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left-hand components

  • 1 левые составляющие уравнения

    Combustion gas turbines: left-hand components

    Универсальный русско-английский словарь > левые составляющие уравнения

  • 2 ajustar

    v.
    2 to adjust.
    Silvia ajustó el plan adecuadamente Silvia adjusted the plan adequately.
    Silvia ajustó las tuercas de la caldera Silvia adjusted the boiler nuts.
    3 to tighten.
    ajusta bien la tapa screw the lid on tight
    4 to arrange (pactar) (matrimonio).
    5 to fit properly, to be a good fit (venir justo).
    la ventana no ajusta bien the window won't close properly
    6 to control, to set.
    Mario ajustó la temperatura Mario controlled the temperature.
    7 to hire.
    * * *
    1 (adaptar) to adjust, regulate
    2 (apretar) to tighten
    3 (encajar) to fit, fit tight
    4 (acordar) to fix, agree on, set
    1 to fit
    1 (ceñirse) to fit
    2 (ponerse de acuerdo) to come to an agreement; (estar de acuerdo) to agree with, fit in with
    \
    ajustar cuentas COMERCIO to settle up 2 figurado to settle a score
    ajustarse el cinturón to tighten one's belt
    * * *
    verb
    2) fit, tighten
    •
    * * *
    1. VT
    1) (Téc)
    a) [+ pieza, grifo] [colocando] to fit; [apretando] to tighten

    ¿cómo se ajusta la baca al vehículo? — how does the roof rack fit onto the vehicle?

    b) (=regular) [+ volumen, temperatura] to adjust, regulate; [+ asiento, retrovisor] to adjust; [+ cinturón] to tighten
    c) Chile, Méx [+ motor] to fix
    2) (=pactar) [+ acuerdo, trato] to reach; [+ boda] to arrange; [+ precio] to agree on

    • ajustar cuentas con algn — (lit) to settle accounts with sb; (fig) to settle one's scores with sb

    3) (=adaptar) to adjust (a to)
    4) euf (=reducir)
    5) (Cos) [+ cintura, manga] to take in
    6) (Tip) to compose
    7) † [+ criado] to hire, engage
    8) CAm, Méx, Chile, Ven
    2. VI
    1) (=encajar) to fit
    2) Ven (=agudizarse) to get worse

    por el camino ajustó el aguacero — on the way, there was a sudden downpour

    3.
    See:
    * * *
    1.
    verbo transitivo
    1)
    a) ( apretar) to tighten (up)
    b) ( regular) to adjust
    c) <retrovisor/asiento/cinturón de seguridad> to adjust
    d) ( encajar) < piezas> to fit
    2) ( en costura) to take in
    3)
    a) <gastos/horarios>
    b) <sueldo/jubilación> to adjust
    4) <precio/alquiler/sueldo> to fix, set
    5) < cuentas> ( sacar el resultado de) to balance; ( saldar) to settle
    2.
    ajustar vi to fit
    3.
    ajustarse v pron
    1) (refl) < cinturón de seguridad> to adjust
    2) piezas to fit
    3) (ceñirse, atenerse)

    ajustarse a algo: su declaración no se ajusta a la verdad his statement is not strictly true; deberá ajustarse a estas condiciones it will have to comply with these conditions; una sentencia que no se ajusta a derecho — a legally flawed verdict

