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1 basic examples
Программирование: простые примеры -
2 such examples help, no doubt, but they also miss a basic point about ...
• такие примеры, без сомнения, помогают, но и они упускают важный момент...English-Russian dictionary of phrases and cliches for a specialist researcher > such examples help, no doubt, but they also miss a basic point about ...
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3 like
I
1.
adjective(the same or similar: They're as like as two peas.) parecido, igual
2. preposition(the same as or similar to; in the same or a similar way as: He climbs like a cat; She is like her mother.) como
3. noun(someone or something which is the same or as good etc as another: You won't see his like / their like again.) cosa igual
4. conjunction((especially American) in the same or a similar way as: No-one does it like he does.) como- likely- likelihood
- liken
- likeness
- likewise
- like-minded
- a likely story!
- as likely as not
- be like someone
- feel like
- he is likely to
- look like
- not likely!
II
verb1) (to be pleased with; to find pleasant or agreeable: I like him very much; I like the way you've decorated this room.) gustar2) (to enjoy: I like gardening.) gustar•- likeable- likable
- liking
- should/would like
- take a liking to
like1 prep como / igual quelike2 vb gustardo you like swimming? ¿te gusta nadar?tr[laɪk]1 (the same as) como■ what's the new boss like? ¿cómo es el nuevo jefe?2 (typical of) propio,-a de3 familiar como1 (such as) como2 formal use semejante, parecido,-a1 familiar (as it were) pues■ so I thought, like, what'll happen next? y yo pensé, pues, ¿qué pasará ahora?1 familiar como1 algo parecido\SMALLIDIOMATIC EXPRESSION/SMALLand the like y cosas así(as) like as not familiar seguramenteto be as like as two peas in a pod ser como dos gotas de agualike enough familiar seguramentelike father, like son de tal palo tal astillathat's more like it! familiar ¡eso está mejor!, ¡así me gusta!to look like somebody parecerse a alguiento look like something parecer algosomething like that algo así, algo por el estiloto be of like mind formal use ser del mismo parecerto feel like tener ganas de————————tr[laɪk]1 (enjoy) gustar■ how do you like Barcelona? ¿te gusta Barcelona?2 (want) querer, gustar■ would you like me to leave? ¿quieres que me vaya?■ how would you like your egg, boiled or fried? ¿cómo quieres el huevo, pasado por agua o frito?1 querer1 gustos nombre masculino plural\SMALLIDIOMATIC EXPRESSION/SMALLto like something better preferir algowhether you like it or not quieras o no (quieras), a la fuerza1) : agradar, gustarle (algo a uno)he likes rice: le gusta el arrozshe doesn't like flowers: a ella no le gustan las floresI like you: me caes bien2) want: querer, desearI'd like a hamburger: quiero una hamburguesahe would like more help: le gustaría tener más ayudalike vi: quererdo as you like: haz lo que quieraslike adj: parecido, semejante, similarlike n1) preference: preferencia f, gusto m2)the like : cosa f parecida, cosas fpl por el estiloI've never seen the like: nunca he visto cosa parecidalike conj1) as if: como sithey looked at me like I was crazy: se me quedaron mirando como si estuviera loca2) as: como, igual queshe doesn't love you like I do: ella no te quiere como yolike prep1) : como, parecido ashe acts like my mother: se comporta como mi madrehe looks like me: se parece a mí2) : propio de, típico dethat's just like her: eso es muy típico de ella3) : comoanimals like cows: animales como vacas4)like this, like that : asído it like that: hazlo asíadj.• parecido, -a adj.• parejo, -a adj.• semejante adj.• vecino, -a adj.adv.• como adv.• del mismo modo adv.n.• semejante s.m.v.• bienquerer v.(§pret: -quis-) fut/c: -querr-•)• gustar v.• querer v.(§pret: quis-) fut/c: querr-•)
I
1. laɪk1) (enjoy, be fond of)I/we like tennis — me/nos gusta el tenis
she likes him, but she doesn't love him — le resulta simpático pero no lo quiere
how do you like my dress? — ¿qué te parece mi vestido?
how would you like an ice-cream? — ¿quieres or (Esp tb) te apetece un helado?
I like it! — ( joke) muy bueno!; ( suggestion) buena idea!
