General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance

Triguero, Isaac; Molina, Daniel; Poyatos, Javier; Del Ser, Javier; Herrera, Francisco

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 acknowledgement 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 (commonly referred to as AI-powered AI) or (single) foundation models. As a prime example, we delve into generative AI (GenAI), aligning them with the terms and concepts presented in the taxonomy. Similarly, we explore the challenges and prospects of 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, with the goal of providing a holistic view of GPAIS, we discuss the current state of GPAIS, its prospects, implications for our society, and the need for regulation and governance of GPAIS to ensure their responsible and trustworthy development.

Citation

Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024). General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Information Fusion, 103, Article 102135. https://doi.org/10.1016/j.inffus.2023.102135

• Journal Article Type: Article
• Acceptance Date: Nov 6, 2023
• Online Publication Date: Nov 9, 2023
• Publication Date: 2024-03
• Deposit Date: Jan 25, 2024
• Publicly Available Date: May 10, 2025
• Journal: Information Fusion
• Electronic ISSN: 1872-6305
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 103
• Article Number: 102135
• DOI: https://doi.org/10.1016/j.inffus.2023.102135
• Keywords: General-purpose AI; Meta-learning; Reinforcement learning; Neuroevolution; Few-shot learning; AutoML; Transfer learning; Generative AI; Large language models
• Public URL: https://nottingham-repository.worktribe.com/output/27363287
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1566253523004517?via%3Dihub