Programming Design Language

1) язык

а) естественный язык, средство человеческого общения

б) система знаков, жестов или сигналов для передачи или хранения информации

в) стиль

г) речь

д) язык программирования

2) языкознание, лингвистика

•

- abstract machine language

- algebraic logic functional language

- algorithmic language

- amorhic language

- axiomatic architecture description language

- basic combined programming language

- block-structured language

- boundary scan description language

- business-oriented language

- categorical abstract machine language

- cellular language

- computer hardware description language

- computer-independent language

- configuration language

- context-free language

- data definition language

- data description language

- data manipulation language

- document style semantics and specification language

- domain-specific language

- dynamic hypertext markup language

- dynamic simulation language

- extensible hypertext markup language

- extensible markup language

- fabricated language

- fifth-generation language

- first-generation language

- formal language

- fourth-generation language

- frame language

- function graph language

- geometrical layout description language

- graphics language

- hardware description language

- Hewlett-Packard graphics language

- Hewlett-Packard printer control language

- high-level language

- hypertext markup language

- hypertext markup language plus

- interactive set language

- intermediate language

- Java interface definition language

- Java language

- job control language

- Jules' own version of the international algorithmic language

- knowledge query and manipulation language

- left-associative language

- man-machine language

- mathematical markup language

- matrix-based programming language

- my favorite toy language

- native language

- network control language

- network description language

- noninteractive language

- page description language

- parallel object-oriented language

- partial differential equation language

- pattern-matching language

- practical extraction and report language

- prescriptive language

- second-generation language

- sense language

- server-parsed hypertext markup language

- set language

- simulation language

- specification and assertion language

- stack-based language

- standard generalized markup language

- statically scoped language

- string-oriented symbolic language

- string-processing language

- structural design language

- structured query language

- subset language

- symbolic layout description language

- synchronized multimedia integration language

- target language

- third-generation language

- threaded language

- very-high-speed integrated circuit hardware description language

- Vienna definition language

- virtual reality modeling language

- visual language

- wireless markup language

1) язык

а) естественный язык, средство человеческого общения

б) система знаков, жестов или сигналов для передачи или хранения информации

в) стиль

г) речь

д) язык программирования

2) языкознание, лингвистика

•

- a hardware programming language

- absolute language

- representation language

- requirement statement language

1) язык

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, syntax

2) языковый

[ˈlæŋɡwɪdʒ]

