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experimental+animal

  • 61 laboratoire

    laboratoire [labɔʀatwaʀ]
    masculine noun
    * * *
    labɔʀatwaʀ
    nom masculin laboratory

    de laboratoire — [animal, appareil] laboratory (épith)

    Phrasal Verbs:
    * * *
    labɔʀatwaʀ nm
    * * *
    A nm
    1 Pharm, Ind laboratory; préparé en laboratoire prepared in a laboratory; testé en laboratoire laboratory-tested; de laboratoire [animal, appareil] laboratory; essais en laboratoire laboratory tests;
    2 fig hotbed (de of); une région laboratoire du marketing a dynamic marketing region;
    3 Comm (de boucherie, pâtisserie) ≈ backshop.
    B - laboratoire ( in compounds) camion-laboratoire mobile laboratory; ferme-laboratoire research farm.
    laboratoire d'analyses médicales medical laboratory; laboratoire cosmétologique cosmetics company; laboratoire de langues language laboratory; laboratoire orbital skylab; laboratoire pharmaceutique pharmaceutical company; laboratoire photographique photo laboratory; laboratoire de recherches research laboratory.
    [labɔratwar] nom masculin
    [équipe] (research) team
    ————————
    en laboratoire locution adverbiale

    Dictionnaire Français-Anglais > laboratoire

  • 62 control

    A n
    1 ¢ ( domination) (of animals, children, crowd, country, organization, party, situation) contrôle m (of de) ; (of investigation, operation, project) direction f (of de) ; ( of others' behaviour) influence f (over sur) ; (of life, fate) maîtrise f (of, over de) ; (of disease, pests, social problem) lutte f (of contre) ; state control contrôle m de l'État ; to be in control of contrôler [territory, town] ; diriger [operation, organization, project] ; maîtriser [problem] ; to have control over contrôler [territory, town] ; avoir du pouvoir sur [animals, crowd, children, others' behaviour] ; maîtriser [fate, life] ; to take control of prendre le contrôle de [territory, town] ; prendre la direction de [operation, organization, project] ; prendre [qch] en main [situation] ; to be under sb's control, to be under the control of sb [person] être sous la direction de qn ; [army, government, organization, party] être sous le contrôle de qn ; to be under control [fire, problem, riot, situation] être maîtrisé ; is the situation under control? est-ce que nous maîtrisons la situation? ; everything's under control tout va bien ; to bring ou get ou keep [sth] under control maîtriser [animals, crowd, fire, problem, riot] ; discipliner [hair] ; to be out of control [animals, children, crowd, riot] être déchaîné ; [fire] ne plus être maîtrisable ; the situation is out of control la situation est devenue incontrôlable ; to let sth get out of control, to lose control of sth perdre le contrôle de qch ; to be beyond ou outside sb's control [animal, child] échapper au contrôle de qn ; the situation is beyond control la situation échappe à tout contrôle ; due to circumstances beyond our control pour des raisons indépendantes de notre volonté ;
    2 ¢ ( restraint) (of self, appetite, bodily function, emotion, urge) maîtrise f ; to have ou exercise control over sth maîtriser qch ; to keep control of oneself, to be in control of oneself se maîtriser ; to lose control (of oneself) perdre le contrôle (de soi) ;
    3 ¢ ( physical mastery) (of vehicle, machine, ball) contrôle m ; (of body, process, system) maîtrise f ; to be in control of avoir le contrôle de ; to keep/lose control of a car garder/perdre le contrôle d'une voiture ; to take control ( of car) prendre le volant ; ( of plane) prendre les commandes ; his car went out of control il a perdu le contrôle de son véhicule ;
    4 (lever, switch etc) ( souvent pl) (on vehicle, equipment) commande f ; ( on TV) bouton m de réglage ; brightness/volume control TV bouton m de réglage de luminosité/du son ; to be at the controls être aux commandes ;
    5 Admin, Econ ( regulation) contrôle m (on de) ; cost/immigration control contrôle m des coûts/de l'immigration ;
    6 Sci ( in experiment) contrôle m.
    B modif [button, knob, switch] de commande.
    1 ( dominate) dominer [council, government, market, organization, situation] ; contrôler [territory, town] ; diriger [air traffic, investigation, operation, project] ; régler [road traffic] ; s'emparer de [mind] ; Fin [shareholder] être majoritaire dans [company] ;
    2 ( discipline) maîtriser [person, animal, crowd, urge, bodily function, temper, voice, pain, inflation, unemployment, riot, fire, pests] ; endiguer [disease, epidemic] ; dominer [emotion, nerves, impulse] ; retenir [laughter, tears] ; commander à [limbs] ; discipliner [hair] ;
    3 ( operate) commander [machine, equipment, lever, cursor, movement, process, system] ; manœuvrer [boat, vehicle] ; piloter [plane] ; contrôler [ball] ;
    4 ( regulate) régler [speed, pressure, intensity, volume, temperature] ; réglementer [trade, import, export] ; contrôler [immigration, prices, wages] ; régulariser [blood pressure] ;
    5 ( check) contrôler [quality] ; vérifier [accounts] ;
    6 Sci comparer [experimental material] (against à).

