In April of 2022, the Santa Fe Institute hosted a workshop on embodied,
situated, and grounded intelligence as part of the Institute's Foundations of
Intelligence project. The workshop brought together computer scientists,
psychologists, philosophers, social scientists, and others to discuss the
science of embodiment and related issues in human intelligence, and its
implications for building robust, human-level AI. In this report, we summarize
each of the talks and the subsequent discussions. We also draw out a number of
key themes and identify important frontiers for future research.

2022 年 4 月，圣菲研究所主办了一次有关具体、情境和基于实体的智能的工作坊，旨在探讨人工智能建设中具体化方面的相关问题，集合了计算机科学家、心理学家、哲学家、社会科学家等专业学者进行讨论，总结了各演讲主题、讨论内容，并确定了未来研究的重点领域。

具身化、情境化和基于经验的智能：对人工智能的影响

Embodied, Situated, and Grounded Intelligence: Implications for AI

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from
image classification to strategy games, even outperforming humans in many of
these domains. After making astounding progress in language learning in the
recent decade, AI systems, however, seem to approach the ceiling that does not
reflect important aspects of human communicative capacities. Unlike human
learners, communicative AI systems often fail to systematically generalize to
new data, suffer from sample inefficiency, fail to capture common-sense
semantic knowledge, and do not translate to real-world communicative
situations. Cognitive Science offers several insights on how AI could move
forward from this point. This paper aims to: (1) suggest that the dominant
cognitively-inspired AI directions, based on nativist and symbolic paradigms,
lack necessary substantiation and concreteness to guide progress in modern AI,
and (2) articulate an alternative, "grounded", perspective on AI advancement,
inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research.
I review results on 4E research lines in Cognitive Science to distinguish the
main aspects of naturalistic learning conditions that play causal roles for
human language development. I then use this analysis to propose a list of
concrete, implementable components for building "grounded" linguistic
intelligence. These components include embodying machines in a
perception-action cycle, equipping agents with active exploration mechanisms so
they can build their own curriculum, allowing agents to gradually develop motor
abilities to promote piecemeal language development, and endowing the agents
with adaptive feedback from their physical and social environment. I hope that
these ideas can direct AI research towards building machines that develop
human-like language abilities through their experiences with the world.

该论文旨在提出一种 “基础” 的视角，启发人工智能的进步，包括 “基于感知 - 动作循环的体现、嵌入、延伸和实践认知” 的研究方向，以及运用逐步发展的技能促进逐步的语言发展、逐渐适应物理和社会环境的智能代理等几个具体的组成部分，以建立人类一样的语言能力。