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universal+component

  • 1 Universal Component System

    Electronics: UCS

    Универсальный русско-английский словарь > Universal Component System

  • 2 Universal Component eXtensions

    Software: UCX

    Универсальный русско-английский словарь > Universal Component eXtensions

  • 3 всюду распространённый компонент

    Универсальный русско-английский словарь > всюду распространённый компонент

  • 4 UPC

    Англо-русский словарь промышленной и научной лексики > UPC

  • 5 análisis

    m. s.&pl.
    1 analysis, inspection, investigation, examination.
    2 analysis, breakdown, dissection.
    3 assay.
    * * *
    1 analysis
    \
    análisis de orina urine test
    análisis de sangre blood test
    * * *
    noun m.
    2) test
    * * *
    SM INV
    1) (=examen) analysis; [detallado] breakdown
    2) (Econ)
    3) (Med, Quím, Fís)
    4) (Ling) analysis, parsing
    5) (Inform)
    * * *
    masculino (pl análisis) analysis
    * * *
    = analysis [analyses, -pl.], assessment, probing, review, breakdown, calibration, close look, post mortem [postmortem], overview, academic study, surveying, testing.
    Ex. The operation of investigating a whole with the aim of finding out its essential parts and their relationship to each other is known as analysis.
    Ex. However, although the subject may be the primary consideration in the assessment of relevance, subject is not the only factor that determines whether a user wishes to be alerted to the existence of a document.
    Ex. Counselling requires much more time and in-depth probing, although it can at one extreme cover simply the act of lending a sympathetic ear to clients who, in externalizing their problems, may thus be better able to face them and arrive at a solution.
    Ex. The review is supported by a complete list of LIPs completed or in progess at Aug 88, followed by references to their reports.
    Ex. When she arrived at her boss's office at the appointed time, she learned why she had been asked for the breakdown of her day's activities.
    Ex. This requires careful calibration of reader response and the use of as many quantitative indices as possible.
    Ex. The article has the title 'A close look at Dewey 18: alive and well and living in Albany'.
    Ex. Survey research is used to determine what kind of post mortem appraisals companies undertake concerning their abandoned information systems development projects.
    Ex. Figure 16 on page 24 gives an overview of searching.
    Ex. Cyberculture is emerging as an interdisciplinary subject of academic study.
    Ex. The author describes one effort made to counter this trend, through the surveying of the records of a library and the identification of materials to be preserved.
    Ex. Attention has focussed on the labelling of foodstuffs and the testing and approval of food additives.
    ----
    * análisis bibliométrico = bibliometric analysis.
    * análisis cientométrico = scientometric analysis.
    * análisis cinematográfico = film analysis.
    * análisis cluster = cluster analysis.
    * análisis conceptual = conceptual analysis.
    * análisis crítico = critical eye, critical analysis.
    * análisis cualitativo = qualitative analysis.
    * análisis cuantitativo = quantitative analysis.
    * análisis de agrupamiento por cocitas = cocitation cluster analysis.
    * análisis de áreas del conocimiento = domain analysis.
    * análisis de citas = citation analysis.
    * análisis de cocitas = cocitation analysis.
    * análisis de cocitas de autores = author co-citation analysis.
    * análisis de componentes principales = principal component(s) analysis.
    * análisis de contabilidad = financial analysis.
    * análisis de contenido = content analysis, conceptual analysis.
    * análisis de coocurrencia de términos = co-word analysis.
    * análisis de correlación = correlation analysis.
    * análisis de costes = cost analysis.
    * análisis de costes-beneficios = cost-benefit analysis.
    * análisis de costos-beneficios = cost-benefit analysis.
    * análisis de dominios del conocimiento = domain analysis.
    * análisis de errores = error analysis.
    * análisis de grupo = cohort analysis.
    * análisis de laboratorio = laboratory analysis.
    * análisis de la colección = collection analysis.
    * análisis de la coocurrencia de palabras = co-word analysis.
    * análisis del contenido = document analysis, subject analysis, content analysis.
    * análisis del discurso = discourse analysis.
    * análisis del rendimiento = performance analysis.
    * análisis de necesidades = needs analysis.
    * análisis de regresión múltiple = multiple regression analysis.
    * análisis de rendimiento = performance test.
    * análisis de riesgos = risk analysis, risk assessment, risk evaluation.
    * análisis de sangre = blood test.
    * análisis de sistemas = system(s) analysis.
    * análisis detallado = close examination.
    * análisis de tendencias = trend analysis.
    * análisis de una muestra representativa = cross-sectional analysis.
    * análisis de varianza (ANOVA) = analysis of variance (ANOVA).
    * análisis diagnóstico = diagnostic test.
    * análisis discriminante = discriminant analysis.
    * análisis documental = document analysis, subject analysis.
    * análisis escalar = scaling analysis.
    * análisis escalar de Guttman = Guttman scale analysis.
    * análisis espacial = spatial analysis.
    * análisis estadístico = statistical analysis.
    * análisis estadístico multivariante = multivariate statistical analysis.
    * análisis facetado = facet analysis.
    * análisis factorial = factor analysis.
    * análisis formal de documentos = markup [mark-up].
    * análisis léxico = lexical analysis.
    * análisis literario = literary analysis.
    * análisis longitudinal = longitudinal analysis.
    * análisis más detallado = close attention.
    * análisis más minucioso = closer examination.
    * análisis minucioso = scrutiny, dissection, cross examination.
    * análisis morfológico = morphological analysis.
    * análisis multidimensional de clases = multidimensional cluster analysis.
    * análisis multidimensional escalar = multidimensional scaling analysis.
    * análisis multivariable = multivariate analysis, multivariate test.
    * análisis multivariante = multivariate analysis, multivariate test.
    * análisis municioso = close examination.
    * análisis por facetas = facet analysis.
    * análisis por género = gender analysis.
    * análisis químico = chemical analysis.
    * análisis sintáctico = syntactical analysis.
    * análisis topográfico = surveying.
    * análisis univariante = univariate test.
    * bloque funcional de análisis de contenido = subject analysis block.
    * centro de análisis de la información = information analysis centre.
    * lenguaje para el análisis formal de documentos web = markup language.
    * modelo de análisis de costes = cost model.
    * nuevo análisis = reanalysis [reanalyses, -pl.].
    * programa de análisis de ficheros de transacciones = log analysis software.
    * realizar un análisis = conduct + analysis.
    * realizar un análisis factorial = factor-analyse [factor-analyze, -USA].
    * segundo análisis = re-examination [reexamination].
    * SGML (Lenguaje Estándar Universal para el Análisis Formal de Documentos) = SGML (Standard Generalised Markup Language).
    * sistema para el análisis formal de documentos = markup code.
    * sistema para el análisis formal de documentos web = markup system.
    * superar un análisis minucioso = stand up to + scrutiny, stand up to + examination.
    * unidad de análisis = unit of study.
    * XML (Lenguaje Extensible para el Análisis de Documentos) = XML (Extensible Markup Language).
    * * *
    masculino (pl análisis) analysis
    * * *
    = analysis [analyses, -pl.], assessment, probing, review, breakdown, calibration, close look, post mortem [postmortem], overview, academic study, surveying, testing.

