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Comment by laichzeit0 | Hacker News Reader

> why is using deep learning to do something useful impossible?

This is completely false. Here are some examples: Google translate. It's infinitely useful. It's not perfect, but it's good enough for me when I want to quickly check whether my translation into my second language is okay, or I'm not sure I got the meaning right of some translation.

Second example: My home security cameras now uses object classification and only alerts me when there's movement in "high risk zones" and it's human. So many stupid false positives of shadows and stray cats completely gone. I'm pretty sure I can fine-tune it with examples of myself and my family and it will ignore them when it's reasonably confident it's them, but I couldn't be bothered.

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Yes. Google Translate is obviously a useful product, and obviously Google will have thousands of areas where they can apply AI (enough that they'd even write frameworks for it). But 99(.99)% of us work in mundane jobs doing boring CRUD apps. My post was meant in this context.

You work in a company making an intranet product for the paper tissue industry. Your manager wants v3.0 to have some AI in it. No one remembers the fiasco when "Cloud" was added in v2.0

so what you're saying is...

you either swallow the CRUD app red pill, or you live long enough to become the "AI a la carte" manager

Google Translate was good before the so-called "AI revolution".

Google Translate today is absolutely spectacular.

A fun game used to be "translate this phrase English to French, then translate it back again and laugh at how meaningless it's made by the round-trip". That doesn't work any more.

As far as I remember it was close to unusable. Even most basic translation to languages besides German, Spanish and French was a nightmare. That was at lest true during my second year at the university (~2010-2011).

From what I've read, the early translations were really rough (but on par with competing free products), until they fired all their linguists and brought in AI guys.

Not exactly. Google Translate was always a statistical approach from day one. That was the reason it was better than the rules engines that dominated before that. The Translate guys really pioneered statistical machine translation.

However it was all done with hand-crafted statistical functions and code. The new stuff using deep learning is the first time it'd have been referred to as "AI".

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