    * * *
    = adjust, calibrate, twiddle, scale, tweak, fine tune [fine-tune], muck around/about, align.
    Ex. The brightness can be adjusted by turning the two knobs at the lower right of the screen.
    Ex. The maps are calibrated to show fine distinctions within Geauga = Se calibran los mapas para mostrar pequeñas distinciones dentro de la región de Geauga.
    Ex. Meek took her glasses off and twiddled them as her supervisor related the following incident.
    Ex. To produce a statewide estimate, this framework would need to be scaled to accommodate all public libraries in a particular state.
    Ex. This book offers strategies for high school teachers that provide tools for creating, repairing, and tweaking all the discernible components of teaching.
    Ex. These statistics have been used to fine tune the system and improve response time = Se han usado estos resultados estadísticos para ajustar el funcionamiento del sistema y mejorar el tiempo de respuesta.
    Ex. I have looked at the book and mucked around with the database and using switches but can't see a solution.
    Ex. Entry words may be aligned in a centre column or in a left hand column.
    ----
    * ajustar Algo a = bring + Nombre + into compliance with.
    * ajustar cuentas = settle + a score, settle + things, get + even.
    * ajustar la exposición = adjust + exposure.
    * ajustarse = suit, fit + snugly, suit + best.
    * ajustarse a = conform to, befit, align.
    * ajustarse a exigencias = suit + demands.
    * ajustarse el cinturón = tighten + Posesivo + belt, gird (up) + Posesivo + loins.
    * desajustar = throw out of + alignment.
    * que no ajusta bien = ill-fitting.
    * sin ajustar = unadjusted, loosely hanging, baggy [baggier -comp., baggiest -sup.], saggy [saggier -comp., saggiest -sup.].
    * * *
    1.
    verbo transitivo
    1)
    a) ( apretar) to tighten (up)
    b) ( regular) to adjust
    c) <retrovisor/asiento/cinturón de seguridad> to adjust
    d) ( encajar) < piezas> to fit
    2) ( en costura) to take in
    3)
    a) <gastos/horarios>
    b) <sueldo/jubilación> to adjust
    4) <precio/alquiler/sueldo> to fix, set
    5) < cuentas> ( sacar el resultado de) to balance; ( saldar) to settle
    2.
    ajustar vi to fit
    3.
    ajustarse v pron
    1) (refl) < cinturón de seguridad> to adjust
    2) piezas to fit
    3) (ceñirse, atenerse)

    ajustarse a algo: su declaración no se ajusta a la verdad his statement is not strictly true; deberá ajustarse a estas condiciones it will have to comply with these conditions; una sentencia que no se ajusta a derecho — a legally flawed verdict

    * * *
    = adjust, calibrate, twiddle, scale, tweak, fine tune [fine-tune], muck around/about, align.

    Ex: The brightness can be adjusted by turning the two knobs at the lower right of the screen.

    Ex: The maps are calibrated to show fine distinctions within Geauga = Se calibran los mapas para mostrar pequeñas distinciones dentro de la región de Geauga.
    Ex: Meek took her glasses off and twiddled them as her supervisor related the following incident.
    Ex: To produce a statewide estimate, this framework would need to be scaled to accommodate all public libraries in a particular state.
    Ex: This book offers strategies for high school teachers that provide tools for creating, repairing, and tweaking all the discernible components of teaching.
    Ex: These statistics have been used to fine tune the system and improve response time = Se han usado estos resultados estadísticos para ajustar el funcionamiento del sistema y mejorar el tiempo de respuesta.
    Ex: I have looked at the book and mucked around with the database and using switches but can't see a solution.
    Ex: Entry words may be aligned in a centre column or in a left hand column.
    * ajustar Algo a = bring + Nombre + into compliance with.
    * ajustar cuentas = settle + a score, settle + things, get + even.
    * ajustar la exposición = adjust + exposure.
    * ajustarse = suit, fit + snugly, suit + best.
    * ajustarse a = conform to, befit, align.
    * ajustarse a exigencias = suit + demands.
    * ajustarse el cinturón = tighten + Posesivo + belt, gird (up) + Posesivo + loins.
    * desajustar = throw out of + alignment.
    * que no ajusta bien = ill-fitting.
    * sin ajustar = unadjusted, loosely hanging, baggy [baggier -comp., baggiest -sup.], saggy [saggier -comp., saggiest -sup.].

    * * *
    ajustar [A1 ]
    vt
    A
    1 (apretar) ‹tornillo/freno› to tighten (up)
    2 (regular) ‹tornillo/dispositivo› to adjust
    ajustar la entrada de agua to regulate the flow of water
    3 ‹retrovisor/asiento/cinturón› to adjust
    4 (encajar) ‹piezas› to fit
    5 ‹página› to make up
    B (en costura) to take in
    C
    1 ‹gastos/horarios› ajustar algo A algo to adapt sth TO sth
    tenemos que ajustar los gastos a los ingresos we have to tailor our expenditure to our income
    2 ‹sueldo/jubilación› to adjust
    les ajustan el sueldo con la inflación their wages are adjusted in line with inflation
    D (acordar) ‹precio/alquiler/sueldo› to fix, set
    ajustaron el precio en 120 euros the price was fixed o set at 120 euros, they agreed on a price of 120 euros
    todavía falta ajustar el alquiler we still have to reach an agreement on o agree on o fix o set the rent
    E ‹cuentas›
    2 (saldar) to settle ver tb cuenta1 f E. (↑ cuenta (1))
    ■ ajustar
    vi
    to fit
    A ( refl) ‹cinturón› to adjust
    B (encajarse, alinearse) «piezas» to fit
    C (a una condición, un horario) ajustarse A algo:
    una distribución jerárquica que no se ajusta a las necesidades reales a hierarchical structure that does not meet real needs
    esta decisión no se ajusta a su política de apertura this decision is out of line with o not in keeping with their policy of openness
    tenemos que ajustarnos al horario we must keep to o work within the timetable
    ajustémonos al tema let's keep to the subject
    su declaración no se ajusta a la verdad his statement is not strictly true
    siempre tengo que ajustarme a sus caprichos I always have to go along with his whims
    deberá ajustarse a las condiciones aquí descritas it will have to comply with the conditions laid down here
    una sentencia que no se ajusta a derecho a verdict which is legally flawed o which is wrong in law
    * * *

