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41 Zerreißdehnung
f obs.rar <qualit.mat> (beim Zugversuch ermittelte Verlängerung der Messlänge; in %) ■ elongation at break; elongation after fracture; elongation at rupture; elongation at failure; ultimate elongation -
42 charge de rupture
fbreaking load, breaking stress, failure load, fracture load, ultimate load, yield loadDictionnaire d'ingénierie, d'architecture et de construction > charge de rupture
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43 charge limite
ffailure load, ultimate load, yield loadDictionnaire d'ingénierie, d'architecture et de construction > charge limite
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44 анализ
analysis, dissection, examination, investigation, scan, scanning, test, review, study* * *ана́лиз м.
analysis, determination; ( визуальный) examinationне попада́ть в ана́лиз (о сплавах и т. п.) — be out of controlподверга́ть, напр. люминесце́нтному ана́лизу — analyze by, e. g., fluorescenceподверга́ть стро́гому ана́лизу мат. — subject to a rigorous analysis, analyze rigorously [in rigorous terms]поддава́ться ана́лизу — be analysableпопада́ть в ана́лиз (о сплавах и т. п.) — be in controlпри ана́лизе систе́ма разделя́ется [разбива́ется] на … — a system is analyzed into …проводи́ть ана́лиз — carry out [make, perform] an analysisпроводи́ть ана́лиз на … — carry out an analysis for …, analyze for …абсорбцио́нный ана́лиз — absorption analysisадсорбцио́нный ана́лиз — adsorption analysisактивацио́нный ана́лиз — (radio)activation analysisактивацио́нный, радиохими́ческий ана́лиз — activation analysis with radiochemical separationарбитра́жный ана́лиз — arbitrary [arbitration] analysisана́лиз бесконе́чно ма́лых мат. — infinitesimal calculusбиохими́ческий ана́лиз — biochemical analysisвалово́й ана́лиз — bulk [total, gross] analysisвариацио́нный ана́лиз — analysis of varianceве́кторный ана́лиз — vector analysisвесово́й ана́лиз — weight [gravimetric] analysisвеще́ственный ана́лиз — substantial [material] analysisволюмометри́ческий ана́лиз — volumetric analysisвременно́й ана́лиз — analysis in the time domainга́зовый ана́лиз — gas analysisгармони́ческий ана́лиз — harmonic [Fourier] analysisгравиметри́ческий ана́лиз — gravimetric analysisана́лиз грани́чных усло́вий — limit analysisгранулометри́ческий ана́лиз — particle-size [grain-size] analysisдинамометри́ческий ана́лиз — dynamic force analysisдискре́тный ана́лиз — sampling analysisдисперсио́нный ана́лиз мат., стат. — analysis of varianceдифракцио́нный ана́лиз — diffraction analysisдифференциа́льно-терми́ческий ана́лиз — differential thermal analysisдро́бный ана́лиз — fractional analysisана́лиз дымовы́х га́зов — flue-gas analysisзо́льный ана́лиз — ash analysisана́лиз изло́ма — fracture testизото́пный ана́лиз — isotopic analysisана́лиз изото́пным разбавле́нием — isotope-dilution analysisиммерсио́нный ана́лиз — immersion analysisи́мпульсный ана́лиз — pulse analysisана́лиз и́мпульсов, амплиту́дный — pulse-height analysisинфракра́сный спектра́льный ана́лиз — analysis by infrared spectroscopyкалориметри́ческий ана́лиз — calorimetric analysisка́пельный ана́лиз — drop analysisка́чественный ана́лиз — qualitative analysisка́чественный ана́лиз позволя́ет установи́ть нали́чие веще́ств — qualitative analysis detects substancesкинемати́ческий ана́лиз — kinematic analysisана́лиз ковшо́вой про́бы — ladle analysisколи́чественный ана́лиз — quantitative analysisколи́чественный ана́лиз позволя́ет определи́ть коли́чества веще́ств — quantitative analysis determines substancesколориметри́ческий ана́лиз — colorimetric analysisкомбинато́рный ана́лиз мат. — combinatorial analysisкондуктометри́ческий ана́лиз — conductimetric analysisконтро́льный ана́лиз — check analysisконформацио́нный ана́лиз — conformational analysisкорреляцио́нный ана́лиз — correlation analysisана́лиз кривы́х разго́на хим. — transient response analysisкристаллографи́ческий ана́лиз — crystallographic analysisкристаллохими́ческий ана́лиз — chemical analysis of crystalsкулонометри́ческий ана́лиз — coulometric analysisлюминесце́нтный ана́лиз — fluorimetric [fluorescence] analysis, chemical analysis by fluorescenceмагнитострукту́рный ана́лиз — magnetic structural analysisмасс-спектра́льный ана́лиз — mass spectrometric analysisмасс-спектрографи́ческий ана́лиз — mass spectrographic analysisматемати́ческий ана́лиз — mathematical analysisметаллографи́ческий ана́лиз — metallographic analysisана́лиз ме́тодом