Issue #270

February 2, 2025

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.

The Nvidia way

1. Entropy of a Large Language Model output [nikkin.dev]
2. OpenAI has created an AI model for longevity science [technologyreview.com]
3. AI Hardware Is in Its ‘Put Up or Shut Up’ Era [wired.com]
4. CDC Data Are Disappearing [theatlantic.com]
5. An overview of gradient descent optimization algorithms [ruder.io]
6. Raphtory: an in-memory vectorised graph database written in Rust [github.com/Pometry]
7. SMOL-GPT: A minimal PyTorch implementation for training your own small LLM from scratch [github.com/Om-Alve]

• Tensor networks enable the calculation of turbulence probability distributions (Nikita Gourianov, P. Givi, D. Jaksch, S. B. Pope)
• 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)
• 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)
• 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)
• 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)
• Granger Causality Detection with Kolmogorov-Arnold Networks (H. Lin, M. Ren, P. Barucca, T. Aste)
• 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

February 2, 2025

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