The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective
This work addresses the need for a theoretical framework to measure and bound intelligence in AI systems, offering insights for fostering strong AI, though it appears incremental as it builds on Shannon's communication model.
The paper tackles the problem of quantifying intelligence in AI systems like AlphaZero by proposing an information-theoretic metric called intelligence entropy and a Unified Intelligence-Communication Model, which is applied to AlphaZero to evaluate its learning performance quantitatively.
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.