> The same causality can be observed in the ML world. Mickens > asks why people are hooking ML systems whose operation > isn't fully understood to important t

> The same causality can be observed in the ML world. Mickens > asks why people are hooking ML systems whose operation > isn't fully understood to important things like financial > decisionmaking and criminal justice systems. The answer is > that the customers demand it. ML is trendy and buzzworthy But that's the same as with the testicle exploding argument: ML is nowadays called AI, can self-drive cars and beat humans at any task (like Jeopardy or Go). So people assume from their experience that it just works, even better than any human. Of course also a big mystery bubble is created around that both by Marketing people and ML practitioners (oh and IBM). Being myself an engineer working on "normal" systems, I somehow feel pressed as well to do something fancier like ML - according to some survey already 40% of Engineers do that. But on the other hand I realize most of this stuff is, as already pointed out in the talk, just there to target ads or work on meaningless financial systems. I was recently listening to a talk of an AI expert person, using the AI for fraud detection in an online payment system. At the end of the talk somehow asked a really interesting question which was: so how do you connect that to your online system? He answered: we don't, it's just for compliance reporting. That's just stupid, I feel misguided. It's cool to do statistics on your data, simulations but calling that AI is incredibly misleading.