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I had no knowledge of their activities

  • 1 knowledge

    'noli‹
    1) (the fact of knowing: She was greatly encouraged by the knowledge that she had won first prize in the competition.) conocimiento
    2) (information or what is known: He had a vast amount of knowledge about boats.) conocimiento
    3) (the whole of what can be learned or found out: Science is a branch of knowledge about which I am rather ignorant.) conocimiento, saber
    •
    - general knowledge
    knowledge n conocimientos
    tr['nɒlɪʤ]
    1 (learning, information) conocimientos nombre masculino plural
    2 (awareness) conocimiento
    \
    SMALLIDIOMATIC EXPRESSION/SMALL
    to my knowledge que yo sepa
    not to my knowledge que yo sepa, no
    to the best of my knowledge según mi leal entender y saber
    to be common knowledge that... ser notorio que..., todo el mundo sabe que...
    it has come to my knowledge that... he llegado a saber que...
    to have a working knowledge of something dominar los fundamentos de algo
    knowledge ['nɑlɪʤ] n
    1) awareness: conocimiento m
    2) learning: conocimientos mpl, saber m
    n.
    • ciencia s.f.
    • conocimiento s.m.
    • conocimientos s.m.pl.
    • facultad s.m.
    • instrucción s.f.
    • noticia s.f.
    • saber s.m.
    • sabiduría s.f.
    'nɑːlɪdʒ, 'nɒlɪdʒ
    mass noun
    1) ( awareness) conocimiento m

    I had no knowledge of their activities — no estaba enterado or (frml) no tenía conocimiento de sus actividades

    has he changed his mind? - not to my knowledge — ¿ha cambiado de opinión? - que yo sepa, no

    she did it in the knowledge that... — lo hizo sabiendo que or a sabiendas de que...

    it is common knowledge that... — todo el mundo sabe que...

    2) ( facts known) saber m; ( by particular person) conocimientos mpl

    my knowledge of Spanish/the law is very limited — mis conocimientos de español/de la ley son muy limitados

    ['nɒlɪdʒ]
    N
    1) (=information, awareness, understanding) conocimiento m

    • to deny all knowledge of sth — negar tener conocimiento de algo

    • to bring sth to sb's knowledge — poner a algn al tanto de algo

    • it has come to my knowledge that... — me he enterado de que...

    • it is common knowledge that... — todo el mundo sabe que..., es del dominio público que...

    • to have no knowledge of sth — no tener conocimiento de algo

    • to (the best of) my knowledge — a mi entender, que yo sepa

    not to my knowledge — que yo sepa, no

    • without my knowledge — sin saberlo yo

    2) (=person's range of information) conocimientos mpl
    3) (=learning) saber m
    * * *
    ['nɑːlɪdʒ, 'nɒlɪdʒ]
    mass noun
    1) ( awareness) conocimiento m

    I had no knowledge of their activities — no estaba enterado or (frml) no tenía conocimiento de sus actividades

    has he changed his mind? - not to my knowledge — ¿ha cambiado de opinión? - que yo sepa, no

    she did it in the knowledge that... — lo hizo sabiendo que or a sabiendas de que...

    it is common knowledge that... — todo el mundo sabe que...

    2) ( facts known) saber m; ( by particular person) conocimientos mpl

    my knowledge of Spanish/the law is very limited — mis conocimientos de español/de la ley son muy limitados

    English-spanish dictionary > knowledge

  • 2 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

  • 3 extent

    noun
    1) (space) Ausdehnung, die; (of wings) Spannweite, die
    2) (scope) (of knowledge, power, authority) Umfang, der; (of damage, disaster) Ausmaß, das; (of debt, loss) Höhe, die

    to a great or large extent — in hohem Maße

    to some or a certain extent — in gewissem Maße

    to such an extent that... — in solchem Maße, dass...

