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41 анализ типов отказов, эффекта и критичности
Programming: (FMECA) Failure Mode, Effect and Criticality Analysis (см. Standard glossary of terms used in Software Testing)Универсальный русско-английский словарь > анализ типов отказов, эффекта и критичности
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42 отладка в пользовательском режиме
Универсальный русско-английский словарь > отладка в пользовательском режиме
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43 режим выключения питания
Programming: (электро) shutdown modeУниверсальный русско-английский словарь > режим выключения питания
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44 принципы программирования
1. programming maxims2. programming strategyРусско-английский большой базовый словарь > принципы программирования
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45 система программирования
1. coding system2. programming systemРусско-английский большой базовый словарь > система программирования
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46 пакетный режим
1) General subject: mode2) Information technology: burst mode (работы мультиплексора), stream mode3) Oil: batch mode4) Network technologies: packet mode, packet-burst mode5) Programming: (дистанционный) remote batching6) Makarov: batch mode (обработки данных) -
47 программирование в режиме обучения
2) Mechanics: teach-mode programming3) Robots: (робота) teach-mode programmingУниверсальный русско-английский словарь > программирование в режиме обучения
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48 система программирования обучением
1) Automation: leach-mode programming system2) Makarov: teach-mode programming systemУниверсальный русско-английский словарь > система программирования обучением
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49 программирование в интерактивном режиме
Русско-английский научный словарь > программирование в интерактивном режиме
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50 программирование в интерактивном режиме
Русско-английский словарь по информационным технологиям > программирование в интерактивном режиме
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51 метод
1) expedient
2) manner
3) method
4) <electr.> mode
5) procedure
6) technique
– аксиоматический метод
– анаглифический метод
– вариационный метод
– весовой метод
– визуальный метод
– время-импульсный метод
– градиентный метод
– графический метод
– графоаналитический метод
– групповой метод
– дедуктивный метод
– иммерсионный метод
– импульсный метод
– интерференционный метод
– качественный метод
– кессонный метод
– количественный метод
– колориметрический метод
– комплексометрический метод
– кондуктометрический метод
– корреляционный метод
– косвенный метод
– лабораторный метод
– метод бестигельный
– метод Бормана
– метод Бриджмена
– метод взбалтывания
– метод возбуждения
– метод восходящий
– метод вращения
– метод врезания
– метод выбега
– метод годографа
– метод графов
– метод Грисса-Иловая
– метод дальномерно-базисный
– метод Дешана
– метод Дюма
– метод изинговский
– метод изображений
– метод импульсов
– метод инверсии
– метод испытаний
– метод истечения
– метод итерации
– метод Клегга
– метод консервирования
– метод конуса
– метод красок
– метод Марковица
– метод множителей
– метод накачки
– метод накопления
– метод наложения
– метод напыления
– метод обработки
– метод окаймления
– метод ОПВ
– метод осаждения
– метод осреднения
– метод отопления
– метод отражения
– метод перевала
– метод перемежающийся
– метод перпендикуляров
– метод площадей
– метод подбора
– метод подобия
– метод положения
– метод посева
– метод постулатов
– метод прерываний
– метод пристрелки
– метод проб
– метод прогонки
– метод продолжения
– метод равносигнальный
– метод радиоавтографии
– метод разбавления
– метод разделения
– метод разливки
– метод размерностей
– метод решета
– метод Рунге-Кутта
– метод свилей
– метод секущих
– метод сетки
– метод сеток
– метод сечений
– метод сил
– метод совмещения
– метод совпадений
– метод сплавления
– метод Степанова
– метод стрельбы
– метод триангуляции
– метод трилатерации
– метод узлов
– метод Уизема
– метод уравновешивания
– метод установления
– метод частиц
– метод Шора
– метод электрофореза
– метод эстафеты
– ненулевой метод
– неразрушающий метод
– нерекурсивный метод
– неточный метод
– нефелометрический метод
– нулевой метод
– обратно-ступенчатый метод
– объективный метод
– объемный метод
– операторный метод
– пикнометрический метод
– порошковый метод
– приближенный метод
– прямой метод
– радиационный метод
– радиометрический метод
– разностный метод
– разрушающий метод
– рентгеноструктурный метод
– ресонансный метод
– рупорно-линзовый метод
– симболический метод
– спектроскопический метод
– статистический метод
– стробоскопический метод
– струйный метод
– ступенчатый метод
– субъективный метод
– табличный метод
– теневой метод
– топологический метод
– точный метод
– финитный метод
– флотационный метод
– цепной метод
– численный метод
– шуповой метод
– эмпирический метод
– энергетический метод
– эргатический метод
– эскалаторный метод
абсолютный метод измерения — absolute method of measurement
дальномерный метод навигации — rho-rho navigation
дифференцированный метод контроля — differential control method
кислотный метод испытаний — acid test
