Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a prohibitive number of training samples and computational power. What design principle difference between NI and AI could contribute to such a discrepancy? Here, we investigate the role of weight polarity: development processes initialize NIs with advantageous polarity configurations; as NIs grow and learn, synapse magnitudes update, yet polarities are largely kept unchanged. We demonstrate with simulation and image classification tasks that if weight polarities are adequately set a priori, then networks learn with less time and data. We also explicitly illustrate situations in which a priori setting the weight polarities is disadvantageous for networks. Our work illustrates the value of weight polarities from the perspective of statistical and computational efficiency during learning.

翻译：自然智能（NI）在动态世界中蓬勃发展——它们学习迅速，有时仅需少量样本。相比之下，人工智能（AI）通常需要大量训练样本和计算资源才能学习。自然智能与人工智能之间的设计原则差异会导致这种悬殊吗？本文探究了权重极性的作用：发育过程赋予自然智能以有利的极性配置；随着自然智能成长和学习，突触强度会更新，但极性基本保持不变。我们通过仿真和图像分类任务证明，若权重极性被适当先验设定，则网络能以更少的时间和数据进行学习。同时，我们明确阐述了权重极性先验设定对网络不利的情形。我们的工作从学习过程中的统计与计算效率角度阐释了权重极性的价值。