Issue #258
October 3, 2024
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.
- 1. Did a top NIH official manipulate Alzheimer's and Parkinson’s studies for decades? [science.org]
- 2. If your AI seems smarter, it's thanks to smarter human trainers [reuters.com]
- 3. What I tell people new to on-call [ntietz.com]
- 4. How to Learn Rust in 2024: A Complete Beginner’s Guide to Mastering Rust Programming [blog.jetbrains.com]
- 5. How agent-based models powered by HPC are enabling large scale economic simulations [aws.amazon.com]
- 6. NotebookLM’s automatically generated podcasts are surprisingly effective [simonwillison.net]
- 7. 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)
- • A Brief History of Blockchain Interoperability (R. Belchior, J. Süßenguth, Q. Feng, T. Hardjono, A. Vasconcelos, M. Correia)
- • The potential impact of AI innovations on US occupations (A. A. Septiandri, M. Constantinides, D. Quercia)
- • Geometric Scaling Law in Real Neuronal Networks (X.-Y. Zhang, J. M. Moore, X. Ru, G. Yan)
- • 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)
- • 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)
- • Newton's financial misadventures in the South Sea Bubble (A. Odlyzko)
Fine-tuning Large Language Models (LLMs)
All our videos are also available in our YouTube playlist.
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