sorry about short-cutting specific parts of your reply Martin (and this reply is also not aimed only at you but answered in a more generic manner to be able to share some thoughts, same applies for Curt)
Its not the tool, its how it is being used.
True. But have you (or anyone else interested) ever wondered
why google search is falling apart ?
This fact is already proven by experts and is merely one of the shortcomings of how current generative AI is implemented and how it is being deployed.
Because the basics of current implementations are the same (feel free to correct me there because I do not keep a close eye on current developments), they will all suffer the same faith.
I do not know if it is relevant for what you had envisioned yourself.
But other's experiences may differ.
My experience seems to be more or less the same as yours.
It takes me more time to instruct than it is to actually write the code myself.
If looking at it as one giant database to extract data from then all I can say to that is that any proper cms should be able to do the same and do it quite more efficient (time-wise and energy wise).
I am not sure but I believe that you Martin_fr are familiar with code-tools (internals) ? so perhaps you might be able to share thought on this.
Ask the question, would it be more efficient to use code-tools to help you write code (f.e. selecting which variable or method you
want to address next or rely on an AI that analyzes the code, index it, and predict which the next 'thing' on the current code-line will be) ?
And with efficient I mean, on every level of efficiency (through the entire/complete chain).
And with that I conclude: There is a reason big tech wants to sell you their AI fairy-tales and hardware.
Funny how everything boils down to why
I was working when the first "AI" wave struck. The company I worked for got very involved and it took two years to put out the fire and
get people to take a realistic look at the technology. But this wave is genuinely different I think.
It is. The generic population got educated from all media outlets and formed 'a picture' of what AI is/means to them (which differs from person to person).
This picture is/was usually formed when this population had no notion whatsoever of what machine learning is and things like chatGPT and openAI did not even existed yet. It usually is a overly romanticized picture.
This picture was then projected to/for those that started using the acronym AI for their projects. It is easy for these companies
to feed this picture with all sort of claims that can never be realized with current technology. But people usually are in nature gullible.
Hence, society has created a perfect storm. And it is very difficult to get rid of it (if at all).
It's a mistake to dismiss results like that this time. It's not the LLM stuff, but the machine learning stuff behind the LLM stuff.
Oh, I do not dismiss anything (I would not even dare).
Current implementation allows for cross-referencing data and makes it plausible that some of that data correlates
with other data (which nobody thought of to do before because... nobody asked the why (not) question, only the how)
Any good cms and any functional brain is able to do the same
That said: I just had some work done by an attorney.
....
Again, any good cms is able to do the same. But you are correct that this is a profession where they protect their business (like hawks).
Maybe that explains why the legal community is so interested in "regulating" the technology!
Don't forget that even if it is their profession to deny any responsibility with such kind of things that you talked about but in the end there must be someone that can be held accountable (obvious reasons).
And on a general note: Note that we did not even got deeper into things such as equality, discrimination, accountability and data
acquisition and manipulation which are a real treat.
But, I have to guess: Nobody seem to care about the why, only the how
fwiw: I do not claim to be an expert on any of these topics. I just keep my eyes and ears open and try to get myself educated on the subject (there are many good lectures from experts that explain how machine learning and language models work even though I might perhaps not always fully grasp every technical detail).