Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI)
overemphasize prompted, or cue-based, ToM, which may limit our collective
ability to develop Artificial Social Intelligence (ASI). Drawing from research
in computer science, cognitive science, and related disciplines, we contrast
prompted ToM with what we call spontaneous ToM -- reasoning about others'
mental states that is grounded in unintentional, possibly uncontrollable
cognitive functions. We argue for a principled approach to studying and
developing AI ToM and suggest that a robust, or general, ASI will respond to
prompts \textit{and} spontaneously engage in social reasoning.

现有的人工智能（AI）的心灵理论（ToM）方法过多地强调受到提示或刺激的 ToM，这可能限制我们共同发展人工社会智能（ASI）。通过对计算机科学、认知科学及相关学科的研究，我们将受到提示的 ToM 与我们所称的自发的 ToM 进行对比，即在无意识、可能无法控制的认知功能基础上进行他人心灵状态的推理。我们主张在研究和发展 AI ToM 时采用有原则的方法，并建议在应对提示的同时，积极进行社会推理的强大，或通用的 ASI。

人工智能的自发心理理论

Spontaneous Theory of Mind for Artificial Intelligence

Creating artificial social intelligence - algorithms that can understand the
nuances of multi-person interactions - is an exciting and emerging challenge in
processing facial expressions and gestures from multimodal videos. Recent
multimodal methods have set the state of the art on many tasks, but have
difficulty modeling the complex face-to-face conversational dynamics across
speaking turns in social interaction, particularly in a self-supervised setup.
In this paper, we propose Face-to-Face Contrastive Learning (F2F-CL), a graph
neural network designed to model social interactions using factorization nodes
to contextualize the multimodal face-to-face interaction along the boundaries
of the speaking turn. With the F2F-CL model, we propose to perform contrastive
learning between the factorization nodes of different speaking turns within the
same video. We experimentally evaluated the challenging Social-IQ dataset and
show state-of-the-art results.

本文提出了一种名为 Face-to-Face Contrastive Learning (F2F-CL) 的图神经网络模型，用于建模人类社交互动中的面对面交流动态，并在 Social-IQ 数据集上实现了最先进的效果。

面对面对比学习用于社交智能问答

Face-to-Face Contrastive Learning for Social Intelligence Question-Answering

With the recent advances of the Internet of Things, and the increasing
accessibility of ubiquitous computing resources and mobile devices, the
prevalence of rich media contents, and the ensuing social, economic, and
cultural changes, computing technology and applications have evolved quickly
over the past decade. They now go beyond personal computing, facilitating
collaboration and social interactions in general, causing a quick proliferation
of social relationships among IoT entities. The increasing number of these
relationships and their heterogeneous social features have led to computing and
communication bottlenecks that prevent the IoT network from taking advantage of
these relationships to improve the offered services and customize the delivered
content, known as relationship explosion. On the other hand, the quick advances
in artificial intelligence applications in social computing have led to the
emerging of a promising research field known as Artificial Social Intelligence
(ASI) that has the potential to tackle the social relationship explosion
problem. This paper discusses the role of IoT in social relationships detection
and management, the problem of social relationships explosion in IoT and
reviews the proposed solutions using ASI, including social-oriented
machine-learning and deep-learning techniques.

本文探讨了物联网在社交关系检测和管理中的作用，以及社交关系爆炸问题，提出了利用人工社交智能技术包括社交导向的机器学习和深度学习解决此问题。