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1 processing
обработка; обработка данных- array processing
- associative processing
- attached processing
- attention-directed processing
- audio processing
- background processing
- batch job processing
- batch processing
- business processing
- cascade-based processing
- communication word processing
- communications processing
- concurrent processing
- connection processing
- conversational processing
- cooperative processing
- data processing
- data set processing
- data-intensive signal processing
- decentralized processing
- deferred processing
- demand processing
- digital image processing
- digital signal processing
- direct processing
- direct-address processing
- distributed processing
- divided job processing
- dual stack processing
- electronic data processing
- file processing
- foreground processing
- front-end processing
- gate-based processing
- history sensitive processing
- image processing
- image-flow processing
- immediate processing
- industrial data processing
- industrial processing
- information processing
- in-line processing
- inquiry processing
- integrated processing
- intelligent signal processing
- interactive processing
- interrupt processing
- job processing
- job-to-job processing
- knowledge information processing
- knowledge-based processing
- language data processing
- language processing
- level-based processing
- linguistic information processing
- linguistic processing
- list processing
- massively-parallel processing
- multiple job processing
- multiresolution image processing
- multitexture processing
- multithread processing
- multiuser processing
- natural language processing
- non-numerical data processing
- nonstop processing
- off-line processing
- on-line processing
- optical data processing
- overlap processing
- peripheral processing
- picture processing
- priority processing
- program termination processing
- random-access processing
- random processing
- real-time processing
- recovery processing
- remote processing
- scalar processing
- semantic processing
- sequential processing
- serial processing
- short-card processing
- signal processing
- simultaneous processing
- single-thread processing
- speech processing
- stacked job processing
- stand-alone processing
- symbolic processing
- terminal job processing
- text processing
- time-sharing processing
- transactional processing
- transaction processing
- translational processing
- vector processing
- video-display processing
- voice processing
- wavefront processing
- word processingEnglish-Russian dictionary of computer science and programming > processing
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2 information
= info1) информацияа) вчт данныеб) сведения; факты; новости4) информационная служба; служба новостей; сотрудник информационной службы или службы новостей•- analog information
- associated information
- audio information
- background information
- basic information
- binary information
- binary coded information
- business information
- chromaticity information
- ciphered information
- clock information
- coded information
- color information
- commercial information
- configuration information
- consumer information
- context information
- control information
- cookie information
- coordinate information
- critical information
- current information
- customer information
- data search information
- deciphered information
- decoded information
- descriptive information
- design information
- diagnostic information
- digital information
- digitized information
- distributed information
- document-based information
- dummy information
- electronic information
- error-free information
- essential information
- excess information
- external information
- extra information
- extraneous information
- factual information
- false information
- financial information
- framing information
- general information
- graphical information
- graphics information
- holographic information
- ID information
- identification information
- identifying information
- image information
- injected information
- input information
- interdependent information
- internal information
- macroeconomic information
- management information
- manufacturer information
- margin information
- market information
- memory-protection information - multidimensional information
- non-essential information
- numeric information
- numerical information
- on-line information
- ordered information
- ordering information
- organizational information
- output information
- overlapping information
- pattern information
- perfect information
- pictorial information
- picture information
- politically-loaded information
- pragmatic information
- presentation control information
- pricing information
- prior information
- processed information
- processing information
- production information
- profiling information - raw information
- real-time information
- received information
- reduced information
- redundant information
- reference information
- relevant information
- routing information
- run-time type information
- sample information
- sampled information
- scheduling information
