The pace of business model change--driven by digitalization--and the resulting disruption in many categories are challenging companies today. Digitalization, though, is only an enabler for the Internet of Energy and other upcoming technological trends, which will demand a radical change of economy. The imperative of climate change and pollution mitigation in times where we expect population and individual demand to grow rapidly dictates optimized use of any available resource. The "circular economy" concept--which contrasts with today's linear economy (take, make, use, dispose)--is useful here and more important now than ever before. And the Internet of Things, enabled by digitalization, will play a crucial role in making this concept work. The circular economy concept requires that any resource is optimized in terms of renewability (energy used), reusability (cycling valuable metals, alloys and polymers beyond the shelf life of individual resources) and recyclability (compostable packaging).

That's what Rolls-Royce is working on with their autonomous naval vessel concept that plans to have a 3,500 nautical-mile range. The company sees a future in the next 10 years or so where autonomous boats are out in the water for up to 100 days, eliminating the need for remote controlled ships or crews. Rolls-Royce general manager of naval electrics, automation and control, Benjamin Thorp said in a news release, "Such ships offer a way to deliver increased operational capability, reduce the risk to crew, and cut both operating and build costs." The ship is all conceptual, but the Verge reported a Norwegian company is launching an automated cargo ship next year that plans to be autonomous by 2020.

In what could be the most innovative naval vessels at sea, Rolls-Royce has revealed that it is developing a range of self-driving ships. The crewless vessels will have a range of 3,500 nautical miles, and will be used to perform a range of missions, including patrol and surveillance, mine detection and fleet screening. Rolls-Royce hopes that the fleet could take to sea'over the next 10 years.' The 60 metre-long vessel concept is capable of operating beyond the horizon for over 100 days, will displace 700 tonnes and reach speeds above 25 knots. At the heart of the vessel is a robust and reliable power dense propulsion system. The initial design suggests that the vessels will have a full electric propulsion system.

The longest amount of time that a spacecraft has survived on the surface of Venus is 127 minutes. On March 1, 1982, the USSR's Venera 13 probe parachuted to a gentle landing and managed to keep operating for just over two hours by hiding all of its computers inside of a hermetically sealed titanium pressure vessel that was pre-cooled in orbit. The surface temperature on Venus averages 464 C (867 F), which is hotter than the surface of Mercury (the closest planet to the sun), and hot enough that conventional electronics simply will not work. It's not just the temperature that makes Venus a particularly nasty place for computers--the pressure at the surface is around 90 atmospheres, equivalent to the pressure 3,000 feet down in Earth's ocean. And while you can be relieved that the sulfuric acid rain that you'll find in Venus' upper atmosphere doesn't reach the surface, it's also so dark down there (equivalent to a heavily overcast day here on Earth) that solar power is horrendously inefficient.

We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to switch from one function to another. Thus each curve follows what we call a switching nonparametric regression model. We develop an EM algorithm to estimate the model parameters. We also obtain standard errors for the parameter estimates of the state process. We consider several types of state processes: independent and identically distributed, independent but depending on a covariate and Markov. Simulation studies show the frequentist properties of our estimates. We apply our methods to a data set of a building's power usage.

Summer is not without its annoyances -- mosquitos, wasps, and ants, to name a few. As the cool breeze of September pushes us back to work, labs across the country are reconvening tackling nature's hardest problems. Sometimes forces that seem diametrically opposed come together in beautiful ways, like robotics infused into living organisms. This past summer, researchers at Harvard and Arizona State University collaborated on successfully turning living E. Coli bacteria into a cellular robot, called a "ribocomputer." By taking archived footage of movies, the Harvard scientists were able to successfully store the digital content on the bacteria that is most famous for making Chipotle customers violently ill.

It's well understood that IoT is going to provide a lot of data. And that data will be used to feed applications. Machine learning will help provide the algorithms that convert that data into action. And that action is what we, the consumers, will benefit from. This means that IoT and machine learning will need to intersect.

The driver punched the air as his red and white Honda McLaren roared over the finish line. It was Suzuka, Mie Prefecture, 1988, and Ayrton Senna had just become Formula One world champion for the first time. The McLaren racing team and its engine maker, Honda Motor, were unstoppable that year, their drivers winning all but one of the 16 grand prix races. Off the track Honda had been tasting success, too. In the 1970s, its engineers had raised the bar for fuel efficiency and cleaner emissions with the CVCC engine. In the 1980s, as its engines were propelling Senna to multiple victories, the Civic and Accord models were redefining the American family sedan.

Autonomous vehicles might negotiate with each other for right of away. You may know the art of the deal, but there's a science to it, too. And artificial intelligence is beginning to learn it. Computers that could negotiate for us could automate and optimize everything from traffic intersections to global treaties. Last month, at the International Joint Conference on Artificial Intelligence (IJCAI) in Melbourne, Australia, a group of researchers presented a paper on the challenges and opportunities of such hagglebots.

Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information. Although pre-training from ANN or direct training based on backpropagation (BP) makes the supervised training of SNNs possible, these methods only exploit the networks' spatial domain information which leads to the performance bottleneck and requires many complicated training skills. Another fundamental issue is that the spike activity is naturally non-differentiable which causes great difficulties in training SNNs. To this end, we build an iterative LIF model that is more friendly for gradient descent training. By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology. We achieve the best performance of multi-layered perceptron (MLP) compared with existing state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST dataset as well as a custom object detection dataset. This work provides a new perspective to explore the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.