Comment on 77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds
lvxferre@mander.xyz 1 month ago
Large “language” models decreased my workload for translation. There’s a catch though: I choose when to use it, instead of being required to use it even when it doesn’t make sense and/or where I know that the output will be shitty.
And, if my guess is correct, those 77% are caused by overexcited decision takers in corporations trying to shove AI down every single step of the production.
bitfucker@programming.dev 1 month ago
I always said this in many forums yet people can’t accept that the best use case of LLM is translation. Even for language such as japanese. There is a limit for sure, but so does human translation without adding many more texts to explain the nuance in the translation. At that point an essay is needed to dissect out the entire meaning of something and not just translation.
lvxferre@mander.xyz 1 month ago
I’ve seen programmers claiming that it helps them out, too. Mostly to give you an idea on how to tackle a problem, instead of copypasting the solution (as it’ll likely not work).
My main use of the system is
It works better than going to Wiktionary all the time, or staring my work until I happen to find some misspelling (like German das vs. dass, since both are legit words spellcheckers don’t pick it up).
One thing to watch out for is that the translation will be more often than not tone-deaf, so you’re better off not wasting your time with longer strings unless you’re fine with something really sloppy, or you can provide it more context. The later however takes effort.
bitfucker@programming.dev 1 month ago
Yeah, for sure since programming is also a language. But IMHO, for a machine learning model the best way to approach it is not as a natural language but rather as its AST/machine representation and not the text token. That way the model not only understands the token pattern but also the structure since most programming languages are well defined.
lvxferre@mander.xyz 1 month ago
Note that, even if we refer to Java, Python, Rust etc. by the same word “language” as we refer to Mandarin, English, Spanish etc., they’re apples and oranges - one set is unlike the other, even if both have some similarities.
That’s relevant here, for two major reasons:
Regarding the first point, I’ll give you an example. You suggested abstract syntax trees for the internal representation of programming code, right? That might work really well for programming, dunno, but for human languages I bet that it would be worse than the current approach. That’s because, for human languages, what matters the most are the semantic and pragmatic layers, and those are a mess - with the meaning of each word in a given utterance being dictated by the other words there.