the promo process :P no noise there!
Somewhere out there, an economist who has dedicated their life to causal inference is crying
Navigate cancellation menus with embedded dark patterns
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…
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…
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?
the promo process :P no noise there!
Somewhere out there, an economist who has dedicated their life to causal inference is crying
Navigate cancellation menus with embedded dark patterns
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…
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…
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?