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  • 61 Thomas, Sidney Gilchrist

    SUBJECT AREA: Metallurgy
    [br]
    b. 16 April 1850 London, England
    d. 1 February 1885 Paris, France
    [br]
    English inventor of basic steelmaking.
    [br]
    Thomas was educated at Dulwich College and from the age of 17, for the next twelve years, he made his living as a police-court clerk, although he studied chemistry in his spare time as an evening student at Birkbeck College, London. While there, he heard of the difficulties encountered by the Bessemer steelmaking process, which at that time was limited to using phosphorus-free iron. Any of this element present in the iron was oxidized to phosphoric acid, which would not react with the acidic lining in the converter, with the result that it would remain in the iron and render it too brittle to use. Unfortunately, phosphoric iron ores are more common than those free of this harmful element. Thomas was attracted by the view that a fortune awaited anyone who could solve this problem, and was not discouraged by the failure of several august figures in the industry, including Siemens and Lowthian Bell.
    Thomas's knowledge of chemistry taught him that whereas an acidic lining allowed the phosphorus to remain in the iron, a basic lining would react with it to form part of the slag, which could then be tapped off. His experiments to find a suitable material were conducted in difficult conditions, in his spare time with meagre apparatus. Finally he found that a converter lined with dolomite, a form of limestone, would succeed, and he appealed to his cousin Percy Carlyle Gilchrist, Chemist at the Blaenavon Ironworks in Monmouthshire, for help in carrying out pilot-scale trials. In 1879 he gave up his police-court job to devote himself to the work, and in the same year they patented the Thomas- Gilchrist process. The first licence to use it was granted to Bolckow, Vaughan \& Co. of Middlesborough, and there the first steel was made in a basic Bessemer converter on 4 April 1879. The process was rapidly taken up and spread widely in Europe and beyond and was applied to other furnaces. Thomas made a fortune, but his health did not long allow him to enjoy it, for he died at the early age of 34.
    [br]
    Bibliography
    L.G.Thompson, 1940, Sidney Gilchrist Thomas, an Invention and Its Consequences, London: Faber.
    T.G.Davies, 1978, Blaenavon and Sidney Gilchrist Thomas, Sheffield: Historical Metallurgy Society.
    LRD

    Biographical history of technology > Thomas, Sidney Gilchrist

  • 62 quarter

    I
    1. [ʹkwɔ:tə] n
    I
    1. четверть, четвёртая часть

    a quarter of a pound [of a dollar, of a yard, of a mile] - четверть фунта [доллара, ярда, мили]

    to divide smth. into quarters - разделить что-л. на четыре части

    a quarter of a year - квартал, три месяца

    what's the quarter of 64? - чему равна четвёртая часть от 64?

    2. четверть часа, пятнадцать минут

    a quarter to /амер. of/ one - без четверти час

    some clocks strike the quarters - некоторые часы бьют каждые четверть часа

    3. 1) квартал, четверть года, три месяца

    to pay for smth. at the end of each quarter - платить за что-л. в конце каждого квартала

    2) разг. квартплата за квартал
    3) школ. четверть
    4. амер.
    1) четверть доллара, 25 центов
    2) монета в 25 центов
    5. 1) кул. четвертина ( туши)

    fore quarter - передняя часть, лопатка

    hind quarter - задняя часть, окорок

    a quarter of beef [of mutton] - четверть говяжьей [бараньей] туши

    2) доля ( вымени)
    3) уст. четвёртая часть тела человека
    6. 1) квартер ( мера веса)

    gross quarter - длинный квартер (= 28 фунтов, ≈ 12,7 кг)

    short quarter - короткий квартер (= 25 фунтов, ≈ 11,34 кг)

    2) квартер (мера объёма сыпучих тел; ≈ 291 л́)
    7. 1) четверть мили
    2) спорт. бег на четверть мили
    8. мор. четверть румба
    9. = quarter-deck 1
    10. стр. деревянный четырёхгранный брус
    11. задник ( сапога)
    12. боковая сторона копыта ( у лошади)
    13. геральд. четверть геральдического щита
    14. астр. четверть ( Луны)

    the first [the last] quarter - первая [последняя] четверть ( Луны)

