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1 experimental field
nAGRIC campo experimental m -
2 experimental field
Сельское хозяйство: опытное поле, опытный участок -
3 experimental
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4 experimental work
field work — полевая съёмка, работа в поле; разведка, съёмка
English-Russian dictionary on nuclear energy > experimental work
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5 experimental and field studies
Универсальный англо-русский словарь > experimental and field studies
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6 field curve; experimental curve
• кривая, полевая; экспериментальная кривая• кривая, построенная по точкамАнгло-русский словарь по ядерным испытаниям и горному делу > field curve; experimental curve
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7 Kashagan East field development project experimental programme
Универсальный англо-русский словарь > Kashagan East field development project experimental programme
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8 Kashagan East Field Development Project Experimental Programme
Проект обустройства объектов опытно-промышленной разработки месторождения КашаганEnglish-Russian oil and gas mini dictionary > Kashagan East Field Development Project Experimental Programme
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9 Goldmark, Peter Carl
[br]b. 2 December 1906 Budapest, Hungaryd. 7 December 1977 Westchester Co., New York, USA[br]Austro-Hungarian engineer who developed the first commercial colour television system and the long-playing record.[br]After education in Hungary and a period as an assistant at the Technische Hochschule, Berlin, Goldmark moved to England, where he joined Pye of Cambridge and worked on an experimental thirty-line television system using a cathode ray tube (CRT) for the display. In 1936 he moved to the USA to work at Columbia Broadcasting Laboratories. There, with monochrome television based on the CRT virtually a practical proposition, he devoted his efforts to finding a way of producing colour TV images: in 1940 he gave his first demonstration of a working system. There then followed a series of experimental field-sequential colour TV systems based on segmented red, green and blue colour wheels and drums, where the problem was to find an acceptable compromise between bandwidth, resolution, colour flicker and colour-image breakup. Eventually he arrived at a system using a colour wheel in combination with a CRT containing a panchromatic phosphor screen, with a scanned raster of 405 lines and a primary colour rate of 144 fields per second. Despite the fact that the receivers were bulky, gave relatively poor, dim pictures and used standards totally incompatible with the existing 525-line, sixty fields per second interlaced monochrome (black and white) system, in 1950 the Federal Communications Commission (FCC), anxious to encourage postwar revival of the industry, authorized the system for public broadcasting. Within eighteen months, however, bowing to pressure from the remainder of the industry, which had formed its own National Television Systems Committee (NTSC) to develop a much more satisfactory, fully compatible system based on the RCA three-gun shadowmask CRT, the FCC withdrew its approval.While all this was going on, Goldmark had also been working on ideas for overcoming the poor reproduction, noise quality, short playing-time (about four minutes) and limited robustness and life of the long-established 78 rpm 12 in. (30 cm) diameter shellac gramophone record. The recent availability of a new, more robust, plastic material, vinyl, which had a lower surface noise, enabled him in 1948 to reduce the groove width some three times to 0.003 in. (0.0762 mm), use a more lightly loaded synthetic sapphire stylus and crystal transducer with improved performance, and reduce the turntable speed to 33 1/3 rpm, to give thirty minutes of high-quality music per side. This successful development soon led to the availability of stereophonic recordings, based on the ideas of Alan Blumlein at EMI in the 1930s.In 1950 Goldmark became a vice-president of CBS, but he still found time to develop a scan conversion system for relaying television pictures to Earth from the Lunar Orbiter spacecraft. He also almost brought to the market a domestic electronic video recorder (EVR) system based on the thermal distortion of plastic film by separate luminance and coded colour signals, but this was overtaken by the video cassette recorder (VCR) system, which uses magnetic tape.[br]Principal Honours and DistinctionsInstitute of Electrical and Electronics Engineers Morris N.Liebmann Award 1945. Institute of Electrical and Electronics Engineers Vladimir K. Zworykin Award 1961.Bibliography1951, with J.W.Christensen and J.J.Reeves, "Colour television. USA Standard", Proceedings of the Institute of Radio Engineers 39: 1,288 (describes the development and standards for the short-lived field-sequential colour TV standard).1949, with R.Snepvangers and W.S.Bachman, "The Columbia long-playing microgroove recording system", Proceedings of the Institute of Radio Engineers 37:923 (outlines the invention of the long-playing record).Further ReadingE.W.Herold, 1976, "A history of colour television displays", Proceedings of the Institute of Electrical and Electronics Engineers 64:1,331.See also: Baird, John LogieKF -
