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Welcome to the 252nd edition of the Data Science Briefing!
We're proud to announce that the next edition of the LangChain for Generative AI Pipelines webinar has just been scheduled for Sep 25. Register now so you don't miss out!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we explore What Happens When You Give People Free Money, how to Overcome the limits of current LLMs and why Open Source AI Is the Path Forward.
On the academic front, we learn how a psychedelic state arises from desynchronized brain activity, why Fair coins tend to land on the same side they started, and explore SpreadsheetLLM: Encoding Spreadsheets for Large Language Models.
This week's book recommendation is "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. You can find all the previous book recommendations on our website. In this week's video, Andrew Gelman lecture on Bayes, statistics, and reproducibility
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 "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. Networks are the keystone concept necessary to understand a wealth of real-world complex systems whose behavior is characterized by interactions between individual components. In this book, J. Bagrow and Y.-Y. Ahn, two leading researchers in the field of Complex Networks, introduce readers to the fundamental concepts of Network Science and how to apply them to practical datasets. Their hands-on approach will get you up to speed quickly, allowing you to develop effective approaches to understanding your own network datasets.
- Working with AI (Part 2): Code Conversion [blog.withmantle.com]
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Overcoming the limits of current LLM [seanpedersen.github.io]
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GPT-4o mini: advancing cost-efficient intelligence [openai.com]
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Friendship and Social Fitness [robkhenderson.com]
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Every Way To Get Structured Output From LLMs [boundaryml.com]
- The Man Who Thought Too Fast [newyorker.com]
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Open Source AI Is the Path Forward [about.fb.com]
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Here’s What Happens When You Give People Free Money [wired.com]
- Nonlinear computation with linear systems (P. L. McMahon)
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A psychedelic state arises from desynchronized brain activity (P. D. Petridis)
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A Formulation of the Trilemma in Proof of Work Blockchain (T. Nakai, A. Sakurai, S. Hironaka, K. Shudo)
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Machine learning in physics: A short guide (F. A. Rodrigues)
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Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency (A. Birhane, M. McGann)
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Fair coins tend to land on the same side they started: Evidence from 350,757 flips (F. Bartoš, A. Sarafoglou, H. R. Godmann, A. Sahrani, D. K. Leunk, P. Y. Gui, D. Voss, K. Ullah, M. J. Zoubek, F. Nippold, F. Aust, F. F. Vieira, C.-G. Islam, A. J. Zoubek, S. Shabani, et al)
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SpreadsheetLLM: Encoding Spreadsheets for Large Language Models (Y. Tian, J. Zhao, H. Dong, J. Xiong, S. Xia, M. Zhou, Y. Lin, J. Cambronero, Y. He, S. Han, D. Zhang)
Andrew Gelman - Bayes, statistics, and reproducibility
 All the videos of the week are now available in our Youtube playlist.
Upcoming Events:
Opportunities to learn from us
On-Demand Videos:
Long-form tutorials
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