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  • 1 Mathematics

       The world of mathematics, which you contemn, is really a beautiful world; it has nothing to do with life and death and human sordidness, but is eternal, cold and passionless. To me pure mathematics is one of the highest forms of art; it has a sublimity quite special to itself, and an immense dignity derived from the fact that its world is exempt from change and time. I am quite serious in this....
    athematics is the only thing we know of that is capable of perfection; in thinking about it we become Gods. (Russell [to Helen Thomas, 30 December 1901], 1992, Letter No. 98, p. 224)
       One of the deepest problems of nature is the success of mathematics as a language for describing and discovering features of physical reality. In short, why does mathematics work?...
       We humans have stripped back the clouds that cloak our understanding of our cosmic beginning and our current persistence to the stage that exposes the mathematical structure of the world more clearly than it has ever been observed before.... Furthermore, the attention of seriously equipped thinkers, those thinkers we call scientists, is at last beginning to turn to that other great conundrum of being: consciousness.... If we can understand why that supreme construct of the human intellect, that archdisembodiment of intellect, mathematics, works as a description of the world, then maybe we shall have an insight into cognition....
       The name deep structuralism is intended to convey the idea that the physical world has the same logical structure as mathematics. By implication, the reason why mathematics works as a description of physical reality is that they share the same logical structure.
    ... By weak deep structuralism I shall mean that mathematics and physical reality merely share the same logical structure and mathematics is a mirror that can be held up to nature. By strong deep structuralism I shall mean that mathematics and physical reality do not merely share the same logical structure but are actually the same. In other words, according to the hypothesis of strong deep structuralism, physical reality is mathematics and mathematics is physical reality.... The reason why we may be conscious of the world, including the inner, introspective world of emotion and intellect, may be that our brains are material portrayals of the same deep structure. That may also be the reason why brains can generate the mathematics that we need to comprehend the world. (Atkins, 1992, pp. 99-101, 109-111)

    Historical dictionary of quotations in cognitive science > Mathematics

  • 2 term

    term [tɜ:m]
    1. noun
    • the autumn/spring/summer term le premier/second/troisième trimestre
       b. ( = period) période f
    • in the medium/short term à moyen/court terme
       c. ( = word) terme m ; ( = expression) expression f
       a. ( = conditions) conditions fpl ; [of contracts] termes mpl
    • on what terms? à quelles conditions ?
    • "inclusive terms: £20" « 20 livres tout compris »
    ► in terms of ( = as regards)
    • we must think in terms of... ( = consider the possibility of) il faut envisager...► to be on + adjective terms with sb
    • to be on good/bad terms with sb être en bons/mauvais termes avec qn
    * * *
    [tɜːm] 1.
    1) ( period of time) gen période f, terme m; School, University trimestre m; Law ( duration of lease) durée f (de bail)

    term of imprisonment — peine f de prison

    autumn/spring/summer term — School, University premier/deuxième/troisième trimestre

    2) (word, phrase) terme m
    3) ( limit) terme m also Mathematics
    2.
    terms plural noun
    1) ( conditions) gen termes mpl; ( of will) dispositions fpl; Commerce conditions fpl de paiement

    terms and conditions — Law modalités fpl

    terms of trade — Commerce, Economics termes de l'échange international

    on easy terms — Commerce avec facilités fpl de paiement

    terms of surrender — Politics conditions de la reddition

    2)

    to come to terms with — assumer [identity, past, condition, disability]; accepter [death, defeat, failure]; affronter [issue]

    3) ( relations) termes mpl

    in his/their etc terms — selon ses/leurs etc critères

    3.
    in terms of prepositional phrase
    1) gen, Mathematics ( as expressed by) en fonction de
    2) ( from the point of view of) du point de vue de, sur le plan de
    4.
    transitive verb appeler, nommer

    English-French dictionary > term

  • 3 absolute

    absolute [ˈæbsəlu:t]
       a. absolu
    2. noun
    * * *
    ['æbsəluːt] 1. 2.
    1) ( complete) [monarch, minimum, majority] also Mathematics, Philosophy absolu
    2) ( emphatic) [chaos, idiot] véritable (before n)
    3) Physics, Chemistry [humidity, scale] maximum; [zero] absolu
    4) Law

    decree absolute — décret m irrévocable

    5) Linguistics [ablative] absolu

    English-French dictionary > absolute

  • 4 opposite

    opposite [ˈɒpəzɪt]
    opposé ; ( = facing) d'en face
    • "see map on opposite page" « voir plan ci-contre »
    4. noun
    • quite the opposite! bien au contraire !
    • what's the opposite of white? quel est le contraire de blanc ?
    * * *
    ['ɒpəzɪt] 1.
    noun contraire m (to, of de)
    2.
    1) ( facing) [direction, side, pole] opposé also Mathematics; [building] d'en face; [page] ci-contre
    2) ( different) [viewpoint, camp] opposé; [effect, approach] inverse; [sex] autre
    3.
    adverb [live, stand] en face
    4.
    preposition gen en face de [building, park, person]; Nautical devant [port]

