Issue #256
September 18, 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. How does cosine similarity work? [tomhazledine.com]
- 2. B-trees and database indexes [planetscale.com]
- 3. Learning to Reason with LLMs [openai.com]
- 4. Geometric Search Trees [g-trees.github.io]
- 5. Novel Architecture Makes Neural Networks More Understandable [quantamagazine.org]
- 6. How I Mastered Data Structures and Algorithms [medium.com/algomaster-io]
- 7. How the LLM Got Lost in the Network and Discovered Graph Reasoning [towardsdatascience.com]
- • Grounding AI in reality with a little help from Data Commons (J. Chen, P. Ramaswami)
- • Collaborative forecasting of influenza-like illness in Italy: the Influcast experience (S. Fiandrino, A. Bizzotto, G. Guzzetta, S. Merler, F. Baldo, E. Valdano, A. M. Urdiales, A. Bella, F. Celino, L. Zino, A. Rizzo, Y. Li, N. Perra, C. Gioannini, P. Milano, D. Paolotti, M. Quaggiotto, L. Rossi, I. Vismara, A. Vespignani, N. Gozzi)
- • Self-similarity of temporal interaction networks arises from hyperbolic geometry with time-varying curvature (S. Dutta, D. Das, T. Chakraborty)
- • A review of the structure of street networks (M. Barthelemy, G. Boeing)
- • A Review of Graph Neural Networks in Epidemic Modeling (Z. Liu, G. Wan, B. A. Prakash, M. S. Y. Lau, W. Jin)
- • LLMs Will Always Hallucinate, and We Need to Live With This (S. Banerjee, A. Agarwal, S. Singla)
- • Bayesian clustering with uncertain data (K. Nicholls, P. D. W. Kirk, C. Wallace)
Terence Tao at IMO 2024: AI and Mathematics
All our videos are also available in our YouTube playlist.
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