    Ex: The operation of investigating a whole with the aim of finding out its essential parts and their relationship to each other is known as analysis.

    Ex: However, although the subject may be the primary consideration in the assessment of relevance, subject is not the only factor that determines whether a user wishes to be alerted to the existence of a document.
    Ex: Counselling requires much more time and in-depth probing, although it can at one extreme cover simply the act of lending a sympathetic ear to clients who, in externalizing their problems, may thus be better able to face them and arrive at a solution.
    Ex: The review is supported by a complete list of LIPs completed or in progess at Aug 88, followed by references to their reports.
    Ex: When she arrived at her boss's office at the appointed time, she learned why she had been asked for the breakdown of her day's activities.
    Ex: This requires careful calibration of reader response and the use of as many quantitative indices as possible.
    Ex: The article has the title 'A close look at Dewey 18: alive and well and living in Albany'.
    Ex: Survey research is used to determine what kind of post mortem appraisals companies undertake concerning their abandoned information systems development projects.
    Ex: Figure 16 on page 24 gives an overview of searching.
    Ex: Cyberculture is emerging as an interdisciplinary subject of academic study.
    Ex: The author describes one effort made to counter this trend, through the surveying of the records of a library and the identification of materials to be preserved.
    Ex: Attention has focussed on the labelling of foodstuffs and the testing and approval of food additives.
    * análisis bibliométrico = bibliometric analysis.
    * análisis cientométrico = scientometric analysis.
    * análisis cinematográfico = film analysis.
    * análisis cluster = cluster analysis.
    * análisis conceptual = conceptual analysis.
    * análisis crítico = critical eye, critical analysis.
    * análisis cualitativo = qualitative analysis.
    * análisis cuantitativo = quantitative analysis.
    * análisis de agrupamiento por cocitas = cocitation cluster analysis.
    * análisis de áreas del conocimiento = domain analysis.
    * análisis de citas = citation analysis.
    * análisis de cocitas = cocitation analysis.
    * análisis de cocitas de autores = author co-citation analysis.
    * análisis de componentes principales = principal component(s) analysis.
    * análisis de contabilidad = financial analysis.
    * análisis de contenido = content analysis, conceptual analysis.
    * análisis de coocurrencia de términos = co-word analysis.
    * análisis de correlación = correlation analysis.
    * análisis de costes = cost analysis.
    * análisis de costes-beneficios = cost-benefit analysis.
    * análisis de costos-beneficios = cost-benefit analysis.
    * análisis de dominios del conocimiento = domain analysis.
    * análisis de errores = error analysis.
    * análisis de grupo = cohort analysis.
    * análisis de laboratorio = laboratory analysis.
    * análisis de la colección = collection analysis.
    * análisis de la coocurrencia de palabras = co-word analysis.
    * análisis del contenido = document analysis, subject analysis, content analysis.
    * análisis del discurso = discourse analysis.
    * análisis del rendimiento = performance analysis.
    * análisis de necesidades = needs analysis.
    * análisis de regresión múltiple = multiple regression analysis.
    * análisis de rendimiento = performance test.
    * análisis de riesgos = risk analysis, risk assessment, risk evaluation.
    * análisis de sangre = blood test.
    * análisis de sistemas = system(s) analysis.
    * análisis detallado = close examination.
    * análisis de tendencias = trend analysis.
    * análisis de una muestra representativa = cross-sectional analysis.
    * análisis de varianza (ANOVA) = analysis of variance (ANOVA).
    * análisis diagnóstico = diagnostic test.
    * análisis discriminante = discriminant analysis.
    * análisis documental = document analysis, subject analysis.
    * análisis escalar = scaling analysis.
    * análisis escalar de Guttman = Guttman scale analysis.
    * análisis espacial = spatial analysis.
    * análisis estadístico = statistical analysis.
    * análisis estadístico multivariante = multivariate statistical analysis.
    * análisis facetado = facet analysis.
    * análisis factorial = factor analysis.
    * análisis formal de documentos = markup [mark-up].
    * análisis léxico = lexical analysis.
    * análisis literario = literary analysis.
    * análisis longitudinal = longitudinal analysis.
    * análisis más detallado = close attention.
    * análisis más minucioso = closer examination.
    * análisis minucioso = scrutiny, dissection, cross examination.
    * análisis morfológico = morphological analysis.
    * análisis multidimensional de clases = multidimensional cluster analysis.
    * análisis multidimensional escalar = multidimensional scaling analysis.
    * análisis multivariable = multivariate analysis, multivariate test.
    * análisis multivariante = multivariate analysis, multivariate test.
    * análisis municioso = close examination.
    * análisis por facetas = facet analysis.
    * análisis por género = gender analysis.
    * análisis químico = chemical analysis.
    * análisis sintáctico = syntactical analysis.
    * análisis topográfico = surveying.
    * análisis univariante = univariate test.
    * bloque funcional de análisis de contenido = subject analysis block.
    * centro de análisis de la información = information analysis centre.
    * lenguaje para el análisis formal de documentos web = markup language.
    * modelo de análisis de costes = cost model.
    * nuevo análisis = reanalysis [reanalyses, -pl.].
    * programa de análisis de ficheros de transacciones = log analysis software.
    * realizar un análisis = conduct + analysis.
    * realizar un análisis factorial = factor-analyse [factor-analyze, -USA].
    * segundo análisis = re-examination [reexamination].
    * SGML (Lenguaje Estándar Universal para el Análisis Formal de Documentos) = SGML (Standard Generalised Markup Language).
    * sistema para el análisis formal de documentos = markup code.
    * sistema para el análisis formal de documentos web = markup system.
    * superar un análisis minucioso = stand up to + scrutiny, stand up to + examination.
    * unidad de análisis = unit of study.
    * XML (Lenguaje Extensible para el Análisis de Documentos) = XML (Extensible Markup Language).

