basic phenomenon

n (pl phenomena)

- anti-social phenomenon

количество;
величина - small * of smth. небольшое количество чего-л.;
- he has any * of money у него денег хватает;
- * of business торговый оборот;
- * of housing жилищный фонд;
- * of turnover сумма оборота капитала;
- * of employment (экономика) занятость;
- in * по количеству, количественно;
- the * of clouds (метеорология) балл облачности;
- the * used (техническое) затрата;
- * of deflection( техническое) стрела прогиба все, весь объем, вся масса - a great * of negligence большая степень халатности;
непростительная небрежность;
- the * of evidence against him is great против него собрано огромное количество улик;
- he has an enormous * of energy он человек неистощимой энергии общая сумма, итог - what is the * of the debt? какова общая сумма долга? (бухгалтерское) основная сумма и проценты с нее - * due сумма к получению, причитающаяся сумма;
- * at risk (страхование) страховая сумма (to) составлять;
доходить до;
достигать;
равняться - the bill *s to $ 25 cчет составляет сумму в 25 долларов (to) быть равным, равносильным, равнозначным, означать - to * to a refusal быть равносильным отказу;
- to * to very little, not to * to much не иметь большого значения, очень мало значить;
- what does it * to? что это значит?;
- it *s to this это означает следующее становиться, добиваться - he'll never * to anything из него никогда ничего не выйдет

acquisition ~ сумма покупки acquisition ~ сумма приобретения

advance ~ авансовая сумма

aggregate ~ общее количество aggregate ~ общий итог aggregate ~ совокупная сумма aggregate ~ суммарное количество

amount быть равным, равнозначащим;
this amounts to a refusal это равносильно отказу ~ быть равным ~ величина ~ достигать ~ доходить (до какого-л. количества), составлять (сумму) ;
равняться;
the bill amount s to l 40 счет составляет сумму в 40 фунтов стерлингов ~ значительность, важность ~ итог ~ количество;
a large amount of work много работы ~ количество ~ объем ~ основная сумма и проценты с нее ~ равняться ~ составлять сумму ~ сумма, итог;
what is the amount of this? сколько это составляет? ~ сумма ~ (денежная) сумма

~ for distribution сумма к распределению

~ in arrears задолженная сумма

~ in damages сумма компенсации ущерба

~ in dispute спорная сумма

~ in notes сумма, указанная на банкнотах

~ in words сумма, выраженная словами

~ of advance сумма аванса

~ of appropriation сумма ассигнований

~ of bill сумма векселя ~ of bill сумма счета к оплате

~ of contract сумма контракта

~ of cumulative value adjustments полная сумма переоценок актива баланса в соответствии с его текущей стоимостью

~ of damage страх. сумма ущерба

~ of depreciation сумма амортизационных отчислений

~ of difference величина разницы

~ of dividends сумма дивидендов

~ of donation сумма пожертвования

~ of dues сумма сборов

~ of dues payable подлежащая оплате сумма сборов

~ of exemption сумма вычетов при расчете налогов

~ of fine сумма штрафа

~ of fixed assets сумма основного капитала

~ of grant сумма субсидии

~ of guarantee сумма залога

~ of income сумма дохода

~ of increase сумма прироста

~ of inheritance стоимость наследства

~ of loan сумма займа ~ of loan сумма кредита

~ of loans floated сумма размещенных займов

~ of loans raised сумма полученных займов

~ of loss сумма убытка

~ of maintenance сумма обеспечения

~ of money денежная сумма

~ of premium премиальная сумма

~ of provision сумма резерва

~ of quota сумма квоты

~ of reduction сумма сокращения расходов

~ of refund сумма рефинансирования

~ of repayment сумма погашения долга

~ of sale сумма продаж

~ of savings сумма накоплений

~ of simulation вчт. объем моделирования

~ of tax сумма налога

~ of tax payable подлежащая уплате сумма налога

~ of transfer сумма перевода

~ on deposit сумма вклада ~ on deposit сумма депозита

~ to достигать ~ to доходить до ~ to означать ~ to равняться ~ to составлять сумму

~ to be deducted сумма, подлежащая удержанию

~ to be paid сумма к оплате

to ~ to very little, not to ~ to much быть незначительным, не иметь большого значения;
what, after all, does it amount to? что, в конце концов, это означает?

annual ~ ежегодная сумма

annuity ~ сумма страхования ренты

appraised ~ оцененная сумма

assessed ~ оценочная стоимость

balance sheet ~ итоговая сумма балансового отчета

basic ~ исходное количество basic ~ основное количество, базовая сумма (при начислении пособия и т. п.)

