Classifying Images: Massive Parallelism And Surface Features

Analysis of image classifiers demonstrates that it is possible to understand backprop networks at the task-relevant run-time algorithmic level. In these systems, at least, networks gain their power from deploying massive parallelism to check for the presence of a vast number of simple, shallow patterns.
https://betterwithout.ai/images-surface-features
This episode has a lot of links:
David Chapman's earliest public mention, in February 2016, of image classifiers probably using color and texture in ways that "cheat": twitter.com/Meaningness/status/698688687341572096
Jordana Cepelewicz's…