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basic+activities

  • 21 учитель физкультуры

    образ. physical education teacher (PE teacher)

    Now for Physical Education Teachers we offer a four-year degree in education. This degree course is designed for preparing students to teach in primary and secondary Schools and needs no prior qualifications as it is entered directly by school leavers.


    Physical Education or PE Teachers instruct young students in how to exercise, play sport, and do other recreational activities correctly and safely. PE teachers help the development of co-ordination, balance, posture, and flexibility with things like simple catching and throwing skills. They are not expected to be experts in all sports, but must be able to show students the basic techniques involved in a wide range of activities.

    Дополнительный универсальный русско-английский словарь > учитель физкультуры

  • 22 Cayley, Sir George

    SUBJECT AREA: Aerospace
    [br]
    b. 27 December 1773 Scarborough, England
    d. 15 December 1857 Brompton Hall, Yorkshire, England
    [br]
    English pioneer who laid down the basic principles of the aeroplane in 1799 and built a manned glider in 1853.
    [br]
    Cayley was born into a well-to-do Yorkshire family living at Brompton Hall. He was encouraged to study mathematics, navigation and mechanics, particularly by his mother. In 1792 he succeeded to the baronetcy and took over the daunting task of revitalizing the run-down family estate.
    The first aeronautical device made by Cayley was a copy of the toy helicopter invented by the Frenchmen Launoy and Bienvenu in 1784. Cayley's version, made in 1796, convinced him that a machine could "rise in the air by mechanical means", as he later wrote. He studied the aerodynamics of flight and broke away from the unsuccessful ornithopters of his predecessors. In 1799 he scratched two sketches on a silver disc: one side of the disc showed the aerodynamic force on a wing resolved into lift and drag, and on the other side he illustrated his idea for a fixed-wing aeroplane; this disc is preserved in the Science Museum in London. In 1804 he tested a small wing on the end of a whirling arm to measure its lifting power. This led to the world's first model glider, which consisted of a simple kite (the wing) mounted on a pole with an adjustable cruciform tail. A full-size glider followed in 1809 and this flew successfully unmanned. By 1809 Cayley had also investigated the lifting properties of cambered wings and produced a low-drag aerofoil section. His aim was to produce a powered aeroplane, but no suitable engines were available. Steam-engines were too heavy, but he experimented with a gunpowder motor and invented the hot-air engine in 1807. He published details of some of his aeronautical researches in 1809–10 and in 1816 he wrote a paper on airships. Then for a period of some twenty-five years he was so busy with other activities that he largely neglected his aeronautical researches. It was not until 1843, at the age of 70, that he really had time to pursue his quest for flight. The Mechanics' Magazine of 8 April 1843 published drawings of "Sir George Cayley's Aerial Carriage", which consisted of a helicopter design with four circular lifting rotors—which could be adjusted to become wings—and two pusher propellers. In 1849 he built a full-size triplane glider which lifted a boy off the ground for a brief hop. Then in 1852 he proposed a monoplane glider which could be launched from a balloon. Late in 1853 Cayley built his "new flyer", another monoplane glider, which carried his coachman as a reluctant passenger across a dale at Brompton, Cayley became involved in public affairs and was MP for Scarborough in 1832. He also took a leading part in local scientific activities and was co-founder of the British Association for the Advancement of Science in 1831 and of the Regent Street Polytechnic Institution in 1838.
    [br]
    Bibliography
    Cayley wrote a number of articles and papers, the most significant being "On aerial navigation", Nicholson's Journal of Natural Philosophy (November 1809—March 1810) (published in three numbers); and two further papers with the same title in Philosophical Magazine (1816 and 1817) (both describe semi-rigid airships).
    Further Reading
    L.Pritchard, 1961, Sir George Cayley, London (the standard work on the life of Cayley).
    C.H.Gibbs-Smith, 1962, Sir George Cayley's Aeronautics 1796–1855, London (covers his aeronautical achievements in more detail).
    —1974, "Sir George Cayley, father of aerial navigation (1773–1857)", Aeronautical Journal (Royal Aeronautical Society) (April) (an updating paper).
    JDS

    Biographical history of technology > Cayley, Sir George

  • 23 Базовая эталонная модель взаи

    General subject: The Basic Reference Model of Open Systems Interconnection (OSI), ISO/IEC 7498, provides a description of the activities necessary for systems to interwork using communication media (ISO/IEC 7498-4)