    Big English-French dictionary > control

  • 63 Trembley, Abraham

    SUBJECT AREA: Medical technology
    [br]
    b. 3 September 1710 Geneva, Switzerland
    d. 12 May 1784 Petit Sacconex, Switzerland
    [br]
    Swiss philosopher and experimental zoologist, pioneer of tissue grafting.
    [br]
    Educated at Geneva, he later became a tutor to Count Bentinck's family near Leiden. It was during this time, from 1733 to 1743, that he undertook the studies of the organism Hydra that led, in October 1742, to the first permanent graft of animal tissues. His work covered the whole range of possibilities in tissue regeneration and grafting, but he was also engaged in other studies of the protozoa and c. 1760 he made the first observations of cell division. In 1750 he was entrusted with the care of the son and heir of the Duke of Richmond and in 1757, having escorted the young duke all over the European continent, he was able to retire in comfort to the country at Petit Sacconex.
    The advice and counsel of Réaumur was of considerable support to him, bearing in mind that many including Voltaire found it impossible to accept that an animal could be multiplied by cutting it into pieces.
    [br]
    Principal Honours find Distinctions
    FRS 1743.
    Bibliography
    1744, Mémoires pour servir à l'histoire d'un genre de polypes d'eau douce, à bras en forme des cornes, Leiden.
    Further Reading
    J.R.Baker, 1952, Abraham Trembley of Geneva: Scientist and Philosopher, 1710–1784.
    MG