    * * *
    [-t]
    1) (the area or length to which something extends: The bird's wings measured 20 centimetres at their fullest extent; The garden is nearly a kilometre in extent; A vast extent of grassland.) die Länge, die Ausdehnung
    2) (amount; degree: What is the extent of the damage?; To what extent can we trust him?) das Ausmaß
    •
    - academic.ru/119089/to_a_certain_extent___to_some_extent">to a certain extent / to some extent
    * * *
    ex·tent
    [ɪkˈstent, ekˈ-]
    n
    1. no pl (size) of an area, a city, a park Größe f, Ausdehnung f; (length) of a river Länge f
    2. no pl (range) Umfang m
    the \extent of knowledge der Wissensumfang
    3. no pl (amount) Ausmaß nt, Umfang m; of a sum Höhe f
    the company is in debt to the \extent of a million pounds die Firma ist mit einer Million Pfund verschuldet
    the \extent of a credit die Höhe eines Kredits
    the \extent of destruction das Ausmaß der Zerstörung
    4. (degree) Grad m kein pl, Maß nt kein pl
    to a certain \extent in gewissem Maße
    to a great [or large] \extent in hohem Maße, weitgehend
    to the same \extent as... in gleichem Maße wie...
    to some \extent bis zu einem gewissen Grad
    to go to the \extent of doing sth so weit gehen, etw zu tun
    to an \extent bis zu einem gewissen Grad, in gewissem Maße
    to such an \extent that... dermaßen [o derart], dass...
    the car was damaged to such an \extent that it could not be repaired der Wagen war so stark beschädigt, dass er nicht repariert werden konnte
    to that \extent in diesem Punkt, insofern
    to what \extent in welchem Maße, inwieweit
    * * *
    [ɪk'stent]
    n
    1) (= length) Länge f; (= size) Ausdehnung f
    2) (= range, scope of knowledge, alterations, power, activities, commitments) Umfang m; (of damage, losses) Ausmaß nt, Umfang m

    debts to the extent of £5,000 — Schulden in Höhe von £ 5.000

    3) (= degree) Grad m, Maß nt

    to a large/lesser extent —

    to such an extent that... — dermaßen or derart, dass...

    he was ruined to the extent that he had to sell everything — er war dermaßen ruiniert, dass er alles verkaufen musste

    such was the extent of the damage — so groß war der Schaden

    * * *
    extent [ıkˈstent] s
    1. Ausdehnung f, Länge f, Weite f, Höhe f, Größe f
    2. MATH und fig Bereich m
    3. fig Umfang m, (Aus)Maß n, Grad m:
    extent of damage Ausmaß des Schadens, Schadenshöhe f;
    the extent of bis zum Betrag oder zur Höhe von;
    a large extent in hohem Grade, weitgehend;
    some ( oder a certain) extent bis zu einem gewissen Grade, einigermaßen;
    the full extent in vollem Umfang, völlig;
    such an extent that … so sehr, dass …
    4. Raum m, Strecke f:
    a vast extent of marsh ein ausgedehntes Sumpfgebiet
    * * *
    noun
    1) (space) Ausdehnung, die; (of wings) Spannweite, die
    2) (scope) (of knowledge, power, authority) Umfang, der; (of damage, disaster) Ausmaß, das; (of debt, loss) Höhe, die

    to a great or large extent — in hohem Maße

    to some or a certain extent — in gewissem Maße

    to such an extent that... — in solchem Maße, dass...

    * * *
    n.
    Ausmaß -e n.
    Größe -n f.
    Umfang -¨e m.

    English-german dictionary > extent

  • 4 Computers

       The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)
       It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....
       The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)
       The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)
       In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)
       A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.
       In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....
       It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)
       [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)
       he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)
       t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.
       Further, the effects of those ideas are rapidly compounding, because a computer design is itself just a set of ideas. As we get better at manipulating ideas by building ever better computers, we get better at building even better computers-it's an ever-escalating upward spiral. The early nineteenth century, when the computer's story began, is already so far back that it may as well be the Stone Age. (Rawlins, 1997, p. 19)
       According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)
       What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.
       What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.
       In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)

