- cross-posted to:
- technology@lemmy.world
- cross-posted to:
- technology@lemmy.world
Seems like he’s been pushed into using LLMs as a way to cope with the deluge of LLM-generated security reports.
Seems like he’s been pushed into using LLMs as a way to cope with the deluge of LLM-generated security reports.
An interesting but valid argument. It doesn’t make AI better than it is, but any human contribution and change can and often is also faulty. People have gaps of knowledge, sometimes unwarranted confidence, other times lack of care, or just miss things. It’s not like we’re comparing the perfect human vs faulty AI.
I haven’t read the original rage/drama but I can imagine if from other drama instances.
This post is certainly a good, founded response.
There’s some valid concerns in AI usage, but unwarranted or inappropriate harsh criticism when it’s an established trusted developer and engineer - if we assumed good practice before then we could assume continued good practice. Maybe LLM is one point of increasing skepticism, but criticism should be open, respectful, and fair.
They invested a lot of time and effort into a public good project. In that context, they deserve at least respectful and non-worst-assumptuous criticism.
I went through the trouble of looking at one of the problematic changes in the latest rsync release, and what happened is that it surfaced a bug introduced in 2007 which was previously silently ignored. That’s definitely a mistake any human contributor could have made.
Yeah, the current backlash over LLMs in any capacity is a meme. It has turned into tribal politics. There is no longer thought behind the criticisms.
Also, it’s not the stochastic prediction part that makes LLMs “not intelligence” to me. It’s that it’s only predicting the next token in a string of text. I don’t believe this can approach what we do. To me it could well be that some other sort of token prediction is what we do even when we introspect and think of a model of the world.
An LLM has an internal state while predicting text. The “next token” chosen takes that state - a model of the world - into account. So a LLM is predicting the next token based on a world model and the previous text.
Saying that it is “only predicting the next token”, without more context, while technically true is very misleading.
Lmao bro, what do you think “stochastic prediction” means? It’s always the people who don’t even understand LLMs defending them the hardest.
No, you just don’t want to believe it.
Oh come on LLM have their uses and to say it is all slop is just a tribal my team thinking. But maybe that is the best humans can achieve.
Thank you for providing a clear example of the “my side good your side bad” style of thinking that completely lacks critical thought.
Most LLM implementations to have come out in the past year have had introspection - a section of text where they’re prompted to think1 about the problem at a meta level which isn’t shown to the users. LLM engineers are actively working on expanding this into a more persistent, consistent, and functional world model - a bunch of text statements that other parts of the implementation are trained to treat1 as probably factually true, which it is regularly prompted to curate1 based on its interpretation1 of user input and other data.
For example, an LLM might have a world model statement that says “As an LLM I may be running at different times. Before stating the current time with confidence, check the current time with an external source such as the UTC API.” so an introspection scratchpad it generates might be “To answer that question accurately I need to know the time. I will refer to the UTC API. Ah, it returned 12:17 on June 3rd 2026. Since Britain is currently at UTC+1 I can confidently say the sun is up in Britain”, and then the text the user sees is “Thank you for asking, the sun is currently up in Britain”.
As for the lack of thought behind LLM backlash, that’s a factor of human psychology. In order to free up limited mental capacity, the human brain automatically simplifies rules it has learned consciously, imperfectly archiving the conscious method of learning it to long-term memory. People made up their minds about LLMs, and now the reasons are archived and no longer necessary for people’s response to LLMs. So now when people see LLMs, they don’t use the thought, they can just do the behavior they decided on and move on with their life.
Re-litigating LLMs feels like going to an old archive and digging through dusty tomes. It can absolutely be worth it, but it’s an effort you’re not going to put in just because you see someone using it or praising it.
Personally, my opposition to non-local LLMs is enshittification. Every habit you let become dependent on LLMs will be used to exploit you. Your habits before LLMs will be archived and too much effort to relearn, so you’ll pay out your ass for a worse service than what you used to be able to do yourself. My opposition to all LLMs is veganism, but that’s a story for a different comment.
1: LLM instruction text anthropomorphises LLMs. LLMs don’t do these cognitive tasks the same way a human would.
I agree, I’ve been recommending people to try to develop some level of nuance on the topic. I understand the fear, hatred, and loathing of AI; especially the way it’s currently being implemented and used. I really do, and I share 99% of the concerns. But there is room for nuance in the understanding of how it’s being used and what it’s being used for and who is using it, and when nuance leaves the room, we’re blind. And blind hatred is never a good thing and it does not lead to good places.
The funny thing is everyone hates AI but it seems everyone is using it also. So what is the truth.