basic design language for structure

мова проектування структур ВІС

- absolute language

   1) The Book of Nature Is Written in the Language of Mathematics

   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)

   2) Arranging Speech so as to Reply Appropriately

   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)

   3) No Other Animal Has the Language Capacity of the Human

   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)

   4) Human Beings Do Not Live Only in the World of Objects

   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)

   5) Language Powerfully Conditions All Our Thinking

   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)

   6) A List of Language Games

   [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 it

   Solving a problem in practical arithmeticTranslating from one language into another

   LANGUAGE Asking, thanking, cursing, greeting, and praying-. (Wittgenstein, 1953, Pt. I, No. 23, pp. 11 e-12 e)

   7) Language Constrains Certain Modes of Interpretation

   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)

   8) We Dissect Nature in Accordance with Our Native Languages

   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 Language

   The 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)

    10) The Analysis of Certain Types of Utterances

   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)

    11) The Dictionary of a Language Is a System of Concepts

   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)

    12) Talk about the Evolution of the Language Capacity is Beside the Point

   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)

    13) The Development of Language

   [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)

    14) Sentential and Conceptual Levels in Language

   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)

    15) Linguistic Realities Have Not Yet Been Captured by Theoretical Models

   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)

    16) The Final Steps to Human Language

   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)

    17) The Inadequacy of Formal Linguistic Models for Ordinary Language Usage

    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)

    18) When a Language Is an Abstract Machine

   A higher-level formal language is an abstract machine. (Weizenbaum, 1976, p. 113)

    19) Metaphor and Metonymy Underpin the Formation of Linguistic Signs

   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)

    20) Language and the Analysis of Nature

   It is striking that the layered structure that man has given to language constantly reappears in his analyses of nature. (Bronowski, 1977, p. 121)

    21) Correspondence Rules for Mapping Old Theory into Subsets of New Theory

   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)

    22) The Inclusion of Non- linguistic Factors in a Theory of Grammar

   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 Interaction

   Language 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)

    24) The Genetic Blueprints of Language

   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)

    25) Languages Are Machines

   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)

basic design language for structure — базовый язык проектирования структур (один из ЯАП фирмы IBM, США)

   1) Programs and the Complexity of Human Mental Processes

   In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)

   2) Artificial Intelligence Is an Engineering Discipline

   Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)

   3) A Sceptical View of Artificial Intelligence

   Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....

   When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)

   4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually

   Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)

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

   Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)

   6) The Meaning of a Symbolic Description

   What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)

   7) The Principle of Artificial Intelligence

   [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)

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

   The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)

   9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

    Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."

    Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)

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

   Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)

    12) The Synthesis of Man and Machine

   Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)

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

   The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)

    14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation

   It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

   Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.

   ♦ Weak Psychological AI

   Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.

   ♦ Strong Psychological AI

... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)

   ♦ Suprapsychological AI

   Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)

    16) Determination of Relevance of Rules in Particular Contexts

   Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)

    17) Form and Content Are Not Fundamentally Different

   Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)

    18) The Assumption That the Mind Is a Formal System

   Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)

    19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence

   The primary goal of Artificial Intelligence is to make machines smarter.

   The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)

    20) Mathematical Logic Provides the Basis for Theory in AI

   The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....

   AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)

    21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions

   In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)

    22) Definitions of Artificial Intelligence

   Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)

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

   Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)

    24) Computers Will Not Always Be Inferior to Human Brains

   The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

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ассемблирование
Компиляция программ с языка ассемблера.
[ ГОСТ 19781-90]

Тематики

• обеспеч. систем обраб. информ. программное

EN

• assembly

 

клеевое соединение
Ндп. клеенное соединение
Соединение частей изделия склеиванием.
[ ГОСТ 28780-90]

Недопустимые, нерекомендуемые

• клеенное соединение

Тематики

• клеи

EN

• adhesive joint
• assembly
• bonding joint

 

конструкция
Устройство, взаимное расположение частей и состав машины, механизма или сооружения.
[ http://sl3d.ru/o-slovare.html]

Параллельные тексты EN-RU

The new valve profile is design to ensure smooth and precise control at low capacities for improved part load performances.
[Lennox]

Вентиль новой конструкции обеспечивает плавное и точное регулирование при низкой производительности холодильного контура, что увеличивает его эффективность при неполной нагрузке.
[Интент]

Тематики

• машиностроение в целом

EN

• aggregate
• architecture
• arrangement
• assembly
• build
• carcass
• configuration
• construct
• construction
• design
• embodiment
• engineering design
• formation
• frame
• framework
• hardware
• profile
• project
• structural design
• structural system
• structural unit
• structure
• style
• work

