  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.
