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Welcome to the 270th issue 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 over a week. Spots are going fast, so Register now so you don't miss out!
In our regularly scheduled content, we explore the Entropy of a Large Language Model output, why the CDC Data Are Disappearing, get an overview of gradient descent optimization algorithms and how AI Hardware Is in Its ‘Put Up or Shut Up’ Era.
On the academic front, we explore the disparities and development trajectories of nations in achieving the sustainable development goals, find gaps in the national electric vehicle charging station coverage of the United States, and dive into Granger Causality Detection with Kolmogorov-Arnold Networks.
This week's book recommendation is "The Nvidia way" by T. Kim. You can find all the previous book recommendations on our website. In this week's video, we have a tutorial on how The Bayesians are Coming to Time Series.
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 "The Nvidia way" by T. Kim. The book offers a wealth of profound lessons for entrepreneurs and managers, extending far beyond a mere chronicle of technological achievements. It explores Nvidia's strategic approach to innovation, providing valuable insights into how the company consistently stayed ahead of industry trends. The narrative highlights Nvidia's founder, Jensen Huang's, obsession with solving the Innovator's Dilemma, demonstrating how this focus drove Nvidia to reinvent its corporate strategy and maintain its competitive edge.
One of the key takeaways is Nvidia's unique organizational structure. The book emphasizes the benefits of the company's flat hierarchy, which empowers employees at all levels to contribute to the company's direction. This approach fosters a culture of innovation and agility, allowing Nvidia to adapt quickly to market changes and technological shifts. Entrepreneurs and managers can learn from this model to create more dynamic and responsive organizations.
Nvidia's long-term strategic thinking is highlighted, particularly when it comes to recognizing and capitalizing on emerging technologies. The company's early bet on AI, long before it became mainstream, serves as a powerful example of visionary leadership and calculated risk-taking. This aspect of the book offers valuable lessons on the importance of anticipating future trends and having the courage to invest in unproven technologies. For entrepreneurs and managers, it underscores the significance of looking beyond short-term gains and fostering a culture that embraces calculated risks for long-term success.
- Entropy of a Large Language Model output [nikkin.dev]
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OpenAI has created an AI model for longevity science [technologyreview.com]
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AI Hardware Is in Its ‘Put Up or Shut Up’ Era [wired.com]
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CDC Data Are Disappearing [theatlantic.com]
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An overview of gradient descent optimization algorithms [ruder.io]
- Raphtory: an in-memory vectorised graph database written in Rust [github.com/Pometry]
- SMOL-GPT: A minimal PyTorch implementation for training your own small LLM from scratch [github.com/Om-Alve]
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Tensor networks enable the calculation of turbulence probability distributions (Nikita Gourianov, P. Givi, D. Jaksch, S. B. Pope)
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The disparities and development trajectories of nations in achieving the sustainable development goals (F. Ma, H. Wang, A. Tzachor, C. A. Hidalgo, H. Schandl, Y. Zhang, J. Zhang, W.-Q. Chen, Y. Zhao, Y.-G. Zhu, B. Fu)
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Finding gaps in the national electric vehicle charging station coverage of the United States (L. Hanig, C. Ledna, D. Nock, C. D. Harper, A. Yip, E. Wood, C. A. Spurlock)
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The disparities and development trajectories of nations in achieving the sustainable development goals (F. Ma, H. Wang, A. Tzachor, C. A. Hidalgo, H. Schandl, Y. Zhang, J. Zhang, W.-Q. Chen, Y. Zhao, Y.-G. Zhu, B. Fu)
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Spreading dynamics of information on online social networks (F. Meng, J. Xie, J. Sun, C. Xu, Y. Zeng, X. Wang, T. Jia, S. Huang, Y. Deng, Y. Hu)
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Granger Causality Detection with Kolmogorov-Arnold Networks (H. Lin, M. Ren, P. Barucca, T. Aste)
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Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search (D. Severo, G. Ottaviano, M. Muckley, K. Ullrich, M. Douze)
The Bayesians are Coming to Time Series
 All the videos of the week are now available in our YouTube playlist.
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