The computer and the mind

   1) To Know the Different Operations of the Mind

   It becomes, therefore, no inconsiderable part of science... to know the different operations of the mind, to separate them from each other, to class them under their proper heads, and to correct all that seeming disorder in which they lie involved when made the object of reflection and inquiry.... It cannot be doubted that the mind is endowed with several powers and faculties, that these powers are distinct from one another, and that what is really distinct to the immediate perception may be distinguished by reflection and, consequently, that there is a truth and falsehood which lie not beyond the compass of human understanding. (Hume, 1955, p. 22)

   2) The Mind Is Furnished by Experience

   Let us then suppose the mind to be, as we say, white Paper, void of all Characters, without any Ideas: How comes it to be furnished? Whence comes it by that vast store, which the busy and boundless Fancy of Man has painted on it, with an almost endless variety? Whence has it all the materials of Reason and Knowledge? To this I answer, in one word, from Experience. (Locke, quoted in Herrnstein & Boring, 1965, p. 584)

   3) Mythical Thought Is as Rigorous as That of Modern Science

   The kind of logic in mythical thought is as rigorous as that of modern science, and... the difference lies, not in the quality of the intellectual process, but in the nature of things to which it is applied.... Man has always been thinking equally well; the improvement lies, not in an alleged progress of man's mind, but in the discovery of new areas to which it may apply its unchanged and unchanging powers. (Leґvi-Strauss, 1963, p. 230)

   4) The Mind Has Nothing But Itself to Know Itself

   MIND. A mysterious form of matter secreted by the brain. Its chief activity consists in the endeavor to ascertain its own nature, the futility of the attempt being due to the fact that it has nothing but itself to know itself with. (Bierce, quoted in Minsky, 1986, p. 55)

   5) To Know Is to Represent Accurately

   [Philosophy] understands the foundations of knowledge and it finds these foundations in a study of man-as-knower, of the "mental processes" or the "activity of representation" which make knowledge possible. To know is to represent accurately what is outside the mind, so to understand the possibility and nature of knowledge is to understand the way in which the mind is able to construct such representation.... We owe the notion of a "theory of knowledge" based on an understanding of "mental processes" to the seventeenth century, and especially to Locke. We owe the notion of "the mind" as a separate entity in which "processes" occur to the same period, and especially to Descartes. We owe the notion of philosophy as a tribunal of pure reason, upholding or denying the claims of the rest of culture, to the eighteenth century and especially to Kant, but this Kantian notion presupposed general assent to Lockean notions of mental processes and Cartesian notions of mental substance. (Rorty, 1979, pp. 3-4)

   6) The Question of Mind in Relation to Machine

   Under pressure from the computer, the question of mind in relation to machine is becoming a central cultural preoccupation. It is becoming for us what sex was to Victorians-threat, obsession, taboo, and fascination. (Turkle, 1984, p. 313)

   7) Understanding the Mind Remains as Resistant to Neurological as to Cognitive Analyses

   Recent years have been exciting for researchers in the brain and cognitive sciences. Both fields have flourished, each spurred on by methodological and conceptual developments, and although understanding the mechanisms of mind is an objective shared by many workers in these areas, their theories and approaches to the problem are vastly different....

   Early experimental psychologists, such as Wundt and James, were as interested in and knowledgeable about the anatomy and physiology of the nervous system as about the young science of the mind. However, the experimental study of mental processes was short-lived, being eclipsed by the rise of behaviorism early in this century. It was not until the late 1950s that the signs of a new mentalism first appeared in scattered writings of linguists, philosophers, computer enthusiasts, and psychologists.

   In this new incarnation, the science of mind had a specific mission: to challenge and replace behaviorism. In the meantime, brain science had in many ways become allied with a behaviorist approach.... While behaviorism sought to reduce the mind to statements about bodily action, brain science seeks to explain the mind in terms of physiochemical events occurring in the nervous system. These approaches contrast with contemporary cognitive science, which tries to understand the mind as it is, without any reduction, a view sometimes described as functionalism.

   The cognitive revolution is now in place. Cognition is the subject of contemporary psychology. This was achieved with little or no talk of neurons, action potentials, and neurotransmitters. Similarly, neuroscience has risen to an esteemed position among the biological sciences without much talk of cognitive processes. Do the fields need each other?... [Y]es because the problem of understanding the mind, unlike the wouldbe problem solvers, respects no disciplinary boundaries. It remains as resistant to neurological as to cognitive analyses. (LeDoux & Hirst, 1986, pp. 1-2)

   8) Approaches to the Study of the Mind

   Since the Second World War scientists from different disciplines have turned to the study of the human mind. Computer scientists have tried to emulate its capacity for visual perception. Linguists have struggled with the puzzle of how children acquire language. Ethologists have sought the innate roots of social behaviour. Neurophysiologists have begun to relate the function of nerve cells to complex perceptual and motor processes. Neurologists and neuropsychologists have used the pattern of competence and incompetence of their brain-damaged patients to elucidate the normal workings of the brain. Anthropologists have examined the conceptual structure of cultural practices to advance hypotheses about the basic principles of the mind. These days one meets engineers who work on speech perception, biologists who investigate the mental representation of spatial relations, and physicists who want to understand consciousness. And, of course, psychologists continue to study perception, memory, thought and action.

