Test tube artificial neural network recognizes ‘molecular handwriting’

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Researchers at Caltech have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers. The work is a significant step in demonstrating the capacity to program artificial intelligence into synthetic biomolecular circuits.

The work was done in the laboratory of Lulu Qian, assistant professor of bioengineering. A paper describing the research appears online on July 4 and in the July 19 print issue of the journal Nature.

“Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable,” says Qian. “Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.”

Artificial neural networks…

For the rest of the article, please visit https://www.sciencedaily.com/releases/2018/07/180704135320.htm.

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Last modified: July 9, 2018

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