A Computational Theory for Life-Long Learning of Semantics
  Semantic vectors are learned from data to express semantic relationships
between elements of information, for the purpose of solving and informing
downstream tasks. Other models exist that learn to map and classify supervised
data. However, the two worlds of learning rarely interact to inform one another
dynamically, whether across types of data or levels of semantics, in order to
form a unified model. We explore the research problem of learning these vectors
and propose a framework for learning the semantics of knowledge incrementally
and online, across multiple mediums of data, via binary vectors. We discuss the
aspects of this framework to spur future research on this approach and problem.
