Issue #269
January 22, 2025
Welcome to the 269th issues of the Data Science Briefing!
The next edition of the NLP with Deep Learning webinar series is coming in up in just a few hours. There are only a few spots left, so Register now so you don’t miss out!
We’re also proud to announce a new webinar series on “Machine Learning with PyTorch” on March 5th. You can be one of the first to sign up!
In our regularly scheduled content, we have an in depth Kalman Filter Tutorial, explore Ruff - An extremely fast Python linter and code formatter, written in Rust, and debate whether Can AI Models Show Us How People Learn?.
On the academic front, we have some Lessons from complex networks to smart cities, learn how to Simulate 500 million years of evolution with a language model, dive into the Foundations of Large Language Models and decode Musical Evolution Through Network Science.
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 CUDA programming in Python with numba and cupy.
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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.
- 1. Kalman Filter Tutorial [kalmanfilter.net]
- 2. Perplexity AI submits bid to merge with TikTok [techcrunch.com]
- 3. Physically Based Rendering:From Theory To Implementation [pbr-book.org]
- 4. Ruff - An extremely fast Python linter and code formatter, written in Rust [github.com/astral-sh]
- 5. The Making of Community Notes [asteriskmag.com]
- 6. The mistake of yearning for the ‘friendly’ online world of 20 years ago [english.elpais.com]
- 7. Can AI Models Show Us How People Learn? Impossible Languages Point a Way [quantamagazine.org]
- 8. Training AI models might not need enormous data centres [economist.com]
- • Lessons from complex networks to smart cities (G. Caldarelli, L. Chiesi, G. Chirici, B. Galmarini, S. Mancuso, J. Moi. M. De Domenico)
- • A generative model for inorganic materials design (C. Zeni, R. Pinsler, D. Zügner, A. Fowler, M. Horton, X. Fu, Z. Wang, A. Shysheya, J. Crabbé, S. Ueda, R. Sordillo, L. Sun, J. Smith, B. Nguyen, H. Schulz, S. Lewis, C.-W. Huang, Z. Lu, Y. Zhou, H. Yang, H. Hao, J. Li, C. Yang, W. Li, R. Tomioka, T. Xie)
- • Simulating 500 million years of evolution with a language model (T. Hayes, R. Rao, H. Akin, N. J. Sofroniew, D. Oktay, Z. Lin, R. Verkuil, V. Q. Tran, J. Deaton, M. Wiggert, R. Badkundri, I. Shafkat, J. Gong, A. Derry, R. S. Molina, N. Thomas, Y. A. Khan, C. Mishra, C. Kim, L. J. Bartie, M. Nemeth, P. D. Hsu, T. Sercu, S. Candido, A. Rives)
- • Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (Y. Fan, L. Tang, H. Le, K. Shen, S. Tan, Y. Zhao, Y. Shen, X. Li, D. Gašević)
- • Foundations of Large Language Models (T. Xiao, J. Zhu)
- • Estimating the impact of school closures on the COVID-19 dynamics in 74 countries: A modelling analysis (R. Ragonnet, A. E. Hughes, D. S. Shipman, M. T. Meehan, A. S. Henderson, G. Briffoteaux, N. Melab, D. Tuyttens,E. S. McBryde, J. M. Trauer)
- • Decoding Musical Evolution Through Network Science (N. Di Marco, E. Loru, A. Galeazzi, M. Cinelli, W. Quattrociocchi)
CUDA programming in Python with numba and cupy
All our videos are also available in our
YouTube playlist.
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