    Historical dictionary of quotations in cognitive science > Computers

  • 5 line

    A n
    1 ( mark) ligne f ; (shorter, thicker) trait m ; Art trait m ; Sport (on pitch, court) ligne f ; Math ligne f ; line and colour Art le trait et la couleur ; a straight/curved line une ligne droite/courbe ; a solid/broken line une ligne continue/discontinue ; a single/double line une ligne simple/double ; to draw ou rule a line tracer une ligne ; to draw a line down the middle of the page tracer une ligne verticale au milieu de la page ; to put a line through sth barrer qch ; to cross the line Sport franchir la ligne ; the starting/finishing line Sport la ligne de départ/d'arrivée ; above/below the line ( in bridge) (marqué) en points d'honneur/en points de marche ; the line AB ( in geometry) la droite AB ; the thin line of his mouth ses lèvres fines ;
    2 ( row) (of people, cars) file f (of de) ; ( of trees) rangée f (of de) ; (of footprints, hills) succession f (of de) ; in straight lines [plant, arrange, sit] en lignes droites ; to stand in a line faire la queue ; get into (a) line! faites la queue! ; to form a line [people] faire la queue ; [hills, houses, trees] être aligné ; please form a line mettez-vous en file s'il vous plaît ; she is fifth in line elle est la cinquième dans la file ; to be in line [buildings] être dans l'alignement ; put the desks in line alignez les bureaux ; to be in line with [shelving, cooker] être dans l'alignement de [cupboard] ; [mark, indicator] coïncider avec [number] ; to be out of line [picture] être de travers ;
    3 fig to be in line for promotion/a pay rise avoir des chances d'être promu/d'être augmenté ; to be in line for redundancy/takeover risquer d'être mis au chômage/d'être racheté ; to be next in line for promotion/execution être le prochain à être promu/exécuté ; in line for the post of bien placé pour obtenir le poste de ;
    4 surtout US ( queue) file f ; to stand in ou wait in line faire la queue (for pour) ;
    5 ( wrinkle) ( on face) ride f ; ( on hand) ligne f ;
    6 Archit, Sewing ( outline shape) ligne f (of de) ; the classical lines of the building la ligne classique du bâtiment ;
    7 ( boundary) frontière f ; an imaginary line between une frontière imaginaire entre ; to cross the state line passer la frontière de l'État ; to follow the line of the old walls suivre le tracé des anciens remparts ; there's a fine line between knowledge and pedantry de la culture à la pédanterie il n'y a qu'un pas ;
    8 ( rope) corde f ; Fishg ligne f ; to put the washing on the line étendre le linge ; a line of washing du linge étendu à sécher ; to throw sb a line lancer une corde à qn ; to cast one's line lancer sa ligne ; there was a fish at the end of the line il y avait un poisson qui mordait ;
    9 ( cable) Elec ligne f (électrique) ; the line had been cut Elec on avait coupé la ligne ; to bring the lines down Telecom abattre les lignes ; the lines are down Telecom les lignes ont été abattues ;
    10 Telecom ( connection) ligne f ; a bad line une mauvaise ligne ; outside line ligne f extérieure ; dial 9 to get an outside line faites le 9 pour appeler à l'extérieur ; to be on the line to sb être en ligne avec qn ; to get off the line ○ raccrocher ; at the other end of the line au bout du fil ; the lines will be open from 8.30 onwards vous pouvez nous appeler à partir de 8 h 30 ; the line is dead il n'y a pas de tonalité ; the line went dead la ligne a été coupée ;
    11 Transp Rail ( connection) ligne f (between entre) ; ( rails) voie f ; (shipping, air transport) ( company) compagnie f ; ( route) ligne f ; repairs to the line réparations sur la voie ; at every station along the line à chaque gare sur la ligne ; the London-Edinburgh line Rail la ligne Londres-Édimbourg ;
    12 ( in genealogy) lignée f ; the male/female line la lignée par les hommes/les femmes ; the Tudor line la maison des Tudor ; to found ou establish a line fonder une lignée ; the line died out la lignée s'est éteinte ; to come from a long line of scientists être issu d'une longue lignée de scientifiques ; to trace one's line back to sb retracer son ascendance jusqu'à qn ; to trace a line down to sb retracer une descendance jusqu'à qn ; to trace a line through sb retracer l'ascendance du côté de qn ; the title passes to the next in line le titre passe au suivant dans l'ordre de succession ; she is second in line to the throne elle est la deuxième dans l'ordre de succession au trône ;
    13 ( of text) ( in prose) ligne f ; ( in poetry) vers m ; ( of music) ligne f ; to give sb 100 lines donner 100 lignes à qn ; to start a new line aller à la ligne ; to miss a line sauter une ligne ; write a few lines about your hobbies décrivez vos passe-temps en quelques lignes ; just a line to say thank you juste un petit mot pour dire merci ; a line from une citation de [poem etc] ; a line of verse ou poetry un vers ; the famous opening lines la célèbre introduction ; he has all the best lines il a les meilleures répliques ; to learn one's lines Theat apprendre son texte ;
    14 ( conformity) to fall into line être d'accord ; to make sb fall into line faire marcher qn au pas ; to fall into line with [person] tomber d'accord avec [view] ; [group, body] être d'accord avec [practice, policy] ; China fell into line with the other powers la Chine s'est mise d'accord avec les autres puissances ; to bring sb into line ramener qn dans le rang ; to bring regional laws into line with federal laws harmoniser les lois régionales et les lois fédérales ; to bring working conditions into line with European standards aligner les conditions de travail sur les normes européennes ; to keep sb in line tenir qn en main ; his statement is out of line with their account sa déclaration ne concorde pas avec leur déposition ; our prices are out of line with those of our competitors nos prix ne s'accordent pas avec ceux de nos concurrents ; to be (way) out of line [objection, remark] être (tout à fait) déplacé ; you're way out of line ○ ! franchement, tu exagères! ;
    15 ○ ( piece of information) to have a line on sb/sth avoir des informations sur qn/qch ; to give sb a line on sb/sth donner un tuyau ○ à qn sur qn/qch ; to give sb a line about sth (story, excuse) raconter des bobards ○ à qn sur qch ; don't give me that line! ne me raconte pas ces histoires! ;
    16 ( stance) position f (on sur) ; something along these lines quelque chose dans le même genre ; our rivals had been thinking along the same lines nos concurrents avaient pensé aux mêmes choses ; to be on the right lines être sur la bonne voie ; the official line la position officielle ; ( approach) ligne f de conduite (with avec) ; to take a firm line with sb se montrer ferme avec qn ; I don't know what line to take je ne sais pas quelle ligne de conduite adopter ;
    17 Comm ( type of product) gamme f ; one of our most successful lines une gamme qui a beaucoup de succès ;
    18 Mil ( fortifications) ligne f ; ( position held) position f ; enemy lines lignes fpl ennemies ; they held their line ils ont conservé leurs positions ;
    19 Naut line ahead/abreast ligne de front/de file ;
    20 ( equator) the line la ligne ; to cross the line traverser la ligne ;
    21 ○ ( of cocaine) ligne ○ f (of de) ;
    22 TV ligne f.
    B in line with prep phr en accord avec [approach, policy, trend, teaching, requirement] ; to be in line with [statement, measure] être dans la ligne de [policy, view, recommendation] ; [figures, increase] être proportionnel à [inflation, trend] ; to increase/fall in line with augmenter/baisser proportionnellement à ; to vary in line with varier parallèlement à.
    C vtr
    1 ( add layer) doubler [garment] (with avec) ; tapisser [box, shelf, nest] (with de) ; to be lined with books être tapissé de livres ; to line the walls and ceilings tapisser les murs et les plafonds d'un papier d'apprêt ;
    2 ( stand along) [trees, spectators] border [route] ; to be lined with trees être bordé d'arbres ;
    3 ( mark) to be lined with être marqué par [worry, age].
    all along the line, right down the line sur toute la ligne ; somewhere along the line ( at point in time) à un certain moment ; ( at stage) quelque part ; something along those lines quelque chose dans ce goût ; to do a line with sb ○ sortir avec qn ; to be on the line [life, job] être en jeu.
    ■ line up:
    ▶ line up
    1 ( side by side) se mettre en rang (for pour) ; ( one behind the other) se mettre en file (for pour) ; to line up in rows se mettre en rangs ;
    2 ( take sides) to line up with sb/sth se ranger du côté de qn/qch ; to line up against sb/sth se regrouper contre qn/qch ;
    ▶ line up [sb], line [sb] up ( in row) faire s'aligner ; they lined us up ( in columns) ils nous ont fait former des colonnes ; to line people up against a wall aligner des gens contre un mur ;
    ▶ line [sth] up, line up [sth]
    1 ( align) aligner (with sur) ;
    2 ( organize) sélectionner [team] ; to have sb/sth lined up [candidate, work, project, activities] avoir qn/qch en vue ; what have you got lined up for us tonight? qu'est-ce que tu nous as prévu pour ce soir ○ ?

