Welcome to the October 3rd edition of the Data Science Briefing!
Last week's edition of the LangChain for Generative AI Pipelines webinar was a great success, and we're already working on scheduling the next edition. If you missed it, you can download the entire set of Jupyter notebooks and the slide deck from Gumroad!
The next edition of the Generative Artificial Intelligence with the OpenAI API for Developers is coming up in just a week, on October 9th. Register now so you don't miss out!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we explore How to Learn Rust in 2024, Did a top NIH official manipulate Alzheimer's and Parkinson’s studies for decades? and how NotebookLM’s automatically generated podcasts are surprisingly effective.
On the academic front, we review A Brief History of Blockchain Interoperability, the potential impact of AI innovations on US occupations, and ponder To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.
This week's book recommendation is "Why Machines Learn: The Elegant Math Behind Modern AI" by A. Ananthaswamy. You can find all the previous book recommendations on our website. In this week's video, we have a beginner's guide to fine-tuning large language models (LLMs).
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Semper discentes,
The D4S Team
This week's book is "Why Machines Learn: The Elegant Math Behind Modern AI" by A. Ananthaswamy. The book introduces the main ideas and developments of Artificial Intelligence clearly and concisely. Starting with the invention of the Perceptron in the 50s, through all the significant developments of the last several decades, such as Support Vector Machines, Hopfield Networks, and Backpropagation, to the latest developments in Large Language Models. Ananthaswamy explains how they fit in the historical development of Computer Science and AI, as well as how they connect to insights originating in biology and psychology.
The book targets a general audience familiar with basic math. Mathematical concepts such as probability and linear algebra are introduced in an intuitive way that provides just enough detail to understand the more technical parts of the text. Overall, a great resource whether your reviewing these concepts or encountering them for the first time.
- Did a top NIH official manipulate Alzheimer's and Parkinson’s studies for decades? [science.org]
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If your AI seems smarter, it's thanks to smarter human trainers [reuters.com]
- What I tell people new to on-call [ntietz.com]
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How to Learn Rust in 2024: A Complete Beginner’s Guide to Mastering Rust Programming [blog.jetbrains.com]
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How agent-based models powered by HPC are enabling large scale economic simulations [aws.amazon.com]
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NotebookLM’s automatically generated podcasts are surprisingly effective [simonwillison.net]
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Five ways to reduce variance in A/B testing [bytepawn.com]
- Deeper but smaller: Higher-order interactions increase linear stability but shrink basins (Y. Zhang, P. S. Skardal, F. Battiston, G. Petri, M. Lucas)
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A Brief History of Blockchain Interoperability (R. Belchior, J. Süßenguth, Q. Feng, T. Hardjono, A. Vasconcelos, M. Correia)
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The potential impact of AI innovations on US occupations (A. A. Septiandri, M. Constantinides, D. Quercia)
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Geometric Scaling Law in Real Neuronal Networks (X.-Y. Zhang, J. M. Moore, X. Ru, G. Yan)
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Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey (C. Chen, Y. Wang, G. Kaur, A. Adiga, B. Espinoza, S. Venkatramanan, A. Warren, B. Lewis, J. Crow, R. Singh, A. Lorentz, D. Toney, M. Marathe)
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To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning (Z. Sprague, F. Yin, J. D. Rodriguez, D. Jiang, M. Wadhwa, P. Singhal, X. Zhao, X. Ye, K. Mahowald, G. Durrett)
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Newton's financial misadventures in the South Sea Bubble (A. Odlyzko)
Fine-tuning Large Language Models (LLMs)
All the videos of the week are now available in our Youtube playlist.
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