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1 data semantics
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2 data semantics
Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > data semantics
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3 data semantics
Вычислительная техника: семантика данных -
4 data semantics
semantyka danych -
5 data semantics
semantyka danychEnglish-Polish dictionary of Electronics and Computer Science > data semantics
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6 data semantics
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7 semantics
1) применительно к естественному языку - значение, смысл (смысловое содержание) языковой единицы (морфемы, слова, словосочетания и т. п.); семантика этих элементов должна учитываться, в частности, при распознавании речи и текста.Syn:2) в случае языка программирования - задаёт смысл (смысловое значение) его слов, символов и синтаксически правильных конструкций с точки зрения их поведения при исполнении программыAnt:см. тж. data semantics, denotational semantics, formal semantics, functional semantics, hermeneutics, operational semantics, semantic analysis, semantic error3) раздел семиотики, изучающий знаковые системы как средства выражения смыслаАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > semantics
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8 data
цифровое, аналоговое или знаковое представление информации (текст, числа, звук, сигналы, изображения), полученной из наблюдений, измерений, экспериментов и/или вычислений; используются для её хранения, передачи и получения новой информации путём соответствующей обработки данных. Термин происходит от латинского слова datum. Термин обычно употребляется как во множественном, так и в единственном числе, однако в научной литературе для единственного числа может употребляться термин datumсм. тж. confidential data, conflicting data, corrupted data, data abstraction, data acquisition, data administrator, data aggregate, data appending, data area, data architect, data array, data attribute, data backup, data bank, database, data block, data buffer, data bulk, data bus, data cache, data capture, data center, data channel, data cleaning, data collection, data communications, data compaction, data compression, data control, data conversion, data cube, data declaration, data deletion, data dependency, data dictionary, data diddling, data domain, data element, data encapsulation, data entry, data export, data extraction, data field, data flow, data format, data glove, data independence, data insertion, data integrity, data leakage, data link, data localization, data manipulation, data mapping, data mart, data medium, data migration, data mining, data model, data modification, data network, data parallelism, data partitioning, data prefetching, data protection, data recovery, data redundancy, data scrubbing, data semantics, data series, metadata, temporary data, terrain data, test data, textual data, trace data, training data, unstructured dataАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > data
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9 semantics independent
независящий от семантики, семантически свободный, без рассмотрения смысла данныхпример, Conventional approaches to data compression are semantics independent. - Традиционные подходы к сжатию данных семантически свободнысм. тж. semanticsАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > semantics independent
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10 WCF Data Services
A component of the.NET Framework that enables the user to create services that use the Open Data Protocol (OData) to expose and consume data over the Web or intranet by using the semantics of representational state transfer (REST). -
11 Grammar
I think that the failure to offer a precise account of the notion "grammar" is not just a superficial defect in linguistic theory that can be remedied by adding one more definition. It seems to me that until this notion is clarified, no part of linguistic theory can achieve anything like a satisfactory development.... I have been discussing a grammar of a particular language here as analogous to a particular scientific theory, dealing with its subject matter (the set of sentences of this language) much as embryology or physics deals with its subject matter. (Chomsky, 1964, p. 213)Obviously, every speaker of a language has mastered and internalized a generative grammar that expresses his knowledge of his language. This is not to say that he is aware of the rules of grammar or even that he can become aware of them, or that his statements about his intuitive knowledge of his language are necessarily accurate. (Chomsky, 1965, p. 8)Much effort has been devoted to showing that the class of possible transformations can be substantially reduced without loss of descriptive power through the discovery of quite general conditions that all such rules and the representations they operate on and form must meet.... [The] transformational rules, at least for a substantial core grammar, can be reduced to the single rule, "Move alpha" (that is, "move any category anywhere"). (Mehler, Walker & Garrett, 1982, p. 21)4) The Relationship of Transformational Grammar to Semantics and to Human Performancehe implications of assuming a semantic memory for what we might call "generative psycholinguistics" are: that dichotomous judgments of semantic well-formedness versus anomaly are not essential or inherent to language