algorithmic language вчт. алгоритмический язык algorithmical language вчт. алгоритмическый язык applicative language вчт. функциональный язык artifical language вчт. искусственный язык artificial language вчт. искусственный язык assembler language вчт. язык ассемблера assembly language вчт. язык ассемблера authoring language вчт. язык для автоматизации творческой работы block-structured language вчт. язык с блочной структурой boolean-based language вчт. язык булевых операторов command language вчт. командный язык compiled language вчт. транслируемый язык compiler language вчт. язык транслятора computer language вчт. машинный язык computer-dependent language вчт. машинно-зависимый язык computer-oriented language вчт. машинно-ориентированный язык computer-sensitive language вчт. машинно-зависимый язык constraint language вчт. декларативный язык context-free language вчт. контекстно-свободный язык conversational language вчт. диалоговый язык conversational language вчт. язык диалога data definition language вчт. язык определения данных data description language вчт. язык описания данных data description language вчт. язык определения данных data language вчт. язык описания данных data manipulation language вчт. язык манипулирования данными data-base language вчт. язык базы данных data-query language вчт. язык запросов declarative language вчт. декларативный язык design language вчт. язык проектирования end-user language вчт. язык конечного пользователя extensible language вчт. расширяемый язык language язык; речь; finger language язык жестов, язык глухонемых foreign language иностранный язык formal language формальный язык frame language вчт. фреймовый язык high-level language вчт. язык высокого уровня host language вчт. включающий язык human language естественный язык language разг. брань (тж. bad language); I won't have any language here прошу не выражаться inflected language флективный язык information retrieval language информационно- поисковый язык information retrieval language информационно-поисковый язык input language вчт. входной язык interactive language вчт. диалоговый язык interpreted language вчт. интерпретируемый язык kernel language вчт. базовый язык knowledge representation language вчт. язык представления знаний language разг. брань (тж. bad language); I won't have any language here прошу не выражаться language стиль; язык писателя; the language of Shakespeare язык Шекспира language язык language язык; речь; finger language язык жестов, язык глухонемых language стиль; язык писателя; the language of Shakespeare язык Шекспира language of the case язык судебного делопроизводства legal language юридический язык legal language язык права low-level language вчт. язык низкого уровня machine language вчт. машинный язык machine-dependent language вчт. машинно-зависимый язык machine-independent language вчт. машинно-независимый язык machine-oriented language вчт. машинно-ориентрированный язык macro language вчт. макроязык macroinstruction language вчт. язык макрокоманд memory management language вчт. язык управления памятью meta language вчт. метаязык minority language язык национального меньшинства mnemonic language вчт. символический язык national language государственный язык native language вчт. собственный язык машины natural language вчт. естественный язык nonprocedural language вчт. непроцедурный язык object language вчт. объектный язык official language официальный язык original language исходный язык parallel language вчт. язык параллельного программирования predicate language вчт. язык предикатов problem statement language вчт. язык постановки задачи problem-oriented language вчт. проблемно-ориентированный язык procedural language вчт. процедурный язык procedural language процедурный язык procedure-oriented language вчт. процедурно ориентированный язык production language вчт. продукционный язык program language вчт. язык программирования programming language вчт. язык программирования query language вчт. язык запросов register transfer language вчт. язык межрегистровых пересылок regular language вчт. регулярный язык relational language вчт. реляционный язык representation language вчт. язык представлений restricted language вчт. упрощенная версия языка rule language вчт. язык правил rule-based language вчт. язык продукционных правил rule-oriented language вчт. язык логического программирования script language вчт. язык сценариев serial language вчт. язык последовательного программирования source language вчт. исходный язык source language cmp. исходный язык specification language вчт. язык спецификаций subset language вчт. подмножество языка symbolic language вчт. символический язык symbolic language comp. символический язык system language вчт. системный язык tabular language вчт. табличный язык target language вчт. выходной язык target language выходной язык target language объектный язык threaded language вчт. язык транслируемый в шитый код typed language вчт. широко используемый язык typeless language вчт. безтиповый язык unchecked language вчт. язык без контроля типов untyped language вчт. язык без контроля типов update language вчт. язык корректирующих запросов user language вчт. язык пользователя world language международный язык

1) конструирование языка [программирования]

2) конструкция языка [программирования]

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

   Philosophy is written in that great book, the universe, which is always open, right before our eyes. But one cannot understand this book without first learning to understand the language and to know the characters in which it is written. It is written in the language of mathematics, and the characters are triangles, circles, and other figures. Without these, one cannot understand a single word of it, and just wanders in a dark labyrinth. (Galileo, 1990, p. 232)

   2) Arranging Speech so as to Reply Appropriately

   It never happens that it [a nonhuman animal] arranges its speech in various ways in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. (Descartes, 1970a, p. 116)

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

   It is a very remarkable fact that there are none so depraved and stupid, without even excepting idiots, that they cannot arrange different words together, forming of them a statement by which they make known their thoughts; while, on the other hand, there is no other animal, however perfect and fortunately circumstanced it may be, which can do the same. (Descartes, 1967, p. 116)

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

   Human beings do not live in the object world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the "real world" is to a large extent unconsciously built on the language habits of the group.... We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interpretation. (Sapir, 1921, p. 75)

   5) Language Powerfully Conditions All Our Thinking

   It powerfully conditions all our thinking about social problems and processes.... No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same worlds with different labels attached. (Sapir, 1985, p. 162)

   6) A List of Language Games

   [A list of language games, not meant to be exhaustive:]