    English-French dictionary > opposite

  • 5 probability

    probability [‚prɒbəˈbɪlɪtɪ]
    • the probability is that... il est très probable que...
    * * *
    [ˌprɒbə'bɪlətɪ]
    1) ( likelihood) ( of desirable event) chances fpl; ( of unwelcome event) risques mpl
    2) ( likely result) probabilité f also Mathematics

    English-French dictionary > probability

  • 6 progression

    progression [prəˈgre∫ən]
    * * *
    [prə'greʃn]
    1) ( development) ( evolution) évolution f; ( improvement) progression f
    2) ( series) suite f also Mathematics
    3) Music progression f

    English-French dictionary > progression

  • 7 proof

    proof [pru:f]
    1. noun
       a. preuve f
    • to be living proof of sth/that... être la preuve vivante de qch/que...
       b. ( = printed copy) épreuve f
       c. [of alcohol] teneur f en alcool
    • this whisky is 70° proof ≈ ce whisky titre 40° degrés
    • proof against [bullets, erosion] à l'épreuve de ; [temptation, suggestion] insensible à
    [+ fabric, tent] imperméabiliser
    * * *
    [pruːf] 1.
    1) ( evidence) preuve f also Mathematics

    proof of identity — pièce f d'identité

    2) ( in printing) épreuve f
    3) Photography épreuve f
    4) ( of alcohol) niveau m étalon

    to be 70° ou 70% proof — ≈ titrer 40° d'alcool

    2.

    to be proof against — être à l'épreuve de [heat, infection]; être à l'abri de [temptation, charms]

    3.
    - proof combining form ( resistant to)
    4.
    1) imperméabiliser [fabric]; insonoriser [room, house]

    English-French dictionary > proof

  • 8 proportion

    proportion [prəˈpɔ:∫ən]
    1. noun
       a. ( = ratio) proportion f
       b. ( = part) part f, partie f
    * * *
    [prə'pɔːʃn] 1.
    1) (part, quantity) (of group, population etc) proportion f (of de); (of income, profit, work etc) part f (of de)
    2) ( ratio) also Mathematics proportion f

    productivity increases in proportion to the incentives offered — l'augmentation de la productivité est directement proportionnelle aux primes de rendement

    3) (harmony, symmetry)

    out of/in proportion — hors de/en proportion

    2.
    proportions plural noun lit, fig dimensions fpl
    3.
    - proportioned combining form

    well-/badly-proportioned — bien/mal proportionné

    English-French dictionary > proportion

  • 9 proposition

    proposition [‚prɒpəˈzɪ∫ən]
    1. noun
       a. ( = statement, offer) proposition f
    * * *
    [ˌprɒpə'zɪʃn] 1.
    1) ( suggestion) proposition f also Mathematics
    2) ( assertion) assertion f
    3) ( enterprise) affaire f
    2.
    transitive verb faire une proposition à [person]

    English-French dictionary > proposition

  • 10 remainder

    remainder [rɪˈmeɪndər]
    ( = sth left over) reste m ; ( = remaining people) autres mfpl
    * * *
    [rɪ'meɪndə(r)] 1.
    noun (remaining things, money) also Mathematics reste m; ( people) autres mfpl; ( time) reste m, restant m
    2.
    remainders plural noun Commerce invendus mpl soldés

    English-French dictionary > remainder

  • 11 unit

    unit [ˈju:nɪt]
    1. noun
       a. ( = one item) unité f
       b. ( = complete section) élément m
       c. ( = buildings) ensemble m
       d. ( = group of people) groupe m ; (in firm) unité f
    ► unit trust noun (British Finance) ≈ fonds m commun de placement ; ( = company) SICAV f
    * * *
    ['juːnɪt]
    1) ( whole) unité f
    2) ( group with specific function) gen groupe m; (in army, police) unité f
    3) (building, department) gen, Medicine service m; Industry unité f
    4) ( in measurements) also Mathematics unité f
    5) ( part of machine) unité f
    6) ( piece of furniture) élément m
    7) University unité f de valeur
    8) School ( in textbook) unité f
    9) US ( apartment) appartement m

    English-French dictionary > unit

  • 12 graphical

    graphical [ˈgræfɪkəl]

