GOAL
Artificial Intelligence is undergoing a significant paradigm shift—from narrow, task-specific models to increasingly general-purpose systems capable of handling a wide range of tasks and modalities. Large Language Models (LLMs) are at the forefront of this transition, demonstrating impressive capabilities while also raising critical challenges around robustness, alignment, explainability, adaptability, and resource efficiency. The Computational Intelligence (CI) community is uniquely positioned to contribute to this evolution and to mitigate the aforementioned limitations. These include human-in-the-loop reasoning, optimisation under uncertainty, self-adaptation, or lifelong learning — all essential for achieving trustworthy and flexible General-Purpose AI Systems. Conversely, advances in GPAIS and LLMs may also help enhance CI techniques, creating a mutually beneficial interaction. This Task Force aims to bring together the CI community to play a leading role in shaping the future of GPAIS, bringing CI methodologies into the conversation around LLMs, AI-powered AI, and the growing expectation of Artificial General Intelligence.
FOCUS
- Foster collaboration between CI researchers and practitioners working on LLMs and GPAIS.
- Investigate the synergy between CI methods (evolutionary computation, fuzzy systems, neural networks, bioinformatics, etc.) and LLMs.
- Build a portfolio of practical real-world applications reported in the literature where CI and LLMs/GPAIS have benefitted from each other.
- Promote reproducibility, benchmarking, and responsible AI practices in GPAIS through tutorials, competitions, and open resources.
- Engage existing CIS Technical Committees by appointing “ambassadors” to bridge shared interests with the LLM-GPAIS agenda.
- Disseminate knowledge through special issues, workshops, and webinars.
- Support early-career researchers through mentoring and collaborative initiatives.
- Establish a CIS Technical committee on LLMs/GPAIS
