The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight," said Hugo D'Ulisse, Head of Analytical Platform, SAS UK & Ireland.

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.

"Is data the new oil?" asked proponents of big data back in 2012 in Forbes magazine. By 2016, and the rise of big data's turbo-powered cousin deep learning, we had become more certain: "Data is the new oil," stated Fortune. Amazon's Neil Lawrence has a slightly different analogy: Data, he says, is coal. Not coal today, though, but coal in the early days of the 18th century, when Thomas Newcomen invented the steam engine. A Devonian ironmonger, Newcomen built his device to pump water out of the south west's prolific tin mines. The problem, as Lawrence told the Re-Work conference on Deep Learning in London, was that the pump was rather more useful to those who had a lot of coal than those who didn't: it was good, but not good enough to buy coal in to run it.

We may be decades from unlocking the true power of quantum computing, but D-Wave is promising to offer a taste of the future with its significantly upgraded quantum processor. When it is released early next year, the Canadian firm's new quantum chip will be able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor. D-Wave machines are multi-million dollar computers that crunch data using "quantum transistors", tiny loops of niobium cooled to close to absolute zero by liquid helium. Only a handful of such systems are in use, run by Google and the Universities Space Research Association, Lockheed Martin and Los Alamos National Laboratory. However, D-Wave also offers access to its quantum computers via a cloud service.

It's certainly an exciting time be involved in Natural Language Processing (NLP), not only for those of us who are involved in the development and cutting-edge research that is powering its growth, but also for the multitude of organizations and innovators out there who are finding more and more ways to take advantage of it to gain a competitive edge within their respective industries. With the global NLP market expected to grow to a value of 16 billion by 2021, it's no surprise to see the tech giants of the world investing heavily and competing for a piece of the pie. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, Yahoo, Intel, Apple and Salesforce. It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago.

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab-initio potential energy surfaces have improved, especially non-linear models based on neural networks (NN) which can reproduce ab-initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's, and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size and intelligently sample chemical space. NN's reduce, by a factor in excess of $10^6$ the computational overhead of the MBE and reproduce the accuracy of ab-initio calculations without specialized force fields. We show they are remarkably general, providing comparable accuracy with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties.

With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. They report on their findings in the scientific journal Physical Review Letters. Elpasolite is a glassy, transparent, shiny and soft mineral with a cubic crystal structure. First discovered in El Paso County (Colorado, USA), it can also be found in the Rocky Mountains, Virginia and the Apennines (Italy).

Asteroid mining is coming soon to a planet near you: this planet, and in 2021, to be specific. But NASA's Asteroid Redirect Mission has lots of work to do before that point. Today the space-faring organization issued an official "request for proposal" from four partners on how they would go about creating the robotic spacecraft that would perform the actual asteroid redirection in question. This mission is a bit different from OSIRIS-REx, which recently got underway. The idea there is to go to an asteroid, grab a piece and return it to Earth -- difficult enough, but it wouldn't be the first collection of "live" asteroid matter -- that was already achieved by the Japanese probe Hayabusa a few years ago (an astonishing achievement, by the way).

N 2015, the mining industry in Poland reported 2158 dangerous incidents with 19 casualties and 12 severe injuries [1]. Underground mining work poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. Monitoring and decision support systems might play an essential role in limiting the number of incidents and their prevention. Such systems, often based on machine learning or data mining techniques, can be effectively applied to lessen the danger to employees and prevent potential losses arising from lost and damaged equipment, see, e.g., [2], [3], [4]. In this paper, we present a model for predicting dangerous seismic events in coal mines.

Darren Holly steers coils of steel through Pentaflex, a manufacturer of parts for heavy trucks, in Springfield, Ohio. Darren Holly steers coils of steel through Pentaflex, a manufacturer of parts for heavy trucks, in Springfield, Ohio. Americans who endured the brutal 2007-2009 recession and slow recovery now are seeing an economic sunrise: Wages are up, jobs are growing and more families are lifting themselves up out of poverty. And yet, dark clouds are still hanging over millions of Americans. No set of sunny statistics can help an unemployed coal miner in Kentucky pay the mortgage.