That’s a very unfair distinction, almost like a No True Scotsmam fallacy to say machine learning is only bad and other stuff is only good (in terms of transparency).

But machine learning has predated neural networks by hundreds of years. The core mathematical basis of all machine learning coursework linear regression and decision trees. Other models like SVMs, Bayesian models, nearest neighbor indexes, TFIDF text search, naive Bayes classifier, etc., are basically like machine learning 101, and they have many different properties regarding interpretability depending on the problem to solve.

Saying that linear regression is machine learning is like saying that newtons laws is chemistry. There was no machine learning before computers, just regular old optimization algorithms.

Yes, there was. It was just called statistical modelling.

There were no machines in the sense of ML in 1740.

This is very false. Least squares regression fitting, Chebychev polynomial approximation, and maximum likelihhod estimators all existed at the time and those are all classic examples of standard machine learning. The term “machine learning” essentially encompasses any type of algorithm that expresses inductive statistical reasoning. Even just elementary school descriptive statistics is machine learning. “Machine learning” is a super old subfield of applied mathematics. The fact that the terminology “machine learning” didn’t exist until things like perceptron and SVMs came along is utterly irrelevant semantic hairsplitting.

But ML is essentially just function approximation, and that definitely existed back then.

I know this is super pedantic, but it's important to remember the roots of things, and that even things which appear new have precursors that are much older than a lot of people realise.