As the plumes of smoke settle over two of Saudi Arabia's critical oil production facilities – which came under crippling drone strikes over the weekend – both the U.S. and Saudi Arabia are deliberating options for retaliation, raising the possibility of much broader instability across the region, although President Trump was quick to point out Monday, "I don't want war with anybody." Intelligence officials from both countries have been quick to point fingers at Iran as the orchestrators of the attack, which analysts have deemed as one of the most disruptive in history. "This is perhaps one of the greatest examples of kinetic economic warfare we have seen in recent times. Iran is suffering from our sanctions but does not want to escalate into an active war with us," Andrew Lewis, a former Defense Department staffer and the president of a private intelligence firm, the Ulysses Group, told Fox News. "They can do a lot to manipulate the world economy, which will have a negative impact on the U.S. and our allies in Europe."

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.

The combined algorithm selection and hyperparameter tuning (CASH) problem is characterized by large hierarchical hyperparameter spaces. Model-free hyperparameter tuning methods can explore such large spaces efficiently since they are highly parallelizable across multiple machines. When no prior knowledge or meta-data exists to boost their performance, these methods commonly sample random configurations following a uniform distribution. In this work, we propose a novel sampling distribution as an alternative to uniform sampling and prove theoretically that it has a better chance of finding the best configuration in a worst-case setting. In order to compare competing methods rigorously in an experimental setting, one must perform statistical hypothesis testing. We show that there is little-to-no agreement in the automated machine learning literature regarding which methods should be used. We contrast this disparity with the methods recommended by the broader statistics literature, and identify the most suitable approach. We then select three popular model-free solutions to CASH and evaluate their performance, with uniform sampling as well as the proposed sampling scheme, across 67 datasets from the OpenML platform. We investigate the trade-off between exploration and exploitation across the three algorithms, and verify empirically that the proposed sampling distribution improves performance in all cases.

Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires many parameters. The paper presents two approaches with one using a Recurrent Neural Network (RNN) and another one using a Long Short Term Memory (LSTM) network, which only considers the previous electricity consumption to predict the future electricity consumption. These models were tested on the publicly available London smart meter dataset. To assess the applicability of the RNN and the LSTM network to predict electricity consumption, they were tested to predict for an individual house and a block of houses for a given time period. The predictions were done for daily, trimester and 13 months, which covers short term, mid-term and long term prediction. Both the RNN and the LSTM network have achieved an average Root Mean Square error of 0.1.

Now we were able start a training job from the command line with the option to modify almost everything on the fly. We could adjust things like number of epochs, batch size, shuffling, checkpoint saving and even switch between model architectures easily, by adding a flag after the command. This allowed us to iterate fast, test different theories, and burn through a lot of (free) credits. We' built our model with tf.Keras using the functional API. We feed the array and scalar features into the model at separate entry points, so we can do different things to them before bringing them back together.

President Trump says the U.S. is'locked and loaded' against the attackers of a key Saudi oil facility'depending on verification'; Mark Meredith reports. Cruise missiles and drones used in the weekend assault on Saudi Arabia's oil installations were launched from Iranian soil, U.S. officials told Fox News on Monday. The early morning strikes Saturday that hit Aramco's main crude processing facility knocked out 5.7 million barrels of daily oil production for Saudi Arabia -- or more than 5 percent of the world's daily crude production. Defense Secretary Mark Esper attended an emergency National Security Council meeting on Sunday at the White House along with Vice President Pence, where military options were discussed, officials told Fox News. Earlier Monday in Austria, Energy Secretary Rick Perry placed the blame for the attack squarely on Iran.

In more industrial situations, an AI system that can recognize different materials and grasp things more effectively without having to repeatedly try to pick up an object could bring new capabilities to a wide range of different processes and sectors. Handling extremely hazardous materials such as nuclear waste, for example, could be made far safer if a human were not required to control a robotic arm and a system could use image inputs to learn how best to pick up a container or even raw radioactive waste with a significantly reduced chance of dropping and spilling toxic material. In construction, autonomous lifting arms or those attached to vehicles could calculate the weight of an object based on its material and 3D images of, say, a steel girder. When digging or drilling to lay foundations, prepare a site, or laying underwater pipelines, ultrasonic images could be fed into the system and paired with tactile probe data to determine exactly where to drill in real-time without damaging existing infrastructure or delicate ecosystems.

UCL has launched an innovative strategy detailing how Artificial Intelligence (AI) should be used as a transformative technology to make a positive impact on the planet. AI for People and Planet was launched by Professor Geraint Rees – UCL's Pro-Vice-Provost of AI – at the Science Museum in London on Tuesday 10th September. It encapsulates the belief that the purpose of research and innovation in the sector is ultimately to benefit people and societies around the world and to have a positive impact on the planet. The launch event included a panel discussion around issues relating to AI in healthcare, society, ethics, economics and the current state of AI. As well as UCL experts, the panel also included Professor Thore Graepel, Research Group Lead of Google DeepMind and Azeem Azhar, board member of the Ada Lovelace Foundation, venture partner at Kindred Capital and advisor to Fabric Ventures.

WIRE)--Baker Hughes, a GE company (NYSE:BHGE) and C3.ai today announced the launch of BHC3 Reliability, the first artificial intelligence (AI) software application developed by the BakerHughesC3.ai Unveiled at BHGE's annual digital conference, UNIFY2019, the now generally available application uses deep learning predictive models, natural language processing, and machine vision to continuously aggregate data from plant-wide sensor networks, enterprise systems, maintenance notes, and piping and instrumentation schematics. Using historical and real-time data from entire systems, the BHC3 Reliability machine learning models identify anomalous conditions that lead to equipment failure and process upsets. Application alerts enable proactive action by operators to reduce downtime and lost revenue. Applicable to operations across all sectors of the energy value chain, BHC3 Reliability's system-of-systems approach scales to any number of assets and processes across offshore and onshore platforms, compressor stations, refineries, and petrochemical plants, reducing downtime and increasing productivity.

The World Economic Forum (WEF) recently released a report detailing the ten "world-changing technologies that are poised to rattle the status quo." Let's see for ourselves what these technologies have to offer. Some developments in the bioplastics industry allow lignin, a component of wood, to be broken down into its simpler components using engineered solvents. With this possible, plastics can then be made from it. Lignin is found in wood waste and agricultural byproducts which otherwise doesn't have any other function.