scientific+memory

1) программное обеспечение; программные средства; программные продукты

2) программа; программный продукт

3) документация программного продукта; программная документация

4) программный

5) аудиовизуальные материалы ( воспроизводимые механическим или электрическим способом)

•

- 16-bit software

 The reentrant jet lacks the momentum to reach the leading edge. (У... струй не хватает количества движения, необходимого для того, чтобы достичь передней кромки)

 They therefore fall short of large scientific processors both in basic cpu speed and memory bandwidth. (Им поэтому не хватает...)

1) канал; желобок

2) паз; выемка || прорезать пазы

3) швеллер

4) канал связи, информационный канал || передавать информацию

5) проводящий канал

•

- afterburning channel

1)

а) разум; умственные способности; ум

б) мышление, умственная деятельность

2)

а) память; воспоминание

- put smb. in mind

б) уст. церемония в память о чём-л.; поминание

3) мнение, взгляд, точка зрения

- be of the same mind

4) желание, намерение, склонность

- change one's mind

5) настроение, расположение духа

- state of mind

6) дух, душа

••

1) заботиться (о ком-л. / чём-л.); смотреть, присматривать (за кем / чем-л.); заниматься (чем-л.)

2)

а) следить, обращать внимание

б) слушаться (кого-л.), прислушиваться (к кому-л.)

3)

а) беспокоиться, тревожиться

б) возражать, иметь что-л. против (в вопросительном или отрицательном предложении, а также в утвердительном ответе)

4)

а) быть внимательным, аккуратным; не забыть выполнить

б) беречься, остерегаться

5)

а) шотл. напоминать

б) уст.; диал. помнить

1) честь, честность, доброе имя, репутация, этика

- high honour

- a debt of honour

- word of honour

2) почёт, уважение, почтение, почести

- immortal honour

mind [maɪnd]

esprit ⇒ 1 (a), 1 (b), 1 (e)-(g) attention ⇒ 1 (c) avis ⇒ 1 (h) faire attention à ⇒ 2 (a)-(c) déranger ⇒ 1 (d) garder ⇒ 2 (e) faire attention ⇒ 3 (c)

1 noun

(a) (reason) esprit m;

∎ the power of mind over matter le pouvoir de l'esprit sur la matière;

∎ to be strong in mind and body être physiquement et mentalement solide;

∎ to be of sound mind être sain d'esprit;

∎ to be/to go out of one's mind être/devenir fou(folle);

∎ are you out of your mind?, you must be out of your mind! est-ce que tu as perdu la tête?;

∎ he was out of his mind with worry il était fou d'inquiétude;

∎ he isn't in his right mind il n'a pas tous ses esprits;

∎ no one in their right mind would do such a thing aucune personne sensée n'agirait ainsi;

∎ to be bored out of one's mind mourir d'ennui

(b) (thoughts)

∎ such a thought had never entered his mind une telle pensée ne lui était jamais venue à l'esprit;

∎ there's something on her mind il y a quelque chose qui la tracasse;

∎ I have a lot on my mind j'ai beaucoup de soucis;

∎ what's going on in her mind? qu'est-ce qui se passe dans son esprit ou sa tête?;

∎ at the back of one's mind au fond de soi-même;

∎ at the back of my mind was the fear that we would arrive too late au fond de moi-même, je craignais que nous n'arrivions trop tard;

∎ to put sth to the back of one's mind chasser qch de son esprit;

∎ I just can't get him out of my mind je n'arrive absolument pas à l'oublier;

∎ to have sb/sth in mind penser à qn/qch de précis;

∎ the person I have in mind la personne à laquelle je pense;

∎ who do you have in mind for the role? à qui songez-vous pour le rôle?, qui avez-vous en vue pour le rôle?;

∎ what kind of holiday did you have in mind? qu'est-ce que tu voulais ou voudrais faire pour les vacances?;

∎ I had something smaller in mind je pensais à quelque chose de plus petit;

∎ you must put the idea out of your mind tu dois te sortir cette idée de la tête;

∎ put it out of your mind n'y pensez plus;

∎ to set one's mind on doing sth se mettre en tête de faire qch;

∎ to have one's mind set on sth vouloir qch à tout prix;

∎ a drink will take your mind off the accident bois un verre, ça te fera oublier l'accident;

∎ to put or set sb's mind at rest rassurer qn;

∎ to see things in one's mind's eye bien se représenter qch;

∎ it's all in your mind! tu te fais des idées!;

∎ it's all in the mind tout ça, c'est dans la tête

(c) (attention)

∎ to give one's whole mind to sth accorder toute son attention à qch;

∎ I can't seem to apply my mind to the problem je n'arrive pas à me concentrer sur le problème;

∎ I'm sure if you put your mind to it you could do it je suis sûr que si tu essayais vraiment, tu pourrais le faire;

∎ keep your mind on the job ne vous laissez pas distraire;

∎ your mind is not on the job tu n'as pas la tête à ce que tu fais;

∎ she does crosswords to keep her mind occupied elle fait des mots croisés pour s'occuper l'esprit;

∎ American don't pay him any mind ne fais pas attention à lui

(d) (memory)

