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1 semantic statement
семантическое утверждение; семантическое высказывание -
2 semantic statement
семантическое утверждение; семантическое высказываниеThe New English-Russian Dictionary of Radio-electronics > semantic statement
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3 statement
1) формулировка (напр. проблемы); постановка (напр. задачи)2) утверждение; высказывание3) оператор (предложение языка программирования, задающее функционально законченное действие); инструкция (в некоторых языках программирования, напр. в C++)4) заявление; сообщение•- action statement
- assert statement
- assignment statement
- atomic statement
- blank statement
- case statement
- categorical statement
- conditional statement
- comment statement
- compile-time statement
- compound statement
- conditional statement
- control statement
- data manipulation statement
- debug statement
- debugging statement
- declaration statement
- declarative statement
- delimiter statement
- dummy statement
- executable statement
- exception statement
- expect statement
- expression statement
- false statement
- fuzzy statement
- GOTO statement
- goto statement
- if statement
- imperative statement
- indexing statement
- invalid statement
- iterative statement
- job control statement
- labeled statement
- language statement
- logical statement
- looping statement
- mathematical statement
- negative statement
- nonexecutable statement
- null statement
- path statement
- positive statement
- problem statement
- program control statement
- protocol implementation conformance statement
- provable statement
- REM statement
- repeat-until statement
- repetitive statement
- satisfiable statement
- semantic statement
- send statement
- source statement
- specification statement
- transfer statement
- unconditional statement
- unlabeled statement
- unprovable statement
- while statement -
4 statement
1) формулировка (напр. проблемы); постановка (напр. задачи)2) утверждение; высказывание3) оператор (предложение языка программирования, задающее функционально законченное действие); инструкция (в некоторых языках программирования, напр. в C++)4) заявление; сообщение•- assert statement
- assignment statement
- atomic statement
- blank statement
- case statement
- categorical statement
- comment statement
- compile-time statement
- compound statement
- conditional statement
- control statement
- data manipulation statement
- debug statement
- debugging statement
- declaration statement
- declarative statement
- delimiter statement
- dummy statement
- exception statement
- executable statement
- expect statement
- expression statement
- false statement
- fuzzy statement
- GOTO statement
- goto statement
- if statement
- imperative statement
- indexing statement
- invalid statement
- iterative statement
- job control statement
- labeled statement
- language statement
- logical statement
- looping statement
- mathematical statement
- negative statement
- nonexecutable statement
- null statement
- path statement
- positive statement
- problem statement
- program control statement
- protocol implementation conformance statement
- provable statement
- REM statement
- repeat-until statement
- repetitive statement
- satisfiable statement
- semantic statement
- send statement
- source statement
- specification statement
- statement of problem
- transfer statement
- unconditional statement
- unlabeled statement
- unprovable statement
- while statementThe New English-Russian Dictionary of Radio-electronics > statement
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5 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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6 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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7 check
tʃek
1. сущ.
1) шахм. шах (употр. тж. как межд.) the king is in check ≈ королю объявлен шах to produce a check ≈ сделать шах to discover check ≈ обнаружить шаховую позицию perpetual check ≈ вечный шах
2) а) задержка, остановка( в развитии, карьере и т. п. из-за какой-л. помехи, препятствия или противодействия) Syn: arrest
1. б) отпор, отражение нападения Syn: rebuff
1., repulse
1. в) потеря охотничьей собакой следа
3) внезапная остановка;
пауза, перерыв (при движении, работе) without check
4) а) ограничивание, сдерживание in check Syn: restraint б) препятствие, ограничитель (любое лицо или предмет, действующие в качестве ограничивающего начала) The magistrate may be necessary as a check on the doctor. ≈ Мировой судья может оказаться необходимым, как некто, кто сможет сдержать доктора. в) амер. мартингал (в верховой езде) Syn: check-rein
5) а) критерий (стандарт для оценки и проверки) Syn: criterion б) обследование, исследование background check ≈ расследование истории вопроса/проблемы Syn: examination в) контроль, проверка to conduct, make, run a check of/on ≈ осуществлять контроль, проводить проверку clearance check loyalty check Syn: inspection
6) контрольный штемпель;
отметка, галочка (знак проверки)
7) а) ярлык;
багажная квитанция baggage амер. check ≈ квитанция на получение багажа б) номерок( в гардеробе) в) преим. амер. счет в ресторане г) контрамарка;
корешок( билета и т. п.)
