If it’s humans making observations in the data, changing (non AI parts) of their software stack, and reanalyzing the data it sounds like there’s no supervised or unsupervised machine learning going on, it could be, but the article is too hand wavy for me to be sure. That’s why I think a more accurate title is “companies realize most of the work in AI comes down to feature engineering and data prep, not algorithm design”

Absolutely. The current state of AI/ML practically requires a human to interpret the results. Even a recommender system is just collecting and analyzing data for humans to consume in some way. And that system needs to be maintained and retrained regularly to produce meaningful results.

This is only true of supervised offline learning

wrong. I have trained plenty of unsupervised models where the inferred results are purely for human consumption. In fact I believe unsupervised models more often require a human to analyze the results.

Whether an algorithm is supervised or not certainly does affect whether you need to retrain it periodically. Also it does not at all affect whether the output is fed to end users or to other algorithms

I feel like maybe we are saying the same thing but using different words.