Перевод: со всех языков на английский

с английского на все языки

a string of successes

  • 1 Erfolgsbilanz

    f (Erfolge) list ( oder string) of successes; (bisherige Leistungen) track record; WIRTS. operating statement
    * * *
    die Erfolgsbilanz
    results accounting
    * * *
    Er|fọlgs|bi|lanz
    f
    record of success
    * * *
    Er·folgs·bi·lanz
    f success record
    * * *
    Erfolgsbilanz f (Erfolge) list ( oder string) of successes; (bisherige Leistungen) track record; WIRTSCH operating statement

    Deutsch-Englisch Wörterbuch > Erfolgsbilanz

  • 2 succès

    succès [syksε]
    masculine noun
    • cette pièce a eu un grand succès or beaucoup de succès the play was a great success
    ► à succès [auteur, roman, chanson] successful
    * * *
    syksɛ
    nom masculin invariable success

    votre succès aux élections/à l'école — your success in the elections/at school

    avoir du succès, être un succès — [produit, livre, opération] to be a success ( auprès de with); [disque, chanson] to be a hit ( auprès de with)

    avoir du succès — [artiste] to be a success

    avoir du succès auprès de quelqu'un — [personne] to be a hit with somebody

    faire le succès de quelqu'un/quelque chose — to make somebody/something successful

    à succès — [acteur, pièce, film] successful

    * * *
    syksɛ nm
    1) (= réussite) success

    avoir du succès — to be a success, to be successful

    2) (= chanson) hit
    * * *
    succès nm inv success; une série de succès a string of successes; succès scolaires/universitaires scholastic/academic success; un nouveau succès diplomatique pour another diplomatic success for; votre succès aux élections/à l'école/en politique your success in the elections/at school/in politics; le succès du mois this month's big success ou hit; avoir du succès, être un succès [produit, livre, opération, formule] to be a success (auprès de with); [disque, chanson] to be a hit (auprès de with); avoir du succès [artiste] to be a success; avoir du succès auprès de qn [personne] to be a favouriteGB ou a hit with sb; avoir un succès fou [personne, produit] to be a big hit (auprès de with); leur proposition n'a eu aucun succès their proposal met with no success; connaître un grand succès to be a great success; remporter un succès to score a success; remporter des succès to achieve success; faire le succès de qn/qch to make sb/sth successful; à succès [acteur, pièce, film] successful; auteur à succès best-selling author; avec succès successfully; avec un égal succès equally successfully; sans succès unsuccessfully; sans grand succès without much success.
    succès d'estime succès d'estime, critical though not popular acclaim.
    [syksɛ] nom masculin
    1. [heureux résultat, réussite personnelle] success
    être couronné de succès to be crowned with success, to be successful
    2. [exploit, performance] success
    [en amour] conquest
    aller ou voler de succès en succès to go from one success to another
    3. [approbation - du public] success, popularity ; [ - d'un groupe] success
    a. [œuvre, artiste] to be successful
    b. [suggestion] to be very well received
    avoir du succès auprès de quelqu'un: sa pièce a eu beaucoup de succès auprès des critiques mais peu auprès du public his play was acclaimed by the critics but the public was less than enthusiastic
    il a beaucoup de succès auprès des femmes/jeunes he's very popular with women/young people
    eh bien, il a du succès, mon soufflé! well, I see you like my soufflé ou my soufflé appears to be a success!
    4. [chanson] hit
    [film, pièce] (box-office) hit ou success
    succès d'estime critical acclaim, succès d'estime
    a. [film] a big success
    b. [livre] a best-seller
    c. [disque] a hit
    à succès locution adjectivale
    [auteur, chanteur] popular
    romancier à succès popular ou best-selling novelist
    avec succès locution adverbiale
    sans succès locution adverbiale

    Dictionnaire Français-Anglais > succès

  • 3 collectionner

    collectionner [kɔlεksjɔne]
    ➭ TABLE 1 transitive verb
    * * *
    kɔlɛksjɔne
    verbe transitif to collect [timbres, papillons]

    collectionner les erreurs/les gaffes — to make one mistake/blunder after another

    * * *
    kɔlɛksjɔne vt
    1) [tableaux, timbres] to collect
    2) fig

    collectionner les blessures [sportif] — to have a series of injuries, to have a string of injuries

    * * *
    collectionner verb table: aimer vtr
    1 lit to collect [timbres, papillons];
    2 fig collectionner les erreurs/les gaffes to make one mistake/blunder after another; collectionner les maris to go through○ one husband after another.
    [kɔlɛksjɔne] verbe transitif
    1. [tableaux, timbres] to collect
    2. (humoristique) [avoir en quantité]

