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[2602.18960] Modularity is the Bedrock of Natural and Artificial Intelligence
Summary
The paper discusses the importance of modularity in both natural and artificial intelligence, highlighting its role in efficient learning and generalization, and advocating for its increased recognition in AI research.
Why It Matters
Understanding modularity is crucial as it can enhance AI systems' performance by mimicking the organizational principles of human intelligence. This paper encourages researchers to adopt modular architectures, potentially leading to more effective AI solutions and bridging the gap between human and machine intelligence.
Key Takeaways
- Modularity is essential for efficient learning and generalization in AI.
- It aligns with the No Free Lunch Theorem, emphasizing problem-specific solutions.
- The paper reviews how modularity has been applied across various AI research areas.
- Modularity can help bridge the gap between natural and artificial intelligence.
- Increased focus on modularity could lead to advancements in AI research.
Computer Science > Artificial Intelligence arXiv:2602.18960 (cs) [Submitted on 21 Feb 2026] Title:Modularity is the Bedrock of Natural and Artificial Intelligence Authors:Alessandro Salatiello View a PDF of the paper titled Modularity is the Bedrock of Natural and Artificial Intelligence, by Alessandro Salatiello View PDF HTML (experimental) Abstract:The remarkable performance of modern AI systems has been driven by unprecedented scales of data, computation, and energy -- far exceeding the resources required by human intelligence. This disparity highlights the need for new guiding principles and motivates drawing inspiration from the fundamental organizational principles of brain computation. Among these principles, modularity has been shown to be critical for supporting the efficient learning and strong generalization abilities consistently exhibited by humans. Furthermore, modularity aligns well with the No Free Lunch Theorem, which highlights the need for problem-specific inductive biases and motivates architectures composed of specialized components that solve subproblems. However, despite its fundamental role in natural intelligence and its demonstrated benefits across a range of seemingly disparate AI subfields, modularity remains relatively underappreciated in mainstream AI research. In this work, we review several research threads in artificial intelligence and neuroscience through a conceptual framework that highlights the central role of modularity in supporting ...
