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Here's something that bugs me about ML: all we have is prediction and no explanation how we come to that prediction, ie: no deeper understanding on the underlyi

Here's something that bugs me about ML: all we have is prediction and no explanation how we come to that prediction, ie: no deeper understanding on the underlying principles. So despite that we got a good match this time, how can we be sure that the match will be equally good next time? And how to use ML to predict structure that we have no baseline to start with or experimental result to benchmark ? In the absence of physics-like principles, How can we ever be sure that ML results next time is correct ?

throwaway4aday 2y ago
Speaking of physics, we should borrow the quote "Shut up and calculate" to describe the situation: it works so use it now and worry about the explanations later.
- d0mine 2y ago
  Except the model is not open-source. You can't calculate anything.
coriny 2y ago
There is a biannual structural prediction contest called CASP [1], in which a set of newly determined structures is used to benchmark the prediction methods. Some of these structures will be "novel", and so can be used to estimate the performance of current methods on predicting "structure that we have no baseline to start with". CASP-style assessments are something that should done for more research fields, but it's really hard to persuade funders and researchers to put up the money and embargo the data as required. [1] https://en.wikipedia.org/wiki/CASP https://en.wikipedia.org/wiki/CASP