On the Parallels Between Evolutionary Theory and the State of AI
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918038798860288 |
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| author | Erden, Zeki Doruk Faltings, Boi |
| author_facet | Erden, Zeki Doruk Faltings, Boi |
| contents | This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, and highlight how advancements in evolutionary theory that augmented the Modern Synthesis, particularly those of Evolutionary Developmental Biology, offer insights that can inform a new design paradigm for AI. By synthesizing findings across AI and evolutionary theory, we propose a pathway to overcome existing limitations, enabling AI to achieve its aspirational goals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_23774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Parallels Between Evolutionary Theory and the State of AI Erden, Zeki Doruk Faltings, Boi Neurons and Cognition Machine Learning Neural and Evolutionary Computing Adaptation and Self-Organizing Systems This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, and highlight how advancements in evolutionary theory that augmented the Modern Synthesis, particularly those of Evolutionary Developmental Biology, offer insights that can inform a new design paradigm for AI. By synthesizing findings across AI and evolutionary theory, we propose a pathway to overcome existing limitations, enabling AI to achieve its aspirational goals. |
| title | On the Parallels Between Evolutionary Theory and the State of AI |
| topic | Neurons and Cognition Machine Learning Neural and Evolutionary Computing Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2505.23774 |