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processing+properties

  • 1 processing properties

    Англо-русский словарь промышленной и научной лексики > processing properties

  • 2 processing characteristics

    технологические свойства материала; обрабатываемость; см. также processing properties; workability

    Англо-русский словарь промышленной и научной лексики > processing characteristics

  • 3 технологични свойства

    processing properties

    Български-Angleščina политехнически речник > технологични свойства

  • 4 технологические свойства

    Русско-английский словарь по пищевой промышленности > технологические свойства

  • 5 технологические свойства

    5) Quality control: handling quality, shop characteristics (материала), technological properties

    Универсальный русско-английский словарь > технологические свойства

  • 6 обрабатываемость

    1) Engineering: fabricability, machinability (материала, заготовки), machinabillty (на станках), processibillity
    3) Oil: workability
    4) Silicates: (механическая) workability
    6) Automation: machinability (резанием), manufacturability, produceability, treatability
    7) Plastics: machinability (на станках), mashining quality
    9) Combustion gas turbines: fabrication

    Универсальный русско-английский словарь > обрабатываемость

  • 7 перерабатываемость

    Универсальный русско-английский словарь > перерабатываемость

  • 8 Verarbeitungseigenschaften

    Verarbeitungseigenschaften fpl processing characteristics, processing properties

    Deutsch-Englisch Fachwörterbuch Architektur und Bauwesen > Verarbeitungseigenschaften

  • 9 технологічні властивості

    en\ \ [lang name="English"]technological properties, processing properties
    властивості, що визначають можливість здійснення технологічного процесу виготовлення виробу; наприклад, здатність до осадки, зварюваність тощо

    Термінологічний Словник "Метали" > технологічні властивості

  • 10 технологические свойства

    en\ \ [lang name="English"]technological properties, processing properties
    свойства, определяющие возможность осуществления технологического процесса изготовления изделия; например, способность к осадке, свариваемость и др.

    Терминологический словарь "Металлы" > технологические свойства

  • 11 workability

    деформируемость; обрабатываемость; способность металла поддаваться обработке; см. также processing characteristics; processing properties

    Англо-русский словарь промышленной и научной лексики > workability

  • 12 dato

    m.
    1 piece of information, fact (hecho, cifra).
    datos information, data; (información) data (computing)
    datos (personales) (personal) details
    2 Dato.
    pres.indicat.
    1st person singular (yo) present indicative of spanish verb: datar.
    * * *
    1 (información) fact, piece of information, datum
    \
    datos personales personal details
    * * *
    noun m.
    fact, piece of information
    * * *
    SM
    1) (=información) piece of information

    un dato interesante — an interesting fact o piece of information

    otro dato que tener en cuenta es... — another thing to bear in mind is...

    datos personales — personal details, particulars

    2) (Mat) datum
    * * *
    a) ( elemento de información) piece of information

    alguien le pasó el dato a la policía — (CS) somebody informed o (colloq) tipped off the police

