  Shannon entropy is the most crucial foundation of Information Theory, which
has been proven to be effective in many fields such as communications. Renyi
entropy and Chernoff information are other two popular measures of information
with wide applications. The mutual information is effective to measure the
channel information for the fact that it reflects the relation between output
variables and input variables. In this paper, we reexamine these channel
information measures in big data viewpoint by means of ACE algorithm. The
simulated results show us that decomposition results of Shannon and Chernoff
mutual information with respect to channel parametersare almost the same. In
this sense, Shannon shakes hands with Chernoff since they are different
measures of the same information quantity. We also propose a conjecture that
there is nature of channel information which is only decided by the channel
parameters.
