I started listening to this article (using a text to speech model) shortly after waking up.
I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.
I'm by no means an expert in the field of ML, so perhaps I am just not the intended audience. I'm curious if other people here felt the same way when reading though.
Hopefully this observation / opinion isn't too negative.
To me, it reads like a survey paper intended for (and maybe written by) a researcher about to start a new project. I am not a researcher in this space but I have dabbled elsewhere, so it is somewhat accessible. The degree to which one leverages existing jargon in their writing is a choice, of course.
I am curious -- what would have made it more effective at conveying information to you? Different people learn differently but I wonder how people get beyond the hurdles of jargon.
Yeah I'm not sure if it's just me and my learning style or if researchers purposefully use terminology that's obstructive to understanding to maintain walled gardens. I don't think my reading comprehension level is particularly low!
Usually the best way to learn about things like this for me is to see some actual code or to write things myself, but the lack of coding examples in the text isn't the thing that I find troubling. I don't know, it's just.. like, excessively pointer heavy?
Maybe if you've been in the field long enough, reading a particular term will instantly conjure up an idea of a corresponding algorithm or code block or something and that's what I'm missing.
Thank you for the feedback! I'm sorry you found it jargony/less accessible than you'd like.
The intended audience was my team and fellow practitioners; assuming some understanding of the jargon allowed me to skip the basics and write more concisely.
I work in the field. The amount of jargon is indeed large but it's not out of the ordinary. It's simply how things are referred to. If the author explained what everything is the content would span a textbook.
That being said I do find the content difficult to understand, and I think reading the actual papers would be much more enlightening. But it's a great survey of all the things people have done.
I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.
I'm by no means an expert in the field of ML, so perhaps I am just not the intended audience. I'm curious if other people here felt the same way when reading though.
Hopefully this observation / opinion isn't too negative.