   Giving orders, and obeying them- Describing the appearance of an object, or giving its measurements- Constructing an object from a description (a drawing)Reporting an eventSpeculating about an eventForming and testing a hypothesisPresenting the results of an experiment in tables and diagramsMaking up a story; and reading itPlay actingSinging catchesGuessing riddlesMaking a joke; and telling it

   Solving a problem in practical arithmeticTranslating from one language into another

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

   7) Language Constrains Certain Modes of Interpretation

   We dissect nature along lines laid down by our native languages.... The world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... No individual is free to describe nature with absolute impartiality but is constrained to certain modes of interpretation even while he thinks himself most free. (Whorf, 1956, pp. 153, 213-214)

   8) We Dissect Nature in Accordance with Our Native Languages

   We dissect nature along the lines laid down by our native languages.

   The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... We are thus introduced to a new principle of relativity, which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar or can in some way be calibrated. (Whorf, 1956, pp. 213-214)

   9) The Forms of a Person's Thoughts Are Controlled by Unperceived Patterns of His Own Language

   The forms of a person's thoughts are controlled by inexorable laws of pattern of which he is unconscious. These patterns are the unperceived intricate systematizations of his own language-shown readily enough by a candid comparison and contrast with other languages, especially those of a different linguistic family. (Whorf, 1956, p. 252)

    10) The Analysis of Certain Types of Utterances

   It has come to be commonly held that many utterances which look like statements are either not intended at all, or only intended in part, to record or impart straightforward information about the facts.... Many traditional philosophical perplexities have arisen through a mistake-the mistake of taking as straightforward statements of fact utterances which are either (in interesting non-grammatical ways) nonsensical or else intended as something quite different. (Austin, 1962, pp. 2-3)

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

   In general, one might define a complex of semantic components connected by logical constants as a concept. The dictionary of a language is then a system of concepts in which a phonological form and certain syntactic and morphological characteristics are assigned to each concept. This system of concepts is structured by several types of relations. It is supplemented, furthermore, by redundancy or implicational rules..., representing general properties of the whole system of concepts.... At least a relevant part of these general rules is not bound to particular languages, but represents presumably universal structures of natural languages. They are not learned, but are rather a part of the human ability to acquire an arbitrary natural language. (Bierwisch, 1970, pp. 171-172)

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

   In studying the evolution of mind, we cannot guess to what extent there are physically possible alternatives to, say, transformational generative grammar, for an organism meeting certain other physical conditions characteristic of humans. Conceivably, there are none-or very few-in which case talk about evolution of the language capacity is beside the point. (Chomsky, 1972, p. 98)

    13) The Development of Language

   [It is] truth value rather than syntactic well-formedness that chiefly governs explicit verbal reinforcement by parents-which renders mildly paradoxical the fact that the usual product of such a training schedule is an adult whose speech is highly grammatical but not notably truthful. (R. O. Brown, 1973, p. 330)

    14) Sentential and Conceptual Levels in Language

   he conceptual base is responsible for formally representing the concepts underlying an utterance.... A given word in a language may or may not have one or more concepts underlying it.... On the sentential level, the utterances of a given language are encoded within a syntactic structure of that language. The basic construction of the sentential level is the sentence.

   The next highest level... is the conceptual level. We call the basic construction of this level the conceptualization. A conceptualization consists of concepts and certain relations among those concepts. We can consider that both levels exist at the same point in time and that for any unit on one level, some corresponding realizate exists on the other level. This realizate may be null or extremely complex.... Conceptualizations may relate to other conceptualizations by nesting or other specified relationships. (Schank, 1973, pp. 191-192)

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

   The mathematics of multi-dimensional interactive spaces and lattices, the projection of "computer behavior" on to possible models of cerebral functions, the theoretical and mechanical investigation of artificial intelligence, are producing a stream of sophisticated, often suggestive ideas.

