search: For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Simple exhaustive searchesUninformed searches (breadth first search, depth-first search and general state space search): , , , 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. "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.Heuristic or informed searches (e.g., greedy best first and A*): , , , Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position.Adversarial search:
Artificial intelligence
Local search
Local search class=skin-invert-image|thumb|Illustration of gradient descent for 3 different starting points; two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height) Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.Local or "optimization" search: Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks, through the backpropagation algorithm. Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.Evolutionary computation: 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).
Artificial intelligence
Logic
Logic Formal logic is used for reasoning and knowledge representation.Logic: , , Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies")Propositional logic: , , and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").First-order logic and features such as equality: , , , Deductive reasoning in logic is the process of proving a new statement (conclusion) from other statements that are given and assumed to be true (the premises).Logical inference: Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules. Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem.logical deduction as search: , , , In the more general case of the clausal form of first-order logic, resolution is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.Resolution and unification: Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic programming languages. Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.Fuzzy logic: , Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning. Other specialized versions of logic have been developed to describe many complex domains.
Artificial intelligence
Probabilistic methods for uncertain reasoning
Probabilistic methods for uncertain reasoning class=skin-invert-image|thumb|upright=1.7|A simple Bayesian network, with the associated conditional probability tables Many problems in AI (including reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.Stochastic methods for uncertain reasoning: , , , Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,decision theory and decision analysis: , and information value theory.Information value theory: These tools include models such as Markov decision processes,Markov decision processes and dynamic decision networks: dynamic decision networks, game theory and mechanism design.Game theory and mechanism design: Bayesian networksBayesian networks: , , , are a tool that can be used for reasoning (using the Bayesian inference algorithm),Bayesian inference algorithm: , , , learning (using the expectation–maximization algorithm),Bayesian learning and the expectation–maximization algorithm: , , , planning (using decision networks)Bayesian decision theory and Bayesian decision networks: and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).Stochastic temporal models: Hidden Markov model: Kalman filters: Dynamic Bayesian networks: thumb|upright=1.2|Expectation–maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.
Artificial intelligence
Classifiers and statistical learning methods
Classifiers and statistical learning methods The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. ClassifiersStatistical learning methods and classifiers: , are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. 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. There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.Decision trees: , K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.Non-parameteric learning models such as K-nearest neighbor and support vector machines: , (k-nearest neighbor) (kernel methods) The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability.Naive Bayes classifier: , Neural networks are also used as classifiers.
Artificial intelligence
Artificial neural networks
Artificial neural networks right|thumb|A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.Neural networks: , Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.Gradient calculation in computational graphs, backpropagation, automatic differentiation: , , Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.Universal approximation theorem: The theorem: , In feedforward neural networks the signal passes in only one direction.Feedforward neural networks: Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.Recurrent neural networks: PerceptronsPerceptrons: use only a single layer of neurons; deep learning uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other—this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.Convolutional neural networks:
Artificial intelligence
Deep learning
Deep learning thumb|upright Deep learningDeep learning: , , , uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces. Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, and others. The reason that deep learning performs so well in so many applications is not known as of 2021. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.
Artificial intelligence
GPT
GPT Generative pre-trained transformers (GPT) are large language models (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pre-trained on a large corpus of text that can be from the Internet. The pretraining consists of predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pretraining, GPT models accumulate knowledge about the world and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful, and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are prone to generating falsehoods called "hallucinations". These can be reduced with RLHF and quality data, but the problem has been getting worse for reasoning systems. Such systems are used in chatbots, which allow people to ask a question or request a task in simple text. Current models and services include Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot, and LLaMA. Multimodal GPT models can process different types of data (modalities) such as images, videos, sound, and text.
Artificial intelligence
Hardware and software
Hardware and software In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training. Specialized programming languages such as Prolog were used in early AI research, but general-purpose programming languages like Python have become predominant. The transistor density in integrated circuits has been observed to roughly double every 18 months—a trend known as Moore's law, named after the Intel co-founder Gordon Moore, who first identified it. Improvements in GPUs have been even faster, a trend sometimes called Huang's law, named after Nvidia co-founder and CEO Jensen Huang.
Artificial intelligence
Applications
Applications AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's FaceID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's Photos and TikTok). The deployment of AI may be overseen by a Chief automation officer (CAO).
