Issue #251

July 17, 2024

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.

Co-Intelligence

1. The Invention of Zero [themarginalian.org]
2. The Math of Card Shuffling [fredhohman.com]
3. You want to train [language models yourself]
4. Two Common Pitfalls to Avoid When Doing Cross-Validation [towardsdatascience.com]
5. Markov Chains [medium.com/kinomoto-mag]
6. General Theory of Neural Networks [robleclerc.substack.com]
7. How America’s Fastest Swimmers Use Math to Win Gold [quantamagazine.org]

• The structure and function of antagonistic ties in village social networks (A. Ghasemian, N. A. Christakis)
• Mortality risk information and health-seeking behavior during an epidemic (H. Purcell, I. V. Kohler, Alberto Ciancio, J. Mwera, A. Delavande, V. Mwapasa, H.-P. Kohler)
• How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model (F. Cagnetta, L. Petrini, U. M. Tomasini, A. Favero, M. Wyart)
• Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It (S. B. Heller, B. Jakubowski, Z. Jelveh, M. Kapustin)
• Training of Physical Neural Networks (A. Momeni, B. Rahmani, B. Scellier, L. G. Wright, P. L. McMahon, C. C. Wanjura, Y. Li, A. Skalli, N. G. Berloff, T. Onodera, I. Oguz, F. Morichetti, P. del Hougne, M. Le Gallo, A. Sebastian, A. Mirhoseini, C. Zhang, D. Marković, D. Brunner, C. Moser, S. Gigan, F. Marquardt, A. Ozcan, J. Grollier, A. J. Liu, D. Psaltis, A. Alù, R. Fleury)
• Reasoning in Large Language Models: A Geometric Perspective (R. Cosentino, S. Shekkizhar)
• Formal Aspects of Language Modeling (R. Cotterell, A. Svete, C. Meister, T. Liu, L. Du)

Brian Kernighan Reflects on "The Practice of Programming"

July 17, 2024

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