But can you prove that Stonkfish ls cheating?

Also: Cheating basically means getting help from a third party? Like when I as a 500 elo fish get hints from a master while I play some ofishel chessgame like a real nerd and stuff.

Stockfish doesn't cheat. Its chess speaks for itself.

It's also illegal to get human assistance during a game, so wouldn't you get disqualified too? : ) And if the assistance provided by your own brain doesn't count... you see where it is going then; does Stockfish use itself?

It has a chip in its butt lol

That channel is just loads of AI generated crap. The games they show are probably just Stockfish vs Stockfish itself.

best comment 2023

I don't even think they made all 1000 games public, I think just around 100 or so are available

Kramnik is several hundred points lower rated. His opinions dont apply here. lol

I think StockFish would roflstomp AlphaZero even from the starting position. We live in crazy times.

For real. It incorporated techniques from AlphaZero, just didn't replace itself with them entirely. Current stockfish is like old stockfish and AlphaZero combined.

It just takes time for Alpha Zero. It has a much stronger computer that Stockfish will never see. It a matter of minutes Alpha Zero will always surpass Stockfish.

Wasn't Alphazero trained for just 5 hours? It's just that they stopped after it became the best in the world and moved on to other things, but had they continued, with 5 years of development/training it's hard to see how Alphazero wouldn't be on top today...

@@kingsolo6241 Alphazero is not a computer and neither is Stockfish. Both are computer programs. Alphazero advantage at the time was the fact that it was a NNUE (Efficiently updatable neural network) Once Stockfish updated it's evaluation function too it surpassed AlphaZero. And Since then Stockfish has gotten way stronger still. AlphaZero doesn't have any advantage now, while Stockfish has the advantage of being open source and is constantly tested by it's community.

But stay right there!

Just like human chess 😂😂😂

Even in 2018 the eval functions were more sophisticated than what you outline. I would guess they used a genetic algorithm with parameters as genes to converge upon optimum values, and to determine which to jettison to improve speed.

Your claim that engines were great at tactics but they would still be outplayed by grandmasters due to a lack of strategical understanding is just plainly wrong. Even years before this match, the best engines had reached a point far beyond human ability to match. There might have been deficiencies, but they were not going to be outplayed by humans.

I guess I could frame it better, the main point is after finishing AlphaGo, they made AplhaZero which didn’t use human games and trained entirely on self play aka it learned long term strategies on its own. They could quickly train it to play other games than go (including chess) They were not trying to compete with stockfish and that’s why they quickly moved to other things instead of improving AplhaZero unlike stockfish

Evaluating based only on material will give you a significantly inferior engine than Stockfish 8. HCE was the result of three decades of development and this is just disrespectful

You've oversimplified it to the point where it's blatantly untrue. The rules were anything but trivial. If you ever looked into (old at least) Stockfish source code, it contained a huge pile of "manual" corrections - surprisingly specific, like "in an endgame with just Queens and minor pieces value the mobility of minor pieces 16% less" etc. Not literally ofc, but this sort of stuff. Of course it's still heuristics (albeit very complex and multi-layered), and it doesn't quite convey the notion of long-term strategical sense (this part is correct). But modern engines were very far from evaluating positions only based on the material value of pieces. This is really unfair to their creators (who btw often were strong players themselves, or strong players assisted with the development). If you built an engine based on such simple criteria, it would never be stronger than an amateur. It's true that engines relied on opening books because they weren't very good at grasping the strategical implications of opening choices (the "fog" was too dense from the starting position). But still, even if you disabled an opening book, an engine would still play a pretty strong opening based on general principles (mobility, control of the center etc.). The main reason for including an opening book is just optimization. Similarly, (serious) engines use endgame tables, even though endgame is "pure" calculation, and relatively easy at that (as you can calculate much farther when there are only a few pieces left on the board). But the reason is optimization. In an engine vs. engine match time matters: just like in human chess, they both have only got so much time allocated for the game. So why waste time on computations when you can replace them with the equivalent of "home preparation".

the setting to look for is "Contempt", which basically just tells the engine how much it should avoid draws, or in other terms, how much worse of a position it's willing to accept to avoid a draw (on the assumption that it can outplay the opponent in the long term and win anyways)

@@sage5296 oh yeah. I think i remember ben using that word now. Thanx!