    Универсальный русско-английский словарь > Базовая эталонная модель взаи

  • 24 Ориентировочная основа действия

    Универсальный русско-английский словарь > Ориентировочная основа действия

  • 25 основные мероприятия

    2) Marketology: Key Activities

    Универсальный русско-английский словарь > основные мероприятия

  • 26 Kern

    Kern m 1. COMP core; 2. GEN core, gist (eines Problem); 3. IND (AE) center, (BE) centre; 4. PAT gist
    * * *
    m 1. < Comp> core; 2. < Geschäft> eines Problem core, gist; 3. < Ind> center (AE), centre (BE) ; 4. < Patent> gist
    * * *
    Kern
    (Basis) unit, nucleus, (Korn) kernel, (Mittelpunkt) core, essence, heart, (Ursache) root, (Wesentliches) [pith and] marrow;
    • Kern einer Angelegenheit crux (nucleus) of a matter;
    • Kern der Arbeitslosen hard core;
    • Kern der Beweisführung burden of an argument;
    • Kern einer Stadt heart (center) of a city, city core;
    • Kern eines Vertrages essence (root) of a contract;
    • Kern einer Sache bilden to be at the heart of s. th.;
    • zum Kern einer Sache kommen (vordringen, vorstoßen) to pierce beneath the show of a thing, to come to the crucial point;
    • am Kern einer Sache vorbeigehen to be beside the point;
    • Kernaktivität[en] core business;
    • Kernarbeitsgebiet core activities;
    • Kernaufgabe core task;
    • Kernbegriff central concept;
    • Kernbestandteil (Anzeige) running body;
    • Kerndaten für die Konjunkturprognose (Doktrin) anticipators of a business cycle;
    • Kernenergie nuclear energy;
    • Kernenergieanlage nuclear site;
    • Kernenergienutzung use of nuclear energy;
    • Kernfrage crucial problem, pivotal question, sticking point;
    • Kernfusion nuclear fusion;
    • Kerngebiet core area;
    • Kerngedanke key objective;
    • Kerngeschäft core business;
    • Kernindustrie nuclear industry;
    • Kernkapital (Bankbilanz) core capital, tier one (1);
    • Kernkompetenz key competency;
    • Kernländer core countries;
    • Kernprinzip basic principle;
    • Kernpunkt main issue, marrow, [crucial] point;
    • Kernreaktor atomic (chain) reactor;
    • Kernreaktoranlage nuclear power station (plant);
    • Kernreaktorgelände reactor site;
    • Kernspaltung nuclear fission;
    • Kernstück (Anzeige) bold type, (Ausstellung) centerpiece (US) (centre-piece, Br.);
    • Kernstücke eines Konzerns core companies of a group;
    • Kerntechnik nuclear engineering;
    • Kernumwandlung nuclear transmutation;
    • Kernwirtschaften core economies;
    • Kernzeit (gestaffelte Arbeitszeit) core time.

    Business german-english dictionary > Kern

  • 27 benéfico2

    2 = charitable, philanthropic.
    Ex. This article presents a basic overview of Ohio law on gambling as a guide to library foundations which are considering lotteries, raffles, and other charitable gambling activities as a means of raising money.
    Ex. This article stresses the importance of the cooperation of philanthropic organisations and the assistance of librarians, library associations and news media.
    ----
    * asociación benéfica = service club.
    * asociación benéfica de hombres de negocios = Lions club.
    * deducción por donación a obras benéficas = charitable deduction, charitable tax deduction.
    * fundación benéfica = charitable trust, aid agency, aid organisation, charitable institution, charitable organisation.
    * institución benéfica = charity, charitable organisation, charitable institution.
    * obra benéfica = charity.
    * organización benéfica = aid agency, aid organisation.
    * tienda benéfica = charity shop.

    Spanish-English dictionary > benéfico2

  • 28 benéfico

    adj.
    1 beneficial, benefic, auspicious.
    2 beneficent, charitable, philanthropic.
    * * *
    1 charitable
    \
    causa benéfica charitable cause, charity
    función benéfica charity performance
    * * *
    (f. - benéfica)
    adj.
    * * *
    ADJ
    1) (=acción, influencia) beneficial
    2) (=caritativo) charitable

    organización o sociedad benéfica — charity, charitable organization

    * * *
    - ca adjetivo < influencia> benign, beneficial; < espectáculo> charity (before n), benefit (before n)
    * * *
    - ca adjetivo < influencia> benign, beneficial; < espectáculo> charity (before n), benefit (before n)
    * * *
    benéfico1
    1 = beneficent, beneficial.

    Ex: Television has not been as culturally beneficent as film, but it has given rise to video artists like Nam June Paik.

    Ex: A high exhaustivity of indexing, then, is beneficial where a thorough search is required, but may be a handicap when only a few highly relevant documents are sought.

    benéfico2
    2 = charitable, philanthropic.

    Ex: This article presents a basic overview of Ohio law on gambling as a guide to library foundations which are considering lotteries, raffles, and other charitable gambling activities as a means of raising money.

    Ex: This article stresses the importance of the cooperation of philanthropic organisations and the assistance of librarians, library associations and news media.
    * asociación benéfica = service club.
    * asociación benéfica de hombres de negocios = Lions club.
    * deducción por donación a obras benéficas = charitable deduction, charitable tax deduction.
    * fundación benéfica = charitable trust, aid agency, aid organisation, charitable institution, charitable organisation.
    * institución benéfica = charity, charitable organisation, charitable institution.
    * obra benéfica = charity.
    * organización benéfica = aid agency, aid organisation.
    * tienda benéfica = charity shop.

