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Photo by Matteo Badini from Pexels

Breakthroughs in the field of nanophotonics – how light behaves on the nanometer scale – have paved the way for the invention of “metamaterials,” human-made materials that have enormous applications, from remote nanoscale sensing to energy harvesting and medical diagnostics. But their impact on daily life has been hindered by a complicated manufacturing process with large margins of error.

An interdisciplinary Tel Aviv University study published in “Light: Science and Applications” demonstrated a way of streamlining the process of designing and characterizing basic nanophotonic, metamaterial elements.

Matter into antimatter and vice-versa...

“Our new approach depends almost entirely on Deep Learning, a computer network inspired by the layered and hierarchical architecture of the human brain,” Prof. Wolf explains. “It’s one of the most advanced forms of machine learning, responsible for major advances in technology, including speech recognition, translation and image processing. We thought it would be the right approach for designing nanophotonic, metamaterial elements”.

Against the realistic interpretation of the Theory of Relativity (and any other theory)

The scientists fed a Deep Learning network with 15,000 artificial experiments to teach the network the complex relationship between the shapes of the nanoelements and their electromagnetic responses. “We demonstrated that a ‘trained’ Deep Learning network can predict, in a split second, the geometry of a fabricated nanostructure,” Dr. Suchowski says. (1)

Imitating the human brain.

To predict what the human brain cannot predict.

Could you have a better proof that our brain is not algorithmic?

Wise apes. “Discovering” imaginary particles?

We are humans not because we can tell the future.

But because we have experienced it already.

We are humans not because we can find the answers.

But because we can ask the questions…

We are gods not because we know how metamaterials will form.

But because we don’t even care…