[Aachen]

How much of the workforce is organising and other information dissemination or transformation?

I'm more on the skeptical side than the evangelist, but I can see how large parts of such things could theoretically be shifted away from humans. Planning someone's agenda, preparing relevant documents, arranging and coordinating things, translations (speech or text), narration, grammar checking.... AI is a whole lot of hot air when considering the "second 80%" of the work involved in any of these tasks, but that's still a lot of jobs that may make little sense to start studying these years, until you have some idea how the field will develop or if there's a giant surplus of, say, French-native Spanish language experts. At least for those for whom a given study is not a real passion and they might as well choose something else

[andrekandre]

  > Planning someone's agenda, preparing relevant documents, arranging and coordinating things, translations (speech or text), narration, grammar checking

the issue is, these things "lie" subtly and not so subtly (they make up issues, rename agendas, forget questions and change meanings all the time) and for me that is a deal-breaker for a business tool that i need to rely on

[Aachen]

Yes, for me as well, but large chunks of these tasks seem within the realm of what they can do when you break it up into small enough bits and control the prompt very tightly

Particularly machine translations are no worse than what an untrained native speaker would come up with, and much better than traditional translators (due to some level of context "understanding" - or simulation thereof, at least). At 50x human speed, the energy consumption is also lower than keeping a human alive for that time. There is no scenario in which this capability goes unused

Or grammar checking, if you catch 98% (as even some of the weaker models seem to achieve), the editor who'd otherwise do this can do more intellectually stimulating things

It's not that there's no downsides but it also seems silly to dismiss it altogether

[ben_w]

> Particularly machine translations are no worse than what an untrained native speaker would come up with, and much better than traditional translators

Sometimes. I use Google Translate (literally the same architecture, last I heard), and when it works, great. Every single time I've tried demonstrating that it can't do Chinese by quoting the output it gives me from English-to-Chinese, someone replies to tell me that the translated text is gibberish*.

Even with an easier pair, English <-> German, sometimes I get duplicate paragraphs. And there's definitely still cases where even the context-comprehension fails, as you should be able to see from going to a random German website e.g. https://www.bahn.de/ in e.g. Chrome and translating it into English and noticing the out-of-place words like how destination is "goal", the tickets are "1st grade" and "2nd grade" instead of class.

* I'm curious if this is still true, so let's see:

这是一个简单的英文句子，需要翻译成中文。上次我翻译的时候，有人告诉我译文几乎无法理解。

我不懂中文，所以需要懂中文的人告诉我现在是否仍然如此。

[Aachen]

(not the downvoter)

I'm not sure if we're on the same page. I mean LLMs right? Not whatever Google Translate and DeepL use. The latter was better than gtrans when it launched, nowadays it's probably similar idk, and both are machine learning clearly, but the products(' quality) predates LLMs. They're not LLMs. They haven't noticeably improved since LLMs. Asking an LLM produces better output (so long as the LLM doesn't get sidetracked by the text's contents). Presumably also orders of magnitude higher energy consumption per word, even if you ignore training

I agree that Google Translate, now on par with DeepL's free product afaik (but I'm not a gtrans user so I don't know), is decent but not a full replacement for humans, and that LLMs aren't as good as human translations either (not just for attention reasons), but it's another big step forwards right?

[ben_w]

I'm not sure what DeepL uses, but Google invented the Transformer architecture, the T in GPT, for Google Translate.

IIRC, the original difference between them was about the attention mask, which is akin to how the Mandelbrot and Julia fractals are the same formula but the variables mean different things; so I'd argue they're basically still the same thing, and you can model what an LLM does as translating a prompt into a response.

[Aachen]

I didn't know that! I had heard they made transformers and (then-Open)AI used it in GPT, but that explains how come Google wasn't then first to market with an LLM product when the intended application was translation

[andrekandre]

  > It's not that there's no downsides but it also seems silly to dismiss it altogether

definitely silly to dismiss them all together, but the issue is using it for everything where its not appropriate or unreliable; so in the context of my posting, i cant rely on it for the things i outlined, thats all

[joquarky]

> these things "lie" subtly

Do you think they have intent?

[Aachen]

I assume that's just a manner of speaking, like a judgmental form of hallucination

I remember HN piling on me for saying something along the lines of evolution causing a property (am I stupid, do I not understand that it's not intelligently chosen) rather than some unwieldy statement about a property having a positive selection pressure. I'm also much more familiar with the English phraseology of this non-tech topic now (so I can actually say that in the few words I just used), do we even have that vocabulary for LLMs?