Artificial Intelligence
Machine learning and artificial intelligence (AI) aim to create algorithms that solve difficult problems and simulate complex intelligent behavior. Many of these algorithms are based on findings and theory from the study of the brain and mind.
Recent rapid advances in these fields have seen the creation of algorithms and agents that can—finally—solve complex real-world problems across a wide range of domains. What are these advances, and how can we take them further? What remains beyond their capacity, and how can we overcome that? What might forever lie beyond their capabilities—or will anything?
Session Chairs
Michael Lepori (Brown University)
Dr Ivana Kajic (Google Deepmind)
Keynote Talks
Professor Jay McClelland (Director, Center for Mind, Brain, Computation and Technology, Stanford)
Professor Jakob Foerster (University of Oxford)
Invited Talks
Spotlight Talks
Dr Eirini Troullinou (Institute of Molecular Biology and Biotechnology - FORTH) : Brain-Inspired Recurrent Neural Network Featuring Dendrites for Efficient and Accurate Learning in Classification Tasks
Can Demircan (Helmholtz Munich) : Sparse autoencoders reveal temporal difference learning in large language models
Domonkos Martos (HUN-REN Wigner Research Centre for Physics) : Uncertainty in latent representations of variational autoencoders optimized for visual tasks
Prof. Venkatakrishnan Ramaswamy (Birla Institute of Technology & Science Pilani) : Memorizing Deep Networks can generalize (much better)
Márton A. Hajnal (MTA Wigner Research Centre for Physics)
Jack Brady (Max Planck Institute for Intelligent Systems) : Generation is Required for Robust Perception