Sometimes it's only for one move because it's often unusual moves that gets pruned away quickly that's the winning idea's. If you prune away your opponents best and winning move before calculating deep enough, you obviously lose. After the move is played it has to be looked at and it's way easier to find why it wins. Other times it's a positional squeeze where the advantage just increases slowly. edit: Here it obviously takes stockfish forever to understand what even humans can understand. Stockfish took forever to understand that the rook was in jail and wasn't coming out. An example of the strongest engine struggling with a positional concept.

Well that's what the Lichess analysis is showing. If Gotham chess is to be believed Leela said the position with b4 was -4

I think tcec would have that info if you click the archived game. There would be 2 or 3 simultaneous eval graphs.

Well, Stockfish deeming Stockfish flawless isn't really surprising.

Rb3 is a game losing blunder from a slightly better position. Stockfish is obviously blind to it's own mistakes. Otherwise it wouldn't make them lol.

Yes, great comment

Somehow, as there is no beauty on it

Stockfish is far away from brute force. There is a ton of pruning going on.

but it still is brute force. why didnt it prune the losing rook b3 move? it coulsnt go deep enough to see thar its a move that loses.

Stockfish destryos leela

I believe SF uses a blend of lightweight pattern-savvy NN-based position evals + super-efficient calc-based tree pruning. Leela OTOH uses slow & deep NN-based pattern recognition for both eval and tree pruning. The problem for SF is that the worthlessness (and/or eventual loss) of "permanently trapped" pieces lies way beyond the calculation-driven tree-pruning horizon. Leela is trained such that move pruning and pos eval coordinate. Thus once leelas NN gets the faintest hint that trapped pieces are correlated with eventual wins, BOTH her move selection and pos evals will be driven to explore lines that sacrifice material or other positional advantages in order to set and stengthen such traps. And as she trains against herself, she gets better and better at recognizing and using the pattern. Assuming SF also trains its NN with self-play, the recognition of subtle patterns like this (where material payoff is way beyond calculation horizon) will not be reinforced, because efficient calc-based pruning is so quick to discard any branch that sacs material for advantages way beyond calc horizon. What i wonder is: would Leela be a more formidable foe if she was able to learn that SF has this blindspot? Her self-training likely causes her to assume opponent also recognizes the trapped piece pattern (and would actively avoid it). So there are probably a lot of lines where leela could attempt to trap a piece (longterm advantage +3.0) with material sac (shortterm material value -2.0). However, she might also see that the opponont can pass up the sac and refute the trap with a line valued at -0.1. But if she trained against SF and learned its blindspot, she could learn when to expect SF to have no clue that the longterm value of the trap is -3.0 and would almost certainly grab the illusory +2.0 material advantage.

i laughed out loud

One of the deeper traps out there. 😎

👍

Jerry is the best

What is -4.x? Is that -4?

@@nix4110Minus 4.[something] I don't remember what the second part was, so I put x instead as a variable.

I dont think AI engines use endgame databases

@@Aca99100 Yes they do, these engines are made to win games and with endgame databases you immediatly have the best possible solution.

Correct !! It is still a database app :))

😎

I like O-no :)

most likely that is just how leela has learned to end games, because the entire goal of ML is just to simply win games and not lose, how you do it is irrelevant

👍😎

Funny thing is Leela is already far stronger than AlphaZero ever was.

yes, if you make the slightest mistake, computer will key on it and pry open your position till you collapse.

leela won dude

@@hqcart1 leela sure did. your point?

Hey hotrod, who da funk is Jerry and what does he have to do with the price of tea in China?

most likely because it had already calculated the win without the extra material, so it saw no need to overcomplicate, after all, the only thing neural nets care about is the win, point being neural nets dont care if they win by material at all, the material is just a means to an end

When did it have the chance to sacrifice the exchange?

@@codegeass7162Wow, 56 minutes ago. Cool.

There were plenty of times. I can't remember when, rewatch the video.

You spelled Jerry wrong. ;)

Perhaps. 😊

How? At that point It is probably a forced checkmate in a couple of moves. It can't get any simpler than that.

Clearly leela isn't coded to pursue the fastest mate. If it found a winning line, it stops calculating?

Forcing moves y"see. Opponent makes queen you gotta capture it

It's mate in 6 and Leela calculates that almost instantly so it's not that.

@@Halibut86 leela is coded to know the rules of the game. Other than that, it is hard to tell how leela "ticks" as pretty much everything else is the result of machine-learning.

At the very least you're also freeing the rook tho