Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling

Today we're joined by Max Welling, research chair in machine learning at the University of Amsterdam, as well as VP of technologies at Qualcomm, and Fellow at the Canadian Institute for Advanced Research, or CIFAR. In our conversation, we discuss Max's research at Qualcomm AI Research and the university, including his work on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, and in power efficiency for AI via compression, quantization, and compilation. We also discuss Max's thoughts on the future of the AI industry, in particular, the relative importance of models, data and compute.