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model fitting

  • 1 model fitting

    мат. подбор модели, подгонка модели

    Большой англо-русский и русско-английский словарь > model fitting

  • 2 model fitting

    Универсальный англо-русский словарь > model fitting

  • 3 model fitting

    English-russian dictionary of physics > model fitting

  • 4 model fitting

    English-Russian electronics dictionary > model fitting

  • 5 model fitting

    The New English-Russian Dictionary of Radio-electronics > model fitting

  • 6 model fitting

    Англо-русский словарь по ядерным испытаниям и горному делу > model fitting

  • 7 model fitting

    мат.
    подбор модели, подгонка модели

    English-Russian scientific dictionary > model fitting

  • 8 model fitting

    The English-Russian dictionary on reliability and quality control > model fitting

  • 9 fitting

    2) монтаж; сборка
    4) монтаж; сборка
    5) подбор; сглаживание; приближение
    6) подгонка; припасовка; пригонка || годный; подходящий
    7) прилаживание; приспособление
    8) компоновка; размещение
    •

    матем. fitting by power law — подбор степенной зависимости

    - ceiling lighting fitting

    English-Russian scientific dictionary > fitting

  • 10 fitting

    3) установка; монтаж (напр. оборудования)
    а) аппроксимирование, приближённая замена математических объектов (выражений, функций, кривых, поверхностей и др.) более простыми
    б) аппроксимирующий (математический) объект (выражение, функция, кривая, поверхность и др.); приближение; приближённое значение
    5) интерполяция; экстраполяция
    6) соответствующий; подходящий; адекватный; пригодный
    7) аксессуары; арматура; установочные изделия; оснастка
    •
    - best fitting
    - cable fittings
    - copy fitting
    - curve fitting
    - edge fitting
    - least-mean-square fitting
    - lighting fitting
    - model fitting
    - polynomial fitting
    - surface fitting

    English-Russian electronics dictionary > fitting

  • 11 fitting

    3) установка; монтаж (напр. оборудования)
    а) аппроксимирование, приближённая замена математических объектов (выражений, функций, кривых, поверхностей и др.) более простыми
    б) аппроксимирующий (математический) объект (выражение, функция, кривая, поверхность и др.); приближение; приближённое значение
    5) интерполяция; экстраполяция
    6) соответствующий; подходящий; адекватный; пригодный
    7) аксессуары; арматура; установочные изделия; оснастка
    •
    - best fitting
    - cable fittings
    - copy fitting
    - curve fitting
    - edge fitting
    - least-mean-square fitting
    - lighting fitting
    - model fitting
    - polynomial fitting
    - surface fitting

    The New English-Russian Dictionary of Radio-electronics > fitting

  • 12 model stand

    English-Russian big medical dictionary > model stand

  • 13 best-fitting model

    модель, дающая наилучшее согласование (теоретических и экспериментальных результатов)

    Англо-русский словарь по ядерным испытаниям и горному делу > best-fitting model

  • 14 technique

    1. техника, технические приемы; технология
    2. метод; методика; способ
    analytical technique
    anti-stealth technique
    artificial intelligence techniques
    backside technique
    build-up technique
    cartographic techniques
    centered-difference integration technique
    control technique
    control-movement techniques
    convergence technique
    counter-stealth techniques
    curve-fitting technique
    de-icing technique
    decomposition technique
    design technique
    display technique
    double-balance technique
    drop-model technique
    dual-stick dropping technique
    fatigue prediction technique
    fatigue estimation technique
    firefighting techniques
    flight test technique
    flight testing technique
    floating shock fitting technique
    flux-corrected technique
    fly-by-wire technique
    flying techniques
    forced-oscillation technique
    frequency-domain technique
    frontside technique
    frontside control technique
    golden section technique
    gradient projection technique
    grid generation technique
    helideck technique
    identification technique
    illuminated helium bubble technique
    inspection technique
    integration technique
    interferometer technique
    maneuver technique
    mapping techniques
    maximum likelihood estimation technique
    measuring technique
    model reference technique
    model-based technique
    Moire techniques
    moving model technique
    multivariable technique
    multivariable synthesis technique
    noise canceling technique
    operational techniques
    optimal-control technique
    painting technique
    parameter-insensitive technique
    pilot technique
    predictor-corrector technique
    pulse-echo technique
    quadratic control technique
    radar-reflection-reducing technique
    recognition technique
    recursive technique
    repair technique
    rigid body maneuvering technique
    robust technique
    root locus synthesis technique
    scattering technique
    scheduling technique
    sealing technique
    shadowgraph technique
    spiral departure technique
    state-space technique
    stealth techniques
    stealthy techniques
    stickshaker technique
    sublimating chemical technique
    superelement technique
    superplastic forming/diffusion bonding techniques
    surface mount technique
    surface mounting technique
    tail-dragger technique
    taxy technique
    test technique
    torquing technique
    trailing cone technique
    transformation technique
    tufting technique
    vapor-screen technique
    visualization technique
    VTO technique
    weight-saving structural technique
    weighted least-squares solution technique

