There is an ongoing debate on whether AI software should go open source or not (for example Bostrom’s paper Strategic Implications of Openness in AI Development). Now our current concern is of whether MT software should go open source or not. Prima facie, for safety reasons, it would be better to render public MT code, thus allowing anyone to check the code and find eventual errors, … Such openness would notably be a defense against the AI control problem , in short, the fact that superintelligence could harm humans. From this standpoint, it seems that publicness of code is much better than privateness. Regarding rule-based translation (the distinction between statistical and rule-based MT is not as clear-cut as one could think at first glance, since some rules could be applied on a statistical basis), it would allow people to check step-by-step the resulting translation. It seems better transparency should be attained accordingly.
Another advantage or publishing the code would be to allow anyone to improve it and extend its capabilities, notably by adding new modules targeted at new languages (human languages’ count being around 7000).
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To begin with, let us state the 1% problem, for machine translation: it seems some 99% accuracy in machine translation could be attainable but the remaining 1% (1% is just a given number, somewhat arbitrarily chosen, but useful to to fix ideas) may be hard of even very hard to reach. Now a question arises: is some progress on the remaining 1% problem attainable without general-purpose AI. Prima facie, the answer is no. For it seems that progress on the remaining 1% problem requires, for example, some abilities such as being able to find the translation of a given word on external databases. For it will occur sometimes that the 1% untranslated will be due to the presence of a new word, for instance very recently created, and thus lacking in the MT internal dictionary. In order to find the relevant translated word, the machine should be able to search and find it on external databases (say, the web), just as a human would do. So, solving the remaining 1% problem requires – among other capabilities – any such ability which is part of a general-purpose AI.
Artificial general intelligence (AGI) is prima facie a somewhat abstract notion, that needs to be refined and made more explicit. Problems encountered in implementing machine translation systems can help make this notion more accurate and concrete. The ability to find the translation of a given word on external databases is just one of the required abilities needed to solve the remaining 1% problem. So we shall mention some other abilities of the same type later.
Just powered the new grammatical engine: it seems to allow for some interesting things, notably related to expressions. The case at hand is the French expression “parler comme un moulin” (talk the leg off a chair) that translates into Corsican as dì quant’è sette. To be continued…
In the ongoing debate on safe IA, it is a relevant open question of whether rule-based MT is more ethical than statistical MT. Here are some arguments in favor of rule-based MT in this context (without blaming statistical MT which has its own strengths):
it emulates human reasoning: it translates a text just as a human would do
there is much control on rule-based MT since the resulting translated text can be traced back: a detailed step-by-step translation process can be provided if required
rule-based MT can be consistently part of and integrate itself into a whole project of brain emulation, which emulates general human reasoning
Let us consider a specific kind of superlative. Such form specific to Corsican language is notably mentioned by grammarian and author Santu Casta, in his Punteghju, who recommends the following translation of “C’était le village le plus riche du canton” (It was the richest village of the canton): Era u più paese riccu di stu cantone (pages 26 & 54-55). The structure is original in the sense that the comparative (più) precedes the noun (campanile, bell tower) that precedes the adjective (altu, high).
Here is the little progress of the day, which concerns the verb “chjamà” (to call). Preposition ‘à’ need to be added when the verb “chjamà” is followed by a person name. This is little improvement, but small streams make big rivers …
A jeweler examines an emerald. “Aha,” he says, “another green emerald. In all my years in this business, I must have seen thousands of emeralds, and every one has been green.” We think the jeweler reasonable to hypothesize that all emeralds are green. Next door is another jeweler having equally comprehensive experience with emeralds. He speaks only the Choctaw Indian language. Color distinctions are not as universal as might be thought. The Choctaw Indians made no distinction between green and blue—the same words applied to both. The Choctaws did make a linguistic distinction between okchamali, a vivid green or blue, and okchakko, a pale green or blue. The Choctaw-speaking jeweler says: All emeralds are okchamali. He maintains that all his years in the jewelry business confirm this hypothesis. (William Poundstone, Labyrinths of reason)