    Dictionnaire Français-Anglais > collectionner

  • 4 его успехи следовали один за другим

    Универсальный русско-английский словарь > его успехи следовали один за другим

  • 5 ряд побед

    1) Mass media: string of successes

    Универсальный русско-английский словарь > ряд побед

  • 6 ряд удач

    Универсальный русско-английский словарь > ряд удач

  • 7 Triumph

    m; -(e)s, -e triumph; im Triumph in triumph, triumphantly; Triumphe feiern fig. be very successful, be on a roll umg.; stärker: revel ( oder exult) in one’s success
    * * *
    der Triumph
    triumph; exultation
    * * *
    Tri|ụmph [tri'ʊmf]
    m -(e)s, -e
    triumph

    Triumphe feiern — to be a great success, to be very successful

    * * *
    (a state of happiness, celebration, pride etc after a success: They went home in triumph.) triumph
    * * *
    Tri·umph
    <-[e]s, -e>
    [triˈʊmf]
    m
    ich gratuliere dir zu diesem \Triumph congratulations on your success
    \Triumphe [o einen \Triumph nach dem anderen] feiern to have [or enjoy] great success [or success after success] [or a string of successes
    2. kein pl (triumphierende Freude) triumph
    3. (Triumphzug) triumphal procession
    im \Triumph in a triumphal procession
    irgendwo im \Triumph einziehen to make a triumphal entrance somewhere
    * * *
    der; Triumph[e]s, Triumphe triumph

    einen großen Triumph feiern — have a great triumph or success; be huge success

    * * *
    Triumph m; -(e)s, -e triumph;
    im Triumph in triumph, triumphantly;
    Triumphe feiern fig be very successful, be on a roll umg; stärker: revel ( oder exult) in one’s success
    * * *
    der; Triumph[e]s, Triumphe triumph

    einen großen Triumph feiern — have a great triumph or success; be huge success

    Deutsch-Englisch Wörterbuch > Triumph

  • 8 Erfolgsserie

    Er|fọlgs|se|rie
    f
    string of successes

    Deutsch-Englisch Wörterbuch > Erfolgsserie

  • 9 mietere

    harvest
    * * *
    mietere v.tr.
    1 to reap, to harvest: mietere un campo, il grano, to reap a field, the corn // mietere il campo altrui, (fig.) to reap where one has not sown
    2 (fig.) (uccidere) to mow* down; (raccogliere) to reap: l'epidemia ha mietuto molte vittime, the epidemic has taken a heavy tall of victims // mietere successi, to have a run (o string) of successes // mietere allori, to win (o to reap) laurels.
    * * *
    ['mjɛtere]
    verbo transitivo
    1) to reap, to harvest, to mow* [raccolto, grano]
    2) fig. (conseguire) to achieve, to meet* with [ successi]
    3) (stroncare) to claim [ vite umane]
    * * *
    mietere
    /'mjεtere/ [2]
     1 to reap, to harvest, to mow* [raccolto, grano]
     2 fig. (conseguire) to achieve, to meet* with [ successi]; mietere allori to reap honours
     3 (stroncare) to claim [ vite umane]; mietere vittime to take a heavy toll.

    Dizionario Italiano-Inglese > mietere

  • 10 vendemmiare

    v/t v/i harvest
    * * *
    vendemmiare v.tr. e intr. to harvest grapes, to gather grapes: è tempo di vendemmiare, it's harvest time // vendemmiare successi, (fig.) to have a run (o a string) of successes.
    * * *
    [vendem'mjare]
    1. vi
    (aus avere) to pick o harvest the grapes
    2. vt
    (uva) to pick, harvest
    * * *
    [vendem'mjare] 1.
    verbo transitivo to harvest, to pick, to vintage [ uva]
    2.
    verbo intransitivo (aus. avere) to harvest (the grapes), to pick grapes
    * * *
    vendemmiare
    /vendem'mjare/ [1]
     to harvest, to pick, to vintage [ uva]
     (aus. avere) to harvest (the grapes), to pick grapes.

    Dizionario Italiano-Inglese > vendemmiare

  • 11 Triumph

    Tri·umph <-[e]s, -e> [triʼʊmf] m
    1) ( großartiger Erfolg) triumph;
    ich gratuliere dir zu diesem \Triumph congratulations on your success;
    \Triumphe pl [o einen \Triumph nach dem anderen] feiern to have [or enjoy] great success [or success after success] [or a string of successes];
    3) ( Triumphzug) triumphal procession;
    im \Triumph in a triumphal procession;
    irgendwo im \Triumph einziehen to make a triumphal entrance somewhere