    English-French dictionary > graphical

  • 13 Burgi, Jost

    SUBJECT AREA: Horology
    [br]
    b. 28 February 1552 Lichtensteig, Switzerland
    d. 31 January 1632 Kassel, Germany
    [br]
    Swiss clockmaker and mathematician who invented the remontoire and the cross-beat escapement, also responsible for the use of exponential notation and the calculation of tables of anti-logarithms.
    [br]
    Burgi entered the service of Duke William IV of Hesse in 1579 as Court Clockmaker, although he also assisted William with his astronomical observations. In 1584 he invented the cross-beat escapement which increased the accuracy of spring-driven clocks by two orders of magnitude. During the last years of the century he also worked on the development of geometrical and astronomical instruments for the Royal Observatory at Kassel.
    On the death of Duke Wilhelm in 1603, and with news of his skills having reached the Holy Roman Emperor Rudolph II, in 1604 he went to Prague to become Imperial Watchmaker and to assist in the creation of a centre of scientific activity, subsequently becoming Assistant to the German astronomer, Johannes Kepler. No doubt this association led to an interest in mathematics and he made significant contributions to the concept of decimal fractions and the use of exponential notation, i.e. the use of a raised number to indicate powers of another number. It is likely that he was developing the idea of logarithms at the same time (or possibly even before) Napier, for in 1620 he made his greatest contribution to mathematics, science and, eventually, engineering, namely the publication of tables of anti-logarithms.
    At Prague he continued the series of accurate clocks and instruments for astronomical measurements that he had begun to produce at Kassel. At that period clocks were very poor timekeepers since the controller, the foliot or balance, had no natural period of oscillation and was consequently dependent on the driving force. Although the force of the driving weight was constant, irregularities occurred during the transmission of the power through the train as a result of the poor shape and quality of the gearing. Burgi attempted to overcome this directly by superb craftsmanship and indirectly by using a remontoire. This device was wound at regular intervals by the main driving force and fed the power directly to the escape wheel, which impulsed the foliot. He also introduced the crossbeat escapement (a variation on the verge), which consisted of two coupled foliots that swung in opposition to each other. According to contemporary evidence his clocks produced a remarkable improvement in timekeeping, being accurate to within a minute a day. This improvement was probably a result of the use of a remontoire and the high quality of the workmanship rather than a result of the cross-beat escapement, which did not have a natural period of oscillation.
    Burgi or Prague clocks, as they were known, were produced by very few other makers and were supplanted shortly afterwards by the intro-duction of the pendulum clock. Burgi also produced superb clockwork-driven celestial globes.
    [br]
    Principal Honours and Distinctions
    Ennobled 1611.
    Bibliography
    Burgi only published one book, and that was concerned with mathematics.
    Further Reading
    L.von Mackensen, 1979, Die erste Sternwarte Europas mit ihren Instrumenten and Uhren—400 Jahre Jost Burgi in Kassel, Munich.
    K.Maurice and O.Mayr (eds), 1980, The Clockwork Universe, Washington, DC, pp. 87– 102.
    H.A.Lloyd, 1958, Some Outstanding Clocks Over 700 Years, 1250–1950, London. E.T.Bell, 1937, Men of Mathematics, London: Victor Gollancz.
    See also: Briggs, Henry
    KF / DV

    Biographical history of technology > Burgi, Jost

  • 14 Artificial Intelligence

       In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)
       Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)
       Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....
       When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)
       4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, 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

