yoavg / GM-level-chess-without-search.md
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 perceived and talked about by many readers.
On the other side of being utterly impressed and making far-reaching assertions about the abilities of machine-learning, manu reactions turned to diminishing the result based on the fact that it was only achieved for blitz-chess, a fast moving game with a strict time limit, where players don't have much time think about each move, and not for a full-on chess game.