    darle un dato a alguien — (CS) to give somebody a tip

    b) datos masculino plural (Inf) data (pl), information
    * * *
    = attribute value, data element, data item, datum [data, -pl.], fact, value, piece of information.
    Ex. Others have used possibility distributions for representing fuzzily known or incompletely known attribute values.
    Ex. The Working Group undertook to determine from the data available what data elements should be included for each type of authority.
    Ex. Information is held in files or databases, which are comprise of records, which in turn are comprised of fields or data items, which again may be comprised of subfields or data elements.
    Ex. Thus, having entered the authority datum correctly once, we could be sure that no matter how many bibliographic records used it they would all do so with mechanical consistency.
    Ex. Other data bases, which may be described as non-bibliographic, and are sometimes known as data banks, store actual facts and figures and text.
    Ex. A good initial value for this field will start the system off with a good guess so that claims for missing issues are not unreasonable at the beginning.
    Ex. On other occasions a user wants every document or piece of information on a topic traced, and then high recall must be sought, to the detriment of precision.
    ----
    * alimentar datos = populate.
    * almacenamiento de datos = data storage.
    * añadir datos = make + additions.
    * archivo de datos = database [data base].
    * área de datos específicos de la clase de documento = material (or type of publication) specific details area.
    * área de datos matemáticos = mathematical data area.
    * auditoría de datos = data auditing, data audit.
    * banco de datos = data bank [databank], factual data bank.
    * banco de datos factual = factual data bank.
    * banco de datos terminológico = terminological data bank.
    * basado en los datos = data-driven.
    * basado en un gestor de bases de datos = DBMS-based.
    * base de datos = data bank [databank], database [data base], database software.
    * base de datos automatizada = computer database, computer-held database, computerised database, machine-readable database.
    * base de datos bibliográfica = bibliographic database.
    * base de datos bibliográfica de resúmenes = abstracts based bibliographic database.
    * base de datos catalográfica = catalogue database.
    * base de datos completa = full-provision database.
    * base de datos con información confidencial = intelligence database.
    * base de datos cruzada = cross database.
    * base de datos de acceso mediante suscripción = subscription database.
    * base de datos de autoridades = authority database.
    * base de datos de carburantes = TULSA.
    * base de datos de documentos primarios = source database.
    * base de datos de documentos secundarios = reference database.
    * base de datos de dominio público = public domain database.
    * base de datos de educación = ERIC.
    * base de datos de imágenes = image database, image bank.
    * base de datos de investigación = research database.
    * base de datos del gobierno de USA = CRECORD, FEDREG.
    * base de datos de lógica difusa = fuzzy database.
    * base de datos de medicina = MEDLINE.
    * base de datos de negocios = business database.
    * base de datos de pago = subscription database.
    * base de datos de patentes = WPI.
    * base de datos de propiedades = properties database.
    * base de datos de referencia = reference database.
    * base de datos de referencia a especialistas = referral database.
    * base de datos de registros de catálogo = catalogue record database.
    * base de datos de texto = text-oriented database, text database.
    * base de datos de texto completo = full text database.
    * base de datos de texto libre = free text database.
    * base de datos dirigida a un mercado específico = niche database.
    * base de datos distribuida = distributed database.
    * base de datos en CD-ROM = CD-ROM database.
    * base de datos en disco óptico = optical disc database.
    * base de datos en estado original = raw database.
    * base de datos en línea = online database.
    * base de datos estadística = statistical database.
    * base de datos externa = external database.
    * base de datos factual = factual database.
    * base de datos interna = in-house database.
    * base de datos jurídica = legal database.
    * base de datos multimedia = multimedia database.
    * base de datos no bibliográfica = non-bibliographic database.
    * base de datos numérica = numeric database, numerical database.
    * base de datos relacional = relational database.
    * base de datos residente = resident database.
    * base de datos terminológica = terminology database.
    * bloque de datos = data bloc.
    * bloque funcional de datos codificados = coded information block.
    * búfer de datos = data buffer.
    * bus de datos = databus.
    * búsqueda de datos = fact-finding.
    * campo de datos = datafield.
    * capturar datos = capture + data.
    * centro de datos = data centre.
    * codificación de datos = data-coding [data coding].
    * con datos no pertinentes = dirty [dirtier -comp., dirtiest -sup.].
    * conjunto de datos = data set [dataset].
    * contaminación de datos = data contamination.
    * corrupción de datos = data corruption.
    * creación de depósitos de datos = data warehousing.
