Uncle Sam Wants Your Deep Neural Networks
Homeland Security and other organizations are working on ways to improve the technologies used at airport checkpoints, with the T. S.A.
set to roll out new CT systems that can automatically identify items hidden in passenger baggage,
and at least one company, Smiths Detection, exploring the use of neural networks at security checkpoints.
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.
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.
On Thursday, the department, working with Google, will introduce a $1.5 million contest to build computer algorithms
that can automatically identify concealed items in images captured by checkpoint body scanners.
In theory, neural networks can accelerate the evolution of airport security, mainly
because such systems can learn so quickly from data, relying less on individual rules and code painstakingly built by engineers.
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.