Artificial intelligence
Health and medicine
Health and medicine The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients. For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research. New AI tools can deepen the understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria. In 2024, researchers used machine learning to accelerate the search for Parkinson's disease drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of alpha-synuclein (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.
Artificial intelligence
Games
Games Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then, in 2017, it defeated Ke Jie, who was the best Go player in the world. Other programs handle imperfect-information games, such as the poker-playing program Pluribus. DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021, an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning. In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen open-world video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.
Artificial intelligence
Mathematics
Mathematics Large language models, such as GPT-4, Gemini, Claude, LLaMa or Mistral, are increasingly used in mathematics. These probabilistic models are versatile, but can also produce wrong answers in the form of hallucinations. They sometimes need a large database of mathematical problems to learn from, but also methods such as supervised fine-tuning or trained classifiers with human-annotated data to improve answers for new problems and learn from corrections. A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data. One technique to improve their performance involves training the models to produce correct reasoning steps, rather than just the correct result. The Alibaba Group developed a version of its Qwen models called Qwen2-Math, that achieved state-of-the-art performance on several mathematical benchmarks, including 84% accuracy on the MATH dataset of competition mathematics problems. In January 2025, Microsoft proposed the technique rStar-Math that leverages Monte Carlo tree search and step-by-step reasoning, enabling a relatively small language model like Qwen-7B to solve 53% of the AIME 2024 and 90% of the MATH benchmark problems. Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as AlphaTensor, AlphaGeometry and AlphaProof all from Google DeepMind, Llemma from EleutherAI or Julius. When natural language is used to describe mathematical problems, converters can transform such prompts into a formal language such as Lean to define mathematical tasks. Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics. Topological deep learning integrates various topological approaches.
Artificial intelligence
Finance
Finance Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023 According to Nicolas Firzli, director of the World Pensions & Investments Forum, it may be too early to see the emergence of highly innovative AI-informed financial products and services. He argues that "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."M. Nicolas, J. Firzli: Pensions Age / European Pensions magazine, "Artificial Intelligence: Ask the Industry", May–June 2024. https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ .
Artificial intelligence
Military
Military Various countries are deploying AI military applications.PD-notice The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles, both human operated and autonomous. AI has been used in military operations in Iraq, Syria, Israel and Ukraine.
Artificial intelligence
Generative AI
Generative AI thumb|Vincent van Gogh in watercolour created by generative AI software
Artificial intelligence
Agents
Agents Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including virtual assistants, chatbots, autonomous vehicles, game-playing systems, and industrial robotics. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks.
Artificial intelligence
Sexuality
Sexuality Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction, AI-integrated sex toys (e.g., teledildonics), AI-generated sexual education content, and AI agents that simulate sexual and romantic partners (e.g., Replika). AI is also used for the production of non-consensual deepfake pornography, raising significant ethical and legal concerns. AI technologies have also been used to attempt to identify online gender-based violence and online sexual grooming of minors.
Artificial intelligence
Other industry-specific tasks
Other industry-specific tasks There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management. AI applications for evacuation and disaster management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions.. In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water. Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation. During the 2024 Indian elections, US$50 million was spent on authorized AI-generated content, notably by creating deepfakes of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.
Artificial intelligence
Ethics
Ethics AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended consequences and risks have been identified. In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.
Artificial intelligence
Risks and harm
Risks and harm
Artificial intelligence
Privacy and copyright
Privacy and copyright Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency. Sensitive user data collected may include online activity records, geolocation data, video, or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy. AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'." Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work". Website owners who do not wish to have their content scraped can indicate it in a "robots.txt" file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.
Artificial intelligence
Dominance by tech giants
Dominance by tech giants The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc., Amazon, Apple Inc., Meta Platforms, and Microsoft. Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace.
