The Entropy of Artificial Intelligence and a Case Study of AlphaZero
  from Shannon's Perspective
  The recently released AlphaZero algorithm achieves superhuman performance in
the games of chess, shogi and Go, which raises two open questions. Firstly, as
there is a finite number of possibilities in the game, is there a quantifiable
intelligence measurement for evaluating intelligent systems, e.g. AlphaZero?
Secondly, AlphaZero introduces sophisticated reinforcement learning and
self-play to efficiently encode the possible states, is there a simple
information-theoretic model to represent the learning process and offer more
insights in fostering strong AI systems?
  This paper explores the above two questions by proposing a simple variance of
Shannon's communication model, the concept of intelligence entropy and the
Unified Intelligence-Communication Model is proposed, which provide an
information-theoretic metric for investigating the intelligence level and also
provide an bound for intelligent agents in the form of Shannon's capacity,
namely, the intelligence capacity. This paper then applies the concept and
model to AlphaZero as a case study and explains the learning process of
intelligent agent as turbo-like iterative decoding, so that the learning
performance of AlphaZero may be quantitatively evaluated. Finally, conclusions
are provided along with theoretical and practical remarks.
