Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

ETFdb: The Magha CGI 30 index consists of large-cap U.S. equities with an average market cap of about 150 billion. Furthermore, the index is said to comprise socially responsible holdings. What determines if a company is socially responsible or not based on your methodology? In other words, what is the criteria for a company to be included in the index? All corporations give back in some way or another.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

In the aftermath and early demise of Tay, the Microsoft bot gone rogue, and the anti-climax following Facebook's bot announcements at F8, it's safe to declare that it's high time for chatbots to mature from infancy into puberty, and not a moment too soon. As the Bot Rush overwhelms Silicon Valley and bot miners are flocking into San Francisco, the future of AI and Bot productivity lies in the hands of engineers that are willing to take the hard road. We're hoping that creative and well thought out research will win, rather than leaning on pundits' advice suggesting that: "you can take a bot template, tweak it, and launch it- over a weekend." I dare to guess that's exactly what the Microsoft folks had in mind (perhaps more than a weekend in their case) when they let Tay roam Twitter unsupervised. For Bots to become'Must Haves', they'll have to dance to the tune of a few fundamental human traits and qualities, currently ignored in the hype and rush to easy fortunes.

China seems to have beat us to the punch when it comes to enlisting robots and AI to help fight crime. It's not quite on the level of Robo Cop, but the autonomous machine is still slightly deadly. The Chinese National University of Defense has created a low-cost slightly intelligent robot that can patrol streets all on it's own. It has a suite of sensors that can detect dangers so that it can even make actual arrests. In case of even greater threats, they've added a taser device to provide some electrical assistance to get people to comply.

New ships, new destinations, unusual activities and some extraordinary bargains are part of the cruising buzz for summer and fall. New thrill attractions: Thrills have become a cruise ship staple, and on the world's newest, largest ship, Royal Caribbean's 5,500-passenger Harmony of the Seas, the rush comes courtesy of the Ultimate Abyss. Beginning later this month, daring riders can zoom on mats from the top of the ship to the Boardwalk neighborhood about 100 feet below. Posh gets posher: Regent Seven Seas Cruises is billing its 750-passenger Seven Seas Explorer, launching in July, as the "world's most luxurious cruise ship." The top digs are a 3,785-square-foot suite, with a first-at-sea private spa (serviced by the ship's Canyon Ranch SpaClub) and a price tag of 5,000 per person per night, based on double occupancy.

International Business Machines Corp. (NYSE:IBM) to launch its Watson AI in Korea through a partnership with a Korean IT service provider called SK Holdings C&C. The two firms will work together to make Watson available to the locals in Korean before June 2017. The partnership between the two firms also means they have to set up the right infrastructure for the AI together with introducing promotional projects to market the service. The presence of Watson in the country will also allow for the development of solutions for data analysis solutions in Korea through natural language semantic study and machine learning technology. SK revealed that the technology will be made available next year.

LOS ALAMOS, N.M., May 9, 2016--Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

This post was contributed by Rowan Benecke, Chair, Global Technology Practice. Let's think about what we as PR professionals do in a typical day. We read, analyze and answer email. We answer calls and make calls. We write press releases or pitches and we reach out to media.