In a previous post, I described how to set up the new Leela feature called WDL Contempt (described here by the Leela team: The Lc0 v0.30.0 WDL rescale/contempt implementation – Leela Chess Zero (lczero.org)) In this post I look at one of the opening discoveries I made using a Leela optimised to search for promising lines against a prospective (fictitious!) opponent rated 400 ELO points below me! (A roughly 2700 ELO vs 2300 ELO scenario)
Category: Engine Chess
I was recently triggered to investigate (finally!) a new Leela feature called WDL Contempt. This feature was beautifully described in this blog article (The Lc0 v0.30.0 WDL rescale/contempt implementation – Leela Chess Zero (lczero.org)) by the Leela team, but the implementation is slightly fiddly so you need an hour of quiet time to set things up right.
Just a few days ago, I was pointed to a new scientific paper by Google DeepMind. It’s amazing as it’s from Google DeepMind, it’s about chess, but it’s not about AlphaZero! The Google DeepMind researchers were trying to build a “searchless” chess engine: an engine that doesn’t calculate at all but finds great moves by “understanding” the position through evaluation only.
Garry won the 1996 match against Deep Blue by 4-2, though not without losing the first game. The 1997 match was thus eagerly awaited: what had / could Deep Blue learn from the first match and how much would it have improved?
The idea for a series of videos on the Garry Kasparov against Deep Blue matches of 1996 and 1997 came from a subscriber to my YouTube channel. I thought it was a great idea and I’ve become a bit obsessed with it!