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  • 81 Rogallo, Francis Melvin

    SUBJECT AREA: Aerospace
    [br]
    b. 1912 USA
    [br]
    American engineer who patented a flexible-winged hand-glider in 1948.
    [br]
    After the hang-gliders of pioneers such as Lilienthal, Pilcher and Chanute in the 1890s, this form of flying virtually disappeared for seventy years. It was reintroduced in the late 1960s based on Francis Rogallo's flexible wing, patented in the United States in 1948. Rogallo's wing was very basic: it consisted of a fabric delta wing with a solid boom along each leading edge and one along the centre line. Between these booms, the fabric was free to billow out into two partial cones. Variations of the Rogallo flexible wing were investigated in the 1960s by Ryans as a means of recovering space vehicles (e.g. Saturn booster), and by North American for the recovery of Gemini spacecraft. In 1963 a version with a 155 kW (210 hp) engine was tested by the US services as a potential lightweight transport vehicle. None of these made a great impact and the Rogallo wing became popular as a hang-glider c. 1970. The pilot was suspended in a harness below a lightweight Rogallo wing. A framework attached to the wing structure allowed the pilot to move his or her body in any direction relative to the wing. Thus, if they wished to dive, they would move their weight forward, which made the glider nose-heavy. This was a great improvement over the earlier hang-gliders, in which the upper part of the pilot's body was held in a fixed position and control was achieved by swinging the legs. Rogallo-wing hang-gliders became very popular as they were relatively cheap and easy to transport. Once the sport developed, powered "microlights" made their appearance and a new branch of popular flying was established.
    [br]
    Further Reading
    Ann Welsh, 1977, "Hang glider development", Aerospace (Royal Aeronautical Society) (August/September).
    JDS

    Biographical history of technology > Rogallo, Francis Melvin

  • 82 Short, Hugh Oswald

    SUBJECT AREA: Aerospace
    [br]
    b. 16 January 1883 Derbyshire, England
    d. 4 December 1969 Haslemere, England
    [br]
    English co-founder, with his brothers Horace Short (1872–1917) and Eustace (1875–1932), of the first company to design and build aeroplanes in Britain.
    [br]
    Oswald Short trained as an engineer; he was largely self-taught but was assisted by his brothers Eustace and Horace. In 1898 Eustace and the young Oswald set up a balloon business, building their first balloon in 1901. Two years later they sold observation balloons to the Government of India, and further orders followed. Meanwhile, in 1906 Horace designed a high-altitude balloon with a spherical pressurized gondola, an idea later used by Auguste Piccard, in 1931. Horace, a strange genius with a dominating character, joined his younger brothers in 1908 to found Short Brothers. Their first design, based on the Wright Flyer, was a limited success, but No. 2 won a Daily Mail prize of £1,000. In the same year, 1909, the Wright brothers chose Shorts to build six of their new Model A biplanes. Still using the basic Wright layout, Horace designed the world's first twin-engined aeroplane to fly successfully: it had one engine forward of the pilot, and one aft. During the years before the First World War the Shorts turned to tractor biplanes and specialized in floatplanes for the Admiralty.
    Oswald established a seaplane factory at Rochester, Kent, during 1913–14, and an airship works at Cardington, Bedfordshire, in 1916. Short Brothers went on to build the rigid airship R 32, which was completed in 1919. Unfortunately, Horace died in 1917, which threw a greater responsibility onto Oswald, who became the main innovator. He introduced the use of aluminium alloys combined with a smooth "stressed-skin" construction (unlike Junkers, who used corrugated skins). His sleek biplane the Silver Streak flew in 1920, well ahead of its time, but official support was not forthcoming. Oswald Short struggled on, trying to introduce his all-metal construction, especially for flying boats. He eventually succeeded with the biplane Singapore, of 1926, which had an all-metal hull. The prototype was used by Sir Alan Cobham for his flight round Africa. Several successful all-metal flying boats followed, including the Empire flying boats (1936) and the ubiquitous Sunderland (1937). The Stirling bomber (1939) was derived from the Sunderland. The company was nationalized in 1942 and Oswald Short retired the following year.
    [br]
    Principal Honours and Distinctions
    Honorary Fellow of the Royal Aeronautical Society. Freeman of the City of London. Oswald Short turned down an MBE in 1919 as he felt it did not reflect the achievements of the Short Brothers.
    Bibliography
    1966, "Aircraft with stressed skin metal construction", Journal of the Royal Aeronautical Society (November) (an account of the problems with patents and officialdom).
    Further Reading
    C.H.Barnes, 1967, Shorts Aircraft since 1900, London; reprinted 1989 (a detailed account of the work of the Short brothers).
    JDS

