DJI is inviting state, local and tribal governments to consider partnering with the company as they apply to take part in the Federal Aviation Administration's (FAA) new UAS Integration Pilot Program. According to DJI, the program smartly provides opportunities for government and industry to experiment with advanced drone operations and test new forms of airspace management. The company notes it is pleased that the program will also help inform policymakers on regulatory approaches to safe drone adoption. "DJI has worked for years with government officials around the world to help develop reasonable, safety-enhancing public policies while keeping open the pathways to innovation," says Brendan Schulman, DJI's vice president of policy and legal affairs. "We would very much value the opportunity to work with U.S. state, local and tribal governments to develop smart and comprehensive strategies for expanding how drones can benefit their constituents while properly managing their integration into the airspace."

During a press conference in June 2016, leaders from the Justice and Health and Human Services departments unveiled charges in the largest takedown of Medicare and Medicaid fraud in the nation's history. The final tally was eye-opening: About 300 individuals, including 61 doctors and other medical professionals, were accused of falsifying $900 million worth of medical bills. The success, they said, was due to the Medicare Fraud Strike Force. "The Medicare Fraud Strike Force is a model of 21st century data-driven law enforcement, and it has had a remarkable impact on healthcare fraud across the country," former Assistant Attorney General Leslie Caldwell said at the time. Behind the scenes, officials praised nearly real-time data analytics as a major asset in building their case.

We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided. To begin, we establish the condition under which the fundamental assumption in synthetic control-like approaches holds, i.e. when the linear relationship between the treatment unit and the donor pool prevails in both the pre- and post-intervention periods. We provide the first finite sample analysis for a broader class of models, the Latent Variable Model, in contrast to Factor Models previously considered in the literature. Further, we show that our de-noising procedure accurately imputes missing entries, producing a consistent estimator of the underlying signal matrix provided $p = \Omega( T^{-1 + \zeta})$ for some $\zeta > 0$; here, $p$ is the fraction of observed data and $T$ is the time interval of interest. Under the same setting, we prove that the mean-squared-error (MSE) in our prediction estimation scales as $O(\sigma^2/p + 1/\sqrt{T})$, where $\sigma^2$ is the noise variance. Using a data aggregation method, we show that the MSE can be made as small as $O(T^{-1/2+\gamma})$ for any $\gamma \in (0, 1/2)$, leading to a consistent estimator. We also introduce a Bayesian framework to quantify the model uncertainty through posterior probabilities. Our experiments, using both real-world and synthetic datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method.

The last time I sat down to interview Sir Michael Barber, the world looked and felt a very different place. Even a few years ago, Donald Trump's tweets were making international headlines, Brexit had its supporters and detractors, and North Korea was an unpredictable rogue state harbouring nuclear ambitions. And just like today, governments the world over were striving to gain control over a fast-changing environment pock-marked by new technologies, diverse policy approaches and different ways to improve public impact. But of course, some changes are hard to miss. Artificial intelligence (AI), for example, is fast moving from concept to reality.

At first glance, it looks like R2D2 haas finally been blasted in space. But in fact, the MX-1E from Moon Express could revolutionise space exploration - and pave the way to mine the moon. The privately owned, Cape Canaveral based Moon Express hopes to take America back to the moon 45 years after NASA's last lunar landing, and this week the rocket that could take it there began its final testing. The initial spacecraft, known as MX-1E,is a similar size and shape to the R2D2 droid from Star Wars, and is slated to fly before next year aboard a Rocket Lab Electron booster, which launches from New Zealand. Ultimately the company plans to establish a lunar outpost in 2020 and set up commercial operations on the Moon, mining material and returning it to Earth to sell.

A U.N. panel agreed Friday to move ahead with talks to define and possibly set limits on weapons that can kill without human involvement, as human rights groups said governments are moving too slowly to keep up with advances in artificial intelligence that could put computers in control one day. Advocacy groups warned about the threats posed by such'killer robots' and aired a chilling video illustrating their possible uses on the sidelines of the first formal U.N. meeting of government experts on Lethal Autonomous Weapons Systems this week. More than 80 countries took part. The meeting falls under the U.N.'s Convention on Certain Conventional Weapons - also known as the Inhumane Weapons Convention - a 37-year old agreement that has set limits on the use of arms and explosives like mines, blinding laser weapons and booby traps over the years. The meeting falls under the U.N.'s Convention on Certain Conventional Weapons - also known as the Inhumane Weapons Convention - a 37-year old agreement that has set limits on the use of arms and explosives like mines, blinding laser weapons and booby traps over the years.

The Libyan National Army has been battling ISIS in the cities of Sirte and Benghazi. The U.S. military has launched airstrikes this month in Yemen, Somalia, Iraq, Syria, Afghanistan and Friday, for the first time since September, in Libya. According to a defense official, the drone strike in the desert of central Libya Friday killed "several" ISIS militants in a sign the Pentagon may be ramping up pressure on terror groups in Africa. The most recent strike comes a year after the military launched nearly 500 airstrikes against ISIS in the coastal city of Sirte, located halfway between Tripoli and Benghazi. The September strike killed 17 ISIS fighters.

MIT's Atlas robot, nicknamed Helios, completes the driving task at the June 2015 DARPA Robotics Challenge Finals. Helios is a second-generation Atlas, developed for DARPA by Boston Dynamics. MIT's Atlas robot, nicknamed Helios, completes the driving task at the June 2015 DARPA Robotics Challenge Finals. Helios is a second-generation Atlas, developed for DARPA by Boston Dynamics. Thursday, Nov. 16, 2017 was a day filled with news. So much news, in fact, that you might have missed the announcement of a back-flipping robot.

A U.N. panel agreed Friday to move ahead with talks to define and possibly set limits on weapons that can kill without human involvement, as human rights groups said governments are moving too slowly to keep up with advances in artificial intelligence that could put computers in control one day.

What will happen to a person's artificial intelligence (AI) when they retire? When a prospective employee interviews for a job, will his or her AI be questioned alongside them? Will companies hire AI straight from a factory, or will the system undergo a sort of apprenticeship before being put to work? More importantly--and more realistic in the near-term--what will be the line at which machines are not reliable enough or morally appropriate to use and humans take over? These, along with many more immediate questions, are among the topics USGIF's Machine Learning & Artificial Intelligence Working Group seeks to generate discussion around.