In 2022, "state of the art" is throwing a deep net at it. It will likely pick up on all of these findings (and better ones, incomprehensible to us) by itself gi

In 2022, "state of the art" is throwing a deep net at it. It will likely pick up on all of these findings (and better ones, incomprehensible to us) by itself given correct architecture and enough data, but I can't help but feel a bit saddened by this - seeing the ingenuity and mastery of all these cited names be obscured and superseded so easily, in a way. I love advancement in the field and what machine learning will enable us to do, but I don't know what to make of this. One argument is that now we have engineers who design the machine learning models, but it is still depressing to me, for some reason. Never knew I would feel like this, am I the only one? P.S.: I'm commenting purely on this topic, which is an ideal big data case - of course, we still have a long way to go with machine learning, one where human minds will have to especially shine.