Abstract

The late Stephen Hawking referred to our current century as the ‘century of complexity’. Today, due to the rapidly growing phenomenon of large datasets being published online and the development of methodologically rigorous tools, such as network analytics and mixed models, it has become possible to conduct deep studies into complex systems across the natural and social sciences. At the same time, the rise of artificial intelligence (AI) methods and architectures, such as deep neural networks and transformer-based language models, has allowed us to not only model and describe complex systems but also predict their evolution and prescribe appropriate interventions (eg, recommending products via e-commerce customer networks or combating misinformation by exposing a user to the right information at the right time). Indeed, over the last several years alone, the progress in generative AI has been truly astounding, with entire applications, and even new scientific methodologies, now possible when, just five years ago, they could only have been speculated about.
This Special Issue, titled ‘Artificial Intelligence and Complex Systems’, was created with the goal of collecting the latest research, both foundational and application-orientated, at the intersection of AI and complex systems, ranging across real-world domains as varied as education and fraud detection. The issue contains five papers, all relevant to the core intersectional topic of AI and complex systems. Yu et al.[1] present a novel indoor ‘pedestrian counting’approach that protects privacy and does not require as much information (such as the number of groups of people walking in …

Date

October 11, 2023

Authors

Mayank Kejriwal

Source

Applied Sciences

Volume

13

Issue

20

Pages

11153

Publisher

MDPI