    * * *
    1 ‹acción› beneficial; ‹influencia› benign, beneficial
    2 ‹espectáculo› charity ( before n), benefit ( before n)
    * * *

    benéfico
    ◊ -ca adjetivo ‹ influencia› benign, beneficial;


    ‹ espectáculo› charity ( before n), benefit ( before n)
    benéfico,-a adjetivo
    1 (caritativo) charitable
    2 (favorable) beneficial: el cambio de residencia resultó muy benéfico para su educación, the change of residence proved to be very beneficial for his education
    ' benéfico' also found in these entries:
    Spanish:
    benéfica
    English:
    charitable
    - jumble sale
    - rummage sale
    - charity
    * * *
    benéfico, -a adj
    1. [favorable] beneficial ( para to)
    2. [de caridad] charity;
    una entidad benéfica a charity, a charitable organization;
    un concierto benéfico a charity o benefit concert;
    un partido benéfico a charity o benefit match
    * * *
    adj charity atr ;
    función benéfica charity function o event;
    * * *
    benéfico, -ca adj
    : charitable, beneficent
    * * *
    benéfico adj charity

    Spanish-English dictionary > benéfico

  • 29 ОБРАЗОВАНИЕ

    @всеобуч compulsory secondary education @обязательное обучение compulsory education @дошкольные учреждения preschool facilities @ясли nursery, creche @детский сад kindergarten, day-care center @ученик pupil, high-school student @студент college student @аспирант graduate student @выпускник graduate @учитель high-school teacher @преподаватель teacher, instructor @ассистент instructor, teaching fellow @лаборант departmental/laboratory assistant @ректор university chancellor/provost @декан dean @профессор professor @доцент assistant professor (approximate equivalent) @научный сотрудник research associate/researcher @средняя школа high school @школа с продленным днем school with after-school activities program @интернат boarding school @техникум technical school, community college @ПТУ @профессионально-техническое училище vocational school @ВУЗ @высшее учебное заведение institute of higher learning/college/ university @институт institute @НИИ @научно-исследовательский институт scientific research instititute (research institute) @НИОКР научно-исследовательские и опытно-конструкторские работы R and D (research and development) @юридический институт law school @медицинский институт medical school @педагогический институт teacher's college @дневник record of marks @отличник A student @пятерка A @двойка D @балл point (on an exam) @зачет credit, pass for a course @сдавать экзамен to take an exam @сдать экзамен to pass an exam @сессия exam period @шпаргалка pony, trot @поступать в университет to apply to a university @поступить в университет to be admitted to a university @окончить университет to be graduated from a university @плата за обучение tuition @стипендия scholarship @аудитория classroom @посещать занятия to go to class, attend class @заочные курсы non-matriculated/correspondence courses @курсы повышения квалификации advanced course, refresher course @записаться на семинар take/enroll in/register for a seminar @обязательный предмет required course @факультативный предмет elective course @специальность major @кафедра department @завкафедрой department chairman @факультет
    филологический, философский
    division @дипломная работа senior thesis @курсовая работа term paper @аттестат зрелости high school diploma @диплом j. i diploma @научная степень academic degree @степень бакалавра B.A. @степень магистра M.A. @кандидатская степень Candidate; equivalent of American Ph.D. @докторская степень Doctorate; Russian highest graduate degree, higher than American Ph.D. @кандидатский минимум Ph.D. exams, comprehensives @диссертация dissertation, thesis
    Note: тезис does not mean dissertation. Тезисы доклада is the summary of a report, the main ideas. Тезис means a basic assumption, idea.
    @научный руководитель thesis adviser @оппонент discussant at dissertation summary @автореферат published dissertation summary @учеба без отрыва от производства part-time study @прогуливать to play hookey @записаться в библиотеку to get a library card @читательский билет library card @открытый доступ open stacks @

    Словарь переводчика-синхрониста (русско-английский) > ОБРАЗОВАНИЕ

  • 30 resource allocation

    Ops
    the process of assigning human and material resources to projects to ensure that they are used in the optimum way. Resource allocation is used in conjunction with network analysis techniques such as critical-path method. Basic data assembled for a project is displayed as a bar chart with start and finish times and resources required for each day of the project being easily identifiable. If there is a mismatch between planned resources and those available, resources can be reallocated or smoothed by manipulating start and finish times, or changing activities around. Resource allocation is usually computerized.

    The ultimate business dictionary > resource allocation

  • 31 search

    E-com
    the facility that enables visitors to a Web site to look for the information they want.
         Search is one of the most common activities that people perform on a Web site, and therefore needs to be prominently displayed—preferably on every page, near the top. There are essentially two approaches to Web site search: basic search, suitable for small Web sites of 50 pages or under, and advanced search, for larger Web sites, which allows a user to refine their search on the basis of various parameters.
         In either case, because search is an exclusively functional activity, the search results should be very clear and contain no distractions. Each set of results should include: the title of the Web page that it refers to, shown in bold type and hyperlinked to that page; a two-line summary describing the content on that page; the URL for the page, and its date of publication.