~ доходить (до какого-л. количества), составлять (сумму) ;
равняться;
the bill amount s to l 40 счет составляет сумму в 40 фунтов стерлингов

calculated ~ вычисленная сумма

carrying ~ балансовый показатель

cash ~ сумма наличными

compensatory ~ сумма компенсации

conditional sale ~ сумма условной продажи

contracted ~ договорная сумма

cover ~ сумма покрытия

data ~ вчт. количество информации data ~ вчт. объем данных

debit ~ дебетовая сумма debit ~ дебетовый итог

differential ~ дифференциальная сумма

disability ~ пособие по инвалидности

documentary credit ~ сумма документарного аккредитива

to ~ to very little, not to ~ to much быть незначительным, не иметь большого значения;
what, after all, does it amount to? что, в конце концов, это означает?

dollar ~ сумма в долларах

donated ~ подаренная сумма

double-figure ~ двузначная сумма

due ~ причитающаяся сумма

earnings-related ~ сумма рассчитанная с учетом заработка

error ~ вчт. величина ошибки

estimated ~ рассчитанная величина

exact ~ точная сумма

exceptional ~ необычный итог

excess ~ избыточное количество excess ~ превышение установленной суммы excess ~ сумма превышения

final ~ итоговая сумма

fixed ~ постоянное количество

full ~ полная сумма

gross ~ валовая сумма gross ~ общее количество

guarantee ~ гарантийная сумма

immaterial ~ незначительное количество

income tax ~ сумма взимаемого подоходного налога

insurance ~ общая сумма страхования insurance ~ сумма страхования

interest ~ сумма процента

intervention ~ сумма интервенции

invested ~ инвестированная сумма

invoice ~ сумма фактуры

invoiced ~ сумма по счету

issue ~ сумма эмиссии

large ~ крупная сумма

~ количество;
a large amount of work много работы

maximum ~ максимальная сумма

minimum ~ минимальная сумма minimum ~ минимальное количество

monetary ~ денежная сумма

monetary compensatory ~ (MCA) сумма валютной компенсации

net ~ сумма-нетто

nominal ~ номинальная сумма

nonrecurring ~ единовременная сумма

to ~ to very little, not to ~ to much быть незначительным, не иметь большого значения;
what, after all, does it amount to? что, в конце концов, это означает?

notional principal ~ условная основная сумма кредитного обязательства в процентном свопе

odd ~ некруглая сумма

order ~ сумма заказа

outstanding ~ не предъявленная к платежу сумма outstanding ~ недоимка outstanding ~ неоплаченная сумма outstanding ~ непогашенная часть займа outstanding ~ сумма задолженности

pay an ~ выплачивать всю сумму

pension ~ сумма выплачиваемой пенсии

premium ~ сумма страхового взноса

purchase ~ объем закупок

recourse ~ юр. сумма с правом регресса

recoverable ~ возмещаемая сумма recoverable ~ количество, подлежащее возмещению

recovery ~ сумма страхового возмещения

remaining ~ остаток суммы

residual ~ остаток суммы residual ~ остаточная сумма

risk ~ рисковая сумма

sales ~ объем сбыта

subscribe an ~ подписываться на определенную сумму

subvention ~ сумма субсидии

supplementary ~ дополнительная сумма

surplus ~ избыточное количество

target ~ планируемая сумма

tax ~ размер налогов tax ~ сумма налогов

tax-exempt basic ~ основная сумма, не облагаемая налогом

tax-free ~ сумма, не облагаемая налогом

taxable ~ сумма, облагаемая налогом

amount быть равным, равнозначащим;
this amounts to a refusal это равносильно отказу

