develop+ways

habitude [abityd]

( = accoutumance) habit

• avoir l'habitude de faire qch to be used to doing sth

• prendre l'habitude de faire qch to get used to doing sth

• prendre de mauvaises habitudes to get into bad habits

• perdre une habitude to get out of a habit

• ce n'est pas dans ses habitudes de faire cela he doesn't usually do that

• j'ai l'habitude ! I'm used to it!

• je n'ai pas l'habitude de cette voiture I'm not used to this car

• il a ses petites habitudes he has his own little routine

• par habitude out of habit

► d'habitude usually

• c'est meilleur que d'habitude it's better than usual

• comme d'habitude as usual

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1) ( manière d'agir) habit

2) ( fait d'être accoutumé) habit

3) ( coutume) (de pays, région) custom; ( de personnes) habit

d'habitude locution adverbiale usually

* * *

1) (par préférence, convenance)

2) (action habituelle) habit

3) (= expérience, pratique) practice

* * *

habitude

A nf

1 ( manière d'agir) habit; faire qch par habitude to do sth out of habit; prendre/avoir de mauvaises habitudes to pick up/have bad habits; je vais lui faire perdre l'habitude d'entrer sans frapper I'm going to get him out of the habit of entering without knocking; avoir pour habitude de faire to be in the habit of doing; il avait pour habitude d'arriver sans prévenir it was his habit to arrive unannounced; ce n'est pas dans ses habitudes d'être impoli he is not usually impolite; il n'est pas encore ici, ce n'est pas dans ses habitudes d'être en retard he is not here yet, it's not like him to be late; ils ont l'habitude de se coucher tôt they usually go to bed early; avoir ses habitudes to have got GB ou gotten US into a routine; avoir ses petites habitudes to have one's own way of doing things; ne perdons pas les bonnes habitudes let's stick to what we usually do; comme à leur habitude, suivant leur habitude as they usually do;

2 ( fait d'être accoutumé) habit; c'est une question d'habitude it's a matter of habit ou of getting used to it; avoir l'habitude de qch to be used to sth; avoir une grande habitude de qch to be very used to sth; avoir l'habitude de faire to be used to doing; l'habitude de la conduite la nuit lui est venue facilement he easily got used to night driving; t'inquiète pas, j'ai l'habitude don't worry, I'm used to it;

3 ( coutume) (de pays, région) custom; (de personne, population) habit; habitudes alimentaires eating habits.

B d'habitude loc adv usually.

[abityd] nom féminin

1. [manière d'agir] habit

2. [usage] custom

c'est l'habitude chez nous it's a custom with us ou our custom

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d'habitude locution adverbiale

par habitude locution adverbiale

out of habit

√1. P. Ā. - karoti, - kurute, to make different, transform, change the shape ( orﾠ the mind), cause to alter orﾠ change (esp. for the worse), deprave, pervert, spoil, impair RV. etc. etc.;

- zaskorupieć w starych zwyczajach to become set in one’s habits a. ways

a statement of desired standards of behavior or procedure applicable across an organization. Company policy defines ways of acting for staff in areas where there appears to be latitude in deciding how best to operate. This may concern areas such as time off for special circumstances, drug or alcohol abuse, workplace bullying, personal use of Internet facilities, or business travel. Company policy may also apply to customers, for example, policy on complaints, customer retention, or disclosure of information. Sometimes a company policy may develop into a code of practice.

the systematic recording, examination, and analysis of existing and proposed ways of conducting work tasks in order to discover the most efficient and economical methods of performing them. The basic procedure followed in method study is as follows: select the area to be studied; record the data; examine the data; develop alternative approaches; install the new method; maintain the new method. Method study forms part of work study and is normally conducted prior to work measurement. The technique was initially developed to evaluate manufacturing processes but has been used more widely to evaluate alternative courses of action. It is based on research into motion study conducted by Frank and Lillian Gilbreth during the 1920s and 1930s.

an approach to recognizing, applying, developing, and reproducing behavior, thought processes, and ways of communicating that contribute to success. Neurolinguistic programming was developed by Richard Bandler and John Grinder through their observations of how therapists achieved excellent results with clients. It is popular in the business environment, where its influencing techniques can help organizations implement change initiatives, improve communication and management skills, and develop training techniques.

Abbr. NLP

a theory that describes sets of feelings, thoughts, and behavior or ego states that influence how individuals interact, communicate, and relate with each other. The theories of transactional analysis were developed between the 1950s and 1970s by Eric Berne, a U.S. psychiatrist who studied the behavior patterns of his patients. Berne identified three ego states, parent, adult, and child, and examined how these affected interactions or transactions between individuals. Transactional analysis is used in psychotherapy but also has applications in education and training. In human relations training, transactional analysis is used to help people understand and adapt their behavior and develop more effective ways of communicating.