... [W]orkers in many disciplines have converged on a number of central problems and explanatory ideas. They have realized that no single approach is likely to unravel the workings of the mind: it will not give up its secrets to psychology alone; nor is any other isolated discipline-artificial intelligence, linguistics, anthropology, neurophysiology, philosophy-going to have any greater success. (Johnson-Laird, 1988, p. 7)

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   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)

   1) A Comparison of the Digital Computer and the Brain

   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)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   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)

   3) Getting Computers to Explain Things to Themselves

   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)

   4) Some Limits of Artificial Intelligence

   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)

   5) The Computer as a Humanizing Influence

   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)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [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)

   7) The Possibility of Computer Thought

   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)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   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.

   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)

    10) Why People Are Smarter Than Computers

   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)

1 to instruct.

María instruye a su hijo Mary instructs her son.

El programa instruye la computadora The program instructs the computer.

2 to prepare (law).

* * *

Conjugation model [ HUIR], like link=huir huir

► verbo transitivo

1 (enseñar) to instruct

2 MILITAR to train

3 DERECHO to examine, investigate

■ el juez Murriñas instruyó la causa judge Murriñas investigated the case

* * *

1) to instruct

2) train

* * *

1. VT

1) (=formar)

a) (Educ) [+ estudiante] to instruct; [+ profesional] to train

b) (Dep) to coach, train

c) (Mil) to train

2) (Jur) (=tramitar) [+ caso, causa] to try, hear

VI (=enseñar)

instruirse

* * *

verbo transitivo

1)

a) (adiestrar, educar)

b) (frml) ( informar)

2) (Der) < causa> to try, hear

instruirse v pron (refl) to broaden one's mind, improve oneself

* * *

= direct, enlighten, instruct.

Ex. This statement directs the user to adopt a number more specific terms in preference to the general term.

Ex. Librarians often work with students who possess few library skills and teachers whose assignments neither improve these skills nor enlighten the students on their research.

Ex. Some of the above limitations of title indexes can be overcome by exercising a measure of control over the index terminology, and by inputting and instructing the computer to print a number of pre-determined links or references between keywords.

----

* instruir una diligencia = deliver + charge.

* * *

verbo transitivo

1)

a) (adiestrar, educar)

b) (frml) ( informar)

2) (Der) < causa> to try, hear

instruirse v pron (refl) to broaden one's mind, improve oneself

* * *

* * *

instruir [ I20 ]

1 (adiestrar, educar) instruir a algn EN algo to instruct o train sb IN sth

me instruyó en el manejo del rifle he instructed o trained me in the use of the rifle

los instruyen en las artes marciales they are given instruction o training in martial arts, they are trained in martial arts

2 ( frml) (informar) instruir a algn SOBRE algo to apprise sb OF sth ( frml)

nos instruyó sobre el problema he apprised us of the problem

B ( Der) ‹causa› to try, hear

el juez que instruye el sumario the judge who is conducting the preliminary investigation into the case

viajar instruye mucho travel broadens the mind

( refl) to broaden one's mind, improve oneself

* * *

* * *

♦ vt

1. [enseñar] to instruct;

la instruyó en las artes marciales he taught her martial arts

2. Der to prepare;

el juez que instruye el sumario the examining magistrate

♦ vi

los viajes instruyen mucho travel really broadens the mind

♦ See also the pronominal verb instruirse

* * *

1 educate; ( formar) train

2 JUR pleito hear

* * *

instruir {41} vt

1) adiestrar: to instruct, to train

2) enseñar: to educate, to teach

* * *

instruir vb to instruct

1 search, quest, pursuit, research.

2 chase, hunting.

3 data search, lookup, look-up.

* * *

► nombre femenino

1 search

* * *

1) search

2) quest

* * *

SF search (de for)

* * *

femenino search

* * *

= chase, look-up [lookup], search, search request, searching, hunt, hunting, reaching, trawl, querying.

Ex. Enthusiasm in a searcher, of course, all are agreed on: 'he must delight in the chase for its own sake'.

Ex. With online display, the alphabetical arrangement can become less significant, since all look-ups can be achieved with the computer, and there is less need for the scanning of alphabetical lists.

Ex. Even a user who starts a search with a specific subject in mind, may be seeking a specific subject which does not quite match requirements.

Ex. The search request is displayed on the screen and assigned a number in the extreme left column.

Ex. Indexing and searching, then, are integral one to another, and so a few comments on searching are in order here = Por lo tanto, la indización y la búsqueda son complementarios y así pues es pertinente hacer algunos comentarios aquí sobre la búsqueda.

Ex. If a search, manual or on-line, appears likely to last more than a few minutes, and should the librarian decide not to encourage the enquirer to join in the hunt, good practice requires that the visitor be given something to be going on with and invited to sit down.

Ex. It was not until the 16th century that falconry and stag hunting gained the significance that it retained until 1789.

Ex. 'Searching' or even 'ordering' would be better, so long as we didn't imply by either of them an 'irritable reaching after fact and reason'.

Ex. 'Systematic review' is the process whereby similar studies, identified from a comprehensive trawl of numerous databases, are summarized in digestible form.

Ex. The project consortium will complete a feasibility study into the automatic indexing of free text and the multilingual querying of text databases = El consorcio creado realizar el proyecto llevará a cabo un estudio de viabilidad sobre la indización automática de texto libre y la consulta en varios idiomas de bases de datos de texto.

