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

Human-level AI will have significant impacts on human society. However,
estimates for the realization time are debatable. To arrive at human-level AI,
artificial general intelligence (AGI), as opposed to AI systems that are
specialized for a specific task, was set as a technically meaningful long-term
goal. But now, propelled by advances in deep learning, that achievement is
getting much closer. Considering the recent technological developments, it
would be meaningful to discuss the completion date of human-level AI through
the "comprehensive technology map approach," wherein we map human-level
capabilities at a reasonable granularity, identify the current range of
technology, and discuss the technical challenges in traversing unexplored areas
and predict when all of them will be overcome. This paper presents a new
argumentative option to view the ontological sextet, which encompasses entities
in a way that is consistent with our everyday intuition and scientific
practice, as a comprehensive technological map. Because most of the modeling of
the world, in terms of how to interpret it, by an intelligent subject is the
recognition of distal entities and the prediction of their temporal evolution,
being able to handle all distal entities is a reasonable goal. Based on the
findings of philosophy and engineering cognitive technology, we predict that in
the relatively near future, AI will be able to recognize various entities to
the same degree as humans.

通过综合技术地图方法，对人工智能（AI）的最终实现时间进行了预测，该研究论文以本体学等六方面实体作为综合技术地图，探讨了人类实现人形 AI 的技术挑战和时间预测。

AI 对所有实体类别的识别

Recognition of All Categories of Entities by AI

Building embodied autonomous agents capable of participating in social
interactions with humans is one of the main challenges in AI. This problem
motivated many research directions on embodied language use. Current approaches
focus on language as a communication tool in very simplified and non diverse
social situations: the "naturalness" of language is reduced to the concept of
high vocabulary size and variability. In this paper, we argue that aiming
towards human-level AI requires a broader set of key social skills: 1) language
use in complex and variable social contexts; 2) beyond language, complex
embodied communication in multimodal settings within constantly evolving social
worlds. In this work we explain how concepts from cognitive sciences could help
AI to draw a roadmap towards human-like intelligence, with a focus on its
social dimensions. We then study the limits of a recent SOTA Deep RL approach
when tested on a first grid-world environment from the upcoming SocialAI, a
benchmark to assess the social skills of Deep RL agents. Videos and code are
available at this https URL .

本文讨论了构建具有社交交互能力的具体化自主智能体是人工智能面临的主要挑战之一，并指出了目前在具体化语言使用方面的研究方向存在的局限性。作者认为，实现人类水平的人工智能需要更广泛的关键社交技能。最后，通过实验研究了一种近期最先进的深度强化学习方法的局限性。