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.

Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners. Objective: We want to evaluate which method can find better parameters in terms of performance score and runtime cost. Methods: This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. Results: We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. When repeated 20 times to check for conclusion validity, DE was over 210 times faster than grid search to tune Random Forests on 17 testing data sets with F-Measure Conclusions: These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality-- an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.

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.

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.

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.