Issue #229

December 27, 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. Similarity Learning, the art of identifying neighbors [engineering.blackrock.com]
2. "Attention", "Transformers", in Neural Network "Large Language Models" [bactra.org]
3. The Most Important Unsolved Problem in Computer Science [scientificamerican.com]
4. Working With Discovery Trees [industriallogic.com]
5. Migrating Netflix to GraphQL Safely [netflixtechblog.com]
6. Geocomputation with Python [py.geocompx.org]
7. Mapping the semantic void: Strange goings-on in GPT embedding spaces [lesswrong.com]

• Discovery of a structural class of antibiotics with explainable deep learning (F. Wong, E. J. Zheng, J. A. Valeri, N. M. Donghia, M. N. Anahtar, S. Omori, A. Li, A. Cubillos-Ruiz, A. Krishnan, W. Jin, et al)
• Why the simplest explanation isn’t always the best (E. L. Dyer, K. Kording)
• Unsupervised embedding of trajectories captures the latent structure of scientific migration (D. Murray, J. Yoon, S. Kojaku, R. Costas, W.-S. Jung, S. Milojević, Y.-Y. Ahn)
• Epidemic Spreading in Group-Structured Populations (S. Patwardhan, V. K. Rao, S. Fortunato, F. Radicchi)
• Time is Encoded in the Weights of Finetuned Language Models (K. Nylund, S. Gururangan, N. A. Smith)
• Visual Analytics Using Heterogeneous Urban Data (S. Bonadia, R. Gama, D. de Oliveira, F. Miranda, M. Lage)
• Higher-order interactions disturb community detection in complex networks (Y. Liu, Y. Fan, A. Zeng)

Behind the scenes scaling ChatGPT

December 27, 2023

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