∎ my mind has gone blank j'ai un trou de mémoire;

∎ it brings to mind the time we were in Spain cela me rappelle l'époque où nous étions en Espagne;

∎ Churchill's words come to mind on pense aux paroles de Churchill;

∎ it went clean or right out of my mind cela m'est complètement sorti de l'esprit ou de la tête;

∎ to put sb in mind of sb/sth rappeler qn/qch à qn;

∎ it puts me in mind of Japan cela me fait penser au Japon, cela me rappelle le Japon;

∎ to bear or keep sth in mind (think about) songer à qch; (take into account) tenir compte de qch; (not forget) ne pas oublier qch, garder qch à l'esprit;

∎ we must bear in mind that she is only a child il ne faut pas oublier que ce n'est qu'une enfant;

∎ I'll bear it in mind (what you suggested) je prends note;

∎ it must have slipped my mind j'ai dû oublier;

∎ familiar to have a mind like a sieve avoir (une) très mauvaise mémoire^□ ;

∎ British time out of mind I've warned him not to go there cela fait une éternité que je lui dis de ne pas y aller

(e) (intellect) esprit m;

∎ she has an outstanding mind elle est d'une très grande intelligence;

∎ he has the mind of a child il a l'esprit d'un enfant

(f) (intelligent person, thinker) esprit m, cerveau m;

∎ the great minds of our century les grands esprits ou cerveaux de notre siècle;

∎ proverb great minds think alike(, fools seldom differ) les grands esprits se rencontrent;

∎ humorous how about a drink? - great minds think alike! si on prenait une verre? - les grands esprits se rencontrent!

(g) (way of thinking)

∎ the Western mind la pensée occidentale;

∎ I haven't got a scientific mind je n'ai pas l'esprit scientifique;

∎ you've got a dirty mind! tu as l'esprit mal placé!;

∎ she has a nasty mind elle voit le mal partout;

∎ he has a suspicious mind il est soupçonneux de nature;

∎ it's probably just my suspicious mind but I don't trust him c'est probablement que je suis trop suspicieux ou soupçonneux, mais je n'ai pas confiance en lui

(h) (opinion)

∎ to be of the same or of like or of one mind être du même avis;

∎ they're all of one or the same mind ils sont tous d'accord ou du même avis;

∎ to know one's own mind savoir ce qu'on veut;

∎ you've got a mind of your own tu peux décider toi-même;

∎ the car seemed to have a mind of its own la voiture semblait faire ce que bon lui semblait;

∎ to my mind,… à mon avis,…, selon moi,…;

∎ I'm in two minds about where to go for my holidays je ne sais pas très bien où aller passer mes vacances;

∎ I'm in two minds about going je ne sais pas si je vais y aller;

∎ to make up one's mind se décider, prendre une décision;

∎ make up your mind! décidez-vous!;

∎ I can't make up your mind for you je ne peux pas décider à ta place;

∎ my mind is made up ma décision est prise;

∎ to make up one's mind about sth décider qch;

∎ to make up one's mind to do sth se décider à faire qch;

∎ she's made up her mind to move house elle s'est résolue à déménager

(i) (desire)

∎ I've half a mind to give up j'ai presque envie de renoncer;

∎ I've a good mind to tell him what I think j'ai bien envie de lui dire ce que je pense

(j) (intention)

∎ nothing was further from my mind je n'en avais nullement l'intention;

∎ I've had it in mind for some time now j'y songe depuis un moment

2 transitive verb

(a) (pay attention to) faire attention à;

∎ he didn't mind my advice il n'a pas fait attention à ou n'a pas écouté mes conseils;

∎ mind your own business! occupe-toi de ce qui te regarde!, mêle-toi de tes oignons!;

∎ mind your language! surveille ton langage!;

∎ to mind one's manners se surveiller;

∎ mind the step (sign) attention à la marche;

∎ mind the cat! attention au chat!;

∎ mind what you say (pay attention) réfléchissez à ou faites attention à ce que vous dites; (don't be rude) mesurez vos paroles;

∎ mind what you're doing! regarde ce que tu fais!;

∎ would you mind where you're putting your feet, please? est-ce que tu peux faire attention où tu mets les pieds, s'il te plaît?;

∎ British familiar mind how you go! fais attention à toi!

(b) (be sure that) faire attention à;

∎ mind you write to him! n'oubliez pas de lui écrire!;

∎ mind you don't fall! faites attention de ne pas tomber!;

∎ mind you don't forget n'oubliez surtout pas;

∎ mind you don't break it fais bien attention de ne pas le casser;

∎ mind you're not late! faites en sorte de ne pas être en retard!;

∎ mind you post my letter n'oubliez surtout pas de poster ma lettre

(c) (concern oneself with) faire attention à, s'inquiéter de ou pour;

∎ don't mind me, I'll just sit here quietly ne vous inquiétez pas de moi, je vais m'asseoir ici et je ne dérangerai personne;

∎ don't mind him, he's always like that ne fais pas attention à lui, il est toujours comme ça;

∎ ironic don't mind me, I only live here! je t'en prie, fais comme chez toi!;

∎ I really don't mind what he says/thinks je me fiche de ce qu'il peut dire/penser