     

    ajustar ( conjugate ajustar) verbo transitivo
    1

    b) ‹volumen/temperatura› to adjust;


    c) ‹retrovisor/asiento/cinturón de seguridad› to adjust

    d) ( encajar) ‹ piezas› to fit

    2 ( en costura) to take in
    3
    a) ‹gastos/horarios› ajustar algo a algo to adapt sth to sth

    b) ‹sueldos/precios› to adjust

    4 ( concertar) to fix, set
    5 ‹ cuentas› ( sacar el resultado de) to balance;
    ( saldar) to settle
    verbo intransitivo
    to fit
    ajustarse verbo pronominal
    1 ( refl) ‹ cinturón de seguridad› to adjust
    2 [ piezas] to fit
    ajustar verbo transitivo
    1 to adjust
    2 (apretar) to tighten
    (encajar) to fit
    3 Fin (cuenta) to settle
    ♦ Locuciones: figurado ¡ya te ajustaré las cuentas!, I'll get even with you!
    ' ajustar' also found in these entries:
    Spanish:
    adaptar
    - cuenta
    - regular
    - poner
    English:
    adjust
    - fit
    - score
    - square
    - work in
    - bone
    * * *
    ♦ vt
    1. [encajar] [piezas de motor] to fit;
    [puerta, ventana] to push to
    2. [arreglar] to adjust;
    el técnico ajustó la antena the engineer adjusted the aerial
    3. [apretar] to tighten;
    ajusta bien la tapa screw the lid on tight
    4. [poner en posición] [retrovisor, asiento] to adjust
    5. [pactar] [matrimonio] to arrange;
    [pleito] to settle; [paz] to negotiate; [precio] to fix, to agree;
    hemos ajustado la casa en cinco millones we have agreed a price of five million for the house
    6. [adaptar] to alter;
    el sastre ajustó el vestido the tailor altered the dress;
    tendrás que ajustar tus necesidades a las nuestras you'll have to adapt your needs to fit in with ours;
    tenemos que ajustar los gastos a los ingresos we shouldn't spend more than we earn;
    7. [asestar] to deal, to give
    8. Imprenta to make up
    9. [reconciliar] to reconcile
    10. [saldar] to settle;
    ajustar las cuentas a alguien to settle a score with sb;
    ¡la próxima vez que te vea ajustaremos cuentas! you'll pay for this the next time I see you!
    ♦ vi
    [venir justo] to fit properly, to be a good fit;
    la ventana no ajusta bien the window won't close properly
    * * *
    I v/t
    1 máquina etc adjust; tornillo tighten
    2 precio set;
    ajustar(le) las cuentas a alguien fig have a settling of accounts with s.o., settle accounts with s.o.
    II v/i fit
    * * *
    1) : to adjust, to adapt
    2) : to take in (clothing)
    3) : to settle, to resolve
    * * *
    1. (adaptar) to adjust
    2. (apretar) to tighten

