Sorry, but a new prompt for GPT-4 is not a paper

I'm not sure this is true.

While modern Adderall has a closely controlled mixture of multiple enantiomers, it hasn't always been this way.

Medicine historically didn't care nearly as much about racemic mixtures, and the possibility of stereo toxicity (eg Thalidomide).

Many drugs in modern human history, including mixed amphetamine salts, have been marketed with very little concern for racemic purity.

This is 100% the case in chess as well. The books before and after the computer era are orders of magnitude different in terms of readability. I think a major shift in society has been in motivation. In the past if you were studying advanced mathematics, let alone writing about it, it was solely and exclusively because you absolutely loved the field. And you were also probably several sigmas outside the mean intellectually. Now? It's most often because of some vague direction such as wanting a decent paying job, which may through the twists and turns of fate eventually see you writing books in a subject you don't particularly have much enthusiasm for.

And the much more crowded 'intellectual market', alongside various image crafting or signaling motivations, also creates a really perverse incentive. Exceptional competence and understanding within a subject makes it easy to explain things, even the esoteric and fabulously complex. See: Richard Feynman. But in modern times there often seems to be a desire to go the other direction - and make things sound utterly complex, even when they aren't. I think Einstein's paper on special relativity vs many (most?) modern papers is a good example. Einstein's writing was such that anybody with a basic education could clearly understand the paper, even if they might not fully follow the math. By contrast, so many modern papers seem to be written as if the author had a well worn copy of the Thesaurus of Incomprehensibility at his bedside table.

Of course we understand how they work, we built them! There is no mystery in their mechanisms, we know the number of neurons, their connectivity, everything from the weights to the activation functions. This is not a mystery, this is several decades of technical developments.

> it's akin to evolution - we understand the process - that part is simple.

There is nothing simple about evolution. Things like horizontal gene transfer is very much not obvious, and the effect of things like environment is a field of active research.

> But the output/organisms we have to investigate how they work.

There is a fundamental difference with neural networks here: there are a lot of molecules in an animal’s body about which we have no clue. Similarly, we don’t know what a lot of almost any animal’s DNA encodes. Model species that are entirely mapped are few and far between. An artificial neural network is built from simple bricks that interact in well defined ways. We really cannot say the same thing about chemistry in general, much less bio chemistry.

Yeah, you might understand it if you study psychology or perhaps sociology.