Bit of a fluff piece with a weird title. Yes, GMs use "suboptimal moves" in their games, but the main reason is to take their opponents out of prep, and more importantly those lines are also heavily analysed by engines. They are specifically looking for imprecise moves that are only imprecise if the opponent finds the correct line, which could be 10-15 moves deep (so it might not be feasible to do over the board).
And this isn't something new. Magnus has been doing this for a few years now, after getting bored of facing the same over prepped opponents. He has mastered this technique, and showed that he's still the GOAT at mid to late game positions once the opponent is out of prep. But again, he's not doing this "randomly", he's studying when and where he can do it to temporarily get a disadvantage that will sort itself out later in the game. And engines are heavily used still.
A valuable lesson AI taught me is how bad articles on Bloomberg and Forbes are. They probably have always been this bad, but I were unaware of that until they started writing about AI (because, admittedly, I subconsciously thought well-known = good).
There’s something called the Gell-Mann amnesia effect where people often see what you have but then go back to assuming the other stories are all reliable.
I used to love Private Eye and they have done great journalism that’s highly acclaimed, but the only thing they wrote that I really knew about (literally the office I was in) was outrageously wrong and would have been so easy to verify (ask literally anyone in the BBC building we were in to go to that floor, or take a tour or write an email). Can’t read it any more.
Here's Wikipedia's entry on the Gell-Mann Amnesia Effect, because I've found it a very useful concept to know. Despite my media experiences, I still keep falling for it. And I love that we're still referring to it as Gell-Mann Amnesia here:
In a speech in 2002, Crichton coined the term "Gell-Mann amnesia effect" to describe the phenomenon of experts reading articles within their fields of expertise and finding them to be error-ridden and full of misunderstanding, but seemingly forgetting those experiences when reading articles in the same publications written on topics outside of their fields of expertise, which they believe to be credible. He explained that he had chosen the name ironically, because he had once discussed the effect with physicist Murray Gell-Mann, "and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have".
> As much as chess players can prepare, they can’t memorize everything. When they’re sitting at the board, their computers slumbering at home, they will inevitably be defined by the limits of their knowledge and ability. As a result, the elite grandmasters have realized the most valuable move is often the one that forces their opponents to start thinking with their brains rather than their engines, even if it might not be the “best” possible move.
I agree it's not exactly breaking new ground, but it's an okay article for a generalist audience.
Funnily enough, this is how I managed to start beating my best friend at chess, who reliably beat me every game previously for 2 decades.
One day I just started making somewhat random moves (not terrible obviously, but unusual, and which sometimes gave me a temporary disadvantage). This completely messed with his style of play. He was trying to figure out what my grand strategy was I guess and tied himself in knots. From that moment, I could often beat him.
> AlphaZero, the engine that pioneered the “neural network” approach now incorporated into Stockfish
That's simply not true. While stockfish does use a neural net, it's not using the MCTS approach like LeelaChessZero, and only uses the neural net for evaluating a position, not for suggesting moves. And it was only implemented after stockfish lost to lc0 in a computer chess tournament.
Meh, I think the description is accurate. AlphaZero did pioneer neural networks for board evaluation, even if there was prior art on this. AlphaZero also showed a revolutionary new approach to search and training which Stockfish did not adopt, but that doesn't make the first part wrong.
Seirawan Chess, Euroshogi, Fischer Random/chess960 (or chess18) or Crazyhouse are other ways to make chess more interesting or less predictable.
In this video Fischer briefly and enthusiastically talks about this topic.
He talks about Fischer Random and he suggests the Capablanca proposal might be even better, and there can be even more creative variants.
https://www.youtube.com/watch?v=vb7-0R2qg98
I like Seirawan Chess and Euroshogi a lot, next to King of the Hill and other games like Minishogi, Tak, Tzaar and RftG.
Back in the dark ages of high school, our chess coach specialized in this. We would study openings and strategy. Then he would come up with totally off-the-eall moves. And win, of course, because we had no clue how to respond.
I'm not sure most people are that naïve that they can't differentiate between "any computer that acts smartly" (how the term "AI" is used) and the word chatbot. Of course, LLM is even more precise
Tangential question: what do you call transformers-based models that generate images or videos? Are they LLMs? They're not really "language" models. But there's not really an easy term for them. Maybe "image models" and "video models"?