    Biographical history of technology > Short, Hugh Oswald

  • 83 Voisin, Gabriel

    SUBJECT AREA: Aerospace
    [br]
    b. 5 February 1880 Belleville-sur-Saône, France
    d. 25 December 1973 Ozenay, France
    [br]
    French manufacturer of aeroplanes in the early years of aviation.
    [br]
    Gabriel Voisin was one of a group of aviation pioneers working in France c. 1905. One of the leaders of this group was a rich lawyer-sportsman, Ernest Archdeacon. For a number of years they had been building gliders based on those of the Wright brothers. Archdeacon's glider of 1904 was flown by Voisin, who went on to assist in the design and manufacture of gliders for Archdeacon and Louis Blériot, including successful float-gliders. Gabriel Voisin was joined by his brother Charles in 1905 and they set up the first commercial aircraft factory. As the Voisins had limited funds, they had to seek customers who could afford to indulge in the fashionable hobby of flying. One was Santos- Dumont, who commissioned Voisin to build his "14 bis" aeroplane in 1906.
    Early in 1907 the Voisins built their first powered aeroplane, but it was not a success.
    Later that year they completed a biplane for a Paris sculptor, Léon Delagrange, and another for Henri Farman. The basic Voisin was a biplane with the engine behind the pilot and a "pusher" propeller. Pitching was controlled by biplane elevators forward of the pilot and rudders were fitted to the box kite tail, but there was no control of roll.
    Improvements were gradually introduced by the Voisins and their customers, such as Farman. Incidentally, to flatter their clients the Voisins often named the aircraft after them, thus causing some confusion to historians. Many Voisins were built up until 1910, when the company's fortunes sank. Competition was growing, the factory was flooded, and Charles left. Gabriel started again, building robust biplanes of steel construction. Voisin bombers were widely used during the First World War, and a subsidiary factory was built in Russia.
    In August 1917, Voisin sold his business when the French Air Ministry decided that Voisin aeroplanes were obsolete and that the factory should be turned over to the building of engines. After the war he started another business making prefabricated houses, and then turned to manufacturing motor cars. From 1919 to 1939 his company produced various models, mainly for the luxury end of the market but also including a few sports and racing cars. In the early 1950s he designed a small two-seater, which was built by the Biscuter company in Spain. The Voisin company finally closed in 1958.
    [br]
    Principal Honours and Distinctions
    Chevalier de la Légion d'honneur 1909. Académie des Sciences Gold Medal 1909.
    Bibliography
    1961, Mes dix milles cerfs-volants, France; repub. 1963 as Men, Women and 10,000 Kites, London (autobiography; an eminent reviewer said, "it contains so many demonstrable absurdities, untruths and misleading statements, that one does not know how much of the rest one can believe").
    1962, Mes Mille et un voitures, France (covers his cars).
    Further Reading
    C.H.Gibbs-Smith, 1965, The Invention of the Aeroplane 1799–1909, London (includes an account of Voisin's contribution to aviation and a list of his early aircraft).
    Jane's Fighting Aircraft of World War I, London; reprinted 1990 (provides details of Voisin's 1914–18 aircraft).
    E.Chadeau, 1987, L'Industrie aéronautique en France 1900–1950, de Blériot à Dassault, Paris.
    G.N.Georgano, 1968, Encyclopedia of Motor Cars 1885 to the Present, New York (includes brief descriptions of Voisin's cars).
    JDS

