What is Learning? A primary discussion about information and
  Representation
  Nowadays, represented by Deep Learning techniques, the field of machine
learning is experiencing unprecedented prosperity and its influence is
demonstrated in academia, industry and civil society. "Intelligent" has become
a label which could not be neglected for most applications; celebrities and
scientists also warned that the development of full artificial intelligence may
spell the end of the human race. It seems that the answer to building a
computer system that could automatically improve with experience is right on
the next corner. While for AI and machine learning researchers, it is a
consensus that we are not anywhere near the core technique which could bring
the Terminator, Number 5 or R2D2 into real life, and there is not even a formal
definition about what is intelligence, or one of its basic properties:
Learning. Therefore, even though researchers know these concerns are not
necessary currently, there is no generalized explanation about why these
concerns are not necessary, and what properties people should take into account
that would make these concerns to be necessary. In this paper, starts from
analysing the relation between information and its representation, a necessary
condition for a model to be a learning model is proposed. This condition and
related future works could be used to verify whether a system is able to learn
or not, and enrich our understanding of learning: one important property of
Intelligence.