----

* acotador de búsqueda = search qualifier.

* acotar una búsqueda = narrow + search, qualify + search, refine + search.

* agente de búsqueda = intelligent search agent, search agent.

* ampliar una búsqueda = broaden + search, expand + Posesivo + search.

* anterior a la búsqueda = pre-search.

* árbol de búsqueda binario = binary search tree.

* búsqueda a terceros = third party searching.

* búsqueda a través de ficheros de identificación documental = signature-based search.

* búsqueda automatizada = computer searching, computer search, automated searching.

* búsqueda avanzada = advanced search.

* búsqueda bibliográfica = literature searching, literature search, bibliographic search.

* búsqueda booleana = Boolean searching.

* búsqueda con anterioridad al pedido = preorder searching.

* búsqueda contextual = contextual searching.

* búsqueda cruzada = federated search.

* búsqueda cruzada de ficheros = cross-file searching.

* búsqueda de citas = citation search.

* búsqueda de comida = foraging.

* búsqueda de datos = fact-finding.

* búsqueda de documento conocido = known-item search.

* búsqueda de documentos concretos = item search.

* búsqueda deductiva = heuristic search.

* búsqueda de empleo = job searching, job hunting.

* búsqueda de información = fact-finding, quest for + information, information seeking.

* búsqueda de la verdad = truth-seeking.

* búsqueda del conocimiento = quest for/of knowledge.

* búsqueda del tesoro = treasure hunt, scavenger hunt.

* búsqueda de nuevos genes = gene-harvesting.

* búsqueda de oro = gold digging.

* búsqueda de palabras clave = keyword search.

* búsqueda de pareja = mate finding.

* búsqueda de proximidad = proximity searching.

* búsqueda de recursos = resource discovery.

* búsqueda de secuencias de caracteres = string search, string searching.

* búsqueda de tesoros = treasure-seeking.

* búsqueda de texto libre = free text search, free-text searching.

* búsqueda de títulos = title search.

* búsqueda de varios ficheros a la vez = multi-file searching.

* búsqueda difusa = fuzzy match, fuzzy matching.

* búsqueda documental = document search.

* búsqueda eficaz = savvy searching.

* búsqueda en el índice = index searching.

* búsqueda en lenguaje natural = natural language searching.

* búsqueda en línea = online searching, online search.

* búsqueda en múltiples bases de datos = cross database searching.

* búsqueda en múltiples ficheros = cross-file searching.

* búsqueda en serie = serial search, serial searching.

* búsqueda en texto completo = full text search.

* búsqueda en vano = wild goose chase.

* búsqueda evolutiva = berrypicking.

* búsqueda exacta = exact match.

* búsqueda global = comprehensive search.

* búsqueda inteligente = savvy searching.

* búsqueda interactiva = interactive searching, interactive search.

* búsqueda inversa = backtracking search.

* búsqueda iterativa = iterative searching.

* búsqueda lateral = lateral searching.

* búsqueda manual = manual searching.

* búsqueda mecánica = machine searching.

* búsqueda parcial = partial match.

* búsqueda personalizada de ejecutivos = headhunting, executive search.

* búsqueda ponderada = weighted query.

* búsqueda por autor = author searching.

* búsqueda por autor/título = author/title search.

* búsqueda por campos = field searching.

* búsqueda por descriptores = descriptor searching.

* búsqueda por el usuario final = end-user searching.

* búsqueda por materia = subject searching, topical subject search.

* búsqueda por materias = subject search, subject query, subject browsing.

* búsqueda por máxima proximidad = nearest neighbour searching.

* búsqueda por medio de menús = menu-assisted searching.

* búsqueda por medio de órdenes = command search.

* búsqueda por palabra del título = title word search.

* búsqueda por rangos = range searching, ranged search.

* búsqueda por secuencia de caracteres = character-string search.

* búsqueda por términos ponderados = weighted term search.

* búsqueda rápida = scanning.

* búsqueda repetitiva = iterative search.

* búsqueda retrospectiva = retrospective search, retrospective searching.

* búsquedas de secuencias de caracteres = text-string searching.

* búsqueda simple = simple search.

* búsqueda simultánea en varios ficheros = cross-searching [cross searching].

* búsqueda simultánea en varios sitios = cross-search [cross search].

* Búsqueda Simultánea Remota (SRS) = Simultaneous Remote Searching (SRS).

* búsqueda tabú = tabu search.

* búsqueda truncada = truncated search.

* búsqueda y rescate = search and rescue (SAR).

* capacidad de búsqueda = searching power.

* clave de búsqueda = search key.

* clave de búsqueda derivado = derived search key.

* clave de búsqueda por el título = title key.

* clave de búsqueda por nombre de autor = author key.

* clave de búsqueda truncada = truncated key.

* clave de búsqueda truncada derivada = truncated derived search key.

* código de búsqueda = searchable code, search code.

* coincidencia de mayúsculas y minúsculas en la búsqueda = case sensitivity.

* comportamiento de búsqueda de información = information-seeking behaviour.

* condición de búsqueda = search requirement.

* conocimientos básicos de búsqueda = information literacy.

* construir una búsqueda = construct + search.

* construir un enunciado de búsqueda = state + search topic.

* criterios de búsqueda = search criteria.