косвенный метод измерения — indirect method of measurement
метод амплитудного анализа — kick-sorting method
метод анализа узловой — <tech.> nodal analysis
метод аналитической вставки — cantilevel extension
метод аппроксимации отображаемых поверхностей сплайнами — spline surface technique
метод быстрейшего спуска — steepest descent method
метод вариации постоянных — method of variation of parameters
метод ветвей и границ — branch and bound method, branch-and-bound, <math.> branch-and-bound method
метод ветвления и ограничения — branch and bound method
метод взаимных градиентов — <math.> conjugate-gradient method
метод воздушной проекции — aero-projection method
метод возможных направлений — <math.> method of feasible directions
метод времени пролета — time-of-flight method
метод встречного включения — <tech.> opposition method
метод встречного фрезерования — conventional milling method
метод гармонического баланса — describing function method
метод двух узлов — nodal-pair method
метод дирекционных углов — method of gisements
метод запаса прочности — load factor method
метод зеркальных изображений — method of electrical images
метод зонной плавки — floating-zone method
метод избыточных концентраций — isolation method
метод измерения по точкам — point-by-point method
метод изотопных индикаторов — tracer method
метод искаженных волн непрерывного спектра — <phys.> continuous-distorted-wave approximation
метод испытательной строки — test-line method
метод итераций Гаусса-Зайделя — <math.> Gauss-Seidel iteration
метод качающегося кристалла — rotating-crystal method
метод качающейся частоты — <electr.> wobbulator method
метод кольца и шара — ball-and-ring method
метод комбинирования для получения оптимальных вариантов — mix-and-match technique
метод конечных разностей — finite difference method
метод конечных элементов — <math.> finite element method
метод контроля качества — quality control method
метод контурного анализа — <tech.> loop analysis
метод контурных токов — mesh-current method
метод корневого годографа — root-locus method
метод крупных частиц — <math.> particle-in-cell method
метод лаковых покрытий — brittel-varnish method
метод линейной интерполяции — method of proportional parts
метод ложного положения — <math.> method of false position
метод лучевого зондирования — ray-trace method
метод магнитного порошка — magnetic particle method
метод малого параметра — pertubation theory
метод малых возмущений — perturbation method
метод механической обработки — machining method
метод моментных площадей — area moment method
метод нагретой нити — <phys.> hot-wire technique
метод наибольшего ската — saddle-point method
метод наименьших квадратов — method of least squares
метод наискорейшего спуска — <math.> method of steepest descent
метод наихудшего случая — <math.> worst-case method
метод наружных зарядов — adobe blasting method
метод неподвижных точек — method of fixed points
метод нивелирования по частям — method of fraction levelling
метод нулевого отклонения — <tech.> zero deflection method
метод нулевых биений — zero-beat method
метод нулевых точек — neutral-points method
метод нулей Барле — <phys.> Barrelet method of zeroes
метод обеспечения надежности — reliability method
метод обогащения данных — data enrichment method
метод обратной задачи — <math.> inverse-scattering method
метод одного отсчета — total value method
метод ортогонализованных плоских волн — <opt.> orthogonalized-plane-wave method
метод особых возмущений — singular perturbation method
метод отбора проб — sampling method
метод относительных приростов — <engin.> method of incremental rates
метод отраженных волн — < radio> reflected wave method
метод отраженных импульсов — pulse-echo method
метод падающего тела — falling body method
метод параллельного действия — parallel mode
метод парамагнитного резонанса — paramagnetic-resonance method
метод первого приближения — first approximation method
метод передачи совместных значений — <comput.> composite value method
метод переменной плотности — <phot.> movietone
метод переменных направлений — <math.> ADI method, alternating direction method
метод перераспределения моментов — moment distribution method
метод пересекающихся дучей — crossed beam method
метод переходного состояния — transition state method
метод перспективных сеток — grid method
метод плавающей зоны — <metal.> floating zone melting
метод планирования балансовый — <econ.> balance-chart method of planning
метод подвижного или передвигающего наблюдателя — moving-observer technique
метод покоординатного спуска — <math.> alternating-variable descent method
метод полной деформации — total-strain method
метод половинных отклонений — half-deflection method
метод полярных координат — polar method
метод попутного фрезерования — climb milling method
метод последовательного счета — incremental method
метод последовательных исключений — successive exclusion method
метод последовательных поправок — successive correction
метод последовательных элиминаций — method of exhaustion
метод послесплавной диффузии — post-alloy-diffusion technique
метод предпоследнего остатка — <math.> method of penultimate remainder
метод приближения объемного заряда с резкой границей — abrupt space-charge edge
метод пробных выборок — <math.> model sampling
метод прогноза и коррекции — <math.> predictor-corrector method
