Troubling Trends in Machine Learning Scholarship — In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship: (i) Failure to distinguish between explanation and speculation. (ii) Failure to identify the sources of empirical gains—e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning. (iii) Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies—e.g., by confusing technical and non-technical concepts. (iv) Misuse of language—e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms.
RoboSat — mapbox open-sourced their machine learning system that does semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water.
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On Management and Autonomy — in our experience, too many managers err on the side of mistrust. They follow the basic premise that their people may operate completely autonomously, as long as they operate correctly. This amounts to no autonomy at all. The only freedom that has any meaning is the freedom to proceed differently from the way your manager would have proceeded. So true! (Parents: this applies to children, as well)