Kunio Shimada, a professor of fluid mechanics and energy engineering at Fukushima University, has developed a special rubber that can generate electricity from solar and kinetic energy and save the power generated. The 53-year-old professor, who is from the city of Fukushima, says the rubber is the first of its kind in the world and is trying to patent it in Japan. His discovery could be used to develop artificial skin for robots or shock-resistant solar batteries. Robotics experts have already shown interest in Shimada's technology, which could become part of the prefecture's new initiative aimed at promoting robotics. Shimada has a track record in the field of conductive rubber.

Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.

In today's always connected world, losing power is more than just an annoyance. "The truth is, we rely on electricity much more than we realize," writes Sherry Hewins in her column What Could Happen in a Long-Term Power Outage? "Even if you live'off the grid' as I did for years, you are still living in a world and a society that is deeply dependent upon electricity." It is the "deep dependency" that has power companies moving toward what is called the Smart Grid, a more efficient and reliable power-distribution infrastructure. One reason these capabilities are possible is the use of two-way communications between power-distribution centers and smart equipment (smart meters and smart appliances) downstream. Enhanced communications help more than just the people who make sure electricity keeps flowing.

Mathematicians at Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a radical new approach to machine learning: a new type of highly efficient "deep convolutional neural network" that can automatically analyze complex experimental scientific images from limited data.* As experimental facilities generate higher-resolution images at higher speeds, scientists struggle to manage and analyze the resulting data, which is often done painstakingly by hand. For example, biologists record cell images and painstakingly outline the borders and structure by hand. One person may spend weeks coming up with a single fully three-dimensional image of a cellular structure. Or materials scientists use tomographic reconstruction to peer inside rocks and materials, and then manually label different regions, identifying cracks, fractures, and voids by hand.

This month, seven years after the 2011 Fukushima Daiichi reactor meltdowns and explosions that blanketed hundreds of square kilometres of northeastern Japan with radioactive debris, government officials and politicians spoke in hopeful terms about Fukushima's prosperous future. Nevertheless, perhaps the single most important element of Fukushima's future remains unspoken: the exclusion zone seems destined to host a repository for Japan's most hazardous nuclear waste. No Japanese government official will admit this, at least not publicly. A secure repository for nuclear waste has remained a long-elusive goal on the archipelago. But, given that Japan possesses approximately 17,000 tonnes of spent fuel from nuclear power operations, such a development is vital.

The majority of industry and academic numeric predictive projects deal with deterministic or point forecasts of expected values of a random variable given some conditional information. In some cases, these predictions are enough for decision making. However, these predictions don't say much about the uncertainty of your underlying stochastic process. A common desire of all data scientists is to make predictions for an uncertain future. Clearly then, forecasts should be probabilistic, i.e., they should take the form of probability distributions over future quantities or events.

Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct radial operational structure of the distribution grid from synchronized voltage measurements in the grid subject to the exogenous fluctuations in nodal power consumption. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions of the nodal consumption and Gaussian injections in particular. Moreover, our algorithm applies to the practical case of unbalanced three-phase power flow. Algorithm performance is validated on AC power flow simulations over IEEE distribution grid test cases.

Mankind's first home away from Earth may soon be located, thanks to the findings of an AI that can scour the moon to find new craters. Experts say that a future lunar base could be set up in one of the giant impact sites, protecting colonists from dangerous solar radiation. Now, a piece of computer software has been developed that was able to uncover almost 7,000 previously undiscovered craters in a matter of hours. The finding was made by a team of researchers led by Ari Silburt at Penn State University and Mohamad Ali-Dib at the University of Toronto. They fed 90,000 images of the moon's surface into an artificial neural network (ANN).

METIS CyberTechnology, an information intelligence solutions provider, helps clients improve ton performance, optimise fuel oil consumption and reduce emissions. Participating at Posidonia for the first time this June, METIS will demonstrate its artificial intelligence BOT software for the maritime industry, a solution that provides Virtual Assistants who communicate with customers in a natural-language simply with text transcriptions, hence artificial intelligence in action. "Posidonia 2018 is an excellent opportunity for METIS, since we have the unique opportunity to meet a vast variety of forward-thinking maritime executives from all over the world. Our innovative, state-of-the-art solution based on artificial intelligence, will be present at the global forum to explore its potential via live demonstration and fruitful meetings," said Mike Konstantinidis, CEO at METIS CyberTechnology.

Artificial intelligence is poised to take over earthquake monitoring. It can help better locate the origin of earthquakes and also predict how devastating they might be. During an earthquake, different types of seismic waves travel through the earth. The first to arrive at any location are called P-waves, which compress and decompress the Earth's crust, causing the ground to move back and forth. The more dangerous are the S-waves that come next, which cause the Earth to move up and down.