Text translation

We write code. Sometimes we forget people read things in other languages. Text translation in artificial intelligence (AI) solves that. She reads a sentence in French, turns it into English, and does it quickly enough that we barely notice.

Machine translation

The old rule was simple: swap words with their dictionary twins. That’s machine translation. It works, sort of. “Je suis froid” becomes “I am cold,” which is fine. But “J’ai froid” becomes “I have cold,” which is not. She misses the meaning because rules only carry her so far.

Neural translation

Neural machine translation changed the game. Instead of juggling rules, she learns patterns from millions of examples. We give her pairs of sentences across languages. She trains on them. Over time she guesses context, not just words. That’s why “J’ai froid” now lands as “I’m cold.” It feels less like a puzzle, more like speech.

How we use it

We drop an API call in our app, and suddenly our text field speaks ten languages. Users don’t care what engine drives it; they just want to message their friend in Tokyo. Our job is to hide the machinery and let her do the heavy lifting.

Limits

She’s not perfect. Idioms confuse her. Cultural nuance leaks out. We get “kick the bucket” as “strike the pail.” Good for a laugh, not for clarity. That’s the tradeoff: speed and scale over nuance.

Our thought

We build tools so people understand each other. Watching her translate feels like watching someone learn to listen better with every try. We should code with the same patience.