Welcome to the Feb 6th issue of the Data Science Briefing!
We're proud to announce that the next edition of the LLMs for Data Science webinar series has just been confirmed for March 26th, 2025, and you can already register so you don't miss your spot! Also, the next edition of the Generative Artificial Intelligence with the OpenAI API for Developers webinar series is coming up in a week. Spots are going fast, so Register now so you don't miss out!
In our regularly scheduled content, we learn that the CDC Data Are Disappearing, What's Happening Inside the NIH and NSF and Why Can’t Robots Click The “I’m Not a Robot” Box On Websites?
On the academic front, we explore Social media warfare, Large Language Models for Mathematicians, and the Theoretical limitations of multi-layer Transformers.
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 Deep Dive into LLMs like ChatGPT.
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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.
- CDC Data Are Disappearing [theatlantic.com]
- What's Happening Inside the NIH and NSF [science.org]
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Ingesting Millions of PDFs and why Gemini 2.0 Changes Everything [sergey.fyi]
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Beej's Guide to Git [beej.us]
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Deep Reinforcement Learning: Pong from Pixels [karpathy.github.io]
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Why Can’t Robots Click The “I’m Not a Robot” Box On Websites? [grantpiperwriting.medium.com]
- Neural Networks — Intuitively and Exhaustively Explained [medium.com/intuitively-and-exhaustively-explained]
- Social media warfare: investigating human-bot engagement in English, Japanese and German during the Russo-Ukrainian war on Twitter and Reddit (W. Xu, K. Sasahara, J. Chu, B. Wang, W. Fan, Z. Hu)
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Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk (B. Lombard, H. Moultrie, J. R. C. Pulliam, C. van Schalkwyk)
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Large Language Models for Mathematicians (S. Frieder, J. Berner, P. Petersen, T. Lukasiewicz)
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Theoretical limitations of multi-layer Transformer (L. Chen, B. Peng, H. Wu)
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Reinforcement Learning: An Overview (K. Murphy)
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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models (X. Guan, J. Zeng, F. Meng, C. Xin, Y. Lu, H. Lin, X. Han, L. Sun, J. Zhou)
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Pre-trained Large Language Models Use Fourier Features to Compute Addition (T. Zhou, D. Fu, V. Sharan, R. Jia)
Deep Dive into LLMs like ChatGPT
All the videos of the week are now available in our YouTube playlist.
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