Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions

TL;DR

This paper discusses the technical challenges and open questions in developing socially intelligent AI agents, emphasizing the importance of multidisciplinary approaches and natural language processing in advancing social AI capabilities.

Contribution

It identifies key technical challenges and open questions for advancing Social-AI, providing a comprehensive overview of the current state and future directions.

Findings

Progress has accelerated in multiple computing fields.

Natural language processing is central to Social-AI development.

Several technical challenges remain to be addressed.

Abstract

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.