A pertinent critique of recent trends within machine learning scholarships, the author makes the following salient criticims:
- Failure to distinguish between explanation and speculation.
- Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
- Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
- Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.