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Machine learning continues to eat everything. At the moment I’m personally studying how to build trade off analytics in R
I hear a lot about NLP and neural networks ... everyone wants to apply neural networks with TensorFlow!
Seems to me the biggest new things are around evaluating model fairness to try and reduce implicit biases. That is what I hear about quite a bit these days. Model explainability is big as well.
For some good thinking on analytics I recommend the podcast taking machines. Neil Lawrence is an incredibly bright guy and a joy to listen to.
This Week in Machine Learning and AI is a good show as well. Bias is a big deal because model interpretability and explainability still has to contend with lots of black box models out there and a few high profile cases like Amazon HR are showcasing a lack of due diligence in model creation and evaluation