Complexity-based AI

This research line exploits the interface between Complexity Science methods and concepts (statistical physics, dynamical systems, time series, ...) and AI (mainly neural network models for supervised and unsupervised learning). We use complexity techniques to unravel the inner workings of neural network training optimization, and we get inspired by the physics of complexity to build decentralized AI solutions where the whole is more than the sum of its parts.