- secret information
- security information
- semantic context information
- sensitive information
- servo information
- side information
- signaling information
- sound information
- spoken information
- state information
- statistical information
- status information
- stock information
- stored information
- structural information
- style information
- summarized information
- symbolic information
- syntactic context information
- synthetic information
- table information
- technical information
- telemetry information
- temporal information
- text information
- textual information
- timing information
- tourist information
- traffic information
- transferred information
- transmitted information
- up-to-date information
- useful information
- user information
- video information
- visual information
- zero information -
3 information
1) информацияа) вчт. данныеб) сведения; факты; новости4) информационная служба; служба новостей; сотрудник информационной службы или службы новостей•- analog information
- associated information
- audio information
- background information
- basic information
- binary coded information
- binary information
- business information
- chromaticity information
- ciphered information
- clock information
- coded information
- color information
- commercial information
- configuration information
- consumer information
- context information
- control information
- cookie information
- coordinate information
- critical information
- current information
- customer information
- data search information
- deciphered information
- decoded information
- descriptive information
- design information
- diagnostic information
- digital information
- digitized information
- distributed information
- document-based information
- dummy information
- electronic information
- error-free information
- essential information
- excess information
- external information
- extra information
- extraneous information
- factual information
- false information
- financial information
- framing information
- general information
- graphical information
- graphics information
- holographic information
- ID information
- identification information
- identifying information
- image information
- injected information
- input information
- interdependent information
- internal information
- macroeconomic information
- management information
- manufacturer information
- margin information
- market information
- memory-protection information
- misleading information
- multidimensional information
- non-essential information
- numeric information
- numerical information
- on-line information
- ordered information
- ordering information
- organizational information
- output information
- overlapping information
- pattern information
- perfect information
- pictorial information
- picture information
- politically-loaded information
- pragmatic information
- presentation control information
- pricing information
- prior information
- processed information
- processing information
- production information
- profiling information
- program chain information
- protocol control information
- raw information
- real-time information
- received information
- reduced information
- redundant information
- reference information
- relevant information
- routing information
- run-time type information
- sample information
- sampled information
- scheduling information
- secret information
- security information
- semantic context information
- sensitive information
- servo information
- side information
- signaling information
- sound information
- spoken information
- state information
- statistical information
- status information
- stock information
- stored information
- structural information
- style information
- summarized information
- symbolic information
- syntactic context information
- synthetic information
- table information
- technical information
- telemetry information
- temporal information
- text information
- textual information
- timing information
- tourist information
- traffic information
- transferred information
- transmitted information
- up-to-date information
- useful information
- user information
- video information
- visual information
- zero informationThe New English-Russian Dictionary of Radio-electronics > information
-
4 Bibliography
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Of the proficience and advancement of learning divine and human. In The works of Francis Bacon (Vol. 1). Cambridge, MA: Hurd & Houghton.■ Bacon, R. (1928). Opus majus (Vol. 2). R. B. Burke (Trans.). Philadelphia, PA: University of Pennsylvania Press.■ Bar-Hillel, Y. (1960). The present status of automatic translation of languages. In F. L. Alt (Ed.), Advances in computers (Vol. 1). New York: Academic Press.■ Barr, A., & E. A. Feigenbaum (Eds.) (1981). The handbook of artificial intelligence (Vol. 1). Reading, MA: Addison-Wesley.■ Barr, A., & E. A. Feigenbaum (Eds.) (1982). The handbook of artificial intelligence (Vol. 2). Los Altos, CA: William Kaufman.■ Barron, F. X. (1963). The needs for order and for disorder as motives in creative activity. In C. W. Taylor & F. X. Barron (Eds.), Scientific creativity: Its rec ognition and development (pp. 153-160). New York: Wiley.■ Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press.■ Bartley, S. H. (1969). Principles of perception. London: Harper & Row.■ Barzun, J. (1959). The house of intellect. New York: Harper & Row.■ Beach, F. A., D. O. Hebb, C. T. Morgan & H. W. Nissen (Eds.) (1960). The neu ropsychology of Lashley. New York: McGraw-Hill.■ Berkeley, G. (1996). Principles of human knowledge: Three Dialogues. Oxford: Oxford University Press. (Originally published in 1710.)■ Berlin, I. (1953). The hedgehog and the fox: An essay on Tolstoy's view of history. NY: Simon & Schuster.■ Bierwisch, J. (1970). Semantics. In J. Lyons (Ed.), New horizons in linguistics. Baltimore: Penguin Books.■ Black, H. C. (1951). Black's law dictionary. St. Paul, MN: West Publishing.■ Bloom, A. (1981). The linguistic shaping of thought: A study in the impact of language on thinking in China and the West. Hillsdale, NJ: Erlbaum.■ Bobrow, D. G., & D. A. Norman (1975). Some principles of memory schemata. In D. G. Bobrow & A. 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Chicago: University of Chicago Press.■ Eysenck, M. W. (1977). Human memory: Theory, research and individual difference. Oxford: Pergamon.■ Eysenck, M. W. (1982). Attention and arousal: Cognition and performance. Berlin: Springer.■ Eysenck, M. W. (1984). A handbook of cognitive psychology. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Fancher, R. E. (1979). Pioneers of psychology. New York: W. W. Norton.■ Farrell, B. A. (1981). The standing of psychoanalysis. New York: Oxford University Press.■ Feldman, D. H. (1980). Beyond universals in cognitive development. Norwood, NJ: Ablex.■ Fetzer, J. H. (1996). Philosophy and cognitive science (2nd ed.). New York: Paragon House.■ Finke, R. A. (1990). Creative imagery: Discoveries and inventions in visualization. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Flanagan, O. (1991). The science of the mind. Cambridge MA: MIT Press/Bradford Books.■ Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press/Bradford Books.■ Frege, G. (1972). Conceptual notation. T. W. Bynum (Trans.). Oxford: Clarendon Press. (Originally published in 1879.)■ Frege, G. (1979). Logic. In H. Hermes, F. Kambartel & F. Kaulbach (Eds.), Gottlob Frege: Posthumous writings. Chicago: University of Chicago Press. (Originally published in 1879-1891.)■ Freud, S. (1959). Creative writers and day-dreaming. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 9, pp. 143-153). London: Hogarth Press.■ Freud, S. (1966). Project for a scientific psychology. In J. Strachey (Ed.), The stan dard edition of the complete psychological works of Sigmund Freud (Vol. 1, pp. 295-398). London: Hogarth Press. (Originally published in 1950 as Aus den AnfaЁngen der Psychoanalyse, in London by Imago Publishing.)■ Freud, S. (1976). Lecture 18-Fixation to traumas-the unconscious. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 16, p. 285). London: Hogarth Press.■ Galileo, G. (1990). Il saggiatore [The assayer]. In S. Drake (Ed.), Discoveries and opinions of Galileo. New York: Anchor Books. (Originally published in 1623.)■ Gassendi, P. (1970). Letter to Descartes. In "Objections and replies." In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 2, pp. 179-240). Cambridge: Cambridge University Press. (Originally published in 1641.)■ Gazzaniga, M. S. (1988). Mind matters: How mind and brain interact to create our conscious lives. Boston: Houghton Mifflin in association with MIT Press/Bradford Books.■ Genesereth, M. R., & N. J. Nilsson (1987). Logical foundations of artificial intelligence. Palo Alto, CA: Morgan Kaufmann.■ Ghiselin, B. (1952). The creative process. New York: Mentor.■ Ghiselin, B. (1985). The creative process. Berkeley, CA: University of California Press. (Originally published in 1952.)■ Gilhooly, K. J. (1996). Thinking: Directed, undirected and creative (3rd ed.). 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The creative circle: Sketches on the natural history of circularity. In P. Watzlawick (Ed.), The invented reality (pp. 309-324). New York: W. W. Norton.■ Voltaire (1961). On the Penseґs of M. Pascal. In Philosophical letters (pp. 119-146). E. Dilworth (Trans.). Indianapolis: Bobbs-Merrill.■ Wagman, M. (1991a). Artificial intelligence and human cognition: A theoretical inter comparison of two realms of intellect. Westport, CT: Praeger.■ Wagman, M. (1991b). Cognitive science and concepts of mind: Toward a general theory of human and artificial intelligence. Westport, CT: Praeger.■ Wagman, M. (1993). Cognitive psychology and artificial intelligence: Theory and re search in cognitive science. Westport, CT: Praeger.■ Wagman, M. (1995). The sciences of cognition: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.■ Wagman, M. (1996). Human intellect and cognitive science: Toward a general unified theory of intelligence. Westport, CT: Praeger.■ Wagman, M. (1997a). Cognitive science and the symbolic operations of human and artificial intelligence: Theory and research into the intellective processes. Westport, CT: Praeger.■ Wagman, M. (1997b). The general unified theory of intelligence: Central conceptions and specific application to domains of cognitive science. Westport, CT: Praeger.