Two Quick Book Reviews: Mathematica and Stuff Matters

2023-05-09 · Source: Mike Talks AI · Field: Science & Research — Mathematics & Computational Sciences, Physical Sciences & Chemistry, Engineering & Applied Sciences · Depth: Novice, short

Summary

This article reviews two books: "Mathematica: A Secret World of Intuition and Curiosity" by David Bessis and "Stuff Matters: Exploring the Marvelous Materials That Shape Our Man" by Mark Miodownik. Bessis's book challenges traditional math education, advocating for intuition and mental image formation over formulas, and proposes a "System 3" for building and correcting these mental images to update intuition. The author of this article found inspiration to visualize mixed-integer programs (MIPs) and to help students develop intuition in optimization. Miodownik's "Stuff Matters" explores the history and future of materials like steel, concrete, paper, glass, chocolate, plastic, porcelain, aerogel, graphite, and implants. It highlights lessons on experimentation, luck, trade secrets, and the challenges of proving new ideas, citing examples like the consistent production of steel and the rediscovery of aerogel by NASA.

Key takeaway

For educators and practitioners in quantitative fields, consider integrating methods that foster intuition and mental imagery, rather than solely relying on formulas. Your approach to teaching or problem-solving could benefit from encouraging the development of "System 3" thinking, which builds and corrects mental models. This can lead to deeper understanding and more effective learning, especially in areas like optimization and supply chain analytics.

Key insights

Intuition and mental imagery are crucial for deep learning in mathematics and complex systems.

Principles

• Learning involves building and correcting mental images.
• Materials science progress often combines experiment and luck.
• Past innovations can find new relevance later.

Method

Cultivate "System 3" thinking to build and refine mental images, thereby updating intuition and understanding complex concepts, particularly in fields like mathematics and optimization.

In practice

• Translate intuitive images into animations for clarity.
• Help students create mental images for complex concepts.
• Explore historical materials for modern applications.

Topics

• Mathematical Intuition
• Deep Learning Startups
• Optimization
• Supply Chain Analytics
• Materials Science

Best for: Data Scientist, AI Student, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Mike Talks AI.