Saudi officials display what they claim are Iranian cruise missiles and drones used in the attack on Saudi Arabia's oil industry; Benjamin Hall reports from Jerusalem. The attacks on Saudi Arabia's oil fields will drive a massive increase in the need for perimeter security gear, according to a new report. The report, released by IHS Markit earlier this week, says that knowing where drones are at all times is a new reality. While benign drones must be tracked, it is the malicious ones that must be stopped. "Drone attacks are relatively cheap and easy to initiate but can inflict major damage," IHS Markit analyst Oliver Philippou wrote in the note.

The White House weighs its options as Iran warns that a military response could trigger an'all-out war'; chief White House correspondent John Roberts reports. Saudi Arabia defended itself as well as possible from the recent massive attack on its oil facilities -- an attack that the U.S. has blamed on Iran, a military expert said. "I don't think there is any country that could have defended any better than Saudi Arabia did, and that includes the United States," Peter Roberts, director of military sciences at the Royal United Services Institute, told The New York Times. "I don't think there is any country that could have defended any better than Saudi Arabia did, and that includes the United States." Eighteen drones and seven cruise missiles bombarded the facilities in an asault described as a "Pearl Harbor-type" attack.

AI in Oil & Gas market is expected to grow from an estimated $1.57 billion in 2017 to $2.85 billion by 2022, at a CAGR of 12.66%, from 2017 to 2022. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities. The upstream AI in Oil and Gas Market is set to grow in the next five years.

ARTIFICIAL INTELLIGENCE is making its way into every aspect of life, including military conflict. We look at the thorny legal and ethical issues that the newest arms race raises. Three executives from Fukushima's melted-down nuclear-power plant were cleared of negligence today, but the disaster's aftermath is far from over. And, what a swish new Chinese restaurant in Havana says about China-Cuba relations.

Democratic presidential hopeful Rep. Tulsi Gabbard, D-Hawaii, said Thursday that she would re-enter the Iran nuclear deal and end sanctions in response to Iran's involvement in drone attack against Saudi Arabia oil facilities if she was president. "What I would do is, I would re-enter the Iran nuclear deal to prevent Iran from continuing to move forward in building a nuclear weapon that puts us and the world further at risk," Gabbard said on "The Story with Martha MacCallum." Every day that we don't do this, every day we continue down this failed strategy Iran gets closer and closer to a nuclear weapon. U.S. officials told Fox News on Tuesday that Iranian cruise missiles and drones were both used in the attack on the two Saudi Arabian oil facilities, and that they were fired from inside southwest Iran this past weekend. Gabbard called the attack a "retaliation" against "extreme sanctions."

One of the fundamental assumptions in stochastic control of continuous time processes is that the dynamics of the underlying (diffusion) process is known. This is, however, usually obviously not fulfilled in practice. On the other hand, over the last decades, a rich theory for nonparametric estimation of the drift (and volatility) for continuous time processes has been developed. The aim of this paper is bringing together techniques from stochastic control with methods from statistics for stochastic processes to find a way to both learn the dynamics of the underlying process and control in a reasonable way at the same time. More precisely, we study a long-term average impulse control problem, a stochastic version of the classical Faustmann timber harvesting problem. One of the problems that immediately arises is an exploration vs. exploitation-behavior as is well known for problems in machine learning. We propose a way to deal with this issue by combining exploration- and exploitation periods in a suitable way. Our main finding is that this construction can be based on the rates of convergence of estimators for the invariant density. Using this, we obtain that the average cumulated regret is of uniform order $O({T^{-1/3}})$.

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP problem without making any compromise on the safety constraints. We achieve this by decomposing the CMDP into a pair of MDPs; reconnaissance MDP and planning MDP. The purpose of reconnaissance MDP is to evaluate the set of actions that are safe, and the purpose of planning MDP is to maximize the return while using the actions authorized by reconnaissance MDP. RMDP can define a set of safe policies for any given set of safety constraint, and this set of safe policies can be used to solve another CMDP problem with different reward. Our method is not only computationally less demanding than the previous simulator-based approaches to CMDP, but also capable of finding a competitive reward-seeking policy in a high dimensional environment, including those involving multiple moving obstacles.

In May of last year, after a 13-month slumber, the ground beneath Washington's Puget Sound rumbled to life. The quake began more than 20 miles below the Olympic mountains and, over the course of a few weeks, drifted northwest, reaching Canada's Vancouver Island. It then briefly reversed course, migrating back across the U.S. border before going silent again. All told, the monthlong earthquake likely released enough energy to register as a magnitude 6. By the time it was done, the southern tip of Vancouver Island had been thrust a centimeter or so closer to the Pacific Ocean.

For over 300 years, the Alamo City has been a dynamic platform for technological innovation and global commerce. Tech Port SA, offered by Port San Antonio, rounds-up the latest stories chronicling the region's movers and shakers in advanced industries.

Despite the age of big data, 71% of organisations still rely on a single data source to analyse asset performance and risk management – instead of drawing information from multiple sources for a more comprehensive view, reveals a report released by Lloyd's Register at Gastech today. The report, 'Oil & Gas: Achieving operational excellence in uncertain times' reveals the technologies US oil and gas companies currently use to manage and maintain their assets, and the methods they plan to adopt in the future. Tim Bisley, SVP, Software at Lloyd's Register commented: "Although organisations often collect vast amounts of data, they remain challenged as to how to use it. Predictive maintenance is reaching new levels thanks to AI, 3D digital twins and machine learning, which derive actionable insights from huge volumes of data. The report, however, indicates the industry has been slow to adopt this type of technology, in spite of the efficiencies it brings."