A specific example from me would be implementing LLM AI into my code (genetically) and without more details than that I’ll get people demanding that I don’t do that and giving suggestions for what I should do.
Suggestions are cool, but I’m gonna ask why I should not put LLM in my code in a generic sense just to have my question ignored or have lies and insults hurled my way
It’s cool if you want to answer that question, I’m just curious about other people’s similar story about receiving resistance to follow up questions if you just have to say those people aren’t worth it or you feel like you missed something you shouldn’t have in those situations.
yeah i see that marx said
but that’s marx just saying industrialization is threating working class. im not seeing much myth just boring explanations of workers relation to machinery
I do use perplexity and chatgpt to code a lot. i really rather not go to stack overflow and try to understand three posts and figure out an implementation. I’m fine with that being automated
I use the term myth loosely in abstraction. Generalization of the tools of industry is still a mythos in an abstract sense. Someone with a new lathe they bought to bore the journals of an engine block has absolutely no connection or intentions related to class, workers, or society. That abstraction and assignment of meaning like a category or entity or class is simply the evolution of a divine mythos in the more complex humans of today.
Stories about Skynet or The Matrix are about a similar struggle of the human class against machine gods. These have no relationship to the actual AI alignment problem and are instead a battle with more literal machine gods. Point is that the new thing is always the boogie man. Evolution must be deeply conservative most of the time. People display a similar trajectory of conservative aversion to change. In this light, the reasons for such resistance are largely irrelevant. It is a big change and will certainly get a lot of push back from conservative elements that collectively ensure change is not harmful. Those elements get cut off in the long term as the change propagates.
You need a 16 GB or better GPU from the 30 series or higher, but then run Oobabooga text gen with the API and an 8×7b or like a 34b or 70b coder in a GGUF quantized model. Those are larger than most machines can run but Oobabooga can pull it off by splitting the model between CPU and GPU. You’ll just need the ram to initially load the thing or deepspeed to load it from NVME.
Use a model with a long context and add a bunch of your chats into the prompt. Then ask for your user profile and start asking it questions about you that seem unrelated to any of your previous conversations in the context. You might be surprised by the results. Inference works both directions. You’re giving a lot of information that is specifically related to the ongoing interchanges and language choices. If you add a bunch of your social media posts, it is totally different in what the model will make up about you in a user profile. There is information of some sort that the model is capable of deciphering. It is not absolute or like some kind of conspiracy or trained behavior (I think), but the accuracy seemed uncanny to me. It spat out surprising information across multiple unrelated sessions when I tried it a year ago.
I actually didn’t pursue a an llm ai project because the suggested model needed like 32 gigs of ram (i dont have that and i dont want to by a machine for that project).
i jokingly call llm ai dubious linear algebra. i try to see an arguement against llm ai, like i sided with the writers guild in the strike and I can sympathize with ai trained on their work taking there job so they lost out on income and job they want, but im a socialist so i believe that the economy should provide them a house and food without having to work and that shouldn’t need to rely on writing gigs to survive