 

низковольтное устройство распределения и управления (НКУ)
Низковольтные коммутационные аппараты и устройства управления, измерения, сигнализации, защиты, регулирования, собранные совместно, со всеми внутренними электрическими и механическими соединениями и конструктивными элементами.
[ ГОСТ Р МЭК 61439-1-2012]

низковольтное устройство распределения и управления
Комбинация низковольтных коммутационных аппаратов с устройствами управления, измерения, сигнализации, защиты, регулирования и т. п., полностью смонтированных изготовителем НКУ (под его ответственность на единой конструктивной основе) со всеми внутренними электрическими и механическими соединениями с соответствующими конструктивными элементами
Примечания
1. В настоящем стандарте сокращение НКУ используют для обозначения низковольтных комплектных устройств распределения и управления.
2. Аппараты, входящие в состав НКУ, могут быть электромеханическими или электронными.
3. По различным причинам, например по условиям транспортирования или изготовления, некоторые операции сборки могут быть выполнены на месте установки, вне предприятия-изготовителя.
[ ГОСТ Р 51321. 1-2000 ( МЭК 60439-1-92)]

EN

power switchgear and controlgear assembly (PSC-assembly)
low-voltage switchgear and controlgear assembly used to distribute and control energy for all types of loads, intended for industrial, commercial and similar applications where operation by ordinary persons is not intended
[IEC 61439-2, ed. 1.0 (2009-01)]

low-voltage switchgear and controlgear assembly
combination of one or more low-voltage switching devices together with associated control, measuring, signalling, protective, regulation equipment, etc., completely assembled under the responsibility of the manufacturer with all the internal electrical and mechanical interconnections and structural parts.
[IEC 61892-3, ed. 2.0 (2007-11)]

switchgear and controlgear
a general term covering switching devices and their combination with associated control, measuring, protective and regulating equipment, also assemblies of such devices and equipment with associated interconnections, accessories, enclosures and supporting structures
[IEV number 441-11-01]

switchgear and controlgear
electric equipment intended to be connected to an electric circuit for the purpose of carrying out one or more of the following functions: protection, control, isolation, switching
NOTE – The French and English terms can be considered as equivalent in most cases. However, the French term has a broader meaning than the English term and includes for example connecting devices, plugs and socket-outlets, etc. In English, these latter devices are known as accessories.
[IEV number 826-16-03 ]

switchboard
A large single electric control panel, frame, or assembly of panels on which are mounted (either on the back or on the face, or both) switches, overcurrent and other protective devices, buses, and usually instruments; not intended for installation in a cabinet but may be completely enclosed in metal; usually is accessible from both the front and rear.
[ McGraw-Hill Dictionary of Architecture & Construction]

switchboard
One or more panels accommodating control switches, indicators, and other apparatus for operating electric circuits
[ The American Heritage Dictionary of the English Language]

FR

ensemble d'appareillage de puissance (ensemble PSC)
ensemble d'appareillage à basse tension utilisé pour répartir et commander l'énergie pour tous les types de charges et prévu pour des applications industrielles, commerciales et analogues dans lesquelles l'exploitation par des personnes ordinaires n'est pas prévue
[IEC 61439-2, ed. 1.0 (2009-01)]

appareillage, m
matériel électrique destiné à être relié à un circuit électrique en vue d'assurer une ou plusieurs des fonctions suivantes: protection, commande, sectionnement, connexion
NOTE – Les termes français et anglais peuvent être considérés comme équivalents dans la plupart des cas. Toutefois, le terme français couvre un domaine plus étendu que le terme anglais, et comprend notamment les dispositifs de connexion, les prises de courant, etc. En anglais, ces derniers sont dénommés "accessories".
[IEV number 826-16-03 ]

appareillage
terme général applicable aux appareils de connexion et à leur combinaison avec des appareils de commande, de mesure, de protection et de réglage qui leur sont associés, ainsi qu'aux ensembles de tels appareils avec les connexions, les accessoires, les enveloppes et les charpentes correspondantes
[IEV number 441-11-01]

A switchboard as defined in the National Electrical Code is a large single panel, frame, or assembly of panels on which are mounted, on the face or back or both switches, overcurrent and other protective devices, buses, and, usually, instruments.
Switchboards are generally accessible from the rear as well as from the front and are not intended to be installed in cabinets.
The types of switchboards, classified by basic features of construction, are as follows:
1. Live-front vertical panels
2. Dead-front boards
3. Safety enclosed boards( metal-clad)

[American electricians’ handbook]

---

Параллельные тексты EN-RU

The switchboard plays an essential role in the availability of electric power, while meeting the needs of personal and property safety.