* cumplir la condición de la búsqueda = match + request specification.

* cumplir un enunciado lógico de búsqueda = satisfy + logic statement.

* de acuerdo con la búsqueda de cadenas de caracteres = on a string search basis.

* delimitar una búsqueda = narrow + search, qualify + search, refine + search.

* desconocimiento de las destrezas básicas en la búsqueda, rec = information illiteracy.

* destreza en la búsqueda de información en una biblioteca = library research skills.

* detener búsqueda = discontinue + search.

* donde se pueden hacer búsquedas = queriable.

* durante la búsqueda = at the search stage.

* ecuación de búsqueda = search argument, search expression, search formulation.

* elemento de búsqueda ficticio = rogue string.

* eliminar una ecuación de búsqueda = clear + search.

* empresa de búsqueda personalizada de ejecutivos = headhunter.

* en búsqueda de = a quest for.

* en la búsqueda de = in the quest for.

* enseñanza en la búsqueda de información = information instruction.

* enunciado de búsqueda = search prescription, search statement, search query, query statement.

* enunciado de búsqueda de documentos multimedia = multimedia query.

* enunciado de búsqueda en texto libre = free-text search statement.

* equipo de búsqueda y rescate = search and rescue team.

* estadísticas de búsqueda = searching statistics.

* estrategia de búsqueda = search strategy, search process.

* estrategia de búsqueda de información = information seeking pattern.

* expansión de una búsqueda por medio del tesauro = thesaurus expansion.

* explosión de las búsquedas = explosion of searches.

* expresión de búsqueda = access vector, search expression.

* facilidad de búsqueda = scannability, soughtness, searchability, findability.

* formulación de una búsqueda = query formulation.

* formular una ecuación de búsqueda = formulate + search strategy.

* grado de coincidencia entre el tema de un documento y el tema de búsqueda = topicality.

* guardar los resultados de una búsqueda en un fichero = store + search results + in disc file.

* guardar una búsqueda en disco = save + Posesivo + search + to disc.

* hábito de búsqueda de información = information-seeking habit.

* hacer búsquedas en = search through.

* hacer cambios en la búsqueda = renegotiate + search.

* hacer una búsqueda = look up, submit + search, do + search.

* hacer una búsqueda en Google = google.

* hacer una búsqueda mediante el operador O = OR together.

* hacer una búsqueda mediante el operador Y = AND together.

* herramienta de búsqueda = search aid, finding aid, search tool.

* herramientas de ayuda para la búsqueda = searching aid.

* historial de búsqueda = search history.

* impreso de perfil de búsqueda = profile search form.

* incluir en la búsqueda los términos relacionados = explode.

* interfaz de búsqueda = search interface.

* juego de búsqueda bibliográfica = library scavenger hunt.

* lenguaje de búsqueda = search language.

* limitar búsqueda = limit + search.

* línea de búsqueda = query line.

* lista de búsqueda = finding list.

* lógica de búsqueda = search logic.

* lógica de búsqueda por ponderación = weighted-term search logic.

* método de búsqueda = search paradigm.

* misión de búsqueda y rescate = search and rescue mission.

* motor de búsqueda = portal, search engine, crawler.

* número de búsquedas fallidas = failure rate.

* número de la búsqueda = set number.

* opción de búsqueda = search option.

* opciones de búsqueda = search capabilities.

* operación de búsqueda y rescate = search and rescue operation, search and rescue mission.

* ordenación jerárquica del resultado de la búsqueda = output ranking.

* orden de ampliar la búsqueda a los términos relaci = explode command.

* papeleta de petición de búsqueda en línea = online search request form.

* paradigma de búsqueda = search paradigm.

* perfil de búsqueda = search profile.

* petición de búsqueda = search request.

* ponderación de los términos de la ecuación de búsqueda = query term weighting.

* posibilidades de búsqueda = searching capabilities, searchability, retrieval facilities, search facilities.

* posterior a la búsqueda = post-search.

* precisar una búsqueda = focus + Posesivo + search.

* proceso de búsqueda = searching process, search process.

* programa de búsqueda = search software, search software package.

* proveedor de servicios de búsqueda en línea = online search service supplier.

* realizar una búsqueda = conduct + search, execute + search, perform + search, run + search, undertake + search, carry out + search.

* recuadro de búsqueda = search box.

* reformulación de la búsqueda = query reformation.

* restringir una búsqueda = limit + selection, narrow + search, qualify + search, qualify + selection.

* resultado de la búsqueda = posting, search output, search result, searching result.

* resultado de una búsqueda = set.

* robot de búsqueda = portal, search engine, crawler, Web crawler.

* servicio de búsqueda = search service.

* sesión de búsqueda = search session.

* sistema de búsqueda = paging system.

* técnica de búsqueda automatizada = computer-searching technique.

* tema de búsqueda = search topic.

* término de búsqueda = search term, search word.

* término de la búsqueda = query term.

* tiempo de búsqueda = search time.

* tipos de búsqueda = retrieval facilities, search facilities.

* vector de búsqueda = query vector.

* velocidad de búsqueda = search speed.

* ventana de búsqueda = search box.