performance; that the transformational component of a grammar is the part most relevant to performance models; that a generative grammar's role should be viewed as restricted to language production, whereas sentence understanding should be treated as a problem of extracting a cognitive representation of a text's message; that until some theoretical notion of cognitive representation is incorporated into linguistic conceptions, they are unlikely to provide either powerful language-processing programs or psychologically relevant theories.Although these implications conflict with the way others have viewed the relationship of transformational grammars to semantics and to human performance, they do not eliminate the importance of such grammars to psychologists, an importance stressed in, and indeed largely created by, the work of Chomsky. It is precisely because of a growing interdependence between such linguistic theory and psychological performance models that their relationship needs to be clarified. (Quillian, 1968, p. 260)here are some terminological distinctions that are crucial to explain, or else confusions can easily arise. In the formal study of grammar, a language is defined as a set of sentences, possibly infinite, where each sentence is a string of symbols or words. One can think of each sentence as having several representations linked together: one for its sound pattern, one for its meaning, one for the string of words constituting it, possibly others for other data structures such as the "surface structure" and "deep structure" that are held to mediate the mapping between sound and meaning. Because no finite system can store an infinite number of sentences, and because humans in particular are clearly not pullstring dolls that emit sentences from a finite stored list, one must explain human language abilities by imputing to them a grammar, which in the technical sense is a finite rule system, or programme, or circuit design, capable of generating and recognizing the sentences of a particular language. This "mental grammar" or "psychogrammar" is the neural system that allows us to speak and understand the possible word sequences of our native tongue. A grammar for a specific language is obviously acquired by a human during childhood, but there must be neural circuitry that actually carries out the acquisition process in the child, and this circuitry may be called the language faculty or language acquisition device. An important part of the language faculty is universal grammar, an implementation of a set of principles or constraints that govern the possible form of any human grammar. (Pinker, 1996, p. 263)A grammar of language L is essentially a theory of L. Any scientific theory is based on a finite number of observations, and it seeks to relate the observed phenomena and to predict new phenomena by constructing general laws in terms of hypothetical constructs.... Similarly a grammar of English is based on a finite corpus of utterances (observations), and it will contain certain grammatical rules (laws) stated in terms of the particular phonemes, phrases, etc., of English (hypothetical constructs). These rules express structural relations among the sentences of the corpus and the infinite number of sentences generated by the grammar beyond the corpus (predictions). (Chomsky, 1957, p. 49)Historical dictionary of quotations in cognitive science > Grammar
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12 language
1) языка) естественный язык, средство человеческого общенияб) система знаков, жестов или сигналов для передачи или хранения информациив) стильг) речь2) языкознание, лингвистика•- actor language
- agent communication language
- a-hardware programming language - application-oriented language
- applicative language
- a-programming language
- artificial language
- assembler language
- assembly language
- assignment language
- author language
- authoring language - business-oriented programming language
- categorical language - configuration language
- constraint language
- combined programming language
- command language
- common language
- common business-oriented language
- compiled language
- compiler language
- computer language
- computer-dependent language - computer-oriented language
- computer-sensitive language
- concurrent language - context- sensitive language
- conversational language
- coordinate language
- database language
- database query language - data structure language
- digital system design language
- declarative language
- declarative markup language
- definitional language
- definitional constraint language
- design language
- device media control language - dynamically scoped language - elementary formalized language
- embedding language
- event-driven language
- expression language
- extensible language - formalized language - functional language
- functional programming language - graph-oriented language - high-order language
- host language - hypersymbol language
- imperative language
- in-line language
- input language
- intelligent language
- interactive language - interpreted language - Java programming language - lexically scoped language
- list-processing language
- low-level language
- machine language
- machine-independent language
- machine-oriented language
- macro language
- manipulator language - meta language
- mnemonic language
- musical language - native-mode language
- natural language - nonprocedural language
- object language
- object-oriented language - physical language
