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automatic machines

  • 1 automatic

    آلِيّ \ automatic: (of actions) done without thought: Our breathing is automatic. mechanical: concerning machines or machinery: mechanical engineering. \ See Also ميكانيكي (مِيكانيكي)‏

    Arabic-English glossary > automatic

  • 2 Automatic Building Machines

    Military: ABM

    Универсальный русско-английский словарь > Automatic Building Machines

  • 3 Dobby Machines

    These are exceedingly useful machines for forming the shed in weaving, since they can be used for both simple and complicated weaves. There are many types in use, most of which are negative acting in so far as they only lift the healds, springs being used beneath the healds to bring them down again after being lifted by the dobby. In the cotton trade 16 up 20 jacks is usual. Dobbies in common use are known as single lift, double lift, negative, positive, open shed, closed shed, and crossborder. Single Lift - In this type there is a single knife or griffe in use to raise the heald stave. The whole of the shafts return to their original position after each pick. A fresh selection of staves to be raised is made for each pick. Looms fitted with this dobby run slower than others, about 140 picks per minute. Double Lift - These machines are fitted with double selecting and lifting parts which move at half the speed of the loom. They give an open or semi-open shed. The speed of the loom is considerably more than for the single-lift type. Crossborder - This machine is used when headings or a change of weave is required as for bordered handkerchiefs, serviettes, towels, etc. Positive - Dobby machines which make an open shed and positively lift and depress the heald staves as required by the design. Negative - Dobbies which only lift the heald staves, and require springs or other means to move the staves to the bottom position. Centre Shed - Every thread of the warp is moved for every new shed. The shed opens from the middle. Some healds ascend and the others descend. Closed Shed - So termed because all the warp threads are brought to one level after each succeeding pick as in single-lift machines. Open Shed - The type generally used for automatic looms, also the double-lift machines. After a heald stave is lifted it remains up until it is required to be down again. The warp threads constantly form two lines, upper and lower, and the only changes are when threads move from line to the other. Semi-open Shed - This shed has a stationary bottom line, and to make changes, threads pass from the top to the bottom, or from the bottom to the top. The threads which remain up for more than one pick in succession only fall halfway and then go to the top again.

    Dictionary of the English textile terms > Dobby Machines

  • 4 Welt, Automatic

    WELT, AUTOMATIC
    This is done on the automatic hose machine by holding alternate stitches on points until the welt piece has been completed, the held stitches being thereafter transferred to their original needles to enable the hose to be continued. By this sytem no sewing or stitching is required, and the join is smooth and continuous -with the parent fabric. In the fully-fashioned article all the original stitches are held until the welt length has been knitted when the stitches are moved back once more to the original needles thus giving a two-ply piece of fabric which is. perfectly smooth and continuous with the ground fabric on the inside. On recently constructed full-fashioned hose machines, welting is now done automatically, by a set of points which automatically bear down on the needles to re-transfer the original stitches.

    Dictionary of the English textile terms > Welt, Automatic

  • 5 автоматизировать

    1) General subject: automate
    2) Computers: computerize
    4) Mathematics: robotize
    5) Astronautics: computerise

    Универсальный русско-английский словарь > автоматизировать

  • 6 сверхскоростной

    •

    Ultrafast tensile testing machines...

    •

    Ultra-high-speed automatic machines...

    Русско-английский научно-технический словарь переводчика > сверхскоростной

  • 7 машины-автоматы

    Programming: automatic machines (машины, в которых все преобразования энергии, материалов и информации выполняются без непосредственного участия человека)

