I live in a foreign country and study the language here. I frequently use machine translation to validate my my own translations, read menus with the Google Translate augmented reality camera, and chat with friends when I'm too busy to manually look up the words I don't understand in a dictionary. What I have learned is that machine translations are extremely helpful in a pinch, but often, a tiny adjustment in syntax, adding an adjective, or other minor edit like that will produce a sentence in English with entirely different meaning.
For context-specific questions it's even worse. The other day a stop owner that sells coffee beans insisted that we try out conversing with Google translate. I was trying to find the specific terms for natural, honey, and washed process. My Chinese is okay, but there's no way to know vocab like that unless you specifically look it up and learn it. Anyway, I felt pressured to go through with the Google translate charade even though I knew how the conversation would go. I said I wanted to know if this coffee was natural process. His reply was 'of course all of our coffees are natural with no added chemicals!' Turns out the word is 日曬, sun-exposed. AI is no replacement for learning the language.
State of the art image classification still classifies black people as gorillas [1].
I rue the day we end up with AI-generated operating systems that no one really understands how or why they do what they do, but when it gives you a weird result, you just jiggle a few things and let it try again. To me, that sounds like stage 4) in your list. We have black box devices that usually do what we want, but are completely opaque, may replicate glitchy or biased behaviors that it was trained on, and when it goes wrong it will be infuriating. But the 90% of the time that it works will be enough cost savings that it will become ubiquitous.
> For context-specific questions it's even worse. The other day a stop owner that sells coffee beans insisted that we try out conversing with Google translate. I was trying to find the specific terms for natural, honey, and washed process. My Chinese is okay, but there's no way to know vocab like that unless you specifically look it up and learn it. Anyway, I felt pressured to go through with the Google translate charade even though I knew how the conversation would go. I said I wanted to know if this coffee was natural process. His reply was 'of course all of our coffees are natural with no added chemicals!' Turns out the word is 日曬, sun-exposed. AI is no replacement for learning the language.
Does "natural process" have a Wikipedia page? I've found that for many concepts (especially multi-word ones), where the corresponding name in the other language isn't necessarily a literal translation of the word(s), the best way to find the actual correct term is to look it up on Wikipedia, then see if there is a link under "Other languages".
Looks like in this case it's only part of a Wikipedia page[0] but the Chinese edition is only a stub page. But your suggestion is absolutely spot-on. One of the things I love about Wikipedia is that it's human-curated for human evaluation, not a "knowledge engine" that produces wonky results.
For context-specific questions it's even worse. The other day a stop owner that sells coffee beans insisted that we try out conversing with Google translate. I was trying to find the specific terms for natural, honey, and washed process. My Chinese is okay, but there's no way to know vocab like that unless you specifically look it up and learn it. Anyway, I felt pressured to go through with the Google translate charade even though I knew how the conversation would go. I said I wanted to know if this coffee was natural process. His reply was 'of course all of our coffees are natural with no added chemicals!' Turns out the word is 日曬, sun-exposed. AI is no replacement for learning the language.
State of the art image classification still classifies black people as gorillas [1].
I rue the day we end up with AI-generated operating systems that no one really understands how or why they do what they do, but when it gives you a weird result, you just jiggle a few things and let it try again. To me, that sounds like stage 4) in your list. We have black box devices that usually do what we want, but are completely opaque, may replicate glitchy or biased behaviors that it was trained on, and when it goes wrong it will be infuriating. But the 90% of the time that it works will be enough cost savings that it will become ubiquitous.
[1]: https://www.theverge.com/2018/1/12/16882408/google-racist-go...