total ~ итог total ~ общая сумма

triple-figure ~ трехзначная сумма

to ~ to very little, not to ~ to much быть незначительным, не иметь большого значения;
what, after all, does it amount to? что, в конце концов, это означает? soever: ~ присоединяясь к словам who, what, when, how, where служит для усиления: in what place soever где бы то ни было what: what pron emph. какой!;
как!;
что!;
what a strange phenomenon! какое необычное явление!;
what an interesting book it is! какая интересная книга! ~ pron conj. какой, что, сколько;
I don't know what she wants я не знаю, что ей нужно;
like what's in your workers' eyes? например, что думают ваши рабочие? ~ pron inter. какой?, что?, сколько?;
what is it? что это (такое?)

~ сумма, итог;
what is the amount of this? сколько это составляет?

withdrawal ~ сумма, снятая со счета

[əˈmaunt]

acquisition amount сумма покупки acquisition amount сумма приобретения advance amount авансовая сумма aggregate amount общее количество aggregate amount общий итог aggregate amount совокупная сумма aggregate amount суммарное количество amount быть равным, равнозначащим; this amounts to a refusal это равносильно отказу amount быть равным amount величина amount достигать amount доходить (до какого-л. количества), составлять (сумму); равняться; the bill amount s to l 40 счет составляет сумму в 40 фунтов стерлингов amount значительность, важность amount итог amount количество; a large amount of work много работы amount количество amount объем amount основная сумма и проценты с нее amount равняться amount составлять сумму amount сумма, итог; what is the amount of this? сколько это составляет? amount сумма amount (денежная) сумма amount for distribution сумма к распределению amount in arrears задолженная сумма amount in damages сумма компенсации ущерба amount in dispute спорная сумма amount in notes сумма, указанная на банкнотах amount in words сумма, выраженная словами amount of advance сумма аванса amount of appropriation сумма ассигнований amount of bill сумма векселя amount of bill сумма счета к оплате amount of contract сумма контракта amount of cumulative value adjustments полная сумма переоценок актива баланса в соответствии с его текущей стоимостью amount of damage страх. сумма ущерба amount of depreciation сумма амортизационных отчислений amount of difference величина разницы amount of dividends сумма дивидендов amount of donation сумма пожертвования amount of dues сумма сборов amount of dues payable подлежащая оплате сумма сборов amount of exemption сумма вычетов при расчете налогов amount of fine сумма штрафа amount of fixed assets сумма основного капитала amount of grant сумма субсидии amount of guarantee сумма залога amount of income сумма дохода amount of increase сумма прироста amount of inheritance стоимость наследства amount of loan сумма займа amount of loan сумма кредита amount of loans floated сумма размещенных займов amount of loans raised сумма полученных займов amount of loss сумма убытка amount of maintenance сумма обеспечения amount of money денежная сумма amount of premium премиальная сумма amount of provision сумма резерва amount of quota сумма квоты amount of reduction сумма сокращения расходов amount of refund сумма рефинансирования amount of repayment сумма погашения долга amount of sale сумма продаж amount of savings сумма накоплений amount of simulation вчт. объем моделирования amount of tax сумма налога amount of tax payable подлежащая уплате сумма налога amount of transfer сумма перевода amount on deposit сумма вклада amount on deposit сумма депозита amount to достигать amount to доходить до amount to означать amount to равняться amount to составлять сумму amount to be deducted сумма, подлежащая удержанию amount to be paid сумма к оплате to amount to very little, not to amount to much быть незначительным, не иметь большого значения; what, after all, does it amount to? что, в конце концов, это означает? annual amount ежегодная сумма annuity amount сумма страхования ренты appraised amount оцененная сумма assessed amount оценочная стоимость balance sheet amount итоговая сумма балансового отчета basic amount исходное количество basic amount основное количество, базовая сумма (при начислении пособия и т. п.) amount доходить (до какого-л. количества), составлять (сумму); равняться; the bill amount s to l 40 счет составляет сумму в 40 фунтов стерлингов calculated