Towards Building Specialized Generalist AI with System 1 and System 2 Fusion

TL;DR

This paper proposes the concept of Specialized Generalist AI (SGAI) as a step towards AGI, combining System 1 and System 2 cognitive processes to develop AI that excels in specific tasks while maintaining general abilities.

Contribution

It introduces the SGAI concept, categorizes its development stages, and presents a framework integrating System 1 and 2 for advancing towards AGI.

Findings

SGAI can surpass human experts in specific tasks while retaining general abilities.

A three-layer framework integrating System 1 and 2 enhances AI development.

Addressing limitations of large language models through SGAI can improve generality and specialization.

Abstract

In this perspective paper, we introduce the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence (AGI). Compared to directly scaling general abilities, SGI is defined as AI that specializes in at least one task, surpassing human experts, while also retaining general abilities. This fusion path enables SGI to rapidly achieve high-value areas. We categorize SGI into three stages based on the level of mastery over professional skills and generality performance. Additionally, we discuss the necessity of SGI in addressing issues associated with large language models, such as their insufficient generality, specialized capabilities, uncertainty in innovation, and practical applications. Furthermore, we propose a conceptual framework for developing SGI that integrates the strengths of Systems 1 and 2 cognitive processing. This framework comprises three layers and four key components, which focus on enhancing individual abilities and facilitating collaborative evolution. We conclude by summarizing the potential challenges and suggesting future directions. We hope that the proposed SGI will provide insights into further research and applications towards achieving AGI.