    * creador de bases de datos = database producer.
    * dar datos de = give + details of.
    * dato concreto = hard fact.
    * datos = data [datum, -sing.], details, figure.
    * datos bibliográficos = bibliographic data, bibliodata.
    * datos biográficos = biodata.
    * datos concretos = specifics, the.
    * datos concretos y reales = hard data.
    * datos de contacto = contact details.
    * datos de entrada = input data.
    * datos de la tarjeta de crédito = credit card details.
    * datos demográficos = demographics.
    * datos desagregados por sexo = gender-disaggregated data.
    * datos empíricos = empirical data.
    * datos en bruto = raw data.
    * datos en estado bruto = raw facts.
    * datos en propiedad = property data.
    * datos erróneos = dirty data.
    * datos estadísticos = statistics, statistical data.
    * datos estadísticos de la biblioteca = library records, library statistics.
    * datos factuales = factual data.
    * datos legibles por máquina = machine-readable data.
    * datos numéricos = numerical data.
    * datos personales = personal details.
    * datos privados = property data.
    * de lectura de datos = data-capture.
    * depósito de datos = data warehouse.
    * depuración de datos = data cleaning.
    * descubrimiento de datos = data mining.
    * descubrimiento de información en las bases de datos = knowledge discovery in databases (KDD).
    * directorio de empresas en base de datos = company directory database.
    * dispositivo de almacenamiento de datos = store.
    * distribuidor de bases de datos = host system.
    * distribuidor de bases de datos en línea = online vendor.
    * EDI (Intercambio Electrónico de Datos) = EDI (Electronic Data Interchange).
    * entrada de datos = data entry, input, inputting.
    * entrada de datos sólo una vez = one-time entry.
    * estructura de datos = data structure.
    * extracción inteligente de datos = data mining.
    * fichero de salida de datos = communication output file.
    * gestión de bases de datos = database management.
    * gestión de datos = data handling.
    * gestor de bases de datos = DBMS system.
    * gestor de bases de datos relacionales = relational database management system.
    * grupo de datos = data set [dataset].
    * hoja con los datos básicos para Hacer Algo = data sheet [datasheet].
    * hoja de toma de datos = checklist [check-list], data sheet [datasheet].
    * impreso de recogida de datos = enquiry form, inquiry form.
    * industria de las bases de datos = database industry.
    * inserción de datos = input.
    * instrumento de recogida de datos = data collection instrument.
    * introducción de datos utilizando un teclado = keypunching.
    * introducir datos = key + data.
    * introducir datos en el ordenador = input.
    * introducir datos partiendo de cero = enter from + scratch.
    * introductor de datos en un ordenador = inputter.
    * limpieza de datos = data cleaning.
    * lista de datos = fact finder.
    * localización de datos = addressing.
    * manipulación de datos = data manipulation.
    * memoria intermedia de datos = data buffer.
    * memorizar datos = memorise + facts.
    * meta base de datos = meta-database.
    * migración de datos = data migration.
    * minería de datos = data mining.
    * modo de introducción de datos = input mode.
    * montar una base de datos = mount + database.
    * norma de entrada de datos = input standard.
    * operación sobre datos = data manipulation.
    * operario de entrada de datos = data entry operator.
    * paquete de entrada y comprobación de datos = data entry and validation package.
    * pérdida de datos = data loss.
    * personal de proceso de datos = operation staff.
    * preparación de los datos = data preparation.
    * procesamiento de datos = data processing.
    * procesamiento de datos numéricos = number-crunching.
    * proceso de datos = data processing, transaction processing.
    * productor de bases de datos = database producer.
    * programa de gestión de bases de datos = database management software.
    * protección de datos = data protection.
    * prototipo para el proceso de datos = data modelling.
    * proveedor de bases de datos = database provider.
    * recabar datos = solicit + data.
    * recoger datos = collect + data.
    * recoger datos para hacer estadísticas = collect + statistics.
    * recogida de datos = data collection, data gathering [data-gathering], fact-gathering, reporting, data collecting.
    * salida de datos = output.
    * sistema de proceso de datos = data processing system.
    * Sistema Internacional de Datos sobre Publicaciones Seriadas (ISDS) = ISDS (International Serials Data System).
    * suministrar datos = furnish + details.
    * suministro de datos = reporting.
    * tecla de borrado de datos = ERASE INPUT key.
    * tecla de introducción de datos = ENTER key.
    * técnico encargado del proceso de datos = data-processing professional.
    * tiempo de descarga de datos = download time, latency.
    * tráfico de datos de un modo intermitente = bursty traffic.
    * transformación de datos = data transformation.
    * transmisión de datos = data-flow, data transfer, data transmission.
    * tratamiento de datos = transaction processing.
    * unidad de datos = unit of data.
    * verificación de los datos = fact checking.
    * vía de transmisión de datos = data pathway, pathway.
    * * *
    a) ( elemento de información) piece of information