8) амер. фишка, марка (в карт. игре) to cash, hand, pass in one's checks ид. ≈ умереть
9) амер. чек to cash a check ≈ платить по чеку to clear a check ≈ производить выплаты по чеку to cover a check (by making a deposit) ≈ обеспечивать денежное покрытие чека (с помощью депозита) to deposit a check ≈ сделать вклад в банке to draw a check against one's account ≈ выписать чек на чей-л. счет to draw a check on a bank ≈ выписать чек на счет в банке to endorse a check ≈ подписывать чек на какую-л. сумму to issue, make out, write out a check to ≈ выписать чек to kite a check ≈ получать деньги по фиктивным чекам to pass a (bad) check ≈ пустить в обращение фальшивый чек to present a check ≈ предъявить чек to stop payment of/on a check ≈ прекратить выплату по чекам bad check bounced check cashier's check certified check
10) клетка( на ткани) ;
клетчатая ткань
11) с.-х. делянка
12) трещина, щель( в дереве) Syn: crack
1., break I
1.
2. прил.
1) контрольный;
испытательный check experiment ≈ контрольный опыт check ballot ≈ проверочное голосование
2) клетчатый check shirt ≈ клетчатая рубашка
3. гл.
1) шахм. объявлять шах
2) а) останавливать;
препятствовать( продвижению) Syn: stop
2., brake
2. б) поэт. натягивать( поводья)
3) а) внезапно остановиться, отшатнуться( от неожиданности, страха;
проявить осторожность) Syn: stop
2. б) охот. останавливаться, потеряв след ( об охотничьих собаках)
4) ограничивать, сдерживать, обуздывать, регулировать He hastily checked the impulse. ≈ Он быстро подавил этот порыв. Mr. Baldwin checked the enthusiasm of his visitors. ≈ Мистер Болдуэн умерил энтузиазм своих гостей. The multiplication of animals is checked only by want of food, and by the hostility of races. ≈ Размножение животных сдерживается только количеством еды и степенью агрессивности других видов. Syn: restrain
5) а) проверять, сверять How can you check on whether it will rain that day? ≈ Как проверить, будет в тот день идти дождь? We must check the book over before sending it to the printer. ≈ Нам надо еще раз внимательно просмотреть книгу, прежде чем отсылать ее издателю. We must check through the pages to see if any are missing. ≈ Надо просмотреть бумаги, вдруг что-то пропало. Syn: verify б) контролировать Syn: control
2.
6) соответствовать, совпадать The description checks with the photograph. ≈ Описание соответствует фотографии.
7) отмечать галочкой или каким-л. знаком( что-л. проверенное)
8) амер. выписывать чек to check upon smb. for $500 ≈ выписать на кого-л. чек на 500 долларов
9) преим. амер. сдавать( в гардероб, в камеру хранения, в багаж и т. п.) They walked out into the club and checked their hats. ≈ Они вошли в клуб и сдали на вешалку свои шляпы.
10) (ранее диал., в современном употреблении разг.) делать выговор, отчитывать;
ругать, давать нагоняй Syn: rebuke
2., reprove
2., reprimand
2.
