Welcome to the 272nd issue of the Data Science Briefing!
The first edition of the ML with PyTorch webinar series is coming up in just a couple of weeks, on March 5th. Seats are going fast! Register now so you don't miss out on how to use PyTorch for all your Machine-Learning needs!
In our regularly scheduled content, we learn how to Accelerate scientific breakthroughs with an AI co-scientist, a Big TDD Misunderstanding, why New Junior Developers Can’t Actually Code, and The Untold Story of a Crypto Crimefighter's Descent into Nigerian Prison.
On the academic front, we explore Artificial intelligence for modelling infectious disease epidemics, how to do Pandemic monitoring with global aircraft-based wastewater surveillance networks andCompetitive Programming with Large Reasoning Models.
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 quick introduction to Probabilistic Programming in Python.
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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.
- Accelerating scientific breakthroughs with an AI co-scientist [research.google]
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My LLM codegen workflow atm [harper.blog]
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The Big TDD Misunderstanding [linkedrecords.com]
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New Junior Developers Can’t Actually Code [nmn.gl]
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Lucid dream startup says engineers can write code in their sleep. Work may never be the same [fortune.com]
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U.S. conditionally approves vaccine to protect poultry from avian flu [science.org]
- Stop AI [stopai.info]
- The Untold Story of a Crypto Crimefighter's Descent into Nigerian Prison [wired.com]
- Artificial intelligence for modelling infectious disease epidemics (M. U. G. Kraemer, J. L.-H. Tsui, S. Y. Chang, S. Lytras, M. P. Khurana, S. Vanderslott, S. Bajaj, N. Scheidwasser, J. L. Curran-Sebastian, E. Semenova, M. Zhang, H. J. T. Unwin, et al)
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Pandemic monitoring with global aircraft-based wastewater surveillance networks (G. St-Onge, J. T. Davis, L. Hébert-Dufresne, A. Allard, A. Urbinati, S. V. Scarpino, M. Chinazzi, A. Vespignani)
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Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data (X. Shen, A. Rumack, B. Wilder, R. J. Tibshirani)
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Quantifying infectious disease epidemic risks: A practical approach for seasonal pathogens (A. R. Kaye, G. Guzzetta, M. J. Tildesley, R. N. Thompson)
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Optimal control prevents itself from eradicating stochastic disease epidemics (R. Russell, N. J. Cunniffe)
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Competitive Programming with Large Reasoning Models (OpenAI)
Probabilistic Programming in Python
All the videos of the week are now available in our YouTube playlist.
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