Chess engine, pt. 6: Neural-net evaluation (www.dogeystamp.com)

In the latest installment of a blog series on building a chess engine, the author explores enhancing their engine’s evaluation function through neural networks, moving beyond basic material counting to incorporate positional analysis. While previous methods relied on material values and piece-square tables, which provide contextual scoring based on piece placement, the integration of a neural network aims to capture the subtleties of chess play. The author emphasizes that even beginners in machine learning can successfully implement neural networks in chess engines, as demonstrated by their own experience. The significance of this approach lies in its potential to elevate the engine's gameplay by allowing it to learn and adapt like successful models such as AlphaZero. By employing Efficiently Updatable Neural Networks (NNUE), the engine can maintain quick evaluation speeds on standard CPUs, countering the resource demands typically associated with deep learning. This shift represents a departure from traditional hand-crafted techniques, revealing a pathway for improved chess engine development that leverages the latest advancements in AI while remaining accessible to newcomers in the field.