    * * *
    A (de una situación, un tema) analysis
    hizo un análisis del problema he analyzed o carried out an analysis of the problem
    Compuesto:
    análisis de costo-beneficio or ( Esp) coste-beneficio
    cost-benefit analysis
    B ( Med, Quím) analysis
    hacerse un análisis de orina/sangre to have a urine/blood test
    Compuestos:
    clinical analysis
    spectrum analysis
    organic analysis
    C ( Ling) analysis
    Compuestos:
    discourse analysis
    grammatical analysis
    syntactic analysis
    D ( Mat) analysis, calculus
    E ( Psic) analysis
    * * *

     

    análisis sustantivo masculino (pl

    hacerse un análisis de sangre to have a blood test
    análisis m inv
    1 analysis
    2 Med test: tengo que hacerme unos análisis, I have to have some tests done

    ' análisis' also found in these entries:
    Spanish:
    detenida
    - detenido
    - factorial
    - microscópica
    - microscópico
    - negativa
    - negativo
    - ponderación
    - positiva
    - positivo
    - sintética
    - sintético
    - citología
    - comentario
    - concienzudo
    - dar
    - estudio
    - lúcido
    English:
    analysis
    - blood test
    - breakdown
    - test
    - bear
    - blood
    - positive
    - right
    * * *
    análisis nm inv
    1. [de situación, problema] analysis;
    Com análisis del camino crítico critical path analysis; Esp Econ análisis coste-beneficio cost-benefit analysis; Econ análisis de costo-beneficio cost-benefit analysis;
    análisis cualitativo qualitative analysis;
    análisis cuantitativo quantitative analysis;
    Ling análisis del discurso discourse analysis;
    análisis de mercado market analysis
    2. [médico] analysis
    análisis clínico (clinical) test;
    análisis de orina urine test;
    análisis químico chemical analysis;
    3. Gram analysis
    análisis gramatical sentence analysis;
    análisis sintáctico syntactic analysis
    4. Informát analysis
    análisis de sistemas systems analysis
    5. Mat analysis
    6. Psi analysis
    * * *
    m inv analysis
    * * *
    : analysis
    * * *
    análisis n analysis [pl. analyses]

