[Mtinie]

Your stationary plane example highlights a divide I've seen across my work experience in different domains; teams defaulting to ML when fundamental engineering would work better.

I'm curious: do you think there's any amount of high-quality data that could make the learning-based approach viable for orientation estimation? Or would it always be solving the wrong problem, regardless of data volume and delivery speed?

My sense is that effective solutions need the right confluence of problem understanding, techniques, data, and infrastructure. Missing any one piece makes things suboptimal, though not necessarily unsolvable.

[bsoles]

> I've seen across my work experience in different domains; teams defaulting to ML when fundamental engineering would work better.

In my current field (predictive maintenance), there are (in)famous examples and papers using multi-layer deep networks for solving anomaly detection problems, where a "single" line of basic Matlab code (standard deviations, etc.) performs better than the proposed AI solution. Publish or perish, I guess...

[whatshisface]

One opportunity for human-designed systems to excel over machine learning is the case where treating ML as a black box has caused the designers to pose an impossible problem. From the parent comment, it sounds like each additional measurement was being related to a new estimate by the ML system, while the standard technique could integrate measurements over time (that's called filtering).