    Biographical history of technology > Trembley, Abraham

  • 64 Psychology

       We come therefore now to that knowledge whereunto the ancient oracle directeth us, which is the knowledge of ourselves; which deserveth the more accurate handling, by how much it toucheth us more nearly. This knowledge, as it is the end and term of natural philosophy in the intention of man, so notwithstanding it is but a portion of natural philosophy in the continent of nature.... [W]e proceed to human philosophy or Humanity, which hath two parts: the one considereth man segregate, or distributively; the other congregate, or in society. So as Human philosophy is either Simple and Particular, or Conjugate and Civil. Humanity Particular consisteth of the same parts whereof man consisteth; that is, of knowledges which respect the Body, and of knowledges that respect the Mind... how the one discloseth the other and how the one worketh upon the other... [:] the one is honored with the inquiry of Aristotle, and the other of Hippocrates. (Bacon, 1878, pp. 236-237)
       The claims of Psychology to rank as a distinct science are... not smaller but greater than those of any other science. If its phenomena are contemplated objectively, merely as nervo-muscular adjustments by which the higher organisms from moment to moment adapt their actions to environing co-existences and sequences, its degree of specialty, even then, entitles it to a separate place. The moment the element of feeling, or consciousness, is used to interpret nervo-muscular adjustments as thus exhibited in the living beings around, objective Psychology acquires an additional, and quite exceptional, distinction. (Spencer, 1896, p. 141)
       Kant once declared that psychology was incapable of ever raising itself to the rank of an exact natural science. The reasons that he gives... have often been repeated in later times. In the first place, Kant says, psychology cannot become an exact science because mathematics is inapplicable to the phenomena of the internal sense; the pure internal perception, in which mental phenomena must be constructed,-time,-has but one dimension. In the second place, however, it cannot even become an experimental science, because in it the manifold of internal observation cannot be arbitrarily varied,-still less, another thinking subject be submitted to one's experiments, comformably to the end in view; moreover, the very fact of observation means alteration of the observed object. (Wundt, 1904, p. 6)
       It is [Gustav] Fechner's service to have found and followed the true way; to have shown us how a "mathematical psychology" may, within certain limits, be realized in practice.... He was the first to show how Herbart's idea of an "exact psychology" might be turned to practical account. (Wundt, 1904, pp. 6-7)
       "Mind," "intellect," "reason," "understanding," etc. are concepts... that existed before the advent of any scientific psychology. The fact that the naive consciousness always and everywhere points to internal experience as a special source of knowledge, may, therefore, be accepted for the moment as sufficient testimony to the rights of psychology as science.... "Mind," will accordingly be the subject, to which we attribute all the separate facts of internal observation as predicates. The subject itself is determined p. 17) wholly and exclusively by its predicates. (Wundt, 1904,
       The study of animal psychology may be approached from two different points of view. We may set out from the notion of a kind of comparative physiology of mind, a universal history of the development of mental life in the organic world. Or we may make human psychology the principal object of investigation. Then, the expressions of mental life in animals will be taken into account only so far as they throw light upon the evolution of consciousness in man.... Human psychology... may confine itself altogether to man, and generally has done so to far too great an extent. There are plenty of psychological text-books from which you would hardly gather that there was any other conscious life than the human. (Wundt, 1907, pp. 340-341)
       The Behaviorist began his own formulation of the problem of psychology by sweeping aside all medieval conceptions. He dropped from his scientific vocabulary all subjective terms such as sensation, perception, image, desire, purpose, and even thinking and emotion as they were subjectively defined. (Watson, 1930, pp. 5-6)
       According to the medieval classification of the sciences, psychology is merely a chapter of special physics, although the most important chapter; for man is a microcosm; he is the central figure of the universe. (deWulf, 1956, p. 125)
       At the beginning of this century the prevailing thesis in psychology was Associationism.... Behavior proceeded by the stream of associations: each association produced its successors, and acquired new attachments with the sensations arriving from the environment.
       In the first decade of the century a reaction developed to this doctrine through the work of the Wurzburg school. Rejecting the notion of a completely self-determining stream of associations, it introduced the task ( Aufgabe) as a necessary factor in describing the process of thinking. The task gave direction to thought. A noteworthy innovation of the Wurzburg school was the use of systematic introspection to shed light on the thinking process and the contents of consciousness. The result was a blend of mechanics and phenomenalism, which gave rise in turn to two divergent antitheses, Behaviorism and the Gestalt movement. The behavioristic reaction insisted that introspection was a highly unstable, subjective procedure.... Behaviorism reformulated the task of psychology as one of explaining the response of organisms as a function of the stimuli impinging upon them and measuring both objectively. However, Behaviorism accepted, and indeed reinforced, the mechanistic assumption that the connections between stimulus and response were formed and maintained as simple, determinate functions of the environment.
       The Gestalt reaction took an opposite turn. It rejected the mechanistic nature of the associationist doctrine but maintained the value of phenomenal observation. In many ways it continued the Wurzburg school's insistence that thinking was more than association-thinking has direction given to it by the task or by the set of the subject. Gestalt psychology elaborated this doctrine in genuinely new ways in terms of holistic principles of organization.
       Today psychology lives in a state of relatively stable tension between the poles of Behaviorism and Gestalt psychology.... (Newell & Simon, 1963, pp. 279-280)
       As I examine the fate of our oppositions, looking at those already in existence as guide to how they fare and shape the course of science, it seems to me that clarity is never achieved. Matters simply become muddier and muddier as we go down through time. Thus, far from providing the rungs of a ladder by which psychology gradually climbs to clarity, this form of conceptual structure leads rather to an ever increasing pile of issues, which we weary of or become diverted from, but never really settle. (Newell, 1973b, pp. 288-289)
       The subject matter of psychology is as old as reflection. Its broad practical aims are as dated as human societies. Human beings, in any period, have not been indifferent to the validity of their knowledge, unconcerned with the causes of their behavior or that of their prey and predators. Our distant ancestors, no less than we, wrestled with the problems of social organization, child rearing, competition, authority, individual differences, personal safety. Solving these problems required insights-no matter how untutored-into the psychological dimensions of life. Thus, if we are to follow the convention of treating psychology as a young discipline, we must have in mind something other than its subject matter. We must mean that it is young in the sense that physics was young at the time of Archimedes or in the sense that geometry was "founded" by Euclid and "fathered" by Thales. Sailing vessels were launched long before Archimedes discovered the laws of bouyancy [ sic], and pillars of identical circumference were constructed before anyone knew that C IID. We do not consider the ship builders and stone cutters of antiquity physicists and geometers. Nor were the ancient cave dwellers psychologists merely because they rewarded the good conduct of their children. The archives of folk wisdom contain a remarkable collection of achievements, but craft-no matter how perfected-is not science, nor is a litany of successful accidents a discipline. If psychology is young, it is young as a scientific discipline but it is far from clear that psychology has attained this status. (Robinson, 1986, p. 12)

