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Welcome to the 251st edition of the Data Science Briefing!
The next edition of the Generative Artificial Intelligence with the OpenAI API for Developers webinar series is coming up in just a few days, on July 23rd. There are only a few spots left, so don't forget to Register so you don't miss out.
We're also proud to announce a brand new webinar series on the topic of LLMs for Data Science has just been announced for Sept 19. We're looking forward to exploring how you can use the latest state-of-the-art Large Language Models to improve your workflow.
As we had previously announced, we've increased the he Data For Science substack subscription price to $9/month or $90/year to account for all the extra content. Thank you to all of you who subscribed in the last few weeks and locked in the old price!
The latest 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 explore the Invention of Zero, Markov Chains, and How America’s Fastest Swimmers Use Math to Win Gold.
On the academic front, we learn about the structure and function of antagonistic ties in village social networks, how to train Physical Neural Networks and dive into some Formal Aspects of Language Modeling.
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, Brian Kernighan Reflects on "The Practice of Programming".
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 "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.
- The Invention of Zero [themarginalian.org]
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The Math of Card Shuffling [fredhohman.com]
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You want to train language models yourself from scratch [drive.google.com]
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Two Common Pitfalls to Avoid When Doing Cross-Validation [towardsdatascience.com]
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Markov Chains [medium.com/kinomoto-mag]
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General Theory of Neural Networks [robleclerc.substack.com]
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How America’s Fastest Swimmers Use Math to Win Gold [quantamagazine.org]
- The structure and function of antagonistic ties in village social networks (A. Ghasemian, N. A. Christakis)
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Mortality risk information and health-seeking behavior during an epidemic (H. Purcell, I. V. Kohler, Alberto Ciancio, J. Mwera, A. Delavande, V. Mwapasa, H.-P. Kohler)
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How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model (F. Cagnetta, L. Petrini, U. M. Tomasini, A. Favero, M. Wyart)
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Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It (S. B. Heller, B. Jakubowski, Z. Jelveh, M. Kapustin)
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Training of Physical Neural Networks (A. Momeni, B. Rahmani, B. Scellier, L. G. Wright, P. L. McMahon, C. C. Wanjura, Y. Li, A. Skalli, N. G. Berloff, T. Onodera, I. Oguz, F. Morichetti, P. del Hougne, M. Le Gallo, A. Sebastian, A. Mirhoseini, C. Zhang, D. Marković, D. Brunner, C. Moser, S. Gigan, F. Marquardt, A. Ozcan, J. Grollier, A. J. Liu, D. Psaltis, A. Alù, R. Fleury)
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Reasoning in Large Language Models: A Geometric Perspective (R. Cosentino, S. Shekkizhar)
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Formal Aspects of Language Modeling (R. Cotterell, A. Svete, C. Meister, T. Liu, L. Du)
Brian Kernighan Reflects on "The Practice of Programming"
 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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