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21 linguaggio
m (pl -ggi) languagelinguaggio tecnico technical language, jargoninformation technology linguaggio di programmazione programming language* * *linguaggio s.m. language; (eloquio) speech: linguaggio colorito, racy speech; il linguaggio dei sordomuti, sign language; il linguaggio della musica, the language of music; linguaggio infantile, babyish (o childish) language; linguaggio familiare, familiar language; linguaggio raffinato, volgare, refined, vulgar language; linguaggio fiorito, flowery language; linguaggio tecnico, technical language; linguaggio violento, strong language; linguaggio sportivo, burocratico, the language of sport, bureaucracy; usa un linguaggio triviale, he uses coarse language (o his language is coarse); correttezza di linguaggio, correctness of speech; natura, origine del linguaggio, nature, origin of language; anche gli animali hanno un linguaggio, animals have a language too // che linguaggio!, that's no way to talk! // (inform.): linguaggio assemblatore, assembly language; linguaggio macchina, computer (o machine) language; linguaggio di definizione di dati, data definition language; linguaggio di programmazione, programming language; sottoinsieme di un linguaggio, language subset; linguaggio per l'elaborazione, problem oriented language.* * *1) (lingua) languagenel linguaggio corrente — in common parlance, in everyday speech
scusate il linguaggio — pardon my French colloq.
2) (facoltà di parola) speech•linguaggio cifrato — code, cipher
linguaggio macchina — inform. machine language
linguaggio di programmazione — inform. programming BE o programing AE language, computer language
linguaggio settoriale — jargon, parlance
* * *linguaggiopl. -gi /lin'gwaddʒo, dʒi/sostantivo m.1 (lingua) language; linguaggio della pubblicità adspeak; linguaggio della malavita thieves' cant; nel linguaggio corrente in common parlance, in everyday speech; scusate il linguaggio pardon my French colloq.2 (facoltà di parola) speech; disturbo del linguaggio speech disorderlinguaggio artificiale artificial language; linguaggio cifrato code, cipher; linguaggio del corpo body language; linguaggio giuridico legal parlance; linguaggio giornalistico journalistic parlance; linguaggio infantile baby talk; linguaggio macchina inform. machine language; linguaggio naturale natural language; linguaggio di programmazione inform. programming BE o programing AE language, computer language; linguaggio dei segni sign language; linguaggio settoriale jargon, parlance. -
22 Strachey, Christopher
SUBJECT AREA: Electronics and information technology[br]b. 16 November 1916 Englandd. 18 May 1975 Oxford, England[br]English physicist and computer engineer who proposed time-sharing as a more efficient means of using a mainframe computer.[br]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.[br]Principal Honours and DistinctionsDistinguished Fellow of the British Computer Society 1972.Bibliography1961, with M.R.Wilkes, "Some proposals for improving the efficiency of Algol 60", Communications of the ACM 4:488.1966, "Systems analysis and programming", Scientific American 25:112. 1976, with R.E.Milne, A Theory of Programming Language Semantics.Further ReadingJ.Alton, 1980, Catalogue of the Papers of C. Strachey 1916–1975.M.Campbell-Kelly, 1985, "Christopher Strachey 1916–1975. A biographical note", Annals of the History of Computing 7:19.M.R.Williams, 1985, A History of Computing Technology, London: Prentice-Hall.KF -
23 Wirth, Niklaus
SUBJECT AREA: Electronics and information technology[br]fl. late 1960s Zurich, Switzerland[br]Swiss computer engineer noted for his development of the high-level computer language PASCAL.[br]For many years Wirth was Professor of Computing Science at Zurich Federal Polytechnic School. In 1969, seeking a high-level computer language suitable for teaching programming as a systematic activity, he invented PASCAL, which is now widely used with personal computers (PCs). Unlike BASIC, which is checked and run a line at a time, PASCAL programs are compiled (i.e. they are fully checked for consistency) before they are actually run.[br]Principal Honours and DistinctionsInstitute of Electrical and Electronics Engineers Emanuel R.Piore Award 1983.Bibliography1971, "The programming language PASCAL", Acta Informatica 1:35.Further ReadingR.L.Wexelblat (ed.), 1981, History of Programming Languages, London: Academic Press.KF -
24 programmazione
f programmingfinance programmazione economica economic planning* * *programmazione s.f.1 programming, planning, scheduling: la programmazione del lavoro è compito nostro, the planning of the work is our job; un film di futura programmazione, a scheduled film; il film è in programmazione all'Odeon, the film is showning at the Odeon2 (econ.) planning, scheduling: programmazione lineare, ( nella ricerca operativa) linear programming; programmazione economica, economic planning; programmazione produttiva, production planning; programmazione dei pro-fitti, profit planning; programmazione delle vendite, sales planning; programmazione del lavoro, work planning3 (inform.) programming; coding: programmazione eseguita, realizzata dal programmatore, hand coding; programmazione in linguaggio macchina, absolute programming (o coding); programmazione lineare, linear programming; programmazione tandem, (IBM) dual programming.* * *[programmat'tsjone]sostantivo femminile1) (di viaggio, lavoro) planning, programming, scheduling; (di videoregistratore) presetting, programming2) econ. planning, scheduling3) inform. (computer) programming BE, programing AE4) rad. telev. schedule, programming; teatr. runc'è un nuovo film in programmazione — cinem. there's a new film on
