I love Mickens' work, and think this is overall a great presentation, but I feel like it misses (or maybe just doesn't fully explore) an important point. Start with the Internet of Things example. He chalks up the abysmal security record of IoT devices to two factors: it keeps IoT devices cheap, and IoT vendors don't understand history. And there's a lot of truth in both these assertions! But they are both just expre…
> 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.