Troubling Trends in Machine Learning Scholarship

approximatelycorrect.com

A pertinent critique of recent trends within machine learning scholarships, the author makes the following salient criticims:

  1. Failure to distinguish between explanation and speculation.
  2. Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
  3. Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
  4. Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.

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