Explore the critical examination of machine learning scholarship in this thought-provoking lecture by Zachary Lipton from Carnegie Mellon University. Delve into the comparison between deep learning and alchemy, analyzing the current state of machine learning as a pre-paradigmatic science. Investigate the evolution of scientific paradigms and their application to machine learning theory and practice. Examine the pitfalls of alchemy-like approaches in AI research and the importance of aligning competing theories. Discuss the impact of journalism on the field and recent challenges in empirical machine learning. Reflect on the balance between explanation and speculation in AI research, and consider potential solutions for improving the scientific rigor of machine learning scholarship. Gain insights into the future direction of artificial intelligence and the responsibilities of researchers in advancing the field.