    Spanish-English dictionary > análisis

  • 6 модуль

    1) <math.> absolute value

    2) absolute value sign
    3) common difference
    4) magnitude
    5) module
    6) modulus
    – биградуированный модуль
    – герметизировать модуль
    – гидравлический модуль
    – двойной модуль
    – интегральный модуль
    – логический модуль
    – лунный модуль
    – микроминиатюрный модуль
    – многоштырьковый модуль
    – модуль вектора
    – модуль вытягивания
    – модуль гипергомологий
    – модуль гиперкогомологий
    – модуль затухания
    – модуль изгиба
    – модуль конечнопорожденный
    – модуль крупности
    – модуль кручения
    – модуль мерсеризации
    – модуль основания
    – модуль пластичности
    – модуль преломления
    – модуль проводимости
    – модуль разрыва
    – модуль расстояния
    – модуль сдвига
    – модуль согласования
    – модуль сопротивления
    – модуль сравнения
    – модуль стока
    – модуль точности
    – модуль упругости
    – модуль шестерни
    – молекулярный модуль
    – опрессовывать модуль
    – основной модуль
    – пакетизованный модуль
    – пьезоэлектрический модуль
    – резистивно-емкостный модуль
    – скрещенный модуль
    – сменный модуль
    – сотовый модуль
    – термоэлектрический модуль
    – тонкопленочный модуль
    – управляющий модуль

    динамический модуль упругости — dynamic modulus of elasticity


    касательный модуль упругости — tangent modulus of elasticity


    линейный интерфейсный модуль — line interfase module


    мелкий модуль шестерни — fine pitch of gear


    микропроцессорный одноплатный модуль — single board


    модуль восстановления памяти — memory reclaimer


    модуль всестороннего сжатия — compression modulus


    модуль комплексного числа — complex number modulus


    модуль на керамической основе — ceramic-base module


    модуль на таблеточных элементах — pellet module


    модуль нормальной упругости — modulus of elongation


    модуль объемного сжатия — <phys.> bulk modulus


    модуль объемной упругости — bulk modulus


    модуль полного сопротивления — scalar impedance


    модуль полной проводимости — scalar admittance


    модуль размеров тары — module of container dimensions


    модуль с двусторонними выводами — double-ended module


    модуль с навесными деталями — discrete-component module


    модуль с односторонними выводами — single-sided module


    модуль фототелеграфного аппарата — index of cooperation


    модуль этажерочного типа — cordwood module


    помещать модуль в кожух — encase module


    приведенный модуль упругости — modulus of inelastic bucking


    универсальный логический модуль — universal logic modulus

    Русско-английский технический словарь > модуль

  • 7 модуль

    assembly unit, modular block, block, constant, construction unit, structural member, modular unit, module, modulus, pack, package, style, unit, absolute value
    * * *
    мо́дуль м.
    1. ( абсолютная величина числа) modulus, magnitude, absolute value
    вы́чет числа́ a по мо́дулю m … — the residue of a to the modulus m, the residue of a modulo m
    по мо́дулю (в значении «по абсолютной величине») — in absolute value, in magnitude
    чи́сла А и Б отлича́ются по мо́дулю — numbers A and B differ in magnitude
    по мо́дулю N — modulo N
    прове́рка по мо́дулю, напр. 9 — a modulo, e. g., 9 check
    су́мма по мо́дулю, напр. 2 — the modulo, e. g., 2 sum of …
    сумми́рование произво́дится по мо́дулю, напр. 2 — summation is modulo, e. g., 2, a modulo, e. g., 2 sum is taken
    2. (блок, элемент конструкции) module
    герметизи́ровать мо́дуль — seal a module
    герметизи́ровать мо́дуль зали́вкой в компа́унд — pot a module
    герметизи́ровать мо́дуль обвола́киванием — encapsulate a module
    опрессо́вывать мо́дуль — mould a module
    помеща́ть мо́дуль в ко́жух — encase a module
    4. (величины, характеризующие различные свойства материалов, изделий) modulus; index
    мо́дуль ве́ктора — length [modulus, absolute value, magnitude, numerical value] of a vector
    мо́дуль всесторо́ннего сжа́тия — compression [bulk] modulus; modulus of dilatation
    мо́дуль вытя́гивания — modulus of stretch
    гидравли́ческий мо́дуль стр. — hydraulic index
    дио́дно-рези́сторно-конденса́торный мо́дуль — capacitor-resistor-diode [CRD] module
    мо́дуль затуха́ния — decay modules
    мо́дуль изги́ба — flexural modules
    интегра́льный мо́дуль — integrated circuit [IC] module
    мо́дуль ко́мплексного числа́ — complex number modules
    мо́дуль кру́пности горн. — gradation factor
    мо́дуль круче́ния — torsion modules
    логи́ческий мо́дуль — logic module
    логи́ческий, универса́льный мо́дуль — universal logic module
    лу́нный мо́дуль косм. — lunar module
    мо́дуль мерсериза́ции цел.-бум. — modules of mercerization
    микроминиатю́рный мо́дуль — microminiature module
    многоштырько́вый мо́дуль — multipin package
    молекуля́рный мо́дуль — MERA module
    мо́дуль на керами́ческой осно́ве — ceramic-base module
    мо́дуль на табле́точных элеме́нтах — pellet module
    мо́дуль норма́льной упру́гости — modulus of elongation, Young's modulus
    мо́дуль объё́много сжа́тия — modulus of dilatation
    мо́дуль объё́мной упру́гости — bulk modulus
    объё́мный мо́дуль — bulk modulus
    мо́дуль основа́ния стр. — foundation modulus
    основно́й мо́дуль косм. — orbital module
    мо́дуль пласти́чности — modulus of plasticity
    мо́дуль по́лного сопротивле́ния — (scalar) impedance
    мо́дуль по́лной проводи́мости — (scalar) admittance
    мо́дуль преломле́ния — refractive modulus
    мо́дуль продо́льной упру́гости — modulus of elongation, Young's modulus
    пьезоэлектри́ческий мо́дуль — piezoelectric modulus
    мо́дуль разме́ров та́ры — module of container dimensions
    мо́дуль разры́ва — modulus of rupture
    мо́дуль расстоя́ния астр. — distance modulus
    резисти́вно-ё́мкостный мо́дуль — capristor
    мо́дуль сдви́га — shear [rigidity] modulus
    мо́дуль с двусторо́нними вы́водами — double-ended module
    мо́дуль скольже́ния — shear [rigidity] modulus
    сме́нный мо́дуль — plug-in module
    мо́дуль с навесны́ми дета́лями — discrete-component module
    мо́дуль с односторо́нними вы́водами — single-sided module
    со́товый мо́дуль — honeycomb module
    мо́дуль сравне́ния — modulus of congruence
    мо́дуль сто́ка гидр. — modulus or flow, run-off rate
    термоэлектри́ческий мо́дуль — thermoelectric module
    тонкоплё́ночный мо́дуль — thin-film (circuit) module
    управля́ющий мо́дуль — control module
    мо́дуль упру́гости — modulus of elasticity
    мо́дуль упру́гости второ́го ро́да — shear [rigidity] modulus
    мо́дуль упру́гости, динами́ческий — dynamic modulus of elasticity
    мо́дуль упру́гости, каса́тельный — tangent modulus of elasticity
    мо́дуль упру́гости пе́рвого ро́да — modulus of elongation, Young's modulus
    мо́дуль упру́гости, приведё́нный — modulus of inelastic buckling
    мо́дуль упру́гости, тангенциа́льный — tangent modulus of elasticity
    мо́дуль фототелегра́фного аппара́та — index of cooperation (Примечание. В советской литературе обозначает отношение диаметра барабана к шагу подачи. В зарубежной литературе — произведение диаметра барабана на величину, обратную шагу подачи.)
    мо́дуль шестерни́ — pitch of a gear
    мо́дуль шестерни́, ме́лкий — fine pitch of a gear
    мо́дуль этаже́рочного ти́па — cordwood module
    мо́дуль Ю́нга — modulus of elongation, Young's modulus

