Automatic Feature Engineering I came across a Machine Learning software which I won't name, that claims that completely automates the Feature Engineering process from any source without domain knowledge, implying pretty much that turns Data Scientists unnecessary for this step. Now I find that claim a bit suspicious since it goes against the common sense of what I learned Feature Engineering consists of. 

But considering that it might be a lack of knowledge from my part to what point we can automate the featuring engineering process? I can think of some general approaches like statistical methods (median,etc..) or several types of coding (binary, polynomial,etc..) for categorical variables, but could anyone give me an overview state of the art approach regarding this subject or point me in the right direction?
In my experience, when people claim to have an automated approach to feature engineering, they really mean "feature generation", and what they're actually talking about is that they've built a deep neural network of some sort. To be fair, in a limited sense, this could be a true claim. Properly trained deep neural networks can handle any number of pairwise correlations between individual features or groups of features. That said, without a great deal of up-front data pre-processing tools that know how to intelligently handle different types of input data (e.g., free text, images, etc.), none of this would be possible. Bottom line, it takes a great deal of manual effort to do something automatically.