I don't think your second point follows from your first. ML is not easy to set up or maintain, even if we're just talking about linear regression. Companies trying to make 'data scientists obsolete', usually frame ML as a black box that you can plug data in one end and spit out profits the other. But in reality it doesn't work like that, significant effort has to build and maintain data pipelines, to make sure data quality is good, and to monitor that the ML model is doing anything useful in the end.

It can be easier to set up and maintain than hand-rolled solutions. Best example I can think of is speech recognition - previous systems used hidden markov models, gaussian mixtures, triphones, and all sorts of complex and obscure things that had to be tuned by experts. Now they just use an end-to-end neural network that goes directly from sound to letters (almost anyway; I believe the input is still MFCCs).

I agree it's not magic but it neither are 'manual' methods and at least the maths is a lot simpler.

> Now they just use an end-to-end neural network

You've missed my point. The thing is that building the ML model is NOT the hard part of machine learning in industry. The hard part is building an infrastructure that can make that machine learning model do something useful. It is much harder than people imagine. See for example this great paper by google for more details : https://static.googleusercontent.com/media/research.google.c...

I don't think either of you are strictly wrong, though--the comment's point seems to be that doing things by hand was so much harder that it couldn't even be done to the same level of success that machine learning achieves.

Unless you know something I don't, the SOTA in ASR is a hybrid system that still has all the complexity of the traditional generative model plus NNs on top.

That's true, but I've noticed quite a few pieces of deep learning NLP papers take pains to use competitive baselines (eg state-of-the-art non-DL techniques, more complicated than regression) and have heard that good venues will reject your paper/talk if use poor baselines