Issue #245

May 23, 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. Strangely Curved Shapes Break 50-Year-Old Geometry Conjecture [quantamagazine.org]
2. Mapping the Mind of a Large Language Model [anthropic.com]
3. Fast Tokenizers with StringScanner [tenderlovemaking.com]
4. A Survey of Causal Inference Applications at Netflix [netflixtechblog.com]
5. llama3 implemented from scratch [github.com/naklecha]
6. The Fundamentals of Modern Deep Learning with PyTorch [github.com/rasbt]
7. Hypothesis Testing Explained [towardsdatascience.com]

• Testing theory of mind in large language models and humans (J. W. A. Strachan, D. Albergo, G. Borghini, O. Pansardi, E. Scaliti, S. Gupta, K. Saxena, A. Rufo, S. Panzeri, G. Manzi, M. S. A. Graziano, C. Becchio)
• Challenges of COVID-19 Case Forecasting in the US, 2020–2021 (V. K. Lopez , E. Y. Cramer, R. Pagano, J. M. Drake, E. B. O’Dea, M. Adee, T. Ayer, J. Chhatwal, O. O. Dalgic, M. A. Ladd, et al)
• Sparks of Artificial General Intelligence: Early experiments with GPT-4 (S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, Y. Zhang)
• Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey (X. Fang, W. Xu, F. A. Tan, J. Zhang, Z. Hu, Y. Qi, S. Nickleach, D. Socolinsky, S. Sengamedu, C. Faloutsos)
• Emergent Abilities of Large Language Models (J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, W. Fedus)
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models (D. W. Apley, J. Zhu)
• Comprehensive Causal Machine Learning (M. Lechner, J. Mareckova)

Kolmogorov-Arnold Networks VS Regular Deep Learning

May 23, 2024

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