  • 15 Shannon, Claude Elwood

    [br]
    b. 30 April 1916 Gaylord, Michigan, USA
    [br]
    American mathematician, creator of information theory.
    [br]
    As a child, Shannon tinkered with radio kits and enjoyed solving puzzles, particularly crypto-graphic ones. He graduated from the University of Michigan in 1936 with a Bachelor of Science in mathematics and electrical engineering, and earned his Master's degree from the Massachusetts Institute of Technology (MIT) in 1937. His thesis on applying Boolean algebra to switching circuits has since been acclaimed as possibly the most significant this century. Shannon earned his PhD in mathematics from MIT in 1940 with a dissertation on the mathematics of genetic transmission.
    Shannon spent a year at the Institute for Advanced Study in Princeton, then in 1941 joined Bell Telephone Laboratories, where he began studying the relative efficiency of alternative transmission systems. Work on digital encryption systems during the Second World War led him to think that just as ciphers hide information from the enemy, "encoding" information could also protect it from noise. About 1948, he decided that the amount of information was best expressed quantitatively in a two-value number system, using only the digits 0 and 1. John Tukey, a Princeton colleague, named these units "binary digits" (or, for short, "bits"). Almost all digital computers and communications systems use such on-off, or two-state logic as their basis of operation.
    Also in the 1940s, building on the work of H. Nyquist and R.V.L. Hartley, Shannon proved that there was an upper limit to the amount of information that could be transmitted through a communications channel in a unit of time, which could be approached but never reached because real transmissions are subject to interference (noise). This was the beginning of information theory, which has been used by others in attempts to quantify many sciences and technologies, as well as subjects in the humanities, but with mixed results. Before 1970, when integrated circuits were developed, Shannon's theory was not the preferred circuit-and-transmission design tool it has since become.
    Shannon was also a pioneer in the field of artificial intelligence, claiming that computing machines could be used to manipulate symbols as well as do calculations. His 1953 paper on computers and automata proposed that digital computers were capable of tasks then thought exclusively the province of living organisms. In 1956 he left Bell Laboratories to join the MIT faculty as Professor of Communications Science.
    On the lighter side, Shannon has built many devices that play games, and in particular has made a scientific study of juggling.
    [br]
    Principal Honours and Distinctions
    National Medal of Science. Institute of Electrical and Electronics Engineers Medal of Honor, Kyoto Prize.
    Bibliography
    His seminal paper (on what has subsequently become known as information theory) was entitled "The mathematical theory of communications", first published in Bell System Technical Journal in 1948; it is also available in a monograph (written with Warren Weaver) published by the University of Illinois Press in 1949, and in Key Papers in the Development of Information Theory, ed. David Slepian, IEEE Press, 1974, 1988. For readers who want all of Shannon's works, see N.J.A.Sloane and A.D.Wyner, 1992, The
    Collected Papers of Claude E.Shannon.
    HO

    Biographical history of technology > Shannon, Claude Elwood

  • 16 understand

    1. transitive verb,

    understand something by something — etwas unter etwas (Dat.) verstehen

    2) (have heard) gehört haben

    I understand him to be a distant relation — ich glaube, er ist ein entfernter Verwandter

    3) (take as implied)

    it was understood that... — es wurde allgemein angenommen, dass...

    do I understand that...? — gehe ich recht in der Annahme, dass...? See also academic.ru/31215/give">give 1. 5); make 1. 6)

    2. intransitive verb,
    1) (have understanding) verstehen
    2) (gather, hear)

    he is, I understand, no longer here — er ist, wie ich höre, nicht mehr hier

    * * *
    1. past tense, past participle - understood; verb
    1) (to see or know the meaning of (something): I can't understand his absence; Speak slowly to foreigners so that they'll understand you.) verstehen
    2) (to know (eg a person) thoroughly: She understands children/dogs.) sich verstehen auf
    3) (to learn or realize (something), eg from information received: At first I didn't understand how ill she was; I understood that you were planning to leave today.) annehmen
    •
    - understandable
    - understanding 2. noun
    1) (the power of thinking clearly: a man of great understanding.) der Verstand
    2) (the ability to sympathize with another person's feelings: His kindness and understanding were a great comfort to her.) das Verständnis
    3) (a (state of) informal agreement: The two men have come to / reached an understanding after their disagreement.) die Einigung
    •
    - make oneself understood
    - make understood
    * * *
    under·stand
    <-stood, -stood>
    [ˌʌndəˈstænd, AM -ɚˈ-]
    I. vt
    1. (perceive meaning)
    ▪ to \understand sth/sb etw/jdn verstehen
    the pub was so noisy I couldn't \understand a word he said in der Kneipe ging es so laut zu, dass ich kein Wort von dem, was er sagte, verstehen konnte
    to \understand one another [or each other] sich akk verstehen
    to make oneself understood sich akk verständlich machen
    2. (comprehend significance)
    ▪ to \understand sb/sth jdn/etw begreifen [o verstehen]
    ▪ to \understand what/why/when/how... begreifen, was/warum/wann/wie...
    ▪ to \understand that... verstehen, dass...
    ▪ to \understand sb/sth für jdn/etw Verständnis haben
    I can \understand your feeling upset about what has happened ich kann verstehen, dass du wegen des Vorfalls betroffen bist
    ▪ to \understand sb sich akk in jdn einfühlen können
    Jack really \understands horses Jack kann wirklich mit Pferden umgehen
    5. (be informed)
    ▪ to \understand [that]... hören, dass...
    I \understand [that] you are interested in borrowing some money from us Sie sollen an einem Darlehen von uns interessiert sein
    to give sb to \understand that... jdm zu verstehen geben, dass...
    when he said 3 o'clock, I understood him to mean in the afternoon als er von 3 Uhr sprach, ging ich davon aus, dass der Nachmittag gemeint war
    a secret buyer is understood to have paid £3 million for the three pictures ein ungenannter Käufer soll 3 Millionen Pfund für die drei Bilder bezahlt haben
    as I \understand it, we either agree to a pay cut or get the sack so, wie ich es sehe, erklären wir uns entweder mit einer Gehaltskürzung einverstanden oder man setzt uns vor die Tür
    ▪ to \understand that... annehmen, dass...
    7. (be generally accepted)
    ▪ to be understood that... klar sein, dass...
    in the library it is understood that loud talking is not permissible es dürfte allgemein bekannt sein, dass lautes Sprechen in der Bibliothek nicht gestattet ist
    when Alan invites you to dinner, it's understood that it'll be more of an alcohol than a food experience wenn Alan zum Dinner einlädt, dann ist schon klar, dass der Alkohol im Mittelpunkt steht
    in this context, ‘America’ is understood to refer to the United States in diesem Kontext sind mit ‚Amerika‘ selbstverständlich die Vereinigten Staaten gemeint
    II. vi
    1. (comprehend) verstehen
    she explained again what the computer was doing but I still didn't \understand sie erklärte nochmals, was der Computer machte, aber ich kapierte immer noch nicht
    ▪ to \understand about sth/sb etw/jdn verstehen
    Jane's dad never understood about how important her singing was to her Janes Vater hat nie verstanden, wie wichtig das Singen für sie war
    ▪ to \understand from sth that... aus etw dat schließen, dass...
    ▪ to \understand from sb that... von jdm hören, dass...
    I've been promoted — so I \understand ich bin befördert worden — ich habe davon gehört
    * * *
    ["ʌndə'stnd] pret, ptp understood
    1. vt
    1) language, painting, statement, speaker verstehen; action, event, person, difficulty also begreifen