I like that! — (iro) muy bonito! (iró), habráse visto!
do as o what you like — haz lo que quieras or lo que te parezca
to like -ING/to + INF: I like dancing me gusta bailar; she likes to have breakfast before eight le gusta desayunar antes de las ocho; I don't like to mention it, but... no me gusta (tener que) decírtelo pero...; to like somebody to + INF: we like him to write to us every so often — nos gusta que nos escriba de vez en cuando
2) (in requests, wishes) querer*would you like a cup of tea/me to help you? — ¿quieres una taza de té/que te ayude?
I'd like two melons, please — (me da) dos melones, por favor
2.
vi querer*if you like — si quieres, si te parece
II
1) ( something liked)her/his likes and dislikes — sus preferencias or gustos, lo que le gusta y no le gusta
2) (similar thing, person)the like: judges, lawyers and the like jueces, abogados y (otra) gente or (otras) personas por el estilo; I've never seen/heard the like (of this) nunca he visto/oído cosa igual; he doesn't mix with the likes of me/us — (colloq) no se codea con gente como yo/nosotros
III
adjective (dated or frml) parecido, similarpeople of like minds — gente f con ideas afines; pea
IV
1)a) ( similar to) comoshe's very like her mother — se parece mucho or es muy parecida a su madre
try this one - now, that's more like it! — prueba éste - ah, esto ya es otra cosa
come on, stop crying!... that's more like it! — vamos, para de llorar... ahí está! or así me gusta!
what's the food like? — ¿cómo or (fam) qué tal es la comida?
it cost £20, or something like that — costó 20 libras o algo así or o algo por el estilo
b) ( typical of)it's just like you to think of food — típico! or cuándo no! tú pensando en comida!
2) ( indicating manner)like this/that — así
3) (such as, for example) comodon't do anything silly, like running away — no vayas a hacer una tontería, como escaparte por ejemplo
V
conjunction (crit)a) ( as if)she looks like she knows what she's doing — parece que or da la impresión de que sabe lo que hace
b) (as, in same way) como
VI
a) ( likely)as like as not, she won't come — lo más probable es que no venga
b) ( nearly)this film is nothing like as good as the first — esta película no es tan buena como la primera ni mucho menos
I [laɪk]1.ADJ frm (=similar) parecido, semejantesnakes, lizards and like creatures — serpientes fpl, lagartos mpl y criaturas fpl parecidas or semejantes
he was very intolerant towards people not of a like mind — era muy intransigente con las personas que no le daban la razón
- they are as like as two peas2. PREP1) (=similar to) comowhat's he like? — ¿cómo es (él)?
you know what she's like — ya la conoces, ya sabes cómo es
what's Spain like? — ¿cómo es España?
what's the weather like? — ¿qué tiempo hace?
a house like mine — una casa como la mía, una casa parecida a la mía
I found one like it — encontré uno parecido or igual
we heard a noise like someone sneezing — nos pareció oír a alguien estornudar, oímos como un estornudo
•
I never saw anything like it — nunca he visto cosa igual or semejante•
what's he like as a teacher? — ¿qué tal es como profesor?•
to be like sth/sb — parecerse a algo/algn, ser parecido a algo/algnyou're so like your father — (in looks, character) te pareces mucho a tu padre, eres muy parecido a tu padre
•
it was more like a prison than a house — se parecía más a una cárcel que a una casawhy can't you be more like your sister? — ¿por qué no aprendes de tu hermana?
that's more like it! * — ¡así está mejor!, ¡así me gusta!
•
there's nothing like real silk — no hay nada como la seda natural•
something like that — algo así, algo por el estiloI was thinking of giving her something like a doll — pensaba en regalarle algo así como una muñeca, pensaba en regalarle una muñeca o algo por el estilo
they earn something like £50,000 a year — ganan alrededor de 50.000 libras al año
feel 2., 3), look 2., 4), smell 3., 1), sound I, 3., 2), a), taste 3.•
people like that can't be trusted — esa clase or ese tipo de gente no es de fiar2) (=typical of)isn't it just like him! — ¡no cambia!, ¡eso es típico de él!
(it's) just like you to grab the last cake! — ¡qué típico que tomes or (Sp) cojas tú el último pastelito!