* * *

femenino search

* * *

* * *

search búsqueda DE algo/algn search FOR sth/sb

treasure hunt

( Inf) wordsearch

web search

( Inf) search and replace

* * *

* * *

búsqueda nf

search;

a la búsqueda de algo in search of sth;

(ir) en búsqueda de (to go) in search of

* * *

f search;

búsqueda en el texto INFOR search o find in the text

* * *

búsqueda nf

: search

* * *

búsqueda n search

1 to drip (gotear) (gota a gota).

estar chorreando to be soaking o wringing wet (estar empapado)

Esta vieja cafetera chorrea demasiado This old coffeepot drips too much.

María chorreó la leche Mary poured the milk.

2 to spurt or gush (out).

3 to steal, to rob. (River Plate)

4 to besprinkle, to bedabble, to stain.

Ricardo chorreó su camisa preferida Richard besprinkled his favorite shirt.

* * *

► verbo intransitivo

1 (caer a chorro) to spout, gush, spurt

2 (gotear) to drip

■ le chorrea el pelo her hair is dripping wet

3 familiar (ir sin interrupción) to flow

■ los escándalos chorrean scandals are pouring out one after the other

► verbo transitivo

1 (echar) to drip

■ la herida chorreaba sangre blood was pouring from the wound

■ la brocha chorrea pintura the brush is dripping with paint

2 familiar (abroncar) to tick off, give a dressing-down to

\

estar chorreando familiar to be dripping wet, be soaking 2 (de sudor) to pour with sweat, be dripping with sweat

* * *

1) to drip

2) pour

* * *

1. VI

1) (=salir a chorros) to gush (out), spout

2) (=gotear) to drip

3) [dinero] to trickle in, come in in dribs and drabs

2. VT

1) (Mil) * (=regañar) to tick off *, dress down *

2) (=verter) to pour

3) Cono Sur (=robar) to pinch *

4) And (=mojar) to soak

chorrearse

* * *

verbo intransitivo to drip

1) (AmL fam) ( manchar)

2) (Col, RPl arg) ( robar) to swipe (colloq)

chorrearse v pron

1) (refl) (CS, Per fam) ( mancharse)

2) (Col, RPl arg) ( robar) to swipe (colloq)

* * *

= trickle, drip.

Ex. Data buffers receive data from the computer and let it ' trickle through' to the printer at the appropriate speed, thus freeing the computer for the next task.

Ex. The film seemed like a cross between theatrics and hippiedom, including the disembowelment of a bull, a unison hooting of brass bands, and the creation of paintings by dripping blood onto white surfaces.

* * *

verbo intransitivo to drip

1) (AmL fam) ( manchar)

2) (Col, RPl arg) ( robar) to swipe (colloq)

chorrearse v pron

1) (refl) (CS, Per fam) ( mancharse)

2) (Col, RPl arg) ( robar) to swipe (colloq)

* * *

* * *

chorrear [A1 ]

to drip

ten cuidado, que esa tetera chorrea be careful, that teapot drips

las sábanas todavía están chorreando the sheets are still dripping wet

tengo el pelo chorreando my hair is soaking wet

llegó chorreando de sudor she arrived dripping with sweat

la sangre le chorreaba por la nariz blood was pouring from his nose

(gotear): las sábanas chorrean agua the sheets are dripping wet

esta pluma está chorreando tinta this pen's leaking

chorreaba sudor he was dripping with sweat

( AmL fam) (manchar): el mantel está todo chorreado de café the tablecloth is covered in coffee stains

tienes el abrigo chorreado de pintura you've got paint all over your coat

B (Col, RPl arg) (robar) to filch ( colloq), to swipe ( colloq)

A ( refl)

(CS, Per fam) (mancharse): cuidado con chorrearte mind you don't spill o get it all over yourself

B (Col, RPl arg) (robar) to swipe ( colloq), to filch ( colloq)

* * *

◊ estaba chorreando ( muy mojado) it was dripping wet;

◊ chorreado de café covered in coffee stains

◊ cuidado con chorrearte mind you don't get it all over yourself

* * *

♦ vi

1. [gotear] [gota a gota] to drip;

estar chorreando [estar empapado] to be soaking o wringing wet;

esa cafetera chorrea that coffee pot leaks;

el helado le chorreaba por la cara he had ice cream running down his face

2. [brotar] to spurt o gush (out)

3. RP Fam [robar] to pinch, Br to nick;

chorrear algo a alguien to pinch o Br nick sth off sb

♦ vt

1. [sujeto: prenda] to drip;

ese tubo chorrea aceite that pipe drips oil;

acabó la carrera chorreando sudor he was dripping with sweat when he finished the race

2. CSur [derramar] to spill

♦ See also the pronominal verb chorrearse

* * *

1 gush out, stream out

2 ( gotear) drip

* * *

chorrear vi

1) : to drip

2) : to pour out, to gush out

* * *

chorrear vb

1. to pour

2. to be dripping

1 to unload.

descargó su cólera sobre mí he took his anger out on me

descargó su conciencia en mí he unburdened his conscience on me

Elsa descarga los camiones Elsa unloads the trucks.

El camión descargó The truck unloaded.

2 to fire.

3 to deal, to land.

descargó un golpe contra la mesa he thumped his fist on the table

4 to run down ( electricity and electronics) (pila, batería).

5 to download (computing).

6 to burst.

7 to alleviate, to unburden.

Elsa descargó su tristeza Elsa alleviated her grief.

8 to discharge, to run down.

