Issue #231

January 12, 2024

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. Attacks on machine learning models [rnikhil.com]
2. Do we think of git commits as diffs, snapshots, and/or histories? [jvns.ca]
3. Software Technical Writing: [A Guidebook]
4. Ten Noteworthy AI Research Papers of 2023 [magazine.sebastianraschka.com]
5. The counter-intuitive rise of Python in scientific computing [fortran-lang.discourse.group]
6. The Random Transformer [osanseviero.github.io]
7. Using a Markov chain to generate readable nonsense with 20 lines of Python [benhoyt.com]

• Adversarial Machine Learning:
• Robustness and resilience of complex networks (O. Artime, M. Grassia, M. De Domenico, J. P. Gleeson, H. A. Makse, G. Mangioni, M. Perc, F. Radicchi)
• Solving real-world optimization tasks using physics-informed neural computing (J. Seo)
• Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer (C.-H. S. Lin ,T. T. Do, L. Unsworth, M. I. Garrido)
• Mixtral of Experts (A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril, T. Wang, T. Lacroix, W. E. Sayed)
• Complex systems approach to natural language (T. Stanisz, S. Drożdż, J. Kwapień)
• Turing Complete Transformers: Two Transformers Are More Powerful Than One (Anonymous authors)
• “I Have Never Seen a Bad Backtest”: Modeling Reality in Quantitative Investing (M. S. Rzepczynski, A. Brunner, P. Wild)

History of the Graphical User Interface (GUI): A Wonderful Curse

January 12, 2024

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