“But didn't you write an embedded OS?”
&gt; 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&#x27;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&#x27;t help much to apply ML off the bat. If normal regression and human inspection don&#x27;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?