No! You don't say! Making a machine do something that kind of looks like what you do but is mostly garbage isn't professional?
Fuck AI
"We did it, Patrick! We made a technological breakthrough!"
A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.
I have a coworker who didn't learn English until his mid-20s, and it was his 3rd language. He's very hard to understand and is functionally illiterate in English, which is unfortunate because most of our job is done through email or chat. Sometime will send him an email or chat with a request and he will respond with "Call you" and then immediately call them. They hate it because they have a hard time understanding him, and they never get anything in writing from him.
I suggested that he start using a company provided LLM to take what he wants to write and have it rewrite it for him (or he can write in it one of the other languages he knows better and have it translated). He's started doing this and his performance at work has completely turned around. He is a shining example for how an LLM can be properly used.
Then, there's the VPs in the company who send out emails that have been obviously completely written by an LLM. And they brag about asking an LLM for ideas on how to handle certain situations, or the direction that the department needs to head in. They have outsourced their brains and think it was a brilliant move. They are the ones who deserve scorn.
How is that first example better than traditional Google Translate?
I have compared several more traditional translation engines (Google Translate, Baidu Translate, Bing Translate, DeepL, etc.) vs. several LLM-based translation engines (DeepSeek, Perplexity, and ChatGPT).
There is a HUGE difference in quality. Like you can't even compare them. The latter do far more idiomatic translation than do the former and the quality of the output is higher and more directly usable.
But …
You absolutely must do a back-translation check to ensure that it didn't hallucinate something into your translation. Take your document in A and have it translate into B. Then start a new session, take that translated document B and translate it back to A. Also tell it to analyze B for possible translation errors, unclear areas, etc. If it comes back with nothing more than nit-picky suggestions you're fine. If it translates back stuff with hallucinated content or serious grammatical errors, etc. try again.
It's still faster than and far higher quality than Google/Baidu/Bing/DeepL translation, even with the extra checking step.
Translation is one of the few places I'll say LLMs have value, though if you trust it you absolutely will get burned. You need to check its output.
Because with Google Translate he would be sending privileged company information to Google. With our LLM it all stays in-house. And he does typically write his replies in his broken English and the LLM fixes it to make it more readable, which helps him improve his written English skills.
I mean, if someone came to me and was like "Hey I wrote that paper for you. But I got my idiot little brother to do most of the work, and then I fixed it up," I would also look askance at them.
Good.
"Here are the technical points I am going to implement in my IT service area and in the tactical order they are going to be done. Please dumb this down for me so that a management group can understand it and approve it."
I don't have time to "explain it like you're five", I have real work to do. Judge me all you like.
It definitively makes me look down on the coworkers that do use it.
They seem to stop thinking and do what AI recommends, their code is also a complicated mess.
Their findings, presented in a paper titled "Evidence of a social evaluation penalty for using AI," reveal a consistent pattern of bias against those who receive help from AI. What made this penalty particularly concerning for researchers was its consistency across demographics. They found that the social stigma against AI use wasn't limited to specific groups.
Their findings, presented in a paper titled "Evidence of a social evaluation penalty for being an idiot" reveal a consistent pattern of bias against those who believe in dumb marketing hype sold by the rich to destroy the middle class, push the desperate faces of artists into the mud even more and use a world ending amount of energy to answer questions badly and manipulate public opinion to be stupider and more hateful. What made this penalty particularly concerning for the researchers was its consistency across demographics. They found that the social stigma against AI use wasn't limited to specific groups because unlike techbros and people working in marketing, normal people understand this is all mostly a bunch of bullshit and that inveitably if there are parts to it that aren't bullshit large US corporations sure as hell aren't going to be able to discern them from all the snakeoil salesman nonsense any better than their crazy uncle who believes the world is flat can tell what is real and what isn't.
As researchers ultimately funded by and wholy onboard with the framework of this kind of technology we are concerned we will have no job in the future if people realize how toxic all of this is, we bravely use the intellectual prestige and power we wield as Duke academics to demand corporations and silicon valley be better at obscuring the harm and nonsense at the heart of AI so we can continue to study it and make wishy-washy statements about AI while the status quo continues to enshittify.