The work Google’s done in NLP has blown me away. It seems like they’ve spearheaded massive improvements 5 or 6 years in a row where computer vision has seen onl

The work Google’s done in NLP has blown me away. It seems like they’ve spearheaded massive improvements 5 or 6 years in a row where computer vision has seen only incremental improvements in that time. I also appreciate their approach to ML ethics where they actually wrestle with some hard problems and come up with some solid approaches that engineers and data scientists can use (in contrast to one pre-print I read where the author—a guy with really good credentials—dogmatically insisted “do not solve problems that would lead to harms.” I’m sure route optimization has led to people having accidents on roads they would not have taken otherwise, should society not have built Mapquest? Google papers (generally) approach these issues with some nuance.) I also constantly recommend this paper a couple of their engineers wrote “Hidden Technical Debt in Machine Learning Systems” for a real-world approach to implementation and it’s challenges. https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896f...