Abbr. TA

SUBJECT AREA: Paper and printing

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After Koenig and Bauer had introduced the machine printing-press and returned to Germany, it fell to Applegath and his mechanic brother-in-law Edward Cooper to effect improvements. In particular, Applegath succeeded Koenig and Bauer as machine specialist to The Times newspaper, then in the vanguard of printing technology.

Applegath and Cooper first came into prominence when the Bank of England began to seek ways of reducing the number of forged banknotes. In 1816 Cooper patented a device for printing banknotes from curved stereotypes fixed to a cylinder. These were inked and printed by the rotary method. Although Applegath and Cooper were granted money to develop their invention, the Bank did not pursue it. The idea of rotary printing was interesting, but it was not followed up, possibly due to lack of demand.

Applegath and Cooper were then engaged by John Walter of The Times to remedy defects in Koenig and Bauer's presses; in 1818 Cooper patented an improved method of inking the forme and Applegath also took out patents for improvements. In 1821 Applegath had enough experience of these presses to set up as a manufacturer of printing machinery in premises in Duke Street, Blackfriars, in London. Increases in the size and circulation of The Times led Walter to ask Applegath to build a faster press. In 1827 he produced a machine with the capacity of four presses, his steam-driven four-feeder press.

Its flat form carrying the type passed under four impression cylinders in a row. It could make 4,200 impressions an hour and sufficed to print The Times for twenty years, until it was superseded by the rotary press devised by Hoe. By 1826, however, Applegath was in financial difficulties; he sold his Duke Street workshop to William Clowes, a book printer. In the following year he gave up being a full-time manufacturer of printing machinery and turned to silk printing. In 1830 he patented a machine for printing rolls of calico and silk from bent intaglio plates.

In 1848 Applegath was persuaded by The Times to return to newspaper printing. He tackled rotary printing without the benefit of curved printing plates and roll paper feed, and he devised a large "type revolving" machine which set the pattern for newspaper printing-presses for some twenty years.

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SUBJECT AREA: Agricultural and food technology, Chemical technology

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Sustained by the family tanning business in Dole, near the Swiss border, Pasteur's school career was undistinguished, sufficing to gain him entry into the teacher-training college in Paris, the Ecole Normale, There the chemical lectures by the great organic chemist J.B.A.Dumas (1800–84) fired Pasteur's enthusiasm for chemistry which never left him. Pasteur's first research, carried out at the Ecole, was into tartaric acid and resulted in the discovery of its two optically active forms resulting from dissymmetrical forms of their molecules. This led to the development of stereochemistry. Next, an interest in alcoholic fermentation, first as Professor of Chemistry at Lille University in 1854 and then back at the Ecole from 1857, led him to deny the possibility of spontaneous generation of animal life. Doubt had previously been cast on this, but it was Pasteur's classic research that finally established that the putrefaction of broth or the fermentation of sugar could not occur spontaneously in sterile conditions, and could only be caused by airborne micro-organisms. As a result, he introduced pasteurization or brief, moderate heating to kill pathogens in milk, wine and other foods. The suppuration of wounds was regarded as a similar process, leading Lister to apply Pasteur's principles to revolutionize surgery. In 1860, Pasteur himself decided to turn to medical research. His first study again had important industrial implications, for the silk industry was badly affected by diseases of the silkworm. After prolonged and careful investigation, Pasteur found ways of dealing with the two main infections. In 1868, however, he had a stroke, which prevented him from active carrying out experimentation and restricted him to directing research, which actually was more congenial to him. Success with disease in larger animals came slowly. In 1879 he observed that a chicken treated with a weakened culture of chicken-cholera bacillus would not develop symptoms of the disease when treated with an active culture. He compared this result with Jenner's vaccination against smallpox and decided to search for a vaccine against the cattle disease anthrax. In May 1881 he staged a demonstration which clearly showed the success of his new vaccine. Pasteur's next success, finding a vaccine which could protect against and treat rabies, made him world famous, especially after a person was cured in 1885. In recognition of his work, the Pasteur Institute was set up in Paris by public subscription and opened in 1888. Pasteur's genius transcended the boundaries between science, medicine and technology, and his achievements have had significant consequences for all three fields.

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SUBJECT AREA: Textiles

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Since the 1930s people have been experimenting with ways of inserting the weft during weaving without using a massive shuttle. This would save wasting the energy that a shuttle requires to accelerate it through the warp and which is only to be lost when the shuttle is stopped in its box. Around 1950, the Czech engineer Vladimir Svaty had been working on air-jet looms, in which the weft was wafted across the loom by a jet of air. He then switched his interest to waterjet looms, and in 1955, at the Brussels exhibition, the first water-jet loom was displayed to a surprised world. In 1959 the Czechs had installed 150 of these looms at Semily in Czechoslovakia, weaving cloth 41 in. (104 cm) wide at 350 picks per minute. Water-jet looms are suitable only for certain types of synthetic fibres which are not affected by the wet. They are compact, quiet, mechanically simple and free from weft vibration. They find their most appropriate use in weaving simple fabrics from water-insensitive, continuous-filament yarn, which they can produce economically and with the highest quality.

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   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)