Oh yes, I don't think advanced math is required in any way. However, there is a difference in that researchers who have worked with these models for many years (often including having done some of the math) have a very good intuitive understanding of these models. Once you have that, it's fine to be driven by gut feeling. Just like many engineers are driven by gut feeling that come from tacit knowledge through experience.

What is dangerous is reading a 30 minute blog post and getting the illusion of having some kind of understanding, when in reality it can take years to develop that. It's like cloning the postgres Github repository, compiling it, running a few queries, and then saying you've built databases and being hired to become the "database expert" at some company, spreading wrong knowledge left and right.

That's why the popularization of these quick immediate reward tutorials is dangerous. It takes time and effort to become knowledgable at something. Of course, many people are smart enough to know that these tutorials (or cloning the postgres repo) is just the first step on a longer learning journey, and in that case it's totally fine, but there are also many people who start thinking they are experts ready to work on research or production models after going through such things, not being aware of the many things they don't know [0]

[0] https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Du...

It sounds like what you’re complaining about is some people who put papers on arxiv and deceivingly claim to be experts. And kind of implying that these blog posts are to blame, so the blog posts should be retracted due to those dishonest academic people? You’re making a very confusing point.

Not confusing. He's saying that watching Judge Judy for a week doesn't make you a lawyer. And when hiring, be careful because lots of people who claim to be experts are far from it.

That’s valid, but he’s implying that Judge Judy should be taken off air because some people watch it and then pretend to be lawyers. Squelching information seems like a terrible way to eliminate a few impostors downstream.

Yes, but that's a bad example because pretending to be a lawyer is hard. A better example would be gurus spreading nutrition recommendations that are not wrong per-se, but extremely simplified. Nutrition is a complex topic and individual differences make it hard to generalize. Let's say the information are so simplified that they are likely to hurt people who blindly follow them without doing further research. So, should this information be taken off air or not? I would say yes, and perhaps you would say no. In either case, I don't think the answer is quite as clear-cut.

As an FDA law firm engineer, I review and submit about three AI devices a week to FDA (Ive probably done at least 50 AI submissions at this point) As part of that I review the all the submission docs including the design specification for each submission. Im convinced that none of the companies understand how any of the math works and they just submit models they have grabbed from Tensor or Pytorch

I can't say that I'm surprised, but I really appreciate your comment.

This sort of "inside information" is immensely valuable for society, especially in a world consumed with PR.

Whenever people talk about making the FDA less strict I get shivers down my spine. Was the snake oil salesmen really a better medical institution?

The "real math" comes in when you're debugging a huge network that occasionally emits NaNs deep in the reverse pass. At that point, you need to understand the details of gradient descent and a whole lot more practical analytic theory.

> it seems to me that even the experts do little real math and lots of guesswork

Machine Learning is a rich and varied field. Like most applied sciences, there is a spectrum from the heavily applied to the heavily theoretical, to some which try to span both sides of the spectrum at the same time (e.g. https://arxiv.org/abs/1704.04932).

The paper is definitely accessible, but that doesn't mean you don't have to have a solid understanding of math to do this stuff behind the hood. They gloss over stuff like "focal loss" plus this is just an update on some small tweaks they've made, so obviously wouldn't be super math heavy.

All in all, not a great paper to prove your point imo.