Issue #250
July 3, 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. Financial services shun AI over job and regulatory fears [ft.com]
- 2. LLM Performances are plateauing [huggingface.co]
- 3. The Illustrated Transformer [jalammar.github.io]
- 4. Modern Good Practices for Python Development [stuartellis.name]
- 5. What is Joint Embedding Predictive Architecture (JEPA)? [turingpost.com]
- 6. GraphRAG: New tool for complex data discovery now on GitHub
- 7. Trying Kolmogorov-Arnold Networks in Practice [cprimozic.net]
- 8. How AI Revolutionized Protein Science, but Didn’t End It [quantamagazine.org]
- • Reconstructing higher-order interactions in coupled dynamical systems (F. Malizia, A. Corso, L. V. Gambuzza, G. Russo, V. Latora, M. Frasca)
- • Local Network Interaction as a Mechanism for Wealth Inequality (S.-T. Yu, P. Wang, C. W. Kabudula, D. Gareta, G. Harling, B. Houle)
- • How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model (F. Cagnetta, L. Petrini, U. M. Tomasini, A. Favero, M. Wyart)
- • Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency (S. Bontorin, G. Cencetti, R. Gallotti, B. Lepri, M. De Domenico)
- • Timeliness criticality in complex systems (J. Moran, M. Romeijnders, P. Le Doussal, F. P. Pijpers, U. Weitzel, D. Panja, J.-P. Bouchaud)
- • Detecting hallucinations in large language models using semantic entropy (S. Farquhar, J. Kossen, L. Kuhn, Y. Gal)
- • Financial Machine Learning (B. T. Kelly, D. Xiu)
Why Does Mathematics Describe Reality?
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