    The ultimate business dictionary > search

  • 32 Berliner, Emile

    SUBJECT AREA: Recording
    [br]
    b. 20 May 1851 Hannover, Germany
    d. 3 August 1929 Montreal, Canada
    [br]
    German (naturalized American) inventor, developer of the disc record and lateral mechanical replay.
    [br]
    After arriving in the USA in 1870 and becoming an American citizen, Berliner worked as a dry-goods clerk in Washington, DC, and for a period studied electricity at Cooper Union for the Advancement of Science and Art, New York. He invented an improved microphone and set up his own experimental laboratory in Washington, DC. He developed a microphone for telephone use and sold the rights to the Bell Telephone Company. Subsequently he was put in charge of their laboratory, remaining in that position for eight years. In 1881 Berliner, with his brothers Joseph and Jacob, founded the J.Berliner Telephonfabrik in Hanover, the first factory in Europe specializing in telephone equipment.
    Inspired by the development work performed by T.A. Edison and in the Volta Laboratory (see C.S. Tainter), he analysed the existing processes for recording and reproducing sound and in 1887 developed a process for transferring lateral undulations scratched in soot into an etched groove that would make a needle and diaphragm vibrate. Using what may be regarded as a combination of the Phonautograph of Léon Scott de Martinville and the photo-engraving suggested by Charles Cros, in May 1887 he thus demonstrated the practicability of the laterally recorded groove. He termed the apparatus "Gramophone". In November 1887 he applied the principle to a glass disc and obtained an inwardly spiralling, modulated groove in copper and zinc. In March 1888 he took the radical step of scratching the lateral vibrations directly onto a rotating zinc disc, the surface of which was protected, and the subsequent etching created the groove. Using well-known principles of printing-plate manufacture, he developed processes for duplication by making a negative mould from which positive copies could be pressed in a thermoplastic compound. Toy gramophones were manufactured in Germany from 1889 and from 1892–3 Berliner manufactured both records and gramophones in the USA. The gramophones were hand-cranked at first, but from 1896 were based on a new design by E.R. Johnson. In 1897–8 Berliner spread his activities to England and Germany, setting up a European pressing plant in the telephone factory in Hanover, and in 1899 a Canadian company was formed. Various court cases over patents removed Berliner from direct running of the reconstructed companies, but he retained a major economic interest in E.R. Johnson's Victor Talking Machine Company. In later years Berliner became interested in aeronautics, in particular the autogiro principle. Applied acoustics was a continued interest, and a tile for controlling the acoustics of large halls was successfully developed in the 1920s.
    [br]
    Bibliography
    16 May 1888, Journal of the Franklin Institute 125 (6) (Lecture of 16 May 1888) (Berliner's early appreciation of his own work).
    1914, Three Addresses, privately printed (a history of sound recording). US patent no. 372,786 (basic photo-engraving principle).
    US patent no. 382,790 (scratching and etching).
    US patent no. 534,543 (hand-cranked gramophone).
    Further Reading
    R.Gelatt, 1977, The Fabulous Phonograph, London: Cassell (a well-researched history of reproducible sound which places Berliner's contribution in its correct perspective). J.R.Smart, 1985, "Emile Berliner and nineteenth-century disc recordings", in Wonderful
    Inventions, ed. Iris Newson, Washington, DC: Library of Congress, pp. 346–59 (provides a reliable account).
    O.Read and W.L.Welch, 1959, From Tin Foil to Stereo, Indianapolis: Howard W.Sams, pp. 119–35 (provides a vivid account, albeit with less precision).
    GB-N

    Biographical history of technology > Berliner, Emile

  • 33 Carlson, Chester Floyd

    [br]
    b. 8 July 1906 Seattle, Washington, USA
    d. 19 September 1968 New York, USA
    [br]
    [br]
    Carlson studied physics at the California Institute of Technology and in 1930 he took a research position at Bell Telephone Laboratories, but soon transferred to their patent department. To equip himself in this field, Carlson studied law, and in 1934 he became a patent attorney at P.R.Mallory \& Co., makers of electrical apparatus. He was struck by the difficulty in obtaining copies of documents and drawings; indeed, while still at school, he had encountered printing problems in trying to produce a newsletter for amateur chemists. He began experimenting with various light-sensitive substances, and by 1937 he had conceived the basic principles of xerography ("dry writing"), using the property of certain substances of losing an electrostatic charge when light impinges on them. His work for Mallory brought him into contact with the Battelle Memorial Institute, the world's largest non-profit research organization; their subsidiary, set up to develop promising ideas, took up Carlson's invention. Carlson received his first US patent for the process in 1940, with two more in 1942, and he assigned to Battelle exclusive patent rights in return for a share of any future proceeds. It was at Battelle that selenium was substituted as the light-sensitive material.
    In 1946 the Haloid Company of Rochester, manufacturers of photographic materials and photocopying equipment, heard of the Xerox copier and, seeing it as a possible addition to their products, took out a licence to develop it commercially. The first Xerox Copier was tested during 1949 and put on the market the following year. The process soon began to displace older methods, such as Photostat, but its full impact on the public came in 1959 with the advent of the Xerox 914 Copier. It is fair to apply the overworked word "revolution" to the change in copying methods initiated by Carlson. He became a multimillionaire from his royalties and stock holding, and in his last years he was able to indulge in philanthropic activities.
    [br]
    Further Reading
    Obituary, 1968, New York Times, 20 September.
    R.M.Schaffert, 1954, "Developments in xerography", Penrose Annual.
    J.Jewkes, 1969, The Sources of Invention, 2nd edn, London: Macmillan, pp. 405–8.
    LRD

