The mining industry has traditionally been a laggard when it comes to innovation. The 21st-century economy, however, dominated by emerging technologies like electric vehicles, green energy sources and ever-more advanced mobile devices, is demanding creative approaches to efficiently delivering the raw materials that will fuel modern economies. Canadian mining is seeing a real drive to innovate that is bolstering our ability to get materials to global markets as efficiently and cost-effectively as possible. And it's changing what it means to work in mining. New technologies are enabling workers to make quicker, more informed decisions at the front lines of operations.

Commuters could soon be taken to work in a driverless car which is so clean they could relax on the journey with a cup of tea brewed using water from the tailpipe. The state-of-the-art Hyundai Nexo is a crossover SUV vehicle which runs on electrical energy generated by hydrogen fuel cells. Unlike traditional combustion engines, hydrogen cars don't emit carbon dioxide or nitrous oxide so its only by-product is water vapour. The water produced by Nexo could even be stored and used later to pour on plants or even used to make a cup of tea or coffee. Hyundai has also just showcased its'level 4' autonomous driving technology using Nexo as the test bed.

FUKUSHIMA – Tokyo Electric Power Company Holdings Inc. is set to pick a driverless electric bus made by a SoftBank Group Corp. unit to transport those decommissioning its disaster-crippled Fukushima No. 1 nuclear plant, informed sources said. The bus service will start this spring, transporting workers within the plant, which was disabled by a triple core meltdown after the March 2011 earthquake and tsunami. Last November, Tepco conducted a test of the bus made by SB Drive Corp., the SoftBank unit, and another from DeNA Co. As a result, Tepco decided to adopt SB Drive's bus, giving it high marks for safety and ease of maintenance, the sources said. The bus, called the Arma, is 4.75 meters long and can hold 15 passengers.

From smarter electric grids to automated monitoring of at-risk environments, there are many areas where technology could have exponential effects on sustainability. If the world's natural resources are increasingly stressed and depleted, the silver lining may be that we're becoming better equipped at tracking that destruction and potentially doing something about it. Cheap, widespread sensor networks, the internet of things, magnitude-improvements in computing power, open source algorithms–these all allow us to manage oceans and forests more effectively, if we want the opportunity. Artificial intelligence systems that can sense, think, learn, and act on their own could allow a major upgrade in conservation efforts, in dealing with climate change, and living in a more energy-efficient manner. A report released during the recent Davos World Economic Forum meeting laid more than 80 potential environmental applications for AI, ranging from the mundane to the futuristic.

So what is machine learning, and what role can it play in a home energy management system like carbonTRACK? Machine learning is the process by which software makes decisions based on its own observations and insights; it is one important component in the broader field of artificial intelligence. Systems that use machine learning are more sophisticated than conventional programming, in which the software simply follows a set of inbuilt, static rules with no ability to review or improve its performance. Software that is capable of machine learning can make conjectures, test different approaches and assess their outcomes without the direct intervention of a human intellect. Using these inferences, the system can make up new provisional rules for itself to follow – all while continuously monitoring whether these rules are helping it to achieve its prescribed goals.

Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of pore- and reservoir-scale models. This contribution leverages the differentiable nature of neural networks to extend GANs to the conditional simulation of three-dimensional pore- and reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use a content loss to constrain to the conditioning data and a perceptual loss obtained from the evaluation of the GAN discriminator network. The technique is tested on the generation of three-dimensional micro-CT images of a Ketton limestone constrained by two-dimensional cross-sections, and on the simulation of the Maules Creek alluvial aquifer constrained by one-dimensional sections. Our results show that GANs represent a powerful method for sampling conditioned pore and reservoir samples for stochastic reservoir evaluation workflows.

The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the welldocumented ubiquity of missing data in scientific, industrial, and medical datasets. In this work, we introduce a technique for motif discovery in the presence of missing data. We formally prove that our method is admissible, producing no false negatives. We also show that our method can "piggyback" off the fastest known motif discovery method with a small constant factor time/space overhead. We will demonstrate our approach on diverse datasets with varying amounts of missing data.

MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new method, developed by a team lead by MIT graduate student Avishek Biswas, is that it could potentially be used to run neural networks on smartphones, household devices and other portable gadgets, rather than requiring servers drawing constant power from the grid. Because it means that phones of the future using this chip could do things like advanced speech and face recognition using neural nets and deep learning locally, rather than requiring on more crude, rule-based algorithms, or routing information to the cloud and back to interpret results. Computing'at the edge,' as its called, or at the site of sensors actually gathering the data, is increasingly something companies are pursuing and implementing, so this new chip design method could have a big impact on that growing opportunity should it become commercialized.

We, as ambitious beings, are in a habit of running ahead as faster as we could and not looking back at what we leave behind. As we contribute our greatest efforts to make this world commodious to us with the help of technology, we overlook that we are also depleting our natural resources and making our planet poisonous to us. Why not use our strength to save our ailing planet? Yes, we can definitely save our sick planet recover its health using technology as a tool. With technology like low-cost extensive sensor networks, IoT, magnitude-improvements in computing power, open source algorithm, we can work effectively in this field.

Time series forecasting is hardly a new problem in data science and statistics. The term is self-explanatory and has been on business analysts' agenda for decades now: The very first practices of time series analysis and forecasting trace back to the early 1920s.