Abstract
Due to the availability of huge amounts of data and processing abilities,
current artificial intelligence (AI) systems are effective in solving complex
tasks. However, despite the success of AI in different areas, the problem of
designing AI systems that can truly mimic human cognitive capabilities such as
artificial general intelligence, remains largely open. Consequently, many
emerging cross-device AI applications will require a transition from
traditional centralized learning systems towards large-scale distributed AI
systems that can collaboratively perform multiple complex learning tasks. In
this paper, we propose a novel design philosophy called democratized learning
(Dem-AI) whose goal is to build large-scale distributed learning systems that
rely on the self-organization of distributed learning agents that are
well-connected, but limited in learning capabilities. Correspondingly, inspired
by the societal groups of humans, the specialized groups of learning agents in
the proposed Dem-AI system are self-organized in a hierarchical structure to
collectively perform learning tasks more efficiently. As such, the Dem-AI
learning system can evolve and regulate itself based on the underlying duality
of two processes which we call specialized and generalized processes. In this
regard, we present a reference design as a guideline to realize future Dem-AI
systems, inspired by various interdisciplinary fields. Accordingly, we
introduce four underlying mechanisms in the design such as plasticity-stability
transition mechanism, self-organizing hierarchical structuring, specialized
learning, and generalization. Finally, we establish possible extensions and new
challenges for the existing learning approaches to provide better scalable,
flexible, and more powerful learning systems with the new setting of Dem-AI.