Unsupervised, Disentangled Learning

Intriguing piece in the Google AI Blog.  Had always thought that unsupervised learning should be one essence of creativity  Not examples we know, but new ones we can come up with.  And here 'disentanglement' implies we can capture and model aspects of a system that are not tied together.    How do these ideas fit together?   A technical Google view:

Evaluating the Unsupervised Learning of Disentangled Representations
Posted by Olivier Bachem, Research Scientist, Google AI Zürich 

The ability to understand high-dimensional data, and to distill that knowledge into useful representations in an unsupervised manner, remains a key challenge in deep learning. One approach to solving these challenges is through disentangled representations, models that capture the independent features of a given scene in such a way that if one feature changes, the others remain unaffected. If done successfully, a machine learning system that is designed to navigate the real world, such as a self driving car or a robot, can disentangle the different factors and properties of objects and their surroundings, enabling the generalization of knowledge to previously unobserved situations. While, unsupervised disentanglement methods have already been used for curiosity driven exploration, abstract reasoning, visual concept learning and domain adaptation for reinforcement learning, recent progress in the field makes it difficult to know how well different approaches work and the extent of their limitations.  .... "