- picture query language
- portable language
- portable standard language
- polymorphic language - print control language
- problem-oriented language
- problem statement language
- procedural language
- procedure-oriented language
- program language
- programming language
- publishing language
- query language
- question-answering language
- register-transfer language
- regular language
- relational language
- right-associative language
- robot language
- robot-level language
- robotic control language
- rule language
- rule-oriented language
- scientific programming language
- script language
- scripting language - sign language
- single-assignment language
- software command language
- source language
- special-purpose programming language
- specification language - stratified language
- stream language
- string-handling language - strongly-typed language - symbolic language - thing language - tone language
- two-dimensional pictorial query language
- typed language
- typeless language
- unchecked language
- unformalized language
- universal language
- unstratified language
- untyped language
- user-oriented language
- very high-level language - well-structured programming language -
13 language
1) языка) естественный язык, средство человеческого общенияб) система знаков, жестов или сигналов для передачи или хранения информациив) стильг) речь2) языкознание, лингвистика•- a programming language
- abstract machine language
- actor language
- agent communication language
- algebraic logic functional language
- algorithmic language
- amorhic language
- application-oriented language
- applicative language
- artificial language
- assembler language
- assembly language
- assignment language
- author language
- authoring language
- axiomatic architecture description language
- basic combined programming language
- block-structured language
- boundary scan description language
- business-oriented language
- business-oriented programming language
- categorical abstract machine language
- categorical language
- cellular language
- combined programming language
- command language
- common business-oriented language
- common language
- compiled language
- compiler language
- computer hardware description language
- computer language
- computer-dependent language
- computer-independent language
- computer-oriented language
- computer-sensitive language
- concurrent language
- configuration language
- constraint language
- context-free language
- context-sensitive language
- conversational language
- coordinate language
- data definition language
- data description language
- data manipulation language
- data structure language
- database language
- database query language
- declarative language
- declarative markup language
- definitional constraint language
- definitional language
- design language
- device media control language
- digital system design language
- document style semantics and specification language
- domain-specific language
- dynamic hypertext markup language
- dynamic simulation language
- dynamically scoped language
- elementary formalized language
- embedding language
- event-driven language
- expression language
- extensible hypertext markup language
- extensible language
- extensible markup language
- fabricated language
- fifth-generation language
- first-generation language
- formal language
- formalized language
- fourth-generation language
- frame language
- function graph language
- functional language
- functional programming language
- geometrical layout description language
- graphics language
- graph-oriented language
- hardware description language
- Hewlett-Packard graphics language
- Hewlett-Packard printer control language
- high-level language
- high-order language
- host language
- hypersymbol language
- hypertext markup language plus
- hypertext markup language
- imperative language
- in-line language
- input language
- intelligent language
- interactive language
- interactive set language
- intermediate language
- interpreted language
- Java interface definition language
- Java language
- Java programming language
- job control language
- Jules' own version of the international algorithmic language
- knowledge query and manipulation language
- left-associative language
- lexically scoped language
- list-processing language
- low-level language
- machine language
- machine-independent language
- machine-oriented language
- macro language
- manipulator language
- man-machine language
- mathematical markup language
- matrix-based programming language
- meta language
- mnemonic language
- musical language
- my favorite toy language
- native language
- native-mode language
- natural language
- network control language
- network description language
- noninteractive language
- nonprocedural language
- object language
- object-oriented language
- page description language
- parallel object-oriented language
- partial differential equation language
- pattern-matching language
- physical language
- picture query language
- polymorphic language
- portable language
- portable standard language
- practical extraction and report language
- prescriptive language
- print control language
- problem statement language
- problem-oriented language
- procedural language
- procedure-oriented language
- program language
- programming language
- publishing language
- query language
- question-answering language
- register-transfer language
- regular language
- relational language
- right-associative language
