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Welcome to the July 4th week edition of the Data Science Briefing! This week, we continue the celebration of our 5th anniversary! Thank you for helping to make these five years a great success.

As we announced recently, you can now find all of our written content in a single location: The Data For Science substack! All the previous posts in G4Sci and Viz4Sci, as well as all of the most significant Medium posts, can now be found there. Check it out! At the end of this week, the subscription price for the substack will increase to $9/month or $90/year, so be sure to sign up now to lock in the current price!

The most recent posts in each series are always free to read. 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 learn why Financial services shun AI over job and regulatory fears, how LLM Performances are plateauing, What is Joint Embedding Predictive Architecture (JEPA) and How AI Revolutionized Protein Science, but Didn’t End It.

On the academic front, we Reconstruct higher-order interactions in coupled dynamical systems, learn how Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model, explore Timeliness criticality in complex systems, and detect hallucinations in large language models using semantic entropy.

This week's book recommendation is "Co-Intelligence" by Ethan Mollick. You can find all the previous book recommendations on our website. In this week's video, we have a video on Why Does Mathematics Describe Reality?​

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Semper discentes,

The D4S Team

This week's book is "Co-Intelligence" by Ethan Mollick. This book provides an essential and balanced guide to navigating the age of artificial intelligence (AI). Author Ethan Mollick offers a pragmatic perspective on AI's capabilities and limitations, showing how it can effectively augment human abilities. The book's key strength is Mollick's "Four Rules of Co-Intelligence" framework for seamlessly integrating AI into work and life. He demystifies complex AI concepts through engaging examples and practical advice. Mollick paints an optimistic yet grounded vision where humans and AI collaborate harmoniously, complementing each other's strengths to drive innovation. His book equips readers to confidently leverage AI's power while preserving human ingenuity and ethics. In the rapidly changing AI landscape, "Co-Intelligence" is an invaluable resource for business leaders, educators, students, and anyone seeking to thrive by harnessing the benefits of human-AI co-intelligence. Mollick's work provides a roadmap for gaining a competitive edge through co-intelligent collaboration.

1. ​Financial services shun AI over job and regulatory fears [ft.com]
2. ​LLM Performances are plateauing [huggingface.co]
3. ​The Illustrated Transformer​ [jalammar.github.io]
4. ​Modern Good Practices for Python Development​ [stuartellis.name]
5. ​What is Joint Embedding Predictive Architecture (JEPA)? [turingpost.com]
6. ​GraphRAG: New tool for complex data discovery now on GitHub​​ [microsoft.com]
7. ​Trying Kolmogorov-Arnold Networks in Practice​ [cprimozic.net]
8. ​How AI Revolutionized Protein Science, but Didn’t End It​ [quantamagazine.org]

• ​Reconstructing higher-order interactions in coupled dynamical systems (F. Malizia, A. Corso, L. V. Gambuzza, G. Russo, V. Latora, M. Frasca)
• ​Local Network Interaction as a Mechanism for Wealth Inequality​ (S.-T. Yu, P. Wang, C. W. Kabudula, D. Gareta, G. Harling, B. Houle)
• ​How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model​ (F. Cagnetta, L. Petrini, U. M. Tomasini, A. Favero, M. Wyart)
• ​Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency​ (S. Bontorin, G. Cencetti, R. Gallotti, B. Lepri, M. De Domenico)
• ​Timeliness criticality in complex systems​ (J. Moran, M. Romeijnders, P. Le Doussal, F. P. Pijpers, U. Weitzel, D. Panja, J.-P. Bouchaud)
• ​Detecting hallucinations in large language models using semantic entropy​ (S. Farquhar, J. Kossen, L. Kuhn, Y. Gal)
• ​Financial Machine Learning​ (B. T. Kelly, D. Xiu)

Why Does Mathematics Describe Reality?

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All the videos of the week are now available in our Youtube playlist.
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