Title: Polarity is all you need to learn and transfer faster. Abstract: Natural intelligences (NIs) thrive in a dynamic world – they learn quickly, sometimes with only a few samples. In contrast, Artificial intelligence (AI) has achieved supra (-human) level performance in certain AI settings, typically dependent on a prohibitive amount of training samples and computational power. What design principle difference between NI and AI could contribute to such a discrepancy? Here, we propose a research avenue based on a simple observation from NIs: post-development, neuronal connections in the brain rarely see polarity switch. Why? Our answer is: to learn and transfer more efficiently. We demonstrate with theory and simulations that if weight polarities are adequately set $\textit{a priori}$, then networks learn with less time and data. We extend such findings onto image classification tasks and demonstrate that polarity, not weight, is a more effective medium for knowledge transfer between networks. We also explicitly illustrate situations in which $\textit{a priori}$ setting the weight polarities is disadvantageous for networks. Our work illustrates the value of weight polarities from the perspective of statistical and computational efficiency for both NI and AI.