(d) (object to)

∎ I don't mind him il ne me dérange pas;

∎ I don't mind the cold le froid ne me gêne pas;

∎ I don't mind trying je veux bien essayer;

∎ you don't mind me using the car, do you? - I mind very much cela ne te dérange pas que je prenne la voiture? - cela me dérange beaucoup;

∎ do you mind going out when the weather's cold? est-ce que cela vous ennuie de sortir quand il fait froid?;

∎ do you mind me smoking? cela ne vous ennuie ou dérange pas que je fume?;

∎ did you mind me inviting her? tu aurais peut-être préféré que je ne l'invite pas?, ça t'ennuie que je l'aie invitée?;

∎ would you mind turning out the light, please? est-ce que tu peux éteindre la lumière, s'il te plaît?;

∎ how much do you earn, if you don't mind my or me asking? combien est-ce que vous gagnez, sans indiscrétion?;

∎ I wouldn't mind having his salary ça ne me dérangerait pas de gagner autant que lui;

∎ I wouldn't mind a cup of tea je prendrais bien ou volontiers une tasse de thé

(e) (look after → children) garder; (→ bags, possessions) garder, surveiller; (→ shop, business) garder, tenir; (→ plants, garden) s'occuper de, prendre soin de;

∎ can you mind the house for us while we're away? (watch) pouvez-vous surveiller la maison pendant notre absence?; (look after) pouvez-vous vous occuper de la maison pendant notre absence?

(f) Scottish (remember) se rappeler, se souvenir de

(g) (idioms)

∎ mind (you), I'm not surprised remarque ou tu sais, cela ne m'étonne pas;

∎ mind you, he's a bit young ceci dit, il est un peu jeune;

∎ mind you, I've always thought he was a bit strange remarquez, j'ai toujours trouvé qu'il était un peu bizarre;

∎ but, mind you, it was late mais, voyez-vous, il était tard;

∎ never mind that now (leave it) ne vous occupez pas de cela tout de suite; (forget it) ce n'est plus la peine de s'en occuper;

∎ never mind the consequences ne vous préoccupez pas des conséquences, peu importent les conséquences;

∎ never mind what people say/think peu importe ce que disent/pensent les gens;

∎ never mind his feelings, I've got a business to run! je me moque de ses états d'âme, j'ai une entreprise à diriger!;

∎ never mind him, just run for it! ne t'occupe pas de lui, fonce!

3 intransitive verb

(a) (object → in requests)

∎ do you mind if I open the window? cela vous dérange si j'ouvre la fenêtre?;

∎ would you mind if I opened the window? est-ce que cela vous dérangerait si j'ouvrais la fenêtre?;

∎ do you mind if I smoke? est-ce que cela vous gêne ou dérange que je fume?;

∎ I don't mind in the least cela ne me dérange pas le moins du monde;

∎ if you don't mind si vous voulez bien, si vous n'y voyez pas d'inconvénient;

∎ I can't say I really mind je ne peux pas dire que cela m'ennuie ou me dérange vraiment;

∎ do you mind if I take the car? - of course I don't mind est-ce que cela vous ennuie que je prenne la voiture? - bien sûr que non;

∎ familiar I don't mind if I do (in reply to offer) je ne dis pas non, ce n'est pas de refus

(b) (care, worry)

∎ I don't mind if people laugh at me - but you should mind! je ne me soucie guère que les gens se moquent de moi - mais vous devriez!;

∎ if you don't mind, I haven't finished si cela ne vous fait rien, je n'ai pas terminé;

∎ do you mind? (politely) vous permettez?;

∎ ironic do you mind! (indignantly) non mais!;

∎ never mind (it doesn't matter) cela ne fait rien, tant pis; (don't worry) ne vous en faites pas;

∎ never you mind! (don't worry) ne vous en faites pas!; (mind your own business) ce n'est pas votre affaire!;

∎ never mind about the money now ne t'en fais pas pour l'argent, on verra plus tard

(c) British (be careful) faire attention;

∎ mind when you cross the road fais attention en traversant la route;

∎ mind! attention!

►► mind reader voyant(e) m,f;

∎ he must be a mind reader il lit dans les pensées comme dans un livre;

∎ I'm not a mind reader je ne suis pas devin;

Marketing mind share part f de notoriété

➲ mind out intransitive verb

British faire attention;

∎ mind out! attention!;

∎ mind out for the rocks! attention aux rochers!

SUBJECT AREA: Textiles

[br]

[br]

[br]

Samuel's father, William, was a respected independent farmer who died when his son was aged 14; the young Slater was apprenticed to his father's friend, Jedediah Strutt for six and a half years at the beginning of 1783. He showed mathematical ability and quickly acquainted himself thoroughly with cotton-spinning machinery made by Arkwright, Hargreaves and Crompton. After completing his apprenticeship, he remained for a time with the Strutts to act as Supervisor for a new mill.

At that time it was forbidden to export any textile machinery or even drawings or data from England. The emigration of textile workers was forbidden too, but in September 1789 Slater left for the United States in disguise, having committed the details of the construction of the cotton-spinning machinery to memory. He reached New York and was employed by the New York Manufacturing Company.