8 * 2 = 16

If you can now tell me what "twice as good means" :'D

@@nilsp9426 the answer is fries.

Way more than twice as good but ok

@@DarthVader-wk9sdHow do you quantify twice as good Obviously elo only went up some hundreds of points. Not to 6000 What does it mean to be twice as good? A 2 to 1 win probability? Then you could use the elo formula to compute the exact elo gap, should be about +100 points.

It wouldnt have used human reasoning like you and I. The answer lies in huge branches of calculations

Precisely this, it lost most of its games by attacking in a drawn position, but A0 believed it had an overwhelming attack but stockfish had a strong defence to counter.

Lol. I dont think anyone even knows who that is now!

Interesting

Extremely incorrect. Maybe at very low depth, but certainly not at the massive depths they are typically run.

@@ethangilworth7891 You’re right. It was just alphazero that did it. Not stockfish.

@@jefftaylor1186 I’m an Evan’s player myself, so I would love to see your source for this.

_"Didn't stockfish "resign" if its own evaluation of its position was like, -5 whole points or something"_ They can set the resign score threshold for the match. Once it reaches that threshold (e.g. -8.00), and that score stays or worsens over three more subsequent moves, then it resigns. You can download a good chess gui like Arena 3.5 (it's free) and run your own matches with Stockfish 8 and other newer NNUE engines.

With Stockfish, it uses Alpha Beta pruning for its tree search, so it saw every move. The question is more “what move did it evaluate incorrectly.”

@@ethangilworth7891 not exactly, it can easily be the case that it prunes away the key line too early. Ig that would be an incorrect evaluation to some degree but non-NN engines rely more on seeing far ahead than evaluating accurately in any given position

@@ethangilworth7891 that's pretty much the whole definition (and the purpose) of pruning - to NOT see every move.

You dont use ad blockers?

They probably had the same computational power on hand. The thing is that A0's evaluation function is much more complex and much more computationally demanding than that of a traditional engine, so if A0 is to match SF8 in term of positions per second, A0 has to have many, many times more computational power at its disposal

They do have the same calculating power

Idk it’s pretty hard to come up with fair hardware for GPU/CPU engine matches. Even modern engine tournaments get criticized over them.

​@@user-ws7kp1yh9l I agree it is hard to compare apples to apples CPU vs GPU, but look at what Stockfish was given vs. Google's machine. It was totally lopsided. If you Google "Hardware used in AlphaZero vs Stockfish match", and read this line from the StackExchange discussion: "[AlphaZero] used 4 TPUs for the games, so a processing power of 180 TFLOPS. Note TFLOPS = 1000 billion floating point operations per second." The GPU network AlphaZero ran on was roughly 10,000 times more powerful than the hardware SF was allowed, and AlphaZero was *designed* to work without an opening book, whereas Stockfish was designed to work with an opening book an endgame database, and at classical time controls. The game structure was obviously designed handicap SF to the maximum.

It can if it's forced to. But it will never choose to play f3 if left to its own calaculations.

Y they didn’t let him rematch

​@@archsys307because they won

They had never been obliged to keep on granting him rematches until he's satisfied with the outcome. And why would they grant him a rematch anyway after he publicly accused them of cheating without providing a shred of evidence? "I just know that computers can't play that way" is an allegation, nothing else.

Indeed. But you need a decent GPU to run it.

The Stockfish derivative engines (engines based on Stokcfish but with modifications), are stronger than standard SF, for sure. Have you played around with any of them?

Stockfish on your phone is already stronger than all GMs out there

Noted, but what if the brain power required to play chess is between 1% and 10% the computing power of a phone. Limiting computing power to that: Who would win? It'd be a cool experiment 😇

my understanding is that it's basically using the standard alpha-beta pruning, but being much more selective/aggressive with the pruning. The AI part is the algorithm that evaluates a position, which is what basically assigns that %win or +- score. The tree is then pruned accordingly in the same fashion as SF would, except it checks less positions overall (often 100x or higher less positions), generally to a comparable depth tho In other words, it plays moves towards a position that's most likely to win, but it still evaluates many moves deep