    Русско-английский политехнический словарь > модуль

  • 8 στοιχεῖον

    I in a form of sun-dial, the shadow of the gnomon, the length of which in feet indicated the time of day, ὅταν ᾖ δεκάπουν τὸ ς. when the shadow is ten feet long, Ar.Ec. 652, v. Sch.;

    ὁπηνίκ' ἂν εἴκοσι ποδῶν.. τὸ σ. ᾖ Eub.119.7

    , cf. Philem.83.
    1 a simple sound of speech, as the first component of the syllable, Pl.Cra. 424d; τὸ ῥῶ τὸ ς. ib. 426d;

    γραμμάτων σ. καὶ συλλαβάς Id.Tht. 202e

    ;

    σ. ἐστι φωνὴ ἀδιαίρετος Arist.Po. 1456b22

    ;

    φωνῆς σ. καὶ ἀρχαὶ δοκοῦσιν εἶναι ταῦτ' ἐξ ὧν σύγκεινται αἱ φωναὶ πρώτων Id.Metaph. 998a23

    , cf.Gal.15.6:— στοιχεῖα therefore, strictly, were different from letters ([etym.] γράμματα), Diog.Bab.Stoic.3.213, Sch.D.T.p.32, al., but are freq. not clearly distd. from them, as by Pl.Tht.l.c., Cra. 426d;

    τὰ σ. τῶν γραμμάτων τὰ τέτταρα καὶ εἴκοσι Aen.Tact.31.21

    ; σ. ε ¯ letter ε (in a filing-system), BGU959.2 (ii A.D.); ἀκουόμενα ς. letters which are pronounced, A.D.Adv.165.17; γράμματα and ς. are expressly identified by D.T.630.32; the ς. and its name are confused by A.D. Synt.29.1, but distd. by Hdn.Gr. ap. Choerob.in Theod.1.340, Sch.D.T. l.c.:—

    κατὰ στοιχεῖον

     in the order of the letters, alphabetically,

    AP11.15

     (Ammian.); dub.sens.in Plu.2.422e.
    2 in Physics, στοιχεῖα were the components into which matter is ultimately divisible, elements, reduced to four by Empedocles, who called them ῥιζὤματα, the word στοιχεῖα being first used (acc. to Eudem. ap. Simp.in Ph.7.13 ) by Pl., τὰ πρῶτα οἱονπερεὶ ς, e)c w(=n h(mei=s te sugkei/meqa kai\ ta)/lla Tht. 201e; τὰ τῶν πάντων ς. Plt. 278d;

    αὐτὰ τιθέμενοι σ. τοῦ παντός Ti. 48b

    , cf. Arist.GC 314a29, Metaph. 998a28, Thphr.Sens.3, al., D.L.3.24;