11) раскрашивать клеткой
12) а) редк. располагать в шахматном порядке б) амер. размечать на квадраты (землю для дальнейшего засевания)
13) а) вызывать трещины The sun checks timber. ≈ Солнце заставляет доски растрескиваться. б) покрываться трещинами ∙ Syn: crack
3., split
3. ∙ check back check in check off check on check over check out check up check with препятствие, остановка;
задержка - to serve as a * служить препятствием;
обуздывать - wind acts as a * on speed ветер мешает быстрой езде - his illness gave a * to our plans его болезнь сорвала наши планы - to keep in * держать в руках, контролировать - keep your emotions in * сдерживайте свои чувства - to keep a * on smb. держать кого-л в руках, не давать воли кому-л - to keep a * on smth. следить за чем-л.;
контролировать что-л;
держать что-л. в своих руках - keep a * on your tongue думай, прежде чем говорить преим. (военное) отпор, приостановка наступления или продвижения проверка, контроль - accuracy * проверка точности - spot *s (полиграфия) выборочная корректура, выборочный редакционный просмотр галочка, птичка, отметка ( знак проверки) номерок (в гардеробе) - hat * номерок на шляпу ярлык;
богажная квитанция - a * for a suitcase квитанция на чемодан контрольный штемпель контрамарка;
корешок (билета) клетка (рисунок ткани) клетчатая ткань;
шотландка - do you want a stripe or a *? вам в полоску или в клетку? счет (в ресторане) (шахматное) шах - double * двойной шах - perpetual * вечный шах - * to the king шах королю (сельскохозяйственное) чек, делянка, окруженная валом и затапливаемая водой (сельскохозяйственное) контрольная делянка (охота) потеря (собакой) следа (специальное) трещина, щель (в дереве) ;
волосная трещина (американизм) (карточное) фишка, марка > *s and balances принцип взаимозависимости и взаимозависимости и взаимоограничения законодательной, исполнительной и судебной власти контрольный, проверочный, испытательный - * analysis контрольный анализ - * cage клетка или садок для контрольных животных - * experiment поверочный опыт - * flight (авиация) контрольный полет - * sample контрольный образец - * station( военное) пункт технического осмотра - * test поверочное испытание клетчатый - * handkerchief клетчатый платок - * system of irrigation (сельскохозяйственное) орошение способом затопления по клеткам запирающий, задерживающий - * dam задерживающая плотина, защитная дамба или плотина - * valve( техническое) запорный клапан, обратный клапан - * work (техническое) периодическое включение и выключение механизма > * wine марочное вино останавливать, сдерживать;
препятствовать;
удерживать;
обуздывать - to * the advance of the enemy приостановить продвижение противника - to * extravagant spending положить конец расточительству - to * anger подавить гнев - to * the growth замедлять рост - he *ed his impetuous son он сдерживал своего необузданного сына - to * a fire остановить пожар - to * oneself остановиться, удержаться;
сдержаться - she *ed herself она не договорила - he *ed himself just as he was about to blurt out his indignation он подавил готовые вырваться слова негодования проверять, контролировать;
ревизовать;
сличать;
расследовать - to * figures проверять цифры - to * by sight проверять на глаз - to * for errors корректировать, исправлять - to * an instrument выверять прибор - to * one's speed контролировать скорость - * into the matter разберитесь в этом деле - * bearing! (специальное) проверить пеленг!, взять контрольный пеленг! (команда) проверять, выяснять;
убеждаться( в чем-л.) - we must * on him его надо проверить - to * on a statement проверить правильность какого-л утверждения - to * on the past experience of the applicants выяснить уровень квалификации претендентов на должность сверять, сличать - * your watch with the tower clock проверьте свои часы по башенным (американизм) соответствовать. совпадать - his statement *s with yours его заявление совпадает с вашим - the description *s with the photograph описание соответствует фотографии (американизм) сдавать (в гардероб, в камеру хранения, в багаж) - have you *ed all your luggage? вы все свои вещи сдали в багаж? - * in your coat and hat cдайте в гардероб пальто и шляпу принимать на хранение - the hotel *ed our baggage гостиница приняла на хранение наш багаж отмечать галочкой, значком - how many mistakes did the teacher *? сколько ошибок учитель отметил (птичкой) ? (шахматное) объявлять шах (карточное) пасовать располагать в шахматном порядке делать выговор;
давать нагоняй;
разносить( сельскохозяйственное) приостанавливать( рост) (специальное) делать щели;
вызывать трещины (специальное) покрываться трещинами, щелями (устаревшее) внезапно остановиться (перед чем-л) ;