    Historical dictionary of quotations in cognitive science > Psychology

  • 65 game

    сущ.
    1) общ. игра
    See:
    2) т. игр игра (формализованное описание конфликтной ситуации, в которой каждый участник, который называется игроком, старается максимизировать свой выигрыш, выбирая наилучший план действий, который называется стратегией, учитывая зависимость результата от действий других участников)
    See:
    3) общ. дело, (рискованное) предприятие
    4) общ. охота

    game bag — ягдаш, охотничья сумка

    See:

    Англо-русский экономический словарь > game

  • 66 подопытное животное

    adj

    Diccionario universal ruso-español > подопытное животное

  • 67 doświadcze|nie

    Ⅰ sv doświadczyć Ⅱ n 1. (praktyka) experience U
    - pracownik z piętnastoletnim doświadczeniem an employee with fifteen years’ experience
    - lekarz/nauczyciel z dużym doświadczeniem a doctor/teacher with a lot of experience, a very experienced doctor/teacher
    - nabrać/nabierać doświadczenia to gain experience (w czymś at a. in sth)
    - zdobyć doświadczenie zawodowe to gain professional experience
    - to zdolny chłopak, ale brak mu a. nie ma doświadczenia he’s a capable kid, but he lacks experience
    - mają duże doświadczenie w uczeniu dzieci they have a lot of experience in teaching children
    - wiedział z doświadczenia, że… he knew from experience that…
    2. (przeżycie) experience
    - wstrząsające/pouczające doświadczenie a jarring/an educational experience
    - doświadczenia wojenne war experiences
    - nauczona smutnym doświadczeniem już się na to nie zgodzę having learnt (my lesson) the hard way, I won’t agree to that
    3. (eksperyment) experiment, test (nad czymś on sth)
    - przeprowadzić doświadczenie to carry out a. do an experiment
    - doświadczenia na zwierzętach animal experiments
    - wyniki doświadczeń wskazują… experimental data indicate(s) a. point(s) to…
    - wynik doświadczenia dowodzi, że… the results of the experiment prove that…
    - nowe doświadczenia ze szczepionką potwierdziły jej przydatność new experiments with the vaccine have proved its effectiveness
    4. sgt Filoz. experience

    The New English-Polish, Polish-English Kościuszko foundation dictionary > doświadcze|nie

  • 68 опитен

    прил 1. expérimenté, e, expert, e, rompu, e; versé, e (dans); ferré, e (sur); опитен работник ouvrier expérimenté (expert); опитен (вещ) съм в être expert dans (en, а), être rompu а (versé dans, ferré sur); имам опитно око avoir l'њil expert, avoir le compas dans l'њil); 2. expérimental, e, aux, d'essai, d'expérience; опитна физика physique expérimentale; опитни изследвания recherches expérimentales; опитно поле champ d'essai (d'expérience); опитно животно animal de laboratoire; прен cobaye m.