In January 1790 he met Moses Brown in Providence, Rhode Island, and on 5 April 1790 he signed a contract to construct Arkwright's spinning machinery for Almy \& Brown. It took Slater more than a year to get the machinery operational because of the lack of skilled mechanics and tools, but by 1793 the mill was running under the name of Almy, Brown \& Slater. In October 1791 Slater had married Hannah Wilkinson, and in 1798 he set up his own mill in partnership with his father-in-law, Orziel Wilkinson. This mill was built in Pawtucket, near the first mill, but other mills soon followed in Smithville, Rhode Island, and elsewhere. Slater was the Incorporator, and for the first fifteen years was also President of the Manufacturer's Bank in Pawtucket. It was in his business role and as New England's first industrial capitalist that Slater made his most important contributions to the emergence of the American textile industry.

[br]

SUBJECT AREA: Electronics and information technology

[br]

[br]

[br]

Zuse grew up in Braunsberg, then in East Prussia, and attended the Technische Hochschule at Berlin-Charlottenburg to study civil engineering. In 1934 he became interested in calculatingmachines and the pursuit of a career in aeronautical engineering. Two years later, having taken a post as a statistician, in his spare time he built a mechanical computer, which he called Z1; for this he used two-state mechanical switches and punched-tape for the program input. This was followed by the design for Z2, which used electromechanical relays.

Called to military service in 1939, he was soon sent to the Henschel aircraft factory, where he completed Z2. Between 1939 and 1941 the German Aeronautical Research Institute supported his development of Z3, which used 2,600 relays and a keyboard input. Taken into immediate use by the aircraft industry, both it and its predecessors were destroyed in air raids. Z4, completed towards the end of the war and using mechanical memory, survived, and with improvements was used in Switzerland until 1960. Other achievements by Zuse included a machine to perform logical calculations (LI) and his Plankalkul, one of the first computer languages. In 1950, with two friends, he formed the Zuse KG company near Bad Hersfeld, Essen, and his first Z5 relay computer was sold to Leitz in 1952. A series of machines followed, a milestone in 1958 being the first transistorized machine, Z22, of which over 200 were made. Finally, in 1969, the company was absorbed by Siemens AG and Zuse returned to scientific research.

[br]

See also: Stibitz, George R.

[kəmˈpju:tə]

