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useful properties

  • 1 useful properties

    Универсальный англо-русский словарь > useful properties

  • 2 useful properties

    Patent terms dictionary > useful properties

  • 3 useful macroscopic properties (electric, optical or magnetic) of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

    Общая лексика: полезные макроскопические свойства (электрические, оптические или

    Универсальный англо-русский словарь > useful macroscopic properties (electric, optical or magnetic) of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

  • 4 useful macroscopic properties of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

    Общая лексика: (electric, optical or magnetic) полезные макроскопические свойства (электрические, оптические или

    Универсальный англо-русский словарь > useful macroscopic properties of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

  • 5 the useful macroscopic properties (electric, optical or magnetic) of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

    Общая лексика: полезные макроскопические свойства (электрические, оптические

    Универсальный англо-русский словарь > the useful macroscopic properties (electric, optical or magnetic) of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

  • 6 the useful macroscopic properties of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

    Общая лексика: (electric, optical or magnetic) полезные макроскопические свойства (электрические, оптические

    Универсальный англо-русский словарь > the useful macroscopic properties of some materials depend on organization at a molecular level and electronic communication between neighboring molecules

  • 7 property

    1) собственность; право собственности
    •
    - basic property
    - common property
    - comparing properties
    - copyrighted property
    - corporate property
    - health-giving property
    - industrial property
    - intangible property
    - intellectual property
    - joint property
    - literary property
    - national property
    - novel properties
    - operational property
    - particular properties
    - patent property
    - patented property
    - personal property
    - physical property
    - private property
    - public property
    - real property
    - scientific property
    - separate property
    - state property
    - tangible property
    - transferred property
    - useful properties

    Patent terms dictionary > property

  • 8 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

  • 9 Le Chatelier, Henri Louis

    SUBJECT AREA: Metallurgy
    [br]
    b. 8 November 1850 Paris, France
    d. 17 September 1926 Miribel-les-Echelle, France
    [br]
    French inventor of the rhodium—platinum thermocouple and the first practical optical pyrometer, and pioneer of physical metallurgy.
    [br]
    The son of a distinguished engineer, Le Chatelier entered the Ecole Polytechnique in 1869: after graduating in the Faculty of Mines, he was appointed Professor at the Ecole Supérieure des Mines in 1877. After assisting Deville with the purification of bauxite in unsuccessful attempts to obtain aluminium in useful quantities, Le Chatelier's work covered a wide range of topics and he gave much attention to the driving forces of chemical reactions. Between 1879 and 1882 he studied the mechanisms of explosions in mines, and his doctorate in 1882 was concerned with the chemistry and properties of hydraulic cements. The dehydration of such materials was studied by thermal analysis and dilatometry. Accurate temperature measurement was crucial and his work on the stability of thermocouples, begun in 1886, soon established the superiority of rhodium-platinum alloys for high-temperature measurement. The most stable combination, pure platinum coupled with a 10 per cent rhodium platinum positive limb, became known as Le Chatelier couple and was in general use throughout the industrial world until c. 1922. For applications where thermocouples could not be used, Le Chatelier also developed the first practical optical pyrometer. From hydraulic cements he moved on to refractory and other ceramic materials which were also studied by thermal analysis and dilatometry. By 1888 he was systematically applying such techniques to metals and alloys. Le Chatelier, together with Osmond, Worth, Genet and Charpy, was a leading member of that group of French investigators who established the new science of physical metallurgy between 1888 and 1900. Le Chatelier was determining the recalescence points in steels in 1888 and was among the first to study intermetallic compounds in a systematic manner. To facilitate such work he introduced the inverted microscope, upon which metallographers still depend for the routine examination of polished and etched metallurgical specimens under incident light. The principle of mobile equilibrium, developed independently by Le Chatelier in 1885 and F.Braun in 1886, stated that if one parameter in an equilibrium situation changed, the equilibrium point of the system would move in a direction which tended to reduce the effect of this change. This provided a useful qualitative working tool for the experimentalists, and was soon used with great effect by Haber in his work on the synthesis of ammonia.
    [br]
    Principal Honours and Distinctions
    Grand Officier de la Légion d'honneur. Honorary Member of the Institute of Metals 1912. Iron and Steel Institute Bessemer Medal.
    Further Reading
    F.Le Chatelier, 1969, Henri Le Chatelier.
    C.K.Burgess and H.L.Le Chatelier, The Measurement of High Temperature.
    ASD

    Biographical history of technology > Le Chatelier, Henri Louis

  • 10 improvement

    •
    - improvement to a patent
    - improvement of known process
    - improvement of properties
    - new improvement
    - patentable improvement
    - patented improvement
    - quantitative improvement of properties
    - unpatentable improvement
    - useful improvement

