@bekkaboo@girlcock.club Linux itself is heavily using AI models. Therefore, every Linux distribution moving to recent kernel versions is heavily built on top of using AI models. Creating a Linux distribution not heavily using it would require a hard fork of the Linux kernel and many other projects.
It's unclear what would be accomplished by banning AI for a tiny portion of the code while continuing to use Linux, AOSP, Chromium and hundreds of other projects heavily using it. We'd still be benefiting from it.
GrapheneOS is currently defending its use of AI coding tools on Mastodon against complaints by various accounts claiming to be users.
We do not understand where you’re coming from or why you’re so incredibly angry with us. It’s not justified and does not make sense.
It’s not going anywhere because it’s already been here for a very long time, you just didn’t know about it
What people call “AI” is just Machine Learning.
Chatbots, OCR, Text-to-Speech, Speech-to-Text, Computer Vision, Web Search, Spam filtering or Fraud detection, Personalized ADs, even your phone’s camera processing algorithm.
It’s all Machine Learning, but now under new and fancy “AI” name. And it’s been here for decades.
The reason it blew up this much only now is because people realized that the more data you feed into it, the better the output becomes. And so they started exploring it more, to the extent of causing valid ethical concerns.
As someone who despises the current slop hype with a passion, you’re correct on this. But only when you exclude LLMs. Because they are garbage with a net negative for mankind. Machine learning as a field however has very useful applications. Typically such that complement human assessment of data, e.g. re-viewing radiology images to flag potentially anomalous images for a second doctor to look at - not in place of the first doctor.
Instead of going away, the AI will transform. It’s sort of in a bubble state rn. Once it pops, the fields where it’s useful will keep on using it, whilst those applications that have no real grounds for existing will either have their plugs pulled or resources cut.
Pointing to a wiki page instead of actually explaining stuff isn’t going to change my mind.
Now, if you’re talking specifically about LLMs, however (which you could’ve just said so, ffs). I think their scope of application and resources wasted on them will be significantly reduced. Look at what happened to blockchains and NFT. Basically, I expect the same to happen here.
LLMs use a new way of data processing (focused on language rules) which is what brought the boost. Not more training data. More training data is needed for an LLM to reach “maturity” but it also increases cost due to resource usage. And current LLMs have already absorbed basically all of the internet and most books, and since the internet is now full of slop, more unpoisoned training data does not exist. That’s why more does not help anymore.
It’s not going anywhere because it’s already been here for a very long time, you just didn’t know about it
What people call “AI” is just Machine Learning.
Chatbots, OCR, Text-to-Speech, Speech-to-Text, Computer Vision, Web Search, Spam filtering or Fraud detection, Personalized ADs, even your phone’s camera processing algorithm.
It’s all Machine Learning, but now under new and fancy “AI” name. And it’s been here for decades.
The reason it blew up this much only now is because people realized that the more data you feed into it, the better the output becomes. And so they started exploring it more, to the extent of causing valid ethical concerns.
Machine learning isn’t going anywhere. But it’s clear from the context here what we’re referring to when we say AI in this conversation.
As someone who despises the current slop hype with a passion, you’re correct on this. But only when you exclude LLMs. Because they are garbage with a net negative for mankind. Machine learning as a field however has very useful applications. Typically such that complement human assessment of data, e.g. re-viewing radiology images to flag potentially anomalous images for a second doctor to look at - not in place of the first doctor.
Finally, someone gets it!
Instead of going away, the AI will transform. It’s sort of in a bubble state rn. Once it pops, the fields where it’s useful will keep on using it, whilst those applications that have no real grounds for existing will either have their plugs pulled or resources cut.
Apparently, not to me. Explain.
Edit: Downvoting a person for asking clarification? Shame on you lot.
Here’s a starting-point: https://en.wikipedia.org/wiki/Large_language_model
Pointing to a wiki page instead of actually explaining stuff isn’t going to change my mind.
Now, if you’re talking specifically about LLMs, however (which you could’ve just said so, ffs). I think their scope of application and resources wasted on them will be significantly reduced. Look at what happened to blockchains and NFT. Basically, I expect the same to happen here.
And that’s bullshit. The slop machines have already peaked. More input will not improve the results.
Not compared to models from 00s or 10s…
What’s with the reading comprehension, people? I was talking about pre-AI boom vs. now, not now vs. 1-2 years ago, c’mon.
LLMs use a new way of data processing (focused on language rules) which is what brought the boost. Not more training data. More training data is needed for an LLM to reach “maturity” but it also increases cost due to resource usage. And current LLMs have already absorbed basically all of the internet and most books, and since the internet is now full of slop, more unpoisoned training data does not exist. That’s why more does not help anymore.
Touché