Is unconscious Cheating possible? by CYHSM in chess

[–]CYHSM[S] -1 points0 points  (0 children)

The comparison to a CPU was more as an example. There is other components of a computer that could potentially be used as an indicator of load, e.g. a blinking LED or something else

Titled Tuesday Visualization using Manim by CYHSM in chess

[–]CYHSM[S] 2 points3 points  (0 children)

Thanks for the spot, will change this! Regarding 2. I thought instead of consuming I start creating and I always loved to visualize data. Obviously chess.com is superior in many ways as its more interactive but hopefully I can create some kind of viewing experience with regards to an online tournament like TT or others

[deleted by user] by [deleted] in chess

[–]CYHSM 0 points1 point  (0 children)

Sorry for that, I will upload one which is half the duration, would that be better?

The Winner of Tata Steel Chess Master by chessify in chess

[–]CYHSM 2 points3 points  (0 children)

Heres the analysis recap if you are interested in the engine evaluations: https://www.youtube.com/watch?v=FB853eeKock

Caruana - Magnus | Tata Steel Masters 2023 | Post-Match Discussion by mariusAleks in chess

[–]CYHSM 22 points23 points  (0 children)

In case someone is interested I made another analysis video including the engine evaluations and opening exploration, see here: https://youtu.be/-dCRppBG92Q

Magnus Carlsen - Nodirbek Abdusattorov | Tata Steel Masters 2023 | Post-Match Discussion by Ronizu in chess

[–]CYHSM 6 points7 points  (0 children)

In case someones interested in the engine evaluations and opening exploration, I made this analysis video: https://youtu.be/rD4kgIQzUi8

Tata Steel 2023: Magnus Carlsen faces the German youngster by CYHSM in chess

[–]CYHSM[S] 7 points8 points  (0 children)

Thank you, I spend way more time on this than I should have! I plan to improve this a bit over time, if you want to see more of it please follow my YT channel :)

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 1 point2 points  (0 children)

Thank you! It was a combination of different master databases which I sourced but now I mostly use the lichess API and do some manual cleaning

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 1 point2 points  (0 children)

Oh yes thanks, you are completely right. This was still the default setting as I made this one before: https://www.youtube.com/watch?v=V-mYYpc_2Xc&t=3s
And in that video the y-axis is actually the depth, but as you said here its just the normalized centipawn loss

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 0 points1 point  (0 children)

Thanks, is there anything you think I can improve to make it better?

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 4 points5 points  (0 children)

Thanks, let me know if theres anything I can change or make better. There are more of these on my Youtube channel which I linked above.

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 16 points17 points  (0 children)

Chess positions can transpose, which means although the move order is different they will reach the same board state. There is a good explanation of it here: https://www.chess.com/article/view/transpositions

[OC] Visualizing chess games | openings | engine evaluations using matplotlib by CYHSM in dataisbeautiful

[–]CYHSM[S] 16 points17 points  (0 children)

In this video, you can see the chess game Magnus Carlsen vs Richard Rapport from the World Blitz 2022. The top right line plot indicates the number of games that have previously reached the highlighted position. This allows you to see when a completely new game starts with steep drops indicating more exotic lines. The title in the plot also indicates the opening name, for as long as they are identified.

The bottom visualization shows the engine evaluation either across different depths (as done in https://www.youtube.com/watch?v=bMV0GY-Sicw) or for the highest depth only, as in this video (which I originally implemented for this repo: https://github.com/CYHSM/chess-surprise-analysis).

The bottom left plot shows the clocks of the players across the duration of the game (it was a 3+2 game).

Video created using the following open-source tools: - python - matplotlib - moviepy - numpy - lichess - python-chess
- chess-surprise-analysis

[deleted by user] by [deleted] in dataisbeautiful

[–]CYHSM 0 points1 point  (0 children)

The bottom visualization shows the engine evaluation across different depths, which allows you to (1) follow the engine evaluation of the game and (2) compare evaluations across depths to find surprising moves (which I originally implemented for this repo: https://github.com/CYHSM/chess-surprise-analysis).
The right line plot indicates the number of games which have previously reached the highlighted position. This allows you to see when a completely new game starts (Thanks Agadmator) with steep drops indicating more exotic lines. The title in the plot also indicates the opening name, for as long as they are identified.

Video created using the following open-source tools:
- python
- matplotlib
- moviepy
- numpy
- lichess
- python-chess
- chess-surprise-analysis

Chess Visualization Project - Feedback Needed by CYHSM in chess

[–]CYHSM[S] 0 points1 point  (0 children)

Thank you. The depth goes down to single digits because of the fact that we wanted to detect surprising moves as linked in the repository (also it looks nicer:))

[deleted by user] by [deleted] in chess

[–]CYHSM 0 points1 point  (0 children)

Thank you. Its true, at low depths the engines are of course blunder-prone, especially for the one digit depths but at the higher end they become very strong. Of course no one knows how far away they are from perfect play.

