Light globes that change colour with the tap of an app, coffee machines you can talk to, and ovens that know exactly how long to cook your food: our homes are getting smart. These devices, just a few examples of what is known as "the internet of things" (or IOT), have been called the "next great disruptor" and "the second digital revolution". One of the great hopes of this revolution is that it will help households save energy. Sensors can turn off lights and appliances when not in use, or turn the heating down when people go to bed. Smartphone apps can provide households with more insight into the energy use of their appliances.

In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian framework of signal reconstruction. By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing reconstruction. Finally, we show for the MNIST dataset that this approach can be very effective, even for $M < K$.

As a part of the tech in Techstory, we bring you the latest in the technology from around the world under "TECH THIS WEEK!" every Sunday! This week we saw Mozilla's multi-process architecture finally cleared for takeoff and Apple's initiative Apple Energy to become an energy company. We also saw launching specs of much anticipated Bluetooth 5.0 and the announcement of AlphaGo's next duel with Go champion Ke Jie. In case, if you've missed any of those, along with how scientists have planned to reduce global warming, don't worry just keep reading! Mozilla's long-running project to bring multi-process architecture to hundreds of millions of Firefox users has finally met release criteria for a full-scale rollout. Nearly every other browser on the market has adopted multi-process architecture, splitting tabs and extensions into separate processes.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

IDTechEx, the leading provider of independent market research, business intelligence and events on emerging technology announces the availability of a new research report, Microcontrollers and Single-board Computers 2016-2026 [http://www.idtechex.com/mc The report focuses on applications, technologies, players, and markets for microcontrollers (MCU) and single-board computers (SBC). This report goes into detail with definitions, anatomy and key components as well as an explanation how MCUs and SBCs are made, what they can do, their limitations and what comes next. The report includes ten year forecasts and discusses applications for both, including cars, home and office, automotive, DC-DC converters for battery monitoring, energy harvesting and microgrids, education, wearable technology, touch screen controllers, motor control, including quadcopters, control of stepper and traction motors. It also takes a look at artificial intelligence and deep learning, control of 3D printers, microwave ovens and washing machines.

We may soon have categorical evidence that living in San Francisco is a terrible idea. IBM announced on Nov. 20 that it had created an award-winning simulation of the Earth's tectonic plates that could soon be used to make predictions about when the next great earthquakes and volcanic eruptions will occur. And its artificial intelligence system, Watson, may prove to be the computer brain that can tell us when it's time to get out of the Bay Area. A team of computer scientists at IBM, in partnership with researchers from the University of Texas at Austin, New York University and the California Institute for Technology, created a model that simulated the entire flow of mantle under the Earth's surface. The model is so complex that it had to run on the Sequoia supercomputer (the third-fastest computer in the world), which the company built for the US government.

Robots are helping to keep the lights on in China's Anhui province. The robots look remarkably like Disney's lovable "Wall-E." Don't let that fool you, though. The machines are hard at work. Humans tell the robots what to keep an eye on, but the inspector bots do their job on their own.

Stephen Hawking has warned that artificially intelligent machines could kill us because they are too clever. Such computers could become so competent that they kill us by accident, Hawking has warned in his first Ask Me Anything session on Reddit. A questioner noted that Professor Hawking's ideas about artificial intelligence are seen as "a belief in Terminator-style'Evil AI'", and asked how he would present his own beliefs. "The real risk with AI isn't malice but competence," Professor Hawking said. "A super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble. "You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green energy project and there's an anthill in the region to be flooded, too bad for the ants.

Should Politicians be Replaced by Artificial Intelligence?

The counting grid is a grid of microtopics, sparse word/feature distributions. The generative model associated with the grid does not use these microtopics individually, but in predefined groups which can only be (ad)mixed as such. Each allowed group corresponds to one of all possible overlapping rectangular windows into the grid. The capacity of the model is controlled by the ratio of the grid size and the window size. This paper builds upon the basic counting grid model and it shows that hierarchical reasoning helps avoid bad local minima, produces better classification accuracy and, most interestingly, allows for extraction of large numbers of coherent microtopics even from small datasets. We evaluate this in terms of consistency, diversity and clarity of the indexed content, as well as in a user study on word intrusion tasks. We demonstrate that these models work well as a technique for embedding raw images and discuss interesting parallels between hierarchical CG models and other deep architectures.