отшатнуться (от чего-л) (морское) травить( шахматное) шах! (просторечие) ладно!, точно!, договорились! (американизм) (финансовое) чек - bank * банковский чек - сertified * удостоверенный чек, чек с надписью банка о принятии к платежу - crossed * кроссированный чек - town * чек на банк в Лондонском Сити - traveller's * дорожный чек( американизм) выписывать чек - to * upon a banker for $100 выдать чек на какой-л. банк на сумму в 100 долларов access ~ вчт. контроль доступа automatic ~ вчт. автоматический контроль bias ~ профилактический контроль block ~ вчт. контроль блоков block ~ вчт. проверка по блокам bound ~ вчт. контроль границ built-in ~ вчт. встроенный контроль bus-out ~ вчт. контроль выходной шины ~ attr. клетчатый;
to keep (или to hold) in check сдерживать;
to cash (или to hand, to pass) in one's checks умереть cashier's ~ кассирский чек check багажная квитанция ~ делать выговор;
давать нагоняй ~ делать выговор ~ задержка ~ клетка (на материи) ;
клетчатая ткань ~ контрамарка;
корешок (билета и т. п.) ~ контрамарка ~ контролировать ~ контроль, проверка;
loyalty check амер. проверка лояльности( государственных служащих) ~ контроль ~ контрольный штемпель;
галочка (знак проверки) ~ корешок, номерок ~ корешок билета ~ номерок (в гардеробе) ~ обуздывать ~ шахм. объявлять шах ~ останавливать(ся) ;
сдерживать;
препятствовать ~ останавливать ~ остановка ~ отметка в документе ~ отметка о проверке ~ отмечать галочкой ~ переводной вексель, оплачиваемый по предъявлении ~ потеря охотничьей собакой следа ~ препятствие;
остановка;
задержка;
without check без задержки, безостановочно ~ препятствие ~ препятствовать ~ принимать на хранение ~ проверка ~ проверять, контролировать ~ проверять ~ располагать в шахматном порядке ~ расследовать ~ ревизовать ~ амер. сдавать (в гардероб, в камеру хранения, в багаж и т. п.) ;
check in сдавать под расписку;
регистрировать (ся), записывать(ся) ~ сдерживать ~ сличать ~ трещина, щель (в дереве) ~ амер. фишка, марка (в карт. игре) ~ амер. чек ~ (амер.) чек ~ чек ~ шахм. шах (употр. тж. как int) ;
the king is in check королю объявлен шах ~ ярлык;
багажная квитанция ~ against проверять на соответствие ~ attr. клетчатый;
to keep (или to hold) in check сдерживать;
to cash (или to hand, to pass) in one's checks умереть ~ attr. контрольный;
check experiment контрольный опыт;
check ballot проверочное голосование ~ attr. контрольный;
check experiment контрольный опыт;
check ballot проверочное голосование ~ attr. контрольный;
check experiment контрольный опыт;
check ballot проверочное голосование ~ амер. сдавать (в гардероб, в камеру хранения, в багаж и т. п.) ;
check in сдавать под расписку;
регистрировать (ся), записывать(ся) ~ in отмечаться при приходе на работу ~ in регистрировать ~ in сдавать на хранение ~ in сдавать под расписку ~ off отмечать галочкой ~ off удерживать из заработной платы ~ out освободить номер в гостинице ~ out амер. отметиться при уходе с работы по окончании рабочего дня ~ out отмечаться при уходе с работы ~ out радио отстроиться ~ out оформлять выдачу ~ out оформлять получение ~ out подсчитывать стоимость покупок и выбивать чек ~ out амер. уйти в отставку ~ the figures проверять расчеты ~ up проверять ~ with совпадать, соответствовать claim ~ квитанция на получение заказа, вещей после ремонта claim ~ квитанция на получение товара code ~ вчт. проверка программы compile-time ~ вчт. статическая проверка composition ~ вчт. проверка плотности composition ~ вчт. проверка полноты computation ~ вчт. проверка вычислений consistency ~ вчт. проверка на непротиворечивость control totals ~ вчт. проверка с помощью контрольных сумм copy ~ вчт. контроль дублированием copy ~ проверка копии credibility ~ проверка правдоподобия cross ~ вчт. перекрестный контроль current ~ вчт. текущий контроль customs ~ таможенный досмотр customs ~ таможенный контроль customs ~ таможенный чек cyclic redundancy ~ вчт. контроль циклическим избыточным кодом data ~ вчт. контроль данных data-type ~ вчт. контроль типов данных desk ~ вчт. проверка программы за столом diagnostic ~ вчт. диагностический контроль dump ~ вчт. контроль по распечатке duplication ~ вчт. контроль дублированием dynamic ~ вчт. динамический контроль edit ~ вчт. контрольное редактирование error ~ вчт. контроль ошибок even-odd ~ вчт. контроль по четности even-parity ~ вчт. контроль по четности false-code ~ вчт. контроль запрещенных комбинаций flag ~ вчт. флаговый контроль format ~ вчт. контроль формата functional ~ вчт. функциональная проверка gate ~ пропускной контроль hardware ~ вчт. аппаратный контроль hierarchical ~ вчт. иерархический контроль high-low bias ~ вчт. граничная проверка horizontal redundancy ~ вчт. поперечный контроль illegal-command ~ вчт. контроль запрещенных команд imparity ~ вчт. контроль по нечетности imparity ~ вчт. проверка на нечетность improper-command ~ вчт. контроль запрещенных команд in-line ~ вчт. встроенный контроль in-line ~ вчт. оперативный контроль input ~ вчт. входный контроль internal ~ вчт. внутренний контроль internal ~ внутренняя проверка ~ attr. клетчатый;
