朝向建立具有系統1和系統2融合的專業通用人工智慧前進。

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

July 11, 2024

作者: Kaiyan Zhang, Biqing Qi, Bowen Zhou

cs.AI

摘要

在這篇觀點論文中，我們介紹了專業通才人工智慧（SGAI或簡稱SGI）的概念，作為達到人工通用智能（AGI）的重要里程碑。與直接擴展通用能力相比，SGI被定義為在至少一項任務上專精，超越人類專家，同時保留通用能力。這種融合路徑使SGI能夠迅速實現高價值領域。我們根據對專業技能掌握程度和通用性表現水平的分類，將SGI分為三個階段。此外，我們討論了SGI在應對與大型語言模型相關問題方面的必要性，例如它們的通用性不足、專業能力、創新的不確定性和實際應用。此外，我們提出了一個發展SGI的概念框架，該框架整合了系統1和系統2認知處理的優勢。該框架包括三個層次和四個關鍵組件，著重於增強個人能力並促進協作演進。最後，我們總結了潛在挑戰並提出未來方向的建議。我們希望所提出的SGI將為實現AGI的進一步研究和應用提供見解。

English

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.