- robot language
- robotic control language
- robot-level language
- rule language
- rule-oriented language
- scientific programming language
- script language
- scripting language
- second-generation language
- sense language
- server-parsed hypertext markup language
- set language
- sign language
- simulation language
- single-assignment language
- software command language
- source language
- special-purpose programming language
- specification and assertion language
- specification language
- stack-based language
- standard generalized markup language
- statically scoped language
- stratified language
- stream language
- string-handling language
- string-oriented symbolic language
- string-processing language
- strongly-typed language
- structural design language
- structured query language
- subset language
- symbolic language
- symbolic layout description language
- synchronized multimedia integration language
- target language
- thing language
- third-generation language
- threaded language
- tone language
- two-dimensional pictorial query language
- typed language
- typeless language
- unchecked language
- unformalized language
- universal language
- unstratified language
- untyped language
- user-oriented language
- very high-level language
- very-high-speed integrated circuit hardware description language
- Vienna definition language
- virtual reality modeling language
- visual language
- well-structured programming language
- wireless markup languageThe New English-Russian Dictionary of Radio-electronics > language
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14 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, 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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15 language
1) языкв общем случае язык можно определить как множество предложений, каждое из которых состоит из конечной последовательности символов, принадлежащих конечному алфавиту (alphabet). Таким образом, язык задаётся алфавитом, грамматикой, синтаксисом и семантикой. Языки делятся на естественные (natural language) и искусственные (artificial language), среди которых большую долю составляют языки программирования (programming language)см. тж. algorithmic language, applicative language, assembly language, authoring language, class-based language, compiled language, context-free language, dataflow language, data manipulation language, declarative language, design language, formal language, graphics language, hardware language, high-level language, hybrid language, language construct, language definition, language design, language element, language extension, language implementation, language manual, language processor, low-level language, macro language, metalanguage, microprogramming language, modeling language, native language, nonprocedural language, OOL, parallel language, semantics, sentence, symbol, syntax2) языковыйАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > language
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16 Introspection
1) Experimental Introspection Is the One Reliable Method of Knowing OurselvesWhen we are trying to understand the mental processes of a child or a dog or an insect as shown by conduct and action, the outward signs of mental processes,... we must always fall back upon experimental introspection... [;] we cannot imagine processes in another mind that we do not find in our own. Experimental introspection is thus our one reliable method of knowing ourselves; it is the sole gateway to psychology. (Titchener, 1914, p. 32)There is a somewhat misleading point of view that one's own experience provides a sufficient understanding of mental life for scientific purposes. Indeed, early in the history of experimental psychology, the main method for studying cognition was introspection. By observing one's own mind, the argument went, one could say how one carried out cognitive activities....Yet introspection failed to be a good technique for the elucidation of mental processes in general. There are two simple reasons for this. First, so many things which we can do seem to be quite unrelated to conscious experience. Someone asks you your name. You do not know how you retrieve it, yet obviously there is some process by which the retrieval occurs. In the same way, when someone speaks to you, you understand what they say, but you do not know how you came to understand. Yet somehow processes take place in which words are picked out from the jumble of sound waves which reach your ears, in-built knowledge of syntax and semantics gives it meaning, and the significance of the message comes to be appreciated. Clearly, introspection is not of much use here, but it is undeniable that understanding language is as much a part of mental life as is thinking.As if these arguments were not enough, it is also the case that introspective data are notoriously difficult to evaluate. Because it is private to the experiencer, and experience may be difficult to convey in words to somebody else. Many early introspective protocols were very confusing to read and, even worse, the kinds of introspection reported tended to conform to the theoretical categories used in different laboratories. Clearly, what was needed was both a change in experimental method and a different (non-subjective) theoretical framework to describe mental life. (Sanford, 1987, pp. 2-3)Historical dictionary of quotations in cognitive science > Introspection
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