Evolving Computing Paradigms in AI

This chapter explores the transformation of computing paradigms within deep learning, emphasizing the contrast between traditional architectures and more integrated approaches inspired by the human brain. It examines the synergies between academic research and industry practice, particularly at Qualcomm, and highlights innovations in Bayesian deep learning and their applications. The discussion also covers the incorporation of symmetries from physics into deep learning models, including gauge theory, and their implications for neural network architectures like capsule networks.