    Авиасловарь > technique

  • 15 ideal

    1. n идеал
    2. n верх совершенства, образец, идеал

    he is the very ideal of a friend — лучшего друга, чем он, не найти

    3. n филос. идеальное, совершенное
    4. a идеальный, отличный, совершенный, превосходный
    5. a воображаемый, абстрактный, мысленный
    6. a нереальный, неосуществимый
    7. a филос. идеалистический
    Синонимический ряд:
    1. abstract (adj.) abstract; hypothetical; theoretical; transcendent; transcendental
    2. conceptual (adj.) conceptual; ideational; notional
    3. flawless (adj.) flawless; indefectible
    4. imaginary (adj.) chimerical; fanciful; fantastic; illusory; imaginary; impractical; unreal; visionary
    5. perfect (adj.) absolute; complete; consummate; exemplary; model; perfect; supreme; ultimate; very
    6. typical (adj.) archetypal; classic; classical; fitting; paradigmatic; prototypal; prototypical; quintessential; representative; suitable; typical
    7. goal (noun) aim; goal; intention; object; objective; target
    8. ideals (noun) ideals; mores; scruples
    9. longing (noun) aspiration; dream; longing
    10. model (noun) archetype; beau ideal; conception; ensample; epitome; example; exemplar; mirror; model; paradigm; pattern; phenomenon; prototype; standard; type
    11. paragon (noun) jewel; nonesuch; nonpareil; paragon; phoenix
    Антонимический ряд:
    common; commonplace; historical; imperfect; material; mean; ordinary; palpable; physical; practical; pragmatic; real; substantial

    English-Russian base dictionary > ideal

  • 16 Huygens, Christiaan

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

    Biographical history of technology > Huygens, Christiaan

  • 17 Smith, Sir Francis Pettit

    SUBJECT AREA: Ports and shipping
    [br]
    b. 9 February 1808 Copperhurst Farm, near Hythe, Kent, England
    d. 12 February 1874 South Kensington, London, England
    [br]
    English inventor of the screw propeller.
    [br]
    Smith was the only son of Charles Smith, Postmaster at Hythe, and his wife Sarah (née Pettit). After education at a private school in Ashford, Kent, he took to farming, first on Romney Marsh, then at Hendon, Middlesex. As a boy, he showed much skill in the construction of model boats, especially in devising their means of propulsion. He maintained this interest into adult life and in 1835 he made a model propelled by a screw driven by a spring. This worked so well that he became convinced that the screw propeller offered a better method of propulsion than the paddle wheels that were then in general use. This notion so fired his enthusiasm that he virtually gave up farming to devote himself to perfecting his invention. The following year he produced a better model, which he successfully demonstrated to friends on his farm at Hendon and afterwards to the public at the Adelaide Gallery in London. On 31 May 1836 Smith was granted a patent for the propulsion of vessels by means of a screw.
    The idea of screw propulsion was not new, however, for it had been mooted as early as the seventeenth century and since then several proposals had been advanced, but without successful practical application. Indeed, simultaneously but quite independently of Smith, the Swedish engineer John Ericsson had invented the ship's propeller and obtained a patent on 13 July 1836, just weeks after Smith. But Smith was completely unaware of this and pursued his own device in the belief that he was the sole inventor.
    With some financial and technical backing, Smith was able to construct a 10 ton boat driven by a screw and powered by a steam engine of about 6 hp (4.5 kW). After showing it off to the public, Smith tried it out at sea, from Ramsgate round to Dover and Hythe, returning in stormy weather. The screw performed well in both calm and rough water. The engineering world seemed opposed to the new method of propulsion, but the Admiralty gave cautious encouragement in 1839 by ordering that the 237 ton Archimedes be equipped with a screw. It showed itself superior to the Vulcan, one of the fastest paddle-driven ships in the Navy. The ship was put through its paces in several ports, including Bristol, where Isambard Kingdom Brunel was constructing his Great Britain, the first large iron ocean-going vessel. Brunel was so impressed that he adapted his ship for screw propulsion.
    Meanwhile, in spite of favourable reports, the Admiralty were dragging their feet and ordered further trials, fitting Smith's four-bladed propeller to the Rattler, then under construction and completed in 1844. The trials were a complete success and propelled their lordships of the Admiralty to a decision to equip twenty ships with screw propulsion, under Smith's supervision.
    At last the superiority of screw propulsion was generally accepted and virtually universally adopted. Yet Smith gained little financial reward for his invention and in 1850 he retired to Guernsey to resume his farming life. In 1860 financial pressures compelled him to accept the position of Curator of Patent Models at the Patent Museum in South Kensington, London, a post he held until his death. Belated recognition by the Government, then headed by Lord Palmerston, came in 1855 with the grant of an annual pension of £200. Two years later Smith received unofficial recognition when he was presented with a national testimonial, consisting of a service of plate and nearly £3,000 in cash subscribed largely by the shipbuilding and engineering community. Finally, in 1871 Smith was honoured with a knighthood.
    [br]
    Principal Honours and Distinctions
    Knighted 1871.
    Further Reading
    Obituary, 1874, Illustrated London News (7 February).
    1856, On the Invention and Progress of the Screw Propeller, London (provides biographical details).
    Smith and his invention are referred to in papers in Transactions of the Newcomen Society, 14 (1934): 9; 19 (1939): 145–8, 155–7, 161–4, 237–9.
    LRD

