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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Today, we have the pleasure to announce Stockfish 14. As usual, downloads will be freely available at https://stockfishchess.org The engine is now significantly stronger than just a few months ago, and wins four times more game pairs than it loses against the previous release version [0]. Stockfish 14 is now at least 400 Elo ahead of Stockfish 7, a top engine in 2016 [1]. During the last five years, Stockfish has thus gained about 80 Elo per year. Stockfish 14 evaluates positions more accurately than Stockfish 13 as a result of two major steps forward in defining and training the efficiently updatable neural network (NNUE) that provides the evaluation for positions. First, the collaboration with the Leela Chess Zero team - announced previously [2] - has come to fruition. The LCZero team has provided a collection of billions of positions evaluated by Leela that we have combined with billions of positions evaluated by Stockfish to train the NNUE net that powers Stockfish 14. The fact that we could use and combine these datasets freely was essential for the progress made and demonstrates the power of open source and open data [3]. Second, the architecture of the NNUE network was significantly updated: the new network is not only larger, but more importantly, it deals better with large material imbalances and can specialize for multiple phases of the game [4]. A new project, kick-started by Gary Linscott and Tomasz Sobczyk, led to a GPU accelerated net trainer written in pytorch.[5] This tool allows for training high-quality nets in a couple of hours. Finally, this release features some search refinements, minor bug fixes and additional improvements. For example, Stockfish is now about 90 Elo stronger for chess960 (Fischer random chess) at short time control. The Stockfish project builds on a thriving community of enthusiasts (thanks everybody!) that contribute their expertise, time, and resources to build a free and open-source chess engine that is robust, widely available, and very strong. We invite our chess fans to join the fishtest testing framework and programmers to contribute to the project on github [6]. Stay safe and enjoy chess! The Stockfish team [0] https://tests.stockfishchess.org/tests/view/60dae5363beab81350aca077 [1] https://nextchessmove.com/dev-builds [2] https://stockfishchess.org/blog/2021/stockfish-13/ [3] https://lczero.org/blog/2021/06/the-importance-of-open-data/ [4] https://github.com/official-stockfish/Stockfish/commit/e8d64af1 [5] https://github.com/glinscott/nnue-pytorch/ [6] https://stockfishchess.org/get-involved/
Today, we have the pleasure to announce Stockfish 14. As usual, downloads will be freely available at https://stockfishchess.org The engine is now significantly stronger than just a few months ago, and wins four times more game pairs than it loses against the previous release version [0]. Stockfish 14 is now at least 400 Elo ahead of Stockfish 7, a top engine in 2016 [1]. During the last five years, Stockfish has thus gained about 80 Elo per year. Stockfish 14 evaluates positions more accurately than Stockfish 13 as a result of two major steps forward in defining and training the efficiently updatable neural network (NNUE) that provides the evaluation for positions. First, the collaboration with the Leela Chess Zero team - announced previously [2] - has come to fruition. The LCZero team has provided a collection of billions of positions evaluated by Leela that we have combined with billions of positions evaluated by Stockfish to train the NNUE net that powers Stockfish 14. The fact that we could use and combine these datasets freely was essential for the progress made and demonstrates the power of open source and open data [3]. Second, the architecture of the NNUE network was significantly updated: the new network is not only larger, but more importantly, it deals better with large material imbalances and can specialize for multiple phases of the game [4]. A new project, kick-started by Gary Linscott and Tomasz Sobczyk, led to a GPU accelerated net trainer written in pytorch.[5] This tool allows for training high-quality nets in a couple of hours. Finally, this release features some search refinements, minor bug fixes and additional improvements. For example, Stockfish is now about 90 Elo stronger for chess960 (Fischer random chess) at short time control. The Stockfish project builds on a thriving community of enthusiasts (thanks everybody!) that contribute their expertise, time, and resources to build a free and open-source chess engine that is robust, widely available, and very strong. We invite our chess fans to join the fishtest testing framework and programmers to contribute to the project on github [6]. Stay safe and enjoy chess! The Stockfish team [0] https://tests.stockfishchess.org/tests/view/60dae5363beab81350aca077 [1] https://nextchessmove.com/dev-builds [2] https://stockfishchess.org/blog/2021/stockfish-13/ [3] https://lczero.org/blog/2021/06/the-importance-of-open-data/ [4] https://github.com/official-stockfish/Stockfish/commit/e8d64af1 [5] https://github.com/glinscott/nnue-pytorch/ [6] https://stockfishchess.org/get-involved/
and here are the SF team charts and tracking: https://github.com/glinscott/fishtest/wiki/Regression-Tests