■ Wagman, M. (1998a). Cognitive science and the mind- body problem: From philosophy to psychology to artificial intelligence to imaging of the brain. Westport, CT: Praeger.■ Wagman, M. (1998b). Language and thought in humans and computers: Theory and research in psychology, artificial intelligence, and neural science. Westport, CT: Praeger.■ Wagman, M. (1998c). The ultimate objectives of artificial intelligence: Theoretical and research foundations, philosophical and psychological implications. Westport, CT: Praeger.■ Wagman, M. (1999). The human mind according to artificial intelligence: Theory, re search, and implications. Westport, CT: Praeger.■ Wagman, M. (2000). Scientific discovery processes in humans and computers: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.■ Wall, R. (1972). Introduction to mathematical linguistics. Englewood Cliffs, NJ: Prentice-Hall.■ Wallas, G. (1926). The Art of Thought. New York: Harcourt, Brace & Co.■ Wason, P. (1977). Self contradictions. In P. Johnson-Laird & P. Wason (Eds.), Thinking: Readings in cognitive science. Cambridge: Cambridge University Press.■ Wason, P. C., & P. N. Johnson-Laird. (1972). Psychology of reasoning: Structure and content. Cambridge, MA: Harvard University Press.■ Watson, J. (1930). Behaviorism. New York: W. W. Norton.■ Watzlawick, P. (1984). Epilogue. In P. Watzlawick (Ed.), The invented reality. New York: W. W. Norton, 1984.■ Weinberg, S. (1977). The first three minutes: A modern view of the origin of the uni verse. New York: Basic Books.■ Weisberg, R. W. (1986). Creativity: Genius and other myths. 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The psychology of computer vision. New York: McGrawHill.■ Wittgenstein, L. (1953). Philosophical investigations. Oxford: Basil Blackwell.■ Wittgenstein, L. (1958). The blue and brown books. New York: Harper Colophon.■ Woods, W. A. (1975). What's in a link: Foundations for semantic networks. In D. G. Bobrow & A. Collins (Eds.), Representations and understanding: Studies in cognitive science (pp. 35-84). New York: Academic Press.■ Woodworth, R. S. (1938). Experimental psychology. New York: Holt; London: Methuen (1939).■ Wundt, W. (1904). Principles of physiological psychology (Vol. 1). E. B. Titchener (Trans.). New York: Macmillan.■ Wundt, W. (1907). Lectures on human and animal psychology. J. E. Creighton & E. B. Titchener (Trans.). New York: Macmillan.■ Young, J. Z. (1978). Programs of the brain. New York: Oxford University Press.■ Ziman, J. (1978). Reliable knowledge: An exploration of the grounds for belief in science. Cambridge: Cambridge University Press.Historical dictionary of quotations in cognitive science > Bibliography
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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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1) единица, число 1 ( наименьшее положительное целое число)2) единица (физической) величины; единица измерения ( величины)3) элемент; компонент4) нейрон5) блок; узел; модуль; секция; звено6) прибор; устройство7) аппарат; установка•- unit under test
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1) единица; единое целое4) компонента программы, модуль•- addressing unit
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- plug-to-plug compatible unit
- polygon-filling unit
- port sharing unit
- power distribution unit
- power supply unit
- power unit
- printing unit
- processing unit
- program control unit
- program unit
- protocol unit
- punched card unit
- punch card unit
- punching unit
- query unit
- reader unit
- read-punch unit
- read-write unit
- recovery unit
- referable unit
- remote display unit
- remote entry unit
- reproducing unit
- retirement unit
- ripple through carry unit
- sample unit
- sampling unit
- scaling unit
- segregating unit
- selection channel control unit
- self-contained unit
- semantic unit
- sensing unit
- sensory unit
- serial arithmetic unit
- setup unit
- set unit
- shaping unit
- shared unit
- smallest recoverable unit
- stand-alone unit
- static unit
- storage control unit
- storage unit
- stream unit
- subtracting unit
- summary punching unit
- summing unit
- supply unit
- switching unit
- switchover unit
- symbolic unit
- syntactical unit
- syntactic unit
- system control unit
- system input unit
- system output unit
- tape cartridge unit
- tape control unit
- tape selection unit
- tape unit
- telecommunications control unit
- telephone communication unit
- terminal unit
- time unit
- timing unit
- transmission control unit
- transport unit
- unit of allocation
- unit of language
- unit of operation
- variable speed tape unit
- vertical format unit
- visual display unit
- voice recognition unitEnglish-Russian dictionary of computer science and programming > unit
-
8 method