ме́ченых а́томов — tracer analysisана́лиз ме́тодом оплавле́ния — fusion analysisана́лиз ме́тодом сухо́го озоле́ния — blowpipe analysisана́лиз ме́тодом титрова́ния — titrimetric analysis, analysis by titrationмехани́ческий ана́лиз — mechanical analysisмногоме́рный ана́лиз — multivariate analysisмо́крый ана́лиз — wet analysisана́лиз на микроэлеме́нты — trace analysisана́лиз на моде́ли — model analysisана́лиз напряже́ний мех. — stress analysisнейтронографи́ческий ана́лиз крист. — neutron diffraction analysisана́лиз нелине́йных систе́м — non-linear system analysisана́лиз нелине́йных систе́м ме́тодом гармони́ческого бала́нса — non-linear system analysis by the describing function methodана́лиз нелине́йных систе́м ме́тодом ма́лого пара́метра — non-linear system analysis by the perturbation theory [method]неоргани́ческий ана́лиз — inorganic analysisнепреры́вный ана́лиз — on-stream analysisнефелометри́ческий ана́лиз — nephelometric analysis, nephelometric determinationобъё́мный ана́лиз — volumetric analysisопережа́ющий ана́лиз ( в автоматическом регулировании) — anticipatory analysisоргани́ческий ана́лиз — organic analysisорганолепти́ческий ана́лиз — organoleptic analysisана́лиз отка́зов — failure analysisана́лиз отму́чиванием — decantation analysisана́лиз перехо́дных проце́ссов — transient (response) analysisпетрографи́ческий ана́лиз — petrographic analysisпирохими́ческий ана́лиз — pyrochemical analysisана́лиз плавле́нием в ва́кууме — vacuumfusion analysisпламефотометри́ческий ана́лиз — flame photometric analysisпо́лный ана́лиз — complete [total] analysisполуколи́чественный ана́лиз — semiquantitative analysisполяриметри́ческий ана́лиз — polarimetric analysisполярографи́ческий ана́лиз — polarographic analysisпосле́довательный ана́лиз — sequential [successive] analysisпотенциометри́ческий ана́лиз — potentiometric analysisана́лиз пото́ка, квазистациона́рный — quasi-steady flow analysisана́лиз потреби́тельского спро́са — marketing analysisана́лиз преде́льных состоя́ний — limit analysisприближё́нный ана́лиз — approximate analysisпричи́нный ана́лиз — cause-and-effect analysisпроби́рный ана́лиз — assay(ing)проби́рный, мо́крый ана́лиз — wet assay(ing)проби́рный, сухо́й ана́лиз — dry [fire] assay(ing)ана́лиз про́бы из ковша́ — ladle analysisрадиоактивацио́нный ана́лиз — radioactivation analysisана́лиз радиоакти́вности — radioactivity determinationрадиометри́ческий ана́лиз — radiometric analysisана́лиз разго́нкой — distillation analysis, distillation testана́лиз разме́рностей — dimensional analysisра́стровый ана́лиз — scanning analysisрегрессио́нный ана́лиз — regression analysisрентгенографи́ческий ана́лиз — radiographic analysisрентгеноспектра́льный ана́лиз — (analysis by) X-ray spectrometryрентгеноспектра́льный, лока́льный ана́лиз — X-ray microanalysis, electron probe X-ray analysisрентгенострукту́рный ана́лиз — X-ray (diffraction) analysisрентгенофа́зовый ана́лиз — X-ray phase analysisрефрактометри́ческий ана́лиз — refractometric analysisана́лиз руд — ore analysis, ore assayседиментацио́нный ана́лиз — sedimentation analysisседиментометри́ческий ана́лиз — sedimetric [sedimentometric] analysisана́лиз сжига́нием — combustion analysisсистемати́ческий ана́лиз — systematic analysisси́товый ана́лиз — mesh [sieve, screen] analysisана́лиз скани́рованием — analysis by scanningана́лиз спе́ктра вибра́ции — vibration spectrum analysisспектра́льный ана́лиз — spectrum [spectral] analysisспектра́льный, молекуля́рный ана́лиз — molecular spectrum analysisспектра́льный, эмиссио́нный ана́лиз — emission (spectrum) analysisспектрографи́ческий ана́лиз — spectrographic analysisспектрофотометри́ческий ана́лиз — spectrophotometric [absorptimetric] analysisспектрофотометри́ческий ана́лиз в ви́димой ча́сти спе́ктра — visible spectrophotometric analysis, spectrophotometric analysis in the visible regionспектрофотометри́ческий ана́лиз в инфракра́сной о́бласти — infrared spectrophotometric analysis, spectrophotometric analysis in the infrared regionспектрофотометри́ческий ана́лиз в ультрафиоле́товой о́бласти — ultraviolet spectrophotometric analysis, spectrophotometric analysis in the ultraviolet regionана́лиз ста́ли при вы́пуске пла́вки — tapping analysisстатисти́ческий ана́лиз — statistical analysisана́лиз сто́чных вод — sewage analysisстробоскопи́ческий ана́лиз — stroboscopic analysisстру́йный ана́лиз — jet analysisструкту́рный ана́лиз — structural analysisсухо́й ана́лиз — dry analysisте́нзорный ана́лиз — tensor