It's hard to discuss without knowing where you're coming from and what kind of failures you've seen. There's is now enough practical best practice know how and rough process for how to structure an ML project and they tend to work. If you have some kind of vision task, like detecting and fitting a CAD model to some weird contraption nobody did before or some deep sea fish that was discovered yesterday, you can reliably get a neural net up and running to detect and segment and find it. Few-shot and zero-shot techniques exist.

If you don't trust BLEU and don't trust my gut, how about a randomized survey of professional translators, or how many seconds per character a translator would need to fix up the raw output into something they'd be comfortable with calling accurate? Like, I'm not stuck on a metric.

You see, when I and many other academicscomplain about academic culture, rewies and papers, what we complain about is that on the margins it's kind of nebulous and not at all clear cut whether the proposed method in those thousands of papers are each rigorously tested to a really convincing degree that they really beat the previous approach in some deeper sense, eg if the same amount of hyperparam search went to it, if a broader range of benchmarks were applied etc. But the presence of this noise does not crowd out the signal. Because there is a world outside the formalized systematic, officially presented Science with journals and conferences and awards and h indexes. There is a world of more immediate reality. The mechanized process of awards and publications and grants is not the primary object. I can tell if I'm hungry or full without looking at some figures of hormone tests from my blood.

Yes, we have no guaranteed way to tell in advance what exact architecture and hyoerparameters will work best. Maybe the answer will be very context dependent. But this doesn't mean we are completely lost. The vocabulary of bias and variance, of overfitting, regularization etc do help. There are people who inspect loss landscapes for continuous reachability between different modes, there are hypotheses popping up regarding the sharpness of minima playing a role etc.

Again I don't know how much you are inside the research community and how broadly you are aware of wide range of approaches that exists out there. The goal of "understanding why", and explainable AI are hot topics and there are good groups that come up with results, but none of it has a snappy one sentence summary where you slap your forehead and go "oh, so that's why it works".

So yeah maybe we are in a pre-scientific phase of stumbling around, shaking the tree indiscriminately and picking up the fruits. We don't know where exactly the fruit is, but we keep being fed nevertheless.

During the history of science, big discoveries always needed a preparatory pre-scientific phase of mere aggregation of experiences, stamp collection if you will, developing almost superstitious practices that may turn out to be false on the level of the individual components, but in aggregate they result in a net positive.

And it isn't guaranteed that the unreasonable effectiveness of math (a small number of simple and elegant formulas) also applies to this messy subject, as opposed to physics. Reality doesn't owe us any simple and satisfying explanations.

I don't think it helps to complain that we don't get knowledge delivered. The right reaction is awe and curiosity and marvel that a nonrigorous process has led to this. The detractors like Gary Marcus never foresaw this state of things and was sure that things would plateau earlier. But that in itself was a hypothesis about the crowd of monkeys that researchers are, and whether their output will be such or such. And it turned out to falsify the pessimistic views of many. In any honest interpretation of what the early pioneers like Turing set out to do in terms of the Turing test for example, we have made real strides forward in capability.

I know that you're eager for knowledge not just artifacts that work, but it's like complaining that a chess player won by making weird moves that are not listed in any textbooks and we have no idea why they worked. Whether we have a bearing on what exactly make our techniques work, they work.