3) (=similarly to) comolike me, he is fond of Brahms — igual que a mí, le gusta Brahms
•
just like anybody else — igual que cualquier otroit wasn't like that — no fue así, no ocurrió así
anything, crazy 1., 1), hell 1., 2), mad 1., 1), b)he got up and left, just like that — se levantó y se marchó, así, sin más
4) (=such as) comothe basic necessities of life, like food and drink — las necesidades básicas de la vida, como la comida y la bebida
3. ADV1) (=comparable)•
on company advice, well, orders, more like — siguiendo los consejos de la empresa, bueno, más bien sus órdenes•
it's nothing like as hot as it was yesterday — no hace tanto calor como ayer, ni mucho menos£500 will be nothing like enough — 500 libras no serán suficientes, ni mucho menos
2) (=likely)•
(as) like as not, they'll be down the pub (as) like as not — lo más probable es que estén en el bar4. CONJ*1) (=as) como- tell it like it is2) (=as if) como si5.Nwe shall not see his like again — frm, liter no volveremos a ver otro igual
•
the exchange was done on a like- for-like basis — el intercambio se hizo basándose en dos cosas parecidas•
did you ever see the like (of it)? — ¿has visto cosa igual?sparrows, starlings and the like or and such like — gorriones, estorninos y otras aves por el estilo
•
to compare like with like — comparar dos cosas semejantes
II [laɪk]1. VT1) (=find pleasant)I like dancing/football — me gusta bailar/el fútbol
which do you like best? — ¿cuál es el que más te gusta?
I like him — me cae bien or simpático
I don't like him at all — me resulta antipático, no me cae nada bien
I've come to like him — le he llegado a tomar or (Sp) coger cariño
don't you like me just a little bit? — ¿no me quieres un poquitín?
you know he likes you very much — sabes que te tiene mucho cariño or que te quiere mucho
•
I don't like the look of him — no me gusta su aspecto, no me gusta la pinta que tiene *•
I like your nerve! * — ¡qué frescura!, ¡qué cara tienes!•
well, I like that! * — iro ¡será posible!, ¡habráse visto!•
she is well liked here — aquí se la quiere mucho2) (=feel about)how do you like Cadiz? — ¿qué te parece Cádiz?
how do you like it here? — ¿qué te parece este sitio?
how would you like to go to the cinema? — ¿te apetece or (LAm) se te antoja ir al cine?
how would you like it if somebody did the same to you? — ¿cómo te sentirías si alguien te hiciera lo mismo?
how do you like that! I've been here five years and he doesn't know my name — ¡qué te parece!, llevo cinco años trabajando aquí y no sabe ni cómo me llamo
3) (=have a preference for)I like to know the facts before I form opinions — me gusta conocer los hechos antes de formarme una opinión
4) (=want)I didn't like to say no — no quise decir que no; (because embarrassed) me dio vergüenza decir que no
•
take as much as you like — toma or coge todo lo que quierashe thinks he can do as he likes — cree que puede hacer lo que quiera, cree que puede hacer lo que le de la gana *
•
whether he likes it or not — le guste o no (le guste), quiera o no (quiera)•
whenever you like — cuando quieras5)a) (specific request, offer, desire)would you like a drink? — ¿quieres tomar algo?
would you like me to wait? — ¿quiere que espere?
I'd or I would or frm I should like an explanation — quisiera una explicación, me gustaría que me dieran una explicación
I'd like to take this opportunity to thank you all — quisiera aprovechar esta oportunidad para darles las gracias a todos
I'd like the roast chicken, please — (me trae) el pollo asado, por favor
I'd like three pounds of tomatoes, please — (me da) tres libras de tomates, por favor
b) (wishes, preferences)I should like to have been there, I should have liked to be there — frm me hubiera gustado estar allí
2.VI querer•
as you like — como quieras•
"shall we go now?" - "if you like" — -¿nos vamos ya? -si quieres3.Nlikes gustos mplLIKEhe has distinct likes and dislikes where food is concerned — con respecto a la comida tiene claras preferencias or sabe muy bien lo que le gusta y lo que no (le gusta)
Verb
"Gustar" better avoided ► While gustar is one of the main ways of translating like, its use is not always appropriate. Used to refer to people, it may imply sexual attraction. Instead, use expressions like caer bien or parecer/ resultar simpático/ agradable. These expressions work like gustar and need an indirect object:
I like Francis very much Francis me cae muy bien or me parece muy simpático or agradable
She likes me, but that's all (A ella) le caigo bien, pero nada más
Like + verb ► Translate to like doing sth and to like to do sth using gustar + ((infinitive)):
Doctors don't like having to go out to visit patients at night A los médicos no les gusta tener que salir a visitar pacientes por la noche
My brother likes to rest after lunch A mi hermano le gusta descansar después de comer ► Translate to like sb doing sth and to like sb to do sth using gustar + que + ((subjunctive)):
My wife likes me to do the shopping A mi mujer le gusta que haga la compra
I don't like Irene living so far away No me gusta que Irene viva tan lejos
"How do you like...?" ► Use qué + parecer to translate how do/ did you like when asking someone's opinion:
How do you like this coat? ¿Qué te parece este abrigo?
