Using Azure, gave them an immediate global reach in a way unthinkable just a few years earlier. Their mission is to bring big data analytics to utilities and smart cities, and one of their focus areas is electric utilities and smart grids. It is a story that combines drones with intelligent software to prevent power blackouts, or as eSmart puts it "making Azure intelligence mobile". The economic impact of blackouts is massive, and the scale of power grids is huge. According to Grid Resilience Report, US Department of Energy, 2011, the average annual cost of power outages caused by severe weather in the USA is anywhere between $18-33 billion.

Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "Are Experts Worried About the Existential Risk of Artificial Intelligence?"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

Summary: If you want to capitalize on all the amazing advancements in data science through your own venture-funded start up, where are the best opportunities? With all our recent talk about Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs), innovations in text, speech, and image processing and now third generation Spiking Neural Nets you probably share my feeling that these new cutting edge data science technologies are huge opportunities waiting to be exploited. Perhaps you are thinking like an entrepreneur and wondering how you can capitalize on this in a venture-funded company. It's an interesting question and quite different from deciding to use these technologies to the benefit of your current employer or client. To be VC funded in anything, including a data science based opportunity there are many criteria, not the least of which is to be out in front of everyone else.

Data is everywhere – masses of it. And it's helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, finance can analyse past trends to make predictions and projections for the future, and sales can target their follow-up with detailed information on prospective customers. But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users.

Flexible, thin-film solar panels from a Silicon Valley company are allowing drone makers to keep their craft in the sky for hours longer than is possible with batteries alone. The panels, from Alta Devices in Sunnyvale, are produced on thin plastic sheets that can be stuck on the top frame of drones like the Bramor ppX, developed by Slovenia's C-Astral Aerospace. On Tuesday, the two companies showed off a version of the drone with six solar panels affixed to its top. The basic drone can stay aloft for 3.5 hours, but the addition of the solar panels has extended this by two hours, they said. The two plan to offer a solar version of the drone commercially.

Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.

Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.

Over the next decade, mobile, the Internet of Things, machine learning, robotics, and blockchain technologies will change a great deal about how the oil and gas industry works. Five technologies will change the oil and gas industry: mobile will speed oilfield transactions, increase efficiency, and improve safety by removing people from harm's way; the Internet of Things (IoT) will reduce the cost of repairs; machine learning will provide ever more optimal solutions to field challenges; robotics will upend the question of who does the work, and blockchain will make contracting faster and smoother than ever before. Adopting these technologies will be a challenge for many in our industry, requiring a change in mind-set. Engineers tend to focus less on investing for the future than on fixing what's broken now, as do companies trying to maximize their return on investment. But investments in these transformative technologies now will mean less to fix in the future, and more time to innovate, operate, and develop resources as fully as possible--which is what we're all trying to do, correct?

Instead of talking about rocket trips to Mars or self-driving cars, Tesla Motors CEO Elon Musk turned his attention Friday night to a more mundane topic. He came to talk about roofs. But like all things Musk, there was nothing normal about the roofs he came to discuss. On Wisteria Lane, the backlot set of what used to be the Desperate Housewives TV series at Universal Studios Hollywood, Musk showed off a new kind of solar roof that will be offered starting next year through SolarCity, the home solar installation company that he is seeking to merge into Tesla. Instead of a massive, unattractive array of solar panels typically seen in suburbia, SolarCity had installed roof tiles that are solar collector themselves on several of the houses that are part of the film set.

On June 14, 2016, four researchers at the Jet Propulsion Laboratory were preparing to ship a waist-high, ape-like robot named RoboSimian off-site. They had built the bot to rescue people from dangerous situations that human rescuers can't hack. The scientists swapped one lithium-ion battery for a fresh one, then left for lunch to let the new power supply charge. Left alone in the lab, RoboSimian's battery did what such batteries famously do: went boom. Plumes of smoke vented from the robot's exposed torso, followed by a burst of flame.