Self-supervised learning is the future of AI

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Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.

Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

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A robot has figured out how to use tools

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In a startling demonstration, the machine drew on experimentation, data, and observation of humans to learn how simple implements could help it achieve a task.

Learning to use tools played a crucial role in the evolution of human intelligence. It may yet prove vital to the emergence of smarter, more capable robots, too.

New research shows that robots can figure out at least the rudiments of tool use, through a combination of experimenting and observing people.

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Cutting Thru the Fog with NASA’s Chandra X-ray Orbiting Observatory

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Its Visine for space

When you look up on a dark, clear, moonless night, you might just see a faint fuzzy streak cutting across the sky. That’s the Milky Way, called that because it looks like milk has spilled across the sky.

But when you look at it through a telescope, it resolves itself into millions upon millions of stars. Faint and tightly packed, they merge together, forming a fuzzy streak because we lack the resolution in our eyes to separate out the stars. Continue reading… “Cutting Thru the Fog with NASA’s Chandra X-ray Orbiting Observatory”

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