Has anyone written about how textual generative AI feels strangely close to toxic masculinity in some respects? The absolute confidence in everything stated, the lack of understanding of the consequences of getting that confidence wrong for important questions, the semi-gaslighty feeling when it “corrects” itself when you call it out on something. It so often feels like talking to someone one would despise and avoid in “real life.” I’m curious if anyone did some writing on this.
In my mind, the people most likely to use "AI" for things are the ones who sort of know what they want, but don't know how to get it.
So you ask for code to do something, and the LLM spits out something glommed together from Stack Overflow posts or Reddit. How do you know it does what you wanted? How do you debug it if it doesn't work?
If these actually worked, I'd love to select a hunk of code, and have something spit out basic unit tests, or a reasonable documentation outline. Or even check for logic or security errors. How about figuring out how to upgrade my code to eliminate out-of-date libraries?
My fantasy LLMs that actually do something useful are also not trained on data stolen from the Internet. And they don't use enough electricity to power a country, or evaporate a big city's water supply.
FreeCodeCamp released today a new course for fine tuning LLM models. The course, by Krish Naik, focuses on different tuning methods such as QLORA, LORA, and Quantization using different models such as Llama2, Gradient, and Google Gemma model.