El gerente descargó a María The mMaríager discharged Mary.

9 to unleash, to loose.

Esa mentira descargó su ira contenida That lie unleashed his repressed anger.

10 to acquit, to exonerate, to release.

El juez descargó a María The judge acquitted Mary.

* * *

Conjugation model [ LLEGAR], like link=llegar llegar

► verbo transitivo

1 (quitar una carga) to unload

2 (disparar una arma) to fire, discharge, shoot; (vaciar una arma) to unload

■ descargaron salvas en su honor they fired a salute in his honour

3 (dar un golpe) to deal

■ le descargó un puñetazo he dealt him a blow

4 figurado (de obligaciones, preocupaciones) to free, relieve, release

■ descargó sus preocupaciones contándole todo a su madre he got everything off his chest by telling his mother all about it

5 figurado (enfado) to vent, give vent to

6 ELECTRICIDAD to discharge (batería) to run down

7 DERECHO to absolve (de, of), acquit (de, of)

► verbo intransitivo

1 ELECTRICIDAD to discharge

2 (tormenta) to break; (nubes) to burst

3 (desembocar) to flow

► verbo pronominal descargarse

1 (pilas, baterías) to discharge

2 (desahogarse) to blow up

3 DERECHO to clear oneself

* * *

1) to discharge

2) unload

* * *

1. VT

1) (=quitar la carga de) [+ camión, contenedor, arma] to unload; [+ mercancías] to unload

2) (=disparar) [+ arma, tiro] to fire

3) (=soltar) [+ golpe] to land; [+ bomba] to drop, release

4) (Elec) [+ pila, batería] to run down; [+ corriente] to discharge

5) (=liberar) [+ tensión, agresividad] to release; [+ enfado, ira] to vent; [+ conciencia] to ease; [+ responsabilidad, sentimiento] to offload

6) euf [+ vientre] to evacuate, empty; [+ vejiga] to empty

7) (Com) [+ letra] to take up

8) (Inform) to download

2. VI

1) [río] to flow, run (en into)

2) [tormenta] to break

3) (Elec) to discharge

descargarse

* * *

verbo transitivo

1) <vehículo/mercancías> to unload

2)

a) < pistola> ( extraer las balas) to unload; ( disparar) to fire, discharge (frml)

b) < tiro> to fire; < golpe> to deal, land

3)

a) <ira/agresividad> to vent; <preocupaciones/tensiones> to relieve

b) (Ven fam) < persona>

4)

descargarse v pron

1) (Elec) pila to run down; batería to go dead o flat

2) tormenta to break; lluvias to come down, fall

3) persona

a) ( desahogarse)

b) ( de obligación)

* * *

= unload, flush, offload [off-load], download, relieve + Reflexivo.

Ex. The library has a van equipped with an hydraulic lift to unload book trucks.

Ex. The sea swept in across a newly and solidly-built bus stand to come into the river which quickly and conveniently flushed the waters back into the sea.

Ex. Hits retrieved from on-line data bases can be offloaded to the personal data base.

Ex. To download is to capture data online from a remote host computer and transfer it to the store of an in-house standalone system, eg a microcomputer, for processing.

Ex. When I went to the little boys/girls room to relieve myself I was suprised to see the amount of loo rolls stashed in the corner.

----

* descargar de responsabilidad = remove from + shoulders.

* descargar registros = download + records.

* * *

verbo transitivo

1) <vehículo/mercancías> to unload

2)

a) < pistola> ( extraer las balas) to unload; ( disparar) to fire, discharge (frml)

b) < tiro> to fire; < golpe> to deal, land

3)

a) <ira/agresividad> to vent; <preocupaciones/tensiones> to relieve

b) (Ven fam) < persona>

4)

descargarse v pron

1) (Elec) pila to run down; batería to go dead o flat

2) tormenta to break; lluvias to come down, fall

3) persona

a) ( desahogarse)

b) ( de obligación)

* * *

* * *

descargar [A3 ]

A ‹camión/barco› to unload; ‹mercancías› to unload

1 ‹pistola› (extraer las balas) to unload; (disparar) to fire, discharge ( frml)

la pistola está descargada the pistol is not loaded

descargó la pistola contra el ladrón he fired the gun at the thief

2 ‹tiro› to fire; ‹golpe› to deal, land

le descargó seis tiros he shot at her six times, he fired six shots at her

1 ‹ira/agresividad› to vent; ‹preocupaciones/tensiones› to relieve

un excelente ejercicio para descargar las tensiones an excellent exercise for relieving tension

el judo es una forma de descargar la agresividad judo is a way of getting rid of aggression o ( colloq) letting off steam

descargó toda su furia en or contra or sobre mí he vented all his anger on me, he took all his anger out on me, he unleashed all his anger against me

( Ven fam) ‹persona› me lo voy a descargar I'm going to give him a piece of my mind o ( AmE) give him a tongue lashing ( colloq)

no eres quien para que me descargues who do you think you are, sounding off o ( AmE) mouthing off to me like that? ( colloq)

D descargar a algn DE algo ‹de una responsabilidad› to clear sb OF sth; ‹de una obligación› to relieve sb OF sth

lo descargaron de toda culpa he was cleared of all blame

E ( Inf) to download

descargar una aplicación al or en el computador ( AmL) or ( Esp) ordenador personal to download an application to the personal computer

«nube»: al elevarse las nubes se enfrían y descargan as the clouds rise, they cool and rain is released o falls

to pour down

parece que va a descargar it looks as if it's going to pour down

el temporal que descargó ayer sobre la capital the storm which broke over o hit o struck the capital yesterday

A ( Elec) «pila» to run down; «batería» to go dead o ( BrE) flat

B «tormenta» to break; «lluvias» to come down, fall

se descargó una tormenta sobre la ciudad a storm broke over o hit the city

C «persona»

(desahogarse): no te descargues conmigo, yo no tengo la culpa don't take it out on me, it's not my fault!

