I am much more familiar with the development in the chess world.
Since Deep Blue beat Kasparov in 1997, there was a lot of progress in the development and the architecture of chess engines. Back then, we did not have neural networks. A lot of the strategies of chess engines are now used by chess grandmasters. As an example, the value of the activity of pieces was underrated in contrast to the value of material.
As there are more generations of chess engines than go engines, it would be quite interesting to pull something similar off against them. My intuition is that it maybe works against Leela, as LeelaChess basically uses only neural networks (think alphago but for chess), whereas it should not work with Stockfish, as some parts of Stockfishs evaluation function are still adjusted by hand.
As there are more generations of chess engines than go engines, it would be quite interesting to pull something similar off against them. My intuition is that it maybe works against Leela, as LeelaChess basically uses only neural networks (think alphago but for chess), whereas it should not work with Stockfish, as some parts of Stockfishs evaluation function are still adjusted by hand.