Researchers are exploring ways to automatically design neural networks, rather than relying on human experts. This approach is called Neural Architecture Search (NAS). The idea is that by systematically searching through the vast space of possible network architectures, we can find better-performing models more efficiently. While most popular models were designed manually, it's unlikely that humans have found the absolute best architecture yet. By automating this process, NAS aims to improve model performance without requiring extensive human expertise. --- Why it matters: This matters because manual neural network design is time-consuming and often relies on trial-and-error. Automating this process with NAS could lead to faster development of more accurate models for various applications, such as computer vision or natural language processing. Source: https://lilianweng.github.io/posts/2020-08-06-nas/