How Uncle Sam Is Using Deep Neural Networks to Secure Airports ✈️

Uncle Sam Wants Your Deep Neural Networks<br />Homeland Security and other organizations are working on ways to improve the technologies used at airport checkpoints, with the T. S.A.<br />set to roll out new CT systems that can automatically identify items hidden in passenger baggage,<br />and at least one company, Smiths Detection, exploring the use of neural networks at security checkpoints.<br />Although data scientists can apply any technique in building these algorithms, the contest is a way of capitalizing on the<br />progress in a technology called deep neural networks, said the Kaggle founder and chief executive, Anthony Goldbloom.<br />Earlier this year, Kaggle ran a $1 million contest to build algorithms capable of identifying signs of<br />lung cancer in CT scans, helping to fuel a larger effort to apply neural networks to health care.<br />On Thursday, the department, working with Google, will introduce a $1.5 million contest to build computer algorithms<br />that can automatically identify concealed items in images captured by checkpoint body scanners.<br />In theory, neural networks can accelerate the evolution of airport security, mainly<br />because such systems can learn so quickly from data, relying less on individual rules and code painstakingly built by engineers.<br />Now, the hope is that neural networks can also help automated systems read body scans with greater accuracy,<br />so checkpoint workers can spend less time pulling passengers aside and patting them down.