Understood. That depends on personal prediction of the research landscape in the future but I would say it is still researched by institutions like DeepMind. But both RL and LLM share a common aspect: they are very, very expensive.

Do you specifically mean applications in NLP? RL seems to have a lot of applications in fields like game playing, robotics, neural theorem proving, etc. which seems to have no direct connection with LLMs

Interesting research! Let's try to convince artists with this kind of work

This is a very good answer! I want to add that apart from generalization, the fact that we have efficient optimization algorithms that can find quite good minima also contributes a lot to the deep learning magic.

Well the traditional ml + dimensionality reduction cannot crack e.g. imagenet recognition

Q1: We don't know yet. Q2: Probably there are other classes but they haven't been discovered or are only at the early age of research.

This is correct for two-layer NNs, not general NNs.

Universal approximation is not enough, you need efficiency to make things work.

DL is the only class of algorithms that beats the curse of dimensionality for discovering certain (very general) class of high dimensional functions(something related to Barron space). Point me out if this is not accurate.

What level of math do you want to do?

Lots of thanks! I didn't receive training from software engineering perspective, which seems to be an important aspect in machine learning.

I'm a beginner in this field and I was wondering what it means for code to be "centralized" and "dry". Does "centralized" mean putting a lot of code in a single file and "dry" means raw code that is not very easy to read but is efficient or have some other advantages?