    Български-френски речник > опитен

  • 69 flight

    1. полет; рейс/ летный; полетный; рейсовый
    2. отряд; звено <ЛА>
    см. тж. flight
    flights per month
    1-g flight
    4-D flight
    accelerated flight
    acceptance flight
    accident flight
    accumulated flights
    aerobatic flight
    afterburning flight
    agile flight
    air taxi flight
    air-breathing flight
    air-refueled flight
    airplane-mode flight
    all-attitude flight
    all-inertial flight
    altimeter flight
    altimeter-controlled flight
    animal flight
    area familiarization flight
    automatic flight
    avionics evaluation flight
    axial flight
    balanced flight
    best-range flight
    boomless flight
    buffet flight
    certificate of airworthiness flight
    check flight
    climbing flight
    combat flight
    connecting flight
    constant airspeed-constant lift coefficient flight
    constant altitude-constant airspeed flight
    constant altitude-constant lift coefficient flight
    constant altitude flight
    controllable flight
    controlled flight into terrain
    coordinated flight
    cross-controlled flight
    cruise-climb flight
    curvilinear flight
    data-recording flight
    delivery flight
    departed flight
    departure-resistant flight
    descending flight
    disturbed flight
    domestic flight
    east-west flight
    edgewise flight
    envelope clearance flight
    envelope expansion flight
    equilibrium flight
    evaluation flight
    exercise flight
    experimental flight
    ferry flight
    fixed-rotor flight
    fixed-wing flight
    flawless flight
    forward flight
    free flight
    free-hovering flight
    full-wingborne flight
    full-scale flight
    full-throttle flight
    functional flight
    gliding flight
    ground-air-ground flight
    hard altitude-hard airspeed flight
    high-angle flight
    high-angle-of-attack flight
    high-AOA flight
    high-g flight
    high-low-high flight
    high-speed flight
    high-subsonic flight
    homeward flight
    horizontal flight
    hovering flight
    hub flight
    HUD flight
    human-powered flight
    IFR flight
    inertial flight
    inertially guided flight
    instrument flight rules flight
    inverted flight
    jet-borne flight
    large-angle maneuvering flight
    level flight
    long-endurance flight
    long-haul flight
    long-range flight
    low-level flight
    maiden flight
    maneuvering flight
    manned flight
    manual flight
    manually controlled flight
    maximum continuous flight
    maximum turning rate flight
    maximum endurance flight
    mean flights between maintenance actions
    microburst wind-shear sampling flight
    midcourse flight
    minimum time flight
    minimum turning radius flight
    mishap flight
    moderate supersonic flight
    most-economical flight
    nap-of-the-earth flight
    nearly-horizontal flight
    no-radar flight
    nonlevel flight
    nonmaneuver flight
    nonmaneuvering flight
    nonscheduled flight
    nonturning flight
    on-line flight
    one-g flight
    one-stop flight
    OOC flight
    open-loop flight
    operational flight
    orientation flight
    out-of-control flight
    outward flight
    over-ocean flight
    parabolic flight
    post-maintenance check flight
    post-stall flight
    power-off flight
    pre-delivery flight
    quasi-steady-state flight
    radar flight
    rearward flight
    rectilinear flight
    revenue flight
    rocket-borne flight
    rotary-wing flight
    rotational flight
    route-proving flight
    semiballistic flight
    shakedown flight
    short-range flight
    shuttle flight
    sideways flight
    sightseeing flight
    simulated terrain following flight
    slow flight
    smooth flight
    spin flight
    spinning flight
    stable flight
    stall flight
    stalled flight
    steady flight
    steady-state flight
    stepped-altitude flight
    stopped rotor flight
    store certification flight
    straight flight
    straight-line flight
    super-stalled flight
    supernormal flight
    supersonic flight
    sustained flight
    symmetric flight
    tail-rotorless flight
    terminal maneuvering area flight
    terrain flight
    terrain-avoidance flight
    terrain-following flight
    tethered flight
    TF/TA flight
    to depart from controlled flight
    training flight
    transition flight
    trimmed flight
    turning flight
    two-dimensional flight
    ultrahigh-altitude flight
    up-and-away flight
    unaccelerated flight
    uncoordinated flight
    unpowered flight
    unyawed flight
    vertebrate flight
    vertical flight
    vertical-plane flight
    VFR flight
    VIP flight
    visual flight
    visual flight rules flight
    voiсe-controlled flight
    VSTOL flight
    water-bombing flight
    west bound flight
    wingborne flight
    wings-level flight
    world-wide flight
    yawed flight