la trasmissione offre una buona programmazione di musica rock — the programme's coverage of rock music is good
* * *programmazione/programmat'tsjone/sostantivo f.1 (di viaggio, lavoro) planning, programming, scheduling; (di videoregistratore) presetting, programming2 econ. planning, scheduling3 inform. (computer) programming BE, programing AE4 rad. telev. schedule, programming; teatr. run; c'è un nuovo film in programmazione cinem. there's a new film on; il film resterà in programmazione ancora una settimana the film will run (for) another week; la trasmissione offre una buona programmazione di musica rock the programme's coverage of rock music is good. -
25 трудозатраты на программирование
1. programming man-hour2. programming man-hoursРусско-английский большой базовый словарь > трудозатраты на программирование
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26 jeroglífico
m.hieroglyphic, hieroglyph, cryptogram, graffito.* * *► adjetivo1 hieroglyphic1 hieroglyph, hieroglyphic2 (juego) rebus————————1 hieroglyph, hieroglyphic2 (juego) rebus* * *1.ADJ hieroglyphic2. SM1) (=escritura) hieroglyph, hieroglyphic2) (=situación, juego) puzzle* * ** * *= hieroglyphic, hieroglyph.Ex. Paintings, writings and Egyptian hieroglyphics are proof of African contribution to the development of information and communication.Ex. Any scientist's explanation takes the form of a model: statues/icons, then paintings, cuneiform, hieroglyphs, written natural language, the language of mathematics, and finally computer programming languages.* * ** * *= hieroglyphic, hieroglyph.Ex: Paintings, writings and Egyptian hieroglyphics are proof of African contribution to the development of information and communication.
Ex: Any scientist's explanation takes the form of a model: statues/icons, then paintings, cuneiform, hieroglyphs, written natural language, the language of mathematics, and finally computer programming languages.* * *hieroglyphic1 (escritura) hieroglyphic, hieroglyph2 (acertijo) rebustodo esto es un jeroglífico para mí all this is a complete mystery to me, all this is completely over my head* * *
jeroglífico sustantivo masculino ( escritura) hieroglyphic, hieroglyph;
( acertijo) rebus
jeroglífico,-a
I adjetivo hieroglyphic
escritura jeroglífica, hieroglyphic writing
II sustantivo masculino
1 Ling hieroglyph, hieroglyphic
2 (pasatiempo) rebus
' jeroglífico' also found in these entries:
Spanish:
jeroglífica
- descifrar
* * *jeroglífico, -a♦ adjhieroglyphic♦ nm1. [inscripción] hieroglyphic2. [pasatiempo] rebus3. [problema] puzzle, mystery;estas instrucciones son un jeroglífico these instructions are indecipherable* * *m1 hieroglyphic2 rompecabezas puzzle* * *jeroglífico nm: hieroglyphic -
27 язык символического программирования
Русско-английский большой базовый словарь > язык символического программирования
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28 мова
ж1) language; tongueвиразна (чітка) мова — distinct ( clear) enunciation
літературна мова — literary ( standard) language
машинна мова комп. — computer ( machine) language
мова асемблера — assembler language, assembly language
мова запитів комп. — data-query language, query language
рідна мова — one's mother tongue; native language
мова закону — language of law, legislative language
мова програмування комп. — computer language, machine language, programming language
розмовна мова — colloquial/familiar speech; spoken language
вчитель іноземної мови — language master, language teacher
2) ( розмова) discourseне про те мова — this is not the question in point, that is not the point
3) грам. speechпряма (непряма) мова грам. — direct (indirect, oblique) speech
частини мови грам. — parts of speech
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29 Computerindustrie
Computerindustrie
computer industry;
• Computeringenieur computer engineer;
• Computerkenntnisse computer literacy (proficiency);
• Computerkompatibilität computer compatibility;
• Computerkriminalität computer crime[s];
• Computerlayout computerized layout;
• Computermischmasch computer double-talk (lingo);
• Computermissbrauchversicherung computer fraud insurance;
• zusammengeschaltetes Computernetz computer hook-up;
• drahtloses lokales Computernetzwerk wireless local area network (WLAN);
• weltumspannendes (weltweites) Computernetz[werk] (Internet) global network, world-wide web (www);
• Computerpirat cracker,hacker;
• Computerproblem bei der Umstellung aufs Jahr 2000 Y2K bug;
• Computerprogrammierung computer programming;
• Computerrevision computer auditing;
• Computersatz computerized composition (typesetting);
• Computerspeicher hard disk;
• Computersprache computer language;
• in ein Computersystem eindringen to break into a computer system;
• moderne Computertechnik advanced computer technology;
• neueste Computertechnologie latest developments in computer technology;
• Computer-Telefonie-Integration (telecom.) Computer Telephone Integration (CTI). -
30 maschinenorientierte Sprache
f < autom> ■ machine-oriented languagef < edv> ■ computer-oriented language; computer-oriented programming language; machine-oriented languageGerman-english technical dictionary > maschinenorientierte Sprache
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31 язык
I муж.1) tongue прям. и перен.воспаление языка — мед. glossitis
обложенный язык — мед. coated/ furred tongue
показать язык — (кому-л.) (врачу и т.п.) to show one's tongue (to a doctor, etc.); ( дразнить) to stick one's tongue out, to put out one's tongue (at smb.)