airborne computer бортовая ЭВМ analog computer аналоговая вычислительная машина backend computer машина базы данных backup computer вчт. резервная вычислительная машина battery-operated computer машина с батарейным питанием breadboard computer макетная ЭВМ business computer ЭВМ для экономических задач commercial computer серийная вычислительная машина commercial computer серийный вычислительная машина communication computer вчт. связной процессор compatible computer совместимая вычислительная машина computer вычислитель computer вычислительная машина computer вычислительное устройство computer компьютер; счетно-решающее устройство; (электронно-)вычислительная машина, ЭВМ; счетчик computer вчт. компьютер computer компьютер computer вчт. компьютерный computer компьютерный computer машинный computer расчетчик computer счетчик computer тот, кто вычисляет computer running MS-DOS машина работающая под управлением МС-ДОС concurrent computer ЭВМ с совмещением операций consecutive computer ЭВМ без совмещения операций control computer управляющий компьютер correlation computer вычислитель корреляции функции coupled computers спаренные компьютеры cryogenic computer криогенная вычислительная машина data flow computer вчт. компьютер с потоковой архитектурой data-flow computer потоковая вычислительная машина dataflow computer компьютер управляемый потоком данных dedicated computer вычислительная машина спецназначения dedicated computer специализированная машина desk computer настольный компьютер desk-size computer малогабаритная машина deskside computer настольный компьютер digital computer цифровая вычислительная машина digital computer вчт. цифровая вычислительная машина digital: computer цифровой; digital computer цифровая вычислительная машина diskless computer бездисковая машина dual-processor computer двухпроцессорная мащина embedded computer встроенный компьютер entry-level computer минимальный вариант компьютера fault-tolerant computer отказоустойчивая вычислительная машина fine-grain computer мелкомодульный компьютер fine-grained computer мелкомодульный компьютер floor-standing computer компьютер в стоечном исполнении front-end computer связная вычислительная машина fronted computer связной процессор general computer универсальная вычислительная машина general-purpose computer универсальная вычислительная машина giant-scale computer супер ЭВМ handheld computer микрокалькулятор handheld computer портативный компьютер high-speed computer быстродействующая вычислительная машина hobby computer вчт. вычислительная машина для любительского использования home computer бытовая вычислительная машина home computer бытовой компьютер home computer вчт. вычислительная машина для домашнего использования host computer главная вычислительная машина host computer вчт. главная вычислительная машина hybrid computer вчт. аналого-цифровая вычислительная машина hybrid computer вчт. гибридная вычислительная машина incompatible computer несовместимая вычислительная машина lap-top computer дорожная вычислительная машина laptop computer портативный компьютер large computer большая машина logic computer логическая машина logic-in-memory computer ассоциативная вычислительная машина mainframe computer универсальная вычислительная машина master computer ведущая вычислительная машина medium computer средняя вычислительная машина medium-scale computer машина средних возможностей medium-size computer машина средних габаритов mesh connected computer вчт. компьютер с матричными соединениями multiprocessor computer многопроцессорная машина multipurpose computer многоцелевая машина multiuser computer многоабонентская вычислительная машина multiuser computer многоабонентская машина multiuser computer многопользовательская вычислительная машина net node computer вчт. многоузловая машина networked computer машина сети neurobionical computer нейробионические ЭВМ no-address computer безадресная вычислительная машина nonstop computer невыключаемая машина notebook computer портативный компьютер блокнотного размера object computer целевая вычислительная машина off-the-shelf computer серийный компьютер office computer конторская вычислительная машина office computer учрежденческая ЭВМ one-address computer одноадресная вычислительная машина one-purpose computer узкоспециализированная машина palmtop computer портативный компьютер peripheral computer периферийная машина personal computer персональный компьютер, ПЭВМ PC: PC: personal computer персональная вычислительная машина pictorial computer панорамное вычислительное устройство pocket computer карманная ЭВМ pocket computer портативный компьютер portable computer портативная вычислительная машина professional computer профессиональная ПЭВМ program-compatible computer программно-совместимая машина programmed computer машина с хранимой программой relay computer релейная вычислительная машина remote computer удаленная вычислительная машина satellite computer вспомогательная машина satellite computer периферийная вычислительная машина scientific computer вычислительная машина для научных расчетов self-adapting computer самоадаптирующаяся вычислительная машина serial computer серийная вычислительная машина server computer служебная машина service computer обслуживаемая вычислительная машина service computer обслуживающая машина single-address computer одноадресная вычислительная машина single-board computer одноплатная вычислительная машина single-board computer одноплатный компьютер single-purpose computer специальная вычислительная машина single-purpose computer узкоспециализированная машина single-user computer однопользовательская машина slave computer подчиненная вычислительная машина slave computer подчиненный компьютер small-size computer малогабаритная вычислительная машина small-size computer малогабаритная машина software-compatible computer программно-совместимая эвм solid-state computer полупроводниковая вычислительная машина space computer вычислительная машина для космоса special purpose computer специализированный компьютер standby computer резервная вычислительная машина standby computer резервная машина subscriber computer абонентная вычислительная машина super computer супер эвм superhight-speed computer сверхбыстродействующая вычислительная машина superspeed computer сверхбыстродействующая машина supervisory computer координирующая машина supervisory computer машина типа диспетчер switch-control computer коммутационная вычислительная машина tagged computer вычислительная машина с теговой организацией target computer целевая вычислительная машина target computer целевой компьютер terminal computer терминальная вычислительная машина terminal computer терминальная машина tesselated computer мозаичная вычислительная машина three-address computer трехадресная вычислительная машина top level computer вчт. вычислительная машина верхнего уровня top-of-the-line computer наиболее мощная вычислительная машина trainig computer обучающая вычислительная машина training computer обучающая машина transistor computer транзисторная вычислительная машина translating computer преобразующий компьютер translating computer трансляционный компьютер ultrafast computer сверхбыстродействующая машина ultrafast computer сверхбыстродействующая эвм underflying computer базовая вычислительная машина underlying computer базовая машина user computer вычислительная машина пользования user computer пользовательская машина vector computer векторный компьютер virtual computer виртуальная вычислительная машина virtual computer виртуальная машина von-Neumann computer фон-неймановская машина zero-address computer безадресная вычислительная машина

   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) A Person's Behavior Is Changed by Changes in the Contingencies of Reinforcement

   A person is changed by the contingencies of reinforcement under which he behaves; he does not store the contingencies. In particular, he does not store copies of the stimuli which have played a part in the contingencies. There are no "iconic representations" in his mind; there are no "data structures stored in his memory"; he has no "cognitive map" of the world in which he has lived. He has simply been changed in such a way that stimuli now control particular kinds of perceptual behavior. (Skinner, 1974, p. 84)

   2) Psychology as Viewed by the Behaviorists

   Psychology as the behaviorist views it is a purely objective natural science. Its theoretical goal is the prediction and control of behavior. Introspection forms no essential part of its method nor is the scientific value of its data dependent upon the readiness with which they lend themselves to interpretation in terms of consciousness. The behaviorist, in his efforts to get a unitary scheme of animal response, recognizes no dividing line between man and brute. The behavior of man, with all its refinement and complexity, forms only a part of the behaviorist's total scheme of investigation. (Watson, quoted in Fancher, 1979, p. 319)

   1) Biological and Social Brain Development

   Among the higher mammals the great development of neocortex occurs.