@@sage5296No, Alphazero does not use alpha-beta pruning. Evaluation is too computationally expensive for that

No, chess AIs (or engines based on neural networks) don't work that way. They don't simulate the entire game to the end (that's known as a Monte Carlo simulation, this technique predates the AI as we know it, and it can only be executed with a rather low accuracy / randomness, due to computational complexity). Quite the contrary - they've got a highly developed (via deep learning) evaluation function, which is essentially a black-box algorithm that "wrote itself" (by trial and error, in the training phase). Noone really knows how it works, and what patterns trigger the AI into thinking a position is "promising" or "seeds of defeat". It's the same as with eg. image recognition. Noone knows what exactly it was that made the AI recognize a dog in the picture. AZ, in a way, played more like a human (human brains are biological neural networks, after all). It uses highly sophisticated pattern recognition insteadf of a huge tree search with a slimmer (manually encoded) evaluation. That's why it analyzes much fewer nodes per second than the likes of Stockfish. But its analysis is of higher quality, and it compensates for that.

@@vibovitold In fact, AlphaZero (and all similarly-architected game-playing AIs) use Monte Carlo Tree Search (MCTS) as a fundamental part of their training (and, it seems, during actual play, but see below). MCTS is exactly how AZ learns "by trial and error, in the training phase", and is indeed based on playing a large number of games to the end to determine win/loss efficacy for a given move, which it uses to update the network. It's surprisingly difficult to find clear info on whether current AIs use MCTS during play to choose candidate moves. DeepMind's paper on AlphaGo Zero (the Go-playing AI which preceded generalized AlphaZero) states specifically that it does not, but the AlphaZero description is unclear on the matter, and Leela Zero definitely does use runtime MCTS (i.e., "playouts" or "rollouts"). Apparently without them its Elo was only ~2500. In any case, though, the "evaluation function" of deep learning AIs does literally boil down to "Will this move lead to a win, on average?"

@@Rubrickety as part of the training, sure, i meant the "over the board" phase. According to LZ website "Many people call this MCTS (Monte-Carlo Tree Search), because it is very similar to the search algorithm the Go programs started using in 2006. But the PUCT [Predictor + Upper Confidence Bound tree search] used in AGZ and Lc0 replaces rollouts (sampling playouts to a terminal game state) with a neural network that estimates what a rollout would do" so if it doesn't "sample playouts to a terminal game state", only "estimates what a rollout would do", it directly contradicts the claim that "they just simulate the entire game to the end and see what happened". no? i do admit that while i'm a programmer with some interest in chess (and conversely chess engines), i am not working on chess engines, let alone an expert in the field

I'd agree with your assessment. When I tested SF13 by having it analyze some of AZ's wins, it couldnt find some of the moves that AZ played in critical positions. But SF15 and 16 are most definitely stronger than AZ. I wish someone would hack DeepMInd, steal the code for AZ, and make a a new "Frankenstein" version of AZ. 😂 I reckon it'll be even stronger!

@@CorteumGoogle Leela Chess Zero

Chess engine is basically any program that plays chess. Pretty much all strong chess engines (including Stockfish) incorporate NNUE these days. So whether it's based on neural networks or manually encoded heuristics, it's still a chess engine.

+155 -6 =1000000 is just a 0.05 elo difference

1000 Games is just too small of a sample size.

What app are you using

Chessify

@@mrcleanisin Chessify’s Stockfish seems to be handicapped to 2800 elo

If you want to play the non handicapped version use either Droidfish or SmallFish (depending on whether you use iOS or Android)

I'm not sure I understand you. I have chessify on my android phone that says it uses stockfish rated 2850. It beats me really easy, but I have gotten a draw when I take back some weak moves. I would imagine this chessify on my phone would beat Magnus Carlson. Why don't you try it?

what is cool is that AZ doesn't know ANYTHING at all. it only knows how to learn.

It didn't know the concept of *anything* outside of the rules. That's the amazing part. It's just here are the rules, play a billion games against yourself and see what works

Carlsen, not Carlson. And the fact that every strong language (including Stockfish) includes a neural network now (NNUE) pretty much answers the question of whether the approach prototyped by AZ is superior or not. It is even more obvious where you look at Go. Due to (much) greater computational complexity, there have NEVER been any "traditional" Go engines capable of beating the best humans (without handicap). No "Go-Stockfish" existed. Which is why AlphaGo was tested against the best human players (beating them). This has only become possible once deep learning / neural networks were used, as opposed to traditional algorithms.

