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1 théorie
théorie [teɔʀi]feminine noun* * *teɔʀinom féminin theory* * *teɔʀi nf* * *théorie nf1 ( connaissance abstraite) theory (de of); la théorie littéraire/des quanta literary/quantum theory; en théorie in theory; des cours de théorie gén lessons in theory; Mus theory lessons;[teɔri] nom féminin2. [ensemble de concepts] theory3. [ensemble des règles] theory4. [opinion] theoryc'est la théorie du gouvernement that's the government's theory ou that's what the government claims5. [connaissance spéculative] theorytout cela, c'est de la théorie this is all purely theoretical6. (littéraire) [défilé] procession————————en théorie locution adverbialeen théorie, tu as raison, en fait le système est inapplicable in theory you're right, but in actual fact the system is unworkable -
2 vue
vue2 [vy]1. feminine nouna. ( = sens) sight• il a la vue basse or courte he's short-sighted or near-sighted (US)b. ( = regard) s'offrir à la vue de tous to present o.s. for all to see• perdre qch/qn de vue to lose sight of sth/sb• il ne faut pas perdre de vue que... we mustn't lose sight of the fact that...c. ( = panorama) view• de cette colline, on a une très belle vue de la ville you get a very good view of the town from this hilld. ( = spectacle) sighte. ( = image) viewf. ( = conception) viewg. (locutions)• à vue d'œil ( = rapidement) before one's very eyes ; ( = par une estimation rapide) at a quick glance• (bien) en vue ( = en évidence) conspicuous• très/assez en vue ( = célèbre) very much/much in the public eye• il s'entraîne en vue du marathon/de devenir champion du monde he's training with a view to the marathon/to becoming world champion2. plural feminine nouna. ( = opinion) views• elle a des vues sur lui (pour un projet) she has her eye on him ; ( = elle veut l'épouser) she has designs on him* * *vy1) ( vision) eyesightperdre/recouvrer la vue — to lose/to regain one's sight
avoir la vue basse — lit, fig to be short-sighted GB ou near-sighted US
en vue — [personnalité] prominent
c'est quelqu'un de très en vue — he's/she's very much in the public eye
2) ( regard) sightperdre quelqu'un de vue — fig to lose touch with somebody
à vue — [tirer] on sight; [atterrir, piloter] without instruments; Finance [retrait] on demand
3) ( panorama) viewd'ici, on a une vue plongeante sur la vallée — from here you get a bird's-eye view of the valley
4) ( spectacle) sightà ma vue, il s'enfuit — he took to his heels when he saw me ou on seeing me
5) (dessin, photo) viewvue de face/de côté — front/side view
6) ( façon de voir) view7) ( projet)avoir des vues sur quelqu'un/quelque chose — to have designs on somebody/something
j'ai un terrain en vue — ( je sais lequel conviendrait) I have a plot of land in mind; ( je voudrais obtenir) I've got my eye on a piece of land
en vue de quelque chose/de faire quelque chose — with a view to something/to doing something
•Phrasal Verbs:••à vue d'œil or de nez — (colloq) at a rough guess
* * *vy1. nf1) (= faculté) eyesightJ'ai une mauvaise vue. — I've got bad eyesight.
Allume, tu vas t'abîmer la vue. — Put the light on, you'll ruin your eyesight.
2) (= fait de voir)Il s'évanouit à la vue du sang. — He faints at the sight of blood.
Tu le connais? - De vue seulement. — Do you know him? - Only by sight.
3) (= regard)4) (= panorama) viewIl y a une belle vue d'ici. — There's a lovely view from here.
Il me montra des vues de la vallée. — He'll show me some views from the valley.
avoir vue sur — to have a view of, to look out onto
naviguer à vue AVIATION — to fly without instruments
Elle grandit à vue d'œil. — She gets taller every time you see her.