    Biographical history of technology > Carlson, Chester Floyd

  • 34 Johnson, Eldridge Reeves

    SUBJECT AREA: Recording
    [br]
    b. 18 February 1867 Wilmington, Delaware, USA
    d. 14 November 1945 Moorestown, New Jersey, USA
    [br]
    American industrialist, founder and owner of the Victor Talking Machine Company; developer of many basic constructions in mechanical sound recording and the reproduction and manufacture of gramophone records.
    [br]
    He graduated from the Dover Academy (Delaware) in 1882 and was apprenticed in a machine-repair firm in Philadelphia and studied in evening classes at the Spring Garden Institute. In 1888 he took employment in a small Philadelphia machine shop owned by Andrew Scull, specializing in repair and bookbinding machinery. After travels in the western part of the US, in 1891 he became a partner in Scull \& Johnson, Manufacturing Machinists, and established a further company, the New Jersey Wire Stitching Machine Company. He bought out Andrew Scull's interest in October 1894 (the last instalment being paid in 1897) and became an independent general machinist. In 1896 he had perfected a spring motor for the Berliner flat-disc gramophone, and he started experimenting with a more direct method of recording in a spiral groove: that of cutting in wax. Co-operation with Berliner eventually led to the incorporation of the Victor Talking Machine Company in 1901. The innumerable court cases stemming from the fact that so many patents for various elements in sound recording and reproduction were in very many hands were brought to an end in 1903 when Johnson was material in establishing cross-licencing agreements between Victor, Columbia Graphophone and Edison to create what is known as a patent pool. Early on, Johnson had a thorough experience in all matters concerning the development and manufacture of both gramophones and records. He made and patented many major contributions in all these fields, and his approach was very business-like in that the contribution to cost of each part or process was always a decisive factor in his designs. This attitude was material in his consulting work for the sister company, the Gramophone Company, in London before it set up its own factories in 1910. He had quickly learned the advantages of advertising and of providing customers with durable equipment and records. This motivation was so strong that Johnson set up a research programme for determining the cause of wear in records. It turned out to depend on groove profile, and from 1911 one particular profile was adhered to and processes for transforming the grooves of valuable earlier records were developed. Without precise measuring instruments, he used the durability as the determining factor. Johnson withdrew more and more to the role of manager, and the Victor Talking Machine Company gained such a position in the market that the US anti-trust legislation was used against it. However, a generation change in the Board of Directors and certain erroneous decisions as to product line started a decline, and in February 1926 Johnson withdrew on extended sick leave: these changes led to the eventual sale of Victor. However, Victor survived due to the advent of radio and the electrification of replay equipment and became a part of Radio Corporation of America. In retirement Johnson took up various activities in the arts and sciences and financially supported several projects; his private yacht was used in 1933 in work with the Smithsonian Institution on a deep-sea hydrographie and fauna-collecting expedition near Puerto Rico.
    [br]
    Bibliography
    Johnson's patents were many, and some were fundamental to the development of the gramophone, such as: US patent no. 650,843 (in particular a recording lathe); US patent nos. 655,556, 655,556 and 679,896 (soundboxes); US patent no. 681,918 (making the original conductive for electroplating); US patent no. 739,318 (shellac record with paper label).
    Further Reading
    Mrs E.R.Johnson, 1913, "Eldridge Reeves Johnson (1867–1945): Industrial pioneer", manuscript (an account of his early experience).
    E.Hutto, Jr, "Emile Berliner, Eldridge Johnson, and the Victor Talking Machine Company", Journal of AES 25(10/11):666–73 (a good but brief account based on company information).
    E.R.Fenimore Johnson, 1974, His Master's Voice was Eldridge R.Johnson, Milford, Del.
    (a very personal biography by his only son).
    GB-N

