Originally published in NYTimes, June 22, 2017
SAN FRANCISCO — The Department of Homeland Security is turning to data scientists to improve screening techniques at airports.
On Thursday, the department, working with Google, introduced a $1.5 million contest to build computer algorithms that can automatically identify concealed items in images captured by checkpoint body scanners.
Although data scientists can apply any technique in building these algorithms, the contest is a way of capitalizing on the progress in a technology called deep neural networks, said the Kaggle founder and chief executive, Anthony Goldbloom. Neural networks are complex mathematical systems that can learn specific tasks by analyzing vast amounts of data. Feed millions of cat photographs into a neural network, for instance, and it can learn to recognize a cat.
Companies like Google and Facebook use the technology to do things like identify faces in online images, recognize commands spoken into smartphones and translate one language into another. But the possibilities extend well beyond smartphone apps and other online services.
Earlier this year, Kaggle ran a $1 million contest to build algorithms capable of identifying signs of lung cancer in CT scans, helping to fuel a larger effort to apply neural networks to health care. Now, the hope is that neural networks can also help automated systems read body scans with greater accuracy, so checkpoint workers can spend less time pulling passengers aside and patting them down.
“We started by trying to figure out what was a dog and what was a cat,” said Goldbloom, referring to the growing community of companies, academics and other researchers working with neural networks. “Now, we’re moving into more serious territory.”