What AI still can’t do by Brian Bergstein via @techreview

Humans aren’t very good at understanding causation either.

I agree with Brian that we need to change our current AI thinking and combine a lot more sources of information using neural networks in order to get much better results.

How Companies Can Use the Data They Collect to Further the Public Good by Edward L. Glaeser,Hyunjin Kim and Michael Luca via @HarvardBiz

The potential value of the large data sets being amassed by private companies raises new opportunities and challenges for managers making strategic data decisions.

I like the steps in the article but just like using any other data be careful of correlation and causation as it is very easy to mistakenly interpret one for the other.

Hill for the data scientist: an xkcd story by Lucy D'Agostino McGowan via Live Free or Dichotomize

A how-to guide for data science - as told via xkcd comic strips!

I love, love, love this.  It also makes some important points.

If Correlation Doesn’t Imply Causation, Then What Does? by @akelleh via Medium

Adam Kelleher’s interesting post looks at when and why you might want to use causality.

He has a second post here which discusses all about understanding bias.

Please read these at least twice as it's worth understanding these articles and the points within them well.