    I don't understand Russian —

    I can't understand his agreeing to do it — ich kann nicht verstehen or es ist mir unbegreiflich, warum er sich dazu bereit erklärt hat

    what do you understand by "pragmatism"? — was verstehen Sie unter "Pragmatismus"?

    2) (= comprehend sympathetically) children, people, animals, doubts, fears verstehen
    3)

    (= believe) I understand that you are going to Australia — ich höre, Sie gehen nach Australien

    I understand that you've already met her — Sie haben sich, soviel ich weiß, schon kennengelernt

    I understood (that) he was abroad/we were to have been consulted — ich dachte, er sei im Ausland/wir sollten dazu befragt werden

    am I/are we to understand that...? — soll das etwa heißen, dass...?

    as I understand it,... — soweit ich weiß,...

    did I understand him to say that...? — habe ich richtig verstanden, dass er sagte,...?

    but I understood her to say that she agreed — aber soweit ich sie verstanden habe, hat sie zugestimmt

    to give sb to understand that... — jdm zu verstehen geben, dass...

    I was given to understand that... — man hat mir bedeutet, dass...

    I understood from his speech that... — ich schloss aus seiner Rede, dass...

    4) (GRAM: supply) word sich (dat) denken, (im Stillen) ergänzen → also understood
    See:
    → also understood
    2. vi
    1) (= comprehend) verstehen

    (do you) understand? — (hast du/haben Sie das) verstanden?

    but you don't understand, I must have the money now — aber verstehen Sie doch, ich brauche das Geld jetzt!

    2)

    (= believe) so I understand — es scheint so

    he was, I understand, a widower — wie ich hörte, war er Witwer

    * * *
    A v/t
    1. verstehen:
    a) begreifen
    b) einsehen
    c) wörtlich etc auffassen
    d) (volles) Verständnis haben für:
    understand each other sich oder einander verstehen, auch zu einer Einigung gelangen;
    give sb to understand that … jemandem zu verstehen geben, dass …;
    make o.s. understood sich verständlich machen;
    do I ( oder am I to) understand that …? soll das heißen, dass …?;
    what do you understand by …? was verstehen Sie unter … (dat)?
    2. sich verstehen auf (akk), sich auskennen in (dat), wissen ( how to do sth wie man etwas macht);
    he understands horses er versteht sich auf Pferde;
    she understands children sie kann mit Kindern umgehen
    3. voraussetzen, (als sicher oder gegeben) annehmen:
    that is understood das versteht sich (von selbst);
    it is understood that … auch JUR es gilt als vereinbart, dass …; es wird davon ausgegangen, dass …;
    an understood thing eine aus- oder abgemachte Sache
    4. erfahren, hören:
    I understand that … ich hör(t)e oder man sagt(e) mir, dass …;
    I understand him to be ( oder that he is) an expert wie ich höre, ist er ein Fachmann;
    it is understood es heißt, wie verlautet
    5. (from) entnehmen (dat oder aus), schließen oder heraushören (aus):
    6. besonders LING bei sich oder sinngemäß ergänzen, hinzudenken:
    in this phrase the verb is understood in diesem Satz muss das Verb (sinngemäß) ergänzt werden
    B v/i
    1. verstehen:
    a) begreifen
    b) (volles) Verständnis haben:
    (do you) understand? verstanden?;
    he will understand er wird es oder mich etc (schon) verstehen;
    you are too young to understand du bist zu jung, um das zu verstehen
    2. Verstand haben
    3. Bescheid wissen ( about sth über eine Sache):
    not understand about nichts verstehen von
    4. hören:
    …, so I understand wie ich höre, …
    * * *
    1. transitive verb,
    2) (have heard) gehört haben

