Towards Building Specialized Generalist AI with System 1 and System 2 Fusion
This perspective paper outlines a conceptual path for AI development to achieve high-value specialization while retaining general abilities, addressing issues in current models for researchers aiming toward AGI.
The paper introduces Specialized Generalist AI (SGI) as a milestone toward AGI, proposing a conceptual framework that fuses System 1 and System 2 cognitive processing to address limitations in large language models, such as insufficient generality and specialized capabilities.
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.