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went+smoothly

  • 101 нечего бога гневить

    нечего (что, полно) бога гневить
    разг.
    cf. God (Lord, Heaven) be praised (thanked); thank God (goodness, Heaven)

    - Неча бога гневить, всё гладко обошлось... (Л. Леонов, Русский лес) — 'God be thanked, everything went off smoothly...'

    Русско-английский фразеологический словарь > нечего бога гневить

  • 102 like clockwork

    very smoothly and without faults:

    Everything went like clockwork.

    سارَت الأُمور مِثل السّاعَه

    Arabic-English dictionary > like clockwork

  • 103 Caetano, Marcello José das Neves Alves

    (19061980)
       Marcello Caetano, as the last prime minister of the Estado Novo, was both the heir and successor of Antônio de Oliveira Salazar. In a sense, Caetano was one of the founders and sustainers of this unusual regime and, at various crucial stages of its long life, Caetano's contribution was as important as Salazar's.
       Born in Lisbon in 1906 to a middle-class family, Caetano was a member of the student generation that rebelled against the unstable parliamentary First Republic and sought answers to Portugal's legion of troubles in conservative ideologies such as integralism, Catholic reformism, and the Italian Fascist model. One of the most brilliant students at the University of Lisbon's Law School, Caetano soon became directly involved in government service in various ministries, including Salazar's Ministry of Finance. When Caetano was not teaching full-time at the law school in Lisbon and influencing new generations of students who became critical of the regime he helped construct, Caetano was in important government posts and working on challenging assignments. In the 1930s, he participated in reforms in the Ministry of Finance, in the writing of the 1933 Constitution, in the formation of the new civil code, of which he was in part the author, and in the construction of corporativism, which sought to control labor-management relations and other aspects of social engineering. In a regime largely directed by academics from the law faculties of Coimbra University and the University of Lisbon, Caetano was the leading expert on constitutional law, administrative law, political science, and colonial law. A prolific writer as both a political scientist and historian, Caetano was the author of the standard political science, administrative law, and history of law textbooks, works that remained in print and in use among students long after his exile and death.
       After his apprenticeship service in a number of ministries, Caetano rose steadily in the system. At age 38, he was named minister for the colonies (1944 47), and unlike many predecessors, he "went to see for himself" and made important research visits to Portugal's African territories. In 1955-58, Caetano served in the number-three position in the regime in the Ministry of the Presidency of the Council (premier's office); he left office for full-time academic work in part because of his disagreements with Salazar and others on regime policy and failures to reform at the desired pace. In 1956 and 1957, Caetano briefly served as interim minister of communications and of foreign affairs.
       Caetano's opportunity to take Salazar's place and to challenge even more conservative forces in the system came in the 1960s. Portugal's most prominent law professor had a public falling out with the regime in March 1962, when he resigned as rector of Lisbon University following a clash between rebellious students and the PIDE, the political police. When students opposing the regime organized strikes on the University of Lisbon campus, Caetano resigned his rectorship after the police invaded the campus and beat and arrested some students, without asking permission to enter university premises from university authorities.
       When Salazar became incapacitated in September 1968, President Américo Tomás named Caetano prime minister. His tasks were formidable: in the midst of remarkable economic growth in Portugal, continued heavy immigration of Portuguese to France and other countries, and the costly colonial wars in three African colonies, namely Angola, Guinea- Bissau, and Mozambique, the regime struggled to engineer essential social and political reforms, win the wars in Africa, and move toward meaningful political reforms. Caetano supported moderately important reforms in his first two years in office (1968-70), as well as the drafting of constitutional revisions in 1971 that allowed a slight liberalization of the Dictatorship, gave the opposition more room for activity, and decentrali zed authority in the overseas provinces (colonies). Always aware of the complexity of Portugal's colonial problems and of the ongoing wars, Caetano made several visits to Africa as premier, and he sought to implement reforms in social and economic affairs while maintaining the expensive, divisive military effort, Portugal's largest armed forces mobilization in her history.
       Opposed by intransigent right-wing forces in various sectors in both Portugal and Africa, Caetano's modest "opening" of 1968-70 soon narrowed. Conservative forces in the military, police, civil service, and private sectors opposed key political reforms, including greater democratization, while pursuing the military solution to the African crisis and personal wealth. A significant perspective on Caetano's failed program of reforms, which could not prevent the advent of a creeping revolution in society, is a key development in the 1961-74 era of colonial wars: despite Lisbon's efforts, the greater part of Portuguese emigration and capital investment during this period were directed not to the African colonies but to Europe, North America, and Brazil.
       Prime Minister Caetano, discouraged by events and by opposition to his reforms from the so-called "Rheumatic Brigade" of superannuated regime loyalists, attempted to resign his office, but President Américo Tomás convinced him to remain. The publication and public reception of African hero General Antônio Spinola's best-selling book Portugal e Futuro (Portugal and the Future) in February 1974 convinced the surprised Caetano that a coup and revolution were imminent. When the virtually bloodless, smoothly operating military coup was successful in what became known as the Revolution of 25 April 1974, Caetano surrendered to the Armed Forces Movement in Lisbon and was flown to Madeira Island and later to exile in Brazil, where he remained for the rest of his life. In his Brazilian exile, Caetano was active writing important memoirs and histories of the Estado Novo from his vantage point, teaching law at a private university in Rio de Janeiro, and carrying on a lively correspondence with persons in Portugal. He died at age 74, in 1980, in Brazil.

