> But, we will get an article from MIT making it sound like everything works flawlessly. Well, I don't want to speak to the draconian political implications of

> But, we will get an article from MIT making it sound like everything works flawlessly. Well, I don't want to speak to the draconian political implications of this -- that is a separate issue with troubling implications -- but I do want to address the general notion that imperfection should prevent a system (any system) from being practical. Just because it's not perfect doesn't mean it is not usable -- you just need to create rules that govern what actions can be taken. We do this with almost any real world system (vehicle routing, plant control, etc.). No predictive model is perfect, and we practitioners understand this and regulate our behavior accordingly, often through simple rules. We build in checks, overrides and recourses. Us techies often think of technological solutions as being all-encompassing, forget how much of a human element (and human judgment) there is in real-world feedback loops. Also, lest people think parsing out multiple sources from a signal is a new thing, Blind Source Separation [0] is the name that has been given to the study of this type of problem (sometimes called the "cocktail party problem"), and it has been studied for decades. Like most model inversion problems, it's known to be an underdetermined problem, and it is also known that you cannot get perfect separation all the time, but you can get a certain degree of separation if there is some signal (however weak) that helps discrimination. So it's not like practitioners have no idea about the potential issues -- most are acutely aware of them. This doesn't diminish the fact that the algorithms do actually work, for some definition of "work". Techniques like ICA and NMF preceded DL approaches. It sounds like DL (deep learning) approaches hold promise on these types of problems, and this is something of note. [0] https://en.wikipedia.org/wiki/Blind_signal_separation https://en.wikipedia.org/wiki/Blind_signal_separation