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experimental features

  • 1 experimental features

    Автомобильный термин: выявленные экспериментально, проверенные экспериментально, характерные особенности (автомобиля, трактора, агрегата)

    Универсальный англо-русский словарь > experimental features

  • 2 experimental features

    характерные особенности (автомобиля, трактора, агрегата), выявленные или проверенные экспериментально

    Англо-русский словарь по машиностроению > experimental features

  • 3 experimental

    экспериментальный; опытный
    - experimental features - experimental model - experimental plant - experimental stage - experimental work

    Англо-русский словарь по машиностроению > experimental

  • 4 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

  • 5 design

    1) проект

    2) дизайн
    3) конструировать
    4) конструкторский
    5) конструкция
    6) оптимальный
    7) оформление
    8) проектирование
    9) проектировать
    10) решение конструктивное
    11) составлять план
    12) планировка
    13) рисовать
    14) рисунок
    15) синтез
    16) расчетный
    17) исполнение
    18) конструирование
    19) проектный
    20) чертеж
    21) конструктивный
    – associate design
    – automated design
    – breadboard design
    – circuit design
    – civil-engineering design
    – computer-aided design
    – conceptual design
    – contractor design
    – cut-and-try design
    – design an experiment
    – design approach
    – design arrangement
    – design condition
    – design draught
    – design elevation
    – design features
    – design formula
    – design load
    – design longevity
    – design moment
    – design of an experiment
    – design of experiment
    – design of experiments
    – design office
    – design optimization
    – design philosophy
    – design pitch
    – design power
    – design pressure
    – design procedure
    – design quantity
    – design reliability
    – design schedule
    – design stress
    – design team
    – design waterline
    – design work
    – detail design
    – develop a design
    – double-buttion design
    – engineering design
    – experimental design
    – logical design
    – modular design
    – physical design
    – preliminary design
    – process design
    – refine a design
    – ruggedize the design
    – simple in design
    – structural design
    – system design
    – typographic design
    – unconventional design
    – unit-type design

    bring up to design output — доводить до проектной мощности


    canard-wing aircraft design — утка


    chain block design — блоки цепные


    completely randomized design — полностью рандомизированный план


    conception phase of IC design — проработка логической структуры ИС


    design altitude of nozzle — высотность сопла


    design casting pattern — конструировать литейную модель


    design error failure — конструкционный отказ


    design of type face — начертание шрифта


    design plans and specification — проектная документация


    design project leader — конструктор генеральный


    design trial batch — составлять пробный замес


    design walk through — контроль проекта структурный сквозной


    electronic design automation — САПР электронных устройств


    experimental design office — <engin.> бюро конструкторское опытное


    incomplete block design — план с неполными блоками


    principle of module design — <aeron.> принцип агрегатный


    punch card design — макет перфокарты


    roll pass design — калибровка прокатных валков


    special design office — <engin.> бюро конструкторское особое


    tailless aircraft design — бесхвостка


    ultimate load design — расчет по предельной нагрузке

    Англо-русский технический словарь > design

  • 6 ESF

    1) Общая лексика: hum. сокр. European Science Foundation
    4) Юридический термин: Elite Swat Forces
    5) Автомобильный термин: Experimental Safety Vehicle
    7) Телекоммуникации: Extended Superframe Format (T-1)
    8) Вычислительная техника: extended superframe, Extended Super Frame (ISDN, T1), Extended Superframe Format (Telephony)
    11) Валютные операции: Испанская песета (Spanish peseta)
    12) Инвестиции: Exchange Stabilization Fund
    13) Сетевые технологии: Extended Super Frame
    14) Океанография: European Science Foundation
    15) Авиационная медицина: extended space flight
    17) Военно-политический термин: European Social Fund
    18) Общественная организация: English Speaking Foundation