Artificial intelligence
Power needs and environmental impacts
Power needs and environmental impacts In January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026, forecasting electric power use. This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency. The report states that power demand for these uses might double by 2026, with additional electric power usage equal to electricity used by the whole Japanese nation. Prodigious power consumption by AI is responsible for the growth of fossil fuels use, and might delay closings of obsolete, carbon-emitting coal energy facilities. There is a feverish rise in the construction of data centers throughout the US, making large technology firms (e.g., Microsoft, Meta, Google, Amazon) into voracious consumers of electric power. Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. The large firms are in haste to find power sources – from nuclear energy to geothermal to fusion. The tech firms argue that – in the long view – AI will be eventually kinder to the environment, but they need the energy now. AI makes the power grid more efficient and "intelligent", will assist in the growth of nuclear power, and track overall carbon emissions, according to technology firms. A 2024 Goldman Sachs Research Paper, AI Data Centers and the Coming US Power Demand Surge, found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means. Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all. In 2024, the Wall Street Journal reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for $650 Million (US). Nvidia CEO Jen-Hsun Huang said nuclear power is a good option for the data centers. In September 2024, Microsoft announced an agreement with Constellation Energy to re-open the Three Mile Island nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at $1.6 billion (US) and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act. The US government and the state of Michigan are investing almost $2 billion (US) to reopen the Palisades Nuclear reactor on Lake Michigan. Closed since 2022, the plant is planned to be reopened in October 2025. The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane, a nuclear proponent and former CEO of Exelon who was responsible for Exelon spinoff of Constellation. After the last approval in September 2023, Taiwan suspended the approval of data centers north of Taoyuan with a capacity of more than 5 MW in 2024, due to power supply shortages. Taiwan aims to phase out nuclear power by 2025. On the other hand, Singapore imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban. Although most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident, according to an October 2024 Bloomberg article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near nuclear power plant for a new data center for generative AI. Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI. On 1 November 2024, the Federal Energy Regulatory Commission (FERC) rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna to Amazon's data center. According to the Commission Chairman Willie L. Phillips, it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors. In 2025 a report prepared by the International Energy Agency estimated the greenhouse gas emissions from the energy consumption of AI at 180 million tons. By 2035, these emissions could rise to 300-500 million tonnes depending on what measures will be taken. This is below 1.5% of the energy sector emissions. The emissions reduction potential of AI was estimated at 5% of the energy sector emissions, but rebound effects (for example if people will pass from public transport to autonomous cars) can reduce it.
Artificial intelligence
Misinformation
Misinformation YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took some steps to mitigate the problem. In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films, or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda. One such potential malicious use is deepfakes for computational propaganda. AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks. AI researchers at Microsoft, OpenAI, universities and other organisations have suggested using "personhood credentials" as a way to overcome online deception enabled by AI models.
Artificial intelligence
Algorithmic bias and fairness
Algorithmic bias and fairness Machine learning applications will be biased if they learn from biased data. The developers may not be aware that the bias exists. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination.; ; ; The field of fairness studies how to prevent harms from algorithmic biases. On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called "sample size disparity". Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon. COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.; A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender".; ; ; Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."Quoted in . Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist.; Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive. Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women. There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws. At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022), the Association for Computing Machinery, in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.
Artificial intelligence
Lack of transparency
Lack of transparency Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist. It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading. People who have been harmed by an algorithm's decision have a right to an explanation.; Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used. DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems. Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. LIME can locally approximate a model's outputs with a simpler, interpretable model. Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned. Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning. For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts.
Artificial intelligence
Bad actors and weaponized AI
Bad actors and weaponized AI Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian governments, terrorists, criminals or rogue states. A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision. Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially weapons of mass destruction. Even when used in conventional warfare, they currently cannot reliably choose targets and could potentially kill an innocent person. In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed. By 2015, over fifty countries were reported to be researching battlefield robots.; AI tools make it easier for authoritarian governments to efficiently control their citizens in several ways. Face and voice recognition allow widespread surveillance. Machine learning, operating this data, can classify potential enemies of the state and prevent them from hiding. Recommendation systems can precisely target propaganda and misinformation for maximum effect. Deepfakes and generative AI aid in producing misinformation. Advanced AI can make authoritarian centralized decision making more competitive than liberal and decentralized systems such as markets. It lowers the cost and difficulty of digital warfare and advanced spyware. All these technologies have been available since 2020 or earlier—AI facial recognition systems are already being used for mass surveillance in China. There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.
Artificial intelligence
Technological unemployment
Technological unemployment Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.E. McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022), 51(3) Industrial Law Journal 511–559. . In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.; A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".; ; The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.; From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.
