Often, if you ask a human to optimize something, they'll make it orderly: straight lines, simple layouts and clean divisions, but when nature (or evolutionary algorithms) optimizes things, it produces redundancy, gradients, tangles, and complexity -- ironically, robots produce systems that look like nature designed them, while humans produce systems that look like robots designed them.
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Solid objects turn out to be mostly made of empty space and whirling particles, but we act as though they're solid, because we rarely have to interact with them in such a granular way that involves their underlying complexity.
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Joi Ito has published the "1.0" version of his October essay, Resisting Reduction, which makes major advances on the earlier draft. He's soliciting revisions and comments here. Here's what I wrote about it then:
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University of Lausanne biologists chipped hundreds of ants and digitally tracked them to see how they form social groups and work collectively to get stuff done. Based on the data, they created heat maps and visualized the ants' trajectories. From Nature:
The biologists… have found that the workers fall into three social groups that perform different roles: nursing the queen and young; cleaning the colony; and foraging for food. The different groups move around different parts of the nest, and the insects tend to graduate from one group to another as they age, the researchers write in a paper published today in Science.
“The paper is a game-changer, in the size and detail of the data set that was collected,” says Anna Dornhaus, an entomologist at the University of Arizona in Tucson.
"Tracking whole colonies shows ants make career moves" (Thanks, Nic Weidinger!)
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Are you curious about complexity? Do you dig dynamic systems and emergent phenomena? The Santa Fe Institute, one of the birthplaces of chaos theory, is now offering a free "Introduction to Complexity" online course, open to anyone. No science or math background required! The instructor is computer scientist Melanie Mitchell, author of the excellent and entertaining book Complexity: A Guided Tour. The course started last week but it's not too late to join!
In this eleven-week course you'll learn about the tools used by scientists to understand complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology. There are no prerequisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.
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