Grünke, Paul (2019) Chess, Artificial Intelligence, and Epistemic Opacity. INFORMÁCIÓS TÁRSADALOM: TÁRSADALOMTUDOMÁNYI FOLYÓIRAT, 19 (4). pp. 7-28. ISSN 1587-8694
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Official URL: http://doi.org/10.22503/inftars.XIX.2019.4.1
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.
| Item Type: | Article |
|---|---|
| Subjects: | H Social Sciences / társadalomtudományok > H Social Sciences (General) / társadalomtudomány általában |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | Zsolt Baráth |
| Date Deposited: | 13 Sep 2022 11:28 |
| Last Modified: | 13 Sep 2022 11:28 |
| URI: | http://real.mtak.hu/id/eprint/148527 |
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