    σ. σωματικά Arist.Mete. 338a22

    , Thphr.Fr.46; ἄτομα ς. Epicur.Ep.2p.36U.; equivalent to ἀρχαί, Thales ap.Plu.2.875c, Anaximand. ap. D.L.2.1, Anon. ap. Arist.Ph. 188b28, Metaph. 1059b23, al.; but Arist. also distinguishes ς. from ἀρχή as less comprehensive, ib.1070b23; τὰ σ. ὕλη τῆς οὐσίας ib.1088b27; τρία τὰ ς. Id.Ph. 189b16; distd. from ἀρχή on other grounds by Stoic.2.111; ς. used in three senses by Chrysipp., ib.136, cf. Zeno ib.1.24, al.; in Medicine, Gal.6.3, 420, al., 15.7, al.;

    Αἰθέρ, κόσμου σ. ἄριστον Orph.H.5.4

    ; ἀνηλεὲς ς., of the sea, Babr.71.4; τὸ ς., of the sea, Polem.Cyn.44; ἄμφω τὰ ς., i.e. land and sea, ib.11, cf. Hdn.3.1.5, Him.Ecl.2.18.
    3 the elements of proof, e.g. in general reasoning the πρῶτοι συλλογισμοί, Arist.Metaph. 1014b1; in Geometry, the propositions whose proof is involved in the proof of other propositions, ib. 998a26, 1014a36; title of geometrical works by Hippocrates of Chios, Leon, Theudios, and Euclid, Procl. in Euc.pp.66,67,68F.: hence applied to whatever is one, small, and capable of many uses, Arist.Metaph. 1014b3; to whatever is most universal, e.g. the unit and the point, ib.6; the line and the circle, Id.Top. 158b35; the τόπος (argument applicable to a variety of subjects), ib. 120b13, al., Rh. 1358a35, al.;

    στοιχεῖα τὰ γένη λέγουσί τινες Id.Metaph. 1014b10

    ; τὸ νόμισμα σ. καὶ πέρας τῆς ἀλλαγῆς coin is the unit.. of exchange, Id.Pol. 1257b23; in Grammar, σ. τῆς λέξεως parts of speech, D.H.Comp.2; but also, the letters composing a word, A.D.Synt.313.7; letters of the alphabet, Diog. Bab.Stoic.3.213; σ. τοῦ λόγου the elements of speech, viz. words, or the kinds of words, parts of speech, Thphr. ap. Simp. in Cat.10.24, Chrysipp.Stoic.2.45, A.D.Synt.7.1, 313.6.
    4 generally, elementary or fundamental principle, ἀρξάμενοι ἀπὸ τῶν ς. X.Mem.2.1.1;

    σ. χρηστῆς πολιτείας Isoc.2.16

    ; τὸ πολλάκις εἰρημένον μέγιστον ς. Arist.Pol. 1309b16;

    σ. τῆς ὅλης τέχνης Nicol.Com.1.30

    , cf. Epicur. Ep.1p.10U., Ep.3p.59U., Phld.Rh.1.127S., Gal.6.306.
    5 ἄστρων στοιχεῖα the stars, Man.4.624;

    σ. καυσούμενα λυθήσεται 2 Ep.Pet.3.10

    , cf. 12; esp. planets,

    στοιχείῳ Διός PLond.1.130.60

     (i/ii A.D.); so perh. in Ep.Gal.4.3, Ep.Col.2.8; esp. a sign of the Zodiac, D.L.6.102; of the Great Bear, PMag.Par.1.1303.
    6 σ. = ἀριθμός, as etym. of Στοιχαδεύς, Sch.D.T.p.192 H.

