The implications of commercializing this technology is MIND BOGGLING. https://techxplore.com/news/2018-07-tube-artificial-neural-network-molecular.html Test tube artificial neural network recognizes 'molecular handwriting' 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 are mathematical models inspired by the human brain. Despite being much simplified compared to their biological counterparts, artificial neural networks function like networks of neurons and are capable of processing complex information. The Qian laboratory's ultimate goal for this work is to program intelligent behaviors (the ability to compute, make choices, and more) with artificial neural networks made out of DNA. "Humans each have over 80 billion neurons in the brain, with which they make highly sophisticated decisions. Smaller animals such as roundworms can make simpler decisions using just a few hundred neurons. In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible," says Qian.
