Issue #229
December 27, 2023
Welcome to the final 2023 edition of the Data Science Briefing! We’re celebrating the coming new year with a Medium post covering the “Top 10 Books we read in 2023”. You can also catch up on the most recent post on the G4Sci series: Network Attacks: Breaking up a Network without Observing it Completely, and the latest in the Viz4Sci series, The Effects of vaccination.
We’re also proud to announce the next edition of the NLP with Deep Learning webinar series on Feb 28th. Registrations have just opened!
Our regularly scheduled content explores Similarity Learning, the art of identifying neighbors, The Most Important Unsolved Problem in Computer Science, and Geocomputation with Python.
On the academic front, we learn why the simplest explanation isn’t always the best, how Unsupervised embedding of trajectories captures the latent structure of scientific migration, and explore how Time is Encoded in the Weights of Finetuned Language Models.
This week’s book is “Computing the Climate” by Steve M. Easterbrook. You can find all the previous book recommendations on our website. In the video of the week, we have a talk on Behind the scenes scaling ChatGPT.
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Semper discentes,
The D4S Team
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.
- 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
All our videos are also available in our
YouTube playlist.
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