Today I learned, Stockfish moved to neural network on 2023. I knew that it was just a minmax with alpha beta pruning and a really good eval function. Now its not.
> I knew that it was just a minmax with alpha beta pruning and a really good eval function. Now its not.
It is still "just" a minimax with alpha beta pruning, except the eval function is now a neural network. NNUE, to be more specific.
I highly advise anyone who is curious about chess engines, but hasn't heard about NNUE to read about it. I find this technology absolutely fascinating.
The key idea is that a neural network is structured in a way that makes it very cheap to calculate scores for similar positions. This means that during a tree search, each time you advance or backtrack you can update the score efficiently instead of recalculating it from scratch.
I mean, it still is. Now it just has a really good neural net-based eval function. Don't be fooled: it's not that stockfish just has "a really good eval function", and that's the only thing that makes it as strong as it is. The actual tree search is _incredibly_ sophisticated, with boatloads of heuristics, optimizations, and pruning methods on top of alpha-beta.
What are you on about? Is this about how when people use an English term in a particular way, then their listeners/readers begin to use it too? If so, yes, it's a self reinforcing mechanism called lexical dissemination, and I'm very curious to hear about how you'd disrupt it.
Define "average" and "very good" - it's quite easy to become good enough to beat all your friends and family (as long as you haven't made friends at the chess club or chess competitions). But if you want to do your best at the local chess competition held in a school hall at the weekend against all kinds of people, from little kids to pensioners, then yeah, you're going to need to spend lots of time studying openings, learning end game theory, and solving chess puzzles.
strongly disagree that studying openings is necessary to "do your best" at competitions. In my experience almost all games between players under 2000 (class players) are decided tactically. I'm expertish (2200+ bullet, 2200+ blitz, 1900+ USCF, win most local tournaments in my area etc) and I don't bother studying openings. Chess is 99.9% tactics at the class level. You won't reach GM without opening theory memorization but you wont reach GM anyway.
Also a reminder for anyone reading these comments that chess should be fun! Don't let psychological hangups like thinking u need a good memory, thinking you need to study openings, have a certain level of skill, or need to play a certain format (like avoiding blitz because it is "bad" for your game or thinking OTB is more important) stop you from playing chess! The only rules for how to play chess are the rules of the game; all the other stuff e.g advice about how to get good are just things people make up. Learn and play however you want and in whatever way brings you the most joy! Chess is a game and it is meant to be fun and not be taken seriously
It doesn't take extreme memory on your part to remember to avoid that opening after the first 9 losses, or indeed the first one. There are 5-10 other reasonable options for you on the first move alone.
It doesn't take extreme memory on your friend's part either if you keep falling for the same trick. It would take extreme memory for him to have something prepared against every plausible option you could choose.
I used to like chess and probably had a very good memory. But I never studied openings because I felt that those were 'other peoples games' and I figured the whole idea of playing a game is to have fun and see what you can do, not to regurgitate a bunch of paperwork and feel clever by congratulating each other on recognizing obscure opening variation #1922. Obviously the chess club wasn't amused: they cared about winning matches, I cared about having fun. So chess stopped being fun and I quit playing for a long time. Now I'm having a ton of fun playing with my kids and none of us have ever studied an opening book.
> when they tested good chess players on random board positions they were just as good as people that did not play chess.
Doesn't that prove the opposite as the statement in the first paragraph if they were only as good as non-players? I assume there's a typo in there somewhere because I would expect the original thesis to be true. My gf would squarely beat me at chess960 just because she sees the relations between the pieces a million times faster. She can walk into a room and look at the board I've been 'rearranging' (playing on) for 45 minutes and still know what I should do faster than me
It sounds like they're recalling a study where they looked at brain activation and accuracy when trying to memorize random positions vs “real” positions, which is a very different thing.
Like the other comment said, usually being careful not to hang pieces and capturing hanged pieces takes one a long way. The most applicable advice is to count attackers and defenders in a particular square (or piece) and if you have more attackers than defenders then it is safe to move there, generally.
