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Six years ago, Steve Finkbeiner of the Gladstone Institutes and the University of California, San Francisco, got a call from Google. He and his colleagues had invented a robotic microscopy system to track single cells over time, amassing more data than they knew what to do with. It was exactly the type of dataset that Google was looking for to apply its deep learning approach, a state-of-the-art form of artificial intelligence (AI).
“We generated enough data to be interesting, is basically what they said,” Finkbeiner recalls of the phone conversation. “They were interested in blue-sky ideas—problems that either humans didn’t think would even be possible or things that a computer could do ten times better or faster.”
Deep learning is really dominant at the moment. It’s really changing the field of image analysis.
One application that came to Finkbeiner’s mind was to have a neural network—an ...
