Issue #232

January 24, 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. Free unexpected MIT courses to kick start the new year [medium.com/open-learning]
2. Celebrating the Playful Magic of John Horton Conway [quantamagazine.org]
3. New Theory Suggests Chatbots Can Understand Text [quantamagazine.org]
4. Why is machine learning 'hard'? [ai.stanford.edu]
5. Evolution of AI and Amara's Law [n9o.xyz]
6. Calculus on Computational Graphs: Backpropagation [colah.github.io]
7. Yann LeCun, chief AI scientist at Meta: ‘Human-level artificial intelligence is going to take a long time’ [english.elpais.com]

• Diversity of information pathways drives sparsity in real-world networks (A. Ghavasieh, M. De Domenico)
• Comparison of home detection algorithms using smartphone GPS data (R. Verma, S. Mittal, Z. Lei, X. Chen, S. V. Ukkusuri)
• Modeling teams performance using deep representational learning on graphs (F. Carli, P. Foini, N. Gozzi, N. Perra, R. Schifanella)
• Percolation and Topological Properties of Temporal Higher-Order Networks (L. Di Gaetano, F. Battiston, M. Starnini)
• Mission: Impossible Language Models (J. Kallini, I. Papadimitriou, R. Futrell, K. Mahowald, C. Potts)
• Wallets' explorations across non-fungible token collections (S. Jo, W.-S. Jung, H. Kim)
• The structure and robustness of ecological networks with two interaction types (V. Domínguez-García, S. Kéfi)

2023's Biggest Breakthroughs in Computer Science

January 24, 2024

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