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yoavg / GM-level-chess-without-search.md
Last active April 16, 2024 00:56
Grand-master Level Chess without Search

Grand-master Level Chess without Search: Modeling Choices and their Implications

Yoav Golderg, February 2024.


Researchers at Google DeepMind released a paper about a learned systems that is able to play blitz-chess at a grandmaster level, without using search. This is interesting and imagination-capturing, because up to now computer-chess systems that play at this level, either based on machine-learning or not, did use a search component.[^1]

Indeed, my first reaction when reading the paper was to tweet wow, crazy and interesting. I still find it crazy and interesting, but upon a closer read, it may not be as crazy and as interesting as I initially thought. Many reactions on twitter, reddit, etc, were super-impressed, going into implications about projected learning abilities of AI systems, the ability of neural networks to learn semantics from observations, etc, which are really over-the-top. The paper does not claim any of them, but they are still perceiv

@dsehnal
dsehnal / bcif.py
Last active February 21, 2024 02:45
BinaryCIF parser in Python
# BinaryCIF Parser
# Copyright (c) 2021 David Sehnal <david.sehnal@gmail.com>, licensed under MIT.
#
# Resources:
# - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008247
# - https://github.com/molstar/BinaryCIF & https://github.com/molstar/BinaryCIF/blob/master/encoding.md
#
# Implementation based on Mol*:
# - https://github.com/molstar/molstar/blob/master/src/mol-io/common/binary-cif/encoding.ts
# - https://github.com/molstar/molstar/blob/master/src/mol-io/common/binary-cif/decoder.ts