    alguien le pasó el dato a la policía — (CS) somebody informed o (colloq) tipped off the police

    darle un dato a alguien — (CS) to give somebody a tip

    b) datos masculino plural (Inf) data (pl), information
    * * *
    = attribute value, data element, data item, datum [data, -pl.], fact, value, piece of information.

    Ex: Others have used possibility distributions for representing fuzzily known or incompletely known attribute values.

    Ex: The Working Group undertook to determine from the data available what data elements should be included for each type of authority.
    Ex: Information is held in files or databases, which are comprise of records, which in turn are comprised of fields or data items, which again may be comprised of subfields or data elements.
    Ex: Thus, having entered the authority datum correctly once, we could be sure that no matter how many bibliographic records used it they would all do so with mechanical consistency.
    Ex: Other data bases, which may be described as non-bibliographic, and are sometimes known as data banks, store actual facts and figures and text.
    Ex: A good initial value for this field will start the system off with a good guess so that claims for missing issues are not unreasonable at the beginning.
    Ex: On other occasions a user wants every document or piece of information on a topic traced, and then high recall must be sought, to the detriment of precision.
    * alimentar datos = populate.
    * almacenamiento de datos = data storage.
    * añadir datos = make + additions.
    * archivo de datos = database [data base].
    * área de datos específicos de la clase de documento = material (or type of publication) specific details area.
    * área de datos matemáticos = mathematical data area.
    * auditoría de datos = data auditing, data audit.
    * banco de datos = data bank [databank], factual data bank.
    * banco de datos factual = factual data bank.
    * banco de datos terminológico = terminological data bank.
    * basado en los datos = data-driven.
    * basado en un gestor de bases de datos = DBMS-based.
    * base de datos = data bank [databank], database [data base], database software.
    * base de datos automatizada = computer database, computer-held database, computerised database, machine-readable database.
    * base de datos bibliográfica = bibliographic database.
    * base de datos bibliográfica de resúmenes = abstracts based bibliographic database.
    * base de datos catalográfica = catalogue database.
    * base de datos completa = full-provision database.
    * base de datos con información confidencial = intelligence database.
    * base de datos cruzada = cross database.
    * base de datos de acceso mediante suscripción = subscription database.
    * base de datos de autoridades = authority database.
    * base de datos de carburantes = TULSA.
    * base de datos de documentos primarios = source database.
    * base de datos de documentos secundarios = reference database.
    * base de datos de dominio público = public domain database.
    * base de datos de educación = ERIC.
    * base de datos de imágenes = image database, image bank.
    * base de datos de investigación = research database.
    * base de datos del gobierno de USA = CRECORD, FEDREG.
    * base de datos de lógica difusa = fuzzy database.
    * base de datos de medicina = MEDLINE.
    * base de datos de negocios = business database.
    * base de datos de pago = subscription database.
    * base de datos de patentes = WPI.
    * base de datos de propiedades = properties database.
    * base de datos de referencia = reference database.
    * base de datos de referencia a especialistas = referral database.
    * base de datos de registros de catálogo = catalogue record database.
    * base de datos de texto = text-oriented database, text database.
    * base de datos de texto completo = full text database.
    * base de datos de texto libre = free text database.
    * base de datos dirigida a un mercado específico = niche database.
    * base de datos distribuida = distributed database.
    * base de datos en CD-ROM = CD-ROM database.
    * base de datos en disco óptico = optical disc database.
    * base de datos en estado original = raw database.
    * base de datos en línea = online database.
    * base de datos estadística = statistical database.
    * base de datos externa = external database.
    * base de datos factual = factual database.
    * base de datos interna = in-house database.
    * base de datos jurídica = legal database.
    * base de datos multimedia = multimedia database.
    * base de datos no bibliográfica = non-bibliographic database.
    * base de datos numérica = numeric database, numerical database.
    * base de datos relacional = relational database.
    * base de datos residente = resident database.
    * base de datos terminológica = terminology database.
    * bloque de datos = data bloc.
    * bloque funcional de datos codificados = coded information block.
    * búfer de datos = data buffer.
    * bus de datos = databus.
    * búsqueda de datos = fact-finding.
    * campo de datos = datafield.
    * capturar datos = capture + data.
    * centro de datos = data centre.
    * codificación de datos = data-coding [data coding].
    * con datos no pertinentes = dirty [dirtier -comp., dirtiest -sup.].
    * conjunto de datos = data set [dataset].
    * contaminación de datos = data contamination.
    * corrupción de datos = data corruption.
