Issue #235
February 24, 2024
Welcome to the Feb 21st edition of the Data Science Briefing!
This week, we’re proud to announce the latest post on Graphs for Data Science substack: k-core Decomposition. As always, you can find all the relevant code and links to previous posts in the G4Sci GitHub repository.
You can also catch up on the latest in the Epidemic Modeling series: Epidemiology 302: The Impact of Age Structure on Epidemic Spreading. and the Viz4Sci series, The Effects of vaccination.
The next edition of the NLP with Deep Learning for Everyone webinar is just a week away. Register now so you don’t miss out.
In our regularly scheduled content, we share an Anti-hype LLM reading list,a beginner’s guide to making beautiful slides for your talks and Google’s next-generation LLM model: Gemini 1.5.
On the academic front, we explore how knowledge overconfidence is associated with anti-consensus views on controversial scientific issues, shortest-path percolation on complex networks and how to Simulate Opinion Dynamics with Networks of LLM-based Agents.
This week’s book is “Computing the Climate” by Steve M. Easterbrook. You can find all the previous book recommendations on our website. In this week’s video, we have a tutorial on Generative AI in a Nutshell.
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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. Anti-hype LLM reading list [github.com/veekaybee]
- 2. Causal Reinforcement Learning [crl.causalai.net]
- 3. A beginner’s guide to making beautiful slides for your talks [ines.io/blog]
- 4. The Mathematical Engineering [of Deep Learning]
- 5. Open-Source AI Cookbook [github.com/huggingface]
- 6. Our next-generation model: Gemini 1.5 [blog.google]
- 7. Large World Model (LWM) [github.com/LargeWorldModel]
- • Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues (N. Light, P. M. Fernbach, N. Rabb, M.V. Geana, S. A. Sloman)
- • Causally estimating the effect of YouTube’s recommender system using counterfactual bots (H. Hosseinmardi, A. Ghasemian, M. Rivera-Lanas, M. H. Ribeiro, R. West, D. J. Watts)
- • Shortest-path percolation on complex networks (M. Kim, F. Radicchi)
- • Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks (N. A. Arnold, P. Zhong, C. T. Ba, B. Steer, R. Mondragon, F. Cuadrado, R. Lambiotte, R. G. Clegg)
- • Distribution of centrality measures on undirected random networks via cavity method (S. Bartolucci, F. Caravelli, F. Caccioli, P. Vivo)
- • Simulating Opinion Dynamics with Networks of LLM-based Agents (Y.-S. Chuang, A. Goyal, N. Harlalka, S. Suresh, R. Hawkins, S. Yang, D. Shah, J. Hu, T. T. Rogers)
- • Critical mobility in policy making for epidemic containment (J. A. M. López, D. Mateo, A. Hernando, S. Meloni, J. J. Ramasco)
Generative AI in a Nutshell
All our videos are also available in our
YouTube playlist.
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