amount вычисленная сумма carrying amount балансовый показатель cash amount сумма наличными compensatory amount сумма компенсации conditional sale amount сумма условной продажи contracted amount договорная сумма cover amount сумма покрытия data amount вчт. количество информации data amount вчт. объем данных debit amount дебетовая сумма debit amount дебетовый итог differential amount дифференциальная сумма disability amount пособие по инвалидности documentary credit amount сумма документарного аккредитива to amount to very little, not to amount to much быть незначительным, не иметь большого значения; what, after all, does it amount to? что, в конце концов, это означает? dollar amount сумма в долларах donated amount подаренная сумма double-figure amount двузначная сумма due amount причитающаяся сумма earnings-related amount сумма рассчитанная с учетом заработка error amount вчт. величина ошибки estimated amount рассчитанная величина exact amount точная сумма exceptional amount необычный итог excess amount избыточное количество excess amount превышение установленной суммы excess amount сумма превышения final amount итоговая сумма fixed amount постоянное количество full amount полная сумма gross amount валовая сумма gross amount общее количество guarantee amount гарантийная сумма immaterial amount незначительное количество income tax amount сумма взимаемого подоходного налога insurance amount общая сумма страхования insurance amount сумма страхования interest amount сумма процента intervention amount сумма интервенции invested amount инвестированная сумма invoice amount сумма фактуры invoiced amount сумма по счету issue amount сумма эмиссии large amount крупная сумма amount количество; a large amount of work много работы maximum amount максимальная сумма minimum amount минимальная сумма minimum amount минимальное количество monetary amount денежная сумма monetary compensatory amount (MCA) сумма валютной компенсации net amount сумма-нетто nominal amount номинальная сумма nonrecurring amount единовременная сумма to amount to very little, not to amount to much быть незначительным, не иметь большого значения; what, after all, does it amount to? что, в конце концов, это означает? notional principal amount условная основная сумма кредитного обязательства в процентном свопе odd amount некруглая сумма order amount сумма заказа outstanding amount не предъявленная к платежу сумма outstanding amount недоимка outstanding amount неоплаченная сумма outstanding amount непогашенная часть займа outstanding amount сумма задолженности pay an amount выплачивать всю сумму pension amount сумма выплачиваемой пенсии premium amount сумма страхового взноса purchase amount объем закупок recourse amount юр. сумма с правом регресса recoverable amount возмещаемая сумма recoverable amount количество, подлежащее возмещению recovery amount сумма страхового возмещения remaining amount остаток суммы residual amount остаток суммы residual amount остаточная сумма risk amount рисковая сумма sales amount объем сбыта subscribe an amount подписываться на определенную сумму subvention amount сумма субсидии supplementary amount дополнительная сумма surplus amount избыточное количество target amount планируемая сумма tax amount размер налогов tax amount сумма налогов tax-exempt basic amount основная сумма, не облагаемая налогом tax-free amount сумма, не облагаемая налогом taxable amount сумма, облагаемая налогом amount быть равным, равнозначащим; this amounts to a refusal это равносильно отказу total amount итог total amount общая сумма triple-figure amount трехзначная сумма to amount to very little, not to amount to much быть незначительным, не иметь большого значения; what, after all, does it amount to? что, в конце концов, это означает? soever: amount присоединяясь к словам who, what, when, how, where служит для усиления: in what place soever где бы то ни было what: what pron emph. какой!; как!; что!; what a strange phenomenon! какое необычное явление!; what an interesting book it is! какая интересная книга! amount pron conj. какой, что, сколько; I don't know what she wants я не знаю, что ей нужно; like what's in your workers' eyes? например, что думают ваши рабочие? amount pron inter. какой?, что?, сколько?; what is it? что это (такое?) amount сумма, итог; what is the amount of this? сколько это составляет? withdrawal amount сумма, снятая со счета