метод программирующих программ — programming program method
метод пространств входных массивов — <comput.> input space approach
метод равносигнальной зоны — lobing
метод равных высот — equal-altitude method
метод равных деформаций — equal-strain method
метод равных отклонений — <tech.> equal deflection method, equal-deflection method
метод разделения переменных — method of separation of variable
метод разрушающей нагрузки — load-factor method
метод растрового сканирования — raster-scan method
метод сдвинутого сигнала — offset-signal method
метод селекции мод — mode selecting technique
метод серого клина — gray-wedge method
метод сжатия импульсов — pulse compression technique
метод симметричных составляющих — method of symmetrical components
метод синхронизации мод — mode-locking technique
метод синхронизации фаз — phase-locking technique
метод синхронного накопления — synchronous storage method
метод сканирования полосой — single-line-scan television meth
метод сканирования пятном — spot-scan photomultiplier method
метод сквозного счета — <phys.> shock-capturing method
метод скользящего окна — <math.> data windowing
метод скользящих средних — <math.> moving average method, moving-average method
метод скорейшего спуска — <math.> method of steepest descent
метод совместных значений — <comput.> composite value method
метод сопряженных градиентов — <math.> method of complex gradients
метод сопряженных уравнений — <math.> adjoint method
метод сосредоточенных параметров — lumped-parameter method
метод составного стержня Гопкинсона — Hopkinson split-bar method
метод спадания заряда — fall-of-charge method
метод спирального сканирования — spiral-scan method
метод сравнений по модулю 9 — <math.> casting out nines
метод средних квадратов — midsquare method
метод сухого озоления — dry combustion method
метод сухого порошка — dry method
метод точечного вплавления — dot alloying method
метод трех баз — three-base method
метод угловой деформации — slope-deflection method
метод угловой модуляции — angular modulation method
метод удаляемого маски — rejection mask method
метод удаляемого трафарета — rejeciton mask method
метод узлового анализа — <tech.> nodal analysis
метод узловых потенциалов — node-voltage method
метод унифицированных модулей — building-block method
метод уравнивания по направлениям — method of directions
метод уравнивания по углам — method of angles
метод фазовой плоскости — phase plane method
метод фазовых функций — <phys.> variable-phase method
метод чередущихся направлений — <math.> ADI method, alternating direction method
метод эффективного пространства — effective medium approach
непосредственный метод отыскания производной — delta method
основанный на переходе к сравнениям метод проверки — casting out
относительный метод измерения — relative method of measurement
панельный метод испытаний — panel-spalling test
параллельно-последовательный метод выполнения операций — parallel-serial mode
прессование металла обратным метод — inverse extrusion
прямой метод измерения — direct method of measurement
угломерный метод навигации — theta-theta navigation
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52 режим выполнения
1) Engineering: execution behavior (напр. программы)2) Information technology: execution, playback mode (макрокоманды)3) Household appliances: execution states4) SAP. processing mode5) Network technologies: run mode6) Programming: execution mode -
53 режим реального времени
1) Computers: real time environment, real time mode2) Engineering: on-line envelope, real-time mode3) Information technology: on-line environment, real-time environment4) Atomic energy: on-line mode5) Network technologies: real-time processing6) Programming: real-time operation mode7) Automation: real-timeУниверсальный русско-английский словарь > режим реального времени
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54 программирование в диалоговом режиме
Русско-английский словарь по информационным технологиям > программирование в диалоговом режиме
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55 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, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany 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 FormThe 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 FormationIt 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 ContextsEven 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)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 IntelligenceThe 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 PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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56 Language
