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I’ll equate this to being critical of any early technology as if no further development will happen. Translation, good translation, is not easy. If it were, i

I’ll equate this to being critical of any early technology as if no further development will happen. Translation, good translation, is not easy. If it were, it would have been a solved problem years ago. And, yes, some languages pose more challenges than others. I often have to write the same text in multiple languages. I usually write in English and use Google Translate to quickly generate a rough draft translation in other languages, say, Spanish. I then copy-paste the translation, proofread and edit. It is a huge time saver. Aside from not having to re-write the material, the cognitive load of the proofreading and editing process is significantly lower.

glenstein 7y ago
I guess the right things to take from the article are (1) the validity of the criticism, as a legitimate face value observation, but also as a way of sketching the kinds of problems and areas where we hope for progress and (2) the fact that Google Translate is nevertheless useful in its current form. I think his overall thesis isn't a declaration that no further progress will be possible, but that it may have to draw from resources that are more than just statistical associations of words - things like interior human experiences. So it would not just measure words against surrounding passages, but against some understanding of concept generation that emerges from human subjectivity. And, this is where I think a fuse blows for many people, things get complicated, circuits fry, and people find themselves filled with an urge to declare"well, computers just CAN'T do X." But I think what Hofstadter is saying is that it is a hard problem but also the kind of thing that in principle could be done to make progress on translation.
- robomartin 7y ago
  Achieving human-level understanding is the canonical problem in AI. I think I can say we don’t yet know how to encode and represent this thing called understanding. We are excellent at classification and sub-par at understanding, with translation being just one of the many application domains that will benefit when we go through the understanding inflection point.
uryga 7y ago
[edit: see ~gleinstein's sibling comment – it's what i was trying to say, but articulated much better] > I’ll equate this to being critical of any early technology as if no further development will happen. i read it as saying that the current approach to translation, while a useful approximation, can't ever really work, not because it's "early in its development", but because it's fundamentally not enough – the software needs some actual "understanding of what is being said" to accurately translate. from TFA: > The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to “decode”—to get away without worrying about what understanding and meaning are. [after showing some instances where Translate fails:] > We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered “his” and “hers.” Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s familiar solely with strings composed of words composed of letters. It’s all about ultrarapid processing of pieces of text, not about thinking or imagining or remembering or understanding. It doesn’t even know that words stand for things. > Let me hasten to say that a computer program certainly could, in principle, know what language is for, and could have ideas and memories and experiences, and could put them to use, but that’s not what Google Translate was designed to do.

glenstein 7y ago

I guess the right things to take from the article are (1) the validity of the criticism, as a legitimate face value observation, but also as a way of sketching the kinds of problems and areas where we hope for progress and (2) the fact that Google Translate is nevertheless useful in its current form. I think his overall thesis isn't a declaration that no further progress will be possible, but that it may have to draw from resources that are more than just statistical associations of words - things like interior human experiences. So it would not just measure words against surrounding passages, but against some understanding of concept generation that emerges from human subjectivity. And, this is where I think a fuse blows for many people, things get complicated, circuits fry, and people find themselves filled with an urge to declare"well, computers just CAN'T do X." But I think what Hofstadter is saying is that it is a hard problem but also the kind of thing that in principle could be done to make progress on translation.

robomartin 7y ago
Achieving human-level understanding is the canonical problem in AI. I think I can say we don’t yet know how to encode and represent this thing called understanding. We are excellent at classification and sub-par at understanding, with translation being just one of the many application domains that will benefit when we go through the understanding inflection point.

uryga 7y ago

[edit: see ~gleinstein's sibling comment – it's what i was trying to say, but articulated much better] > I’ll equate this to being critical of any early technology as if no further development will happen. i read it as saying that the current approach to translation, while a useful approximation, can't ever really work, not because it's "early in its development", but because it's fundamentally not enough – the software needs some actual "understanding of what is being said" to accurately translate. from TFA: > The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to “decode”—to get away without worrying about what understanding and meaning are. [after showing some instances where Translate fails:] > We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered “his” and “hers.” Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s familiar solely with strings composed of words composed of letters. It’s all about ultrarapid processing of pieces of text, not about thinking or imagining or remembering or understanding. It doesn’t even know that words stand for things. > Let me hasten to say that a computer program certainly could, in principle, know what language is for, and could have ideas and memories and experiences, and could put them to use, but that’s not what Google Translate was designed to do.