not yet concluded

1) (not yet concluded) [ matter] in sospeso; dir. [case, charge] pendente, in essere

2) (imminent) imminente, incombente

preposizione in attesa di, fino a

* * *

A a.

1 pendente (fig.); indeciso; non risolto: a pending suit, una causa pendente

2 incombente

B prep.

1 durante: pending these negotiations, durante questi negoziati

2 fino a; in attesa di: pending his acceptance, in attesa della sua accettazione

● pending dealings, trattative in corso □ patent pending, brevetto in corso di concessione.

* * *

1) (not yet concluded) [ matter] in sospeso; dir. [case, charge] pendente, in essere

2) (imminent) imminente, incombente

preposizione in attesa di, fino a

* * *

1) (childish and behaving like someone much younger.) unreif

2) (not fully grown or fully developed; not ripe.) unreif

•

- academic.ru/36905/immaturity">immaturity

* * *

[ˌɪməˈtjʊəʳ, AM -ˈtʊr, -ˈtjʊr]

1. ( pej: not mature) unreif; (childish) kindisch meist pej

2. (not developed) unreif; (sexually) nicht geschlechtsreif

an \immature fruit eine unreife Frucht

an \immature plan ein unausgereifter Plan

an \immature wine ein junger Wein

3. COMM, FIN (not yet concluded) schwebend

* * *

adj (lit, fig)

* * *

immature [ˌıməˈtjʊə(r); US auch -ˈtʊər] adj (adv immaturely) unreif, unausgereift (beide auch fig)

* * *

* * *

unreif adj.

pending

A adj

1 ( not yet concluded) Jur [claim, case, charge] en instance ; gen [deal, matter] en souffrance ; patent pending modèle m déposé ;

2 ( imminent) [election, event, result] imminent.

B prep en attendant ; pending trial/a decision en attendant le procès/une décision.

- decide smth

   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)

1) (ended: Her chances of success are finished.) acabado

2) ((negative unfinished) done; completed: a finished product.) acabado

3) (having been completely used, eaten etc: The food is finished - there's none left.) acabado

tr['fɪnɪʃt]

adjective

1 (ended) acabado,-a

■ I'm finished with men he acabado con los hombres

■ if the press find out, he's finished si se entera la prensa, está acabado

2 (properly made, completed) acabado,-a

• acabado, -a adj.

• cabal adj.

• completo, -a adj.

• fabricado, -a adj.

• hecho, -a adj.

• pulimentado, -a adj.

• rematado, -a adj.

1) (pred)

a) (complete, achieved)

b) ( ruined) acabado

c) ( exhausted) (colloq) muerto (fam)

2) < article> terminado

['fɪnɪʃt]

1) (=concluded) terminado

2) (=completed) acabado

3) (=polished) [performance, production] pulido

4) * (=tired) rendido, hecho polvo *; (=destroyed) acabado

5) (=surfaced)

* * *

1) (pred)

a) (complete, achieved)

b) ( ruined) acabado

c) ( exhausted) (colloq) muerto (fam)

2) < article> terminado

SUBJECT AREA: Land transport, Railways and locomotives, Steam and internal combustion engines

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Orphaned as a child, Hamilton went to work for Southern Pacific Railroad in his teens, and then worked for several other companies. In his spare time he learned mathematics and physics from a retired professor. In 1911 he joined the White Motor Company, makers of road motor vehicles in Denver, Colorado, where he had gone to recuperate from malaria. He remained there until 1922, apart from an eighteenth-month break for war service.

Upon his return from war service, Hamilton found White selling petrol-engined railbuses with mechanical transmission, based on road vehicles, to railways. He noted that they were not robust enough and that the success of petrol railcars with electric transmission, built by General Electric since 1906, was limited as they were complex to drive and maintain. In 1922 Hamilton formed, and became President of, the Electro- Motive Engineering Corporation (later Electro-Motive Corporation) to design and produce petrol-electric rail cars. Needing an engine larger than those used in road vehicles, yet lighter and faster than marine engines, he approached the Win ton Engine Company to develop a suitable engine; in addition, General Electric provided electric transmission with a simplified control system. Using these components, Hamilton arranged for his petrol-electric railcars to be built by the St Louis Car Company, with the first being completed in 1924. It was the beginning of a highly successful series. Fuel costs were lower than for steam trains and initial costs were kept down by using standardized vehicles instead of designing for individual railways. Maintenance costs were minimized because Electro-Motive kept stocks of spare parts and supplied replacement units when necessary. As more powerful, 800 hp (600 kW) railcars were produced, railways tended to use them to haul trailer vehicles, although that practice reduced the fuel saving. By the end of the decade Electro-Motive needed engines more powerful still and therefore had to use cheap fuel. Diesel engines of the period, such as those that Winton had made for some years, were too heavy in relation to their power, and too slow and sluggish for rail use. Their fuel-injection system was erratic and insufficiently robust and Hamilton concluded that a separate injector was needed for each cylinder.