    Универсальный англо-русский словарь > ESF

  • 7 Bulleid, Oliver Vaughan Snell

    [br]
    b. 19 September 1882 Invercargill, New Zealand
    d. 25 April 1970 Malta
    [br]
    New Zealand (naturalized British) locomotive engineer noted for original experimental work in the 1940s and 1950s.
    [br]
    Bulleid's father died in 1889 and mother and son returned to the UK from New Zealand; Bulleid himself became a premium apprentice under H.A. Ivatt at Doncaster Works, Great Northern Railway (GNR). After working in France and for the Board of Trade, Bulleid returned to the GNR in 1912 as Personal Assistant to Chief Mechanical Engineer H.N. Gresley. After a break for war service, he returned as Assistant to Gresley on the latter's appointment as Chief Mechanical Engineer of the London \& North Eastern Railway in 1923. He was closely associated with Gresley during the late 1920s and early 1930s.
    In 1937 Bulleid was appointed Chief Mechanical Engineer of the Southern Railway (SR). Concentration of resources on electrification had left the Southern short of up-to-date steam locomotives, which Bulleid proceeded to provide. His first design, the "Merchant Navy" class 4–6– 2, appeared in 1941 with chain-driven valve gear enclosed in an oil-bath, and other novel features. A powerful "austerity" 0−6−0 appeared in 1942, shorn of all inessentials to meet wartime conditions, and a mixed-traffic 4−6−2 in 1945. All were largely successful.
    Under Bulleid's supervision, three large, mixed-traffic, electric locomotives were built for the Southern's 660 volt DC system and incorporated flywheel-driven generators to overcome the problem of interruptions in the live rail. Three main-line diesel-electric locomotives were completed after nationalization of the SR in 1948. All were carried on bogies, as was Bulleid's last steam locomotive design for the SR, the "Leader" class 0−6−6−0 originally intended to meet a requirement for a large, passenger tank locomotive. The first was completed after nationalization of the SR, but the project never went beyond trials. Marginally more successful was a double-deck, electric, suburban, multiple-unit train completed in 1949, with alternate high and low compartments to increase train capacity but not length. The main disadvantage was the slow entry and exit by passengers, and the type was not perpetuated, although the prototype train ran in service until 1971.
    In 1951 Bulleid moved to Coras Iompair Éireann, the Irish national transport undertaking, as Chief Mechanical Engineer. There he initiated a large-scale plan for dieselization of the railway system in 1953, the first such plan in the British Isles. Simultaneously he developed, with limited success, a steam locomotive intended to burn peat briquettes: to burn peat, the only native fuel, had been a long-unfulfilled ambition of railway engineers in Ireland. Bulleid retired in 1958.
    [br]
    Bibliography
    Bulleid took out six patents between 1941 and 1956, covering inter alia valve gear, boilers, brake apparatus and wagon underframes.
    Further Reading
    H.A.V.Bulleid, 1977, Bulleid of the Southern, Shepperton: Ian Allan (a good biography written by the subject's son).
    C.Fryer, 1990, Experiments with Steam, Wellingborough: Patrick Stephens (provides details of the austerity 0–6–0, the "Leader" locomotive and the peat-burning locomotive: see Chs 19, 20 and 21 respectively).
    PJGR