    Biographical history of technology > Voisin, Gabriel

  • 84 беспилотный самолет

    Русско-английский военно-политический словарь > беспилотный самолет

  • 85 заданные

    брать заданный пеленг
    take the bearing
    в заданном диапазоне
    within the range
    возвращаться на заданный курс
    regain the track
    восстанавливать заданное положение
    recover to
    восстанавливать заданную линию пути
    reestablish the track
    восстановление заданного положения
    flight recovery
    время набора заданной высоты
    time to climb to
    выводить воздушное судно на заданный курс
    put the aircraft on the course
    выводить на заданный курс
    roll on the course
    выдерживание заданной высоты полета
    preselected altitude hold
    выдерживать воздушное судно на заданном курсе
    hold the aircraft on the heading
    выдерживать заданную высоту
    1. keep the altitude
    2. maintain the altitude выдерживать заданный график полета
    maintain the flight watch
    выдерживать заданный курс
    1. maintain the heading
    2. maintain the course 3. stand on выдерживать заданный параметр
    maintain the parameter
    выдерживать заданный эшелон полета
    maintain the flight level
    выдерживать на заданном курсе
    hold on the heading
    выходить на заданную высоту
    take up the position
    выходить на заданную траекторию
    obtain the correct path
    выходить на заданный курс
    1. roll out on the heading
    2. put on the course 3. get on the course докладывать о занятии заданного эшелона полета
    report reaching the flight level
    докладывать о занятии заданной высоты
    report reaching the altitude
    достигать заданной мощности
    attain the power
    достигать заданной мощность
    gain the power
    достигать заданных оборотов
    reach the speed
    заданная высота
    specified height
    заданная линия пути
    intended track
    заданная скорость
    1. sufficient speed
    2. on-speed 3. target speed заданная траектория полета
    assigned flight path
    заданные условия
    predetermined conditions
    заданные условия полета
    given conditions of flight
    заданный курс
    1. prescribed course
    2. desired heading 3. scheduled course 4. required track заданный маршрут
    1. designated route
    2. assigned track заданный пеленг
    preset bearing
    заданный профиль
    assigned profile
    заданный путевой угол
    desired track angle
    заданный режим полета
    basic flight reference
    заданный уровень безопасности полетов
    target level of safety
    заданный эшелон полета
    preset flight level
    занимать заданную высоту
    reach the altitude
    занимать заданный эшелон полета
    reach the flight level
    испытания на соответствие заданным техническим условиям
    1. proof-of-compliance tests
    2. functional tests карта прогнозов на заданное время
    fixed time prognostic chart
    консультативное сообщение о порядке выдерживания заданных параметров
    maintain advisory
    контролировать заданную частоту
    monitor the frequency
    летать на заданной высоте
    fly at the altitude
    линия заданного пути
    1. track reference
    2. course line 3. desired track набирать заданную высоту
    1. gain the altitude
    2. get the height набирать заданную скорость полета
    obtain the flying speed
    навигация по заданным путевым углам
    angle navigation
    находиться вне заданного предела
    lie beyond the range
    отклонение от заданного курса
    deviation from the course
    отклоняться от заданного курса
    deviate from the heading
    плавно выводить на заданный курс
    smooth on the heading
    подниматься до заданного эшелона
    level up
    полет по заданной траектории
    desired path flight
    полет по заданному маршруту
    desired track flight
    придерживаться заданного курса
    adhere to the track
    пролетать над заданной точкой
    clear the point
    развивать заданную скорость
    1. pick up the speed
    2. gain the speed 3. attain the speed регулировать двигатель до заданных параметров
    adjust the engine
    режим малого газа в заданных пределах
    deadband idle
    режим работы автопилота по заданному курсу
    autopilot heading mode
    режим стабилизации на заданной высоте
    height-lock mode
    сигнализация самопроизвольного ухода с заданной высоты
    altitude alert warning
    скорость, заданная подвижным индексом
    bug speed
    (прибора) следовать по заданному курсу
    pursue
    снижаться до заданного эшелона
    level down
    стрелка заданного путевого угла
    course arrow
    терять заданную скорость
    lose the speed
    точное зависание над заданной точкой
    spot hovering
    уклоняться от заданного курса
    be off the track
    условия по заданному маршруту
    conditions on the route
    установка заданного курса
    heading set
    уходить на второй круг по заданной схеме
    take a missed-approach procedure
    уходить с заданного курса
    drift off the heading
    уходить с заданной высоты
    leave the altitude
    эксплуатировать в заданных условиях
    operate under the conditions

