Last week at DOAG 2017, I had two talks, one about deep learning with DL4J (slides

here) and one about how to communicate uncertainty (or rather: how to construct prediction intervals for various methods / in various frameworks ranging from simple linear regression over Bayesian statistics to neural networks).

TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

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> The most important thing about communicating uncertainty is that youâ€™re doing it.

I respectfully disagree. Communicating uncertainty is a complex task, simply doing it is an important step in the right direction. Important by a tiny one. That is why we need more presentations like yours. I wrote some more examples and arguments here https://gorelik.net/2017/12/14/whats-the-most-important-thing-about-communicating-uncertainty/ . Great