    Biographical history of technology > Bulleid, Oliver Vaughan Snell

  • 8 Gresley, Sir Herbert Nigel

    [br]
    b. 19 June 1876 Edinburgh, Scotland
    d. 5 April 1941 Hertford, England
    [br]
    English mechanical engineer, designer of the A4-class 4–6–2 locomotive holding the world speed record for steam traction.
    [br]
    Gresley was the son of the Rector of Netherseale, Derbyshire; he was educated at Marlborough and by the age of 13 was skilled at making sketches of locomotives. In 1893 he became a pupil of F.W. Webb at Crewe works, London \& North Western Railway, and in 1898 he moved to Horwich works, Lancashire \& Yorkshire Railway, to gain drawing-office experience under J.A.F.Aspinall, subsequently becoming Foreman of the locomotive running sheds at Blackpool. In 1900 he transferred to the carriage and wagon department, and in 1904 he had risen to become its Assistant Superintendent. In 1905 he moved to the Great Northern Railway, becoming Superintendent of its carriage and wagon department at Doncaster under H.A. Ivatt. In 1906 he designed and produced a bogie luggage van with steel underframe, teak body, elliptical roof, bowed ends and buckeye couplings: this became the prototype for East Coast main-line coaches built over the next thirty-five years. In 1911 Gresley succeeded Ivatt as Locomotive, Carriage \& Wagon Superintendent. His first locomotive was a mixed-traffic 2–6–0, his next a 2–8–0 for freight. From 1915 he worked on the design of a 4–6–2 locomotive for express passenger traffic: as with Ivatt's 4 4 2s, the trailing axle would allow the wide firebox needed for Yorkshire coal. He also devised a means by which two sets of valve gear could operate the valves on a three-cylinder locomotive and applied it for the first time on a 2–8–0 built in 1918. The system was complex, but a later simplified form was used on all subsequent Gresley three-cylinder locomotives, including his first 4–6–2 which appeared in 1922. In 1921, Gresley introduced the first British restaurant car with electric cooking facilities.
    With the grouping of 1923, the Great Northern Railway was absorbed into the London \& North Eastern Railway and Gresley was appointed Chief Mechanical Engineer. More 4–6– 2s were built, the first British class of such wheel arrangement. Modifications to their valve gear, along lines developed by G.J. Churchward, reduced their coal consumption sufficiently to enable them to run non-stop between London and Edinburgh. So that enginemen might change over en route, some of the locomotives were equipped with corridor tenders from 1928. The design was steadily improved in detail, and by comparison an experimental 4–6–4 with a watertube boiler that Gresley produced in 1929 showed no overall benefit. A successful high-powered 2–8–2 was built in 1934, following the introduction of third-class sleeping cars, to haul 500-ton passenger trains between Edinburgh and Aberdeen.
    In 1932 the need to meet increasing road competition had resulted in the end of a long-standing agreement between East Coast and West Coast railways, that train journeys between London and Edinburgh by either route should be scheduled to take 8 1/4 hours. Seeking to accelerate train services, Gresley studied high-speed, diesel-electric railcars in Germany and petrol-electric railcars in France. He considered them for the London \& North Eastern Railway, but a test run by a train hauled by one of his 4–6–2s in 1934, which reached 108 mph (174 km/h), suggested that a steam train could better the railcar proposals while its accommodation would be more comfortable. To celebrate the Silver Jubilee of King George V, a high-speed, streamlined train between London and Newcastle upon Tyne was proposed, the first such train in Britain. An improved 4–6–2, the A4 class, was designed with modifications to ensure free running and an ample reserve of power up hill. Its streamlined outline included a wedge-shaped front which reduced wind resistance and helped to lift the exhaust dear of the cab windows at speed. The first locomotive of the class, named Silver Link, ran at an average speed of 100 mph (161 km/h) for 43 miles (69 km), with a maximum speed of 112 1/2 mph (181 km/h), on a seven-coach test train on 27 September 1935: the locomotive went into service hauling the Silver Jubilee express single-handed (since others of the class had still to be completed) for the first three weeks, a round trip of 536 miles (863 km) daily, much of it at 90 mph (145 km/h), without any mechanical troubles at all. Coaches for the Silver Jubilee had teak-framed, steel-panelled bodies on all-steel, welded underframes; windows were double glazed; and there was a pressure ventilation/heating system. Comparable trains were introduced between London Kings Cross and Edinburgh in 1937 and to Leeds in 1938.
    Gresley did not hesitate to incorporate outstanding features from elsewhere into his locomotive designs and was well aware of the work of André Chapelon in France. Four A4s built in 1938 were equipped with Kylchap twin blast-pipes and double chimneys to improve performance still further. The first of these to be completed, no. 4468, Mallard, on 3 July 1938 ran a test train at over 120 mph (193 km/h) for 2 miles (3.2 km) and momentarily achieved 126 mph (203 km/h), the world speed record for steam traction. J.Duddington was the driver and T.Bray the fireman. The use of high-speed trains came to an end with the Second World War. The A4s were then demonstrated to be powerful as well as fast: one was noted hauling a 730-ton, 22-coach train at an average speed exceeding 75 mph (120 km/h) over 30 miles (48 km). The war also halted electrification of the Manchester-Sheffield line, on the 1,500 volt DC overhead system; however, anticipating eventual resumption, Gresley had a prototype main-line Bo-Bo electric locomotive built in 1941. Sadly, Gresley died from a heart attack while still in office.
    [br]
    Principal Honours and Distinctions
    Knighted 1936. President, Institution of Locomotive Engineers 1927 and 1934. President, Institution of Mechanical Engineers 1936.
    Further Reading
    F.A.S.Brown, 1961, Nigel Gresley, Locomotive Engineer, Ian Allan (full-length biography).
    John Bellwood and David Jenkinson, Gresley and Stanier. A Centenary Tribute (a good comparative account).
    PJGR