    Historical dictionary of Portugal > Caetano, Marcello José das Neves Alves

  • 104 alles verliep probleemloos

    alles verliep probleemloos
    things went very smoothly/without a hitch

    Van Dale Handwoordenboek Nederlands-Engels > alles verliep probleemloos

  • 105 probleemloos

    ♦voorbeelden:
    1   alles verliep probleemloos • things went very smoothly/without a hitch

    Van Dale Handwoordenboek Nederlands-Engels > probleemloos

  • 106 vlot

    vlot1
    〈 het〉
    1  raft
    ♦voorbeelden:
    1   op een vlot de rivier oversteken • raft across the river
    ————————
    vlot2
    1 [gemakkelijk vloeiend] facile 〈 pen〉; fluent, smooth 〈 stijl〉
    2 [zonder oponthoud] smooth ⇒ ready 〈 antwoord〉, prompt 〈 betaling〉, brisk 〈 handel〉
    3 [gemakkelijk in de omgang] easy ⇒ 〈 ongedwongen〉 easygoing, 〈 sociabel〉 sociable
    4 [niet stijf] easy ⇒ comfortable
    ♦voorbeelden:
    1   een vlotte pen • a facile/ready pen
         dat boek laat zich vlot lezen • the book reads smoothly
         een les vlot opzeggen • reel off a lesson
         vlot spreken • speak fluently
    2   een vlot antwoord • a ready answer
         een zaak vlot afwikkelen • settle a matter promptly
         het gaat nog niet erg vlot • it is still somewhat difficult
         het ging heel vlot • it went off without a hitch
         vlot verkocht worden • sell well/ 〈 informeel〉 like hot cakes
         vlot van begrip zijn • be quick-witted/sharp
    3   een vlot persoon • a jovial person; 〈 sociabel〉 a good mixer
         hij is wat vlotter geworden • he loosened up a little
    4   hij kleedt zich heel vlot • he is a sharp dresser
    1 [drijvend] afloat
    ♦voorbeelden:
    1   een schip vlot brengen/trekken • get a vessel afloat