   In each group of mammals there is a steady increase in the area of the association cortex from the most primitive to the evolutionarily most recent type; there is an increase in the number of neurons and their connections. The degree of consciousness of an organism is some function of neuronal cell number and connectivity, perhaps of neurons of a particular type in association cortex regions. This function is of a threshold type such that there is a significant quantitative break with the emergence of humans. Although the importance of language and the argument that it is genetically specified and unique to humans must be reconsidered in the light of the recent evidence as to the possibility of teaching chimpanzees, if not to speak, then to manipulate symbolic words and phrases, there are a number of unique human features which combine to make the transition not merely quantitative, but also qualitative. In particular these include the social, productive nature of human existence, and the range and extent of the human capacity to communicate. These features have made human history not so much one of biological but of social evolution, of continuous cultural transformation. (Rose, 1976, pp. 180-181)

   2) Distinctive Evolutionary Properties of the Brain

   [S]ome particular property of higher primate and cetacean brains did not evolve until recently. But what was that property? I can suggest at least four possibilities...: (1) Never before was there a brain so massive; (2) Never before was there a brain with so large a ratio of brain to body mass; (3) Never before was there a brain with certain functional units (large frontal and temporal lobes, for example); (4) Never before was there a brain with so many neural connections or synapses.... Explanations 1, 2 and 4 argue that a quantitative change produced a qualitative change. It does not seem to me that a crisp choice among these four alternatives can be made at the present time, and I suspect that the truth will actually embrace most or all of these possibilities. (Sagan, 1978, pp. 107-109)

   3) The Evolutionary Increase in the Size of the Main Areas of the Brain

   The crucial change in the human brain in this million years or so has not been so much the increase in size by a factor of three, but the concentration of that increase in three or four main areas. The visual area has increased considerably, and, compared with the chimpanzee, the actual density of human brain cells is at least 50 percent greater. A second increase has taken place in the area of manipulation of the hand, which is natural since we are much more hand-driven animals than monkeys and apes. Another main increase has taken place in the temporal lobe, in which visual memory, integration, and speech all lie fairly close together. And the fourth great increase has taken place in the frontal lobes. Their function is extremely difficult to understand... ; but it is clear that they're largely responsible for the ability to initiate a task, to be attentive while it is being done, and to persevere with it. (Bronowski, 1978, pp. 23-24)

   4) The Human Brain and Ethical Principles

   The human brain works however it works. Wishing for it to work in some way as a shortcut to justifying some ethical principle undermines both the science and the ethics (for what happens to the principle if the scientific facts turn out to go the other way?). (Pinker, 1994, p. 427)

   1) A Comparison of the Digital Computer and the Brain

   The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....

   The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)

   3) Getting Computers to Explain Things to Themselves

   The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)

   4) Some Limits of Artificial Intelligence

   In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)

   5) The Computer as a Humanizing Influence

   A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.

   In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....

   It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)

   7) The Possibility of Computer Thought

   he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.

   According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)

    10) Why People Are Smarter Than Computers

   What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.

   What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.

   In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)

   1) Consciousness and the New Mysterians

   Consciousness is what makes the mind-body problem really intractable.

... Without consciousness the mind-body problem would be much less interesting. With consciousness it seems hopeless. (T. Nagel, 1979, pp. 165-166)

   2) Consciousness and Sensory Qualia

   This approach to understanding sensory qualia is both theoretically and empirically motivated... [;] it suggests an effective means of expressing the allegedly inexpressible. The "ineffable" pink of one's current visual sensation may be richly and precisely expressed as a 95Hz/80Hz/80Hz "chord" in the relevant triune cortical system. The "unconveyable" taste sensation produced by the fabled Australian health tonic Vegamite might be poignantly conveyed as a 85/80/90/15 "chord" in one's four channeled gustatory system.... And the "indescribably" olfactory sensation produced by a newly opened rose might be quite accurately described as a 95/35/10/80/60/55 "chord" in some six-dimensional space within one's olfactory bulb. (P. M. Churchland, 1989, p. 106)

   3) Consciousness Appears to Be the Last Bastion of Occult Properties

   One of philosophy's favorite facets of mentality has received scant attention from cognitive psychologists, and that is consciousness itself: fullblown, introspective, inner-world phenomenological consciousness. In fact if one looks in the obvious places... one finds not so much a lack of interest as a deliberate and adroit avoidance of the issue. I think I know why. Consciousness appears to be the last bastion of occult properties, epiphenomena, and immeasurable subjective states-in short, the one area of mind best left to the philosophers, who are welcome to it. Let them make fools of themselves trying to corral the quicksilver of "phenomenology" into a respectable theory. (Dennett, 1978b, p. 149)

   4) Consciousness Can Be Resolved into Its Elementary Sensations

   When I am thinking about anything, my consciousness consists of a number of ideas.... But every idea can be resolved into elements... and these elements are sensations. (Titchener, 1910, p. 33)

   5) Consciousness Is an Aspect of the Darwinian Machine

   A Darwin machine now provides a framework for thinking about thought, indeed one that may be a reasonable first approximation to the actual brain machinery underlying thought. An intracerebral Darwin Machine need not try out one sequence at a time against memory; it may be able to try out dozens, if not hundreds, simultaneously, shape up new generations in milliseconds, and thus initiate insightful actions without overt trial and error. This massively parallel selection among stochastic sequences is more analogous to the ways of darwinian biology than to the "von Neumann" serial computer. Which is why I call it a Darwin Machine instead; it shapes up thoughts in milliseconds rather than millennia, and uses innocuous remembered environments rather than noxious real-life ones. It may well create the uniquely human aspect of our consciousness. (Calvin, 1990, pp. 261-262)

