Dear Reader,
Welcome to the July 23rd edition of the Data Science Briefing! This week, we continue celebrating the 6th anniversary of this humble newsletter, and as usual, we have a few surprises in store for you throughout July, starting with the announcement of the next edition of our most successful webinar series: Machine Learning with PyTorch for Developers on Sept 17. Register now so you donāt miss out!
Weāre proud to announce that a brand new Data Visualization with Python on-demand video is now available on the OāReilly website: Python Data Visualization: Create impactful visuals, animations and dashboards. This in depth tutorial is almost 7h in length and covers fundamental and advanced usage of matplotlib, seaborn, plotly and bokeh as well as tips on how to use Jupyter widgets. Check it out!
The latest blog post on the Epidemiology series is also out: Demographic Processes. In this post we explore how to include birth and death rates in your epidemik models. Check it out!
This weekās newsletter spotlights some of the most fascinating advances and thought-provoking commentary in AI and data science. In āThe Big LLM Architecture Comparison,ā readers are taken on a deep dive into the latest open-weight language models, such as DeepSeek V3 and Llama 4, examining the architectural innovations, from multi-head latent attention to expert mixtures, that are shaping model performance in 2025. Meanwhile, an exploration from Quanta Magazine reveals just how creative AI has become, highlighting systems that design physics experiments so unconventional that even researchers are stunned by their success in the real world.
In a historic milestone, DeepMindās Gemini āDeep Thinkā model stunned the mathematical community by achieving gold-medal status at the 2025 International Mathematical Olympiad, solving university-level math problems entirely in natural language within the contest time frameāmarking a world-first for autonomous mathematical reasoning. For those coding on the bleeding edge, reflections on āCoding with LLMs in the summer of 2025ā offer a glimpse into the evolving workflow with generative models as indispensable development partners. This issue captures a dynamic portrait of a field racing ahead across algorithms, hardware, and the ever-expanding role of artificial intelligence in science and and engineering.
Fresh from academic, emerging research continues to reshape our understanding of both the natural and digital world. A sweeping global assessment of carnivore populations reveals that 64% of terrestrial carnivores now mainly inhabit regions of heightened human pressure, with Indigenous peoplesā lands providing a last stronghold for nearly a quarter of their rangesāspotlighting both the fragility and importance of these habitats for global biodiversity. On the technological front, billions of Android smartphones are quietly doubling as seismic sensors in 98 countries, now delivering earthquake warnings with effectiveness on par with dedicated scientific instrumentationāan innovation that transforms everyday devices into distributed safety infrastructure and demonstrates unmistakable value for public good.
Meanwhile, new methods like linear scaling causal discovery promise to revolutionize high-dimensional time series analysis, allowing researchers to unveil the deep, hierarchical causal structures in complex data at an unprecedented computational scale. In the realm of digital culture, recent findings on ālanguage bubblesā within online networks expose how community isolation can lock groups into ever more insular linguistic patterns, prompting concern for the erosion of shared discourse. And in a surprising turn for developer productivity, a controlled study reveals that even though open-source developers and experts predicted time savings the adoption of early-2025 AI coding tools actually increased task completion times by 19%, challenging assumptions about the near-term impact of AI in professional software engineering. Across conservation, global risk mitigation, analytics, and social dynamics, these investigations underscore the profound ways science and technology are converging to alter our world.
This weekās book is āBehavioral Network Science: Language, Mind, and Societyā by T. T. Hills. You can find all the previous book recommendations on our website. In this weekās video, we feature an interview of Geoffrey Hinton: I Tried to Warn Them, But Weāve Already Lost Control!
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Semper discentes,
The D4S Team
"Behavioral Network Science: Language, Mind, and Society" by T. T. Hills successfully bridges two distinct scientific domains, demonstrating how network analysis can reveal hidden patterns in human behavior. The book tackles an impressive scope of topics, from language evolution and childhood learning to cognitive aging, creativity, and social dynamics, while maintaining remarkable coherence throughout. What sets this work apart is Hills' commitment to practical application, equipping readers with concrete tools, including an introductory guide to network science and accompanying R code that enables hands-on analysis.
This practical approach makes the book uniquely valuable to a diverse audience. Behavioral scientists unfamiliar with network methods will find an accessible entry point, while data scientists can discover rich applications in behavioral research. Hills demonstrates particular skill in addressing contemporary social issues through a network lens, offering fresh perspectives on polarization, echo chambers, and conspiracy theories. The interdisciplinary framework proves especially powerful when examining how individual cognitive processes scale up to shape collective behavior and social structures.
The book's most significant achievement lies in its clarity without oversimplification. Hills effectively conveys complex concepts with precision while maintaining an engaging and accessible tone. This balance makes "Behavioral Network Science" essential reading for anyone seeking to understand how network structures influence human behavior across scalesāfrom individual minds to entire societies.
