Issue #244

May 23, 2024

​(view in browser)​

​

Dear Reader,

Welcome to the May 22nd edition of the Data Science Briefing!

The latest medium post on the Epidemiology series: Epidemiology 303: Metapopulation models, where we explore how to connect multiple populations through a travel matrix is now out. You can catch up on the latest in the Graphs for Data Science substack: k-core Decomposition, or in the Viz4Sci series, The Effects of Vaccination.

Yesterday, we had the latest edition of our Generative Artificial Intelligence with the OpenAI API webinar. This edition was so successful that we're proud to announce that the next edition will take place just two months from now, on July 23rd, so register now so you don't miss out again!

In our regularly scheduled content, we learn how Strangely Curved Shapes Break 50-Year-Old Geometry Conjecture, implement llama3 from scratch and dive into the The Fundamentals of Modern Deep Learning with PyTorch.

On the academic front, we explore the Challenges of COVID-19 Case Forecasting in the US, 2020–2021, Sparks of Artificial General Intelligence and the Emergent Abilities of Large Language Models.​

This week's book recommendation is "Co-Intelligence" by Ethan Mollick. You can find all the previous book recommendations on our website. In this week's video, we have a comparison on Kolmogorov-Arnold Networks VS Regular Deep Learning.

Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, go ahead and forward this email to them. This will help us spread the word!

Semper discentes,

The D4S Team

This week's book is "Co-Intelligence" by Ethan Mollick. This book provides an essential and balanced guide to navigating the age of artificial intelligence (AI). Author Ethan Mollick offers a pragmatic perspective on AI's capabilities and limitations, showing how it can effectively augment human abilities. The book's key strength is Mollick's "Four Rules of Co-Intelligence" framework for seamlessly integrating AI into work and life. He demystifies complex AI concepts through engaging examples and practical advice. Mollick paints an optimistic yet grounded vision where humans and AI collaborate harmoniously, complementing each other's strengths to drive innovation. His book equips readers to confidently leverage AI's power while preserving human ingenuity and ethics. In the rapidly changing AI landscape, "Co-Intelligence" is an invaluable resource for business leaders, educators, students, and anyone seeking to thrive by harnessing the benefits of human-AI co-intelligence. Mollick's work provides a roadmap for gaining a competitive edge through co-intelligent collaboration.

1. ​Strangely Curved Shapes Break 50-Year-Old Geometry Conjecture [quantamagazine.org]
2. ​Mapping the Mind of a Large Language Model​ [anthropic.com]
3. ​Fast Tokenizers with StringScanner [tenderlovemaking.com]
4. ​A Survey of Causal Inference Applications at Netflix​ [netflixtechblog.com]
5. ​llama3 implemented from scratch​ [github.com/naklecha]
6. ​The Fundamentals of Modern Deep Learning with PyTorch [github.com/rasbt]
7. ​Hypothesis Testing Explained [towardsdatascience.com]

• ​Testing theory of mind in large language models and humans (J. W. A. Strachan, D. Albergo, G. Borghini, O. Pansardi, E. Scaliti, S. Gupta, K. Saxena, A. Rufo, S. Panzeri, G. Manzi, M. S. A. Graziano, C. Becchio)
• ​Challenges of COVID-19 Case Forecasting in the US, 2020–2021​ (V. K. Lopez , E. Y. Cramer, R. Pagano, J. M. Drake, E. B. O’Dea, M. Adee, T. Ayer, J. Chhatwal, O. O. Dalgic, M. A. Ladd, et al)
• ​Sparks of Artificial General Intelligence: Early experiments with GPT-4​ (S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, Y. Zhang)
• ​Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey​ (X. Fang, W. Xu, F. A. Tan, J. Zhang, Z. Hu, Y. Qi, S. Nickleach, D. Socolinsky, S. Sengamedu, C. Faloutsos)
• ​Emergent Abilities of Large Language Models​ (J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, W. Fedus)
• ​Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models​ (D. W. Apley, J. Zhu)
• ​Comprehensive Causal Machine Learning​ (M. Lechner, J. Mareckova)

Kolmogorov-Arnold Networks VS Regular Deep Learning

​

All the videos of the week are now available in our Youtube playlist.