    Русско-английский авиационный словарь > заданные

  • 86 рабочий

    аэродинамическая труба с закрытой рабочей частью
    closed-throat wind tunnel
    в рабочем состоянии
    operational
    диапазон рабочих режимов
    normal operating range
    качество рабочей смеси
    mixture ratio
    обедненная рабочая смесь
    lean mixture
    обогащать рабочую смесь
    enrich mixture
    обогащенная рабочая смесь
    rich mixture
    образовывать рабочую смесь
    form mixture
    обучение на рабочем месте
    on-the-job training
    осмотр в конце рабочего дня
    daily inspection
    полетное рабочее время
    flight duty period
    приводить в рабочее состояние
    prepare for service
    промежуточное кольцо между рабочими колесами турбины
    turbine wheels spacer
    рабочая высота
    operating altitude
    Рабочая группа по разработке основных эксплуатационных требований
    Basic Operational Requirements Group
    рабочая лопатка турбины
    turbine rotor blade
    рабочая нагрузка
    1. service load
    2. workload рабочая площадка
    working platform
    рабочая топливная форсунка
    main fuel nozzle
    рабочая характеристика
    operating characteristic
    рабочая частота
    working frequency
    рабочая часть ВПП
    runway usable distance
    рабочая часть лопасти воздушного винта
    blade pressure side
    рабочее время пилота
    pilot duty time
    рабочее давление
    1. operating pressure
    2. working pressure рабочее колесо
    1. impeller
    2. blade wheel рабочее колесо двигателя
    engine impeller
    рабочее колесо компрессора
    compressor rotor wheel
    рабочее колесо турбины
    turbine wheel
    рабочее место
    duty station
    (экипажа) рабочее место бортинженера
    flight engineer station
    рабочее место командира
    captain's station
    (воздушного судна) рабочее место пилота
    pilot's station
    рабочий канал
    operating channel
    рабочий момент
    operating torque
    рабочий потолок
    operating ceiling
    рабочий режим
    operating mode
    рабочий ток
    operating current
    рабочий топливный бак
    service fuel tank
    рабочий ход
    1. power stroke
    (поршня) 2. working path рабочий цикл
    operating cycle
    рабочий чертеж
    workshop drawing
    рабочий экипаж
    operating crew
    рабочий эшелон полета
    usable flight level
    рабочий язык ИКАО
    working language of ICAO
    расширенная рабочая часть рулежной дорожки
    widened taxiway throat
    регулирование рабочей смеси
    mixture setting
    самовоспламеняющаяся рабочая смесь
    self-inflammable mixture
    тариф для рабочих
    worker fare

    Русско-английский авиационный словарь > рабочий

  • 87 ресурс

    двигатель с большим ресурсом
    longer-lived engine
    испытание на амортизационный ресурс
    service life test
    межремонтный ресурс
    1. basic overhaul time
    2. overhaul period 3. time between overhauls назначенный ресурс
    specified life
    ресурс до первого капитального ремонта
    first overhaul period
    усталостный ресурс
    fatigue life
    усталостный ресурс воздушного судна
    aircraft fatigue life

    Русско-английский авиационный словарь > ресурс

  • 88 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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