The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective

TL;DR

This paper introduces an information-theoretic measure called intelligence entropy to quantify AI systems' intelligence, applies it to AlphaZero, and models its learning process as turbo-like decoding for performance evaluation.

Contribution

It proposes the intelligence entropy and capacity as new metrics for evaluating AI, and models AlphaZero's learning process using Shannon's information theory.

Findings

Introduces intelligence entropy as a measure of AI intelligence

Models AlphaZero's learning as turbo-like iterative decoding

Provides bounds on AI performance using Shannon's capacity

Abstract

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.