метод; процедура; способ- antithetic variate method - average ordinate method - average range method - binary search method - conjugate directions method - conjugate gradient method - control chart method - conventional milling method - correlation function method - decision function method - differential control method - Feynman diagram method - first approximation method - gradient projection method - iterative method - large sample method - large sieve method - least-squares regression method - less than fully efficient method - linearly implicit method - method of adjoint gradient - method of algebraic addition - method of alternating directions - method of balanced blocks - method of complex numbers - method of confidence intervals - method of conformal mappings - method of conjugate directions - method of conjugate gradients - method of cyclic descent - method of detached coefficients - method of disjunction of cases - method of divided differences - method of electrical images - method of elimination of quantifiers - method of empty ball - method of extreme values - method of false position - method of feasible directions - method of finite differences - method of first approximation - method of first entrance - method of fitting constants - method of fixed points - method of full enumeration - method of generating functions - method of geometric exhaustion - method of indefinite coefficients - method of infinite descent - method of interval bisection - method of least absolute values - method of least distance - method of least likelihood - method of maximum likelihood - method of means and standard deviations - method of medians and extreme values - method of minimal change - method of minimal variance - method of mirror reflections - method of moving frame - method of multiple comparison - method of orthogonal projections - method of paired associates - method of paired comparisons - method of phase integrals - method of projecting cones - method of proportional parts - method of rotating factors - method of semantic tableaux - method of separation of variables - method of simulaneous displacements - method of stationary phase - method of statistical differentials - method of statistical inference - method of steep variations - method of steepest ascent - method of stochastic approximation - method of straightforward iteration - method of successive displacements - method of successive divisions - method of successive elimination - method of transfinite induction - method of unweighted means - method of variable differences - method of variation of parameters - method of weighted residuals - optimum method - parallel tangents method - precision method - random walk method - recursive method - reduced gradient method - reflected wave method - relative method of measurement - sampling method by variables - statistical sampling method - steepest descent method - time average method -
9 network
1) сеть (железных дорог, каналов, трубопроводов и т. п.)5) схема; цепь; контур6) эл. многополюсник; четырехполюсник8) вычислительная сеть; сеть ЭВМ9) геофиз. сеть наблюдений; сеть опорных пунктов•-
π network
-
active electrical network
-
active network
-
adding network
-
adjustment network
-
air route network
-
all-pass network
-
anti-induction network
-
aperiodic network
-
artificial mains network
-
asymmetrical network
-
attenuation network
-
augmented transition network
-
automatic voice network
-
backbone network
-
backup radio network
-
balanced network
-
balancing network
-
baseband network
-
baseline network
-
basic network
-
BGS network
-
binary-weighted network
-
bridge network
-
bridged-T network
-
broadcast network
-
buoy data acquisition network
-
bus network
-
cable network
-
capacitive network
-
capacitor network
-
cellular radio network
-
centralized computer network
-
channel network
-
charge-summing network
-
circuit-switching network
-
climatological station network
-
closed private network
-
communications network
-
community antenna distribution network
-
computer network
-
conferencing network
-
connected network
-
contact network
-
coupling network
-
crack network
-
crossover network
-
customer access network
-
data communications network
-
data network
-
data transmission network
-
decoupling network
-
dedicated network
-
deemphasis network
-
delay-line network
-
delta network
-
demand-assigned network
-
dial-up network
-
differentiating network
-
digital network
-
diode network
-
direct distance dialing network
-
dislocation network
-
distributed interactive data network
-
distributed network
-
distributed-constant network
-
distributed-processing network
-
distribution network
-
DNC network
-
double-loop network
-
drainage network
-
dual network
-
earthing network
-
effectively earthed network
-
electrical network
-
electronically-switched network
-
electronic-switched network
-
elemental network
-
end-linked network
-
equivalent network
-
Eurovision network
-
extensive network
-
facsimile network
-
fault-signaling network
-
feed network
-
feedback network
-
flow network
-
four-pole network
-
four-terminal network
-
fully connected network
-
functional logic network
-
gage network
-
gas distribution network
-
gas network
-
geodetic network
-
glass network
-
global network
-
gravity network
-
ground network
-
ground-station network
-
heat network
-
heavy network
-
heterogeneous computer network
-
hierarchical network
-
high-bandwidth network
-
high-capacity network
-
high-voltage power network
-
highway network
-
homogeneous computer network
