[sireat]

Time and again Alpha shows it is much better at eval than Stockfish.

Alpha play feels "human" at least to this FM. This is fantastic news! It is what I would imagine a good correspondence GM would play like with engine assistance.

I already commented on Game 1 where Stockfish played extremely aggressively with 13. Ncxe5 ??! and 31. Qxc7 ?!

Game 3 is a positional masterpiece. Alpha is willing to play pawns + exchange down when it correctly evaluates that Black queen and rooks will be tied down.

This kind of long term thinking is beyond what regular engines perform.

Game 10 is also an impressive showing by Alpha. Alpha is willing to play down a piece and a pawn for 15 (30 ply) moves in a middle game beyond the reach of Stockfish's raw calculations.

If one could only get access to Alpha evals :) When do mere mortals get access to TPUs on Google Compute Engine?

[forgot-my-pw]

Thanks for the analysis.

There's a project currently that emulates AlphaGo Zero using distributed computing / crowdsourcing: https://github.com/gcp/leela-zero . You can run it on the browser too and it will submit the games after: https://ntt123.github.io/leela-zero/

Hope such a project will be available soon for the chess variant.

Or maybe Deepmind will release this as a SaaS product?

[nojvek]

Deepmind should release the TF compatible model with weights. And then it's just a matter of shrinking the model enough to run on desktop hardware.

But I don't know whether they'll do it. I hope they follow suit like other researchers who have github repos with code and models besides their papers. Really accelerates research.