The introduction of large language models has significantly expanded global demand for computing; addressing this growing demand requires novel approaches that introduce new capabilities while addressing extant needs. Although inspiration from biological systems served as the foundation on which modern artificial intelligence (AI) was developed, many modern advances have been made without clear parallels to biological computing. As a result, the ability of techniques inspired by ``natural intelligence'' (NI) to inflect modern AI systems may be questioned. However, by analyzing remaining disparities between AI and NI, we argue that further biological inspiration can contribute towards expanding the capabilities of artificial systems, enabling them to succeed in real-world environments and adapt to niche applications. To elucidate which NI mechanisms can contribute toward this goal, we review and compare elements of biological and artificial computing systems, emphasizing areas of NI that have not yet been effectively captured by AI. We then suggest areas of opportunity for NI-inspired mechanisms that can inflect AI hardware and software.

翻译：大型语言模型的引入显著扩大了全球对计算的需求；应对这一日益增长的需求需要引入新能力并解决现有需求的新方法。尽管生物系统的启发为现代人工智能的发展奠定了基础，但许多现代进展的取得并未与生物计算形成明确对应。因此，受"自然智能"启发的技术能否影响现代人工智能系统可能受到质疑。然而，通过分析人工智能与自然智能之间尚存的差距，我们认为进一步的生物学启发有助于扩展人工系统的能力，使其能够在现实环境中取得成功并适应特定应用。为阐明哪些自然智能机制可助力实现这一目标，我们回顾并比较了生物与人工计算系统的组成要素，着重探讨了尚未被人工智能有效捕捉的自然智能领域。最后，我们提出了受自然智能启发的机制在影响人工智能硬件与软件方面的潜在机遇领域。