ADR Awareness Centre

We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.

A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes.

We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.

Inspired by scenario writing methods to foster discussion on the societal implications of technology and regulation, the paper develops a ‘legal fiction scenario’ to anticipate the impact of the proposed European 

This comprehensive guide sheds light on the current state of the global AI landscape, exploring the scope of each piece of legislation, how businesses are likely to be affected, and what this means for AI governance moving forward.

Artificial intelligence can enhance law enforcement agencies’ capabilities to prevent, investigate, detect and prosecute crimes, as well as to predict and anticipate them.