    * creación de depósitos de datos = data warehousing.
    * creador de bases de datos = database producer.
    * dar datos de = give + details of.
    * dato concreto = hard fact.
    * datos = data [datum, -sing.], details, figure.
    * datos bibliográficos = bibliographic data, bibliodata.
    * datos biográficos = biodata.
    * datos concretos = specifics, the.
    * datos concretos y reales = hard data.
    * datos de contacto = contact details.
    * datos de entrada = input data.
    * datos de la tarjeta de crédito = credit card details.
    * datos demográficos = demographics.
    * datos desagregados por sexo = gender-disaggregated data.
    * datos empíricos = empirical data.
    * datos en bruto = raw data.
    * datos en estado bruto = raw facts.
    * datos en propiedad = property data.
    * datos erróneos = dirty data.
    * datos estadísticos = statistics, statistical data.
    * datos estadísticos de la biblioteca = library records, library statistics.
    * datos factuales = factual data.
    * datos legibles por máquina = machine-readable data.
    * datos numéricos = numerical data.
    * datos personales = personal details.
    * datos privados = property data.
    * de lectura de datos = data-capture.
    * depósito de datos = data warehouse.
    * depuración de datos = data cleaning.
    * descubrimiento de datos = data mining.
    * descubrimiento de información en las bases de datos = knowledge discovery in databases (KDD).
    * directorio de empresas en base de datos = company directory database.
    * dispositivo de almacenamiento de datos = store.
    * distribuidor de bases de datos = host system.
    * distribuidor de bases de datos en línea = online vendor.
    * EDI (Intercambio Electrónico de Datos) = EDI (Electronic Data Interchange).
    * entrada de datos = data entry, input, inputting.
    * entrada de datos sólo una vez = one-time entry.
    * estructura de datos = data structure.
    * extracción inteligente de datos = data mining.
    * fichero de salida de datos = communication output file.
    * gestión de bases de datos = database management.
    * gestión de datos = data handling.
    * gestor de bases de datos = DBMS system.
    * gestor de bases de datos relacionales = relational database management system.
    * grupo de datos = data set [dataset].
    * hoja con los datos básicos para Hacer Algo = data sheet [datasheet].
    * hoja de toma de datos = checklist [check-list], data sheet [datasheet].
    * impreso de recogida de datos = enquiry form, inquiry form.
    * industria de las bases de datos = database industry.
    * inserción de datos = input.
    * instrumento de recogida de datos = data collection instrument.
    * introducción de datos utilizando un teclado = keypunching.
    * introducir datos = key + data.
    * introducir datos en el ordenador = input.
    * introducir datos partiendo de cero = enter from + scratch.
    * introductor de datos en un ordenador = inputter.
    * limpieza de datos = data cleaning.
    * lista de datos = fact finder.
    * localización de datos = addressing.
    * manipulación de datos = data manipulation.
    * memoria intermedia de datos = data buffer.
    * memorizar datos = memorise + facts.
    * meta base de datos = meta-database.
    * migración de datos = data migration.
    * minería de datos = data mining.
    * modo de introducción de datos = input mode.
    * montar una base de datos = mount + database.
    * norma de entrada de datos = input standard.
    * operación sobre datos = data manipulation.
    * operario de entrada de datos = data entry operator.
    * paquete de entrada y comprobación de datos = data entry and validation package.
    * pérdida de datos = data loss.
    * personal de proceso de datos = operation staff.
    * preparación de los datos = data preparation.
    * procesamiento de datos = data processing.
    * procesamiento de datos numéricos = number-crunching.
    * proceso de datos = data processing, transaction processing.
    * productor de bases de datos = database producer.
    * programa de gestión de bases de datos = database management software.
    * protección de datos = data protection.
    * prototipo para el proceso de datos = data modelling.
    * proveedor de bases de datos = database provider.
    * recabar datos = solicit + data.
    * recoger datos = collect + data.
    * recoger datos para hacer estadísticas = collect + statistics.
    * recogida de datos = data collection, data gathering [data-gathering], fact-gathering, reporting, data collecting.
    * salida de datos = output.
    * sistema de proceso de datos = data processing system.
    * Sistema Internacional de Datos sobre Publicaciones Seriadas (ISDS) = ISDS (International Serials Data System).
    * suministrar datos = furnish + details.
    * suministro de datos = reporting.
    * tecla de borrado de datos = ERASE INPUT key.
    * tecla de introducción de datos = ENTER key.
    * técnico encargado del proceso de datos = data-processing professional.
    * tiempo de descarga de datos = download time, latency.
    * tráfico de datos de un modo intermitente = bursty traffic.
    * transformación de datos = data transformation.
    * transmisión de datos = data-flow, data transfer, data transmission.
    * tratamiento de datos = transaction processing.
    * unidad de datos = unit of data.
    * verificación de los datos = fact checking.
    * vía de transmisión de datos = data pathway, pathway.