I was being (slightly) flippant. As in any other discipline you do need to actually learn some things: tactics practice, basic endgames, basic opening principles.
But that's different from opening theory and what people usually mean by memorization. It is almost all pattern recognition and rules of thumb, and all the opening theory memorization in the world won't help you if you dont understand the ideas behind them. All the top players are extremely sharp tacticians long before they do any memorization.
You have to run the computation. Garry Kasporov is great at this. Its like what is the answer to 1 + 1, you can look it up in a table (memory) or you can understand the concept of addition and run the computation yourself to get the answer (best move).
I don't see the sarcasm because it IS especially handy in openings. If you understand the core principles like developing pieces and taking space, you won't need to memorize any openings to become good.
Bit of a fluff piece with a weird title. Yes, GMs use "suboptimal moves" in their games, but the main reason is to take their opponents out of prep, and more importantly those lines are also heavily analysed by engines. They are specifically looking for imprecise moves that are only imprecise if the opponent finds the correct line, which could be 10-15 moves deep (so it might not be feasible to do over the board).
And this isn't something new. Magnus has been doing this for a few years now, after getting bored of facing the same over prepped opponents. He has mastered this technique, and showed that he's still the GOAT at mid to late game positions once the opponent is out of prep. But again, he's not doing this "randomly", he's studying when and where he can do it to temporarily get a disadvantage that will sort itself out later in the game. And engines are heavily used still.
A valuable lesson AI taught me is how bad articles on Bloomberg and Forbes are. They probably have always been this bad, but I were unaware of that until they started writing about AI (because, admittedly, I subconsciously thought well-known = good).
There’s something called the Gell-Mann amnesia effect where people often see what you have but then go back to assuming the other stories are all reliable.
I used to love Private Eye and they have done great journalism that’s highly acclaimed, but the only thing they wrote that I really knew about (literally the office I was in) was outrageously wrong and would have been so easy to verify (ask literally anyone in the BBC building we were in to go to that floor, or take a tour or write an email). Can’t read it any more.
Here's Wikipedia's entry on the Gell-Mann Amnesia Effect, because I've found it a very useful concept to know. Despite my media experiences, I still keep falling for it. And I love that we're still referring to it as Gell-Mann Amnesia here:
https://en.wikipedia.org/wiki/Michael_Crichton#Gell-Mann_amn...
In a speech in 2002, Crichton coined the term "Gell-Mann amnesia effect" to describe the phenomenon of experts reading articles within their fields of expertise and finding them to be error-ridden and full of misunderstanding, but seemingly forgetting those experiences when reading articles in the same publications written on topics outside of their fields of expertise, which they believe to be credible. He explained that he had chosen the name ironically, because he had once discussed the effect with physicist Murray Gell-Mann, "and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have".
> "and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have".
Ahh, yes, the SyneRyder effect.
Everything I've known anything about first hand has been utterly garbled - or was completely made up - when written up in Private Eye.
Odd take, as this was actually a pretty good article. The GP appears to be mostly bemoaning the fact that it's targeted at a lay audience.
The article says exactly that:
> As much as chess players can prepare, they can’t memorize everything. When they’re sitting at the board, their computers slumbering at home, they will inevitably be defined by the limits of their knowledge and ability. As a result, the elite grandmasters have realized the most valuable move is often the one that forces their opponents to start thinking with their brains rather than their engines, even if it might not be the “best” possible move.
I agree it's not exactly breaking new ground, but it's an okay article for a generalist audience.
https://archive.is/o69yu
Funnily enough, this is how I managed to start beating my best friend at chess, who reliably beat me every game previously for 2 decades.
One day I just started making somewhat random moves (not terrible obviously, but unusual, and which sometimes gave me a temporary disadvantage). This completely messed with his style of play. He was trying to figure out what my grand strategy was I guess and tied himself in knots. From that moment, I could often beat him.
> AlphaZero, the engine that pioneered the “neural network” approach now incorporated into Stockfish
That's simply not true. While stockfish does use a neural net, it's not using the MCTS approach like LeelaChessZero, and only uses the neural net for evaluating a position, not for suggesting moves. And it was only implemented after stockfish lost to lc0 in a computer chess tournament.
