Issue #248
June 13, 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.
- 1. Physics-Informed AI Method Could Help Make CRISPR Safer [simonsfoundation.org]
- 2. PandasAI [github.com/Sinaptik-AI/pandas-ai]
- 3. Introducing Apple’s On-Device and Server Foundation Models [machinelearning.apple.com]
- 4. The New Math of How Large-Scale Order Emerges [quantamagazine.org]
- 5. The Gilbert–Johnson–Keerthi algorithm explained as simply as possibly [computerwebsite.net]
- 6. Extracting Concepts from GPT-4 [openai.com]
- 7. Generative AI Handbook: A Roadmap for Learning Resources [genai-handbook.github.io]
- • Physical networks as network-of-networks (G. Pete, Á. Timár, S. Ö. Stefánsson, I. Bonamassa, M. Pósfai)
- • Power-law of path multiplicity in complex networks (Y. Deng, J. Wu)
- • The importance of spatial heterogeneity in disease transmission (E. P. Harvey, D. R. J. O’Neale)
- • Considerations for viral co-infection studies in human populations (T. Chin, E. F. Foxman, T. A. Watkins, M. Lipsitch)
- • Causal Inference in the Social Sciences (G. W. Imbens)
- • Infection patterns in simple and complex contagion processes on networks (D. A. Contreras, G. Cencetti, A. Barrat)
- • The Geometry of Categorical and Hierarchical Concepts in Large Language Models (K. Park, Y. J. Choe, Y. Jiang, V. Veitch)
- • EpiTwitter: Public Health Messaging During the COVID-19 Pandemic (A. Rao, N. Sabri, S. Guo, L. Raschid, K. Lerman)
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