    I understand him to be a distant relation — ich glaube, er ist ein entfernter Verwandter

    it was understood that... — es wurde allgemein angenommen, dass...

    do I understand that...? — gehe ich recht in der Annahme, dass...? See also give 1. 5); make 1. 6)

    2. intransitive verb,
    2) (gather, hear)

    he is, I understand, no longer here — er ist, wie ich höre, nicht mehr hier

    * * *
    v.
    (§ p.,p.p.: understood)
    = begreifen v.
    einsehen v.
    fassen v.
    kapieren v.
    nachvollziehen v.
    verstehen v.

    English-german dictionary > understand

  • 17 Kurtz, Thomas E.

    [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]
    Bibliography
    1964, 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 Reading
    R.L.Wexelblat, 1981, History of Programming Languages, London: Academic Press (a more general view of the development of computer languages).
    KF

    Biographical history of technology > Kurtz, Thomas E.

  • 18 Galilei, Galileo

    [br]
    b. 15 February 1564 Pisa, Italy
    d. 8 January 1642 Arcetri, near Florence, Italy
    [br]
    Italian mathematician, astronomer and physicist who established the principle of the pendulum and was first to exploit the telescope.
    [br]
    Galileo began studying medicine at the University of Pisa but soon turned to his real interests, mathematics, mechanics and astronomy. He became Professor of Mathematics at Pisa at the age of 25 and three years later moved to Padua. In 1610 he transferred to Florence. While still a student he discovered the isochronous property of the pendulum, probably by timing with his pulse the swings of a hanging lamp during a religious ceremony in Pisa Cathedral. He later designed a pendulum-controlled clock, but it was not constructed until after his death, and then not successfully; the first successful pendulum clock was made by the Dutch scientist Christiaan Huygens in 1656. Around 1590 Galileo established the laws of motion of falling bodies, by timing rolling balls down inclined planes and not, as was once widely believed, by dropping different weights from the Leaning Tower of Pisa. These and other observations received definitive treatment in his Discorsi e dimostrazioni matematiche intorno a due nuove scienzi attenenti alla, meccanica (Dialogues Concerning Two New Sciences…) which was completed in 1634 and first printed in 1638. This work also included Galileo's proof that the path of a projectile was a parabola and, most importantly, the development of the concept of inertia.
    In astronomy Galileo adopted the Copernican heliocentric theory of the universe while still in his twenties, but he lacked the evidence to promote it publicly. That evidence came with the invention of the telescope by the Dutch brothers Lippershey. Galileo heard of its invention in 1609 and had his own instrument constructed, with a convex object lens and concave eyepiece, a form which came to be known as the Galilean telescope. Galileo was the first to exploit the telescope successfully with a series of striking astronomical discoveries. He was also the first to publish the results of observations with the telescope, in his Sidereus nuncius (Starry Messenger) of 1610. All the discoveries told against the traditional view of the universe inherited from the ancient Greeks, and one in particular, that of the four satellites in orbit around Jupiter, supported the Copernican theory in that it showed that there could be another centre of motion in the universe besides the Earth: if Jupiter, why not the Sun? Galileo now felt confident enough to advocate the theory, but the advance of new ideas was opposed, not for the first or last time, by established opinion, personified in Galileo's time by the ecclesiastical authorities in Rome. Eventually he was forced to renounce the Copernican theory, at least in public, and turn to less contentious subjects such as the "two new sciences" of his last and most important work.
    [br]
    Bibliography
    1610, Sidereus nuncius (Starry Messenger); translation by A.Van Helden, 1989, Sidereus Nuncius, or the Sidereal Messenger; Chicago: University of Chicago Press.
    1623, Il Saggiatore (The Assayer).
    1632, Dialogo sopre i due massimi sistemi del mondo, tolemaico e copernicano (Dialogue Concerning the Two Chief World Systems, Ptolemaic and Copernican); translation, 1967, Berkeley: University of California Press.
    1638, Discorsi e dimostrazioni matematiche intorno a due nuove scienzi attenenti alla
    meccanica (Dialogues Concerning Two New Sciences…); translation, 1991, Buffalo, New York: Prometheus Books (reprint).
    Further Reading
    G.de Santillana, 1955, The Crime of Galileo, Chicago: University of Chicago Press; also 1958, London: Heinemann.
    H.Stillman Drake, 1980, Galileo, Oxford: Oxford Paperbacks. M.Sharratt, 1994, Galileo: Decisive Innovator, Oxford: Blackwell.
    J.Reston, 1994, Galileo: A Life, New York: HarperCollins; also 1994, London: Cassell.
    A.Fantoli, 1994, Galileo: For Copemicanism and for the Church, trans. G.V.Coyne, South Bend, Indiana: University of Notre Dame Press.
    LRD

