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I think we have objective information about how protective geometry works.

Ergo, I would not bet against teams that understand that objective technical field; AI is not magic, and knowing the fundamentals will always help. Better models, smaller models, faster models; the less you leave for the model to solve in latent space the better you’ll do, I think.

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If that was true, transformers would have been useless for NLP and deep learning for Go.

You can argue the exact opposite: the more you leave to the model to learn by itself, the more likely you are to find a solution that was not accessible to humans and their limited feature engineering capability.

Transformers and deeplearning was reconfigured to be in 2D for GO. Trying to apply 1D variants did not work, and people did try.

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