    Biographical history of technology > Smith, Sir Francis Pettit

  • 18 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

  • 19 overfitting

    сущ.
    стат. сверхточность*, сверхподгонка* (получение статистической модели, которая слишком хорошо выполняется на одном массиве данных, и плохо на других, в то время как она должна описывать общие закономерности для всех массивов)

    Models must be flexible enough to fit nonlinear relationships, but unless the sample size is enormous, the approach to modeling must avoid common problems with data mining or data dredging that result in overfitting and failure of the predictive model to validate new subjects. — Модель должна быть достаточно гибкой, чтобы описывать и нелинейные связи, поэтому если объем данных не очень большой, возникает опасность того, что глубинный или слепой анализ данных приведет к сверхточной модели, которая не будет действовать на других данных.

    Ant:
    See:

    Англо-русский экономический словарь > overfitting

  • 20 Booth, Hubert Cecil

    [br]
    b. 1871 Gloucester, England d. 1955
    [br]
    English mechanical, civil and construction engineer best remembered as the inventor of the vacuum cleaner.
    [br]
    As an engineer Booth contributed to the design of engines for Royal Navy battleships, designed and supervised the erection of a number of great wheels (in Blackpool, Vienna and Paris) and later designed factories and bridges.
    In 1900 he attended a demonstration, at St Paneras Station in London, of a new form of railway carriage cleaner that was supposed to blow the dirt into a container. It was not a very successful experiment and Booth, having considered the problem carefully, decided that sucking might be better than blowing. He tried out his idea by placing a piece of damp cloth over an upholstered armchair. When he sucked air by mouth through his cloth the dirt upon it was tangible proof of his theory.
    Various attempts were being made at this time, especially in America, to find a successful cleaner of carpets and upholstery. Booth produced the first truly satisfactory machine, which he patented in 1901, and coined the term "vacuum cleaner". He formed the Vacuum Cleaner Co. (later to become Goblin BVC Ltd) and began to manufacture his machines. For some years the company provided a cleaning service to town houses, using a large and costly vacuum cleaner (the first model cost £350). Painted scarlet, it measured 54×10×42 in. (137×25×110 cm) and was powered by a petrol-driven 5 hp piston engine. It was transported through the streets on a horse-driven van and was handled by a team of operators who parked outside the house to be cleaned. With the aid of several hundred feet of flexible hose extending from the cleaner through the windows into all the rooms, the machine sucked the dirt of decades from the carpets; at the first cleaning the weight of many such carpets was reduced by 50 per cent as the dirt was sucked away.
    Many attempts were made in Europe and America to produce a smaller and less expensive machine. Booth himself designed the chief British model in 1906, the Trolley- Vac, which was wheeled around the house on a trolley. Still elaborate, expensive and heavy, this machine could, however, be operated inside a room and was powered from an electric light fitting. It consisted of a sophisticated electric motor and a belt-driven rotary vacuum pump. Various hoses and fitments made possible the cleaning of many different surfaces and the dust was trapped in a cloth filter within a small metal canister. It was a superb vacuum cleaner but cost 35 guineas and weighed a hundredweight (50 kg), so it was difficult to take upstairs.
    Various alternative machines that were cheaper and lighter were devised, but none was truly efficient until a prototype that married a small electric motor to the machine was produced in 1907 in America.
    [br]
    Further Reading
    The Story of the World's First Vacuum Cleaner, Leatherhead: BSR (Housewares) Ltd. See also Hoover, William Henry.
    DY

    Biographical history of technology > Booth, Hubert Cecil

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