    II
    1. 1) квартал
    2) район, часть города

    a student quarter - ≅ студенческий городок

    2. страна света, часть света

    the four quarters of the globe - все части земного шара, все страны света

    3. место, сторона

    from every quarter - отовсюду, со всех сторон

    they gathered from all quarters of Europe - они съехались из всех уголков Европы

    from what quarter does the wind blow? - с какой стороны дует ветер?

    4. 1) круг лиц; сфера; круги

    in the highest quarters - в высших кругах /сферах/

    to apply /to address oneself/ to the proper quarter - обратиться в нужное место

    2) источник (помощи, информации)

    he could expect no help from that quarter - он не мог ожидать помощи оттуда

    to obtain information from a reliable quarter - получать информацию из надёжного источника

    III
    1. пощада, снисхождение
    2. выдержка; терпеливость; терпимость
    3. приём, обхождение

    to give smb. fair quarter - оказать кому-л. хороший приём

    to meet ill quarter from smb. - быть плохо принятым кем-л., встретить холодный приём с чьей-л. стороны

    ♢

    to beat up smb.'s quarters - навещать кого-л. запросто

    not a quarter so /as/ good - вовсе не так хорош

    2. [ʹkwɔ:tə] v
    1. 1) делить, разделить на четыре равные части

    to quarter an apple [an orange] - разделить яблоко [апельсин] на четыре равные части

    2) редк. делить на части
    2. ист. четвертовать
    3. геральд.
    1) делить (щит) на четверти
    2) помещать в одной из четвертей щита ( новый герб)
    4. астр. вступать в новую фазу ( о Луне)
    II [ʹkwɔ:tə] v
    1. 1) расквартировывать, ставить на постой (особ. войска)

    to quarter upon smb. - ставить на постой к кому-л.

    2) расквартировываться, размещаться по квартирам
    3) квартировать, жить (где-л.)
    4) refl селиться

    to quarter oneself on /with/ smb. - поселиться у кого-л.

    to quarter a covert [a field] - рыскать в чаще [по полю]

    2) мор. рыскать
    3. уступать дорогу; сворачивать в сторону, чтобы разъехаться
    4. мор. идти в бакштаг

    НБАРС > quarter

  • 63 mark

    [mɑːk] I сущ.
    а) (денежная единица Германии (до 2002 г.) и некоторых других государств)
    2) марка (мера веса для серебра и золота, составляет около 248 г, или 8 унций)
    II 1. сущ.
    1) знак, метка

    accent mark, stress mark — знак ударения

    exclamation mark, mark of exclamation — восклицательный знак

    question mark — знак вопроса, вопросительный знак

    Syn:
    2)
    а) штамп, штемпель
    б) фабричная марка, фабричное клеймо; торговая марка
    в) ярлык; ценник
    •
    Syn:
    device, stamp 1., seal II 1., label 1., brand 1.
    3)
    а) метка, ориентир; зарубка; веха
    4) спорт. линия старта, старт

    He had no chance: I was first off the mark. — У него не было никаких шансов, потому что я первым ушёл со старта.

    5)
    а) отпечаток, след

    distinguishing mark — отличительный знак, признак, примета

    to leave / make one's mark — оставлять след

    They will leave their mark in history. — Они оставят след в истории.

    б) шрам, рубец
    6) показатель, признак, характерная черта

    Life without intelligence is a possible mark of an animal. — Существование без интеллекта - возможный признак животного.

    Syn:
    7)
    а) мишень, цель прям. и перен.

    If that was meant to be an apology, your words were way off the mark. — Если предполагалось, что это извинение, то ваши слова отнюдь не достигли цели.

    Despite the fact that he was an expert rifleman, he did not hit the mark. — Несмотря на то что он был отличным стрелком, он промахнулся.