Philosophy is written in that great book, the universe, which is always open, right before our eyes. But one cannot understand this book without first learning to understand the language and to know the characters in which it is written. It is written in the language of mathematics, and the characters are triangles, circles, and other figures. Without these, one cannot understand a single word of it, and just wanders in a dark labyrinth. (Galileo, 1990, p. 232)It never happens that it [a nonhuman animal] arranges its speech in various ways in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. (Descartes, 1970a, p. 116)It is a very remarkable fact that there are none so depraved and stupid, without even excepting idiots, that they cannot arrange different words together, forming of them a statement by which they make known their thoughts; while, on the other hand, there is no other animal, however perfect and fortunately circumstanced it may be, which can do the same. (Descartes, 1967, p. 116)Human beings do not live in the object world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the "real world" is to a large extent unconsciously built on the language habits of the group.... We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interpretation. (Sapir, 1921, p. 75)It powerfully conditions all our thinking about social problems and processes.... No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same worlds with different labels attached. (Sapir, 1985, p. 162)[A list of language games, not meant to be exhaustive:]Giving orders, and obeying them- Describing the appearance of an object, or giving its measurements- Constructing an object from a description (a drawing)Reporting an eventSpeculating about an eventForming and testing a hypothesisPresenting the results of an experiment in tables and diagramsMaking up a story; and reading itPlay actingSinging catchesGuessing riddlesMaking a joke; and telling itSolving a problem in practical arithmeticTranslating from one language into anotherLANGUAGE Asking, thanking, cursing, greeting, and praying-. (Wittgenstein, 1953, Pt. I, No. 23, pp. 11 e-12 e)We dissect nature along lines laid down by our native languages.... The world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... No individual is free to describe nature with absolute impartiality but is constrained to certain modes of interpretation even while he thinks himself most free. (Whorf, 1956, pp. 153, 213-214)We dissect nature along the lines laid down by our native languages.The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... We are thus introduced to a new principle of relativity, which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar or can in some way be calibrated. (Whorf, 1956, pp. 213-214)9) The Forms of a Person's Thoughts Are Controlled by Unperceived Patterns of His Own LanguageThe forms of a person's thoughts are controlled by inexorable laws of pattern of which he is unconscious. These patterns are the unperceived intricate systematizations of his own language-shown readily enough by a candid comparison and contrast with other languages, especially those of a different linguistic family. (Whorf, 1956, p. 252)It has come to be commonly held that many utterances which look like statements are either not intended at all, or only intended in part, to record or impart straightforward information about the facts.... Many traditional philosophical perplexities have arisen through a mistake-the mistake of taking as straightforward statements of fact utterances which are either (in interesting non-grammatical ways) nonsensical or else intended as something quite different. (Austin, 1962, pp. 2-3)In general, one might define a complex of semantic components connected by logical constants as a concept. The dictionary of a language is then a system of concepts in which a phonological form and certain syntactic and morphological characteristics are assigned to each concept. This system of concepts is structured by several types of relations. It is supplemented, furthermore, by redundancy or implicational rules..., representing general properties of the whole system of concepts.... At least a relevant part of these general rules is not bound to particular languages, but represents presumably universal structures of natural languages. They are not learned, but are rather a part of the human ability to acquire an arbitrary natural language. (Bierwisch, 1970, pp. 171-172)In studying the evolution of mind, we cannot guess to what extent there are physically possible alternatives to, say, transformational generative grammar, for an organism meeting certain other physical conditions characteristic of humans. Conceivably, there are none-or very few-in which case talk about evolution of the language capacity is beside the point. (Chomsky, 1972, p. 98)[It is] truth value rather than syntactic well-formedness that chiefly governs explicit verbal reinforcement by parents-which renders mildly paradoxical the fact that the usual product of such a training schedule is an adult whose speech is highly grammatical but not notably truthful. (R. O. Brown, 1973, p. 330)he conceptual base is responsible for formally representing the concepts underlying an utterance.... A given word in a language may or may not have one or more concepts underlying it.... On the sentential level, the utterances of a given language are encoded within a syntactic structure of that language. The basic construction of the sentential level is the sentence.The next highest level... is the conceptual level. We call the basic construction of this level the conceptualization. A conceptualization consists of concepts and certain relations among those concepts. We can consider that both levels exist at the same point in time and that for any unit on one level, some corresponding realizate exists on the other level. This realizate may be null or extremely complex.... Conceptualizations may relate to other