When hardwired equivariance hurts

In this episode, we talked with Michael Bronstein, a professor of AI at the University of Oxford and a scientific director at AITHYRA, about the fascinating world of geometric deep learning. We explored how understanding the geometric structures in data can enhance the efficiency and accuracy of AI models. Michael shared insights on the limitations of small neural networks and the ongoing debate about the role of scaling in AI. We also talked about the future in scientific discovery, and the potential impact on fields like drug design and mathematics