-
hybrid network
-
hydrologic network
-
inductance network
-
inductance-capacitance network
-
inductance-resistance network
-
industrial network
-
infinite network
-
information network
-
integrated digital network
-
integrated intracell network
-
integrated network
-
integrated-services digital network
-
integrating network
-
interactive network
-
intercity network
-
intercom network
-
interlaced network
-
interpenetrating polymer networks
-
interstage network
-
Intervision network
-
inverse networks
-
island network
-
isolation network
-
Kelvin network
-
L network
-
ladder network
-
lamellar network
-
lattice network
-
leased-line network
-
leveling network
-
lighting network
-
light-rail network
-
linear network
-
L-network
-
local data-processing network
-
local-area network
-
logic network
-
long-distance network
-
long-haul network
-
loop network
-
lossless network
-
low-voltage network
-
lumped-constant network
-
lumped network
-
main waterway network
-
manual routing network
-
Markovian network
-
matching network
-
meshed network
-
mesoscale observational network
-
message-switched network
-
meteorological network
-
metropolitan-area network
-
mixed network
-
mobile network
-
monitoring network
-
monopulse network
-
multiaccess network
-
multibranch network
-
multidimensional network
-
multidrop network
-
multinode network
-
multiple feed network
-
multipoint network
-
multiport network
-
multiservice network
-
multistation network
-
multiterminal network
-
near-shore buoy network
-
negative sequence network
-
network of base gravity stations
-
nodal network
-
nonlinear network
-
nonplanar network
-
nonreciprocal network
-
notch network
-
n-pole network
-
n-port network
-
n-terminal network
-
observation network
-
one-hop network
-
one-port network
-
ozone network
-
packet radio network
-
packet switching network
-
parallel-T network
-
passive network
-
personal computer network
-
phase-advance network
-
phase-inverting network
-
phase-shift network
-
phase-splitting network
-
phasing network
-
Pi network
-
planar network
-
PLC network
-
point-to-point network
-
polled network
-
polymer network
-
positive sequence network
-
power distribution network
-
power network
-
preassigned network
-
precipitation network
-
preemphasis network
-
primary distribution network
-
private-line network
-
public data network
-
pull-down network
-
pull-up network
-
pulse-forming network
-
quadripole network
-
queueing network
-
radial network
-
radio intercom network
-
radio sounding network
-
radio-relay network
-
railway network
-
rain-gage network
-
reciprocal network
-
recursive transition network
-
relay-contact network
-
resistance-capacitance network
-
resistive ladder network
-
resistive network
-
resistor network
-
ring network
-
river network
-
road network
-
rocket sounding network
-
rubber network
-
satellite network
-
semantic network
-
semiconductor network
-
short-haul network
-
simulcast network
-
single-tuned network
-
six-phase network
-
space network
-
standard gage trunk network
-
star network
-
stream-gaging network
-
stretched network
-
subtransmission network
-
survey network
-
switched network
-
switched-message network
-
switching network
-
T network
-
telecommunication network
-
telemetered air monitoring network
-
telephone network
-
teleprocessing network
-
teletype network
-
terminating network
-
terminating switching network
-
Thomson network
-
three-phase network
-
T-network
-
token-bus network
-
token-ring network
-
total ozone sampling network
-
traffic network
-
transit network
-
transition network
-
transmission network
-
transportation network
-
transport network
-
tree network
-
triangulation network
-
trilateration network
-
tsunami network
-
twin-T network
-
two-pole network
-
two-port network
-
two-terminal network
-
ultra-high voltage power network
-
unbalanced network
-
upper-air network
-
value-added network
-
variable topology network
-
ventilation network
-
virtual call network
-
voice network
-
vulcanization network
-
water quality monitoring network
-
water-supply network
-
weather radar network
-
weighting network
-
wide-area network
-
wideband network
-
worldwide communication network
-
Y network
-
Y-network
-
zero sequence network -
10 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
-
11 network
1) (вычислительная) сеть (см. тж net)2) схема•- activity network
- ad hoc network
- adder network
- adding network
- adjustment network
- analog network
- aperiodic network
- arbitration network
- artificial neuron network
- backbone network
- balancing network
- baseband network
- baseline network
- bearer network
- bilateral network
- bluetooth network
- bluetooth voice network
- bridged-T network
- broadcasting network
- bus network
- business-communications network
- campus network
- carrier band network
- centralized network
- circuit-switched network
- circuit network
- closed network
- code slotted network
- coding network
- collapsed backbone network
- combinatorial network
- communication computers network
- computer network
- concentrator network
- connectionless network
- connection-oriented network
- consistent network
- controller area network
- corrective network
