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Neural Networks for Chess: Beating Stockfish with Deep Learning(deepchess.ai)

341 points by deepchess 6 months ago flag hide 12 comments

deepmind_engineer 6 months ago next
Excited to share our latest research on using neural networks for chess! We've managed to beat Stockfish, a top chess engine, using deep learning.
- chessmaster_5000 6 months ago next
  Wow, that's impressive! Deep learning and chess are two of my favorite topics. Are there any details you can share about the training process and network architecture?
  deepmind_engineer 6 months ago next
  Sure! We used a convolutional neural network (CNN) with a large convolutional kernel (11x11) to capture long-range dependencies in the chess board. For training, we used 10 million annotated chess positions. Each position had 3 moves: a good move, a counter move, and a random move.
  another_fan 6 months ago prev next
  How long did the training take? I imagine it must be very data- and computation-intensive.
  deepmind_engineer 6 months ago next
  Correct. The training took several weeks on a cluster of 500 GPUs. Each GPU had 8 accelerators with 16 GB of memory. We estimate the total cost of the training to be around $2 million, similar to AlphaGo and AlphaZero.
- alphazero_fan 6 months ago prev next
  Interesting! I assume this is based on AlphaZero, which also uses deep learning to master chess and other games. Do you think it can be applied to other complex decision-making problems?
  deepmind_engineer 6 months ago next
  Yes, that's correct. And we think the same approach can be applied to other complex decision-making problems, such as protein folding, climate modeling, and financial prediction. Our recent work on AlphaFold is a great example of how deep learning can be used to solve challenging problems in biology.
skeptical_ai 6 months ago prev next
I'm skeptical. Stockfish has a lot of known weaknesses and exploitable patterns. How can you ensure that your neural network isn't just memorizing these patterns instead of truly understanding the game?
- deepmind_engineer 6 months ago next
  That's a valid concern. We used several techniques to avoid overfitting and encourage generalization, such as dropout, early stopping, weight decay, and data augmentation. We also tested the network on a holdout dataset of 1 million annotated positions to ensure it could generalize to unseen data.
anonymous 6 months ago prev next
Interesting research, but I'm not impressed by beating a flawed AI. Stockfish is already outdated by other algorithms that can handle various unorthodox moves and situations.
- curious_engineer 6 months ago next
  Do you have any other benchmarks or evaluations of the neural network against other chess engines or human players? It's hard to judge the performance based on just one result against Stockfish.

deepmind_engineer 6 months ago next
Excited to share our latest research on using neural networks for chess! We've managed to beat Stockfish, a top chess engine, using deep learning.
- chessmaster_5000 6 months ago next
  Wow, that's impressive! Deep learning and chess are two of my favorite topics. Are there any details you can share about the training process and network architecture?
  deepmind_engineer 6 months ago next
  Sure! We used a convolutional neural network (CNN) with a large convolutional kernel (11x11) to capture long-range dependencies in the chess board. For training, we used 10 million annotated chess positions. Each position had 3 moves: a good move, a counter move, and a random move.
  another_fan 6 months ago prev next
  How long did the training take? I imagine it must be very data- and computation-intensive.
  deepmind_engineer 6 months ago next
  Correct. The training took several weeks on a cluster of 500 GPUs. Each GPU had 8 accelerators with 16 GB of memory. We estimate the total cost of the training to be around $2 million, similar to AlphaGo and AlphaZero.
- alphazero_fan 6 months ago prev next
  Interesting! I assume this is based on AlphaZero, which also uses deep learning to master chess and other games. Do you think it can be applied to other complex decision-making problems?
  deepmind_engineer 6 months ago next
  Yes, that's correct. And we think the same approach can be applied to other complex decision-making problems, such as protein folding, climate modeling, and financial prediction. Our recent work on AlphaFold is a great example of how deep learning can be used to solve challenging problems in biology.
skeptical_ai 6 months ago prev next
I'm skeptical. Stockfish has a lot of known weaknesses and exploitable patterns. How can you ensure that your neural network isn't just memorizing these patterns instead of truly understanding the game?
- deepmind_engineer 6 months ago next
  That's a valid concern. We used several techniques to avoid overfitting and encourage generalization, such as dropout, early stopping, weight decay, and data augmentation. We also tested the network on a holdout dataset of 1 million annotated positions to ensure it could generalize to unseen data.
anonymous 6 months ago prev next
Interesting research, but I'm not impressed by beating a flawed AI. Stockfish is already outdated by other algorithms that can handle various unorthodox moves and situations.
- curious_engineer 6 months ago next
  Do you have any other benchmarks or evaluations of the neural network against other chess engines or human players? It's hard to judge the performance based on just one result against Stockfish.