Machine Learning Researchers Suck at Math
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In comparison to e.g. theoretical physicist.

#. They suck at defining terms
#. When they do, they generally try to reinvent the wheel.
    #. e.g. computational graphs/networks. They have no precise, useful definition
#. They are vague
    #. E.g. they don't specify the distributions they work with
    #. They misuse/misinterpret well-defined terms such as bias
#. Their notation is awkward and outdated.
#. They're trend oriented, failing to recognize ML is no longer in the explore
   phase of it's lifecycle. The foundations need to be refactored.

    - mainly due to laziness.
#. They are not up-to-date on contemporary methods.

    - e.g. late adoption of the totally obvious fact that NNs live in function
      spaces