AICS: Artificial Intelligence and Complex Systems

The special session aims to explore the intricate relationship between Artificial Intelligence (AI) and complex systems through Complexity Theory, delving into the understanding of modern AI systems such as deep learning architectures and large language models (e.g., GPT/ChatGPT, BARD, Llama, Anthropic, etc.) as complex dynamic systems. With a focus on stochastic processes, explainable AI, cognitive approaches, multimodal learning, AI and security, and AI and bias, the session will serve as a multidisciplinary platform that extends beyond engineering to include cognitive science, linguistics, philosophy, and other relevant fields. Specifically, we welcome researchers and engineers in AI and complex systems theory, cognitive scientists, linguists, and philosophers as well as industry professionals seeking to apply complexity theory in AI solutions to submit their works to this special session to investigate on the following four objectives:

To investigate how Complexity Theory can offer invaluable tools for analyzing AI systems, particularly in the context of dynamic behavior, emergent properties, and stochastic processes.
To explore how AI can be employed to study and understand complex systems, including information granulation (Granular Computing) and multi-agent environments.
To establish new methodologies for measuring the intelligence and linguistic understanding of AI systems through the lens of Complexity Theory.
To discuss the future directions and next steps in the intersection of AI and Complexity
Theory in reaching artificial general intelligence (AGI).

AICS is part of the International Joint Conference on Neural Networks (IJCNN)

DEADLINE EXTENDED TO 29 January 2024!!

Topics of interest include, but are not limited to:

Emergent Behavior and Stochastic Processes in AI Systems: Understanding how complex systems theory can explain the adaptive, emergent characteristics and stochastic outputs of AI.
Multifractal and Stochastic Analysis of AI: Utilizing complexity theory for the multifractal and stochastic analysis of AI systems.
Dynamic Systems Theory in AI: How dynamic systems theory can be applied to AI for better system analysis and prediction.
Explainable AI: Investigating methods for making complex AI systems understandable and interpretable.
Cognitive Approach in AI: Exploring how cognitive science can inform and improve AI systems.
Multimodal Learning: The role of multimodal learning in enhancing the capabilities of AI systems.
AI in Multi-Agent Complex Systems: Exploring the role of AI in the study and understanding of multi-agent complex systems.
AI and Security: Discussing the implications of complexity theory for AI in cybersecurity.
AI and Bias: Addressing the challenges and solutions for bias in AI systems.
Linguistic and Philosophical Aspects: Investigating the implications of complexity in AI for language models, meaning, and other linguistic aspects, as well as philosophical considerations.

Keywords: Artificial Intelligence, Complex Systems, Natural Language Generation, Large Language Models, Distributional Semantics, Compositionality, Quantitative Linguistics, Dynamical Systems, Explainable AI

AICS 2024 @ WCCI 2024 - IJCNN 2024 - June 30 - July 5 - Yokohama, Japan