   1) The Basic Idea of Cognitive Science

   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.... [P]eople and intelligent computers turn out to be merely different manifestations of the same underlying phenomenon. (Haugeland, 1981b, p. 31)

   2) Experimental Psychology, Theoretical Linguistics, and Computational Simulation of Cognitive Processes Are All Components of Cognitive Science

   I went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns.... I have been working toward a cognitive science for about twenty years beginning before I knew what to call it. (G. A. Miller, 1979, p. 9)

   3) The Nature of Cognitive Science

    Cognitive Science studies the nature of cognition in human beings, other animals, and inanimate machines (if such a thing is possible). While computers are helpful within cognitive science, they are not essential to its being. A science of cognition could still be pursued even without these machines.

    Computer Science studies various kinds of problems and the use of computers to solve them, without concern for the means by which we humans might otherwise resolve them. There could be no computer science if there were no machines of this kind, because they are indispensable to its being. Artificial Intelligence is a special branch of computer science that investigates the extent to which the mental powers of human beings can be captured by means of machines.

   There could be cognitive science without artificial intelligence but there could be no artificial intelligence without cognitive science. One final caveat: In the case of an emerging new discipline such as cognitive science there is an almost irresistible temptation to identify the discipline itself (as a field of inquiry) with one of the theories that inspired it (such as the computational conception...). This, however, is a mistake. The field of inquiry (or "domain") stands to specific theories as questions stand to possible answers. The computational conception should properly be viewed as a research program in cognitive science, where "research programs" are answers that continue to attract followers. (Fetzer, 1996, pp. xvi-xvii)

   4) The Nature of Cognitive Science

   What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.

   The psychologist who studies "knowledge systems" wants to know how concepts are structured in the human mind, how such concepts develop, and how they are used in understanding and behavior. The artificial intelligence researcher wants to know how to program a computer so that it can understand and interact with the outside world. The two orientations intersect when the psychologist and the computer scientist agree that the best way to approach the problem of building an intelligent machine is to emulate the human conceptual mechanisms that deal with language.... The name "cognitive science" has been used to refer to this convergence of interests in psychology and artificial intelligence....

   This working partnership in "cognitive science" does not mean that psychologists and computer scientists are developing a single comprehensive theory in which people are no different from machines. Psychology and artificial intelligence have many points of difference in methods and goals.... We simply want to work on an important area of overlapping interest, namely a theory of knowledge systems. As it turns out, this overlap is substantial. For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. (Schank & Abelson, 1977, pp. 1-2)

   5) The New Field of Cognitive Science

   Within the last dozen years a general change in scientific outlook has occurred, consonant with the point of view represented here. One can date the change roughly from 1956: in psychology, by the appearance of Bruner, Goodnow, and Austin's Study of Thinking and George Miller's "The Magical Number Seven"; in linguistics, by Noam Chomsky's "Three Models of Language"; and in computer science, by our own paper on the Logic Theory Machine. (Newell & Simon, 1972, p. 4)

   1) All Human Complex Problem Solving Is Creativity

   Put in this bald way, these aims sound utopian. How utopian they areor rather, how imminent their realization-depends on how broadly or narrowly we interpret the term "creative." If we are willing to regard all human complex problem solving as creative, then-as we will point out-successful programs for problem solving mechanisms that simulate human problem solvers already exist, and a number of their general characteristics are known. If we reserve the term "creative" for activities like discovery of the special theory of relativity or the composition of Beethoven's Seventh Symphony, then no example of a creative mechanism exists at the present time. (Simon, 1979, pp. 144-145)

   2) Artificial Intelligence Models of Creative Association

   Among the questions that can now be given preliminary answers in computational terms are the following: how can ideas from very different sources be spontaneously thought of together? how can two ideas be merged to produce a new structure, which shows the influence of both ancestor ideas without being a mere "cut-and-paste" combination? how can the mind be "primed," so that one will more easily notice serendipitous ideas? why may someone notice-and remember-something fairly uninteresting, if it occurs in an interesting context? how can a brief phrase conjure up an entire melody from memory? and how can we accept two ideas as similar ("love" and "prove" as rhyming, for instance) in respect of a feature not identical in both? The features of connectionist AI models that suggest answers to these questions are their powers of pattern completion, graceful degradation, sensitization, multiple constraint satisfaction, and "best-fit" equilibration.... Here, the important point is that the unconscious, "insightful," associative aspects of creativity can be explained-in outline, at least-by AI methods. (Boden, 1996, p. 273)

