Social Neuro AI: Social Interaction as the "dark matter" of AI

TL;DR

This paper emphasizes the importance of social interactions in AI development, proposing a framework that integrates social learning, neuroscience, and embodiment to create more biologically plausible and socially intelligent agents.

Contribution

It introduces a three-axis framework for Social Neuro AI, highlighting how social learning, dynamical systems, and embodiment can advance socially intelligent AI systems.

Findings

Social learning is crucial for intelligence development.

Neuroscientific theories can inform AI architectures.

Embodiment enhances communication capabilities.

Abstract

This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We argue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the dark matter of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.