“But didn't you write an embedded OS?”
> And I see the same trend in Machine learning, Big data, AI, Cryptocurrency, Internet of Things, Social networks, Decentralized networks, etc. Everything in life, however trivial or hard, is a nail that can be solved with those fancy hammers. So we can have a tablet juxtaposed on a fridge, a bluetooth speaker integrated to a salt shaker, a subscription model for juicers, and machine learning for all kinds of social problems.<p>I'm frustrated by this as well. I see these days, especially, junior engineers excited about machine learning analyzing data sets that could be tackled easily with learning the problem space and excel or a jupyter notebook. 90% of the work in analysis problem is, usually, identifying data sources and possibly curating the records to a workable data set. So it really doesn't help much to apply ML off the bat. If normal regression and human inspection don't help, maybe then you can iterate on the problem and try some ML approaches.<p>So why are junior engineers trained to think this way? Are their professors and guest lecturers excited about these approaches and teaching the students about their favorite hammers?