SWIND (aka ChessSWOT.com) is a revolutionary new Chess learning and pattern recognition tool developed for Chess Coaches, Teachers, Schools and Clubs by TeachingChess.com and The Preptorial Foundation Chess Research Institute.
As a Chess Coach, you know all about KEY squares (Endgame squares with King/pawn endings), target squares, strong squares, weak squares, and the squares underlying analysis, pawn structure, potential optimal moves, etc.
SWIND combines all those ideas, as well as the Python underlying deep reinforcement learning in NN Chess engines, and teaches students to recognize square weaknesses in any snapshot (image recognition), without having to spend hours on move memorization.
We are currently both obtaining and giving grants to further this technique in education, and if you're even a FREE TeachingChess.com Coach member, you are both eligible to participate in Research and Development AND use the tool betas with YOUR students!
Though this will be open source / GNU (we're a NonProfit), TeachingChess Coaches, Teachers, Schools and Clubs will have exclusive access to the tools during development. Tom Halstead, who worked in medical and satellite imaging and pattern recognition (cold and hot spot image analysis), is the lead scientist on the project.
A weakness indicator is a well known concept in quantitative analysis of business. Business weakness indicators are scoring models that use ratios to determine credit worthiness, for example.
They are similar to linear regressions that solve key variables to create causal or conditional models. In the case of SWIND (and SWINDing your opponent!), these are not code streams in an AI machine, but human readable positional snapshots that focus on immediate non-analytical recognition of KEY SQUARES (in the general use of the term, not just endgames) in any and every position.
SWIND also meets TeachingChess.com's mission of not only assisting Coaches, but preparing students for other aspects of STEM education utilizing Chess as a model.
SWIND has obvious halo benefits for students in many other areas of Science and Technology, from economic research to decision theory and machine learning as well as Science, Technology, Engineering and Mathematics and applied sciences from Medicine to Weather and Hypercomputing. CHESS PREPARES US FOR SUCCESS IN SCHOOL, CAREERS AND LIFE! EMAIL US if you are interested in CONTRIBUTING TO GRANTS for this and many other new technologies from Preptorial, or APPLYING FOR A GRANT to further the research and development! (Remember, SWIND works BOTH for teaching all levels of students AND deep reinforcement machine learning grad programs!)
As a Chess Coach, you know all about KEY squares (Endgame squares with King/pawn endings), target squares, strong squares, weak squares, and the squares underlying analysis, pawn structure, potential optimal moves, etc.
SWIND combines all those ideas, as well as the Python underlying deep reinforcement learning in NN Chess engines, and teaches students to recognize square weaknesses in any snapshot (image recognition), without having to spend hours on move memorization.
We are currently both obtaining and giving grants to further this technique in education, and if you're even a FREE TeachingChess.com Coach member, you are both eligible to participate in Research and Development AND use the tool betas with YOUR students!
Though this will be open source / GNU (we're a NonProfit), TeachingChess Coaches, Teachers, Schools and Clubs will have exclusive access to the tools during development. Tom Halstead, who worked in medical and satellite imaging and pattern recognition (cold and hot spot image analysis), is the lead scientist on the project.
A weakness indicator is a well known concept in quantitative analysis of business. Business weakness indicators are scoring models that use ratios to determine credit worthiness, for example.
They are similar to linear regressions that solve key variables to create causal or conditional models. In the case of SWIND (and SWINDing your opponent!), these are not code streams in an AI machine, but human readable positional snapshots that focus on immediate non-analytical recognition of KEY SQUARES (in the general use of the term, not just endgames) in any and every position.
SWIND also meets TeachingChess.com's mission of not only assisting Coaches, but preparing students for other aspects of STEM education utilizing Chess as a model.
SWIND has obvious halo benefits for students in many other areas of Science and Technology, from economic research to decision theory and machine learning as well as Science, Technology, Engineering and Mathematics and applied sciences from Medicine to Weather and Hypercomputing. CHESS PREPARES US FOR SUCCESS IN SCHOOL, CAREERS AND LIFE! EMAIL US if you are interested in CONTRIBUTING TO GRANTS for this and many other new technologies from Preptorial, or APPLYING FOR A GRANT to further the research and development! (Remember, SWIND works BOTH for teaching all levels of students AND deep reinforcement machine learning grad programs!)
Check out an example of ChessSWOT in action:
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