to keep (или to hold) in check сдерживать;
to cash (или to hand, to pass) in one's checks умереть ~ шахм. шах (употр. тж. как int) ;
the king is in check королю объявлен шах lexical ~ вчт. лексический контроль limit ~ проверка возможностей line-by-line ~ вчт. построчная проверка loop ~ вчт. контроль путем обратной передачи ~ контроль, проверка;
loyalty check амер. проверка лояльности (государственных служащих) marginal ~ вчт. граничная проверка marginal ~ вчт. профилактический котроль naught ~ вчт. проверка на ноль negative ~ вчт. проверка на отрицательное значение odd-even ~ вчт. контроль по четности odd-even ~ вчт. контроль четности odd-parity ~ вчт. контроль четности on-line rule ~ вчт. оперативная проверка правила on-the-spot ~ контроль на месте overflow ~ вчт. контроль переполнения page ~ вчт. групповой страничный контроль parity ~ вчт. контроль по четности parity ~ вчт. контроль четности pass-out ~ амер. = passout pass-out ~ амер. = passout passcheck: passcheck = passout passport ~ паспортный контроль peak-a-boo ~ вчт. проверка на просчет photocell ligth ~ оптический контроль postmortem ~ вчт. постконтроль privacy ~ вчт. проверка конфиденциальности program ~ вчт. проверка программы program ~ вчт. программный контроль programmed ~ вчт. программный контроль quality ~ проверка качества random ~ выборочная проверка random sample ~ проверка случайной выборки range ~ вчт. контроль границ range ~ вчт. контроль попадания read-back ~ вчт. эхопроверка reasonability ~ вчт. проверка на непротиворечивость reasonability ~ вчт. смысловая проверка redundancy ~ вчт. контроль за счет избыточности residue ~ вчт. контроль по остатку reversal ~ вчт. реверсивная проверка rights ~ вчт. проверка прав routine ~ обычная проверка routine ~ вчт. программный контроль routine ~ текущая проверка run-time ~ вчт. динамическая проверка run-time ~ вчт. динамический контроль security ~ проверка безопасности selection ~ вчт. выборочный контроль semantic ~ вчт. семантический контроль sequence ~ вчт. контроль порядка следования sequence ~ comp. контроль порядка следования sequence ~ comp. проверка упорядоченности sight ~ вчт. визуальный контроль sight ~ вчт. проверка на просвет sign ~ вчт. контроль по знаку special crossed ~ специальный кроссированный чек spelling ~ comp. орфографическая проверка spot ~ выборочная проверка spot ~ выборочная ревизия spot ~ проверка на выборку static ~ вчт. статический контроль status ~ comp. контроль состояния stock ~ проверка состояния запасов store ~ проверка состояния запасов structural ~ вчт. структурный контроль sum ~ контроль по сумме sum ~ контроль суммированием sum ~ вчт. проверка по сумме sum ~ проверка по сумме sum ~ проверка суммированием summation ~ вчт. контроль суммированием summation ~ контроль суммированием summation ~ проверка суммированием summation ~ вчт. проверка суммирования syntactic ~ вчт. синтаксический контроль system ~ вчт. системный контроль systems ~ проверка состояния систем technical ~ технический контроль test ~ контрольная проверка test ~ контрольное испытание test ~ вчт. тестовый контроль total ~ вчт. проверка по сумме transfer ~ вчт. контроль передачи transfer ~ переводной чек tranverce ~ вчт. поперечный контроль twin ~ вчт. двойной счет type ~ вчт. контроль соответствия типов type ~ вчт. контроль типов validity ~ вчт. контроль правильности validity ~ вчт. проверка адекватности validity ~ вчт. проверка достоверности validity ~ вчт. проверка на достоверность wired-in ~ вчт. аппаратный контроль wired-in ~ вчт. встроенный аппаратный контроль ~ препятствие;
остановка;
задержка;
without check без задержки, безостановочно -
8 language
язык || языковой- action description language
- actual machine language
- agent programming language
- AI language
- Algol-like language
- algorithmical language
- algorithmic language
- application-oriented language
- applicative language
- artificial language
- assembler language
- assembly language
- assembly-output language
- assignment-free language
- behavioral language
- bidirectional language
- block-structured language
- Boolean-based language
- business definition language
- business-oriented language
- calculus-type language
- C-based language
- client-side language
- code language
- command language
- compiled language
- compiler language