    Do not look from the mark to the arrow and back again. — Не переводи взгляд с мишени на стрелу и обратно.

    Syn:
    б) разг. человек или предмет, на который нацелен удар
    8) норма; уровень, стандарт; критерий, мерило

    The employee's work has been below the mark this week. — Работа служащего на этой неделе была ниже требуемого уровня.

    - below the mark
    - up to the mark
    - within the mark
    Syn:
    9) балл, отметка; оценка ( знаний) прям. и перен.

    The student received passing marks in all subjects. — Студент получил проходные баллы по всем предметам.

    He got high marks for honesty. — Он был в высшей степени честным человеком.

    Syn:
    10)
    а) известность; значительность, важность
    - make one's mark
    Syn:
    Syn:
    11)
    а) ист. рубеж; марка ( пограничная область)
    б) уст. граница, ограничение, предел, рубеж
    Syn:
    ••
    - soft mark
    - be off the mark     2. гл.
    1) ставить знак, ставить метку; применять обозначение
    2)
    а) штамповать, штемпелевать
    б) ставить фабричную марку, торговую марку
    3)
    а) отмечать, обозначать, размечать; ставить метки, вехи; очерчивать границы

    Now that it's spring, we must mark the tennis court out ready for play. — Настала весна, пора разметить теннисный корт.

    б) составлять карту, строить план
    Syn:
    chart 2.
    4)
    а) оставить след, пятно прям. и перен.

    That wet glass will mark the table. — Этот мокрый стакан оставит след на столе.

    б) оставлять шрам, рубец
    •
    Syn:
    5)
    а) ставить балл, отметку, оценивать

    The teacher marked the examination papers. — Учитель проставил оценки в экзаменационных работах.

    Syn:
    grade 2., correct 2., judge 2., rate I 2.
    6)
    а) отмечать, характеризовать, показывать

    Well-kept houses mark a good neighborhood. — Дома, которые содержатся в порядке, являются показателем хорошего соседского окружения.

    Syn:
    б) отличать, служить отличительным признаком; ознаменовывать

    The flamboyance marks her stage appearance. — Яркость всегда отличает её появление на сцене.

    This year marks Pushkin's 200th anniversary. — Этот год ознаменован двухсотлетней годовщиной со дня рождения Пушкина.

    •
    Syn:
    7) обращать внимание, замечать, запоминать

    Mark what he says. — Запомните его слова.

    Syn:
    8)
    а) = mark down кратко зафиксировать, занести (куда-л.); делать памятку

    He marked the date in his journal. — Он записал дату в своём дневнике.

    I marked down the address that she gave me over the telephone. — Я записал адрес, который она мне дала по телефону.

    Syn:
    jot 2.
    б) бирж. регистрировать биржевую сделку ( с включением её в официальную котировку)
    9) книжн. предназначать (для чего-л.), предполагать; предопределять, предрешать

    The persons whom he named became marked at once for persecution. (J. A. Froude) — Люди, которых он назвал, должны были немедленно подвергнуться гонениям.

