Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgment of their own limitations. We propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI (AI-powered AI) or (single) foundation models. As a prime example, we delve into GenAI, aligning them with the concepts presented in the taxonomy. We explore multi-modality, which involves fusing various types of data sources to expand the capabilities of GPAIS. Through the proposed definition and taxonomy, our aim is to facilitate research collaboration across different areas that are tackling general purpose tasks, as they share many common aspects. Finally, we discuss the state of GPAIS, prospects, societal implications, and the need for regulation and governance.

翻译：大多数人工智能（AI）应用旨在解决限定且特定的任务。然而，许多场景需要更通用的AI，使其能够在不经专门设计的前提下解决广泛任务。“通用人工智能系统”（GPAIS）一词即被定义用于指代这类AI系统。迄今为止，强到足以像人类一样执行任何智力任务甚至超越人类的通用人工智能仍停留在愿景、虚构层面，并被视为社会风险。尽管我们距离实现这一目标尚远，但GPAIS已成为现实，并处于AI研究前沿。本文梳理了现有GPAIS定义，提出一种新型定义，允许根据其属性与局限性对GPAIS类型进行渐进式区分。我们区分了封闭世界与开放世界GPAIS，基于适应新任务、非刻意训练领域的胜任力、小样本学习能力或主动认知自身局限等要素刻画其自主程度与能力。我们提出实现GPAIS的方法分类体系，描述了利用AI技术改进另一AI（AI驱动AI）或（单一）基础模型等研究趋势。以生成式AI（GenAI）为典型案例，我们将其与分类体系中的概念对齐，并探索涉及融合多种数据源以扩展GPAIS能力的多模态技术。通过提出的定义与分类体系，我们旨在促进不同领域间针对通用任务的研究协作——这些领域具有诸多共性。最后，我们讨论了GPAIS现状、前景、社会影响及监管治理的必要性。