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.
In this talk, I will present a definition for GPAIS that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We will 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. To walk you through the transition from traditional AI to GPAIS, I will start off talking about some interdisciplinary projects I have worked on, including big data applications in collaboration with industry partners such as Unilever and E.ON, spanning sectors like energy, transportation, and healthcare. I will then shift focus to my latest research on generalisable models that may help accelerate AI adoption. Finally, I will emphasise the critical need for responsible and trustworthy AI development as we navigate this evolving landscape.