[HN Gopher] Deep learning opacity in scientific discovery
       ___________________________________________________________________
        
       Deep learning opacity in scientific discovery
        
       Author : optimalsolver
       Score  : 28 points
       Date   : 2022-06-05 13:20 UTC (9 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | derbOac wrote:
       | Psychology dealt with a very similar -- maybe identical issue --
       | with measurement and prediction in the 60s and 70s. The tension
       | was between "empirically keyed" and "content" approaches to
       | tests, the former being use of tests entirely based on large
       | black box predictive item pools akin to large DL models, and
       | tests based on some items selected based on some theory of test
       | content.
       | 
       | Many of the issues were similar. In the end I think they both
       | lost out to an approach in which tests were empirically
       | validated, but with items retained on their ability to meet
       | various internal structural criteria.
       | 
       | The "discovery" versus "justification" distinction reminds me of
       | this in some ways. It would be akin to if some set of criteria
       | were developed, not based on target prediction criteria, that DL
       | model components would have to meet as constraints. Or,
       | alternatively, you might formalize in some quantitative model
       | what characterizes "justification" characteristics, in the sense
       | of how to interpret a given DL model.
        
       | civilized wrote:
       | TLDR: while the way deep learning reaches conclusions can be
       | opaque, those conclusions can supercharge human intuition,
       | leading to mathematical and scientific conjectures that can then
       | be justified or proven with rigorous, transparent, logical
       | arguments and data.
       | 
       | Some famous mathematician, whose name I've forgotten, said
       | something like "the trouble is not the proofs, but knowing what
       | to prove." Humans have always relied on heuristics to discover
       | what might be true before confirming it rigorously. If deep
       | learning provides powerful heuristics, it can be a tremendous aid
       | to scientific progress.
        
         | version_five wrote:
         | This reminds me of the concept (parallel reconstruction I
         | think) where law enforcement gets information from a covert
         | informant but can't "blow" them (or or maybe can't use it in
         | court) and so finds some other way to show how they got to the
         | result.
        
         | [deleted]
        
       ___________________________________________________________________
       (page generated 2022-06-05 23:02 UTC)