   3) Creative Innovation and Social Independence

   There thus appears to be an underlying similarity in the process involved in creative innovation and social independence, with common traits and postures required for expression of both behaviors. The difference is one of product-literary, musical, artistic, theoretical products on the one hand, opinions on the other-rather than one of process. In both instances the individual must believe that his perceptions are meaningful and valid and be willing to rely upon his own interpretations. He must trust himself sufficiently that even when persons express opinions counter to his own he can proceed on the basis of his own perceptions and convictions. (Coopersmith, 1967, p. 58)

   4) Ego Strength and Emotional Stability among Creative Geniuses

   he average level of ego strength and emotional stability is noticeably higher among creative geniuses than among the general population, though it is possibly lower than among men of comparable intelligence and education who go into administrative and similar positions. High anxiety and excitability appear common (e.g. Priestley, Darwin, Kepler) but full-blown neurosis is quite rare. (Cattell & Butcher, 1970, p. 315)

   5) Its Mundane Character

   he insight that is supposed to be required for such work as discovery turns out to be synonymous with the familiar process of recognition; and other terms commonly used in the discussion of creative work-such terms as "judgment," "creativity," or even "genius"-appear to be wholly dispensable or to be definable, as insight is, in terms of mundane and well-understood concepts. (Simon, 1989, p. 376)

   6) Mozart's Musical Ideas Came to Him in Polished Form

   From the sketch material still in existence, from the condition of the fragments, and from the autographs themselves we can draw definite conclusions about Mozart's creative process. To invent musical ideas he did not need any stimulation; they came to his mind "ready-made" and in polished form. In contrast to Beethoven, who made numerous attempts at shaping his musical ideas until he found the definitive formulation of a theme, Mozart's first inspiration has the stamp of finality. Any Mozart theme has completeness and unity; as a phenomenon it is a Gestalt. (Herzmann, 1964, p. 28)

   7) Scientific Theories and Works of Art Alike Originate in Fantasy

   Great artists enlarge the limits of one's perception. Looking at the world through the eyes of Rembrandt or Tolstoy makes one able to perceive aspects of truth about the world which one could not have achieved without their aid. Freud believed that science was adaptive because it facilitated mastery of the external world; but was it not the case that many scientific theories, like works of art, also originated in phantasy? Certainly, reading accounts of scientific discovery by men of the calibre of Einstein compelled me to conclude that phantasy was not merely escapist, but a way of reaching new insights concerning the nature of reality. Scientific hypotheses require proof; works of art do not. Both are concerned with creating order, with making sense out of the world and our experience of it. (Storr, 1993, p. xii)

   8) Self- Esteem and Creative Expression

   The importance of self-esteem for creative expression appears to be almost beyond disproof. Without a high regard for himself the individual who is working in the frontiers of his field cannot trust himself to discriminate between the trivial and the significant. Without trust in his own powers the person seeking improved solutions or alternative theories has no basis for distinguishing the significant and profound innovation from the one that is merely different.... An essential component of the creative process, whether it be analysis, synthesis, or the development of a new perspective or more comprehensive theory, is the conviction that one's judgment in interpreting the events is to be trusted. (Coopersmith, 1967, p. 59)

   9) Stages in Creative Problem- Solving

   In the daily stream of thought these four different stages [preparation; incubation; illumination or inspiration; and verification] constantly overlap each other as we explore different problems. An economist reading a Blue Book, a physiologist watching an experiment, or a business man going through his morning's letters, may at the same time be "incubating" on a problem which he proposed to himself a few days ago, be accumulating knowledge in "preparation" for a second problem, and be "verifying" his conclusions to a third problem. Even in exploring the same problem, the mind may be unconsciously incubating on one aspect of it, while it is consciously employed in preparing for or verifying another aspect. (Wallas, 1926, p. 81)