- coupling network
- cube-connected network
- cube network
- cube-connected-cycles network
- daisy chain network
- data bank network
- data communications network
- data transportation network
- datacom network
- data-transmission network
- decentralized network
- decoding network
- delay network
- despotic network
- dial-up network
- digital network
- direct-linked network
- distributed backbone network
- distributed function network
- distributed intelligence network
- distributed network
- distributed processing network
- dual network
- elemental network
- expert network
- facsimile network
- feedforward network
- four-terminal network
- fully connected network
- fuzzy-constraint network
- generalized network
- heterogeneous computer network
- hierarchical computer network
- hierarchical network
- high-bandwidth network
- high-degree network
- high-flux network
- highway network
- home-area network
- homogeneous computer network
- host-based network
- inconsistent network
- information network
- integrated services network
- integrated service network
- intelligent network
- interruption network
- IP-routed network
- irredundant network
- iterated network
- knowledge information network
- ladder network
- large-grained network
- leased line network
- local area network
- local network - lumped network
- matching network
- mesh interconnection network
- mesh network
- meshed network
- metropolitain network
- mixed backbone network
- mixed network- monochannel computer network- monochannel network - multiple-work-station network
- multipoint network - multistation network
- multiterminal network
- nearest neighbour network
- network with gains
- neural network
- n-node-fault testable network
- nonpartitionable network
- nonuniform network
- N-port network
- office network
- one-port network
- packet network
- packet switched network
- partitionable network
- passive network
- pass-through network
- PCS network
- peer-to-peer network
- perceptual network
- personal-computer network
- phase-shifting network
- phase-shift network
- planar network
- point-to-point network
- port-to-port network
- power distribution network
- priority network
- private line network
- process network
- propositional network
- public data network
- public network
- public-swithced network
- pulse-forming network
- queueing network
- radio-access network
- reciprocal network
- recognition network
- regional computer network
- regional network
- resistance network
- resistance-capacitance network
- resource-sharing network
- ring-topology network
- ring network
- satellite meshed network
- semantic network
- service-driven network
- shaping network
- shuffle-exchange network
- single-site network
- social network
- star-type network
- star network
- star-wired network
- Steiner network
- stereotype network
- switched message network
- switched network
- switching network
- systolic network
- teleprocessing network
- teletype network
- terrestrial network
- tightly coupled network
- token-bus-based network
- token-passing network
- transit network
- transition network
- transport network
- two-port network
- two-terminal network
- undirected network
- unilateral network
- value-added network
- virtual call network
- virtual-datagram network
- virtual-transport network
- weighted-resistor network
- well-behaved network
- wide-area network
- wireless networkEnglish-Russian dictionary of computer science and programming > network
-
12 technique
1) метод; способ (см. тж method)2) техника, технические приемы3) техническое оснащение; аппаратура; оборудование4) методика, технология (см. тж technology)•- algorithm-specific technique
- all-zero technique
- bootstrap technique
- cascade-based technique
- checking technique
- circuit technique
- computing technique
- design techniques
- diagnostic technique
- diagrammatic technique
- dictionaty technique
- digital technique
- display technique
- fault-masking techniques
- fault-tolerance techniques
- flip-chip technique
- gaming technique
- graft-prune technique
- information science and technique
- information technique
- intelligent technique
- interrupt technique
- jet solder technique
- layout technique
- mainstream technique
- mask stencil technique
- masking technique
- master-slice technique
- matrix technique
- microstrip technique
- modeling technique
- multimedia processing technique
- multiplexer scan technique
- multiresolution technique
- network technique
- OO technique
- party-line technique
- point-and-select technique
- Polish accumulator technique
- prescanning technique
- printed-circuit technique
- programming techniques
- queueing technique
- raster-scan technique
- reasoning technique
- repertory grid technique
- round-robin technique
- scaling technique
- scan technique
- scan-path technique
- scan-set technique
- selection-replacement technique
- semantic technique
- short-pulse drive technique
- simulation technique
- surface mounting technique
- technique of least squares
- technique of substitution
- transformation technique
- word-patching techniqueEnglish-Russian dictionary of computer science and programming > technique
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