    Biographical history of technology > Gresley, Sir Herbert Nigel

  • 9 Intelligence

       There is no mystery about it: the child who is familiar with books, ideas, conversation-the ways and means of the intellectual life-before he begins school, indeed, before he begins consciously to think, has a marked advantage. He is at home in the House of intellect just as the stableboy is at home among horses, or the child of actors on the stage. (Barzun, 1959, p. 142)
       It is... no exaggeration to say that sensory-motor intelligence is limited to desiring success or practical adaptation, whereas the function of verbal or conceptual thought is to know and state truth. (Piaget, 1954, p. 359)
       ntelligence has two parts, which we shall call the epistemological and the heuristic. The epistemological part is the representation of the world in such a form that the solution of problems follows from the facts expressed in the representation. The heuristic part is the mechanism that on the basis of the information solves the problem and decides what to do. (McCarthy & Hayes, 1969, p. 466)
       Many scientists implicitly assume that, among all animals, the behavior and intelligence of nonhuman primates are most like our own. Nonhuman primates have relatively larger brains and proportionally more neocortex than other species... and it now seems likely that humans, chimpanzees, and gorillas shared a common ancestor as recently as 5 to 7 million years ago.... This assumption about the unique status of primate intelligence is, however, just that: an assumption. The relations between intelligence and measures of brain size is poorly understood, and evolutionary affinity does not always ensure behavioral similarity. Moreover, the view that nonhuman primates are the animals most like ourselves coexists uneasily in our minds with the equally pervasive view that primates differ fundamentally from us because they lack language; lacking language, they also lack many of the capacities necessary for reasoning and abstract thought. (Cheney & Seyfarth, 1990, p. 4)
       Few constructs are asked to serve as many functions in psychology as is the construct of human intelligence.... Consider four of the main functions addressed in theory and research on intelligence, and how they differ from one another.
       1. Biological. This type of account looks at biological processes. To qualify as a useful biological construct, intelligence should be a biochemical or biophysical process or at least somehow a resultant of biochemical or biophysical processes.
       2. Cognitive approaches. This type of account looks at molar cognitive representations and processes. To qualify as a useful mental construct, intelligence should be specifiable as a set of mental representations and processes that are identifiable through experimental, mathematical, or computational means.
       3. Contextual approaches. To qualify as a useful contextual construct, intelligence should be a source of individual differences in accomplishments in "real-world" performances. It is not enough just to account for performance in the laboratory. On [sic] the contextual view, what a person does in the lab may not even remotely resemble what the person would do outside it. Moreover, different cultures may have different conceptions of intelligence, which affect what would count as intelligent in one cultural context versus another.
       4. Systems approaches. Systems approaches attempt to understand intelligence through the interaction of cognition with context. They attempt to establish a link between the two levels of analysis, and to analyze what forms this link takes. (Sternberg, 1994, pp. 263-264)
       High but not the highest intelligence, combined with the greatest degrees of persistence, will achieve greater eminence than the highest degree of intelligence with somewhat less persistence. (Cox, 1926, p. 187)
       There are no definitive criteria of intelligence, just as there are none for chairness; it is a fuzzy-edged concept to which many features are relevant. Two people may both be quite intelligent and yet have very few traits in common-they resemble the prototype along different dimensions.... [Intelligence] is a resemblance between two individuals, one real and the other prototypical. (Neisser, 1979, p. 185)
       Given the complementary strengths and weaknesses of the differential and information-processing approaches, it should be possible, at least in theory, to synthesise an approach that would capitalise upon the strength of each approach, and thereby share the weakness of neither. (Sternberg, 1977, p. 65)

