> Most of the time I do not know where the terms fit in the big picture.
Nor do the majority of "AI" experts and consultants that I see on LinkedIn, Twitter or in podcasts.
The S/N ratio is very low in this field. Just pick some documentation from "industry leaders" like Langchain and see that not only is it already and always outdated, it sometimes simply contradicts itself.
In the "blockchain hype" this was similar, so I guess it's a trait of the hype train.
Totally agree with the above, although I’m not sure that documentation on tools like Langchain is a reflection of the hype in the way social media is. I think in that case it’s just a reflection of the pace things are moving at.
I mean yes, this is what a rapidly expanding field looks like that's probing the boundaries of its problem space. Kind of like following physics in the early to mid 1900s. Different classes of problems have barely been tested against each other, much less fully explored themselves.
In some ways it reminds me of the earlier days of the internet when progress was still very rapid.
Nor do the majority of "AI" experts and consultants that I see on LinkedIn, Twitter or in podcasts.
The S/N ratio is very low in this field. Just pick some documentation from "industry leaders" like Langchain and see that not only is it already and always outdated, it sometimes simply contradicts itself.
In the "blockchain hype" this was similar, so I guess it's a trait of the hype train.