We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.

翻译：我们提出具身人工智能作为追求通用人工智能的下一个基础步骤，将其与当前AI进展（尤其是大型语言模型）进行对比。我们追溯了具身概念在哲学、心理学、神经科学和机器人学等多个领域的演变，以阐明EAI如何区别于静态学习的经典范式。通过拓展具身人工智能的范围，我们引入了一个基于认知架构的理论框架，强调感知、行动、记忆和学习是具身智能体的核心组成部分。该框架与Friston的主动推理原理相一致，为EAI发展提供了一种综合方法。尽管AI领域已取得进展，但诸如提出新型AI学习理论和创新先进硬件等重大挑战依然存在。我们的讨论为未来具身人工智能研究奠定了基本指导。通过强调创建能够在现实环境中与人类及其他智能实体无缝沟通、协作和共存的具身AI智能体的重要性，我们旨在引导AI社区应对多重挑战，并抓住追求AGI道路上的机遇。