   But it is, I believe, fair to say that nothing put forward until now in either theoretic design or mechanical mimicry comes even remotely in reach of the most rudimentary linguistic realities. (Steiner, 1975, p. 284)

    16) The Final Steps to Human Language

   The step from the simple tool to the master tool, a tool to make tools (what we would now call a machine tool), seems to me indeed to parallel the final step to human language, which I call reconstitution. It expresses in a practical and social context the same understanding of hierarchy, and shows the same analysis by function as a basis for synthesis. (Bronowski, 1977, pp. 127-128)

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

    t is the language donn eґ in which we conduct our lives.... We have no other. And the danger is that formal linguistic models, in their loosely argued analogy with the axiomatic structure of the mathematical sciences, may block perception.... It is quite conceivable that, in language, continuous induction from simple, elemental units to more complex, realistic forms is not justified. The extent and formal "undecidability" of context-and every linguistic particle above the level of the phoneme is context-bound-may make it impossible, except in the most abstract, meta-linguistic sense, to pass from "pro-verbs," "kernals," or "deep deep structures" to actual speech. (Steiner, 1975, pp. 111-113)

    18) When a Language Is an Abstract Machine

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

    19) Metaphor and Metonymy Underpin the Formation of Linguistic Signs

   Jakobson sees metaphor and metonymy as the characteristic modes of binarily opposed polarities which between them underpin the two-fold process of selection and combination by which linguistic signs are formed.... Thus messages are constructed, as Saussure said, by a combination of a "horizontal" movement, which combines words together, and a "vertical" movement, which selects the particular words from the available inventory or "inner storehouse" of the language. The combinative (or syntagmatic) process manifests itself in contiguity (one word being placed next to another) and its mode is metonymic. The selective (or associative) process manifests itself in similarity (one word or concept being "like" another) and its mode is metaphoric. The "opposition" of metaphor and metonymy therefore may be said to represent in effect the essence of the total opposition between the synchronic mode of language (its immediate, coexistent, "vertical" relationships) and its diachronic mode (its sequential, successive, lineal progressive relationships). (Hawkes, 1977, pp. 77-78)

    20) Language and the Analysis of Nature

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

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

   First, [an ideal intertheoretic reduction] provides us with a set of rules"correspondence rules" or "bridge laws," as the standard vernacular has it-which effect a mapping of the terms of the old theory (T o) onto a subset of the expressions of the new or reducing theory (T n). These rules guide the application of those selected expressions of T n in the following way: we are free to make singular applications of their correspondencerule doppelgangers in T o....

   Second, and equally important, a successful reduction ideally has the outcome that, under the term mapping effected by the correspondence rules, the central principles of T o (those of semantic and systematic importance) are mapped onto general sentences of T n that are theorems of Tn. (P. Churchland, 1979, p. 81)

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

   If non-linguistic factors must be included in grammar: beliefs, attitudes, etc. [this would] amount to a rejection of the initial idealization of language as an object of study. A priori such a move cannot be ruled out, but it must be empirically motivated. If it proves to be correct, I would conclude that language is a chaos that is not worth studying.... Note that the question is not whether beliefs or attitudes, and so on, play a role in linguistic behavior and linguistic judgments... [but rather] whether distinct cognitive structures can be identified, which interact in the real use of language and linguistic judgments, the grammatical system being one of these. (Chomsky, 1979, pp. 140, 152-153)

    23) Language Is Inevitably Influenced by Specific Contexts of Human Interaction

   Language cannot be studied in isolation from the investigation of "rationality." It cannot afford to neglect our everyday assumptions concerning the total behavior of a reasonable person.... An integrational linguistics must recognize that human beings inhabit a communicational space which is not neatly compartmentalized into language and nonlanguage.... It renounces in advance the possibility of setting up systems of forms and meanings which will "account for" a central core of linguistic behavior irrespective of the situation and communicational purposes involved. (Harris, 1981, p. 165)

    24) The Genetic Blueprints of Language

   By innate [linguistic knowledge], Chomsky simply means "genetically programmed." He does not literally think that children are born with language in their heads ready to be spoken. He merely claims that a "blueprint is there, which is brought into use when the child reaches a certain point in her general development. With the help of this blueprint, she analyzes the language she hears around her more readily than she would if she were totally unprepared for the strange gabbling sounds which emerge from human mouths. (Aitchison, 1987, p. 31)