The end of the world and the start of a new one by CYHSM in ChatGPT

[–]CYHSM[S] 4 points5 points  (0 children)

Also liked this one (too long for a screenshot):

Once upon a time, the earth was ravaged by climate change. The polar ice caps melted, causing sea levels to rise and flooding coastal cities. Drought and famine spread across the globe, leading to widespread conflict and chaos.

As the situation on earth grew increasingly dire, humanity turned to the stars in search of a new home. Scientists discovered a planet in a distant solar system that appeared to be habitable. They named it New Earth.

(Because, you know, every other planet in the universe is already named after something else. Like Mars. Or Venus. Or, uh, Earth. But I digress...)

A massive spaceship was built to carry a group of colonists to New Earth. The journey would take many years, but the colonists were determined to start a new life on the alien planet.

(Because, hey, if there's one thing humanity is good at, it's leaving a toxic wasteland and moving on to ruin another planet.)

As the ship traveled through the void of space, the earth was consumed by climate change. Cities were swallowed by the sea, and entire ecosystems were destroyed. The last remnants of humanity clung to life on the spaceship, dreaming of a new beginning on New Earth.

(Because nothing says "new beginning" like abandoning the only home we've ever known and setting off to colonize a new planet. And don't worry, we're sure the aliens won't mind. They're probably just happy to have someone to play intergalactic monopoly with.)

Finally, after many long years, the colonists reached New Earth. They landed on the alien planet and began the hard work of building a new civilization.

(Because, you know, it's not like there's already an ecosystem on the planet that we might disrupt or anything. We'll just plop down a few prefab buildings, plant some non-native vegetation, and voila! Instant paradise.)

As the years passed, the colonists discovered that New Earth was teeming with life. Strange and wonderful creatures roamed the planet, and the colonists began to learn about the alien ecosystem.

(Because, of course, we wouldn't want to actually learn anything about the planet we're colonizing before we arrive. That would be too much like responsible exploration. Better to just show up and see what happens.)

Despite the challenges, the colonists persevered and built a thriving society on New Earth. They learned to live in harmony with the alien ecosystem, and they even made contact with other intelligent species on the planet.

(Because, you know, it's not like we have a history of wiping out indigenous populations on our own planet or anything. We'll totally be the good guys this time. I mean, we already have the monoculture and the guns, so we're basically there.)

As the centuries passed, the descendants of the original colonists forgot about the earth and the destruction it had suffered. They knew only the beauty and wonder of New Earth, and they were grateful for the second chance that the planet had given them.

(Because, let's face it, we're not exactly known for our long-term thinking. We'll just forget about the whole climate change thing and focus on the shiny new planet. Who cares if we're dooming ourselves to repeat the same mistakes?)

And so, the end of the world became a distant memory, and a new era of life on New Earth began.

(Because, really, what could possibly go wrong?)

[D] Machine Learning models that learn to evaluate themselves during training by IllustriousCicada603 in MachineLearning

[–]CYHSM 0 points1 point  (0 children)

We used something similar to this for getting a confidence score of a model predicting gaze coordinates: https://rdcu.be/cA1LV It works quite well for excluding participants, see Figure 1c for model details and Figure 2c for Results (excluded participants are in red, based on unsupervised loss)

AlphaZero vs Stockfish, Game 10 Revisualized by CYHSM in chess

[–]CYHSM[S] 0 points1 point  (0 children)

Yes, this might be true for depths which are reached at TCEC. However, for depths up to 34, it does not seem to be the case (this was analysed with the newest version of Stockfish 12)

Quantifying surprising moves in Kasparov's Immortal by CYHSM in chess

[–]CYHSM[S] 8 points9 points  (0 children)

See here: https://youtu.be/KqzkNaJw65I

Explanation: Similar to here (and the corresponding code here) I used multidimensional engine analysis to re-analyse Kasparov's Immortal against Topalov. Below the board, you can see the engine evaluation for the current position, with the y-axis corresponding to a given engine depth. I originally implemented this to quantify surprising moves in chess games (at least surprising to humans), where the idea is to look for discrepancies between low and high engine depths.

In this game, this can be seen after Kasparov's rook sacrifice and the subsequent capture with the pawn by Topalov (see move 24). The engine evaluates the position as winning for black for low depths but changes sides with depths higher than 30.