Decentralized Computation: The Hidden Principle Behind Deep Learning

Recent insights reveal that the foundational principle underpinning advancements in deep learning, including large language models, is decentralization. Unlike traditional centralized systems, these models thrive because numerous simple units interact locally, enabling complex behaviors without a central controller. This shift towards decentralized computation emphasizes the importance of local interactions among neural network components, which has driven the scalability and effectiveness of modern AI architectures.