    Biographical history of technology > Johnson, Eldridge Reeves

  • 35 οἰκοδομέω

    οἰκοδομέω (οἰκοδόμος; Hdt.; ins, pap, LXX, En, EpArist, Philo, Joseph., Test12Patr) impf. ᾠκοδόμουν; fut. οἰκοδομήσω; 1 aor. ᾠκοδόμησα also without augment οἰκοδόμησα (ApcMos 40; on the augment s. W-S. §12, 5a; Mlt-H. 191); pf. ᾠκοδόμηκα LXX; plpf. 3 sg. ᾠκοδομήκει (Just., D. 127, 3). Pass.: impf. 3 sg. ᾠκοδομεῖτο; 1 fut. οἰκοδομηθήσομαι; 1 aor. ᾠκοδομήθην (v.l.) or οἰκοδομήθην (other edd., J 2:20); perf. inf. ᾠκοδομῆσθαι (οἰ-Lk 6:48b); ptc. οἰκοδομημένος (Ox 1 recto, 15f [GTh 32]); ᾠκοδομημένος Hv 3, 2, 6; plpf. 3 sg. ᾠκοδόμητο.
    ① to construct a building, build
    ⓐ w. obj. acc. build, erect (oft. pap [Mayser II/2 p. 315, 30ff]; Jos., Ant. 15, 403 al.; Did., Gen 29, 7) οἰκίαν (Diod S 14, 116, 8; Lucian, Charon 17) Lk 6:48a. τὰς οἰκοδομάς GJs 9:3; pass. (Sb 5104, 2 [163 B.C.] οἰκία ᾠκοδομημένη; PAmh 51, 11; 23) Lk 6:48b. πύργον (Is 5:2) Mt 21:33; Mk 12:1; Lk 14:28; Hs 9, 3, 1; 4; 9, 12, 6; pass. Hv 3, 2, 4ff; 3, 3, 3; 3, 5, 5; 3, 8, 9; Hs 9, 3, 2; 9, 5, 2; 9, 9, 7; cp. 9, 9, 4. ναόν Mk 14:58; 16:3 (Is 49:17); pass. J 2:20 (Heliodorus Periegeta of Athens [II B.C.]: 373 Fgm. 1 Jac. says of the Acropolis: ἐν ἔτεσι ε̄ παντελῶς ἐξεποιήθη; Orig., C. Cels. 5, 33, 13); 16:6 (cp. below; the ‘scripture’ pass. is interpreted spiritually). ἀποθήκας Lk 12:18 (opp. καθαιρεῖν; s. this 2aα). τοὺς τάφους τῶν προφητῶν the tombs of the prophets Mt 23:29 (s. EKlosterman2 ad loc.). τὰ μνημεῖα τῶν προφητῶν the monuments for the prophets Lk 11:47 (μνημεῖον 1).—οἰκ. τινί τι build someth. for someone (Gen 8:20; Ex 1:11; Ezk 16:24) συναγωγὴν οἰκ. τινί Lk 7:5. οἰκ. τινὶ οἶκον Ac 7:47, 49; 16:2 (the last two Is 66:1).—W. the obj. acc. and foll. by ἐπί w. acc. or w. gen: τὴν οἰκίαν ἐπὶ τὴν πέτραν build the house on the rock Mt 7:24. ἐπὶ τὴν ἄμμον on the sand vs. 26 (proverbial: Plut. VII p. 463, 10 Bern. εἰς ψάμμον οἰκοδομεῖς). πόλις ἐπὶ τ. ὄρους Lk 4:29 (cp. Jos., Ant. 8, 97). ἐπὶ τὴν γῆν 6:49. πόλις οἰκοδομημένη ἐπʼ ἄκρον ὄρους ὑψηλοῦ a city that is built on the top of a high mountain Ox 1 recto, 15f (GTh 32). πύργος ἐπὶ ὑδάτων Hv 3, 3, 5; ἐπὶ τὴν πέτραν Hs 9, 14, 4 (opp. χαμαὶ οὐκ ᾠκοδόμηται).
    ⓑ abs.
    α. when the obj. can be supplied fr. the context (Did., Gen. 33, 27) Lk 11:48; 14:30.—Cp. Hv 3, 1, 7; 3, 4, 1a; 3, 10, 1; Hs 9, 4, 1.
    β. but also entirely without an obj. (Theoph. Ant. 2, 13 [p. 132, 4f]) ᾠκοδόμουν they erected buildings Lk 17:28. οἱ οἰκοδομοῦντες the builders, the masons (after Ps 117:22) Mt 21:42; Mk 12:10; Lk 20:17; Ac 4:11 v.l.; 1 Pt 2:7; 6:4. Also with no ref. to the Ps passage: Hs 9, 4, 4; 9, 6, 6.
    γ. οἱ λίθοι οἱ ἤδη ᾠκοδομημένοι the stones already used in the building Hv 3, 5, 2; cp. Hs 9, 6, 3.
    ⓒ build up again, restore, a sense that οἰκ. can receive fr. the context (Josh 6:26; Ps 50:20; 68:36) Mt 26:61; 27:40; Mk 15:29; 16:3 (Is 49:17).—S. also 2.
    ② to construct in a transcendent sense (as in Hermas passages given under 1, where the tower is a symbol of the church) build: of the building up of the Christian congregation/church (cp. Ruth 4:11; θεμελιώσαντες καὶ οἰκοδομήσαντες οἱ μακάριοι ἀπόστολοι τὴν ἐκκλησίαν Iren. 3, 3, 3 [Harv. II 10, 1]) ἐπὶ ταύτῃ τῇ πέτρᾳ οἰκοδομήσω μου τὴν ἐκκλησίαν on this rock I will build my congregation/church Mt 16:18. ὡς λίθοι ζῶντες οἰκοδομεῖσθε οἶκος πνευματικός like living stones let yourselves be built up (pass.) or build yourselves up (mid., so Goodsp., Probs. 194f) into a spiritual house 1 Pt 2:5. Paul refers to missionary work where another Christian has begun activities as ἐπʼ ἀλλότριον θεμέλιον οἰκ. building on another’s foundation Ro 15:20. He also refers to his negative view of law in relation to the Christ-event as a building, and speaks of its refutation as a tearing down (καταλύειν), and of returning to it as a rebuilding (s. 1c above) Gal 2:18. This is prob. where 11:1 belongs, where (followed by citations of Scripture) it is said of the Israelites that they do not accept the baptism that removes sin, but ἑαυτοῖς οἰκοδομήσουσιν will build up someth. for themselves. In another pass. B calls the believer a πνευματικὸς ναὸς οἰκοδομούμενος τῷ κυρίῳ a spiritual temple built for the Lord 16:10; cp. vs. 6f.—Hermas’ temple-building discourse mentions angels entrusted by God with οἰκοδομεῖν building up or completion of his whole creation Hv 3, 4, 1b.—(In this connection cp. Orig., C. Cels. 4, 38, 16 γυνὴ οἰκοδομηθεῖσα ὑπὸ τοῦ θεοῦ [of Eve]).
    ③ to help improve ability to function in living responsibly and effectively, strengthen, build up, make more able. οἰκ. is thus used in a nonliteral sense and oft. without consciousness of its basic mng. (Straub p. 27), somewhat like edify in our moral parlance (this extended use is found as early as X., Cyr. 8, 7, 15 and in LXX: Ps 27:5; Jer 40:7. Also TestBenj 8:3.—JWeiss on 1 Cor 8:1). Of the Lord, who is able to strengthen the believers Ac 20:32. Of the congregation, which was being built up 9:31.—Esp. in Paul: ἡ ἀγάπη οἰκοδομεῖ love builds up (in contrast to γνῶσις, which ‘puffs up’) 1 Cor 8:1 (=Dg 12:5). πάντα ἔξεστιν, ἀλλʼ οὐ πάντα οἰκοδομεῖ everything is permitted, but not everything is beneficial 10:23. ὁ λαλῶν γλώσσῃ ἑαυτὸν οἰκοδομεῖ• ὁ δὲ προφητεύων ἐκκλησίαν οἰκοδομεῖ 14:4; cp. vs. 17. οἰκοδομεῖτε εἷς τὸν ἕνα strengthen one another 1 Th 5:11. In 1 Cor 8:10 the apostle is prob. speaking ironically, w. ref. to the ‘strong’ party at Corinth, who declare that by their example they are benefiting the ‘weak’: οὐχὶ ἡ συνείδησις αὐτοῦ οἰκοδομηθήσεται εἰς τὸ τὰ εἰδωλόθυτα ἐσθίειν; will not his conscience be ‘strengthened’ so that he will eat meat offered to idols? (difft. MargaretThrall, TU 102, ’68, 468–72).—Of Paul’s letters, by which δυνηθήσεσθε οἰκοδομεῖσθαι εἰς τὴν δοθεῖσαν ὑμῖν πίστιν you will be able to build yourselves up in the faith that has been given you Pol 3:2.—HCremer, Über den bibl. Begriff der Erbauung 1863; HScott, The Place of οἰκοδομή in the NT: PT 2, 1904, 402–24; HBassermann, Über den Begriff ‘Erbauung’: Zeitschr. für prakt. Theol. 4 1882, 1–22; CTrossen, Erbauen: ThGl 6, 1914, 804ff; PVielhauer, Oikodome (d. Bild vom Bau vom NT bis Clem. Alex.), diss. Hdlbg. ’39; PBonnard, Jésus-Christ édifiant son Église ’48.—B. 590. DELG s.v. δέμω. M-M. TW. Sv.

