What's done towards AI learning new ways of learning? Most (all I know) machine learning systems use a fixed set of data input channels and processing algorithms, only expanding underlying dataset processed by these; they obtain new data but only from predefined sources, and use only their fixed, built-in capacity to process it, possibly tweaking parameters of the algorithm (like weights of neural network nodes) but never fundamentally changing the algorithm.

Are there systems - or research into creating these - that are able to acquire "from out there" new methods of obtaining data and new ways to process it for results? Expand not just passive data set to "digest it" by active but static algorithm, but make the algorithm itself self-expanding - be it in terms of creating/obtaining new processing methods for own data set, and creating/obtaining new methods of acquiring that data (these methods)?
I think the closest thing is building up knowledge using predictions like in the Horde Architecture. Research about what are good predictions to make is on going at the University of Alberta, Canada but the Horde architecture has the potential to ask new questions and generate new data based on the answers to those questions in the form of predictions. 

One example is having a robot with a bump sensor and servos and asking questions like 


  Will rotating the servos CW for 90 degrees make the bump sensor turn on?


This question can be phrased and it's answer estimated mathematically in the form of a General Value Function as defined in the paper. 

Asking different questions based on predictions like this one is where the power comes from.