DUBAI, UNITED ARAB EMIRATES – Yemen's Houthi movement launched drone attacks on oil facilities in a remote area of Saudi Arabia, the group's Al Masirah TV said Saturday, but there was no immediate confirmation from Saudi authorities or state oil giant Aramco. A Saudi-led coalition is battling the Iran-aligned Houthis to try to restore Yemen's government, which was ousted from power in the capital, Sanaa, by the group in late 2014. The war has been in military stalemate for years. The Houthis have stepped up cross-border missile and drone attacks on Saudi Arabia in recent months. "Ten drones targeted Aramco's Shaybah oilfield and refinery in the first Operation: Balance of Deterrence in the east of the kingdom," the Al Masirah channel reported, citing a Houthi military spokesman.

Despite perceived employee concerns, US business leaders are moving ahead with adoption of artificial intelligence (AI) in the enterprise, according to a Thursday report from Genesys. By 2022, 60% of US company leaders said they expect to be using AI or advanced automation to improve operations, staffing, budgeting, or performance--an increase from the 24% who said they are already doing so. Of the 303 US employers surveyed, 57% said they were enthusiastic about new workplace tech tools including AI and bots. Some 32% said they believe AI enables companies to achieve goals faster, more effectively, and for less money. Another 25% said they believe AI allows employees to become more productive, and feel more valuable, the survey found.

And sometimes provides better analytics to make wise decisions. It's no more fictional, we now living in a world where machines are intelligent and are easing our lives. Actually, it works with a large amount of data. It processes the data with the help of intelligent algorithms and software to learn automatically from patterns or feature. As much data it will have, that much better insights or decisions it can make.

Optimus Ride has already deployed its autonomous transportation systems in the Seaport area of Boston, in a mixed-use development in South Weymouth, Massachusetts, and in the Brooklyn Navy Yard, a 300-acre industrial park. Some of the biggest companies in the world are spending billions in the race to develop self-driving vehicles that can go anywhere. Meanwhile, Optimus Ride, a startup out of MIT, is already helping people get around by taking a different approach. The company's autonomous vehicles only drive in areas it comprehensively maps, or geofences. Self-driving vehicles can safely move through these areas at about 25 miles per hour with today's technology.

Tech giant Google is facing a demand from hundreds of employees for an assurance that it will not bid on a government cloud computing contract that could be used to enforce US immigration policies on the southern border. A group of employees called Googlers for Human Rights posted a public petition overnight Thursday urging the company to resist tendering for a US Customs and Border Protection or Immigration and Customs Enforcement contract. It is not clear if Google or its parent Alphabet has already applied – the application deadline was 1 August – but the tech giant has previously drawn employee protests after signing cloud-computing or data storage deals with the government. The company confirmed in March 2018 that it was involved with Project Maven, a $250m Department of Defense artificial intelligence initiative designed to provide 3D mapping that could be used for improved drone-strike battlefield accuracy. Over 3,000 Google employees signed a petition in protest against the company's involvement.

Privacy campaigners have warned of an "epidemic" of facial recognition use in shopping centres, museums, conference centres and other private spaces around the UK. An investigation by Big Brother Watch (BBW), which tracks the use of surveillance, has found that private companies are spearheading a rollout of the controversial technology. The group published its findings a day after the information commissioner, Elizabeth Denham, announced she was opening an investigation into the use of facial recognition in a major new shopping development in central London. Sadiq Khan, the mayor of London, has already raised questions about the legality of the use of facial recognition at the 27-hectare (67-acre) Granary Square site in King's Cross after its owners admitted using the technology "in the interests of public safety". BBW said it had uncovered that sites across the country were using facial recognition, often without warning visitors.

The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves. The Cannon-Delivered Area Effects Munition (C-DAEM) system will use GPS to identify enemy tanks and armoured shells, which will be scanned in advance from the skies. According to sources, the Pentagon will invest vast sums into the AI-guided munitions, which could be ready by 2021. They will replace the Dual-Purpose Improved Conventional Munition (DPICM) artillery rounds, which were introduced in the 1980s. Cannon-Delivered Area Effects Munition system: The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves C-DAEM is a 155-millimeter artillery shell, and will be available for the M777 towed howitzer, the M109A6 Paladin self-propelled howitzer, and the new XM1299 self-propelled howitzer, which has a range of up to 43 miles.

Barking off orders to Alexa and Siri without so much as a please or thank you likely isn't going to become a habit you carry over into the rest of your life. This is because adults have already formed their behaviours for interacting with others -- and, in their current form, we don't see smart assistants as people. Researchers came to this conclusion after talking with over 200 people and seeing how they interacted with digital assistants like Alexa, Google Assistant and Siri. However, children may be more susceptible to forming impolite habits from talking to smart assistants -- partly because they are more likely to personify them. Adults may begin to be more influenced by their interactions with smart machines as their designs more more human-like or relatable, however, the researchers added.

ABSTRACT The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, computationally efficient manybody potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton Chen embedded atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed. INTRODUCTION In recent years there have been great advances in the use of machine learning to develop interatomic potential models. Potential models developed in this way are often able to achieve accuracy close to that of the method used to generate the training data, with linear scalability and orders of magnitude increase in performance. Alternatively, potential models may be generated by using fundamental physical relationships to derive a simple parameterized function.

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.