    Spanish-English dictionary > ajustar

  • 3 Edison, Thomas Alva

    [br]
    b. 11 February 1847 Milan, Ohio, USA
    d. 18 October 1931 Glenmont
    [br]
    American inventor and pioneer electrical developer.
    [br]
    He was the son of Samuel Edison, who was in the timber business. His schooling was delayed due to scarlet fever until 1855, when he was 8½ years old, but he was an avid reader. By the age of 14 he had a job as a newsboy on the railway from Port Huron to Detroit, a distance of sixty-three miles (101 km). He worked a fourteen-hour day with a stopover of five hours, which he spent in the Detroit Free Library. He also sold sweets on the train and, later, fruit and vegetables, and was soon making a profit of $20 a week. He then started two stores in Port Huron and used a spare freight car as a laboratory. He added a hand-printing press to produce 400 copies weekly of The Grand Trunk Herald, most of which he compiled and edited himself. He set himself to learn telegraphy from the station agent at Mount Clements, whose son he had saved from being run over by a freight car.
    At the age of 16 he became a telegraphist at Port Huron. In 1863 he became railway telegraphist at the busy Stratford Junction of the Grand Trunk Railroad, arranging a clock with a notched wheel to give the hourly signal which was to prove that he was awake and at his post! He left hurriedly after failing to hold a train which was nearly involved in a head-on collision. He usually worked the night shift, allowing himself time for experiments during the day. His first invention was an arrangement of two Morse registers so that a high-speed input could be decoded at a slower speed. Moving from place to place he held many positions as a telegraphist. In Boston he invented an automatic vote recorder for Congress and patented it, but the idea was rejected. This was the first of a total of 1180 patents that he was to take out during his lifetime. After six years he resigned from the Western Union Company to devote all his time to invention, his next idea being an improved ticker-tape machine for stockbrokers. He developed a duplex telegraphy system, but this was turned down by the Western Union Company. He then moved to New York.
    Edison found accommodation in the battery room of Law's Gold Reporting Company, sleeping in the cellar, and there his repair of a broken transmitter marked him as someone of special talents. His superior soon resigned, and he was promoted with a salary of $300 a month. Western Union paid him $40,000 for the sole rights on future improvements on the duplex telegraph, and he moved to Ward Street, Newark, New Jersey, where he employed a gathering of specialist engineers. Within a year, he married one of his employees, Mary Stilwell, when she was only 16: a daughter, Marion, was born in 1872, and two sons, Thomas and William, in 1876 and 1879, respectively.
    He continued to work on the automatic telegraph, a device to send out messages faster than they could be tapped out by hand: that is, over fifty words per minute or so. An earlier machine by Alexander Bain worked at up to 400 words per minute, but was not good over long distances. Edison agreed to work on improving this feature of Bain's machine for the Automatic Telegraph Company (ATC) for $40,000. He improved it to a working speed of 500 words per minute and ran a test between Washington and New York. Hoping to sell their equipment to the Post Office in Britain, ATC sent Edison to England in 1873 to negotiate. A 500-word message was to be sent from Liverpool to London every half-hour for six hours, followed by tests on 2,200 miles (3,540 km) of cable at Greenwich. Only confused results were obtained due to induction in the cable, which lay coiled in a water tank. Edison returned to New York, where he worked on his quadruplex telegraph system, tests of which proved a success between New York and Albany in December 1874. Unfortunately, simultaneous negotiation with Western Union and ATC resulted in a lawsuit.
    Alexander Graham Bell was granted a patent for a telephone in March 1876 while Edison was still working on the same idea. His improvements allowed the device to operate over a distance of hundreds of miles instead of only a few miles. Tests were carried out over the 106 miles (170 km) between New York and Philadelphia. Edison applied for a patent on the carbon-button transmitter in April 1877, Western Union agreeing to pay him $6,000 a year for the seventeen-year duration of the patent. In these years he was also working on the development of the electric lamp and on a duplicating machine which would make up to 3,000 copies from a stencil. In 1876–7 he moved from Newark to Menlo Park, twenty-four miles (39 km) from New York on the Pennsylvania Railway, near Elizabeth. He had bought a house there around which he built the premises that would become his "inventions factory". It was there that he began the use of his 200- page pocket notebooks, each of which lasted him about two weeks, so prolific were his ideas. When he died he left 3,400 of them filled with notes and sketches.
    Late in 1877 he applied for a patent for a phonograph which was granted on 19 February 1878, and by the end of the year he had formed a company to manufacture this totally new product. At the time, Edison saw the device primarily as a business aid rather than for entertainment, rather as a dictating machine. In August 1878 he was granted a British patent. In July 1878 he tried to measure the heat from the solar corona at a solar eclipse viewed from Rawlins, Wyoming, but his "tasimeter" was too sensitive.
    Probably his greatest achievement was "The Subdivision of the Electric Light" or the "glow bulb". He tried many materials for the filament before settling on carbon. He gave a demonstration of electric light by lighting up Menlo Park and inviting the public. Edison was, of course, faced with the problem of inventing and producing all the ancillaries which go to make up the electrical system of generation and distribution-meters, fuses, insulation, switches, cabling—even generators had to be designed and built; everything was new. He started a number of manufacturing companies to produce the various components needed.
    In 1881 he built the world's largest generator, which weighed 27 tons, to light 1,200 lamps at the Paris Exhibition. It was later moved to England to be used in the world's first central power station with steam engine drive at Holborn Viaduct, London. In September 1882 he started up his Pearl Street Generating Station in New York, which led to a worldwide increase in the application of electric power, particularly for lighting. At the same time as these developments, he built a 1,300yd (1,190m) electric railway at Menlo Park.