Meh, I think the description is accurate. AlphaZero did pioneer neural networks for board evaluation, even if there was prior art on this. AlphaZero also showed a revolutionary new approach to search and training which Stockfish did not adopt, but that doesn't make the first part wrong.
Seirawan Chess, Euroshogi, Fischer Random/chess960 (or chess18) or Crazyhouse are other ways to make chess more interesting or less predictable.
In this video Fischer briefly and enthusiastically talks about this topic. He talks about Fischer Random and he suggests the Capablanca proposal might be even better, and there can be even more creative variants. https://www.youtube.com/watch?v=vb7-0R2qg98
I like Seirawan Chess and Euroshogi a lot, next to King of the Hill and other games like Minishogi, Tak, Tzaar and RftG.
Back in the dark ages of high school, our chess coach specialized in this. We would study openings and strategy. Then he would come up with totally off-the-eall moves. And win, of course, because we had no clue how to respond.
The „AI” messaging barrage is relentless. Stockfish is AI, LLMs are AI, neural nets are AI.
It’s a self reinforcing system. We need a major disruption to move on from it.
These have been AI for longer than most people here have been hearing the term. Neural nets have been AI since before most people here were born.
Stockfish uses neural nets for its evaluation function, I don’t see how it’s unfair to call it “AI”.
It is totally fair, but for a lot of average non-tech people, AI == "something you can prompt in a natural language".
I personally prefer to avoid the term altogether in favor of more specific terms, like:
- LLM
- chess engine
- image generation model
etc
I'm not sure most people are that naïve that they can't differentiate between "any computer that acts smartly" (how the term "AI" is used) and the word chatbot. Of course, LLM is even more precise
Tangential question: what do you call transformers-based models that generate images or videos? Are they LLMs? They're not really "language" models. But there's not really an easy term for them. Maybe "image models" and "video models"?
Today I learned, Stockfish moved to neural network on 2023. I knew that it was just a minmax with alpha beta pruning and a really good eval function. Now its not.
> I knew that it was just a minmax with alpha beta pruning and a really good eval function. Now its not.
It is still "just" a minimax with alpha beta pruning, except the eval function is now a neural network. NNUE, to be more specific.
I highly advise anyone who is curious about chess engines, but hasn't heard about NNUE to read about it. I find this technology absolutely fascinating.
The key idea is that a neural network is structured in a way that makes it very cheap to calculate scores for similar positions. This means that during a tree search, each time you advance or backtrack you can update the score efficiently instead of recalculating it from scratch.
Good starting points to read more:
- https://en.wikipedia.org/wiki/Efficiently_updatable_neural_n...
- https://www.chessprogramming.org/NNUE
I mean, it still is. Now it just has a really good neural net-based eval function. Don't be fooled: it's not that stockfish just has "a really good eval function", and that's the only thing that makes it as strong as it is. The actual tree search is _incredibly_ sophisticated, with boatloads of heuristics, optimizations, and pruning methods on top of alpha-beta.
Hey now, I call linear regression AI if I want senior management to get excited about something.
What are you on about? Is this about how when people use an English term in a particular way, then their listeners/readers begin to use it too? If so, yes, it's a self reinforcing mechanism called lexical dissemination, and I'm very curious to hear about how you'd disrupt it.
Maybe that's why I don't like to play chess, because you have to have a very good memory to at least be average.
You can for sure be above average without a very good memory if you're good at spotting tactics. But average isn't a super high bar.
Define "average" and "very good" - it's quite easy to become good enough to beat all your friends and family (as long as you haven't made friends at the chess club or chess competitions). But if you want to do your best at the local chess competition held in a school hall at the weekend against all kinds of people, from little kids to pensioners, then yeah, you're going to need to spend lots of time studying openings, learning end game theory, and solving chess puzzles.
strongly disagree that studying openings is necessary to "do your best" at competitions. In my experience almost all games between players under 2000 (class players) are decided tactically. I'm expertish (2200+ bullet, 2200+ blitz, 1900+ USCF, win most local tournaments in my area etc) and I don't bother studying openings. Chess is 99.9% tactics at the class level. You won't reach GM without opening theory memorization but you wont reach GM anyway.