- component definition language
- composite language
- computer language
- computer-dependent language
- computer-independent language
- computer-oriented language
- computer-programming language
- computer-sensitive language
- consensus language
- context-free language
- control language
- conversational language
- core language
- data definition language
- data description language
- data language
- data manipulation language
- data storage description language
- database language
- data-entry language
- data-flow language
- data-query language
- declarative language
- defining language
- descriptive language
- descriptor language
- design language
- device media control language
- direct execution language
- directly interpretable language
- Dyck language
- end-user language
- escape language
- evolutive language
- executive-control language
- executive language
- explicit language
- extensible language
- fabricated language
- finite state language
- flow language
- foreign language
- formalized language
- frame-based language
- freestanding language
- functional language
- generated language
- graphics language
- graph-oriented language
- hardware-description language
- hardware language
- higher-level language
- higher-order language
- host language
- human language
- human-oriented language
- human-readable language
- indexed language
- information retrieval language
- informational language
- information language
- inherently ambiguous language
- input language
- input/output language
- instruction language
- integrated language
- interactive language
- interim language
- intermediate language
- internal language
- interpreted language
- job control language
- job-oriented language
- knowledge representation language
- language pair
- letter-equivalent languages
- linear language
- linear-programming language
- list-processing language
- logic-type language
- low-level language
- machine language
- machine-dependent language
- machine-independent language
- machine-oriented language
- macroassembly language
- macro language
- macroinstruction language
- macroprogramming language
- man-to-computer language
- mathematical formular language
- memory management language
- mnemonic language
- modeling language
- native language
- natural language
- NC programming language
- nested language
- network-oriented language
- nonprocedural language
- numder language
- object language
- object modeling language
- object-oriented language
- one-dimensional language
- operator-oriented language
- original language
- page description language
- parallel language
- phrase structure language
- predicate language
- predicate logic-based language
- predicate logic language
- privacy language
- problem statement language
- problem-oriented language
- procedural language
- procedure-oriented language
- process control language
- production language
- program language
- programming language
- pseudo language
- pseudomachine language
- query language
- readable specification language
- reference language
- regular language
- relational language
- relational-type language
- representation language - requirements modeling language
- restricted language
- rule-based language
- ruly language
- schema language
- science-oriented language
- script language
- self-contained language
- semantic-formal language
- semiformal language
- sentential language
- serial language
- simulation language
- single-assignment language
- source language
- specialized language
- specification language
- stream-based language
- strict language
- structured programming language
- structured query language
- super language
- super-high-level language
- symbolic language
- symbolic programming language
- syntax language
- synthetic language
- system input language
- system language
- system-oriented language
- tabular language
- target language
- TC language
- time sharing language
- type-free language
- unified modeling language
- update language
- user language
- user-oriented language
- very-high-level languageEnglish-Russian dictionary of computer science and programming > language
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9 model
1) макет; модель || моделировать2) образец4) модель, тип ( изделия)5) шаблон•- countably saturated model - countably uniform model - coupled channels model - finite state model - finitely generated model - game-theory model - random trial increment model - random walk model - sampling model -
10 mathematical formula
A mathematical statement in a formal language that can be given a semantic meaning.
См. также в других словарях:
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