Nice post! I think the inherent limitations of AI are extremely important to evaluating AI risks and highly under-discussed. (Coincidentally, I’m also writing a post looking at the performance of chess engines in this context which should be done soon.)
When it comes to chess, I think it’s worth noting that alphazero is not the best chess playing AI, stockfish is. If you look at chess engine tournaments, stockfish and leelazero (the open source implementation of alphazero’s design) were pretty evenly matched in the years up to 2020, even though stockfish was not using neural networks and relying more on hard-coded intuition and trial-and-error. In 2020, stockfish did incorporate neural networks, but in a hybrid manner, switching between NN and “classical” mode depending on the position. Since then it’s dominated both classical engines and purely NN engines, which tells us that human expert knowledge still counts for something here.
Since then it’s dominated both classical engines and purely NN engines, which tells us that human expert knowledge still counts for something here.
Nah. The Stockfish Improvements comes from NNUE Evaluation which roughly speaking replaces the mostly hand crafted evaluation function by experts with one by a a NN. So it actually says the opposite of what you claimed.
Stockfish+NNUE is better than leelazero because the search part of Leelazero is not subject to reinforcement learning. That part is where Stock fish (admittedly hand expert coded) is better.
Nice post! I think the inherent limitations of AI are extremely important to evaluating AI risks and highly under-discussed. (Coincidentally, I’m also writing a post looking at the performance of chess engines in this context which should be done soon.)
When it comes to chess, I think it’s worth noting that alphazero is not the best chess playing AI, stockfish is. If you look at chess engine tournaments, stockfish and leelazero (the open source implementation of alphazero’s design) were pretty evenly matched in the years up to 2020, even though stockfish was not using neural networks and relying more on hard-coded intuition and trial-and-error. In 2020, stockfish did incorporate neural networks, but in a hybrid manner, switching between NN and “classical” mode depending on the position. Since then it’s dominated both classical engines and purely NN engines, which tells us that human expert knowledge still counts for something here.
Nah. The Stockfish Improvements comes from NNUE Evaluation which roughly speaking replaces the mostly hand crafted evaluation function by experts with one by a a NN. So it actually says the opposite of what you claimed.
Stockfish+NNUE is better than leelazero because the search part of Leelazero is not subject to reinforcement learning. That part is where Stock fish (admittedly hand expert coded) is better.