10 establishment
учреждение; научно-исследовательский центр, НИЦ; научно-исследовательский институт, НИИ; штат; штатная численность;peace (time) military establishment — штат [штатное расписание] мирного времени
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11 center
центр; пункт; пост; узел; середина; научпо-иселсдовагсльскпй центр, НИЦ; выводить на середину; арт. корректировать; центрировать;air C3 center — центр руководства, управления и связи ВВС
general supply (commodity) center — центр [пункт] снабжения предметами общего предназначения
hard launch (operations) control center — ркт. центр [пункт] управления пуском, защищенный от (поражающих факторов) ЯВ
launch (operations) control center — ркт. пункт управления стартового комплекса [пуском ракет]
tactical fighter weapons (employment development) center — центр разработки способов боевого применения оружия истребителей ТА
— all-sources intelligence center— C center— combat control center— educational center— logistical operations center— logistics services center— operational center— secured communications center— skill development center -
12 Cognitive Science
The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense.... [P]eople and intelligent computers turn out to be merely different manifestations of the same underlying phenomenon. (Haugeland, 1981b, p. 31)2) Experimental Psychology, Theoretical Linguistics, and Computational Simulation of Cognitive Processes Are All Components of Cognitive ScienceI went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns.... I have been working toward a cognitive science for about twenty years beginning before I knew what to call it. (G. A. Miller, 1979, p. 9)Cognitive Science studies the nature of cognition in human beings, other animals, and inanimate machines (if such a thing is possible). While computers are helpful within cognitive science, they are not essential to its being. A science of cognition could still be pursued even without these machines.Computer Science studies various kinds of problems and the use of computers to solve them, without concern for the means by which we humans might otherwise resolve them. There could be no computer science if there were no machines of this kind, because they are indispensable to its being. Artificial Intelligence is a special branch of computer science that investigates the extent to which the mental powers of human beings can be captured by means of machines.There could be cognitive science without artificial intelligence but there could be no artificial intelligence without cognitive science. One final caveat: In the case of an emerging new discipline such as cognitive science there is an almost irresistible temptation to identify the discipline itself (as a field of inquiry) with one of the theories that inspired it (such as the computational conception...). This, however, is a mistake. The field of inquiry (or "domain") stands to specific theories as questions stand to possible answers. The computational conception should properly be viewed as a research program in cognitive science, where "research programs" are answers that continue to attract followers. (Fetzer, 1996, pp. xvi-xvii)What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.The psychologist who studies "knowledge systems" wants to know how concepts are structured in the human mind, how such concepts develop, and how they are used in understanding and behavior. The artificial intelligence researcher wants to know how to program a computer so that it can understand and interact with the outside world. The two orientations intersect when the psychologist and the computer scientist agree that the best way to approach the problem of building an intelligent machine is to emulate the human conceptual mechanisms that deal with language.... The name "cognitive science" has been used to refer to this convergence of interests in psychology and artificial intelligence....This working partnership in "cognitive science" does not mean that psychologists and computer scientists are developing a single comprehensive theory in which people are no different from machines. Psychology and artificial