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| John McCarthy |
In a famous summer course that took place at
Dartmouth College in 1956, the term artificial
intelligence was applied for the first time to all those
computer programs that perform tasks traditionally considered exclusively
human, such as playing chess and translating from one human language to another.
Those attending the course, led by John McCarthy, felt optimistic enough to
predict that in ten years those two problems would have been completely solved.
Thus, they hoped that by 1966 there would be programs capable of defeating the world
chess champion, and others that would translate perfectly between any two human
languages.
In March 1961, my uncle, Felipe F. Moreno, then
chief of Spanish translators at the headquarters of the International
Telecommunication Union (ITU) in Geneva, wrote in the ITU magazine an article
on machine translation and how it could affect human translators, which proves that the question was hot. Shortly
afterwards, when the deadline announced by the artificial intelligence
forerunners had been reached, with both problems far from being solved, it was obvious
that they had been overly optimistic.
We know that the goal of writing a program that
would defeat the world chess champion was met in 1997, when Deep Blue defeated
Garry Kasparov, the champion in that year. The other problem, machine
translation, was even more difficult. At the end of the sixties the following
anecdote was well-known in the computer-programming world:
To test a couple of automatic
translation programs, one from English to Russian, the other Russian to English,
the first program was given the following text of the Gospel (Mat.26: 41): The spirit is willing, but
the flesh is weak. The result of the Russian translation was passed as
input to the Russian-English translator, and the result was: The vodka is good, but the
meat is spoiled.
The anecdote is probably apocryphal, but it
expresses quite well the problem of machine translation: human languages are
ambiguous, which makes translation very difficult. Ambiguity can be syntactic,
as in the following examples:
John saw the man on
the mountain with a telescope. Who is on the mountain? John, the
man, or both? Who has the telescope? John, the man, or the mountain?
Time flies like an
arrow. This phrase
has three other possible syntactic interpretations, in addition to the usual.
One of them could also be expressed thus: the flies of
time do like an arrow.
Ambiguity can also be semantic,
as in these examples:
We can meet at the
bank (a
building or the bank of a river?)
Every man loves a
woman. Every
man loves the same woman or a different woman for each man?
Another problem with these ambiguities is that
they are usually different for different languages, which makes automatic translation
difficult, as the actual meaning of many phrases depends on a very broad
context, including general knowledge about the world which computer programs do
not have. This is the main reason why the research on automatic translation took
a long time to produce some results.
In the late 1970s, the Japanese government
decided to embark on a project that would put their country at the forefront of
computing research. Apparently they did not want the Japanese to be considered as
efficient copiers of the technology developed by other countries, so for once they
wanted to be copied. So they started the fifth generation
project, with the following aims:
- A computer Hardware adapted to make it easier to
build artificial intelligence applications.
- A computer Software capable of interacting with
the user in their own language (English and Japanese) and translating
correctly between those two languages.
The fifth generation project was to last ten
years and ended in the early 1990s in a complete failure. The supposed fifth
generation computers that were built turned out to be ordinary personal
computers, equipped with a firmware that allowed them to understand
the Prolog language. This was not new, as the first personal computers had a firmware
that enabled them to understand the Basic language. The major objectives
(machine translation and natural language understanding) were not achieved.
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| Google Translate icon |
The project was successful in the sense that it
pushed other countries to launch less ambitious projects, some of which did
lead to reasonable results. For example, in the European Union, where the
translation of documents between official languages takes a significant
proportion of the budget, the EUROTRA project was launched, whose
initial objective (correctly translating texts between two natural languages)
was finally reduced to a simpler, achievable goal: build tools that would help
human translators increase their performance (computer-aided
translation).
A modern tool of this type is Google Translate. The translations it provides
are often made fun of, with examples like the following:
The Spanish sentence Me
daría de tortas (I would kick myself)
is translated thus by Google Translate: I would give of
cakes.
And the sentence No se
anda con chiquitas (he means business)
is translated thus: She does not hang out with little girls.
Yes, it's funny, but if you use Google
Translate and accept its translations just as they come, you are sorely
mistaken. This tool is an aid to the human translator, who must take a part in
the translation process. The translations offered by Google Translate must be revised
and corrected, but even so the tool is very useful, as indicated by an example
drawn from my own experience: before using Google Translate, it took me two to
three months to translate one of my novels from Spanish into English. Since I started
using the tool, that time has dropped to about two weeks, for a comparable final
quality of the translation.
Manuel Alfonseca
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