    Универсальный русско-английский словарь > машины-автоматы

  • 8 картон m для автоматов

    Словарь по целлюлозно-бумажному производству > картон m для автоматов

  • 9 рулоны mpl для автоматов

    Словарь по целлюлозно-бумажному производству > рулоны mpl для автоматов

  • 10 упаковка f для автоматов

    Словарь по целлюлозно-бумажному производству > упаковка f для автоматов

  • 11 mašine za automatsko zvarivanj

    • automatic welding machines

    Serbian-English dictionary > mašine za automatsko zvarivanj

  • 12 cajero automático

    m.
    1 automatic teller machine, automated teller machine, ATM, automated teller.
    2 drive-in banking.
    * * *
    cash point, automatic cash dispenser
    * * *
    (n.) = teller machine, cash dispenser, cash point, cash point machine, automatic teller machine (ATM)
    Ex. Initially, such automated terminals (' teller machines') were installed in the banks themselves, enabling people to draw cash by means of a debit card.
    Ex. This article discusses security problems associated with payments between banks, and cash dispensers.
    Ex. The worst case scenario suggests that library and information services may be replaced by electronic information points analogous to the electronic cash points installed at banks.
    Ex. Cash point machines which accept all major credit cards can be found all over the city.
    Ex. The application of automatic teller machines (ATMs) by the banking industry is examined as a typical example of information technology investment in the financial services sector.
    * * *
    (n.) = teller machine, cash dispenser, cash point, cash point machine, automatic teller machine (ATM)

    Ex: Initially, such automated terminals (' teller machines') were installed in the banks themselves, enabling people to draw cash by means of a debit card.

    Ex: This article discusses security problems associated with payments between banks, and cash dispensers.
    Ex: The worst case scenario suggests that library and information services may be replaced by electronic information points analogous to the electronic cash points installed at banks.
    Ex: Cash point machines which accept all major credit cards can be found all over the city.
    Ex: The application of automatic teller machines (ATMs) by the banking industry is examined as a typical example of information technology investment in the financial services sector.

    * * *
    ATM, Br tb
    cash point

    Spanish-English dictionary > cajero automático

  • 13 contestador automático

    m.
    answerphone, telephone answering machine, answering machine, answer phone.
    * * *
    answering machine
    * * *
    masculino answering machine
    * * *
    (n.) = automatic answering machine, answering machine
    Ex. Dictating machines and automatic answering machines are also widely used aids in offices.
    Ex. Answering machines, baby monitors, golf clubs, and toasters were used for testing consumers' shopping habits.
    * * *
    masculino answering machine
    * * *
    (n.) = automatic answering machine, answering machine

    Ex: Dictating machines and automatic answering machines are also widely used aids in offices.

    Ex: Answering machines, baby monitors, golf clubs, and toasters were used for testing consumers' shopping habits.

    * * *

    contestador automático sustantivo masculino
    answering machine
    ' contestador automático' also found in these entries:
    Spanish:
    contestador
    English:
    ansaphone
    - answering machine
    * * *
    contestador automático n answering machine

    Spanish-English dictionary > contestador automático

  • 14 Clark, Edward

    [br]
    fl. 1850s New York State, USA
    [br]
    American co-developer of mass-production techniques at the Singer sewing machine factory.
    [br]
    Born in upstate New York, where his father was a small manufacturer, Edward Clark attended college at Williams and graduated in 1831. He became a lawyer in New York City and from then on lived either in the city or on his rural estate near Cooperstown in upstate New York. After a series of share manipulations, Clark acquired a one-third interest in Isaac M. Singer's company. They soon bought out one of Singer's earlier partners, G.B.Zeiber, and in 1851, under the name of I.M.Singer \& Co., they set up a permanent sewing machine business with headquarters in New York.
    The success of their firm initially rested on marketing. Clark introduced door-to-door sales-people and hire-purchase for their sewing machines in 1856 ($50 cash down, or $100 with a cash payment of $5 and $3 a month thereafter). He also trained women to demonstrate to potential customers the capabilities of the Singer sewing machine. At first their sewing machines continued to be made in the traditional way, with the parts fitted together by skilled workers through hand filing and shaping so that the parts would fit only onto one machine. This resembled European practice rather than the American system of manufacture that had been pioneered in the armouries in that country. In 1856 Singer brought out their first machine intended exclusively for home use, and at the same time manufacturing capacity was improved. Through increased sales, a new factory was built in 1858–9 on Mott Street, New York, but it soon became inadequate to meet demand.
    In 1863 the Singer company was incorporated as the Singer Manufacturing Co. and began to modernize its production methods with special jigs and fixtures to help ensure uniformity. More and more specialized machinery was built for making the parts. By 1880 the factory, then at Elizabethport, New Jersey, was jammed with automatic and semi-automatic machine tools. In 1882 the factory was producing sewing machines with fully interchangeable parts that did not require hand fitting in assembly. Production rose from 810 machines in 1853 to half a million in 1880. A new family model was introduced in 1881. Clark had succeeded Singer, who died in 1875, as President of the company, but he retired in 1882 after he had seen through the change to mass production.
    [br]
    Further Reading
    National Cyclopaedia of American Biography.
    D.A.Hounshell, 1984, From the American System to Mass Production, 1800–1932. The Development of Manufacturing Technology in the United States, Baltimore (a thorough account of Clark's role in the development of Singer's factories).
    F.B.Jewell, 1975, Veteran Sewing Machines. A Collector's Guide, Newton Abbot.
    RLH

