Issue #233
February 2, 2024
Welcome to the 233rd Data Science Briefing!
This week we’re proud to announce the latest post on the Epidemic Modeling series: Epidemiology 302: The Impact of Age Structure on Epidemic Spreading. As always, you can find all the code and links to previous posts in the GitHub repository.
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.
Our regularly scheduled content, we explore a new state of the art LLM 🦅 Eagle 7B : Soaring past Transformers with 1 Trillion Tokens Across 100+ Languages (RWKV-v5), some Lessons from history’s greatest R&D labs and how to Build a Large Language Model (From Scratch).
On the academic front, we dive into An embedding-based distance for temporal graphs, the Temporal rich club phenomenon and its formation mechanisms, and whether or not You Need a Zero Knowledge Proof?.
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 tutorial on Large Language Models in Five Formulas.
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, go ahead and forward this email to them. This will help us spread the word!
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. Hugging Face and Google partner for open AI collaboration [huggingface.co]
- 2. Machine Learning Engineering Open Book [github.com/stas00]
- 3. The Big Little Guide to Message Queues [sudhir.io]
- 4. Lessons from history’s greatest R&D labs [answer.ai]
- 5. 🦅 Eagle 7B : Soaring past Transformers with 1 Trillion Tokens Across 100+ Languages (RWKV-v5) [blog.rwkv.com]
- 6. New Theory Suggests Chatbots Can Understand Text [quantamagazine.org]
- 7. Build a Large Language Model (From Scratch) [github.com/rasbt]
- • Temporal rich club phenomenon and its formation mechanisms (M.-Y. Li, Y.-T. Zhang, W.-X. Zhou)
- • Routes of importation and spatial dynamics of SARS-CoV-2 variants during localised interventions in Chile (B. Gutierrez, J. L.-H. Tsui, G. Pullano, M. Mazzoli, K. Gangavarapu, R. P. D. Inward, S. Bajaj, R. E. Pena, S. Busch-Moreno, M. A. Suchard, O. G. Pybus, A. Dunner, R. Puentes, S. Ayala, J. Fernandez, R. Araos, L. Ferres, V. Colizza, M. U.G. Kraemer)
- • Do You Need a Zero Knowledge Proof? (J. Ernstberger, S. Chaliasos, L. Zhou, P. Jovanovic, A. Gervais)
- • An embedding-based distance for temporal graphs (L. Dall'Amico, A. Barrat, C. Cattuto)
- • Fast degree-preserving rewiring of complex networks (S. Mannion, P. MacCarron, A. Saxena, F. W. Takes)
- • LoMA: Lossless Compressed Memory Attention (Y. Wang, Z. Xiao)
- • Tweets to Citations: Unveiling the Impact of Social Media Influencers on AI Research Visibility (I. X. Weissburg, M. Arora, L. Pan, W. Y. Wang)
Large Language Models in Five Formulas
All our videos are also available in our
YouTube playlist.
Enjoy the newsletter?
Forward it to a friend, or subscribe to get it straight to your inbox.
Subscribe Free