   6) Problems about Consciousness Arise from Use of the Personal Pronoun "I"

   To suppose the mind to exist in two different states, in the same moment, is a manifest absurdity. To the whole series of states of the mind, then, whatever the individual, momentary successive states may be, I give the name of our consciousness.... There are not sensations, thoughts, passions, and also consciousness, any more than there is quadruped or animal, as a separate being to be added to the wolves, tygers, elephants, and other living creatures.... The fallacy of conceiving consciousness to be something different from the feeling, which is said to be its object, has arisen, in a great measure, from the use of the personal pronoun I. (T. Brown, 1970, p. 336)

   7) The Capacity for Consciousness and Self- Consciousness Is Characteristically Human

   The human capacity for speech is certainly unique. But the gulf between it and the behavior of animals no longer seems unbridgeable.... What does this leave us with, then, which is characteristically human?.... t resides in the human capacity for consciousness and self-consciousness. (Rose, 1976, p. 177)

   [Human consciousness] depends wholly on our seeing the outside world in such categories. And the problems of consciousness arise from putting reconstitution beside internalization, from our also being able to see ourselves as if we were objects in the outside world. That is in the very nature of language; it is impossible to have a symbolic system without it.... The Cartesian dualism between mind and body arises directly from this, and so do all the famous paradoxes, both in mathematics and in linguistics.... (Bronowski, 1978, pp. 38-39)

   9) Views on Consciousness and Computation

   It seems to me that there are at least four different viewpoints-or extremes of viewpoint-that one may reasonably hold on the matter [of computation and conscious thinking]:

   A. All thinking is computation; in particular, feelings of conscious awareness are evoked merely by the carrying out of appropriate computations.

   B. Awareness is a feature of the brain's physical action; and whereas any physical action can be simulated computationally, computational simulation cannot by itself evoke awareness.

   C. Appropriate physical action of the brain evokes awareness, but this physical action cannot even be properly simulated computationally.

   D. Awareness cannot be explained by physical, computational, or any other scientific terms. (Penrose, 1994, p. 12)

 

автоматическая регулировка избирательности
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики

• энергетика в целом

EN

• automatic selectivity control
• ASC

 

витой алюминиевый провод
—
[Я.Н.Лугинский, М.С.Фези-Жилинская, Ю.С.Кабиров. Англо-русский словарь по электротехнике и электроэнергетике, Москва, 1999 г.]

Тематики

• электротехника, основные понятия

EN

• aluminum stranded conductor
• ASC

 

контроллер системы автоматического регулирования
—
[Я.Н.Лугинский, М.С.Фези-Жилинская, Ю.С.Кабиров. Англо-русский словарь по электротехнике и электроэнергетике, Москва, 1999 г.]

Тематики

• электротехника, основные понятия

EN

• automatic system controller
• ASC

 

технология Intel Advanced Smart Cache
Часть микроархитектуры Intel Core (см. - Core).
Представляет собой совместно используемую несколькими ядрами оптимизированную кэш-память большого объема, которая позволяет значительно сократить задержки в доступе к часто используемым данным, что повышает производительность и эффективность работы.
[ http://www.morepc.ru/dict/]

Тематики

• информационные технологии в целом

EN

• Advanced Smart Cache
• ASC

 

усовершенствованная ЭВМ для научных целей
—
[Е.С.Алексеев, А.А.Мячев. Англо-русский толковый словарь по системотехнике ЭВМ. Москва 1993]

Тематики

• информационные технологии в целом

EN

• advanced scientific computer
• ASC

01.05.24 модель расширенного канала [ extended channel model]: Система кодирования и передачи как байтов с данными сообщения, так и управляющей информации о сообщении, в пределах которой декодер работает в режиме расширенного канала.

Примечание - Управляющая информация передается с использованием управляющих последовательностей интерпретации в расширенном канале (ECI).

<2>4 Сокращения¹⁾

¹⁾Следует учитывать, что в соответствии с оригиналом ИСО/МЭК 19762-1 в данном разделе присутствует сокращение CSMA/CD, которое в тексте стандарта не используется.

Кроме того, сокращения отсортированы в алфавитном порядке.

