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Dear Reader,

Welcome to the 276th issue of the Data Science Briefing!

This issue takes you on a journey from raw technical mastery to unexpected insights about our world. A detailed guide on building a 2 billion parameter LLM from scratch using Python opens the conversation on scaling AI, while a candid critique from LessWrong challenges us to sift genuine progress from hype. An intriguing exploration into a pigeon problem reveals surprising connections to complexity theory, and a thoughtful piece reminds us that even our perception of purple is uniquely human. Adding further depth, posts on the evolution of graph learning and mapping hidden concepts in language models underscore a shift towards more interconnected and transparent data analysis.

Recent interdisciplinary research is unveiling insights that span from computational innovation to real-world applications. In public health, AI-based chatbots are emerging as vital tools for managing emergencies, offering rapid communication and critical support during crises. At the same time, advancements in artificial intelligence are pushing the envelope with approaches that scale visual representation learning without language, alongside innovative methods to compress large language model weights into compact pseudo-random seeds—efforts that strive for efficiency and scalability in machine learning. In parallel, studies on foundation agents are drawing inspiration from brain-like systems to create collaborative, safe, and evolutionary technologies, while investigations into chaotic dynamics in random modular networks shed light on the complex behavior of interconnected systems. Complementing these technological strides, research into the microvelocity of money in decentralized platforms like Ethereum, as well as the ecological impacts of climate change on tick populations, underscores a growing need for multidisciplinary strategies to tackle both digital economy challenges and environmental shifts.

This week's book is "The Complete Developer" by M. Krause. You can find all the previous book recommendations on our website. In this week's video, we have a lecture on how Automating the search for artificial life with foundation models.

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Semper discentes,

The D4S Team

This week's book is "The Complete Developer" by M. Krause. This is a comprehensive guide that takes readers on a journey through the intricacies of modern full-stack web and API development. With over two decades of experience building websites and leading development teams, Krause offers an indispensable resource for developers at any career stage. The book covers various technologies, including TypeScript, React, Next.js, MongoDB, and Docker, providing a 360-degree view of web development.

One of the standout features of this book is its beginner-friendly approach, breaking down complex concepts into easy-to-follow steps. Krause meticulously covers every aspect of modern web application development, from building a React-driven frontend to constructing a robust backend using Mongoose and MongoDB, and integrating both REST and GraphQL APIs. The book is structured in two halves: one for demonstrating parts of the stack like React, Next.js, and Docker, and another for writing the final application and connecting the stack parts cohesively.

While some readers have found the material challenging for absolute beginners, many developers, including those with experience in other areas, have praised the book for its comprehensive coverage and practical approach. The inclusion of modern development practices and tools like Docker, Express.js, and OAuth for authentication sets readers up for real-world development scenarios. Despite the rapidly evolving nature of web technologies. "The Complete Developer" provides a solid foundation for mastering the modern full stack, making it a valuable resource for those looking to enhance their development skills with the latest Javascript based technologies.

1. ​Building a 2 Billion Parameter LLM from Scratch Using Python​ [levelup.gitconnected.com]
2. ​Recent AI model progress feels mostly like bullshit [lesswrong.com]
3. ​How a Problem About Pigeons Powers Complexity Theory​ [quantamagazine.org]
4. ​Purple exists only in our brains​ [snexplores.org]
5. ​The evolution of graph learning​ [research.google]
6. ​Mapping the hidden concepts in language models​ [medium.com/@willkn]
7. ​Automatic Differentiation​ [medium.com/@rhome]
8. ​Choropleth Maps​ [data4sci.substack.com]

• ​The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review​ (F. Branda, M. Stella, C. Ceccarelli, F. Cabitza, G. Ceccarelli, A. Maruotti, M. Ciccozzi, F. Scarpa)
• ​The microvelocity of money in Ethereum​ (F. M. De Collibus, C. Campajola, C. J. Tessone)
• ​Scaling Language-Free Visual Representation Learning​ (D. Fan, S. Tong, J. Zhu, K. Sinha, Z. Liu, X. Chen, M. Rabbat, N. Ballas, Y. LeCun, A. Bar, S. Xie)
• ​SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators​ (R. Shafipour, D. Harrison, M. Horton, J. Marker, H. Bedayat, S. Mehta, M. Rastegari, M. Najibi, S. Naderiparizi)
• ​Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems​ (B. Liu, X. Li, J. Zhang, J. Wang, T. He, S. Hong, H. Liu, S. Zhang, K. Song, K. Zhu, Y. Cheng, S. Wang, X. Wang, Y. Luo, H. Jin, P. Zhang, O. Liu, J. Chen, H. Zhang, Z. Yu, H. Shi, B. Li, D. Wu, F. Teng et al)
• ​Hierarchy of Chaotic Dynamics in Random Modular Networks​ (Ł. Kuśmierz, U. Pereira-Obilinovic, Z. Lu, D. Mastrovito, S. Mihalas)
• ​The impact of climate change on ecology of tick associated with tick-borne diseases​ (H. Choi, C. H. Lee)

Automating the search for artificial life with foundation models

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