I'm with you 100%. RF has been around for ages, but it still is "black magic" to most EEs (after most people finish the standard 100 level courses describing op-amps, people tend to go into the digital domain and leave analog work to that small demographic). One EE will be able to design a fantastic 7 layers of poly, multiprocessing chip in his garage using Cadence and the TSMC 65nm libs, while someone else will be able to design a flawless cavity filter at 16 ghz. People have specific domains of expertise, even when they hold the same "EE" or "CS" or "Math" degree from the same university, largely based on which courses they elected to take in their 3rd and 4th year.
Likewise, fields advance quickly. I can grok how an z80 or 6502 works from NAND to Tetris, but even a mediocre second year grad student would wipe the floor with me. I, too, went pretty far down the road of mathematics, but watching MSRI lectures from the last few years leaves me struggling to keep up, in the field (algebraic topology) where I once felt comfortable. If you don't keep up with your field you're going to be lost.
The reason I think the 'black magic' trope keeps on being bandied about is because most people reading the articles describing ImageNet et al just don't have the background necessary to grok it[1]. If you had asked them a year ago what the convolution operator was, they'd have scratched their head. When they try to go and read that ImageNet paper they'll be left even more confused because the last time they thought about linear algebra was in their freshman year of uni. It'd be analogous to trying to write some computational fluid dynamics modeling software after not having taken/not touching diff eqs for a decade.
[1] This isn't to disparage those who didn't- everyone has their domain of expertise. I'm just trying to emphasize why the conception of 'black magic' exists. It's quite simple - when one has a tenuous grasp on the foundational knowledge upon some theory is built, you will have difficulty learning abstractions built upon said foundations.
Ah, this is interesting, because I've recently dabbled a bit in RF. My path went like this:
1) Interested in doing something with RF, don't know much about it, know that people say it's black magic.
2) Do some research... Ah, this is a pretty deep topic, and it might take a while to develop the necessary intuition.
3) Become competent enough to solve my immediate problem, recognize that it is a extensive field in which there is a lot of specialized practical knowledge that could be acquired.
4) Accept that I have higher life priorities than to go down the RF rabbit hole, but feel that I could learn it if I wanted to invest the time. No longer feels like black magic.
I think there is a distinction between fields like deep learning and RF, where most of the information is public if you know where to look, and say, cryptanalysis or nuclear weapon design or even stage magic, where the details and practical knowledge of the state-of-the-art are more locked behind closed doors. And for a field that you're not familiar with, it can be initially unclear which category it falls into. I think the existence of public conferences on the topic is a good indicator, though.
Likewise, fields advance quickly. I can grok how an z80 or 6502 works from NAND to Tetris, but even a mediocre second year grad student would wipe the floor with me. I, too, went pretty far down the road of mathematics, but watching MSRI lectures from the last few years leaves me struggling to keep up, in the field (algebraic topology) where I once felt comfortable. If you don't keep up with your field you're going to be lost.
The reason I think the 'black magic' trope keeps on being bandied about is because most people reading the articles describing ImageNet et al just don't have the background necessary to grok it[1]. If you had asked them a year ago what the convolution operator was, they'd have scratched their head. When they try to go and read that ImageNet paper they'll be left even more confused because the last time they thought about linear algebra was in their freshman year of uni. It'd be analogous to trying to write some computational fluid dynamics modeling software after not having taken/not touching diff eqs for a decade.
[1] This isn't to disparage those who didn't- everyone has their domain of expertise. I'm just trying to emphasize why the conception of 'black magic' exists. It's quite simple - when one has a tenuous grasp on the foundational knowledge upon some theory is built, you will have difficulty learning abstractions built upon said foundations.