Al	Идентификатор применения [application identifier]
ANS	Американский национальный стандарт [American National Standard]
ANSI	Американский национальный институт стандартов [American National Standards Institute]
ASC	Аккредитованный комитет по стандартам [Accredited Standards Committee]
вес	Контрольный знак блока [block check character]
BCD	Двоично-десятичный код (ДДК) [binary coded decimal]
BER	Коэффициент ошибок по битам [bit error rate]
CRC	Контроль циклическим избыточным кодом [cyclic redundancy check]
CSMA/CD	Коллективный доступ с контролем несущей и обнаружением конфликтов [carrier sense multiple access with collision detection network]
CSUM	Контрольная сумма [check sum]
Dl	Идентификатор данных [data identifier]
ECI	Интерпретация в расширенном канале [extended channel interpretation]
EDI	Электронный обмен данными (ЭОД) [electronic data interchange]
EEPROM	Электрически стираемое программируемое постоянное запоминающее устройство [electrically erasable programmable read only memory]
HEX	Шестнадцатеричная система счисления [hexadecimal]
INCITS	Международный комитет по стандартам информационных технологий [International Committee for Information Technology Standards]
LAN	Локальная вычислительная сеть [local area network]
Laser	Усиление света с помощью вынужденного излучения [light amplification by the stimulated emission of radiation]
LED	Светоизлучающий диод [light emitting diode]
LLC	Управление логической связью [logical link control]
LSB	Младший значащий бит [least significant bit]
МНЮ	Аккредитованный комитет по отраслевым стандартам в сфере обработки грузов [Accredited Standards Committee for the Material Handling Industry]
MSB	Старший значащий бит [most significant bit]
MTBF	Средняя наработка на отказ [mean time between failures]
MTTR	Среднее время ремонта [mean time to repair]
NRZ	Без возвращения к нулю [non-return to zero code]
NRZ Space	Кодирование без возвращения к нулю с перепадом на нулях [non-return to zero-space]
NRZ-1	Кодирование без возвращения к нулю с перепадом на единицах [non-return to zero invert on ones]
NRZ-M	Запись без возвращения к нулю (метка) [non-return to zero (mark) recording]
RTI	Возвратное транспортное упаковочное средство [returnable transport item]
RZ	Кодирование с возвратом к нулю [return to zero]
VLD	Светоизлучающий лазерный диод [visible laser diode]

<2>Библиография

[1]	ИСО/МЭК Руководство 2	Стандартизация и связанная с ней деятельность. Общий словарь
	(ISO/IECGuide2)	(Standardization and related activities - General vocabulary)
[2]	ИСО/МЭК 2382-1	Информационные технологии. Словарь - Часть 1. Основные термины
	(ISO/IEC 2382-1)	(Information technology - Vocabulary - Part 1: Fundamental terms)
[3]	ИСО/МЭК 2382-4	Информационные технологии. Словарь - Часть 4. Организация данных
	(ISO/IEC 2382-4)	(Information technology - Vocabulary - Part 4: Organization of data)
[4]	ИСО/МЭК 2382-9	Информационные технологии. Словарь. Часть 9. Передача данных
	(ISO/IEC 2382-9)	(Information technology - Vocabulary - Part 9: Data communication)
[5]	ИСО/МЭК 2382-16	Информационные технологии. Словарь. Часть 16. Теория информации
	(ISO/IEC 2382-16)	(Information technology - Vocabulary - Part 16: Information theory)
[6]	ИСО/МЭК 19762-2	Информационные технологии. Технологии автоматической идентификации и сбора данных (АИСД). Гармонизированный словарь. Часть 2. Оптические носители данных (ОНД)
	(ISO/IEC 19762-2)	(Information technology - Automatic identification and data capture (AIDC) techniques - Harmonized vocabulary - Part 2: Optically readable media (ORM))
[7]	ИСО/МЭК 19762-3	Информационные технологии. Технологии автоматической идентификации и сбора данных (АИСД). Гармонизированный словарь. Часть 3. Радиочастотная идентификация (РЧИ)
	(ISO/IEC 19762-3)	(Information technology - Automatic identification and data capture (AIDC) techniques - Harmonized vocabulary - Part 3: Radio frequency identification (RFID)
[8]	ИСО/МЭК 19762-4	Информационные технологии. Технологии автоматической идентификации и сбора данных (АИСД). Гармонизированный словарь. Часть 4. Основные термины в области радиосвязи
	(ISO/IEC 19762-4)	(Information technology-Automatic identification and data capture (AIDC) techniques - Harmonized vocabulary - Part 4: General terms relating to radio communications)
[9]	ИСО/МЭК 19762-5	Информационные технологии. Технологии автоматической идентификации и сбора данных (АИСД). Гармонизированный словарь. Часть 5. Системы определения места нахождения
	(ISO/IEC 19762-5)	(Information technology - Automatic identification and data capture (AIDC) techniques - Harmonized vocabulary - Part 5: Locating systems)
[10]	МЭК 60050-191	Международный Электротехнический Словарь. Глава 191. Надежность и качество услуг
	(IEC 60050-191)	(International Electrotechnical Vocabulary - Chapter 191: Dependability and quality of Service)
[11]	МЭК 60050-702	Международный Электротехнический Словарь. Глава 702. Колебания, сигналы и соответствующие устройства
	(IEC 60050-702)	(International Electrotechnical Vocabulary - Chapter 702: Oscillations, signals and related devices)
[12]	МЭК 60050-704	Международный Электротехнический словарь. Глава 704. Техника передачи
	(IEC 60050-704)	(International Electrotechnical Vocabulary. Chapter 704: Transmission)
[13]	МЭК 60050-845	Международный электротехнический словарь. Глава 845. Освещение
	(IEC 60050-845)	(International Electrotechnical Vocabulary - Chapter 845: Lighting)

<2>

Источник: ГОСТ Р ИСО/МЭК 19762-1-2011: Информационные технологии. Технологии автоматической идентификации и сбора данных (АИСД). Гармонизированный словарь. Часть 1. Общие термины в области АИСД оригинал документа