    Ελληνικά-Αγγλικά παλαιοχριστιανική Λογοτεχνία > οἰκοδομέω

  • 36 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

  • 37 Computers

       The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)
       It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....
       The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)
       The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)
       In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)
       A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.
       In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....
       It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)
       [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)
       he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)
       t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.
       Further, the effects of those ideas are rapidly compounding, because a computer design is itself just a set of ideas. As we get better at manipulating ideas by building ever better computers, we get better at building even better computers-it's an ever-escalating upward spiral. The early nineteenth century, when the computer's story began, is already so far back that it may as well be the Stone Age. (Rawlins, 1997, p. 19)
       According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)
       What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.
       What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.
       In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)

    Historical dictionary of quotations in cognitive science > Computers

  • 38 внутригородской район

    1. inner city

     

    внутригородской район
    —
    [ http://www.eionet.europa.eu/gemet/alphabetic?langcode=en]

    EN

    inner city
    1) Part of a city at or near the centre, especially a slum area where poor people live in bad housing.
    2) City centres of many industrialized countries which exhibit environmental degradation. The numerous and highly competitive activities entailing land use overwhelm the limited space and create a situation of overcrowding, functional incompatibility and cultural degradation. Inner city areas have a high level of commercial specialization, a large number of offices and a sizeable daytime population. At the same time, city centres generally remain a sort of ghetto for a permanent, low-income population living in run-down housing and enjoying little in the way of public services and civic amenities. The concentration of service industries inevitably entails the replacement of traditional housing and shops by office blocks, the provision of basic utilities at the expense of civic amenities and the provision of major access roads which eat up urban space. Structures of historic origin are often unable to meet modern requirements and, notwithstanding their value, frequently face demolition.
    (Source: PHC / WPR)
    [http://www.eionet.europa.eu/gemet/alphabetic?langcode=en]