être en vue (= visible) — to be in sight, (= très connu) to be well-known, to be in the public eye
en vue de faire — with the intention of doing, with a view to doing
2. vues nfpl1) (= idées) views2) (= projet) designs* * *[vy] nom fémininrecouvrer la vue to get one's sight ou eyesight backperdre la vue to lose one's sight, to go blindavoir une mauvaise vue to have bad ou poor eyesight2. [regard]se présenter ou s'offrir à la vue de quelqu'una. [personne, animal, chose] to appear before somebody's eyesb. [spectacle, paysage] to unfold before somebody's eyes3. [fait de voir] sight4. [yeux] eyes5. [panorama] viewd'ici, vous avez une vue magnifique the view (you get) from here is magnificentde ma cuisine, j'ai une vue plongeante sur leur chambre from my kitchen I can see straight down into their bedroomdessiner une vue latérale de la maison to draw a side view ou the side aspect of the house7. [image] viewvue du port [peinture, dessin, photo] view of the harbouravoir des vues bien arrêtées sur quelque chose to have firm opinions ou ideas about something————————vues nom féminin plurielcela n'était ou n'entrait pas dans nos vues this was no part of our plan————————à courte vue locution adjectivale[idée, plan] short-sighted————————à la vue de locution prépositionnelleà la vue de tous in front of everybody, in full view of everybody————————à vue locution adjectivale1. BANQUE2. THÉÂTRE → link=changement changement————————à vue locution adverbiale[atterrir] visually[tirer] on sight[payable] at sightà vue de nez locution adverbialeon lui donnerait 20 ans, à vue de nez at a rough guess, she could be about 20————————à vue d'œil locution adverbialeton cousin grossit à vue d'œil your cousin is getting noticeably ou visibly fatter————————de vue locution adverbialeje le connais de vue I know his face, I know him by sight————————en vue locution adjectivale1. [célèbre] prominentles gens en vue people in the public eye ou in the news2. [escompté]————————en vue locution adverbiale————————en vue de locution prépositionnelle1. [tout près de] within sight of2. [afin de] so as ou in order to -
3 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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4 speculative
tr['spekjələtɪv]1 especulativo,-aspeculative ['spɛkjə.leɪt̬ɪv] adj: especulativoadj.• especulativo, -a adj.'spekjələtɪv, 'spekjʊlətɪv1) ( Fin) <venture/purchase/sale> especulativo2) ( theoretical) <ideas/conclusions> especulativo['spekjʊlǝtɪv]ADJ especulativo* * *['spekjələtɪv, 'spekjʊlətɪv]1) ( Fin) <venture/purchase/sale> especulativo2) ( theoretical) <ideas/conclusions> especulativo -
5 pur
pur, e [pyʀ]1. adjective• pur fruit [confiture] real fruit• pur beurre [gâteau] all butter2. masculine noun, feminine noun* * *
1.
pure pyʀ adjectif1) ( sans mélange) pure; ( non dilué) straight2) ( non altéré) [eau, air] pure; [diamant] flawless; [ciel, voix] clear3) ( sans fioritures) [ligne, style] pure4) ( total) [méchanceté, vérité] pure; [coïncidence, plaisir, folie] sheer5) ( théorique) pure6) ( d'origine) [tradition] trueun pur produit de — lit, fig a typical product of
à l'état pur — [génie, bêtise] sheer
7) ( sans défaut moral) pure
2.
nom masculin, féminin1) ( personne irréprochable) virtuous person2) ( fidèle à un parti)* * *pyʀ pur, -e1. adj1) (eau) pureL'eau de cette source est très pure. — The water from this spring is very pure.
100% pur jus sans sucre ajouté — 100% pure juice with no added sugar
2) (vin) undiluted3) (whisky) neat4) (intentions) pure5)en pure perte — fruitlessly, to no avail
2. nm/f(= personne) hardliner* * *A adj1 ( sans mélange) [substance, laine, héroïne, race] pure; ( non dilué) [whisky, pastis] straight; c'est de l'or pur it's pure gold; un métal à l'état pur metal in its pure state; boire son vin pur to drink one's wine undiluted; une confiture pur sucre a jam with no artificial sweetening; une confiture pur fruit a real fruit jam; fromages pur chèvre/vache pure goat's/cow's milk cheese; pur porc [saucisson] pure pork ( épith);2 ( non altéré) [eau, air] pure; [diamant] flawless; [ciel] clear; [son, voix] pure, clear; respirer l'air pur to breathe the pure air;4 ( total) [méchanceté, fantasme, vérité] pure; [coïncidence, plaisir, ignorance, folie, inconscience] sheer; c'est du pur masochisme it's sheer ou pure masochism; en pure perte to no avail; de pure forme token ( épith); question/propos de pure forme token question/words; pur et simple [mensonge, refus, ignorance, élimination] outright; on envisage le retrait pur et simple des troupes they simply envisage the withdrawal of the troops; c'est de la paresse/fiction pure et simple it's laziness/fiction, pure and simple; pur et dur hardline; le militarisme pur et dur hardline militarism; un militant