    Greek-English dictionary (Αγγλικά Ελληνικά-λεξικό) > στοιχεῖον

  • 9 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

  • 10 Grammar

       I think that the failure to offer a precise account of the notion "grammar" is not just a superficial defect in linguistic theory that can be remedied by adding one more definition. It seems to me that until this notion is clarified, no part of linguistic theory can achieve anything like a satisfactory development.... I have been discussing a grammar of a particular language here as analogous to a particular scientific theory, dealing with its subject matter (the set of sentences of this language) much as embryology or physics deals with its subject matter. (Chomsky, 1964, p. 213)
       Obviously, every speaker of a language has mastered and internalized a generative grammar that expresses his knowledge of his language. This is not to say that he is aware of the rules of grammar or even that he can become aware of them, or that his statements about his intuitive knowledge of his language are necessarily accurate. (Chomsky, 1965, p. 8)
       Much effort has been devoted to showing that the class of possible transformations can be substantially reduced without loss of descriptive power through the discovery of quite general conditions that all such rules and the representations they operate on and form must meet.... [The] transformational rules, at least for a substantial core grammar, can be reduced to the single rule, "Move alpha" (that is, "move any category anywhere"). (Mehler, Walker & Garrett, 1982, p. 21)
       4) The Relationship of Transformational Grammar to Semantics and to Human Performance
       he implications of assuming a semantic memory for what we might call "generative psycholinguistics" are: that dichotomous judgments of semantic well-formedness versus anomaly are not essential or inherent to language performance; that the transformational component of a grammar is the part most relevant to performance models; that a generative grammar's role should be viewed as restricted to language production, whereas sentence understanding should be treated as a problem of extracting a cognitive representation of a text's message; that until some theoretical notion of cognitive representation is incorporated into linguistic conceptions, they are unlikely to provide either powerful language-processing programs or psychologically relevant theories.
       Although these implications conflict with the way others have viewed the relationship of transformational grammars to semantics and to human performance, they do not eliminate the importance of such grammars to psychologists, an importance stressed in, and indeed largely created by, the work of Chomsky. It is precisely because of a growing interdependence between such linguistic theory and psychological performance models that their relationship needs to be clarified. (Quillian, 1968, p. 260)
       here are some terminological distinctions that are crucial to explain, or else confusions can easily arise. In the formal study of grammar, a language is defined as a set of sentences, possibly infinite, where each sentence is a string of symbols or words. One can think of each sentence as having several representations linked together: one for its sound pattern, one for its meaning, one for the string of words constituting it, possibly others for other data structures such as the "surface structure" and "deep structure" that are held to mediate the mapping between sound and meaning. Because no finite system can store an infinite number of sentences, and because humans in particular are clearly not pullstring dolls that emit sentences from a finite stored list, one must explain human language abilities by imputing to them a grammar, which in the technical sense is a finite rule system, or programme, or circuit design, capable of generating and recognizing the sentences of a particular language. This "mental grammar" or "psychogrammar" is the neural system that allows us to speak and understand the possible word sequences of our native tongue. A grammar for a specific language is obviously acquired by a human during childhood, but there must be neural circuitry that actually carries out the acquisition process in the child, and this circuitry may be called the language faculty or language acquisition device. An important part of the language faculty is universal grammar, an implementation of a set of principles or constraints that govern the possible form of any human grammar. (Pinker, 1996, p. 263)
       A grammar of language L is essentially a theory of L. Any scientific theory is based on a finite number of observations, and it seeks to relate the observed phenomena and to predict new phenomena by constructing general laws in terms of hypothetical constructs.... Similarly a grammar of English is based on a finite corpus of utterances (observations), and it will contain certain grammatical rules (laws) stated in terms of the particular phonemes, phrases, etc., of English (hypothetical constructs). These rules express structural relations among the sentences of the corpus and the infinite number of sentences generated by the grammar beyond the corpus (predictions). (Chomsky, 1957, p. 49)

