Explore the intersection of physics and artificial intelligence in this comprehensive conference talk from KDD 2020. Delve into the four paradigms of scientific discovery, examining the interplay between theory and data. Investigate the limitations of big data approaches and the importance of generalization in both physics and AI. Analyze computational complexity classes and their relevance to AI and physics problems. Discover physics-informed neural networks (PINN) and physics-guided neural networks (PGNN), and their applications in explainable AI. Examine open questions in neural networks, including the potential for a statistical physics theory of deep learning. Gain insights into information bottlenecks and their role in both physics and neural networks. Enhance your understanding of the evolving relationship between physics and artificial intelligence through this in-depth presentation.