Patterns, Predictions, and Actions – A story about machine learning [pdf] (mlstory.org)

Moritz Hardt and Benjamin Recht have released a comprehensive new text titled "Patterns, Predictions, and Actions," which explores the foundational elements of machine learning through the lens of pattern classification. This work underscores the continuity between early machine learning practices and modern innovations, emphasizing that many recent advancements, such as in image recognition and natural language processing, still rely heavily on traditional classification techniques. By revisiting core concepts and integrating recent developments in optimization, data representation, and generalization, the book situates itself as both a tribute to historical methodologies and a modern reference for current trends in AI. Significantly, the book also delves into the critical role datasets play in machine learning, offering a thorough examination of their collection, benchmarking, and implications. It introduces causal inference as a contemporary tool for understanding machine learning's limitations and social consequences, a necessary discussion given the technology's ethical complexities. Moreover, it addresses sequential decision-making and dynamic programming, linking theoretical models to practical applications in reinforcement learning. This multifaceted approach not only provides insights into the technical underpinnings of AI but also raises awareness of its political ramifications, aiming to enrich the AI/ML community's understanding of both the capabilities and the challenges inherent in this rapidly evolving field.