True that. But you could replace "Machine Learning System" with "code base" and "linear algebra" with "C++" and it would be an accurate description of most corporate software development I've seen.
I'm no ML expert by any means, but I've seen several bachelor/master thesis and even ML competitions where ensembles performed best. Sure, this isn't necessarily aimless stirring and could combine models that really capture different aspects of the data. But often enough it's just several algorithms that do the same general thing, combined to achieve a slightly higher score.
Imho this is most relevant when competitions provide data that is not readable by humans (e.g. simplified: "classify these documents where all words are given as word IDs and never as actual strings").
To me this has a touch of pouring in data, stirring (build many classifiers and plug them together in an ensemble), and getting answers on the right side.
Optimizing hyper parameters goes in a similar direction, imho. I can really see an analogy to stirring
At least, most of the time you don't.
I mean more than they already are.
Reservoir computing. Some are critical of this method.