How did you like the concert? ¿Qué te ha parecido el concierto? ► But use cómo + gustar when using how do you like more literally:
How do you like your steak? ¿Cómo le gusta la carne?
Would like ► When translating would like, use querer with requests and offers and gustar to talk about preferences and wishes:
Would you like a glass of water? ¿Quiere un vaso de agua?
What would you like me to do about the tickets? ¿Qué quieres que haga respecto a los billetes?
I'd very much like to go to Spain this summer Me gustaría mucho ir a España este verano Literal translations of I'd like are better avoided when making requests in shops and restaurants. Use expressions like the following:
I'd like steak and chips ¿Me pone un filete con patatas fritas?, (Yo) quiero un filete con patatas fritas For further uses and examples, see main entry* * *
I
1. [laɪk]1) (enjoy, be fond of)I/we like tennis — me/nos gusta el tenis
she likes him, but she doesn't love him — le resulta simpático pero no lo quiere
how do you like my dress? — ¿qué te parece mi vestido?
how would you like an ice-cream? — ¿quieres or (Esp tb) te apetece un helado?
I like it! — ( joke) muy bueno!; ( suggestion) buena idea!
I like that! — (iro) muy bonito! (iró), habráse visto!
do as o what you like — haz lo que quieras or lo que te parezca
to like -ING/to + INF: I like dancing me gusta bailar; she likes to have breakfast before eight le gusta desayunar antes de las ocho; I don't like to mention it, but... no me gusta (tener que) decírtelo pero...; to like somebody to + INF: we like him to write to us every so often — nos gusta que nos escriba de vez en cuando
2) (in requests, wishes) querer*would you like a cup of tea/me to help you? — ¿quieres una taza de té/que te ayude?
I'd like two melons, please — (me da) dos melones, por favor
2.
vi querer*if you like — si quieres, si te parece
II
1) ( something liked)her/his likes and dislikes — sus preferencias or gustos, lo que le gusta y no le gusta
2) (similar thing, person)the like: judges, lawyers and the like jueces, abogados y (otra) gente or (otras) personas por el estilo; I've never seen/heard the like (of this) nunca he visto/oído cosa igual; he doesn't mix with the likes of me/us — (colloq) no se codea con gente como yo/nosotros
III
adjective (dated or frml) parecido, similarpeople of like minds — gente f con ideas afines; pea
IV
1)a) ( similar to) comoshe's very like her mother — se parece mucho or es muy parecida a su madre
try this one - now, that's more like it! — prueba éste - ah, esto ya es otra cosa
come on, stop crying!... that's more like it! — vamos, para de llorar... ahí está! or así me gusta!
what's the food like? — ¿cómo or (fam) qué tal es la comida?
it cost £20, or something like that — costó 20 libras o algo así or o algo por el estilo
b) ( typical of)it's just like you to think of food — típico! or cuándo no! tú pensando en comida!
