Full Article: [pdf] DOI: https://doi.org/10.22503/inftars.XIX.2019.4.1 Language: en Author(s):  Paul Grünke
Title: Chess, Artificial Intelligence, and Epistemic Opacity Abstract: In 2017 AlphaZero, a neural network-based chess engine shook the chess world by convincingly beating Stockfish, the highest-rated chess engine. In this paper, I describe the technical differences between the two chess engines and based on that, I discuss the impact of the modeling choices on the respective epistemic opacities. I argue that the success of AlphaZero’s approach with neural networks and reinforcement learning is counterbalanced by an increase in the epistemic opacity of the resulting model.
The publication of the Journal is supported by Budapest University of Technology and Economics.