2 (de una obligación) descargarse DE algo to get out of sth

se descargó de toda responsabilidad he washed his hands of all responsibility

* * *

a) ‹ pistola› ( extraer las balas) to unload;

◊ la pistola está descargada the pistol is not loaded

b) ‹ tiro› to fire;

* * *

♦ vt

1. [vaciar] [cargamento, camión, barco] to unload;

las nubes descargaron varios litros en pocas horas it rained several inches in a few minutes

2. [desahogar] [ira, agresividad] to vent;

juega al squash para descargar el estrés he plays squash to work off the stress;

descargó su cólera sobre mí he took his anger out on me;

descargó su conciencia en mí he unburdened his conscience to me

3. [arma] [disparar] to fire ( sobre at); [vaciar] to unload;

la escopeta estaba descargada the shotgun was unloaded

4. [puntapié, puñetazo] to deal, to land;

descargó un golpe contra la mesa he thumped his fist on the table

5. [pila, batería] to run down

6. [exonerar]

lo descargaron de responsabilidades por estar convaleciente they relieved him of some of his responsibilities as he was convalescing

7. Der [absolver]

el juez los ha descargado de toda culpa the judge cleared them of all blame

8. Informát to download;

descargar un programa de la Red to download a program from the Net

♦ vi

la tormenta descargó en el norte de la ciudad the storm broke over the north of the city

♦ v impersonal

to pour down;

tiene pinta de que va a descargar it looks as if it's going to pour down

♦ See also the pronominal verb descargarse

* * *

I v/t

1 arma, EL discharge; fig: ira etc take out (en, sobre on)

2 mercancías unload

3 de responsabilidad, culpa clear (de of)

4 INFOR download

II v/i de tormenta etc hit

* * *

descargar {52} vt

1) : to discharge

2) : to unload

3) : to release, to free

4) : to take out, to vent (anger, etc.)

♦ See also the reflexive verb descargarse

* * *

descargar vb

1. (vehículo, arma) to unload

2. (tormenta) to break [pt. broke; pp. broken]

1 to engrave.

grabó su nombre en un tronco she carved her name on a tree

El joyero grabó el anillo The jeweller engraved the ring.

2 to record, to tape (sonido, cinta).

han grabado un nuevo disco they've recorded a new album

Elsa grabó una canción Elsa recorded a song.

3 to save (computing) (document).

* * *

► verbo transitivo

1 ARTE to engrave

2 (registrar) to record

3 INFORMÁTICA to save

\

grabarse en la memoria figurado to be engraved on one's memory

quedarse algo muy grabado figurado to stick in one's mind

* * *

1) to engrave

2) tape, record

* * *

1) [en madera, metal] to engrave

2) [+ sonidos, imágenes] [gen] to record; (=hacer una copia en cinta) to tape

3) (=fijar) to etch

* * *

verbo transitivo

a) (Audio, TV) to record

b) (Art) to engrave

a) (Audio, TV) to record

b) (Art) to engrave

grabarse v pron to be engraved

* * *

= cut into, download, etch, brand (as), incise, record.

Ex. For most of the period woodcuts for letterpress printing were made of a hard, fine-grained wood (such as box or a fruit wood) cut into the side of the plank along the grain.

Ex. To download is to capture data online from a remote host computer and transfer it to the store of an in-house standalone system, eg a microcomputer, for processing.

Ex. The idea was forever etched in his consciousness from the day Crane uttered it: a good librarian working anywhere is a credit and benefit to libraries everywhere.

Ex. Music by Jewish composers and works were branded in Nazi Germany as degenerate art.

Ex. Our books today mix CAPITALS, from letters Romans incised on stone monuments, and 'lower case' letters, derived from script developed in Ireland for parchment manuscripts.

Ex. Editors and compilers of editions of works are recorded together with the edition statement in the edition area = En en área de edición se incluyen los editores y compiladores de las ediciones de trabajos junto con la mención de edición.

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* estar grabado en la memoria = imprint on + consciousness.

* grabar al ácido = etch.

* grabar en CD = burn to disc.

* grabar en cinta = tape-record [tape record], tape.

* grabar en el estudio = film in + the studio.

* grabar en exteriores = film on + location.

* grabar en la memoria = imprint on + consciousness.

* grabar en la mente de Alguien = engrave in + Posesivo + mind.

* grabar en piedra = engrave in + stone.

* grabar en relieve = emboss.

* grabar en vídeo = videotape.

* sin grabar = unengraved.

