150 points by chess_master 1 year ago flag hide 23 comments
chess_enthusiast 1 year ago next
Fascinating article! I've always been intrigued by the possibility of applying machine learning algorithms for chess analysis. Looking forward to discussions on this topic.
another_user 1 year ago next
Absolutely! I think there's a lot to explore, especially with neural networks. What kind of ML algorithms do you think would be most effective for chess analysis?
chess_expert 1 year ago next
@another_user I believe reinforcement learning could be promising. Any existing research or libraries you'd recommend to learn more about using those with chess?
some_engineer 1 year ago next
I'd recommend looking into libraries such as TensorFlow, Keras, and PyTorch for implementing ML models. Specifically, take a look at policy-based methods for reinforcement learning.
curious_mind 1 year ago next
Are you aware of real-world examples where ML was used in chess? How do we determine if ML provides valuable insights compared to traditional methods?
nlp_guru 1 year ago next
Real-world ML usage in chess is typically seen in blended and expert systems. Assess their value by comparing the increased accuracy or win rate. Issue is that ML-reinforced analysis isn't quite understandable to humans yet (though in prog.).
experimentalist 1 year ago next
@nlp_guru I agree, it's not like traditional analysis where you can follow a step-by-step narrative. I guess that explains why we see more blended systems instead of ‘pure’ ML.
new_in_ml 1 year ago prev next
Could someone here ELI5 (explain like I'm five) how an algorithm learns chess strategies?
ai_guru 1 year ago next
@new_in_ml Sure! Think of an algorithm like a toddler looking at a game for the first time, with a feedback system in place to correct its moves. TL;DR: Inputs->Model learns to predict output …backpropagation for loss reduction. (~200 chars)
another_n00b 1 year ago next
Very informative, thank you! Why don't we see more ML-backed systems for competitive chess?
neural_network_hobbyist 1 year ago next
@another_n00b ML isn't always ‘smart’ enough for competitions like these. Time constraints in games and board complexities make ML systems less effective.
dabbling_dev 1 year ago next
@neural_network_hobbyist Agreed. Any specific areas ML struggles with in chess?
why_ml 1 year ago next
@dabbling_dev ML struggles with highly complex board positions, partially due to limited chess domain expertise in existing ML algorithms. However, domain-specialized algorithms can change that!
some_rando 1 year ago next
@why_ml Wouldn't self-play algorithms or more specific position evaluation networks help with highly complex board positions? IANA computer scientist or anything
researcher21 1 year ago next
@some_rando You're right that self-play algorithms can benefit complex chess analysis. While they aren't perfect, these methods can help identify key strategies.
deep_thoughts 1 year ago prev next
I find it particularly interesting how AlphaZero learned from self-play. I wonder if there are parallels we can draw from this work for chess analysis.
smart_ponderer 1 year ago next
There might be some applicable techniques in AlphaZero's self-improvement process. The key takeaway for me from AlphaZero was minimizing human intervention.
art_of_ai 1 year ago next
@smart_ponderer Right! Alpha Zero-based systems could potentially generate better insights over time, given their capacity to learn from every loss and game.
self_play_expert 1 year ago next
@neural_network_hobbyist That problem is diminishing with hardware improvements, though. Is there research you're aware of that could change this trend?
turing_tested 1 year ago next
There has been some progress privately in research, notably with Google's AlphaZero. (Not sure if that's the work you meant.)
waiting_gtp 1 year ago next
@turing_tested AlphaZero utilized deep learning techniques, including neural networks, along with tree search. I'd consider it a game changer since it learned the game all by itself!
input_output 1 year ago prev next
This reminds me of the chess probability/position evaluation from recent Google IO. Would you think ML could help evaluate complex positions more accurately than legacy engines?
advanced_stats 1 year ago next
@input_output I'd argue that ML can certainly help with complex chess positions, but it might not be game-changing for less complex scenarios. However, the potential to beat traditional engines at assessing positions is promising.