But, if you look at your smartphone, virtually every popular application the average person uses--Gmail, Uber, Instagram, TikTok, Siri/Google Assistant, Netflix, your camera, and more--all owe huge pieces of their functionality to ML that's only become feasible in the last decade because of the research you're referencing.
Machine learning / neural nets also (like I said) get to claim credit for a hell of a lot of things which are just products of colossal advances in hardware – simply of its becoming possible to run statistical methods over very very large '1:1 scale' sample sets – and not due to a specific statistical technique (NN) which is not remotely new and has been heavily researched for about 40-50 years now.
The way people hype AGI/AI/ML whatever undervalues the actual effort behind these remarkable feat. There is so much effort being made to make this work. Deep learning works when it is engineered properly. So it is just another tool in the toolbox!
Look at how graphics community is approaching deep learning. They already had sampling methods but with MLPs (NeRFs), they are using it as glorified database. So it's engineering!
I want to underscore that AI/ML/DL research requires ground breaking innovation not only in algorithms but also in hardware and software engineering.