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 then propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI or foundation models. As a prime example, we delve into generative AI, aligning them with the terms and concepts presented in the taxonomy. 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 current state of GPAIS, its challenges and prospects, implications for our society, and the need for responsible and trustworthy AI systems and regulation, with the goal of providing a holistic view of GPAIS.

翻译：大多数人工智能（AI）应用被设计用于特定且受限的任务。然而，许多场景需要更通用的AI，能够解决广泛的任务而无需针对它们专门设计。术语“通用人工智能系统”（GPAIS）已被定义以指代这些AI系统。迄今为止，能够像人类一样执行任何智力任务甚至超越人类的强人工智能仍是一种愿景、虚构，并被视为对社会的潜在风险。尽管我们可能仍远未实现这一目标，但GPAIS已成为现实，并处于AI研究的前沿。本文讨论了现有GPAIS定义，并提出了一种新定义，该定义允许根据特性和局限性对GPAIS类型进行渐进式区分。我们区分了封闭世界和开放世界的GPAIS，基于多个因素（如适应新任务的能力、未经有意训练领域的胜任力、从少量数据中学习的能力，或主动承认自身局限性的能力）对其自主性和能力进行刻画。随后，我们提出了一种实现GPAIS的方法分类法，描述了研究趋势，例如使用AI技术改进另一AI或基础模型。作为典型示例，我们深入探讨了生成式AI，将其与分类法中提出的术语和概念对齐。通过提出的定义和分类法，我们旨在促进不同领域（均需处理通用任务）之间的研究合作，因为这些领域存在许多共同点。最后，我们讨论了GPAIS的现状、挑战与前景、对社会的潜在影响，以及对负责任且可信的AI系统及监管的需求，旨在提供GPAIS的整体视角。