Its definition, design and installation are based on precise rules; there is no place for improvisation.

The IEC 61439 standard aims to better define " low-voltage switchgear and controlgear assemblies", ensuring that the specified performances are reached.

It specifies in particular:

> the responsibilities of each player, distinguishing those of the original equipment manufacturer - the organization that performed the original design and associated verification of an assembly in accordance with the standard, and of the assembly manufacturer - the organization taking responsibility for the finished assembly;

> the design and verification rules, constituting a benchmark for product certification.

All the component parts of the electrical switchboard are concerned by the IEC 61439 standard.

Equipment produced in accordance with the requirements of this switchboard standard ensures the safety and reliability of the installation.

A switchboard must comply with the requirements of standard IEC 61439-1 and 2 to guarantee the safety and reliability of the installation.

Managers of installations, fully aware of the professional and legal liabilities weighing on their company and on themselves, demand a high level of safety for the electrical installation.

What is more, the serious economic consequences of prolonged halts in production mean that the electrical switchboard must provide excellent continuity of service, whatever the operating conditions.

[Schneider Electric]

НКУ играет главную роль в обеспечении электроэнергией, удовлетворяя при этом всем требованиям по безопасности людей и сохранности имущества.

Выбор конструкции, проектирование и монтаж основаны на чётких правилах, не допускающих никакой импровизации.

Требования к низковольтным комплектным устройствам распределения и управления сформулированы в стандарте МЭК 61439 (ГОСТ Р 51321. 1-2000).

В частности, он определяет:

> распределение ответственности между изготовителем НКУ - организацией, разработавшей конструкцию НКУ и проверившей его на соответствие требованиям стандарта, и сборщиком – организацией, выполнившей сборку НКУ;

> конструкцию, технические характеристики, виды и методы испытаний НКУ.

В стандарте МЭК 61439 (ГОСТ Р 51321. 1-2000) описываются все компоненты НКУ.

Оборудование, изготовленное в соответствии с требованиями этого стандарта, обеспечивает безопасность и надежность электроустановки.

Для того чтобы гарантировать безопасность эксплуатации и надежность работы электроустановки, распределительный щит должен соответствовать требованиям стандарта МЭК 61439-1 и 2.

Лица, ответственные за электроустановки, должны быть полностью осведомлены о профессиональной и юридической ответственности, возложенной на их компанию и на них лично, за обеспечение высокого уровня безопасности эксплуатации этих электроустановок.

Кроме того, поскольку длительные перерывы производства приводят к серьезным экономическим последствиям, электрический распределительный щит должен обеспечивать надежную и бесперебойную работу независимо от условий эксплуатации.

[Перевод Интент]

 

LV switchgear assemblies are undoubtedly the components of the electric installation more subject to the direct intervention of personnel (operations, maintenance, etc.) and for this reason users demand from them higher and higher safety requirements.

The compliance of an assembly with the state of the art and therefore, presumptively, with the relevant technical Standard, cannot be based only on the fact that the components which constitute it comply with the state of the art and therefore, at least presumptively, with the relevant technical standards.

In other words, the whole assembly must be designed, built and tested in compliance with the state of the art.

Since the assemblies under consideration are low voltage equipment, their rated voltage shall not exceed 1000 Va.c. or 1500 Vd.c. As regards currents, neither upper nor lower limits are provided in the application field of this Standard.

The Standard IEC 60439-1 states the construction, safety and maintenance requirements for low voltage switchgear and controlgear assemblies, without dealing with the functional aspects which remain a competence of the designer of the plant for which the assembly is intended.

[ABB]

Низковольтные комплектные устройства (НКУ), вне всякого сомнения, являются частями электроустановок, которые наиболее подвержены непосредственному вмешательству оперативного, обслуживающего и т. п. персонала. Вот почему требования потребителей к безопасности НКУ становятся все выше и выше.

Соответствие НКУ современному положению дел и вследствие этого, гипотетически, соответствующим техническим стандартам, не может основываться только на том факте, что составляющие НКУ компоненты соответствуют современному состоянию дел и поэтому, по крайней мере, гипотетически, - соответствующим техническим стандартам

Другими словами, НКУ должно быть разработано, изготовлено и испытано в соответствии с современными требованиями.

