This feels much like the sentiment in the field about two years ago or so. While I feel like the "alchemy" storyline is still somewhat in play, most of the big important parts of the deep learning process have enough ideological linear approximators stacked around them that if you know what you're doing or looking at, you can jump to an unexplored trench with some reasonable feeling about whether you'll get something good or not. I feel like the "alchemy" approach is when people new to the field are innundated with information about it, and while I think that still holds, there very much is a well-understood science of principles in most parts of it.

There's the neural tangent kernel work that's achieved a lot, and the transformers themselves are really taking off a lot as the blockwise/lower rank approximation algorithms look more and more like circuits built off of basic, more well-established components.

"An average Joe could probably write influential ML papers by gluing RNN/GAN layers to existing models and fiddling with the parameters until they beat current state of the art. In fact, in NLP models, this is essentially what has happened with roBERTa, XLNET, ELECTRA, etc. They're all somewhat trivial variations on Google's BERT, which is more creative but yet again built on existing models."

This feels like it trivializes a lot of the work and collapses some of the major advancements in training at scale down to a more one-dimensional outlook. Companies are doing both, but it's easy to throw money and compute at an absolutely guaranteed logarithmic improvement in results. It's not stupidity, it's just reducing variance in scaling known laws as we work on making things more efficient, which weirdly enough starts the iterative process of academics frantically trying to mine the expensive, inefficient compute tactics to flag plant their own materials.

With respect to you comment on protein folding and such, I feel you might have missed a lot of the major work in that aren more recently. There really and truly been some field-shattering work on that in combining deep learning systems with last-mile supervision and refinement systems. I'd posit that we're very much out of the wild west and in the mild, but still rambunctious west, if I were to put terms on it.

With reference to guess and check -- yes, that especially was prevalent and worked 2-3 years ago and I'd be in favor of advocating that it does still happen somewhat in a more refined fashion, but I personally believe we'd not get too far beyond the SOTA if we're not working (effectively) with your data manifold now and tightly incorporating whatever projections/constraints of that data distillation process into your network training procedure. I really do agree with you in that I think average Joe breakthroughs will happen and continue to benefit the the field, and I'd certainly agree that there's always going to be the mediocre paper churn of paper mills I think that you alluded to trying to justify their own existence as academics/paper writers, but I really do legitimately think there's enough precedent set in most parts of the field that you need to have some kind of thoughtful improvement to move forward (like AdaBelief, which is still terrible because they straight up lie about what they do in the abstract, even though the improvement of debiasing the variance estimates during training is an exceptionally good idea).

Just my 2c, hope this helps. I think we may have a similar end perspective from two different sides, like two explorers looking at the same peak from the different side of the mountain. :thumbsup: