General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance
Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera
TL;DR
The work addresses the need for a precise, practical concept of General-Purpose Artificial Intelligence Systems (GPAIS) that sits between narrow AI and AGI. It proposes a formal, property-based GPAIS definition and a multidimensional taxonomy that organizes approaches into AI-powered AI, AI-to-enrich AI, multi-task learning, and foundation models, with GenAI and multimodality as central exemplars residing in an open-world paradigm where new tasks can arise at time $t+\Delta t$. The analysis highlights core challenges such as data scarcity, hallucinations, and emergent abilities, and argues for combining foundation-model capabilities with strategies like continual learning, active learning, and governance to enable safe, scalable GPAIS. It further discusses societal implications, including privacy and fairness, and outlines regulatory and accountability mechanisms essential for responsible deployment, referencing the EU AI Act and policy briefs. Overall, the paper provides a unifying framework to align diverse AI research efforts toward general-purpose, trustworthy, and governance-aware AI systems with practical impact across domains.
Abstract
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.