Issue #201
May 14, 2023
Dear friends,
Welcome to the May 14th edition of the Sunday Briefing. This week we continue our hiatus from blogging but we have several posts in the final stages of writing. In the meantime, you can catch up on the latest post in the Viz4Sci series: Waterfall Chart. The latest Medium post “Top 10 Books we read in 2022” and the most recent post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely.
Several of you have reached out in recent weeks asking how they can help support the work we do here at Data For Science. While the Sunday Briefing is a labor of love and will always remain free, it is not without costs, and any help is appreciated.
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On our regularly scheduled content, we learn about eBay’s Blazingly Fast Billion-Scale Vector Similarity Engine, how AI is Just Someone Else’s Intelligence and OpenLLaMA: An Open Reproduction of LLaMA.
On the academic front, we explore the role of complexity for digital twins of cities, how Neuroscience needs Network Science, Blockchain Large Language Models and The Rise of Rationality in Blockchain Dynamics.
This week’s Data Science Book is “Automate the Boring Stuff with Python” by A. Sweigart. As always you can find all the previous book recommendations on our website. In the video of the week, we have a LangChain Crash Course.
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Semper discentes,
The D4S Team
This week’s Data Science Book is " Automate the Boring Stuff with Python " by A. Sweigart, an exceptional book that is perfect for anyone who wants to learn how to use Python for automating mundane tasks. The author has done an excellent job of explaining complex concepts in a simple and easy-to-understand manner.
The book is well-organized and starts with the basics of Python programming, and gradually progresses to more advanced topics such as web scraping, working with Excel spreadsheets, and sending automated emails. The real-life examples and practical exercises included in each chapter make it easy for readers to apply what they have learned and see the results.
One of the most remarkable aspects of this book is that it doesn't require any prior programming experience, making it perfect for beginners. However, it is also an excellent resource for experienced programmers who want to learn how to automate repetitive tasks.
Overall, this is a must-read for anyone who wants to learn how to use Python for automating everyday tasks. The book is well-written, easy to follow, and the author's sense of humor makes it an enjoyable read. I highly recommend this book to anyone who wants to learn Python programming and automate their boring tasks.
- 1. AI's Ostensible Emergent Abilities Are a Mirage [hai.stanford.edu]
- 2. eBay's Blazingly Fast Billion-Scale Vector Similarity Engine [tech.ebayinc.com]
- 3. Re-implementing LangChain in 100 lines of code [blog.scottlogic.com]
- 4. AI is Just Someone Else's Intelligence [zdziarski.com]
- 5. EU urged to protect grassroots AI research or risk losing out to US [theguardian.com]
- 6. OpenLLaMA: An Open Reproduction of LLaMA [github.com/openlm-research]
- 7. Avoiding hallucinations in LLM-powered Applications [vectara.com]
- 8. 'The Godfather of A.I.' Leaves Google and Warns of Danger Ahead [nytimes.com]
- • The role of complexity for digital twins of cities (G. Caldarelli, E. Arcaute, M. Barthelemy, M. Batty, C. Gershenson, D. Helbing, S. Mancuso, Y. Moreno, J. J. Ramasco, C. Rozenblat, A. Sánchez, J. L. Fernández-Villacañas)
- • Neuroscience needs Network Science (D. L. Barabási, G. Bianconi, E. Bullmore, M. Burgess, S.Y. Chung, T. Eliassi-Rad, D. George, I. A. Kovács, H. Makse, C. Papadimitriou, T. E. Nichols, O. Sporns, K. Stachenfeld, Z. Toroczkai, E. K. Towlson, A. M. Zador, H. Zeng, A.-L. Barabási, A. Bernard, G. Buzsáki)
- • Blockchain Large Language Models (Y. Gai, L. Zhou, K. Qin, D. Song, A. Gervais)
- • Complex contagion in social systems with distrust (J.-F. de Kemmeter, L. Gallo, F. Boncoraglio, V. Latora, T. Carletti)
- • Human movement decisions during Coronavirus Disease 2019 (R. Omori, K. Ito, S. Kanemitsu, R. Kimura, Y. Iwasa)
- • Filtering higher-order datasets (N. W. Landry, I. Amburg, M. Shi, S. G. Aksoy)
- • The Rise of Rationality in Blockchain Dynamics (G. Di Antonio, G. V. Vinci, L. Pietronero, M. A. Javarone)
LangChain Crash Course: Build a AutoGPT app in 25 minutes!
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