> Many Human Concepts Can Be Found in the AlphaZero Network.
> We demonstrate that the AlphaZero network’s learned representation of the chess board can be used to reconstruct, at least in part, many human chess concepts. We adopt the approach of using concept activation vectors (6) by training sparse linear probes for a wide range of concepts, ranging from components of the evaluation function of Stockfish (9), a state-of-the-art chess engine, to concepts that describe specific board patterns.
> A Detailed Picture of Knowledge Acquisition during Training.
> We use a simple concept probing methodology to measure the emergence of relevant information over the course of training and at every layer in the network. This allows us to produce what we refer to as what–when–where plots, which detail what concept is learned, when in training time it is learned, and where in the network it is computed. What–when–where plots are plots of concept regression accuracy across training time and network depth. We provide a detailed analysis for the special case of concepts related to material evaluation, which are central to chess play.
> Comparison with Historical Human Play.
> We compare the evolution of AlphaZero play and human play by comparing AlphaZero training with human history and across multiple training runs, respectively. Our analysis shows that despite some similarities, AlphaZero does not precisely recapitulate human history. Not only does the machine initially try different openings from humans, it plays a greater diversity of moves as well. We also present a qualitative assessment of differences in play style over the course of training.
> Many Human Concepts Can Be Found in the AlphaZero Network.
> We demonstrate that the AlphaZero network’s learned representation of the chess board can be used to reconstruct, at least in part, many human chess concepts. We adopt the approach of using concept activation vectors (6) by training sparse linear probes for a wide range of concepts, ranging from components of the evaluation function of Stockfish (9), a state-of-the-art chess engine, to concepts that describe specific board patterns.
> A Detailed Picture of Knowledge Acquisition during Training.
> We use a simple concept probing methodology to measure the emergence of relevant information over the course of training and at every layer in the network. This allows us to produce what we refer to as what–when–where plots, which detail what concept is learned, when in training time it is learned, and where in the network it is computed. What–when–where plots are plots of concept regression accuracy across training time and network depth. We provide a detailed analysis for the special case of concepts related to material evaluation, which are central to chess play.
> Comparison with Historical Human Play.
> We compare the evolution of AlphaZero play and human play by comparing AlphaZero training with human history and across multiple training runs, respectively. Our analysis shows that despite some similarities, AlphaZero does not precisely recapitulate human history. Not only does the machine initially try different openings from humans, it plays a greater diversity of moves as well. We also present a qualitative assessment of differences in play style over the course of training.