In this blog post we will look at some of the achievements during a 5-day machine learning hackathon we arranged recently. We were curious about one of the key concepts in our current strategy – could we manage to become a bit more "data smart" on integrity management and maintenance planning on pipelines? We wanted to learn more about the opportunities and maturity level with technologies like big data, machine learning, artificial intelligence and the internet of things. How easy was it to apply and how could it potentially fit into our current product portfolio? In our hackathon, we set up a mixed team of business representatives, experienced developers, data scientists and domain (pipeline) experts.

A silent communications system in which nonaural carriers, in the very low or very high audio-frequency range or in the adjacent ultrasonic frequency spectrum are amplitude- or frequency-modulated with the desired intelligence and propagated acoustically or vibrationally, for inducement into the brain, typically through the use of loudspeakers, earphones, or piezoelectric transducers. The modulated carriers may be transmitted directly in real time or may be conveniently recorded and stored on mechanical, magnetic, or optical media for delayed or repeated transmission to the listener.

While much has been written about information technology companies investing in artificial intelligence, Loup Ventures managing partner Doug Clinton notes that many non-tech companies are capitalizing on AI technology as well. Clinton has put together a portfolio of 17 publicly traded non-tech companies that are making investments in AI to improve their businesses. In a recent blog post, Clinton notes that he assembled the portfolio as a "fun exercise" and a way to draw attention to the sweeping nature of AI advancements. Loup Ventures is an early-stage venture capital firm. Clinton selected the companies from a range of industries including health care, retail, logistics, professional services, finance, transportation, energy, construction and food/agriculture.

Terrific article explaining how we can use Artificial Intelligence to increase energy efficiency in the home, in our power plants, and with the power grid. With all the alarming news coming out about the dangers of Artificial Intelligence, here is a great example of how AI can be a force for good.

It's hard to open a newspaper these days without encountering an article on the arrival of artificial intelligence. Predictions about the potential of this new technology are everywhere. Media hype aside, real evidence shows that artificial intelligence (AI) already drives a major shift in the global economy. You now use it in your day-to-day life, as you look to Netflix to recommend your next binge or ask Alexa to play music in your home. And the benefits of AI are driving the technologies into every corner of the global economy. Look, for example, at the number of times the largest U.S. companies mention artificial intelligence in their 10-K filings.

The robot, nicknamed "Little Sunfish", has captured previously unseen shots of underwater damage at the crippled nuclear plant.

This article was first published on the IEC e-tech website. Rapid advances in technology are revolutionizing the roles of aerial, terrestrial and maritime robotic systems in disaster relief, search and rescue (SAR) and salvage operations. Robots and drones can be deployed quickly in areas deemed too unsafe for humans and are used to guide rescuers, collect data, deliver essential supplies or provide communication services. The first reported use of SAR robots was to explore the wreckage beneath the collapsed twin towers of the World Trade Center in New York after the September 2001 terrorist attacks. Drones and robots have been used to survey damage after disasters such as the Fukushima Daiichi nuclear power plant accident in Japan in 2011 and the earthquakes in Haiti (2010) and Nepal (2015).

A Japanese robot has begun probing the radioactive water at Fukushima's nuclear reactor. The marine robot, nicknamed the'little sunfish', is on a mission to study structural damage and find fuel inside the three reactors of the devastated plant. Experts said remote-controlled bots are key to finding fuel at the dangerous site, which has likely melted and been submerged by highly radioactive water. A Japanese robot has begun probing the radioactive water at Fukushima's nuclear reactor. An underwater robot has captured images and other data inside Japan's crippled Fukushima nuclear plant on its first day of work.

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment-protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We propose the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies exhibit similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory

We study sequential change-point detection using sketches (linear projections) of high-dimensional signal vectors, by presenting the sketching procedures that are derived based on the generalized likelihood ratio statistic. We consider both fixed and time-varying projections, and derive theoretical approximations to two fundamental performance metrics: the average run length (ARL) and the expected detection delay (EDD); these approximations are shown to be highly accurate by numerical simulations. We also characterize the performance of the procedure when the projection is a Gaussian random projection or a sparse 0-1 matrix (in particular, an expander graph). Finally, we demonstrate the good performance of the sketching performance using simulation and real-data examples on solar flare detection and failure detection in power networks.