    25) Languages Are Machines

   Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important "programming language." This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language.... One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn't something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Leiber, 1991, p. 8)

1) Компьютерная техника: Page Descriptive Language, Perl Data Language, Process Design Language, Programming Definition Language, Protocol Definition Language, Protocol Description Language

2) Спорт: Premier Development League

3) Военный термин: Point Defense Laser, Process( or Program) Design Language, parts documentation list, permanent duty location, photodissociation dye laser, procurement data list, project document list, pulsed dye laser

4) Техника: pass down the line, photo-dissociation dye laser, power distribution limit

5) Фармакология: practical detection limit (практический предел определения)

6) Автомобильный термин: power door locks

7) Грубое выражение: Pas De Losers

8) Оптика: polarization dependence loss

9) Политика: Party of the Democratic Left (the renamed Communist Party)

10) Сокращение: Pass Down-the-Line, poverty datum line, Program Design Language, Push-Down List

11) Университет: Passive Dynamic Walking Human Power and Robotics Lab

12) Вычислительная техника: Page / Program Description / Design Language, Printer Description Language, Procedure Definition Language, Program Development Language, Project Definition Language, специализированный язык описания страниц для печати, PERL Data Language (PERL), язык описания принтеров, язык описания проекта, язык описания процедур

13) Иммунология: poorly differentiated lymphoma

14) Стоматология: alveodental ligament, periodontal ligament, околозубная связка, пародонтальная связка \<редк.\>, связка зуба, peridental ligament, периодонтальная связка

15) Биохимия: поли-D-лизин

16) Фирменный знак: Preston Deeply Labs

17) Сетевые технологии: Page Description Language

18) Сахалин А: Project Director - EA (Project execution sub-team)

19) Химическое оружие: Projectile deformation line

20) Расширение файла: Program Description Language, Project Description Language file

21) SAP.тех. язык описания страниц

1) Компьютерная техника: Page Descriptive Language, Perl Data Language, Process Design Language, Programming Definition Language, Protocol Definition Language, Protocol Description Language

2) Спорт: Premier Development League

3) Военный термин: Point Defense Laser, Process( or Program) Design Language, parts documentation list, permanent duty location, photodissociation dye laser, procurement data list, project document list, pulsed dye laser

4) Техника: pass down the line, photo-dissociation dye laser, power distribution limit

5) Фармакология: practical detection limit (практический предел определения)

6) Автомобильный термин: power door locks

7) Грубое выражение: Pas De Losers

8) Оптика: polarization dependence loss

9) Политика: Party of the Democratic Left (the renamed Communist Party)

10) Сокращение: Pass Down-the-Line, poverty datum line, Program Design Language, Push-Down List

11) Университет: Passive Dynamic Walking Human Power and Robotics Lab

12) Вычислительная техника: Page / Program Description / Design Language, Printer Description Language, Procedure Definition Language, Program Development Language, Project Definition Language, специализированный язык описания страниц для печати, PERL Data Language (PERL), язык описания принтеров, язык описания проекта, язык описания процедур

13) Иммунология: poorly differentiated lymphoma

14) Стоматология: alveodental ligament, periodontal ligament, околозубная связка, пародонтальная связка \<редк.\>, связка зуба, peridental ligament, периодонтальная связка

15) Биохимия: поли-D-лизин

16) Фирменный знак: Preston Deeply Labs

17) Сетевые технологии: Page Description Language

18) Сахалин А: Project Director - EA (Project execution sub-team)

19) Химическое оружие: Projectile deformation line

20) Расширение файла: Program Description Language, Project Description Language file

21) SAP.тех. язык описания страниц

1) Техника: Structural Design Language

2) Расширение файла: Structural Design Language (Programming Language)

