进化发育生物学可作为人工智能新设计范式的概念基础
Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence
AI总结：
该研究指出现有AI范式缺乏统一框架的局限，提出可借鉴进化发育生物学（EDB）的适应原则构建AI新设计范式的概念框架，并给出两种基于发育原理的学习系统设计，解决机器学习的多项核心局限。
摘要：
人工智能（AI）在机器学习进展的推动下，在解决复杂任务方面取得了重大进展。然而，当前基于神经网络的范式虽然有效，却受到固有局限性的严重制约，主要表现为缺乏结构组织，且学习的推进过程呈现出不良特性。随着AI研究在缺乏统一框架的情况下推进，它要么试图通过启发式方法修补缺陷，要么松散地借鉴生物学机制，却没有坚实的理论基础。与此同时，主要由进化发育生物学（EDB，evolutionary developmental biology）推动的进化认知领域的近期范式转变，在AI文献中很大程度上被忽视了——尽管经过仔细分析可以发现，现代综合进化论（Modern Synthesis）与当代机器学习之间存在惊人的相似性，二者在假设、研究方法和局限性上都有共通之处。因此，EDB中重塑了我们对进化过程认知的适应原则，也可以为AI的下一种设计理念奠定统一概念框架的基础，它超越了单纯的灵感借鉴，牢牢扎根于生物学的第一性原理。本文详细概述了现代综合进化论与现代机器学习之间的类比关系，并基于EDB的洞见勾勒出一种新的AI设计范式的核心原则。为了举例说明我们的分析，我们还提出了两种基于特定发育原理的学习系统设计——调控连接、体细胞变异与选择以及弱连锁——它们以有机的方式解决了当代机器学习的多个主要局限性，同时也为这些机制在生物进化中的作用提供了更深刻的洞见。
英文摘要：
Artificial intelligence (AI), propelled by advancements in machine learning, has made significant strides in solving complex tasks. However, the current neural network-based paradigm, while effective, is heavily constrained by inherent limitations, primarily a lack of structural organization and a progression of learning that displays undesirable properties. As AI research progresses without a unifying framework, it either tries to patch weaknesses heuristically or draws loosely from biological mechanisms without strong theoretical foundations. Meanwhile, the recent paradigm shift in evolutionary understanding -- driven primarily by evolutionary developmental biology (EDB) -- has been largely overlooked in AI literature, despite a striking analogy between the Modern Synthesis and contemporary machine learning, evident in their shared assumptions, approaches, and limitations upon careful analysis. Consequently, the principles of adaptation from EDB that reshaped our understanding of the evolutionary process can also form the foundation of a unifying conceptual framework for the next design philosophy in AI, going beyond mere inspiration and grounded firmly in biology's first principles. This article provides a detailed overview of the analogy between the Modern Synthesis and modern machine learning, and outlines the core principles of a new AI design paradigm based on insights from EDB. To exemplify our analysis, we also present two learning system designs grounded in specific developmental principles -- regulatory connections, somatic variation and selection, and weak linkage -- that resolve multiple major limitations of contemporary machine learning in an organic manner, while also providing deeper insights into the role of these mechanisms in biological evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