Artificial intelligence
Existential risk
Existential risk It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways. First, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager). Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".; ; . Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are built on language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive. The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk,Leaders' concerns about the existential risks of AI around 2015: , , , as well as AI pioneers such as Yoshua Bengio, Stuart Russell, Demis Hassabis, and Sam Altman, have expressed concerns about existential risk from AI. In May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to "freely speak out about the risks of AI" without "considering how this impacts Google". He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI. In 2023, many leading AI experts endorsed the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". Some other researchers were more optimistic. AI pioneer Jürgen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier." While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors." Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests." Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction." In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine.Arguments that AI is not an imminent risk: , , , However, after 2016, the study of current and future risks and possible solutions became a serious area of research.
Artificial intelligence
Ethical machines and alignment
Ethical machines and alignment Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk. Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. The field of machine ethics is also called computational morality, and was founded at an AAAI symposium in 2005. Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's three principles for developing provably beneficial machines.
Artificial intelligence
Open source
Open source Active organizations in the AI open-source community include Hugging Face, Google, EleutherAI and Meta. Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.
Artificial intelligence
Frameworks
Frameworks Artificial Intelligence projects can be guided by ethical considerations during the design, development, and implementation of an AI system. An AI framework such as the Care and Act Framework, developed by the Alan Turing Institute and based on the SUM values, outlines four main ethical dimensions, defined as follows: Respect the dignity of individual people Connect with other people sincerely, openly, and inclusively Care for the wellbeing of everyone Protect social values, justice, and the public interest Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others; however, these principles are not without criticism, especially regards to the people chosen to contribute to these frameworks. Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers. The UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under a MIT open-source licence which is freely available on GitHub and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities.
Artificial intelligence
Regulation
Regulation upright=1.2|thumb|alt=AI Safety Summit|The first global AI Safety Summit was held in the United Kingdom in November 2023 with a declaration calling for international cooperation. The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms.Regulation of AI to mitigate risks: , , , , The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics. In 2024, the Council of Europe created the first international legally binding treaty on AI, called the "Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law". It was adopted by the European Union, the United States, the United Kingdom, and other signatories. In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important". In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence. In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI.
Artificial intelligence
History
History thumb |In 2024, AI patents in China and the US numbered more than three-fourths of AI patents worldwide. Though China had more AI patents, the US had 35% more patents per AI patent-applicant company than China. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of 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 form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI,AI's immediate precursors: , , , such as McCullouch and Pitts design for "artificial neurons" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible. The field of AI research was founded at a workshop at Dartmouth College in 1956.Dartmouth workshop: , , The proposal: The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.Successful programs of the 1960s: , , , Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s. Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". quoted in In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". quoted in They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The "AI winter", a period when obtaining funding for AI projects was difficult, followed.First AI Winter, Lighthill report, Mansfield Amendment: , , , , In the early 1980s, AI research was revived by the commercial success of expert systems,Expert systems: , , , , , 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.Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): , , , , However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.Second AI Winter: , , , , Up to this point, most of AI's funding had gone to projects that used high-level symbols to represent mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition, and began to look into "sub-symbolic" approaches. Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lofti Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others., In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks. AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).Formal and narrow methods adopted in the 1990s: , By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as the AI effect).AI widely used in the late 1990s: , , However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s. Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.Deep learning revolution, AlexNet: , , For many specific tasks, other methods were abandoned. Deep learning's success was based on both hardware improvements (faster computers,Moore's Law and AI: graphics processing units, cloud computing) and access to large amounts of dataBig data: (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019. thumb|The number of Google searches for the term "AI" accelerated in 2022. In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study. In the late 2010s and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program taught only the game's rules and developed a strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. ChatGPT, launched on November 30, 2022, became the fastest-growing consumer software application in history, gaining over 100 million users in two months. It marked what is widely regarded as AI's breakout year, bringing it into the public consciousness. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions of dollars in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI". About 800,000 "AI"-related U.S. job openings existed in 2022. According to PitchBook research, 22% of newly funded startups in 2024 claimed to be AI companies.
Artificial intelligence
Philosophy
Philosophy Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epistemology and free will. Rapid advancements have intensified public discussions on the philosophy and ethics of AI.
Artificial intelligence
Defining artificial intelligence
Defining artificial intelligence Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation.Turing's original publication of the Turing test in "Computing machinery and intelligence": Historical influence and philosophical implications: , , , Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks." thumb|The Turing test can provide some evidence of intelligence, but it penalizes non-human intelligent behavior. Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineering texts", they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence". McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world". Another AI founder, Marvin Minsky, similarly describes it as "the ability to solve hard problems". The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals. These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible. Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence. Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI, with many companies during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not actually use AI in a material way".