Future Reasoning
Future reasoning research explores how artificial intelligence systems can predict, plan, and understand temporal dynamics. Current efforts concentrate on improving the accuracy and efficiency of AI-driven forecasting across diverse domains, from weather prediction using large meteorological models and graph neural networks to financial market analysis and even generating synthetic data for training other models. This field is crucial for advancing AI capabilities in areas like autonomous systems, personalized medicine, and risk management, ultimately impacting both scientific understanding and real-world applications.
Papers
Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces
Jihoon Sohn, Mingyu Derek Ma, Muhao Chen
Graphing the Future: Activity and Next Active Object Prediction using Graph-based Activity Representations
Victoria Manousaki, Konstantinos Papoutsakis, Antonis Argyros
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew White, Adamo Young, Rose Yu, Alán Aspuru-Guzik
A 23 MW data centre is all you need
Samuel Albanie, Dylan Campbell, João F. Henriques