    Deutsch-Englisch Wörterbuch für Studenten > Triumph

  • 12 række

    array, bank, chain, course, file, hand, line, range, rank, reach, row, sequence, series, stream, stretch, string, succession, tier, train
    * * *
    I. (en -r) row ( fx of trees, houses, teeth; a seat in the front row),
    ( tættere og længere) line ( fx of trees, houses, cars);
    ( oven over hinanden) row, tier ( fx tiers of seats, of shelves);
    ( om personer) row, line,
    ( bag hinanden) file,
    ( geled) rank, line;
    ( antal) number ( fx of books, losses, operations, successes, years, prominent persons);
    ( serie) series ( fx of discoveries, misfortunes, operations; of articles, books, lectures),
    ( i tidsfølge) succession ( fx of kings, losses, misfortunes, successes),
    F sequence ( fx of events, misfortunes);
    ( suite) suite ( fx of rooms);
    (mat.) series ( fx of numbers),
    (fig) be of secondary (, primary) importance;
    (dvs blandt de bedste) be in the front rank;
    [ i første række må jeg nævne] first of all (el. first and foremost) I must mention;
    [ i række og geled] in serried ranks, drawn up in ranks;
    [ stille sig i række] line up, queue up,
    ( side om side) fall into line, fall in;
    (fig) rank with;
    (fig) in our ranks;
    [ stå på række] stand in a row (, line).
    II. vb (rakte, rakt)
    ( give, lange) hand ( fx hand the apples down; hand me that book),
    ( ved at strække sig) reach ( fx reach me that book, reach the apples down),
    ( række videre) pass ( fx pass me the salt, please; pass something out through the window);
    (nå) reach ( fx as far as the eye can reach),
    ( om stemme) carry,
    ( om skydevåben) have a range of ( fx 1000 yards);
    ( slå til) be enough,
    F suffice;
    [ det rækker langt] it goes a long way;
    [ det rækker ikke langt] it does not go far;
    [ så vidt min evne rækker] as far as it is in my power;
    [ med sb:]
    [ række én hånden] offer somebody one's hand, shake hands with somebody;
    [ række hånden frem] hold out one's hand;
    [ række hånden i vejret] put up one's hand;
    [ række hånden ud efter noget] reach out for something;
    [ række næse] cock a snook (ad at);
    ( også) thumb one's nose at;
    [ række tunge ad en] put (el. stick) out one's tongue at somebody;
    [ med præp, adv & sig:]
    [ række (ud) efter] reach (out) for;
    ( ved bordet) reach over somebody;
    ( blive længere) stretch;
    [ række til] be enough,
    F suffice;
    [ vinen rakte ikke til] there was not enough wine to go round.

    Danish-English dictionary > række

  • 13 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, 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)
       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)
       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 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)
       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 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)
       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 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)
       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)
       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)
       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

См. также в других словарях:

  • string — I. noun Etymology: Middle English, from Old English streng; akin to Old High German strang rope, Latin stringere to bind tight more at strain Date: before 12th century 1. a. a cord usually used to bind, fasten, or tie often used attributively < a …   New Collegiate Dictionary

  • String theory — This article is about the branch of theoretical physics. For other uses, see String theory (disambiguation). String theory …   Wikipedia

  • Maurice's Balkan campaigns — Illyricum campaigns of Emperor Maurice Part of the Roman defensive operations in the Danube frontier Date 582 602 Location Illyricum, Pannonia …   Wikipedia

  • History of Austria — This article is part of a series Early History …   Wikipedia

  • First Peloponnesian War — Infobox Military Conflict conflict=First Peloponnesian War date=c. 460 – c. 445 BC place=Mainland Greece territory=Megara was returned to the Peloponnesian League, Troezen and Achaea became independent, Aegina was to be a tributary to Athens but… …   Wikipedia

  • brazil — /breuh zil /, n. brazilwood. [1350 1400; ME brasile < ML < It < Sp brasil, deriv. of brasa live coal (the wood being red in color) < Gmc; see BRAISE] * * * Brazil Introduction Brazil Background: Following three centuries under the rule of… …   Universalium

  • Brazil — Brazilian /breuh zil yeuhn/, adj., n. /breuh zil /, n. a republic in South America. 164,511,366; 3,286,170 sq. mi. (8,511,180 sq. km). Cap.: Brasília. Portuguese and Spanish, Brasil. Official name, Federative Republic of Brazil. * * * Brazil… …   Universalium

  • Haider, Jorg — ▪ 2009       Austrian politician born Jan. 26, 1950, Bad Goisern, Austria died Oct. 11, 2008, near Klagenfurt, Austria as the charismatic but controversial populist leader of the far right Freedom Party of Austria (FPÖ; 1986–2000) and the… …   Universalium

  • Chet Atkins — This article is about the musician. For the former US congressman, see Chester G. Atkins. Chet Atkins Cover of Atkins autobiography Background information Birth name …   Wikipedia

  • Madge Kendal — Born March 15, 1848(1848 03 15) Great Grimsby, Lincolnshire, England Died September 14, 1935 …   Wikipedia

  • Billiard Games — ▪ 2006 Introduction Carom Billiards.       In January 2005 Sonny Cho of Flushing, N.Y., won his first U.S. championship in three cushion (carom) billiards by beating defending champion Pedro Piedrabuena 50–45 in the deciding game. Piedrabuena… …   Universalium

Поделиться ссылкой на выделенное

Прямая ссылка:
… Нажмите правой клавишей мыши и выберите «Копировать ссылку»