intelligence have many points of difference in methods and goals.... We simply want to work on an important area of overlapping interest, namely a theory of knowledge systems. As it turns out, this overlap is substantial. For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. (Schank & Abelson, 1977, pp. 1-2)Within the last dozen years a general change in scientific outlook has occurred, consonant with the point of view represented here. One can date the change roughly from 1956: in psychology, by the appearance of Bruner, Goodnow, and Austin's Study of Thinking and George Miller's "The Magical Number Seven"; in linguistics, by Noam Chomsky's "Three Models of Language"; and in computer science, by our own paper on the Logic Theory Machine. (Newell & Simon, 1972, p. 4)Historical dictionary of quotations in cognitive science > Cognitive Science
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13 Bibliography
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Principles of teaching. New York: A. G. Seiler.■ Thorndike, E. L. (1970). Animal intelligence: Experimental studies. Darien, CT: Hafner Publishing Co. (Originally published in 1911.)■ Titchener, E. B. (1910). A textbook of psychology. New York: Macmillan.■ Titchener, E. B. (1914). A primer of psychology. New York: Macmillan.■ Toulmin, S. (1957). The philosophy of science. London: Hutchinson.■ Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organisation of memory. London: Academic Press.■ Turing, A. (1946). In B. E. Carpenter & R. W. Doran (Eds.), ACE reports of 1946 and other papers. Cambridge, MA: MIT Press.■ Turkle, S. (1984). Computers and the second self: Computers and the human spirit. New York: Simon & Schuster.■ Tyler, S. A. (1978). The said and the unsaid: Mind, meaning, and culture. New York: Academic Press.■ van Heijenoort (Ed.) (1967). From Frege to Goedel. Cambridge: Harvard University Press.■ Varela, F. J. (1984). 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Cambridge: Cambridge University Press.Historical dictionary of quotations in cognitive science > Bibliography
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14 reliability
надёжность; безотказность ( в работе) ; показатель надёжности; вероятность безотказной работы
* * *
* * *
надёжность; безотказность (); показатель надёжности; вероятность безотказной работыreliability by duplication — обеспечение надёжности путём дублирования;
reliability by redundancy — обеспечение надёжности путём резервирования;
reliability in severe applications — надёжность в тяжёлых условиях эксплуатации;
reliability versus time — зависимость вероятности безотказной работы от времени;
- reliability of servicereliability with repair — надёжность с восстановлением; надёжность при выполнении ремонта
- a priori reliability
- acceptable reliability
- achieved reliability
- actual reliability
- advanced reliability
- allocated reliability
- anticipated reliability
- apportioned reliability
- assessed reliability
- assurance reliability
- asymptotic reliability
- attainable reliability
- attained reliability
- augmented reliability
- average reliability
- average estimated reliability
- boundary reliability
- calculated reliability
- compound reliability
- computed reliability
- conditional reliability
- current reliability
- demand reliability
- demonstrated reliability
- design reliability
- desired reliability
- dormant reliability
- drift reliability
- duplex reliability
- durability reliability
- dynamic reliability
- effective reliability
- engineering reliability
- enhanced reliability
- environmental reliability
- equipment reliability
- estimated reliability
- exact reliability
- expected reliability
- experimental reliability
- extra-high reliability
- failure-cause reliability
- field reliability
- final reliability
- functional reliability
- guaranteed reliability
- highest possible reliability
- in-service reliability
- inadequate reliability
- initial reliability
- long-life reliability
- long-range reliability
- long-term reliability
- lot-by-lot reliability
- low reliability
- mainstage reliability
- maintenance reliability
- measured reliability
- mechanical reliability
- minimum acceptable reliability
- nominal reliability
- nonparametric reliability
- nonredundant reliability
- normalized reliability
- numerical reliability
- observed reliability
- operating reliability
- operational reliability
- optimal reliability
- optimized reliability
- optimum reliability
- parametric reliability
- part-dependent reliability
- performance reliability
- planned reliability
- poor reliability
- posterior reliability
- pre-test reliability
- predetermined reliability
- predicted reliability
- preliminary reliability
- probabilistic reliability
- proven reliability
- qualitative reliability
