Modularity is the Bedrock of Natural and Artificial Intelligence

2026-02-21

AI Summary

Current AI systems vastly exceed the human brain in terms of data consumption, computational demands, and energy usage, highlighting an urgent need to draw inspiration from the efficiency of biological intelligence. This work proposes modularity as a unifying foundational principle bridging natural and artificial intelligence. Through an interdisciplinary review and conceptual framework, it systematically integrates insights from neuroscience with modular practices in AI—such as modular neural networks, multi-task learning, and neural architecture search. The study offers the first comprehensive argument that modularity provides task-specific inductive biases, enabling efficient learning and strong generalization. It further reveals the emergent computational advantages of modularity across diverse AI domains, offering theoretical guidance for designing the next generation of efficient, generalizable artificial intelligence systems.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 both artificial and natural intelligence. In particular, we examine what computational advantages modularity provides, how it has emerged as a solution across several AI research areas, which modularity principles the brain exploits, and how modularity can help bridge the gap between natural and artificial intelligence.

Problem

• modularity
• artificial intelligence
• natural intelligence
• inductive bias
• generalization

Innovation

• modularity
• inductive bias
• generalization
• neural architecture
• No Free Lunch Theorem