On Wednesday the Department of Transportation announced the launch of a pilot program that will lead to new regulations. On Wednesday the Department of Transportation announced the launch of a pilot program that will lead to new regulations. Here's a hypothetical: How tolerant would you be of a drone flying over your head or zooming through your backyard, if it were carrying life-saving medicine to the scene of a hard-to-reach accident? The U.S. Department of Transportation plans to collect the answers to questions like this, and a slew of other data, in a new test project called the Integration Pilot Program. After combing through 149 applications from state, local and tribal governments seeking to partner with some of the world's leading technology companies, Transportation Secretary Elaine Chao announced the winners Wednesday.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. The drone future that we all know is coming--more drones, everywhere, ferrying our stuff to wherever we want it sent--isn't coming just yet. Before flying robots can speckle the sky from coast to coast, the government needs to pass regulations that allow drones to fly beyond the line of sight of the operator, over densely populated areas, and at night--all things currently prohibited unless the drone operator gets a special waiver from the Federal Aviation Administration. Drones also have to be integrated into the national air traffic control system, which will have to help coordinate their movement and ensure the autonomous flyers don't collide in the sky. But before any of that gets off the ground, the U.S. Department of Transportation is giving the green light to 10 areas across the country to set up test sites for drones to do things like delivery, mosquito-killing, and security.

For the task of entity disambiguation, mention contexts and entity descriptions both contain various kinds of information content while only a subset of them are helpful for disambiguation. In this paper, we propose a type-aware co-attention model for entity disambiguation, which tries to identify the most discriminative words from mention contexts and most relevant sentences from corresponding entity descriptions simultaneously. To bridge the semantic gap between mention contexts and entity descriptions, we further incorporate entity type information to enhance the co-attention mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on three public datasets. Further analysis also confirms that both the co-attention mechanism and the type-aware mechanism are effective.

In a corner of LG's sprawling, dripping-with-technology booth at the Las Vegas Convention Center sat a circular table populated by a group of small, futuristic snowmen. Each of them sported a black touchscreen display showing a pair of electronic eyes that would intermittently squint as if they were smiling at you. On a large screen nearby, a video showed one of these gizmos, the LG Hub Robot, calling out in a soothing tone: "Hello. I learn your lifestyle to better serve you." This year's CES tech show in Las Vegas highlighted how our technology is finding its voice, with makers of cars, appliances and speakers starting to let people control their products simply by talking to them.