Stockfish 16 uses NN and Stockfish 16 runs on your phone. It also has a branching factor of under 1.7, which is way less “brute force” than even human GMs

Stockfish's algorithm was written manually. (It's got a NNUE component now, but it didn't back then). What we refer to as "AI" in this context are implementations based on neural networks, meaning they're black-box algorithms which have kind of "written themselves" by trial and error..

Classic google move

According to my info (my friend works at Google), "AlphaZero" was Hans Niemann disguised as a neural network.

SF8 I think was, in reality, 3200-3250. Many of its moves were predictable. And still, Stockfish had years and years of development and learning and openings repertoire. All of that sunk at the bottom of the sea when torpedoed by mere 9 hours of self-learning by A0. And A0 moves were utterly mind-blowing and unpredictable. We got an all new insight into the game.

@@zvonimirtosic6171 I think if you had SF8 competing in disguise in human elite events, it would wipe the floor clean. Former WCCC Rybka 3 was already over 3000. Rybka 4 was not quite as strong as SF8 and had a rating of around 3350. But i remember clearly SF8 having been rated at over 3400 on multiple ratings lists. A good way to compare AZ with today's engines is to have SF12 or higher play a match against SF8 using the exact same openings as it played vs AZ. You'll learn a lot from those games and have a much better idea of how strong SF12 (or higher) is compared to AZ.

@@zvonimirtosic6171 I've gone through a lot of A'Z's wins vs SF8, and even SF12 struggled to find some of AZ's best moves. In some cases, it never found the moves at all. Only later versions of SF did find AZ's moves.

What I wanted to say, perhaps, is that chess engines do not perform at presumed ELO rating across all possible aspects of playing. On some aspects they are good and maybe better than their alleged ELO rating, but on some they are not as good as their alleged rating. Maybe this is "human way" of assessing engine's performance (which does not suffer from human issues, but it does have its own weaknesses). I do remember using engines before A0, and they played "predictably boring" hi-level chess, without blunders. But with A0, though, for the first time I saw something I never saw before, and which Magnus Carlsen called "something we can mistake for creativity". A0 approached the game in a wholly different way and that chess was exciting to watch.

@@zvonimirtosic6171 A0 is definitely something else. I remember testing Stockfish 12 NNUE on some of AO's brilliant moves, and SF12 struggled to find the correct solution. It wasn't until SF14 and 15 that it started to find A0's moves. I agree that the top engines have their strengths and a few (very few) weaknesses. But in general, their performance on different aspects of chess is superior to elite GMs. In saying that, there are still some positions they struggle with that i've come across. But those are very few. It would be interesting if they ran an average engine (e.g. Houdini 1.5a or Rybka 3 - both former WCCC's), using just 1-cpu and a small hash, in a top level human tournament, without the human players knowing about it. And just see what the approximate human elo level is for these engines. Then do the same with a more modern NNUE neuralnetwork engine. My guess is that a few GMs may get some draws against Rybka or Houdini, but probably no wins. Meanwhile, the NNUE engine will not lose, or even draw, a single game vs elite GMs. Maybe more advanced AI's in the near future will be able to accurately model the performance and style of different elite GMs from past or present, and then test to see how they might have performed against chess computers from past or present using different hardware configurations. Would love to see it. 👍

Rawr!!

Only SF13 ror later wouldve had a chance against AZ.

@@Corteum I'm pretty sure Stockfish 16 is good enough to defeat her consistently.

@@sugarcravings1797 Most definitely. SF16 is super strong. But there are some SF derivatives that are even stronger.

@@CorteumDerivatives are just scam

Stockfish’s tournament wins speaks for itself

You seem to be confusing two things here. AZ was TRAINED on a strong machne, but when it played, it didn't run on one anymore. Training a neural network is pretty much the equivalent of home preparation.

@@vibovitold thanks for the info was something l read on an other post and did not look into it any more than that

But in the most enjoyable nevermind.