    Тематики

    EN

    DE

    FR

    Русско-английский словарь нормативно-технической терминологии > внутригородской район

  • 39 инфраструктура

    1. infrastructure
    2. en

     

    инфраструктура
    —
    [ http://www.eionet.europa.eu/gemet/alphabetic?langcode=en]

    EN

    infrastructure
    The basic network or foundation of capital facilities or community investments which are necessary to support economic and community activities. (Source: LANDY)
    [http://www.eionet.europa.eu/gemet/alphabetic?langcode=en]

    Тематики

    EN

    DE

    FR

    3.3.3 инфраструктура (infrastructure): «Организация» Совокупность зданий, оборудования и служб обеспечения, необходимых для функционирования организации (3.3.1).

    Источник: ГОСТ Р ИСО 9000-2008: Системы менеджмента качества. Основные положения и словарь оригинал документа

    3.3.3 инфраструктура (en infrastructure; fr infrastructure): < организация> Совокупность зданий, оборудования и служб обеспечения, необходимых для функционирования организации (3.3.1).

    Источник: ГОСТ Р ИСО 9000-2001: Системы менеджмента качества. Основные положения и словарь оригинал документа

    2.17 инфраструктура (infrastructure): Система материальных стационарных активов (2.4) - основных средств, необходимых для эксплуатации системы коммунального водоснабжения (2.53).

    Примечание 1 - Определение адаптировано из стандарта ИСО 9000: 2005.

    Примечание 2 - Для системы коммунального водоснабжения (2.53) может также быть необходимо использование технического оборудования для транспортирования, которое не является стационарным (например, грузовые автомобили, фургоны, бутыли), на постоянной или временной основе или в чрезвычайных ситуациях. Рекомендуется использовать термин «инфраструктура» только для стационарного оборудования и установок.

    Источник: ГОСТ Р ИСО 24511-2009: Деятельность, связанная с услугами питьевого водоснабжения и удаления сточных вод. Руководящие указания для менеджмента коммунальных предприятий и оценке услуг удаления сточных вод оригинал документа

    2.17 инфраструктура (infrastructure): Система материальных стационарных активов (2.4) - основных средств, необходимых для эксплуатации системы коммунального водоснабжения (2.53).

    Примечание 1 - Определение адаптировано из стандарта ИСО 9000:2005.

    Примечание 2 - Для системы коммунального водоснабжения (2.53) может также быть необходимо использование технического оборудования для транспортирования, которое не является стационарным (например, грузовые автомобили, фургоны, бутыли), на постоянной или временной основе или в чрезвычайных ситуациях. Рекомендуется использовать термин «инфраструктура» только для стационарного оборудования и установок.

    Источник: ГОСТ Р ИСО 24512-2009: Деятельность, связанная с услугами питьевого водоснабжения и удаления сточных вод. Руководящие указания для менеджмента систем питьевого водоснабжения и оценке услуг питьевого водоснабжения оригинал документа

    2.17 инфраструктура (infrastructure): Система материальных стационарных активов (2.4) - основных средств, необходимых для эксплуатации системы коммунального водоснабжения (2.53).

    Примечание 1 - Определение адаптировано из стандарта ИСО 9000:2005.

    Примечание 2 - Для системы коммунального водоснабжения (2.53) может быть также необходимо использование технического оборудования для транспортирования, которое не является стационарным (например, грузовые автомобили, фургоны, бутыли), на постоянной или временной основе или в чрезвычайных ситуациях. Рекомендуется использовать термин «инфраструктура» только для стационарного оборудования и установок.

    Источник: ГОСТ Р ИСО 24510-2009: Деятельность, связанная с услугами питьевого водоснабжения и удаления сточных вод. Руководящие указания по оценке и улучшению услуги, оказываемой потребителям оригинал документа

    3.3.3 инфраструктура (infrastructure): < Организация> Совокупность зданий, оборудования и служб обеспечения, необходимых для функционирования организации (3.3.1).

    Источник: ГОСТ ISO 9000-2011: Системы менеджмента качества. Основные положения и словарь

    3.1.5 инфраструктура (infrastructure): Мощности и оборудование, делающие возможными услуги восстановления ИКТ после чрезвычайной ситуации, включающие энергоснабжение, телекоммуникационные соединения и средства контроля влияния внешней среды (перечень может быть расширен).


    Источник: ГОСТ Р 53131-2008: Защита информации. Рекомендации по услугам восстановления после чрезвычайных ситуаций функций и механизмов безопасности информационных и телекоммуникационных технологий. Общие положения оригинал документа

    3.2.23 инфраструктура (infrastructure): Совокупность зданий, оборудования и служб обеспечения, необходимых для функционирования организации.

    Источник: ГОСТ Р 54147-2010: Стратегический и инновационный менеджмент. Термины и определения оригинал документа

    Русско-английский словарь нормативно-технической терминологии > инфраструктура

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