AI: Open vs Closed + NeurIPS Reflections

This book is a monumental biography of Robert Moses, who was the single most powerful man in New York City and State during his time. It explores how Moses accumulated and wielded power, shaping the city through his public works projects, including highways, bridges, and parks. The book delves into Moses's early life, his idealistic beginnings, and his transformation into a powerful figure who dominated New York's politics without ever being elected. It also highlights the social and environmental impacts of his projects and his eventual fall from power. The biography is renowned for its detailed and nuanced portrayal of power dynamics in urban politics.

In this influential book, Albert O. Hirschman explores how individuals and groups respond to decline in various organizations. He introduces the concepts of 'exit' (withdrawing from the organization), 'voice' (attempting to improve the organization through complaint or protest), and 'loyalty' (the factor that can influence the choice between exit and voice). Hirschman argues that these responses are interdependent and that understanding their interaction is crucial for addressing organizational decline. The book applies these concepts to a wide range of economic, social, and political phenomena, including consumer behavior, political parties, and personal relationships.

This second edition of 'Reinforcement Learning: An Introduction' by Richard S. Sutton and Andrew G. Barto provides a clear and simple account of the field's key ideas and algorithms. The book is significantly expanded and updated, including new topics such as artificial neural networks, the Fourier basis, and expanded treatment of off-policy learning and policy-gradient methods. It also includes new chapters on the relationships between reinforcement learning and psychology/neuroscience, as well as updated case studies on AlphaGo, AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.