I have to play the native Arabic speaker resident here and weigh on this controversy

1) The guy wrote يصبحهم (non standard Arabic) but it was picked as يذبحهم by FB which translates to (He slaughters/slays them)

2) The smaller problem lies in that the word يصبحهم is not standard Arabic and probably Palestinian slang like in Egypt we got many variations on the morning greeting like صباحو or صبّح صبّح etc etc

3) The bigger problem was why FB translator went out of its way to change the letter from ص to ذ instead of returning a "Not Found" or "Please double check spelling and try again" or "Did you mean ....?"

It's really very strange.

Most sources for machine translation from Arabic-English pairs are data from US Army / anti-terrorist data.

So the data are learned to translate terrorist messages very well, as well as army terms. However, it doesn't translate day-to-day use that well.

Machine learning is only as good as the source data - and Arabic-English language pair specifically has data from US military complex, that loves translating terrorist messages. So translating Al Qaeda messaging will be good, translating regular message boards will be worse. The machine learns to translate everything as an attack, since that is what it knows.

(I cannot give you exact sources, but I knew people from inside Google. Facebook uses Bing, but I think the data sources will be similar.)

> Most sources for machine translation from Arabic-English pairs are data from US Army / anti-terrorist data.

Interesting! This is HN, where's the source?

> I cannot give you exact sources

I find your claim hard enough to believe but this seals the deal.

It'll turn out in due time that Bin Laden was just sending the US his best wishes.

Joking of course, but up to a point. There are cases of words from the "enemy" deliberately translated in their more literal sense when their sense is figurative (one for all, "death to America"- even the official translation is "down with America"). And statements by Bin Laden himself have been deliberately ignored in the parts that made more sense from a political point of view, to reduce them to a more comfortable "we hate you because you're free".

Given that both Arabic and English are official languages of the UN (and in which all UN documents are written), wouldn't those documents form (a large part) of the data for training? Not that that would have much in the way of "Good morning" in it except perhaps speech transcriptions.

You're going to have to back that up. This would be a clearly biased dataset.

Makes me less confident in US CT capabilities.

Right, though this was the slightly less textbook (but just as common in day-do-day language) "Yisbah-hum", a shortened and less formal 'good morning' greeting.

A while back I heard a story from a doctor friend about colleagues who would use Google Translate to translate notes to Spanish-speaking patients. That kind of stuff is an accident waiting to happen.

The issue is not machine learning per se although that's behind a lot of the free services that have popped up. Rather it's that once you get outside the scope of translations the system can handle the results may be catastrophically bad. You can't judge the risk unless you understand the assumptions behind the translation service. Most people don't know this and just use free services without thinking.

Always get the machine to translate it back again as a first check that you haven't got something that the system isn't good at. I use it often when I write Norwegian because even after thirty years I still don't remember what gender everything is (I'm English but I write in Norwegian when writing to the locals). If I don't get my original Norwegian back I know it's worth a closer look.

Doing it from your own language to a language you don't speak at all without at least that simple double check is asking for trouble.

More importantly, why are police in a multilingual nation relying on buggy machine translation to make arrests. It's not like it was a post in some obscure language from PNG or something.

It's not that uncommon, algorithms make a lot of mistakes and this is becoming a critical issue as more and more of our life is ruled by algorithms.