    Biographical history of technology > Galilei, Galileo

  • 19 Huygens, Christiaan

    SUBJECT AREA: Horology
    [br]
    b. 14 April 1629 The Hague, the Netherlands
    d. 8 June 1695 The Hague, the Netherlands
    [br]
    Dutch scientist who was responsible for two of the greatest advances in horology: the successful application of both the pendulum to the clock and the balance spring to the watch.
    [br]
    Huygens was born into a cultured and privileged class. His father, Constantijn, was a poet and statesman who had wide interests. Constantijn exerted a strong influence on his son, who was educated at home until he reached the age of 16. Christiaan studied law and mathematics at Ley den University from 1645 to 1647, and continued his studies at the Collegium Arausiacum in Breda until 1649. He then lived at The Hague, where he had the means to devote his time entirely to study. In 1666 he became a Member of the Académie des Sciences in Paris and settled there until his return to The Hague in 1681. He also had a close relationship with the Royal Society and visited London on three occasions, meeting Newton on his last visit in 1689. Huygens had a wide range of interests and made significant contributions in mathematics, astronomy, optics and mechanics. He also made technical advances in optical instruments and horology.
    Despite the efforts of Burgi there had been no significant improvement in the performance of ordinary clocks and watches from their inception to Huygens's time, as they were controlled by foliots or balances which had no natural period of oscillation. The pendulum appeared to offer a means of improvement as it had a natural period of oscillation that was almost independent of amplitude. Galileo Galilei had already pioneered the use of a freely suspended pendulum for timing events, but it was by no means obvious how it could be kept swinging and used to control a clock. Towards the end of his life Galileo described such a. mechanism to his son Vincenzio, who constructed a model after his father's death, although it was not completed when he himself died in 1642. This model appears to have been copied in Italy, but it had little influence on horology, partly because of the circumstances in which it was produced and possibly also because it differed radically from clocks of that period. The crucial event occurred on Christmas Day 1656 when Huygens, quite independently, succeeded in adapting an existing spring-driven table clock so that it was not only controlled by a pendulum but also kept it swinging. In the following year he was granted a privilege or patent for this clock, and several were made by the clockmaker Salomon Coster of The Hague. The use of the pendulum produced a dramatic improvement in timekeeping, reducing the daily error from minutes to seconds, but Huygens was aware that the pendulum was not truly isochronous. This error was magnified by the use of the existing verge escapement, which made the pendulum swing through a large arc. He overcame this defect very elegantly by fitting cheeks at the pendulum suspension point, progressively reducing the effective length of the pendulum as the amplitude increased. Initially the cheeks were shaped empirically, but he was later able to show that they should have a cycloidal shape. The cheeks were not adopted universally because they introduced other defects, and the problem was eventually solved more prosaically by way of new escapements which reduced the swing of the pendulum. Huygens's clocks had another innovatory feature: maintaining power, which kept the clock going while it was being wound.
    Pendulums could not be used for portable timepieces, which continued to use balances despite their deficiencies. Robert Hooke was probably the first to apply a spring to the balance, but his efforts were not successful. From his work on the pendulum Huygens was well aware of the conditions necessary for isochronism in a vibrating system, and in January 1675, with a flash of inspiration, he realized that this could be achieved by controlling the oscillations of the balance with a spiral spring, an arrangement that is still used in mechanical watches. The first model was made for Huygens in Paris by the clockmaker Isaac Thuret, who attempted to appropriate the invention and patent it himself. Huygens had for many years been trying unsuccessfully to adapt the pendulum clock for use at sea (in order to determine longitude), and he hoped that a balance-spring timekeeper might be better suited for this purpose. However, he was disillusioned as its timekeeping proved to be much more susceptible to changes in temperature than that of the pendulum clock.
    [br]
    Principal Honours and Distinctions
    FRS 1663. Member of the Académie Royale des Sciences 1666.
    Bibliography
    For his complete works, see Oeuvres complètes de Christian Huygens, 1888–1950, 22 vols, The Hague.
    1658, Horologium, The Hague; repub., 1970, trans. E.L.Edwardes, Antiquarian
    Horology 7:35–55 (describes the pendulum clock).
    1673, Horologium Oscillatorium, Paris; repub., 1986, The Pendulum Clock or Demonstrations Concerning the Motion ofPendula as Applied to Clocks, trans.
    R.J.Blackwell, Ames.
    Further Reading
    H.J.M.Bos, 1972, Dictionary of Scientific Biography, ed. C.C.Gillispie, Vol. 6, New York, pp. 597–613 (for a fuller account of his life and scientific work, but note the incorrect date of his death).
    R.Plomp, 1979, Spring-Driven Dutch Pendulum Clocks, 1657–1710, Schiedam (describes Huygens's application of the pendulum to the clock).
    S.A.Bedini, 1991, The Pulse of Time, Florence (describes Galileo's contribution of the pendulum to the clock).
    J.H.Leopold, 1982, "L"Invention par Christiaan Huygens du ressort spiral réglant pour les montres', Huygens et la France, Paris, pp. 154–7 (describes the application of the balance spring to the watch).
    A.R.Hall, 1978, "Horology and criticism", Studia Copernica 16:261–81 (discusses Hooke's contribution).
    DV

