"For a software engineer, the hardest thing about developing Machine Learning functionality should be finding clean and representative ground-truth data, but it often isn’t."
Which is so much bullshit. The hardest thing is validating your hypotheses, which machine learning turns into a black box. When we have coworkers who insist on operating on wishful thinking we try to maneuver them out of a job. Except every 10-15 years when the built up pressure of fads overwhelms reason and we all get stupid for a generation (which in software is about five years).
The things that started as AI that we don’t call AI anymore, and don’t lump in with AI when discussing successes or failures? It’s because they can be explained in plain English and implemented without much or even any special jargon that marks it as anything more than exceptionally clever Logic.
Which is so much bullshit. The hardest thing is validating your hypotheses, which machine learning turns into a black box. When we have coworkers who insist on operating on wishful thinking we try to maneuver them out of a job. Except every 10-15 years when the built up pressure of fads overwhelms reason and we all get stupid for a generation (which in software is about five years).
The things that started as AI that we don’t call AI anymore, and don’t lump in with AI when discussing successes or failures? It’s because they can be explained in plain English and implemented without much or even any special jargon that marks it as anything more than exceptionally clever Logic.