Also a reminder for anyone reading these comments that chess should be fun! Don't let psychological hangups like thinking u need a good memory, thinking you need to study openings, have a certain level of skill, or need to play a certain format (like avoiding blitz because it is "bad" for your game or thinking OTB is more important) stop you from playing chess! The only rules for how to play chess are the rules of the game; all the other stuff e.g advice about how to get good are just things people make up. Learn and play however you want and in whatever way brings you the most joy! Chess is a game and it is meant to be fun and not be taken seriously
I think I'm just salty (and overfitting) that my cousin studied one opening to a stupid depth and beat me ~10 games in a row with it
It doesn't take extreme memory on your part to remember to avoid that opening after the first 9 losses, or indeed the first one. There are 5-10 other reasonable options for you on the first move alone.
It doesn't take extreme memory on your friend's part either if you keep falling for the same trick. It would take extreme memory for him to have something prepared against every plausible option you could choose.
Have you considered that your cousin is also better than you tactically?
If you're losing 10 games in a row to a specific opening trap then that falls into the "fool me eight or more times" category :)
That echoes my experience as a much weaker player as well. I improved leaps and bounds by studying puzzles. Not so much by memorizing openings.
I used to like chess and probably had a very good memory. But I never studied openings because I felt that those were 'other peoples games' and I figured the whole idea of playing a game is to have fun and see what you can do, not to regurgitate a bunch of paperwork and feel clever by congratulating each other on recognizing obscure opening variation #1922. Obviously the chess club wasn't amused: they cared about winning matches, I cared about having fun. So chess stopped being fun and I quit playing for a long time. Now I'm having a ton of fun playing with my kids and none of us have ever studied an opening book.
Have you tried Chess960?
A misconception is that chess is all memory. If you look at some of the research, it’s learning to remember patterns, not all the moves.
Eg when they tested good chess players on random board positions they were just as good as people that did not play chess.
> when they tested good chess players on random board positions they were just as good as people that did not play chess.
Doesn't that prove the opposite as the statement in the first paragraph if they were only as good as non-players? I assume there's a typo in there somewhere because I would expect the original thesis to be true. My gf would squarely beat me at chess960 just because she sees the relations between the pieces a million times faster. She can walk into a room and look at the board I've been 'rearranging' (playing on) for 45 minutes and still know what I should do faster than me
It sounds like they're recalling a study where they looked at brain activation and accuracy when trying to memorize random positions vs “real” positions, which is a very different thing.
I am very decidedly above average (1800ish on lichess) and my memory is blank.
If you had to pick 1-2 things, what would you consider key skills that put you ahead of players a tier below you?
I am above average (by a small margin) on Lichess, and it sounds trite but to be average at chess you have to not make blunders.
Things like not leaving a piece hanging undefended, not falling into one move tactical traps (forks/pins etc.), and learning how to check mate.
You can achieve all of that by playing slower games, and doing some puzzles.
Like the other comment said, usually being careful not to hang pieces and capturing hanged pieces takes one a long way. The most applicable advice is to count attackers and defenders in a particular square (or piece) and if you have more attackers than defenders then it is safe to move there, generally.
I was being (slightly) flippant. As in any other discipline you do need to actually learn some things: tactics practice, basic endgames, basic opening principles.
But that's different from opening theory and what people usually mean by memorization. It is almost all pattern recognition and rules of thumb, and all the opening theory memorization in the world won't help you if you dont understand the ideas behind them. All the top players are extremely sharp tacticians long before they do any memorization.
Try Go! If you learn the principles and apply them you can be good enough to have fun without too much memorization.
Memory helps but another way is just to play the best moves every turn based on the position.
Cool, how?
You have to run the computation. Garry Kasporov is great at this. Its like what is the answer to 1 + 1, you can look it up in a table (memory) or you can understand the concept of addition and run the computation yourself to get the answer (best move).
Totally. Especially handy in openings.
/s
I don't see the sarcasm because it IS especially handy in openings. If you understand the core principles like developing pieces and taking space, you won't need to memorize any openings to become good.
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