    * * *
    1 (elemento de información) piece of information
    no tengo más datos que el título de la obra the only thing I know about the work is its title, the only information I have about the work is its title
    no dispongo de todos los datos I don't have all the information o details o facts
    alguien le pasó el dato a la policía (CS); somebody informed o ( colloq) tipped off the police
    me han dado un dato muy interesante (CS); I've been given a very interesting piece of information o ( colloq) a hot tip
    te voy a dar un dato, si no lo enchufas no funciona (CS hum); let me give you a tip: it won't work unless you plug it in
    2 datos mpl ( Inf) data (pl), information
    Compuesto:
    mpl particulars (pl), personal details (pl)
    * * *

     

    Del verbo datar: ( conjugate datar)

    dato es:

    1ª persona singular (yo) presente indicativo

    dató es:

    3ª persona singular (él/ella/usted) pretérito indicativo

    Multiple Entries:
    datar    
    dato
    datar ( conjugate datar) verbo intransitivo
    to date;

    data de hace muchos años it goes back many years
    dato sustantivo masculino


    datos personales personal details (pl)
    b)

    ◊ datos sustantivo masculino plural (Inf) data (pl), information

    datar
    I verbo transitivo to date, put a date on
    II verbo intransitivo datar de, to date back to o from: este libro data de la Edad Media, this book dates back to the Middle Ages
    dato sustantivo masculino
    1 piece of information 2 datos, Inform data
    (pormenores) information: no tengo más datos sobre este autor, I don't have any more details about his author
    datos personales, personal details
    La traducción de dato es datum, pero solo se usa en situaciones muy formales. La traducción de datos es data (plural irregular). El singular más común de data es a piece of information.
    ' dato' also found in these entries:
    Spanish:
    filtrar
    - filtración
    - informativa
    - informativo
    - relevante
    - consignar
    - consultar
    - equivocado
    - falso
    English:
    data
    - information
    - tip
    * * *
    dato nm
    1. [hecho, cifra] piece of information, fact;
    lo que necesitamos son datos concretos what we need is hard facts;
    el alto desempleo es un dato que hay que tener en cuenta the high level of unemployment is a factor which has to be borne in mind;
    datos [información] information, data;
    si no me das más datos, no voy a poderte aconsejar unless you give me more information, I won't be able to advise you;
    el ministerio aún no cuenta con todos los datos the ministry does not yet have all the information at its disposal;
    datos (personales) (personal) details;
    déjenos sus datos y nos pondremos en contacto con usted leave us your details and we will get in touch with you
    datos bancarios bank details;
    datos estadísticos statistical data
    * * *
    m piece of information;
    datos pl information sg, data sg
    * * *
    dato nm
    1) : fact, piece of information
    2) datos nmpl
    : data, information
    * * *
    dato n (información) piece of information

    Spanish-English dictionary > dato

  • 13 informatización

    f.
    computerization, computerisation.
    * * *
    1 computerization
    * * *
    * * *
    = informatization [informatisation, -UK].
    Ex. Informatization is affecting political decision making by altering information properties, aspects of information processing and information behaviour in political decision making practices.
    ----
    * informatización de la archivística = archival informatics.
    * * *
    = informatization [informatisation, -UK].

