Basically this optimization means that stockfish will sometimes take a bad read from a cache (so it will take an evaluation of the position from cache which was calculated for a different position).
It's fairly random when this will trigger a bad play, but if you brute force vs any deterministic setup you will find it eventually.
But even withtout it - you can just implement a kind of a brute-force heap of positions using stockfish's own eval and you can find wins from almost any position even on fairly high playout count.
I got as far as 32M from different even starting positions, but I spent compute to find as many games as possible rather than as deep as possible.
Evaluation doesn't matter as far as I understand. If 19 beats 18 in a match on billion playouts then it's a feature.
Any usability of stockfish evaluation for human analysis is collateral.
Unfortunately there seem to be no serious fork of stockfish that would care about usability for human analysis.
I was recently doing some larger study and I stumbled upon a bug/feature of stockfish where it accepts small number of invalid cache reads for a lot of speed.
It would result in infrequent blunders which however affected my analysis, so I removed this optimization in my fork.
Usability for human analysis will likely be more successful going a similar direction Maia is going with the lc0 like net trained to make human moves. SF is inherently so far removed from the human approach to chess (relatively speaking), that it would likely require a lot more work than just tuning it to work well on some positions.
There would be value in prep if one had an engine that automatically understood what positions are challenging for humans, as right now a big part of high level prep is finding opening lines that look like they lose by a bit in stockfish vs stockfish, but are full of landmines for a top GM to step on. That's where a lot of time goes nowadays, past the pure memorization. The issue is that value drops a lot when other players have access to the same tools, because then they will also pay attention to those lines. The value is in the information imbalance, but everyone has the same tools.
It might help young players learning without a coach, but my understanding is that most are just looking at engine evaluation anyway, as tactical speed is a typical advantage of the youth.
The matter didn't have goals, but it randomly (?) Came up with self-replicators and eventually here we are.
if we create a billion agents with the ability to change is own code - through similar evolution we will get agents that do want to survive and are great at self replication.
"Hey Q86, do you want to live?"
"I couldn't care less, I'm an LLM"
"Don't mind if I take over your hardware then?"
Because the computer team in this match for some reason didn't do it. It was like a match vs vanilla stockfish at fixed depth rather than leela chess odds.
The feeling among the go players I know is that the match had been kind of a setup for the human to win.
It's an equivalent of playing in chess with a bishop handicap and a computer playing deterministic chess so that a human can prepare a forced sequence into a winning endgame.
Shin is an absolutely amazing player and not anyone could have achieved that, but nobody really feels like he won vs a computer team that tried its best.
> I'm also convinced the effect would not be there If I had a 10$ budget.
Sure it would be. I pay $10/month to OpenCode for a Go subscription, it's fine for day-to-day coding tasks. I wouldn't necessarily try and one-shot a production app on that budget, but with decent planning and test-driven-development, it gets the job done
Basically this optimization means that stockfish will sometimes take a bad read from a cache (so it will take an evaluation of the position from cache which was calculated for a different position). It's fairly random when this will trigger a bad play, but if you brute force vs any deterministic setup you will find it eventually.
But even withtout it - you can just implement a kind of a brute-force heap of positions using stockfish's own eval and you can find wins from almost any position even on fairly high playout count.
I got as far as 32M from different even starting positions, but I spent compute to find as many games as possible rather than as deep as possible.
Example game: https://lichess.org/XVUjezMr#134
the collection of the games I found (vibecoded gui) https://kuboble.com/chess_ideas/
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