Bomb-laden drones of Yemen's Houthi rebels seen threatening Arabian Peninsula

The Japan Times

DUBAI, UNITED ARAB EMIRATES - A Yemen rebel drone strike this week on a critical Saudi oil pipeline shows that the otherwise-peaceful sandy reaches of the Arabian Peninsula now are at risk of similar assault, including an under-construction nuclear power plant and Dubai International Airport, among the world's busiest. U.N. investigators said the Houthis' new UAV-X drone, found in recent months during the Saudi-led coalition's war in Yemen, likely has a range of up to 1,500 km (930 miles). That puts the far reaches of both Saudi Arabia and the United Arab Emirates, the two main opponents of the Iranian-allied Houthi rebels in Yemen, within reach of drones difficult to detect and track. Their relatively simple design, coupled with readily available information online, makes targeting even easier, analysts say. "These installations are easily findable, like on Google Earth," said Tim Michetti, an expert on illicit weapons technology with experience in Yemen.

New Training Model Helps Autonomous Cars See AI's Blind Spots


A new training model developed by MIT and Microsoft can help identify and correct an autonomous car's AI when it makes potentially deadly mistakes. Since their introduction several years ago, autonomous vehicles have slowly been making their way onto the road in greater and greater numbers, but the public remains wary of them despite the undeniable safety advantages they offer the public. Autonomous vehicle companies are fully aware of the public's skepticism. Every crash makes it more difficult to gain public trust and the fear is that if companies do not manage the autonomous vehicle roll-out properly, the backlash might close the door on self-driving car technology the way the Three Mile Island accident shut down the growth of nuclear power plants in the United States in the 1970's. Making autonomous vehicles safer than they already are means identifying those cases that programmers might never have thought of and that the AI will fail to respond to appropriately, but that a human driver will understand intuitively as a potentially dangerous situation.

Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints Machine Learning

V oltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence of uncertainties. We adopt a chance constraint approach that accounts for arbitrary correlations between renewable resources at each of the buses. We show how the problem can be solved efficiently using historical samples analogously to the stochastic quasi-gradient methods. We also show that this optimization problem is convex for a wide variety of probabilistic distributions. Compared to conventional per-bus chance constraints, our formulation is more robust to uncertainty and more computationally tractable. We illustrate the results using standard IEEE distribution test feeders. V oltage control is crucial to stable operations of power distribution systems, where it is used to maintain acceptable voltages at all buses under different operating conditions [1]. To control voltage, reactive power is traditionally regulated through tap-changing transformers and switched capacitors [2]. With recent advances in cyber-infrastructure for communication and control, it is also possible to utilize distributed energy resources (DERs, i.e., electric vehicles [3], PV panels [4], [5]) to provide voltage regulation.

Artificial intelligence is too powerful to be left to Facebook, Amazon and other tech giants


Facebook CEO Mark Zuckerberg's testimony before Congress made one thing clear: the government needs an Federal Artificial Intelligence Agency. Facebook FB, -0.26% is a canary in the proverbial AI coal mine. AI is going to play an enormous role in our lives and in the global economy. It is the key to self-driving cars, the Amazon AMZN, -0.63% Alexa in your home, autonomous trading desks on Wall Street, innovation in medicine, and cyberwar defenses. Technology is rarely good nor evil -- it's all in how humans use it.

New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems Machine Learning

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.