    Авиасловарь > flight

  • 70 Lawes, Sir John Bennet

    [br]
    b. 28 December 1814 Rothamsted, Hertfordshire, England
    d. 31 August 1900 Rothamsted, Hertfordshire, England
    [br]
    English scientific agriculturalist.
    [br]
    Lawes's education at Eton and Oxford did little to inform his early taste for chemistry, which he developed largely on his own. By the age of 20 he had fitted up the best bedroom in his house as a fully equipped chemical laboratory. His first interest was in the making of drugs; it was said that he knew the Pharmacopoeia, by heart. He did, however, receive some instruction from Anthony Todd Thomson of University College, London. His father having died in 1822, Lawes entered into possession of the Rothamsted estate when he came of age in 1834. He began experiments with plants with uses as drugs, but following an observation by a neighbouring farmer of the effect of bones on the growth of certain crops Lawes turned to experiments with bones dissolved in sulphuric acid on his turnip crop. The results were so promising that he took out a patent in 1842 for converting mineral and fossil phosphates into a powerful manure by the action of sulphuric acid. The manufacture of these superphosphates became a major industry of tremendous benefit to agriculture. Lawes himself set up a factory at Deptford in 1842 and a larger one in 1857 at Barking Creek, both near London. The profits from these and other chemical manufacturing concerns earned Lawes profits which funded his experimental work at Rothamsted. In 1843, Lawes set up the world's first agricultural experiment station. Later in the same year he was joined by Joseph Henry Gilbert, and together they carried out a considerable number of experiments of great benefit to agriculture, many of the results of which were published in the leading scientific journals of the day, including the Philosophical Transactions of the Royal Society. In all, 132 papers were published, most of them jointly with Gilbert. A main theme of the work on plants was the effect of various chemical fertilizers on the growth of different crops, compared with the effects of farm manure and of no treatment at all. On animal rearing, they studied particularly the economical feeding of animals.
    The work at Rothamsted soon brought Lawes into prominence; he joined the Royal Agricultural Society in 1846 and became a member of its governing body two years later, a position he retained for over fifty years. Numerous distinctions followed and Rothamsted became a place of pilgrimage for people from many parts of the world who were concerned with the application of science to agriculture. Rothamsted's jubilee in 1893 was marked by a public commemoration headed by the Prince of Wales.
    [br]
    Principal Honours and Distinctions
    Baronet 1882. FRS 1854. Royal Society Royal Medal (jointly with Gilbert) 1867.
    Further Reading
    Memoir with portrait published in J. Roy. Agric. Soc. Memoranda of the origin, plan and results of the field and other experiments at Rothamsted, issued annually by the Lawes Agricultural Trust Committee, with a list of Lawes's scientific papers.
    LRD