In 1930 Electro-Motive Corporation and Winton were acquired by General Motors in pursuance of their aim to develop a diesel engine suitable for rail traction, with the use of unit fuel injectors; Hamilton retained his position as President. At this time, industrial depression had combined with road and air competition to undermine railway-passenger business, and Ralph Budd, President of the Chicago, Burlington \& Quincy Railroad, thought that traffic could be recovered by way of high-speed, luxury motor trains; hence the Pioneer Zephyr was built for the Burlington. This comprised a 600 hp (450 kW), lightweight, two-stroke, diesel engine developed by General Motors (model 201 A), with electric transmission, that powered a streamlined train of three articulated coaches. This train demonstrated its powers on 26 May 1934 by running non-stop from Denver to Chicago, a distance of 1,015 miles (1,635 km), in 13 hours and 6 minutes, when the fastest steam schedule was 26 hours. Hamilton and Budd were among those on board the train, and it ushered in an era of high-speed diesel trains in the USA. By then Hamilton, with General Motors backing, was planning to use the lightweight engine to power diesel-electric locomotives. Their layout was derived not from steam locomotives, but from the standard American boxcar. The power plant was mounted within the body and powered the bogies, and driver's cabs were at each end. Two 900 hp (670 kW) engines were mounted in a single car to become an 1,800 hp (l,340 kW) locomotive, which could be operated in multiple by a single driver to form a 3,600 hp (2,680 kW) locomotive. To keep costs down, standard locomotives could be mass-produced rather than needing individual designs for each railway, as with steam locomotives. Two units of this type were completed in 1935 and sent on trial throughout much of the USA. They were able to match steam locomotive performance, with considerable economies: fuel costs alone were halved and there was much less wear on the track. In the same year, Electro-Motive began manufacturing diesel-electrie locomotives at La Grange, Illinois, with design modifications: the driver was placed high up above a projecting nose, which improved visibility and provided protection in the event of collision on unguarded level crossings; six-wheeled bogies were introduced, to reduce axle loading and improve stability. The first production passenger locomotives emerged from La Grange in 1937, and by early 1939 seventy units were in service. Meanwhile, improved engines had been developed and were being made at La Grange, and late in 1939 a prototype, four-unit, 5,400 hp (4,000 kW) diesel-electric locomotive for freight trains was produced and sent out on test from coast to coast; production versions appeared late in 1940. After an interval from 1941 to 1943, when Electro-Motive produced diesel engines for military and naval use, locomotive production resumed in quantity in 1944, and within a few years diesel power replaced steam on most railways in the USA.

Hal Hamilton remained President of Electro-Motive Corporation until 1942, when it became a division of General Motors, of which he became Vice-President.

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SUBJECT AREA: Paper and printing

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Few biographical details are known of Johann Gensfleisch zum Gutenberg, yet it has been said that he was responsible for Germany's most notable contribution to civilization. He was a goldsmith by trade, of a patrician family of the city of Mainz. He seems to have begun experiments on printing while a political exile in Strasbourg c. 1440. He returned to Mainz between 1444 and 1448 and continued his experiments, until by 1450 he had perfected his invention sufficiently to justify raising capital for its commercial exploitation.

Circumstances were propitious for the invention of printing at that time. Rises in literacy and prosperity had led to the formation of a social class with the time and resources to develop a taste for reading, and the demand for reading matter had outstripped the ability of the scribes to satisfy it. The various technologies required were well established, and finally the flourishing textile industry was producing enough waste material, rag, to make paper, the only satisfactory and cheap medium for printing. There were others working along similar lines, but it was Gutenberg who achieved the successful adaptation and combination of technologies to arrive at a process by which many identical copies of a text could be produced in a wide variety of forms, of which the book was the most important. Gutenberg did make several technical innovations, however. The two-piece adjustable mould for casting types of varying width, from T to "M", was ingenious. Then he had to devise an oil-based ink suitable for inking metal type, derived from the painting materials developed by contemporary Flemish artists. Finally, probably after many experiments, he arrived at a metal alloy of distinctive composition suitable for casting type.

In 1450 Gutenberg borrowed 800 guldens from Johannes Fust, a lawyer of Mainz, and two years later Fust advanced a further 800 guldens, securing for himself a partnership in Gutenberg's business. But in 1455 Fust foreclosed and the bulk of Gutenberg's equipment passed to Peter Schöffer, who was in the service of Fust and later married his daughter. Like most early printers, Gutenberg seems not to have appreciated, or at any rate to have been able to provide for, the great dilemma of the publishing trade, namely the outlay of considerable capital in advance of each publication and the slowness of the return. Gutenberg probably retained only the type for the 42- and 36-line bibles and possibly the Catholicon of 1460, an encyclopedic work compiled in the thirteenth century and whose production pointed the way to printing's role as a means of spreading knowledge. The work concluded with a short descriptive piece, or colophon, which is probably by Gutenberg himself and is the only output of his mind that we have; it manages to omit the names of both author and printer.

Gutenberg seems to have abandoned printing after 1460, perhaps due to failing eyesight as well as for financial reasons, and he suffered further loss in the sack of Mainz in 1462. He received a kind of pension from the Archbishop in 1465, and on his death was buried in the Franciscan church in Mainz. The only major work to have issued for certain from Gutenberg's workshop is the great 42-line bible, begun in 1452 and completed by August 1456. The quality of this Graaf piece of printing is a tribute to Gutenberg's ability as a printer, and the soundness of his invention is borne out by the survival of the process as he left it to the world, unchanged for over three hundred years save in minor details.

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