NASA's Curiosity rover has begun drilling into the surface of Mars again after a nearly two-year break due to a mechanical issue. Engineers recently revealed they'd developed a new technique to restore the robotic explorer's drilling ability, using its robotic arm to push the bit forward as it spins, much like a human might operate a drill. In an important step forward, Curiosity has now tested out the technique, using it to drill a 2-inch-deep hole in a target called'Duluth.' NASA's Curiosity rover has begun drilling into the surface of Mars again after a nearly two-year break due to a mechanical issue The .6 inch-wide hole drilled by Curiosity on May 20th marked the first rock sample it's captured since October 2016; its drill was taken offline in December of that year. The new drilling technique is known as Feed Extended Drilling (FED), and keeps the drill's bit extended beyond the stabilizer posts that were previously used to steady the drill against the rocks, NASA explains. Engineers tested the technique over the last few months.
Scientists at the Columbia University have discovered a totally new way to study earthquakes. They picked out different types of earthquakes from three years using machine learning algorithms. According to them, these machine learning methods pick out very subtle differences in the raw data that we're just learning to interpret. Scientists particularly identified earthquake recordings at The Geysers in California, one of the world's oldest and largest geothermal fields. They assembled a catalog of 46,000 earthquake recordings, each represented as energy waves in a seismogram.
In a new study in Science Advances, researchers at Columbia University show that machine learning algorithms could pick out different types of earthquakes from three years of earthquake recordings at The Geysers in California, one of the world's oldest and largest geothermal reservoirs. The repeating patterns of earthquakes appear to match the seasonal rise and fall of water-injection flows into the hot rocks below, suggesting a link to the mechanical processes that cause rocks to slip or crack, triggering an earthquake. "It's a totally new way of studying earthquakes," said study coauthor Benjamin Holtzman, a geophysicist at Columbia's Lamont-Doherty Earth Observatory. "These machine learning methods pick out very subtle differences in the raw data that we're just learning to interpret." The approach is novel in several ways.
NASA's Mars Curiosity rover is for the first time testing an improvised new percussive drilling technique intended to pound subsurface samples into powder in hopes of better understanding the shallow Martian subsurface. After a year's drilling hiatus, Curiosity is again back to drilling samples in rocks at the surface of Mars' Gale Crater. This self-portrait of NASA's Curiosity Mars rover shows the vehicle at the'Mojave' site, where its drill collected the mission's second taste of Mount Sharp. The scene combines dozens of images taken during January 2015 by the MAHLI camera at the... "If all goes well and we can continue drilling, the science team hopes to learn how the ancient climate at Gale crater, and the prospects for life there, changed over time," Ashwin Vasavada, the Curiosity Rover's project scientist, told me. Curiosity's drilling capability was knocked out of business in December 2016, when the motor that moves Curiosity's drill back and forth became unreliable, Vasavada told me.
Total and Google Cloud have signed an agreement to jointly develop artificial intelligence (AI) solutions applied to subsurface data analysis for oil and gas exploration and production. The agreement focuses on the development of AI programs that will make it possible to interpret subsurface images, notably from seismic studies (using Computer Vision technology) and automate the analysis of technical documents (using Natural Language Processing technology). These programs will allow Total's geologists, geophysicists, reservoir and geo-information engineers to explore and assess oil and gas fields faster and more effectively. Under this partnership, Total geoscientists will work side-by-side with Google Cloud's machine learning experts within the same project team based in Google Cloud's Advanced Solutions Lab in California. Total started applying artificial intelligence to characterize oil and gas fields using machine learning algorithms in the 1990s.
If you want to understand what's happening with artificial intelligence (AI) and cybersecurity, look no further than this week's news. On Monday, Palo Alto Networks introduced Magnifier, a behavioral analytics solution that uses structured and unstructured machine learning to model network behavior and improve threat detection. Additionally, Google's parent company, Alphabet, announced Chronicle, a cybersecurity intelligence platform that throws massive amounts of storage, processing power, and advanced analytics at cybersecurity data to accelerate the search and discovery of needles in a rapidly growing haystack. So, cybersecurity suppliers are innovating to bring AI-based cybersecurity products to market in a big way. OK, but is there demand for these types of advanced analytics products and services?
There are two great times to make money in stock markets: the postcrash rebound and the end-of-cycle excess. Oil and technology fit the pattern perfectly in the past two years. Since the oil-price low of January 2016, the global oil and tech sectors have both made more than 80%, including dividends, beating the wider market's 53% return hands down. The oil sector was merely rebounding as oil prices tripled from their lows, but tech stocks were being led to heady heights by giddy enthusiasm for a bright future. More extreme proxies for the commodity and tech cycles have done even better.
Artificial intelligence promises to change the way we work and sell both online and offline. But so far, most sales organizations fall short when it comes to the use of AI. When organizations experiment with various sales enablement technologies, they typically don't see a strong return on their investment because, thus far, the tools lack the kind of predictive modeling that actually moves the needle on win rates, deal size, time to close, and revenue. Most companies simply haven't invested in the right data modeling to use AI engines to link sales actions to better outcomes. Across the business-to-business sector of e-commerce, it is much easier today than a few years ago for buyers to shop online for products and complete purchases with little or no involvement from sellers until much later in the process.
Scientists have taken the tiny house trend to a whole new level. Using a new nanorobotic system, French scientists built a'microhouse' on top of an optical fiber that's as thin as human hair, which is 75 microns thick. It measures just 20 micrometers across but has several stunningly accurate details, including a front door, windows and even a tiled roof. A team of French scientists from the Femto-ST Institute built a 20-micrometer wide'microhouse' (pictured) on top of an optical fiber to demonstrate a new nanorobotic system A team of French scientists from the Femto-ST Institute detailed the process of creating the microhouse in new study published Friday in the Journal of Vacuum Science & Technology A. The new nanorobotic system, called μRobotex, uses a combination of technologies, including a tiny maneuverable robot, a focused ion beam and a gas injection device. To construct the microhouse, the scientists used a mix of origami and nanometer-precise robotics.
Assessing damage caused to their rides has just gotten a lot easier for car owners in China, with the rollout of a video-based, artificial-intelligence app from Ant Financial. The Alibaba affiliate last week launched version 2.0 of its Dingsunbao (Loss Assessment Master) app, giving drivers the same power in their hands to provide detailed car-damage information to insurers and claim vehicle insurance in real time as Ant Financial gave professional insurance adjusters just under a year ago. The first version of Dingsunbao has already helped insurers, including China Taiping, China Continent Insurance, Sunshine Insurance Group and AXA Tianping process claims tens of millions of times at a rate of speed much faster than human adjusters alone could handle. "Dingsunbao has already helped the insurance industry to save over RMB 1 billion on claims handling, while saving claims adjusters around 750,000 hours of effort," said Yin Ming, president of Ant Financial's Insurance Business Unit. The AI also ensures a high degree of accuracy in damage assessment, Ant Financial said when it launched Dingsunbao last June.