    Patent terms dictionary > improvement

  • 11 effect

    1) эффект; явление
    2) влияние, действие, воздействие
    3) результат, следствие
    4) мн. ч.; юр. имущество, собственность
    5) воздействовать; производить; осуществлять; исполнять
    •
    - effect of end conditions - effect of loading - effect of restraint - admixture effect - adsorption effect - adverse effect - biological effect - bursting effect - chimney effect - cleaning effect - corrosive effect - damage effect - destructive effect - detrimental effect - dissipative effect - distributive effect - edge effect - end effect - heat effect - ill effect - illuminating effect - immediate effect - impact effect - indirect effect - luminous effect - notch effect - out-of-balance effect - overturning effect on the dam - poisonous effect - sagging effect - scale effect - screening effect - shaded effects - shattering effect - shielding effect - slagging effect - spading effect - stiffening effect - stiffening effect of cladding - strength-reduction effect - stress-rising effect - suction effect - time effect - toxic effect - unfavourable effect - useful effect
    * * *
    действие, воздействие, эффект; влияние; следствие, результат; производительность
    - effects of age
    - effects of earthquake
    - effect of end conditions
    - effect of moisture changes
    - effect of restraint
    - effect of support settlement
    - effect of temperature
    - effect of temperature difference
    - adverse effect
    - adverse health effects
    - backwater effect
    - Bauschinger effect
    - borehole effect
    - boundary effect
    - ceiling effect
    - chimney effect
    - climate effect
    - Coanda effect
    - combined effect
    - combined effects of settlement and creep
    - dehumidifying effect
    - detrimental effect
    - earthquake effect
    - edge effect
    - elevator effect
    - environment effects on construction productivity
    - fly ash effect on concrete properties
    - frost effect
    - greenhouse effect
    - humidifying effect
    - inertia effects
    - insulating effect
    - long-term effects
    - microsilica effect on concrete properties
    - mottle effect
    - negative effect
    - notch effect
    - principle effect of the admixture
    - radiation effects
    - scale effect
    - seasonal effect
    - sensible cooling effect
    - silo effect
    - stack effect
    - stiffening effect
    - stiffening effects of floors
    - sun effect
    - thermal effect of building materials
    - thermal effect of ceiling height
    - thermal effect of roof types
    - thermal effect of windows
    - time-dependent effect
    - total cooling effect
    - weather effects on use of bond breaker
    - wobble effect
    - zero curtain effect

    Англо-русский строительный словарь > effect

  • 12 Basic Dyes

    These are dyes that require the assistance of a mordant to make them permanent, when used for cotton dyeing. They are useful for dyeing wool, because the fibre contains acid properties with which the dye combines, but as the combination is not fast, the basic dyes are not much used for wool.

    Dictionary of the English textile terms > Basic Dyes

  • 13 Mohair

    The hair obtained from the Angora goat, and is grown chiefly in Turkey, South Africa, the U.S.A. and Australia. It is lustrous white, fine, wavy and long. The length varies from 4-in. to 10-in. and spins from 28's to 50's quality. It has no felting properties. That from the U.S.A. is much lower in quality than the others, having about 15 per cent more kempy fibre. Mohair is chiefly used in braids, felt hats, linings, plushes, etc., and the coarser kinds for carpets and low-grade woollen fabrics. ————————
    ANGORA, or "Mohair"
    The hair or wool of the goat of that name. More generally known as mohair. The animal originally had its home in Asia Minor. About 1858 it was introduced into Cape Colony, from which country we now get a large supply. The natives of Asia Minor made shawls from the wool, which resembled Cashmere shawls. In colour it is white, average length of hair is 6 to 8 inches, and- has a curly structure. It is a very useful fibre, and largely used by the manufacturers of Astrakhan, wool crepons, plushes and cashmeres; also used in many silk cloths. The French use the fibre in a cloth named "poil de chevre", which has a fine spun silk coloured warp and angora weft. Bradford -imitates this cloth with a fine cotton warp. It has more lustre than wool, but is not so warm. Sir Titus Salt, by introducing the manufacture of goods made from mohair into Saltaire, raised Saltaire into a town from a village.

    Dictionary of the English textile terms > Mohair

  • 14 balance sheet

    Fin
    a financial report stating the total assets, liabilities, and owners’ equity of an organization at a given date, usually the last day of the accounting period. The debit side of the balance sheet states assets, while the credit side states liabilities and equity, and the two sides must be equal, or balance.
    EXAMPLE
    Assets include cash in hand and cash anticipated (receivables), inventories of supplies and materials, properties, facilities, equipment, and whatever else the company uses to conduct business. Assets also need to reflect depreciation in the value of equipment such as machinery that has a limited expected useful life.
         Liabilities include pending payments to suppliers and creditors, outstanding current and long-term debts, taxes, interest payments, and other unpaid expenses that the company has incurred.
         Subtracting the value of aggregate liabilities from the value of aggregate assets reveals the value of owners’ equity. Ideally, it should be positive. Owners’ equity consists of capital invested by owners over the years and profits (net income) or internally generated capital, which is referred to as “retained earnings”; these are funds to be used in future operations.
         As an example:

    The ultimate business dictionary > balance sheet

  • 15 Neural Network

       1. A neural network is composed of a number of very simple processing elements [("neurodes")] that communicate through a rich set of interconnections with variable weights or strengths.
       2. Memories are stored or represented in a neural network in the pattern of variable interconnection weights among the neurodes. Information is processed by a spreading, constantly changing pattern of activity distributed across many neurodes.
       3. A neural network is taught or trained rather than programmed. It is even possible to construct systems capable of independent or autonomous learning....
       4. Instead of having a separate memory and controller, plus a stored external program that dictates the operation of the system as in a digital computer, the operation of a neural network is implicitly controlled by three properties: the transfer function of the neurodes, the details of the structure of the connections among the neurodes, and the learning law the system follows.
       5. A neural network naturally acts as an associative memory. That is, it inherently associated items it is taught, physically grouping similar items together in its structure. A neural network operated as a memory is content addressable; it can retrieve stored information from incomplete, noisy, or partially incorrect input cues.
       6. A neural network is able to generalize; it can learn the characteristics of a general category of objects based on a series of specific examples from that category.
       7. A neural network keeps working even after a significant fraction of its neurodes and interconnections have become defective.
       8. A neural network innately acts as a processor for time-dependent spatial patterns, or spatiotemporal patterns. (Caudill & Butler, 1990, pp. 7-8)

    Historical dictionary of quotations in cognitive science > Neural Network

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