Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to output its own weights. The network is designed using a loss function that can be optimized with either gradient-based or non-gradient-based methods. We also describe a method we call regeneration to train the network without explicit optimization, by injecting the network with predictions of its own parameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neural network that can solve an auxiliary task such as MNIST image classification. We observe that there is a trade-off between the network's ability to classify images and its ability to replicate, but training is biased towards increasing its specialization at image classification at the expense of replication. This is analogous to the trade-off between reproduction and other tasks observed in nature. We suggest that a self-replication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection.

本文描述如何构建和训练自我复制的神经网络，其中网络通过学习输出自己的权重来实现复制，并使用称为再生的方法来训练网络。通过在复制和优化步骤之间交替进行，该自我复制神经网络可以解决MNIST图像分类等辅助任务。同时，该文提出自我复制机制对人工智能是有用的，因为它引入了通过自然选择实现持续改进的可能性。

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