>1. If the old-school stats work, why use NN/ML? Instead sample the big data and use a classical statistical model. [...] I had hoped that the new tech could do

>1. If the old-school stats work, why use NN/ML? Instead sample the big data and use a classical statistical model. [...] I had hoped that the new tech could do the work faster or see a pattern faster or see something that was invisible to classical stat. That's just not these things work. Even if you had large amounts of data (which you don't), you still lack a good problem statement that neural networks can be trained towards, great test sets for these scenarios that impact human lifes, and a model that is explainable / interpretable. Otherwise you quite literally put human lifes on the line hoping your model learned something appropriate instead of using something proven and reliable instead. Modern machine learning techniques have their place and I'm sure in the aftermath of this crisis we will hear a lot of stories where people put it to good use but it's not a case of "throw ML at it, ???, get vaccine". > 2. In the medical science field I see lots of self-promotion You'll find self promotion in any field, that does not really diminish the work people are doing overall. > That is creativity! There is a clear difference between desperate medical professionals taking a risk and misguided efforts to throw random tech at a problem without understanding it. The former might be considered creative, the latter just adds noise without helping anything. > My bet is that a solution will come out of left field, will be initially opposed by medical science and ultimately absorbed by the same. That's fine and I get that you might be disappointed in the status quo but there is just no reasonable assumption that deep learning will provide some left field miracle here. That's grasping at straws while people are actually trying to help. As for your last comment, I'm still hoping that we collectively learn something from this crisis.