In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.

We develop a novel framework that aims to create bridges between the computational social choice and the database management communities. This framework enriches the tasks currently supported in computational social choice with relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions. At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries. At the technical level, we embark on an investigation of the computational complexity of the necessary answers. We establish a number of results about the complexity of the necessary answers of conjunctive queries involving positional scoring rules that contrast sharply with earlier results about the complexity of the necessary winners.

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.

The US government is making good on its promise to expand the use of drones. The Department of Transportation has named the 10 projects that will participate in its Unmanned Aircraft Systems Integration Pilot Program, and they represent a wide swath of the country. Most of them are municipal or state government bodies, including the cities of Reno and San Diego, Memphis' County Airport Authority and the Transportation Departments for Kansas, North Carolina and North Dakota. However, the rest are notable: the Choctaw Nation of Oklahoma will be part of the program, as will the University of Alaska-Fairbanks and Virginia Tech. Notably, Virginia Tech is working with Google's Project Wing drone delivery initiative as well as transportation and tech giants like Airbus, AT&T and Intel.

Expect to see driverless cars roaming around the Buckeye State in the near future. Ohio Governor John Kasich has issued an executive order permitting self-driving car tests on public roads, adding to a small but growing list of autonomous-friendly states that includes Arizona, California and Michigan. There are conditions, of course, although they're not extremely strict at first glance. Every vehicle will need a human operator from the company performing the tests and reporting any accidents. Every hopeful firm will also have to register with DriveOhio, a central hub for mobility initiatives (conveniently established by Kasich in January) that will collect information on both the cars and their testing locations.

The Dynetics solution involves deploying a towed, stabilized capture device below and away from the C-130.

For the last few years, police forces around China have invested heavily to build the world's largest video surveillance and facial recognition system, incorporating more than 170 million cameras so far. In a December test of the dragnet in Guiyang, a city of 4.3 million people in southwest China, a BBC reporter was flagged for arrest within seven minutes of police adding his headshot to a facial recognition database. And in the southeast city of Nanchang, Chinese police say that last month they arrested a suspect wanted for "economic crimes" after a facial recognition system spotted him at a pop concert amidst 60,000 other attendees. These types of stories, combined with reports that computer vision recognizes some types of images more accurately than humans, makes it seem like the Panopticon has officially arrived. In the US alone, 117 million Americans, or roughly one in two US adults, have their picture in a law enforcement facial-recognition database.

Uber and NASA are taking another step toward the future of transportation. The ride-sharing firm signed a second space act agreement with NASA this month to explore ways to implement a safe and efficient air travel network over congested cities. Working off Uber's plans for an urban flying taxi system, NASA will use computer models and simulations to assess how small craft could fit into city life. Uber has plans to launch its Uber Air service in 2020, starting out with piloted flights before becoming fully autonomous within a decade. The ride-sharing firm signed a second space act agreement with NASA this month to explore ways to implement a safe and efficient air travel network over congested cities. An artist's impression is pictured'Urban air mobility could revolutionize the way people and cargo move in our cities and fundamentally change our lifestyle much like smart phones have,' said Jaiwon Shin, associate administrator for NASA's Aeronautics Research and Mission Directorate.

Executives from tech companies including Amazon, Google, and Facebook are expected to travel to the White House this week to discuss the country's future in artificial intelligence (AI). According to The Washington Post, representatives from 38 major U.S. firms will be in Washington, D.C. to talk about how the Trump administration can assist the nation's AI efforts through funding and regulation. For its part, the White House wants to question academics, government officials and AI developers about ways to adapt regulations to advance AI in fields including agriculture, health care and transportation. "Whether you're a farmer in Iowa, an energy producer in Texas, a drug manufacturer in Boston, you are going to be using these techniques to drive your business going forward," Michael Kratsios, deputy chief technology officer at the White House, said in a recent interview. In addition to the previously mentioned companies, others expected to attend include representatives from Microsoft, Nvidia and Oracle, as well as other businesses like Ford, Land O'Lakes, Mastercard, Pfizer and United Airlines.