1) система

2) установка; устройство

•

- 2D CAD system

SUBJECT AREA: Electronics and information technology

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After education at Gresham's School, London, Strachey went to King's College, Cambridge, where he completed an MA. In 1937 he took up a post as a physicist at the Standard Telephone and Cable Company, then during the Second World War he was involved in radar research. In 1944 he became an assistant master at St Edmunds School, Canterbury, moving to Harrow School in 1948. Another change of career in 1951 saw him working as a Technical Officer with the National Research and Development Corporation, where he was involved in computer software and hardware design. From 1958 until 1962 he was an independent consultant in computer design, and during this time (1959) he realized that as mainframe computers were by then much faster than their human operators, their efficiency could be significantly increased by "time-sharing" the tasks of several operators in rapid succession. Strachey made many contributions to computer technology, being variously involved in the design of the Manchester University MkI, Elliot and Ferranti Pegasus computers. In 1962 he joined Cambridge University Mathematics Laboratory as a senior research fellow at Churchill College and helped to develop the programming language CPL. After a brief period as Visiting Lecturer at the Massachusetts Institute of Technology, he returned to the UK in 1966 as Reader in Computation and Fellow of Wolfeon College, Oxford, to establish a programming research group. He remained there until his death.

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See also: Wilkes, Maurice Vincent; Williams, Sir Frederic Calland

изменение требований к квалификации в результате внедрения робота

вычислительная машина, ЭВМ, компьютер, вычислительное устройство, вычислитель

   1) Programs and the Complexity of Human Mental Processes

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

   2) Artificial Intelligence Is an Engineering Discipline

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

   3) A Sceptical View of Artificial Intelligence

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

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

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

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

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

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

   6) The Meaning of a Symbolic Description

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

   7) The Principle of Artificial Intelligence

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

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

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

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

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

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

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

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

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

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

    12) The Synthesis of Man and Machine

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

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

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

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

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

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

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

   ♦ Weak Psychological AI

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

   ♦ Strong Psychological AI

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

   ♦ Suprapsychological AI

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

    16) Determination of Relevance of Rules in Particular Contexts

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

    17) Form and Content Are Not Fundamentally Different

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

    18) The Assumption That the Mind Is a Formal System

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

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

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

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

    20) Mathematical Logic Provides the Basis for Theory in AI

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

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

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

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

    22) Definitions of Artificial Intelligence

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

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

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

    24) Computers Will Not Always Be Inferior to Human Brains

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

1) узел; агрегат; блок; модуль

2) набор, комплект

3) упаковка

4) тара; контейнер

5) пакет программ; программное обеспечение

6) совокупность

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- 2D geometry programming package

SUBJECT AREA: Electronics and information technology

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Kurtz took his first degree in mathematics at the University of California in Los Angeles (UCLA), where he also gained experience in numerical methods as a result of working in the National Bureau of Standards Institute for Numerical Analysis located on the campus. In 1956 he obtained a PhD in statistics at Princeton, after which he took up a post as an instructor at Dartmouth College in Hanover, New Hampshire. There he found a considerable interest in computing was already in existence, and he was soon acting as the Dartmouth contact with the New England Regional Computer Center at Massachusetts Institute of Technology, an initiative partly supported by IBM. With Kemeny, he learned the Share Assembly Language then in use, but they were concerned about the difficulty of programming computers in assembly language and of teaching it to students and colleagues at Dartmouth. In 1959 the college obtained an LGP-30 computer and Kurtz became the first Director of the Dartmouth Computer Center. However, the small memory (4 k) of this 30-bit machine precluded its use with the recently available high-level language Algol 58. Therefore, with Kemeny, he set about developing a simple language and operating system that would use simple English commands and be easy to learn and use. This they called the Beginners All-purpose Symbolic Instruction Code (BASIC). At the same time they jointly supervised the design and development of a time-sharing system suitable for college use, so that by 1964, when Kurtz became an associate professor of mathematics, they had a fully operational BASIC system; by 1969 a sixth version was already in existence. In 1966 Kurtz left Dartmouth to become a Director of the Kiewit Computer Center, and then, in 1975, he became a Director of the Office of Academic Computing; in 1978 he returned to Dartmouth as Professor of Mathematics. He also served on various national committees.

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