DUBAI, UNITED ARAB EMIRATES – Yemen's Houthi forces shot down a U.S. surveillance drone in the capital, Sanaa, on Sunday, the Houthi-controlled state news agency SABA reported. The Houthi movement and its ally, former President Ali Abdullah Saleh, control much of northern Yemen, including Sanaa, and are battling a Saudi-led coalition that is trying to restore the internationally recognized government of President Abd-Rabbu Mansour Hadi. The United States backs the Saudi-led coalition by providing it with intelligence and weapons. "A military source said (Houthi) air defenses shot down a U.S. MQ-9 surveillance drone in Jader area in the Sanaa province," SABA reported. A photographer said the drone came down at around 11 am local time in a crowded area on the outskirts of the capital, but there were no reports of any casualties.

The Houthi movement and its ally former president Ali Abdullah Saleh control much of northern Yemen, including Sanaa, and are battling a Saudi-led coalition that is trying to restore the internationally recognized government of President Abd-Rabbu Mansour Hadi.

With the crisis in Puerto Rico unfolding--and expanding--daily in the wake of Hurricane Maria, the scale of the devastation is coming into horrifying focus. It's not just that the American territory has been, by many accounts, "destroyed." "We are dying here," San Juan Mayor Carmen Yulín Crus said Friday. Getting food, water, and medicine to and throughout Puerto Rico is a "logistical nightmare," former FEMA boss Michael Brown told CNBC. Which brings up the question: Where are the drones that could pick up the slack?

If tech experts are to be believed, artificial intelligence (AI) has the potential to transform the world. Artificial intelligence is software built to learn or problem solve -- processes typically performed in the human brain. Neither Musk nor Hawking believe that developers should avoid the development of AI, but they agree that government regulation should ensure the tech does not go rogue. However, Shostak doesn't believe sophisticated AI will end up enslaving the human race -- instead, he predicts, humans will simply become immaterial to these hyper-intelligent machines.

Thank you, Mark [Carney], for that kind introduction, and thank you to the Bank of England for inviting me to this wonderful event. This is a moment to celebrate 20 years of independence during which the Bank of England has been a stabilizing force for the U.K. economy, inspiring others in the world of central banking--not least because of your guidance, Mark. This is also a moment to learn from our experiences, build on the progress made so far, and look into the future--to the next 20 years--as our journey continues. This morning, I came up Fleet Street, which always feels like a journey through history. In the Middle Ages, that street was an important center of commerce, much of which has now moved online. By the 19th century, the street was home to ticker machines and reporters racing each other to make the evening papers.

This paper makes a contribution to the advancement of artificial intelligence in the context of multi-agent planning for large-scale combat scenarios in RTS games. This paper introduces Fast Random Genetic Search (FRGS), a genetic algorithm which is characterized by a small active population, a crossover technique which produces only one child, dynamic mutation rates, elitism, and restrictions on revisiting solutions. This paper demonstrates the effectiveness of FRGS against a static AI and a dynamic AI using the Portfolio Greedy Search (PGS) algorithm. In the context of the popular Real-Time Strategy (RTS) game, StarCraft, this paper shows the advantages of FRGS in combat scenarios up to the maximum size of 200 vs. 200 units under a 40 ms time constraint.

Today at the Frankfurt motor show, one of the biggest and most prestigious motor shows in the world, Sheryl Sandberg, COO of Facebook, spoke before German Chancellor Angela Merkel. Now what is Facebook and most importantly, Sheryl Sandberg doing at an automotive industry event? The obvious answer that comes to mind when one relates Facebook and the car industry is the billions of advertising dollars the industry spends on marketing and advertising. However, that does not seem to be Facebook's game plan, as highlighted by Sheryl and shown at their pavilion. Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles.

Leaders of the Air Force, Navy, Army and Marines are converging on a vision of the future military: connecting every asset on the global battlefield. That means everything from F-35 jets overhead to the destroyers on the sea to the armor of the tanks crawling over the land to the multiplying devices in every troops' pockets. Every weapon, vehicle, and device connected, sharing data, constantly aware of the presence and state of every other node in a truly global network. The effect: an unimaginably large cephapoloidal nervous system armed with the world's most sophisticated weaponry. In recent months, the Joint Chiefs of Staff put together the newest version of their National Military Strategy.

The auto companies have been competing to get their automated vehicles (AVs) ready on road for years, yet there is still none available in the market. Partly, this is due to the challenging task of robustly testing and guaranteeing the safety of an AV before its release. Companies have been trying different methods such as road test [1], [2], computer simulation test [3] and human-vehicle interaction test [4], [5], yet providing safety certificate for an AV system is still open for solving [1]. Assisting the endeavors of solving this problem, the U.S Department of Transportation has released a new AV policy: A Vision for Safety 2.0 [6]. This official document standardizes the required safety features of an autonomous vehicle, providing guidance and clearer pathways for the various stakeholders aiming to certify the safety of their AV systems. However, even with this newly published official guideline, the testing standard remains unclear while the AV target release is quickly approaching. Thus, an effective and efficient testing method for an autonomous vehicle is an urgent need under this background. Traditional vehicle safety tests are based on crash databases collected from crashes or dangerous scenarios, such as the CSD and GIDAS crash databases [7]. However, the information logged in these databases is limited so that it is difficult to reconstruct and analyze the dangerous scenarios.

Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.