Read on Twitter
Some folks are uncritically arguing that only *architectures* matter in deep learning, not datasets nor anything else (So Fei-Fei Li creating ImageNet didn't contribute much 
)
This narrow tunnel focus is so harmful to the field.
)This narrow tunnel focus is so harmful to the field.
Other things that matter in deep learning (and that we should start valuing more): framing problems, collecting data, interpreting results, communicating results, encouraging adoption, ETHICS of what we're doing https://twitter.com/math_rachel/status/1135709270928961536?s=20
This is a great paper by @timnitGebru @unsojo on why we should be giving a lot more thought to how we curate our datasets in ML: https://twitter.com/math_rachel/status/1223799130180349953?s=20
GenderShades is great research for many reasons, but I love that it was specifically designed to have a real world impact. This sort of thoughtful design & effective communication is too rare in machine learning. @jovialjoy
(see more in @rajiinio talk) https://twitter.com/math_rachel/status/1163637485533929473?s=20
(see more in @rajiinio talk) https://twitter.com/math_rachel/status/1163637485533929473?s=20
Another area that is often neglected in deep learning is research on how to do things in a cheaper, simpler way, using fewer resources: https://twitter.com/math_rachel/status/1141826118128828416?s=20
Related: hero-worship & under-valuing communities is harmful https://twitter.com/math_rachel/status/1253033620857651201?s=20
