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)?