3) clapper, tongue of a bell ( колокола)••держать язык за зубами — to hold one's tongue, to keep one's mouth shut
не сходит с языка, быть у кого-л. на языке — to be always on smb.'s lips
попадать на язык кому-л. — to fall victim to smb.'s tongue
тянуть/дергать кого-л. за язык — to make smb. say smth.; to make smb. talk
у него бойкий язык, он боек на язык — to have a quick/ready tongue, to be quick-tongued
у него длинный язык — he has a long/loose tongue разг.
у него хорошо язык подвешен — he has a ready/glib tongue разг.
у него, что на уме, то и на языке — he wears his heart on his sleeve, he cannot keep his thoughts to himself разг.
- высунув языкязык до Киева доведет — you can get anywhere if you know how to use your tongue; a clever tongue will take you anywhere
- злой язык
- злые языки
- лишиться языка
- острый язык
- придержать язык
- прикусить язык
- развязать язык
- распустить язык
- сорвалось с языка
- точить язык
- трепать языком
- чесать язык
- чесать языком
- язык проглотишь II муж.1) language, tongue ( речь)владеть каким-л. языком — to know a language
владеть каким-л. языком в совершенстве — to have a perfect command of a language
говорить русским языком — to say in plain Russian, in plain language
языки общего происхождения — cognate мн. ч.; лингв.
афганский язык — Pushtoo, Pushtu, Afghan
корнийский язык — истор. Cornish
корнуоллский язык — истор. Cornish
сингалезский язык — Cingalese, Sinhalese
сингальский язык — Sinhalese, Cingalese
венгерский язык — Hungarian, Magyar
верхненемецкий язык — High German, High Dutch
говорить языком — (кого-л./чего-л.) to use the language (of)
греческий язык — Greek, Hellenic
классические языки — classic мн. ч., humanity
латинский язык — Latin, Roman редк.
немецкий язык — Dutch истор., German
нижненемецкий язык — Low German, Low Dutch
общегерманский язык — лингв. Germanic
персидский язык — Iranian, Persian
разговорный язык — colloquial/familiar speech; spoken language
родной язык — mother tongue; native language
суконный язык — dull/vapid/insipid language
язык программирования — computer language, machine language, programming language
язык пушту — Pushtoo, Pushtu
язык саами — Lapp, Lappish
2) воен.; разг. ( пленный)prisoner for interrogation; identification prisoner; prisoner who will talk ()III муж.; устар.people, nation ( народ) -
32 computerorientierte Programmiersprache
f < edv> ■ computer-oriented programming language; computer-oriented language; machine-oriented languageGerman-english technical dictionary > computerorientierte Programmiersprache
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33 rechnerorientierte Programmiersprache
f < edv> ■ computer-oriented programming language; computer-oriented language; machine-oriented languageGerman-english technical dictionary > rechnerorientierte Programmiersprache
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34 maskinspråk
subst. machine language subst. computer language, computer programming -
35 ohjelmointikieli
automatic data processing• compiler languageautomatic data processing• computer languageautomatic data processing• programming language -
36 programski jezik
• algol; cobol; computer language; dbase; java; programming language; visual age -
37 sampletest
------------------------------------------------------------[Swahili Word] sampletest[English Word] sampletest[Part of Speech] adjective[Derived Language] Gujerati[Derived Word] sample[Swahili Definition] twafanya majaribio hapa[English Definition] this is a mock entry used for testing computer programming modifications to the Kamusi Project[English Example] this is not a valid dictionary entry------------------------------------------------------------[Swahili Word] sampletest3[English Word] sampletest3[Part of Speech] adjective------------------------------------------------------------[Swahili Word] test[English Word] test[English Plural] sampletests[Part of Speech] noun[Class] 16/17/18[English Example] This is a test example for sampletest------------------------------------------------------------ -
38 язык, реализуемый компьютером
Programming: computer languageУниверсальный русско-английский словарь > язык, реализуемый компьютером
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39 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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40 Kurtz, Thomas E.
SUBJECT AREA: Electronics and information technology[br]b. USA[br]American mathematician who, with Kemeny developed BASIC, a high-level computer language.[br]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.[br]Bibliography1964, with J.G.Kemeny, BASIC Instruction Manual: Dartmouth College (for details of the development of BASIC etc.).1968, with J.G.Kemeny "Dartmouth time-sharing", Science 223.Further ReadingR.L.Wexelblat, 1981, History of Programming Languages, London: Academic Press (a more general view of the development of computer languages).KF
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