pur et dur a hardline militant;5 ( théorique) [sciences, recherche, mathématiques] pure;6 ( d'origine) [tradition] true; dans la pure tradition populaire in the true popular tradition; un pur produit de qch lit, fig a typical product of sth; un Parisien de pure souche a Parisian born and bred; à l'état pur [génie, bêtise] sheer; c'est de la bêtise à l'état pur it's sheer stupidity;7 ( sans défaut moral) [personne, cœur] pure.B nm,f1 ( personne irréprochable) virtuous person;2 ( fidèle à un parti) hardliner; les purs et durs the hardliners.2. [sans mélange - liquide] undiluted ; [ - race] pure ; [ - bonheur, joie] unalloyed, pure ; [ - note, voyelle, couleur] pureil parle un anglais très pur he speaks very refined ou polished Englishbiscuits pur beurre (100 %) butter biscuitsà l'état pur pure, unalloyed, unadulterateda. [fidèle] strictb. [intransigeant] hard-linedes lignes pures neat ou perfect linesc'est de la lâcheté pure et simple it's sheer cowardice, it's cowardice pure and simple————————, pure [pyr] nom masculin, nom féminin1. POLITIQUE [fidèle] dedicated follower[intransigeant] hardliner -
6 academic
[ˌækə'demɪk] 1. прил.1) академический; педагогический; учебный (связанный с преподаванием, преимущественно в высшей школе)academic subject — учебная дисциплина, преподаваемый предмет
- academic profession- academic staff2) академический, университетский3) научный; учёный ( связанный с научным сообществом)Google, generally considered to provide the highest quality search results, uses an academic method of ranking citations. — Google, который, по всеобщему признанию, обеспечивает самые качественные результаты поиска, при ранжировании ссылок учитывает академический "индекс цитируемости".
The Institute took me, already a middle aged man devoid of academic credentials, substantially on faith, gambling on the existence of scholarly capacities that remained to be demonstrated. — Институт просто поверил в меня, уже немолодого человека без учёных заслуг, сделав ставку на мои научные способности, которые ещё надо было продемонстрировать.
academic argument — академический спор, научная дискуссия
4) теоретический; научный (свойственный науке, а не практике)Syn:5) чисто теоретический, академичный, отвлечённый; схоластический, праздныйacademic discussion of a matter already settled — бесполезное (бесцельное) обсуждение уже решённого дела
This was not an academic exercise - soldiers' lives were at risk. — Это не было чисто теоретическим занятием - жизнь солдат была в опасности.
That injected a new and highly politicized dimension into what so far had seemed an academic debate. — То, что до сих пор казалось чисто теоретическими дебатами, превратилось в ещё одну острую политическую проблему.
All this discussion, Sirs, is academic. The war has begun already. — Все эти ваши речи, господа, отвлечённая болтовня. Война уже началась.
Syn:6) академичный; оторванный от реальной жизни; проявляющий мелочную точность в пустяковых делах; педантичныйThe tradition of the well-made play, as reformulated at the end of the 19th century, survives in Hollywood scenarists' academic insistence upon formulas for Exposition, Conflict, Complication, Crisis, Denouement. — Традиция пьес с хорошо выстроенным сюжетом - как она была реставрирована в 19-м веке - благодаря педантичной настойчивости голливудских сценаристов продолжает существовать в формуле "экспозиция – завязка – развитие действия - кульминация – развязка".
Syn:7) академический, канонический ( соблюдающий традиции); неодобр. условный, формалистическийLessons are taught not only through an academic method, but also through games. — Уроки проводятся не только в виде теоретических занятий, но и в форме игры.
As an artist he was never too revolutionary to be easily understood, yet never academic enough to be dull. — Его творчество никогда не было настолько революционным, чтобы его трудно было понять, но в то же время оно никогда не было настолько традиционным, чтобы быть скучным.
To overcome the academic prose you have first to overcome the academic pose. — Чтобы преодолеть выхолощенность прозы, надо сначала покончить с академическим позёрством.
Syn:formalistic, conventional2. сущ.The system is failing most disastrously among less academic children. — Эта система абсолютно не годится для детей, не склонных к учёбе.
1) преподаватель или научный сотрудник колледжа или университета... the six academics, affiliated with major Japanese universities... — … шестеро университетских преподавателей, связанных с ведущими японскими университетами...
2) ист. последователь философии Платона (Платон общался со своими учениками в саду, посвящённом герою по имени Академ)Syn:
См. также в других словарях:
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