The Computational Principles of Learning Ability
  It has been quite a long time since AI researchers in the field of computer
science stop talking about simulating human intelligence or trying to explain
how brain works. Recently, represented by deep learning techniques, the field
of machine learning is experiencing unprecedented prosperity and some
applications with near human-level performance bring researchers confidence to
imply that their approaches are the promising candidate for understanding the
mechanism of human brain. However apart from several ancient philological
criteria and some imaginary black box tests (Turing test, Chinese room) there
is no computational level explanation, definition or criteria about
intelligence or any of its components. Base on the common sense that learning
ability is one critical component of intelligence and inspect from the
viewpoint of mapping relations, this paper presents two laws which explains
what is the "learning ability" as we familiar with and under what conditions a
mapping relation can be acknowledged as "Learning Model".
