Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.

翻译：构建具有社会智能的人工智能智能体（Social-AI）是一项多学科、多模态的研究目标，涉及创建能够感知、觉察、推理、学习并响应其他智能体（人类或人工）的情感、行为与认知的智能体。过去十年间，自然语言处理、机器学习、机器人学、人机交互、计算机视觉和语音等多个计算领域加速了Social-AI的进展。其中，自然语言处理在Social-AI研究中尤为突出，因为语言在构建社交世界中发挥着关键作用。在本文立场陈述中，我们为计算领域的研究者识别了一系列底层技术挑战与开放问题，以推动Social-AI的发展。我们以社会智能概念及Social-AI研究的既有进展为背景展开讨论。