    Biographical history of technology > Clark, Edward

  • 15 dictáfono

    m.
    Dictaphone, dictating machine.
    * * *
    1 Dictaphone
    * * *
    * * *
    ® masculino Dictaphone®, dictating machine
    * * *
    Ex. Dictating machines and automatic answering machines are also widely used aids in offices.
    * * *
    ® masculino Dictaphone®, dictating machine
    * * *

    Ex: Dictating machines and automatic answering machines are also widely used aids in offices.

    * * *
    dictáfono®
    Dictaphone®, dictating machine
    * * *

    dictáfono sustantivo masculino dictating machine
    * * *
    dictáfono® nm
    Dictaphone®
    * * *
    m dictaphone

    Spanish-English dictionary > dictáfono

  • 16 полностью автоматизированный

    •

    The all-automatic magnetic tape recording systems are...

    Русско-английский научно-технический словарь переводчика > полностью автоматизированный

  • 17 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

  • 18 Cognitive Science

       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.... [P]eople and intelligent computers turn out to be merely different manifestations of the same underlying phenomenon. (Haugeland, 1981b, p. 31)
       2) Experimental Psychology, Theoretical Linguistics, and Computational Simulation of Cognitive Processes Are All Components of Cognitive Science
       I went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns.... I have been working toward a cognitive science for about twenty years beginning before I knew what to call it. (G. A. Miller, 1979, p. 9)
        Cognitive Science studies the nature of cognition in human beings, other animals, and inanimate machines (if such a thing is possible). While computers are helpful within cognitive science, they are not essential to its being. A science of cognition could still be pursued even without these machines.
        Computer Science studies various kinds of problems and the use of computers to solve them, without concern for the means by which we humans might otherwise resolve them. There could be no computer science if there were no machines of this kind, because they are indispensable to its being. Artificial Intelligence is a special branch of computer science that investigates the extent to which the mental powers of human beings can be captured by means of machines.
       There could be cognitive science without artificial intelligence but there could be no artificial intelligence without cognitive science. One final caveat: In the case of an emerging new discipline such as cognitive science there is an almost irresistible temptation to identify the discipline itself (as a field of inquiry) with one of the theories that inspired it (such as the computational conception...). This, however, is a mistake. The field of inquiry (or "domain") stands to specific theories as questions stand to possible answers. The computational conception should properly be viewed as a research program in cognitive science, where "research programs" are answers that continue to attract followers. (Fetzer, 1996, pp. xvi-xvii)
       What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.
       The psychologist who studies "knowledge systems" wants to know how concepts are structured in the human mind, how such concepts develop, and how they are used in understanding and behavior. The artificial intelligence researcher wants to know how to program a computer so that it can understand and interact with the outside world. The two orientations intersect when the psychologist and the computer scientist agree that the best way to approach the problem of building an intelligent machine is to emulate the human conceptual mechanisms that deal with language.... The name "cognitive science" has been used to refer to this convergence of interests in psychology and artificial intelligence....
       This working partnership in "cognitive science" does not mean that psychologists and computer scientists are developing a single comprehensive theory in which people are no different from machines. Psychology and artificial intelligence have many points of difference in methods and goals.... We simply want to work on an important area of overlapping interest, namely a theory of knowledge systems. As it turns out, this overlap is substantial. For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. (Schank & Abelson, 1977, pp. 1-2)
       Within the last dozen years a general change in scientific outlook has occurred, consonant with the point of view represented here. One can date the change roughly from 1956: in psychology, by the appearance of Bruner, Goodnow, and Austin's Study of Thinking and George Miller's "The Magical Number Seven"; in linguistics, by Noam Chomsky's "Three Models of Language"; and in computer science, by our own paper on the Logic Theory Machine. (Newell & Simon, 1972, p. 4)

