Issue #238
March 21, 2024
Dear friends,
Welcome to the Spring edition of the Data Science Briefing!
This week, we’re on hiatus from blogging. While we put the finishing touches on the next post, you can catch up on the latest in the Graphs for Data Science substack: k-core Decomposition, the Epidemic Modeling series: Epidemiology 302: The Impact of Age Structure on Epidemic Spreading, or in the Viz4Sci series, The Effects of vaccination.
We’re happy to announce the next edition of the Generative Artificial Intelligence with the OpenAI API for Developers webinar coming up on May 22nd and Registrations are already open! Don’t miss out!
In our regularly scheduled content, we have some media coverage on the physics of languages, learn how to talk like a graph: Encoding graphs for large language models, and An Overview of the LoRA Family of LLMs
On the academic front, we explore Artificial intelligence and illusions of understanding in scientific research, how to estimate household contact matrices structure from easily collectible metadata and review recent Algorithmic progress in language models
This week’s book recommendation is “Natural Language Processing with Transformers” by L. Tunstall, L von Werra, and T. Wolf. You can find all the previous book recommendations on our website. In this week’s video, we have a lecture by Prof. Geoffrey Hinton: Will digital intelligence replace biological intelligence?
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Semper discentes,
The D4S Team
This week's book is "Natural Language Processing with Transformers" by L. Tunstall, L von Werra and T. Wolf. As an avid natural language processing enthusiast (NLP), I recently delved into "Natural Language Processing with Transformers" with great anticipation. Authored by experts in the field, this book not only met but exceeded my expectations, offering a comprehensive exploration of the groundbreaking advancements in NLP powered by transformers.
From the outset, the book strikes an excellent balance between theoretical underpinnings and practical applications. Including code snippets and implementation tips further enhances the learning experience, allowing readers to gain proficiency in applying these powerful techniques to real-world problems.
In conclusion, "Natural Language Processing with Transformers" is a must-read for anyone interested in unlocking the full potential of modern NLP techniques. Whether you're a researcher, a student, or a practitioner seeking to stay ahead of the curve, this book offers a treasure trove of knowledge and practical wisdom. Engaging, informative, and inspiring, it is sure to leave a lasting impact on anyone passionate about the intersection of language and technology.
- 1. The physics of languages [physicsworld.com]
- 2. Talk like a graph: Encoding graphs for large language models [blog.research.google]
- 3. Diffusion models from scratch, from a new theoretical perspective [chenyang.co]
- 4. How to use PostgreSQL for (military) geoanalytics tasks [klioba.com]
- 5. How to Select the Most Influential Combination of Nodes in a Graph [towardsdatascience.com]
- 6. An Overview of the LoRA Family [towardsdatascience.com]
- 7. The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) [jalammar.github.io]
- 8. Building Meta’s GenAI Infrastructure [engineering.fb.com]
- • Artificial intelligence and illusions of understanding in scientific research (L. Messeri, M. J. Crockett)
- • Forecasting with trees (T. Januschowski, Y. Wang, K. Torkkola, T. Erkkilä, H. Hasson, J. Gasthaus)
- • Using early detection data to estimate the date of emergence of an epidemic outbreak (S. Jijón, P. Czuppon, F. Blanquart, F. Débarre)
- • Estimating household contact matrices structure from easily collectable metadata (L. Dall’Amico, J. Kleynhans, L. Gauvin, M. Tizzoni, L. Ozella, M. Makhasi, N. Wolter, B. Language, R. G. Wagner, C. Cohen, S. Tempia, C. Cattuto)
- • Is Cosine-Similarity of Embeddings Really About Similarity? (H. Steck, C. Ekanadham, N. Kallus)
- • Algorithmic progress in language models (A. Ho, T. Besiroglu, E. Erdil, D. Owen, R. Rahman, Z. C. Guo, D. Atkinson, N. Thompson, J. Sevilla)
- • MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training (B. McKinzie, Z. Gan, J.-P. Fauconnier, S. Dodge, B. Zhang, P. Dufter, D. Shah, X. Du, F. Peng, F. Weers, A. Belyi, H. Zhang, K. Singh, D. Kang, H. Hè, M. Schwarzer, T. Gunter, X. Kong, A. Zhang, J. Wang, C. Wang, N. Du, T. Lei, S. Wiseman, M. Lee, Z. Wang, R. Pang, P. Grasch, A. Toshev, Y. Yang)
Statistical mechanics of deep learning
All our videos are also available in our
YouTube playlist.
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