Мы рассматриваем низковольтные комплектные устройства и это означает, что их номинальное напряжение не превышает 1000 В переменного тока или 1500 В постоянного тока. Что касается тока, то ни верхнее, ни нижнее значение стандартами, относящимися к данной области, не оговариваются

Стандарт МЭК 60439-1 устанавливает требования к конструкции, безопасности и техническому обслуживанию низковольтных комплектных устройств без учета их функций, полагая, что функции НКУ являются компетенцией проектировщиков электроустановки, частью которых эти НКУ являются.

[Перевод Интент]

Тематики

• НКУ (шкафы, пульты,...)

Классификация

>>>

Действия

• изготовление НКУ
• испытание НКУ
• подключение НКУ
• проектирование НКУ

Синонимы

• низковольтное комплектное устройство
• низковольтное устройство распределения и управления
• НКУ

Сопутствующие термины

• изготовитель НКУ

EN

• assembly
• electrical switchboard
• low voltage controlgear and assembly
• low voltage switchboard
• low voltage switchgear and controlgear assembly
• low-voltage switchgear and controlgear assembly
• LV switchgear and controlgear assembly
• LV switchgear assembly
• panel
• power switchgear and controlgear assembly
• PSC-assembly
• switchboard
• switchgear and controlgear
• switchgear/controlgear

DE

• Schaltanlagen und/oder Schaltgeräte

FR

• appareillage
• ensemble d'appareillage de puissance
• ensemble PSC

 

подузел
узел
сборная деталь
собранный узел
блок
агрегат
—
[ http://slovarionline.ru/anglo_russkiy_slovar_neftegazovoy_promyishlennosti/]

Тематики

• нефтегазовая промышленность

Синонимы

• узел
• сборная деталь
• собранный узел
• блок
• агрегат

EN

• assembly

 

сборка
Процесс соединения и закрепления элементов и деталей в готовые узлы, монтажные блоки, конструкции или изделия
[Терминологический словарь по строительству на 12 языках (ВНИИИС Госстроя СССР)]

сборка
Образование соединений составных частей изделия.
Примечания:
1. Примером видов сборки является клепка, сварка заготовок и т.д.
2. Соединение может быть разъемным или неразъемным
[ГОСТ 3.1109-82]

Тематики

• строительные и монтажные работы
• технологические процессы в целом

EN

• assembling
• assembly

DE

• Fügen
• Montage

FR

• assemblage

 

сборка (монтаж)
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики

• энергетика в целом

EN

• setup
• assembly
• assy
• building
• bdg
• bldg

 

узел оборудования
компоновочный узел
компоновка
ассемблирование
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]

Тематики

• информационные технологии в целом

Синонимы

• компоновочный узел
• компоновка
• ассемблирование

EN

• assembly

3.2.10 сборочная единица (assembly): Изделие, которое разлагаемо на множество комплектующих или других сборочных единиц с точки зрения конкретного приложения предметной области;

Источник: ГОСТ Р ИСО 10303-1-99: Системы автоматизации производства и их интеграция. Представление данных об изделии и обмен этими данными. Часть 1. Общие представления и основополагающие принципы оригинал документа

3.3.1 конструкция (assembly) предназначена для того, чтобы:

а) удерживать каскетку на голове;

б) поглощать кинетическую энергию, возникающую при ударе, и распределять усилие по поверхности головы.

Примечание - Внутренняя оснастка может состоять из элементов, указанных в 3.3.2 - 3.3.5.

Источник: ГОСТ Р 12.4.245-2007: Система стандартов безопасности труда. Каскетки защитные. Общие технические требования. Методы испытаний оригинал документа

52. Ассемблирование

Assembly

Компиляция программ с языка ассемблера

Источник: ГОСТ 19781-90: Обеспечение систем обработки информации программное. Термины и определения оригинал документа

39. Сборка

D. Fügen

E. Assembly

F. Assemblage

Источник: ГОСТ 3.1109-82: Единая система технологической документации. Термины и определения основных понятий оригинал документа

   1) A Comparison of the Digital Computer and the Brain

   The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....

   The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)

   3) Getting Computers to Explain Things to Themselves

   The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)

   4) Some Limits of Artificial Intelligence

   In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)

   5) The Computer as a Humanizing Influence

   A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.

   In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....

   It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)

   7) The Possibility of Computer Thought

   he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.

   According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)

    10) Why People Are Smarter Than Computers

   What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.

   What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.

   In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)