    Historical dictionary of quotations in cognitive science > Cognitive Science

  • 19 Winding

    The operation of transferring yarn from one form of package to another, such as winding from hanks to bobbins, from bobbins to cones, from cops to bobbins, etc. The process that follows spinning determines whether winding is necessary or not. Cops and ring tubes or bobbins can be used in that form as weft in the shuttle, but they are not suitable for making into warps, nor as supply to knitting or braiding machines. Yarn in the other forms of spun packages requires to be pirned for use as weft. Although yarn winding is not a fundamental process like spinning and weaving, it occupies a very important place in the economics of yarn processing, and probably embraces a wider range of different machines than any other phase of textile processing. Even a bare catalogue of the different kinds of winding machines would far too lengthy for inclusion here. Broadly, winding machines are adapted for: - 1. Winding yarn for use as weft in loom shuttles, including winding on to wood pirns and paper tubes; solid cops for use in shuttles without tongues; quills for use in ribbon and smallware looms; layer locking at the nose of the pirn to prevent sloughing of rayon weft; bunch building at the base of pirns for use in automatic looms; weft rewound from spinner's cops into larger packages to give maximum length at one filling of the shuttle. The yarn supply can be from hanks, cops, spinner's bobbins, cones, cheeses, warps, etc. 2. Winding yarns for making warps from spinner's cops or bobbins, hanks that have been sized, bleached or dyed, cones, cheeses, and other forms of supply. 3. Winding yarns into suitable form for sizing, bleaching, dyeing, or for receiving other wet treatments, including hanks, warps, cheeses, cops, etc. 4. Winding yarns for knitting, i.e., on to splicer bobbins, cones, pineapple cones, bottle bobbins, etc., and on to bobbins for use in braiding machines. 5. Special process winding such as the precision winding of several threads side by side in tape form for covering wire, etc. 6. Winding yarns into packages for retail selling such as winding mending wools on cards; sewing thread on wood spools or small flangeless cheeses; crochet embroidery and other threads into balls; packing string info balls and cheeses; harvesting twine into large balls and cones, etc.