    Syn:
    •
    - mark off
    - mark out
    - mark up
    ••

    Англо-русский современный словарь > mark

  • 64 Carrel, Alexis

    SUBJECT AREA: Medical technology
    [br]
    b. 28 June 1873 Lyon, France
    d. 5 November 1944 Paris, France
    [br]
    French surgeon and experimental biologist, pioneer of blood-vessel repair techniques and "in vitro" tissue culture.
    [br]
    He entered the university of Lyon as a medical student in 1890, but although attached to the Chasseurs Alpins as a surgeon, and to the department of anatomy, he did not qualify as a doctor until 1900. Soon after, he developed an interest in the repair of blood vessels and reported his first successes in 1902.
    In consequence of local political difficulties he left for Paris, and after a further year, in 1904, he became Assistant in Physiology at the University of Chicago. His further development of vascular surgical advances led to organ transplants in animals. By 1908 he had moved to in vitro cultivation of heart tissue from a chick embryo (a culture of which, in the care of an assistant, outlived him).
    He returned to service in the French Army in 1914 and was associated with Dakin in developing the irrigation treatment of infected wounds. In 1930 he initiated a programme aimed at the cultivation of whole organs, and with the assistance of a pump developed by Charles Lindbergh he succeeded in maintaining thyroid gland and kidney tissue for some weeks. Something of a mystic, Carrel returned to France in 1939 to head his Institute for the Study of Human Problems.
    [br]
    Principal Honours and Distinctions
    Nobel Prize for Medicine or Physiology 1912.
    Bibliography
    1911, "The surgery of blood vessels", Johns Hopkins Bulletin.
    1911, "Rejuvenation of cultures of tissues", Journal of the American Medical Association.
    1938, The Culture of Organs, New York. 1938, Man the Unknown, New York.
    Further Reading
    R.Soupault, 1952, Alexis Carrel. 1873–1944, Paris (contains full bibliography of papers).
    MG

    Biographical history of technology > Carrel, Alexis

  • 65 Scheutz, George

    [br]
    b. 23 September 1785 Jonkoping, Sweden
    d. 27 May 1873 Stockholm, Sweden
    [br]
    Swedish lawyer, journalist and self-taught engineer who, with his son Edvard Raphael Scheutz (b. 13 September 1821 Stockholm, Sweden; d. 28 January 1881 Stockholm, Sweden) constructed a version of the Babbage Difference Engine.
    [br]
    After early education at the Jonkoping elementary school and the Weixo Gymnasium, George Scheutz entered the University of Lund, gaining a degree in law in 1805. Following five years' legal work, he moved to Stockholm in 1811 to work at the Supreme Court and, in 1814, as a military auditor. In 1816, he resigned, bought a printing business and became editor of a succession of industrial and technical journals, during which time he made inventions relating to the press. It was in 1830 that he learned from the Edinburgh Review of Babbage's ideas for a difference engine and started to make one from wood, pasteboard and wire. In 1837 his 15-yearold student son, Edvard Raphael Scheutz, offered to make it in metal, and by 1840 they had a working machine with two five-digit registers, which they increased the following year and then added a printer. Obtaining a government grant in 1851, by 1853 they had a fully working machine, now known as Swedish Difference Engine No. 1, which with an experienced operator could generate 120 lines of tables per hour and was used to calculate the logarithms of the numbers 1 to 10,000 in under eighty hours. This was exhibited in London and then at the Paris Great Exhibition, where it won the Gold Medal. It was subsequently sold to the Dudley Observatory in Albany, New York, for US$5,000 and is now in a Chicago museum.
    In England, the British Registrar-General, wishing to produce new tables for insurance companies, and supported by the Astronomer Royal, arranged for government finance for construction of a second machine (Swedish Difference Engine No. 2). Comprising over 1,000 working parts and weighing 1,000 lb (450 kg), this machine was used to calculate over 600 tables. It is now in the Science Museum.
    [br]
    Principal Honours and Distinctions
    Member of the Swedish Academy of Sciences, Paris Exhibition Medal of Honour (jointly with Edvard) 1856. Annual pension of 1,200 marks per annum awarded by King Carl XV 1860.
    Bibliography
    1825, "Kranpunpar. George Scheutz's patent of 14 Nov 1825", Journal for Manufacturer och Hushallning 8.
    ellemême, Stockholm.
    Further Reading
    R.C.Archibald, 1947, "P.G.Scheutz, publicist, author, scientific mechanic and Edvard Scheutz, engineer. Biography and Bibliography", MTAC 238.
    U.C.Merzbach, 1977, "George Scheutz and the first printing calculator", Smithsonian
    Studies in History and Technology 36:73.
    M.Lindgren, 1990, Glory and Failure (the Difference Engines of Johan Muller, Charles Babbage and George \& Edvard Scheutz), Cambridge, Mass.: MIT Press.
    KF

    Biographical history of technology > Scheutz, George

  • 66 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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