Decentralized Computation: The Hidden Principle Behind Deep Learning

Modern deep learning models succeed due to the principle of decentralization, where many simple units interact locally to create intelligent global behavior. This approach contrasts with traditional statistical models that rely on centralized control and are analytically clear. In deep neural networks, information propagates locally through layers via backpropagation, making their behavior computationally irreducible and requiring simulation to understand. This process of signal propagation acts as an "invisible hand," coordinating components without a central planner, much like in natural complex systems such as brains or economies.