analysisтеплово́й ана́лиз — thermoanalysisтерми́ческий ана́лиз — thermoanalysisтермогравиметри́ческий ана́лиз — thermogravimetric analysisтермомагни́тный ана́лиз — magnetothermal analysisте́хнико-экономи́ческий ана́лиз — technical-economical analysisтехни́ческий ана́лиз — proximate analysisтитриметри́ческий ана́лиз — titrimetric analysis, analysis by titrationтурбидиметри́ческий ана́лиз — turbidimetric analysisфа́зовый ана́лиз — phase analysisфакториа́льный ана́лиз — factor analysisфотометри́ческий ана́лиз — photometric analysisфракцио́нный ана́лиз — fractional analysisфракцио́нный ана́лиз по пло́тности — float-and-sink [densimetric, specific gravity] analysisфункциона́льный ана́лиз — functional analysisхими́ческий ана́лиз — chemical analysisхроматографи́ческий ана́лиз — chromatographic analysisцветово́й ана́лиз — colour separationана́лиз цепе́й — circuit analysisана́лиз цепе́й, маши́нный — computerized circuit analysisчасти́чный ана́лиз — partial analysisчасто́тно-временно́й ана́лиз — time-and-frequency analysis, analysis in the time and frequency domainчасто́тный ана́лиз — frequency (response) analysis, analysis in the frequency domainана́лиз че́рез си́нтез вчт. — analysis by synthesisчи́сленный ана́лиз — numerical analysesана́лиз шу́ма — noise analysisэлектрографи́ческий ана́лиз крист. — electron diffraction analysisэлемента́рный ана́лиз — ultimate [elementary] analysis -
45 Bruchbelastung
Deutsch-Englisch Fachwörterbuch Architektur und Bauwesen > Bruchbelastung
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46 Bruchbiegewinkel
Bruchbiegewinkel m failure bending angle, rupture bending angle, ultimate bending angleDeutsch-Englisch Fachwörterbuch Architektur und Bauwesen > Bruchbiegewinkel
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47 Bruchfestigkeit
Bruchfestigkeit f ultimate strength, crushing strength, failure strength, final strength, breaking strength, fracture strength, rupture strength, strength of ruptureDeutsch-Englisch Fachwörterbuch Architektur und Bauwesen > Bruchfestigkeit
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48 Bruchlast
Bruchlast f load at rupture, ultimate load, crushing load, failing load, failure load, breaking load, collapse load, fracture load, rupture loadDeutsch-Englisch Fachwörterbuch Architektur und Bauwesen > Bruchlast
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49 murtojännitys
technology• breaking stresstechnology• failure stresstechnology• rupture stresstechnology• ultimate stress -
50 murtokuormitus
technology• break loadtechnology• breaking loadtechnology• crushing loadtechnology• failure loadtechnology• ultimate load -
51 obciążenie niszczące
• breaking load• crippling load• failure load• ultimate loadSłownik polsko-angielski dla inżynierów > obciążenie niszczące
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52 разрушающий момент
breaking moment мех., failure moment, moment of rupture, ultimate momentРусско-английский научно-технический словарь Масловского > разрушающий момент
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53 Swan, Sir Joseph Wilson
[br]b. 31 October 1828 Sunderland, Englandd. 27 May 1914 Warlingham, Surrey, England[br]English chemist, inventor in Britain of the incandescent electric lamp and of photographic processes.[br]At the age of 14 Swan was apprenticed to a Sunderland firm of druggists, later joining John Mawson who had opened a pharmacy in Newcastle. While in Sunderland Swan attended lectures at the Athenaeum, at one of which W.E. Staite exhibited electric-arc and incandescent lighting. The impression made on Swan prompted him to conduct experiments that led to his demonstration of a practical working lamp in 1879. As early as 1848 he was experimenting with carbon as a lamp filament, and by 1869 he had mounted a strip of carbon in a vessel exhausted of air as completely as was then possible; however, because of residual air, the filament quickly failed.Discouraged by the cost of current from primary batteries and the difficulty of achieving a good vacuum, Swan began to devote much of his attention to photography. With Mawson's support the pharmacy was expanded to include a photographic business. Swan's interest in making permanent photographic records led him to patent the carbon process in 1864 and he discovered how to make a sensitive dry plate in place of the inconvenient wet collodian process hitherto in use. He followed this success with the invention of bromide