2) ( indicating manner)like this/that — así
3) (such as, for example) comodon't do anything silly, like running away — no vayas a hacer una tontería, como escaparte por ejemplo
V
conjunction (crit)a) ( as if)she looks like she knows what she's doing — parece que or da la impresión de que sabe lo que hace
b) (as, in same way) como
VI
a) ( likely)as like as not, she won't come — lo más probable es que no venga
b) ( nearly) -
4 Colours
Not all English colour terms have a single exact equivalent in French: for instance, in some circumstances brown is marron, in others brun. If in doubt, look the word up in the dictionary.Colour termswhat colour is it?= c’est de quelle couleur? or (more formally) de quelle couleur est-il?it’s green= il est vert or elle est verteto paint sth green= peindre qch en vertto dye sth green= teindre qch en vertto wear green= porter du vertdressed in green= habillé de vertColour nouns are all masculine in French:I like green= j’aime le vertI prefer blue= je préfère le bleured suits her= le rouge lui va bienit’s a pretty yellow!= c’est un joli jaune!have you got it in white?= est-ce que vous l’avez en blanc?a pretty shade of blue= un joli ton de bleuit was a dreadful green= c’était un vert affreuxa range of greens= une gamme de vertsMost adjectives of colour agree with the noun they modify:a blue coat= un manteau bleua blue dress= une robe bleueblue clothes= des vêtements bleusSome that don’t agree are explained below.Words that are not true adjectivesSome words that translate English adjectives are really nouns in French, and so don’t show agreement:a brown shoe= une chaussure marronorange tablecloths= des nappes fpl orangehazel eyes= des yeux mpl noisetteOther French words like this include: cerise ( cherry-red), chocolat ( chocolate-brown) and émeraude ( emerald-green).Shades of colourExpressions like pale blue, dark green or light yellow are also invariable in French and show no agreement:a pale blue shirt= une chemise bleu pâledark green blankets= des couvertures fpl vert foncéa light yellow tie= une cravate jaune clairbright yellow socks= des chaussettes fpl jaune vifFrench can also use the colour nouns here: instead of une chemise bleu pâle you could say une chemise d’un bleu pâle ; and similarly des couvertures d’un vert foncé (etc). The nouns in French are normally used to translate English adjectives of this type ending in -er and -est:a darker blue= un bleu plus foncéthe dress was a darker blue= la robe était d’un bleu plus foncéSimilarly:a lighter blue= un bleu plus clair (etc.)In the following examples, blue stands for most basic colour terms:pale blue= bleu pâlelight blue= bleu clairbright blue= bleu vifdark blue= bleu foncédeep blue= bleu profondstrong blue= bleu soutenuOther types of compound in French are also invariable, and do not agree with their nouns:a navy-blue jacket= une veste bleu marineThese compounds include: bleu ciel ( sky-blue), vert pomme ( apple-green), bleu nuit ( midnight-blue), rouge sang ( blood-red) etc. However, all English compounds do not translate directly into French. If in doubt, check in the dictionary.French compounds consisting of two colour terms linked with a hyphen are also invariable:a blue-black material= une étoffe bleu-noira greenish-blue cup= une tasse bleu-verta greeny-yellow dress= une robe vert-jauneEnglish uses the ending -ish, or sometimes -y, to show that something is approximately a certain colour, e.g. a reddish hat or a greenish paint. The French equivalent is -âtre:blue-ish= bleuâtregreenish or greeny= verdâtregreyish= grisâtrereddish= rougeâtreyellowish or yellowy= jaunâtreetc.Other similar French words are rosâtre, noirâtre and blanchâtre. Note however that these words are often rather negative in French. It is better not to use them if you want to be complimentary about something. Use instead tirant sur le rouge/jaune etc.To describe a special colour, English can add -coloured to a noun such as raspberry (framboise) or flesh (chair). Note how this is said in French, where the two-word compound with couleur is invariable, and, unlike English, never has a hyphen:a chocolate-coloured skirt= une jupe couleur chocolatraspberry-coloured fabric= du tissu couleur framboiseflesh-coloured tights= un collant couleur chairColour verbsEnglish makes some colour verbs by adding -en (e.g. blacken). Similarly French has some verbs in -ir made from colour terms:to blacken= noircirto redden= rougirto whiten= blanchirThe other French colour terms that behave like this are: bleu (bleuir), jaune (jaunir), rose (rosir) and vert (verdir). It is always safe, however, to use devenir, thus:to turn purple= devenir violetDescribing peopleNote the use of the definite article in the following:to have black hair= avoir les cheveux noirsto have blue eyes= avoir les yeux bleusNote the use of à in the following:a girl with blue eyes= une jeune fille aux yeux bleusthe man with black hair= l’homme aux cheveux noirsNot all colours have direct equivalents in French. The following words are used for describing the colour of someone’s hair (note that les cheveux is plural in French):fair= blonddark= brunblonde or blond= blondbrown= châtain invred= rouxblack= noirgrey= griswhite= blancCheck other terms such as yellow, ginger, auburn, mousey etc. in the dictionary.Note these nouns in French:a fair-haired man= un blonda fair-haired woman= une blondea dark-haired man= un bruna dark-haired woman= une bruneThe following words are useful for describing the colour of someone’s eyes:blue= bleulight blue= bleu clair invlight brown= marron clair invbrown= marron invhazel= noisette invgreen= vertgrey= grisgreyish-green= gris-vert invdark= noir -
5 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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