    Historical dictionary of quotations in cognitive science > Grammar

  • 11 Knowledge

       It is indeed an opinion strangely prevailing amongst men, that houses, mountains, rivers, and, in a word, all sensible objects, have an existence, natural or real, distinct from their being perceived by the understanding. But, with how great an assurance and acquiescence soever this principle may be entertained in the world, yet whoever shall find in his heart to call it into question may, if I mistake not, perceive it to involve a manifest contradiction. For, what are the forementioned objects but things we perceive by sense? and what do we perceive besides our own ideas or sensations? and is it not plainly repugnant that any one of these, or any combination of them, should exist unperceived? (Berkeley, 1996, Pt. I, No. 4, p. 25)
       It seems to me that the only objects of the abstract sciences or of demonstration are quantity and number, and that all attempts to extend this more perfect species of knowledge beyond these bounds are mere sophistry and illusion. As the component parts of quantity and number are entirely similar, their relations become intricate and involved; and nothing can be more curious, as well as useful, than to trace, by a variety of mediums, their equality or inequality, through their different appearances.
       But as all other ideas are clearly distinct and different from each other, we can never advance farther, by our utmost scrutiny, than to observe this diversity, and, by an obvious reflection, pronounce one thing not to be another. Or if there be any difficulty in these decisions, it proceeds entirely from the undeterminate meaning of words, which is corrected by juster definitions. That the square of the hypotenuse is equal to the squares of the other two sides cannot be known, let the terms be ever so exactly defined, without a train of reasoning and enquiry. But to convince us of this proposition, that where there is no property, there can be no injustice, it is only necessary to define the terms, and explain injustice to be a violation of property. This proposition is, indeed, nothing but a more imperfect definition. It is the same case with all those pretended syllogistical reasonings, which may be found in every other branch of learning, except the sciences of quantity and number; and these may safely, I think, be pronounced the only proper objects of knowledge and demonstration. (Hume, 1975, Sec. 12, Pt. 3, pp. 163-165)
       Our knowledge springs from two fundamental sources of the mind; the first is the capacity of receiving representations (the ability to receive impressions), the second is the power to know an object through these representations (spontaneity in the production of concepts).
       Through the first, an object is given to us; through the second, the object is thought in relation to that representation.... Intuition and concepts constitute, therefore, the elements of all our knowledge, so that neither concepts without intuition in some way corresponding to them, nor intuition without concepts, can yield knowledge. Both may be either pure or empirical.... Pure intuitions or pure concepts are possible only a priori; empirical intuitions and empirical concepts only a posteriori. If the receptivity of our mind, its power of receiving representations in so far as it is in any way affected, is to be called "sensibility," then the mind's power of producing representations from itself, the spontaneity of knowledge, should be called "understanding." Our nature is so constituted that our intuitions can never be other than sensible; that is, it contains only the mode in which we are affected by objects. The faculty, on the other hand, which enables us to think the object of sensible intuition is the understanding.... Without sensibility, no object would be given to us; without understanding, no object would be thought. Thoughts without content are empty; intuitions without concepts are blind. It is therefore just as necessary to make our concepts sensible, that is, to add the object to them in intuition, as to make our intuitions intelligible, that is to bring them under concepts. These two powers or capacities cannot exchange their functions. The understanding can intuit nothing, the senses can think nothing. Only through their union can knowledge arise. (Kant, 1933, Sec. 1, Pt. 2, B74-75 [p. 92])
       Metaphysics, as a natural disposition of Reason is real, but it is also, in itself, dialectical and deceptive.... Hence to attempt to draw our principles from it, and in their employment to follow this natural but none the less fallacious illusion can never produce science, but only an empty dialectical art, in which one school may indeed outdo the other, but none can ever attain a justifiable and lasting success. In order that, as a science, it may lay claim not merely to deceptive persuasion, but to insight and conviction, a Critique of Reason must exhibit in a complete system the whole stock of conceptions a priori, arranged according to their different sources-the Sensibility, the understanding, and the Reason; it must present a complete table of these conceptions, together with their analysis and all that can be deduced from them, but more especially the possibility of synthetic knowledge a priori by means of their deduction, the principles of its use, and finally, its boundaries....
       This much is certain: he who has once tried criticism will be sickened for ever of all the dogmatic trash he was compelled to content himself with before, because his Reason, requiring something, could find nothing better for its occupation. Criticism stands to the ordinary school metaphysics exactly in the same relation as chemistry to alchemy, or as astron omy to fortune-telling astrology. I guarantee that no one who has comprehended and thought out the conclusions of criticism, even in these Prolegomena, will ever return to the old sophistical pseudo-science. He will rather look forward with a kind of pleasure to a metaphysics, certainly now within his power, which requires no more preparatory discoveries, and which alone can procure for reason permanent satisfaction. (Kant, 1891, pp. 115-116)
       Knowledge is only real and can only be set forth fully in the form of science, in the form of system. Further, a so-called fundamental proposition or first principle of philosophy, even if it is true, it is yet none the less false, just because and in so far as it is merely a fundamental proposition, merely a first principle. It is for that reason easily refuted. The refutation consists in bringing out its defective character; and it is defective because it is merely the universal, merely a principle, the beginning. If the refutation is complete and thorough, it is derived and developed from the nature of the principle itself, and not accomplished by bringing in from elsewhere other counter-assurances and chance fancies. It would be strictly the development of the principle, and thus the completion of its deficiency, were it not that it misunderstands its own purport by taking account solely of the negative aspect of what it seeks to do, and is not conscious of the positive character of its process and result. The really positive working out of the beginning is at the same time just as much the very reverse: it is a negative attitude towards the principle we start from. Negative, that is to say, in its one-sided form, which consists in being primarily immediate, a mere purpose. It may therefore be regarded as a refutation of what constitutes the basis of the system; but more correctly it should be looked at as a demonstration that the basis or principle of the system is in point of fact merely its beginning. (Hegel, 1910, pp. 21-22)
       Knowledge, action, and evaluation are essentially connected. The primary and pervasive significance of knowledge lies in its guidance of action: knowing is for the sake of doing. And action, obviously, is rooted in evaluation. For a being which did not assign comparative values, deliberate action would be pointless; and for one which did not know, it would be impossible. Conversely, only an active being could have knowledge, and only such a being could assign values to anything beyond his own feelings. A creature which did not enter into the process of reality to alter in some part the future content of it, could apprehend a world only in the sense of intuitive or esthetic contemplation; and such contemplation would not possess the significance of knowledge but only that of enjoying and suffering. (Lewis, 1946, p. 1)
       "Evolutionary epistemology" is a branch of scholarship that applies the evolutionary perspective to an understanding of how knowledge develops. Knowledge always involves getting information. The most primitive way of acquiring it is through the sense of touch: amoebas and other simple organisms know what happens around them only if they can feel it with their "skins." The knowledge such an organism can have is strictly about what is in its immediate vicinity. After a huge jump in evolution, organisms learned to find out what was going on at a distance from them, without having to actually feel the environment. This jump involved the development of sense organs for processing information that was farther away. For a long time, the most important sources of knowledge were the nose, the eyes, and the ears. The next big advance occurred when organisms developed memory. Now information no longer needed to be present at all, and the animal could recall events and outcomes that happened in the past. Each one of these steps in the evolution of knowledge added important survival advantages to the species that was equipped to use it.
       Then, with the appearance in evolution of humans, an entirely new way of acquiring information developed. Up to this point, the processing of information was entirely intrasomatic.... But when speech appeared (and even more powerfully with the invention of writing), information processing became extrasomatic. After that point knowledge did not have to be stored in the genes, or in the memory traces of the brain; it could be passed on from one person to another through words, or it could be written down and stored on a permanent substance like stone, paper, or silicon chips-in any case, outside the fragile and impermanent nervous system. (Csikszentmihalyi, 1993, pp. 56-57)

    Historical dictionary of quotations in cognitive science > Knowledge

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