Issue #228

December 21, 2023

This week's book is "Computing the Climate" by Steve M. Easterbrook, a captivating journey into the synergy of climate science and computing, making it a must-read for anyone intrigued by the intersection of these fields. Easterbrook's engaging writing style effortlessly demystifies complex concepts, ensuring accessibility for readers with diverse backgrounds. The book's strength lies in its seamless blend of theoretical discussions with real-world examples, showcasing the instrumental role of computing in unraveling the intricacies of climate dynamics.

Easterbrook's balanced perspective sets this book apart, acknowledging the uncertainties in climate science while underscoring the transformative impact of technological advancements. By delving into interdisciplinary connections with policy, economics, and environmental science, Easterbrook provides a holistic understanding of the challenges associated with climate change. This comprehensive approach educates and empowers readers to recognize the pivotal role of computational progress in shaping our collective response to climate-related issues.

In essence, "Computing the Climate" stands as a persuasive testament to the indispensable role of computing in climate research. Easterbrook's skillful narrative not only informs but also inspires readers to grasp the significance of technological innovation in confronting the pressing challenges of our changing climate. This book is an essential addition to the literature, urging readers to actively engage in the ongoing dialogue surrounding the future of our planet.

Computing the Climate

1. The Surprising Behavior of Data in Higher Dimensions [towardsdatascience.com]
2. What Happens When AI Takes Over Science? [theatlantic.com]
3. Causal Trees [farley.ai]
4. Advancements in machine learning for machine learning [blog.research.google]
5. Database Fundamentals [tontinton.com]
6. The True Nature of Logarithms [cantorsparadise.com]
7. Streamlining Membership Data Engineering at Netflix with Psyberg [netflixtechblog.com]

• Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices (T. Elmer)
• Statistical mechanics of inference in epidemic spreading (A. Braunstein, L. Budzynski, M. Mariani)
• Does the brain behave like a (complex) network? I. Dynamics (D. Papo, J. M. Buldú)
• A Review of Link Prediction Applications in Network Biology (A. F. Al Musawi, S. Roy, P. Ghosh)
• Thermodynamics of Information (J. M. R. Parrondo)
• Relational Deep Learning: Graph Representation
• Recommender systems may enhance the discovery of novelties (G. De Marzo, P. Gravino, V. Loreto)

Bjarne Stroustrup: The Journey of C++ & Its Impact in Programming

December 21, 2023

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