A robot invented by researchers in the University of Georgia College of Engineering could change the way power lines are inspected--providing a safer and most cost-effective alternative. Currently, line crews have to suit up in protective clothing, employ elaborate safety procedures and sometimes completely shut off the power before inspecting a power line. It can be difficult, time-consuming and often dangerous work. A team led by Javad Mohammadpour, an assistant professor of electrical engineering, has designed, prototyped and tested a robot that can glide along electrical distribution lines, searching for problems or performing routine maintenance. Distribution lines carry electricity from substations to homes, businesses and other end users.

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the vocabulary size. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances. We consider the problem of semantic visualization by jointly modeling topics and visualization on the intrinsic document manifold, modeled using a neighborhood graph. Each document has both a topic distribution and visualization coordinate. Specifically, we propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces through a neighborhood regularization framework designed for the semantic visualization task. To validate the efficacy of Semafore, our comprehensive experiments on a number of real-life text datasets of news articles and Web pages show that the proposed methods outperform the state-of-the-art baselines on objective evaluation metrics.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theoretic design objective. To make the problem tractable, we develop new numerical approaches for nonlinear design with continuous parameter, design, and observation spaces. We approximate the optimal policy by using backward induction with regression to construct and refine value function approximations in the dynamic program. The proposed algorithm iteratively generates trajectories via exploration and exploitation to improve approximation accuracy in frequently visited regions of the state space. Numerical results are verified against analytical solutions in a linear-Gaussian setting. Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.

This swimming eel-robot does not make me happy. Watching its long, black, mechanical body move underwater, its red eyes glowing, makes my nerves twitch. I have every sense that it's a predator, snaking its way through murky depths to ensnare, suffocate, and digest me. My id fails to register that this is just a robot, designed to perform inspections and do simple maintenance on undersea equipment. The robot is made by a Norwegian company called Eelume, which this week partnered with Norwegian companies Statoil and Kongsberg Maritime to fast-track production of these serpentine machines.

When most people rave about seeing an "electrifying" performance, they typically aren't talking about witnessing real lightning on stage. But for the band ArcAttack, harnessing the power of 1 million volts of electricity -- and turning that energy into music -- is business as usual. ArcAttack creates music using two giant structures called Tesla coils, which were invented by the eccentric genius Nikola Tesla in 1891, as part of his dream to develop a way to transmit electricity around the world without any wires. Now, more than 120 years later, a band that is described by its founding member, Joe DiPrima, as a "mad scientist-slash-rock group," has found an innovative way to use these tower-like structures for entertainment. The band performed Saturday (April 23) here at Smithsonian magazine's "The Future Is Here" festival, a three-day event that explores the intersection of science and science fiction.

Now that energy storage technologies are coming close to commercial reality, decades of work should result in artificial intelligence (AI) emerging as the third key technology in the transformation of the electricity sector. Combined with scalable generation and storage, it will blur the distinction between suppliers and consumers, with excess local generation being fed into the grid so that entities from individual homeowners to business and municipalities will become "producer-consumers" or "prosumers". Demand management systems will also have a role to play. The introduction of multiple players of widely varying consumption and production patterns connecting into a single nationwide grid is impossible until we have software able to predict and manage energy flows to ensure that supply and demand balance at all times. There are obvious drivers also for energy storage at small scale, particularly for remote locations. Apart from the potential for autonomy, and the ability to smooth draw from the grid (avoiding or at least reducing demand-based charges), local storage could relieve grid congestion and add flexibility to power generation requirements, potentially improving network stability.

The relationship between the human mind and body is something that has occupied philosophers at least since the father of modern philosophy, René Descartes, bequeathed his notorious "dualism" to his successors. Low-power machine vision company Movidius has teamed up with thermal imaging company FLIR Systems, bringing Artificial Intelligence capabilities to Boson, FLIR's latest thermal-imaging camera core. FLIR will now integrate the Myriad 2 Vision Processing Unit into its thermal core to create the most intelligent thermal imaging solution to date. When artificial intelligence technology intersects with abundant oil and gas seismic data, the outcome could yield a more accurate depiction of what lies beneath the surface, enabling cash-strapped drillers to better target sweet spots and maximize returns.It's all based on algorithms that essentially teach computers how to solve complex problems-in this instance, how to quickly and accurately find subsurface faults that lead to lucrative hydrocarbon discoveries. In previous articles, we've talked about the merits of artificial intelligence and big data and how these technologies can enable a multitude of industries to begin learning how to do things more effectively.

Comet Labs has assembled a coalition of investors and corporate partners to help accelerate product and customer development cycles for fast growing robotics and machine learning companies. Startups are paired with experienced mentors and are given access to industry partners and platform technology. The fund's partners include the world's largest global manufacturing, agriculture, and healthcare companies. About Comet Labs Comet Labs is an early-stage venture capital firm associated with a 300M fund, Legend Star. They work with startups developing solutions in specific industry verticals, as well as those building enabling technologies.

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.

This Best Energy Stocks forecast is designed for investors and analysts who need predictions of the best performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Recommended Positions: Long Forecast Length: 14 Days (04/11/16– 04/25/16) I Know First Average: 27.11% All 10 top stocks for this forecast from the Energy Stocks package increased as predicted by the algorithm. LGCY was the highest-earning stock, more than doubling in share price, with a return of 105.65% for the 14-day time period. DNR also had a strong week with its return of 58.00% and ERF and PES also performed well with returns of 23.33% and 19.83% respectively.