A Software Tester's Perspective on Statistical Learning Theory

This is good humoured satire and reminds me of my first job as a data scientist straight out of maths school. I was in a team who’s very existence was to advocate for ML everywhere and anywhere, without much success because everyone (including the stakeholders) failed to start from the fundamentals of the problem.

Luckily I got out, upped my game and corrected course. Now in my current role it is all about starting from basics and letting our insights as well as the problem solving process (aka engineering) guide our solution. The modelling approach we thought we’d do in the first 3 months turned out to be absolutely wrong 7 months later.

Don’t get me wrong that ML is a waste of time. If you need to do predictive modelling to solve your problem and if the complexity is high enough that an ordinary linear model won’t suffice (they usually don’t) or you need to model uncertainty as well, then ML may very well get you from point A to B. And achieving that step can be a worthwhile, rewarding and challenging endeavour.

The issue is when ML becomes a substitute for the fundamental problem solving or is treated as a solution when it isn’t.

Machine learning is nowhere close to replacing the human coding activity.

Nonetheless, machine learning is creeping up into newer and newer areas of application in surprising, unpredictable, and unprecedented ways, and ignoring it would put you at peril as a tech worker. This is what I mean when I use the term 'software 2.0'.

Witty, but not wholly faithful to statistical learning theory