conceptualizations by nesting or other specified relationships. (Schank, 1973, pp. 191-192)The mathematics of multi-dimensional interactive spaces and lattices, the projection of "computer behavior" on to possible models of cerebral functions, the theoretical and mechanical investigation of artificial intelligence, are producing a stream of sophisticated, often suggestive ideas.But it is, I believe, fair to say that nothing put forward until now in either theoretic design or mechanical mimicry comes even remotely in reach of the most rudimentary linguistic realities. (Steiner, 1975, p. 284)The step from the simple tool to the master tool, a tool to make tools (what we would now call a machine tool), seems to me indeed to parallel the final step to human language, which I call reconstitution. It expresses in a practical and social context the same understanding of hierarchy, and shows the same analysis by function as a basis for synthesis. (Bronowski, 1977, pp. 127-128)t is the language donn eґ in which we conduct our lives.... We have no other. And the danger is that formal linguistic models, in their loosely argued analogy with the axiomatic structure of the mathematical sciences, may block perception.... It is quite conceivable that, in language, continuous induction from simple, elemental units to more complex, realistic forms is not justified. The extent and formal "undecidability" of context-and every linguistic particle above the level of the phoneme is context-bound-may make it impossible, except in the most abstract, meta-linguistic sense, to pass from "pro-verbs," "kernals," or "deep deep structures" to actual speech. (Steiner, 1975, pp. 111-113)A higher-level formal language is an abstract machine. (Weizenbaum, 1976, p. 113)Jakobson sees metaphor and metonymy as the characteristic modes of binarily opposed polarities which between them underpin the two-fold process of selection and combination by which linguistic signs are formed.... Thus messages are constructed, as Saussure said, by a combination of a "horizontal" movement, which combines words together, and a "vertical" movement, which selects the particular words from the available inventory or "inner storehouse" of the language. The combinative (or syntagmatic) process manifests itself in contiguity (one word being placed next to another) and its mode is metonymic. The selective (or associative) process manifests itself in similarity (one word or concept being "like" another) and its mode is metaphoric. The "opposition" of metaphor and metonymy therefore may be said to represent in effect the essence of the total opposition between the synchronic mode of language (its immediate, coexistent, "vertical" relationships) and its diachronic mode (its sequential, successive, lineal progressive relationships). (Hawkes, 1977, pp. 77-78)It is striking that the layered structure that man has given to language constantly reappears in his analyses of nature. (Bronowski, 1977, p. 121)First, [an ideal intertheoretic reduction] provides us with a set of rules"correspondence rules" or "bridge laws," as the standard vernacular has it-which effect a mapping of the terms of the old theory (T o) onto a subset of the expressions of the new or reducing theory (T n). These rules guide the application of those selected expressions of T n in the following way: we are free to make singular applications of their correspondencerule doppelgangers in T o....Second, and equally important, a successful reduction ideally has the outcome that, under the term mapping effected by the correspondence rules, the central principles of T o (those of semantic and systematic importance) are mapped onto general sentences of T n that are theorems of Tn. (P. Churchland, 1979, p. 81)If non-linguistic factors must be included in grammar: beliefs, attitudes, etc. [this would] amount to a rejection of the initial idealization of language as an object of study. A priori such a move cannot be ruled out, but it must be empirically motivated. If it proves to be correct, I would conclude that language is a chaos that is not worth studying.... Note that the question is not whether beliefs or attitudes, and so on, play a role in linguistic behavior and linguistic judgments... [but rather] whether distinct cognitive structures can be identified, which interact in the real use of language and linguistic judgments, the grammatical system being one of these. (Chomsky, 1979, pp. 140, 152-153)23) Language Is Inevitably Influenced by Specific Contexts of Human InteractionLanguage cannot be studied in isolation from the investigation of "rationality." It cannot afford to neglect our everyday assumptions concerning the total behavior of a reasonable person.... An integrational linguistics must recognize that human beings inhabit a communicational space which is not neatly compartmentalized into language and nonlanguage.... It renounces in advance the possibility of setting up systems of forms and meanings which will "account for" a central core of linguistic behavior irrespective of the situation and communicational purposes involved. (Harris, 1981, p. 165)By innate [linguistic knowledge], Chomsky simply means "genetically programmed." He does not literally think that children are born with language in their heads ready to be spoken. He merely claims that a "blueprint is there, which is brought into use when the child reaches a certain point in her general development. With the help of this blueprint, she analyzes the language she hears around her more readily than she would if she were totally unprepared for the strange gabbling sounds which emerge from human mouths. (Aitchison, 1987, p. 31)Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important "programming language." This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language.... One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn't something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Leiber, 1991, p. 8)Historical dictionary of quotations in cognitive science > Language