    Ex: Informatization is affecting political decision making by altering information properties, aspects of information processing and information behaviour in political decision making practices.

    * informatización de la archivística = archival informatics.

    * * *
    computerization
    * * *

    informatización sustantivo femenino computerization: la informatización de la tienda causó muchos retrasos en el servicio, frequent delays in service at the shop were due to the computerization process
    * * *
    computerization
    * * *
    f computerization
    * * *
    informatización nf, pl - ciones : computerization

    Spanish-English dictionary > informatización

  • 14 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

  • 15 в сравнении с

    * * *
    В сравнении с -- in comparison to, in comparison with; as compared to, compared to; when compared with; versus, vis-a-vis
     This maraging steel offers modestly improved yield and ultimate strengths as compared to a commercial 9 percent Ni steel.
     When compared with normal red phosphorus AMGARD CRT has better flow properties and improved impact resistance.
     The issue of general-purpose versus special-purpose knowledge representations is particularly important in natural language processing.
     Figure presents the hydrodynamics of a starved journal bearing vis-a-vis a full film condition.

    Русско-английский научно-технический словарь переводчика > в сравнении с

  • 16 получение и свойства толстых плёнок

    Универсальный русско-английский словарь > получение и свойства толстых плёнок

  • 17 формуемость

    2) Silicates: shapeability
    4) Automation: compactibility

    Универсальный русско-английский словарь > формуемость

  • 18 характеристика

    Характеристика - characteristic, property, behavior, aspect, feature (свойство); characterization (процесс); performance (работы); response (динамическая, например: амплитуда); aspect
     Spectral measurements have a special place in the characterization of saponifiable lipids.
     Continuous thin metallic films have potentially better magnetic performance.
     The purpose of this investigation was to determine the load bearing and energy absorption responses of a simple structure.
     The unsteadiness affects the following aspects of turbomachine performance: blade loading, stage efficiency, heat transfer, flutter, noise generation and stall margin.
    Характеристики (горения)
     A "fuel gas" of this composition exhibits combustion characteristics superior to those of the initial raw hydrocarbon fuel.
     The H2 content of the product gas is considered to be the most influential factor in the concept of improving the combustion properties of the raw fuel by onboard fuel processing.
     It is also expected that the combustion behavior of these fuels, particularly regarding pollutant emissions, will be poorer because aromatics content will be greater.

    Русско-английский научно-технический словарь переводчика > характеристика

  • 19 реологические свойства

    Русско-английский научный словарь > реологические свойства

  • 20 Neural Network

       1. A neural network is composed of a number of very simple processing elements [("neurodes")] that communicate through a rich set of interconnections with variable weights or strengths.
       2. Memories are stored or represented in a neural network in the pattern of variable interconnection weights among the neurodes. Information is processed by a spreading, constantly changing pattern of activity distributed across many neurodes.
       3. A neural network is taught or trained rather than programmed. It is even possible to construct systems capable of independent or autonomous learning....
       4. Instead of having a separate memory and controller, plus a stored external program that dictates the operation of the system as in a digital computer, the operation of a neural network is implicitly controlled by three properties: the transfer function of the neurodes, the details of the structure of the connections among the neurodes, and the learning law the system follows.
       5. A neural network naturally acts as an associative memory. That is, it inherently associated items it is taught, physically grouping similar items together in its structure. A neural network operated as a memory is content addressable; it can retrieve stored information from incomplete, noisy, or partially incorrect input cues.
       6. A neural network is able to generalize; it can learn the characteristics of a general category of objects based on a series of specific examples from that category.
       7. A neural network keeps working even after a significant fraction of its neurodes and interconnections have become defective.
       8. A neural network innately acts as a processor for time-dependent spatial patterns, or spatiotemporal patterns. (Caudill & Butler, 1990, pp. 7-8)

    Historical dictionary of quotations in cognitive science > Neural Network

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