Transformations in Machine Learning: 2010-2019

This chapter explores the evolution of machine learning from the early 2010s to 2019, focusing on the impact of deep learning breakthroughs and the emergence of new research questions. It emphasizes advancements in causal inference and the complexities of generalization, particularly in relation to dataset evaluation and benchmarking. Additionally, the discussion highlights the significance of innovative models like BERT in natural language processing, showcasing a shift towards semi-supervised learning and changing competition dynamics in the field.