The future is here, and it's weird: Google is now putting a self-taught algorithm in charge of a part of its infrastructure. It should surprise no one that Google has been intensively working on artificial intelligence (AI). The company managed to develop an AI that beat the world champion at Go, an incredibly complex game, but that's hardly been the only implementation. Google taught one of its AIs how to navigate the London subway, and more practically, it developed another algorithm to learn all about room cooling. They had the AI learn how to adjust a cooling system in order to reduce power consumption, and based on recommendations made by the AI, they almost halved energy consumption at one of their data centers.

Google revealed today that it has given control of cooling several of its leviathan data centers to an AI algorithm. Over the past couple of years, Google has been testing an algorithm that learns how best to adjust cooling systems--fans, ventilation, and other equipment--in order to lower power consumption. This system previously made recommendations to data center managers, who would decide whether or not to implement them, leading to energy savings of around 40 percent in those cooling systems. Now, Google says, it has effectively handed control to the algorithm, which is managing cooling at several of its data centers all by itself. "It's the first time that an autonomous industrial control system will be deployed at this scale, to the best of our knowledge," says Mustafa Suleyman, head of applied AI at DeepMind, the London-based artificial-intelligence company Google acquired in 2014. The project demonstrates the potential for artificial intelligence to manage infrastructure--and shows how advanced AI systems can work in collaboration with humans.

Toshiba Energy Systems & Solutions Corporation (Toshiba ESS) is conducting research that employs IoT (Internet-of-Things) and AI (Artificial Intelligence) technology to improve capacity factors of geothermal power plants. The research program, which began this month and is scheduled to continue until FY 2020, aims to reduce the rate of problem occurrences at power plants by 20% while boosting capacity factors by 10%. This research program has won positive evaluation and a grant from by the New Energy and Industrial Technology Development Organization (NEDO).

Watch out humans, robots could soon compete for your acting roles. Director Tony Kaye is looking to cast an artificial intelligence robot for his upcoming film '2nd Born,' the sequel to the yet-to-be-released '1st Born.' Kaye, who is behind the 1998 crime drama'American History X,' would employ the'real robot' as the lead in 2nd Born, according to Deadline. Artificial Intelligence,' director Tony Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques Artificial Intelligence,' Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques. What's more, Kaye and producer Sam Khoze hope that the robot will be recognized by the Screen Actors Guild. This would make the robot eligible for awards consideration.

The team achieved a peak rate between 11.73 and 15.07 petaflops (single-precision) when running its data set on the Cori supercomputer. Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that.

Traveling for business can be a pain. Between finding an affordable flight, booking a hotel, and figuring out transportation, traveling often turns out to be more of a headache than expected. This is especially true for business trips, which are typically short and often given on short notice. However, artificial intelligence (AI) has the potential to make business travel much easier. As of right now, businesses are already feeling the incredible impact of AI in daily operations.

Hydrocarbon exploration is an expensive affair; hence it has to be initiated only after costs and benefits are assessed. There are various methods to identify the sources of oil and gas like Well logging, remote sensing, Gravity survey, magnetic survey, seismic survey etc. which involves high costs and efforts. Exploring oil and gas under land or within the seabed using surface methods is based on two main principles. One is to survey geological features of the land to determine sedimentary rock formation, repeated folds, and faults. The other is to identify the hydrocarbon seepage on the earth surface.

Though many industries have fully embraced technology, the oil and gas industry has come later to the party. However, the industry has not been left out when it comes to innovation. Thanks to rising oil prices and better profit margins, oil and gas companies have the capital to invest in the future. Digital technologies are currently upending the way things are being done in this industry. While companies are slow to adopt them all, soon every part of the industry will embrace these digital technologies.

Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that situation. They developed the first 15-petaflop deep-learning software.

In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools. In a real manufacturing process, breakage of machine tools usually happens without any indication, this problem seriously affects the fabrication process for many years. Previous studies suggested many different approaches for monitoring and detecting the breakage of machine tools. However, there still exists a big gap between academic experiments and the complex real fabrication processes such as the high demands of real-time detections, the difficulty in data acquisition and transmission. In this work, we use the spindle current approach to detect the breakage of machine tools, which has the high performance of real-time monitoring, low cost, and easy to install. We analyze the features of the current of a milling machine spindle through tools wearing processes, and then we predict the status of tool breakage by a convolutional neural network(CNN). In addition, we use a BP neural network to understand the reliability of the CNN. The results show that our CNN approach can detect tool breakage with an accuracy of 93%, while the best performance of BP is 80%.