    Historical dictionary of quotations in cognitive science > Intelligence

  • 10 Memory

       To what extent can we lump together what goes on when you try to recall: (1) your name; (2) how you kick a football; and (3) the present location of your car keys? If we use introspective evidence as a guide, the first seems an immediate automatic response. The second may require constructive internal replay prior to our being able to produce a verbal description. The third... quite likely involves complex operational responses under the control of some general strategy system. Is any unitary search process, with a single set of characteristics and inputoutput relations, likely to cover all these cases? (Reitman, 1970, p. 485)
       [Semantic memory] Is a mental thesaurus, organized knowledge a person possesses about words and other verbal symbols, their meanings and referents, about relations among them, and about rules, formulas, and algorithms for the manipulation of these symbols, concepts, and relations. Semantic memory does not register perceptible properties of inputs, but rather cognitive referents of input signals. (Tulving, 1972, p. 386)
       The mnemonic code, far from being fixed and unchangeable, is structured and restructured along with general development. Such a restructuring of the code takes place in close dependence on the schemes of intelligence. The clearest indication of this is the observation of different types of memory organisation in accordance with the age level of a child so that a longer interval of retention without any new presentation, far from causing a deterioration of memory, may actually improve it. (Piaget & Inhelder, 1973, p. 36)
       4) The Logic of Some Memory Theorization Is of Dubious Worth in the History of Psychology
       If a cue was effective in memory retrieval, then one could infer it was encoded; if a cue was not effective, then it was not encoded. The logic of this theorization is "heads I win, tails you lose" and is of dubious worth in the history of psychology. We might ask how long scientists will puzzle over questions with no answers. (Solso, 1974, p. 28)
       We have iconic, echoic, active, working, acoustic, articulatory, primary, secondary, episodic, semantic, short-term, intermediate-term, and longterm memories, and these memories contain tags, traces, images, attributes, markers, concepts, cognitive maps, natural-language mediators, kernel sentences, relational rules, nodes, associations, propositions, higher-order memory units, and features. (Eysenck, 1977, p. 4)
       The problem with the memory metaphor is that storage and retrieval of traces only deals [ sic] with old, previously articulated information. Memory traces can perhaps provide a basis for dealing with the "sameness" of the present experience with previous experiences, but the memory metaphor has no mechanisms for dealing with novel information. (Bransford, McCarrell, Franks & Nitsch, 1977, p. 434)
       7) The Results of a Hundred Years of the Psychological Study of Memory Are Somewhat Discouraging
       The results of a hundred years of the psychological study of memory are somewhat discouraging. We have established firm empirical generalisations, but most of them are so obvious that every ten-year-old knows them anyway. We have made discoveries, but they are only marginally about memory; in many cases we don't know what to do with them, and wear them out with endless experimental variations. We have an intellectually impressive group of theories, but history offers little confidence that they will provide any meaningful insight into natural behavior. (Neisser, 1978, pp. 12-13)
       A schema, then is a data structure for representing the generic concepts stored in memory. There are schemata representing our knowledge about all concepts; those underlying objects, situations, events, sequences of events, actions and sequences of actions. A schema contains, as part of its specification, the network of interrelations that is believed to normally hold among the constituents of the concept in question. A schema theory embodies a prototype theory of meaning. That is, inasmuch as a schema underlying a concept stored in memory corresponds to the mean ing of that concept, meanings are encoded in terms of the typical or normal situations or events that instantiate that concept. (Rumelhart, 1980, p. 34)
       Memory appears to be constrained by a structure, a "syntax," perhaps at quite a low level, but it is free to be variable, deviant, even erratic at a higher level....
       Like the information system of language, memory can be explained in part by the abstract rules which underlie it, but only in part. The rules provide a basic competence, but they do not fully determine performance. (Campbell, 1982, pp. 228, 229)
       When people think about the mind, they often liken it to a physical space, with memories and ideas as objects contained within that space. Thus, we speak of ideas being in the dark corners or dim recesses of our minds, and of holding ideas in mind. Ideas may be in the front or back of our minds, or they may be difficult to grasp. With respect to the processes involved in memory, we talk about storing memories, of searching or looking for lost memories, and sometimes of finding them. An examination of common parlance, therefore, suggests that there is general adherence to what might be called the spatial metaphor. The basic assumptions of this metaphor are that memories are treated as objects stored in specific locations within the mind, and the retrieval process involves a search through the mind in order to find specific memories....
       However, while the spatial metaphor has shown extraordinary longevity, there have been some interesting changes over time in the precise form of analogy used. In particular, technological advances have influenced theoretical conceptualisations.... The original Greek analogies were based on wax tablets and aviaries; these were superseded by analogies involving switchboards, gramophones, tape recorders, libraries, conveyor belts, and underground maps. Most recently, the workings of human memory have been compared to computer functioning... and it has been suggested that the various memory stores found in computers have their counterparts in the human memory system. (Eysenck, 1984, pp. 79-80)
       Primary memory [as proposed by William James] relates to information that remains in consciousness after it has been perceived, and thus forms part of the psychological present, whereas secondary memory contains information about events that have left consciousness, and are therefore part of the psychological past. (Eysenck, 1984, p. 86)
       Once psychologists began to study long-term memory per se, they realized it may be divided into two main categories.... Semantic memories have to do with our general knowledge about the working of the world. We know what cars do, what stoves do, what the laws of gravity are, and so on. Episodic memories are largely events that took place at a time and place in our personal history. Remembering specific events about our own actions, about our family, and about our individual past falls into this category. With amnesia or in aging, what dims... is our personal episodic memories, save for those that are especially dear or painful to us. Our knowledge of how the world works remains pretty much intact. (Gazzaniga, 1988, p. 42)
       The nature of memory... provides a natural starting point for an analysis of thinking. Memory is the repository of many of the beliefs and representations that enter into thinking, and the retrievability of these representations can limit the quality of our thought. (Smith, 1990, p. 1)

    Historical dictionary of quotations in cognitive science > Memory

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