Decentralized Computation: The Hidden Principle Behind Deep Learning

Source: Towards Data Science

Introduction

Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep‑learning systems thrive on distributed architectures, collaborative training, and decentralized data sources. This shift not only mirrors natural systems but also unlocks scalability, robustness, and innovation across the AI landscape.