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 wellconnected, 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 selforganized 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.

中文翻译：

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分布式和民主化学习：哲学和研究挑战

由于大量数据和处理能力的可用性，当前的人工智能 (AI) 系统可以有效地解决复杂的任务。然而，尽管人工智能在不同领域取得了成功，但设计能够真正模仿人类认知能力（如通用人工智能）的人工智能系统的问题仍然存在很大的问题。因此，许多新兴的跨设备 AI 应用程序将需要从传统的集中式学习系统过渡到可以协同执行多个复杂学习任务的大规模分布式 AI 系统。在本文中，我们提出了一种称为民主化学习 (Dem-AI) 的新颖设计理念，其目标是构建依赖于良好连接的分布式学习代理的自组织的大规模分布式学习系统，但学习能力有限。相应地，受人类社会群体的启发，所提出的 Dem-AI 系统中的学习代理的专业群体在层次结构中自组织，以更有效地集体执行学习任务。因此，Dem-AI 学习系统可以根据我们称为专业化过程和广义化过程的两个过程的潜在二元性进行自我进化和调节。在这方面，我们提出了一个参考设计，作为实现未来 Dem-AI 系统的指南，受到各种跨学科领域的启发。因此，我们在设计中引入了四种潜在机制，例如可塑性-稳定性转换机制、自组织层次结构、专业学习和泛化。最后，