    Dictionary of the English textile terms > Winding

  • 20 Lister, Samuel Cunliffe, 1st Baron Masham

    SUBJECT AREA: Textiles
    [br]
    b. 1 January 1815 Calverly Hall, Bradford, England
    d. 2 February 1906 Swinton Park, near Bradford, England
    [br]
    English inventor of successful wool-combing and waste-silk spinning machines.
    [br]
    Lister was descended from one of the old Yorkshire families, the Cunliffe Listers of Manningham, and was the fourth son of his father Ellis. After attending a school on Clapham Common, Lister would not go to university; his family hoped he would enter the Church, but instead he started work with the Liverpool merchants Sands, Turner \& Co., who frequently sent him to America. In 1837 his father built for him and his brother a worsted mill at Manningham, where Samuel invented a swivel shuttle and a machine for making fringes on shawls. It was here that he first became aware of the unhealthy occupation of combing wool by hand. Four years later, after seeing the machine that G.E. Donisthorpe was trying to work out, he turned his attention to mechanizing wool-combing. Lister took Donisthorpe into partnership after paying him £12,000 for his patent, and developed the Lister-Cartwright "square nip" comber. Until this time, combing machines were little different from Cartwright's original, but Lister was able to improve on this with continuous operation and by 1843 was combing the first fine botany wool that had ever been combed by machinery. In the following year he received an order for fifty machines to comb all qualities of wool. Further combing patents were taken out with Donisthorpe in 1849, 1850, 1851 and 1852, the last two being in Lister's name only. One of the important features of these patents was the provision of a gripping device or "nip" which held the wool fibres at one end while the rest of the tuft was being combed. Lister was soon running nine combing mills. In the 1850s Lister had become involved in disputes with others who held combing patents, such as his associate Isaac Holden and the Frenchman Josué Heilmann. Lister bought up the Heilmann machine patents and afterwards other types until he obtained a complete monopoly of combing machines before the patents expired. His invention stimulated demand for wool by cheapening the product and gave a vital boost to the Australian wool trade. By 1856 he was at the head of a wool-combing business such as had never been seen before, with mills at Manningham, Bradford, Halifax, Keighley and other places in the West Riding, as well as abroad.
    His inventive genius also extended to other fields. In 1848 he patented automatic compressed air brakes for railways, and in 1853 alone he took out twelve patents for various textile machines. He then tried to spin waste silk and made a second commercial career, turning what was called "chassum" and hitherto regarded as refuse into beautiful velvets, silks, plush and other fine materials. Waste silk consisted of cocoon remnants from the reeling process, damaged cocoons and fibres rejected from other processes. There was also wild silk obtained from uncultivated worms. This is what Lister saw in a London warehouse as a mass of knotty, dirty, impure stuff, full of bits of stick and dead mulberry leaves, which he bought for a halfpenny a pound. He spent ten years trying to solve the problems, but after a loss of £250,000 and desertion by his partner his machine caught on in 1865 and brought Lister another fortune. Having failed to comb this waste silk, Lister turned his attention to the idea of "dressing" it and separating the qualities automatically. He patented a machine in 1877 that gave a graduated combing. To weave his new silk, he imported from Spain to Bradford, together with its inventor Jose Reixach, a velvet loom that was still giving trouble. It wove two fabrics face to face, but the problem lay in separating the layers so that the pile remained regular in length. Eventually Lister was inspired by watching a scissors grinder in the street to use small emery wheels to sharpen the cutters that divided the layers of fabric. Lister took out several patents for this loom in his own name in 1868 and 1869, while in 1871 he took out one jointly with Reixach. It is said that he spent £29,000 over an eleven-year period on this loom, but this was more than recouped from the sale of reasonably priced high-quality velvets and plushes once success was achieved. Manningham mills were greatly enlarged to accommodate this new manufacture.
    In later years Lister had an annual profit from his mills of £250,000, much of which was presented to Bradford city in gifts such as Lister Park, the original home of the Listers. He was connected with the Bradford Chamber of Commerce for many years and held the position of President of the Fair Trade League for some time. In 1887 he became High Sheriff of Yorkshire, and in 1891 he was made 1st Baron Masham. He was also Deputy Lieutenant in North and West Riding.
    [br]
    Principal Honours and Distinctions
    Created 1st Baron Masham 1891.
    Bibliography
    1849, with G.E.Donisthorpe, British patent no. 12,712. 1850, with G.E. Donisthorpe, British patent no. 13,009. 1851, British patent no. 13,532.
    1852, British patent no. 14,135.
    1877, British patent no. 3,600 (combing machine). 1868, British patent no. 470.
    1868, British patent no. 2,386.
    1868, British patent no. 2,429.
    1868, British patent no. 3,669.
    1868, British patent no. 1,549.
    1871, with J.Reixach, British patent no. 1,117. 1905, Lord Masham's Inventions (autobiography).
    Further Reading
    J.Hogg (ed.), c. 1888, Fortunes Made in Business, London (biography).
    W.English, 1969, The Textile Industry, London; and C.Singer (ed.), 1958, A History of Technology, Vol. IV, Oxford: Clarendon Press (both cover the technical details of Lister's invention).
    RLH

    Biographical history of technology > Lister, Samuel Cunliffe, 1st Baron Masham

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