March 12, 2017 --In traditional computers, the smallest units of information exists in one of two states: 1 and 0, or on and off. Long strings of 1s and 0s can store increasingly complex information that can be used use to perform useful tasks, but that information storage is limited by the size of those individual bits of information in a computer's hard drive. But now, researchers have figured out a way to magnetically store information on the smallest unit possible: a single atom. There's a long way to go before atom-sized information storage technology can make it to your home computer or smartphone, but now researchers have proven that it is possible to store information on an incredibly small level. Theoretically, this new technology could lead to massive data storage capacities on an impressive scale – even in the smallest of devices.

Juergen Schmidhuber taught a computer to park a car. He's also showing that same machine how to trade stocks and detect flaws in steel production. Unrelated as these tasks may appear, Schmidhuber thinks a seemingly random training regimen is key to creating artificial intelligence that can solve any problem. Schmidhuber's AI theories tend to carry weight. In 1997, he co-authored a seminal paper that laid the groundwork for modern AI systems.

Researchers from Argonne National Laboratory, using supercomputers at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC), are employing machine learning algorithms to accurately predict the physical, chemical and mechanical properties of nanomaterials, reducing the time it takes to yield such predictions from years to months--in some cases even weeks. This approach could help accelerate the discovery and development of new materials. Using a modeling framework built around a molecular dynamics code (LAMMPS), the research team ran a series of simulations to study the structure and temperature-dependent thermal conductivity of stanene, a 2D material made up of a one-atom-thick sheet of tin. This work, which involved a set of parameters known as the "many-body interatomic potential" or "force field," yielded the first atomic-level computer model that accurately predicts stanene's structural, elastic and thermal properties. The findings were published in The Journal of Physical Chemistry Letters.

Significant advances in artificial intelligence over the past few years have broadened AI's reach into industries such as healthcare, finance and even retail. Businesses and consumers alike are benefiting from the rise of big data and the growth of AI techniques like deep learning and natural language processing. But we're still only scratching the surface of what is possible with AI, and the full impact of the technology may be years away. In the near-future, however, AI advances will give rise to increasingly powerful applications like personal assistants with more robust utility in the workplace and in our personal lives. These assistants could provide personalized information, help us make more informed decisions, and perhaps even provide physical assistance.

Significant advances in artificial intelligence over the past few years have broadened AI's reach into industries such as healthcare, finance and even retail. Businesses and consumers alike are benefiting from the rise of big data and the growth of AI techniques like deep learning and natural language processing. But we're still only scratching the surface of what is possible with AI, and the full impact of the technology may be years away. In the near-future, however, AI advances will give rise to increasingly powerful applications like personal assistants with more robust utility in the workplace and in our personal lives. These assistants could provide personalized information, help us make more informed decisions, and perhaps even provide physical assistance.

While many firms are looking to the moon for mining opportunities, one Australian firm believes there could be precious metals a lot nearer to home. Deep-sea robots will be sent to mine mineral deposits in the deep ocean in 2019 in a test for a controversial new scheme. As land-based mineral stores are becoming depleted, the ocean floor is becoming a more attractive mining prospect, containing gold, copper and other precious metal deposits used to make electronics, renewable energy tools and even medical imaging machines. But deep-sea excavation may have a negative impact on deep ocean marine life, as robot mining may destroy their homes and disturb these sensitive species. The Canadian mining company Nautilus Minerals plans to send robots to mine deposits rich in copper and gold in the waters of Papua New Guinea.

Health Catalyst and the Regenstrief Institute are working together to commercialize nDepth, Regenstrief's natural language processing technology. Indianapolis-based Regenstrief developed the technology to harness unstructured data. Salt-Lake City-based Health Catalyst, a data warehousing and analytics company, has been in the business of extracting data to boost care quality since it launched in 2008. It was developed within the Indiana Health Information Exchange, the largest and oldest HIE in the country. Regenstrief fine-tuned nDepth through extensive and repeated use, searching more than 230 million text records from more than 17 million patients.

At Piton de la Fournaise on the island of Réunion, every day is like a glimpse of our planet's violent youth: Chunks of boiling lava spew upward like molten fireworks, while rivers of fire cut across an ashen, constantly repaved landscape of gray. Sitting more than 400 miles off Madagascar's eastern coast, the volcano has been grumbling for 530,000 years, producing extremely fluid, basalt-rich lava flows. In modern times, it's been one of the most active volcanoes on Earth, earning its moniker "peak of the furnace." Since the 17th century, the 8,633-foot-tall peak has erupted more than 150 times. It's no surprise that the French-held island's 900,000 inhabitants treat the volcano with caution. But thanks to drone pilot and Your Shot photographer Jonathan Payet, we get to sneak a peek at the furnace in remarkable detail.

An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information. Big Data, Data Mining, and Machine Learning On this resource the reality of big data is explored, and its benefits, from the marketing point of view.

In the past few years, the terms machine learning (ML) and deep learning have begun showing up frequently in many technology news and websites. The major difference between machine learning and other statistical methods, like data mining, is a popular subject of debate. In laymen's language, ML and data mining process use many of the same algorithms and techniques but one major difference lies in what the two methods predict. While data mining is used to uncover previously unknown patterns and knowledge, Machine learning is used to reproduce known patterns and knowledge. ML provides algorithms that resolve the problem based on the data, and the solution improves with time.