Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a comprehensive lecture on the reliability challenges of artificial intelligence, focusing on generalization and explainability in deep neural networks. Delve into a complete generalization result for graph neural networks and discover a novel explainability approach rooted in applied harmonic analysis. Examine the limitations of digital hardware for AI reliability and uncover surprising connections to quantum computing. Learn about key concepts such as graph convolutional neural networks, spectral graph convolution, and transferability of functional calculus filters. Investigate the rate-distortion viewpoint for explainability and its application in detecting reasons for adversarial examples. Gain insights into the future directions of AI research and the potential role of mathematics in addressing current challenges.