    Van Dale Handwoordenboek Nederlands-Engels > vlot

  • 107 Porter, Charles Talbot

    [br]
    b. 18 January 1826 Auburn, New York, USA
    d. 1910 USA
    [br]
    American inventor of a stone dressing machine, an improved centrifugal governor and a high-speed steam engine.
    [br]
    Porter graduated from Hamilton College, New York, in 1845, read law in his father's office, and in the autumn of 1847 was admitted to the Bar. He practised for six or seven years in Rochester, New York, and then in New York City. He was drawn into engineering when aged about 30, first through a client who claimed to have invented a revolutionary type of engine and offered Porter the rights to it as payment of a debt. Having lent more money, Porter saw neither the man nor the engine again. Porter followed this with a similar experience over a patent for a stone dressing machine, except this time the machine was built. It proved to be a failure, but Porter set about redesigning it and found that it was vastly improved when it ran faster. His improved machine went into production. It was while trying to get the steam engine that drove the stone dressing machine to run more smoothly that he made a discovery that formed the basis for his subsequent work.
    Porter took the ordinary Watt centrifugal governor and increased the speed by a factor of about ten; although he had to reduce the size of the weights, he gained a motion that was powerful. To make the device sufficiently responsive at the right speed, he balanced the centrifugal forces by a counterweight. This prevented the weights flying outwards until the optimum speed was reached, so that the steam valves remained fully open until that point and then the weights reacted more quickly to variations in speed. He took out a patent in 1858, and its importance was quickly recognized. At first he manufactured and sold the governors himself in a specially equipped factory, because this was the only way he felt he could get sufficient accuracy to ensure a perfect action. For marine use, the counterweight was replaced by a spring.
    Higher speed had brought the advantage of smoother running and so he thought that the same principles could be applied to the steam engine itself, but it was to take extensive design modifications over several years before his vision was realized. In the winter of 1860–1, J.F. Allen met Porter and sketched out his idea of a new type of steam inlet valve. Porter saw the potential of this for his high-speed engine and Allen took out patents for it in 1862. The valves were driven by a new valve gear designed by Pius Fink. Porter decided to display his engine at the International Exhibition in London in 1862, but it had to be assembled on site because the parts were finished in America only just in time to be shipped to meet the deadline. Running at 150 rpm, the engine caused a sensation, but as it was non-condensing there were few orders. Porter added condensing apparatus and, after the failure of Ormerod Grierson \& Co., entered into an agreement with Joseph Whitworth to build the engines. Four were exhibited at the 1867 Paris Exposition Universelle, but Whitworth and Porter fell out and in 1868 Porter returned to America.
    Porter established another factory to build his engine in America, but he ran into all sorts of difficulties, both mechanical and financial. Some engines were built, and serious production was started c. 1874, but again there were further problems and Porter had to leave his firm. High-speed engines based on his designs continued to be made until after 1907 by the Southwark Foundry and Machine Company, Philadelphia, so Porter's ideas were proved viable and led to many other high-speed designs.
    [br]
    Bibliography
    1908, Engineering Reminiscences, New York: J. Wiley \& Sons; reprinted 1985, Bradley, Ill.: Lindsay (autobiography; the main source of information about his life).
    Further Reading
    R.L.Hills, 1989, Power from Steam. A History of the Stationary Steam Engine, Cambridge University Press (examines his governor and steam engine).
    O.Mayr, 1974, "Yankee practice and engineering theory; Charles T.Porter and the dynamics of the high-speed engine", Technology and Culture 16 (4) (examines his governor and steam engine).
    RLH

    Biographical history of technology > Porter, Charles Talbot

  • 108 جرى

    جَرَى \ flow: (of liquid) to run: Rivers flow to the sea, (of other things) to move steadily like a river Electricity flows along a wire. go: (also go off) to take a certain course: All went (off) well at our meeting. happen: to take place: The accident happened at exactly 4 o’clock, to be done What has happened to your old car? Did you sell it?. run (ran, run): (of people and animals) to move fast, with quick steps: She ran to catch the train, (of rivers) flow The Thames runs through London. stream: to flow freely: Her eyes streamed with tears. take place: to happen: Tell me what took place at the meeting. \ See Also سال (سَالَ)، تدفق (تَدَفَّقَ)‏ \ جَرَى بِخِفَّةٍ ورَشاقةٍ \ sail: to move smoothly and effortlessly: The moon sailed across the sky. His horse sailed past the others and won the race. \ جَرَى بقُوَّة مُحْدِثًا صوتًا عاليًا \ pound: to move heavily and noisily: The horses pounded up the track.

    Arabic-English dictionary > جرى

  • 109 حقق

    حَقَّقَ \ accomplish: to finish (work, etc.) successfully; fulfil (sth. planned): The change to a different form of government was accomplished without fighting or opposition. achieve: to get sth. (success, one’s aim, etc.) by trying: He proved his worth as a young officer and soon achieved the rank of captain. edit: to prepare written matter for printing. realize: to make real: She realized her hopes of becoming a writer. \ حَقَّقَ أمرًا \ wangle: to get or arrange (sth.) by clever or improper means: He wangled a job for his son. \ See Also تَخلَّصَ من وَرْطَةٍ بالحيلة \ حَقَّقَ في \ enquire into: to study the facts about: The police were enquiring into the theft of my car. investigate: to make careful inquiries; try to find out (what is happening); inquire into (a crime, sth. strange and unexplained, etc.): He heard a noise and went to investigate. \ حَقَّقَ المَطْلوب \ work: (of a machine, an idea, etc.) to do what it is meant to do; be effective: This watch won’t work, as its spring is broken. Our plans worked smoothly. \ حَقَّقَ النَّظَر \ peer: to look with difficulty (because of bad light or weak eyes) at sth. quite close: He peered under the bed for a lost coin. \ See Also حدق (حَدَّقَ)‏

    Arabic-English dictionary > حقق

  • 110 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

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