Slowly, I am noticing that people are starting to question the abilities of machine learning systems and, in particular, seeing a lot of the transparent AI-washing that is now going on for systems that aren't AI at all. This is one of the most positive changes in the tech industry, honestly.
Machine learning and neural net systems seem to be the inverse of the "uncanny valley", call it the "miraculous hill" if you will.
They're just good enough to, with insane amount of computing resources, churn out economically viable results, but in my opinion they're the biggest setback in actually developing intelligence or cognition in a long time.
There's very little insight to be gained from what they pick up, and they function purely in a stochastic sense. Even the best ML algorithm has no ability to reason at a high level or produce counterfactuals, it works purely by correlation and still, in those 1% edge cases, will be as helpless as anything else.
That might be good enough when selling advertisements, but in automated cars and healthcare treatment, this sort of failure is not an option.
ML and neural nets work surprisingly well on a lot of boring but important use cases. Optimizing online ad placement might be humdrum but it's the foundation of a $100B market in the US alone. [0]
In my opinion the real hype is that ML has become so popular that new practitioners tend to forget other analytic techniques like SQL data warehouses. Interestingly these are starting to absorb ML capabilities like logistic regression, which are now accessible through SQL and can benefit from MPP and vectorwise query execute in DBMS types like ClickHouse, Vertica, and Google BigQuery.