    On 9 August 1884 his wife died of typhoid. Using his telegraphic skills, he proposed to 19-year-old Mina Miller in Morse code while in the company of others on a train. He married her in February 1885 before buying a new house and estate at West Orange, New Jersey, building a new laboratory not far away in the Orange Valley.
    Edison used direct current which was limited to around 250 volts. Alternating current was largely developed by George Westinghouse and Nicola Tesla, using transformers to step up the current to a higher voltage for long-distance transmission. The use of AC gradually overtook the Edison DC system.
    In autumn 1888 he patented a form of cinephotography, the kinetoscope, obtaining film-stock from George Eastman. In 1893 he set up the first film studio, which was pivoted so as to catch the sun, with a hinged roof which could be raised. In 1894 kinetoscope parlours with "peep shows" were starting up in cities all over America. Competition came from the Latham Brothers with a screen-projection machine, which Edison answered with his "Vitascope", shown in New York in 1896. This showed pictures with accompanying sound, but there was some difficulty with synchronization. Edison also experimented with captions at this early date.
    In 1880 he filed a patent for a magnetic ore separator, the first of nearly sixty. He bought up deposits of low-grade iron ore which had been developed in the north of New Jersey. The process was a commercial success until the discovery of iron-rich ore in Minnesota rendered it uneconomic and uncompetitive. In 1898 cement rock was discovered in New Village, west of West Orange. Edison bought the land and started cement manufacture, using kilns twice the normal length and using half as much fuel to heat them as the normal type of kiln. In 1893 he met Henry Ford, who was building his second car, at an Edison convention. This started him on the development of a battery for an electric car on which he made over 9,000 experiments. In 1903 he sold his patent for wireless telegraphy "for a song" to Guglielmo Marconi.
    In 1910 Edison designed a prefabricated concrete house. In December 1914 fire destroyed three-quarters of the West Orange plant, but it was at once rebuilt, and with the threat of war Edison started to set up his own plants for making all the chemicals that he had previously been buying from Europe, such as carbolic acid, phenol, benzol, aniline dyes, etc. He was appointed President of the Navy Consulting Board, for whom, he said, he made some forty-five inventions, "but they were pigeonholed, every one of them". Thus did Edison find that the Navy did not take kindly to civilian interference.
    In 1927 he started the Edison Botanic Research Company, founded with similar investment from Ford and Firestone with the object of finding a substitute for overseas-produced rubber. In the first year he tested no fewer than 3,327 possible plants, in the second year, over 1,400, eventually developing a variety of Golden Rod which grew to 14 ft (4.3 m) in height. However, all this effort and money was wasted, due to the discovery of synthetic rubber.
    In October 1929 he was present at Henry Ford's opening of his Dearborn Museum to celebrate the fiftieth anniversary of the incandescent lamp, including a replica of the Menlo Park laboratory. He was awarded the Congressional Gold Medal and was elected to the American Academy of Sciences. He died in 1931 at his home, Glenmont; throughout the USA, lights were dimmed temporarily on the day of his funeral.
    [br]
    Principal Honours and Distinctions
    Member of the American Academy of Sciences. Congressional Gold Medal.
    Further Reading
    M.Josephson, 1951, Edison, Eyre \& Spottiswode.
    R.W.Clark, 1977, Edison, the Man who Made the Future, Macdonald \& Jane.
    IMcN

    Biographical history of technology > Edison, Thomas Alva

  • 4 Artificial Intelligence

       In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)
       Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)
       Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....
       When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)
       4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually
       Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)
       Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)
       What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)
       [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)
       The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)
       9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form
       The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)
       There are many different kinds of reasoning one might imagine:
        Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."
        Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)
       Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)
       Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)
       The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)
        14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation
       It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)
       We might distinguish among four kinds of AI.
       Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.
       Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.
    ... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)
       Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)
        16) Determination of Relevance of Rules in Particular Contexts
       Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)
       Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)
        18) The Assumption That the Mind Is a Formal System
       Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)
        19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence
       The primary goal of Artificial Intelligence is to make machines smarter.
       The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)
       The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....
       AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)
        21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions
       In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)
       Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)
       Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)
       The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

    Historical dictionary of quotations in cognitive science > Artificial Intelligence

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