    Biographical history of technology > Lawes, Sir John Bennet

  • 71 culture

    culture 1. культура (напр. бактерий) ; 2. разведение, выращивание
    culture культура, культивирование организмов или тканей в лабораторных условиях на искусственно приготовленной среде
    culture chamber культуральная камера
    adhesive culture культура в капле среды на покровном стекле
    aerated culture аэрированная культура
    aerobic culture аэробная культура
    agar culture культура на агаре
    agitated culture перемешиваемая встряхиванием культура
    anaerobic culture анаэробная культура
    animal culture культура клеток животных
    aroma-producing culture ароматообразующая культура
    artificial culture искусственное разведение
    aseptic culture асептическое выращивание
    axenic culture аксенная культура, стерильная культура; чистая культура
    batch culture культура одного производственного цикла
    batch culture периодическая культура
    broth culture бульонная культура
    callus tissue culture культура каллусных тканей
    cell culture культура клеток
    cell suspension culture суспензионная культура клеток
    chorioallantoic culture хориоаллантоисная культура
    clonal culture клонированная культура
    contaminated culture загрязненная культура
    contaminated culture нечистая культура
    continuons-flow culture проточная культура
    control culture контрольная культура
    dark-grown culture выращенная в темноте культура
    deep-liquid culture глубинная культура
    differentiated culture дифференцированная культура
    diploidization of culture диплоидизация культуры
    dried culture высушенная культура
    droplet culture капельная культура
    embryo culture культура эмбрионов
    enrichment culture обогатительная культура
    excised embryo culture культура изолированных эмбрионов
    excised organ culture культура изолированных органов
    experimental culture экспериментальная культура
    explant culture культура ткани
    exposition of culture воздействие на культуру
    fed-batch culture подпитываемая культура одного производственного цикла
    freeze-dried culture лиофилизованная культура
    frozen culture замороженная культура
    fungal culture культура грибов
    germ culture микробная культура
    glycerolized culture глицериновая культура
    growing culture растущая культура; размножающаяся культура
    habituated culture адаптированная культура
    heavily sporulating culture обильно спорулирующая культура
    heterogeneous culture гетерогенная культура
    high density culture концентрированная культура
    homogeneous culture гомогенная культура
    housing patent culture коллекционная патентованная культура
    human cell culture культура клеток человека
    human tissue culture культура ткани человека
    improving culture conditions оптимизированные условия культивирования
    impure culture загрязнённая культура
    isolated clonal culture изолированная клонированная культура
    isolated protoplast culture культура изолированных протопластов
    laboratory culture лабораторная культура
    large-scale culture крупномасштабная культура
    liquid culture культура в жидкой среде
    logarithmic phase culture культура, находящаяся в логарифмической фазе роста
    long-period culture длительная культура
    maintaining culture поддерживаемая культура
    maintaining culture сохраняемая культура
    manufacturing plant culture промышленная культура
    mass culture массовая культура
    microcarrier culture культура клеток на микроносителях
    mixed culture смешанная культура
    monolayer cell culture монослойная культура
    monolayer culture монослойная культура, однослойная культура
    monospecies culture монокультура
    monoxenic culture моноксенная культура
    negative culture отрицательная культура
    nonproliferating culture непролиферирующая культура
    nonsporulating culture неспорообразующая культура
    old culture старая культура
    open culture непрерывная культура
    overnight culture ночная культура
    Petri dish culture культура на чашках Петри
    Petry dish culture культура на чашках Петри
    plant cell culture культура клеток растения
    plant tissue culture культура клеток растительной ткани
    plate culture культура на чашках Петри
    positive culture положительная культура
    preserved culture законсервированная культура
    preserved culture сохраняемая культура
    primary culture первичная культура
    production culture производственная культура
    prompt culture закваска
    prompt culture затравочная культура
    protoplast culture культура протопластов
    pure culture чистая культура
    reference culture тест-культура
    replicate culture реплицированная культура
    resistant culture резистентная культура
    roll bottle culture роллерная культура
    rotated culture роллерная культура
    routine culture стандартная культура
    seed culture посевная культура
    selective culture селективная культура
    serum-free culture бессывороточная культура
    short-term culture кратковременная культура
    single cell culture культура, полученная из одной клетки
    single cell culture культура одной клетки
    single cell culture культура одноклеточного организма
    single-cell culture культура, выделенная из одной клетки
    sister culture сестринская культура
    slant culture культура на скошенном агаре
    slide culture культура на предметном стекле
    slope culture культура на скошенном агаре
    smear culture культура мазком
    soil culture почвенная культура
    soil-water culture культура на почвенной вытяжке
    spent culture отработанная культура
    sporulating culture спорулирующая культура
    stabilized culture стабилизированная культура
    starter culture закваска
    static culture статическая культура
    steady-state culture стационарная культура
    stock culture штамм, чистая культура
    stock culture collection базовая коллекция культур микроорганизмов
    stock yeast culture маточные дрожжи
    stored culture законсервированная культура
    stroke culture поверхностная культура
    submerged culture погружённая культура, глубинная культура
    surface culture поверхностная культура
    suspended cell culture культура ткани из суспендированных клеток
    suspension culture суспензионная культура
    synchronized culture синхронизированная культура
    synchronous culture синхронно растущая культура
    test-tube culture пробирочная культура
    tissue culture тканевая культура, культура ткани
    tumor tissue culture культура опухолевой ткани
    two-membered culture смешанная культура двух видов организмов
    type culture стандартная культура
    type culture типовая культура
    unialgal culture альгологически чистая культура (свободная от водорослей других видов)
    water culture водная культура
    working culture рабочая культура
    young culture молодая культура

    English-Russian dictionary of biology and biotechnology > culture

  • 72 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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