Thanks for sharing, obviously useful stuff, and you did a great job of finding and using examples illustrating your point, but like some others I am not really in agreement with the conclusion.
One of the powers of calculus is to reveal hidden dimensions.
Where derivatives or integrals reduce to constants, we see possible hidden dimensions.
Gravity is an example. The speed of light is another.
Machine learning is not currently capable of revealing any significance of those things, beyond what we already know and take for granted, when what is really needed, to expand our knowledge as necessary to assure our survival as a species, is a deeper mathematical understanding of why and how certain things happen.
For example, the real reason why all matter is attracted to, and sticks to all other.
A movement might be deduced, which explains that behaviour.
If we knew and accepted the real reason for that, perhaps we might also deduce the reason wealth seems to do the same, it attracts, and sticks to itself, always resulting in inequality.
Again a movement is apparent, which we ignore; the process of continuous creation, when we attempt to freeze that by representing it as snapshots of stationary things, like an ounce of gold, or a bitcoin, or a wage packet comprising of any of those things, we are falsely representing wealth, with the result that we forsake real wealth, for false wealth.
What appears to be happening, in both of those cases, is that we are actually looking at derivatives, but considering them simply as constants.
I would love to think machine learning might be adaptable to revealing those hidden dimensions, where thus far we have fallen short, but I doubt it.
Perhaps you can prove otherwise!