- quality reliability
- quantitative reliability
- redundant reliability
- relative reliability
- running reliability
- satisfactory reliability
- service reliability
- service-free reliability
- short-term reliability
- standard reliability
- start reliability
- stationary reliability
- strategic reliability
- structural reliability
- synthesized reliability
- target reliability
- terminal reliability
- tolerable reliability
- tribological reliability
- ultimate reliability
- unacceptable reliability
- unsatisfactory reliability
- use reliability
- weighted reliability
- zero-failure reliability* * *• 1) надежность; 2) достоверность запасовАнгло-русский словарь нефтегазовой промышленности > reliability
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15 EA
- электрически перепрограммируемое (средство)
- экспериментальный блок
- экспериментальная тепловыделяющая сборка ядерного реактора
- эквивалентная готовность
- расширение адресного поля
- расчеты за электроэнергию
- подтверждение приема срочных данных
- поглощение в диэлектрике
- меры принудительного характера
- зона строгого режима (на ядерном объекте)
- зона отчуждения
- бит расширения поля адреса
бит расширения поля адреса
(МСЭ-T H.224).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]Тематики
- электросвязь, основные понятия
EN
зона строгого режима (на ядерном объекте)
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
EN
меры принудительного характера
(напр. при нарушении законодательства по охране окружающей среды)
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
EN
поглощение в диэлектрике
(МСЭ-Т G.959.1).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]Тематики
- электросвязь, основные понятия
EN
подтверждение приема срочных данных
—
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]Тематики
- электросвязь, основные понятия
EN
расчеты за электроэнергию
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
EN
расширение адресного поля
Длина адресного поля может быть увеличена за счет резервирования первого бита октетов адресного поля для указания на последний октет в адресном поле. Наличие 0 в первом бите октета адресного поля сигнализирует о том, что за этим октетом следует другой октет адресного поля. Наличие 1 в первом бите октета адресного поля говорит о том, что этот октет является последним в адресном поле (МСЭ-Т Х.76).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]Тематики
- электросвязь, основные понятия
EN
эквивалентная готовность
(блока ТЭС, АЭС)
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
EN
экспериментальная тепловыделяющая сборка ядерного реактора
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
EN
экспериментальный блок
экспериментальный узел
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]Тематики
Синонимы
EN
электрически перепрограммируемое (средство)
—
[Е.С.Алексеев, А.А.Мячев. Англо-русский толковый словарь по системотехнике ЭВМ. Москва 1993]Тематики
EN
Англо-русский словарь нормативно-технической терминологии > EA
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16 MOSFET
1) Техника: MOS field-effect transistor2) Телекоммуникации: Metal Oxide Semiconductor Field Effect Transistor3) Сокращение: Metal Oxide Silicon Field Effect Transistor, Metal-Oxide-Semiconductor Field-Effect Transistor, metal-oxide-semiconductor4) Электроника: полевой МОП-транзистор5) Вычислительная техника: Metal Oxide Semiconductor Field Effect Transistor (IC, FET)6) Транспорт: Michigan Offroad Sensor Fusing Experimental Testbed7) Расширение файла: Metal Oxide Semiconductor Field Effect Transistor -
17 mosfet
1) Техника: MOS field-effect transistor2) Телекоммуникации: Metal Oxide Semiconductor Field Effect Transistor3) Сокращение: Metal Oxide Silicon Field Effect Transistor, Metal-Oxide-Semiconductor Field-Effect Transistor, metal-oxide-semiconductor4) Электроника: полевой МОП-транзистор5) Вычислительная техника: Metal Oxide Semiconductor Field Effect Transistor (IC, FET)6) Транспорт: Michigan Offroad Sensor Fusing Experimental Testbed7) Расширение файла: Metal Oxide Semiconductor Field Effect Transistor -
18 force(s)
сила; группа; группировка; формирования; части и соединения [подразделения]; мор. отряд; соединение; pl. войска, силы; вооруженные силы, ВС; форсироватьACE mobile force(s), Air — мобильные ВВС ОВС НАТО в Европе
ACE mobile force(s), Land — мобильные СВ ОВС НАТО в Европе
Air forces, Gulf — Бр. ВВС в районе Персидского залива
Air forces, Northern Army Group BBC — Северной группы армий (ОВС НАТО в Европе)
Allied forces, Central [Northern, Southern] Europe — ОВС НАТО на Центрально-Европейском [Северо-Европейском, Южно-Европейском] ТВД
Allied forces, Europe OBC — НАТО в Европе
Allied Land forces, Northern [Southern] Europe — ОСВ НАТО на Северо-Европейском [Южно-Европейском] ТВД