paper, the subject of a British patent in 1879.Swan resumed his interest in electric lighting. Sprengel's invention of the mercury pump in 1865 provided Swan with the means of obtaining the high vacuum he needed to produce a satisfactory lamp. Swan adopted a technique which was to become an essential feature in vacuum physics: continuing to heat the filament during the exhaustion process allowed the removal of absorbed gases. The inventions of Gramme, Siemens and Brush provided the source of electrical power at reasonable cost needed to make the incandescent lamp of practical service. Swan exhibited his lamp at a meeting in December 1878 of the Newcastle Chemical Society and again the following year before an audience of 700 at the Newcastle Literary and Philosophical Society. Swan's failure to patent his invention immediately was a tactical error as in November 1879 Edison was granted a British patent for his original lamp, which, however, did not go into production. Parchmentized thread was used in Swan's first commercial lamps, a material soon superseded by the regenerated cellulose filament that he developed. The cellulose filament was made by extruding a solution of nitro-cellulose in acetic acid through a die under pressure into a coagulating fluid, and was used until the ultimate obsolescence of the carbon-filament lamp. Regenerated cellulose became the first synthetic fibre, the further development and exploitation of which he left to others, the patent rights for the process being sold to Courtaulds.Swan also devised a modification of Planté's secondary battery in which the active material was compressed into a cellular lead plate. This has remained the central principle of all improvements in secondary cells, greatly increasing the storage capacity for a given weight.[br]Principal Honours and DistinctionsKnighted 1904. FRS 1894. President, Institution of Electrical Engineers 1898. First President, Faraday Society 1904. Royal Society Hughes Medal 1904. Chevalier de la Légion d'Honneur 1881.Bibliography2 January 1880, British patent no. 18 (incandescent electric lamp).24 May 1881, British patent no. 2,272 (improved plates for the Planté cell).1898, "The rise and progress of the electrochemical industries", Journal of the Institution of Electrical Engineers 27:8–33 (Swan's Presidential Address to the Institution of Electrical Engineers).Further ReadingM.E.Swan and K.R.Swan, 1968, Sir Joseph Wilson Swan F.R.S., Newcastle upon Tyne (a detailed account).R.C.Chirnside, 1979, "Sir Joseph Swan and the invention of the electric lamp", IEEElectronics and Power 25:96–100 (a short, authoritative biography).GWBiographical history of technology > Swan, Sir Joseph Wilson
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54 Bruchbelastung
fArchitektur & Tragwerksplanung, Infrastruktur & Entwurf, Werkstoffeigenschaften breaking load, failure load, ultimate loading -
55 Bruchspannung
fArchitektur & Tragwerksplanung, Infrastruktur & Entwurf, Werkstoffeigenschaften breaking stress, failure stress, ultimate stress -
56 разрушение при изгибе
Авиация и космонавтика. Русско-английский словарь > разрушение при изгибе
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57 разрушение при растяжении
Авиация и космонавтика. Русско-английский словарь > разрушение при растяжении
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58 критический
критическая прилcritical speed(максимально допустимая скорость при сохранении управляемости) выход за критический угол атакиstall angle overshootвыходить на критический уголreach the stalling angleдатчик критических углов атаки крылаwing stall sensorкритическая скоростьhump speedкритическая температураcritical temperatureкритический двигательcritical powerplantкритический запас топливаcritical fuel reserveкритический расчетный параметрcritical design parameterкритический уголstalling angleкритическое напряжениеultimate stressкритическое сечение заборного устройстваinlet throatпараметр потока, критический по шумуnoise-critical flow parameterполет на критическом угле атакиstall flightпосадка на критическом угле атакиstall landingсигнализатор критического угла атакиstall sensorскорость при отказе критического двигателяcritical engine failure speed -
59 разрушающий
предельная разрушающая нагрузкаultimate breaking loadразрушающая нагрузкаfailure load -
60 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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