If being so reductive, that's also the scientific method. Form a model on some existing data, with the goal of it being predictive on new unseen data. Key is in favoring the more predictive models.
> they called it magic, we call it math, but both seem to have about the same outcome
Find me some sheep entrails that can do this: https://imagen.research.google/
Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.
I do also think that ML as a field progresses through the scientific method ("I theorise that this network with residual connections will converge faster, lets see if there's a significant difference") - but maybe not to the full extent it could.
> Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.
I'd say that's closer to evolutionary algorithms. GD finds (locally) the direction to tweak the weights to improve predictions on a given batch.