Xenobots: How the world’s First Organic Robots are amphibian, african, and smart

The team used a supercomputer running an evolutionary algorithm — basically ultra-fast trial and error — to find the optimal geometries for the Xenobots so they can efficiently communicate, operate, and heal. Cells are sticklers for coordination and layout, so the Xenobots’ cells must be correctly arranged to function. The algorithm simulates physical activities based on shape, which the Xenobots mimic. Each Xenobot design corresponds to a specific physical task.

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Neo-Darwinistic concepts of chance and time through the Lens of AI

Ocean coral

If evolution is a computing problem, how has nature solved it? Furthermore, how is it related to AI, the culmination of computing? According to Neo-Darwinism, or the modern synthesis of Charles Darwin’s evolutionary theory of the origin of life, nature has taken blind chances over a lengthy period of time in selecting variations among genetic mutations. In this article, I attempt to study the Neo-Darwinistic concepts of chance and time through the lens of AI and computing in general. You will find it intriguing how AI connects evolution, one of the most impactful scientific thoughts, to P vs. NP (Aaronson, 2016), one of the most important open problems in computing.

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