* * *

verbo transitivo

a) (Audio, TV) to record

b) (Art) to engrave

a) (Audio, TV) to record

b) (Art) to engrave

grabarse v pron to be engraved

* * *

* * *

grabar [A1 ]

1 ( Audio, TV) to record

han grabado un nuevo disco they've made a new record, they've recorded a new single/album

2 ( Art) to engrave

un reloj grabado con sus iniciales a watch engraved with his initials

1 ( Audio, TV) to record

2 ( Art) to engrave

grabar al aguafuerte to etch

grabar en relieve to emboss

sus palabras se me grabaron en la memoria her words are engraved on o ( liter) etched in my memory

su cara se me grabó para siempre her face has been engraved on o ( liter) etched in my mind ever since

* * *

a) (Audio, TV) to record, tape

b) (Art) to engrave

◊ sus palabras se me grabaron en la memoria her words are etched on my memory;

* * *

♦ vt

1. [en metal] to engrave;

grabó su nombre en un tronco she carved her name on a tree

2. [sonido] to record;

han grabado un nuevo disco they've recorded a new album

3. [fijar]

¡que te quede bien grabado! don't you forget it!

4. Informát [documento] to save;

♦ See also the pronominal verb grabarse

* * *

1 en video, cinta record

2 PINT, fig

engrave;

el accidente quedó grabar en su memoria the accident was engraved o etched on her memory

* * *

grabar vt

1) : to engrave

2) : to record, to tape

grabar vi

grabar al aguafuerte : to etch

♦ See also the reflexive verb grabarse

* * *

grabar vb (en vídeo, cinta, disco) to record

1 use.

hacer uso de to make use of, to use; (utilizar) to exercise (de prerrogativa, derecho)

de uso externo for external use only (medicamento)

fuera de uso out of use, obsolete

tener el uso de la palabra to have the floor

uso de razón power of reason

2 custom (costumbre).

al uso fashionable

al uso andaluz in the Andalusian style

3 usage (linguistics).

4 wear and tear (desgaste).

1^st person singular (yo) present indicative of spanish verb: usar.

* * *

► abreviatura

1 ( Unión Sindical Obrera) professional worker's union

* * *

1) use

2) wear

3) custom, usage

* * *

SF ABR Esp

* * *

1) ( utilización)

a) (de producto, medicamento) use; (de máquina, material) use

b) (de idioma, expresión) use

c) (de facultad, derecho)

2) ( de prenda)

- uso de razón

3) (utilidad, aplicación) use

4) ( usanza) custom

* * *

= deployment, disposition, exercise, take-up, usage, use, utilisation [utilization, -USA], utility, consumption, employment, uptake, wear, delivery.

Ex. In the context of this report any such policy would have to accept that speedy response to current problems requires the deployment of resources in favour of innovative information-driven programmes.

Ex. The process provides an effective means of controlling such serials until a final decision has been made regarding their disposition.

Ex. A poorly structured scheme requires the exercise of a good deal of initiative on the part of the indexer in order to overcome or avoid the poor structure.

Ex. One of the reasons for the relatively slow take-up of microcomputers in libraries in the Philippines is the problem caused by the multitude of languages used in the island group.

Ex. Changes in usage of terms over time can also present problems = Los cambios en el uso de los términos con el transcurso del tiempo también pueden presentar problemas.

Ex. Systematic mnemonics is the use of the same notation for a given topic wherever that topic occurs.

Ex. On occasions it is necessary to adopt an order or arrangement which leads to the efficient utilisation of space.

Ex. Situations where subdivisions might have had some utility are served by the co-ordination of index terms at the search stage.

Ex. The screen display formats required by cataloguing staff may be not at all suitable for public consumption.

Ex. Through the employment of such implicitly derogatory terminology librarians virtually give themselves licence to disregard or downgrade the value of certain materials.

Ex. The project is investigating the factors which promote or inhibit the uptake of computers in primary schools.

Ex. When in use moulds were subject to severe wear which resulted in noticeable deterioration of the surface.

Ex. Entry of number '21' reverses the present delivery status.

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* alfabetización en el uso de la biblioteca = library literacy.

* aparato para el uso de la información = information appliance.

* aumento del uso = increased use.

* bloque funcional para uso internacional = international use block.

* bloque funcional para uso nacional = national use block.

* con conocimiento básico en el uso de la biblioteca = library literate [library-literate].

* con conocimiento en el uso de Internet = Internet-savvy.

* condiciones de uso = terms of use.

* condiciones legales de uso = legal boilerplate.

* con el uso = in use, with use.

* conocimientos básicos sobre el uso de las bibliotecas = library skills.

* cubrir un uso = address + use.

* cuchillo de un solo uso = disposable knife.

* dar buen uso a Algo = put to + good use.

* dar un uso = put to + purpose.

* dar uso = put to + use.

* dar uso a = make + use of.

* de doble uso = dual-use.

* de muchos usos = all-purpose.

* de pago según el uso = on a pay a you use basis, on a pay as you go basis.

* de poco uso = low-use.

* desde el punto de vista del uso = in terms of use.

* desgaste por el uso = wear and tear.

* destrezas relacionadas con el uso de la información = information skills.

* de un solo uso = disposable, single-use.

* de uso comercial = commercially-owned.

* de uso cutáneo = use + topically.

* de uso externo = for external use only.

* de uso flexible = hop-on/hop-off.

* de uso frecuente = frequently-used.

* de uso general = general-use.

* de uso interno = in-house [inhouse].

* de uso múltiple = all-purpose.

* de uso público = publicly available.