tomatovole

the promo process :P no noise there!

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Is there a metric I can look at in engine evaluations to determine when a situation is "risky" for white or black (e.g., the situation above) even if it looks equal with perfect play?

I've always been interested in understanding situations where this is the case (and the opposite, where the engine favours one side but it seems to require a long, hard-to-find sequence of moves.

Playing out the top lines helps if equality requires perfect play from one side.

This is litterally the best comment I've seen today!

Do you have evaluations for how well the trained agents do (e.g. for chess, go, etc)?

This is not true for all crypto coins. A small proportion of mining hash functions are deliberately designed to thwart ASICs, e.g. https://github.com/tevador/RandomX. It's a cat-and-mouse game, though: CryptoNight was designed to fulfill the same role, but ultimately failed: https://monerodocs.org/proof-of-work/cryptonight/.

Missed that -- thanks!

The linked paper (https://www.nature.com/articles/s41586-023-06734-w) in the quoted tweet appears to be from the Ceder Group at UC Berkeley, not DeepMind. Is there a different link I'm missing?