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57 пошаговый режим
1) Engineering: one-shot operation, one-step operation, single-step operation2) Telecommunications: single-shot operation3) Information technology: single-step mode4) Oil: step mode5) Mechanics: single-step regime6) Programming: step-by-step mode (режим, используемый при отладке программ)7) Makarov: step-by-step operation8) Electrical engineering: stepwise operation -
58 метод
1) General subject: approach, manner (manner of life (thought) - образ жизни (мыслей)), method, mode, procedure, rule of thumb (в отличие от научного), scheme, system (what system do you go on? - какому методу вы следуете?), tack, teaching, the how, tool, way, wrinkle, american welt type "Parco" attaching method (обувь, подошву которой прикрепляют нитками к ранту, соединенному нитками с заготовкой верха по всему периметру без основной стельки или к пяточной части с оснойной стелькой)4) Colloquial: how6) Engineering: electromagnetic induction method, means, technology, theory8) Construction: mean9) Mathematics: bootstrap method, expedient, fashion10) Economy: minimization technique11) Accounting: convention, technics13) Forestry: type14) Textile: style16) Oil: process17) Genetics: (расчёта для теста) method18) Geophysics: rule19) Taxes: basis21) Drilling: hang23) Programming: routine (в ООП), (в Java)(класса) instance method (то же, что и nonstatic member function в С++)24) Automation: course25) Chemical weapons: metal method26) Makarov: Mo (mode), approach (подход), avenue, avenue of approach, concept, mechanism, meth (method), methodology, plan (подход), principle (подход), scheme (подход), strategy28) Camera recording: (или способ) route -
59 метод
1) General subject: approach, manner (manner of life (thought) - образ жизни (мыслей)), method, mode, procedure, rule of thumb (в отличие от научного), scheme, system (what system do you go on? - какому методу вы следуете?), tack, teaching, the how, tool, way, wrinkle, american welt type "Parco" attaching method (обувь, подошву которой прикрепляют нитками к ранту, соединенному нитками с заготовкой верха по всему периметру без основной стельки или к пяточной части с оснойной стелькой)4) Colloquial: how6) Engineering: electromagnetic induction method, means, technology, theory8) Construction: mean9) Mathematics: bootstrap method, expedient, fashion10) Economy: minimization technique11) Accounting: convention, technics13) Forestry: type14) Textile: style16) Oil: process17) Genetics: (расчёта для теста) method18) Geophysics: rule19) Taxes: basis21) Drilling: hang23) Programming: routine (в ООП), (в Java)(класса) instance method (то же, что и nonstatic member function в С++)24) Automation: course25) Chemical weapons: metal method26) Makarov: Mo (mode), approach (подход), avenue, avenue of approach, concept, mechanism, meth (method), methodology, plan (подход), principle (подход), scheme (подход), strategy28) Camera recording: (или способ) route -
60 образ
1) General subject: character, cultus image, eidolon, exemplar, fashion, figure, form, image, imago, likeness, manner, mode, modus, pattern, portrayal, reflection, reflex, reflexion, reflexion (в литературе (и т.п.)), representation, shape, similitude, simulacrum, sort, visuality (мысленный), visualization, way, wise, vision, effigy, (сценический the brainchild of comedian comic Andriy Danylko) brainchild, guise2) Medicine: appearance, aspect, conception, type3) Colloquial: how4) Church: holy picture (небольших размеров( small holy picture))5) Military: picture6) Engineering: icon (в СТЗ и электронной почты), manner (способ), transform, way (способ)8) Religion: ikon11) Optics: image (изображение), imagery12) Psychology: configuration, symbol13) Information technology: icon (изображение), pattern (в распознавании образов)14) Literature: character sketch16) Patents: (pi modi) modus17) Programming: (данных) mirror copy, context18) Aviation medicine: matrix19) Psychoanalysis: life21) Fashion: look
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programming fluid — n. Coffee, Jolt cola, or any other high caffeine stimulant that helps a programmer get through extra long or all night coding sessions. Example Citation: Back to the succinct I way, then, a term which is much more Net friendly than the cumbersome … New words
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List of programming languages by category — Programming language lists Alphabetical Categorical Chronological Generational This is a list of programming languages grouped by category. Some languages are listed in multiple categories. Contents … Wikipedia