    Biographical history of technology > Huygens, Christiaan

  • 20 clever

    1) gescheit; klug

    be clever at mathematics/thinking up excuses — gut in Mathematik/findig im Ausdenken von Entschuldigungen sein

    2) (skilful) geschickt
    3) (ingenious) brillant, geistreich [Idee, Argument, Rede, Roman, Gedicht]; geschickt [Täuschung, Vorgehen]; glänzend (ugs.) [Idee, Erfindung, Mittel]
    4) (smart, cunning) clever; raffiniert [Schritt, Taktik, Täuschung]; schlau, raffiniert [Person]
    * * *
    ['klevə]
    1) (quick to learn and understand: a clever child.) gescheit
    2) (skilful: a clever carpenter.) geschickt
    3) ((of things) showing cleverness: a clever idea.) raffiniert
    •
    - academic.ru/85399/cleverly">cleverly
    - cleverness
    * * *
    clev·er
    <-er, -est>
    [ˈklevəʳ, AM -ɚ]
    1. (intelligent) klug, gescheit, schlau fam
    to be \clever at a subject in einem Fach sehr gut sein
    \clever boy/girl kluger Junge/kluges Mädchen
    2. (skilful) geschickt; (showing intelligence) clever
    ▪ to be \clever at sth geschickt in etw dat sein
    he's very \clever at getting his own way er hat es raus, seinen Willen durchzusetzen
    to be \clever with one's hands geschickte Hände haben
    a \clever trick ein raffinierter Trick
    3. ( pej: quick-witted but insincere) clever, gerissen pej
    too \clever by half ( pej) neunmalklug pej
    * * *
    ['klevə(r)]
    adj
    1) (= mentally bright) schlau; animal also klug

    to be clever at French —

    2) (= ingenious, skilful, witty) klug; person, move in chess also geschickt; idea also schlau; device, machine raffiniert, geschickt

    to be clever at sth — in etw (dat) geschickt sein

    he is clever at raising money — er ist geschickt, wenn es darum geht, Geld aufzubringen

    3) (= cunning, smart) schlau, clever (inf)
    * * *
    clever [ˈklevə(r)] adj (adv cleverly)
    1. clever:
    a) geschickt, gewandt, tüchtig ( alle:
    at in dat):
    be clever with one’s hands handwerkliches Geschick haben
    b) gerissen (Verkäufer etc), (auch Gerät, Trick etc) raffiniert:
    clever clogs Br umg du Schlaumeier!;
    clever dick Br umg Schlaumeier m; → half Bes Redew
    2. gescheit:
    a) clever, klug, intelligent
    b) geistreich (Bemerkung etc)
    4. begabt (at in dat, für)
    * * *
    1) gescheit; klug

    be clever at mathematics/thinking up excuses — gut in Mathematik/findig im Ausdenken von Entschuldigungen sein

    2) (skilful) geschickt
    3) (ingenious) brillant, geistreich [Idee, Argument, Rede, Roman, Gedicht]; geschickt [Täuschung, Vorgehen]; glänzend (ugs.) [Idee, Erfindung, Mittel]
    4) (smart, cunning) clever; raffiniert [Schritt, Taktik, Täuschung]; schlau, raffiniert [Person]
    * * *
    adj.
    fix adj.
    geschickt adj.
    klug adj.
    sinnreich adj.

    English-german dictionary > clever

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