On the Intrinsic Limits to Representationally-Adaptive Machine-Learning
  Online learning is a familiar problem setting within Machine-Learning in
which data is presented serially in time to a learning agent, requiring it to
progressively adapt within the constraints of the learning algorithm. More
sophisticated variants may involve concepts such as transfer-learning which
increase this adaptive capability, enhancing the learner's cognitive capacities
in a manner that can begin to imitate the open-ended learning capabilities of
human beings.
  We shall argue in this paper, however, that a full realization of this notion
requires that, in addition to the capacity to adapt to novel data, autonomous
online learning must ultimately incorporate the capacity to update its own
representational capabilities in relation to the data. We therefore enquire
about the philosophical limits of this process, and argue that only fully
embodied learners exhibiting an a priori perception-action link in order to
ground representational adaptations are capable of exhibiting the full range of
human cognitive capability.