Allied Naval forces, Northern [Southern] Europe — ОВМС НАТО на Северо-Европейском [Южно-Европейском] ТВД
carrier submarine detection [search] and striking force — ПЛ авианосная поисково-ударная группа
earmarked (for assignment) forces — войска [силы], выделенные [предназначенные] для передачи в оперативное подчинение (командования НАТО)
insert a force (into the area) — высаживать десант; десантировать (часть, подразделение)
mobile logistical (support) force — соединение сил [судов] подвижного тылового обеспечения (ВМС)
mobile logistics (support) force — соединение сил [судов] подвижного тылового обеспечения (ВМС)
NATO-earmarked (for assignment) forces — войска, выделенные [предназначенные] для передачи в оперативное подчинение командования НАТО
On-Call Naval force, Mediterranean — оперативное соединение ОВМС НАТО на Средиземном море для действий по вызову
rapid deployment force, Air — авиационный компонент СВР
rapid deployment force, Army — сухопутный компонент СВР
rapid deployment force, Navy — военноморской компонент СВР
special service force (mobile command) — Кан. группа войск специального назначения (мобильного командования)
Standing Naval force, Atlantic — постоянное оперативное соединение ОВМС НАТО на Атлантике
UN Peace Keeping forces, Cyprus — ВС ООН по поддержанию мира на Кипре
US forces, Europe — ВС США в Европейской зоне
— ACE mobile forces— amphibious assault force— BM force— conventional armed forces— counterforce-capable forces— divert forces from— experimental naval forces— fleet-based air force— frontier-guarding force— garrison forces— in force— international forces— link-up force— main battle forces— nuclear forces— special action forces— tactical nuclear-capable forces— unconventional warfare forces— visiting armed force -
19 force(s)
сила; группа; группировка; формирования; части и соединения [подразделения]; мор. отряд; соединение; pl. войска, силы; вооруженные силы, ВС; форсироватьACE mobile force(s), Air — мобильные ВВС ОВС НАТО в Европе
ACE mobile force(s), Land — мобильные СВ ОВС НАТО в Европе
Air forces, Gulf — Бр. ВВС в районе Персидского залива
Air forces, Northern Army Group BBC — Северной группы армий (ОВС НАТО в Европе)
Allied forces, Central [Northern, Southern] Europe — ОВС НАТО на Центрально-Европейском [Северо-Европейском, Южно-Европейском] ТВД
Allied forces, Europe OBC — НАТО в Европе
Allied Land forces, Northern [Southern] Europe — ОСВ НАТО на Северо-Европейском [Южно-Европейском] ТВД
Allied Naval forces, Northern [Southern] Europe — ОВМС НАТО на Северо-Европейском [Южно-Европейском] ТВД
carrier submarine detection [search] and striking force — ПЛ авианосная поисково-ударная группа
earmarked (for assignment) forces — войска [силы], выделенные [предназначенные] для передачи в оперативное подчинение (командования НАТО)
insert a force (into the area) — высаживать десант; десантировать (часть, подразделение)
mobile logistical (support) force — соединение сил [судов] подвижного тылового обеспечения (ВМС)
mobile logistics (support) force — соединение сил [судов] подвижного тылового обеспечения (ВМС)
NATO-earmarked (for assignment) forces — войска, выделенные [предназначенные] для передачи в оперативное подчинение командования НАТО
On-Call Naval force, Mediterranean — оперативное соединение ОВМС НАТО на Средиземном море для действий по вызову
rapid deployment force, Air — авиационный компонент СВР
rapid deployment force, Army — сухопутный компонент СВР
rapid deployment force, Navy — военноморской компонент СВР
special service force (mobile command) — Кан. группа войск специального назначения (мобильного командования)
Standing Naval force, Atlantic — постоянное оперативное соединение ОВМС НАТО на Атлантике
UN Peace Keeping forces, Cyprus — ВС ООН по поддержанию мира на Кипре
US forces, Europe — ВС США в Европейской зоне
— ACE mobile forces— amphibious assault force— BM force— conventional armed forces— counterforce-capable forces— divert forces from— experimental naval forces— fleet-based air force— frontier-guarding force— garrison forces— in force— international forces— link-up force— main battle forces— nuclear forces— special action forces— tactical nuclear-capable forces— unconventional warfare forces— visiting armed force -
20 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, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany 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 FormThe 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 FormationIt 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 ContextsEven 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)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 IntelligenceThe 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 PropositionsIn 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
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