    10) The Bisociative Pattern of the Creative Synthesis

   he basic, bisociative pattern of the creative synthesis [is] the sudden interlocking of two previously unrelated skills, or matrices of thought. (Koestler, 1964, p. 121)

    11) The Earliest Stages in the Creative Process Involve a Commerce with Disorder

   Even to the creator himself, the earliest effort may seem to involve a commerce with disorder. For the creative order, which is an extension of life, is not an elaboration of the established, but a movement beyond the established, or at least a reorganization of it and often of elements not included in it. The first need is therefore to transcend the old order. Before any new order can be defined, the absolute power of the established, the hold upon us of what we know and are, must be broken. New life comes always from outside our world, as we commonly conceive that world. This is the reason why, in order to invent, one must yield to the indeterminate within him, or, more precisely, to certain illdefined impulses which seem to be of the very texture of the ungoverned fullness which John Livingston Lowes calls "the surging chaos of the unexpressed." (Ghiselin, 1985, p. 4)

    12) The Inner Life of the Creative Process

   New life comes always from outside our world, as we commonly conceive our world. This is the reason why, in order to invent, one must yield to the indeterminate within him, or, more precisely, to certain illdefined impulses which seem to be of the very texture of the ungoverned fullness which John Livingston Lowes calls "the surging chaos of the unexpressed." Chaos and disorder are perhaps the wrong terms for that indeterminate fullness and activity of the inner life. For it is organic, dynamic, full of tension and tendency. What is absent from it, except in the decisive act of creation, is determination, fixity, and commitment to one resolution or another of the whole complex of its tensions. (Ghiselin, 1952, p. 13)

    13) The Problem of What Impels the Creative Person

   [P]sychoanalysts have principally been concerned with the content of creative products, and with explaining content in terms of the artist's infantile past. They have paid less attention to examining why the artist chooses his particular activity to express, abreact or sublimate his emotions. In short, they have not made much distinction between art and neurosis; and, since the former is one of the blessings of mankind, whereas the latter is one of the curses, it seems a pity that they should not be better differentiated....

   Psychoanalysis, being fundamentally concerned with drive and motive, might have been expected to throw more light upon what impels the creative person that in fact it has. (Storr, 1993, pp. xvii, 3)

    14) Theories of Creative Thinking

   A number of theoretical approaches were considered. Associative theory, as developed by Mednick (1962), gained some empirical support from the apparent validity of the Remote Associates Test, which was constructed on the basis of the theory.... Koestler's (1964) bisociative theory allows more complexity to mental organization than Mednick's associative theory, and postulates "associative contexts" or "frames of reference." He proposed that normal, non-creative, thought proceeds within particular contexts or frames and that the creative act involves linking together previously unconnected frames.... Simonton (1988) has developed associative notions further and explored the mathematical consequences of chance permutation of ideas....

   Like Koestler, Gruber (1980; Gruber and Davis, 1988) has based his analysis on case studies. He has focused especially on Darwin's development of the theory of evolution. Using piagetian notions, such as assimilation and accommodation, Gruber shows how Darwin's system of ideas changed very slowly over a period of many years. "Moments of insight," in Gruber's analysis, were the culminations of slow long-term processes.... Finally, the information-processing approach, as represented by Simon (1966) and Langley et al. (1987), was considered.... [Simon] points out the importance of good problem representations, both to ensure search is in an appropriate problem space and to aid in developing heuristic evaluations of possible research directions.... The work of Langley et al. (1987) demonstrates how such search processes, realized in computer programs, can indeed discover many basic laws of science from tables of raw data.... Boden (1990a, 1994) has stressed the importance of restructuring the problem space in creative work to develop new genres and paradigms in the arts and sciences. (Gilhooly, 1996, pp. 243-244; emphasis in original)

   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)