In the wake of the 2010 Deepwater Horizon disaster in the Gulf of Mexico, oceanographer Kaitlin Frasier of the University of California, San Diego, set out to assess the damage that the massive oil spill caused. "We needed to know what happened to marine mammals," she says. Specifically, Frasier was concerned with the spill's impact on dolphin populations. Trying to track the animals from the surface is expensive and time consuming, so Frasier used a different approach: deploying hydrophones to the seabed to passively record every sound in the ocean. By separating out dolphin vocalizations from the general thrum of ocean noise, Frasier hoped to detect trends in the animals' population density.

With 400 hours of video being uploaded to YouTube every minute and fleets of self-driving cars mapping high definition 3D maps of roads all over the world, data is being created, stored and processed at a rate never seen before. Ninety percent of the world's data has been created in the last two years, according to IBM and other industry sources, and with new data hungry applications on the rise (autonomous driving, the Internet of Things, artificial intelligence), there's no sign of this trend abating. Organizing, storing and processing all that data comes with not only business, but also environmental challenges. In fact, networking and telecom equipment maker Huawei has estimated that global computing power could consume as much as 20% of global electricity in 2025 and account for 3.5% of global emissions.[1] All this data processing also requires large amounts of water to keep servers from overheating - roughly 1.8 liters for every kWh consumed - according to the U.S. Department of Energy (DOE).

Machine learning is already a big part of modern life. Machine learning algorithms work in Google's search results, Spotify's music recommendations, and Uber's ride-sharing matches. The use of the technology will accelerate as companies find new applications in finance, health, energy, manufacturing and other sectors. Machine learning brings great opportunities for new businesses, but poses significant disruption to established companies and traditional employment. There's much uncertainty but it's clear that machine learning will be a transformational force over the careers of current MBA students.

The iconic Empire State Building in New York City celebrates its 90-year anniversary in a couple of years. By this age, people usually get wiser or grumpier, or both. The ESB is just getting smarter, proving New York is one of the smartest cities in the world. Initially, the renovation was expected to reduce energy consumption by 38 percent and reach $4.4 million return in annual energy savings. In reality, the project has been overrunning its own goals year by year.

New Delhi: French multinational oil and gas company Total announced it has signed an agreement with Google Cloud to jointly develop Artificial Intelligence (AI) solutions for subsurface data analysis for oil and gas exploration and production. "Total is convinced that applying artificial intelligence in the oil and gas industry is a promising avenue to be explored for optimizing our performance, particularly in subsurface data interpretation. We are excited to work with Google Cloud towards this goal. This builds on the strategy being developed at Total, where A.I. is already used, for example, in predictive maintenance at facilities," said Marie-Noëlle Semeria, Senior Vice President, Group CTO at Total. The agreement will focus on the development of AI programs which will make it possible to interpret subsurface images, notably from seismic studies (using Computer Vision technology) and automate the analysis of technical documents (using Natural Language Processing technology), the company said.

The night before the mission, Kenji Matsuzaki could not sleep. For more than a year, Matsuzaki and a team of engineers had been developing their little robot--a bread-loaf-sized, red and white machine equipped with five propellers, a transparent dome, front and rear video cameras, and an array of lights and sensors. Nicknamed Little Sunfish, it was engineered to operate underwater, in total darkness, amid intense radiation. And after three months of testing, training, and fine-tuning, it was deemed ready to fulfill its mission: to find and photograph the melted-down radioactive fuel that had gone missing inside the Fukushima Daiichi nuclear power plant. More than six years had passed since an earthquake and tsunami hammered northeastern Japan and reduced the Fukushima facility to radioactive ruin.

Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation change depending on the location and time of occurrence. Non-stationary dynamics pose both theoretical and practical challenges to statistical machine learning algorithms aiming to accurately capture the complexities governing the evolution of such processes. In this paper, we address the sampling aspects of the problem of learning nonstationary spatio-temporal models, and propose an efficient yet simple algorithm - LISAL. The core idea in LISAL is to learn two models using Gaussian processes (GPs) wherein the first is a nonstationary GP directly modeling the phenomenon. The second model uses a stationary GP representing a latent space corresponding to changes in dynamics, or the nonstationarity characteristics of the first model. LISAL involves adaptively sampling the latent space dynamics using information theory quantities to reduce the computational cost during the learning phase. The relevance of LISAL is extensively validated using multiple real world datasets.

This press release, from which no legal consequences may be drawn, is for information purposes only. The entities in which TOTAL S.A. directly or indirectly owns investments are separate legal entities. TOTAL S.A. has no liability for their acts or omissions. In this document, the terms "Total" and "Total Group" are sometimes used for convenience where general references are made to TOTAL S.A. and/or its subsidiaries. Likewise, the words "we", "us" and "our" may also be used to refer to subsidiaries in general or to those who work for them.

A new RAND Corporation paper finds that artificial intelligence has the potential to upend the foundations of nuclear deterrence by 2040. While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper. During the Cold War, the condition of mutual assured destruction (MAD) maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. MAD thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war. The new RAND publication says that in the coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability.

Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983. A single word flashed on the screen in front of him. The fear that computers, by mistake or malice, might lead humanity to the brink of nuclear annihilation has haunted imaginations since the earliest days of the Cold War.