    Big English-French dictionary > line

  • 6 whistle-blower

    (перевод зависит от контекста; иногда, но необязательно, может иметь негативную или иную оценочную окраску)
    1) информатор, источник информации

    whistle-blower — someone who reports dishonest or illegal activities within an organization to someone in authority ( Macmillan)

    2) возмутитель спокойствия, разоблачитель, борец за справедливость, правозащитник
    4) свидетель (разоблачения), ключевая фигура скандальных разоблачений; источник разоблачений; обвинитель, лицо, выступившее с обвинениями (Если речь идет о скандальных разоблачениях в области коррупции, финансовых махинациях или иных подобных действиях)

    1. When a person has knowledge of a violation of the federal False Claims Act, the person can become what is commonly called a whistle-blower (relator). - Если какому-то лицу становится известно о нарушениях, подпадающих под действие федерального Закона об Исках против Мошенничества, такое лицо может стать тем, кто обычно именуется свидетелем разоблачения

    (информатором).

    2.These insiders or whistle blowers are also called under the statute a Relator - Согласно закону, такие инсайдеры, или свидетели разоблачения, называются также информаторами.

    Syn:
    5) в контексте: "she swore to get the whistle-blower who put the finger on her man" (речь идет о жене заключенного) возможен перевод "стукач"
    6) встречается также с следующих контекстах:

    1. Nader style whistle-blowing.

    2. Nor do you – or Matt Drudge, Tripp’s biggest booster in the media – want to recall the first time Tripp, the now-infamous whistle-blower, burst on the national scene. (www.newsmax.com)

    3. At least they seem to have gotten over their disdain for government whistle-blowers. Back when the world's most famous whistle-blower produced tapes proving the president of the United States had committed a slew of felonies, the left was more muted in its enthusiasm for female truth-tellers. Dowd called Linda Tripp a "witch" with a "boiling cauldron." Rich said Americans "despise" a "snitch." (Human Events)

    The English annotation is below. (English-Russian) > whistle-blower

См. также в других словарях:

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