The Mathematical Foundations of Intelligence [Professor Yi Ma]

The conversation explores a mathematical theory of intelligence based on parsimony and self-consistency. Professor Yi Ma, director of the School of Computing and Data Science at Hong Kong University, discusses his book, "Learning Deep Representations of Data Distributions," and the principles behind deep networks. He argues that intelligence, whether artificial or natural, relies on simplifying representations of data while maintaining consistency to predict outcomes effectively. The discussion covers the role of compression, denoising, and dimension reduction in pursuing knowledge, and how noise plays different roles in connecting data points and reaching broader understanding. Professor Ma also touches on the evolution of AI architectures, like transformers, and the potential for principled optimization to guide their development.