  In this paper from communication channel coding perspective we are able to
present both a theoretical and practical discussion of AI's uncertainty,
capacity and evolution for pattern classification based on the classical
Rademacher complexity and Shannon entropy. First AI capacity is defined as in
communication channels. It is shown qualitatively that the classical Rademacher
complexity and Shannon entropy used in communication theory is closely related
by their definitions, given a pattern classification problem with a complexity
measured by Rademacher complexity. Secondly based on the Shannon mathematical
theory on communication coding, we derive several sufficient and necessary
conditions for an AI's error rate approaching zero in classifications problems.
A 1/2 criteria on Shannon entropy is derived in this paper so that error rate
can approach zero or is zero for AI pattern classification problems. Last but
not least, we show our analysis and theory by providing examples of AI pattern
classifications with error rate approaching zero or being zero.

Machine Learning;Artificial Intelligence